### HARRY_READ_ME.txt

File from FOI2009.zip Apparently written between 2006 and 2009 by Ian Harris of the University of East Anglia Climate Research Unit Norwich, UKHARRY_READ_ME

READ ME for Harry's work on the CRU TS2.1/3.0 datasets, 2006-2009!

1. Two main filesystems relevant to the work:

/cru/dpe1a/f014 /cru/tyn1/f014

Both systems copied in their entirety to /cru/cruts/

Nearly 11,000 files! And about a dozen assorted 'read me' files addressing individual issues, the most useful being:

fromdpe1a/data/stnmon/doc/oldmethod/f90_READ_ME.txt fromdpe1a/code/linux/cruts/_READ_ME.txt fromdpe1a/code/idl/pro/README_GRIDDING.txt

(yes, they all have different name formats, and yes, one does begin '_'!)

2. After considerable searching, identified the latest database files for

tmean:

fromdpe1a/data/cruts/database/+norm/tmp.0311051552.dtb fromdpe1a/data/cruts/database/+norm/tmp.0311051552.dts

(yes.. that is a directory beginning with '+'!)

3. Successfully ran anomdtb.f90 to produce anomaly files (as per item 7 in the '_READ_ME.txt' file). Had to make some changes to allow for the move back to alphas (different field length from the 'wc -l' command).

4. Successfully ran the IDL regridding routine quick_interp_tdm.pro (why IDL?! Why not F90?!) to produce '.glo' files.

5. Currently trying to convert .glo files to .grim files so that we can compare with previous output. However the progam suite headed by globulk.f90 is not playing nicely - problems with it expecting a defunct file system (all path widths were 80ch, have been globally changed to 160ch) and also no guidance on which reference files to choose. It also doesn't seem to like files being in any directory other than the current one!!

6. Temporarily abandoned 5., getting closer but there's always another problem to be evaded. Instead, will try using rawtogrim.f90 to convert straight to GRIM. This will include non-land cells but for comparison purposes that shouldn't be a big problem... [edit] noo, that's not gonna work either, it asks for a 'template grim filepath', no idea what it wants (as usual) and a serach for files with 'grim' or 'template' in them does not bear useful fruit. As per usual. Giving up on this approach altogether.

7. Removed 4-line header from a couple of .glo files and loaded them into Matlab. Reshaped to 360r x 720c and plotted; looks OK for global temp (anomalies) data. Deduce that .glo files, after the header, contain data taken row-by-row starting with the Northernmost, and presented as '8E12.4'. The grid is from -180 to +180 rather than 0 to 360. This should allow us to deduce the meaning of the co-ordinate pairs used to describe each cell in a .grim file (we know the first number is the lon or column, the second the lat or row - but which way up are the latitudes? And where do the longitudes break? There is another problem: the values are anomalies, wheras the 'public' .grim files are actual values. So Tim's explanations (in _READ_ME.txt) are incorrect..

8. Had a hunt and found an identically-named temperature database file which did include normals lines at the start of every station. How handy - naming

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two different files with exactly the same name and relying on their location to differentiate! Aaarrgghh!! Re-ran anomdtb:

crua6[/cru/cruts/rerun1/data/cruts/rerun1work] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required: .tmp > Select the .cts or .dtb file to load: tmp.0311051552.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 8 > Select the generic .txt file to save (yy.mm=auto): rr2.txt > Select the first,last years AD to save: 1901,2002 > Operating... > Failed to find file. > Enter the file, with suffix: .dts tmp.0311051552.dts Values loaded: 1255171542; No. Stations: 12155 > NORMALS MEAN percent STDEV percent > .dtb 5910325 86.6 > .cts 575661 8.4 6485986 95.0 > PROCESS DECISION percent %of-chk > no lat/lon 12043 0.2 0.2 > no normal 335741 4.9 4.9 > out-of-range 31951 0.5 0.5 > duplicated 341323 5.0 5.3 > accepted 6107721 89.4 > Dumping years 1901-2002 to .txt files...

crua6[/cru/cruts/rerun1/data/cruts/rerun1work]

9. Ran the IDL function:

IDL> quick_interp_tdm2,1901,2002,'rr2glofiles/rr2grid.',1200,gs=0.5,dumpglo='dumpglo',pts _prefix='rr2txtfiles/rr2.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2002

IDL>

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This produces anomoly files even when given a normals-added database.. doesn't create the CLIMATOLOGY. However we do have it, both in the 'normals' directory of the user data directory, and in the dpe1a 'cru_cl_1.0' folder! The relevant file is 'clim.6190.lan.tmp'. Obviously this is for land only.

10. Trying to compare .glo and .grim Wrote several programs to assist with this process. Tried creating anomalies from the .grim files, using the published climatology. Then tried to compare with the glo files I'd produced (this is all for 1961-1970). Couldn't get a sensible grid layout for the glo files! Eventually resorted to visualisation - looks like the .glo files are 'regular' grid format after all (longitudes change fastest). Don't understand why the comparison program had so much trouble getting matched cells!

11. Decided to concentrate on Norwich. Tim M uses Norwich as the example on the website, so we know it's at (363,286). Wrote a prog to extract the relevant 1961-1970 series from the published output, the generated .glo files, and the published climatology. Prog is norwichtest.for. Prog also creates anomalies from the published data, and raw data from the generated .glo data. Then Matlab prog plotnorwich.m plots the data to allow comparisons. First result: works perfectly, except that the .glo data is all zeros. This means I still don't understand the structure of the .glo files. Argh!

12. Trying something *else*. Will write a prog to convert the 1961-1970 .glo files to a single file with 120 columns and a row for each non-zero cell. It will be slow. It is a nuisance because the site power os off this weekend (and it's Friday afternoon) so I will get it running at home. Program is glo2vec.for, and yup it is slow. Started a second copy on uealogin1 and it's showing signs of overtaking the crua6 version that started on Friday (it's Tuesday now). I'm about halfway through and the best correlation so far (as tested by norwichcorr.for) is 0.39 at (170,135) (lon,lat).

13. Success! I would crack open a bottle of bubbly but it's only 11.25am. The program norwichcorr.for found a correlation for the norwich series at (363, 286) of 1.00! So we have found the published Norwich series in the grids I produced. A palpable sense of relief pervades the office :-) It's also the grid reference given by Tim for Norwich. So how did I miss it earlier??

14. Wrote a program ('glo2grim.for') to do what I cannot get Tim's 'raw2grim.f90', ie, convert .glo files to GRIM format. It's slow but sure. In parallel, a quick prog called grimcmp.for which compares two GRIM-format files. It produces brief stats. At time of writing, just over 4000 cells have been converted, and the output of grimcmp is:

uealogin1[/cru/cruts/rerun1/data/cruts/rerun1work] ./grimcmp

Welcome to the GRIM Comparer

Please enter the first grim file (must be complete!): cru_ts_2_10.1961-1970.tmp Please enter the second grim file (may be incomplete): glo2grim1.out

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File glo2grim1.out terminated prematurely after 4037 records.

SUMMARY FROM GRIMCMP

Files compared: 1. cru_ts_2_10.1961-1970.tmp 2. glo2grim1.out

Total Cells Compared 4037 Total 100% Matches 0 Cells with Corr. == 1.00 0 ( 0.0%) Cells with 0.90<=Corr<=0.99 3858 (95.6%) Cells with 0.80<=Corr<=0.89 119 ( 2.9%) Cells with 0.70<=Corr<=0.79 25 ( 0.6%)

..which is good news! Not brilliant because the data should be identical.. but good because the correlations are so high! This could be a result of my mis-setting of the parameters on Tim's programs (although I have followed his recommendations wherever possible), or it could be a result of Tim using the Beowulf 1 cluster for the f90 work. Beowulf 1 is now integrated in to the latest Beowulf cluster so it may not be practical to test that theory.

15. All change! My 'glo2grim1' program was presciently named as it's now up to v3! My attempt to speed up early iterations by only reading as much of each glo file as was needed was really stupidly coded and hence the poor results. Actually they're worryingly good as the data was effectively random :-0 We are now on-beam and initial results are very very promising:

uealogin1[/cru/cruts/rerun1/data/cruts/rerun1work] ./grimcmp3x

File glo2grim3.out terminated prematurely after 143 records.

SUMMARY FROM GRIMCMP

Files compared: 1. cru_ts_2_10.1961-1970.tmp 2. glo2grim3.out

Total Cells Compared 143 Total 100% Matches 12 Cells with Corr. == 1.00 12 ( 8.4%) Cells with 0.96<=Corr<=0.99 130 (90.9%) Cells with 0.90<=Corr<=0.95 1 ( 0.7%) Cells with 0.80<=Corr<=0.89 0 ( 0.0%) Cells with 0.70<=Corr<=0.79 0 ( 0.0%)

..so all correlations are >= 0.9 and all but one are >=0.96! with 12 complete (100% identical) matches I think we can safely say we are producing the data Tim produced. The variations can be accounted for as rounding errors due to different hardware and compilers, I reckon..

16. So, it seemed like a good time to start a Precip run. With a bit of luck this would go as smoothly as the Temperature run, ho, ho, ho. The first problem was that anomdtb kept crashing:

crua6[/cru/cruts/rerun1/data/cruts/rerun2work] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

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> Enter the suffix of the variable required:
.pre > Will calculate percentage anomalies. > Select the .cts or .dtb file to load: pre.0312031600.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 8 > Select the generic .txt file to save (yy.mm=auto): rr2pre.txt > Select the first,last years AD to save: 1901,2002 > Operating... Values loaded: 1258818288; No. Stations: 12732 > NORMALS MEAN percent STDEV percent > .dtb 2635549 29.6 forrtl: error (75): floating point exception IOT trap (core dumped) crua6[/cru/cruts/rerun1/data/cruts/rerun2work]

..not good! Tried recompiling for uealogin1.. AARGGHHH!!! Tim's code is not 'good' enough for bloody Sun!! Pages of warnings and 27 errors! (full results in 'anomdtb.uealogin1.compile.results').

17. Inserted debug statements into anomdtb.f90, discovered that a sum-of-squared variable is becoming very, very negative! Key output from the debug statements:

OpEn= 16.00, OpTotSq= 4142182.00, OpTot= 7126.00 DataA val = 93, OpTotSq= 8649.00 DataA val = 172, OpTotSq= 38233.00 DataA val = 950, OpTotSq= 940733.00 DataA val = 797, OpTotSq= 1575942.00 DataA val = 293, OpTotSq= 1661791.00 DataA val = 83, OpTotSq= 1668680.00 DataA val = 860, OpTotSq= 2408280.00 DataA val = 222, OpTotSq= 2457564.00 DataA val = 452, OpTotSq= 2661868.00 DataA val = 561, OpTotSq= 2976589.00 DataA val = 49920, OpTotSq=-1799984256.00 DataA val = 547, OpTotSq=-1799684992.00 DataA val = 672, OpTotSq=-1799233408.00 DataA val = 710, OpTotSq=-1798729344.00 DataA val = 211, OpTotSq=-1798684800.00 DataA val = 403, OpTotSq=-1798522368.00 OpEn= 16.00, OpTotSq=-1798522368.00, OpTot=56946.00 forrtl: error (75): floating point exception IOT trap (core dumped)

..so the data value is unbfeasibly large, but why does the sum-of-squares parameter OpTotSq go negative?!!

Probable answer: the high value is pushing beyond the singleprecision default for Fortran reals?

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Value located in pre.0312031600.dtb:

-400002 3513 3672 309 HAMA SYRIA 1985 2002 -999 -999 6190 842 479 3485 339 170 135 106 0 9 243 387 737 1985 887 582 93 16 17 0 0 0 0 352 221 627 1986 899 252 172 527 173 30 0 0 0 84 496 570 1987 578 349 950 191 4 0 0 0 0 343 462 929 1988 1044 769 797 399 11 903 218 0 0 163 517 1181 1989 269 62 293 3 13 0 0 0 0 101 292 342 1990 328 276 83 135 224 0 0 0 0 87 343 230 1991 1297 292 860 320 70 0 0 0 0 206 298 835 1992 712 1130 222 39 339 301 0 0 0 0 909 351 1993 726 609 452 82 672 3 0 0 0 34 183 351 1994 625 661 561 41 155 0 0 0 22 345 953 1072 1995 488-9999-9999 182-9999 0-9999 0 0 0 754-9999 1996-9999 40949920-9999 82 0-9999 0 36 414 112 312 1997-9999 339 547-9999 561-9999 0 0 54 155 265 962 1998 1148 289 672 496-9999 0 0-9999 9 21-9999 1206 1999 343 379 710 111 0 0 0-9999-9999-9999 132 285 2000 1518 399 211 354 27 0-9999 0 27 269 316 1057 2001 370-9999-9999 273 452 0-9999-9999-9999 290 356-9999 2002 871 329 403 111 233-9999 0 0-9999-9999 377 1287

(value is for March 1996)

Action: value replaced with -9999 and file renamed:

pre.0312031600H.dtb (to indicate I've fixed it)

.dts file also renamed for consistency.

anomdtb then runs fine!! Producing the usual txt files.

18. Ran the IDL gridding routine for the precip files:

quick_interp_tdm2,1901,2002,'rr2preglofiles/rr2pregrid.',450,gs=0.5,dumpglo='dumpglo ',pts_prefix='rr2pretxtfiles/rr2pre.'

..and this is where it gets CRAZY. Instead of running normally, this time I get:

IDL> quick_interp_tdm2,1901,1910,'rr2glofiles2/rr2grid.',1200,gs=0.5,dumpglo='dumpglo',pt s_prefix='rr2txtfiles/rr2.'

limit=glimit(/all) ; sets limit to global field ^ % Syntax error. At: /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro, Line 38

lim=glimit(/all) ^ % Syntax error. At: /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro, Line 122

r=area_grid(pts2(n,1),pts2(n,0),pts2(n,2),gs*2.0,bounds,dist,angular=angular) ^ % Syntax error. At: /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro, Line 183 % Compiled module: QUICK_INTERP_TDM2. % Attempt to call undefined procedure/function: 'QUICK_INTERP_TDM2'. % Execution halted at: $MAIN$

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IDL>

.. WHAT?! Now it's not precompiling its functions for some reason! What's more - I cannot find the 'glimit' function anywhere!!

Eventually (the following day) I found glimit and area_grid, they are in Mark New's folder: /cru/u2/f080/Idl. Since this is in $IDL_PATH I have no idea why they're not compiling! I manually compiled them with .compile, and the errors vanished! Though not for long:

IDL> .compile /cru/u2/f080/Idl/glimit.pro % Compiled module: GLIMIT. IDL> .compile /cru/u2/f080/Idl/area_grid.pro % Compiled module: AREA_GRID.

IDL> quick_interp_tdm2,1901,1910,'rr2glofiles2/rr2grid.',1200,gs=0.5,dumpglo='dumpglo',pt s_prefix='rr2txtfiles/rr2.' % Compiled module: QUICK_INTERP_TDM2. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Variable is undefined: STRIP. % Execution halted at: QUICK_INTERP_TDM2 215 /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro % $MAIN$

IDL>

Was this a similar problem? Unfortunately not:

IDL> .compile /cru/u2/f080/Idl/strip.pro

% Compiled module: STRIP.

IDL> quick_interp_tdm2,1901,1910,'rr2glofiles2/rr2grid.',1200,gs=0.5,dumpglo='dumpglo',pt s_prefix='rr2txtfiles/rr2.' Defaults set 1901 % Variable is undefined: STRIP. % Execution halted at: QUICK_INTERP_TDM2 215 /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro % QUICK_INTERP_TDM2 215 /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro % $MAIN$

IDL>

..so it looks like a path problem. I wondered if the NFS errors that have been plagueing crua6 work for some time now might have prevented IDL from adding the correct directories to the path? After all the help file does mention that IDL discards any path entries that are inaccessible.. so if the timeout is a few seconds that would explain it. So I restarted IDL, and PRESTO! It worked. I then tried the precip veriosn - and it worked too!

IDL> quick_interp_tdm2,1901,2002,'rr2preglofiles/rr2pregrid.',450,gs=0.5,dumpglo='dumpglo ',pts_prefix='rr2pretxtfiles/rr2pre.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP.

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% Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2001 2002

IDL>

I then ran glo2grim4.for to convert from percentage anomalies to real (10ths of a mm) values. Initial results are not as good as temperature, but mainly above 0.96 so obviously on the right track.

However..

19. Here is a little puzzle. If the latest precipitation database file contained a fatal data error (see 17. above), then surely it has been altered since Tim last used it to produce the precipitation grids? But if that's the case, why is it dated so early? Here are the dates:

/cru/dpe1a/f014/data/cruts/database/+norm/pre.0312031600.dtb - directory date is 23 Dec 2003

/cru/tyn1/f014/ftpfudge/data/cru_ts_2.10/data_dec/cru_ts_2_10.1961-1970.pre.Z - directory date is 22 Jan 2004 (original date not preserved in zipped file) - internal (header) date is also '22.01.2004 at 17:57'

So what's going on? I don't see how the 'final' precip file can have been produced from the 'final' precipitation database, even though the dates imply that. The obvious conclusion is that the precip file must have been produced before 23 Dec 2003, and then redated (to match others?) in Jan 04.

20. Secondary Variables - Eeeeeek!! Yes the time has come to attack what even Tim seems to have been unhappy about (reading between the lines). To assist me I have 12 lines in the gridding ReadMe file.. so par for the course. Almost immediately I hit that familiar feeling of ambiguity: the text suggests using the following three IDL programs: frs_gts_tdm.pro rd0_gts_tdm.pro vap_gts_anom.pro So.. when I look in the code/idl/pro/ folder, what do I find? Well:

3447 Jan 22 2004 fromdpe1a/code/idl/pro/frs_gts_anom.pro 2774 Jun 12 2002 fromdpe1a/code/idl/pro/frs_gts_tdm.pro

2917 Jan 8 2004 fromdpe1a/code/idl/pro/rd0_gts_anom.pro 2355 Jun 12 2002 fromdpe1a/code/idl/pro/rd0_gts_tdm.pro

5880 Jan 8 2004 fromdpe1a/code/idl/pro/vap_gts_anom.pro

In other words, the *anom.pro scripts are much more recent than the *tdm scripts. There is no way of knowing which Tim used to produce the current public files. The scripts differ internally but - you guessed it! - the descriptions at the start are identical. WHAT IS GOING ON? Given that the 'README_GRIDDING.txt' file is dated 'Mar 30 2004' we will have to assume that the originally-stated scripts must be used.

To begin with, we need binary output from quick_interp_tdm2, so it's run again for tmp and pre, and (for the first time) for dtr. This time, the command line looks like this for tmp:

IDL> quick_interp_tdm2,1901,2002,'idlbinout/idlbin',1200,gs=2.5,dumpbin='dumpbin',pts_pre fix='tmp_txt_4idl/tmp.'

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This gives screen output for each year, typically: 1991 grid 1991 non-zero 0.9605 2.0878 2.1849 cells= 27048 And produces output files (in, in this case, 'idlbinout/'), like this: -rw------- 1 f098 cru 248832 Sep 21 12:20 idlbin_tmp/idlbin_tmp1991

At this point, did some logical renaming. So.. .txt files (pre-IDL) are typically 'tmp.1901.01.txt' in 'tmp_txt_4idl/' binary files (post-IDL) are typically 'idlbin_tmp1991' in 'idlbin_tmp/'. These changes rolled back to the quoted command lines, to avoid confusion.

Next, precip command line:

IDL> quick_interp_tdm2,1901,2002,'idlbin_pre/idlbin_pre',450,gs=2.5,dumpbin='dumpbin',pts _prefix='pre_txt_4idl/pre.' (note new filenaming schema) This gives example screen output: 1991 grid 1991 non-zero -4.8533 36.2155 51.0738 cells= 51060 And produces output files like: -rw------- 1 f098 cru 248832 Sep 21 12:50 idlbin_pre/idlbin_pre1991

Finally for the primaries, the first stab at dtr. Ran anomdtb with the database file dtr.0312221128.dtb, and the standard/recommended responses. Screen output: > NORMALS MEAN percent STDEV percent > .dtb 0 0.0 > .cts 3375441 84.1 3375441 84.1 > PROCESS DECISION percent %of-chk > no lat/lon 3088 0.1 0.1 > no normal 638538 15.9 15.9 > out-of-range 70225 1.7 2.1 > duplicated 135457 3.4 4.1 > accepted 3167636 78.9 > Dumping years 1901-2002 to .txt files...

Then for the gridding:

IDL> quick_interp_tdm2,1901,2002,'idlbin_dtr/idlbin_dtr',750,gs=2.5,dumpbin='dumpbin',pts _prefix='dtr_txt_4idl/dtr.' Giving screen output: 1991 grid 1991 non-zero -0.3378 1.6587 1.7496 cells= 3546 And files such as: -rw------- 1 f098 cru 248832 Sep 21 13:39 idlbin_dtr/idlbin_dtr1991

And.. at this point, I read the ReadMe file properly. I should be gridding at 2.5 degrees not 0.5 degrees! For some reason, secondary variables are not derived from the 0.5 degree grids. Re-did all three generations (the sample command lines and outputs above have been altered to reflect this, to avoid confusion).

So, to the generation of the synthetic grids.

Tried running frs_gts_tdm but it complained it couldn't find the normals file:

IDL> frs_gts_tdm,dtr_prefix='idlbin_dtr/idlbin_dtr',tmp_prefix='idlbin_tmp/idlbin_tmp',19 01,2002,outprefix='syngrid_frs/syngrid_frs' % Compiled module: FRS_GTS_TDM. % Attempt to call undefined procedure/function: 'FRS_GTS_TDM'. % Execution halted at: $MAIN$

IDL>

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frs_gts,dtr_prefix='idlbin_dtr/idlbin_dtr',tmp_prefix='idlbin_tmp/idlbin_tmp',1901,2 002,outprefix='syngrid_frs/syngrid_frs' % Compiled module: RDBIN. % Compiled module: STRIP. ls: /home/cru/f098/m1/gts/frs/glo/glo.frs.norm not found ls: /home/cru/f098/m1/gts/frs/glo/glo.frs.norm.Z not found ls: /home/cru/f098/m1/gts/frs/glo/glo.frs.norm.gz not found % READF: End of file encountered. Unit: 99, File: foo % Execution halted at: RDBIN 25 /cru/u2/f080/Idl/rdbin.pro % FRS_GTS 18 /cru/cruts/fromdpe1a/code/idl/pro/frs_gts_tdm.pro % $MAIN$

IDL>

However when I eventually found what I hope is the normals file:

/cru/cruts/fromdpe1a/data/grid/twohalf/glo25.frs.6190

..and altered the IDL prog to read it.. same error! Turns out it's preferring to pick up Mark N's version so tried explicitly compiling, ('.compile xxxxxx.pro') that worked, in that the error changed:

IDL> frs_gts,dtr_prefix='idlbin_dtr/idlbin_dtr',tmp_prefix='idlbin_tmp/idlbin_tmp',1901,2 002,outprefix='syngrid_frs/syngrid_frs' % Compiled module: RDBIN. % Compiled module: STRIP.
yes % Variable is undefined: NF. % Execution halted at: RDBIN 68 /cru/u2/f080/Idl/rdbin.pro % FRS_GTS 21 /cru/cruts/fromdpe1a/code/idl/pro/frs_gts_tdm.pro % $MAIN$

IDL>

So what is this mysterious variable 'nf' that isn't being set? Well strangely, it's in Mark N's 'rdbin.pro'. I say strangely because this is a generic prog that's used all over the place! Nonetheless it does have what certainly looks like a bug:

38 if keyword_set(gridsize) eq 0 then begin 39 info=fstat(lun) 40 if keyword_set(seas) then info.size=info.size*2.0 41 if keyword_set(ann) then info.size=info.size*12.0 42 nlat=sqrt(info.size/48.0) 43 gridsize=180.0/nlat 44 if keyword_set(quiet) eq 0 then print,'filesize=',info.size 45 if keyword_set(quiet) eq 0 then print,'gridsize=',gridsize 46 endif 47 if keyword_set(had) then had=1 else had=0 48 if keyword_set(echam) then echam=1 else echam=0 49 if keyword_set(gfdl) then gfdl=1 else gfdl=0 50 if keyword_set(ccm) then ccm=1 else ccm=0 51 if keyword_set(csiro) then csiro=1 else csiro=0 52 ;create array to read data into 53 if keyword_set(seas) then nf=6 else nf=12 54 if keyword_set(ann) then nf=1 55 defxyz,lon,lat,gridsize,grid=grid,nf=nf,had=had,echam=echam,gfdl=gfdl,ccm=ccm,csiro= csiro 56 if keyword_set(quiet) eq 0 then help,grid 57 grid=fix(grid) 58 ;read data

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59 readu,lun,grid 60 close,lun 61 spawn,string('rm -f ',fff) 62 endif else begin 63 openr,lun,fname 64 ; check file size and work out grid spacing if gridsize isn't set 65 if keyword_set(gridsize) eq 0 then begin 66 info=fstat(lun) 67 if keyword_set(quiet) eq 0 then print,'yes' 68 nlat=sqrt((info.size/nf)/4.0) 69 gridsize=180.0/nlat 70 if keyword_set(quiet) eq 0 then print,'filesize=',info.size 71 if keyword_set(quiet) eq 0 then print,'gridsize=',gridsize 72 endif 73 if keyword_set(seas) then nf=6.0 else nf=12.0 74 if keyword_set(ann) then nf=1

In other words, 'nf' is set in the first conditional set of statements, but in the alternative (starting on #62) it is only set AFTER it's used (set #73,#74; used #68). So I shifted #73 and #74 to between #64 and #65, and.. with precompiling to pick up the local version of rdbin, too.. it worked! Er, perhaps.

Lots of screen output, and lots of files. A set of synthetic grids in 'syngrid_frs/' as requested, typically:

-rw------- 1 f098 cru 20816 Sep 17 22:10 syngrid_frs/syngrid_frs1991.Z

..but also a set of some binariy files in the working directory! They look like this:

-rw------- 1 f098 cru 51542 Sep 17 22:10 glo.frs.1991.Z

Having read the program it looks as though the latter files are absolutes, whereas the former are anomalies. With this in mind, they are renamed:

glo.frs.1991 -> glo.frs.abs.1991

..and put into folder syngrid_frs_abs/

Then - a real setback. Looked for a database file for frost.. nothing. Is this a real secondary parameter? Answer: yes. Further digging revealed that quick_interp_tdm2.pro has a 'nostn' command line option. It's undocumented, as usual, but it does seem to avoid the use of the 'pts_prefix' option.. so I set it, and it at least *ran* for the full term (though very slow compared to primary variables)!

IDL> quick_interp_tdm2,1901,2002,'glo_frs_grids/frs.grid.',750,gs=0.5,dumpglo='dumpglo',n ostn=1,synth_prefix='syngrid_frs/syngrid_frs'

It does produce output grids. Without converting to absolutes with the normals file, it's hard to know if they're realistic.

Then, I moved on to rd0 (wet-day frequency). This time, when I searched for the normals files required ('glo.pre.norm' and 'glo.rd0.norm'), I could not (as before) find exact matches. The difference this time is that the program checks that the normals file supplied is a 0.5-degree grid, so glo25.pre.6190 failed. This implies to me that my approach to frs (above) was wrong as well. Where is the documenatation to explain all this?!

Finally - a breakthrough. A search of Mark New's old directory hierarchy revealed what look like the required files:

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crua6[/cru/mark1/f080] find . -name 'glo.*.norm*' ./gts/cld/glo/glo.cld.norm.Z ./gts/dtr/glo_old/glo.dtr.norm.Z ./gts/frs/glo.frs.norm.Z ./gts/frs/glo/glo.frs.norm.Z find: cannot open < ./gts/frs/glo_txt > ./gts/pre/glo_quick_abs/glo.pre.norm.Z ./gts/pre/glo_quick_log/glo.pre.norm.Z ./gts/pre/glo_spl/glo.pre.norm.Z find: cannot open < ./gts/pre_perc/station_list > ./gts/rad/glo/glo.rad.norm.Z ./gts/rd0/glo/glo.rd0.norm.Z ./gts/rd0/glo_old/glo.rd0.norm.Z ./gts/sunp/glo/glo.sunp.norm ./gts/sunp/means/glo.sunp.norm.Z ./gts/tmp/glo/glo.tmp.norm.Z ./gts/tmp/glo_old/glo.tmp.norm.Z find: cannot open < ./gts/tmp/station_list > ./gts/vap/glo/glo.vap.norm.Z ./gts/wnd/glo/glo.wnd.norm.Z

A listing of /cru/mark1/f080/gts gives:

drwxr-x--- 2 f080 cru 1024 Sep 12 2005 cdrom drwxr-x--- 10 f080 cru 57344 Nov 1 2001 cld drwxr-xr-x 19 f080 cru 24576 Feb 27 2001 dtr drwxr-x--- 2 f080 cru 8192 Feb 25 1998 elev drwxr-x--- 2 f080 cru 8192 Jun 8 1998 euroclivar -rw-r----- 1 f080 cru 0 Aug 3 1999 foo drwxr-x--- 6 f080 cru 8192 Aug 6 2002 frs -rw-r-x--- 1 f080 cru 438 May 12 1998 gts.errors -rw-r----- 1 f080 cru 10 Jul 21 1999 in drwxr-x--- 5 f080 cru 8192 Jan 6 1999 jiang drwxr-x--- 2 f080 cru 8192 Apr 7 1998 landsea -rw-r----- 1 f080 cru 240 May 12 1998 normal.errors drwxr-x--- 5 f080 cru 8192 Aug 6 2002 plots drwxr-xr-x 12 f080 cru 106496 May 22 2000 pre drwxr-x--- 9 f080 cru 114688 Aug 6 2002 pre_perc drwxr-x--- 4 f080 cru 1024 Jan 6 1999 rad drwxr-x--x 6 f080 cru 8192 Nov 1 2001 rd0 -rwxr-xr-- 1 f080 cru 1779 Dec 5 1997 readme.txt drwxr-x--- 8 f080 cru 1024 Apr 5 2000 reg_series drwxr-x--- 3 f080 cru 1024 Oct 18 1999 reh drwxr-x--- 2 f080 cru 8192 Jan 19 2000 scengen drwxr-x--- 5 f080 cru 24576 Nov 5 1998 sunp drwxr-x--- 2 f080 cru 1024 Aug 6 2002 test drwxr-x--- 4 f080 cru 1024 Aug 3 1999 tmn drwxr-xr-x 20 f080 cru 122880 Mar 19 2002 tmp drwxr-x--- 4 f080 cru 1024 Aug 3 1999 tmx drwxr-x--- 6 f080 cru 1024 Jul 8 1998 ukcip drwxr-x--- 5 f080 cru 8192 Nov 5 2001 vap drwxr-x--- 4 f080 cru 1024 Jul 2 1998 wnd

And a listing of, for example, the 'frs' directory:

drwxr-x--- 2 f080 cru 16384 Jul 18 2002 glo -rw-r-x--- 1 f080 cru 433393 Aug 12 1998 glo.frs.1961.Z -rw-r-x--- 1 f080 cru 321185 Aug 12 1998 glo.frs.ano.1961.Z -rw-r-x--- 1 f080 cru 740431 Aug 12 1998 glo.frs.norm.Z drwxr-xr-x 2 f080 cru 16384 Jul 27 1999 glo25 drwx------ 2 f080 cru 8192 Jul 18 2002 glo_txt drwxr-xr-x 2 f080 cru 8192 Aug 28 1998 means

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So, the following were copied to the working area:

cp /cru/mark1/f080/gts/frs/glo.frs.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/ cp /cru/mark1/f080/gts/cld/glo/glo.cld.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/ cp /cru/mark1/f080/gts/dtr/glo_old/glo.dtr.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/

precip looked like it might be a problem (3 matching files, see above), but on investigation they were found to be identical! Wonderful.

cp /cru/mark1/f080/gts/pre/glo_quick_log/glo.pre.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/ cp /cru/mark1/f080/gts/rad/glo/glo.rad.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/ cp /cru/mark1/f080/gts/rd0/glo/glo.rd0.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/

There were two 'sunp' norm files, but one was 0 bytes in length.

cp /cru/mark1/f080/gts/sunp/means/glo.sunp.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/ cp /cru/mark1/f080/gts/tmp/glo/glo.tmp.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/ cp /cru/mark1/f080/gts/vap/glo/glo.vap.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/ cp /cru/mark1/f080/gts/wnd/glo/glo.wnd.norm.Z /cru/cruts/rerun1/data/cruts/rerun_synth/

The synthetics generation was then re-run for frs (records above have been modified to reflect this).

Next, rd0. Synthetics generated OK..

IDL> rd0_gts,1901,2002,1961,1990,outprefix="syngrid_rd0/syngrid_rd0",pre_prefix="idlbin_p re/idlbin_pre"

..until the end:

2001
yes filesize= 248832 gridsize= 2.50000 2002
yes filesize= 248832 gridsize= 2.50000 % Program caused arithmetic error: Floating divide by 0 % Program caused arithmetic error: Floating illegal operand

IDL>

However, all synthetic grids appear to have been written OK, including 2002.

Grid generation proceeded without error:

IDL> quick_interp_tdm2,1901,2002,'glo_rd0_grids/rd0.grid.',450,gs=0.5,dumpglo='dumpglo',n ostn=1,synth_prefix='syngrid_rd0/syngrid_rd0'

Onto vapour pressure, and the crunch. For here, the recommended program for

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synthetic grid production is 'vap_gts_anom.pro'. In fact, there is no sign of a 'vap_gts_tdm.pro'. And, in the program notes, it reads:

; required inputs are: ; ** vapour pressure and temperature normals on 2.5deg grid ; (these come ready-supplied for a 1961-90 normal period) ; ** temp and dtr monthly anomalies on 2.5deg grid, including normal period

So, we face a situation where some synthetics are built with 0.5-degree normals, and others are built with 2.5-degree normals. I can find no documentation of this. There are '*_anom.pro' versions of the frs and rd0 programs, both of which use 2.5-degree normals, however they are dated Jan 2004, and Tim's Read_Me (which refers to the '*_tdm.pro' 0.5-degree versions) is dated end March 2004, so we have to assume these are his best suggestions.

The 2.5 normals are found here:

> ls -l /cru/cruts/fromdpe1a/data/grid/twohalf/ total 1248 -rwxr-xr-x 1 f098 cru 248832 Jan 9 2004 glo25.frs.6190 -rwxr-xr-x 1 f098 cru 248832 Jan 8 2004 glo25.pre.6190 -rwxr-xr-x 1 f098 cru 248832 Jan 8 2004 glo25.rd0.6190 -rwxr-xr-x 1 f098 cru 248832 Jan 7 2004 glo25.tmp.6190 -rwxr-xr-x 1 f098 cru 248832 Jan 6 2004 glo25.vap.6190 -rwxr-xr-x 1 f098 cru 86 Feb 25 2004 readme.txt

readme.txt: 2.5deg climatology files Tim Mitchell, 25.2.04

These are in Mark New's binary format (end)

Set up the required inputs, and ran it:

IDL> vap_gts_anom,dtr_prefix='idlbin_dtr/idlbin_dtr',tmp_prefix='idlbin_tmp/idlbin_tmp',1 901,2002,outprefix='syngrid_vap/syngrid_vap',dumpbin=1

Producing screen output like this: 1991 vap (x,s2,<<,>>): 0.000493031 0.000742087 -0.0595093 1.86497

And output files like this: -rw------- 1 f098 cru 248832 Sep 22 10:56 syngrid_vap/syngrid_vap1991

On, without further ado, to the gridding. For this secondary, there *are* database files, so the 'nostn' option is not used, and anomdtb.f is wheeled out again to construct .txt files for the run:

crua6[/cru/cruts/rerun1/data/cruts/rerun_vap] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:
.vap > Select the .cts or .dtb file to load: vap.0311181410.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23

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> Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 8 > Select the generic .txt file to save (yy.mm=auto): vap.txt > Select the first,last years AD to save: 1901,2002 > Operating... Values loaded: 1239868112; No. Stations: 7691 > NORMALS MEAN percent STDEV percent > .dtb 887754 46.9 > .cts 34175 1.8 921929 48.7 > PROCESS DECISION percent %of-chk > no lat/lon 105 0.0 0.0 > no normal 969384 51.3 51.3 > out-of-range 2661 0.1 0.3 > duplicated 25557 1.4 2.8 > accepted 893711 47.3 > Dumping years 1901-2002 to .txt files...

crua6[/cru/cruts/rerun1/data/cruts/rerun_vap]

Moved straight onto the gridding, which, of course, failed:

IDL> quick_interp_tdm2,1901,2002,'glo_vap_grids/vap.grid.',1000,gs=0.5,dumpglo='dumpglo', synth_prefix='syngrid_vap/syngrid_vap',pts_prefix='../rerun_vap/vap_txt_4idl/vap.' Defaults set 1901 1902 % Array dimensions must be greater than 0. % Execution halted at: QUICK_INTERP_TDM2 88 /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro % QUICK_INTERP_TDM2 88 /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro % $MAIN$

IDL>

This turns out to be because of the sparcity of VAP station measurements in the early years. The program cannot handle anom files of 0 length, even though it checks the length! Bizarre. The culprit is 'vap.1902.03.txt', the only month to have no station reading at all (45 months have only 1 however). I decided to mod the program to use the 'nostn' option if the length is 0. Hope that's right - the synthetics are read in first and the station data is added to that grid so this should be OK.. and it looks OK:

IDL> quick_interp_tdm2,1901,2002,'vap.grid.',1000,gs=0.5,dumpglo='dumpglo',synth_prefix=' syngrid_vap/syngrid_vap',pts_prefix='../rerun_vap/vap_txt_4idl/vap.' % Compiled module: GLIMIT. Defaults set 1901 1902 no stations found in: ../rerun_vap/vap_txt_4idl/vap.1902.03.txt 1903

(..etc..)

Pause for reflection: the list of CRU_TS_2.1 parameters is as follows: pre primary, done

Page 15HARRY_READ_ME

tmp primary, done tmx derived, not done tmn derived, not done dtr primary, done vap secondary, done cld/spc secondary, not done wet secondary, done frs secondary, done

Now the interesting thing is that the 'Read Me' file for gridding only mentions frs, rd0 (which I'm assuming == wet) and vap. How, then, do I produce cld/spc and the two derived vars??

Well, there's a /cru/cruts/fromdpe1a/code/idl/pro/cal_cld_gts_tdm.pro, also: /cru/cruts/fromdpe1a/code/idl/pro/cloudcorrspc.pro /cru/cruts/fromdpe1a/code/idl/pro/cloudcorrspcann.pro /cru/cruts/fromdpe1a/code/idl/pro/cloudcorrspcann9196.pro

Loading just the first program opens up another huge can o' worms. The program description reads:

pro cal_cld_gts_tdm,dtr_prefix,outprefix,year1,year2,info=info ; calculates cld anomalies using relationship with dtr anomalies ; reads coefficients from predefined files (*1000) ; reads DTR data from binary output files from quick_interp_tdm2.pro (binfac=1000) ; creates cld anomaly grids at dtr grid resolution ; output can then be used as dummy input to splining program that also ; includes real cloud anomaly data

So, to me this identifies it as the program we cannot use any more because the coefficients were lost. As it says in the gridding read_me:

Bear in mind that there is no working synthetic method for cloud, because Mark New lost the coefficients file and never found it again (despite searching on tape archives at UEA) and never recreated it. This hasn't mattered too much, because the synthetic cloud grids had not been discarded for 1901-95, and after 1995 sunshine data is used instead of cloud data anyway.

But, (Lord how many times have I used 'however' or 'but' in this file?!!), when you look in the program you find that the coefficient files are called:

rdbin,a,'/cru/tyn1/f709762/cru_ts_2.0/_constants/_7190/a.25.7190',gridsize=2.5 rdbin,b,'/cru/tyn1/f709762/cru_ts_2.0/_constants/_7190/b.25.7190',gridsize=2.5

And, if you do a search over the filesystems, you get:

crua6[/cru/cruts] ls fromdpe1a/data/grid/cru_ts_2.0/_makecld/_constants/_7190/spc2cld/_ann/ a.25.01.7190.glo.Z a.25.05.7190.glo.Z a.25.09.7190.glo.Z a.25.7190.eps.Z b.25.04.7190.glo.Z b.25.08.7190.glo.Z b.25.12.7190.glo.Z a.25.02.7190.glo.Z a.25.06.7190.glo.Z a.25.10.7190.glo.Z b.25.01.7190.glo.Z b.25.05.7190.glo.Z b.25.09.7190.glo.Z b.25.7190.eps.Z a.25.03.7190.glo.Z a.25.07.7190.glo.Z a.25.11.7190.glo.Z b.25.02.7190.glo.Z b.25.06.7190.glo.Z b.25.10.7190.glo.Z a.25.04.7190.glo.Z a.25.08.7190.glo.Z a.25.12.7190.glo.Z b.25.03.7190.glo.Z b.25.07.7190.glo.Z b.25.11.7190.glo.Z crua6[/cru/cruts] ls fromdpe1a/data/grid/cru_ts_2.0/_makecld/_constants/_7190/spc2cld/_mon/ a.25.01.7190.glo.Z a.25.05.7190.glo.Z a.25.09.7190.glo.Z a.25.7190.eps.Z b.25.04.7190.glo.Z b.25.08.7190.glo.Z b.25.12.7190.glo.Z a.25.02.7190.glo.Z a.25.06.7190.glo.Z a.25.10.7190.glo.Z b.25.01.7190.glo.Z b.25.05.7190.glo.Z b.25.09.7190.glo.Z b.25.7190.eps.Z

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a.25.03.7190.glo.Z a.25.07.7190.glo.Z a.25.11.7190.glo.Z b.25.02.7190.glo.Z b.25.06.7190.glo.Z b.25.10.7190.glo.Z a.25.04.7190.glo.Z a.25.08.7190.glo.Z a.25.12.7190.glo.Z b.25.03.7190.glo.Z b.25.07.7190.glo.Z b.25.11.7190.glo.Z

So.. we don't have the coefficients files (just .eps plots of something). But what are all those monthly files? DON'T KNOW, UNDOCUMENTED. Wherever I look, there are data files, no info about what they are other than their names. And that's useless.. take the above example, the filenames in the _mon and _ann directories are identical, but the contents are not. And the only difference is that one directory is apparently 'monthly' and the other 'annual' - yet both contain monthly files.

Lots of further investigation.. probably the most useful program found is cal_cld_gts_tdm.pro, the description of which reads as follows:

pro cal_cld_gts_tdm,dtr_prefix,outprefix,year1,year2,info=info ; calculates cld anomalies using relationship with dtr anomalies ; reads coefficients from predefined files (*1000) ; reads DTR data from binary output files from quick_interp_tdm2.pro (binfac=1000) ; creates cld anomaly grids at dtr grid resolution ; output can then be used as dummy input to splining program that also ; includes real cloud anomaly data

It also tellingly contains: ; unnecessary because 61-90 normals have already been created ; print, "@@@@@ looking for 2.5 deg DTR 1961-90 @@@@@" ; mean_gts,'~/m1/gts/dtr/glo25/glo25.dtr.',nor1,nor2 ; mean_gts_tdm,'/cru/mark1/f080/gts/dtr/glo25/glo25.dtr.',nor1,nor2 ;print, "@@@@@ looking for 2.5 deg DTR normal @@@@@"

;; rdbin,dtrnor,'~/m1/gts/dtr/glo25/glo25.dtr.'+string(nor1-1900,nor2-1900,form='(2i2.2 )') ;dtrnorstr='/cru/mark1/f080/gts/dtr/glo25/glo25.dtr.'+string(nor1-1900,nor2-1900,for m='(2i2.2)') ;rdbin,dtrnor,dtrnorstr

The above has seemingly been replaced with: rdbin,a,'/cru/tyn1/f709762/cru_ts_2.0/_constants/_7190/a.25.7190',gridsize=2.5 rdbin,b,'/cru/tyn1/f709762/cru_ts_2.0/_constants/_7190/b.25.7190',gridsize=2.5

These are the files that have been lost according to the gridding read_me (see above).

The conclusion of a lot of investigation is that the synthetic cloud grids for 1901-1995 have now been discarded. This means that the cloud data prior to 1996 are static.

Edit: have just located a 'cld' directory in Mark New's disk, containing over 2000 files. Most however are binary and undocumented..

Eventually find fortran (f77) programs to convert sun to cloud:

sh2cld_tdm.for converts sun hours monthly time series to cloud percent sp2cld_m.for converts sun percent monthly time series to cloud oktas

There are also programs to convert sun parameters:

sh2sp_m.for sun hours to sun percent sh2sp_normal.for sun hours monthly .nrm to sunshine percent sh2sp_tdm.for sun hours monthly time series to sunshine percent

AGREED APPROACH for cloud (5 Oct 06).

Page 17HARRY_READ_ME

For 1901 to 1995 - stay with published data. No clear way to replicate process as undocumented.

For 1996 to 2002: 1. convert sun database to pseudo-cloud using the f77 programs; 2. anomalise wrt 96-00 with anomdtb.f; 3. grid using quick_interp_tdm.pro (which will use 6190 norms); 4. calculate (mean9600 - mean6190) for monthly grids, using the published cru_ts_2.0 cloud data; 5. add to gridded data from step 3.

This should approximate the correction needed.

On we go.. firstly, examined the spc database.. seems to be in % x10. Looked at published data.. cloud is in % x10, too. First problem: there is no program to convert sun percentage to cloud percentage. I can do sun percentage to cloud oktas or sun hours to cloud percentage! So what the hell did Tim do?!! As I keep asking.

Examined the program that converts sun % to cloud oktas. It is complicated! Have inserted a line to multiple the result by 12.5 (the result is in oktas*10 and ranges from 0 to 80, so the new result will range from 0 to 1000).

Next problem - which database to use? The one with the normals included is not appropriate (the conversion progs do not look for that line so obviously are not intended to be used on +norm databases). The non normals databases are either Jan 03 (in the '_ateam' directory) or Dec 03 (in the regular database directory). The newer database is smaller! So more weeding than planting in 2003. Unfortunately both databases contain the 6190 normals line, just unpopulated. So I willgo with the 'spc.0312221624.dtb' database, and modify the alreadymodified conversion program to process the 6190 line.

Then - comparing the two candidate spc databases:

spc.0312221624.dtb spc.94-00.0312221624.dtb

I find that they are broadly similar, except the normals lines (which both start with '6190') are very different. I was expecting that maybe the latter contained 94-00 normals, what I wasn't expecting was that thet are in % x10 not %! Unbelievable - even here the conventions have not been followed. It's botch after botch after botch. Modified the conversion program to process either kind of normals line.

Decided to go with the 'spc.94-00.0312221624.dtb' database, as it hopefully has some of the 94-00 normals in. I just wish I knew more.

Conversion was hampered by the discovery that some stations have a mix of % and % x10 values! So more mods to Hsp2cldp_m.for. Then conversion, producing cldfromspc.94000312221624.dtb. Copied the .dts file across as is, not sure what it does unfortunately (or can't remember!).

After conversion, ran anomdtb:

crua6[/cru/cruts/rerun1/data/cruts/rerun_cld] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required: .cld

Page 18HARRY_READ_ME

> Select the .cts or .dtb file to load: cldfromspc.94000312221624.dtb

> Specify the start,end of the normals period: 1994,2000 > Specify the missing percentage permitted: 25 > Data required for a normal: 6 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 8 > Select the generic .txt file to save (yy.mm=auto): cldfromspc.txt > Select the first,last years AD to save: 1994,2002 > Operating...

> .cts 96309 19.6 280712 57.2 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 209619 42.8 42.8 > out-of-range 177298 36.2 63.2 > duplicated 154 0.0 0.1 > accepted 103260 21.1 > Dumping years 1994-2002 to .txt files...

crua6[/cru/cruts/rerun1/data/cruts/rerun_cld]

Then ran quick_interp_tdm2:

IDL> .compile /cru/cruts/fromdpe1a/code/idl/pro/quick_interp_tdm2.pro % Compiled module: QUICK_INTERP_TDM2. IDL> .compile /cru/cruts/fromdpe1a/code/idl/pro/rdbin.pro % Compiled module: RDBIN.

IDL> quick_interp_tdm2,1994,2002,'glo_from_idl/cld.',600,gs=0.5,pts_prefix='txt_4_idl/cld fromspc.',dumpglo='dumpglo' Defaults set 1994 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1995 1996 1997 1998 1999 2000 2001 2002

IDL>

Tadaa: .glo files produced for 1994 to 2002.

Then retracked to produce regular 0.5-degree grids for dtr (having only produced 2.5-degree binaries for synthetics earlier):

IDL>

Page 19HARRY_READ_ME

quick_interp_tdm2,1901,2002,'glo_dtr_grids/dtr.',750,gs=0.5,pts_prefix='dtr_txt_4idl /dtr.',dumpglo='dumpglo'

That went off without any apparent hitches, so I wrote a fortran prog, 'maxminmaker.for', to produce tmn and tmx grids from tmp and dtr. It ran.

However - yup, more problems - when I checked the inputs and outputs I found that in numerous instances there was a value for mean temperature in the grid, with no corresponding dtr value. This led to tmn = tmx = tmp for thos cells.

NOT GOOD.

Actually, what was NOT GOOD was my grasp of context. Oh curse this poor memory! For the IDL gridding program produces ANOMALIES not ACTUALS.

Wrote a program, 'glo2abs.for' does a file-for-file conversion of .glo files (as produced by quick_interp_tdm2.pro) to absolute-value files (also gridded and with headers). After some experiments realised that the .glo anomalies are in degrees, but the normals are in 10ths of a degree :-)

Produced absolutes for TMP. Then wrote a program, 'cmpcruts.for', to compare the absolute grids with the published cru_ts_2.10 data. The comparison simply measures absolute differences between old and new, and categorises as either (1) identical, (2) within 0.5 degs, (3) within 1 deg, (4) over 1 deg apart. Results for temperature (TMP):

Identical <0.5deg 0.5-1deg >1deg 30096176 48594200 2755281 1076423

And for temperature range (DTR):

45361058 31267870 3893754 1999398

These are very promising. The vast majority in both cases are within 0.5 degrees of the published data. However, there are still plenty of values more than a degree out.

The total number of comparisons is 67420*102*12 = 82,522,080

It seems prudent to add percentage calculations..

TMP: Final Diff Totals: 30096176 48594200 2755281 1076423 Percentages: 36.47 58.89 3.34 1.30

TMP has a comforting 95%+ within half a degree, though one still wonders why it isn't 100% spot on..

DTR: Final Diff Totals: 45361058 31267870 3893754 1999398 Percentages: 54.97 37.89 4.72 2.42

DTR fares perhaps even better, over half are spot-on, though about 7.5% are outside a half.

However, it's not such good news for precip (PRE): Final Diff Totals: 11492331 21163924 9264554 40601271 Percentages: 13.93 25.65 11.23 49.20

21. A little experimentation goes a short way..

I tried using the 'stn' option of anomdtb.for. Not completely sure what it's supposed to do, but no matter as it didn't work:

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crua6[/cru/cruts/rerun1/data/cruts/rerun_pre] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:
.pre > Will calculate percentage anomalies. > Select the .cts or .dtb file to load: pre.0312031600H.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 5 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 4 > Check for duplicate stns after anomalising? (0=no,>0=km range) 8 > Select the .stn file to save: pre.fromanomdtb.stn > Enter the correlation decay distance: 450 > Submit a grim that contains the appropriate grid. > Enter the grim filepath: cru_ts_2_10.1961-1970.pre

> Grid dimensions and domain size: 720 360 67420 > Select the first,last years AD to save: 1901,2002 > Operating...

> NORMALS MEAN percent STDEV percent > .dtb 2635548 29.6 > .cts 4711327 52.8 7325296 82.2 > PROCESS DECISION percent %of-chk > no lat/lon 20761 0.2 0.2 > no normal 1585342 17.8 17.8 > out-of-range 20249 0.2 0.3 > duplicated 317035 3.6 4.3 > accepted 6972308 78.2 > Calculating station coverages... > ##### WithinRange: Alloc: DataB ##### forrtl: severe (174): SIGSEGV, segmentation fault occurred crua6[/cru/cruts/rerun1/data/cruts/rerun_pre]

..knowing how long it takes to debug this suite - the experiment endeth here. The option (like all the anomdtb options) is totally undocumented so we'll never know what we lost.

22. Right, time to stop pussyfooting around the niceties of Tim's labyrinthine software suites - let's have a go at producing CRU TS 3.0! since failing to do that will be the definitive failure of the entire project..

Firstly, we need to identify the updated data files. I acquired the following:

iran_asean_GHCN_WWR-CD_save50_CLIMAT_MCDW_updat_merged renamed to pre.0611301502.dat newbigfile0606.dat renamed to tmp.0611301507.dat glseries_tmn_final_merged renamed to tmn.0611301516.dat

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glseries_tmx_final_merged renamed to tmx.0611301516.dat anders9106m.dat renamed to tmp9106.0612011708.dat

..and established a directory hierarchy under /cru/cruts/version_3_0

Next step, convert the various db formats to the CRU TS one. Made a visual comparison which indicated that it would work. Unfortunately it will mean losing the 'extra' fields that have been tacked onto the headers willy-nilly as they are undocumented. Furthermore the two extra fields in the CRU TS format are undocumented, as far as I can see! So I wrote headergetter.for to produce stats on the CRU TS headers. It looks for violations of the mandatory blank spaces, and for variations in the two extra fields. Sample output for temperature and precip:

Header report for tmp.0311051552.dtb Produced by headgetter.for Total Records Read: 12155

BLANKS (expected at 8,14,21,26,47,61,66,71,78) position missed 8 0 14 0 21 0 26 0 47 0 61 0 66 0 71 0 78 2

EXTRA FIELD 1 (72:77) type detected counted Missing Value Code 12155 Possible F.P. Value 0 Possible Exp. Value 0 Integer Value Found 0 Real Value Found 0 Unidentifiable 0

EXTRA FIELD 2 (79:86) type detected counted Missing Value Code 709 Possible F.P. Value 697 Possible Exp. Value 0 Integer Value Found 10749 Real Value Found 0 Unidentifiable 0

ENDS

Header report for pre.0312031600.dtb Produced by headgetter.for Total Records Read: 12732

BLANKS (expected at 8,14,21,26,47,61,66,71,78) position missed 8 0 14 0 21 0 26 0 47 0 61 0 66 0

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71 0 78 154

EXTRA FIELD 1 (72:77) type detected counted Missing Value Code 12732 Possible F.P. Value 0 Possible Exp. Value 0 Integer Value Found 0 Real Value Found 0 Unidentifiable 0

EXTRA FIELD 2 (79:86) type detected counted Missing Value Code 3635 Possible F.P. Value 437 Possible Exp. Value 0 Integer Value Found 8660 Real Value Found 0 Unidentifiable 0

ENDS

As can be seen, there are no unidentifiable headers - hurrah! - but quite a few violations of the boundary between the two extra fields, particularly in the precip database. On examination, the culprits are all African stations. The two tmp exceptions:

641080 -330 1735 324 BANDUNDU DEM REP CONGO 1961 1990 -99908 642200 -436 1525 445 KINSHASA/BINZA DEM REP CONGO 1960 1990 -99920

And samples of the pre exceptions:

-656002 698 -958 150 SUAKOKO LIBERIA 1951 1970 -999123008050 -655327 727 -723 350 KOUIBLY IVORY COAST 1977 1990 -999109001290 -655001 1320 -235 332 GOURCY BURKINA FASO 1956 1980 -999120001240 -618504 788 -1118 -999 KENEMA/FARM SIERRA LEONE 1951 1972 -999139003500 -612067 1407 -307 253 KORO MALI 1958 1989 -999127002650

So the first extra field is apparently unused! It would be a handy place for the 6-character data-code and valid-start-year from the temperature db.

On to a more detailed look at the cru precip format; not sure whether there are two extra fields or one, and what the sizes are. A quick hack through the headers is not pleasing. There appears to be only one field, but it can have up to nine (9) digits in it, and at least three missing value codes:

6785300-1863 2700 1080HWANGE/N.P.A. ZIMBABWE 19621996 40 8100100 680 -5820 2GEORGETOWN GUYANA 18462006 -99 6274000 1420 2460 1160KUTUM SUDAN 19291990 194 6109200-9999-99999 -999UNKNOWN NIGER 19891989 -999 6542000 945 -2 197YENDI GHANA 19071997 8010 6544200 672 -160 293KUMASI GHANA 19062006 17009 6122306 1670 -299 267KABARA MALI 19231989 270022 6193128 32 672 -999SAO TOME SAO TOME 19391973 8888888 6266000 1850 3180 249KARIMA SUDAN 19172006 18315801 6109905 1208 -367 315OUARKOYE BURKINA FASO 19601980 120002470

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*unimpressed*

This is irritating as it means precip has only 9 fields and I can't do a generic mapping from any cru format to cru ts.

As a glutton for punishment I then looked at the tmin/tmax db format. Looks like two extra fields (i6,i7) with mvcs of 999999 and 8888888 respectively. However *sigh* inspection reveals the following two possibilities:

851300 3775 -2568 17PONTA DELGADA PORTUGAL 18652004 9999998888888 851500 3697 -2517 100SANTA MARIA A ACORES 19542006 -77777 8888888

Isn't that marvellous? These can't even be read with a consistent header format!

So, the approach will be to read exactly ONE extra field. For cru tmp that will be the i2+i4 anders/best-start codes as one. For cru pre it will be the amazing multipurpose, multilength field. For cru tmnx it will be the first field, which is at least stable at i6.

Conversions/corrections performed:

Temperature

Converted tmp.0611301507.dat to tmp.0612081033.dat

Found one corrupted station name:

BEFORE 911900 209 1564 20 HI*KAHULUI WSO (PUU NENE) 1954 1990 101954 -999.00

AFTER 911900 209 1564 20 KAHULUI ARPT/MAUI HAWAII 1954 1990 101954 -999.00

Precipitation

Converted pre.0611301502.dat to pre.0612081045.dat

Found one corrupted station name:

BEFORE 4125600 2358 5828 15SEEB AP./=MUSCAT*0.9OMAN 18932006 301965

AFTER 4125600 2358 5828 15 SEEB INTL/MUSCAT OMAN 1893 2006 -999 -999.00

(DL later reported that the name wasintended to signify that the data had been corrected by a factor of 0.9 when data from another station was incorporated to extend the series - this was Mike Hulme's work)

Write db2dtb.for, which converts any of the CRU db formats to the CRU TS format.

Started work on mergedb.for, which should merge a primary database with and incoming database of the same (CRU TS) format. Quite complicated. No operator interventions, just a log file of failed attempts - but hooks left in for op sections in case this turns out to be the main programmatic deliverable to BADC!

23. Interrupted work on mergedb.for in order to trial a precip gridding for 3.0. This required another new proglet, addnormline.for, which adds a normals line below each header. It fills in the normals values if the condisions are met (75% of values, or 23 for the 30 year period).

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Initial results promising.. ran it for precip, it added normals lines OK, a total of 15942 with 6003 missing value lines. No errors, and no ops interventions because the file didn't have normals lines before!

'Final' precip file: pre.0612151458.dtb

Tried running anomdtb.f90.. failed because it couldn't find the .dts file! No matter that it doesn't need it - argh!

Examined existing .dts files.. not sure what they're for. Headers are identical to the .dtb file, all missing values are retained, all other values are replaced with one of several code numbers, no idea what they mean.

Wrote 'falsedts.for' to produce dummy .dts files with all zeros in place of real data values. Produced pre.0612151458.dts.

Added normals line, producing: pre.0612181221.dtb Re-produced matching pre.0612181221.dts file.

Tried running anomdtb.f90 again. This time it crashed at record #1096. Wrote a proglet 'findstn.for' to find the n-th station in a dtb file, pulled out 1096:

0 486 10080 1036 BUKIT LARUT MALAYSIA 1951 1988 -999 -999.00 6190 2094 2015 2874 3800 4619 3032 5604 3718 4626 5820 5035 3049 1951 3330 2530 2790 5660 4420 4030 1700 2640 8000 5950 6250 2020

(snipped normal years)

1979 110 1920 1150 5490 3140 308067100 2500 4860 4280 4960 1600

Uh-oh! That's 6.7m of rain in July 1979? Looks like a factor-of-10 problem. Confirmed with DL and changed to 6710.

Next run, crashed at #4391, CHERRAPUNJI, the wettest place in the world. So here, the high values are realistic. However I did notice that the missing value code was -10 instead of -9999! So modified db2dtb.for to fix that and re-produced the precip database as pre.0612181214.dat. This then had to have normals recalculated for it (after fixing #1096).

Finally got it through anomdtb.for AND quick_interp_tdm2 - without crashing! IDL was

even on the ball with the missing months at the end of 2006:

IDL> quick_interp_tdm2,1901,2006,'preglo/pregrid.',450,gs=0.5,dumpglo='dumpglo',pts_prefi x='preanoms/pre.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902

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1903 (etc) 2005 2006 no stations found in: preanoms/pre.2006.09.txt no stations found in: preanoms/pre.2006.10.txt no stations found in: preanoms/pre.2006.11.txt no stations found in: preanoms/pre.2006.12.txt

All good. Wrote mergegrids.for to create the more-familiar decadal and full-series files from the monthly *.glo.abs ones.

Then.. like an idiot.. I had to test the data! Duh.

Firstly, wrote mmeangrid.for and cmpmgrids.m to get a visual comparison of old and new precip grids (old being CRU TS 2.10). This showed variations in 'expected' areas where changes had been made, it the Southern tip of Greenland.

Next, Phil requested some statistical plots of percentage change in annual totals, and long-term trends. Wrote 'anntots.for' to convert monthly gridded files into yearly totals files. Then tried to write precipchecker.m to do the rest in Matlab.. it wasn't having it, OUT OF MEMORY! Bah. So wrote 'prestats.for' to calculate the final stats, for printing with an emasculated precipchecker.m. BUT.. it wouldn't work, and on investigating I found 200-odd stations with zero precipitation for the entire 1901-2006 period! Modified anntots.for to dump a single grid with those cells that remained at zero marked, then plotted.

Zero cells in North Africa and the Western coast of South America. None in the CRU TS 2.10 precip grids :-(

Next step, produce a list of cell centres of the offending cells. wrote a quick proglet, 'idzerocells.for'. Then 'getcellstations.for', which, given a CRUTS DB file and a list of lat/lon values, extracts all stations lying inside the cells listed.

Uh-oh. Looked in the new pre db and found 15 stations for 257 zero cells! They are:

6061170 2810 670 381 FT FLATTERS ALGERIA 1925 1965 -999 -999.00 6064000 2650 840 559 FORT POLIGNAC ALGERIA 1925 2006 -999 -999.00 6262000 2080 3260 470 STATION NO. 6 SUDAN 1950 1988 -999 -999.00 8450100 -810 -7900 26 TRUJILLO PERU 1961 2006 -999 -999.00 8453100 -920 -7850 10 CHIMBOTE PERU 1961 2006 -999 -999.00 8462800 -1200 -7710 13 LIMA-CALLAO/INTL.AP. PERU 1961 2006 -999 -999.00 8463100 -1210 -7700 137 LIMATAMBO/C.DE MARTE PERU 1927 1980 -999 -999.00 8469100 -1380 -7630 6 PISCO PERU 1942 2006 -999 -999.00 8540600 -1850 -7030 29 ARICA/CHACALLUTA CHILE 1903 2006 -999 -999.00 8541700 -2020 -7020 6 IQUIQUE/CAVANCHA CHILE 1886 1986 -999 -999.00 8541800 -2053 -7018 52 IQUIQUE DIEGO ARACEN CHILE 1989 2006 -999 -999.00 8700494 -707 -7957 150 CAYALTI PERU 1934 1959 -999 -999.00 8700562 -1203 -7703 137 LIMA PERU 1929 1963 -999 -999.00

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8700581 -1207 -7717 13 LA PUNTA (NA PERU 1939 1963 -999 -999.00 9932040 2810 670 381 FT FLATTER ALGERIA 1925 1965 -999 -999.00

Looked for the same zero cell stations in the old pre db (pre.0312031600.dtb) and only found 10:

-854031 -2021 -7015 5 IQUIQUE/CAVANCHA CHILE 1899 1986 -999 0.00 -843002 -1210 -7700 135 LIMATAMBO PERU 1927 1980 -999

-603550 2810 670 381 FT FLATTER ALGERIA 1925 1965 -999 -999.00 606400 2650 841 558 ILLIZI/ILLIRANE ALGERIA 1925 2002 -999 -999 626200 2075 3255 468 STATION NO. 6 SUDAN 1950 1988 -999 -999.00 845010 -810 -7903 30 TRUJILLO/MARTINEZ PERU 1961 2002 -999 -999 845310 -916 -7851 11 CHIMBOTE/TENIENTE PERU 1961 2001 -999

846280 -1200 -7711 13 LIMA/JORGE CHAVEZ PERU 1961 2002 -999 -999 846910 -1375 -7628 7 PISCO (CIV/MIL) PERU 1942 2002 -999 -999 854180 -2053 -7018 52 IQUIQUE/DIEGO ARAC CHILE 1989 2002 -999 -999.00

So why does the old db result in no 'zero' cells, and the new db give us over 250? I wondered if normals might be the answer, but none of the 10 stations from the old db have in-db normals, wheras three of the new db have:

8453100 -920 -7850 10 CHIMBOTE PERU 1961 2006 -999 -999.00 6190 19 59 36 18 5 0 3 0 0 1 10 5 8469100 -1380 -7630 6 PISCO PERU 1942 2006 -999 -999.00 6190 3 0 3 0 0 1 1 3 1 4 0 0 8540600 -1850 -7030 29 ARICA/CHACALLUTA CHILE 1903 2006 -999 -999.00 6190 1 3 0 0 0 2 2 2 2 0 0 0

So these alone ought to guarantee three of the cells being nonzero - they should have the bloody normals in! So the next check has to be the climatology, that which provides the cell-by-cell normals..

A check of the gridded climatology revealed that all 257 'zero cells' have their climatologies set to zero, too. This was partially checked in the GRIM-format climatology just in case!

Next, a focus: on CHIMBOTE (see header line above). This has real data (not just zeros). It is in cell (162,203), or (-9.25,-78.75) [lat, lon in both cases]. So we extract the full timeseries for that cell from the published 2.10 (1901-2002) GRIM file:

Grid-ref= 203, 162 2 0 0 0 2 0 2 0 0 5 0 3

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2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 0 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 7 0 3 2 0 0 0 2 0 0 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 0 0 0 5 0 3 2 0 0 0 2 0 2 0 0 5 0 3 2 0 0 0 2 0 0 0 0 5 0 3 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 2 5 6 0 0 0 0 3 2 3 0 0 0 0 17 0 0 4 0 3 2 0 0 0 3 0 2 0 0 2 0 3 0 0 0 0 0 0 14 0 0 9 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 0 12 0 0 0 0 5 0 0 0 0 0 0 0 0 0 3 0 2 0 0 0 0 0 0 10 0 0 0 0 2 0 0 0 0 3 0 11 0 0 2 0 3 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 4 0 0 3 0 0 0 0 0 15 0 0 0 0 2 0 0 0 0 0 0 0 0 0 4 3 2 5 0 0 0 0 0 0 0 0 12 0 3 0 0 2 2 4 2 0 0 2 3 0 3 0 0 0 0 3 0 0 2 0 2 2 3 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 3 0 0 0 0 0 0 0 0 2 3 0 0 0 4 0 0 12 0

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0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 2 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 7 0 0 0 2 0 0 0 3 2 0 7 0 0 2 0 0 2 0 0 0 0 0 0 7 0 0 2 2 2 0 0 0 8 0 2 0 0 0 0 2 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 10 0 3 0 0 0 0 0 9 0 0 0 0 3 0 0 0 0 0 0 0 0 0 3 0 5 4 0 0 2 10 2 0 0 0 0 0 4 0 0 0 0 0 0 0 0 2 5 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 3 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 8 2 0 0 0 0 0 3 0 0 2 0 2 0 0 0 0 0 2 3 0 0 0 0 2 0 2 0 0 5 0 2 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 2 0 0 2 0 0 0 0 2 0 2 0 0 0 0 0 0 5 0 0 0 0 0 0 11 0 2 0 0 4 0 3 2 3 2 0 13 0 0 0 0 0 0 0 2 6 0 3 0 0 0 0 2 3 0 7 2 0 0 0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3

..yet in the 3.00 version, it's all zeros!

Only one thing for it.. examine the attempt at regenerating 2.10. Unfortunately - well, interestingly then - this gave the same zero cells as the 3.00 generation! So it's something to do with the process, not the database (or the climatology, assuming that has remained constant, which I gather it has).

Update: aha! Phil pointed out that for precip the climatology is used as a MULTIPLIER. So if the clim hasn't changed, the cells should always have been zero regardless of actual data.

As I should have remembered:

crua6[/cru/cruts/version_3_0/primaries/precip] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.pre Enter a name for the gridded climatology file: clim.6190.lan.pre.grid Enter the path and stem of the .glo files: preglo/pregrid. Enter the starting year: 1901 Enter the ending year: 2006

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Enter the path (if any) for the output files: pregrid/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Right, erm.. off I jolly well go! pregrid.01.1901.glo pregrid.02.1901.glo (etc)

Decided to read Mitchell & Jones 2005 again. Noticed that the limit for SD when anomalising should be 4 for precip, not 3! So re-ran with that:

crua6[/cru/cruts/version_3_0/primaries/precip] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:
.pre > Will calculate percentage anomalies. > Select the .cts or .dtb file to load: pre.0612181221.dtb pre.0612181221.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/precip/pre.0612181221.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 4 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 8 > Select the generic .txt file to save (yy.mm=auto): pre4sd.txt > Select the first,last years AD to save: 1901,2006 > Operating... /tmp_mnt/cru-auto/cruts/version_3_0/primaries/precip/pre.0612181221.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/precip/pre.0612181221.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/precip/pre.0612181221.dts

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/precip/pre.0612181221.dts

> NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 made it to here > .cts 299359 3.0 7613600 76.8 > PROCESS DECISION percent %of-chk > no lat/lon 17527 0.2 0.2 > no normal 2355659 23.8 23.8 > out-of-range 13253 0.1 0.2 > duplicated 586206 5.9 7.8

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> accepted 6934807 70.0 > Dumping years 1901-2006 to .txt files...

This is not as good a percentage as for 2.10:

> NORMALS MEAN percent STDEV percent > .dtb 0 0.0 > .cts 3375441 84.1 3375441 84.1 > PROCESS DECISION percent %of-chk > no lat/lon 3088 0.1 0.1 > no normal 638538 15.9 15.9 > out-of-range 70225 1.7 2.1 > duplicated 135457 3.4 4.1 > accepted 3167636 78.9 > Dumping years 1901-2002 to .txt files...

But the actual number of accepted values is more than TWICE 2.10!

Of course, the same 257 gridcells are zeros, because the multiplicative normals are still zero.

For reference, these are the results for the 3 SD limit of 3.00:

> NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 made it to here > .cts 284160 2.9 7598401 76.7 > PROCESS DECISION percent %of-chk > no lat/lon 17527 0.2 0.2 > no normal 2370858 23.9 24.0 > out-of-range 32379 0.3 0.4 > duplicated 583193 5.9 7.8 > accepted 6903495 69.7 > Dumping years 1901-2006 to .txt files...

So we've only gained 0.3% of values, a real figure of 31312 values.

Conclusion: stick with a 3 Standard Deviation limit, like the Read_Me says.

24. (cont of 22 really)

Restarted work on mergedb.for. Decided I was taking the wrong approach, so the interruption was probably a GOOD THING.

The process now is to read in the header lines AND line numbers from the main database, and to then process the incoming database one record at a time. It's more logical and haivng the line numbers will speed things up enormously (well it has done on previous occasions).

The biggest immediate problem was the loss of an hour's edits to the program, when the network died.. no explanations from anyone, I hope it's not a return to last year's troubles.

(some weeks later)

well, it compiles OK, and even runs enthusiastically. However there are loads of bugs that I now have to fix. Eeeeek. Timesrunningouttimesrunningout.

(even later)

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Getting there.. still ironing out glitches and poor programming.

25. Wahey! It's halfway through April and I'm still working on it. This surely is the worst project I've ever attempted. Eeeek. I think the main problem is the rather nebulous concept of the automatic updater. If I hadn't had to write it to add the 1991-2006 temperature file to the 'main' one, it would probably have been a lot simpler. But that one operation has proved so costly in terms of time, etc that the program has had to bend over backwards to accommodate it. So yes, in retrospect it was not a brilliant idea to try and kill two birds with one stone - I should have realised that one of the birds was actually a pterodactyl with a temper problem.

Success! crua6[/cru/cruts/version_3_0/db/testmergedb] ./mergedb

************************************************** * MERGEDB * * * * Merging of two database files * * Ops ID: f098xxxx * * Date: 12:17 25/04/07 * * The Session ID is: 0704251217.f098xxxx * * (log file 'mergedb.0704251217.f098xxxx.log') * * * * Please choose the mode of working. * * This program can either run.. * * [1] Interactively, (in which case an operator * * must be present throughout to make decision), * * or [2] in Batch mode, (in which case it may * * be left unattended). If Batch mode is used, a * * file of outstanding issues will be saved for * * later [3] resolution by an operator. * * * * [1] Interactive (operator) processing * * [2] Batch (no operator) processing * * [3] Operator processing of saved batch * * [4] Run a previously-saved action file * * * * Please enter 1,2,3 or 4: 4 * RUN ACTION FILE MODE * * * * Enter the ACTion filename, or 'x' for a list: x * The 1 most recent ACT files: * 1. mergedb.0704201343.f098xxxx.act * * Enter a number or 0 for none of the above: 1 * Enter 'Y' to run this file or 'N' to abort: Y * * * Creation date/time: 13:43 20/04/07 * * Batch initiator was: f098 *

* Number of actions/requests: 2586 * This ACT file derived from original OPS file: * * mergedb.0704201210.f098xxxx.ops * * Main (existing) Database: tmp.0702091122.dtb * Secondary (incoming) Database: tmp.0612081519.dat * Parameter is 'tmp' - confirm (Y/N): Y

* Actions Completed! * * Thank You for using MERGEDB! *

**************************************************

..well, 'success' in the sense that it ran and apparently all the data's in the right place, in tmp.0704251819.dtb.

Page 32HARRY_READ_ME

26. OK, now to merge in the US stations. First, wrote 'us2cru' to convert the marksusanonwmocru.dat file to the 'standard' format we're using. That worked OK. Then used 'addnormline' to, well - add a normals line. Only 17 out of 1035 stations ended up with missing normals, which is pretty good!

The with-normals US database file is tmp.0704251654.dat.

Now, I knew that using mergedb as it stands would not work. It expects to be updating the existing records, and actions like 'addnew' require OPSto confirm each one. So I thought it best to add an OPS clause to autoconfirm additions where there's no WMO match and the data density is OK, say 50% or higher. Unfortunately, that didn't work either, and rather than spend even more time debugging mergedb.for, I knocked off simpleaddnew.for, which adds two non-overlapping databases. The resultant file, with all three partial databases, is tmp.0704271015.dtb.

27. Well, enough excuses - time to remember how to do the anomalising and gridding things! Fisrtly, ran 'addnormline' just to ensure all normals are up to date. The result was 8 new sets of normals, so well worth doing. The database is now:

tmp.0704292158.dtb

Ran 'anomdtb' - got caught out by the requirement for a companion '.dts' file again, ran 'falsedts.for' and carried on.. would still be nice to be sure that it's not something meaningful **sigh**.

Output: <BEGIN QUOTE> crua6[/cru/cruts/version_3_0/primaries/temp] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required: .tmp > Select the .cts or .dtb file to load: tmp.0704292158.dtb tmp.0704292158.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/temp/tmp.0704292158.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 8 > Select the generic .txt file to save (yy.mm=auto): tmp.txt > Select the first,last years AD to save: 1901,2006 > Operating... /tmp_mnt/cru-auto/cruts/version_3_0/primaries/temp/tmp.0704292158.dtb

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/tmp_mnt/cru-auto/cruts/version_3_0/primaries/temp/tmp.0704292158.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/temp/tmp.0704292158.dts

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/temp/tmp.0704292158.dts

> Failed to find file. > Enter the file, with suffix: .dts tmp.0704292158.dts tmp.0704292158.dts

/tmp_mnt/cru-auto/cruts/version_3_0/primaries/temp/tmp.0704292158.dts

> NORMALS MEAN percent STDEV percent > .dtb 3330007 81.3 made it to here > .cts 92803 2.3 3422810 83.6 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 671592 16.4 16.4 > out-of-range 744 0.0 0.0 > duplicated 4102723 100.2 119.9 > accepted -680657 -16.6 > Dumping years 1901-2006 to .txt files...

crua6[/cru/cruts/version_3_0/primaries/temp] <END QUOTE>

.. which is a trifle worrying! And looking at the .txt files, they look rather odd as well - for instance, tmp.1953.03.txt starts like this:

7.09 0.87 10.0 0.10000 10010 7.83 -1.55 28.0 -4.80000 10080 6.97 -1.89 10.0 0.90000 -999 6.97 -1.89 100.0 0.50000 10260 7.45 -1.90 16.0 -3.10000 10280 6.95 -2.55 129.0 3.70000 10650 7.04 -3.11 14.0 0.00000 10980 6.60 -0.20 0.0 1.20000 11000 6.73 -1.44 13.0 1.60000 -999 6.68 -1.40 39.0 2.20000 11530

Now, do those first two columns look like lat & lon to you? Me neither, here's what the old version of the same file looks like:

60.00 -20.00 -999.0 0.40000-990007 62.00 -33.00 -999.0 -0.40000-990002 56.50 -51.00 0.0 -0.50000-990000 6.90 122.06 6.0 -0.60000 -999 13.13 123.73 17.0 0.20000 -999 14.52 121.00 15.0 0.60000 -999 18.37 121.63 4.0 1.10000 -999 6.90 122.00 6.0 -0.60000 -999 10.70 122.50 14.0 -0.10000 -999 13.13 123.73 19.0 0.10000 -999

In fact, the first two columns never get outside of +/- 30. Oh bugger. What the HELL is going on?!

Page 34HARRY_READ_ME

Decided to pursue that worrying (and impossible) 'duplicates' figure.

The function 'sort' was used to sort the database so that any duplicate lines would be together - then 'uniq' was used to pull out duplicates. There were quite a few dupes, and one or two triples too, like these:

crua6[/cru/cruts/version_3_0/primaries/temp] grep -n '1984 \-83 \-46 22 55 126 154 222 215 159 63 32 \-62' tmp.0704292158.dtb 195789:1984 -83 -46 22 55 126 154 222 215 159 63 32 -62 254265:1984 -83 -46 22 55 126 154 222 215 159 63 32 -62 254380:1984 -83 -46 22 55 126 154 222 215 159 63 32 -62

These are from the following stations: 720344 408 1158 1539 ELKO-FAA-AP---------USA--------- 1870 1996 301870 -999.00 725837 408 1158 1549 NV ELKO FAA AP 1930 1990 101930 -999.00 725910 401 1223 103 RED BLUFF USA 1878 2006 101878 -999.00

The past two are consecutive stations.

Looking at the last two.. it seems that 725910 has 725837's data!

1977 71 124 118 184 167 275 283 280 230 190 126 99 1978 107 114 149 144 208 248 289 282 232 220 118 72 1979 85 99 139 150 218 256 282 258 253 189 117 94 1980 99 121 119 156 192 216 275 262 241 196 128 102 1981 14 19 49 90 123 196 233 227 164 71 47 11 1982 -49 -14 32 57 114 164 206 214 148 74 11 -23 1983 -9 -1 54 59 114 167 204 223 170 104 25 -19 1984 -83 -46 22 55 126 154 222 215 159 63 32 -62

Ascan be seen, 1981 sees a complete chance in range, especially for Autumn/Winter. In fact, from 1981 to 1990, 725910 is a copy of 725837! It then reverts to the original range for the rest of the run. So.. did the merging program do this? Unfortunately, yes. Check dates:

crua6[/cru/cruts/version_3_0/db/testmergedb] grep -n 'RED BLUFF' tmp.0*.* tmp.0612081519.dat:28595: 725910 401 1223 103 RED BLUFF USA 1991 2006 101991 -999.00 tmp.0702091122.dtb:171674: 725910 401 1223 103 RED BLUFF USA 1878 1980 101878 -999.00 tmp.0704251819.dtb:200331: 725910 401 1223 103 RED BLUFF USA 1878 2006 101878 -999.00 tmp.0704271015.dtb:254272: 725910 401 1223 103 RED BLUFF USA 1878 2006 101878 -999.00 tmp.0704292158.dtb:254272: 725910 401 1223 103 RED BLUFF USA 1878 2006 101878 -999.00 crua6[/cru/cruts/version_3_0/db/testmergedb]

The first file is the 1991-2006 update file. The second is the original temperature database - note that the station ends in 1980.

It has *inherited* data from the previous station, where it had -9999 before! I thought I'd fixed that?!!!

/goes off muttering to fix mergedb.for for the five hundredth time

Miraculously, despite being dog-tired at nearly midnight on a Sunday, I did find the problem. I was clearing the data array but not close enough to the action - when stations were being passed through (ie no data to

Page 35HARRY_READ_ME

add to them) they were not being cleaned off the array afterwards. Meh.

Wrote a specific routine to clear halves of the data array, and back to square one. Re-ran the ACT file to merge the x-1990 and 1991-2006 files. Created an output file exactly the same size as the last time (phew!) but with..

crua6[/cru/cruts/version_3_0/db/testmergedb] comm -12 tmp.0704292355.dtb tmp.0704251819.dtb |wc -l 285516 crua6[/cru/cruts/version_3_0/db/testmergedb] wc -l tmp.0704292355.dtb 285829 tmp.0704292355.dtb

.. 313 lines different. Typically:

14881,14886c14881,14886 < 1965-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1966-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1967-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1968-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1969-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1970-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

--- > 1965 -221 -177 -234 -182 -5 6 24 36 -15 -91 -100 -221 > 1966 -272 -194 -248 -192 -66 10 27 45 -12 -75 -139 -228 > 1967 -201 -243 -196 -158 -26 1 40 30 -18 -89 -183 -172 > 1968 -253 -256 -253 -107 -42 10 46 33 -21 -64 -134 -195 > 1969 -177 -202 -248 -165 -33 8 42 50 -1 -89 -157 -204 > 1970 -237 -192 -217 -160 -87 6 30 25 -5 -55 -143 -222

ie, what should have been missing data is now missing data again:

200436,200445c200436,200445 < 1981-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1982-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1983-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1984-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1985-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1986-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1987-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1988-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1989-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 < 1990-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

--- > 1981 14 19 49 90 123 196 233 227 164 71 47 11 > 1982 -49 -14 32 57 114 164 206 214 148 74 11 -23 > 1983 -9 -1 54 59 114 167 204 223 170 104 25 -19 > 1984 -83 -46 22 55 126 154 222 215 159 63 32 -62 > 1985 -57 -29 17 89 122 181 244 188 121 79 -11 -50 > 1986 2 31 66 72 113 187 194 214 116 78 11 -39 > 1987 -59 -5 30 97 131 177 193 192 153 101 21 -35 > 1988 -65 -15 29 80 108 184 222 198 138 116 8 -57 > 1989 -113 -54 53 94 113 164 215 186 143 78 8 -24 > 1990 -24 -30 49 100 100 166 214 194 177 77 9 -97

Hurrah!

So the interim database file is tmp.0704292355.dtb. Now to re-add the US station dataset with simpleaddnew.for.

crua6[/cru/cruts/version_3_0/db/testmergedb] ./simpleaddnew

SIMPLYADDNEW - add stations to a database

Page 36HARRY_READ_ME

This program assumes the two databases have NO COMMON STATIONS and will fail (stop) if any are found.

Please enter the main database: tmp.0704292355.dtb

Please enter the new database: tmp.0704251654.dat Please enter a 3-character parameter code: tmp Output database is: tmp.0704300053.dtb crua6[/cru/cruts/version_3_0/db/testmergedb]

So now we have the combined database again, a bit quicker than last time: tmp.0704300053.dtb. Pity we slid into May: I was hoping to only be FIVE MONTHS late.

What's worse - there are STILL duplicate non-missing lines, 210 of them. The first example is this:

1835 92 73 141 187 260 279 281 288 241 195 183 106

Which belongs to this in the original database (tmp.0702091122.dtb):

722080 329 800 15 CHARLESTON, S. CAROL UNITED STATES 1823 1990 101823 -999.00 6190 84 100 142 180 224 257 274 270 245 191 145 104

..and to this in the US database (tmp.0704251654.dat):

720467 328 799 3 CHARLESTON-CITY-----USA--------- 1835 1996 301835 -999.00 6190 91 106 144 186 227 260 277 272 249 199 154 112

These two stations obviously have a lot in common - though not everything, as their normals (shown) differ. In fact, on examination the US database record is a poor copy of the main database one, it has more missing data and so forth. By 1870 they have diverged, so in this case it's probably OK.. but what about the others? I just do not have the time to follow up everything. We'll have to take 210 year repetitions as 'one of those things'.

..actually, I decided in the end to follow up all 210 of them. The likelihood is that the number is far greater, since the filtering that gave the 210 figure excluded any lines with two or more consecutive missing values (to avoid hundreds of just-missing-value lines). Also I spotted some instances where data lines would be identical but for one or more missing values in one of the stations.

After checking, I found that the majority of the duplications were between the original database and the US database, with just a couple of 'linked' stations within the original database, and half a dozen in the 1991-2006 update file. One surprise was that stations I'm sure I rejected ended up marked as 'addnew' in the .act file - quite unsettling!

Rather foolishly, perhaps, I decided to have a go at interactively incorporating the US data rather than using 'simplyaddnew'. However, progress was so slow (because of the high number of 'near matches') that this approach was abandoned.

Tried 'anomdtb' with the fixed final file (tmp.0704300053.dtb)... no better! The crucial bits:

<BEGIN QUOTE>

Page 37HARRY_READ_ME

> NORMALS MEAN percent STDEV percent > .dtb 3323823 81.3 made it to here > .cts 91963 2.2 3415786 83.5 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 675037 16.5 16.5 > out-of-range 744 0.0 0.0 > duplicated 4100117 100.2 120.1 > accepted -685075 -16.7 > Dumping years 1901-2006 to .txt files... > Failed to create file. Try again. > Enter the file, with suffix: .ann tmp.ann > Failed to create file. Try again. > Enter the file, with suffix: .ann h.ann

crua6[/cru/cruts/version_3_0/primaries/temp] <END QUOTE>

So the 'duplicated' figure is slightly lower.. but what's this error with the '.ann' file?! Never seen before. Oh GOD if I could start this project again and actually argue the case for junking the inherited program suite!!

OK.. the .ann file was simply that it refuses to overwrite any existing one. Meh. It's happy to overwrite the log file of course - nice bit of logic there.

and the duplicates? Well I inserted a debug line where the decision is made. Here's an example:

712600 vs. 727340: 4.7 8.4 4.7 8.4 -> 0.0km

Here the two WMO codes look OK (though others are -999 which seems unlikely) but the two lat/lon pairs? Ooops. Here are the actual headers:

712600 465 845 187 Sault Ste Marie A CANADA 1945 2006 361945 -999.00 727340 465 844 220 SAULT-STE-MARIE----- USA--------- 1888 2006 101888 -999.00

So, uhhhh.. what in tarnation is going on? Just how off-beam are these datasets?!!

Not sure why the lats & lons are a factor of 10 too low - may be intentional though it wasn't happening before.

Ran with the original database:

<BEGIN QUOTE> > NORMALS MEAN percent STDEV percent > .dtb 2113609 81.7 made it to here > .cts 0 0.0 2113608 81.7 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 474422 18.3 18.3 > out-of-range 68179 2.6 3.2 > duplicated 923258 35.7 45.1 > accepted 1122172 43.4

Page 38HARRY_READ_ME

> Dumping years 1901-1990 to .txt files... <END QUOTE>

The lats & lons look the same.. but a lot less duplicates!

WHY? Well, it could just be those pesky US stations.. so why not compare the two bespoke log files (as excerpted above)?

Immediately, another baffler: the log file from the run of the 'final' database has lots of 'DEBUG DETAIL' information, but the log file from the run of the original database does not! So cropping those away with a judicious 'tail'.. I ran comm:

crua6[/cru/cruts/version_3_0/primaries/temp] comm -23 log_anomdtb_H.0702091122.dat barelog_anomdtb_H.0704300053.dat |wc -l 200 crua6[/cru/cruts/version_3_0/primaries/temp] comm -13 log_anomdtb_H.0702091122.dat barelog_anomdtb_H.0704300053.dat | wc -l 2572 crua6[/cru/cruts/version_3_0/primaries/temp] comm -12 log_anomdtb_H.0702091122.dat barelog_anomdtb_H.0704300053.dat | wc -l 1809

So 200 duplication events are unique to the older database, and 2572 are unique to the new database - with 1809 common to both. A quick look at the 2572 'new' ones showed a majority of those with the first WMO as -999: this is the key. The databases do not have any records with WMO=-999 as far as I know, so something is going on..

28. With huge reluctance, I have dived into 'anomdtb' - and already I have that familiar Twilight Zone sensation.

I have found that the WMO Code gets set to -999 if *both* lon and lat are missing. However, the following points are relevant:

* LoadCTS multiplies non-missing lons by 0.1, so they range from -18 to +18 with missing value codes passing through AS LONG AS THEY ARE -9999. If they are -999 they will be processed and become -99.9. It is not clear why lats are not treated in the same way!

* The subroutine 'Anomalise' in anomdtb checks lon and lat against a simple 'MissVal', which is defined as -999. This will catch lats of -999 but not lons of -9999.

* This does still not explain how we get so many -999 codes.. unless we don't and it's just one or two?

And the real baffler:

* If the code is -999 because lat and lon are both missing - how the bloody hell does it know there's a duplication within 8km?!!!

.. ah, OK. well for a start, the last point above does not apply - not one case of the code being set to -999 because of lat/lon missing. In fact, I hate to admit it, bit it is *sort of* clever - the code is set to -999 to prevent it being used again, because the distance/duplication checker will not make a distance comparison if either code is -999. So HOW COME loads of the duplicates have a code of -999?!!!

The plot thickens.. I changed the exclusion tests in the duplication loops from: if (AStn(XAStn).NE.MissVal) then

Page 39HARRY_READ_ME

to: if (int(AStn(XAStn)).NE.-999) then

This made NO DIFFERENCE. So having tested to ensure that the first of the pair hasn't already been used - we then use it! What's more I've noticed that it's usually the one 'incorporated' in the previous iteration!

Consider:

67700 vs. 160660: 4.6 -0.9 4.6 -0.9 -> 5.4km -999 vs. 160707: 4.6 -0.9 4.6 -0.9 -> 2.2km -999 vs. 160800: 4.6 -0.9 4.5 -0.9 -> 7.3km -999 vs. 160811: 4.6 -0.9 4.6 -0.9 -> 5.8km

Here we can see (check the first set of lat/lons) that, after being incorporated into 160660, 67700 goes on to also be incorporated into 160707, 160800 and 160811! So the same data could end up in three other stations. It gets worse!! Because later on, we find:

160660 vs. 160707: 4.6 -0.9 4.6 -0.9 -> 7.9km -999 vs. 160800: 4.6 -0.9 4.5 -0.9 -> 7.0km -999 vs. 160811: 4.6 -0.9 4.6 -0.9 -> 5.8km 160707 vs. 160800: 4.6 -0.9 4.5 -0.9 -> 7.9km -999 vs. 160811: 4.6 -0.9 4.6 -0.9 -> 6.6km 160800 vs. 160811: 4.5 -0.9 4.6 -0.9 -> 2.2km

So three of those recipients have gone on to be incorporated into one of them (160811). But although in this case 67700 is within 8km of 160811, there is no guarantee! Indeed, with this system, the 'chosen' station may hop all over the place in <8km steps, collecting data as it goes. In a densely-packed area this could drastically reduce the number of stations. Then there's these:

85997 vs. 390000: -10.0 -20.0 -10.0 -20.0 -> 0.0km -999 vs. 685807: -10.0 -20.0 -10.0 -20.0 -> 0.0km -999 vs. 688607: -10.0 -20.0 -10.0 -20.0 -> 0.0km -999 vs. 967811: -10.0 -20.0 -10.0 -20.0 -> 0.0km -999 vs. 968531: -10.0 -20.0 -10.0 -20.0 -> 0.0km

as might be guessed, they all end up incorporated into 968531 - but no surprise seeing as their lats & lons are rubbish!!! Oh Tim what have you done, man? [actually - what he's done is to let missing lats & lons through. Missing lon code is -1999 not -9999 so these figures are the roundings]

All that said, the biggest worry is still the lats & lons themselves. They just don't look realistic. Lats appear to have been reduced by a factor of 10 too, even though I can't find the code for that. And (from the top example) is 67700 really 5.4km from 160660?

67700 460 -90 273 LUGANO SWITZERLAND 1864 2006 101864 -999.00 160660 456 -87 -999 MILANO MALPENSA ITALY 1961 1970 101961 -999.00

Of course not! It's just over 50km. I do not understand why the lats & lons have been scaled, when the stated distance threshold has not.

At least I've found *where* they are scaled, in LoadCTS (crutsfiles.f90):

if (StnInfo(XStn,2).NE.LatMissVal) Lat (XStn) = real(StnInfo(XStn,2)) / real(LatFactor) if (StnInfo(XStn,3).NE.LonMissVal) Lon (XStn) = real(StnInfo(XStn,3)) /

Page 40HARRY_READ_ME

real(LonFactor)

Looking at how LoadCTS is called from anomdtb..

subroutine LoadCTS (StnInfo,StnLocal,StnName,StnCty,Code,Lat,Lon,Elv,OldCode,Data,YearAD,&

NmlData,DtbNormals,CallFile,Hulme,Legacy,HeadOnly,HeadForm,LongType,Silent,Extra,Phi lJ, & YearADMin,YearADMax,Source,SrcCode,SrcSuffix,SrcDate, & LatMV,LonMV,ElvMV,DataMV,LatF,LonF,ElvF,NmlYr0,NmlYr1,NmlSrc,NmlInc)

call LoadCTS (StnInfoA,StnLocalA,StnNameA,StnCtyA,Code=AStn,OldCode=AStnOld, & Lat=ALat,Lon=ALon,Elv=AElv,DtbNormals=DtbNormalsA, & Data=DataA,YearAD=AYearAD,CallFile=LoadFileA,silent=1) ! get .dtb file

.. we see that Legacy is not passed. This means that.. (from LoadCTS):

LatFactor=100 ; LonFactor=100 ; ElvFactor=1 ! usual/hulme hdr factors if (present(Legacy)) then LatFactor=10 ; LonFactor=10 ; ElvFactor=1 ! legacy hdr factors end if if (present(LatF)) LatFactor = LatF ! custom hdr factors if (present(LonF)) LonFactor = LonF if (present(ElvF)) ElvFactor = ElvF

..LatFactor and LonFactor are set to 100.

So I added a specific pair of arguments, LatF=10,LonF=10, and got:

> NORMALS MEAN percent STDEV percent > .dtb 3323823 81.3 made it to here > .cts 91963 2.2 3415786 83.5 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 675037 16.5 16.5 > out-of-range 744 0.0 0.0 > duplicated 53553 1.3 1.6 > accepted 3361489 82.2 > Dumping years 1901-2006 to .txt files...

Hurrah! Looking at the log it is still ignoring the -999 Code and re-intgrating stations.. but not to any extent worth worrying about. Not when duplications are down to 1.3% :-)))

Then got a mail from PJ to say we shouldn't be excluding stations inside 8km anyway - yet that's in IJC - Mitchell & Jones 2005! So there you go. Ran again with 0km as the distance:

> NORMALS MEAN percent STDEV percent > .dtb 3323823 81.3 made it to here > .cts 91963 2.2 3415786 83.5 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 675037 16.5 16.5 > out-of-range 744 0.0 0.0 > accepted 3415042 83.5

Page 41HARRY_READ_ME

> Dumping years 1901-2006 to .txt files...

Which hasn't saved much as it turns out. In fact, I must conclude that an inquiring mind is a very dangerous thing - I decided to see what difference it made, turning off the proximity duplicate detection and elimination:

crua6[/cru/cruts/version_3_0/primaries/temp] wc -l */*1962.12.txt 2773 oldtxt/old.1962.12.txt 3269 tmptxt0km/tmp.1962.12.txt 3308 tmptxt8km/tmp.1962.12.txt

So.. 'oldtxt' is before I fixed the lat/lon scaling problem. But look at the last

two - I got MORE results when I used an elimination radius! Whaaaaaaaaat?!!!

/goes home in a huff

/gets out of huff and goes into house, checks things and thinks hard

Okay, I guess if we don't do the roll-duplicates-together thing, then we could lose data because the 'rolled' station (ie the one subsumed into its neighbour) might have useful years but no normals, so that data would be lost?

29. I suddenly thought - what about the Australian data? But luckily that's just tmax/tmin so I can roll that into the next database work.

30. Being an idiot much experience I decided to go back to the 'perfectly-good' precip generation for v3.0 and re-do the anomalies with the new anomdtb. At 8km, we got the duplicates down from 5.9% to 2.1%:

<OLD ANOMDTB WITH LATLON PROBS> > NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 made it to here > .cts 299359 3.0 7613600 76.8 > PROCESS DECISION percent %of-chk > no lat/lon 17527 0.2 0.2 > no normal 2355659 23.8 23.8 > out-of-range 13253 0.1 0.2 > duplicated 586206 5.9 7.8 > accepted 6934807 70.0 > Dumping years 1901-2006 to .txt files...

<NEW ANOMDTB WITH LATLON 'FIXED'> > NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 made it to here > .cts 299359 3.0 7613600 76.8 > PROCESS DECISION percent %of-chk > no lat/lon 17527 0.2 0.2 > no normal 2355659 23.8 23.8 > out-of-range 13253 0.1 0.2 > duplicated 207391 2.1 2.8 > accepted 7313622 73.8 > Dumping years 1901-2006 to .txt files...

And, of course, all in with 0km range:

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> NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 made it to here > .cts 299359 3.0 7613600 76.8 > PROCESS DECISION percent %of-chk > no lat/lon 17527 0.2 0.2 > no normal 2355659 23.8 23.8 > out-of-range 13253 0.1 0.2 > accepted 7521013 75.9 > Dumping years 1901-2006 to .txt files...

Happy? well.. no. Because something is happening for precip that does not happen for temp! But of course. Here are the first few lines from various 1962.12 text files..

tmptxt8km/tmp.1962.12.txt 70.90 8.70 10.0 2.10000 10010 78.30 -15.50 28.0 -3.30000 10080 69.70 -18.90 10.0 -1.40000 -999 69.70 -18.90 100.0 -1.50000 10260 74.50 -19.00 16.0 -1.20000 10280 69.50 -25.50 129.0 -3.10000 10650 70.40 -31.10 14.0 -0.20000 10980 66.00 -2.00 0.0 0.50000 11000 67.30 -14.40 13.0 -1.00000 11520 66.80 -14.00 39.0 -0.70000 11530

tmptxt0km/tmp.1962.12.txt 70.90 8.70 10.0 2.10000 10010 78.30 -15.50 28.0 -3.30000 10080 69.70 -18.90 10.0 -1.40000 10250 69.70 -18.90 100.0 -1.50000 10260 74.50 -19.00 16.0 -1.20000 10280 69.50 -25.50 129.0 -3.10000 10650 70.40 -31.10 14.0 -0.20000 10980 66.00 -2.00 0.0 0.50000 11000 67.30 -14.40 13.0 -1.00000 11520 66.80 -14.00 39.0 -0.70000 11530

preanoms/pre.1962.12.txt (old anomdtb output) 61.00 10.60 190.0 48.20000-511900 54.45 -6.07 116.0 -3.70000 -999 50.83 -4.55 15.0 -22.40000-389870 50.22 -5.30 76.0 39.70000 -999 50.63 -3.45 9.0 -28.10000-388730 51.43 -2.67 51.0 -36.90000 -999 51.05 -3.60 314.0 -27.80000-386030 51.72 -2.77 245.0 -37.70000-385850 51.62 -3.97 10.0 -46.10000-384130 52.35 -3.82 301.0 -4.40000-380860

pretxt8km/pre.1962.12.txt 610.00 106.00 190.0 48.20000-511900 544.50 -60.70 116.0 -3.70000-392380 508.30 -45.50 15.0 -22.40000-389870 502.20 -53.00 76.0 39.70000-389280 506.30 -34.50 9.0 -28.10000-388730 514.30 -26.70 51.0 -36.90000-386780 510.50 -36.00 314.0 -27.80000-386030 517.20 -27.70 245.0 -37.70000-385850 516.20 -39.70 10.0 -46.10000-384130 523.50 -38.20 301.0 -4.40000-380860

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pretxt0km/pre.1962.12.txt 610.00 106.00 190.0 48.20000-511900 544.50 -60.70 116.0 -3.70000-392380 508.30 -45.50 15.0 -22.40000-389870 502.20 -53.00 76.0 39.70000-389280 506.30 -34.50 9.0 -28.10000-388730 514.30 -26.70 51.0 -36.90000-386780 510.50 -36.00 314.0 -27.80000-386030 517.20 -27.70 245.0 -37.70000-385850 516.20 -39.70 10.0 -46.10000-384130 523.50 -38.20 301.0 -4.40000-380860

..As a result of fixing the lats and lons for temperature, and indeed precip it seems, we have buggered up the outputs!!! Obviously the correction factor is expecting 100 not 10, but why isn't this a problem for temperature?! Went back and ran exactly the same version of anomdtb on temperature - exactly the same as last time (2nd from top above). So it is precip specific (or, erm, .not.temp specific?).

On the other hand, we've fixed the -999 WMO codes..

..and actually, those anomalies had better be percentage anomalies!

(checks a few) - yes, they are :-)

So oookay, LoadCTS reports the divisor is still 10 for lon/lat, so the stored values for the first station (-511900, BIRI) should be 61 and 10.6, sounds about right for Norway. The bit in anomdtb (actually the subroutine 'Dumping', LOL) that writes the .txt files just writes directly from the arrays.. so they must have been modified somewhere in 'Anomalise' (there's nothing else in 'Dumping'). Modified anomdtb to dump the first station's lat & lon at key stages - they were too high throughout, so LoadCTS assumed to be the troublemaker. Modified LoadCTS in the same way, and it was holding them at x100 from their true values, ie 61.0 -> 6100. It was about now that I spotted something I'd not thought to examine before: precip headers use two decimal places for their coordinates!

Temperature header: 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00

Precipitation header: 100100 7093 -867 10 JAN MAYEN NORWAY 1921 2006 -999 -999.00

So.. this begs the question, how does the software suite know which it's got? By rights it should look at the most extreme values for each.. something tells me that's not the case. Decided to look at the ranges of values for different versions of the databases, starting with temperature:

crua6[/cru/cruts] head -1 fromdpe1a/data/cruts/database/+norm/tmp.0311051552.dtb -990017 -9999 -99999 -999 UNKNOWN MARINE 1948 1990 -999 -999.00 crua6[/cru/cruts] head -1 fromdpe1a/data/cruts/database/+norm/_old/tmp.0310311715.dtb -176000 3520 3330 220 NICOSIA CYPRUS 1932 1974 -999 nocode crua6[/cru/cruts] head -1 rerun1/data/cruts/rerun_tmp/tmp.0311051552.dtb -990017 -9999 -99999 -999 UNKNOWN MARINE 1948 1990 -999 -999.00 crua6[/cru/cruts] head -1 rerun1/data/cruts/rerun_tmp/tmp.0311051552n.dtb -990017 -9999 -99999 -999 UNKNOWN MARINE 1948 1990 -999 -999.00

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crua6[/cru/cruts] head -1 rerun1/data/cruts/rerun_tmp/database/+norm/_old/tmp.0310311715.dtb -176000 3520 3330 220 NICOSIA CYPRUS 1932 1974 -999 nocode crua6[/cru/cruts] head -1 rerun1/data/cruts/rerun_tmp/database/+norm/tmp.0311051552.dtb -990017 -9999 -99999 -999 UNKNOWN MARINE 1948 1990 -999 -999.00 crua6[/cru/cruts] head -1 rerun1/data/cruts/rerun_tmp/database/tmp.0311051552.dtb -990017 -9999 -99999 -999 UNKNOWN MARINE 1948 1990 -999 -999.00 crua6[/cru/cruts] head -1 version_3_0/primaries/temp/tmp.0702091122.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 1990 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/primaries/temp/tmp.0704300053.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/testmergedb/tmp.0702091122.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 1990 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/testmergedb/tmp.0704292355.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/testmergedb/badtimeline/tmp.0704251819.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/testmergedb/badtimeline/tmp.0704271015.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/testmergedb/badtimeline/tmp.0704292158.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/testmergedb/tmp.0704300053.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/tmp.0702091122.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 1990 341921 -999.00 crua6[/cru/cruts] head -1 version_3_0/db/tmp.0704300053.dtb 10010 709 87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00

Without going any further, it's obvious that LoadCTS is going to have to autosense the lat and lon ranges. Missing value codes can then be derived - if it always returns actual (unscaled) degrees (to one or two decimal places) then any value lower than -998 will suffice for both parameters. However, this does make me wonder why it wasn't done like that. Is there a likelihood of the programs being used on a spatial subset of stations? Say, English? Then lon would never get into double figures, though lat would.. well let's just hope not! *laughs hollowly*

Okay.. so I wrote extra code into LoadCTS to detect Lat & Lon ranges. It excludes
any values for which the modulus of 100 is -99, so hopefully missing value codes do not conribute. The factors are set accordingly (to 10 or 100). I had to default to 1 which is a pity. Once you've got the factors, detection of missing values can be a simple out-of-range test.

However *sigh* this led me to examine the detection of 'non-standard longitudes' - a small section of code that converts PJ-style reversed longitudes, or 0-360 ones, to regular -180 (W) to +180 (E). This code is switched on by the presence of the 'LongType' flag in the LoadCTS call - the trouble is, THAT FLAG IS NEVER SET BY

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ANOMDTB. There is a declaration 'integer :: QLongType' but that is never referred to again. Just another thing I cannot understand, and another reason why this should all have been rewritten from scratch a year ago!

So, I wrote 'revlons.for' - a proglet to reverse all longitude values in a database file. Ran it on the temperature database (final):

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/testmergedb] ./revlons REVLONS - Reverse All Longitudes!

This nifty little proglet will fix all of your longitudes so that they point the right way, ie, positive = East of Greenwich, negative = West.

..of course, if they are already fixed, this will UNfix them. I am not that smart! So be careful!!

Please enter the database to be fixed: tmp.0704300053.dtb

Output file will be: tmp.0705101334.dtb Confirm this filename (Y/N): Y

Log file will be: tmp.0705101334.log

5065 stations written to tmp.0705101334.dtb

<END QUOTE>

Thus the 'final' temperature database is now tmp.0705101334.dtb.

Re-ran anomdtb - with working lat/lon detection and missing lat/lon value detection - for both precip and temperature. This should ensure that all WMO codes are present and all lats and lons are correct.

Temp: <BEGIN QUOTE> > ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required: .tmp > Select the .cts or .dtb file to load: tmp.0705101334.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): tmp.txt > Select the first,last years AD to save: 1901,2006 > Operating...

> NORMALS MEAN percent STDEV percent

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> .dtb 3323823 81.3 > .cts 91963 2.2 3415786 83.5 > PROCESS DECISION percent %of-chk > no lat/lon 1993 0.0 0.0 > no normal 673044 16.5 16.5 > out-of-range 744 0.0 0.0 > accepted 3415042 83.5 > Dumping years 1901-2006 to .txt files... <END QUOTE>

Precip: <BEGIN QUOTE> crua6[/cru/cruts/version_3_0/primaries/precip] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:
.pre > Will calculate percentage anomalies. > Select the .cts or .dtb file to load: pre.0612181221.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 4 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): pre.txt > Select the first,last years AD to save: 1901,2006 > Operating...

> NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 > .cts 299359 3.0 7613600 76.8 > PROCESS DECISION percent %of-chk > no lat/lon 17911 0.2 0.2 > no normal 2355275 23.8 23.8 > out-of-range 13253 0.1 0.2 > accepted 7521013 75.9 > Dumping years 1901-2006 to .txt files... <END QUOTE>

Note that precip accepted values is up to 75.9%, I honestly don't think we'll get higher.

Decided to process temperature all the way. Ran IDL:

IDL> quick_interp_tdm2,1901,2006,'tmpglo/tmpgrid.',1200,gs=0.5,dumpglo='dumpglo',pts_pref ix='tmp0km0705101334txt/tmp.'

then glo2abs, then mergegrids, to produce monthly output grids. It apparently worked:

-rw------- 1 f098 cru 138964083 May 13 20:42

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cru_ts_3_00.1901.2006.tmp.dat.gz -rw------- 1 f098 cru 7852589 May 13 20:42 cru_ts_3_00.2001.2006.tmp.dat.gz -rw------- 1 f098 cru 13108065 May 13 20:39 cru_ts_3_00.1991.2000.tmp.dat.gz -rw------- 1 f098 cru 13106515 May 13 20:36 cru_ts_3_00.1981.1990.tmp.dat.gz -rw------- 1 f098 cru 13106963 May 13 20:33 cru_ts_3_00.1971.1980.tmp.dat.gz -rw------- 1 f098 cru 13123939 May 13 20:30 cru_ts_3_00.1961.1970.tmp.dat.gz -rw------- 1 f098 cru 13120586 May 13 20:26 cru_ts_3_00.1951.1960.tmp.dat.gz -rw------- 1 f098 cru 13120691 May 13 20:23 cru_ts_3_00.1941.1950.tmp.dat.gz -rw------- 1 f098 cru 13130077 May 13 20:20 cru_ts_3_00.1931.1940.tmp.dat.gz -rw------- 1 f098 cru 13104881 May 13 20:16 cru_ts_3_00.1921.1930.tmp.dat.gz -rw------- 1 f098 cru 13094948 May 13 20:13 cru_ts_3_00.1911.1920.tmp.dat.gz -rw------- 1 f098 cru 13085509 May 13 17:08 cru_ts_3_00.1901.1910.tmp.dat.gz

As a reminder, these output grids are based on the tmp.0705101334.dtb database, with

no merging of neighbourly stations and a limit of 3 standard deviations on anomalies.

Decided to (re-) process precip all the way, in the hope that I was in the zone or something. Started with IDL:

IDL> quick_interp_tdm2,1901,2006,'preglo/pregrid.',450,gs=0.5,dumpglo='dumpglo',pts_prefi x='pre0km0612181221txt/pre.'

Then glo2abs, then mergegrids.. all went fine, apparently.

31. And so.. to DTR! First time for generation I think.

Wrote 'makedtr.for' to tackle the thorny problem of the tmin and tmax databases not being kept in step. Sounds familiar, if worrying. am I the first person to attempt to get the CRU databases in working order?!! The program pulls no punches. I had already found that tmx.0702091313.dtb had seven more stations than tmn.0702091313.dtb, but that hadn't prepared me for the grisly truth:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/dtr] ./makedtr

MAKEDTR - Produce a DTR database

This program takes as its input a database of of minimum temperatures and another of maximum temperatures, and produces a database of diurnal temperatures. If the input databases are found to be out of synchronisation, the option is also given to save synchronised versions.

So, may I please have the tmin database? tmn.0702091139.dtb

May I please now have the tmax database? tmx.0702091313.dtb

The output database will now be called: dtr.0705152339.dtb

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IMPORTANT: PLEASE READ! (it's good for you)

The databases you gave are NOT synchronised!

tmn.0702091139.dtb has 42 'extra' stations

tmx.0702091313.dtb has 49 'extra' stations You have the choice of quitting, or of allowing me to create two new synchronised databases, which will be saved and used to create the dtr db

Enter Q to Quit, S to Synchronise: S New tmin database is: tmn.0705152339.dtb Discarded tmin stations here: tmn.0702091139.dtb.del New tmax database is: tmx.0705152339.dtb Discarded tmax stations here: tmx.0702091313.dtb.del Number of stations to process: 14267 <END QUOTE>

Yes, the difference is a lot more than seven! And the program helpfully dumps a listing of the surplus stations to the log file. Not a pretty sight.

Unfortunately, it hadn't worked either. It turns out that there are 3518 stations in each database with a WMO Code of ' 0'. So, as the makedtr program indexes on the WMO Code.. you get the picture. *cries*

Rewrote as makedtr2, which uses the first 20 characters of the header to match:

<BEGIN QUOTE> MAKEDTR2 - Produce a DTR database

This program takes as its input a database of of minimum temperatures and another of maximum temperatures, and produces a database of diurnal temperatures. If the input databases are found to be out of synchronisation, the option is also given to save synchronised versions.

So, may I please have the tmin database? tmn.0702091139.dtb

May I please now have the tmax database? tmx.0702091313.dtb

The output database will now be called: dtr.0705162028.dtb

IMPORTANT: PLEASE READ! (it's good for you)

The databases you gave are NOT synchronised!

tmn.0702091139.dtb has 203 'extra' stations

tmx.0702091313.dtb has 209 'extra' stations You have the choice of quitting, or of allowing me to create two new synchronised databases, which will be saved and used to create the dtr db

Enter Q to Quit, S to Synchronise: S New tmin database is: tmn.0705162028.dtb Discarded tmin stations here: tmn.0702091139.dtb.del New tmax database is: tmx.0705162028.dtb Discarded tmax stations here: tmx.0702091313.dtb.del

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<END QUOTE>

The big jump in the number of 'surplus' stations is because we are no longer automatically matching stations with WMO=0.

Here's what happened to the tmin and tmax databases, and the new dtr database:

Old tmin: tmn.0702091139.dtb Total Records Read: 14309 New tmin: tmn.0705162028.dtb Total Records Read: 14106 Del tmin: tmn.0702091139.dtb.del Total Records Read: 203

Old tmax: tmx.0702091313.dtb Total Records Read: 14315 New tmax: tmx.0705162028.dtb Total Records Read: 14106 Del tmax: tmx.0702091313.dtb.del Total Records Read: 209

New dtr: dtr.0705162028.dtb Total Records Read: 14107

*sigh* - one record out! Also three header problems:

BLANKS (expected at 8,14,21,26,47,61,66,71,78) position missed 8 1 14 1 21 0 26 0 47 1 61 0 66 0 71 0 78 0

Why?!! Well the sad answer is.. because we've got a date wrong. All three 'header' problems relate to this line:

6190 94 95 98 100 101 101 102 103 102 97 94 94

..and as we know, this is not a conventional header. Oh bum. But, but.. how? I know we do muck around with the header and start/end years, but still..

Wrote filtertmm.for, which simply steps through one database (usually tmin) and looks for a 'perfect' match in another database (usually tmax). 'Perfect' here means a match of WMO Code, Lat, Lon, Start-Year and End-Year. If a match is found, both stations are copied to new databases:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/dtr] ./filtertmm

FILTERTMM - Create GOOD tmin/max databases

Please enter the tmin database: tmn.0702091139.dtb

Please enter the tmax database: tmx.0702091313.dtb

working..

Old tmin database: tmn.0702091139.dtb had 14309 stations New tmin database: tmn.0705182204.dtb has 13016 stations Old tmax database: tmx.0702091313.dtb had 14315 stations New tmax database: tmx.0705182204.dtb has 13016 stations <END QUOTE>

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I am going to *assume* that worked! So now.. to incorporate the Australian monthly data packs. Ow. Most future-proof strategy is probably to write a converter that takes one or more of the packs and creates CRU-format databases of them. Edit: nope, thought some more and the *best* strategy is a program that takes *pairs* of Aus packs and updates the actual databases. Bearing in mind that these are trusted updates and won't be used in any other context.

From Dave L - who incorporated the initial Australian dump - for the tmin/tmax bulletins, he used a threshold of 26 days/month or greater for inclusion.

Obtained two files from Dave - an email that explains some of the Australian bulletin data/formatting, and a list of Austraian headers matched with their internal codes (the latter being generated by Dave).

Actually.. although I was going to assume that filtertmm had done the synching job OK, a brief look at the Australian stations in the databases showed me otherwise. For instance, I pulled all the headers with 'AUSTRALIA' out of the two 0705182204 databases. Now because these were produced by filtertmm, we know that the codes (if present), lats, lons and dates will all match. Any differences will be in altitude and/or name. And so they were:

crua6[/cru/cruts/version_3_0/db/dtr] diff tmn.0705182204.dtb.oz tmx.0705182204.dtb.oz | wc -l 336

..so roughly 100 don't match. They are mostly altitude discrepancies, though there

are an alarming number of name mismatches too. Examples of both:

74c74 < 0 -3800 14450 11 AVALON AIRPORT AUSTRALIA 2000 2006 -999 -999.00

--- > 0 -3800 14450 8 AVALON AIRPORT AUSTRALIA 2000 2006 -999 -999.00

16c16 < 0 -4230 14650 585 TARRALEAH VILLAGE AUSTRALIA 2000 2006 -999 -999.00

--- > 0 -4230 14650 595 TARRALEAH CHALET AUSTRALIA 2000 2006 -999 -999.00

Examples of the second kind (name mismatch) are most concerning as they may well be different stations. Looked for all occurences in all tmin/tmax databases:

crua6[/cru/cruts/version_3_0/db/dtr] grep 'TARRALEAH' *dtb tmn.0702091139.dtb: 0 -4230 14650 585 TARRALEAH VILLAGE AUSTRALIA 2000 2006 -999 -999.00 tmn.0702091139.dtb:9597000 -4230 14645 595 TARRALEAH CHALET AUSTRALIA 1991 2000 -999 -999.00 tmn.0705182204.dtb: 0 -4230 14650 585 TARRALEAH VILLAGE AUSTRALIA 2000 2006 -999 -999.00 tmn.0705182204.dtb:9597000 -4230 14645 595 TARRALEAH CHALET AUSTRALIA 1991 2000 -999 -999.00 tmx.0702091313.dtb: 0 -4230 14650 595 TARRALEAH CHALET AUSTRALIA 2000 2006 -999 -999.00 tmx.0702091313.dtb:9597000 -4230 14645 595 TARRALEAH CHALET AUSTRALIA 1991

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2000 -999 -999.00 tmx.0705182204.dtb: 0 -4230 14650 595 TARRALEAH CHALET AUSTRALIA 2000 2006 -999 -999.00 tmx.0705182204.dtb:9597000 -4230 14645 595 TARRALEAH CHALET AUSTRALIA 1991 2000 -999 -999.00

This takes a little sorting out. Well first, recognise that we are dealing with four files: tmin and tmax, early and late (before and after filtertmm.for). We see there are two TARRALEAH entries in each of the four files. We see that 'TARRALEAH VILLAGE' only appears in the tmin file. We see, most importantly perhaps, that they are temporally contiguous - that is, each pair could join with minimal overlap, as one is 1991-2000 and the other 2000-2006. Also, we note that the 'early' one of each pair has a slightly different longitude and altitude (the former being the thing that distinguished the stations in filtertmm.for).

Finally, this, from the tmax.2005120120051231.txt bulletin:

95018, 051201051231, -42.30, 146.45, 18.0, 00, 31, 31, 585,
TARRALEAH VILLAGE

So we can resolve this case - a single station called TARRALEAH VILLAGE, running from 1991 to 2006.

But what about the others?! There are close to 1000 incoming stations in the bulletins, must every one be identified in this way?!! Oh God. There's nothing for it - I'll have to write a prog to find matches for the incoming Australian bulletin stations in the main databases. I'll have to use the databases from before the filtertmm application, so *0705182204.dtb. And it will only need the Australian headers, so I used grep to create *0705182204.dtb.auhead files. The other input is the list of stations taken from the monthly bulletins. Now these have a different number of stations each month, so the prog will build an array of all possible stations based on the files we have. Oh boy. And the program shall be called, 'auminmaxmatch.for'.

Assembled some information:

crua6[/cru/cruts/version_3_0/db] wc -l *auhead 1518 glseries_tmn_final_merged.auhead 1518 tmn.0611301516.dat.auhead 1518 tmn.0612081255.dat.auhead 1518 tmn.0702091139.dtb.auhead 1518 tmn.0705152339.dtb.auhead 1426 tmn.0705182204.dtb.auhead

(the 'auhead' files were created with <grep 'AUSTRALIA'>)

Actually, stopped work on that. Trying to match over 800 'bulletin' stations against over 3,000 database stations *in two unsynchronised files* was just hurting my brain. The files have to be properly synchronised first, with a more lenient and interactive version of filtertmm. Or... could I use mergedb?! Pretend to merge tmin into tmax and see what pairings it

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managed? No roll through obviously. Well it's worth a play.

..unfortunately, not. Because when I tried, I got a lot of odd errors followed by a crash. The reason, I eventually deduced, was that I didn't build mergedb with the idea that WMO codes might be zero (many of the australian stations have wmo=0). This means that primary matching on WMO code is impossible. This just gets worse and worse: now it looks as though I'll have to find WMO Codes (or pseudo-codes) for the *3521* stations in the tmin file that don't have one!!!

OK.. let's break the problem down. Firstly, a lot of stations are going to need WMO codes, if available. It shouldn't be too hard to find any matches with the existing WMO coded stations in the other databases (precip, temperature). Secondly, we need to exclude stations that aren't synchronised between the two databases (tmin/tmax). So can mergedb be modified to treat WMO codes of 0 as 'missing'? Had a look, and it does check that the code isn't -999 OR 0.. but not when preallocating flags in subroutine 'countscnd'. Fixed that and tried running it again.. exactly the same result (crash). I can't see anything odd about the station it crashes on:

0 -2810 11790 407 MOUNT MAGNET AERO AUSTRALIA 2000 2006 -999 -999.00 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2000 339 344 280 252 214 202 189 196 262 291 316 377 2001 371 311 310 300 235 212 201 217 249 262 314 333 2002-9999-9999 339 297 258 209 205 212 246 299 341 358 2003 365 367 336 296 249 195 193 200 238 287 325 368 2004 395 374 321 284 219 214 173 188 239 309 305 370 2005 389 396 358 315 251 182 189 201 233 267 332 341 2006 366 331 314 246 240-9999-9999-9999-9999-9999-9999-9999

.. it's very similar to preceding (and following) stations, and the station before has even less real data (the one before that has none at all and is auto-deleted). The nature of the crash is 'forrtl: error (65): floating invalid' - so a type mismatch possibly. The station has a match in the tmin database (tmn.0702091139.dtb) but the longitude is different:

tmn.0702091139.dtb: 0 -2810 11780 407 MOUNT MAGNET AERO AUSTRALIA 2000 2006 -999 -999.00 tmx.0702091313.dtb: 0 -2810 11790 407 MOUNT MAGNET AERO AUSTRALIA 2000 2006 -999 -999.00

It also appears in the tmin/tmax bulletins, eg: 7600, 070401070430, -28.12, 117.84, 16.0, 00, 30, 30, 407, MOUNT
MAGNET AERO

Note that the altitude matches (as distinct from the station below).

Naturally, there is a further 'MOUNT MAGNET' station, but it's probably distinct:

tmn.0702091139.dtb:

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9442800 -2807 11785 427 MOUNT MAGNET (MOUNT AUSTRALIA 1956 1992 -999 -999.00 tmx.0702091313.dtb: 9442800 -2807 11785 427 MOUNT MAGNET (MOUNT AUSTRALIA 1957 1992 -999 -999.00

I am at a bit of a loss. It will take a very long time to resolve each of these 'rogue' stations. Time I do not have. The only pragmatic thing to do is to dump any stations that are too recent to have normals. They will not, after all, be contributing to the output.

So I knocked out 'goodnorm.for', which simply uses the presence of a valid normals line

to sort. The results were pretty scary:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/dtr] ./goodnorm

GOODNORM: Extract stations with non-missing normals

Please enter the input database name: tmn.0702091139.dtb The output database will be called: tmn.0705281724.dtb

(removed stations will be placed in: tmn.0705281724.del)

FINISHED.

Stations retained: 5026 Stations removed: 9283

crua6[/cru/cruts/version_3_0/db/dtr] ./goodnorm

GOODNORM: Extract stations with non-missing normals

Please enter the input database name: tmx.0702091313.dtb The output database will be called: tmx.0705281724.dtb

(removed stations will be placed in: tmx.0705281724.del)

FINISHED.

Stations retained: 4997 Stations removed: 9318

<END QUOTE>

Essentially, two thirds of the stations have no normals! Of course, this still leaves us with a lot more stations than we had for tmean (goodnorm reported 3316 saved, 1749 deleted) though still far behind precipitation (goodnorm reported 7910 saved, 8027 deleted).

I suspect the high percentage lost reflects the influx of modern Australian data. Indeed, nearly 3,000 of the 3,500-odd stations with missing WMO codes were excluded by this operation. This means that, for tmn.0702091139.dtb, 1240 Australian stations were lost, leaving only 278.

This is just silly. I can't dump these stations, they are needed to potentially match with the bulletin stations. I am now going to try the following:

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1. Attempt to pair bulletin stations with existing in the tmin database. Mark pairings in the database headers and in a new 'Australian Mappings' file. Program auminmatch.for.

2. Run an enhanced filtertmm to synchronise the tmin and tmax databases, but prioritising the 'paired' stations from step 1 (so they are not lost). Mark the same pairings in the tmax headers too, and update the 'Australian Mappings' file.

3. Add the bulletins to the databases.

OK.. step 1. Modified auminmaxmatch.for to produce auminmatch.for. Hit a semi-philosophical problem: what to do with a positive match between a bulletin station and a zero-wmo database station? The station must have a real WMO code or it'll be rather hard to describe the match!

Got a list of around 12,000 wmo codes and stations from Dave L; unfortunately there was a problem with its formatting that I just couldn't resolve.

So.. current thinking is that, if I find a pairing between a bulletin station and a zero-coded Australain station in the CRU database, I'll give the CRU database station the Australian local (bulletin) code twice: once at the end of the header, and once as the WMO code *multiplied by -1* to avoid implying that it's legitimate. Then if a 'proper' code is found or allocated later, the mapping to the bulletin code will still be there at the end of the header. Of course, an initial check will ensure that a match can't be found, within the CRU database, between the zero-coded station and a properly-coded one.

Debated header formats with David. I think we're going to go with (i8,a8) at the end of the header, though really it's (2x,i6,a8) as I remember the Anders code being i2 and the real start year being i4 (both from the tmean database). This will mean post-processing existing databases of course, but that's not a priority.

A brief (hopefully) diversion to get station counts sorted. David needs them so might as well sort the procedure. In the upside-down world of Mark and Tim, the numbers of stations contributing to each cell during the gridding operation are calculated not in the IDL gridding program - oh, no! - but in anomdtb! Yes, the program which reads station data and writes station data has a second, almost-entirely unrelated function of assessing gridcell contributions. So, to begin with it runs in the usual way:

crua6[/cru/cruts/version_3_0/primaries/precip] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:

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.pre > Will calculate percentage anomalies. > Select the .cts or .dtb file to load: pre.0612181221.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 4

But then, we choose a different output, and it all shifts focus and has to ask all the IDL questions!!

> Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 4 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the .stn file to save: pre.stn > Enter the correlation decay distance: 450 > Submit a grim that contains the appropriate grid. > Enter the grim filepath: clim.6190.lan.pre

> Grid dimensions and domain size: 720 360 67420 > Select the first,last years AD to save: 1901,2006 > Operating...

> NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 > .cts 299359 3.0 7613600 76.8 > PROCESS DECISION percent %of-chk > no lat/lon 17911 0.2 0.2 > no normal 2355275 23.8 23.8 > out-of-range 13253 0.1 0.2 > accepted 7521013 75.9 > Calculating station coverages...

And then.. it unhelpfully crashes:

> ##### WithinRange: Alloc: DataB ##### forrtl: severe (174): SIGSEGV, segmentation fault occurred

Ho hum. I did try this last year which is why I'm not tearing my hair out. The plan is to use the outputs from the regular anomdtb runs - ie, the monthly files of valid stations. After all we need to know the station counts on a per month basis. We can use the lat and lon, along with the correlation decay distance.. shouldn't be too awful. Just even more programming and work. So before I commit to that, a quick look at the IDL gridding prog to see if it can dump the figures instead: after all, this is where the actual 'station count' information is assembled and used!!

..well that was, erhhh.. 'interesting'. The IDL gridding program calculates whether

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or not a station contributes to a cell, using.. graphics. Yes, it plots the station sphere of influence then checks for the colour white in the output. So there is no guarantee that the station number files, which are produced *independently* by anomdtb, will reflect what actually happened!!

Well I've just spent 24 hours trying to get Great Circle Distance calculations working in Fortran, with precisely no success. I've tried the simple method (as used in Tim O's geodist.pro, and the more complex and accurate method found elsewhere (wiki and other places). Neither give me results that are anything near reality. FFS.

Worked out an algorithm from scratch. It seems to give better answers than the others, so we'll go with that. Also decided that the approach I was taking (pick a gridline of latitudeand reverseengineer the GCD algorithm so the unknown is the second lon) was overcomplicated, when we don't need to know where it hits, just that it does. Since for any cell the nearest point to the station will be a vertex, we can test candidate cells for the distance from the appropriate vertex to the station. Program is stncounts.for, but is causing immense problems.

The problem is, really, the huge numbers of cells potentially involved in one station, particularly at high latitudes. Working out the possible bounding box when you're within cdd of a pole (ie, for tmean with a cdd of 1200, the N-S extent is over 20 cells (10 degs) in each direction. Maybe not a serious problem for the current datasets but an example of the complexity. Also, deciding on the potential bounding box is nontrivial, because of cell 'width' changes at high latitudes (at 61 degs North, the half-degree cells are only 27km wide! With a precip cdd of 450 km this means the bounding box is dozens of cells wide - and will be wider at the Northern edge!

Clearly a large number of cells are being marked as covered by each station. So in densely-stationed areas there will be considerable smoothing, and in sparsely-stationed (or empty) areas, there will be possibly untypical data. I might suggest two station counts - one of actual stations contributing from within the cell, one for stations contributing from within the cdd. The former being a subset of the latter, so the latter could be used as the previous release was used.

Well, got stncounts.for working, finally. And, out of malicious interest, I dumped the first station's coverage to a text file and counted up how many cells it 'influenced'. The station was at 10.6E, 61.0N. The total number of cells covered was a staggering 476! Or, if you prefer, 475 indirect and one direct.

Ran for the first month (01/1901). Compared the resulting grid with that from CRU TS 2.1. Seems to compare fine, some higher, some lower. Example:

2.10: 139 142 146 154 156 157 165 170

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3.00: 141 148 154 153 153 159 163 168

(data are on latitude #265 and longitudes #163-170)

Wrote 'makelsmask.for' to, well, make a land-sea mask. It'll work with any gridded data file that uses -999 for sea. The mask is called 'lsmask.halfdeg.dat'. Adapted stncounts.for to read it and use it to mask the output files.

Still a bit disturbed by the large number of cells marked as 'influenced' by a single station. IDL seems to use the inbuilt 'TRIGRID' function to interpolate the grid, so there's no way of getting the station count for a particular cell that way anyway. Not that it would mean much, since there is bound to be some kind of weighting (it's not clear what that weighting is, though, from the IDL website). So the figures in the station count files are really rather loose. What might be useful as a companion dataset would be the ACTUAL station counts. Counts for cells with stations actually INSIDE them. Of course, that might be rather sensitive information..

Managed a full run of stncounts. It took over five and a half hours, which is a bit much!

Back to the gridding. I am seriously worried that our flagship gridded data product is produced by Delaunay triangulation - apparently linear as well. As far as I can see, this renders the station counts totally meaningless. It also means that we cannot say exactly how the gridded data is arrived at from a statistical perspective - since we're using an off-the-shelf product that isn't documented sufficiently to say that. Why this wasn't coded up in Fortran I don't know - time pressures perhaps? Was too much effort expended on homogenisation, that there wasn't enough time to write a gridding procedure? Of course, it's too late for me to fix it too. Meh.

Well, it's been a real day of revelations, never mind the week. This morning I discovered that proper angular weighted interpolation was coded into the IDL routine, but that its use was discouraged because it was slow! Aaarrrgghh. There is even an option to tri-grid at 0.1 degree resolution and then 'rebin' to 720x360 - also deprecated! And now, just before midnight (so it counts!), having gone back to the tmin/tmax work, I've found that most if not all of the Australian bulletin stations have been unceremoniously dumped into the files without the briefest check for existing stations. A classic example would be these 'two' stations:

0 -1570 12870 31 KIMBERLEY RES.STATIO AUSTRALIA 2000 2006 -999 -999.00 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2000 245 243 243 232 184 143 138 155 193 231 249 249 2001 245 247 241 216 156 167 163 129 201 238 246 247 2002 244 246 230 208 167 122 92 119 202 217 248 259 2003 253 249 222 220 169 151 144 158 203 216 248 250 2004 252 247 244 209 202 135 129 140 176 230 248 257 2005 245 246 237-9999-9999-9999-9999-9999-9999-9999-9999-9999 2006-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

0 -1565 12871 31 KIMBERLEY RES.STATIO AUSTRALIA 1971 2000 -999 -999.00 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

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1971 254 249 239 218 166 147 142 169 214 246 253 241 1972 246 244 226 198 175 158 126 182 200 222 244 259 1973 255 259 252 232 215 186 171 189 216 240 256 246 1974 247 243 240 217 183 144 134 171 216 247 248 246 1975 239 239 237 216 180 157 168 171 223 233 243 246 1976 235 244 227 190 148 142 142 144 177 236 252 250 1977 253 249 245 218 177 135 130 137 187 226 250 248 1978 247 244 239 199 218 174 162 186 195 233 245 253 1979 247 246 238 217 205 166 147 178 216 234 248 254 1980 249 245 240 221 186 161 141 171 192 241 249 252 1981-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1982-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1983-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1984-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1985-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1986-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1987-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1988-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1989-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1990-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1991 248 244 234 224 169 160 160 140 210 225 252 260 1992 253 251 247 239 206-9999 141 173 218 237 246 260 1993 247-9999 242 225 207 172 149 170 204 237 249 258 1994 253-9999 214 196 171 140 130 141 171 222 248 247 1995 245 249 234 205 186 155 148 151 198 217 245 244 1996 245 238 220 208 159 166 136 161 179 225 233 247 1997 245 243 217 195 186 149 138 156 195 230 242 247 1998 248 250 245 229 188 167 177 158 200 247 253 250 1999 250 245 242 216 144 150 123-9999 188 239 240 251 2000 245 243 243 232 184 143 138 154 194 231 249 249

Now, I admit the lats and lons aren't spot on. But c'mon, what are the chances of them being different? The two year 2000s are almost identical. What about:

0 -1550 12450 12 KURI BAY AUSTRALIA 2000 2006 -999 -999.00 9420800 -1548 12452 29 KURI BAY AUSTRALIA 1965 1992 -999 -999.00

Or:

0 -1550 12810 11 WYNDHAM AUSTRALIA 2000 2006 -999 -999.00 0 -1550 12820 4 WYNDHAM AERO AUSTRALIA 2000 2006 -999 -999.00 9421400 -1549 12812 11 WYNDHAM POST OFFICE AUSTRALIA 1968 2000 -999 -999.00 9421401 -1547 12810 20 WYNDHAM (WYNDHAM POR AUSTRALIA 1898 1966 -999 -999.00

Come On!! This is one station isn't it.

I'd be content to leave it - but I have to match the bulletins! And I can match to the long, stable series or to the loose, flapping ones put in for the purpose! Meh II.

So.. in the end I matched to the 2000-2006 stations, where they actually did match. Unfortunately the huge bulk of the bulletins still had to have new entries created for them, which is a shame, and begs the question of why the Australian update bulletins don't match the original 'catch-up' block they sent us.

For some reason, the auminmatch program is causing no end of grief. I thought I'd

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managed a complete run, and it did produce a good-looking tmin database with lots of new station stubs tacked on the end:

-1009 -6628 11054 12 KURI BAY AUSTRALIA 2007 2007 -999 1009 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2006-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 -1019 -6628 11054 23 KALUMBURU AUSTRALIA 2007 2007 -999 1019 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2006-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 -1020 -6628 11054 51 TRUSCOTT AUSTRALIA 2007 2007 -999 1020 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2006-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

However, it doesn't seem to have put the bulletin codes on the (a8) header field, for some of the matches only!

Not sure why this is yet.. but have found also that there are cases of duplicated lat/lon pairs, so multiple matches are being made.. argh.. will have to further augment auminmatch. Not happy.

An interesting aside.. David was looking at the v3.00 precip to help National Geographic with an enquiry. I produced a second 'station' file with the 'honest' counts (see above) and he used that to mask out cells with a 0 count (ie that only had indirect data from 'nearby' stations). There were some odd results.. with certain months havign data, and others being missing. After considerable debate and investigation, it was understood that anomdtb calculates normals on a monthly basis. So, where there are 7 or 8 missing values in each month (1961-1990), a station may end up contributing only in certain months of the year, throughout its entire run! This was noticed in the Seychelles, where only October has real data (the remaining months being relaxed to the climatology but excluded by David using the 'tight' station mask). There is
no easy solution, because essentially it's an honest result: only October has sufficient values to form a normal, so only October gets anomalised. It's an unfortunate concidence that it's the only station in the cell, but it's not the only one. A 'solution' could be for anomdtb to get a bit more involved in the gridding, to check that if a cell only has one station (for one

or more years) then it's all-or-nothing. Maybe if only one month has a normal then it's dumped and the whole reverts to climatology. Maybe if 4 or more months have normals.. maybe if >0 months have normals and the rest can be brought in with a minor relaxation of the '75% rule'.. who knows.

Back to auminmatch.for, and a (philosophical) breakthrough. Built a loop to find 'fuzzy' matches and group them together. The user then processes one group at a time, pairing up matches until the potential for further matches is zero (or the user decides it is).

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Uses a FSM to work out each chain (all db matches for a bulletin, then all bulletins that match each of those db stations, then.. etc). To understand it, either read the code (especially the comments) or just look at this mind-boggling example from the first run of it:

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+- User Match Decision(s) Please! Bulletin stations: 8 1. 9021 -3193 11598 15 PERTH AIRPORT 2. 9225 -3192 11587 25 PERTH METRO 3. 9106 -3205 11598 10 GOSNELLS CITY 4. 9240 -3201 11614 384 BICKLEY 5. 9172 -3210 11588 30 JANDAKOT AERO 6. 9215 -3196 11576 41 SWANBOURNE 7. 9194 -3222 11581 14 MEDINA RESEARCH CENT 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 18 1. 0 -3190 11590 25 PERTH METRO 2000 2006 2. 0 -3190 11600 15 PERTH AIRPORT 2000 2006 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992 6. 0 -3200 11580 41 SWANBOURNE 2000 2006 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 11. 0 -3210 11590 30 JANDAKOT AERO 2000 2006 12. 0 -3210 11600 10 GOSNELLS CITY 2000 2006 13. 0 -3200 11610 384 BICKLEY 2000 2006 14. 0 -3220 11580 14 MEDINA RESEARCH CENT 2000 2006 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000 17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 1,2 Bulletin stations: 7 2. 9225 -3192 11587 25 PERTH METRO 3. 9106 -3205 11598 10 GOSNELLS CITY 4. 9240 -3201 11614 384 BICKLEY 5. 9172 -3210 11588 30 JANDAKOT AERO 6. 9215 -3196 11576 41 SWANBOURNE 7. 9194 -3222 11581 14 MEDINA RESEARCH CENT 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 17 1. 0 -3190 11590 25 PERTH METRO 2000 2006 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992 6. 0 -3200 11580 41 SWANBOURNE 2000 2006 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 11. 0 -3210 11590 30 JANDAKOT AERO 2000 2006 12. 0 -3210 11600 10 GOSNELLS CITY 2000 2006 13. 0 -3200 11610 384 BICKLEY 2000 2006 14. 0 -3220 11580 14 MEDINA RESEARCH CENT 2000 2006 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000

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17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 2,1 Bulletin stations: 6 3. 9106 -3205 11598 10 GOSNELLS CITY 4. 9240 -3201 11614 384 BICKLEY 5. 9172 -3210 11588 30 JANDAKOT AERO 6. 9215 -3196 11576 41 SWANBOURNE 7. 9194 -3222 11581 14 MEDINA RESEARCH CENT 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 16 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992 6. 0 -3200 11580 41 SWANBOURNE 2000 2006 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 11. 0 -3210 11590 30 JANDAKOT AERO 2000 2006 12. 0 -3210 11600 10 GOSNELLS CITY 2000 2006 13. 0 -3200 11610 384 BICKLEY 2000 2006 14. 0 -3220 11580 14 MEDINA RESEARCH CENT 2000 2006 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000 17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 3,12 Bulletin stations: 5 4. 9240 -3201 11614 384 BICKLEY 5. 9172 -3210 11588 30 JANDAKOT AERO 6. 9215 -3196 11576 41 SWANBOURNE 7. 9194 -3222 11581 14 MEDINA RESEARCH CENT 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 15 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992 6. 0 -3200 11580 41 SWANBOURNE 2000 2006 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 11. 0 -3210 11590 30 JANDAKOT AERO 2000 2006 13. 0 -3200 11610 384 BICKLEY 2000 2006 14. 0 -3220 11580 14 MEDINA RESEARCH CENT 2000 2006 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000 17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 4,13 Bulletin stations: 4 5. 9172 -3210 11588 30 JANDAKOT AERO 6. 9215 -3196 11576 41 SWANBOURNE 7. 9194 -3222 11581 14 MEDINA RESEARCH CENT 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 14 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992

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6. 0 -3200 11580 41 SWANBOURNE 2000 2006 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 11. 0 -3210 11590 30 JANDAKOT AERO 2000 2006 14. 0 -3220 11580 14 MEDINA RESEARCH CENT 2000 2006 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000 17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 5,11 Bulletin stations: 3 6. 9215 -3196 11576 41 SWANBOURNE 7. 9194 -3222 11581 14 MEDINA RESEARCH CENT 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 13 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992 6. 0 -3200 11580 41 SWANBOURNE 2000 2006 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 14. 0 -3220 11580 14 MEDINA RESEARCH CENT 2000 2006 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000 17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 6,6 Bulletin stations: 2 7. 9194 -3222 11581 14 MEDINA RESEARCH CENT 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 12 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 14. 0 -3220 11580 14 MEDINA RESEARCH CENT 2000 2006 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000 17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 7,14 Bulletin stations: 1 8. 9256 -3224 11568 6 GARDEN ISLAND HSF Database stations: 11 3. 9461000 -3190 11600 20 PERTH AIRPORT COMPAR 1944 2006 4. 9461001 -3190 11600 18 PERTH AIRPORT 1944 2004 5. 9461501 -3198 11607 210 KALAMUNDA (KALAMUNDA 1908 1992 7. 0 -3200 11580 20 SUBIACO TREATMENT PL 2000 2006 8. 0 -3196 11579 20 SUBIACO TREATMENT PL 1991 1999 9. 9460800 -3195 11587 19 PERTH (PERTH REGIONA 1897 1992 10. 9460801 -3195 11587 19 PERTH-(PERTH-REGIONA 1897 1992 15. 0 -3220 11580 4 KWINANA BP REFINERY 2000 2006 16. 0 -3223 11576 4 KWINANA BP REFINERY 1961 2000

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17. 9560800 -3222 11581 14 MEDINA RESEARCH CENT 1991 2000 18. 0 -3220 11570 6 GARDEN ISLAND HSF 2000 2006

Enter a matching pair, (bulletin,database) or 'n' to end: 8,18

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

Amazing, huh? Most are actually more like this:

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+- User Match Decision(s) Please! Bulletin stations: 1 1. 9053 -3167 11602 40 PEARCE RAAF Database stations: 2 1. 0 -3170 11600 40 PEARCE RAAF 2000 2006 2. 9461200 -3167 11602 49 BULLSBROOK (PEARCE A 1940 1992

Enter a matching pair, (bulletin,database) or 'n' to end: 1,1

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

However.. still teething troubles, with previously-paired stations reappearing for a second chance sometimes! So more debugging.. fixed. Also added a test before the user gets a chain, to anticipate what the user (er, I) would do. For instance, I generally match to a 200-2006, WMO=0 database station if the names match, as they're the ones David L put in from the Aus update files. I, er, the user then gets ambiguities and nearby but unconnected stations. Fine, until you get a nasty surprise like this one:

User Match Decision(s) Please! Bulletin stations: 2 1. 58009 -2864 15364 95 BYRON BAY (CAPE BYRO 2. 58216 -2864 15364 95 BYRON BAY (CAPE BYRO Database stations: 3 1. 0 -2860 15360 95 BYRON BAY (CAPE BYRO 2000 2006 2. 0 -2860 15360 95 BYRON BAY (CAPE BYRO 2000 2006 3. 9459500 -2863 15363 98 CAPE BYRON 1974 1992

Looking in the files I see that Bulletin 58009 is 'BYRON BAY (CAPE BYRON LIGHTHOUSE)', and 58216 is 'BYRON BAY (CAPE BYRON AWS)'. But the database stubs that have been entered have not been intelligently named, just truncated - so I have no way of knowing which is which! CRU NEEDS A DATA MANAGER. In this case I had to assume that the updates were processed in .au code order, so 1-1 and 2-2. Argh. A few doubles found, too:

Bulletin stations: 1 1. 33106 -2037 14895 59 HAMILTON ISLAND AIRP Database stations: 3 1. 0 -2040 14900 23 HAMILTON ISLAND AIRP 2000 2006 2. 0 -2040 14900 59 HAMILTON ISLAND AIRP 2000 2006 3. 9436800 -2035 14895 23 HAMILTON ISLAND AIRP 1991 2000

Bulletin stations: 1 1. 90186 -3829 14245 71 WARRNAMBOOL AIRPORT Database stations: 4 1. 0 -3830 14250 71 WARRNAMBOOL AIRPORT 2000 2006 2. 0 -3830 14240 75 WARRNAMBOOL AIRPORT 2000 2006 3. 0 -3840 14248 21 WARRNAMBOOL (POST OF 1961 1980 4. 0 -3828 14243 76 WARRNAMBOOL A 1983 1999

And the results? Strictly average, I thought.. but I'd forgotten to count the extra 'anticipated match' routine achievements! So I grepped the match-by-match file, matches.0706281447.dat, and got:

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crua6[/cru/cruts/version_3_0/db/dtr] grep 'AUTO\:' matches.0706281447.dat |wc -l 232 crua6[/cru/cruts/version_3_0/db/dtr] grep 'AUTO FROM CHAIN' matches.0706281447.dat | wc -l 514 crua6[/cru/cruts/version_3_0/db/dtr] grep 'MANUAL' matches.0706281447.dat | wc -l 12

In other words, all that sweat was worth it - 746 stations matched automatically, and a further 12 manually! Only (797-758=) 39 bulletins unmatched! Wheeee! And here they

are:

-6072 -2303 11504 111 EMU CREEK STATION AUSTRALIA 2007 2007 -999 6072 -12044 -3355 12070 220 MUNGLINUP WEST AUSTRALIA 2007 2007 -999 12044 -12241 -2888 12132 370 LEONORA AERO AUSTRALIA 2007 2007 -999 12241 -17031 -2965 13806 50 MARREE COMPARISON AUSTRALIA 2007 2007 -999 17031 -21118 -3323 13800 10 PORT PIRIE AERODROME AUSTRALIA 2007 2007 -999 21118 -22801 -3575 13659 143 CAPE BORDA COMPARISO AUSTRALIA 2007 2007 -999 22801 -23122 -3451 13868 65 ROSEWORTHY AWS AUSTRALIA 2007 2007 -999 23122 -24521 -3512 13926 33 MURRAY BRIDGE COMPAR AUSTRALIA 2007 2007 -999 24521 -25509 -3533 14052 99 LAMEROO COMPARISON AUSTRALIA 2007 2007 -999 25509 -26026 -3716 13976 3 ROBE COMPARISON AUSTRALIA 2007 2007 -999 26026 -32004 -1826 14602 5 CARDWELL MARINE PDE AUSTRALIA 2007 2007 -999 32004 -35019 -2282 14764 260 CLERMONT SIRIUS ST AUSTRALIA 2007 2007 -999 35019 -48243 -2943 14797 154 LIGHTNING RIDGE VISI AUSTRALIA 2007 2007 -999 48243 -55024 -3103 15027 307 GUNNEDAH RESOURCE CE AUSTRALIA 2007 2007 -999 55024 -56037 -3053 15167 987 ARMIDALE (TREE GROUP AUSTRALIA 2007 2007 -999 56037 -60013 -3218 15251 4 FORSTER - TUNCURRY R AUSTRALIA 2007 2007 -999 60013 -63039 -3371 15031 1015 KATOOMBA (MURRI ST) AUSTRALIA 2007 2007 -999 63039 -63226 -3348 15013 900 LITHGOW (COOERWULL) AUSTRALIA 2007 2007 -999 63226 -68257 -3406 15077 112 CAMPBELLTOWN (MOUNT AUSTRALIA 2007 2007 -999 68257 -70263 -3475 14970 670 GOULBURN TAFE AUSTRALIA 2007 2007 -999 70263 -82170 -3655 14600 171 BENALLA AIRPORT AUSTRALIA 2007 2007 -999 82170 -84150 -3787 14801 4 LAKES ENTRANCE (EAST AUSTRALIA 2007 2007 -999 84150 -85099 -3863 14581 3 POUND CREEK AUSTRALIA 2007 2007 -999 85099 -88023 -3723 14591 230 LAKE EILDON AUSTRALIA 2007 2007 -999 88023 -200001 -2166 15027 209 MIDDLE PERCY ISLAND AUSTRALIA 2007 2007 -999 200001

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-200100 -2066 11558 24 VARANUS ISLAND AUSTRALIA 2007 2007 -999 200100 -200212 -1061 12598 -999 NORTHERN ENDEAVOUR AUSTRALIA 2007 2007 -999 200212 -200283 -1629 14997 8 WILLIS ISLAND AUSTRALIA 2007 2007 -999 200283 -200288 -2904 16794 112 NORFOLK ISLAND AERO AUSTRALIA 2007 2007 -999 200288 -200731 -1176 13003 7 POINT FAWCETT AUSTRALIA 2007 2007 -999 200731 -200783 -1772 14845 3 FLINDERS REEF AUSTRALIA 2007 2007 -999 200783 -200790 -1045 10569 261 CHRISTMAS ISLAND AER AUSTRALIA 2007 2007 -999 200790 -200824 -1753 21040 2 PAPEETE AUSTRALIA 2007 2007 -999 200824 -200838 -3922 14698 116 HOGAN ISLAND AUSTRALIA 2007 2007 -999 200838 -200851 -52 16692 7 NAURU ARCS-2 AUSTRALIA 2007 2007 -999 200851 -200852 -206 14743 4 MANUS ARCS-1 AUSTRALIA 2007 2007 -999 200852 -300000 -6858 7797 18 DAVIS AUSTRALIA 2007 2007 -999 300000 -300001 -6760 6287 10 MAWSON AUSTRALIA 2007 2007 -999 300001 -300017 -6628 11054 40 CASEY AUSTRALIA 2007 2007 -999 300017

Resultant database: tmn.0707021605.dtb

[edit: found another fault, had to re-run. Headers weren't being modded if the WMO code was already there]

32. The next stage *heart falls* will be to synchronise tmax *against* tmin, sweeping up duplicates in the process. How long's THIS gonna take? Well actually, it might be fairly easy, if we use a similar approach. We can base it all around the user being given a 'cloud' of related stations to pick pairs from, only they will be uniquely numbered so that two from the same database can be selected. The user can in this way 'pair up' stations in
groups.

Of course, this comes with the downside of complexity (and therefore bugs). And both databases will almost certainly have to be preloaded in their entirety because of the need for the user to be able to confirm header and data precedence info when stations within a database are merged.

Oh - and I'll have to move bloody quick. So more bugs.

Well.. it's written, and debugging. Around 1500 lines of code, or 1000 without all the comments ;-) It does indeed read in all the data, so has to be compiled on uealogin1 (as crua6 doesn't have enough memory!). Reusing code from auminmatch.for did speed things up a bit, though

two new subroutines had to be written to carry out checking for merges (within a database) and for

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matches (between the databases). Also introduced a user decision at the start to allow the TMin database to take precedence in terms of station metadata. Here's the current state of play:

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/dtr] ./auminmaxsync

WELCOME TO THE TMIN/TMAX SYNCHRONISER

Before we get started, an important question: Should TMin header info take precedence over TMax?

This will significantly reduce user decisions later, but is a big step as TMax settings may be silently overridden!

To let TMin header values take precedence over those of TMax, enter 'YES': YES Please enter the tmin database name: tmn.0707021605.dtb Please enter the tmax database name: tmx.0702091313.dtb

Reading in both databases.. TMin database stations: 14349 TMax database stations: 14315

Processing one-to-one matches..

Initial scan found: one-to-one matches: 7875 of which confirmed: 7691 in a station cloud: 6411 (tmin) in a station cloud: 6392 (tmax) unmatchable: 63 (tmin) unmatchable: 48 (tmax) Processing match clouds..

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+- User Match Decision(s) Please! TMin stations: 2 1. -401000 3178 3522 783 JERUSALEM 1863 2000 -999 0 2. 4018400 3178 3522 809 JERUSALEM 1977 1995 -999 0 TMax stations: 2 3. -401000 3178 3522 783 JERUSALEM 1863 2000 -999 0 4. 4018400 3178 3522 809 JERUSALEM 1977 1995 -999 0

*** Remember: Merge first, Match second! *** Enter ANY pair to match or merge, or 'n' to end: <END QUOTE>

So stats pretty much as expected/hoped. The one-to-one matches should, of course, be 100%.. but as the databases aren't synchronised, and as there are hundreds of 'duplicate' entries.. only around 50% match straight away. The situation isn't as bleak as it looks, though - there is further automatching at the beginning of each cloud, so the user can still be spared the obvious. If the merging gets too onerous, though, I might have to automate that - with associated risks.

And of course - if you look closely - things are still a little offbeam :-/

Found another database bug by chance.. a <tab> instead of a space after 'CRANWELL':

-324320 5303 -50 62 CRANWELL UK 1961 1995 -999

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-999.00

Doesn't show up in reads as it's a white space character. Argh. Fixed in tmin & tmax. Now to find out why some matched stations STILL don't have the backref in the last header field!! ..found it, not my problem, it's the ones that *pre-existed* in the databases, there's 84 in total I think. So I can write a proglet to check that any with negative WMO codes have the positive version in that last field. And I did - 'fixtnxrefs.for'. Fixed: tmn.0702091139.dtb (84 fixed) tmn.0707021605.dtb (651 'fixed' - includes all with negative WMOs regardless of end field) tmx.0702091313.dtb (84 fixed)

So why, when we matched 758 bulletins in the first place, did this program only 'fix' 651, of which 84 were preexisting? Because, of course, the matches only get a negative WMO code if the original WMO code is missing (zero). The 'missing' stations would be ones that already had a WMO code.

So, try again, and it's looking good!

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/dtr] ./auminmaxsync

WELCOME TO THE TMIN/TMAX SYNCHRONISER

Before we get started, an important question: Should TMin header info take precedence over TMax?

This will significantly reduce user decisions later, but is a big step as TMax settings may be silently overridden!

To let TMin header values take precedence over those of TMax, enter 'YES': YES Please enter the tmin database name: tmn.0702091139.dtb Please enter the tmax database name: tmx.0702091313.dtb

Reading in both databases.. TMin database stations: 14309 TMax database stations: 14315

Processing one-to-one matches..

Initial scan found: one-to-one matches: 7889 of which confirmed: 7702 in a station cloud: 6365 (tmin) in a station cloud: 6378 (tmax) unmatchable: 55 (tmin) unmatchable: 48 (tmax) Processing match clouds..

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+- User Match Decision(s) Please! TMin stations: 2 1. -401000 3178 3522 783 JERUSALEM 1863 2000 -999 401000 2. 4018400 3178 3522 809 JERUSALEM 1977 1995 -999 0 TMax stations: 2 3. -401000 3178 3522 783 JERUSALEM 1863 2000 -999 401000 4. 4018400 3178 3522 809 JERUSALEM 1977 1995 -999 0

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*** Remember: Merge first, Match second! *** Enter ANY pair to match or merge, or 'n' to end: 1,2 Merging two stations from the TMin database: Stn 1: -401000 3178 3522 783 JERUSALEM ISRAEL 1863 2000 -999 401000 Stn 2: -401000 3178 3522 783 JERUSALEM ISRAEL 1863 2000 -999 401000 Please resolve the following inconsistencies: Overlap: Station A) -401000 3178 3522 783 JERUSALEM ISRAEL 1863 2000 -999 401000 Station B) 4018400 3178 3522 809 JERUSALEM ISRAEL 1977 1995 -999 -999.00

You must decide which station's data takes precedence. The intercorrelation for the period is: 0.99 Enter A or B, or undo pair(X):

<END QUOTE>

Well.. it's kinda working. I found some idiotic bugs, though it is a fearsomely complicated program with lots of indirect pointers (though I do try and resolve them at the first opportunity). One thing that's making debugging frustratingly difficult is something that must be a uealogin1 feature, and I haven't seen it before: the program doesn't actually flush the output channels whenever you write! For example, as I write this the program has dispensed with auto-matching:

Initial scan found: one-to-one matches: 7875 of which confirmed: 7691 in a station cloud: 6411 (tmin) in a station cloud: 6392 (tmax) unmatchable: 63 (tmin) unmatchable: 48 (tmax)

(yes, it's a little tighter now)

Anyway, since then I've merged two pair (JERUSALEM) then paired the remainder. That activity has generated match reports on channel 31 BUT THEY ARE NOT IN THE FILE YET. Here is the tail of channel 31:

crua6[/cru/cruts/version_3_0/db/dtr] tail mat.0707121500.dat TMax: 9929470 4330 1340 342 MACERATA ITALY 1953 1975 -999 -999.00
AUTO PAIRING FROM ONE-TO-ONE SCAN: TMin: 9929480 4030 880 585 MACOMER ITALY 1952 1978 -999 -999.00 TMax: 9929480 4030 880 585 MACOMER ITALY 1952 1978 -999 -999.00
AUTO PAIRING FROM ONE-TO-ONE SCAN: TMin: 9929500 4010 1850 86 PALASCIA AERO ITALY 1952 1978 -999 -999.00 TMax: 9929500 4010 1850 86 PALASCIA AERO ITALY 1952 1978 -999 -999.00
AUTO PAIRING FROM ONE-TO-ONE SCAN: TMin: 9929520 4060 1490 30 PONTECAGNANO ITALY 1951 1978 -999 -999.00 TMax: 9929520 4060 1490 30 PONTECAGNANO ITALY 1951 1978 -999 -999.00

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In addition, the log file is EMPTY, yet at least 416 bytes have been written to it. How the hell can I debug if I can't monitor what's being written to the log files?!! Of course, once I force-quit the program, and wait a bit.. the missing info appears. Similarly if I carry on using the program, the files get more info. It's as if there's a write buffer that runs FIFO. Must look at the 'help'.. why is it that whenever I crack the programming, the systems themselves step in the screw it up? And computer support is away of course.

Looked at f77 -help.. nothing. well nothing obvious. Anyway, more debugging and..

Seems to be working. But it's going to take ages. Here is an example of the problem:

<BEGIN QUOTE>

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+- User Match Decision(s) Please! TMin stations: 2 1. -315770 5638 -287 10 LEUCHARS UK 1959 1995 -999 315770 2. 317100 5640 -287 12 LEUCHARS UNITED KINGDO 1997 2006 -999 0 TMax stations: 2 3. -315770 5638 -287 10 LEUCHARS UK 1959 1995 -999 315770 4. 317100 5638 -287 12 LEUCHARS RAF UK UK 1973 2006 -999 0

*** Remember: Merge first, Match second! *** Enter ANY pair to match or merge, or 'n' to end: <END QUOTE>

Not only do both databases have unnecessary duplicates, introduced for external mapping purposes by the look of it, but the 'main' stations (2 and 4) have different station name & country. In fact one of the country names is illegal! Dealing with things like this cannot be automated as they're the results of non-automatic decisions.

Something new - a listing of 147 Australian 'bulletin' stations, most of which have mappings to WMO codes. Decided to xref against the (mapped) TMin database, for a laugh. Then decided to take it more seriously. Wrote a prog to IMPOSE the mappings onto tmn.0707021605.dtb, overriding existing mappings as necessary. What a bloody mess.

Decided to be vaguely sensible and let the program, auwmoxref.for, evolve. so to begin with it just did a scan between the mappings file (au_mapping_to_wmo.dat) and the tmin database with my mappings in (tmn.0707021605.dtb). Results:

crua6[/cru/cruts/version_3_0/db/dtr] ./auwmoxref

<BEGIN QUOTE> AUWMOXREF: Check Australian cross-references

Enter the file of WMO mappings: au_mapping_to_wmo.dat 115 mappings read

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Enter the mapped TMin database: tmn.0707021605.dtb 14349 database headers read

RESULTS:

WMO Matches: 92 (multiples) ( 0) > Ref matches: 60 > Ref empty: 31 > Ref WRONG: 1

Ref Matches: 114 (multiples) ( 0) > WMO matches: 60 > WMO -1*Ref: 41 > WMO WRONG: 13 <END QUOTE>

So first the good news - no duplicates. Well there shouldn't have been any anyway of course, but the way things are going I'm taking nothing for granted. See, I count something turning out as expected as 'good news'. So anyway.. I also extracted the statistic that 26 mappings matched both Ref and WMO, but to separate database entries. Thus the 115 mappings are allocated as follows:

60 Mapping found to be correctly implemented (over half, excellent) 41 WMO Missing, of which: 26 WMO found elsewhere (one of which has an unmapped ref attached to it) 15 WMO not in database (can add wmo codes for these) 13 WMO wrong, of which: 5 Can be merged with real WMO (effectively same station) 8 WMO not in database 1 Completely unmatched (96003 -> 949500)

For the purposes of actions to take, the 13 'WMO Wrong' refs can simply be unmapped from their incorrect mappings and be rolled into the 41 'WMO Missing' refs.

So:

60 Mapping found to be correctly implemented (over half, excellent) 54 WMO Missing or wrong, of which: 31 WMO found elsewhere (one of which has an unmapped ref attached to it) 23 WMO not in database but pairing made (can add wmo codes for these) 8 WMO not in database and no pairing (can add new stations for these) 1 Completely unmatched (96003 -> 949500)

So, actions to take:

1. For the first 60, no action required. 2. For the 13 with incorrectly-assigned WMOs, disengage and roll into the rest below 3. For the 1 WMO with an unmapped ref attached, disengage and roll into the rest below 3. For the 31 with dislocated WMOs, print a list and ref when doing the tmin/tmax syncing 4. For the 23 with WMO-less stations, add the WMO codes.. 5. For the 8 with no WMO found and no pairing found, create new stations.

For the disengagements, decided to work directly with an editor rather than craft another program! So changes made to tmn.0707021605.dtb (after a suitable backup was made of course!).

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The following assignments were disengaged (and replaced with -999.00). Where a WMO code follows in brackets, the ref was reassigned there.

1. 9460300 -3200 11550 43 ROTTNEST ISLAND AUSTRALIA 1898 2006 -999 9193 (9460200) 2. 9464600 -3090 12810 159 FORREST AUSTRALIA 1946 2006 -999 11052 (no) 3. 9432200 -2020 13000 340 RABBIT FLAT AUSTRALIA 1969 2006 -999 15666 (no) 4. 9557400 -2640 15300 6 TEWANTIN RSL PARK AUSTRALIA 1949 2006 -999 40908 (no) 5. 9451600 -2810 14860 199 ST GEORGE AIRPORT AUSTRALIA 1938 2006 -999 43109 (9451700) 6. 9452700 -2950 14990 213 MOREE AERO AUSTRALIA 1964 2006 -999 53115 (9552700) 7. 9454100 -2980 15110 582 INVERELL (RAGLAN ST) AUSTRALIA 1907 2006 -999 56242 (no) 8. 9478700 -3140 15290 4 PORT MACQUARIE AIRPO AUSTRALIA 1907 2006 -999 60139 (no) 9. 9475800 -3210 15090 216 SCONE SCS AUSTRALIA 2000 2006 -999 61089 (9473800) 10. 9494000 -3510 15080 85 JERVIS BAY (POINT PE AUSTRALIA 1907 2006 -999 68151 (no) 11. 9491600 -3590 14840 1482 CABRAMURRA SMHEA AWS AUSTRALIA 1962 2006 -999 72161 (no) 12. 9482700 -3630 14160 133 NHILL AUSTRALIA 1897 2006 -999 78031 (9582900) 13. 9597900 -4300 14710 63 GROVE (COMPARISON) AUSTRALIA 1961 2006 -999 94069 (no)

The 'mismatched WMO code' station was disengaged from it's reference and given 48027 instead: 1. 9471100 -3150 14580 218 COBAR AIRPORT AWS AUSTRALIA 1962 2006 -999 48237 -> 48027

I mailed BOM as we have 94711 = COBAR AWS but they have *94710* for AWS and 94711 for COBAR MO. The reply was as follows:

<BEGIN QUOTE> On 18 Jul 2007, at 8:51, Matthew Bastin wrote:

Hi Ian,

I hope this table helps

Name BoM No. WMO No. Opened Closed Cobar Comparison 48244 94711 1/11/1997 15/11/2000 Cobar MO 48027 94711 1/01/1962 Cobar Airport AWS 48237 94710 11/06/1993 Cobar PO 48030 1/1/1881 31/12/1965

The blank in the Closed column means that the site is still open When Cobar Comparison site closed it transferred its WMO number to Cobar MO A blank in the WMO No. column means that the site never had a WMO number.

I am not sure of the overlap between the assignment of 94711 between 48244 and 48027. I will find out and get back to you. <END QUOTE>

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Here are our current 'COBAR' headers:

0 -3150 14580 260 COBAR COMPARISON AUSTRALIA 2000 2006 -999 -999.00 0 -3150 14580 260 COBAR MO AUSTRALIA 2000 2006 -999 -999.00 0 -3148 14582 265 COBAR AUSTRALIA 1962 2004 -999 -999.00 0 -3150 14580 251 COBAR POST OFFICE AUSTRALIA 1902 1960 -999 -999.00 9471100 -3150 14580 218 COBAR AIRPORT AWS AUSTRALIA 1962 2006 -999 48027

Now looking at the dates.. something bad has happened, hasn't it. COBAR AIRPORT AWS

cannot start in 1962, it didn't open until 1993! Looking at the data - the COBAR station 1962-2004 seems to be an exact copy of the COBAR AIRPORT AWS station 1962-2004, except that the latter has more missing values. Now, COBAR AIRPORT AWS has 15 months of missing value codes beginning Oct 1993.. coincidence? No. I think that that series should start there. Furthermore, the overlap between COBAR and COBAR MO (2000-2004) is *almost* identical:

0 -3148 14582 265 COBAR AUSTRALIA 1962 2004 -999 -999.00 2000 177 209 183 135 80 51 45 52 105 122 166 186 2001 223 214 159 126 72 61 43 52 105 110 148 181 2002 195 185 168 148 88 58 49 63 101 128 186 192 2003 222 216 161 137 97 71 56 61 92 113 159 208 2004 207 226 175 141 74 69 46 69 90 136 160 186

0 -3150 14580 260 COBAR MO AUSTRALIA 2000 2006 -999 -999.00 2000 178 209 184 136 80 52 45 55 105 122 166 186 (7/12) 2001 223 214 159 126 72 61 43 52 105 110 148 181 (12/12) 2002 195 185 168 148 88 58 49 63 101 128 187 192 (11/12) 2003 222 216 161 137 97 71 56 61 92 113 159 208 (12/12) 2004 207 226 175 141 74 69 46 69 90 136 160 186 (12/12)

I therefore propose to extend COBAR MO using COBAR, and to truncate COBAR AIRPORT AWS at 1993. All BOM codes will be appended for completeness. So the new headers (with lat/lon from BOM too) are:

0 -3149 14583 260 COBAR COMPARISON AUSTRALIA 2000 2006 -999 48244 (closed) 9471100 -3149 14583 260 COBAR MO AUSTRALIA 1962 2006 -999 48027 0 -3150 14583 251 COBAR POST OFFICE AUSTRALIA 1902 1960 -999 48030 (closed) 9471000 -3154 14580 218 COBAR AIRPORT AWS AUSTRALIA 1995 2006 -999 48237

Deleted: 0 -3148 14582 265 COBAR AUSTRALIA 1962 2004 -999 -999.00

The remaining 26 dislocated references were reassigned as for the 13 above. Legitimate mappings:

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1. 3003 9420300 2. 4032 9431200 3. 5007 9430200 4. 7176 9431700 5. 9021 9461000 6. 14508 9415000 7. 14932 9413100 8. 17031 9448000 9. 22801 9480500 10. 26026 9481200 11. 27045 9417000 12. 32040 9429400 13. 40842 9457800 14. 50052 9470700 15. 55024 9474000 16. 67105 9575300 17. 68072 9475000 18. 71041 9590800 19. 86282 9486600 20. 200283 9429900 21. 200288 9499600 22. 200790 9699500 23. 200839 9499500 24. 300000 8957100 25. 300001 8956400 26. 300017 8961100

WMO codes were added to these uncoded sites as shown:

1. 9410000 -1430 12670 23 KALUMBURU AUSTRALIA 2000 2006 -999 1019 2. 9562500 -3160 11720 217 CUNDERDIN AIRFIELD AUSTRALIA 2000 2006 -999 10286 3. 9564000 -3270 11670 275 WANDERING AUSTRALIA 2000 2006 -999 10917 4. 9567000 -3380 13820 109 SNOWTOWN (RAYVILLE P AUSTRALIA 2000 2006 -999 21133 5. 9481400 -3530 13890 58 STRATHALBYN RACECOUR AUSTRALIA 2000 2006 -999 24580 6. 9548200 -2590 13940 47 BIRDSVILLE AIRPORT AUSTRALIA 2000 2006 -999 38026 7. 9552900 -2670 15020 305 MILES CONSTANCE STRE AUSTRALIA 2000 2006 -999 42112 8. 9549200 -2800 14380 132 THARGOMINDAH AIRPORT AUSTRALIA 2000 2006 -999 45025 9. 9578400 -3190 15250 8 TAREE AIRPORT AWS AUSTRALIA 2000 2006 -999 60141 10. 9571900 -3220 14860 284 DUBBO AIRPORT AWS AUSTRALIA 2000 2006 -999 65070 11. 9586900 -3560 14500 94 DENILIQUIN AIRPORT A AUSTRALIA 2000 2006 -999 74258 12. 9495400 -4070 14470 94 CAPE GRIM BAPS AUSTRALIA 2000 2006 -999 91245 13. 9596400 -4110 14680 3 LOW HEAD AUSTRALIA 2000 2006 -999 91293 14. 9595900 -4190 14670 1055 LIAWENEE AUSTRALIA 2000 2006 -999 96033

The following was corrected (ref had been mistyped as 78013): 1. 9582900 -3783 14206 200 HAMILTON RESEARCH ST AUSTRALIA 1971 1998 -999 78031

Now the results look like this:

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WMO Matches: 106 > Ref matches: 106 > Ref empty: 0 > Ref WRONG: 0 Ref Matches: 106 > WMO matches: 106 > WMO -1*Ref: 0 > WMO WRONG: 0

In other words, there are (115-106=) 9 mappings unfulfilled. The ref hasn't been matched and WMO code isn't in the database. However, that didn't mean they weren't in the database with a missing WMO code, did it? The following were found and augmented with both WMO code and ref.

9457000 -2639 15304 6 TEWANTIN RSL PARK AUSTRALIA 2000 2004 -999 40908 9594000 -3509 15080 85 JERVIS BAY (PT PERP AWS) AUSTRALIA 2000 2006 -999 68151

The following were added as new station stubs: 9532200 -2018 13001 340 RABBIT FLAT AUSTRALIA 2007 2007 -999 15666 9554100 -2978 15111 582 INVERELL (RAGLAN ST) AUSTRALIA 2007 2007 -999 56242 9478600 -3143 15287 4 PORT MACQUARIE AIRPT AUSTRALIA 2007 2007 -999 60139 9591600 -3594 14838 1482 CABRAMURRA SMHEA AWS AUSTRALIA 2007 2007 -999 72161 9597100 -4298 14708 63 GROVE (COMPARISON) AUSTRALIA 2007 2007 -999 94069

The following was complicated by the fact that two versions of the station appear to have been concatenated. This is the station as it already exists in the TMin database: 9464600 -3085 12811 159 FORREST AUSTRALIA 1946 2006 -999 -999.00 However, the current 'live' FORREST station (11052) started in 1993, according to bom.au records. And wouldn't you know it, the data for this station has missing data between 12/92 and 12/99 inclusive. So I reckon it's the old FORREST AERO station (WMO 9464600, .au ID 11004), with the new Australian bulletin updates tacked on (hence starting in 2000). Especially as the old station started in 1946 (http://www.bom.gov.au/climate/averages/tables/cw_011004.shtml). The trouble is that the bom.au mappings all agree that FORREST is now WMO=9564600. So.. do I split off the 2000-present data to a new station with the new number, or accept that whoever joined them (Dave?) looked into it and decided it would be OK? The BOM website says they're 800m apart. Decided to be brave and split the data back into two stations, with both codes attached (in case we ever get replacement data for the closed station, the site says it went to 1995 after all). So there are now two FORREST stations:

9464600 -3085 12811 159 FORREST AERO AUSTRALIA 1946 1992 -999 11004

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9564600 -3085 12811 159 FORREST AUSTRALIA 2000 2006 -999 11052

Hope that's right..

The following mapping was added, though the station does not currently feature in the bulletins. 9495900 -4228 14628 -999 BUTLERS GORGE AUSTRALIA 2007 2007 -999 96003 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2007-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

Also ran a risky search&replace to left-justify the 'AUSTRALIA' in its field, provided the field wasn't touched by an extended station name. Seems to have been 100% successful.

All 115 refs now matched in the TMin database. Confidence in the fidelity of the Australian station in the database drastically reduced. Likelihood of invalid merging of Australian stations high. Let's go..

Well OK, made some final 'improvements' to the syncing program. Now, after it forms a cloud, it should automatically merge stations provided the criteria are met and no others are possibles. It also records, in a separate 'action' file (act.*), every relevant action performed during the run, so that if interrupted I should be able to hack in something to enable a 'resume'. It's been done a bit hastily so no guarantees that enough information's been saved!

Debugging is still a big issue, unfortunately. It's a complicated program to sort out and the possibilities for indexing errors are many. In fact, for the first time ever, it's just locked up! That's a first (it was due to getmos not defaulting to months 1 & 12 if the data was all missing).

Another problem solved - spent ages wondering how the start & end years for a particular station (WARATAH) were being corrupted. Turns out they weren't - I'd written 'getmos' to trim empty years, but forgot to check the return flag! Duh.

So.. perhaps a debugged run through? I'm quickly realising that the Australian stations are in such a state that I'm having to constantly refer to the station descriptions on the BOM website, which are individual PDFs:

http://www.bom.gov.au/climate/cdo/metadata/pdf/metadata088110.pdf

It takes time.. time I don't have! Though I'm pleased to see that the second FSM is helpfully chipping in to pair things up when possible.

getting seriously fed up with the state of the Australian data. so many new stations have been introduced, so many false references.. so many changes that aren't documented. Every time a cloud forms I'm presented with a bewildering selection of similar-sounding sites,

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some with references, some with WMO codes, and some with both. And if I look up the station metadata with one of the local references, chances are the WMO code will be wrong (another station will have it) and the lat/lon will be wrong too. I've been at it for well over an hour, and I've reached the 294th station in the tmin database. Out of over 14,000. Now even accepting that it will get easier (as clouds can only be formed of what's ahead of you), it is still very daunting. I go on leave for 10 days after tomorrow, and if I leave it running it isn't likely to be there when I return! As to whether my 'action dump' will work (to save repetition).. who knows?

Yay! Two-and-a-half hours into the exercise and I'm in Argentina!

Pfft.. and back to Australia almost immediately :-( .. and then Chile. Getting there.

Unfortunately, after around 160 minutes of uninterrupted decision making, my screen has started to black out for half a second at a time. More video cable problems - but why now?!! The count is up to 1007 though.

I am very sorry to report that the rest of the databases seem to be in nearly as poor a state as Australia was. There are hundreds if not thousands of pairs of dummy stations, one with no WMO and one with, usually overlapping and with the same station name and very similar coordinates. I know it could be old and new stations, but why such large overlaps if that's the case? Aarrggghhh! There truly is no end in sight. Look at this:

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+- User Match Decision(s) Please! TMin stations: 4 1. 0 153 12492 80 MENADO/DR. SA INDONESIA 1960 1975 -999 0 2. 0 153 12492 80 MENADO/ SAM RATULANG INDONESIA 1986 2004 -999 0 4. 9701400 153 12492 80 MENADO/DR. SAM RATUL INDONESIA 1995 2006 -999 0 5. 9997418 153 12492 81 SAMRATULANGI MENADO INDONESIA 1973 1989 -999 0 TMax stations: 4 6. 0 153 12492 80 MAPANGET/MANADO INDONESIA 1960 1975 -999 0 7. 0 153 12492 80 MENADO/ SAM RATULANG ID ID 1957 2004 -999 0 9. 9701400 153 12492 80 MENADO/DR. SAM RATUL INDONESIA 1995 2006 -999 0 10. 9997418 153 12492 81 SAMRATULANGI MENADO INDONESIA 1972 1989 -999 0

*** Remember: Merge first, then Match *** Enter ANY pair to match or merge, 'a' to auto-match (no merges), or 'x' to end:

I honestly have no idea what to do here. and there are countless others of equal

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bafflingness.

I'll have to go home soon, leaving it running and hoping none of the systems die overnight :-(((

.. it survived, thank $deity. And a long run of duplicate stations, each requiring multiple decisions concerning spatial info, exact names, and data precedence for overlaps. If for any reason this has to be re-run, it can certainly be speeded up! Some large clouds, too - this one started with 59 members from each database:

-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+- User Match Decision(s) Please! TMin stations: 7 11. 7101965 4362 -7940 78 TORONTO ISLAND 1905 1959 -999 0 14. 7163427 4363 -7940 77 TORONTO ISLAND A CANADA 1957 1994 -999 0 23. 7101987 4380 -7955 194 TORONTO MET RES STN 1965 1988 -999 0 24. 7163434 4380 -7955 194 TORONTO MET RES STN CANADA 1965 1988 -999 0 36. 0 4388 -7944 233 RICHMOND HILL 1959 2003 -999 0 39. 7163408 4388 -7945 233 RICHMOND HILL CANADA 1959 1990 -999 0 40. 7163409 4387 -7943 218 RICHMOND HILL WPCP 1960 1981 -999 0 TMax stations: 8 70. 7101965 4362 -7940 78 TORONTO ISLAND 1905 1959 -999 0 71. 7126500 4363 -7940 77 TORONTO ISLAND A 1957 1994 -999 0 73. 7163427 4363 -7940 77 TORONTO ISLAND A CANADA 1957 1990 -999 0 82. 7101987 4380 -7955 194 TORONTO MET RES STN 1965 1988 -999 0 83. 7163434 4380 -7955 194 TORONTO MET RES STN CANADA 1965 1988 -999 0 95. 0 4388 -7944 233 RICHMOND HILL 1959 2003 -999 0 98. 7163408 4388 -7945 233 RICHMOND HILL CANADA 1959 1990 -999 0 99. 7163409 4387 -7943 218 RICHMOND HILL WPCP 1960 1981 -999 0

There were even larger clouds later.

One thing that's unsettling is that many of the assigned WMo codes for Canadian stations do not return any hits with a web search. Usually the country's met office, or at least the Weather Underground, show up - but for these stations, nothing at all. Makes me wonder if these are long-discontinued, or were even invented somewhere other than Canada! Examples:

7162040 brockville 7163231 brockville 7163229 brockville 7187742 forestburg

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7100165 forestburg

Here's a heartwarming example of a cloud which self-paired completely (debug ines included):

<BEGIN QUOTE> DBG: cloud formed with ( 6, 6) members DBG: automerging done, leaving ( 6, 6) DBG: pot.auto i,j: 1 1 DBG: i,ncs2m,cs2m(1-5): 1 1 1 8578 8582 8596 0 DBG: paired: 1 1 108 MILE HOUSE ABEL

Attempting to pair stations: From TMin: 0 5170 -12140 994 108 MILE HOUSE ABEL 1987 2002 -999 -999.00 From TMax: 0 5170 -12140 994 108 MILE HOUSE ABEL 1987 2002 -999 -999.00 DBG: AUTOPAIRED: 1 1 DBG: pot.auto i,j: 2 2 DBG: i,ncs2m,cs2m(1-5): 2 1 2 8578 8582 8596 0 DBG: paired: 2 2 100 MILE HOUSE

Attempting to pair stations: From TMin: 7194273 5165 -12130 1059 100 MILE HOUSE CANADA 1970 1999 -999 -999.00 From TMax: 7194273 5165 -12130 1059 100 MILE HOUSE CANADA 1970 1999 -999 -999.00 DBG: AUTOPAIRED: 2 2 DBG: pot.auto i,j: 3 3 DBG: i,ncs2m,cs2m(1-5): 3 1 3 8578 8582 8596 0 DBG: paired: 3 3 HORSE LAKE

Attempting to pair stations: From TMin: 7103611 5160 -12120 994 HORSE LAKE 1983 1994 -999 -999.00 From TMax: 7103611 5160 -12120 994 HORSE LAKE 1983 1994 -999 -999.00 DBG: AUTOPAIRED: 3 3 DBG: pot.auto i,j: 4 4 DBG: i,ncs2m,cs2m(1-5): 4 1 4 8578 8582 8596 0 DBG: paired: 4 4 LONE BUTTE 2

Attempting to pair stations: From TMin: 7103629 5155 -12120 1145 LONE BUTTE 2 1981 1991 -999 -999.00 From TMax: 7103629 5155 -12120 1145 LONE BUTTE 2 1981 1991 -999 -999.00 DBG: AUTOPAIRED: 4 4 DBG: pot.auto i,j: 5 5 DBG: i,ncs2m,cs2m(1-5): 5 1 5 8578 8582 8596 0 DBG: paired: 5 5 100 MILE HOUSE 6NE

Attempting to pair stations: From TMin: 7103637 5168 -12122 928 100 MILE HOUSE 6NE 1987 2002 -999 -999.00 From TMax: 7103637 5168 -12122 928 100 MILE HOUSE 6NE 1987 2002 -999 -999.00 DBG: AUTOPAIRED: 5 5 DBG: pot.auto i,j: 6 6 DBG: i,ncs2m,cs2m(1-5): 6 1 6 8578 8582 8596 0 DBG: paired: 6 6 WATCH LAKE NORTH

Attempting to pair stations:

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From TMin: 7103660 5147 -12112 1069 WATCH LAKE NORTH 1987 1996 -999 -999.00 From TMax: 7103660 5147 -12112 1069 WATCH LAKE NORTH 1987 1996 -999 -999.00 DBG: AUTOPAIRED: 6 6 <END QUOTE>

Now arguably, the MILE HOUSE ABEL stations should have rolled into one of the other MILE HOUSE ones with a WMO code.. but the lat/lon/alt aren't close enough. Which is as intended.

*

*

Well, it *kind of* worked. Thought the resultant files aren't exactly what I'd expected:

-rw------- 1 f098 cru 12715138 Jul 25 15:25 act.0707241721.dat -rw------- 1 f098 cru 435839 Jul 25 15:25 log.0707241721.dat -rw------- 1 f098 cru 4126850 Jul 25 15:25 mat.0707241721.dat -rw------- 1 f098 cru 6221390 Jul 25 15:25 tmn.0707021605.dtb.lost -rw------- 1 f098 cru 2962918 Jul 25 15:25 tmn.0707241721.dat -rw------- 1 f098 cru 0 Jul 25 15:25 tmx.0702091313.dtb.lost -rw------- 1 f098 cru 2962918 Jul 25 15:25 tmx.0707241721.dat

act.0707241721.dat: hopefully-complete record of all activities

log.0707241721.dat: hopefully-useful log of odd happenings (and mergeinfo() trails)

mat.0707241721.dat: hopefully-complete list of all merges and pairings

tmn.0707021605.dtb.lost: too-small collection of unpaired stations

tmn.0707241721.dat: too-small output database

tmx.0702091313.dtb.lost: MUCH too-small collection of unpaired stations!!!

tmx.0707241721.dat: too-small (but hey, the same size as the twin) output database

ANALYSIS

Well, LOL, the reason the output databases are so small is that every station looks like this:

9999810 -748 10932 114 SEMPOR INDONESIA 1971 2000 -999 -999.00 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1971 229 225 225 229 229-9999 223 221 222 225 224-9999

Yes - just one line of data. The write loops went from start year to start year. Ho hum :-/

Not as easy to fix as you might think, seeing as the data may well be the result of a merge and so can't just be pasted in from the source database.

As for the 'unbalanced' 'lost' files: well for a start, the same error as above (just one line of data), then on top of that, both sets written to the same file. what time did I write that bit, 3am?!! Ecch.

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33. So, as expected.. I'm gonna have to write in clauses to make use of the log, act and mat files. I so do not want to do this.. but not as much as I don't want to do a day's interacting again!!

Got it to work.. sort of. Turns out I had included enough information in the ACT file, and so was able to write auminmaxresync.for. A few teething troubles, but two new databases ('tm[n|x].0707301343.dtb') created with 13654 stations in each. And yes - the headers are identical :-)

[edit: see below - the 'final' databases are tm*.0708071548.dtb]

Here are the header counts, demonstrating that something's still not quite right..

Original: 14355 tmn.0707021605.dtb.heads

New: 13654 tmn.0707301343.dtb.heads

Lost/merged: 14318 tmn.0707021605.dtb.lost.heads (should be 14355-13654-37 = 664?) 37 tmn.0707021605.dtb.merg.heads (seems low)

Original: 14315 tmx.0702091313.dtb.heads

New: 13654 tmx.0707301343.dtb.heads

Lost/merged: 14269 tmx.0702091313.dtb.lost.heads (should be 14315-13654-46 = 615?) 46 tmx.0702091313.dtb.merg.heads (seems low)

In fact, looking at the original ACT file that we used:

crua6[/cru/cruts/version_3_0/db/dtr] grep 'usermerg' act.0707241721.dat | wc -l 258 crua6[/cru/cruts/version_3_0/db/dtr] grep 'automerg' act.0707241721.dat | wc -l 889

..so will have to look at how the db1/2xref arrays are prepped and set in the program. Nonetheless the construction of the new databases looks pretty good. There's aminor problem where the external reference field is sometimes -999.00 and sometimes 0. Not sure which is best, probably 0, as the field will usually be used for reference numbers/characters rather than real data values. Used an inline perl command to fix.

..after some rudimentary corrections:

uealogin1[/cru/cruts/version_3_0/db/dtr] wc -l *.heads 14355 tmn.0707021605.dtb.heads 122 tmn.0707021605.dtb.lost.heads 579 tmn.0707021605.dtb.merg.heads 13654 tmn.0708062250.dtb.heads 14315 tmx.0702091313.dtb.heads 93 tmx.0702091313.dtb.lost.heads 570 tmx.0702091313.dtb.merg.heads 13654 tmx.0708062250.dtb.heads

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Almost perfect! But unfortunately, there is a slight discrepancy, and they have a habit of being tips of icebergs. If you add up the header/station counts of the new tmin database, merg and lost files, you get 13654 + 579 + 122 = 14355, the original station count. If you try the same check for tmax, however, you get 13654 + 570 + 93 = 14317, two more than the original count! I suspected a couple of stations were being counted twice, so using 'comm' I looked for identical headers. Unfortunately there weren't any!! So I have invented two stations, hmm. Got the program to investigate, and found two stations in the cross-reference array which had cross refs *and* merge flags:

ERROR: db2xref( 126) = 127 -14010 : 126> 9596400 -4110 14680 3 LOW HEAD AUSTRALIA 2000 2006 -999 91293 14010> 9596900 -4170 14710 150 CRESSY RESEARCH STAT AUSTRALIA 1971 2006 -999 91306

and

ERROR: db2xref(13948) = 227 -226 : 13948> 9570600 -3470 14650 145 NARRANDERA AIRPORT AUSTRALIA 1971 2006 -999 0 226> 0 -3570 14560 110 FINLEY (CSIRO) AUSTRALIA 2000 2001 -999 0

So in the first case, LOW HEAD has been merged with another station (#14010) AND paired with #127. Similarly, NARRANDERA AIRPORT has been mreged with #226 and paired with #227. However, these apparent merges are false! As we see in the first case, 14010 is not LOW HEAD. Similarly for the second case.

Looking in the relevant match file from the process (mat.0707241721.dat) we find:

AUTO MERGE FROM CHAIN: TMax Stn 1: 0 -4110 14680 3 LOW HEAD AUSTRALIA 2000 2006 -999 -999.00 TMax Stn 2: 0 -4105 14678 4 LOW HEAD AUSTRALIA 2000 2004 -999 -999.00 New Header: 0 -4110 14680 3 LOW HEAD AUSTRALIA 2000 2006 -999 0 Note: Stn 1 data overwrote Stn 2 data

MANUAL PAIRING FROM CHAIN: TMin: 9596400 -4110 14680 3 LOW HEAD AUSTRALIA 2000 2006 -999 91293 TMax: 0 -4110 14680 3 LOW HEAD AUSTRALIA 2000 2006 -999 0 New Header: 9596400 -4110 14680 3 LOW HEAD AUSTRALIA 2000 2006 -999 91293

and

AUTO MERGE FROM CHAIN: TMax Stn 1: 0 -3470 14650 145 NARRANDERA AIRPORT AUSTRALIA 2000 2006 -999 -999.00 TMax Stn 2: 9570600 -3471 14651 145 NARRANDERA AIRPORT AUSTRALIA 1972 1980 -999 -999.00 New Header: 9570600 -3470 14650 145 NARRANDERA AIRPORT AUSTRALIA 1972 2006 -999 0

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Note: Stn 2 data overwrote Stn 1 data

MANUAL PAIRING FROM CHAIN: TMin: 9570600 -3470 14650 145 NARRANDERA AIRPORT AUSTRALIA 1971 2003 -999 0 TMax: 9570600 -3470 14650 145 NARRANDERA AIRPORT AUSTRALIA 1972 2006 -999 0 New Header: 9570600 -3470 14650 145 NARRANDERA AIRPORT AUSTRALIA 1971 2006 -999 0

Found the problem - mistyping of an assignment.. and so:

crua6[/cru/cruts/version_3_0/db/dtr] wc -l *.heads

14355 tmn.0707021605.dtb.heads 122 tmn.0707021605.dtb.lost.heads 579 tmn.0707021605.dtb.merg.heads 13654 tmn.0708071548.dtb.heads

14315 tmx.0702091313.dtb.heads 93 tmx.0702091313.dtb.lost.heads 568 tmx.0702091313.dtb.merg.heads 13654 tmx.0708071548.dtb.heads

Phew! Well the headers are identical for the two new databases:

crua6[/cru/cruts/version_3_0/db/dtr] cmp tmn.0708071548.dtb.heads tmx.0708071548.dtb.heads |wc -l 0

34. So the to the real test - converting to DTR! Wrote tmnx2dtr.for, which does exactly that. It reported 233 instances where tmin > tmax (all set to missing values) and a handful where tmin == tmax (no prob). Looking at the 233 illogicals, most of the stations look as though considerable work is needed on them. This highlights the fact that all I've done is to synchronise the tmin and tmax databases with each other, and with the Australian stations - there is still a lot of data cleansing to perform at some stage! But not right now :-)

Input Files TMin: tmn.0708071548.dtb TMax: tmx.0708071548.dtb

Output file DTR: dtr.0708071924.dtb

Cases of identical values: 39 Cases of min > max (BAD!): 233 All illegals written to: illdtr.0708071924.dat

Example of 'illegal' values to demonstrate quality of station data:

station: 9600100 587 9532 126 SABANG/CUT BAU ID ID 1984 2006 -999 0 min data: 2006 203 -197 200-9999 -211 207 233-9999-9999-9999-9999-9999 max data: 2006 290 -299 307-9999 -315 309 308-9999-9999-9999-9999-9999

Doesn't look very likely!

Normals added:

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crua6[/cru/cruts/version_3_0/db/dtr] ./addnormline

**** ADDNORMLINE  

Calculates monthly normals for 1961-1990, provided at least 75% of values are present. Results go into a normals line coming after the header. Operator called if different normals exist!

Please enter the input database: dtr.0708071924.dtb

Proposed output database name: dtr.0708081052.dat

ACCEPT/REJECT (A/R): A Output database name: dtr.0708081052.dat Derived logfile name: dtr.0708081052.log

So the final DTR database is dtr.0708081052.dtb.

And so to the main process:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/primaries/dtr] ./anomdtb

> ** *** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required: .dtr > Select the .cts or .dtb file to load: dtr.0708081052.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): dtr.txt > Select the first,last years AD to save: 1901,2006 > Operating...

> NORMALS MEAN percent STDEV percent > .dtb 3746373 65.9 > .cts 178161 3.1 3924534 69.0 > PROCESS DECISION percent %of-chk > no lat/lon 650 0.0 0.0 > no normal 1763302 31.0 31.0 > out-of-range 24 0.0 0.0 > accepted 3924510 69.0 > Dumping years 1901-2006 to .txt files...

<END QUOTE>

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So a lower pewrcentage than last time (69.0 vs. 78.9), but then, more data overall so a better result (3924510 vs. 3167636).

Gridding:

IDL> quick_interp_tdm2,1901,2006,'dtrglo/dtr.',750,gs=0.5,pts_prefix='dtrtxt/dtr.',dumpgl o='dumpglo'

Convert from .glo: crua6[/cru/cruts/version_3_0/primaries/dtr] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.dtr Enter a name for the gridded climatology file: clim.6190.lan.dtr.grid Enter the path and stem of the .glo files: dtrglo/dtr. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: Now, CONCENTRATE. Addition or Percentage (A/P)? A Right, erm.. off I jolly well go! dtr.01.1901.glo (etc) dtr.12.2006.glo

Finally, gridding: Writing cru_ts_3_00.1901.1910.dtr.dat Writing cru_ts_3_00.1911.1920.dtr.dat Writing cru_ts_3_00.1921.1930.dtr.dat Writing cru_ts_3_00.1931.1940.dtr.dat Writing cru_ts_3_00.1941.1950.dtr.dat Writing cru_ts_3_00.1951.1960.dtr.dat Writing cru_ts_3_00.1961.1970.dtr.dat Writing cru_ts_3_00.1971.1980.dtr.dat Writing cru_ts_3_00.1981.1990.dtr.dat Writing cru_ts_3_00.1991.2000.dtr.dat Writing cru_ts_3_00.2001.2006.dtr.dat Writing cru_ts_3_00.1901.2006.dtr.dat

35. Onto the secondaries, working from the rerun methodology (see section 20 above).

Began with temperature, using the anomaly txt files from the half-degree generation:

IDL> quick_interp_tdm2,1901,2006,'tmpbin/tmpbin',1200,gs=2.5,dumpbin='dumpbin',pts_prefix ='tmp0km0705101334txt/tmp.'

This produced binaries such as 'tmpbin1901'.

Then precipitation:

IDL> quick_interp_tdm2,1901,2006,'prebin/prebin',450,gs=2.5,dumpbin='dumpbin',pts_prefix= 'pre0km0612181221txt/pre.'

Finally, dtr:

IDL> quick_interp_tdm2,1901,2006,'dtrbin/dtrbin',50,gs=2.5,dumpbin='dumpbin',pts_prefix='

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dtrtxt/dtr.'

*** EEEK! Is that '50' a mistype? Meaning that anything using binary DTR will need re-doing? (RAL, Dec 07) ***

And so to the synthetics.

FRS:

IDL> .compile /cru/cruts/fromdpe1a/code/idl/pro/rdbin.pro % Compiled module: RDBIN. IDL> .compile /cru/cruts/fromdpe1a/code/idl/pro/frs_gts_tdm.pro % Compiled module: FRS_GTS.

IDL> frs_gts,dtr_prefix='dtrbin/dtrbin',tmp_prefix='tmpbin/tmpbin',1901,2006,outprefix='f rssyn/frssyn'

IDL> quick_interp_tdm2,1901,2006,'frsgrid/frsgrid',750,gs=0.5,dumpglo='dumpglo',nostn=1,s ynth_prefix='frssyn/frssyn'

crua6[/cru/cruts/version_3_0/secondaries/frs] ../glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.frs Enter a name for the gridded climatology file: clim.6190.lan.frs.grid Enter the path and stem of the .glo files: frsgrid/frsgrid. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: Now, CONCENTRATE. Addition or Percentage (A/P)? A Right, erm.. off I jolly well go! frsgrid.01.1901.glo (etc) frsgrid.12.2006.glo

crua6[/cru/cruts/version_3_0/secondaries/frs] ../mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: frsgridabs/frsgrid.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.frs.dat Writing cru_ts_3_00.1901.1910.frs.dat Writing cru_ts_3_00.1911.1920.frs.dat Writing cru_ts_3_00.1921.1930.frs.dat Writing cru_ts_3_00.1931.1940.frs.dat Writing cru_ts_3_00.1941.1950.frs.dat Writing cru_ts_3_00.1951.1960.frs.dat Writing cru_ts_3_00.1961.1970.frs.dat Writing cru_ts_3_00.1971.1980.frs.dat Writing cru_ts_3_00.1981.1990.frs.dat Writing cru_ts_3_00.1991.2000.frs.dat Writing cru_ts_3_00.2001.2006.frs.dat

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RD0:

IDL> .compile /cru/cruts/fromdpe1a/code/idl/pro/rdbin.pro % Compiled module: RDBIN. IDL> .compile /cru/cruts/fromdpe1a/code/idl/pro/rd0_gts_tdm.pro % Compiled module: RD0_GTS.

IDL> rd0_gts,1901,2006,1961,1990,outprefix='rd0syn/rd0syn',pre_prefix='prebin/prebin' Reading precip and rd0 normals % Compiled module: STRIP.
yes filesize= 6220800 gridsize= 0.500000 % Compiled module: DEFXYZ.
yes filesize= 6220800 gridsize= 0.500000 % Compiled module: DAYS. Calculating synthetic Rd0 normal 1961
yes filesize= 248832 gridsize= 2.50000 % Compiled module: RD0CAL. 1962
yes

(etc)

2006
yes filesize= 248832 gridsize= 2.50000 % Program caused arithmetic error: Floating divide by 0 % Program caused arithmetic error: Floating illegal operand

IDL>

(as before, see section 20.)

IDL> quick_interp_tdm2,1901,2006,'rd0grid/rd0grid',450,gs=0.5,dumpglo='dumpglo',nostn=1,s ynth_prefix='rd0syn/rd0syn'

crua6[/cru/cruts/version_3_0/secondaries/rd0] ../glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: forrtl: error (69): process interrupted (SIGINT) crua6[/cru/cruts/version_3_0/secondaries/rd0] mkdir rd0gridabs crua6[/cru/cruts/version_3_0/secondaries/rd0] ../glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim.6190.lan.wet.grid Enter the path and stem of the .glo files: rd0grid/rd0grid. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: rd0gridabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A

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Right, erm.. off I jolly well go! rd0grid.01.1901.glo (etc) rd0grid.12.2006.glo

crua6[/cru/cruts/version_3_0/secondaries/rd0] ../mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: rd0gridabs/rd0grid.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.rd0.dat Writing cru_ts_3_00.1901.1910.rd0.dat (etc)

I have to admit, I still don't understand secondary parameter generation. I've read the papers, and the miniscule amount of 'Read Me' documentation, and it just doesn't make sense. In particular, why use 2.5 degree grids of the primaries instead of 0.5? Why deliberately lose spatial resolution, only to have to reinterpolate later?

No matter; on to Vapour Pressure. Here's the complete output from the initial binary gridding,using dtr and tmp:

IDL> vap_gts_anom,dtr_prefix='dtrbin/dtrbin',tmp_prefix='tmpbin/tmpbin',1901,2006,outpref ix='vapsyn/vapsyn',dumpbin=1 % Compiled module: VAP_GTS_ANOM. % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. Land,sea: 56016 68400 Calculating tmn normal % Compiled module: TVAP. Calculating synthetic vap normal % Compiled module: ESAT. Calculating synthetic anomalies % Compiled module: MOMENT. 1901 vap (x,s2,<<,>>): 1.61250e-05 6.15570e-06 -0.160607 0.222689 % Compiled module: WRBIN. 1902 vap (x,s2,<<,>>): -0.000123188 3.46116e-05 -0.268891 0.0261283 1903 vap (x,s2,<<,>>): 6.86689e-05 4.52675e-06 -0.121429 0.123995 1904 vap (x,s2,<<,>>): -1.30788e-05 1.83887e-05 -0.454975 0.0919596 1905 vap (x,s2,<<,>>): 1.94645e-05 1.32224e-05 -0.408679 0.0498396 1906 vap (x,s2,<<,>>): 3.22279e-05 3.74796e-06 -0.178658 0.0261283 1907 vap (x,s2,<<,>>): -2.56545e-05 1.68228e-05 -0.268768 0.0498040 1908 vap (x,s2,<<,>>): 6.39573e-05 3.49149e-06 -0.173230 0.354836 1909 vap (x,s2,<<,>>): 3.50080e-05 3.21530e-06 -0.201157 0.0261283 1910 vap (x,s2,<<,>>): 3.45249e-05 6.15026e-06 -0.130285 0.144744 1911 vap (x,s2,<<,>>): 3.99470e-05 5.85673e-06 -0.360082 0.0261283 1912 vap (x,s2,<<,>>): -7.91931e-06 1.06891e-05 -0.279282 0.0261283 1913 vap (x,s2,<<,>>): 6.07153e-05 7.10663e-07 -0.0148902 0.0261283 1914 vap (x,s2,<<,>>): 7.22507e-05 2.52354e-06 -0.130205 0.124774

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1915 vap (x,s2,<<,>>): -2.11176e-05 1.59592e-05 -0.308456 0.0579963 1916 vap (x,s2,<<,>>): -8.95735e-05 2.41852e-05 -0.247123 0.140438 1917 vap (x,s2,<<,>>): -0.000105104 2.43058e-05 -0.229282 0.282290 1918 vap (x,s2,<<,>>): 1.14711e-05 7.76188e-06 -0.248782 0.0261283 1919 vap (x,s2,<<,>>): 2.51597e-05 5.75406e-06 -0.295303 0.215085 1920 vap (x,s2,<<,>>): -2.78549e-06 1.81183e-05 -0.373193 0.0261283 1921 vap (x,s2,<<,>>): 6.07153e-05 7.10663e-07 -0.0148902 0.0261283 1922 vap (x,s2,<<,>>): -1.86602e-05 1.22345e-05 -0.275667 0.0261283 1923 vap (x,s2,<<,>>): 5.76800e-05 1.22728e-06 -0.170021 0.0261283 1924 vap (x,s2,<<,>>): 6.07153e-05 7.10663e-07 -0.0148902 0.0261283 1925 vap (x,s2,<<,>>): 8.32519e-05 5.55618e-06 -0.109315 0.186182 1926 vap (x,s2,<<,>>): 0.000106602 5.15263e-06 -0.105764 0.206929 1927 vap (x,s2,<<,>>): 5.23023e-05 2.64333e-06 -0.194649 0.0498040 1928 vap (x,s2,<<,>>): 5.50934e-05 2.47944e-06 -0.314917 0.0261283 1929 vap (x,s2,<<,>>): -0.000524952 0.000155755 -0.417342 0.215959 1930 vap (x,s2,<<,>>): 8.28323e-05 1.87314e-05 -0.328074 0.193805 1931 vap (x,s2,<<,>>): -7.80687e-05 3.63543e-05 -0.315060 0.215417 1932 vap (x,s2,<<,>>): 5.62579e-05 3.81547e-06 -0.249130 0.120583 1933 vap (x,s2,<<,>>): -3.47433e-05 1.69009e-05 -0.218800 0.148224 1934 vap (x,s2,<<,>>): 0.000156604 1.56121e-05 -0.173230 0.152809 1935 vap (x,s2,<<,>>): 6.69520e-05 4.91451e-06 -0.160529 0.120391 1936 vap (x,s2,<<,>>): -0.000255663 6.63373e-05 -0.398866 0.0261283 1937 vap (x,s2,<<,>>): 6.99402e-05 2.70766e-05 -0.328074 0.201202 1938 vap (x,s2,<<,>>): 5.91796e-05 6.70722e-06 -0.215017 0.155977 1939 vap (x,s2,<<,>>): 4.88266e-05 5.25789e-06 -0.173294 0.0893239 1940 vap (x,s2,<<,>>): 9.63896e-06 7.45103e-06 -0.214763 0.0758103 1941 vap (x,s2,<<,>>): 4.11127e-05 4.15525e-06 -0.234030 0.0261283 1942 vap (x,s2,<<,>>): -9.97969e-05 3.88466e-05 -0.288682 0.148893 1943 vap (x,s2,<<,>>): 8.38607e-05 3.48416e-06 -0.0148902 0.163562 1944 vap (x,s2,<<,>>): 7.96681e-05 7.91305e-06 -0.227413 0.104055 1945 vap (x,s2,<<,>>): 3.37215e-05 3.99524e-06 -0.248782 0.0261283 1946 vap (x,s2,<<,>>): 5.31976e-05 2.63755e-06 -0.128263 0.163584 1947 vap (x,s2,<<,>>): 0.000131113 1.66296e-05 -0.353903 0.193758 1948 vap (x,s2,<<,>>): 6.80941e-05 1.62353e-06 -0.0148902 0.163624 1949 vap (x,s2,<<,>>): 2.47925e-05 2.45819e-05 -0.328074 0.237848 1950 vap (x,s2,<<,>>): -9.57348e-05 7.78468e-05 -0.366764 0.726541 1951 vap (x,s2,<<,>>): -6.54446e-06 1.35656e-05 -0.446058 0.0261283 1952 vap (x,s2,<<,>>): -0.000158974 5.02732e-05 -0.262313 0.193617 1953 vap (x,s2,<<,>>): 1.18525e-05 4.22691e-05 -0.282204 0.230629 1954 vap (x,s2,<<,>>): -0.000151975 6.78713e-05 -0.373235 0.230602 1955 vap (x,s2,<<,>>): -0.000134153 5.23124e-05 -0.298578 0.0841820 1956 vap (x,s2,<<,>>): -9.61671e-05 5.20484e-05 -0.492004 0.0888951 1957 vap (x,s2,<<,>>): -1.18048e-05 1.31769e-05 -0.220902 0.0261283 1958 vap (x,s2,<<,>>): -8.61762e-06 1.12079e-05 -0.207799 0.148170 1959 vap (x,s2,<<,>>): 8.27399e-05 4.88857e-06 -0.0929929 0.170919 1960 vap (x,s2,<<,>>): 3.38773e-05 1.53901e-05 -0.207944 0.155940 1961 vap (x,s2,<<,>>): 5.72571e-05 9.01807e-07 -0.0653905 0.0261283 1962 vap (x,s2,<<,>>): 8.20891e-05 3.78016e-06 -0.240435 0.126662 1963 vap (x,s2,<<,>>): -0.000108489 3.85148e-05 -0.266356 0.0836364 1964 vap (x,s2,<<,>>): 3.02043e-05 6.37207e-06 -0.240547 0.150816 1965 vap (x,s2,<<,>>): 5.76898e-05 2.48022e-06 -0.279282 0.143283 1966 vap (x,s2,<<,>>): -0.000300312 5.32054e-05 -0.622719 0.0261283 1967 vap (x,s2,<<,>>): 6.43500e-05 8.58218e-07 -0.0148902 0.0496181 1968 vap (x,s2,<<,>>): -0.000241750 4.22773e-05 -0.214442 0.271730 1969 vap (x,s2,<<,>>): -0.000568502 9.92260e-05 -0.385322 0.0732047 1970 vap (x,s2,<<,>>): 6.07153e-05 7.10663e-07 -0.0148902 0.0261283 1971 vap (x,s2,<<,>>): 2.15333e-05 4.77100e-06 -0.188071 0.0261283 1972 vap (x,s2,<<,>>): -7.14160e-05 3.56948e-05 -0.365803 0.201611 1973 vap (x,s2,<<,>>): 5.77503e-05 1.17079e-06 -0.160550 0.0261283 1974 vap (x,s2,<<,>>): 3.49354e-05 4.93069e-06 -0.149678 0.144313 1975 vap (x,s2,<<,>>): 6.14429e-05 7.36204e-07 -0.0148902 0.0380432 1976 vap (x,s2,<<,>>): 6.49657e-05 3.25410e-06 -0.266356 0.165472 1977 vap (x,s2,<<,>>): 0.000107180 1.92804e-05 -0.304625 0.208459

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1978 vap (x,s2,<<,>>): -4.80106e-05 3.28909e-05 -0.285492 0.105108 1979 vap (x,s2,<<,>>): -0.000102001 2.35900e-05 -0.214390 0.112952 1980 vap (x,s2,<<,>>): 4.16963e-05 2.70211e-06 -0.144913 0.0864268 1981 vap (x,s2,<<,>>): 0.000274196 1.86668e-05 -0.0148902 0.222522 1982 vap (x,s2,<<,>>): 8.57426e-07 7.08135e-06 -0.161781 0.0831981 1983 vap (x,s2,<<,>>): -5.84499e-06 1.76470e-05 -0.234194 0.128289 1984 vap (x,s2,<<,>>): -0.000106476 2.97454e-05 -0.335850 0.150833 1985 vap (x,s2,<<,>>): 9.32757e-06 4.35533e-05 -0.323331 0.222522 1986 vap (x,s2,<<,>>): 7.22110e-05 4.76179e-06 -0.141725 0.185658 1987 vap (x,s2,<<,>>): -2.27107e-05 2.09631e-05 -0.291446 0.103599 1988 vap (x,s2,<<,>>): 6.58090e-05 9.21014e-07 -0.0148902 0.0670816 1989 vap (x,s2,<<,>>): 9.54406e-05 1.72599e-05 -0.266297 0.160293 1990 vap (x,s2,<<,>>): 0.000218826 3.56583e-05 -0.174187 0.236204 1991 vap (x,s2,<<,>>): 5.93288e-05 8.18618e-07 -0.0776650 0.0261283 1992 vap (x,s2,<<,>>): 7.57687e-05 4.27091e-06 -0.174292 0.215085 1993 vap (x,s2,<<,>>): -1.69378e-05 2.36942e-05 -0.314882 0.0420169 1994 vap (x,s2,<<,>>): 6.36348e-05 1.18760e-06 -0.0148902 0.163543 1995 vap (x,s2,<<,>>): 0.000281573 6.09912e-05 -0.463574 0.259426 1996 vap (x,s2,<<,>>): 5.03362e-05 5.47691e-06 -0.224751 0.124774 1997 vap (x,s2,<<,>>): 0.000132649 2.97693e-05 -0.446455 0.281070 1998 vap (x,s2,<<,>>): 5.96544e-07 3.39098e-05 -0.359037 0.201228 1999 vap (x,s2,<<,>>): 5.91499e-05 2.37232e-06 -0.166206 0.215985 2000 vap (x,s2,<<,>>): 4.06034e-05 4.61604e-06 -0.0898572 0.191977 2001 vap (x,s2,<<,>>): 0.000138230 8.53512e-06 -0.0512625 0.206929 2002 vap (x,s2,<<,>>): 0.000218003 4.36873e-05 -0.760830 0.282290 2003 vap (x,s2,<<,>>): 7.00864e-05 7.67472e-06 -0.301868 0.237875 2004 vap (x,s2,<<,>>): 5.49200e-06 2.13246e-05 -0.500544 0.112129 2005 vap (x,s2,<<,>>): 6.05939e-06 5.83817e-05 -0.885566 0.199814 2006 vap (x,s2,<<,>>): 9.02885e-05 3.60834e-05 -0.455230 0.607388

How very useful! No idea what any of that means. although it's heartwarming to see that it's nothing like the results of the 2.10 rerun, where 1991 looked like this:

1991 vap (x,s2,<<,>>): 0.000493031 0.000742087 -0.0595093 1.86497

Now, of course, it looks like this:

1991 vap (x,s2,<<,>>): 5.93288e-05 8.18618e-07 -0.0776650 0.0261283

From this I can deduce.. err.. umm..

Anyway now I need to use whatever VAP station data we have. And here I'm a little flaky (again), the vap database hasn't been updated, is it going to be? Asked Dave L and he supplied summaries he'd produced of CLIMAT bulletins from 2000-2006. Slightly odd format but very useful all the

same.

And now, a brief interlude. As we've reached the stage of thinking about secondary variables, I wondered about the CLIMAT updates, as one of the outstanding work items is to write routines to convert CLIMAT and MCDW bulletins to CRU format (so that mergedb.for can read them). So I look at a CLIMAT bulletin, and what's the first thing I notice? It's that there is absolutely no station identification information apart from the WMO code. None. No lat/lon, no name, no country. Which means that all the bells and whistles I built into mergedb, (though they were needed for the db merging of course) are surplus to requirements. The data must simply be added to

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whichever station has the same number at the start, and there's no way to check it's right. I don't appear to have a copy of a MCDW bulletin yet, only a PDF.. I wonder if that's the same? Anyway, back to the main job.

As I was examining the vap database, I noticed there was a 'wet' database. Could I not use that to assist with rd0 generation? well.. it's not documented, but then, none of the process is so I might as well bluff my way into it! Units seem to vary:

CLIMAT bulletins have day counts:

SURFACE LAND 'CLIMAT' DATA FOR 2006/10. MISSING DATA=-32768 MET OFFICE, HADLEY CENTRE CROWN COPYRIGHT WMO BLK WMO STN STNLP MSLP TEMP VAP P DAYS RN RAIN R QUINT SUN HRS SUN % MIN_T MAX_T 01 001 10152 10164 5 52 9 63 2 -32768 -32768 -12 20

Dave L's CLIMAT update has days x 10:

100100 7093 -867 9JAN MAYEN(NOR-NAVY) NORWAY 20002006 -7777777 2000 150 120 180 60 150 20 30 130 120 150 70 70

The existing 'wet' database (wet.0311061611.dtb) has days x 100:

10010 7093 -866 9 JAN MAYEN(NOR NAVY) NORWAY 1990 2003 -999 -999 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1990-9999-9999-9999-9999 400 600 600 1800 1500 1100 800 1800

The published climatology has days x 100 as well:

Tyndall Centre grim file created on 13.01.2004 at 15:22 by Dr. Tim Mitchell .wet = wet day frequency (days) 0.5deg lan clim:1961-90 MarkNew but adj so that wet=<pre [Long=-180.00, 180.00] [Lati= -90.00, 90.00] [Grid X,Y= 720, 360] [Boxes= 67420] [Years=1975-1975] [Multi= 0.0100] [Missing=-999] Grid-ref= 1, 148 1760 1580 1790 1270 890 510 470 290 430 400 590 1160

So I guess we go with days x100. Dave's files will have to be reformatted anyway so it's a negligible overhead. Okaaaay..

Wrote dave2cru.for to convert Dave L's CLIMAT composites to CRU-format files in the appropriate units. One problem is the significant number of stations without names or countries: they are simply 'xxxxxxxxxx' and I'm not sure how mergedb is going to take to that! Well only one way to find out.. so I converted the rain days data:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db] ./dave2cru

DAVE2CRU - convert Dave L CLIMAT composites to dtb files Enter the CLIMAT composite to be converted: CLIMAT_MCDW_MCDW_rdy_updat_merged

Example data line from that file:

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2000 150 120 180 60 150 20 30 130 120 150 70 70

Please enter a factor to apply (or 1): 10 Please enter the 3-ch parameter code: rd0

The output file will be: rd0.0708151122.dtb

3411 stations written. <END QUOTE>

Then tried to merge that into wet.0311061611.dtb, and immediately hot formatting issues - that pesky last field has been badly abused here, taking values including:

-999.00 0.00 nocode (yes, really!)

Had a quick review of mergedb; it won't be trivial to update it to treat that field as a8. So reluctantly, changed all the 'nocode' entries to '0':

crua6[/cru/cruts/version_3_0/db/rd0] perl -pi -e 's/nocode/ 0/g' wet.0311061611.dt*

Unfortunately, that didn't solve the problems.. as there are alphanumerics in that field later on:

-712356 5492 -11782 665 SPRING CRK WOLVERINE CANADA 1969 1988 -999 307F0P9

So.. ***sigh***.. will have to alter mergedb.for to treat that field as alpha. Aaarrgghhh.

Did that. Next problem is best summarised with an example:

************************************************** * * * OPERATOR DECISION REQUIRED: * * * 100100 7093 -867 9 JAN MAYEN(NOR-NAVY) NORWAY 2000 2006 -999 0 * * * This incoming station has a possible match in * * the current database, but either the WMO code * * or the lat/lon values differ. * * * * Incoming: * 100100 7093 -867 9 JAN MAYEN(NOR-NAVY) NORWAY 2000 2006 -999 0 * Potential match: * 10010 7093 -866 9 JAN MAYEN(NOR NAVY) NORWAY 1990 2003 -999 -999

Yes, the 'wet' database features old-style 5-digit WMO codes. The best approach is probably to alter mergedb again, to multiply any 5-digit codes by 10. Not sure if there is a similar problem with 7-digit codes, hopefully not.

Oh, more bloody delays. Modified mergedb to 'adjust' the WMO codes, fine. But then a proper run of it

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just demonstrated that it's far too picky. Even a 0.01-degree difference in coordinates required ops intervention. What we need for updates is an absolute priority for WMO codes, and only a shout if the name or the spatial coordinates are waaay off. I am seriously considering scrapping mergedb in favour of a version of auminmaxresync - its cloud-based approach and 'intelligent' matching is far more efficient than mergedb's brute-force attack, as you'd expect from a program built on top of that knowledge. And it does save all its actions. But I don't know that I have the wherewithal.. okay, I do.

Derived newmergedb.for from auminmaxresync.for. Should be fairly robust. Doesn't offer as many bells and whistles as mergedb.for, but should be faster and more helpful all the same.

Well.. it works.. but the data doesn't. It's that old devil called WMO numbering again:

Comparing Update: 718000 4868 622 217 NANCY/ESSEY FRANCE 2001 2002 -999 0 ..with Master: 718000 4665 -5306 28 CAPE RACE (MARS) CANADA 1920 1969 -999 -999

Now what's happened here? Well the CLIMAT numbering only gives five digits (71 800) and so an extra zero has been added to bring it up to six. Unfortunately, that's the wrong thing to do, because that's the code of CAPE RACE. The six-digit code for NANCY/ESSEY is 071800. Mailed Phil and DL as this could be a big problem - many of the Update stations have no other metadata!

Also noticed that some of the CLIMAT data seemed to be missing, eg for NANCY/ESSEY:

718000 4868 622 217NANCY/ESSEY FRANCE 20002006 -7777777 2000-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2001-9999 110-9999-9999-9999-9999-9999 120 150 110 130 90 2002 80 160 70 70 80 30 60 120 100 130 180 140 2003-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2004-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2005-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2006-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

I have the CLIMAT bulletin for 10/2006, which gives data for Rain Days (12 in this case). It doesn't seem likely that nothing was reported after 2002.

I am now wondering whether it would be best to go back to the MCDW and CLIMAT bulletins themselves and work directly from those.

--
Well, information is always useful. And I probably did know this once.. long ago. All official WMO codes are five digits, countrycountrystationstationstation. However, we use seven-digit codes, because when no official code is available we improvise with two extra digits. Now I can't see why we didn't leave the rest at five digits, that would have been clear. I also can't see why, if we had to make them all seven digits, we extended the 'legitimate' five-digit codes by multiplying by 100, instead of

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adding two numericallymeaningless zeros at the most significant (left) end. But, that's what happened, and like everything else that's the way it's staying.

So - incoming stations with WMO codes can only match stations with codes ending '00'. Put another way, for comparison purposes any 7-digit codes ending '00' should be truncated to five digits.

Also got the locations of the original CLIMAT and MCDW bulletins.

CLIMAT are here: http://hadobs.metoffice.com/crutem3/data/station_updates/

MCDW are here: ftp://ftp1.ncdc.noaa.gov/pub/data/mcdw http://www1.ncdc.noaa.gov/pub/data/mcdw/

Downloaded all CLIMAT and MCDW bulletins (CLIMAT 01/2003 to 07/2007; MCDW 01/2003 to 06/2007 (with a mysterious extra called 'ssm0302.Apr211542' - which turns out to be identical to ssm0302.fin)).

Wrote mcdw2cru.for and climat2cru.for, just guess what they do, go on..

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/MCDW] ./mcdw2cru

MCDW2CRU: Convert MCDW Bulletins to CRU Format

Enter the earliest MCDW file: ssm0301.fin Enter the latest MCDW file (or <ret> for single files): ssm0706.fin

All Files Processed tmp.0709071541.dtb: 2407 stations written vap.0709071541.dtb: 2398 stations written pre.0709071541.dtb: 2407 stations written sun.0709071541.dtb: 1693 stations written

Thanks for playing! Byeee! <END QUOTE>

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/CLIMAT] ./climat2cru

CLIMAT2CRU: Convert MCDW Bulletins to CRU Format

Enter the earliest CLIMAT file: climat_data_200301.txt Enter the latest CLIMAT file (or <ret> for single file): climat_data_200707.txt

All Files Processed tmp.0709071547.dtb: 2881 stations written vap.0709071547.dtb: 2870 stations written pre.0709071547.dtb: 2878 stations written sun.0709071547.dtb: 2020 stations written tmn.0709071547.dtb: 2800 stations written tmx.0709071547.dtb: 2800 stations written

Thanks for playing! Byeee! <END QUOTE>

Of course, it wasn't quite that simple. MCDW has an inexplicably complex format,

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which I'm sure will vary over time and eventually break the converter. For instance, most text is left-justified, except the month names for the overdue data, which are right-justified. Also, there is no missing value code, just blank space if a value is absent. This necessitates reading everything as strings and then testing for content. Oh, and a small amount of rain is marked 'T'.. as are small departures from the mean!!

So moan over, now we have a set of updates for the secondary databases. And, indeed for the primary ones - except that I've already processed those, as updated by Dave L.. er.. ah well. So as I'm running stupidly late anyway - why not find out? It's that Imp of the Perverse on my shoulder again.

Actually as I examined all the databases in the tree to work out what was wheat and what chaff, I had my awful memory jogged quite nastily: WE NEED RAIN DAYS. So both conversion progs will need adjusting and re-running!! Waaaaah! And frankly at 18:45 on a Friday evening.. it's not gonna happen right now.

..okay, a another week, another razorblade to slide down. Modified mcdw2cru to include rain days:

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/MCDW] ./mcdw2cru

MCDW2CRU: Convert MCDW Bulletins to CRU Format

Enter the earliest MCDW file: ssm0301.fin Enter the latest MCDW file (or <ret> for single files): ssm0706.fin

All Files Processed tmp.0709111032.dtb: 2407 stations written vap.0709111032.dtb: 2398 stations written rdy.0709111032.dtb: 2407 stations written pre.0709111032.dtb: 2407 stations written sun.0709111032.dtb: 1693 stations written

Thanks for playing! Byeee! <END QUOTE>

Checked, and the four preexisting databases match perfectly with their counterparts, so I didn't break anything in the adjustments. and the rdy file looks good too (actually the above is the *final* run; there were numerous bugs as per).

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/CLIMAT] ./climat2cru

CLIMAT2CRU: Convert MCDW Bulletins to CRU Format

Enter the earliest CLIMAT file: climat_data_200301.txt Enter the latest CLIMAT file (or <ret> for single file): climat_data_200707.txt

All Files Processed tmp.0709101706.dtb: 2881 stations written vap.0709101706.dtb: 2870 stations written rdy.0709101706.dtb: 2876 stations written pre.0709101706.dtb: 2878 stations written

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sun.0709101706.dtb: 2020 stations written tmn.0709101706.dtb: 2800 stations written tmx.0709101706.dtb: 2800 stations written

Thanks for playing! Byeee! <END QUOTE>

Again, existing outputs are unchanged and the new rdy file looks OK (though see bracketed note above for MCDW).

So.. to the incorporation of these updates into the secondary databases. Oh, my.

Beginning with Rain Days, known variously as rd0, rdy, pdy.. this allowed me to modify newmergedb.for to cope with various 'freedoms' enjoyed by the existing databases (such as six-digit WMO codes). And then, when run, an unexpected side-effect of my flash correlation display thingy: it shows up existing problems with the data!

Here is the first 'issue' encountered by newmergedb, taken from the top and with my comments in <anglebrackets>:

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/rd0] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: Should the incoming 'update' header info and data take precedence over the existing database? Or even vice-versa? This will significantly reduce user decisions later, but is a big step!

Enter 'U' to give Updates precedence, 'M' to give Masters precedence, 'X' for equality: U Please enter the Master Database name: wet.0311061611.dtb Please enter the Update Database name: rdy.0709111032.dtb

Reading in both databases.. Master database stations: 4988 Update database stations: 2407

Looking for WMO code matches..

***** OPERATOR ADJUDICATION REQUIRED *****

In attempting to pair two stations, possible data incompatibilities have been found.

MASTER: 221130 6896 3305 51 MURMANSK EX USSR 1936 2003 -999 -999 UPDATE: 2211300 6858 3303 51 MURMANSK RUSSIAN FEDER 2003 2007 -999 0

CORRELATION STATISTICS (enter 'C' for more information): > -0.60 is minimum correlation coeff. > 0.65 is maximum correlation coeff. > -0.01 is mean correlation coeff.

Enter 'Y' to allow, 'N' to deny, or an information code letter: C

<OKAY - SO I'VE REQUESTED A DISPLAY OF THE LAGGED CORRELATIONS>

Master Data: Correlation with Update first year aligned to this year -v

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1936 900 600 1000 800 1000 900 1300 1700 2100 1800 900 1000 0.27 1937 300 1400 1300 800 1400 1800 500 1200 1600 1000 1100 1500 0.15 1938 900 1000 1500 1800 1200 1500 1200 1700 500 700 1600 700 -0.13 1939 1500 1300 1100 1400 1200 1200 1000 1300 1800 1600 1100 1300 0.24 1940 1000 1500 1000 1200 1100 1700 2600 1500 1500 1400 1700 1100 0.15 1941 1800 1200 1000 1200 900 1100 900 1200 1900 1500 1000 1400 0.48 1942 900 900 1700 900 1600 1000 600 1100 1400 1300 700 700 0.51 1943 800 1000 1000 1300 900 800 1500 1600 1400 1500 1300 1200 0.44 1944 1000 400 900 800 1200 600 900 2000 900 1100 1000 900 0.32 1945 500 400 700 700 800 1800 900 1100 1200 1100 1300 700 0.19 1946 1200 1200 100 700 900 1200 400 900 800 1900 1300 1400 0.16 1947 900 1300 1300 1100 1600 1000 800 1400 1400 1700 2100 1900 0.09 1948 1100 1400 1400 1200 1300 1800 1200 1700 1500 2200 2100 1900 0.10 1949 1100 1100 500 1500 1600 1100 1500 1200 2200 2500 900 1600 0.04 1950 1300 800 1000 1100 1700 1200 1500 800 1100 1300 1500 1400 -0.04 1951 1100 600 1400 1400 1500 1600 2100 1300 1500 1700 2000 1700 -0.13 1952 2100 800 1100 1800 1300 1200 2400 2200 1600 1000 1000 2300 -0.23 1953 2100 1400 2100 1500 900 300 1300 1700 1500 800 1200 800 -0.24 1954 2100 600 1300 1000 1300 1700 1600 2000 1800 1300 1400 1200 -0.40 1955 2200 1300 900 1000 1600 2000 1100 1400 1000 2100 2300 1600 -0.20 1956 1300 1100 1300 400 1600 1300 900 1500 2000 1300 2000 1400 -0.30 1957 1700 1600 1100 1100 1900 1900 1400 1600 1400 1700 2300 2600 -0.27 1958 1300 2200 1900 700 1500 1200 2100 1000 1900 1700 1600 1000 -0.21 1959 2500 1800 1300 900 900 1600 1600 1500 2200 1700 1000 900 -0.33 1960 1800 1700 1500 400 1300 1500 400 1000 1300 1500 1000 1400 -0.21 1961 2100 1800 2200 1500 800 1400 1600 1100 1900 1200 1200 2100 -0.59 1962 2100 1100 1000 1500 1300 1100 1300 1700 1200 2000 1600 2300 -0.37 1963 2100 2100 2000 1000 700 2000 1400 1800 1400 1600 2000 2400 -0.56 1964 2400 1100 1000 1700 1100 1400 1400 1400 2000 1200 2100 1800 -0.42 1965 1400 2100 1300 1000 1700 1700 1400 2400 1300 2100 1900 2100 -0.41 1966 1600 1600 2000 2000 1700 1200 2000 2500 2500 2700 1600 600 -0.34 1967 2200 1700 1600 1200 1000 1400 1600 1300 1700 1500 1200 2100 -0.21 1968 1600 1800 1800 1800 1500 1800 1400 2100 1000 2000 2100 2000 -0.28 1969 1100 300 1900 1200 1000 1300 1500 1200 1200 2000 1700 800 -0.25 1970 1900 1400 1200 900 600 1200 1500 700 2300 1700 1700 2100 -0.23 1971 2000 1300 1600 1600 1200 1100 1400 1800 2000 1600 1700 1500 -0.39 1972 1300 1200 1300 1200 1700 800 1400 1800 1900 2000 1700 1600 -0.26 1973 1800 1100 1700 900 1200 1500 500 1800 1200 2000 2100 2100 -0.36 1974 1100 2400 700 1600 1300 1300 1800 2000 1900 1200 1400 2400 -0.29 1975 1500 2200 1400 1700 2500 2200 2300 1600 1700 2300 1800 2600 -0.47 1976 1900 800 1100 1500 1000 900 1300 1800 2200 1600 1400 1600 -0.33 1977 1800 1400 2200 1200 1600 1900 1300 1500 1500 1900 1500 2000 -0.40 1978 1500 1800 1400 2100 700 1000 1100 1900 1700 2300 1500 2200 -0.24 1979 1700 1700 1700 1200 1500 1800 900 1200 1800 1600 1500 2300 -0.39 1980 1900 1300 1300 1000 1400 900 700 1100 1300 1600 2200 1700 -0.36 1981 2600 500 1900 2000 800 1900 1500 2000 1400 1500 1800 1600 -0.46 1982 2200 1800 1100 1600 1500 2200 1800 1400 1700 1700 1900 1400 -0.60 1983 2400 1900 1700 1200 800 1500 1200 2000 1400 2100 2000 2500 -0.23 1984 1900 800 1500 2000 1100 1600 2000 1700 1100 1400 1000 1200 1985-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1986-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1987-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1988-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1989-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 0.65 1990-9999-9999-9999-9999-9999 500 1300 900 700 900 1300 700 0.62 1991-9999 900 500 300 700 1000 1500 700 1700 1000 1300 1300 0.54 1992 800 1000 600 500 700 900-9999 1300-9999 700 900 1200 0.60 1993 600 900 400 500 900 1500 1000 800 800 1000 400 1000 0.55 1994 1300 1000 300 600 700 1000 900 600 1200 0 1400 600 0.43 1995 900 900 600 700 700 900 1100 1300 600 1800 1300 500 0.61 1996 500 1100 400 700 700 1200 1200 1100 1100 900 1000 1400 0.54 1997 1200 800 1300 600 600 100 500 1100 900-9999 1000 900 0.61 1998 1200 1300 800 1100 1100 1100 800 600 1200 1100 600 1200 0.52

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1999 600 400 600 1000 700 700 1800 1400 700 1600 800 1200 0.62 2000 1100 600 1500 1700 900 1500 800 800 1000 1000 600 600 0.40 2001 600 500 700 700 600 500 1200 1200 700 1300 900 1000 0.63 2002 1000 800 1300 200 900 1100 1400 1200 1400 1800 1100 700 2003 1100-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 Update Data: 2003 1100 700 700 500 1000 400 700 1100 1200 2100 800 1900 2004 900 700 600 600 1300 1200 1000 1200 1400 900 1000 1000 2005 1000 400 800 1100 900 600 1200 1000 1600 1000 1300 1200 2006 700 500 1300 400 600 1200 1600 700 1000-9999 600 1500 2007 1400 400 400 1300 1200 1200-9999-9999-9999-9999-9999-9999

<DO YOU SEE? THERE'S THAT OH-SO FAMILIAR BLOCK OF MISSING CODES IN THE LATE 80S, THEN THE DATA PICKS UP AGAIN. BUT LOOK AT THE CORRELATIONS ON THE RIGHT, ALL GOOD AFTER THE BREAK, DECIDEDLY DODGY BEFORE IT. THESE ARE TWO DIFFERENT STATIONS, AREN'T THEY? AAAARRRGGGHHHHHHH!!!!!>

MASTER: 221130 6896 3305 51 MURMANSK EX USSR 1936 2003 -999 -999 UPDATE: 2211300 6858 3303 51 MURMANSK RUSSIAN FEDER 2003 2007 -999 0

CORRELATION STATISTICS (enter 'C' for more information): > -0.60 is minimum correlation coeff. > 0.65 is maximum correlation coeff. > -0.01 is mean correlation coeff.

Enter 'Y' to allow, 'N' to deny, or an information code letter: <END QUOTE>

So.. should I really go to town (again) and allow the Master database to be 'fixed' by this program? Quite honestly I don't have time - but it just shows the state our data holdings have drifted into. Who added those two series together? When? Why? Untraceable, except anecdotally.

It's the same story for many other Russian stations, unfortunately - meaning that (probably) there was a full Russian update that did no data integrity checking at all. I just hope it's restricted to Russia!!

There are, of course, metadata issues too. Take:

<BEGIN QUOTE> MASTER: 206740 7353 8040 47 DIKSON ISLAND EX USSR 1936 2003 -999 -999 UPDATE: 2067400 7330 8024 47 OSTROV DIKSON RUSSIAN FEDER 2003 2007 -999 0

CORRELATION STATISTICS (enter 'C' for more information): > -0.70 is minimum correlation coeff. > 0.81 is maximum correlation coeff. > -0.01 is mean correlation coeff. <END QUOTE>

This is pretty obviously the same station (well OK.. apart from the duff early period, but I've got used to that now). But look at the longitude! That's probably 20km! LUckily I selected 'Update wins' and so the metadata aren't compared. This is still going to take ages,

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because although I can match WMO codes (or should be able to), I must check that the data correlate adequately - and for all these stations there will be questions. I don't think it would be a good idea to take the usual approach of coding to avoid the situation, because (a) it will be non-trivial to code for, and (b) not all of the situations are the same. But I am beginning to wish I could just blindly merge based on WMO code.. the trouble is that then I'm continuing the approach that created these broken databases. Look at this one:

<BEGIN QUOTE> ***** OPERATOR ADJUDICATION REQUIRED *****

In attempting to pair two stations, possible data incompatibilities have been found.

MASTER: 239330 6096 6906 40 HANTY MANSIJSK EX USSR 1936 1984 -999 -999 UPDATE: 2393300 6101 6902 46 HANTY-MANSIJSK RUSSIAN FEDER 2003 2007 -999 0

CORRELATION STATISTICS (enter 'C' for more information): > -0.42 is minimum correlation coeff. > 0.39 is maximum correlation coeff. > -0.02 is mean correlation coeff.

Enter 'Y' to allow, 'N' to deny, or an information code letter: C Master Data: Correlation with Update first year aligned to this year -v 1936 1400 800 1700 900 1200 800 700 800 1800-9999-9999-9999 0.33 1937 1400 800 500 1700 1500 800 1200 1000 1700 1300 700 1200 0.32 1938 1000 1700 1200 1100 1100 800 800 1300 1400 1900 1800 1300 0.04 1939 1100 1700 1600 1800 1500 800 1500 1900 1700 1800 1300 1300 0.09 1940 1300 700 900 900 1800 1200 900 1300 1200 2200 1900 1800 0.08 1941 1400 1100 1800 1000 1400 1900 1400 700 1300 1200 1900 2000 0.02 1942 1700 900 1600 900 1200 1500 1300 1500 1200 1900 1500 1500 -0.06 1943 1400 1300 1300 800 1400 1600 1300 1500 1900 2000 700 1900 -0.17 1944 1900 1500 2000 1100 1200 1300 1500 1700 1800 1200 1500 1900 -0.32 1945 1300 1000 1400 2100 2000 1100 1700 700 1600 1800 2300 1700 -0.42 1946 2300 1900 1500 1100 1100 2000 1800 1000 1200 2100 2000 1800 -0.35 1947 1900 1400 1600 1000 2100 1900 2100 1000 1200 2000 2100 1500 -0.35 1948 1700 1500 1800 800 1300 1800 1700 1300 1800 2200 2000 2100 -0.15 1949 2300 2100 1000 700 1600 1400 1200 800 2100 2000 1100 1400 -0.07 1950 2100 2300 1000 1100 1500 1600 1600 2300 1900 1200 1100 1500 0.00 1951 1600 1000 1500 800 1500 1400 1200 600 1800 1800 1400 2400 -0.07 1952 1600 400 1100 1300 1100 1400 800 2000 1500 2300 1300 1600 -0.04 1953 2000 1200 1500 500 1300 1500 1100 1200 2300 2200 1600 2100 -0.02 1954 1700 1800 700 700 1000 1300 1200 1600 2000 1800 1800 600 0.01 1955 2400 1400 1000 1100 1700 1200 1000 1300 1500 1300 2300 1600 -0.08 1956 1300 800 1000 1100 1000 1000 1400 1800 1900 1900 2600 2000 -0.29 1957 1900 1200 1700 1000 1100 1100 1100 700 800 2300 1900 2200 -0.18 1958 1300 1600 1500 400 1500 1100 1300 1400 1900 2400 2000 1600 -0.28 1959 1700 1600 700 1300 1700 1100 1100 1600 2000 2100 1900 1600 -0.04 1960 1800 1600-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 0.24 1961-9999-9999-9999-9999-9999-9999-9999 1600 1600 1700 1900 1600 0.33 1962 1700 800 1200 600 400 1100 900 2000 1100 1900 1700 1500 0.25 1963 1200 1300 1700 700 1100 1600 900 1000 1100 1400 1800 2000 -0.04 1964 1900 500 1300 1300 1200 1200 1100 1100 1700 1500 2000 1800 0.13 1965 1200 1400 700 900 1200 1100 1300 1400 1800 2500 1000 1700 0.23 1966 1800 1600 2100 1300 1500 2100 900 1800 1500 2400 1900 800 0.11 1967 1600 1200 1100 600 800 1100 1100 700 1300 1200 1300 1900 0.39 1968 1600 1400 1600 1200 900 1300 1400 1000 1700 1300 1400 1200 0.24

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1969 900 1000 1100 1500 1700 1700 1000 1800 1200 1400 1900 1300 0.04 1970 1500 1200 1600 1400 700 1600 700 1600 1000 1500 1900 1600 -0.02 1971 1700 400 1100 1700 1300 1700 700 2000 900 2100 2000 1900 -0.11 1972 1200 1500 1400 800 1700 1300 1700 2000 2100 1700 2500 1900 -0.08 1973 1200 1100 1100 700 800 1300 2100 1000 2400 1900 1800 2300 -0.11 1974 700 1200 1800 1800 1400 1200 1000 1300 1100 1600 1900 700 -0.14 1975 2200 1800 1400 1300 1500 1500 1400 1500 1400 2300 1900 2100 -0.15 1976 2000 1500 600 700 1100 1600 1300 1100 1500 1800 1600 1200 -0.11 1977 1900 1700 1800 1400 1000 1100 1000 1300 1500 1800 1700 2100 -0.15 1978 1600 1000 800 1400 1400 800 1600 1600 2300 2200 2200 1800 0.03 1979 1600 1600 1600 900 900 1900 1200 1700 1200 2100 1600 2000 0.00 1980 1600 1200 500 800 1500 1100 800 1700 1200 600 2200 2200 -0.05 1981 2000 1000 1700 1300 1500 1100 800 400 1500 800 1500 1900 0.06 1982 2400 1800 1100 1200 1200 1100 1000 1700 1200 2100 1800 2000 0.03 1983 2500 2100 1800 1300 1400 1200 1200 1300 1300 1900 2300 1900 0.10 1984 1200 700 500 1300 900 800 1100 1000 1700 1600 1600 1300 Update Data: 2003 1500 900 600 400 900 1200 500 700 1100 600 700 1500 2004 700 600 700 400 600 1100 500 900 900 1400 1500 600 2005 700 400 800 1400 300 900 800 800 900 500 1200 600 2006 800 700 900 1000 800 500 1000 500 1300 1100 700 1600 2007 1100 1100 900 700 1300 1500-9999-9999-9999-9999-9999-9999 <END QUOTE>

Here, the expected 1990-2003 period is MISSING - so the correlations aren't so hot!

Yet the WMO codes and station names /locations are identical (or close). What the hell is supposed to happen here? Oh yeah - there is no 'supposed', I can make it up. So I have :-)

If an update station matches a 'master' station by WMO code, but the data is unpalatably inconsistent, the operator is given three choices:

<BEGIN QUOTE> You have failed a match despite the WMO codes matching. This must be resolved!! Please choose one:

1. Match them after all. 2. Leave the existing station alone, and discard the update. 3. Give existing station a false code, and make the update the new WMO station.

Enter 1,2 or 3: <END QUOTE>

You can't imagine what this has cost me - to actually allow the operator to assign false WMO codes!! But what else is there in such situations? Especially when dealing with a 'Master' database of dubious provenance (which, er, they all are and always will be).

False codes will be obtained by multiplying the legitimate code (5 digits) by 100, then adding 1 at a time until a number is found with no matches in the database. THIS IS NOT PERFECT but as there is no central repository for WMO codes - especially made-up ones - we'll have to chance duplicating one that's present in one of the other databases. In any case, anyone comparing WMO codes between databases - something I've studiously avoided doing except for tmin/tmax where I had to - will be treating the false codes with suspicion anyway. Hopefully.

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Of course, option 3 cannot be offered for CLIMAT bulletins, there being no metadata with which to form a new station.

This still meant an awful lot of encounters with naughty Master stations, when really I suspect nobody else gives a hoot about. So with a somewhat cynical shrug, I added the nuclear option - to match every WMO possible, and turn the rest into new stations (er, CLIMAT excepted). In other words, what CRU usually do. It will allow bad databases to pass unnoticed, and good databases to become bad, but I really don't think people care enough to fix 'em, and it's the main reason the project is nearly a year late.

And there are STILL WMO code problems!!! Let's try again with the issue. Let's look at the first station in most of the databases, JAN MAYEN. Here it is in various recent databases:

dtr.0705152339.dtb: 100100 7093 -867 9 JAN MAYEN NORWAY 1998 2006 -999 -999.00 pre.0709111032.dtb:0100100 7056 -840 9 JAN MAYEN NORWAY 2003 2007 -999 0 sun.0709111032.dtb:0100100 7056 -840 9 JAN MAYEN NORWAY 2003 2007 -999 0 tmn.0702091139.dtb: 100100 7093 -867 9 JAN MAYEN NORWAY 1998 2006 -999 -999.00 tmn.0705152339.dtb: 100100 7093 -867 9 JAN MAYEN NORWAY 1998 2006 -999 -999.00 tmp.0709111032.dtb:0100100 7056 -840 9 JAN MAYEN NORWAY 2003 2007 -999 0 tmx.0702091313.dtb: 100100 7093 -867 9 JAN MAYEN NORWAY 1998 2006 -999 -999.00 tmx.0705152339.dtb: 100100 7093 -867 9 JAN MAYEN NORWAY 1998 2006 -999 -999.00 vap.0709111032.dtb:0100100 7056 -840 9 JAN MAYEN NORWAY 2003 2007 -999 0

As we can see, even I'm cocking it up! Though recoverably. DTR, TMN and TMX need to be written as (i7.7).

Anyway, here it is in the problem database:

wet.0311061611.dtb: 10010 7093 -866 9 JAN MAYEN(NOR NAVY) NORWAY 1990 2003 -999 -999

You see? The leading zero's been lost (presumably through writing as i7) and then a zero has been added at the trailing end. So it's a 5-digi WMO code BUT NOT THE RIGHT ONE. Aaaarrrgghhhhhh!!!!!!

I think this can only be fixed in one of two ways:

1. By hand.

2. By automatic comparison with other (more reliable) databases.

As usual - I'm going with 2. Hold onto your hats.

Actually, a brief interlude to churn out the tmin & tmax primaries, which got

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sort-of forgotten after dtr was done:

<BEGIN ABRIDGED QUOTES (separated by '#####')> > ***** AnomDTB: converts .dtb to anom .txt for gridding ***** > Enter the suffix of the variable required:

.tmn > Select the .cts or .dtb file to load: tmn.0708071548.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto):

tmn.txt > Select the first,last years AD to save: 1901,2006 > Operating... > NORMALS MEAN percent STDEV percent > .dtb 3814210 65.5 > .cts 210801 3.6 4025011 69.2 > PROCESS DECISION percent %of-chk > no lat/lon 650 0.0 0.0 > no normal 1793923 30.8 30.8 > out-of-range 976 0.0 0.0 > accepted 4024035 69.1 > Dumping years 1901-2006 to .txt files... #####

IDL> quick_interp_tdm2,1901,2006,'tmnglo/tmn.',750,gs=0.5,pts_prefix='tmntxt/tmn.',dumpgl o='dumpglo' ##### Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: gunzip clim.6190.lan.tmn FILE NOT FOUND - PLEASE TRY AGAIN: clim.6190.lan.tmn Enter a name for the gridded climatology file: clim.6190.lan.tmn.grid Enter the path and stem of the .glo files: tmnglo/tmn. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: tmnabs Now, CONCENTRATE. Addition or Percentage (A/P)? A Right, erm.. off I jolly well go! tmn.01.1901.glo (etc) tmn.12.2006.glo ##### Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for. Enter a gridfile with YYYY for year and MM for month: tmnabs/tmn.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006

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Enter End Month: 12 Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.tmn.dat Writing cru_ts_3_00.1901.1910.tmn.dat (etc) ##### > ***** AnomDTB: converts .dtb to anom .txt for gridding ***** > Enter the suffix of the variable required:

.tmx > Select the .cts or .dtb file to load: tmx.0708071548.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto):

tmx.txt > Select the first,last years AD to save: 1901,2006 > Operating... > NORMALS MEAN percent STDEV percent > .dtb 3795470 65.4 > .cts 205607 3.5 4001077 68.9 > PROCESS DECISION percent %of-chk > no lat/lon 652 0.0 0.0 > no normal 1805313 31.1 31.1 > out-of-range 471 0.0 0.0 > accepted 4000606 68.9 > Dumping years 1901-2006 to .txt files... #####

IDL> quick_interp_tdm2,1901,2006,'tmxglo/tmx.',750,gs=0.5,pts_prefix='tmxtxt/tmx.',dumpgl o='dumpglo' ##### Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.tmx Enter a name for the gridded climatology file: clim.6190.lan.tmx.grid Enter the path and stem of the .glo files: tmxglo/tmx. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: tmxabs Now, CONCENTRATE. Addition or Percentage (A/P)? A Right, erm.. off I jolly well go! tmx.01.1901.glo (etc) tmx.12.2006.glo ##### Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for. Enter a gridfile with YYYY for year and MM for month: tmxabs/tmx.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01

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Enter End Year: 2006 Enter End Month: 12 Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.tmx.dat Writing cru_ts_3_00.1901.1910.tmx.dat (etc) <END ABRIDGED QUOTES>

This took longer than hoped.. running out of disk space again. This is why Tim didn't save more of the intermediate products - which would have made my detective work easier. The ridiculous process he adopted - and which we have dutifully followed - creates hundreds of intermediate files at every stage, none of which are automatically zipped/unzipped. Crazy. I've filled a 100gb disk!

So, anyway, back on Earth I wrote wmocmp.for, a program to - you guessed it - compare WMO codes from a given set of databases. Results were, ah.. 'interesting':

<BEGIN QUOTE> REPORT:

Database Title Exact Match Close Match Vague Match Awful Match Codes Added WMO = 0 ../db/pre/pre.0612181221.dtb n/a n/a n/a n/a 14397 1540 ../db/dtr/tmn.0708071548.dtb 1865 3389 57 77 5747 2519 ../db/tmp/tmp.0705101334.dtb 0 4 28 106 4927 0 <END QUOTE>

So the largest database, precip, contained 14397 stations with usable WMO codes (and 1540 without). The TMin, (and TMax and DTR, which were tested then excluded as they matched TMin 100%) database only agreed perfectly with precip for 1865 stations, nearby 3389, believable 57, worrying 77. TMean fared worse, with NO exact matches (WMO misformatting again) and over 100 worrying ones.

The big story is the need to fix the tmean WMO codes. For instance:

10010 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00

is illegal, and needs to become one of: 01001 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 0001001 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 0100100 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00

I favour the first as it's technically accurate. Alternatively we seem to have widely adopted the third, which at least has the virtue of being consistent. Of course it's the only one that will match the precip:

100100 7093 -867 10 JAN MAYEN NORWAY 1921 2006 -999 -999.00

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..which itself should be either:

0100100 7093 -867 10 JAN MAYEN NORWAY 1921 2006 -999 -999.00

or:

01001 7093 -867 10 JAN MAYEN NORWAY 1921 2006 -999 -999.00

Aaaaarrrggghhhh!!!!

And the reason this is so important is that the incoming updates will rely PRIMARILY on matching the WMO codes! In fact CLIMAT bulletins carry no other identification, of course. Clearly I am going to need a reference set of 'qenuine WMO codes'.. and wouldn't you know it, I've found four!

Location N. Stations Notes http://weather.noaa.gov/data/nsd_bbsss.txt 11548 Full country names, ';' delim http://www.htw-dresden.de/~kleist/wx_stations_ct.html 13000+ *10, leading zeros kept, fmt probs From Dave Lister 13080 *10 and leading zeros lost, country codes From Philip Brohan 11894 2+3, No countries

The strategy is to use Dave Lister's list, grabbing country names from the Dresden list. Wrote getcountrycodes.for and extracted an imperfect but useful-as-a-reference list. Hopefully in the main the country will not need fixing or referring to!!

Wrote 'fixwmos.for' - probably not for the first time, but it's the first prog of that name in my repository so I'll have to hope for the best. After an unreasonable amount of teething troubles (due to my forgetting that the tmp database stores lats & lons in degs*100 not degs*10, and also to the presence of a '-99999' as the lon for GUATEMALA in the reference set) I managed to sort-of fix the tmp database:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/tmp] ./fixwmos

FIXWMOS - Fix WMO Codes in a Database

Enter the database to be fixed: tmp.0705101334.dtb

The operation completed successfully.

2263 WMO Codes were 'fixed' and all were rewritten as (i7.7)

The output database is tmp.0709281456.dtb

crua6[/cru/cruts/version_3_0/db/tmp] <END QUOTE>

The first records have changed as follows:

crua6[/cru/cruts/version_3_0/db/tmp] diff tmp.0705101334.dtb tmp.0709281456.dtb |head -30 1c1

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< 10010 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00

--- > 0100100 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00

So far so good.. but records that weren't matched with the reference set didn't fare so well:

89c89 < 10050 780 142 9 ISFJORD RADIO NORWAY 1912 1979 101912 -999.00

--- > 0010050 780 142 9 ISFJORD RADIO NORWAY 1912 1979 101912 -999.00

This is misleading because, although there probably won't BE any incoming updates for ISFJORD RADIO, we can't say for certain that there will never be updates for any station outside the current reference set. In fact, we can say with confidence that there will be!

So, what to do? Do we assume a particular factor to adjust ALL codes by, based on the matches? Or do we attempt (note careful use of verb) to use the country codes database to work out the most significant 'real' digits of these codes?

Well, I fancy the first one. We'll make two passes through the data, the first pass changes nothing but saves counts of the successful factors in bins: *0.01, *0.1, *1, *10, *100 should do it. I sure hope all the results are in one bin!

It worked. An initial 'verbose' run showed a consistent choice of factor, though it'll exit with an error code if multiple factors are registered in one database.

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/tmp] ./fixwmos

FIXWMOS - Fix WMO Codes in a Database

Enter the database to be fixed: tmp.0705101334.dtb locfac set to: 10 First ref: 0100100

The operation completed successfully.

2263 WMO Codes were 'matched' All codes were modified with a factor of 10 Lons/lats were modified with a factor of 10

The output database is tmp.0710011359.dtb

crua6[/cru/cruts/version_3_0/db/tmp] <END QUOTE>

Example results: <BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/tmp] diff tmp.0705101334.dtb tmp.0710011359.dtb | head -12 1c1 < 10010 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00

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--- > 0100100 7090 -870 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 89c89 < 10050 780 142 9 ISFJORD RADIO NORWAY 1912 1979 101912 -999.00

--- > 0100500 7800 1420 9 ISFJORD RADIO NORWAY 1912 1979 101912 -999.00 159c159 < 10080 783 155 28 Svalbard Lufthavn NORWAY 1911 2006 341911 -999.00

--- > 0100800 7830 1550 28 Svalbard Lufthavn NORWAY 1911 2006 341911 -999.00 <END QUOTE>

Then.. attacked the wet database! And immediately found this beauty:

0 -9999 -99999 -999 UNKNOWN UNKNOWN 1994 2003 -999 0 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1994 500 800 600 400 600 100 0 100 200 400 1000 1300 1995 400 100 1100 900 1200 800 200 100 200 400 800 500 1996 500 1100 1500 600 900-9999 0 300 400 700 0 1100 1997 800 1000 700 1000 1000 1000 200 200 400 700 200 1000 1998 700 700 1000 1000-9999 800 100 100 0 200 400 700 1999 300 1000 800-9999 700 800 0 200-9999 600 400 200 2000 1100 600 900 900 1000 400-9999 100 200 300 0 400 2001 0 800 300 500 1200 0 0 0 200 200 500 800 2002 800 300 600 1300 800 500 400 100 300 400 400 600 2003 300-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

Gotta love the system! Like this is ever going to be a blind bit of use. Modified the code to leave such stations unmolested, but identified in a separate file so they can be 'cleansed', it being a little too risky to auto-cleanse such things.

Hopefully the final attack on 'wet':

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/db/rd0] ./fixwmos

FIXWMOS - Fix WMO Codes in a Database

Enter the database to be fixed: wet.0311061611.dtb

The operation completed successfully.

1920 WMO Codes were 'matched' All codes were modified with a factor of 10 Lons/lats were modified with a factor of 1

The output database is wet.0710021341.dtb

IMPORTANT: the following WMO codes were not altered: False codes (wmo<0): 2917 Illegal codes (0<=wmo<1000): 1 (illegals written to wet.0311061611.bad) crua6[/cru/cruts/version_3_0/db/rd0] <END QUOTE>

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I then removed the sole illegal (see above) from wet.0710021341.dtb, which becomes the 'new old' wet/rd0 database.

So.. to incorporate the updates! Finally. First, the MCDW, metadata-rich ones:

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/rd0] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, ian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: wet.0710021341.dtb Please enter the Update Database name: rdy.0709111032.dtb

Reading in both databases.. Master database stations: 4987 Update database stations: 2407

Looking for WMO code matches.. * new header 0100100 7056 -840 9 JAN MAYEN NORWAY 1990 2007 -999 -999 * 2 reject(s) from update process 0710041559

Writing wet.0710041559.dtb

OUTPUT(S) WRITTEN

New master database: wet.0710041559.dtb

Update database stations: 2407 > Matched with Master stations: 1556 (automatically: 1556) (by operator: 0) > Added as new Master stations: 0 > Rejected: 2 Rejects file: rdy.0709111032.dtb.rejected Note: IEEE floating-point exception flags raised: Inexact; Invalid Operation; See the Numerical Computation Guide, ieee_flags(3M) uealogin1[/cru/cruts/version_3_0/db/rd0] <END QUOTE>

(also knocked up rrstats.for at this stage, to analyse replication rates by latitude band for a given database - needs a Matlab prog to drive really)

[a bit of debugging here as the last records weren't being written properly, filenames adjusted above accordingly]

Then, the CLIMAT, nothing-but-the-code ones:

*WARNING: ignore this, the CLIMAT bulletins were later improved with metadata and newmergedb rerun*

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<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/rd0] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: wet.0710041559.dtb Please enter the Update Database name: rdy.0709101706.dtb

Reading in both databases.. Master database stations: 5836 Update database stations: 2876

Looking for WMO code matches.. 378 reject(s) from update process 0710081508

Writing wet.0710081508.dtb

OUTPUT(S) WRITTEN

New master database: wet.0710081508.dtb

Update database stations: 2876 > Matched with Master stations: 2498 (automatically: 2498) (by operator: 0) > Added as new Master stations: 0 > Rejected: 378 Rejects file: rdy.0709101706.dtb.rejected Note: IEEE floating-point exception flags raised: Inexact; Invalid Operation; See the Numerical Computation Guide, ieee_flags(3M) uealogin1[/cru/cruts/version_3_0/db/rd0] <END QUOTE>

Now of course, we can't add any of the CLIMAT bulletin stations as 'new' stations because we don't have any metadata! so.. is it worth using the lookup table? Because although I'm thrilled at the high match rate (87%!), it does seem worse when you realise that you lost the rest..

* see below, CLIMAT metadata fixed! *

At this stage I knocked up rrstats.for and the visualisation companion tool, cmprr.m. A simple process to show station counts against time for each 10-degree latitude band (with 20-degree bands at the North and South extremities). A bit basic and needs more work - but good for a quick & dirty check.

Wrote dllist2headers.for to convert the 'Dave Lister' WMO list to CRU header format - the main difficulty being the accurate conversion of the two-character 'country codes' - especially since many are actually state codes for the US! Ended up with wmo.0710151633.dat as our reference WMO set.

Incorporated the reference WMO set into climat2cru.for. Successfully reprocessed the CLIMAT bulletins

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into databases with at least SOME metadata:

pre.0710151817.dtb rdy.0710151817.dtb sun.0710151817.dtb tmn.0710151817.dtb tmp.0710151817.dtb tmx.0710151817.dtb vap.0710151817.dtb

In fact, it was far more successful than I expected - only 11 stations out of 2878 without metadata!

Re-ran newmergedb:

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/rd0] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: wet.0710041559.dtb Please enter the Update Database name: rdy.0710151817.dtb

Reading in both databases.. Master database stations: 5836 Update database stations: 2876

Looking for WMO code matches.. 71 reject(s) from update process 0710161148

Writing wet.0710161148.dtb

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

OUTPUT(S) WRITTEN

New master database: wet.0710161148.dtb

Update database stations: 2876 > Matched with Master stations: 2498 (automatically: 2498) (by operator: 0) > Added as new Master stations: 307 > Rejected: 71 Rejects file: rdy.0710151817.dtb.rejected Note: IEEE floating-point exception flags raised: Inexact; Invalid Operation; See the Numerical Computation Guide, ieee_flags(3M) uealogin1[/cru/cruts/version_3_0/db/rd0] <END QUOTE>

307 stations rescued! and they'll be there in future of course, for metadata-free CLIMAT bulletins to match with.

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So where were we.. Rain Days. Family tree:

wet.0311061611.dtb

+ rdy.0709111032.dtb (MCDW composite)

+ rdy.0710151817.dtb (CLIMAT composite with metadata added)

V
V wet.0710161148.dtb

Now it gets tough. The current model for a secondary is that it is derived from one or more primaries, plus their normals, plus the normals for the secondary.

The IDL secondary generators do not allow 'genuine' secondary data to be incorporated. This would have been ideal, as the gradual increase in observations would have gradually taken precedence over the primary-derived synthetics.

The current stats for the wet database were derived from the new proglet, dtbstats.for:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/secondaries/rd0] ./dtbstat

DTBSTAT: Database Stats Report

Please enter the (18ch.) database name: wet.0710161148.dtb

Report for: wet.0710161148.dtb

Stations in Northern Hemisphere: 5365 Stations in Southern Hemisphere: 778 Total: 6143

Maximum Timespan in Northern Hemisphere: 1840 to 2007 Maximum Timespan in Southern Hemisphere: 1943 to 2007 Global Timespan: 1840 to 2007

crua6[/cru/cruts/version_3_0/secondaries/rd0] <END QUOTE>

So, without further ado, I treated RD0 as a Primary and derived gridded output from the database:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/secondaries/rd0] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required: .rd0 > Select the .cts or .dtb file to load: wet.0710161148.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data:

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3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): rd0.txt > Select the first,last years AD to save: 1901,2007 > Operating...

> NORMALS MEAN percent STDEV percent > .dtb 0 0.0 > .cts 731118 45.4 730956 45.4 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 878015 54.6 54.6 > out-of-range 56 0.0 0.0 > accepted 731062 45.4 > Dumping years 1901-2007 to .txt files...

crua6[/cru/cruts/version_3_0/secondaries/rd0] <END QUOTE>

Not particularly good - the bulk of the data being recent, less than half had valid normals (anomdtb calculates normals on the fly, on a per-month basis). However, this isn't so much of a problem as the plan is to screen it for valid station contributions anyway.

<BEGIN QUOTE>
IDL> quick_interp_tdm2,1901,2007,'rd0glo/rd0.',450,gs=0.5,dumpglo='dumpglo',pts_prefix='r d0txt/rd0.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2007 no stations found in: rd0txt/rd0.2007.08.txt no stations found in: rd0txt/rd0.2007.09.txt no stations found in: rd0txt/rd0.2007.10.txt no stations found in: rd0txt/rd0.2007.11.txt no stations found in: rd0txt/rd0.2007.12.txt

IDL> <END QUOTE>

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/secondaries/rd0] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim.6190.lan.wet.grid2 Enter the path and stem of the .glo files: rd0glo/rd0.

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Enter the starting year: 1901 Enter the ending year: 2007 Enter the path (if any) for the output files: rd0abs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A ! this was a guess! We'll see how the results look Right, erm.. off I jolly well go! rd0.01.1901.glo (etc) <END QUOTE>

Then.. wait a minute! I checked back, and sure enough, quick_interp_tdm.pro DOES allow both synthetic and 'real' data to be included in the gridding. From the program description:

<BEGIN QUOTE> ; TDM: the dummy grid points default to zero, but if the synth_prefix files are present in call, ; the synthetic data from these grids are read in and used instead <END QUOTE>

And so.. (after some confusion, and renaming so that anomdtb selects percentage anomalies)..

IDL> quick_interp_tdm2,1901,2006,'rd0pcglo/rd0pc',450,gs=0.5,dumpglo='dumpglo',synth_pref ix='rd0syn/rd0syn',pts_prefix='rd0pctxt/rd0pc.'

The trouble is, we won't be able to produce reliable station count files this way. Or can we use the same strategy, producing station counts from the wet database route, and filling in 'gaps' with the precip station counts? Err.

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/secondaries/rd0] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim.grid Enter the path and stem of the .glo files: rd0pcglo/rd0pc. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: rd0pcgloabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Right, erm.. off I jolly well go! rd0pc.01.1901.glo (etc) <END QUOTE>

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/secondaries/rd0] ./mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: rd0pcgloabs/rd0pc.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

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Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.rd0.dat Writing cru_ts_3_00.1901.1910.rd0.dat Writing cru_ts_3_00.1911.1920.rd0.dat Writing cru_ts_3_00.1921.1930.rd0.dat Writing cru_ts_3_00.1931.1940.rd0.dat Writing cru_ts_3_00.1941.1950.rd0.dat Writing cru_ts_3_00.1951.1960.rd0.dat Writing cru_ts_3_00.1961.1970.rd0.dat Writing cru_ts_3_00.1971.1980.rd0.dat Writing cru_ts_3_00.1981.1990.rd0.dat Writing cru_ts_3_00.1991.2000.rd0.dat Writing cru_ts_3_00.2001.2006.rd0.dat crua6[/cru/cruts/version_3_0/secondaries/rd0] <END QUOTE>

All according to plan.. except the values themselves!

For January, 2001:

Minimum = 0 Maximum = 32630 Vals >31000 = 1

For the whole of 2001:

Minimum = 0 Maximum = 56763 Vals >31000 = 5

Not good. We're out by a factor of at least 10, though the extremes are few enough to just cap at DiM. So where has this factor come from?

Well here's the January 2001 climatology:

Minimum = 0 Maximum = 3050 Vals >3100 = 0

That all seems fine for a percentage normals set. Not entirly sure about 0 though.

so let's look at the January 2001 gridded anomalies file:

Minimum = -48.046 Maximum = 0.0129

This leads to a show-stopper, I'm afraid. It looks as though the calculation I'm using for percentage anomalies is, not to put too fine a point on it, cobblers.

This is what I use to build actuals from anomalies in glo2abs.for:

absgrid(ilon(i),ilat(i)) = nint(normals(i,imo) + * anoms(ilon(i),ilat(i)) * normals(i,imo) / 100)

or, to put it another way, V = N(A+N)/100

This is what anomdtb.f90 uses to build anomalies from actuals:

DataA(XAYear,XMonth,XAStn) = nint(1000.0*((real(DataA(XAYear,XMonth,XAStn)) / & real(NormMean(XMonth,XAStn)))-1.0))

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or, in the same terms, A = 1000((V/N)-1)

which reverses to: V = N(A+1000)/1000

This could well explain things. It could also mean that I have to reproduce v3.00 precip AFTER it's been used (against my wishes) by Dave L and Dimitrious.

Well to start with, I'll try the new calculation in glo2abs to reproduce the rd0 data.

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/secondaries/rd0] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: c.grid Enter the path and stem of the .glo files: rd0pcglo/rd0pc. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: rd0pcgloabs Now, CONCENTRATE. Addition or Percentage (A/P)? P Right, erm.. off I jolly well go! rd0pc.01.1901.glo (etc) <END QUOTE>

This *does* improve matters considerably. Now, for January 2001:

Minimum = 0 Maximum = 5090 (a little high but not fatal) Vals >3100 = 556 Vals >3500 = 110 Vals >4000 = 2 (so the bulk of the excessions are only a few days over)

In fact the 2nd highest Max is 4369, well below 5090.

So, good news - but only in the sense that I've found the error. Bad news in that it's a further confirmation that my abilities are short of what's required here.

Rushed back to precip. Found the .glo files in /cru/cruts/version_3_0/primaries/precip/pre0km0612181221glo/, and re-ran glo2abs with the revised percentage anomaly equation:

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/primaries/precip] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.pre Enter a name for the gridded climatology file: clim.6190.lan.pre.gridded2 Enter the path and stem of the .glo files: pre0km0612181221glo/pregrid. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: pre0km0612181221abs/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Right, erm.. off I jolly well go! pregrid.01.1901.glo

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(etc) <END QUOTE>

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/primaries/precip] ./mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: pre0km0612181221abs/pregrid.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.pre.dat Writing cru_ts_3_00.1901.1910.pre.dat Writing cru_ts_3_00.1911.1920.pre.dat Writing cru_ts_3_00.1921.1930.pre.dat Writing cru_ts_3_00.1931.1940.pre.dat Writing cru_ts_3_00.1941.1950.pre.dat Writing cru_ts_3_00.1951.1960.pre.dat Writing cru_ts_3_00.1961.1970.pre.dat Writing cru_ts_3_00.1971.1980.pre.dat Writing cru_ts_3_00.1981.1990.pre.dat Writing cru_ts_3_00.1991.2000.pre.dat Writing cru_ts_3_00.2001.2006.pre.dat crua6[/cru/cruts/version_3_0/primaries/precip] <END QUOTE>

Then back to finish off rd0. Modified glo2abs to allow the operator to set minima and maxima, with a specific option to set wet day limits (DiM*100):

<BEGIN QUOTE> crua6[/cru/cruts/version_3_0/secondaries/rd0] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim...grid Enter the path and stem of the .glo files: rd0pcglo/rd0pc. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: rd0pcgloabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set a single minimum and maximum 3. Set monthly minima and maxima (for wet/rd0)

Choose: 3 Right, erm.. off I jolly well go! rd0pc.01.1901.glo (etc) <END QUOTE>

Output was checked.. and as expected, January 2001 had 556 values of 3100 :-)

<BEGIN QUOTE>

Page 116HARRY_READ_ME

crua6[/cru/cruts/version_3_0/secondaries/rd0] ./mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: rd0pcgloabs/rd0pc.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.rd0.dat Writing cru_ts_3_00.1901.1910.rd0.dat Writing cru_ts_3_00.1911.1920.rd0.dat Writing cru_ts_3_00.1921.1930.rd0.dat Writing cru_ts_3_00.1931.1940.rd0.dat Writing cru_ts_3_00.1941.1950.rd0.dat Writing cru_ts_3_00.1951.1960.rd0.dat Writing cru_ts_3_00.1961.1970.rd0.dat Writing cru_ts_3_00.1971.1980.rd0.dat Writing cru_ts_3_00.1981.1990.rd0.dat Writing cru_ts_3_00.1991.2000.rd0.dat Writing cru_ts_3_00.2001.2006.rd0.dat crua6[/cru/cruts/version_3_0/secondaries/rd0] <END QUOTE>

Back to where this all started - Vapour Pressure.

We have:

1. 'Master' (ie original) database vap.0311181410.dtb 2. MCDW updates database vap.0709111032.dtb 3. CLIMAT updates database *with added metadata* vap.0710151817.dtb

so first we incorporate the MCDW updates..

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/vap] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: vap.0311181410.dtb Please enter the Update Database name: vap.0709111032.dtb

Reading in both databases.. Master database stations: 7691 Update database stations: 2398

Looking for WMO code matches.. 2 reject(s) from update process 0710241541

Writing vap.0710241541.dtb

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

Page 117HARRY_READ_ME

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

OUTPUT(S) WRITTEN

New master database: vap.0710241541.dtb

Update database stations: 2398 > Matched with Master stations: 1847 (automatically: 1847) (by operator: 0) > Added as new Master stations: 549 > Rejected: 2 Rejects file: vap.0709111032.dtb.rejected uealogin1[/cru/cruts/version_3_0/db/vap] <END QUOTE>

Then, the CLIMAT ones:

<BEGIN QUOTE> uealogin1[/cru/cruts/version_3_0/db/vap] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: vap.0710241541.dtb Please enter the Update Database name: vap.0710151817.dtb

Reading in both databases.. Master database stations: 8240 Update database stations: 2870

Looking for WMO code matches.. 68 reject(s) from update process 0710241549

Writing vap.0710241549.dtb

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

OUTPUT(S) WRITTEN

New master database: vap.0710241549.dtb

Update database stations: 2870 > Matched with Master stations: 2599 (automatically: 2599) (by operator: 0) > Added as new Master stations: 203 > Rejected: 68 Rejects file: vap.0710151817.dtb.rejected uealogin1[/cru/cruts/version_3_0/db/vap] <END QUOTE>

So, not as good as the MCDW update.. lost 68.. but then of course we are talking about station data that arrived with NO metadata AT ALL.

Page 118HARRY_READ_ME

So we will try the unaltered rd0 process on vap. It should be the same; a mix of synthetic and observed.

************************************************************************************

* * PRIORITY INTERRUPT * PRIORITY INTERRUPT * PRIORITY INTERRUPT * PRIORITY INTERRUPT *

************************************************************************************

*

After an email enquiry from Wladimir J. Alonso (alonsow@mail.nih.gov), in which unusual behaviour of CRU TS 2.10 Vapour Pressure data was observed, I discovered that some of the Wet Days and Vepour Pressure datasets had been swapped!!

The files I was looking at were decadal, 1981-1990.

Vapour Pressure, January: Min 0 Max 310 Vapour Pressure, February: Min 0 Max 280

Wet Days, January: Min 0 Max 3220 Wet days, February: Min 0 Max 3240

So I wrote crutsstats.for, whioch returns monthly and annual minima, maxima and means for any gridded output file.

Tried it on the full runs, and they look OK:

crua6[/cru/cruts/vap_wet_investigation] head -90 cru_ts_2_10.1901-2002.vap.grid.stats |tail -10 1981 0 322 82 0 324 84 0 320 90 0 335 99 0 352 111 0 356 130 0 349 144 0 344 143 0 360 124 0 323 105 0 320 90 0 321 83 0 360 107 1982 0 312 80 0 323 83 0 318 88 0 329 98 0 348 111 0 357 126 0 365 143 0 364 140 0 355 124 0 318 105 0 321 90 0 318 84 0 365 106 1983 0 348 82 0 340 85 0 330 90 0 505 99 0 348 112 0 364 130 0 360 145 0 362 143 0 368 126 0 323 105 0 318 91 0 317 82 0 505 108 1984 0 312 80 0 320 82 0 315 89 0 329 97 0 347 112 0 359 130 0 353 144 0 343 140 0 353 122 0 324 105 0 318 89 0 316 81 0 359 106 1985 0 314 80 0 320 81 0 319 88 0 359 98 0 352 111 0 367 128 0 358 141 0 355 141 0 353 123 0 323 105 0 322 90 0 323 82 0 367 106 1986 0 312 81 0 330 83 0 316 89 0 321 99 0 366 112 0 394 129 0 371 143 0 342 139 0 354 122 0 323 104 0 318 90 0 316 82 0 394 106 1987 0 320 81 0 318 85 0 318 88 0 335 98 0 363 112 0 366 130 0 397 147 0 356 142 0 354 126 0 345 105 0 325 91 0 365 84 0 397 107 1988 0 413 83 0 324 84 0 352 90 0 323 99 0 346 113 0 363 131 0 367 148 0 358 144 0 387 126 0 342 105 0 320 89 0 315 83 0 413 108

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1989 0 336 80 0 320 83 0 327 90 0 324 98 0 343 112 0 366 130 0 365 145 0 349 142 0 353 124 0 323 105 0 324 90 0 332 84 0 366 107 1990 0 320 83 0 323 85 0 476 92 0 413 101 0 361 113 0 363 132 0 371 146 0 353 143 0 371 124 0 327 106 0 318 93 0 317 84 0 476 108

crua6[/cru/cruts/vap_wet_investigation] head -90 cru_ts_2_10.1901-2002.wet.grid.stats | tail -10 1981 0 3100 1018 0 2800 919 0 3100 980 0 3000 911 0 3100 945 0 3000 1010 0 3100 1051 0 3100 1040 0 3000 981 0 3100 1017 0 3000 1021 0 3100 1003 0 3100 992 1982 0 3100 983 0 2800 894 0 3100 967 0 3000 925 0 3100 927 0 3000 941 0 3100 979 0 3100 1054 0 3000 1007 0 3100 1055 0 3000 996 0 3100 1044 0 3100 981 1983 0 3100 1035 0 2800 863 0 3100 941 0 3000 919 0 3100 929 0 3000 949 0 3100 990 0 3100 1039 0 3000 996 0 3100 1026 0 3000 1034 0 3100 1057 0 3100 982 1984 0 3100 981 0 2900 848 0 3100 920 0 3000 841 0 3100 932 0 3000 973 0 3100 1048 0 3100 1057 0 3000 1023 0 3100 1057 0 3000 992 0 3100 1016 0 3100 974 1985 0 3100 969 0 2800 896 0 3100 952 0 3000 896 0 3100 928 0 3000 938 0 3100 1057 0 3100 1043 0 3000 993 0 3100 1043 0 3000 1066 0 3100 1029 0 3100 984 1986 0 3100 988 0 2800 908 0 3100 950 0 3000 895 0 3100 922 0 3000 962 0 3100 1022 0 3100 1052 0 3000 1037 0 3100 1052 0 3000 1048 0 3100 986 0 3100 985 1987 0 3100 1011 0 2800 909 0 3100 930 0 3000 856 0 3100 954 0 3000 972 0 3100 1021 0 3100 1064 0 3000 978 0 3100 991 0 3000 1002 0 3100 1047 0 3100 978 1988 0 3100 1033 0 2900 924 0 3100 971 0 3000 903 0 3100 938 0 3000 980 0 3100 1039 0 3100 1101 0 3000 1014 0 3100 1017 0 3000 1007 0 3100 1054 0 3100 998 1989 0 3100 1019 0 2800 936 0 3100 1015 0 3000 892 0 3100 978 0 3000 1020 0 3100 1054 0 3100 1075 0 3000 1023 0 3100 1070 0 3000 1046 0 3100 1053 0 3100 1015 1990 0 3100 996 0 2800 959 0 3100 1011 0 3000 953 0 3100 928 0 3000 907 0 3100 983 0 3100 986 0 3000 915 0 3100 968 0 3000 949 0 3100 959 0 3100 960

So the monthly maxima are fine here. But for the decadal files?

crua6[/cru/cruts/vap_wet_investigation] cat cru_ts_2_10.1981-1990.vap.grid.stats0 1981 0 310 102 0 280 92 0 310 98 0 300 91 0 310 95 0 300 101 0 310 105 0 310 104 0 300 98 0 310 102 0 300 102 0 310 100 0 310 99 1982 0 310 98 0 280 89 0 310 97 0 300 93 0 310 93 0 300 94 0 310 98 0 310 105 0 300 101 0 310 106 0 300 100 0 310 104 0 310 98

Page 120HARRY_READ_ME

1983 0 310 104 0 280 86 0 310 94 0 300 92 0 310 93 0 300 95 0 310 99 0 310 104 0 300 100 0 310 103 0 300 103 0 310 106 0 310 98 1984 0 310 98 0 290 85 0 310 92 0 300 84 0 310 93 0 300 97 0 310 105 0 310 106 0 300 102 0 310 106 0 300 99 0 310 102 0 310 97 1985 0 310 97 0 280 90 0 310 95 0 300 90 0 310 93 0 300 94 0 310 106 0 310 104 0 300 99 0 310 104 0 300 107 0 310 103 0 310 98 1986 0 310 99 0 280 91 0 310 95 0 300 90 0 310 92 0 300 96 0 310 102 0 310 105 0 300 104 0 310 105 0 300 105 0 310 99 0 310 99 1987 0 310 101 0 280 91 0 310 93 0 300 86 0 310 95 0 300 97 0 310 102 0 310 106 0 300 98 0 310 99 0 300 100 0 310 105 0 310 98 1988 0 310 103 0 290 92 0 310 97 0 300 90 0 310 94 0 300 98 0 310 104 0 310 110 0 300 101 0 310 102 0 300 101 0 310 105 0 310 100 1989 0 310 102 0 280 94 0 310 101 0 300 89 0 310 98 0 300 102 0 310 105 0 310 107 0 300 102 0 310 107 0 300 105 0 310 105 0 310 101 1990 0 310 100 0 280 96 0 310 101 0 300 95 0 310 93 0 300 91 0 310 98 0 310 99 0 300 91 0 310 97 0 300 95 0 310 96 0 310 96

crua6[/cru/cruts/vap_wet_investigation] cat cru_ts_2_10.1981-1990.wet.grid.stats 1981 0 3220 819 0 3240 842 0 3200 903 0 3350 992 0 3520 1113 0 3560 1304 0 3490 1440 0 3440 1427 0 3600 1236 0 3230 1048 0 3200 898 0 3210 833 0 3600 1071 1982 0 3120 801 0 3230 827 0 3180 881 0 3290 982 0 3480 1108 0 3570 1264 0 3650 1432 0 3640 1405 0 3550 1239 0 3180 1048 0 3210 901 0 3180 835 0 3650 1060 1983 0 3480 820 0 3400 850 0 3300 898 0 5050 993 0 3480 1125 0 3640 1295 0 3600 1451 0 3620 1428 0 3680 1259 0 3230 1050 0 3180 912 0 3170 822 0 5050 1075 1984 0 3120 803 0 3200 823 0 3150 887 0 3290 971 0 3470 1124 0 3590 1299 0 3530 1437 0 3430 1404 0 3530 1218 0 3240 1053 0 3180 894 0 3160 812 0 3590 1060 1985 0 3140 803 0 3200 815 0 3190 882 0 3590 978 0 3520 1113 0 3670 1277 0 3580 1405 0 3550 1411 0 3530 1233 0 3230 1048 0 3220 900 0 3230 821 0 3670 1057 1986 0 3120 809 0 3300 827 0 3160 889 0 3210 990 0 3660 1120 0 3940 1294 0 3710 1428 0 3420 1393 0 3540 1220 0 3230 1041 0 3180 895 0 3160 821 0 3940 1061 1987 0 3200 810 0 3180 849 0 3180 880 0 3350 980 0 3630 1124 0 3660 1296 0 3970 1466 0 3560 1423 0 3540 1260 0 3450 1054 0 3250 910 0 3650 844 0 3970 1075 1988 0 4130 829 0 3240 835 0 3520 902 0

Page 121HARRY_READ_ME

3230 989 0 3460 1133 0 3630 1311 0 3670 1475 0 3580 1441 0 3870 1264 0 3420 1054 0 3200 889 0 3150 832 0 4130 1079 1989 0 3360 804 0 3200 825 0 3270 898 0 3240 978 0 3430 1120 0 3660 1301 0 3650 1447 0 3490 1421 0 3530 1240 0 3230 1052 0 3240 900 0 3320 836 0 3660 1069 1990 0 3200 827 0 3230 853 0 4760 918 0 4130 1005 0 3610 1127 0 3630 1322 0 3710 1462 0 3530 1428 0 3710 1236 0 3270 1062 0 3180 930 0 3170 844 0 4760 1084

Much confusion! The orders of magnitude have changed to reflect the expected ranges - but the data have clearly been swapped!

Another decade:

crua6[/cru/cruts/vap_wet_investigation]cat cru_ts_2_10.1921-1930.vap.grid.stats 1921 0 310 102 0 280 89 0 310 100 0 300 88 0 310 95 0 300 97 0 310 101 0 310 104 0 300 102 0 310 104 0 300 97 0 310 101 0 310 98 1922 0 310 95 0 280 93 0 310 97 0 300 89 0 310 95 0 300 98 0 310 105 0 310 107 0 300 98 0 310 104 0 300 102 0 310 103 0 310 99 1923 0 310 100 0 280 88 0 310 97 0 300 90 0 310 97 0 300 98 0 310 101 0 310 101 0 300 100 0 310 104 0 300 101 0 310 103 0 310 98 1924 0 310 97 0 290 89 0 310 95 0 300 90 0 310 91 0 300 97 0 310 100 0 310 102 0 300 101 0 310 102 0 300 102 0 310 100 0 310 97 1925 0 310 98 0 280 89 0 310 98 0 300 87 0 310 90 0 300 96 0 310 101 0 310 103 0 300 103 0 310 101 0 300 103 0 310 100 0 310 97 1926 0 310 99 0 280 87 0 310 95 0 300 87 0 310 95 0 300 93 0 310 103 0 310 104 0 300 99 0 310 102 0 300 102 0 310 101 0 310 97 1927 0 310 96 0 280 87 0 310 96 0 300 89 0 310 94 0 300 97 0 310 103 0 310 104 0 300 102 0 310 103 0 300 102 0 310 99 0 310 98 1928 0 310 97 0 290 89 0 310 91 0 300 88 0 310 90 0 300 96 0 310 101 0 310 104 0 300 97 0 310 99 0 300 99 0 310 96 0 310 96 1929 0 310 95 0 280 84 0 310 95 0 300 86 0 310 91 0 300 95 0 310 100 0 310 102 0 300 98 0 310 102 0 300 98 0 310 98 0 310 95 1930 0 310 98 0 280 88 0 310 97 0 300 88 0 310 93 0 300 93 0 310 99 0 310 103 0 300 99 0 310 105 0 300 101 0 310 97 0 310 97 crua6[/cru/cruts/vap_wet_investigation] cat cru_ts_2_10.1921-1930.wet.grid.stats 1921 0 3120 805 0 3190 814 0 3140 874 0 3210 969 0 3800 1106 0 3590 1289 0 3600 1439 0 3440 1390 0 3530 1220 0 3230 1032 0 3180 877 0 3160 824 0 3800 1053

Page 122HARRY_READ_ME

1922 0 3120 794 0 3220 813 0 3140 874 0 3210 971 0 3470 1104 0 3590 1280 0 3560 1420 0 3440 1387 0 3530 1211 0 3230 1025 0 3180 896 0 3140 812 0 3590 1049 1923 0 3070 799 0 3140 808 0 3140 871 0 3210 947 0 3460 1082 0 3660 1276 0 3560 1410 0 3440 1392 0 3530 1222 0 3230 1048 0 3180 907 0 3160 826 0 3660 1049 1924 0 3270 792 0 3230 817 0 3160 879 0 3340 955 0 3460 1094 0 3710 1264 0 3560 1415 0 3440 1386 0 3530 1228 0 3160 1034 0 3180 892 0 3140 806 0 3710 1047 1925 0 3110 786 0 3190 815 0 3140 873 0 3210 966 0 3470 1084 0 3590 1253 0 3560 1408 0 3460 1397 0 3530 1231 0 3230 1025 0 3160 896 0 3220 828 0 3590 1047 1926 0 3260 815 0 3290 842 0 3310 889 0 3310 957 0 3460 1085 0 3950 1266 0 3560 1406 0 3450 1402 0 3530 1237 0 3230 1042 0 3250 899 0 3150 811 0 3950 1054 1927 0 3120 795 0 3300 822 0 3170 873 0 3360 959 0 3540 1096 0 3610 1271 0 3550 1424 0 3450 1390 0 3530 1233 0 3230 1053 0 3180 897 0 3280 814 0 3610 1052 1928 0 3200 809 0 3240 823 0 3140 875 0 3400 963 0 3470 1095 0 3590 1263 0 3560 1425 0 3450 1397 0 3530 1228 0 3230 1039 0 3180 902 0 3160 824 0 3590 1054 1929 0 3150 794 0 3190 802 0 3160 867 0 3310 950 0 3600 1084 0 3580 1250 0 3550 1399 0 3440 1385 0 3530 1218 0 3230 1049 0 3180 897 0 3160 806 0 3600 1042 1930 0 3190 798 0 3190 824 0 3150 881 0 3210 965 0 3470 1099 0 3590 1276 0 3530 1424 0 3440 1409 0 3540 1220 0 3200 1042 0 3300 907 0 3280 829 0 3590 1056

The same story. And the final two years:

crua6[/cru/cruts/vap_wet_investigation] cat cru_ts_2_10.2001-2002.vap.grid.stats 2001 0 310 87 0 280 84 0 310 90 0 300 81 0 310 87 0 300 93 0 310 95 0 310 95 0 300 89 0 310 95 0 300 95 0 310 87 0 310 90 2002 0 310 91 0 280 85 0 310 92 0 300 83 0 310 88 0 300 89 0 310 93 0 310 94 0 300 92 0 310 93 0 300 88 0 310 86 0 310 90 crua6[/cru/cruts/vap_wet_investigation] cat cru_ts_2_10.2001-2002.wet.grid.stats 2001 0 3320 834 0 3250 841 0 3180 913 0 3490 1010 0 3490 1147 0 4380 1323 0 3660 1487 0 5120 1466 0 3530 1266 0 3460 1088 0 3620 932 0 3410 843 0 5120 1096 2002 0 3310 837 0 3390 863 0 3270 918 0 3370 1012 0 3930 1151 0 4140 1339 0 3750 1503 0 5110 1453 0 3530 1261 0 3310 1067 0 3470 922 0 3300 833 0 5110 1096

It looks like a consistent problem: all the decadal VAp and WET files should be discarded, and only the 'full run' 1901-2002 files used. But my theory that the error occurred when the 1901-2002 files were converted to decadal doesn't sound true now, because why would the precision levels change? Surely, if the decadal files are

Page 123HARRY_READ_ME

derived from the 1901-2002 files, it's just a case of copying data across?

Let's look at *just* 1981, to try and assess this issue:

FULL 1901-2002 FILE

VAP: 1981 0 322 82 0 324 84 0 320 90 0 335 99 0 352 111 0 356 130 0 349 144 0 344 143 0 360 124 0 323 105 0 320 90 0 321 83 0 360 107

WET: 1981 0 3100 1018 0 2800 919 0 3100 980 0 3000 911 0 3100 945 0 3000 1010 0 3100 1051 0 3100 1040 0 3000 981 0 3100 1017 0 3000 1021 0 3100 1003 0 3100 992

DECADAL 1981-1990 FILE

VAP: 1981 0 310 102 0 280 92 0 310 98 0 300 91 0 310 95 0 300 101 0 310 105 0 310 104 0 300 98 0 310 102 0 300 102 0 310 100 0 310 99

WET: 1981 0 3220 819 0 3240 842 0 3200 903 0 3350 992 0 3520 1113 0 3560 1304 0 3490 1440 0 3440 1427 0 3600 1236 0 3230 1048 0 3200 898 0 3210 833 0 3600 1071

It's evident that the data have not only been swapped - they've been scaled too. Aaaarrrgghhhhhh!!!!!

******************************************************************************* * PRIORITY INTERRUPT ENDS * PRIORITY INTERRUPT ENDS * PRIORITY INTERRUPT ENDS *

*******************************************************************************

Now, where were we.. ah yes, Vapour Pressure. So far:

Original: vap.0311181410.dtb

+ MCDW: vap.0709111032.dtb

v

v Intermediate: vap.0710241541.dtb

+ CLIMAT: vap.0710151817.dtb

v

v Final: vap.0710241549.dtb

Produce anomalies:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:
.vap > Select the .cts or .dtb file to load: vap.0710241549.dtb > Specify the start,end of the normals period: 1961,1990

Page 124HARRY_READ_ME

> Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): vap.txt > Select the first,last years AD to save: 1901,2006 > Operating...

> NORMALS MEAN percent STDEV percent > .dtb 908812 45.2 > .cts 35390 1.8 944202 47.0 > PROCESS DECISION percent %of-chk > no lat/lon 105 0.0 0.0 > no normal 1064261 53.0 53.0 > out-of-range 49 0.0 0.0 > accepted 944153 47.0 > Dumping years 1901-2006 to .txt files...

crua6[/cru/cruts/version_3_0/secondaries/vap] <END_QUOTE>

Well.. 47% accepted, 53% no normals.. pretty much as expected, and unlikely to improve no matter how many new CLIMAT and MCDW updates there are. We need back data for 1961-1990.

Synthetic production:

<BEGIN_QUOTE>
IDL> vap_gts_anom,dtr_prefix='../dtrbin/dtrbin',tmp_prefix='../tmpbin/tmpbin',1901,2006,o utprefix='vapsyn/vapsyn',dumpbin=1 % Compiled module: VAP_GTS_ANOM. % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. Land,sea: 56016 68400 Calculating tmn normal % Compiled module: TVAP. Calculating synthetic vap normal % Compiled module: ESAT. Calculating synthetic anomalies % Compiled module: MOMENT. 1901 vap (x,s2,<<,>>): 1.61250e-05 6.15570e-06 -0.160607 0.222689 % Compiled module: WRBIN. 1902 vap (x,s2,<<,>>): -0.000123188 3.46116e-05 -0.268891 0.0261283 1903 vap (x,s2,<<,>>): 6.86689e-05 4.52675e-06 -0.121429 0.123995 (etc) <END_QUOTE>

(also produced, vapsyn/vapsyn1901 .. vapsyn/vapsyn2006)

Gridding with both observed and synthetic data:

IDL> quick_interp_tdm2,1901,2006,'vapglo/vap.',1000,gs=0.5,dumpglo='dumpglo',synth_prefix ='vapsyn/vapsyn',pts_prefix='vaptxt/vap.'

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Create absolute grids from anomaly grids:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.grid Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set a single minimum and maximum 3. Set monthly minima and maxima (for wet/rd0)

Choose: 1 Right, erm.. off I jolly well go! vap.01.1901.glo vap.02.1901.glo (etc) <END_QUOTE>

and finally, create the output files:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapabs/vap.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.YYYY.vap.dat Try again.. read instructions this time?

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.vap.dat Writing cru_ts_3_00.1901.1910.vap.dat Writing cru_ts_3_00.1911.1920.vap.dat Writing cru_ts_3_00.1921.1930.vap.dat Writing cru_ts_3_00.1931.1940.vap.dat Writing cru_ts_3_00.1941.1950.vap.dat Writing cru_ts_3_00.1951.1960.vap.dat Writing cru_ts_3_00.1961.1970.vap.dat Writing cru_ts_3_00.1971.1980.vap.dat Writing cru_ts_3_00.1981.1990.vap.dat Writing cru_ts_3_00.1991.2000.vap.dat Writing cru_ts_3_00.2001.2006.vap.dat <END_QUOTE>

Ah - and I was really hoping this time that it would just WORK. But of course not - nothing works first

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time in this project. I ran crutsstats on cru_ts_3_00.1901.2006.vap.dat, and:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./crutsstats

CRUTSSTATS: Stats for CRU TS gridded files

Enter the monthly gridded data file: cru_ts_3_00.1901.2006.vap.dat

Please enter the start year: 1901

106 years from 1901 to 2006

Output file is cru_ts_3_00.1901.2006.vap.dat.stats 1901 1 358 106 1902 1 358 106 1903 1 358 106 1904 1 358 106 1905 1 358 106 (etc) 2002 1 358 106 2003 1 358 106 2004 1 358 106 2005 1 358 106 2006 1 358 106 <END_QUOTE>

What?! Every year has the same min (fine, VAP of 0 is probably impossible), max (I can just about believe, if there's a cell with no stations inside the cdd and the normal for it happens to be the highest value, and MEAN (oh no, NO WAY!). What's odder - the .glo files are different:

crua6[/cru/cruts/version_3_0/secondaries/vap/vapabs] diff vap.06.1974.glo.abs.nh vap.06.1975.glo.abs.nh |wc -l 56

Admittedly, 56 lines different out of 360 isn't hugely different. And looking, they are only slight and infrequent differences. But the monthly stats are all cloned as well:

1901 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1902 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1903 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1904 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1905 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1906 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356

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143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1907 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106

Well the first thing to do, after the inevitable wailing and gnashing of teeth, is to re-run glo2abs without the 'zero minimum' flag (just in case I coded that badly, I was in a hurry):

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.grid2 Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): N Right, erm.. off I jolly well go! vap.01.1901.glo vap.02.1901.glo (etc) <END_QUOTE>

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapabs/vap.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.vap.dat Writing cru_ts_3_00.1901.1910.vap.dat Writing cru_ts_3_00.1911.1920.vap.dat Writing cru_ts_3_00.1921.1930.vap.dat Writing cru_ts_3_00.1931.1940.vap.dat Writing cru_ts_3_00.1941.1950.vap.dat Writing cru_ts_3_00.1951.1960.vap.dat Writing cru_ts_3_00.1961.1970.vap.dat Writing cru_ts_3_00.1971.1980.vap.dat Writing cru_ts_3_00.1981.1990.vap.dat Writing cru_ts_3_00.1991.2000.vap.dat Writing cru_ts_3_00.2001.2006.vap.dat <END_QUOTE>

Sadly, that gave the same result. So what of the published (v2.10) VAP dataset? That looks ~ok:

<BEGIN_QUOTE>

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crua6[/cru/cruts/version_3_0/secondaries/vap] ./crutsstats

CRUTSSTATS: Stats for CRU TS gridded files

Enter the monthly gridded data file: cru_ts_2_10.1901-2002.vap.grid

Please enter the start year: 1901

102 years from 1901 to 2002

Output file is cru_ts_2_10.1901-2002.vap.grid.stats 1901 0 411 105 1902 0 413 104 1903 0 465 104 1904 0 359 104 1905 0 383 104 1906 0 376 105 1907 0 387 104 (etc) <END_QUOTE>

Not good at all. Or, rather, good that it must be a solvable problem. Except that it's 10 to 5 on a Sunday afternoon and it's me that's got to solve it.

Where to start? Well, retrace your steps, that's how you get out of a minefield. So first up, to compare similar months in the anomaly files. Though I already know what I'm going to find, don't I? Because glo2abs isn't going to do anything unusual, it just adds the normal and there you go. So if the absolutes are very similar, the anomalies will be, too.. hmm. Well, I *suppose* I could try producing two more copies of the output files - one with just synthetic data and one with just observed data? It's only a couple of re-runs of the quick_interp_tdm2.pro IDL routine..

Started with the synthetic-only run:

<BEGIN_QUOTE>
IDL> quick_interp_tdm2,1901,2006,'vapsynglo/vapsyn.',1000,gs=0.5,dumpglo='dumpglo',nostn= 1,synth_prefix='vapsyn/vapsyn'

crua6[/cru/cruts/version_3_0/secondaries/vap/syn_only] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: ../clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.grid Enter the path and stem of the .glo files: vapsynglo/vapsyn. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapsynabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): N Right, erm.. off I jolly well go! vapsyn.01.1901.glo vapsyn.02.1901.glo (etc)

crua6[/cru/cruts/version_3_0/secondaries/vap/syn_only] ./mergegrids

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Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapsynabs/vapsyn.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.vap.syn.dat Writing cru_ts_3_00.1901.1910.vap.syn.dat Writing cru_ts_3_00.1911.1920.vap.syn.dat Writing cru_ts_3_00.1921.1930.vap.syn.dat Writing cru_ts_3_00.1931.1940.vap.syn.dat Writing cru_ts_3_00.1941.1950.vap.syn.dat Writing cru_ts_3_00.1951.1960.vap.syn.dat Writing cru_ts_3_00.1961.1970.vap.syn.dat Writing cru_ts_3_00.1971.1980.vap.syn.dat Writing cru_ts_3_00.1981.1990.vap.syn.dat Writing cru_ts_3_00.1991.2000.vap.syn.dat Writing cru_ts_3_00.2001.2006.vap.syn.dat <END_QUOTE>

And then the observed-only:

<BEGIN_QUOTE>
IDL> quick_interp_tdm2,1901,2006,'vapobsglo/vapobs.',1000,gs=0.5,dumpglo='dumpglo',pts_pr efix='vaptxt/vap.'

crua6[/cru/cruts/version_3_0/secondaries/vap/obs_only] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: ../clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.grid Enter the path and stem of the .glo files: vapobsglo/vapobs. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapobsabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): N Right, erm.. off I jolly well go! vapobs.01.1901.glo vapobs.02.1901.glo (etc)

crua6[/cru/cruts/version_3_0/secondaries/vap/obs_only] ./mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapobsabs/vapobs.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

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Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.vap.obs.dat Writing cru_ts_3_00.1901.1910.vap.obs.dat Writing cru_ts_3_00.1911.1920.vap.obs.dat Writing cru_ts_3_00.1921.1930.vap.obs.dat Writing cru_ts_3_00.1931.1940.vap.obs.dat Writing cru_ts_3_00.1941.1950.vap.obs.dat Writing cru_ts_3_00.1951.1960.vap.obs.dat Writing cru_ts_3_00.1961.1970.vap.obs.dat Writing cru_ts_3_00.1971.1980.vap.obs.dat Writing cru_ts_3_00.1981.1990.vap.obs.dat Writing cru_ts_3_00.1991.2000.vap.obs.dat Writing cru_ts_3_00.2001.2006.vap.obs.dat <END_QUOTE>

So.. how do the stats look for these two datasets?

Synthetic-only:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap/syn_only] ./crutsstats

CRUTSSTATS: Stats for CRU TS gridded files

Enter the monthly gridded data file: cru_ts_3_00.1901.2006.vap.syn.dat

Please enter the start year: 1901

106 years from 1901 to 2006

Output file is cru_ts_3_00.1901.2006.vap.syn.dat.stats 1901 1 358 106 1902 1 358 106 1903 1 358 106 1904 1 358 106 1905 1 358 106 1906 1 358 106 (etc) <END_QUOTE>

Observed-only:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap/obs_only] ./crutsstats

CRUTSSTATS: Stats for CRU TS gridded files

Enter the monthly gridded data file: cru_ts_3_00.1901.2006.vap.obs.dat

Please enter the start year: 1901

106 years from 1901 to 2006

Output file is cru_ts_3_00.1901.2006.vap.obs.dat.stats 1901 1 358 106 1902 1 358 106 1903 1 358 106 1904 1 358 106 1905 1 358 106 1906 1 358 106 (etc) <END_QUOTE>

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Oh, GOD. What is going on? Are we data sparse and just looking at the climatology? How can a synthetic dataset derived from tmp and dtr produce the same statistics as an 'real' dataset derived from observations?

Let's be logical. Here are the two 'separated' gridding runs:

IDL> quick_interp_tdm2,1901,2006,'vapsynglo/vapsyn.',1000,gs=0.5,dumpglo='dumpglo',nostn= 1,synth_prefix='vapsyn/vapsyn'

IDL> quick_interp_tdm2,1901,2006,'vapobsglo/vapobs.',1000,gs=0.5,dumpglo='dumpglo',pts_pr efix='vaptxt/vap.'

Well they look fine. The synthetic run has no other data inputs ('nostn=1'), and the observed run has no references to the synthetic data. So.. either quick_interp_tdm2.pro is doing something 'unusual', or, or.. hang on, let's try the climatology for stats:

1961 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106

Ah, Bingo was his name-o! as I was hoping (well OK it's a bad kind of hope), the reason it's all the same is that it is by and large defaulting to the climatology. Which means that not much (any?) data is getting through, no matter if we use synthetic, observed, or both together. What's odd about that conclusion is that the synthetic data is derived from TMP and DTR - two very well-populated datasets! So synthetics alone should pretty much fill the.. hang on, just though of something horrendous.. oh, okay, probably not that. I was wondering if glo2abs.for was factoring the normals so that the anomalies were insignificant, but the equation is:

absgrid(ilon(i),ilat(i)) = * nint(anoms(ilon(i),ilat(i))*10) + normals(i,imo)

..so the anomaly is getting the weight! But still - - not a wise thing to leave to automatics. So glo2abs should prompt the user.. but with what? Just one anomaly and normal? Several? The same one from different timesteps? Eeek. Let's look at this actual case.

January 1961, lines 11103, 11104 in the glo file (11099, 11100 without header, putting it on about 33.5 degs N) 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 4.7173E-04 4.7224E-03 5.4273E-03 6.1323E-03 6.8372E-03 7.5422E-03 8.2472E-03 1.9677E-03 0.0000E+00 0.0000E+00

Those anomalies are mighty tiny, given that the absolutes are three-digit integers! Hardly surprising they're not really appearing on the radar when added to normals typically two orders of magnitude higher! Even with the *10 in the glo2abs prog, we're still looking at values around 0.06.

Looked at the observed anomalies (output from anomdtb.f90) - here the anomalies are larger! Between -5 and +5, roughly, which is what I'm used to seeing in .txt files.

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To investigate the synthetics, I needed to look at re-run vap_gts_tdm.pro. It says,

; Note that anomalies are in hPa*10 (bin) or hPa (glo)

So the binary file anomaly units - the ones we're using - are in hPa*10. Let's get one o' them synthetic glo files:

IDL> vap_gts_anom,dtr_prefix='../dtrbin/dtrbin',tmp_prefix='../tmpbin/tmpbin',1961,1961,o utprefix='vapsynglo/vapsyn.',dumpglo=1 Land,sea: 56016 68400 Calculating tmn normal % Compiled module: TVAP. Calculating synthetic vap normal % Compiled module: ESAT. Calculating synthetic anomalies % Compiled module: MOMENT. 1961 vap (x,s2,<<,>>): 5.72571e-05 9.01807e-07 -0.0653905 0.0261283 % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL.

For Jan 1961 (may as well stick with it), -999 is the missing value code. The range is -0.0149 to +0.0222 (remember this is an anomaly in hPa according to the program comment). So if it's telling the truth, the binary anomalies presented to quick_interp_tdm2.pro will range from roughly -0.3 to +0.3. still nt going to impinge on normals between 1 and 358, is it?

So, what are the normals in? Well according to clim.6190.lan.vap:

crua6[/cru/cruts/version_3_0/secondaries/vap] head -11 clim.6190.lan.vap Tyndall Centre grim file created on 12.01.2004 at 11:47 by Dr. Tim Mitchell .vap = vapour pressure (hPa) 0.5deg lan clim:1961-90 MarkNew [Long=-180.00, 180.00] [Lati= -90.00, 90.00] [Grid X,Y= 720, 360] [Boxes= 67420] [Years=1975-1975] [Multi= 0.1000] [Missing=-999] Grid-ref= 1, 148 291 294 296 293 287 279 265 262 271 279 286 287 Grid-ref= 1, 311 14 11 13 21 44 69 92 90 65 37 22 14 Grid-ref= 1, 312 13 10 12 20 43 67 90 87 63 35 21 13

That's what I've been missing! D'oh. That '[Multi= 0.1000]'. That would still only give a range of 0.1 to 35.8 hPa, and my anomalies are still around 0.006 (or 0.3 for synthetics).

Two things, then. Firstly to get glo2abs to read the multiplicative factor from the climatology header and impose it on the output. Secondly to work out why all the anomalies have different magnitudes! Or is vapour pressure really so teeny?

Working on glo2abs. Well my theory for additive anomalies is this: I read in the normals, and apply the multiplicative factor in the header (for VAP it's 0.1). I assume the anomalies are already in the relevant units (ie require no factoring). This looks to be the case for .txt files anyway. So I can add the anomaly to the adjusted normal. Then (because I need integer output) I can DIVIDE by the factor (because that got us from integer to real before). Fine in theory but it all depends on the anomalies being in regular 'units' (why wouldn't they be? They're reals!). OK, check from the beginning, obs first:

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Database: hPa*10 (typically 3-digit integers)

anomdtb.for calls subroutine CheckVariSuffix, which contains:

<BEGIN_QUOTE> else if (Suffix.EQ.".vap") then Variable="vapour pressure (hPa)" Factor = 0.1 <END_QUOTE>

And how does anomdtb.f90 use the Factor? well in the original version:

<BEGIN_QUOTE> crua6[/cru/cruts/untouched/code/linux/cruts] grep 'Factor' anomdtb.f90 real :: MissThresh,StdevThresh,DistanceThresh,Factor, ExeSpace,WyeSpace call CheckVariSuffix (LoadSuffix,Variable,Factor) OpTot = OpTot + (real(DataA(XAYear,XMonth,XAStn))/Factor) OpTotSq = OpTotSq + ((real(DataA(XAYear,XMonth,XAStn))/Factor) ** 2) NormMean (XMonth,XAStn) = Factor*OpTot/OpEn if (OpTotSq.GT.0) NormStdev (XMonth,XAStn) = Factor*sqrt((OpTotSq/OpEn)-((OpTot/OpEn)**2)) OpTot = OpTot + (real(DataA(XAYear,XMonth,XAStn))/Factor) OpTotSq = OpTotSq + ((real(DataA(XAYear,XMonth,XAStn))/Factor) ** 2) NormMean (XMonth,XAStn) = Factor*OpTot/OpEn NormStdev (XMonth,XAStn) = Factor*sqrt((OpEn/(OpEn-1))*((OpTotSq/OpEn)-((OpTot/OpEn)**2))) OpTot = OpTot + (DataA(XAYear,XMonth,XAStn)/Factor) OpTotSq = OpTotSq + (DataA(XAYear,XMonth,XAStn)/Factor) ** 2 OpStDev = Factor*sqrt((OpEn/(OpEn-1))*((OpTotSq/OpEn)-((OpTot/OpEn)**2))) OpMean = Factor*(OpTot/OpEn)

ALat(XAStn),ALon(XAStn),AElv(XAStn),real(DataA(XAYear,XMonth,XAStn))*Factor,AStn(XAS tn) <END_QUOTE>

I *think* the factor is being used multiplicatively. I don't understand why it's being used as a divisor though.. I must have understood last December because I managed to rewrite the 'standard deviation' section, also using it as a divisor!

One obvious thing to try is to use the revised glo2abs. That should now be working in 'units' (but saving in whatever range the normals are in). After that I could try comparing the old and 'new' (ie modded by me) versions of anomdtb.f90 to ensure I didn't break something (sure I didn't, but still..)

So, I revised glo2abs. It now reads the 'Multi' factor from the climatology header, and applies it to the normals before they're used.

So, re-ran quick_interp+tdm2.pro:

IDL> quick_interp_tdm2,1901,2006,'vapglo/vap.',1000,gs=0.5,dumpglo='dumpglo',synth_prefix ='vapsyn/vapsyn',pts_prefix='vaptxt/vap.'

A sample of the outputs, vap.12.1962.glo, had a range of values from -2.3006 to +1.8388, with the majority being 0. A total of 56387 cells were nonzero, which given that there are 67420 land cells, isn't too bad. It's a pretty gaussian distribution, too. It still seems like a small variation (typically +/- 0.5). For the cell where I

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live (Norwich, 363,286), the normals are:

Grid-ref= 363, 286 71 69 76 86 107 129 147 149 135 115 88 77

Or in hPa:

Grid-ref= 363, 286 7.1 6.9 7.6 8.6 10.7 12.9 14.7 14.9 13.5 11.5 8.8 7.7

The nearest station (well based on a quick search) is LOWESTOFT. Taking 1962 and 1963 and scaling:

62 7.6 6.9 6.5 9.2 10.9 12.6 14.4 15.0 13.6 12.3 8.9 6.5 63 5.4 5.5 7.9 9.9 11.1 14.8 15.8 15.1 14.6 11.7 10.3 6.9

The ranges: 2.2 1.4 1.4 0.7 0.2 2.2 1.4 0.1 1.0 0.6 1.4 0.4

Well our sample December 1962 range of anomalies was -2.3006 to +1.8388, and the January range is -3.3640 to +2.1250. So, I have to admit, that's the same order of magnitude for our particular cell, year and month(s).

So, assuming these .glo files are OK, we'll try glo2abs again:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: deleteme1 Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set a single minimum and maximum 3. Set monthly minima and maxima (for wet/rd0) Choose: 1 Right, erm.. off I jolly well go! vap.01.1901.glo vap.02.1901.glo (etc) <END_QUOTE>

..and the result.. look good! For (again) December 1962:

Min 0 (well I did set that, see above) Max 315

Number of zeros: 1078, perfectly respectable although I do wonder if VAP=0 is illegal.. hmm.. OK, added an option in glo2abs:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also

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a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: deleteme3 Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set a single minimum and maximum 3. Set monthly minima and maxima (for wet/rd0) 4. Set all values >0, (ie, positive) Choose: 4 Right, erm.. off I jolly well go! vap.01.1901.glo vap.02.1901.glo (etc) <END_QUOTE>

Result for December 1962: Min 1, Max 315. A good spread of values, without a disproportionate number of '1's, I'm please to say.

So, to generate the output files. Again.

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./mergegrids Welcome! This is the MERGEGRIDS program. I will create decadal and full gridded files from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapabs/vap.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE: cru_ts_3_00.SSSS.EEEE.vap.dat Writing cru_ts_3_00.1901.1910.vap.dat Writing cru_ts_3_00.1911.1920.vap.dat Writing cru_ts_3_00.1921.1930.vap.dat Writing cru_ts_3_00.1931.1940.vap.dat Writing cru_ts_3_00.1941.1950.vap.dat Writing cru_ts_3_00.1951.1960.vap.dat Writing cru_ts_3_00.1961.1970.vap.dat Writing cru_ts_3_00.1971.1980.vap.dat Writing cru_ts_3_00.1981.1990.vap.dat Writing cru_ts_3_00.1991.2000.vap.dat Writing cru_ts_3_00.2001.2006.vap.dat <END_QUOTE>

And what of the statistics. Well by now I've realised that we don't have complete coverage! So the normals are bound to poke through quite a bit. In fact, the story is as it was in the beginning! *cries*

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/vap] ./crutsstats

CRUTSSTATS: Stats for CRU TS gridded files

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Enter the monthly gridded data file: cru_ts_3_00.1901.2006.vap.dat

Please enter the start year: 1901

106 years from 1901 to 2006

Output file is cru_ts_3_00.1901.2006.vap.dat.stats 1901 1 358 106 1902 1 358 106 1903 1 358 106 1904 1 358 106 1905 1 358 106 1906 1 358 106 1907 1 358 106 1908 1 358 106 (etc) <END_QUOTE>

Now admittedly, the 106 mean does vary.. it hioits the dizzying heights of 107 on occasion! With a couple of 105s thrown in to balance the books. Had a look at the stats in detail, compared to those for CRU TS 2.10. And guess what? Yes.. the old stats are better! Here's the first decade:

CRU TS 2.10 1901 0 324 79 0 338 82 0 314 88 0 321 97 0 411 110 0 378 128 0 358 143 0 343 140 0 353 122 0 332 103 0 318 88 0 314 81 0 411 105 1902 0 312 80 0 319 82 0 314 87 0 321 96 0 413 109 0 366 125 0 356 141 0 343 138 0 353 122 0 323 102 0 318 88 0 315 80 0 413 104 1903 0 314 79 0 331 82 0 315 88 0 334 95 0 465 109 0 359 125 0 371 141 0 359 139 0 353 122 0 323 102 0 318 88 0 315 80 0 465 104 1904 0 310 78 0 319 81 0 312 86 0 321 95 0 347 109 0 359 126 0 355 140 0 344 138 0 354 121 0 323 103 0 318 89 0 316 81 0 359 104 1905 0 314 79 0 319 79 0 321 86 0 326 95 0 346 109 0 383 127 0 356 142 0 344 139 0 353 122 0 330 103 0 318 90 0 321 82 0 383 104 1906 0 328 80 0 330 81 0 323 87 0 335 98 0 376 111 0 359 128 0 356 142 0 343 140 0 353 122 0 323 103 0 318 89 0 316 82 0 376 105 1907 0 312 79 0 327 80 0 314 87 0 321 94 0 387 106 0 359 125 0 379 140 0 343 139 0 353 122 0 323 104 0 318 87 0 316 81 0 387 104 1908 0 312 79 0 323 81 0 330 86 0 338 95 0 346 109 0 359 127 0 353 142 0 343 138 0 353 122 0 316 102 0 318 87 0 316 81 0 359 104 1909 0 312 79 0 319 81 0 323 87 0 321 94 0 346 107 0 359 125 0 355 141 0 343 140 0 354 122 0 320 103 0 318 90 0 316 81 0 359 104 1910 0 312 80 0 319 82 0 315 86 0 321 95 0 347 109 0 359 126 0 383

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142 0 343 139 0 353 122 0 318 102 0 318 87 0 316 80 0 383 104

CRU TS 3.00 1901 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1902 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1903 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1904 1 311 80 1 320 82 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1905 1 311 80 1 320 83 1 315 88 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1906 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1907 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 141 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1908 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 129 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1909 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106 1910 1 311 80 1 320 83 1 315 89 1 320 98 1 346 111 1 358 128 1 356 143 1 342 140 1 354 123 1 323 104 1 318 90 1 315 82 1 358 106

..and here's a more recent decade:

CRU TS 2.10 1991 0 314 82 0 322 84 0 331 90 0 672 100 0 523 113 0 540 134 0 607 147 0 424 143 0 353 125 0 328 106 0 386 91 0 350 83 0 672 108 1992 0 337 82 0 383 84 0 450 90 0 613 98 0 347 112 0 359 128 0 373 140 0 345 140 0 353 122 0 347 103 0 414 89 0 384 83 0 613 106 1993 0 324 81 0 403 83 0 449 90 0 622 98 0 518 113 0 534 131 0 652 147 0 398 143 0 353 122 0 333 105 0 408 89 0 339 84 0 652 107 1994 0 346 82 0 396 82 0 457 90 0 626 100 0 524 113 0 507 132 0 605 146 0 416 143 0 349 125 0 332 107 0

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397 93 0 341 84 0 626 108 1995 0 369 83 0 406 86 0 461 90 0 686 100 0 505 114 0 565 134 0 673 146 0 492 147 0 364 127 0 342 108 0 427 91 0 339 82 0 686 109 1996 0 334 81 0 431 83 0 548 88 0 634 97 0 524 113 0 530 131 0 645 147 0 422 142 0 366 124 0 337 106 0 413 91 0 344 84 0 645 107 1997 0 367 82 0 322 84 0 348 90 0 323 99 0 344 113 0 484 133 0 426 147 0 523 145 0 353 126 0 348 108 0 345 93 0 370 86 0 523 109 1998 0 339 84 0 345 89 0 338 92 0 355 104 0 361 116 1 531 137 1 356 152 0 560 149 0 370 128 0 347 108 0 369 92 0 334 85 0 560 111 1999 0 323 83 0 334 86 0 324 90 0 336 100 0 362 113 0 487 132 0 362 148 0 357 143 1 353 127 0 331 107 0 337 91 0 316 85 0 487 109 2000 0 319 82 0 319 85 0 319 91 0 328 102 0 356 114 0 476 133 0 358 146 0 520 146 0 353 124 0 333 107 0 335 91 0 334 84 0 520 109

CRU TS 3.00 1991 1 311 81 1 320 83 1 320 90 1 320 100 1 346 113 1 358 132 1 356 146 1 342 143 1 354 125 1 323 105 1 318 91 1 315 82 1 358 108 1992 1 311 82 1 319 84 1 315 90 1 320 97 1 346 111 1 358 127 1 356 141 1 342 140 1 354 122 1 323 102 1 317 89 1 315 83 1 358 106 1993 1 313 81 1 315 83 1 315 89 1 320 98 1 346 112 1 358 131 1 356 146 1 342 142 1 354 122 1 323 103 1 323 88 1 317 83 1 358 106 1994 1 311 82 1 322 82 1 315 89 1 320 99 1 346 112 1 358 131 1 356 146 1 346 142 1 354 125 1 323 106 1 318 92 1 315 83 1 358 107 1995 1 311 82 1 318 85 1 320 90 1 324 99 1 346 112 1 358 131 1 356 146 1 345 144 1 354 124 1 323 107 1 321 90 1 315 81 1 358 108 1996 1 311 80 1 321 82 1 320 87 1 320 96 1 346 111 1 358 130 1 356 145 1 343 141 1 354 122 1 323 105 1 318 90 1 319 82 1 358 106 1997 1 311 81 1 320 84 1 315 90 1 320 99 1 346 113 1 358 131 1 356 145 1 342 143 1 354 123 1 323 106 1 318 90 1 315 83 1 358 107 1998 1 311 81 1 334 85 1 326 89 1 338 100 1 346 114 1 358 134 1 356 148 1 342 145 1 354 125 1 323 105 1 318 89 1 315 84 1 358 108 1999 1 316 82 1 320 85 1 322 88 1 320 99 1 346 112 1 358 131 1 356 148 1 342 142 1 354 125 1 323 106 1 318 91 1 315 84 1 358 108

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2000 1 317 82 1 320 84 1 315 90 1 320 100 1 346 113 1 358 131 1 356 146 1 342 144 1 354 123 1 323 105 1 318 90 1 315 83 1 358 108

I DON'T UNDERSTAND!!!!!

Well, OK - I see that a VAP of zero is acceptable. Though as it's a pressure, I don't believe it! I'll stick with 1.

The issue is that the earlier dataset has a variability (in the maximum) that we just don't have in the new one. And I feel that I've been through every bloody phase of the process and checked we're doing it right!!!

~~~
Right. Let's look at the distributions of values in each dataset. We'll take Jan 1910 and Jun 2000. And as this is a textual document, I'll have to describe the results.

Offsets. Well each month has 360 lines, so each year has 4320 lines. So for Jan 1910 we need to skip nine years, or 38880 lines, then take the next 360. For Jun 2000 we need to skip 99 years, or 427680 lines, then another five months, or 1800 lines, then take the next 360. So:

head -39240 cru_ts_2.10.1901-2002.vap.dat |tail -360 > cru_ts_2.10.Jan.1910.vap.dat head -39240 cru_ts_3.00.1901.2006.vap.dat |tail -360 > cru_ts_3.00.Jan.1910.vap.dat

head -428040 cru_ts_2.10.1901-2002.vap.dat |tail -360 > cru_ts_2.10.Jun.2000.vap.dat head -428040 cru_ts_3_00.1901.2006.vap.dat |tail -360 > cru_ts_3_00.Jun.2000.vap.dat

I loaded the resultant monthly files into Matlab, and played with them mercilessly.

Well to start with, they all look the same. Truly. I've got a 4-plot page with TS 2.10 in the left-hand column, and TS 3.00 on the right. January 1910 on the top, June 2000 on the bottom. and they look pretty much inseparable, though if I had to Spot The Difference, the TS 2.10 June 2000 distribution is a little flatter (that is, the massive spike at the low end is a little shorter, and the rest of the entourage are a little taller.

What are particularly worthy of note are the maximums. Because they don't match those produced by crutsstats.for.

Month Model Max (Matlab) Max (crutsstats) Jan 1910 TS 2.10 312 312 Jan 1910 TS 3.00 311 311 Jun 2000 TS 2.10 319 476 Jun 2000 TS 3.00 317 358

Not entirely sure why the latter ones would be wrong. But I suspect crutsstats - because otherwise I miscounted the line numbers to extract June 2000 with! Actually, OK, that does seem more likely.

Let's try it from the 1991-2000 files. The offset will be 9*4320 + 5*360 + 360 = 41040.

gunzip -c /cru/cruts/fromtyn1/data/cru_ts_2.10/newly_gridded/data_dec/cru_ts_2_10.1991-2000.va

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p.grid.gz | head -41040 | tail -360 > cru_ts_2_10.Jun.2000.vap.dat gunzip -c cru_ts_3_00.1991.2000.vap.dat.gz | head -41040 | tail -360 > cru_ts_3_00.Jun.2000.vap.dat

Well - looks like I did miscount, because the new files are different! And so are the Maxima:

Month Model Max (Matlab) Max (crutsstats) Jun 2000 TS 2.10 300 476 Jun 2000 TS 3.00 358 358

..so almost perfect. At least the stats for the file I'm creating match.

And now the June 2000 histograms are much more interesting! And of course (for this is THIS project), much more worrying. The June 2000 plot for the new data (3.00) shows a fall at VAP ->0. This is in contrast to the other three, which show a more expotential decline from a high near 0 (though admittedly the 2.10 version does have a second peak at around 120). In fact, the June 2000 3.00 series has peaks at ~90 and ~300! Oh, help.

The big question must be, why does it have so little representation in the low numbers? Especially given that I'm rounding erroneous negatives up to 1!!

Oh, sod it. It'll do. I don't think I can justify spending any longer on a dataset, the previous version of which was completely wrong (misnamed) and nobody noticed for five years.

So.. one week to go before handover, and I'm just STARTING the Sun/Cloud parameter, the one I thought would cause the most trouble! Oh, boy. Let's try and work out the scenario.

Historically, we've issued Cloud:

crua6[/cru/cruts/fromtyn1/data/cru_ts_2.10/data_all] gunzip -c cru_ts_2_10.1901-2002.cld.Z |head -10 Tyndall Centre grim file created on 22.01.2004 at 13:52 by Dr. Tim Mitchell .cld = cloud cover (percentage) CRU TS 2.1 [Long=-180.00, 180.00] [Lati= -90.00, 90.00] [Grid X,Y= 720, 360] [Boxes= 67420] [Years=1901-2002] [Multi= 0.1000] [Missing=-999] Grid-ref= 1, 148 725 750 750 700 638 600 613 613 663 675 713 725

..so data is in % x10.

Then, of course, there's the relevant read_me text (from /cru/cruts/fromdpe1a/code/idl/pro/read_me_GRIDDING.txt):

"Bear in mind that there is no working synthetic method for cloud, because Mark New lost the coefficients file and never found it again (despite searching on tape archives at UEA) and never recreated it. This hasn't mattered too much, because the synthetic cloud grids had not been discarded for 1901-95, and after 1995 sunshine data is used instead of cloud data anyway."

So that's alright then! See also the earlier attempts to recreate TS 2.10 cloud.

The main gridding prog for cloud appears to be cal_cld_gts_tdm.pro:

pro cal_cld_gts_tdm,dtr_prefix,outprefix,year1,year2,info=info

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; calculates cld anomalies using relationship with dtr anomalies ; reads coefficients from predefined files (*1000) ; reads DTR data from binary output files from quick_interp_tdm2.pro (binfac=1000) ; creates cld anomaly grids at dtr grid resolution ; output can then be used as dummy input to splining program that also ; includes real cloud anomaly data

As for converting sun hours to cloud cover.. we only appear to have interactive, file-by-file programs. Herewith all the relevant progs I can find:

IDL ./idl/pro/cal_cld_gts_tdm.pro (synthetic cloud from DTR) ./idl/pro/cloudcorr.pro (construct cloud correlation coefficients with DTR) ./idl/pro/cloudcorrspc.pro (construct cloud correlation coefficients with sunshine %) ./idl/pro/cloudcorrspcann.pro (construct cloud correlation coefficients with sunshine %) ./idl/pro/cloudcorrspcann9196.pro (construct cloud correlation coefficients with sunshine %)

(the 'ann' versions above include the assumption that the relationships remain constant through the year)

F77 ./f77/mnew/sh2cld_tdm.for (this one needs to be modded as for sp2cldp_m.for I think) ./f77/mnew/Hsp2cldp_m.for (one I wrote last year which seems to almost do what we need) ./f77/mnew/sp2cld_m.for (this one needs to be modded as for sp2cldp_m.for I think) ./f77/mnew/sh2sp_m.for ./f77/mnew/sh2sp_normal.for ./f77/mnew/sh2sp_tdm.for

Aaaand - another head-banging shocker! The program sh2cld_tdm.for, which describes itself thusly:

program sunh2cld c converts sun hours monthly time series to cloud percent (n/N)

Does NO SUCH THING!!! Instead it creates SUN percentages! This is clear from the variable names and user interactions.

So.. if I add the sunh -> sun% process from sh2cld_tdm.for into Hsp2cldp_m.for, I should end up with a sun hours to cloud percent convertor. Possibly. Except that the sun% to cld% engine looks like it's creating oktas instead:

do im=1,12 ratio = (real(sunp(im))/100) if (ratio.ge.0.95) cldp(im) = 0 if (ratio.lt.0.95.and.ratio.ge.0.35) * cldp(im) = (0.95-ratio)*100 if (ratio.lt.0.35.and.ratio.ge.0.15) * cldp(im) = ((0.35-ratio)*50)+60 if (ratio.lt.0.15) cldp(im) = ((0.15-ratio)*100)+70 if (cldp(im).gt.80.0) cldp(im) = 80.0 if (ratio.lt.0) cldp(im) = -9999

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enddo

Added the previous '*12.5' mod to approximate true percentages (*10).

Looking back I see we found cloud and sunpercent databases (line counts shown):

228936 cld.0301081434.dtb 104448 cld.0312181428.dtb 111989 combo.cld.dtb 57395 spc.0301201628.dtb 51551 spc.0312221624.dtb 51551 spc.94-00.0312221624.dtb

And agreed a strategy:

<BEGIN_QUOTE> AGREED APPROACH for cloud (5 Oct 06).

For 1901 to 1995 - stay with published data. No clear way to replicate process as undocumented.

For 1996 to 2002: 1. convert sun database to pseudo-cloud using the f77 programs; 2. anomalise wrt 96-00 with anomdtb.f; 3. grid using quick_interp_tdm.pro (which will use 6190 norms); 4. calculate (mean9600 - mean6190) for monthly grids, using the published cru_ts_2.0 cloud data; 5. add to gridded data from step 3.

This should approximate the correction needed. <END_QUOTE>

This is confusing. I can only use one (observed) cloud database in the final gridding. The above agreement seems to assume that all data after 1996 will come from sun. But dtbstat.for reports:

<BEGIN_QUOTE> Report for: spc.0312221624.dtb (it's similar for the other spcs, except the earlier one goes to 2002)

Stations in Northern Hemisphere: 1750 Stations in Southern Hemisphere: 350 Total: 2100

Maximum Timespan in Northern Hemisphere: 1889 to 2003 Maximum Timespan in Southern Hemisphere: 1944 to 2003 Global Timespan: 1889 to 2003

Minimum Data Value: 0 Maximum Data Value: 1000 <END_QUOTE>

So the Sun Percent databases run for long periods. Similarly, for cloud:

<BEGIN_QUOTE> Report for: cld.0312181428.dtb

Stations in Northern Hemisphere: 3286 Stations in Southern Hemisphere: 319 Total: 3605

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Maximum Timespan in Northern Hemisphere: 1905 to 1996 Maximum Timespan in Southern Hemisphere: 1959 to 1996 Global Timespan: 1905 to 1996

Minimum Data Value: 0 Maximum Data Value: 1000 <END_QUOTE>

Not as long a run, and it sure ends at 1996! So 1901 to 1995 will, as agreed, remain untouched.

Well.. let's try converting the MCDW and CLIMAT Sun hours to Sun percents, then adding to the SPC database (spc.0312221624.dtb). Modified Hsh2cld .for to save sun percent too. Lots of debugging.. eventually dug out:

Doorenbos, J., Pruitt, W.O., 1977. Guidelines for predicting crop water requirements. FAO irrigation and drainage paper no. 24. Food and Agriculture Organization of the United Nations,

Rome.

This was used to inform the Fortran conversion programs by indicating the latitude-potential_sun and sun-to-cloud relationships. It also assisted greatly in understanding what was wrong - Tim was in fact calculating Cloud Percent, despite calling it Sun Percent!! Just awful.

And so..

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/db/cld] ./Hsh2cld

Hsh2cld - Convert a Sun Hours database to a Cloud Percent one

Please enter the Sun Hours database: sun.0709111032.dtb Data Factor detected: *1.000

Completed - 1693 stations converted.

Sun Percentage Database: spc.0711271420.dtb Cloud Percentage Database: cld.0711271420.dtb

crua6[/cru/cruts/version_3_0/db/cld] ./Hsh2cld

Hsh2cld - Convert a Sun Hours database to a Cloud Percent one

Please enter the Sun Hours database: sun.0710151817.dtb Data Factor detected: *0.100

Completed - 2020 stations converted.

Sun Percentage Database: spc.0711271421.dtb Cloud Percentage Database: cld.0711271421.dtb

crua6[/cru/cruts/version_3_0/db/cld] <END_QUOTE>

So, now the luxury of a little experiment.. I merged the MCDW and CLIMAT 'spc' databases into the existing one *separately*. Here were the results:

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MCDW: <BEGIN_QUOTE> uealogin1[/cru/cruts/version_3_0/db/cld] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: spc.0312221624.dtb Please enter the Update Database name: spc.0711271420.dtb

Reading in both databases.. Master database stations: 2100 Update database stations: 1693

New master database: spc.0711271504.dtb

Update database stations: 1693 > Matched with Master stations: 867 (automatically: 867) (by operator: 0) > Added as new Master stations: 826 > Rejected: 0 <END_QUOTE>

CLIMAT: <BEGIN_QUOTE> Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: spc.0312221624.dtb Please enter the Update Database name: spc.0711271421.dtb

Reading in both databases.. Master database stations: 2100 Update database stations: 2020

98 reject(s) from update process 0711271505

New master database: spc.0711271505.dtb

Update database stations: 2020 > Matched with Master stations: 917 (automatically: 917) (by operator: 0) > Added as new Master stations: 1005 > Rejected: 98 Rejects file: spc.0711271421.dtb.rejected <END_QUOTE>

So, as expected, a few of the CLIMAT stations couldn't be matched for metadata.. no worries. what's interestng is that roughly the same ratio of stations were matched with existing in both cases (867/1693 vs 917/2020). Slightly better for MCDW though.

Now, as our updates only start in 2003, that means we've just lost between 826 and 1005 sets of data (added as new). We can't be exact as we don't know the overlap between the MCDW and the CLIMAT

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bulletins.. but we will have a better idea when I try the anomdtb experiment on the combined update. First, add the CLIMAT update again, this time to the MCDW-updated database:

CLIMAT: <BEGIN_QUOTE> Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: spc.0711271504.dtb Please enter the Update Database name: spc.0711271421.dtb

Reading in both databases.. Master database stations: 2926 Update database stations: 2020

38 reject(s) from update process 0711271514

New master database: spc.0711271514.dtb

Update database stations: 2020 > Matched with Master stations: 1736 (automatically: 1736) (by operator: 0) > Added as new Master stations: 246 > Rejected: 38 Rejects file: spc.0711271421.dtb.rejected <END_QUOTE>

Note several bits of good news! Firstly, rejects are down to 38 (60 having matched with MCDW stations). That's not *that* good of course - those will be new and so 2003 onwards only. Similarly, (1005-246=) 759 CLIMAT bulletins matched MCDW ones, they will also be 2003 onwards only. In other words, there were only (1736-759=) 977 updates to existing stations. So.. yes I'm being sidetracked again.. I found and downloaded ALL the MCDW bulletins, back to 1994!

<BEGIN_QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/MCDW] ./mcdw2cru

MCDW2CRU: Convert MCDW Bulletins to CRU Format

Enter the earliest MCDW file: ssm9409.fin Enter the latest MCDW file (or <ret> for single files): ssm0708.fin

All Files Processed tmp.0711271645.dtb: 2785 stations written *** SEE LATER RUNS *** vap.0711271645.dtb: 2786 stations written *** SEE LATER RUNS *** rdy.0711271645.dtb: 2781 stations written *** SEE LATER RUNS *** pre.0711271645.dtb: 2791 stations written *** SEE LATER RUNS *** sun.0711271645.dtb: 2184 stations written *** SEE LATER RUNS ***

Thanks for playing! Byeee! <END_QUOTE>

Now I'm not planning to re-run all the previous parameters! Hell, they should have had the older data in already! But for sun/cloud, this could help enormously. Here's the plan:

1. Merge the CLIMAT-sourced database into the new MCDW-sourced database. 2. Convert this modern sun hours database into a modern cloud percent database. 3. Add normals for 95-02. 4. Use the new program 'normshift.for' to calculate 95-02 normals from TS 2.10 CLD.

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5. Calculate difference between TS 2.10 6190 normls and the above. 6. Modify the in-database normals (step 3) with the difference (step 5). 7. Carry on as before?

No.. this won't work. anomdtb.for calculates normals on the fly - it would have to know too much.

The next opportunity comes at the output from anomdtb - the normalised values in the *.txt files that the IDL gridder reads. These are just files - one per month - with lists of coordinates and values, so ideal to add normalised values to. Decided that this will be the process:

Modern SunH DB -> Hsh2cld.for -> Modern Cld% DB Modern Cld% DB -> newprog.for -> 6190anomalies.txt

..meanwhile, as before..

Normal Cld% DB -> anomdtb.for -> 6190anomalies.txt

So we then just have to merge the two 6190 anomaly sets! Which could just be a concatenation.

Easy, then.. the only thing we need is the miraculous 'newprog.for'! With three days before delivery.

No, no, no - HANG ON. Let's not try and boil the ocean! How about:

1901-2002 Static, as published, leave well alone (or recalculate with better DTR). 2003-2006/7 Calc from modern SunH and use the suggested mods after gridding.

This is what was originally intended. But there will be problems:

1. MCDW only goes back to 2006, so what's the data density for 2003-2005? Should this also use synthetic cloud from DTR? I guess yes.

2. No guarantee of continuity from 2002 to 2003. This could be the real stickler. Moving from one system to the other - this is why it might be better to re-run 1901-2002 as well.

OKAY.. normshift.for now creates a gridded set of conversion data between whatever period you choose and 1961-1990. Such that it can be added to the gridded output of the process run with the 'false' normalisation period.

So.. first, merge your bulletins:

Well FIRSTLY, you realise that your databases don't have normals lines, so you modify mcdw2cru.for and climat2cru.for to optionally add them, then you re-run them on the bulletins, ending up with:

<BEGIN_QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/MCDW] ./mcdw2cru

MCDW2CRU: Convert MCDW Bulletins to CRU Format

Enter the earliest MCDW file: ssm9409.fin Enter the latest MCDW file (or <ret> for single files): ssm0708.fin Add a dummy normals line? (Y/N): Y

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All Files Processed tmp.0711272156.dtb: 2785 stations written vap.0711272156.dtb: 2786 stations written rdy.0711272156.dtb: 2781 stations written pre.0711272156.dtb: 2791 stations written sun.0711272156.dtb: 2184 stations written

Thanks for playing! Byeee! <END_QUOTE>

<BEGIN_QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/CLIMAT] ./climat2cru

CLIMAT2CRU: Convert MCDW Bulletins to CRU Format

Enter the earliest CLIMAT file: climat_data_200301.txt Enter the latest CLIMAT file (or <ret> for single file): climat_data_200707.txt Add a dummy normals line? (Y/N): Y

All Files Processed tmp.0711272219.dtb: 2881 stations written vap.0711272219.dtb: 2870 stations written rdy.0711272219.dtb: 2876 stations written pre.0711272219.dtb: 2878 stations written sun.0711272219.dtb: 2020 stations written tmn.0711272219.dtb: 2800 stations written tmx.0711272219.dtb: 2800 stations written

Thanks for playing! Byeee! <END_QUOTE>

So.. NOW can I merge CLIMAT into MCDW?!

As expected, thank goodness:

<BEGIN_QUOTE> uealogin1[/cru/cruts/version_3_0/incoming/merge_CLIMAT_into_MCDW] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: sun.0711272156.dtb Please enter the Update Database name: sun.0711272219.dtb

Reading in both databases.. Master database stations: 2184 Update database stations: 2020

Looking for WMO code matches.. 28 reject(s) from update process 0711272225

Writing sun.0711272225.dtb

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

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OUTPUT(S) WRITTEN

New master database: sun.0711272225.dtb

Update database stations: 2020 > Matched with Master stations: 1775 (automatically: 1775) (by operator: 0) > Added as new Master stations: 217 > Rejected: 28 Rejects file: sun.0711272219.dtb.rejected <END_QUOTE>

Wahey! Lots of stations to play with!

So, next.. convert to cloud!

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/db/cld] ./Hsh2cld

Hsh2cld - Convert a Sun Hours database to a Cloud Percent one

Please enter the Sun Hours database: sun.0711272225.dtb Data Factor detected: *1.000

Completed - 2401 stations converted.

Sun Percentage Database: spc.0711272230.dtb Cloud Percentage Database: cld.0711272230.dtb <END_QUOTE>

So.. bated breath..

..and yay!

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/cld] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required: .cld > Select the .cts or .dtb file to load: cld.0711272230.dtb

> Specify the start,end of the normals period: 1995,2002 > Specify the missing percentage permitted: 12.5 > Data required for a normal: 7 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): cld.txt > Select the first,last years AD to save: 1995,2007 > Operating...

/tmp_mnt/cru-auto/cruts/version_3_0/secondaries/cld/cld.0711272230.dts

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> NORMALS MEAN percent STDEV percent > .dtb 0 0.0 > .cts 83961 49.3 83961 49.3 > PROCESS DECISION percent %of-chk > no lat/lon 95 0.1 0.1 > no normal 86174 50.6 50.7 > out-of-range 28 0.0 0.0 > accepted 83933 49.3 > Dumping years 1995-2007 to .txt files... <END_QUOTE>

Well.. a 'qualified' yay.. only half got normals! But I don't like to raise the 'missing percentage' limit to 25% because we're only talking about 8 values to begin with!!

The output files look OK.. between 400 and 600 values in each, not a lot really but hey, better than nowt. So onto the conversion data (must stop calling 'em factors, they're not multiplicative).

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/secondaries/cld] ./normshift

NORMSHIFT - Normals from any period

Please enter the source file: cru_ts_2_10.1901-2002.cld.grid Enter the start year of this file: 1901 Enter the end year of this file: 2002 Enter the normal period start year: 1995 Enter the normal period end year: 2002 Enter the 3-character parameter: cld

Normals file will be: clim.9502.to.6190.grid.cld <END_QUOTE>

So, erm.. now we need to create our synthetic cloud from DTR. Except that's the thing we CAN'T do because pro cal_cld_gts_tdm.pro needs those bloody coefficients (a.25.7190, etc) that went AWOL. Frustratingly we do have some of the outputs from the program (ie, a.25.01.7190.glo), but that's obviously no use.

So, erm. We need synthetic cloud for 2003-2007, or we won't have enough data to run with. And yes it's taken me this long to realise that. Oh, bugger.

Had a detailed search around Mark New's old disk (still online thankfully). Found this:

<BEGIN_QUOTE> crua6[/cru/mark1/markn/gts/cld/val] ls -l total 7584 lrwxrwxrwx 1 f080 cru 25 Sep 12 2005 c1 -> /cru/u1/f080/isccp/c1_mon -rw-r--r-- 1 f080 cru 1290 Mar 24 1998 cld_corr.j -rw-r--r-- 1 f080 cru 938 Mar 17 1998 cld_scat.j -rw-r----- 1 f080 cru 922584 Mar 24 1998 cru_hahn_corr.ps -rw-r----- 1 f080 cru 922588 Mar 24 1998 cru_isccp_corr.ps -rw-r----- 1 f080 cru 533 Mar 27 1998 cruobs_hahn_corr.j -rw-r----- 1 f080 cru 868561 Mar 27 1998 cruobs_hahn_corr.ps -rw-r--r-- 1 f080 cru 697 Mar 20 1998 dtr_corr.j -rw-r----- 1 f080 cru 50 Mar 27 1998 foo

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-rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1980 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1981 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1982 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1983 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1984 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1985 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1986 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1987 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1988 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1989 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1990 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1991 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1992 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1993 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1994 -rw-r----- 1 f080 cru 248832 Mar 27 1998 glo25.cld.1995 -rw-r----- 1 f080 cru 922592 Mar 24 1998 hahn_isccp_corr.ps -rw-r----- 1 f080 cru 2378 Mar 24 1998 test.j <END_QUOTE>

..which looks to me like the place where he calculated the coefficients. The *.j files are IDL 'Journal' files, so can be run from within IDL. This was my first attempt:

<BEGIN_QUOTE> IDL> .run cld_corr.j % Compiled module: $MAIN$. % Compiled module: RD25_GTS. YEAR: 1981 % Compiled module: RDBIN. % Compiled module: STRIP. foo: Permission denied. foo: Permission denied. foo: Permission denied. % OPENR: Error opening file. Unit: 99, File: /home/cru/f098/u1/hahn/hahn25.1981 No such file or directory % Execution halted at: RDBIN 63 /cru/u2/f080/Idl/rdbin.pro % RD25_GTS 11 /cru/u2/f080/Idl/rd25_gts.pro % $MAIN$ 1 /tmp_mnt/cru-auto/mark1/f080/gts/cld/val/cld_corr.j

IDL> <END_QUOTE>

I then had to chase around to find three sets of missing files.. to fulfil these five conditions:

if keyword_set(hgrid) eq 0 then rd25_gts,$ hgrid,'~/u1/hahn/hahn25.',1981,1991 if keyword_set(rgrid) eq 0 then rd25_gts,$ rgrid,'../glo_reg_25/glo.cld.',1981,1991 if keyword_set(hgrid2) eq 0 then rd25_gts,$ hgrid2,'~/u1/hahn/hahn25.',1983,1991 if keyword_set(igrid) eq 0 then rdisccp_gts,$ igrid,'c1/isccp.',1983,1991 if keyword_set(rgrid2) eq 0 then rd25_gts,$ rgrid2,'../glo_reg_25/glo.cld.',1983,1991

I managed to find the hahn25 files (on Mark's disk), and some likely-looking isccp files (also on Mark's disk). But although there were plenty of files with 'glo', 'cld' and '25' in them, there were none matching the filename construction above. However, as some of those were in the same directory - I'll take that chance!!

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I did try, honestly. Very hard. I found all the files, and put them in directories. I made a local copy of the job file, 'H_cld_corr.j', with the local directory refs in. Hell, I even precompiled the correct version of rdbin!

All for nothing, as usual. It runs quite happily, zipping through things, until:

% Compiled module: RDISCCP_GTS. YEAR: 1983 % Compiled module: RDISCCP. c1/isccp.83.07.72 c1/isccp.83.07.72.Z: No such file or directory c1/isccp.83.08.72 c1/isccp.83.08.72.Z: No such file or directory c1/isccp.83.09.72 c1/isccp.83.09.72.Z: No such file or directory c1/isccp.83.10.72 c1/isccp.83.10.72.Z: No such file or directory c1/isccp.83.11.72 c1/isccp.83.11.72.Z: No such file or directory c1/isccp.83.12.72 c1/isccp.83.12.72.Z: No such file or directory YEAR: 1984 c1/isccp.84.01.72 c1/isccp.84.01.72.Z: No such file or directory (etc)

It isn't seeing the isccp files EVEN THOUGH THEY ARE THERE. Odd. If I create Z files it says they aren't compressed.

It ends with:

YEAR: 1991
yes filesize= 248832 gridsize= 2.50000 % Compiled module: MARK_CORRELATE. % Compiled module: CORRELATE.

I have no idea what it's actually done though. It doesn't appear to have produced anything.. ah:

IDL> help % At $MAIN$ 1 /tmp_mnt/cru-auto/cruts/version_3_0/cloud_synthetics/H_cld_corr.j CRU_HAHN_CORR FLOAT = Array[144, 72, 12] CRU_ISCCP_CORR FLOAT = Array[144, 72, 12] HGRID LONG = Array[144, 72, 12, 11] HGRID2 LONG = Array[144, 72, 12, 9] IGRID LONG = Array[144, 72, 12, 9] ILAT INT = 72 ILON INT = 144 IM INT = 12 ISCCP_HAHN_CORR FLOAT = Array[144, 72, 12] N LONG = Array[5225] NN LONG = 5225 RGRID LONG = Array[144, 72, 12, 11] RGRID2 LONG = Array[144, 72, 12, 9] Compiled Procedures: $MAIN$ DEFXYZ RD25_GTS RDBIN RDISCCP RDISCCP_GTS

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Compiled Functions:
CORRELATE MARK_CORRELATE STRIP

IDL>

..so this is one of a set of tools *that you have to know how to use*. All the work's done in the IDl data space.

Well as we don't have any instructions, that's a complete waste of two-and-a-half days' time.

Let's forget about CLD and start worrying about NetCDF.

NETDCF

Well now, we have to make the data available in NetCDF and ASCII grid formats. At the moment, it might be best to just post-process the final ASCII grids into NetCDF; though more elegant to have mergegrids.for produce both! As it has the data there anyway.. so I modified mergegrids.for into makegrids.for, with added NetCDF goodness. as usual, lots of problems getting the syntax right..

************************************************************************ BADC Work.. at RAL, 3-5 December 2007

Finally got NetCDF & Fortran working on the chosen server here (damp.badc.rl.ac.uk). I am definitely not a chamaeleonic life form when it comes to unfamiliar computer systems. Shame. The elusive command line compile statement is:

gfortran -I/usr/local/netcdf-3.6.2/include/ -o fileout.o filein.f /usr/local/netcdf-3.6.2/lib/libnetcdf.a

Hunting for CDDs I found a potential problem with binary DTR (used in the construction of Frost Days, Vapour Pressure, and (eventually) Cloud. It looks as though there was a mistyping when the 2.5-degree binaries were constructed:

IDL> quick_interp_tdm2,1901,2006,'dtrbin/dtrbin',50,gs=2.5,dumpbin='dumpbin',pts_prefix=' dtrtxt/dtr.'

That '50' should have been.. 750! Oh bugger. Well, might as well see if generation does work here. DTR/bin/2.5:

..er.. hang on while I try and get IDL to recognise a path.. meh. As usual I find this effectively impossible, so have to issue manual .compile statements. The suite of progs required to compile for quick_interp_tdm2.pro is:

glimit.pro area_grid.pro strip.pro wrbin.pro

..and, of course, quick_interp_tdm2.pro. Actually, others need others.. so I wrote a generic IDL script to load all of Satan's little helpers:

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IDL> @../../programs/idl/loads4idl.j % Compiled module: GLIMIT. % Compiled module: AREA_GRID. % Compiled module: STRIP. % Compiled module: WRBIN. % Compiled module: RDBIN. % Compiled module: DEFXYZ. % Compiled module: FRSCAL. % Compiled module: DAYS. % Compiled module: RNGE. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. % Compiled module: TVAP. % Compiled module: ESAT.

IDL>

This is just because I still don't have IDL_PATH working so having to issue each of the above as manual compile statements (in that order) was getting tedious. n00b. [this now fixed - ed] Anyway, here's the corrected binary DTR production:

IDL> quick_interp_tdm2,1901,2006,'dtrbin/dtrbin',750,gs=2.5,dumpbin='dumpbin',pts_prefix= 'dtrtxt/dtr.' Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: MEAN. % Compiled module: MOMENT. % Compiled module: STDDEV. grid 1901 non-zero 0.9415 1.8771 1.8417 cells= 5608 1902 grid 1902 non-zero 0.8608 1.8713 1.8752 cells= 5569 (etc)

And so to regenerate FRS:

<BEGIN_QUOTE>
IDL> frs_gts,dtr_prefix='dtrbin/dtrbin',tmp_prefix='tmpbin/tmpbin',1901,2006,outprefix='f rssyn/frssyn' IDL> quick_interp_tdm2,1901,2006,'frsgrid/frsgrid',750,gs=0.5,dumpglo='dumpglo', nostn=1,synth_prefix='frssyn/frssyn' -bash-3.00$ ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.frs Enter a name for the gridded climatology file: clim.6190.lan.frs.delme Enter the path and stem of the .glo files: frsgrid/frs. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: frsabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set a single minimum and maximum 3. Set monthly minima and maxima (for wet/rd0) 4. Set all values >0, (ie, positive) Choose: 3 Right, erm.. off I jolly well go!

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frs.01.1901.glo frs.02.1901.glo (etc) <END_QUOTE>

Now looking to get makegrids.for working.. managed to get the data to write by declaring REALs as DOUBLE PRECISION - later realising I could/should have changed the NetCDF interface calls to REAL instead! Ah well. Still tussling with the 'time' variable.. not clear how to handle observations. Luckily, Mike S knew what the standard was:

<BEGIN_QUOTE> >I need to define the time parameter in the NetCDF version of the CRUTS >dataset. I suspect I need to use 'Gregorian' (which to all intents and >porpoises is accurate although it reverts to Julian before xx/yy/1582) but >I wondered if there was a convention (in CRU, or wider) for allocating >standard timestamps to observations?

For the gridded temperature we use

short time(time) ; time:units = "months since 1870-1-1" ;

Remember to start with zero!

Mike <END_QUOTE>

And that seems to now be working! Here's the run with the compile statement included:

<BEGIN_QUOTE> -bash-3.00$ gfortran -I/usr/local/netcdf-3.6.2/include/ -o makegrids ../../programs/fortran/makegrids.for /usr/local/netcdf-3.6.2/lib/libnetcdf.a -bash-3.00$ ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Writing: cru_ts_3_00.1901.1910.frs.dat cru_ts_3_00.1901.1910.frs.nc Writing: cru_ts_3_00.1911.1920.frs.dat cru_ts_3_00.1911.1920.frs.nc Writing: cru_ts_3_00.1921.1930.frs.dat cru_ts_3_00.1921.1930.frs.nc Writing: cru_ts_3_00.1931.1940.frs.dat cru_ts_3_00.1931.1940.frs.nc Writing: cru_ts_3_00.1941.1950.frs.dat cru_ts_3_00.1941.1950.frs.nc Writing: cru_ts_3_00.1951.1960.frs.dat cru_ts_3_00.1951.1960.frs.nc Writing: cru_ts_3_00.1961.1970.frs.dat cru_ts_3_00.1961.1970.frs.nc Writing: cru_ts_3_00.1971.1980.frs.dat cru_ts_3_00.1971.1980.frs.nc Writing: cru_ts_3_00.1981.1990.frs.dat cru_ts_3_00.1981.1990.frs.nc Writing: cru_ts_3_00.1991.2000.frs.dat cru_ts_3_00.1991.2000.frs.nc Writing: cru_ts_3_00.2001.2006.frs.dat cru_ts_3_00.2001.2006.frs.nc

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-bash-3.00$ <END_QUOTE>

And here, for a combination of posterity and boredom, is a (curtailed) dump from ncdump:

<BEGIN_QUOTE> -bash-3.00$ ncdump cru_ts_3_00.1901.2006.frs.nc |head -300 netcdf cru_ts_3_00.1901.2006.frs { dimensions: lon = 720 ; lat = 360 ; time = UNLIMITED ; // (1272 currently) variables: double lon(lon) ; lon:long_name = "longitude" ; lon:units = "degrees_east" ; double lat(lat) ; lat:long_name = "latitude" ; lat:units = "degrees_north" ; int time(time) ; time:long_name = "time" ; time:units = "months since 1870-1-1" ; time:calendar = "standard" ; double frs(time, lat, lon) ; frs:long_name = "ground frost frequency" ; frs:units = "days" ; frs:scale_factor = 0.00999999977648258 ; frs:correlation_decay_distance = 750. ; frs:_FillValue = -9999. ; frs:missing_value = -9999. ;

// global attributes: :title = "CRU TS 3.00 Mean Temperature" ; :institution = "BADC" ; :contact = "BADC <badc@rl.ac.uk>" ; data:

lon = -179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75, -176.25, -175.75, -175.25, (etc) 170.75, 171.25, 171.75, 172.25, 172.75, 173.25, 173.75, 174.25, 174.75, 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25, 178.75, 179.25, 179.75 ;

lat = -89.75, -89.25, -88.75, -88.25, -87.75, -87.25, -86.75, -86.25, -85.75, -85.25, -84.75, (etc) 79.75, 80.25, 80.75, 81.25, 81.75, 82.25, 82.75, 83.25, 83.75, 84.25, 84.75, 85.25, 85.75, 86.25, 86.75, 87.25, 87.75, 88.25, 88.75, 89.25, 89.75 ;

time = 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, (etc) 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643 ;

frs = -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, (etc - probably some real data there somewhere)

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-bash-3.00$ <END_QUOTE>

And VAP: <BEGIN_QUOTE>
IDL> vap_gts_anom,dtr_prefix='dtrbin/dtrbin',tmp_prefix='tmpbin/tmpbin',1901,2006,outpref ix='vapsyn/vapsyn.',dumpbin=1 % Compiled module: VAP_GTS_ANOM. % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. Land,sea: 56016 68400 Calculating tmn normal % Compiled module: TVAP. Calculating synthetic vap normal % Compiled module: ESAT. Calculating synthetic anomalies % Compiled module: MOMENT. 1901 vap (x,s2,<<,>>): 1.67770e-05 6.23626e-06 -0.160509 0.222689 1902 vap (x,s2,<<,>>): -0.000122533 3.46933e-05 -0.268891 0.0644855 (etc) <END_QUOTE>

These numbers are different from the original runs - so that was a genuine mistyping. Eek, that's not very promising, is it?

<BEGIN_QUOTE>
IDL> quick_interp_tdm2,1901,2006,'vapglo/vap.',1000,gs=0.5,dumpglo='dumpglo',synth_prefix ='vapsyn/vapsyn.',pts_prefix='vaptxt/vap.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc)

-bash-3.00$ ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.delme Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set a single minimum and maximum 3. Set monthly minima and maxima (for wet/rd0) 4. Set all values >0, (ie, positive)

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Choose: 4 Right, erm.. off I jolly well go! vap.01.1901.glo vap.02.1901.glo (etc)

-bash-3.00$ ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapabs/vap.MM.YYYY.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.vap.dat

Now please enter the 3-ch parameter code: vap Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Vapour Pressure Writing: cru_ts_3_00.1901.1910.vap.dat cru_ts_3_00.1901.1910.vap.nc Writing: cru_ts_3_00.1911.1920.vap.dat cru_ts_3_00.1911.1920.vap.nc Writing: cru_ts_3_00.1921.1930.vap.dat cru_ts_3_00.1921.1930.vap.nc Writing: cru_ts_3_00.1931.1940.vap.dat cru_ts_3_00.1931.1940.vap.nc Writing: cru_ts_3_00.1941.1950.vap.dat cru_ts_3_00.1941.1950.vap.nc Writing: cru_ts_3_00.1951.1960.vap.dat cru_ts_3_00.1951.1960.vap.nc Writing: cru_ts_3_00.1961.1970.vap.dat cru_ts_3_00.1961.1970.vap.nc Writing: cru_ts_3_00.1971.1980.vap.dat cru_ts_3_00.1971.1980.vap.nc Writing: cru_ts_3_00.1981.1990.vap.dat cru_ts_3_00.1981.1990.vap.nc Writing: cru_ts_3_00.1991.2000.vap.dat cru_ts_3_00.1991.2000.vap.nc Writing: cru_ts_3_00.2001.2006.vap.dat cru_ts_3_00.2001.2006.vap.nc -bash-3.00$ <END_QUOTE>

A quick look at the VAP NetCDF headers & data looked good. So - yay, that's the damage repaired, pity it took over a day of the time at RAL. But I didn't have to fix it now - it was an opportunity to get the process working in this environment.

Next problem - station counts. I had this working fine in CRU - here it's insisting on stopping indefinitely at January 1957. Discovered - after 36 hours of fretting and debugging - that it's popping its clogs on the South Polar base:

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890090 -900 0 2853 AMUNDSEN-SCOTT ANTARCTICA 1957 2006 101957 -999.00

And what d'you know, when I debug it, it's as simple as being too close to the pole and not having any loop restrictions in the East and West hunts for valid cells.. just looping forever! Added a few simple conditionals and all seems to run.. but outputs don't look right, the Jan 1957 station counts have missing values for the polar regions.

Managed to get anomdtb compiled with gfortran, after altering a few lines (in anomdtb and its mods) where Tim had shrugged off the surly bounds of strict F90.. it must be compiled in programs/fortran/ though, with the line (embedded in the anomdtb comments too):

gfortran -o anomdtb filenames.f90 time.f90 grimfiles.f90 crutsfiles.f90 loadperfiles.f90 saveperfiles.f90 annfiles.f90 cetgeneral.f90 basicfun.f90 wmokey.f90 gridops.f90 grid.f90 ctyfiles.f90 anomdtb.f90

As part of the modifications I removed the unused options - meaning that a .dts file is no longer required (and, of course, neither is 'falsedts.for'). Ran it for the temperature database and got apparently-identical anomaly files to the ones I generated in CRU :-))) Ran quick_interp_tdm2, glo2abs and makegrids, ended up with grids very similar (though sadly not identical) to the originals (could be IDL, could be compiler, could be system).

Scripting. Now this was always going to be the challenge, for a large suite of highly-interactive programs in F77, F90 and IDL which didn't follow universal file naming conventions. So to start with, I thought it might be 'fun' to compile an exhaustive/ing list of the commands needed to make a complete update. Headings begin with *, notes are in brackets

* Add MCDW Updates mcdw2cru (interactive) newmergedb (per parameter, interactive) * Add CLIMAT Updates climat2cru (interactive) newmergedb (per parameter, interactive) * Add BOM Updates au2cru (unfinished, interactive, should do whole job) * Regenerate DTR Database tmnx2dtr (interactive) * Produce Primary Parameters (TMP, TMN, TMX, DTR, PRE) anomdtb (per parameter, interactive) quick_interp_tdm2 (per parameter) glo2abs (per parameter, interactive) makegrids (per parameter, interactive) * Prepare Binary Grids (TMP, DTR, PRE) for Synthetics quick_interp_tdm2 (per parameter) * Produce Secondary Parameter (FRS, uses TMP,DTR) frs_gts_tdm quick_interp_tdm2 glo2abs (interactive) makegrids (interactive)

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* Produce Secondary Parameter (VAP, uses TMP,DTR) vap_gts_anom anomdtb (interactive) quick_interp_tdm2 glo2abs (interactive) makegrids (interactive) * Produce Secondary Parameter (WET/RD0, uses PRE) rd0_gts_anom anomdtb (interactive) quick_interp_tdm2 glo2abs (interactive) makegrids (interactive)

*** BACK AT CRU ***

Tried to compile makegrids.for on uealogin1 (as crua6 is being retired). Got an odd

error:

ld: warning: file /usr/local/netcdf-3.6.1/lib/libnetcdf.a(fort-attio.o): wrong ELF class: ELFCLASS64

..which was cured with the addition of '-xarch=native64' to the compile statement:

f77 -xarch=native64 -I/usr/local/netcdf-3.6.1/include/ -o makegrids ../../BADC_AREA/programs/fortran/makegrids.for /usr/local/netcdf-3.6.1/lib/libnetcdf.a

Then had to play around to try and reduce the size of the NetCDF files - they were bigger than the uncompressedASCII ones! This was because the variable was declared as DOUBLE, which is 64 bits, or 8 bytes, per datum. A waste, since we deal with the data as integers and use factors to restore 'real' values. So redeclared as INT. Considering re-redeclaring as SHORT, which is 16 bits to INT's 32.. however, that only gives me signed -32,768 to 32,767 or unsigned 0 to 65,535. That's enough for our datasets but only if precip has a positive missing value code, which I don't like the sound of.

Reproduced all primaries and secondaries with INT typing for the NetCDF component.

Simultaneously trying to work out why stncounts.for is apparently ignoring the South Pole station (Amundsen-Scott) even though the rest of the output looks fine.. eventually realised that the land/sea mask is blobking it!!

Station counts work continues.. should the NetCDF files be written as INT to match the data files, or SHORT to save a lot of space? In fact, should the station counts be in the same NetCDF files as their data?!!

Finished off the local regenration of VAP and FRS from the corrected dtrbin files:

DTR fix:

IDL> quick_interp_tdm2,1901,2006,'dtrbin/dtrbin',750,gs=2.5,dumpbin='dumpbin',pts_prefix= 'dtrtxt/dtr.'

VAP binary regen:

IDL> vap_gts_anom,dtr_prefix='dtrbin/dtrbin',tmp_prefix='tmpbin/tmpbin',1901,2006,outpref ix='vapsyn/vapsyn.',dumpbin=1

Note that, as expected, results ARE different for synthetic vap!

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First two lines of the CRUA6 run:

1901 vap (x,s2,<<,>>): 1.53894e-05 6.16462e-06 -0.160509 0.222662 1902 vap (x,s2,<<,>>): -0.000123921 3.46215e-05 -0.268891 0.0261302

First two lines of the damp.badc.rl.ac.uk run:

1901 vap (x,s2,<<,>>): 1.67770e-05 6.23626e-06 -0.160509 0.222689 1902 vap (x,s2,<<,>>): -0.000122533 3.46933e-05 -0.268891 0.0644855

Different compilers.. different architectures?.. different OS.. whichever.

IDL> quick_interp_tdm2,1901,2006,'vapglo/vap.',1000,gs=0.5,dumpglo='dumpglo',synth_prefix ='vapsyn/vapsyn.',pts_prefix='vaptxt/vap.'

Paused here as waiting for completion of makegrids2.for (includes station counts in NetCDF files). One early ramification of this is that glo2abs.for now saves filenames with year.month, rather than replicating the loopy month.year that quick_interp_tdm2.pro gives. This will allow file lists to be compiled with 'ls >tmpfile', so that timespan and missing files can be detected at the start. Have also had to amend (and re-run at great length!) stnounts.for to do the same.

Just realised I still haven't worked out how to do the station counts for the secondaries. I can't work out how Tim did it either, since he worked out counts at the same stage I do. Did he let the primary parameter's counts override? Or backfill? Well we could take the approach that the gridding routine takes, namely to use observed data and only refer to synthetic when that fails (and only refer to normals when that fails, obviously).

So, stncounts will have to accept TWO sets of .txt files. At each timestep it will have to first count the secondary parameter's stations, then the primary parameter(s) will be counted and fill in any zeros in the grid. However, we will need different information for the paper - what use is the effective station count as some is of higher 'quality' than the rest? So will probably need the regular observed-only count as well.. which could be a separate run of stncounts but faaar more sensible to be a side effect of this run.

Oh Gods.. I've got to modify another program. *cries*

I think the mods to stncounts will be the turning point for all the programs. So far, they have all been generic, but this is not tenable if the system is to be automated - they need to be aware of the parameters and what they mean. Otherwise stncounts will have to be told how many primaries produced the synthetic grids, etc, etc - stupid. So I need to devise a directory structure and file naming schema that will support the entire update process. Eeeeeeeek.

** time passes.. other projects given higher priority.. **

Back to precip, it seems the variability is too low. This points to a problem with the percentage anomaly routines. See earlier escapades - will the Curse of Tim never be lifted?

A reminder. I started off using a 'conventional' calculation:

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absgrid(ilon(i),ilat(i)) = nint(normals(i,imo) + * anoms(ilon(i),ilat(i)) * normals(i,imo) / 100) which is: V = N + AN/100

This was shown to be delivering unrealistic values, so I went back to anomdtb to see how the anomalies were contructed in the first place, and found this:

DataA(XAYear,XMonth,XAStn) = nint(1000.0*((real(DataA(XAYear,XMonth,XAStn)) / & real(NormMean(XMonth,XAStn)))-1.0)) which is: A = 1000((V/N)-1)

So, I reverse engineered that to get this: V = N(A+1000)/1000

And that is apparently also delivering incorrect values. Bwaaaahh!!

Modified anomdtb to dump the precip anomaly calculation. It seems to be working with raw values, eg:

DataA: 1050, NormMean: 712.00, Anom: 475 DataA: 270, NormMean: 712.00, Anom: -621 DataA: 710, NormMean: 712.00, Anom: -3 DataA: 430, NormMean: 712.00, Anom: -396 DataA: 280, NormMean: 712.00, Anom: -607 DataA: 830, NormMean: 712.00, Anom: 166 DataA: 0, NormMean: 712.00, Anom: -1000 DataA: 270, NormMean: 712.00, Anom: -621 DataA: 280, NormMean: 712.00, Anom: -607 DataA: 450, NormMean: 712.00, Anom: -368 DataA: 180, NormMean: 712.00, Anom: -747 DataA: 1380, NormMean: 712.00, Anom: 938

However, that -1000 is interesting for zero precip. It looks as though the anomalies are in mm*10, like the precip database raw values.. but no! These are dumped from within the program, the output .txt files have -100 instead of -1000. That's because of the CheckVariSuffix routine, which returns a factor based on the parameter code, including:

else if (Suffix.EQ.".pre") then Variable="precipitation (mm)" Factor = 0.1

Factor is then used when the anomaly files are written:

do XAStn = 1, NAStn if (DataA(XAYear,XMonth,XAStn).NE.-9999) write (9,"(2f8.2,f8.1,f13.5,i7)"), &

ALat(XAStn),ALon(XAStn),AElv(XAStn),real(DataA(XAYear,XMonth,XAStn))*Factor,AStn(XAS tn) end do

So, the anomalies are in real units (presumably mm/day).

So.. we grid these values. The resulting anomalies (for Jan 1980) look like this:

max = 1612.4 min = -100

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These should be applied to the climatology (normals). I think they can be applied to either unscaled 'real' value normals or to normals which are mm*10. Results will be scaled accordingly. So.. let's look at glo2abs. Again.

The current formula to convert to real values is:

absgrid(ilon(i),ilat(i)) = nint((rnormals(i,imo)* * (anoms(ilon(i),ilat(i))+1000)/1000)/rmult) V = N(A+1000)/1000*rmult

Not happy with that anyway. The multiplicative factor.. should that be there at all?

Now, if these are 'genuine' percentage anomalies - ie, they represent the percentage change from the mean, then the formula to convert them using unscaled normals would be:

V = N + N(A/100)

For instance, -100 would give V = 0, and 100 would give V = 2N. Now if the normals are *10, surely the results will be *10 too? As each term has N as a multiplicative factor anyway. This makes me wonder about the prevailing theory that the anomalies need to be *10. They are fractions of the normal, so it shouldn't matter whether the normal is real or *10. The variability should be the same (ie, the variability of the anomalies).

So, a run (just for 1980) of glo2abs, then, using the following formula:

absgrid(ilon(i),ilat(i)) = nint(rnormals(i,imo) + * (anoms(ilon(i),ilat(i)) * rnormals(i,imo)) / 100) V = N + A*N/100

This should give integer values of mm*10 (because the normals are mm*10 and uncorrected).

So, working backwards, what *should* the original anomalising routine in anomdtb.f90 look like? It should look like this:

A = 100*(V-N)/N, or: A = 100(V/N) - 100, or: A = 100((V/N)-1)

The last version is REMARKABLY similar to the original (A = 1000((V/N)-1)), in fact

I think we can call equivalence if V and N are in mm? Complicated. The '100' is not a scaling factor, it's the number that determines a percentage calculation. If we use 1000, what are we saying? The percentage anomalies we would have got are now 10x higher. Where V=0, A will be -1000, a meaningless percentage anomaly for precip. That's where the CheckVariSuffix factor pops up, multiplying by 0.1 and getting us back where we started.

So.. Tim's original looks right, once you understand the correction factor applied later.

In which case, my original algorithm should have worked!!

A slightly Heath-Robinson attempt to verify.. extracted the cell for Wick from the

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1980 output. Wick is 58.45N, 3.08W - I reckon that's (297,353). I get:

106 69 91 22 16 74 79 80 129 156 177 151

The station says:

1980 763 582 718 153 95 557 587 658 987 1162 1346 1113

**sigh** Yes, they do follow a similar shape.. it's just that the original station data are much higher. In terms of Up/Down (following direction):

Station DUDDUUUUUUD Gridded DUDDUUUUUUD

So, once again I don't understand statistics. Quel surprise, given that I haven't had any training in stats in my entire life, unless you count A-level maths.

Normals for the same cell:

Grid-ref= 353, 297 1060 740 870 600 610 670 700 910 990 1070 1200 1110

Now, these look like mm*10, the same units as the station data and what I expected. If I apply the anomalies to these normals, it looks like I'll get what I'm after.. the trouble is that glo2abs.for deliberately works with real values and so applies the factor in the clim header before using the values. I just don't think that works with percentages.. hmm.

Actually, it does. I ran through the algorithms and because the normal is multiplicative, you can do the scaling before or after. In other words, if V' is V produced with scaled normals (N*0.1) then we do end up with V = 10V'. So I just need to include the factor in the final equation:

V = (N + A*N/100)/F

Ran it, and the results were good. So - as it's the only change - I won't have to regrid precip after all! Just re-run from glo onwards.. did so, then used the old (but working) version of makegrids to produce the gridded ASCII and NetCDF files.

So.. comparisons. Well I want to compare with both 2.0 and 2.1, because they do differ. So

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I will need to convert 2.0 to regular-gridded, as I did with 2.1. If I could only remember the program I wrote to do it!!

**

Another problem. Apparently I should have derived TMN and TMX from DTR and TMP, as that's what v2.10 did and that's what people expect. I disagree with publishing datasets that are simple arithmetic derivations of other datasets published at the same time, when the real data could be published instead.. but no.

This introduces the problem of derivation. TMN = TMP -DTR/2 and TMX = TMP + DTR/2, but this does not tell us what to do when either or both values (TMP, DTR) are missing. One thing to check is the climatologies. Here are the first two cell normals for all four parameters:

.tmp = near-surface temperature (degrees Celsius) Grid-ref= 1, 148 270 274 269 265 259 253 246 242 248 252 261 268 Grid-ref= 1, 311 -187 -213 -195 -137 -30 47 90 81 33 -56 -132 -186

.dtr = diurnal temperature range (degrees Celsius) Grid-ref= 1, 148 56 71 54 59 55 50 54 51 57 61 61 73 Grid-ref= 1, 311 74 71 72 77 59 65 64 56 49 51 63 71

.tmn = near-surface temperature minimum (degrees Celsius) Grid-ref= 1, 148 242 238 242 236 232 228 219 217 220 222 230 232 Grid-ref= 1, 311 -224 -249 -231 -175 -59 15 58 53 8 -81 -163 -222

.tmx = near-surface temperature maximum (degrees Celsius) Grid-ref= 1, 148 298 309 296 295 287 278 273 268 277 283 292 305 Grid-ref= 1, 311 -150 -178 -159 -98 0 80 122 109 58 -31 -100 -151 Grid-ref= 1, 312

Well, making allowances for rounding errors, they do seem to hold to the relationship.

Wrote maketmnx.for to derive TMN and TMX from TMP and DTR grids. Works with the output files from glo2abs.for. Ran makegrids to produce .dat and .nc files ( still pre-station

count inclusion).

On to precip problems. Tim O ran some comparisons between 2.10 and 3.00, in general things are much improved but there are a few hair raisers (asterisked for special concern):

Cape Verde Isl, MAM & DJF, P Galapagos, All, T&P Guinea, MAM 1901, P Bangladesh, All 1991-2000, P **

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Bhutan, DJF 1939 & 1945, P Laos/Vietnam, DJF 1991, P **

Looked at Bangladesh first. Here, the 1990s show a sudden drop that really can only be some stations having data a factor of 10 too low. This ties in with the WWR station data that DL added for 1991-2000, which aprently was prone to scaling issues. Wrote stnx10.for to scale a file of WWR Bangladesh records, then manually C&P'd the decade over the erroneous ones in the database. Also fixed country name from 'BNGLADESH'!

Then Laos/Vietnam. Here we have an anomalously high peak for 1991 DJF. Used getllstations.for to extract all stations in a box around Laos & Vietnam (8 to 25N, 100 to 110E), a total of 96 stations from Thailand, Vietnam, Laos, Kampuchea, and China. Eeeek. Tim O's program only worked with boxes though. Also, I'm not 100% sure which year DJF belongs to in Tim's world.. hopefully it's the December year (as it was the fourth column in his plot table). However.. plotted *all* the data as overlapping years, and there is no trace of a spike in DJF. Uh-oh.

I'm not actually convinced that the 'country box' approach is much cop. Better to examine each land cell and automagically mark any with excessions? Say 5 SD to begin with. Could then be extra clever and pull the relevant stations and find the source of the excession? Of course, this shouldn't happen, since there is a 4SD limit imposed by anomdtb.f90 for precip (3SD for others).

Wrote vietlaos.for to run through the lists of Vietnam and Laos cells (provided by Tim O) and extract the DJF precip values for each (from the 1901-2006 gridded file). It then calculates the standard deviation of each series, normalises, and notes any values over 6.0 SDs (1991 onwards).

Result.. some very high values (up to 11.3 standard deviations!) in 1991/2. Worst cells: Row Column Index StdDev 212 571 273 11.21 213 571 273 11.30 214 571 273 10.15 212 572 273 11.11 213 572 273 11.20 214 572 273 10.58 212 573 273 10.84 213 573 273 11.10 212 574 273 10.76 215 572 273 10.06 214 573 273 10.53 213 574 273 10.94 214 574 273 10.44 212 575 273 10.65 213 575 273 10.66 211 576 273 10.96 212 576 273 10.51

Index 273 can be related to time as follows. The series begins in 1901 and we take

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three values per year (J,F,D). So 1990 would be the 90th year and the 268th-270th values. Thus 273 = Dec 1991.

The cells are all contiguous, implying a single station's influence via the gridding
process:

570 571 572 573 574 575 576 211 n/a n/a 6.37 7.56 8.36 9.71 10.96 212 n/a 11.21 11.11 10.84 10.76 10.65 10.51 213 5.52 11.30 11.20 11.10 10.94 10.66 n/a 214 5.34 10.15 10.58 10.53 10.44 n/a n/a 215 4.37 9.97 10.06 n/a n/a n/a n/a

'n/a' means the cell isn't in the Laos or Vietnam areas.

The 'epicentre' of the anomaly looks to be cell (213,571), which is in the Laos file: Box Column Row Lon Lat 205773 571 213 105.75 16.75

So we're looking for stations in the vicinity of 105.75E, 16.75N. Well the precip database has a total of EIGHT Laos stations, so that should be straightforward:

4893000 1990 10210 304 LUANG PRABANG LAOS 1951 2006 -999 -999.00 4893800 1920 10170 323 SAYABOURY LAOS 1969 2006 -999 -999.00 4894000 1800 10260 170 VIENTIANE LAOS 1941 2006 -999 -999.00 4894600 1738 10465 152 THAKHEK LAOS 1989 2006 -999 -999.00 4894700 1660 10480 155 SAVANNAKHET LAOS 1970 2006 -999 -999.00 4894800 1670 10500 184 SENO LAOS 1951 2006 -999 -999.00 4895200 1568 10643 168 SARAVANE LAOS 1989 2006 -999 -999.00 4895500 1510 10580 93 PAKSE LAOS 1968 2006 -999 -999.00

Well, SENO has to be the prime candidate. Unfortunately, this is from SENO:

4894800 1670 10500 184 SENO LAOS 1951 2006 -999 -999.00 <snip> 1989 0 0-9999 1910-9999 1010 4450 2690 2880 1340 0 0 1990 60 1560 150 420 1110 4830 3620 3690-9999 780 30 0 1991 0 0 400 0 690 1907 1890 5308 3238 805 0 366 1992 488 280 50 80 1883 2503 2644 2935 2039 131 0 89 1993 0 0 139 280 2324 1163 1949 4460 2145 0 29 0

A most undistinguished set. So, the net widens:

4894700 1660 10480 155 SAVANNAKHET LAOS 1970 2006 -999 -999.00 <snip> 1989-9999 0-9999-9999 1080-9999-9999-9999-9999 1490-9999-9999 1990 30-9999 240-9999-9999-9999 1920-9999-9999-9999-9999 0 1991 0 0 127 49 952 2508 1681 4034 4006 1690 0 338 1992 324 338 93 691 1932 2344 2048 4464 756 607 0 197 1993 0 5 335 263 2665 921 2884 2204 1834 17 23 0

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..nope..

4894600 1738 10465 152 THAKHEK LAOS 1989 2006 -999 -999.00 <snip> 1989-9999 0-9999-9999 2030-9999-9999-9999-9999 1490-9999-9999 1990 10-9999 520-9999-9999-9999-9999-9999-9999-9999-9999 0 1991 0 0 905 119 861 6058 3578 7092 2417 373 0 324 1992 105 318 125 456 2140 2978 4623 4595 3376 425 0 854 1993 0 108 52 1343 5835 2999 6285 4375 1017 467 8 0

..nope.. unless these values *are* unusual? Let's look at the highest two Decembers from each station:

4893000 1990 10210 304 LUANG PRABANG LAOS 1951 2006 -999 -999.00 1992 193 911 0 497 657 1246 2971 2837 929 584 95 1372 1994 0 54 1107 291 1702 2436 2025 3636 1516 316 185 816

4893800 1920 10170 323 SAYABOURY LAOS 1969 2006 -999 -999.00 1992 411 719 0 816 754 1252 2573 1671 1686 991 351 879 1994 0 208 1695 503 2262 1607 1743 2562 3205 118 193 454

4894000 1800 10260 170 VIENTIANE LAOS 1941 2006 -999 -999.00 1971 0 70 140 340 2940 2750 2890 2260 1630 1030 0 180 1992 381 273 11 424 2372 4878 4381 3676 3091 630 0 212 1994 0 300 921 322 2685 2725 4698 1932 4000 3031 1016 166 (inc for comparison with previous)

4894600 1738 10465 152 THAKHEK LAOS 1989 2006 -999 -999.00 1991 0 0 905 119 861 6058 3578 7092 2417 373 0 324 1992 105 318 125 456 2140 2978 4623 4595 3376 425 0 854 1994 0 612 952 558 1697 7092 5121 4276 2428 486 20 2 (inc for comparison with previous)

4894700 1660 10480 155 SAVANNAKHET LAOS 1970 2006 -999 -999.00 1991 0 0 127 49 952 2508 1681 4034 4006 1690 0 338 1992 324 338 93 691 1932 2344 2048 4464 756 607 0 197 1994 0 734 390 494 1381 3377 1525 5651 1881 600 0 0 (inc for comparison with previous)

4894800 1670 10500 184 SENO LAOS 1951 2006 -999 -999.00 1971 0 880 130 370 1270 4010 2200 2860 1930 410 0 140 1991 0 0 400 0 690 1907 1890 5308 3238 805 0 366 1992 488 280 50 80 1883 2503 2644 2935 2039 131 0 89 (inc for comparison with previous) 1994 0 532 318 969 2065 1937 1197 4552 1934 197 0 0 (inc for comparison with previous)

4895200 1568 10643 168 SARAVANE LAOS 1989 2006 -999 -999.00 1992 287 33 52 222 1072 5444 2998 8899 2243 1070 0 0 (inc for comparison with previous) 1994 0 10 354 686 1743 3387 5829 3254 4219 408 41 4 (inc for comparison with previous) 1998 26 619 0 574 2386 2871 1530 2308 2680 913 463 73

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2005 0 0 120 1230 1990 2860 4350 8060 3770 280 140 70

4895500 1510 10580 93 PAKSE LAOS 1968 2006 -999 -999.00 1972-9999-9999-9999-9999-9999-9999-9999-9999 2610 870 280 140 1992 166 101 0 210 665 1898 2574 6448 2942 648 10 31 (inc for comparison with previous) 1994 0 0 134 220 2537 3596 5161 5384 7693 1513 236 94

Summary: LUANG PRABANG shows a significant anomaly of 1372 for Dec 1992. Unfortunately, this finds echoes both temporal (1994 has 816) and spatial (SAYABOURY's 1992 is 879). So, if these values are causing the spike, it's genuine (if exaggerated in a way yet to be determined).

Wrote vietlaos2, to gather data from the cells AND stations. It also gets the climatology. Initially it only gathered 13 stations with data in 1991/2, using 'VIETNAM' and 'LAOS' to select on country name. However, taking the cell [214,574] in December 1991 as the peak incident, we can use those coordinates (17.25N, 107.25E) to centre a bounding box for station selection. A box 10degs square yields only 17 stations, none of which have anything remotely spikey in Dec 1991. A box 20degs square (some would say unfeasibly large) yields 98 stations, one of which does have a bit of a spike in Dec 91.. not impressively so though, and it's a long way away:

4855200 853 9993 3 NAKHON SI THAMMARAT THAILAND 1912 2000 -999 -999.00

Over 10.5 degrees South and over 7 degrees West of the target cell. Not very convincing, especially as closer stations are bound to have masked it.

One FINAL try with vietlaos3.for. Just looking at December, now, and getting the original station normals as well as the climatological ones. The whole chain. This proves to be surprisingly complicated.

On a parallel track (this would really have been better as a blog), Tim O has found that the binary grids of primary vars (used in synthetic production of secondary parameters) should be produced with 'binfac' set to 10 for TMP and DTR. This may explain the poor performance and coverage of VAP in particular.

Back to VietLaos.. the station output from vietlaos3.for had a couple of stations with missing anomaly values:

LAT LON ALT NORM VAL ANOM 17.15 104.13 171.00 29.00 62.00 -9999.00 15.80 102.03 182.00 45.00 40.70 -9999.00

I eventually worked out that I hadn't collapsed a universal probability, it was just the 4 standard deviation screen in anomdtb (4 for precip, 3 for temp). To confirm, I did a short

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anomdtb run (just for 1991) with the sd limit set to 10, and sure enough:

17.15 104.13 171.0 2037.900024835600 15.80 102.03 182.0 804.400024840300

They both look high enough to trigger the 4sd cap. However, since the spike we're investigating is from a regular process run, where that limit was in place, we can't use those values. Program is thus amended to omit any stations without anomalies (for Dec 1991)

Next issue is to make sense of the output. The first line from the station file is (headings added):

LAT LON ALT NORM VAL ANOM 22.60 114.10 25.00 29.00 21.60 -25.50

Remembering it's percentage anomalies! So 25.5% of 29 is 29*.255 = 7.395. Add that to 21.6? 29.0 :-)

By contract, the cell file looks like this:

ROW COL LAT LON VAL NORM 220 561 20.25 100.75 12.90 15.00

There are 63 stations and 204 cells (196 when missing values (sea) eliminated). I guess one approach would be to grid the anomalies, to see if a peak is visible. I did. It is. The simple interpolation in Matlab puts the peak at 17.25N, 105.25E - matches the grid peak for lat and a little west for lon. The nearest high station anomaly is 2369.2, that's from:

4838300 1653 10472 138 MUKDAHAN THAILAND 1934 2000 -999 -999.00 6190 34 127 290 907 1813 2900 2271 3353 2596 886 84 13 1934 0 100 0 500 3150 2940 2460 2980 3320 350 0 20 1935 0 0 0 440 1920 1560 3220 580 1770 0 170 0 1936 0 0 820 0 2320 1900 3460 810 2120 0 0 0 1937 0 0 350 660 3640 740 1920 2890 4470 330 0 0 1938 0 0 550 1300 730 1720 2340 400 2030 810 0 0 1939 0 280 700 230 1320 420 2480 4190 2130 0 0 0 1940-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1941 0 0 530 590 1800 2710 420 2790 650 750 0 0 1942 0 0 540 660 1650 3200 1200 1730 1990 0 0 0 1943 0 0 1600 1300 1960 1880 2000 2200 2600 0 0 0 1944 0-9999 0 320 2210 1040 1700 2500 2150 820 50 0 1945 70 600 0 340 0 2470 3400 2780 1620 20 330 500 1946 0 0 1360 0-9999 1720 1070 3330 2870 1260 0 0 1947 0 180 50 1390 3200 1530 3520 1150-9999 50 0 0 1948-9999-9999 0 1630 3520 1040 2900 3980 2160 380 10 0 1949 0 200 170 470 3000 2720 3110 4920 360 690 90-9999 1950 0 70 0 250 1610 2090 1040 1390 3500 1960 20 0 1951 0 340 770 1380 530 3380 1590 1950 3580 1430 20 0 1952 0 0 1170 660 1640 3160 2320 4150 3510 860 30 0 1953 260 110 430 630 1010 2200 1480 2780 1180 310 10 0 1954 460 10 30 1100 1950 2870 1120 2640 4220 620 0 0 1955 0 0 280 580 2180 4100 2900 2570 1810 270 20 0 1956 0 420 150 1000 3000 2930 3980 3840 2020 220 0 0 1957 0 30 1210 630 1690 2130 2090 3030 3240 460 0 0 1958 70 50 10 1090 1060 1690 2670 910 2750 700 0 0 1959 0 430 730 290 2300 1540 2080 2030 3910 280 0 0

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1960 0 190 550 650 1230 1750 3750 5090 2190 700 90 0 1961 0 0 590 660 2190 5880 2150 4310 4030 1140 0 0 1962 0 20 120 880 2200 2690 2780 4770-9999 360 50 0 1963 0 0 610 600 1010 3480 2130 3410 1250 220 150 0 1964 0 0 740 910 3370 2600 630 2050 4700 1120 20 0 1965 0 250 660 780 2120 2700 2110 2810 2210 1350 0 0 1966 0 610 310 730 3340 1370 3100 4010 2020 510 40 110 1967 0 0 50 870 1810 1800 1540 1960 3270 150 130 0 1968 10 70 50 170 1770 2320 1140 2360 5140 700 0 0 1969 0 20 290 260 1850 1990 3430 2060 3470 290 30 0 1970 0 10 90 1150 2210 3620 2610 4310 1290 340 10 0 1971 0 730 570 740 2130 3580 4060 2100 3240 510 30 170 1972 0 550 460 1280 1040 3470 3250 3640 2980 2340 20 0 1973 0 0 10 1080 2650 1990 2460 2050 2090 190 0 0 1974 400 0 60 2160 1160 2520 3070 6110 1920 570 260 0 1975 20 350 360 410 2200 3340 3230 3560 940 520 20 40 1976 0 210 380 1700 1160 1460 2430 3720 3250 780 60 0 1977 20 10 90 1000 620 620 1470 3980 4010 100 0 0 1978 0 100 920 650 1710 3690 2960 4420 2190 110 0 0 1979 10 140 50 900 2000 4230 1230 2540 2910 0 0 0 1980 0 50 190 1040 1440 3490 3310 930 6130 1830 170 20 1981 0 210 220 720 2630 4730 2490 1750 610 1260 90 0 1982 0 0 290 840 1330 2160 390 4390 3400 1720 370 0 1983 90 30 0 550 1360 2700 830 5200 1380 1680 0 0 1984 10 0 350 1270 2030 2290 2900 3880 2130 1380 650 0 1985 380 50 170 860 1100 4270 1580 3350 900 1170 0 0 1986 0 0 120 1650 2120 2210 1830 2980 1760 1700 240 10 1987 0 110 290 360 1090 4210 2670 3640 2140 1040 20 0 1988 0 30 90 1170 1130 1790 1800 3580 740 1730 0 0 1989-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1990-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1991 0-9999 105 226 1370 2079 1452 4190 3799 1610-9999 321 1992 328 314 150 637 1968 1906 2366 4973 1287 238 0 216 1993-9999 5 476 768 2438 1169 3671 2463 2215 13 22-9999 1994 0 781 274 409 1837 2297 1625 5755 1709 216-9999-9999 1995 0 140 834 672 1556 1606 4439 2848 681 857 69 0 1996 12 2 660 2394 1566 1526 1960 3350 4843 724 476-9999 1997 35 321 458 642 1154 2832 1197 4071 1722 1800 0-9999 1998 0 346 154 241 2174 3348 813 2153 2231 276 85 37 1999 63 0 182 1025 3449 1207 3681 1570 2628 299 109-9999 2000-9999 95 48 2742 2816 1852 1725 1903 2903 391 0 0

Note that the Dec 1991 value is anomalous, but not as extreme as the 1945 datum, which would get the same treatment with normals and climatologies, so should produce an even bigger spike for 1945 DJF! Unless of course it's screened out by the 4SD rule.. which it is! Well - no value in pre.1945.12.txt for this location.

Anyway.. this is the highest value in the Vietnam/Laos cells for Dec 1991:

ROW COL LAT LON VAL NORM 198 571 9.25 105.75 63.50 130.00

With a normal of 130, that makes the anomaly -48.85. Now I'm confused. How can an anomalously high value be well below the 61-90 mean? Aaarrgghhhh. Perhaps I should look at the highest anomaly. That turns out to be 80, from here:

216 563 18.25 101.75 1.80 1.00

Not exactly a show stopper. Time to look at the .glo files, which glo2abs processes into absolutes. Here's a Far-Eastern region with a spike:

>> glod3(210:216,567:573)

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567 568 569 570 571 572 573 216 1393.6 1791.6 1757.4 1723.2 1674.5 1553.2 1431.9 215 1501.7 1899.8 1927.3 1893.1 1786.3 1665 1505.3 214 1609.9 2007.9 2097.2 2019.5 1885.4 1712.8 1540.2 213 1359.4 2116.1 2252.6 2092.9 1920.3 1747.7 1575.1 212 80.145 1195.5 1796.1 1882 1955.2 1782.6 1610 211 -6.125 -36.614 563.99 649.87 735.75 821.63 907.5 210 -59.833 -90.333 -89.649 -83.283 -76.929 -70.576 -64.223

The spike is at [213,569]. Yes, I know, it's the n-th set of coordinates. You should see the plots! But looking at the anomalies is the closest we'll get to what Tim's program was doing, ie, calculating DJF standard deviations. Or something. Now, the coordinates are 16.75N, 104.75E. And wouldn't you know it, our prime suspect (see above) is on top of it:

4838300 1653 10472 138 MUKDAHAN THAILAND 1934 2000 -999 -999.00

So OK, here we go with the full run-down for December 1991, in the 16.75N,105.75E region:

TYPE VALUE COMMENT Raw data: 321 Highest unscreened December for this station (67 years) Normal: 13 Looks right - of course, very low for the target data! Anomaly: 2369.2 Correctly calculated Gridded anomaly: 2252.6 Believable interpolation Gridded actual: er... Strangely, it seems to be 0.

Ah well - had enough. It looks like it's an extreme but believable event in a Thai station, let's leave it like that. Re-running precip, with the new updated database pre.0803271802.dtb:

<BEGIN_QUOTE> crua6[/cru/cruts/version_3_0/primaries/precip] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:
.pre > Will calculate percentage anomalies. > Select the .cts or .dtb file to load: pre.0803271802.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 4 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3

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> Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): pre.txt > Select the first,last years AD to save: 1901,2006 > Operating... /tmp_mnt/cru-auto/cruts/version_3_0/primaries/precip/pre.0803271802.dtb > NORMALS MEAN percent STDEV percent > .dtb 7315040 73.8 > .cts 299359 3.0 7613600 76.8 > PROCESS DECISION percent %of-chk > no lat/lon 17911 0.2 0.2 > no normal 2355275 23.8 23.8 > out-of-range 13249 0.1 0.2 > accepted 7521017 75.9 > Dumping years 1901-2006 to .txt files...

IDL> quick_interp_tdm2,1901,2006,'preglo/pregrid.',450,gs=0.5,dumpglo='dumpglo',pts_prefi x='pretxt/pre.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2006

IDL>

crua6[/cru/cruts/version_3_0/primaries/precip] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.pre Enter a name for the gridded climatology file: clim.6190.lan.pre.grid4 Enter the path and stem of the .glo files: preglo/pregrid. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: preabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 1 Right, erm.. off I jolly well go! pregrid.01.1901.glo (etc) pregrid.12.2006.glo

uealogin1[/cru/cruts/version_3_0/primaries/precip] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from

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the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: preabs/pregrid.YYYY.MM.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.pre.dat

Now please enter the 3-ch parameter code: pre Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Precipitation Writing: cru_ts_3_00.1901.1910.pre.dat cru_ts_3_00.1901.1910.pre.nc Writing: cru_ts_3_00.1911.1920.pre.dat cru_ts_3_00.1911.1920.pre.nc Writing: cru_ts_3_00.1921.1930.pre.dat cru_ts_3_00.1921.1930.pre.nc Writing: cru_ts_3_00.1931.1940.pre.dat cru_ts_3_00.1931.1940.pre.nc Writing: cru_ts_3_00.1941.1950.pre.dat cru_ts_3_00.1941.1950.pre.nc Writing: cru_ts_3_00.1951.1960.pre.dat cru_ts_3_00.1951.1960.pre.nc Writing: cru_ts_3_00.1961.1970.pre.dat cru_ts_3_00.1961.1970.pre.nc Writing: cru_ts_3_00.1971.1980.pre.dat cru_ts_3_00.1971.1980.pre.nc Writing: cru_ts_3_00.1981.1990.pre.dat cru_ts_3_00.1981.1990.pre.nc Writing: cru_ts_3_00.1991.2000.pre.dat cru_ts_3_00.1991.2000.pre.nc Writing: cru_ts_3_00.2001.2006.pre.dat cru_ts_3_00.2001.2006.pre.nc <END_QUOTE>

On to the reproduction of binaries for TMP and DTR, and subsequent regeneration of VAP and FRS.

TMP Binaries:

IDL> quick_interp_tdm2,1901,2006,'tmpbin/tmpbin',1200,gs=2.5,dumpbin='dumpbin',binfac=10, pts_prefix='tmp0km0705101334txt/tmp.' Defaults set 1901 % Compiled module: MEAN. % Compiled module: MOMENT. % Compiled module: STDDEV. grid 1901 non-zero -0.1127 0.9472 1.3993 cells= 46444 % Compiled module: STRIP. % Compiled module: WRBIN. 1902 grid 1902 non-zero -0.4378 1.0267 1.4712 cells= 48321 1903 grid 1903 non-zero -0.3462 0.9586 1.3704 cells= 48803 1904 grid 1904 non-zero -0.4288 0.9753 1.3866 cells= 49493 1905

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grid 1905 non-zero -0.2732 0.9620 1.3722 cells= 49845 1906 grid 1906 non-zero -0.1910 0.9176 1.3323 cells= 49568 1907 grid 1907 non-zero -0.5290 1.0338 1.4636 cells= 50458 1908 grid 1908 non-zero -0.3546 0.9165 1.2607 cells= 50449 1909 grid 1909 non-zero -0.4005 0.9998 1.4222 cells= 50163 1910 grid 1910 non-zero -0.3404 0.9459 1.3879 cells= 50489 1911 grid 1911 non-zero -0.3868 0.9445 1.3154 cells= 50752 1912 grid 1912 non-zero -0.4441 1.0156 1.4580 cells= 51731 1913 grid 1913 non-zero -0.3315 0.9125 1.2647 cells= 51634 1914 grid 1914 non-zero -0.1896 0.9655 1.4205 cells= 50734 1915 grid 1915 non-zero -0.1935 1.0501 1.5537 cells= 51773 1916 grid 1916 non-zero -0.3523 1.0092 1.4885 cells= 52042 1917 grid 1917 non-zero -0.5407 1.1470 1.6613 cells= 54151 1918 grid 1918 non-zero -0.4379 1.0228 1.4534 cells= 52579 1919 grid 1919 non-zero -0.3663 1.0327 1.5279 cells= 51872 1920 grid 1920 non-zero -0.2173 1.0092 1.4856 cells= 50540 1921 grid 1921 non-zero -0.1261 0.9004 1.2767 cells= 52468 1922 grid 1922 non-zero -0.2355 0.9258 1.3934 cells= 54202 1923 grid 1923 non-zero -0.1942 1.0040 1.4733 cells= 54975 1924 grid 1924 non-zero -0.1418 0.9416 1.3491 cells= 55664 1925 grid 1925 non-zero -0.1033 1.0121 1.5038 cells= 55677 1926 grid 1926 non-zero 0.0471 0.9750 1.4242 cells= 55826 1927 grid 1927 non-zero -0.1290 0.9839 1.4396 cells= 57033 1928 grid 1928 non-zero -0.0254 0.9581 1.3929 cells= 56950 1929 grid 1929 non-zero -0.2651 1.1120 1.7327 cells= 58284 1930 grid 1930 non-zero -0.0233 1.0157 1.5554 cells= 57481 1931 grid 1931 non-zero 0.0072 1.0705 1.6009 cells= 57932 1932 grid 1932 non-zero 0.0407 1.0426 1.5664 cells= 57752 1933 grid 1933 non-zero -0.2517 1.1010 1.6794 cells= 59297 1934 grid 1934 non-zero 0.0858 1.0705 1.6510 cells= 58932 1935 grid 1935 non-zero -0.0383 1.0498 1.5969 cells= 59316 1936 grid 1936 non-zero -0.0118 1.0867 1.6457 cells= 59676

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1937 grid 1937 non-zero 0.1841 1.0572 1.6419 cells= 59702 1938 grid 1938 non-zero 0.2843 1.0094 1.4853 cells= 59478 1939 grid 1939 non-zero 0.0828 1.0270 1.5633 cells= 60643 1940 grid 1940 non-zero 0.1223 1.0033 1.5251 cells= 60381 1941 grid 1941 non-zero 0.0049 1.0253 1.4988 cells= 63950 1942 grid 1942 non-zero 0.0486 1.0061 1.5799 cells= 61984 1943 grid 1943 non-zero 0.1795 1.0288 1.5243 cells= 63082 1944 grid 1944 non-zero 0.1993 0.9783 1.4922 cells= 62327 1945 grid 1945 non-zero -0.0306 1.0840 1.5827 cells= 62977 1946 grid 1946 non-zero -0.0376 1.0094 1.4989 cells= 63193 1947 grid 1947 non-zero 0.1326 1.0977 1.7075 cells= 64854 1948 grid 1948 non-zero 0.0276 0.9783 1.4466 cells= 66490 1949 grid 1949 non-zero -0.0873 1.0422 1.5665 cells= 68159 1950 grid 1950 non-zero -0.2032 1.0344 1.5841 cells= 67736 1951 grid 1951 non-zero -0.0537 0.9777 1.4482 cells= 70202 1952 grid 1952 non-zero -0.0112 0.9952 1.5704 cells= 69668 1953 grid 1953 non-zero 0.2020 1.0140 1.5735 cells= 70734 1954 grid 1954 non-zero -0.0062 1.0381 1.6387 cells= 71309 1955 grid 1955 non-zero -0.1527 1.0281 1.6167 cells= 73181 1956 grid 1956 non-zero -0.2183 1.0542 1.5788 cells= 77564 1957 grid 1957 non-zero -0.0142 0.9929 1.5067 cells= 77649 1958 grid 1958 non-zero -0.0257 1.0166 1.5491 cells= 80430 1959 grid 1959 non-zero 0.0019 1.0058 1.5493 cells= 79903 1960 grid 1960 non-zero -0.0661 0.9628 1.4564 cells= 80353 1961 grid 1961 non-zero 0.0016 0.9440 1.3960 cells= 81093 1962 grid 1962 non-zero 0.0660 0.9249 1.4273 cells= 77733 1963 grid 1963 non-zero -0.0850 1.0164 1.5649 cells= 80869 1964 grid 1964 non-zero -0.3518 0.9639 1.4553 cells= 82284 1965 grid 1965 non-zero -0.2396 0.9097 1.3315 cells= 82512 1966 grid 1966 non-zero -0.2748 1.0171 1.5915 cells= 81405 1967 grid 1967 non-zero -0.0372 0.9385 1.4324 cells= 81573 1968

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grid 1968 non-zero -0.2106 0.9665 1.5115 cells= 81706 1969 grid 1969 non-zero -0.1505 1.0751 1.7496 cells= 81490 1970 grid 1970 non-zero -0.0771 0.8111 1.1569 cells= 80462 1971 grid 1971 non-zero -0.1102 0.9128 1.3577 cells= 82451 1972 grid 1972 non-zero -0.1992 1.0147 1.5390 cells= 82070 1973 grid 1973 non-zero 0.0914 0.9087 1.3303 cells= 81625 1974 grid 1974 non-zero -0.1369 0.9896 1.5273 cells= 81687 1975 grid 1975 non-zero -0.0400 0.9258 1.3720 cells= 81390 1976 grid 1976 non-zero -0.2595 0.9596 1.4088 cells= 82439 1977 grid 1977 non-zero 0.0718 0.9855 1.5405 cells= 80143 1978 grid 1978 non-zero -0.0233 0.9729 1.5545 cells= 80118 1979 grid 1979 non-zero -0.0921 1.0054 1.6126 cells= 79714 1980 grid 1980 non-zero 0.1600 0.9471 1.4078 cells= 80417 1981 grid 1981 non-zero 0.4437 1.0207 1.5695 cells= 81226 1982 grid 1982 non-zero -0.0664 0.9502 1.4287 cells= 80230 1983 grid 1983 non-zero 0.2325 0.9886 1.4907 cells= 82258 1984 grid 1984 non-zero 0.0904 1.0216 1.6368 cells= 81431 1985 grid 1985 non-zero 0.0625 0.9590 1.5123 cells= 81731 1986 grid 1986 non-zero 0.1007 0.8952 1.3674 cells= 81016 1987 grid 1987 non-zero 0.1116 0.9654 1.4412 cells= 84529 1988 grid 1988 non-zero 0.3365 0.9242 1.3069 cells= 82070 1989 grid 1989 non-zero 0.2451 1.0428 1.6170 cells= 79951 1990 grid 1990 non-zero 0.4275 1.0581 1.5828 cells= 82418 1991 grid 1991 non-zero 0.4279 0.9504 1.3005 cells= 80068 1992 grid 1992 non-zero 0.0265 0.9827 1.4543 cells= 80204 1993 grid 1993 non-zero 0.1614 0.9820 1.4965 cells= 78945 1994 grid 1994 non-zero 0.2598 0.9699 1.3958 cells= 77509 1995 grid 1995 non-zero 0.5222 1.0500 1.5943 cells= 80001 1996 grid 1996 non-zero 0.3733 0.9745 1.4383 cells= 78304 1997 grid 1997 non-zero 0.3947 1.0250 1.4473 cells= 80450 1998 grid 1998 non-zero 0.6010 1.1707 1.5968 cells= 82794 1999 grid 1999 non-zero 0.4249 1.0485 1.5105 cells= 80640

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2000 grid 2000 non-zero 0.4574 1.0256 1.4596 cells= 78883 2001 grid 2001 non-zero 0.5695 1.0596 1.4876 cells= 78391 2002 grid 2002 non-zero 0.6281 1.1662 1.6404 cells= 80200 2003 grid 2003 non-zero 0.6715 1.0531 1.3475 cells= 77636 2004 grid 2004 non-zero 0.5362 0.9762 1.2947 cells= 79600 2005 grid 2005 non-zero 0.8050 1.1350 1.4605 cells= 80465 2006 no stations found in: tmp0km0705101334txt/tmp.2006.11.txt no stations found in: tmp0km0705101334txt/tmp.2006.12.txt grid 2006 non-zero 0.6621 1.1017 1.5361 cells= 65396

IDL>

DTR Binaries:

IDL> quick_interp_tdm2,1901,2006,'dtrbin/dtrbin',750,gs=2.5,dumpbin='dumpbin',binfac=10,p ts_prefix='dtrtxt/dtr.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: MEAN. % Compiled module: MOMENT. % Compiled module: STDDEV. grid 1901 non-zero 0.3357 0.8741 1.1669 cells= 18526 % Compiled module: STRIP. % Compiled module: WRBIN. 1902 grid 1902 non-zero 0.2560 0.8530 1.1640 cells= 19088 1903 grid 1903 non-zero 0.2015 0.8514 1.1841 cells= 19063 1904 grid 1904 non-zero 0.2647 0.8584 1.1827 cells= 19155 1905 grid 1905 non-zero 0.1753 0.9056 1.2595 cells= 20808 1906 grid 1906 non-zero 0.2458 0.9003 1.2127 cells= 20892 1907 grid 1907 non-zero 0.2658 0.9124 1.2370 cells= 21621 1908 grid 1908 non-zero 0.3003 0.8911 1.1912 cells= 22028 1909 grid 1909 non-zero 0.2063 0.8791 1.1952 cells= 22253 1910 grid 1910 non-zero 0.2524 0.8563 1.1561 cells= 23297 1911 grid 1911 non-zero 0.2317 0.8808 1.2201 cells= 24153 1912 grid 1912 non-zero 0.1748 0.9544 1.3196 cells= 24284 1913 grid 1913 non-zero 0.1990 0.9047 1.2463 cells= 24366 1914 grid 1914 non-zero 0.1395 0.9195 1.2747 cells= 24431 1915 grid 1915 non-zero 0.0185 0.9405 1.2969 cells= 26178

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1916 grid 1916 non-zero 0.0178 0.8761 1.1904 cells= 27095 1917 grid 1917 non-zero 0.1518 0.9108 1.2619 cells= 26614 1918 grid 1918 non-zero 0.1303 0.9134 1.2533 cells= 26447 1919 grid 1919 non-zero 0.1300 0.8856 1.2029 cells= 25701 1920 grid 1920 non-zero 0.0500 0.8480 1.1650 cells= 26563 1921 grid 1921 non-zero 0.1660 0.8213 1.1246 cells= 26549 1922 grid 1922 non-zero 0.1133 0.8468 1.1707 cells= 26701 1923 grid 1923 non-zero 0.1794 0.8962 1.2293 cells= 27643 1924 grid 1924 non-zero 0.1309 0.8549 1.1583 cells= 28642 1925 grid 1925 non-zero 0.2252 0.9167 1.2254 cells= 28841 1926 grid 1926 non-zero 0.1076 0.8559 1.1687 cells= 30671 1927 grid 1927 non-zero 0.1285 0.8715 1.1766 cells= 30962 1928 grid 1928 non-zero 0.1279 0.8576 1.1773 cells= 31156 1929 grid 1929 non-zero 0.1784 0.8826 1.1974 cells= 32021 1930 grid 1930 non-zero 0.1344 0.8711 1.1830 cells= 33360 1931 grid 1931 non-zero 0.0238 0.8470 1.1548 cells= 32726 1932 grid 1932 non-zero 0.0872 0.8489 1.1546 cells= 33396 1933 grid 1933 non-zero 0.1012 0.8560 1.1597 cells= 34574 1934 grid 1934 non-zero 0.0295 0.8591 1.1676 cells= 34203 1935 grid 1935 non-zero 0.1092 0.8682 1.1665 cells= 34561 1936 grid 1936 non-zero 0.2106 0.8947 1.2055 cells= 35632 1937 grid 1937 non-zero 0.1686 0.8463 1.1182 cells= 35921 1938 grid 1938 non-zero 0.0845 0.8242 1.1125 cells= 35718 1939 grid 1939 non-zero 0.1428 0.8362 1.1277 cells= 37525 1940 grid 1940 non-zero 0.1891 0.8662 1.1493 cells= 38227 1941 grid 1941 non-zero 0.1334 0.8502 1.1411 cells= 38486 1942 grid 1942 non-zero 0.1176 0.8344 1.1001 cells= 39312 1943 grid 1943 non-zero 0.1844 0.8476 1.1191 cells= 40361 1944 grid 1944 non-zero 0.1564 0.8195 1.0904 cells= 40010 1945 grid 1945 non-zero 0.1566 0.8164 1.0740 cells= 40172 1946 grid 1946 non-zero 0.1648 0.8529 1.1288 cells= 40305 1947

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grid 1947 non-zero 0.0668 0.8305 1.1166 cells= 41045 1948 grid 1948 non-zero 0.2204 0.8015 1.0473 cells= 41004 1949 grid 1949 non-zero 0.1192 0.8185 1.1029 cells= 41329 1950 grid 1950 non-zero 0.1349 0.7915 1.0675 cells= 42803 1951 grid 1951 non-zero 0.1881 0.8003 1.0647 cells= 47064 1952 grid 1952 non-zero 0.1751 0.7776 1.0556 cells= 46868 1953 grid 1953 non-zero 0.1331 0.7763 1.0518 cells= 48129 1954 grid 1954 non-zero 0.0824 0.7668 1.0349 cells= 47912 1955 grid 1955 non-zero 0.0963 0.7733 1.0468 cells= 49105 1956 grid 1956 non-zero 0.0901 0.7695 1.0440 cells= 50637 1957 grid 1957 non-zero 0.0689 0.7536 1.0279 cells= 50456 1958 grid 1958 non-zero 0.0050 0.7504 1.0291 cells= 52013 1959 grid 1959 non-zero 0.0439 0.7329 1.0020 cells= 52162 1960 grid 1960 non-zero 0.0674 0.7049 0.9542 cells= 52787 1961 grid 1961 non-zero 0.0445 0.6810 0.9111 cells= 56188 1962 grid 1962 non-zero 0.1297 0.6877 0.9156 cells= 54897 1963 grid 1963 non-zero 0.1449 0.7088 0.9661 cells= 55755 1964 grid 1964 non-zero 0.0955 0.6719 0.9029 cells= 54909 1965 grid 1965 non-zero 0.0913 0.6638 0.8950 cells= 54906 1966 grid 1966 non-zero 0.0878 0.6566 0.8813 cells= 54751 1967 grid 1967 non-zero 0.0805 0.6626 0.8876 cells= 54393 1968 grid 1968 non-zero 0.0826 0.6611 0.8923 cells= 54602 1969 grid 1969 non-zero 0.0253 0.6787 0.9309 cells= 55176 1970 grid 1970 non-zero 0.0576 0.6232 0.8301 cells= 55444 1971 grid 1971 non-zero 0.0987 0.6340 0.8426 cells= 54610 1972 grid 1972 non-zero 0.0472 0.6631 0.8979 cells= 55812 1973 grid 1973 non-zero 0.0287 0.6424 0.8741 cells= 54755 1974 grid 1974 non-zero -0.0119 0.6782 0.9289 cells= 56105 1975 grid 1975 non-zero 0.0287 0.6259 0.8458 cells= 54696 1976 grid 1976 non-zero 0.0740 0.6565 0.8966 cells= 55239 1977 grid 1977 non-zero -0.0454 0.6600 0.9026 cells= 54227 1978 grid 1978 non-zero -0.1045 0.6529 0.8775 cells= 55036

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1979 grid 1979 non-zero -0.0749 0.6510 0.8753 cells= 54990 1980 grid 1980 non-zero -0.0565 0.6269 0.8300 cells= 55430 1981 grid 1981 non-zero -0.0498 0.6704 0.8970 cells= 55023 1982 grid 1982 non-zero -0.0828 0.6622 0.8874 cells= 56028 1983 grid 1983 non-zero -0.1380 0.6808 0.9142 cells= 55854 1984 grid 1984 non-zero -0.1530 0.6675 0.8850 cells= 55751 1985 grid 1985 non-zero -0.1156 0.6359 0.8438 cells= 55066 1986 grid 1986 non-zero -0.0822 0.6412 0.8583 cells= 55111 1987 grid 1987 non-zero -0.0579 0.6599 0.8919 cells= 55607 1988 grid 1988 non-zero -0.0691 0.6615 0.8752 cells= 54503 1989 grid 1989 non-zero -0.0165 0.6685 0.9064 cells= 55615 1990 grid 1990 non-zero -0.0655 0.6734 0.9057 cells= 54497 1991 grid 1991 non-zero -0.0899 0.6602 0.8722 cells= 53533 1992 grid 1992 non-zero -0.1171 0.6951 0.9349 cells= 53739 1993 grid 1993 non-zero -0.1314 0.6882 0.9188 cells= 51271 1994 grid 1994 non-zero -0.0480 0.6934 0.9282 cells= 50211 1995 grid 1995 non-zero -0.0357 0.7596 1.2498 cells= 52366 1996 grid 1996 non-zero -0.0723 0.7666 1.1579 cells= 51667 1997 grid 1997 non-zero -0.1340 0.7854 1.1636 cells= 52195 1998 grid 1998 non-zero -0.1627 0.8328 1.1992 cells= 52907 1999 grid 1999 non-zero -0.1237 0.7134 0.9716 cells= 51050 2000 grid 2000 non-zero -0.1765 0.7575 1.0412 cells= 51137 2001 grid 2001 non-zero -0.1305 0.7564 1.0658 cells= 49146 2002 grid 2002 non-zero -0.0984 0.7549 1.0967 cells= 46178 2003 grid 2003 non-zero -0.1072 0.7128 1.0087 cells= 46904 2004 grid 2004 non-zero -0.1628 0.8113 1.2574 cells= 47399 2005 grid 2005 non-zero -0.1150 0.8546 1.3540 cells= 43715 2006 no stations found in: dtrtxt/dtr.2006.09.txt no stations found in: dtrtxt/dtr.2006.10.txt no stations found in: dtrtxt/dtr.2006.11.txt no stations found in: dtrtxt/dtr.2006.12.txt grid 2006 non-zero -0.0087 0.9041 1.6077 cells= 28592

IDL>

VAP synthetics:

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IDL> vap_gts_anom,dtr_prefix='../dtrbin/dtrbin',tmp_prefix='../tmpbin/tmpbin',1901,2006,o utprefix='vapsyn/vapsyn.',dumpbin=1 % Compiled module: VAP_GTS_ANOM. % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. Land,sea: 56016 68400 Calculating tmn normal % Compiled module: TVAP. Calculating synthetic vap normal % Compiled module: ESAT. Calculating synthetic anomalies % Compiled module: MOMENT. 1901 vap (x,s2,<<,>>): -0.0450600 0.230521 -4.50663 4.92927 % Compiled module: WRBIN. 1902 vap (x,s2,<<,>>): -0.102633 0.271200 -4.14974 4.92341 1903 vap (x,s2,<<,>>): -0.107597 0.242152 -5.74305 5.58190 1904 vap (x,s2,<<,>>): -0.123137 0.221801 -4.30042 3.71240 1905 vap (x,s2,<<,>>): -0.0799978 0.267905 -4.54584 6.04190 1906 vap (x,s2,<<,>>): -0.0343380 0.240282 -4.86007 6.45160 1907 vap (x,s2,<<,>>): -0.137421 0.284412 -5.06625 6.02255 1908 vap (x,s2,<<,>>): -0.105214 0.234139 -6.99258 5.15916 1909 vap (x,s2,<<,>>): -0.103285 0.252210 -5.48791 5.12214 1910 vap (x,s2,<<,>>): -0.104377 0.225462 -4.64360 7.19087 1911 vap (x,s2,<<,>>): -0.109270 0.255412 -4.35549 6.99751 1912 vap (x,s2,<<,>>): -0.127306 0.287857 -4.63037 5.84822 1913 vap (x,s2,<<,>>): -0.107747 0.271437 -4.73097 4.98110 1914 vap (x,s2,<<,>>): -0.0481493 0.274125 -4.43114 6.19318 1915 vap (x,s2,<<,>>): -0.0343964 0.332448 -5.31914 6.04190 1916 vap (x,s2,<<,>>): -0.0947238 0.293859 -6.31417 4.63692 1917 vap (x,s2,<<,>>): -0.170714 0.396007 -8.69476 3.86362 1918 vap (x,s2,<<,>>): -0.133214 0.311174 -5.48035 6.19711 1919 vap (x,s2,<<,>>): -0.0687798 0.307508 -6.14869 9.28416 1920 vap (x,s2,<<,>>): -0.0619862 0.262944 -5.76398 4.10598 1921 vap (x,s2,<<,>>): -0.0319013 0.301257 -5.73133 7.07691 1922 vap (x,s2,<<,>>): -0.0621843 0.231719 -5.03104 4.18061 1923 vap (x,s2,<<,>>): -0.0626035 0.285980 -5.70781 7.32851 1924 vap (x,s2,<<,>>): -0.0720660 0.281999 -4.88423 7.32851 1925 vap (x,s2,<<,>>): -0.0673457 0.292218 -5.70781 8.66163 1926 vap (x,s2,<<,>>): -0.0264075 0.297545 -4.88423 6.07568 1927 vap (x,s2,<<,>>): -0.0400396 0.277274 -4.26864 9.46017 1928 vap (x,s2,<<,>>): -0.0376556 0.250591 -4.88423 4.32885 1929 vap (x,s2,<<,>>): -0.0885709 0.313796 -5.52846 5.63952 1930 vap (x,s2,<<,>>): -0.0261154 0.252065 -4.93317 5.76016 1931 vap (x,s2,<<,>>): 0.00818994 0.340814 -5.13082 6.25571 1932 vap (x,s2,<<,>>): -0.000556678 0.290796 -4.36993 6.39834 1933 vap (x,s2,<<,>>): -0.0732750 0.299654 -4.85280 5.68388 1934 vap (x,s2,<<,>>): -0.0230768 0.306463 -4.27863 6.18267 1935 vap (x,s2,<<,>>): -0.0294056 0.256452 -4.08567 5.28612 1936 vap (x,s2,<<,>>): -0.0166144 0.312561 -4.76875 6.14991 1937 vap (x,s2,<<,>>): 0.00628125 0.273150 -4.94706 8.10473 1938 vap (x,s2,<<,>>): 0.0538196 0.277858 -3.83216 5.16185 1939 vap (x,s2,<<,>>): -0.0101522 0.261258 -4.26058 6.09505 1940 vap (x,s2,<<,>>): -0.0340492 0.270330 -5.28004 4.99802 1941 vap (x,s2,<<,>>): -0.00272590 0.359517 -4.30990 7.86635 1942 vap (x,s2,<<,>>): -0.0107379 0.256225 -4.31849 5.04972 1943 vap (x,s2,<<,>>): -0.00847952 0.306959 -4.05096 5.09181 1944 vap (x,s2,<<,>>): 0.0135632 0.286842 -6.32264 5.44941 1945 vap (x,s2,<<,>>): -0.0348352 0.339675 -5.77718 6.11337 1946 vap (x,s2,<<,>>): -0.0308651 0.304056 -6.39576 6.89301 1947 vap (x,s2,<<,>>): 0.0119494 0.347709 -4.52023 7.91578

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1948 vap (x,s2,<<,>>): -0.00279501 0.269904 -4.20986 8.68827 1949 vap (x,s2,<<,>>): -0.0392110 0.341484 -5.71766 6.29158 1950 vap (x,s2,<<,>>): -0.0805553 0.315878 -4.60512 9.89166 1951 vap (x,s2,<<,>>): -0.0455985 0.286749 -5.21167 5.09294 1952 vap (x,s2,<<,>>): -0.0285279 0.310278 -6.19114 5.91316 1953 vap (x,s2,<<,>>): 0.0164626 0.328090 -5.10127 6.91089 1954 vap (x,s2,<<,>>): -0.0270003 0.325794 -4.60512 5.86057 1955 vap (x,s2,<<,>>): -0.0427618 0.309960 -6.32375 5.67290 1956 vap (x,s2,<<,>>): -0.141589 0.324435 -5.33854 4.30108 1957 vap (x,s2,<<,>>): -0.0322534 0.290902 -6.51552 5.47146 1958 vap (x,s2,<<,>>): -0.00254215 0.284311 -4.56239 5.24883 1959 vap (x,s2,<<,>>): -0.0136386 0.273182 -4.56765 7.10322 1960 vap (x,s2,<<,>>): -0.0268859 0.251590 -4.74454 5.61688 1961 vap (x,s2,<<,>>): 0.00428266 0.256116 -3.58335 5.09660 1962 vap (x,s2,<<,>>): -0.0169736 0.241635 -5.13969 4.88660 1963 vap (x,s2,<<,>>): -0.0132022 0.280225 -4.86808 6.25239 1964 vap (x,s2,<<,>>): -0.0801179 0.230050 -4.37340 5.66511 1965 vap (x,s2,<<,>>): -0.0818651 0.255070 -4.92358 5.56393 1966 vap (x,s2,<<,>>): -0.0280720 0.231274 -5.94548 5.55636 1967 vap (x,s2,<<,>>): -0.0181908 0.260895 -4.50197 5.60940 1968 vap (x,s2,<<,>>): -0.0724171 0.263899 -7.34842 4.57032 1969 vap (x,s2,<<,>>): -0.0251546 0.277463 -5.45023 5.64616 1970 vap (x,s2,<<,>>): -0.0310875 0.183015 -5.20684 4.72542 1971 vap (x,s2,<<,>>): -0.0584375 0.247623 -4.25373 5.45411 1972 vap (x,s2,<<,>>): -0.0703370 0.291676 -4.58312 5.34509 1973 vap (x,s2,<<,>>): 0.0333626 0.249423 -4.92304 4.40218 1974 vap (x,s2,<<,>>): -0.0588555 0.269075 -5.21312 7.79163 1975 vap (x,s2,<<,>>): -0.0141657 0.248957 -5.68768 6.99932 1976 vap (x,s2,<<,>>): -0.122256 0.269952 -6.63680 3.87281 1977 vap (x,s2,<<,>>): 0.0307466 0.242061 -4.42668 4.59490 1978 vap (x,s2,<<,>>): -0.0234150 0.225811 -5.53217 5.65761 1979 vap (x,s2,<<,>>): 0.0121905 0.234581 -3.81796 8.11386 1980 vap (x,s2,<<,>>): 0.0243552 0.225838 -4.95252 10.0661 1981 vap (x,s2,<<,>>): 0.0730062 0.273207 -6.21452 5.02121 1982 vap (x,s2,<<,>>): 0.00410785 0.225016 -4.32441 5.82923 1983 vap (x,s2,<<,>>): 0.0990013 0.349267 -5.66664 7.11964 1984 vap (x,s2,<<,>>): 0.0240819 0.257495 -4.72244 5.45411 1985 vap (x,s2,<<,>>): 0.00794266 0.232222 -4.08511 5.98364 1986 vap (x,s2,<<,>>): 0.0247494 0.226368 -4.53393 6.49533 1987 vap (x,s2,<<,>>): 0.0702606 0.326509 -6.49176 5.65761 1988 vap (x,s2,<<,>>): 0.108340 0.262510 -4.71399 5.24402 1989 vap (x,s2,<<,>>): 0.0558202 0.259338 -5.43051 5.26850 1990 vap (x,s2,<<,>>): 0.142205 0.288092 -5.08627 4.54985 1991 vap (x,s2,<<,>>): 0.123237 0.286160 -4.73092 7.81615 1992 vap (x,s2,<<,>>): 0.00923000 0.309573 -4.60065 7.87882 1993 vap (x,s2,<<,>>): 0.0506631 0.270131 -4.38424 5.70831 1994 vap (x,s2,<<,>>): 0.119075 0.311620 -3.41067 5.64707 1995 vap (x,s2,<<,>>): 0.153732 0.311293 -5.12451 6.16339 1996 vap (x,s2,<<,>>): 0.0641272 0.281972 -7.07159 5.34453 1997 vap (x,s2,<<,>>): 0.171295 0.432609 -6.05247 7.83205 1998 vap (x,s2,<<,>>): 0.316044 0.580134 -5.70816 6.81637 1999 vap (x,s2,<<,>>): 0.154989 0.339337 -5.84341 5.00767 2000 vap (x,s2,<<,>>): 0.151733 0.320928 -5.67183 5.19904 2001 vap (x,s2,<<,>>): 0.207467 0.427043 -5.60383 10.6589 2002 vap (x,s2,<<,>>): 0.224772 0.487041 -10.9627 10.6589 2003 vap (x,s2,<<,>>): 0.230009 0.420027 -4.31180 6.60522 2004 vap (x,s2,<<,>>): 0.196860 0.370951 -6.88193 9.91745 2005 vap (x,s2,<<,>>): 0.294053 0.485357 -10.8553 7.02645 2006 vap (x,s2,<<,>>): 0.193683 0.411812 -6.28115 9.19209

IDL>

VAP Gridding:

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IDL> quick_interp_tdm2,1901,2006,'vapglo/vap.',1000,gs=0.5,dumpglo='dumpglo',synth_prefix ='vapsyn/vapsyn.',pts_prefix='vaptxt/vap.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2006

IDL>

VAP Gridded Absolutes:

crua6[/cru/cruts/version_3_0/secondaries/vap] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.grid4 Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 4 Right, erm.. off I jolly well go! vap.01.1901.glo (etc) vap.12.2006.glo

VAP Output Files:

uealogin1[/cru/cruts/version_3_0/secondaries/vap] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapabs/vap.YYYY.MM.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.vap.dat

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Now please enter the 3-ch parameter code: vap Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Vapour Pressure Writing: cru_ts_3_00.1901.1910.vap.dat cru_ts_3_00.1901.1910.vap.nc Writing: cru_ts_3_00.1911.1920.vap.dat cru_ts_3_00.1911.1920.vap.nc Writing: cru_ts_3_00.1921.1930.vap.dat cru_ts_3_00.1921.1930.vap.nc Writing: cru_ts_3_00.1931.1940.vap.dat cru_ts_3_00.1931.1940.vap.nc Writing: cru_ts_3_00.1941.1950.vap.dat cru_ts_3_00.1941.1950.vap.nc Writing: cru_ts_3_00.1951.1960.vap.dat cru_ts_3_00.1951.1960.vap.nc Writing: cru_ts_3_00.1961.1970.vap.dat cru_ts_3_00.1961.1970.vap.nc Writing: cru_ts_3_00.1971.1980.vap.dat cru_ts_3_00.1971.1980.vap.nc Writing: cru_ts_3_00.1981.1990.vap.dat cru_ts_3_00.1981.1990.vap.nc Writing: cru_ts_3_00.1991.2000.vap.dat cru_ts_3_00.1991.2000.vap.nc Writing: cru_ts_3_00.2001.2006.vap.dat cru_ts_3_00.2001.2006.vap.nc

FRS synthetics:

IDL> frs_gts,dtr_prefix='../dtrbin/dtrbin',tmp_prefix='../tmpbin/tmpbin',1901,2006,outpre fix='frssyn/frssyn' % Compiled module: RDBIN. % Compiled module: STRIP. filesize= 6220800 gridsize= 0.500000 % Compiled module: DEFXYZ. Calculating synthetic frs normal 1961 filesize= 248832 gridsize= 2.50000 (etc) 1990 filesize= 248832 gridsize= 2.50000 % Compiled module: DAYS. Calculating synthetic anomalies 1901 filesize= 248832 gridsize= 2.50000 (etc) 2006 filesize= 248832 gridsize= 2.50000

IDL>

FRS gridding:

IDL> quick_interp_tdm2,1901,2006,'frsgrid/frsgrid',750,gs=0.5,dumpglo='dumpglo',nostn=1,s ynth_prefix='frssyn/frssyn' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT.

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Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. ls: frssyn/frssyn1901 not found ls: frssyn/frssyn1901.Z not found found: frssyn/frssyn1901.gz % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 ls: frssyn/frssyn1902 not found ls: frssyn/frssyn1902.Z not found found: frssyn/frssyn1902.gz (etc) 2006 ls: frssyn/frssyn2006 not found ls: frssyn/frssyn2006.Z not found found: frssyn/frssyn2006.gz

IDL>

VAP Problems again.. this time it's too high **sigh**

Tim suggested the 'synthfac' parameter in quick_interp_tdm2. The note for it says:

; multi factor to obtain synth file actual values

..so I reckon it should be 0.1 - but was wrong. The note is misleading at best, since the actual code looks like this:

dummygrid=dummygrid/synthfac

..so the factor should be 10:

IDL> quick_interp_tdm2,1901,2006,'vapglo/vap.',1000,gs=0.5,dumpglo='dumpglo',synthfac=10, synth_prefix='vapsyn/vapsyn.',pts_prefix='vaptxt/vap.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/vap] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology.

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Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.grid9 Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 1 Right, erm.. off I jolly well go! vap.1901.01.glo (etc) vap.2006.12.glo

uealogin1[/cru/cruts/version_3_0/secondaries/vap] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapabs/vap.YYYY.MM.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.vap.dat

Now please enter the 3-ch parameter code: vap Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Vapour Pressure Writing: cru_ts_3_00.1901.1910.vap.dat cru_ts_3_00.1901.1910.vap.nc Writing: cru_ts_3_00.1911.1920.vap.dat cru_ts_3_00.1911.1920.vap.nc Writing: cru_ts_3_00.1921.1930.vap.dat cru_ts_3_00.1921.1930.vap.nc Writing: cru_ts_3_00.1931.1940.vap.dat cru_ts_3_00.1931.1940.vap.nc Writing: cru_ts_3_00.1941.1950.vap.dat cru_ts_3_00.1941.1950.vap.nc Writing: cru_ts_3_00.1951.1960.vap.dat cru_ts_3_00.1951.1960.vap.nc Writing: cru_ts_3_00.1961.1970.vap.dat cru_ts_3_00.1961.1970.vap.nc Writing: cru_ts_3_00.1971.1980.vap.dat cru_ts_3_00.1971.1980.vap.nc Writing: cru_ts_3_00.1981.1990.vap.dat cru_ts_3_00.1981.1990.vap.nc Writing: cru_ts_3_00.1991.2000.vap.dat cru_ts_3_00.1991.2000.vap.nc Writing: cru_ts_3_00.2001.2006.vap.dat cru_ts_3_00.2001.2006.vap.nc

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Re-doing FRS as well:

IDL> quick_interp_tdm2,1901,2006,'frsgrid/frsgrid',750,gs=0.5,dumpglo='dumpglo',nostn=1,s ynthfac=10,synth_prefix='frssyn/frssyn' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2006

IDL>

Also re-doing WET/RD0:

IDL> quick_interp_tdm2,1901,2006,'prebin/prebin',450,gs=2.5,dumpbin='dumpbin',binfac=10,p ts_prefix='pretxt/pre.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: MEAN. % Compiled module: MOMENT. % Compiled module: STDDEV. grid 1901 non-zero -0.2703 38.0775 72.6735 cells= 36537 % Compiled module: STRIP. % Compiled module: WRBIN. 1902 grid 1902 non-zero -1.8369 38.8160 105.0312 cells= 37346 (etc) 2006 no stations found in: pretxt/pre.2006.09.txt no stations found in: pretxt/pre.2006.10.txt no stations found in: pretxt/pre.2006.11.txt no stations found in: pretxt/pre.2006.12.txt grid 2006 non-zero 2.2381 38.8997 70.0935 cells= 32663

IDL>

There then followed a produciton run for WET, resulting in unrealistic, banded output. This was tracked down to the sythetic gridder, rd0_gts_tdm.pro, using half-degree normals with a 2.5-degree output. So it was modified to read the 2.5 normals, and

rerun:

IDL> .compile /cru/cruts/version_3_0/BADC_AREA/programs/idl/rd0_gts_tdm.pro % Compiled module: RD0_GTS.

IDL> rd0_gts,1901,2006,1961,1990,outprefix='rd0syn/rd0syn',pre_prefix='../prebin/prebin' Reading precip and rd0 normals

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% Compiled module: RDBIN. % Compiled module: STRIP.
yes filesize= 248832 gridsize= 2.50000 % Compiled module: DEFXYZ.
yes filesize= 248832 gridsize= 2.50000 % Compiled module: DAYS. Calculating synthetic Rd0 normal 1961 filesize= 248832 gridsize= 2.50000 (etc) 1990 filesize= 248832 gridsize= 2.50000 Calculating synthetic anomalies 1901 filesize= 248832 gridsize= 2.50000 % Compiled module: WRBIN. 1902 (etc) 2006 filesize= 248832 gridsize= 2.50000 % Program caused arithmetic error: Floating divide by 0 % Program caused arithmetic error: Floating illegal operand

IDL>

..which is what happened last time. And, again - all synthetics produced, apparently OK. I think it's just the last few empty months of 2006..

IDL> quick_interp_tdm2,1901,2006,'rd0pcglo/rd0pc',450,gs=0.5,dumpglo='dumpglo',synth_pref ix='rd0syn/rd0syn',synthfac=10,pts_prefix='rd0pctxt/rd0pc.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/wet] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim.6190.lan.wet.grid4 Enter the path and stem of the .glo files: rd0pcglo/rd0pc. Enter the starting year: 1901

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Enter the ending year: 2006 Enter the path (if any) for the output files: rd0pcabs Now, CONCENTRATE. Addition or Percentage (A/P)? P Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 3 Right, erm.. off I jolly well go! rd0pcglo/rd0pc.01.1901.glo rd0pcglo/rd0pc.1901.01.glo rd0pc.1901.01.glo (etc) rd0pc.2006.12.glo

uealogin1[/cru/cruts/version_3_0/secondaries/wet] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: rd0pcabs/rd0pc.YYYY.MM.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.wet.dat

Now please enter the 3-ch parameter code: wet Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Rain Days Writing: cru_ts_3_00.1901.1910.wet.dat cru_ts_3_00.1901.1910.wet.nc Writing: cru_ts_3_00.1911.1920.wet.dat cru_ts_3_00.1911.1920.wet.nc Writing: cru_ts_3_00.1921.1930.wet.dat cru_ts_3_00.1921.1930.wet.nc Writing: cru_ts_3_00.1931.1940.wet.dat cru_ts_3_00.1931.1940.wet.nc Writing: cru_ts_3_00.1941.1950.wet.dat cru_ts_3_00.1941.1950.wet.nc Writing: cru_ts_3_00.1951.1960.wet.dat cru_ts_3_00.1951.1960.wet.nc Writing: cru_ts_3_00.1961.1970.wet.dat cru_ts_3_00.1961.1970.wet.nc Writing: cru_ts_3_00.1971.1980.wet.dat cru_ts_3_00.1971.1980.wet.nc Writing: cru_ts_3_00.1981.1990.wet.dat cru_ts_3_00.1981.1990.wet.nc Writing: cru_ts_3_00.1991.2000.wet.dat cru_ts_3_00.1991.2000.wet.nc Writing: cru_ts_3_00.2001.2006.wet.dat cru_ts_3_00.2001.2006.wet.nc

VAP - three stations deleted with unbelievable data:

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6450000 45 942 15 LIBREVILLE/LEON MBA GABON 1979 2007 -999 -999 6451000 208 1148 599 BITAM GABON 1971 2007 -999 -999 6275000 1400 3233 378 ED DUEIM SUDAN 1971 2007 -999 -999

VAP then re-run with new database vap.0804231150.dtb:

crua6[/cru/cruts/version_3_0/secondaries/vap] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required:
.vap > Select the .cts or .dtb file to load: vap.0804231150.dtb > Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 > Select outputs (1=.cts,2=.ann,3=.txt,4=.stn): 3 > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): vap.txt > Select the first,last years AD to save: 1901,2006 > Operating...

> NORMALS MEAN percent STDEV percent > .dtb 907894 45.2 > .cts 35390 1.8 943284 47.0 > PROCESS DECISION percent %of-chk > no lat/lon 105 0.0 0.0 > no normal 1064261 53.0 53.0 > out-of-range 49 0.0 0.0 > accepted 943235 47.0 > Dumping years 1901-2006 to .txt files...

IDL> quick_interp_tdm2,1901,2006,'vapglo/vap.',1000,gs=0.5,dumpglo='dumpglo',synthfac=10, synth_prefix='vapsyn/vapsyn.',pts_prefix='vaptxt/vap.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/vap] ./glo2abs Welcome! This is the GLO2ABS program.

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I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.vap Enter a name for the gridded climatology file: clim.6190.lan.vap.grid2 Enter the path and stem of the .glo files: vapglo/vap. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: vapabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 1 Right, erm.. off I jolly well go! vapglo/vap.01.1901.glo vapglo/vap.1901.01.glo vap.1901.01.glo (etc) vap.2006.12.glo

uealogin1[/cru/cruts/version_3_0/secondaries/vap] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: vapabs/vap.YYYY.NN.glo.abs Enter a gridfile with YYYY for year and MM for month: vapabs/vap.YYYY.MM.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.vap.dat

Now please enter the 3-ch parameter code: vap Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Vapour Pressure Writing: cru_ts_3_00.1901.1910.vap.dat cru_ts_3_00.1901.1910.vap.nc Writing: cru_ts_3_00.1911.1920.vap.dat cru_ts_3_00.1911.1920.vap.nc Writing: cru_ts_3_00.1921.1930.vap.dat cru_ts_3_00.1921.1930.vap.nc Writing: cru_ts_3_00.1931.1940.vap.dat cru_ts_3_00.1931.1940.vap.nc Writing: cru_ts_3_00.1941.1950.vap.dat cru_ts_3_00.1941.1950.vap.nc Writing: cru_ts_3_00.1951.1960.vap.dat cru_ts_3_00.1951.1960.vap.nc Writing: cru_ts_3_00.1961.1970.vap.dat cru_ts_3_00.1961.1970.vap.nc Writing: cru_ts_3_00.1971.1980.vap.dat cru_ts_3_00.1971.1980.vap.nc Writing: cru_ts_3_00.1981.1990.vap.dat cru_ts_3_00.1981.1990.vap.nc

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Writing: cru_ts_3_00.1991.2000.vap.dat cru_ts_3_00.1991.2000.vap.nc Writing: cru_ts_3_00.2001.2006.vap.dat cru_ts_3_00.2001.2006.vap.nc

Next round of problems..

Well, VAP finally looks OK, which is good news.

WET looks better, but variability is still too low. It's complicated by the synthetic elements in combination with percentage anomalies!

I found that I could examine the 'mark New binary files' with Matlab, using (ie):

>> fid = fopen('glo25.rd0.6190','r'); >> [d,c] = fread(fid,inf,'int16'); >> whos Name Size Bytes Class

c 1x1 8 double array d 124416x1 995328 double array fid 1x1 8 double array

Grand total is 124418 elements using 995344 bytes

>> c

c =

124416

>> min(d)

ans =

0

>> max(d)

ans =

303

>> hmean(d)

ans =

123.7939

So we can deduce that the rd0 2.5 degree normals are in days*10. Similarly for the others of interest:

FILE MIN MAX MEAN UNITS glo25.rd0.6190 0 303 106 days*10 glo25.pre.6190 0 391 21 mm? glo.rd0.norm 0 310 124 days*10 glo.pre.norm 0 1244 58 mm clim.6190.lan.wet.grid 0 3090 1018 days*100 clim.6190.lan.pre.grid 0 12430 574 mm*10

My guess is that glo25.pre.6190 has a lower max because the wider coverage of each

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cell is squashing the peaks. So.. an experimental run with half-degree synthetics!

First, generate half-degree binaries for pre:

IDL> quick_interp_tdm2,1901,2006,'prebin05/prebin05.',450,gs=0.5,dumpbin='dumpbin',binfac =10,pts_prefix='pretxt/pre.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: MEAN. % Compiled module: MOMENT. % Compiled module: STDDEV. grid 1901 non-zero -0.0524 38.0853 75.9241 cells= 921834 (etc)

Then re-generate synthetic rd0 on a half-degree grid:

IDL> rd0_gts,1901,2006,1961,1990,outprefix='rd0syn05/rd0syn.',pre_prefix='../prebin05/pre bin05.' Reading precip and rd0 normals
yes filesize= 248832 gridsize= 2.50000
yes filesize= 248832 gridsize= 2.50000 Calculating synthetic Rd0 normal 1961
yes filesize= 6220800 gridsize= 0.500000 (etc.. as before but with appropriately larger numbers) 2006 filesize= 6220800 gridsize= 0.500000 % Program caused arithmetic error: Floating divide by 0 % Program caused arithmetic error: Floating illegal operand

IDL>

Then the gridding, using the new half-degree synthetics:

IDL> quick_interp_tdm2,1901,2006,'rd0pcglo05/rd0pc05.',450,gs=0.5,dumpglo='dumpglo',synth _prefix='rd0syn05/rd0syn.',synthfac=10,pts_prefix='rd0pctxt/rd0pc.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc)

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RIGHT, stop all that.. Tim O has recalculated the 2.5-degree binary normals for PRE (from half degree) and WET (from TS 2.1). So.. time to try out the OTHER synthetic rd0 generator, the one that reads precip anomalies:

IDL> .compile ../../BADC_AREA/programs/idl/rd0_gts_anom.pro % Compiled module: RD0_GTS_ANOM. IDL> .compile ../../BADC_AREA/programs/idl/ % Error opening file. File: ../../BADC_AREA/programs/idl/ No such file or directory IDL> .compile ../../BADC_AREA/programs/idl/rdbin.pro % Compiled module: RDBIN.

IDL> rd0_gts_anom,1901,2006,1961,1990,outprefix='rd0syn/rd0syn.',pre_prefix='../prebin/pr ebin' Reading precip and rd0 normals % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: DAYS. Calculating synthetic rd0 normal % Compiled module: RD0CAL. Calculating synthetic rd0 anomalies

IDL>

IDL> quick_interp_tdm2,1901,2006,'rd0glo/rd0.',450,gs=0.5,dumpglo='dumpglo',synth_prefix= 'rd0syn/rd0syn.',synthfac=10,pts_prefix='rd0pctxt/rd0pc.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/wet] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim.6190.lan.wet.grid7 Enter the path and stem of the .glo files: rd0glo/rd0. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: rd0abs/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits

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Choose: 3 Right, erm.. off I jolly well go! rd0glo/rd0.01.1901.glo rd0glo/rd0.1901.01.glo rd0.01.1901.glo (etc) rd0.12.2006.glo

uealogin1[/cru/cruts/version_3_0/secondaries/wet] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: rd0abs/rd0.YYYY.MM.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.wet.dat

Now please enter the 3-ch parameter code: wet Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Wet Days Writing: cru_ts_3_00.1901.1910.wet.dat cru_ts_3_00.1901.1910.wet.nc Writing: cru_ts_3_00.1911.1920.wet.dat cru_ts_3_00.1911.1920.wet.nc Writing: cru_ts_3_00.1921.1930.wet.dat cru_ts_3_00.1921.1930.wet.nc Writing: cru_ts_3_00.1931.1940.wet.dat cru_ts_3_00.1931.1940.wet.nc Writing: cru_ts_3_00.1941.1950.wet.dat cru_ts_3_00.1941.1950.wet.nc Writing: cru_ts_3_00.1951.1960.wet.dat cru_ts_3_00.1951.1960.wet.nc Writing: cru_ts_3_00.1961.1970.wet.dat cru_ts_3_00.1961.1970.wet.nc Writing: cru_ts_3_00.1971.1980.wet.dat cru_ts_3_00.1971.1980.wet.nc Writing: cru_ts_3_00.1981.1990.wet.dat cru_ts_3_00.1981.1990.wet.nc Writing: cru_ts_3_00.1991.2000.wet.dat cru_ts_3_00.1991.2000.wet.nc Writing: cru_ts_3_00.2001.2006.wet.dat cru_ts_3_00.2001.2006.wet.nc

Wrong again! The saga continues.. actually I'm beginning to wonder if it'll still be going when I JOIN SAGA.

This time, the 'real' areas have variability 10x too low, and the 'synthetic' areas have variability sqrt(10) too low. The latter can be explained by the binary precip being in %age anoms *10, so rd0_gts_anom.pro modified to divide by 1000 when calculating (instead of 100). Example (from the normals calculation):

Before: pregrd(nland)=((pregrd(nland)/100.0)+1.0)*prenorm(nland) ; make pre anom into abs

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After: pregrd(nland)=((pregrd(nland)/1000.0)+1.0)*prenorm(nland) ; make pre anom into abs (mm)

'Synthfac=10' will also not be needed in the final gridding, that should take care of the 'real' area variability.

So..

IDL> .compile ../../BADC_AREA/programs/idl/rd0_gts_anom.pro % Compiled module: RD0_GTS_ANOM. IDL> .compile ../../BADC_AREA/programs/idl/rdbin.pro % Compiled module: RDBIN.

IDL> rd0_gts_anom,1901,2006,1961,1990,outprefix='rd0syn/rd0syn.',pre_prefix='../prebin/pr ebin' Reading precip and rd0 normals % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: DAYS. Calculating synthetic rd0 normal % Compiled module: RD0CAL. Calculating synthetic rd0 anomalies % Compiled module: WRBIN.

IDL>

IDL> quick_interp_tdm2,1901,2006,'rd0glo/rd0.',450,gs=0.5,dumpglo='dumpglo',synth_prefix= 'rd0syn/rd0syn.',pts_prefix='rd0pctxt/rd0pc.'

<snip!>

That didn't work, real areas 10x too small (synth areas OK though). So..

IDL> quick_interp_tdm2,1901,2006,'rd0glo/rd0.',anomfac=10,450,gs=0.5,dumpglo='dumpglo',sy nth_prefix='rd0syn/rd0syn.',pts_prefix='rd0pctxt/rd0pc.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1901 % Compiled module: RDBIN. % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1902 (etc) 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/wet] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim.6190.lan.wet.grid9 Enter the path and stem of the .glo files: rd0glo/rd0. Enter the starting year: 1901

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Enter the ending year: 2006 Enter the path (if any) for the output files: rd0abs/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 3 Right, erm.. off I jolly well go! rd0glo/rd0.01.1901.glo rd0glo/rd0.1901.01.glo rd0.1901.01.glo (etc) rd0.2006.12.glo

uealogin1[/cru/cruts/version_3_0/secondaries/wet] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: rd0abs/rd0.YYYY.MM.glo.abs Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.wet.dat

Now please enter the 3-ch parameter code: wet Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Rain Days Writing: cru_ts_3_00.1901.1910.wet.dat cru_ts_3_00.1901.1910.wet.nc Writing: cru_ts_3_00.1911.1920.wet.dat cru_ts_3_00.1911.1920.wet.nc Writing: cru_ts_3_00.1921.1930.wet.dat cru_ts_3_00.1921.1930.wet.nc Writing: cru_ts_3_00.1931.1940.wet.dat cru_ts_3_00.1931.1940.wet.nc Writing: cru_ts_3_00.1941.1950.wet.dat cru_ts_3_00.1941.1950.wet.nc Writing: cru_ts_3_00.1951.1960.wet.dat cru_ts_3_00.1951.1960.wet.nc Writing: cru_ts_3_00.1961.1970.wet.dat cru_ts_3_00.1961.1970.wet.nc Writing: cru_ts_3_00.1971.1980.wet.dat cru_ts_3_00.1971.1980.wet.nc Writing: cru_ts_3_00.1981.1990.wet.dat cru_ts_3_00.1981.1990.wet.nc Writing: cru_ts_3_00.1991.2000.wet.dat cru_ts_3_00.1991.2000.wet.nc Writing: cru_ts_3_00.2001.2006.wet.dat cru_ts_3_00.2001.2006.wet.nc

Hmmm.. still some problems. In several areas, including a swathe of Russia, the mean values drop around 1991 - just when MCDW comes in.

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Targeted the area and found several candidates:

uealogin1[/cru/cruts/version_3_0/db/rd0] ./getllstations

GETCELLSTATIONS

Extracts stations from a CRU TS Database.

Please define the bounding box in tenths of a degree, ie 55 degrees North would be 550: Southern Edge (-900 to 899): 570 Northern Edge ( 570 to 900): 630 Western Edge (-1800 to 1799): 900 Eastern Edge ( 900 to 1800): 1050

Enter the CRU TS Database file: wet.0710161148.dtb

And finally, an output filename: wet.0710161148.57_63N.90_105E.dat

Done. Found 7 matching stations out of 6143

2388400 6160 9000 63 BOR RUSSIA (ASIA) 1936 2007 -999 -999 2389100 6167 9637 261 BAJKIT RUSSIA (ASIA) 1936 2007 -999 -999 2490800 6033 10227 260 VANAVARA RUSSIA (ASIA) 1936 2007 -999 -999 2926300 5845 9215 78 JENISEJSK RUSSIA (ASIA) 1936 2007 -999 -999 2928200 5842 9740 134 BOGUCANY RUSSIA (ASIA) 1936 2007 -999 -999 2398600 6047 9302 521 SEVERO-JENISEJSK RUSSIA (ASIA) 2004 2007 -999 0 2937900 5720 9488 147 TASEJEVA RIVER RUSSIA (ASIA) 2004 2007 -999 0

The last two are too short to have any meaning. The second and third have missing data over the entire period of concern. That leaves BOR, JENISEJSK and BOGUCANY, the latter of which we'll examine closer. Here's the series, lifted directly from wet.0710161148.dtb:

2928200 5842 9740 134 BOGUCANY RUSSIA (ASIA) 1936 2007 -999 -999 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1936 1200 500 800 500 1100 1400 900 1200 400 1300 1600 1700 1937 1400 1000 1300 400 800 1200 1100 1300 1800 2000 1300 1000 1938 1100 1100 500 1000 1000 800 600 1300 1400 2300 1800 1500 1939 1200 700 900 700 1500 1300 1100 1600 800 1900 1800 1900 1940 800 1500 500 1400 1400 1300 1400 1600 1500 2400 2000 1600 1941 2200 1500 1800 1100 1400 1300 1700 1600 900 1600 1900 1700 1942 1100 1600 1500 1100 1000 1900 600 1600 500 2000 1700 1900 1943 1000 1000 800 0 1500 1100 1000 1100 1200 2000 2100 2100 1944 1300 1100 1100 400 1400 1200-9999 1100 1500 1600 2000 2900 1945 2100 900 1000-9999 900 2100 1700 1200 900 1700 1900 1600 1946 2400 1400 1500 1000 1800 1700 1300 1800 700 1700 2200 2300 1947 1400 1300 1700 1500 900 600 1000 700 2000 600 1300 1200 1948 1700 1100 900 1100 1100 1100 1100 1900 1400 1300 1200 1500 1949 2100 1100 1000 700 1900 1600 800 1500 1600 1100 1600 1200 1950 1500 1100 800 800 1400 600 600 800 1600 1500 1900 2500 1951 1100 1200 1400 500 1000 1400 1200 2000 1100 1400 1100 1400 1952 1500 1200 1100 700 1600 1100 1300 1200 1200 2100 1200 1300

Page 199HARRY_READ_ME

1953 1200 600 1300 700 800 800 1100 1100 1400 2100 1500 2100 1954 1900 1300 1100 1100 800 400 1700 1100 1300 1800 2000 1500 1955 1100 1600 1100 1000 1500 1400 1000 1100 1500 1400 1600 2000 1956 1800 1200 1000 1200 800 900 1900 800 1100 2100 800 1200 1957 1200 300 700 1200 1300 900 1300 1200 1700 1700 1900 2200 1958 2000 1000 1200 900 1400 1100 800 1000 1200 2000 2200 1900 1959 2100 1000 900 1400 1800 700 1600 1300 1300 1600 2300 1900 1960 1700 1800 1000 1600 1000 1500 1400 1500 2300 1100 1900 1200 1961 1700 1400 600 500 1000 1400 1400 1700 1800 1500 1600 1800 1962 1200 1300 700 700 800 1000 1300 1200 1200 900 2100 2000 1963 900 600 700 800 1300 1000 1300 1300 1400 1300 1100 2100 1964 1000 800 1200 500 800 1400 1000 800 700 1900 1000 2300 1965 1500 1100 800 500 1000 1100 800 1500 1900 1200 1600 1400 1966 2100 2100 1400 1000 1800 1200 1000 1000 1200 2100 2700 2400 1967 1800 1700 900 900 1200 900 1100 1100 800 1100 1300 1700 1968 1000 1500 800 900 800 1700 600 1200 1600 800 2200 1400 1969 1400 1500 1000 1500 1300 1300 1100 1100 1400 1200 1500 1700 1970 2000 1300 200 1000 1100 900 1100 1700 900 1500 1500 1800 1971 2100 900 1300 700 1200 500 1000 1600 2000 1400 1600 1600 1972 1600 1600 800 500 1400 1200 600 1700 1600 1500 2400 2300 1973 1400 1200 1600 1600 1200 900 1000 1400 600 800 1700 1800 1974 1800 1400 900 1100 1500 1400 1000 1500 1500 1700 2100 2200 1975 2600 1500 1100 1000 1200 1200 1100 1700 1300 1200 2200 1200 1976 1300 1400 1000 600 1300 600 700 1300 1800 1900 1900 1800 1977 1800 1100 2000 900 1400 1400 900 1700 1000 1600 2500 1600 1978 2500 1100 1000 1300 900 1400 1100 1300 1300 1500 1600 2000 1979 2100 1700 1900 1200 1200 600 600 1500 1000 2200 2400 1500 1980 1700 1100 600 700 900 1100 1300 800 1300 1500 2100 2200 1981 1600 1800 1100 1200 1300 700 1100 1200 1200 1300 1300 1800 1982 1300 1000 1500 800 1200 1300 1700 1400 1700 1800 2800 2500 1983 2200 1200 1400 1700 1200 1000 700 1600 1200 1200 1900 2400 1984 1400 1900 1000 800 1300 700 700 200 400 1500 2400 2100 1985-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1986-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1987-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1988-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1989-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1990-9999-9999-9999-9999 1000 1100 300 700 1000 700 800 400 1991-9999 400 400 600 800 900 1200 600 800 700 300 700 1992 600 100 200 800 500-9999-9999 700 500 200 1000 600 1993 100 200 200 200 700 1300 100 1100 900 800 300 900 1994 700 200 300 500 1000 700 300 600 1400 900 300 900 1995 900 900 600 900 500 1100 800 0 1000 100 1100 400 1996 600 100 300 1000 600 300 200 1100 1100 600 1000 1500 1997 700 500 600 400 600 1200 500 1500 700 1100 900 1000 1998 700 500 0 1000 1100 1000-9999 900 1300 1500 900 1600 1999 900 400 200 700 200 900 700 900 600 1000 800 700 2000 400 700 600 500 1400 1000 700 600 900 1000 900 1200 2001 400 700 700 300 1100 1000 1300 400 1000 900 900 1000 2002 800 1100 600 600 400 1500-9999 1100 900 700 1100 500 2003 800 800 400 600 500 200 400 900 1200 900 1100 500 2004 500 900 1000 600 800 900 800 1000 1500 1200 900 400 2005 700 300 100 900 600 1500 900 600 900 1600 800 500 2006 600 600 900 400 700 500 500 1000 900 1100 800 1300 2007 500 1000 900 200 1200 900 700-9999-9999-9999-9999-9999

You can see that the data after 1990 are for some months significantly lower than the period before.. which would be the period the normals would be based on! I used Matlab to calculate the normals for this series:

6190 1667 1342 1063 933 1172 1080 980 1288 1272 1412 1852 1840

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I then look in the 1995 anomaly files, rd0pctxt/rd0pc.1995.[01-12].txt.

Tabulated process for 1995:

raw norm anom 900 1666.7 -4.60 900 1341.7 -3.29 600 1062.5 -4.35 900 933.33 -0.36 500 1172 -5.73 1100 1080 0.19 800 980 -1.84 0 1288 -10.00 1000 1272 -2.14 100 1412 -9.29 1100 1852 -4.06 400 1840 -7.83

They aren't percentage anomalies! They are percentage anomalies /10. This could explain why the real data areas had variability 10x too low. BUT it shouldn't be - they should be regular percentage anomalies! This whole process is too convoluted and created myriad problems of this kind. I really think we should change it.

Back on the case. I need to find where the post-1990 data came from for these three stations. I already know the geneaology of the database:

wet.0311061611.dtb

+ rdy.0709111032.dtb (MCDW composite)

+ rdy.0710151817.dtb (CLIMAT composite with metadata added)

V
V wet.0710161148.dtb

I was going to do further backtracing, but it's been revealed that the same issues were in 2.1 - meaning that I didn't add the duff data. The suggested way forward is to not use any observations after 1989, but to allow synthetics to take over. I'm not keen on this approach as it's likely (imo) to introduce visible jumps at 1990, since we're effectively introducing a change of data source just after calculating the normals. My compromise is to try it - but to also try a straight derivation from half-degree synthetics.

So, first, we need synthetic-only from 1990 onwards, that can be married with the existing glos from pre-1990.

Actually, we might as well produce a full series of gridded syn-only rd0. Hell, we can do both options in one go!

No point in using the final gridding routine, rd0_gts_anom can produce glo files itself, let's give it a go.

Well - not straightforward. rd0_gts_anom.pro is quite resistant to the idea that it might produce half-degree synthetics, to the point where I'm really not sure what's left to modify! Eventually found it.. the .glo saving

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routine takes a second argument which is a code for the grid size. Because just giving it the grid size just wouldn't be the same, would it? Here it is:

SaveGlo,23,rd0month,CallFile=Savefile,CallTitle=SaveTitle

Now that 23 is the key, but you have to look in quick_interp_tdm2.pro to decode it:

if (gs[0] eq 0.5) then SaveGrid=12 if (gs[0] eq 2.5) then SaveGrid=22 if (gs[0] eq 5.0) then SaveGrid=23

So actually, this was saving with a gridsize of 5 degrees! Disquietingly, this isn't born out by the file sizes, but we'll gloss over that. So, with '23' changed to '12', we have rd0_gts_anom_05.pro.

Produced half-degree synthetic-only WET:

IDL> .compile ../../BADC_AREA/programs/idl/rd0_gts_anom_05.pro % Compiled module: RD0_GTS_ANOM. IDL> .compile ../../BADC_AREA/programs/idl/rdbin.pro % Compiled module: RDBIN.

IDL> rd0_gts_anom,1901,2006,1961,1990,outprefix='rd0syn05glo/rd0syn05.',pre_prefix='../pr ebin05/prebin05.' Reading precip and rd0 normals % Compiled module: STRIP. % Compiled module: DEFXYZ. % Compiled module: DAYS. Calculating synthetic rd0 normal % Compiled module: RD0CAL. Calculating synthetic rd0 anomalies % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL.

IDL>

crua6[/cru/cruts/version_3_0/secondaries/wet] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.wet Enter a name for the gridded climatology file: clim.6190.lan.wet.grid14 Enter the path and stem of the .glo files: rd0syn05glo/rd0syn05. Enter the starting year: 1901 Enter the ending year: 2006 Enter the path (if any) for the output files: rd0syn05abs/ Now, CONCENTRATE. Addition or Percentage (A/P)? P Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 3 Right, erm.. off I jolly well go! rd0syn05glo/rd0syn05.01.1901.glo rd0syn05glo/rd0syn05.1901.01.glo rd0syn05.1901.01.glo (etc) rd0syn05.2006.12.glo

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There was then some copying around of decades' worth chunks of .abs files, to make a set with obs/syn to 1989 and syn from 1990 onwards. Then:

uealogin1[/cru/cruts/version_3_0/secondaries/wet] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: rd0abs_mixed/rd0.YYYY.MM.glo.abs.gz Enter Start Year: 1901 Enter Start Month: 01 Enter End Year: 2006

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.wet.dat

Now please enter the 3-ch parameter code: wet Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Rain Days synth 1990 on Writing: cru_ts_3_00.1901.1910.wet.dat cru_ts_3_00.1901.1910.wet.nc Writing: cru_ts_3_00.1911.1920.wet.dat cru_ts_3_00.1911.1920.wet.nc Writing: cru_ts_3_00.1921.1930.wet.dat cru_ts_3_00.1921.1930.wet.nc Writing: cru_ts_3_00.1931.1940.wet.dat cru_ts_3_00.1931.1940.wet.nc Writing: cru_ts_3_00.1941.1950.wet.dat cru_ts_3_00.1941.1950.wet.nc Writing: cru_ts_3_00.1951.1960.wet.dat cru_ts_3_00.1951.1960.wet.nc Writing: cru_ts_3_00.1961.1970.wet.dat cru_ts_3_00.1961.1970.wet.nc Writing: cru_ts_3_00.1971.1980.wet.dat cru_ts_3_00.1971.1980.wet.nc Writing: cru_ts_3_00.1981.1990.wet.dat cru_ts_3_00.1981.1990.wet.nc Writing: cru_ts_3_00.1991.2000.wet.dat cru_ts_3_00.1991.2000.wet.nc Writing: cru_ts_3_00.2001.2006.wet.dat cru_ts_3_00.2001.2006.wet.nc

This is the dataset we're going with.

Now, we're on to STATISTICS. Specifically, the ones needed for the paper. They include:

Cell coverage - the percentage of cells in each region that have stations, are near stations, or are not covered;

Station counts - the number of contributing stations in each region;

Data sources - the observed/synthetic split for secondary parameters.

All statistics will be required at a yearly timestep and broken down by region (ie, Europe, Africa, Asia..).

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Cell coverage would ideally come from the IDL gridder - however, as we know, the approach of quick_interp_tdm2.pro does not lend itself to revealing such information! The best solution will be to use the paired cell/cdd station counts, as produced by stncounts.for.

Station counts should be straightforward to derive from the anomaly files (.txt), as output by anomdtb.f90. This, however, will only work for Primary parameters, since Secondaries are driven from synthetic data as well. Further, the synthetic element in this is usually at 2.5 degrees, so a direct relationship with half-degree coverage will be hard to establish.

Data sources will not be easy (see Station counts above). One approach could be to analyse the anomaly files for the Primary parameter(s), and make the assumption that their half-degree coverage will carry through (via the 2.5-degree synthetic stage and the gridding) to the final gridded data.

Actually, I think the most logical approach is to produce secondary station files that just record the observed contributions (as opposed to the derived ones). Users will be free to use these in tandem with the appropriate primary counts, which they can assume will have 'contributed' to the unfilled cells but to a less reliable extent (both because of the indirect derivation and the lower resolution).

So - about time we had a drains-up on the update procedures. I've already established a logical hierarchy:

/cru/cruts/final_structure /cru/cruts/final_structure/database * Repository for databases, might need subdirs /cru/cruts/final_structure/incoming /cru/cruts/final_structure/incoming/BOM /cru/cruts/final_structure/incoming/CLIMAT /cru/cruts/final_structure/incoming/MCDW /cru/cruts/final_structure/incoming/other /cru/cruts/final_structure/primary /cru/cruts/final_structure/primary/tmp /cru/cruts/final_structure/primary/tmp/txt /cru/cruts/final_structure/primary/tmp/glo /cru/cruts/final_structure/primary/tmp/abs /cru/cruts/final_structure/primary/tmp/stn /cru/cruts/final_structure/primary/tmp/stn/cdd0 /cru/cruts/final_structure/primary/tmp/stn/cddn /cru/cruts/final_structure/primary/pre /cru/cruts/final_structure/primary/pre/txt /cru/cruts/final_structure/primary/pre/glo /cru/cruts/final_structure/primary/pre/abs /cru/cruts/final_structure/primary/pre/stn /cru/cruts/final_structure/primary/pre/stn/cdd0 /cru/cruts/final_structure/primary/pre/stn/cddn /cru/cruts/final_structure/primary/tmn /cru/cruts/final_structure/primary/tmn/txt /cru/cruts/final_structure/primary/tmn/glo /cru/cruts/final_structure/primary/tmn/abs /cru/cruts/final_structure/primary/tmn/stn /cru/cruts/final_structure/primary/tmn/stn/cdd0 /cru/cruts/final_structure/primary/tmn/stn/cddn /cru/cruts/final_structure/primary/tmx

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/cru/cruts/final_structure/primary/tmx/txt /cru/cruts/final_structure/primary/tmx/glo /cru/cruts/final_structure/primary/tmx/abs /cru/cruts/final_structure/primary/tmx/stn /cru/cruts/final_structure/primary/tmx/stn/cdd0 /cru/cruts/final_structure/primary/tmx/stn/cddn /cru/cruts/final_structure/primary/dtr /cru/cruts/final_structure/primary/dtr/txt /cru/cruts/final_structure/primary/dtr/glo /cru/cruts/final_structure/primary/dtr/abs /cru/cruts/final_structure/primary/dtr/stn /cru/cruts/final_structure/primary/dtr/stn/cdd0 /cru/cruts/final_structure/primary/dtr/stn/cddn /cru/cruts/final_structure/secondary /cru/cruts/final_structure/secondary/vap /cru/cruts/final_structure/secondary/vap/syn /cru/cruts/final_structure/secondary/vap/txt /cru/cruts/final_structure/secondary/vap/glo /cru/cruts/final_structure/secondary/vap/abs /cru/cruts/final_structure/secondary/vap/stn /cru/cruts/final_structure/secondary/vap/stn/observed_only /cru/cruts/final_structure/secondary/vap/stn/observed_only/cdd0 /cru/cruts/final_structure/secondary/vap/stn/observed_only/cddn /cru/cruts/final_structure/secondary/vap/stn/all * might not do these /cru/cruts/final_structure/secondary/vap/stn/all/cdd0 /cru/cruts/final_structure/secondary/vap/stn/all/cddn /cru/cruts/final_structure/secondary/wet /cru/cruts/final_structure/secondary/wet/syn /cru/cruts/final_structure/secondary/wet/txt /cru/cruts/final_structure/secondary/wet/glo /cru/cruts/final_structure/secondary/wet/abs /cru/cruts/final_structure/secondary/wet/stn /cru/cruts/final_structure/secondary/wet/stn/observed_only /cru/cruts/final_structure/secondary/wet/stn/observed_only/cdd0 /cru/cruts/final_structure/secondary/wet/stn/observed_only/cddn /cru/cruts/final_structure/secondary/wet/stn/all * might not do these /cru/cruts/final_structure/secondary/wet/stn/all/cdd0 /cru/cruts/final_structure/secondary/wet/stn/all/cddn /cru/cruts/final_structure/secondary/frs /cru/cruts/final_structure/secondary/frs/syn /cru/cruts/final_structure/secondary/frs/txt /cru/cruts/final_structure/secondary/frs/glo /cru/cruts/final_structure/secondary/frs/abs /cru/cruts/final_structure/secondary/frs/stn /cru/cruts/final_structure/secondary/frs/stn/observed_only /cru/cruts/final_structure/secondary/frs/stn/observed_only/cdd0 /cru/cruts/final_structure/secondary/frs/stn/observed_only/cddn /cru/cruts/final_structure/secondary/frs/stn/all * might not do these /cru/cruts/final_structure/secondary/frs/stn/all/cdd0 /cru/cruts/final_structure/secondary/frs/stn/all/cddn /cru/cruts/final_structure/secondary/cld /cru/cruts/final_structure/secondary/cld/syn /cru/cruts/final_structure/secondary/cld/txt /cru/cruts/final_structure/secondary/cld/glo /cru/cruts/final_structure/secondary/cld/abs /cru/cruts/final_structure/secondary/cld/stn /cru/cruts/final_structure/secondary/cld/stn/observed_only /cru/cruts/final_structure/secondary/cld/stn/observed_only/cdd0 /cru/cruts/final_structure/secondary/cld/stn/observed_only/cddn /cru/cruts/final_structure/secondary/cld/stn/all * might not

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do these /cru/cruts/final_structure/secondary/cld/stn/all/cdd0 /cru/cruts/final_structure/secondary/cld/stn/all/cddn /cru/cruts/final_structure/static /cru/cruts/final_structure/static/climatology /cru/cruts/final_structure/static/mask

Then, there's the list of procedures (which probably need checking):

* Add MCDW Updates mcdw2cru (interactive) newmergedb (per parameter, interactive) * Add CLIMAT Updates climat2cru (interactive) newmergedb (per parameter, interactive) * Add BOM Updates au2cru (unfinished, interactive, should do whole job) * Regenerate DTR Database tmnx2dtr (interactive) * Produce Primary Parameters (TMP, TMN, TMX, DTR, PRE) anomdtb (per parameter, interactive) quick_interp_tdm2 (per parameter) glo2abs (per parameter, interactive) makegrids (per parameter, interactive) * Prepare Binary Grids (TMP, DTR, PRE) for Synthetics quick_interp_tdm2 (per parameter) * Produce Secondary Parameter (FRS, uses TMP,DTR) frs_gts_tdm quick_interp_tdm2 glo2abs (interactive) makegrids (interactive) * Produce Secondary Parameter (VAP, uses TMP,DTR) vap_gts_anom anomdtb (interactive) quick_interp_tdm2 glo2abs (interactive) makegrids (interactive) * Produce Secondary Parameter (WET/RD0, uses PRE) rd0_gts_anom anomdtb (interactive) quick_interp_tdm2 glo2abs (interactive) makegrids (interactive)

Now, in terms of methodology, we obviously need this to be portable. So either a system parameter or a text file with local info is going to be needed. Since reading system parameters is less than easy (syntax differs between platforms) a text file might be the way forward. At a pinch, all it would need to contain would be the root to the hierarchy (ie, '/cru/cruts/final_structure/' in the above example). Might be an idea to provide a sandbox for users to compile into.. and a file of compile lines? I wonder how far off I am from a makefile? That would help with the frightening anomdtb.f linkages. Tried 'make' with anomdtb and it doesn't automatically find the includes, even when they're in the same directory!

I guess I need to finish the fortran gridder program. That would allow steamlining. Notes on that work are mainly in the file 'gridder.sandpit'. Suffice to say, it works :-) Needs tweaking, and

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a few philosophical questions resolving, but apart from that..

So, you release a dataset that people have been clamouring for, and the buggers only

start using it! And finding problems. For instance:

<QUOTE> Hi Tim (good start! -ed)

I realise you are likely to be very busy at the moment, but we have come across something in the CRU TS 3.0 data set which I hope you can help out with.

We have been looking at the monthly precipitation totals over southern Africa (Angola, to be precise), and have found some rather large differences between precipitation as specified in the TS 2.1 data set, and the new TS 3.0 version. Specifically, April 1967 for the cell 12.75 south, 16.25 east, the monthly total in the TS 2.1 data set is 251mm, whereas in TS 3.0 it is 476mm. The anomaly does not only appear in this cell, but also in a number of neighbouring cells. This is quite a large difference, and the new TS 3.0 value doesn't entirely tie in with what we might have expected from the station-based precip data we have for this

area. Would it be possible for you could have a quick look into this issue?

Many thanks,

Daniel.

-------------------------------------------------------- Dr Daniel Kingston Post Doctoral Research Associate Department of Geography University College London

Gower Street London WC1E 6BT

UK Email d.kingston@ucl.ac.uk Tel. +44 (0)20 7679 0510

<END>

Well, it's a good question! And it took over two weeks to answer. I wrote angola.m, which pretty much established that three local stations had been augmented for 3.0, and that April 1967 was anomalously wet. Lots of non-reporting stations (ie too few years to form normals) also had high values. As part of this, I also wrote angola3.m, which added

two rather interesting plots: the climatology, and the output from the Fortran gridder I'd just completed. This raised a couple of points of interest:

1. The 2.10 output doesn't look like the climatology, despite there being no stations in the area. It ought to have simply relaxed to the clim, instead it's wetter.

2. The gridder output is lower than 3.0, and much lower than the stations!

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I asked Tim and Phil about 1., they couldn't give a definitive opinion. As for 2., their guesses were correct, I needed to mod the distance weighting. As usual, see gridder.sandpit for the full info.

So to CLOUD. For over a year, rumours have been circulating that money had been found to pay somebody for a month to recreate Mark New's coefficients. But it never quite gelled. Now, at last, someone's producing them! Unfortunately.. it's me.

The idea is to derive the coefficients (for the regressing of cloud against DTR) using the published 2.10 data. We'll use 5-degree blocks and years 1951-2002, then produce coefficients for each 5-degree latitude band and month. Finally, we'll interpolate

to get half-degree coefficients. Apparently.

Lots of 'issues'. We need to exclude 'background' stations - those that were relaxed

to the climatology. This is hard to detect because the climatology consists of valid values, so testing for equivalence isn't enough. It might have to be the station files *shudder*.

Using station files was OK, actually. A bigger problem was the inclusion of strings of consecutive, identical values (for cloud and/or dtr). Not sure what the source is, as they are not == to the climatology (ie the anoms are not 0). Discussed with Phil - decided to try excluding any cell with a string like that of >10 values. Cloud only for now. The result of that was, unfortunately, the loss of several output values, ie:

lat band: 19 month: 7 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -36.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -37.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -41.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00

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3.00 -38.00 3.00 -38.00 3.00 -43.00 3.00 -38.00 3.00 -38.00 3.00 -41.00 3.00 -38.00 3.00 -39.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -43.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -44.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00 3.00 -38.00

Results (n= 52): nan nan

As can be seen, neither the dtr (left) nor the cloud (right) look 'sensible', even

as anomalies. Several other months in lat band #19 are either nan or -999 (count=0).

However, if we push the duplicates limit up to, say, 20, we get:

lat band: 19 month: 7 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -53.50 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 13.50 -50.00 17.00 -65.00

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8.50 -42.00 11.50 -49.00 18.00 -71.00

////////////////////

1.00 33.50 1.00 40.00 1.00 32.00 1.00 42.50 1.00 38.00 1.00 38.00 1.00 32.50 1.00 52.50 1.00 44.00 1.00 36.50 1.00 41.00 1.00 30.50 1.00 38.00 1.00 36.00 1.00 38.00 1.00 38.50 1.00 39.00 1.00 31.50 1.00 40.00 1.00 38.00 1.00 31.00 1.00 44.00 1.00 43.00 1.00 37.00 1.00 31.00 1.00 31.00 1.00 30.50

Results (n= 988): 37.59707 -6.05338

So, we can have a proper result, but only by including a load of garbage! In fact, I might print out this cell as an example. Let's see:

limit nvals factor intercept 10 52 nan nan 20 728 -7.68581 33.30551 none 1716 -8.32450 34.28972

Hmm.. also tried just removing duplicate strings (rather than whole cells):

limit nvals factor intercept 10 1160 -6.99748 26.31960

This 'looks' better - not so steep, and the intercept is a shade closer to 0. The Matlab script plotcld.m allows comparison of scatter diagrams, these are fed from example data files manually extracted from the cloudreg.log file after varying the duplicate limit and/or strategy.

Showed Phil - and now sidetracked into producing global mean series from the 3.0 parameters (DTR first).

OK, got cloud working, have to generate it now.. but distracted by starting on the mythical 'Update' program. As usual, it's much more complicated than it seems. So, let's work out the order of events.

Well first, some ground rules: should this be 'dumb'? Should the operator say what they want to happen, and walk away, coming back later to check it worked? Or should

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it be interactive, to the extent of the operator deciding on station matches and so forth? At the moment, the introduction of new data (MCDW, CLIMAT, BOM) is highly interactive, and, though BOM should be fully automatic in the future, the same cannot be said for MCDW and CLIMAT. Hmmm. well I guess there are two possibilities:

1. Operator selects 'interactive' additions. Script proceeds, calling merge programs as necessary, some of which may ask the operator to decide on matches. This could take hours, or even days, depending on the quality of the incoming metadata.

2. Operator selects 'automatic' additions. Script proceeds, calling special versions of the merge programs. These have a fixed threshold of confidence for adding new data to exisitng databases. When the threshold is crossed, the data is not added but stored in a new database, which might of course be later added under option 1. Note that the threshold would be higher than that in 1. that initiates operator involvement.

Is this sufficient? It certainly means more coding, but not a huge amount. In a

worst case scenario (where the operator always chooses '2.'), we still have the unused data updates that can be interactively merged in at any time (even yaesr in the future).

This all avoids the big questions, of course. When do updates happen, and how far back do they go? For instance, let's say there are six-month published updates. So say the full 1901-present files are published yearly, with six-month update files as interims. What happens in any of the following circumstances?

A. Updates for, say, 1965, are available.

B. The data used in the January-to-June update is further updated after publication and is present in the next 'full' release (so that the early Jan-Jun grids differ from those in the 1901-present publication).

(in both A. and B., it would usually be MCDW updates that carried retrospective data, this is marked as 'OVERDUE').

Luckily, this isn't really up to me. Or.. is it? If the operator specifies a time period to update, it ought to warn if it finds earlier updates in those files. So further mods to mcdw2cruauto are required.. its results file must list extras. Or - ooh! How about a SECOND output database for the MCDW updates, containing just the OVERDUE stuff?

Back.. think.. even more complicated. My head hurts. No, it actually does. And I ought to be on my way home. But look, we create a new master database (for each parameter) every time we update, don't we? What we ought to do is provide a log file for each new database, identifying which data have been added. Oh, God. OK, let's go..

NEW DATA PROCESS

1. Ops runs 'Update', and chooses 'New Data'.

2. Ops selects MCDW, CLIMAT, and/or BOM data and gives update dates

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3. Ops selects 'interactive' or 'automatic' database merging.

4. Update checks source files are present and initiates conversion to CRU format.

5. Update runs the merging program to join the new data to the existing databases, creating new databases. If data for previous periods is included in the update files, it will be included.

5a. If Ops selected 'automatic', merging program asks for decisions on 'difficult' matches. These are all logged of course.

6. Merge program creates log of changes between old databases and new ones, inc. source of the data.

UPDATE PROCESS

1. Ops runs 'Update', and chooses 'Update'. Yes, I know.

2. Ops gives parameter(s) and time period to update.

3. Ops specifies six-month interim or full update.

4. Update provides candidate databases for the update, Ops chooses.

5. Update runs the anomaly and gridding programs for the specified period.

Note. The following system command will find the number of stations reporting in a given year from a given database.

grep '^2006 ' tmp/tmp.0710011359.dtb | grep -v '\-9999\-9999\-9999\-9999\-9999\-9999\-9999\-9999\-9999\-9999\-9999\-9999' | wc -l

Discovered ('remembered' would be better; sadly I didn't) that I never got round to writing a BOM-to-CRU converter. It got overtaken by the drastic need to get the tmin and tmax databases synchronised (see above, somewhere). There was a barely-started thing, so I cannibalised it for bom2cruauto.for, which eventually worked. In fact, it was a good entry into the fraught world of automatic, script-fed programs.

Got bom2cruauto.for working, then climat2cruauto.for and mcdw2cruauto.for in quick succession (the latter two having their output databases compared successfully with those generated in Nov 2007).

Next, I suppose it's the next in the sequence: mergedb. This is where I'm anxious: I want it all to be plain sailing and automatic, but I don't think there's any practical way to obviate the operator from the need to make judgements on the possible mapping of stations.

---
Back to get CLD sorted out. Need a break from the updater! Though much the same difficulties, trying to work out the process (it's anything but straightforward for cloud, seeing as the incoming updates are in Sun Hours, and we have to apply our own regressions to DTR). Knocked up a subroutine, 'sh2cp', to convert sun hours to cloud percentage on the fly, and added it to mcdw2cruauto and climat2cruauto. Of

course, one of the problems is that you need a latitude value to perform the conversion - so

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the CLIMAT bulletins lose the value if they can't be matched in the WMO list! Not much I can do about that, and let's face it those stations are going to end up as 'new' stations with no possibility of a 61-90 normal.

So.. using the new converters (which are built to be driven by the update program),

I set about converting MCDW and CLIMAT bulletins:

uealogin1[/cru/cruts/version_3_0/update_top] ./mcdw2cruauto uealogin1[/cru/cruts/version_3_0/update_top] cat results/results.0901101032/mcdw.0901101032.res

OK uealogin1[/cru/cruts/version_3_0/update_top]

uealogin1[/cru/cruts/version_3_0/update_top] ./climat2cruauto uealogin1[/cru/cruts/version_3_0/update_top] cat results/results.0901101032/climat.0901101032.res

OK uealogin1[/cru/cruts/version_3_0/update_top]

Gotta love silent running :=)

The output cld databases both look OK, and pretty much equivalent except that MCDW goes back further (to 1994). CLIMAT is 2000 onwards because that's what's on Phil Brohan's website.

Now we have to merge. This is where it gets hairy.

Looking at existing cld databases, we have the originals (cld.0301081434.dtb and cld.0312181428.dtb), and the 2007 version (cld.0711272230.dtb). Looking back through the notes, this was the product of processing the MCDW and CLIMAT bulletins into sun hours databases (sun.0711272156.dtb and sun.0711272219.dtb respectiverly), then merging those to form sun.0711272225.dtb, then converting to cloud using Hsp2cld.for, giving us cld.0711272230.dtb. So the new cloud databases I've just produced should be, if not identical, very similar? Oh, dear. There is a passing similarity, though this seems to break down in Winter. I don't have time to do detailed comparisons, of course, so we'll just run with the new one. After all, I have tested the conversion for the latest programs, I'm not sure how much testing was done last time.

The procedure last time - that is, when I was trying to re-produce TS 2.10, we have no idea what the procedure was for its initial production! - was to incorporate the sun percent data from the bulletins into the existing sun percent db (spc.0312221624.dtb). The trouble is, the existing cloud dbs are bigger. They stop at 1996, but that's no problem, since we have MCDW from then.

228936 cld.0301081434.dtb 104448 cld.0312181428.dtb 111989 combo.cld.dtb 57395 spc.0301201628.dtb 51551 spc.0312221624.dtb 51551 spc.94-00.0312221624.dtb

So, how about merging our new MCDW cloud database into cld.0312181428.dtb, then merging the CLIMAT one into that? The logic here is that the cloud must be from observations, because the sun databases are much smaller. Well, the ones we know about! It might be worth checking the station numbers for each year though. Unfortunately, we don't

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have a lot of luck merging MCDW updates into the Dec 2003 CLD database:

uealogin1[/cru/cruts/version_3_0/db/cld] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: cld.0312181428.dtb Please enter the Update Database name: mcdw.cld.0901101032.dtb

Reading in both databases.. Master database stations: 3605 Update database stations: 2204

Looking for WMO code matches.. 5 reject(s) from update process 0902101404

Writing cld.0902101404.dtb

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

OUTPUT(S) WRITTEN

New master database: cld.0902101404.dtb

Update database stations: 2204 > Matched with Master stations: 858 (automatically: 858) (by operator: 0) > Added as new Master stations: 1341 > Rejected: 5 Rejects file: mcdw.cld.0901101032.dtb.rejected uealogin1[/cru/cruts/version_3_0/db/cld]

Of course, as we are only generating from 1996 onwards, this probably isn't of much significance. Luckily.

Next, merge CLIMAT into the new database. well of course this is much more satisfactory, because it matches with the MCDW stations:

uealogin1[/cru/cruts/version_3_0/db/cld] ./newmergedb

WELCOME TO THE DATABASE UPDATER

Before we get started, an important question: If you are merging an update - CLIMAT, MCDW, Australian - do you want the quick and dirty approach? This will blindly match on WMO codes alone, ignoring data/metadata checks, and making any unmatched updates into new stations (metadata permitting)?

Enter 'B' for blind merging, or <ret>: B Please enter the Master Database name: cld.0902101404.dtb Please enter the Update Database name: climat.cld.0901101032.dtb

Reading in both databases..

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Master database stations: 4946 Update database stations: 2038

Looking for WMO code matches.. 2 reject(s) from update process 0902101409

Writing cld.0902101409.dtb

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

OUTPUT(S) WRITTEN

New master database: cld.0902101409.dtb

Update database stations: 2038 > Matched with Master stations: 1858 (automatically: 1858) (by operator: 0) > Added as new Master stations: 178 > Rejected: 2 Rejects file: climat.cld.0901101032.dtb.rejected uealogin1[/cru/cruts/version_3_0/db/cld]

So we now have cld.0902101409.dtb, a database consisting of cld.0312181428.dtb, updated first with derived-cloud data from MCDW (1994-2008), then with derived-cloud data from CLIMAT (2000-2008). This should be anomalised, then fed into quick_interp_tdm2.pro along with synthetic, DTR-derived cloud. So that is the next hurdle.

Well, we have the program. And we've played with it, but forgot to c&p those runs into here (well they were only a few days ago!) so here they are now:

crua6[/cru/cruts/version_3_0/secondaries/cld/cldfromdtrtxt] ./dtr2cld Please enter the path/file of the FIRST dtr txt file: /cru/cruts/version_3_0/primaries/dtr/dtrtxt/dtr.1901.01.txt Please enter the path/file of the LAST dtr txt file: /cru/cruts/version_3_0/primaries/dtr/dtrtxt/dtr.2006.12.txt crua6[/cru/cruts/version_3_0/secondaries/cld/cldfromdtrtxt]

Then an experimental IDL gridding using half degree and glo output. It was late at night, I think I had an idea of visualising or comparing.. no such luck.

crua6[/cru/cruts/version_3_0/secondaries/cld] idl IDL Version 5.4 (OSF alpha). (c) 2000, Research Systems, Inc. Installation number: 66286. Licensed for use by: Climatic Research Unit

IDL> quick_interp_tdm2,1995,2006,'cld.',750,gs=0.5,dumpglo='dumpglo',pts_prefix='cldfromd trtxt/cld.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1995 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1996 1997

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1998 1999 2000 2001 2002 2003 2004 2005 2006 no stations found in: cldfromdtrtxt/cld.2006.09.txt no stations found in: cldfromdtrtxt/cld.2006.10.txt no stations found in: cldfromdtrtxt/cld.2006.11.txt no stations found in: cldfromdtrtxt/cld.2006.12.txt

IDL>

So now we need to try that last step again - this time going for 2.5-degree binary outputs, suitable for feeding back into it for the full cloud gridding. Oh, my.

IDL> quick_interp_tdm2,1996,2006,'cldfromdtr25bin/cld.',750,gs=2.5,dumpbin='dumpbin',pts_ prefix='cldfromdtrtxt/cld.'

<output removed as re-done below with CDD=600>

Okay, that's the synthetic binary cloud ready. Now we need to run anomdtb on the 'observed' cloud database:

crua6[/cru/cruts/version_3_0/secondaries/cld] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required (eg, .tmp): .cld > Select the .dtb file to load: cld.0902101409.dtb cld.0902101409.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/secondaries/cld/cld.0902101409.dtb

> Specify the start,end of the normals period: 1961,1990 > Specify the missing percentage permitted: 25 > Data required for a normal: 23 > Specify the no. of stdevs at which to reject data: 3 The output selection is tied to 3 (ungridded anomalies) > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): cldnew.txt > Select the first,last years AD to save: 1996,2006 > Operating... /tmp_mnt/cru-auto/cruts/version_3_0/secondaries/cld/cld.0902101409.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/secondaries/cld/cld.0902101409.dtb

> NORMALS MEAN percent STDEV percent

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> .dtb 0 0.0 > .cts 288328 22.2 288328 22.2 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 1010030 77.8 77.8 > out-of-range 24 0.0 0.0 > accepted 288304 22.2 > Dumping years 1996-2006 to .txt files...

crua6[/cru/cruts/version_3_0/secondaries/cld]

Unfortunately, that isn't working. Too many stations outside the usual normals period (1961-1990). My notes from the last attempt are less than inspiring.. it looks as though we need the program 'normshift.for', and normalise 95-02. So:

crua6[/cru/cruts/version_3_0/secondaries/cld] ./anomdtb

> ***** AnomDTB: converts .dtb to anom .txt for gridding *****

> Enter the suffix of the variable required (eg, .tmp): .cld > Select the .dtb file to load: cld.0902101409.dtb cld.0902101409.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/secondaries/cld/cld.0902101409.dtb

> Specify the start,end of the normals period: 1995,2002 > Specify the missing percentage permitted: 25 > Data required for a normal: 6 > Specify the no. of stdevs at which to reject data: 3 The output selection is tied to 3 (ungridded anomalies) > Check for duplicate stns after anomalising? (0=no,>0=km range) 0 > Select the generic .txt file to save (yy.mm=auto): cldupdate.txt > Select the first,last years AD to save: 1996,2006 > Operating... /tmp_mnt/cru-auto/cruts/version_3_0/secondaries/cld/cld.0902101409.dtb

/tmp_mnt/cru-auto/cruts/version_3_0/secondaries/cld/cld.0902101409.dtb

> NORMALS MEAN percent STDEV percent > .dtb 0 0.0 > .cts 271495 20.9 271495 20.9 > PROCESS DECISION percent %of-chk > no lat/lon 0 0.0 0.0 > no normal 1026863 79.1 79.1 > out-of-range 474 0.0 0.2 > accepted 271021 20.9 > Dumping years 1996-2006 to .txt files...

crua6[/cru/cruts/version_3_0/secondaries/cld]

Hmm.. that's giving us between 670 and 790 stations per month.. not too bad

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I suppose seeing as it's a secondary parameter. Now for normshift, which has already been run (search back in this file), producing clim.9502.to.6190.grid.cld, which is your standard 12-grids-360rx720c, giving the diffs between 1995-2002 normals and 1961-1990 normals. So after gridding we could add these.. except that after gridding we'll have incorporated the DTR_derived synthetic cloud, which is of course based on the 1961-1990 normals as it's derived from DTR!! Arrrrggghh.

So.. {sigh}.. another problem. Well we can't change the updates side, that has to use 1995-2002 normals. But maybe we'll have to adjust the station anomalies, prior to gridding? I don't see an alternative.

Wrote movenorms.for, using the engine of dtr2cld (as it's processing the same kind of files and also needs to map stations to cells). However we quickly hit a problem:

crua6[/cru/cruts/version_3_0/secondaries/cld] ./movenorms

Please enter the adjustment file: clim.9502.to.6190.grid.cld

Please enter a generic source file with MM for month and YYYY for year: cldupdatetxt/cldupdate.YYYY.MM.txt Start YEAR: 1996 Start MONTH: 01 End YEAR: 2006 End MONTH: 12

Please enter a generic destination file with MM for month and YYYY for year: cldupdate6190/cldupdate6190.YYYY.MM.txt

ERROR. Station in sea: File: cldupdate6190/cldupdate6190.1996.01.txt Offending line: 18.54 72.49 11.0 -6.600004305700 Resulting indices ilat,ilon: 218 505 crua6[/cru/cruts/version_3_0/secondaries/cld]

This is a station on the West coast of India; probably Mumbai. Unfortunately, as a coastal station it runs the risk of missing the nearest land cell. The simple movenorms program is about to become less simple.. but was do-able. The log file was empty at the end, indicating that all 'damp' stations had found dry land:

crua6[/cru/cruts/version_3_0/secondaries/cld] ./movenorms

Please enter the adjustment file: clim.9502.to.6190.grid.cld

Please enter a generic source file with MM for month and YYYY for year: cldupdatetxt/cldupdate.YYYY.MM.txt Start YEAR: 1996 Start MONTH: 1 End YEAR: 2006 End MONTH: 12

Please enter a generic destination file with MM for month and YYYY for year: cldupdate6190/cldupdate6190.YYYY.MM.txt crua6[/cru/cruts/version_3_0/secondaries/cld] wc -l movenorms.log 0 movenorms.log crua6[/cru/cruts/version_3_0/secondaries/cld]

So.. now I should be able to do the final gridding of cloud for 1996-2006.

IDL> quick_interp_tdm2,1996,2006,'cloudcomboglo/cld.',750,gs=0.5,dumpglo='dumpglo',synth_

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prefix='cldfromdtr25bin/cld.',pts_prefix='cldupdate6190/cldupdate6190.'

<output removed as re-done below with CDD=600>

crua6[/cru/cruts/version_3_0/secondaries/cld] ./glo2abs

<output removed as re-done below with CDD=600>

uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./makegrids

<output removed as re-done below with CDD=600>

All files look alright. BUT. The NetCDF attributes (which are still bad) do say that the CDD for cloud is 600. If it is, I will eat my screen, because I'll have to do all the gridding ops again :-((( and.. it is :-o

So.. binary re-production:

IDL> quick_interp_tdm2,1996,2006,'cldfromdtr25bin/cld.',600,gs=2.5,dumpbin='dumpbin',pts_ prefix='cldfromdtrtxt/cld.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1996 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: MEAN. % Compiled module: MOMENT. % Compiled module: STDDEV. grid 1996 non-zero -22.2967 46.5119 134.6274 cells= 53787 % Compiled module: STRIP. % Compiled module: WRBIN. 1997 grid 1997 non-zero -22.7474 47.1535 131.6472 cells= 53374 1998 grid 1998 non-zero -24.3090 50.5343 155.2392 cells= 53557 1999 grid 1999 non-zero -21.0658 46.2280 127.9565 cells= 52391 2000 grid 2000 non-zero -23.3182 50.4612 154.3142 cells= 51948 2001 grid 2001 non-zero -25.4712 50.3292 147.5332 cells= 50464 2002 grid 2002 non-zero -22.6252 49.8823 153.3252 cells= 46980 2003 grid 2003 non-zero -21.8382 48.3537 136.5305 cells= 48279 2004 grid 2004 non-zero -23.9221 47.9721 145.4819 cells= 48179 2005 grid 2005 non-zero -26.0049 48.8422 145.3165 cells= 44448 2006 no stations found in: cldfromdtrtxt/cld.2006.09.txt no stations found in: cldfromdtrtxt/cld.2006.10.txt no stations found in: cldfromdtrtxt/cld.2006.11.txt no stations found in: cldfromdtrtxt/cld.2006.12.txt grid 2006 non-zero -17.3353 45.5259 135.7803 cells= 29194

IDL>

..and finals re-produced:

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IDL> quick_interp_tdm2,1996,2006,'cloudcomboglo/cld.',600,gs=0.5,dumpglo='dumpglo',synth_ prefix='cldfromdtr25bin/cld.',pts_prefix='cldupdate6190/cldupdate6190.' Defaults set 1996 % Compiled module: RDBIN. % Compiled module: DEFXYZ. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/cld] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.cld Enter a name for the gridded climatology file: clim.6190.lan.cld.toothbrush Enter the path and stem of the .glo files: cloudcomboglo/cld. Enter the starting year: 1996 Enter the ending year: 2006 Enter the path (if any) for the output files: cloudcomboabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 2

Enter minimum value: 0

Enter maximum value: 1000 Right, erm.. off I jolly well go! cloudcomboglo/cld.01.1996.glo cloudcomboglo/cld.1996.01.glo cld.1996.01.glo cld.1996.02.glo

(etc)

cld.2006.11.glo cld.2006.12.glo crua6[/cru/cruts/version_3_0/secondaries/cld]

uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

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Enter a gridfile with YYYY for year and MM for month: cloudcomboabs/cld.YYYY.MM.glo.abs Enter Start Year: 1996 Enter Start Month: 01 Enter End Year: 2006 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.cld.dat

Now please enter the 3-ch parameter code: cld Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Percentage Cloud Cover Writing: cru_ts_3_00.1996.2000.cld.dat cru_ts_3_00.1996.2000.cld.nc Writing: cru_ts_3_00.2001.2006.cld.dat cru_ts_3_00.2001.2006.cld.nc uealogin1[/cru/cruts/version_3_0/secondaries/cld]

The question is, IS THIS ANY GOOD? Well, we currently have published cloud data to 2002. So we can make comparisons between 1996 and 2002. Oh, my. I am sure I've written plenty of comparison routines, but as to their location or name..ah. There is cmpmgrids.m, which I modified away from its precipitation-only mentality. I used mmeangrid.for to calculate monthly mean fields (1996-2002) for both 2.10 and 3.00 cloud. The resulting mean files, cru_ts_2_10.1996.2002.cld.dat.mmeans and cru_ts_3_00.1996.2002.cld.dat.mmeans, were fed into cmpmgrids.m. The results were less than ideal, though they could have been much worse. Essentially, North America is totally different - cloudier in Feb/Mar/Apr, sunnier the rest of the year. There are other differences, particularly in Northern Asia, but these are oatchier and don't extend throughout the year. So.. the obvious cause would be the inclusion of DTR-derived cloud, since that would have significant station counts in North America compared to CLD? Also, there seems to be horizontal banding.. not a good sign given the nature of the DTR-to-CLD conversion! Naturally, the way to test this is to make comparisons between FIVE different datasets:

1. CRU TS 2.10 1996-2002 monthly means 2. CRU TS 3.00 1996-2002 monthly means 3. CRU TS 3.00 1996-2002 (synthetic only) monthly means 4. CRU TS 3.00 1996-2002 (observed only) monthly means 5. CLD Climatology

The inclusion of 5 will show the extent of missing data, perhaps.. so I'm suggesting the following tests:

2-1 Basic comparison of old and new (already done) 3-1 How the DTR-derived synthetic CLD relates to 2.1 4-1 How the sun-hours-derived observed CLD relates to 2.1 3-2 How the DTR-derived synthetic CLD relates to the 'combo' CLD 1-5 How 2.1 relates to the climatology 2-5 How 3.0 relates to the climatology

So, to making datasets 3 and 4:

IDL> quick_interp_tdm2,1996,2002,'cldfromdtrglo05/cld.',600,gs=0.5,dumpglo='dumpglo',pts_ prefix='cldfromdtrtxt/cld.' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 1996 % Compiled module: MAP_SET.

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% Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 no stations found in: cldfromdtrtxt/cld.2006.09.txt no stations found in: cldfromdtrtxt/cld.2006.10.txt no stations found in: cldfromdtrtxt/cld.2006.11.txt no stations found in: cldfromdtrtxt/cld.2006.12.txt

IDL> quick_interp_tdm2,1996,2002,'cldfromupdate6190glo/cld.',600,gs=0.5,dumpglo='dumpglo' ,pts_prefix='cldupdate6190/cldupdate6190.' Defaults set 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/cld] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.cld Enter a name for the gridded climatology file: clim.6190.lan.cld.dunconvertin Enter the path and stem of the .glo files: cldfromdtrglo05/cld. Enter the starting year: 1996 Enter the ending year: 2002 Enter the path (if any) for the output files: cldfromdtrglo05abs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 2

Enter minimum value: 0

Enter maximum value: 1000 Right, erm.. off I jolly well go! cldfromdtrglo05/cld.01.1996.glo cldfromdtrglo05/cld.1996.01.glo cld.1996.01.glo cld.1996.02.glo

Page 222HARRY_READ_ME

(etc)

cld.2002.11.glo cld.2002.12.glo crua6[/cru/cruts/version_3_0/secondaries/cld] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.cld Enter a name for the gridded climatology file: clim.6190.lan.cld.notuagain Enter the path and stem of the .glo files: cldfromupdate6190glo/cld. Enter the starting year: 1996 Enter the ending year: 2002 Enter the path (if any) for the output files: cldfromupdate6190gloabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 2

Enter minimum value: 0

Enter maximum value: 1000 Right, erm.. off I jolly well go! cldfromupdate6190glo/cld.01.1996.glo cldfromupdate6190glo/cld.1996.01.glo cld.1996.01.glo cld.1996.02.glo

(etc)

cld.2002.11.glo cld.2002.12.glo crua6[/cru/cruts/version_3_0/secondaries/cld]

uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: cldfromdtrglo05abs/cld.YYYY.MM.glo.abs Enter Start Year: 1996 Enter Start Month: 01 Enter End Year: 2002 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.cld_from_dtr_only.dat

Now please enter the 3-ch parameter code: cld Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Percentage Cloud Cover from DTR only Writing: cru_ts_3_00.1996.2000.cld_from_dtr_only.dat cru_ts_3_00.1996.2000.cld_from_dtr_only.nc

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Writing: cru_ts_3_00.2001.2002.cld_from_dtr_only.dat cru_ts_3_00.2001.2002.cld_from_dtr_only.nc uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: cldfromupdate6190gloabs/cld.YYYY.MM.glo.abs Enter Start Year: 1996 Enter Start Month: 01 Enter End Year: 2002 Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.cld_from_sunobs_only.dat

Now please enter the 3-ch parameter code: cld Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Percentage Cloud Cover from SUN obs only Writing: cru_ts_3_00.1996.2000.cld_from_sunobs_only.dat cru_ts_3_00.1996.2000.cld_from_sunobs_only.nc Writing: cru_ts_3_00.2001.2002.cld_from_sunobs_only.dat cru_ts_3_00.2001.2002.cld_from_sunobs_only.nc uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./mmeangrid

MMEANGRID - Calculate monthly means for CRU_TS grids

Please enter the gridded data filename: cru_ts_3_00.1996.2002.cld_from_dtr_only.dat

Writing monthly mean grids to: cru_ts_3_00.1996.2002.cld_from_dtr_only.dat.mmeans

uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./mmeangrid

MMEANGRID - Calculate monthly means for CRU_TS grids

Please enter the gridded data filename: cru_ts_3_00.1996.2002.cld_from_sunobs_only.dat

Writing monthly mean grids to: cru_ts_3_00.1996.2002.cld_from_sunobs_only.dat.mmeans

uealogin1[/cru/cruts/version_3_0/secondaries/cld]

Giving us (numbering from before):

1. cru_ts_2_10.1996.2002.cld.dat.mmeans 2. cru_ts_3_00.1996.2002.cld.dat.mmeans 3. cru_ts_3_00.1996.2002.cld_from_dtr_only.dat.mmeans 4. cru_ts_3_00.1996.2002.cld_from_sunobs_only.dat.mmeans 5. clim.6190.lan.cld.grid

And here are our target comparisons again, this time with notes:

2-1 Basic comparison of old and new (already done)

-> Major diffs in N America, all months (lat. striping)

3-1 How the DTR-derived synthetic CLD relates to 2.1

-> major diffs globally, all months (lat. striping)

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4-1 How the sun-hours-derived observed CLD relates to 2.1

-> minor patchy diffs globally, all months

3-2 How the DTR-derived synthetic CLD relates to the climatology

-> major diffs globally, all months (lat. striping) c/w 3-1

1-5 How 2.1 relates to the climatology

-> minor patchy diffs globally - equiv for

2-5 How 3.0 relates to the climatology

-> Pretty much as for 2-1

The deduction so far is that the DTR-derived CLD is waaay off. The DTR looks OK, well OK in the sense that it doesn;t have prominent bands! So it's either the factors and offsets from the regression, or the way they've been applied in dtr2cld.

Well, dtr2cld is not the world's most complicated program. Wheras cloudreg is, and I immediately found a mistake! Scanning forward to 1951 was done with a loop that, for completely unfathomable reasons, didn't include months! So we read 50 grids instead of 600!!! That may have had something to do with it. I also noticed, as I was correcting THAT, that I reopened the DTR and CLD data files when I should have been opening the bloody station files!! I can only assume that I was being interrupted continually when I was writing this thing. Running with those bits fixed improved matters somewhat, though now there's a problem in that one 5-degree band (10S to 5S) has no stations! This will be due to low station counts in that region, plus removal of duplicate values.

Had a think. Phil advised averaging the bands either side to fill the gap, but yuk! And also the band to the North (ie, 5S to equator) is noticeably lower (extreme, even).

So after some investigation I found that, well, here's the email:

<MAIL QUOTE> Phil,

I've looked at why we're getting low counts for valid cloud cells in certain 5-degree latitude bands.

The filtering algorithm omits any cell values where the station count is zero, for either CLD or DTR. In general, it's the CLD counts that are zero and losing us the data.

However, in many cases, the cloud value in that cell on that month is not equal to the climatology. And there is plenty of DTR data. So I'm wondering how accurate the station counts are for secondary variables, given that they have to reflect observed and synthetic inputs. Here's a brief example:

(all values are x10)

CLD------------------- DTR------------------- val stn anom val stn anom 553.00 0.00 -10.00 134.00 20.00 -1.00 558.00 0.00 -17.00 139.00 20.00 2.00 565.00 0.00 -23.00 137.00 20.00 5.00 581.00 0.00 -32.00 139.00 16.00 8.00

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587.00 0.00 -38.00 137.00 16.00 9.00 567.00 0.00 -46.00 127.00 15.00 6.00 564.00 0.00 -49.00 120.00 14.00 3.00 552.00 0.00 -48.00 111.00 12.00 0.00 543.00 0.00 -45.00 105.00 12.00 -1.00 535.00 0.00 -40.00 99.00 10.00 -1.00

So, I'm proposing to filter on only the DTR counts, on the assumption that PRE was probably available if DTR was, so synthesis of CLD was likely to have happened, just not shown in the station counts which are probably 'conservative'? <END MAIL QUOTE>

I didn't get an email back but he did verbally consent. So away we go!

Running with a DTR-station-only screening gives us lots of station values, even with duplicate filtering turned back on. Niiice. It's still not exactly smooth, but it might be enough to 'fix' the synthetic cloud.

So, moved all existing directories to <name>_old_badcoeffs, and..

..reproduced the synthetic cloud:

crua6[/cru/cruts/version_3_0/secondaries/cld] ./dtr2cld Please enter the path/file of the FIRST dtr txt file: /cru/cruts/version_3_0/primaries/dtr/dtrtxt/dtr.1996.01.txt Please enter the path/file of the LAST dtr txt file: /cru/cruts/version_3_0/primaries/dtr/dtrtxt/dtr.2006.12.txt crua6[/cru/cruts/version_3_0/secondaries/cld]

Binary 2.5 grid production:

IDL> quick_interp_tdm2,1996,2006,'cldfromdtr25bin/cld.',600,gs=2.5,dumpbin='dumpbin',pts_ prefix='cldfromdtrtxt/cld.' Defaults set 1996 grid 1996 non-zero -28.0336 35.2211 155.5659 cells= 35824 1997 grid 1997 non-zero -28.7817 35.5387 155.2398 cells= 36027 1998 grid 1998 non-zero -31.6090 41.7502 182.8359 cells= 36481 1999 grid 1999 non-zero -26.8934 35.6123 151.3076 cells= 34127 2000 grid 2000 non-zero -28.3765 41.1417 180.7935 cells= 34846 2001 grid 2001 non-zero -31.7763 40.9542 172.0433 cells= 33724 2002 grid 2002 non-zero -29.6498 42.6404 182.7383 cells= 31683 2003 grid 2003 non-zero -27.8828 38.8903 161.9822 cells= 32227 2004 grid 2004 non-zero -30.5593 38.4767 174.0231 cells= 32315 2005 grid 2005 non-zero -33.2088 40.1841 170.8421 cells= 30951 2006 no stations found in: cldfromdtrtxt/cld.2006.09.txt no stations found in: cldfromdtrtxt/cld.2006.10.txt no stations found in: cldfromdtrtxt/cld.2006.11.txt no stations found in: cldfromdtrtxt/cld.2006.12.txt grid 2006 non-zero -27.2999 36.2585 161.9338 cells= 20383

IDL>

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Final gridding with obs as well:

IDL> quick_interp_tdm2,1996,2006,'cloudcomboglo/cld.',600,gs=0.5,dumpglo='dumpglo',synth_ prefix='cldfromdtr25bin/cld.',pts_prefix='cldupdate6190/cldupdate6190.' Defaults set 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

IDL>

crua6[/cru/cruts/version_3_0/secondaries/cld] ./glo2abs Welcome! This is the GLO2ABS program. I will create a set of absolute grids from a set of anomaly grids (in .glo format), also a gridded version of the climatology. Enter the path and name of the normals file: clim.6190.lan.cld Enter a name for the gridded climatology file: clim.6190.lan.cld.speeeeew Enter the path and stem of the .glo files: cloudcomboglo/cld. Enter the starting year: 1996 Enter the ending year: 2006 Enter the path (if any) for the output files: cloudcomboabs/ Now, CONCENTRATE. Addition or Percentage (A/P)? A Do you wish to limit the output values? (Y/N): Y 1. Set minimum to zero 2. Set single minimum and maximum values 3. Set minima and maxima based on days in month 4. Set integer values >=1, (ie, positive) 5. Changed my mind, no limits Choose: 2

Enter minimum value: 0

Enter maximum value: 1000 Right, erm.. off I jolly well go! cloudcomboglo/cld.01.1996.glo cloudcomboglo/cld.1996.01.glo cld.1996.01.glo cld.1996.02.glo (etc) cld.2006.11.glo cld.2006.12.glo crua6[/cru/cruts/version_3_0/secondaries/cld]

uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./makegrids Welcome! This is the MAKEGRIDS program. I will create decadal and full gridded files, in both ASCII text and NetCDF formats, from the output files of (eg) glo2abs.for.

Enter a gridfile with YYYY for year and MM for month: cloudcomboabs/cld.YYYY.MM.glo.abs Enter Start Year: 1996 Enter Start Month: 01 Enter End Year: 2006

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Enter End Month: 12

Please enter a sample OUTPUT filename, replacing start year with SSSS and end year with EEEE, and ending with '.dat', eg: cru_ts_3_00.SSSS.EEEE.tmp.dat : cru_ts_3_00.SSSS.EEEE.cld.dat

Now please enter the 3-ch parameter code: cld Enter a generic title for this dataset, eg: CRU TS 3.00 Mean Temperature : CRU TS 3.00 Percentage Cloud Cover Writing: cru_ts_3_00.1996.2000.cld.dat cru_ts_3_00.1996.2000.cld.nc Writing: cru_ts_3_00.2001.2006.cld.dat cru_ts_3_00.2001.2006.cld.nc uealogin1[/cru/cruts/version_3_0/secondaries/cld]

uealogin1[/cru/cruts/version_3_0/secondaries/cld] head -30240 cru_ts_3_00.1996.2006.cld.dat >cru_ts_3_00.1996.2002.cld.dat uealogin1[/cru/cruts/version_3_0/secondaries/cld] ./mmeangrid

MMEANGRID - Calculate monthly means for CRU_TS grids

Please enter the gridded data filename: cru_ts_3_00.1996.2002.cld.dat

Writing monthly mean grids to: cru_ts_3_00.1996.2002.cld.dat.mmeans

uealogin1[/cru/cruts/version_3_0/secondaries/cld]

Back with cmpmgrids.m.. and things look MUCH better. Differences with the climatology, or with the 2.10 release, are patchy and generally below 30%. Of course it would be nice if the differences with the 2.10 release were negligable, since our regression coefficients were based on 2.10 DTR and CLD.. though of course the sun hours component is an unknown there, as is the fact that 2.10 used PRE as well as DTR for the synthetics. Anyway it gets the thumbs-up. The strategy will be to just produce it for 2003-2006.06, to tie in with the rest of the 3.00 release. So I just need to.. argh. I don't have
any way to create NetCDF files 1901-2006 without the .glo.abs files to work from! I'd have to specially code a version that swallowed the existing 1901-2002 then added ours. Meh. Well it's no problem to release a concatenated ASCII file, so I'll do that:

crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] ls -l total 2414884 -rw------- 1 f098 cru 1904005440 Feb 13 10:35 cru_ts_2_10.1901-2002.cld.grid -rw------- 1 f098 cru 7542905 Feb 12 17:50 cru_ts_3_00.1996.2000.cld.dat.gz -rw------- 1 f098 cru 62217668 Feb 12 17:50 cru_ts_3_00.1996.2000.cld.nc -rw------- 1 f098 cru 273762720 Feb 12 17:51 cru_ts_3_00.1996.2006.cld.dat -rw------- 1 f098 cru 136867556 Feb 12 17:51 cru_ts_3_00.1996.2006.cld.nc -rw------- 1 f098 cru 8893264 Feb 12 17:51 cru_ts_3_00.2001.2006.cld.dat.gz -rw------- 1 f098 cru 74659316 Feb 12 17:51 cru_ts_3_00.2001.2006.cld.nc crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] gunzip

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cru_ts_3_00.2001.2006.cld.dat.gz crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] wc -l cru_ts_3_00.2001.2006.cld.dat 25920 cru_ts_3_00.2001.2006.cld.dat crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] tail -17280 cru_ts_3_00.2001.2006.cld.dat >cru_ts_3_00.2003.2006.cld.dat crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] wc -l cru_ts_3_00.2003.2006.cld.dat 17280 cru_ts_3_00.2003.2006.cld.dat crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] mv cru_ts_2_10.1901-2002.cld.grid cru_ts_3.00.1901.2006.cld.dat crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] cat cru_ts_3_00.2003.2006.cld.dat >>cru_ts_3.00.1901.2006.cld.dat crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] wc -l cru_ts_3.00.1901.2006.cld.dat 457920 cru_ts_3.00.1901.2006.cld.dat crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final] cmp ../cru_ts_2_10.1901-2002.cld.grid cru_ts_3.00.1901.2006.cld.dat cmp: EOF on ../cru_ts_2_10.1901-2002.cld.grid crua6[/cru/cruts/version_3_0/secondaries/cld/cld_final]

Done and tested.

So.. back to the update process :-(

Well to take a slightly different tack, I thought I'd look at the gridding end of things. Specifically, how to run IDL in batch mode. I think I've got it: you create a batch file with the command(s) in, then setenv IDL_STARTUP [name of batch file]. When you type 'idl' it runs the batch file, unfortunately it doesn't quit afterwards, though adding an 'exit' line to the batch file does the trick! Of course, there is

no easy way to check it's working properly, since the random element (used when relaxing to the climatology) ensures that each run gives different results:

crua6[/cru/cruts/version_3_0/secondaries/cld] cmp testglo/cld.2004.11.glo testglo2/cld.2004.11.glo testglo/cld.2004.11.glo testglo2/cld.2004.11.glo differ: char 9863, line 104 crua6[/cru/cruts/version_3_0/secondaries/cld]

Still, the mechanism is so similar to that used to run other Fortran progs that we can carry on, I guess. Naturally I would prefer to use the gridder I wrote, partly because it does a much better, *documentable* job, but mainly because I don'y want all that effort wasted!

Also looked at NetCDF production, as it's still looming. ncgen looks quite good, it can work from a 'CDL' file (format is the same as the output from ncdump). It can even produce fortran code to reproduce the file!!

Ah well. Back to the 'incoming data' process. The fact that the mcdw2cruauto and climat2cruauto programs worked fine for CLD is a big bonus, they read their runs and date files andthey wrote their results. Though the results didn't include the names of the output databases, I've had second thoughts about that. I want the update program to be in charge, so it should know what files have been produced (assuming the result is 'OK'). If the conversion program sends back a list, then the update program will have to parse it to find out which parameter is which, and that's silly when it should know anyway!! The situation is different for merging. I don't have a full strategy for file naming yet. Let's look at a typical process for an unnamed (not tmn or tmx) primary parameter, ie simple case:

File(s) Process mcdw update(s)

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convert mcdw mcdw db current db merge mcdw into current current+mcdw db

climat update(s) convert climat climat db merge climat into current+mcdw current+mcdw+climat db anomalise anomaly files grid gridded anomalies climatology actualise gridded actuals reformat into .dat and .nc final output files

So, naming. Well the governing principle of the update process is that all files have the same 10-digit datestamp. So the run can be uniquely identified, as can all its files (data, log, etc). I am NOT changing that! A main problem is that we will have to depart from the rigid database naming schema ('tla.datestr.dtb') because we will have lots of databases in a single run. In the above example, four databases will all have the same datestamp. Here's a possible name system:

mcdw db mcdw.tla.datestr.dtb current+mcdw db int1.tla.datestr.dtb climat db clmt.tla.datestr.dtb current+mcdw+climat db int2.tla.datestr.dtb

The final db would then be copied or renamed to:

tla.datestr.dtb pre.0902161401.dtb

For secondary parameters it's even worse! I'm not super-keen on the use of 'int1' ('interim 1') and so on.. they give no useful information. But a more complicated schema isn't going to be uderstood by anyone else anyway! And we should have the Database Master List to refer to at all times.. okay. All interim databases will be labeled 'int1', 'int2', and so forth. The update program will have to keep track of numbering. And, of course - it will have to tell the merging program what to call the output database! Bah.

It gets WORSE. The update program has to know which 'Master' database to pass to the merge program. For MCDW, it's going to be the 'current' database for that parameter. But for CLIMAT and BOM, it depends on whether MCDW or CLIMAT (respectively) merges have gone before. And only for those parameters that are precursored! More complexity. Well, I suppose I can take one of two approaches:

1. Test at each stage for each parameter (ie for BOM, test whether CLIMAT tmx/tmn have just been done). This could be done by testing for the filenames or by setting flags. 2. Maintain a list in memory of 'latest' databases for each parameter. A bit less elegant, but easier to understand and use.

Well, as we already HAVE (2), we'll go with that one ;0).

Okay. Because it is so complicated (well, for my brain anyway), I'm going to write out the filenames that update is using and expecting, so I can check that the conversion and merging programs tie in.

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INITS/ASSUMPTIONS dtstr = 0902161655 par = TMP source = MCDW prev db = db/tmp/tmp.0809111204.dtb

CONVERSION runs/runs.0902161655/conv.mcdw.0902161655.dat Run information updates/MCDW/db/db.0902161655 Dir for output dbs results/results.0902161655/conv.mcdw.0902161655.res Expected results file updates/MCDW/db/db.0902161655/mcdw.tmp.0902161655.dtb Expected output db logs/logs.0902161655/conv.mcdw.0902161655.log Expected log file

MERGING db/tmp/tmp.0809111204.dtb Current/latest db updates/MCDW/db/db.0902161655/mcdw.tmp.0902161655.dtb New db to be merged in updates/MCDW/db/db.0902161655/int1.tmp.0902161655.dtb Interim output db runfile.latest.dat Contains name of current run file runs/runs.0902161655/merg.mcdw.0902161655.dat Run information (read from above) results/results.0902161655/merg.mcdw.0902161655.res Expected results file updates/MCDW/db/db.0902161655/int1.tmp.0902161655.dtb Expected output db logs/logs.0902161655/merg.mcdw.0902161655.log Expected log file

These all seem to match up with the respective programs! Not sure that all the necessary directories are being created yet, though.. they are now. Some modifications to the above have been made (and retrospectively updated).

So, with half of the update program written, I got it all compiled, reset all the incoming data to 'unprocessed', and.. got it working!

Of course, I immediately realised that I'd missed out the DTR conversion at the end. And that.. didn't go any better than the rest of it, despite a quick conversion of tmnx2dtrauto.for.

Well, keen-eyed viewers will remember that all the tmin/tmax/dtr/back-to-tmin-and-tmax stuff revolves around the tmin and tmax databases being kept in absolute step. That is, same stations, same coordinates and names, same data spans. Otherwise the job of synching, and of converting to DTR, becomes horrendous. But look at what happens to the line counts of the databases as they're mangled through the system:

originals ** identical metadata ** 606244 tmn/tmn.0708071548.dtb 606244 tmx/tmx.0708071548.dtb

climat conversions 27090 climat.tmn.0902192248.dtb 27080 climat.tmx.0902192248.dtb

climat merged interims 607692 int2.tmn.0902192248.dtb 604993 int2.tmx.0902192248.dtb

bom conversions ** identical metadata ** 5388 bom.tmn.0902192248.dtb 5388 bom.tmx.0902192248.dtb

bom merged (into climat interims) interims

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607692 int3.tmn.0902192248.dtb 604993 int3.tmx.0902192248.dtb

Sometimes life is just too hard. It's after midnight - again. And I'm doing all this over VNC in 256 colours, which hurts. Anyway, the above line counts. I don't know which is the more worrying - the fact that adding the CLIMAT updates lost us 1251 lines from tmax but gained us 1448 for tmin, or that the BOM additions added sod all. And yes - I've checked, the int2 and int3 databases are IDENTICAL. Aaaarrgghhhhh.

I guess.. I am going to need one of those programs I wrote to sync the tmin and tmax databases, aren't I?

Actually, it's worse than that. The CLIMAT merges for TMN and TMX look very similar:

<QUOTE CLIMAT TMN MERGE INTO LATEST DB> New master database: updates/CLIMAT/db/db.0902192248/int2.tmn.0902192248.dtb

Update database stations: 2922 > Matched with Master stations: 2227 (automatically: 2227) (by operator: 0) > Added as new Master stations: 566 > Rejected: 129 Rejects file: updates/CLIMAT/db/db.0902192248/climat.tmn.0902192248.dtb.rejected <END QUOTE>

<QUOTE CLIMAT TMX MERGE INTO LATEST DB> New master database: updates/CLIMAT/db/db.0902192248/int2.tmx.0902192248.dtb

Update database stations: 2921 > Matched with Master stations: 2226 (automatically: 2226) (by operator: 0) > Added as new Master stations: 566 > Rejected: 129 Rejects file: updates/CLIMAT/db/db.0902192248/climat.tmx.0902192248.dtb.rejected <END QUOTE>

I don't see how we end up with such drastic differences in line counts!!

Well the first thing to do was to fix climat2cruauto so that it treated tmin and

tmax as inseparable. Thus the CLIMAT databases for these two should be identical (um, apart from the data values).

OK, this is getting SILLY. Now the BOM and CLIMAT conversions are in sync, and the original databases are in synch, yet the processing creates massive divergence!!

originals 606244 db/tmn/tmn.0708071548.dtb 606244 db/tmx/tmx.0708071548.dtb

climat conversions 27080 updates/CLIMAT/db/db.0902201023/climat.tmn.0902201023.dtb 27080 updates/CLIMAT/db/db.0902201023/climat.tmx.0902201023.dtb

climat merged interims 607687 updates/CLIMAT/db/db.0902201023/int2.tmn.0902201023.dtb

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604987 updates/CLIMAT/db/db.0902201023/int2.tmx.0902201023.dtb

bom conversions ** identical metadata ** 5388 updates/BOM/db/db.0902201023/bom.tmn.0902201023.dtb 5388 updates/BOM/db/db.0902201023/bom.tmx.0902201023.dtb

bom merged (into climat interims) interims 607687 updates/BOM/db/db.0902201023/int3.tmn.0902201023.dtb 604987 updates/BOM/db/db.0902201023/int3.tmx.0902201023.dtb

So the behaviour of newmergedbauto is, for want of a better word, unpredictable. Oh, joy. And, as indicated, the BOM updates are totally rejected:

<QUOTE BOM TMN MERGE INTO INTERIM DB> New master database: updates/BOM/db/db.0902201023/int3.tmn.0902201023.dtb

Update database stations: 898 > Matched with Master stations: 0 (automatically: 0) (by operator: 0) > Added as new Master stations: 0 > Rejected: 898 Rejects file: updates/BOM/db/db.0902201023/bom.tmn.0902201023.dtb.rejected <END QUOTE>

<QUOTE BOM TMX MERGE INTO INTERIM DB> Update database stations: 898 > Matched with Master stations: 0 (automatically: 0) (by operator: 0) > Added as new Master stations: 0 > Rejected: 898 Rejects file: updates/BOM/db/db.0902201023/bom.tmx.0902201023.dtb.rejected <END QUOTE>

I really thought I was cracking this project. But every time, it ends up worse than before.

OK, let's try and work out the order of events. I'm using getheads to look at metadata only.

1. CLIMAT conversions. These seem to be working fine:

crua6[/cru/cruts/..CLIMAT/db/db.0902201023] cmp climat.tmn.0902201023.hds climat.tmx.0902201023.hds crua6[/cru/cruts/..CLIMAT/db/db.0902201023]

2. Original databases. They look OK:

crua6[/cru/cruts/version_3_0/update_top/db] cmp tmn/tmn.0708071548.hds tmx/tmx.0708071548.hds crua6[/cru/cruts/version_3_0/update_top/db]

3. CLIMAT merging into original databases. Bad, bad, bad.

crua6[/cru/cruts/..CLIMAT/db/db.0902201023] diff int2.tmn.0902201023.hds int2.tmx.0902201023.hds |wc -l 4848 crua6[/cru/cruts/../CLIMAT/db/db.0902201023]

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Something is very poorly. It's my programming skills, isn't it.

Looking at the log files for the CLIMAT merging, they give identical stats! what differ are the dates, ie:

<QUOTE DIFFS BETWEEN CLIMAT MERGE LOGS> crua6[/cru/cruts/version_3_0/update_top/logs/logs.0902201023] diff merg.climat.tmn.0902201023.log merg.climat.tmx.0902201023.log |more 1,2c1,2 < Master file: db/tmn/tmn.0708071548.dtb < Update file: updates/CLIMAT/db/db.0902201023/climat.tmn.0902201023.dtb

--- > Master file: db/tmx/tmx.0708071548.dtb > Update file: updates/CLIMAT/db/db.0902201023/climat.tmx.0902201023.dtb 281c281 < code match with: 1033800 5247 970 55 HANNOVER DL GM 1927 2006 -999 0

--- > code match with: 1033800 5247 970 55 HANNOVER DL GM 1930 2006 -999 0 287c287 < code match with: 1038400 5247 1340 49 BERLIN-TEMPELHOF GERMANY 1991 2006 -999 0

--- > code match with: 1038400 5247 1340 49 BERLIN-TEMPELHOF GERMANY 1929 2006 -999 0 <END QUOTE>

..and so on. What's got me stumped is that the headers of both pairs of input databases are IDENTICAL. These dates are spurious! Look:

crua6[/cru/cruts/version_3_0/update_top/db] grep '55 HANNOVER' tmn/tmn.0708071548.dtb 1033800 5247 970 55 HANNOVER DL GM 1927 2006 -999 0 crua6[/cru/cruts/version_3_0/update_top/db] grep '55 HANNOVER' tmx/tmx.0708071548.dtb 1033800 5247 970 55 HANNOVER DL GM 1927 2006 -999 0 crua6[/cru/cruts/version_3_0/update_top/db] grep '49 BERLIN-TEMPELHOF' tmn/tmn.0708071548.dtb 1038400 5247 1340 49 BERLIN-TEMPELHOF GERMANY 1929 2006 -999 0 crua6[/cru/cruts/version_3_0/update_top/db] grep '49 BERLIN-TEMPELHOF' tmx/tmx.0708071548.dtb 1038400 5247 1340 49 BERLIN-TEMPELHOF GERMANY 1929 2006 -999 0 crua6[/cru/cruts/version_3_0/update_top/db]

You see? The HANNOVER 1930 date, and the BERLIN-TEMPELHOF 1991 date, are wrong!! Christ. That's not even consistent, one's supposedly in the tmin file, the other, the tmax

one.

So, an apparently-random pollution of the start dates. And.. FOUND IT! As usual, the program is doing exactly what I asked it to do. When I wrote it I simply didn't consider the possibility of tmin and tmax needing to sync. So one of the first things it does, when reading in the exisitng database, is to truncate station data series where whole years are missing

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values. And for HANNOVER, tmax has 1927-1929 missing, but tmin has (some) data in those years. A-ha!

What to do.. I guess the logical thing to do is to not truncate for tmin and tmax! So I added a flag to newmergedbauto, that it passes to the 'getmos' subroutine, that stops it from replacing start and end years, and.. it worked!! Hurrah! Or, well.. it ran without giving any

errors or crashing horribly. Yes, that's it. And here are all the 142 files (and directories) it created:

crua6[/cru/cruts/version_3_0/update_top] find . -name '*0902201545*' ./results/results.0902201545 ./results/results.0902201545/conv.mcdw.0902201545.res ./results/results.0902201545/merg.mcdw.tmp.0902201545.res ./results/results.0902201545/merg.mcdw.pre.0902201545.res ./results/results.0902201545/merg.mcdw.vap.0902201545.res ./results/results.0902201545/merg.mcdw.wet.0902201545.res ./results/results.0902201545/merg.mcdw.cld.0902201545.res ./results/results.0902201545/conv.climat.0902201545.res ./results/results.0902201545/merg.climat.tmp.0902201545.res ./results/results.0902201545/merg.climat.vap.0902201545.res ./results/results.0902201545/merg.climat.wet.0902201545.res ./results/results.0902201545/merg.climat.pre.0902201545.res ./results/results.0902201545/merg.climat.cld.0902201545.res ./results/results.0902201545/merg.climat.tmn.0902201545.res ./results/results.0902201545/merg.climat.tmx.0902201545.res ./results/results.0902201545/conv.bom.0902201545.res ./results/results.0902201545/merg.bom.tmn.0902201545.res ./results/results.0902201545/merg.bom.tmx.0902201545.res ./results/results.0902201545/mdtr.0902201545.res ./runs/runs.0902201545 ./runs/runs.0902201545/conv.mcdw.0902201545.dat ./runs/runs.0902201545/merg.mcdw.0902201545.dat ./runs/runs.0902201545/conv.climat.0902201545.dat ./runs/runs.0902201545/merg.climat.0902201545.dat ./runs/runs.0902201545/conv.bom.0902201545.dat ./runs/runs.0902201545/merg.bom.0902201545.dat ./runs/runs.0902201545/mdtr.0902201545.dat ./db/tmp/tmp.0902201545.dtb ./db/tmn/tmn.0902201545.dtb ./db/tmx/tmx.0902201545.dtb ./db/dtr/dtr.0902201545.dtb ./db/pre/pre.0902201545.dtb ./db/vap/vap.0902201545.dtb ./db/wet/wet.0902201545.dtb ./db/cld/cld.0902201545.dtb ./updates/BOM/db/db.0902201545 ./updates/BOM/db/db.0902201545/bom.tmn.0902201545.dtb ./updates/BOM/db/db.0902201545/bom.tmx.0902201545.dtb ./updates/BOM/db/db.0902201545/int3.tmn.0902201545.dtb ./updates/BOM/db/db.0902201545/bom.tmn.0902201545.dtb.rejected ./updates/BOM/db/db.0902201545/int3.tmx.0902201545.dtb ./updates/BOM/db/db.0902201545/bom.tmx.0902201545.dtb.rejected ./updates/BOM/db/db.0902201545/int3.dtr.0902201545.dtb ./updates/BOM/mergefiles/merg.bom.tmn.0902201545.mat ./updates/BOM/mergefiles/merg.bom.tmn.0902201545.act ./updates/BOM/mergefiles/merg.bom.tmn.0902201545.xrf ./updates/BOM/mergefiles/merg.bom.tmx.0902201545.mat ./updates/BOM/mergefiles/merg.bom.tmx.0902201545.act ./updates/BOM/mergefiles/merg.bom.tmx.0902201545.xrf

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./updates/CLIMAT/mergefiles/merg.climat.tmp.0902201545.mat ./updates/CLIMAT/mergefiles/merg.climat.tmp.0902201545.act ./updates/CLIMAT/mergefiles/merg.climat.tmp.0902201545.xrf ./updates/CLIMAT/mergefiles/merg.climat.vap.0902201545.mat ./updates/CLIMAT/mergefiles/merg.climat.vap.0902201545.act ./updates/CLIMAT/mergefiles/merg.climat.vap.0902201545.xrf ./updates/CLIMAT/mergefiles/merg.climat.wet.0902201545.mat ./updates/CLIMAT/mergefiles/merg.climat.wet.0902201545.act ./updates/CLIMAT/mergefiles/merg.climat.wet.0902201545.xrf ./updates/CLIMAT/mergefiles/merg.climat.pre.0902201545.mat ./updates/CLIMAT/mergefiles/merg.climat.pre.0902201545.act ./updates/CLIMAT/mergefiles/merg.climat.pre.0902201545.xrf ./updates/CLIMAT/mergefiles/merg.climat.cld.0902201545.mat ./updates/CLIMAT/mergefiles/merg.climat.cld.0902201545.act ./updates/CLIMAT/mergefiles/merg.climat.cld.0902201545.xrf ./updates/CLIMAT/mergefiles/merg.climat.tmn.0902201545.mat ./updates/CLIMAT/mergefiles/merg.climat.tmn.0902201545.act ./updates/CLIMAT/mergefiles/merg.climat.tmn.0902201545.xrf ./updates/CLIMAT/mergefiles/merg.climat.tmx.0902201545.mat ./updates/CLIMAT/mergefiles/merg.climat.tmx.0902201545.act ./updates/CLIMAT/mergefiles/merg.climat.tmx.0902201545.xrf ./updates/CLIMAT/db/db.0902201545 ./updates/CLIMAT/db/db.0902201545/climat.tmp.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.vap.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.wet.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.pre.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.cld.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.tmn.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.tmx.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/int2.tmp.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.tmp.0902201545.dtb.rejected ./updates/CLIMAT/db/db.0902201545/int2.vap.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.vap.0902201545.dtb.rejected ./updates/CLIMAT/db/db.0902201545/int2.wet.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.wet.0902201545.dtb.rejected ./updates/CLIMAT/db/db.0902201545/int2.pre.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.pre.0902201545.dtb.rejected ./updates/CLIMAT/db/db.0902201545/int2.cld.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.cld.0902201545.dtb.rejected ./updates/CLIMAT/db/db.0902201545/int2.tmn.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.tmn.0902201545.dtb.rejected ./updates/CLIMAT/db/db.0902201545/int2.tmx.0902201545.dtb ./updates/CLIMAT/db/db.0902201545/climat.tmx.0902201545.dtb.rejected ./updates/MCDW/mergefiles/merg.mcdw.tmp.0902201545.mat ./updates/MCDW/mergefiles/merg.mcdw.tmp.0902201545.act ./updates/MCDW/mergefiles/merg.mcdw.tmp.0902201545.xrf ./updates/MCDW/mergefiles/merg.mcdw.pre.0902201545.mat ./updates/MCDW/mergefiles/merg.mcdw.pre.0902201545.act ./updates/MCDW/mergefiles/merg.mcdw.pre.0902201545.xrf ./updates/MCDW/mergefiles/merg.mcdw.vap.0902201545.mat ./updates/MCDW/mergefiles/merg.mcdw.vap.0902201545.act ./updates/MCDW/mergefiles/merg.mcdw.vap.0902201545.xrf ./updates/MCDW/mergefiles/merg.mcdw.wet.0902201545.mat ./updates/MCDW/mergefiles/merg.mcdw.wet.0902201545.act ./updates/MCDW/mergefiles/merg.mcdw.wet.0902201545.xrf ./updates/MCDW/mergefiles/merg.mcdw.cld.0902201545.mat ./updates/MCDW/mergefiles/merg.mcdw.cld.0902201545.act ./updates/MCDW/mergefiles/merg.mcdw.cld.0902201545.xrf ./updates/MCDW/db/db.0902201545 ./updates/MCDW/db/db.0902201545/mcdw.tmp.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.vap.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.wet.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.pre.0902201545.dtb

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./updates/MCDW/db/db.0902201545/mcdw.sun.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.cld.0902201545.dtb ./updates/MCDW/db/db.0902201545/int1.tmp.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.tmp.0902201545.dtb.rejected ./updates/MCDW/db/db.0902201545/int1.pre.0902201545.dtb ./updates/MCDW/db/db.0902201545/int1.vap.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.vap.0902201545.dtb.rejected ./updates/MCDW/db/db.0902201545/int1.wet.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.wet.0902201545.dtb.rejected ./updates/MCDW/db/db.0902201545/int1.cld.0902201545.dtb ./updates/MCDW/db/db.0902201545/mcdw.cld.0902201545.dtb.rejected ./logs/logs.0902201545 ./logs/logs.0902201545/conv.mcdw.0902201545.log ./logs/logs.0902201545/merg.mcdw.tmp.0902201545.log ./logs/logs.0902201545/merg.mcdw.pre.0902201545.log ./logs/logs.0902201545/merg.mcdw.vap.0902201545.log ./logs/logs.0902201545/merg.mcdw.wet.0902201545.log ./logs/logs.0902201545/merg.mcdw.cld.0902201545.log ./logs/logs.0902201545/conv.climat.0902201545.log ./logs/logs.0902201545/merg.climat.tmp.0902201545.log ./logs/logs.0902201545/merg.climat.vap.0902201545.log ./logs/logs.0902201545/merg.climat.wet.0902201545.log ./logs/logs.0902201545/merg.climat.pre.0902201545.log ./logs/logs.0902201545/merg.climat.cld.0902201545.log ./logs/logs.0902201545/merg.climat.tmn.0902201545.log ./logs/logs.0902201545/merg.climat.tmx.0902201545.log ./logs/logs.0902201545/conv.bom.0902201545.log ./logs/logs.0902201545/merg.bom.tmn.0902201545.log ./logs/logs.0902201545/merg.bom.tmx.0902201545.log ./logs/logs.0902201545/mdtr.0902201545.log crua6[/cru/cruts/version_3_0/update_top]

So, this leaves the new databases in the db/xxx/ directories, and db/latest.versions.dat telling us which ones they are. Which should be all the next suite of programs needs to create the final output files. Eeeeeeeek.

Well for this 'half' of the process it's going to be 90% planning and strategy - because that's how the first half ended up.

Let's revisit the process list from earlier - just the database-onwards bits and interactivity removed:

* Produce Primary Parameters (TMP, TMN, TMX, DTR, PRE) anomdtb (per parameter) quick_interp_tdm2 (per parameter) glo2abs (per parameter) makegrids (per parameter) * Prepare Binary Grids (TMP, DTR, PRE) for Synthetics quick_interp_tdm2 (per parameter) * Produce Secondary Parameter (FRS, uses TMP,DTR) frs_gts_tdm quick_interp_tdm2 glo2abs makegrids * Produce Secondary Parameter (VAP, uses TMP,DTR) vap_gts_anom anomdtb quick_interp_tdm2 glo2abs makegrids

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* Produce Secondary Parameter (WET/RD0, uses PRE) rd0_gts_anom anomdtb quick_interp_tdm2 glo2abs makegrids * Produce Secondary Parameter (CLD, uses DTR) anomdtb (95-02 norm period)

movenorms dtr2cld quick_interp_tdm2 glo2abs makegrids

Having drawn out the process flowchart, I wondered if quick_interp_tdm2.pro would be kind enough to output both .glo and binary gridded files, simultaneously? This would simplify and speed things up a bit. So, with absolutely no alarm bells ringing at all, I decided to make a sample run for DTR, just for 2006, to compare simultaneous outputs with the original ones. You idiot.

IDL> quick_interp_tdm2,2006,2006,'testdtrglo/dtr.',750,gs=0.5,pts_prefix='dtrtxt/dtr.',du mpglo='dumpglo',dumpbin='dumpbin' % Compiled module: QUICK_INTERP_TDM2. % Compiled module: GLIMIT. Defaults set 2006 % Compiled module: MAP_SET. % Compiled module: CROSSP. % Compiled module: STRIP. % Compiled module: SAVEGLO. % Compiled module: SELECTMODEL. no stations found in: dtrtxt/dtr.2006.09.txt no stations found in: dtrtxt/dtr.2006.10.txt no stations found in: dtrtxt/dtr.2006.11.txt no stations found in: dtrtxt/dtr.2006.12.txt % Compiled module: MEAN. % Compiled module: MOMENT. % Compiled module: STDDEV. grid 2006 non-zero 0.1125 2.1122 2.9219 cells= 202010 % Compiled module: WRBIN. IDL> exit crua6[/cru/cruts/version_3_0/primaries/dtr] ls -l testdtrglo/ total 43048 -rw------- 1 f098 cru 6220800 Feb 23 11:06 dtr.2006 -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.01.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.02.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.03.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.04.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.05.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.06.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.07.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.08.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.09.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.10.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.11.glo -rw------- 1 f098 cru 3142986 Feb 23 11:06 dtr.2006.12.glo crua6[/cru/cruts/version_3_0/primaries/dtr]

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So there, as hoped-for, binary and text output files. BUT. Comparisons with earlier versions from the same database.. are depressingly awful:

crua6[/cru/cruts/version_3_0/primaries/dtr] diff dtr.2006.01.glo testdtrglo/dtr.2006.01.glo |wc -l 33484 crua6[/cru/cruts/version_3_0/primaries/dtr]

Sample comparison of lines 700-710 from old and new glo files:

crua6[/cru/cruts/version_3_0/primaries/dtr] head -710 dtr.2006.01.glo |tail -11 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 3.9705E-04 1.1257E-02 2.2117E-02 3.1641E-02 8.9739E-03 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 crua6[/cru/cruts/version_3_0/primaries/dtr] head -710 testdtrglo/dtr.2006.01.glo | tail -11 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 1.7088E-03 8.5614E-04 3.4384E-06 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 crua6[/cru/cruts/version_3_0/primaries/dtr]

They're NOTHING LIKE EACH OTHER. I really do hate this whole project. Ran the gridder again, just for text output.. and..

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crua6[/cru/cruts/version_3_0/primaries/dtr] head -710 testdtrglo2/dtr.2006.01.glo | tail -11 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 3.1268E-03 6.5528E-03 9.9787E-03 1.3405E-02 1.6831E-02 9.7796E-03 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 crua6[/cru/cruts/version_3_0/primaries/dtr]

Different again! Can this just be the random seed used in the gridding algorithm? If so, why aren't we seeing a consistent pattern of 0.0 vs non-0.0 values? Another reason - if one were needed - why we should dump this gridding approach altogether. But, er, not yet! No time to finish and test the fortran gridder, which will doubtless sink to some depth and never be seen again, we'll carry on with this mediocre approach.

Spent a whole day knocking up an anomaly program - as I felt anomdtb was vastly overweight and supremely complicated to compile. Unfortunately, I got stuck trying to work out data and latlon factors for different parameters, (argh! why?), and what percentage anomalies really were, and in the end GAVE UP and now I have to modify anomdtb after all. Actually - that looked

even worse, so went back to anomauto and finished it off. And.. it works. Actually, a bit too well. For example, when deriving anomalies from the CLD database, this was the original (a few weeks ago!):

uealogin1[/cru/cruts/version_3_0/update_top] wc -l cld.2000.11.txt 606 cld.2000.11.txt

..and this is the new one, from the same source database of course:

uealogin1[/cru/cruts/version_3_0/update_top] wc -l interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt 1282 interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt

..so, um - more than twice as many got through! Erk. Screening not tough enough! Results also not exactly identical (> indicates potential match):

OLD:

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uealogin1[/cru/cruts/version_3_0/update_top] head -10 cld.2000.11.txt > 68.27 22.30 327.0 -21.20000 208000 > 65.83 24.15 6.0 -41.00000 219600 > 63.18 14.50 370.0 -45.30000 222600 > 59.37 13.47 55.0 -43.90000 241800 > 57.78 11.88 53.0 -35.50000 251200 > 57.67 18.35 47.0 -42.10000 259000 69.75 27.03 101.0 -23.80000 280500 67.37 26.65 179.0 -24.30000 283600 64.93 25.37 15.0 -33.40000 287500 62.40 25.68 145.0 -0.80000 293500

NEW: uealogin1[/cru/cruts/version_3_0/update_top/interim_data/anoms/anoms.0902201545/cld] head -10 cld.2000.11.txt 67.27 14.37 13.0 4.85715 115200 > 68.27 22.30 327.0 8.56250 208000 > 65.83 24.15 6.0 16.59999 219600 > 63.18 14.50 370.0 3.26250 222600 > 59.37 13.47 55.0 15.33749 241800 > 57.78 11.88 53.0 8.93749 251200 > 57.67 18.35 47.0 6.58749 259000 60.13 -1.18 84.0 -7.02500 300500 58.22 -6.32 13.0 -1.22501 302600 57.20 -2.22 65.0 -7.80000 309100

OK, let's look at the means being used. Here's an example:

lat lon alt anom wmo mean 68.27 22.30 327.0 8.56250 208000 90.14

and the actual Nov 2000 value for this station (KARESUANDO, SWEDEN) is 987:

2000 887 800 900-9999-9999 812 762 825 625 825 987-9999

OK. So we read in 987. Then we multiply by the factor, which should be 0.1, giving us 98.7.

Then we subtract the mean, giving us 98.7-90.14 = 8.56, which is what we're getting.

So no mismatches between data, time, and metadata. Good. and the 95/02 mean is right, too (90.1375).

So, er. AH! solved it. Looking at the wrong 'old' cloud text files. tadaa:

OLD BUT CORRECT: crua6[/cru/cruts/version_3_0/update_top] head -10 ../secondaries/cld/cldupdatetxt/cldupdate.2000.11.txt 68.27 22.30 327.0 8.50000 208000 65.83 24.15 6.0 16.60000 219600 63.18 14.50 370.0 3.20000 222600 59.37 13.47 55.0 15.30000 241800 57.78 11.88 53.0 8.90000 251200 57.67 18.35 47.0 6.50000 259000 69.75 27.03 101.0 8.90000 280500 67.37 26.65 179.0 9.40000 283600 64.93 25.37 15.0 11.20000 287500 62.40 25.68 145.0 5.90000 293500

Hurrah. Now I need to know why I'm producing too many. It's not as bad, though:

OLD BUT CORRECT: crua6[/cru/cruts/version_3_0/update_top] wc -l

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../secondaries/cld/cldupdatetxt/cldupdate.2000.11.txt 760 ../secondaries/cld/cldupdatetxt/cldupdate.2000.11.txt

NEW: uealogin1[/cru/cruts/version_3_0/update_top] wc -l interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt 1282 interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt

Let's look at the first example, a station we let through that anomdtb kicked back:

0115200 6727 1437 13 BODO VI (CIV/MIL) NORWAY 1995 2008 -999 0 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1995-9999-9999-9999-9999-9999-9999-9999-9999-9999 875-9999-9999 1996-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1997-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 1998-9999-9999-9999-9999 575 675 762 762 675 837 775-9999 1999 1000 812 762 750 550 750 862 775 637 825 1000-9999 2000 1000 912 800 750 812 850 737 825 700 737 862-9999 2001 875 750 475 650 775 775 825 825 750 900 1000-9999 2002 800 862 750 737 612 612-9999 562 800 462 762-9999 2003 850 825 862 550 712-9999 525 775 762 750 825-9999 2004 937 875 762 525 637 725 787 675 837 750 1000-9999 2005 1000 812 762 700 737 775 687 800 850 850-9999-9999 2006-9999 850 500 612-9999-9999 800 575 812 750 962-9999 2007 1000 712 750 837 762 687 675 812 850 975 950-9999 2008 1000 887 687-9999 750 775 675 612 725 887-9999-9999

Now, our limit for a valid normal is 75%, which for 1995-2002 should mean 6.

BODO VI has five valid values in November. So our limit is either wrong, or not being applied.

..yup:

uealogin1[/cru/cruts/version_3_0/update_top] ./anomauto minn calculated as 7

Ho hum. Recalculated it to 6 (whilst checking that 1961-1990 still gave 23). Re-ran.

To my horror - if not surprise - that let EVEN MORE IN! Well of course it did you silly sausage. This still doesn't explain how BODO VI gets in with 5 values:

uealogin1[/cru/cruts/version_3_0/update_top] wc -l interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt 1404 interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt

Aha. I wonder if I'm initialising the onestn() array in the wrong place? Because data is only added if not -9999, so it has to be prefilled with -9999 *every time*.. dammit. If I fix that, I get:

uealogin1[/cru/cruts/version_3_0/update_top] wc -l interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt 746 interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt

14 stations LESS than the previous exercise. That'll do, surely?

OLD RELIABLE: crua6[/cru/cruts/version_3_0/update_top] head -10

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../secondaries/cld/cldupdatetxt/cldupdate.2000.11.txt 68.27 22.30 327.0 8.50000 208000 65.83 24.15 6.0 16.60000 219600 63.18 14.50 370.0 3.20000 222600 59.37 13.47 55.0 15.30000 241800 57.78 11.88 53.0 8.90000 251200 57.67 18.35 47.0 6.50000 259000 69.75 27.03 101.0 8.90000 280500 67.37 26.65 179.0 9.40000 283600 64.93 25.37 15.0 11.20000 287500 62.40 25.68 145.0 5.90000 293500

NEW LATEST: uealogin1[/cru/cruts/version_3_0/update_top] head interim_data/anoms/anoms.0902201545/cld/cld.2000.11.txt 68.27 22.30 327.0 8.56250 208000 90.14 65.83 24.15 6.0 16.59999 219600 83.40 63.18 14.50 370.0 3.26250 222600 85.44 59.37 13.47 55.0 15.33749 241800 84.66 57.78 11.88 53.0 8.95714 251200 86.04 57.67 18.35 47.0 6.58749 259000 85.91 69.75 27.03 101.0 8.95714 280500 91.04 67.37 26.65 179.0 9.47143 283600 90.53 64.93 25.37 15.0 11.27142 287500 88.73 62.40 25.68 145.0 5.90000 293500 94.10

It's not going to be easy to find 14 missing stations, is it? Since the anomalies aren't exactly the same.

Should I be worried about 14 lost series? Less than 2%. Actually, I noticed something interesting.. look at the anomalies. The anomdtb ones aren't *rounded* to 1dp, they're *truncated*! So, er - wrong!

So let's say, anomalies are done. Hurrah. Onwards, plenty more to do!

Got the gridding working, I think. IDL of course. I modified quick_interp_tdm2.pro to accept start and end months, otherwise it just produces whole years with files of zeros for months with no anomaly file. And errors. And since this is likely to be a six-month update..

Re-planned the program layout. Not a major exercise, just putting different loops in to speed up and simplify operations. It now runs as follows (note this is simplified!!):

1. User chooses update databases or update datasets. Dates, parameters, etc.

2. Update Databases 2.1 Convert any MCDW bulletins to CRU format; merge into existing databases 2.2 Convert any CLIMAT bulletins to CRU format; merge into databases from 2.1 2.3 Convert any BOM bulletins to CRU format; merge into databases from 2.2

3. Update datasets 3.1 Convert databases to anomalies 3.2 Grid primary parameters 3.3 Generate synthetic secondary parameters 3.4 Grid secondary parameters 3.5 Convert gridded anomalies to actuals 3.6 Produce final datasets

1876 lines including subrotuines and notes. Ten Fortran and four IDL programs (plus indirect ones). All

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Fortran programs are mine, now. Top-level listing:

drwx------ 10 f098 cru 4096 Feb 19 20:55 db drwx------ 3 f098 cru 4096 Feb 28 17:01 reference drwx------ 3 f098 cru 4096 Mar 1 15:41 runs drwx------ 4 f098 cru 4096 Feb 23 12:15 gridded_finals drwx------ 4 f098 cru 4096 Feb 27 17:56 results drwx------ 5 f098 cru 4096 Mar 1 15:40 logs drwx------ 6 f098 cru 4096 Dec 18 11:00 updates drwx------ 8 f098 cru 4096 Feb 28 16:15 interim_data -rw------- 1 f098 cru 11 Feb 27 17:48 newdata.latest.date -rwxr-xr-x 1 f098 cru 132425 Mar 1 14:41 update -rwxr-xr-x 1 f098 cru 16465 Mar 1 14:41 dtr2cldauto -rwxr-xr-x 1 f098 cru 17990 Mar 1 14:55 tmnx2dtrauto -rwxr-xr-x 1 f098 cru 19427 Mar 1 15:43 glo2absauto -rwxr-xr-x 1 f098 cru 20929 Mar 1 14:42 movenormsuato -rwxr-xr-x 1 f098 cru 23350 Mar 1 15:42 anomauto -rwxr-xr-x 1 f098 cru 29076 Mar 1 14:50 climat2cruauto -rwxr-xr-x 1 f098 cru 29481 Mar 1 14:50 bom2cruauto -rwxr-xr-x 1 f098 cru 29867 Mar 1 14:49 mcdw2cruauto -rwxr-xr-x 1 f098 cru 323870 Mar 1 15:52 makegridsauto -rwxr-xr-x 1 f098 cru 89515 Mar 1 16:10 newmergedbauto

So, to station counts. These will have to mirror section 3 above. Coverage of secondary parameters is particularly difficult - what is the best approach? To include synthetic coverage, when it's only at 2.5-degree?

No. I'm going to back my previous decision - all station count files reflect actualy obs for that parameter only. So for secondaries, you get actual obs of that parameter (ie naff all for FRS). You get the info about synthetics that enables you to use the relevant primary counts if you want to. Of course, I'm going to have to provide a combined TMP and DTR station count to satisfy VAP & FRS users. The problem is that the synthetics are incorporated at 2.5-degrees, NO IDEA why, so saying they affect particular 0.5-degree cells is harder than it should be. So we'll just gloss over that entirely ;0)

ARGH. Just went back to check on synthetic production. Apparently - I have no memory of this at all - we're not doing observed rain days! It's all synthetic from 1990 onwards. So I'm going to need conditionals in the update program to handle that. And separate gridding before 1989. And what TF happens to station counts?

OH FUCK THIS. It's Sunday evening, I've worked all weekend, and just when I thought it was done I'm hitting yet another problem that's based on the hopeless state of our databases. There is no uniform data integrity, it's just a catalogue of issues that continues to grow as they're found.

rd0_gts_anom_05 will produce half-degree .glo files from gridded pre anoms. So if we call that, we can use it, and stncounts for PRE will be authentic (as it's the sole input). Final decision: coded update.for to produce WET from obs+syn until 12/1989, syn only thereafter. WET station counts only

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produced until 1989, PRE must be used (with caveats) after that point.

Wrote tmpdtrstnsauto.for to produce tmp.and.dtr station counts (ie you only get a count when both parameters have a count, and even then it's the min()). The resulting counts are the effective FRS counts, and the synthetic VAP counts.

Onto PET. Tracked down the PET program from Dimitrios, way back in 2007! It uses TMP, TMN, TMX, VAP, CLD and WND (the latter as 61-90 normals from IPCC). Converted to f77 'automatic' (makepetauto.for).

Onto whole runs of the update program. With a lot of debugging!

Discovered that WMO codes are still a pain in the arse. And that I'd forgotten to match Australian updates by BOM code (last field in header) instead of WMO code - so I had to modify newmergedbauto. Also found that running fixwmos.for was less than successful on VAP, because it's already screwed:

uealogin1[/cru/cruts/version_3_0/update_top/db/vap] grep -i 'jan mayen' vap.0804231150.dtb 0100100 7093 -867 9 JAN MAYEN(NOR-NAVY) NORWAY 2003 2007 -999 0 1001000 7093 -866 9 JAN MAYEN(NOR NAVY) NORWAY 1971 2003 -999 -999 uealogin1[/cru/cruts/version_3_0/update_top/db/vap]

Started work on fixdupes.for, to cleanse a given database of obvious duplicatestations, but selfdiverted back onto getting the whole update process compiled and running end to end. Almost immediately found that match rated in the merging were mixed. Added a section to newmergedbauto that did a quick matchmaking exercise on any update stations that failed the code matching. Just lat/lon and character fields really. Didn't seem to make a lot of difference. Here are the merge results for all updates and parameters, in the order they would have happened:

uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.mcdw.tmp.0903091631.log OUTPUT(S) WRITTEN

New master database: updates/MCDW/db/db.0903091631/int1.tmp.0903091631.dtb

Update database stations: 2802 > Matched with Master stations: 1759 (automatically: 1759) (by operator: 0) > Added as new Master stations: 1043 > Rejected: 0 uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.mcdw.pre.0903091631.log OUTPUT(S) WRITTEN

New master database: updates/MCDW/db/db.0903091631/int1.pre.0903091631.dtb

Update database stations: 2807 > Matched with Master stations: 2783

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(automatically: 2783) (by operator: 0) > Added as new Master stations: 24 > Rejected: 0 uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.mcdw.vap.0903091631.log

New master database: updates/MCDW/db/db.0903091631/int1.vap.0903091631.dtb

Update database stations: 2804 > Matched with Master stations: 2677 (automatically: 2677) (by operator: 0) > Added as new Master stations: 124 > Rejected: 3 Rejects file: updates/MCDW/db/db.0903091631/mcdw.vap.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.mcdw.wet.0903091631.log

New master database: updates/MCDW/db/db.0903091631/int1.wet.0903091631.dtb

Update database stations: 2801 > Matched with Master stations: 2634 (automatically: 2634) (by operator: 0) > Added as new Master stations: 163 > Rejected: 4 Rejects file: updates/MCDW/db/db.0903091631/mcdw.wet.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.mcdw.cld.0903091631.log

New master database: updates/MCDW/db/db.0903091631/int1.cld.0903091631.dtb

Update database stations: 2204 > Matched with Master stations: 2199 (automatically: 2199) (by operator: 0) > Added as new Master stations: 0 > Rejected: 5 Rejects file: updates/MCDW/db/db.0903091631/mcdw.cld.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.climat.tmp.0903091631.log

New master database: updates/CLIMAT/db/db.0903091631/int2.tmp.0903091631.dtb

Update database stations: 3065 > Matched with Master stations: 2629 (automatically: 2629) (by operator: 0) > Added as new Master stations: 345 > Rejected: 91 Rejects file: updates/CLIMAT/db/db.0903091631/climat.tmp.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.climat.vap.0903091631.log

New master database: updates/CLIMAT/db/db.0903091631/int2.vap.0903091631.dtb

Update database stations: 3039 > Matched with Master stations: 2912

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(automatically: 2912) (by operator: 0) > Added as new Master stations: 38 > Rejected: 89 Rejects file: updates/CLIMAT/db/db.0903091631/climat.vap.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.climat.wet.0903091631.log

New master database: updates/CLIMAT/db/db.0903091631/int2.wet.0903091631.dtb

Update database stations: 3047 > Matched with Master stations: 2718 (automatically: 2718) (by operator: 0) > Added as new Master stations: 232 > Rejected: 97 Rejects file: updates/CLIMAT/db/db.0903091631/climat.wet.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.climat.pre.0903091631.log

New master database: updates/CLIMAT/db/db.0903091631/int2.pre.0903091631.dtb

Update database stations: 3054 > Matched with Master stations: 2801 (automatically: 2801) (by operator: 0) > Added as new Master stations: 229 > Rejected: 24 Rejects file: updates/CLIMAT/db/db.0903091631/climat.pre.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.climat.cld.0903091631.log

New master database: updates/CLIMAT/db/db.0903091631/int2.cld.0903091631.dtb

Update database stations: 2038 > Matched with Master stations: 1964 (automatically: 1964) (by operator: 0) > Added as new Master stations: 71 > Rejected: 3 Rejects file: updates/CLIMAT/db/db.0903091631/climat.cld.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.climat.tmn.0903091631.log

New master database: updates/CLIMAT/db/db.0903091631/int2.tmn.0903091631.dtb

Update database stations: 2921 > Matched with Master stations: 2406 (automatically: 2406) (by operator: 0) > Added as new Master stations: 387 > Rejected: 128 Rejects file: updates/CLIMAT/db/db.0903091631/climat.tmn.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.climat.tmx.0903091631.log

New master database: updates/CLIMAT/db/db.0903091631/int2.tmx.0903091631.dtb

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Update database stations: 2921 > Matched with Master stations: 2406 (automatically: 2406) (by operator: 0) > Added as new Master stations: 387 > Rejected: 128 Rejects file: updates/CLIMAT/db/db.0903091631/climat.tmx.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.bom.tmn.0903091631.log

New master database: updates/BOM/db/db.0903091631/int3.tmn.0903091631.dtb

Update database stations: 906 > Matched with Master stations: 783 (automatically: 783) (by operator: 0) > Added as new Master stations: 120 > Rejected: 3 Rejects file: updates/BOM/db/db.0903091631/bom.tmn.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631] tail merg.bom.tmx.0903091631.log

New master database: updates/BOM/db/db.0903091631/int3.tmx.0903091631.dtb

Update database stations: 906 > Matched with Master stations: 783 (automatically: 783) (by operator: 0) > Added as new Master stations: 120 > Rejected: 3 Rejects file: updates/BOM/db/db.0903091631/bom.tmx.0903091631.dtb.rejected uealogin1[/cru/cruts/version_3_0/update_top/logs/logs.0903091631]

Probably the worst story is temperature, particularly for MCDW. Over 1000 new stations! Highly unlikely. I am tempted to blame the different lat/lon scale, but for now it will have to rest.

Still hitting the problem with TMP lats and lons being a mix of deg*10 and deg*100, it's screwing up the station counts work (of course). Unfortunately, I did some tests and the 'original' TMP database has the trouble, it's not my update suite :-(((

Then.. I worked it out. Sample headers from the 'original' TMP db tmp.0705101334.dtb:

10010 709 -87 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 10050 780 142 9 ISFJORD RADIO NORWAY 1912 1979 101912 -999.00 10080 783 155 28 Svalbard Lufthavn NORWAY 1911 2006 341911 -999.00 10100 693 162 -999 ANDENES 1868 1955 101868 -999.00 10250 697 189 10 TROMSO/LANGNES NORWAY 1949 2006 101949 -999.00 10260 697 189 100 Tromsoe NORWAY 1890 2006 341890 -999.00

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10280 745 190 16 Bjoernoeya NORWAY 1920 2006 341920 -999.00

And from the fixwmos-wmo-fixed version tmp.0903081416.dtb:

0100100 7090 -870 10 Jan Mayen NORWAY 1921 2006 341921 -999.00 0100500 7800 1420 9 ISFJORD RADIO NORWAY 1912 1979 101912 -999.00 0100800 7830 1550 28 Svalbard Lufthavn NORWAY 1911 2006 341911 -999.00 0101000 6930 1620 -999 ANDENES 1868 1955 101868 -999.00 0102500 6970 1890 10 TROMSO/LANGNES NORWAY 1949 2006 101949 -999.00 0102600 6970 1890 100 Tromsoe NORWAY 1890 2006 341890 -999.00 0102800 7450 1900 16 Bjoernoeya NORWAY 1920 2006 341920 -999.00

FM! fixwmos fixed coordinates as well! Here's the snippet:

locfac = 10 ! init location factor assuming lat & lon are in degs*10 do i=1,10000 read(10,'(a86)',end=12)buffy read(buffy,fmt)wmo,lat,lon,alt,sname,ctry,sy,ey,flag,extref if (lat.gt.maxlat) maxlat = lat if (lat.gt.900) goto 12 do j=sy,ey+norml read(10,'()') enddo enddo 12 if (maxlat.gt.900) locfac = 1 ! lat & lon are in degs *100

So it was written with TMP in mind! Oh, for a memory. So we don't need to fret about

TMP lat/lon being *10 any more!!

And it's taken me until NOW to realise that the IDL synthetic generators (vap_gts_anom, frs_gts_tdm, rd0_gts_anom) all need to calculate 1961-1990 normals! So they will need TMP, DTR and/or PRE binary normals for 1961 to 1990. Which means anomalies will have

to be automatically generated for that period regardless of the requested period!!! *Cries*

Introduced suitable conditionals to ensure that 61-90 anomalies and gridded binaries

are automatically produced if the relevant secondary parameters are requested.

More run-time issues, VAP is still giving an apparently non-fatal error:

% Program caused arithmetic error: Floating underflow % Program caused arithmetic error: Floating overflow % Program caused arithmetic error: Floating illegal operand

(this only appears once the vap_gts_anom.pro program has finished, so can't be identified)

Stuck on WET production, getting an error from rd0_gts_anom_05.pro, from the same bit of code that works fine in rd0_gts_anom.pro! The error:

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% Attempt to subscript RD0SYN with NSEA is out of range. % Execution halted at: RD0_GTS_ANOM_05 34 /cru-auto/cruts/version_3_0/BADC_AREA/programs/idl/rd0_gts_anom_05.pro % $MAIN$

Line 34:

rd0syn=float(prenorm)*0.0 & rd0syn(nsea)=-9999

Do you know, I actually worked this one out by myself. Preen. It turned out that nsea was -1, which meant that it was finding no hits here:

nsea =where(rd0norm eq -9999 or prenorm eq -9999)

When I looked - the min and max of rd0norm and prenorm were:

-32768 32514

..and I thought, what a coincidence, that's 2^16. Aha! Must be an Endian problem. Looked it up on the web, abd the IDL ref manual, and found that adding:

,/swap_if_big_endian

..to the end of the openr statements in rdbin.pro, it all worked! :-)))

and then, of course, another problem I should have anticipated: half-degree gridding of synthetics needs half-degree primary binaries. So the precip binaries must be half-degree for WET (after 1989) and the usual 2.5-degrees earlier. More modifications to update.for!! And it took me a further 24 hours to cotton on that I'd need half-degree TMP and DTR binaries for FRS. VAP won't mind as it's using the synthetics as an adjunct to the observations - the exceptions are those secondaries where no observations can be used. WET after 1989, FRS, and CLD after 2002 (but CLD considerately works from DTR anomalies, ungridded).

So I'm going to have to produce half-degree gridded binary TMP and DTR anomalies, adding HALF AN HOUR to the run time. Bollocks. Though I could be clever and save it.. then I'd have to monitor when 1961-1990 databases were altered, and compare, and.. wibble.

Got that done. Then got it all working (though outputs not tested). Whooo. Now for

BADC.

... Actually, BADC wasn't too bad. Took a day or so to get everything to compile, mainly having to shift to gfortran rather than f77, and also to use -w to suppress warnings. Discovered that the IDL there didn't look at IDL_STARTUP, bah, but then found a way to specify a startup file on the command line, ie:

call system('idl -e @runs/runs.'//dtstr//'/'// * 'idl.syn1.wet.txt.'//dtstr//'.dat -quiet') ! idl

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no startup

..so that's all right then. Got it all running without errors at BADC. Well, I say that, I'm still getting this for VAP:

gridding PRE anomalies at 0.5 for synthetics Producing secondary: VAP % Program caused arithmetic error: Floating overflow % Program caused arithmetic error: Floating illegal operand gridding VAP anomalies and synthetics Producing secondary: WET

I haven't been able to identify what's causing that. Um.

Anyway the next items are the tricky saving of 2.5 and 0.5 binaries for 1961-1990, only regenerating then if the dbs have been altered. Requires multi-process cooperation, since we can't tell from the database timestamps which years were potentially changed. Admittedly, with this system that only accepts MCDW/CLIMAT/BOM updates, a pre-1991 change is all but impossible, but build for the case you can't anticipate..! Also up next is the deconstruction of the early cloud data (ie to 2002) so we can generate NetCDF files for the whole shebang. degroupcld.for will do the honours.

Did CLD first. Having reverse-engineered gabs ('gridded absolute') files for 1901-2002, I then modified update (extensively) to skip anything to do with CLD (including station counts) before 2003. Then, at the anoms-to-absolutes stage, unzipped and copied over any pre-2003 CLD gabs files from the reference repository.

I suppose I'll have to do CLD station counts (just 'n' obviously) at some stage,

too.

Ran update, just for CLD, just for 1901-06/2006. Realised halfway through that I'd really have to do station counts as well because update does 'em for DTR anyway! That ought to cut out but doesn't at the moment.

It's getting faster.. implementing the 'saved binaries' was easier than I thought as well. Lots to change but straightforward. Now the IDL synthetics generators will always look in the reference area for 1961-1990 gridded binaries, whether 2.5-degree or 0.5-degree. And those datasets *should* be regenerated if flags are set that 1961-1990 data has been changed in the databases.

Then, a big problem. Lots of stars ('*********') in the PET gridded absolutes. Wrote sidebyside.m to display the five input parameters; VAP looks like being the culprit, with unfeasibly large values (up to 10034 in fact). And that's after the standard /10. So, erm.. a drains-up on VAP is now required. Oh, joy. And CLD also looks unacceptable, despite all that work -

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big patches of 100% and 0% dominate, no doubt a result of clipping by glo2absauto. The clipping is necessary, but shouldn't be needed so often!

Reassuringly, the 3_00 VAP and CLD that are published look fine, so it's soemthing I've done in the automation process. Mis-scaling is most likely.

Started chaining back through (initially) VAP. The gabs files were identical to the finals (now, if that had failed it would have been a problem!). The gridded anomaly files were a lot more interesting, because although they looked just as bad, their max values were exactly 9999. That ain't no coincidence!

Trailing fiurther back.. VAP anoms are OK, so suspicion falls on the synthetics. And lo and behold, re-running the TMP and DTR 2.5-grid binary productions with quick_interp_tdm2 gives:

IDL> quick_interp_tdm2,2006,2006,'interim_data/gbins/gbins.0903201540/tmp/tmp.',1200,gs=2 .5,pts_prefix='interim_data/anoms/anoms.0903201540/tmp/tmp.',dumpbin='dumpbin',start m=07,endm=12 Defaults set 2006 grid 2006 non-zero-8341.4424 8341.4424 3712.6726 cells= 74509 IDL> quick_interp_tdm2,2006,2006,'interim_data/gbins/gbins.0903201540/dtr/dtr.', 750,gs=2.5,pts_prefix='interim_data/anoms/anoms.0903201540/dtr/dtr.',dumpbin='dumpbi n',startm=07,endm=12 Defaults set 2006 grid 2006 non-zero-9116.9639 9116.9639 2825.0928 cells= 68171

IDL>

Those strings of numbers? They're supposed to be mean, average magnitude, and std dev! Should look something like this:

IDL> quick_interp_tdm2,2006,2006,'testdtrglo/dtr.',750,gs=0.5,pts_prefix='dtrtxt/dtr.',du mpglo='dumpglo',dumpbin='dumpbin' Defaults set 2006 grid 2006 non-zero 0.1125 2.1122 2.9219 cells= 202010

If I run with info='info', I get:

MEAN AV MAG STD DEV 2006 data 2006 month 7 0.0981 1.1909 1.7665 data 2006 month 8 0.3129 1.3504 1.9677 data 2006 month 9 0.2413 1.2774 2.0954 data 2006 month 10 -0.0024 1.3375 2.0739 data 2006 month 11 0.2594 1.1632 2.0542 data 2006 month 12 0.0874 1.3236 2.2353

..confirming that the DTR (in this case) incoming anomalies are all within expected tolerances.

Ooh! Just found this a few thousand lines back, which may be relevant:

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<QUOTE> On a parallel track (this would really have been better as a blog), Tim O has found that the binary grids of primary vars (used in synthetic production of secondary parameters) should be produced with 'binfac' set to 10 for TMP and DTR. This may explain the poor performance and coverage of VAP in particular. <END_QUOTE>

Did that help? Not much at all, unfortunately. This is frustrating, I can't see what's different. I even enabled a commented-out line that prints the ranges of pts2(*,2) and r, and they look OK:

IDL> quick_interp_tdm2,2006,2006,'interim_data/gbins/gbins.0903201540/dtr/dtr.', 750,gs=2.5,pts_prefix='interim_data/anoms/anoms.0903201540/dtr/dtr.',dumpbin='dumpbi n',startm=07,endm=12,info='info'

MEAN AV MAG STD DEV 2006 data 2006 month 7 0.0981 1.1909 1.7665 -10.4367 17.5867 -7.15403 14.5088 data 2006 month 8 0.3129 1.3504 1.9677 -7.00800 25.6929 -4.89867 15.7781 data 2006 month 9 0.2413 1.2774 2.0954 -18.4621 26.2400 -15.1905 22.7162 data 2006 month 10 -0.0024 1.3375 2.0739 -8.61333 18.5400 -6.05684 15.7678 data 2006 month 11 0.2594 1.1632 2.0542 -6.91852 33.3200 -5.10848 28.5915 data 2006 month 12 0.0874 1.3236 2.2353 -8.76667 24.4500 -6.03609 21.9419 grid 2006 non-zero-9124.9951 9124.9951 2813.1790 cells= 68111

IDL>

OK, I *think* I've got it. It's the fact that we're writing a yearly binary file but only have data for the second half of that year:

minmax Jan-Jun: -9999.00 -9999.00 minmax Jul-Dec: -15.1905 28.5915

Now, I don't see how we get:

grid 2006 non-zero-9125.1289 9125.1289 2813.0332 cells= 68110

..but I do see how vap_gts_anom might just read the *first* six months, which would all be -9999.

So, we need to be able to write six-month binaries. Oh, my giddy aunt. What a crap crap system. We'll have to switch to monthly binaries, it's the only unambiguous way. Meaning major modifications to numerous IDL proglets. Fuck. Everything from the main progs (vap_gts_anom, quick_interp_tdm2, etc) to the supporting ones (rdbin for one).

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After HOURS, I think I've sussed it, at least for VAP. The incoming *integer* binaries had to be constructed with binfac=10, because otherwise the integer bit renders most anomalies 0. Then, in the vap_gts_anom script, the values have to be divided by 100, to give degrees. any other combination of scaling factors throws the sat vap pressure calculations into the weeds. Of course, monthly binaries are still required. Ho hum.

Modified quick_interp_tdm2 to take another additional command-line option: dumpmobin, which if set will see that binaries are saved monthly, not yearly. Of course, the 2.5-degree TMP and DTR binary grids are only used by VAP - FRS uses 0.5-degree.

So, rather than carry on with mods, I thought I'd mod update enough to fix VAP, then run it all again.

Well, it ran. Until PET production, where it crashed with the same (understandable) read error as before ('********' not being an integer). However, when I invoked the Matlab sidebyside proglet to examine the VAP, it was much improved on the previous VAP. The max was still 10000, just a shade too high, but the actual spatial pollution was much reduced. There's hope! I think this all stems from the sensitivity of the saturated vapour pressure calculations, where a factor of 10 error in an input can make a factor of 1000 difference to the output.

Had to briefly divert to trick makegridsauto into thinking it was in the middle of a full 1901-2006 update, to get CLD NetCDF files produced for the whole period to June '06. Kept some important users in Bristol happy.

So, back to VAP. Tried dividing the incoming TMP 7 DTR binaries by 1000! Still no joy. Then had the bright idea of imposing a threshold on the 3.00 vap in the Matlab program. The result was that quite a lot of data was lost from 3.00, but what remained was a very good match for the 2.10 data (on which the thresholds were based).

I think I've got it! Hey - I might be home by 11. I got quick_interp_tdm2 to dump a min/max for the synthetic grids. Guess what? Our old friend 32767 is here again, otherwise known as big-endian trauma. And sure enough, the 0.5 and 2.5 binary normals (which I inherited, I've never produced them), both need to be opened for reading with:

openr,lun,fname,/swap_if_big_endian

..so I added that as an argument to rdbin, and used it wherever rdbin is called to open these normals.

So, I went through all the IDL routines. I added an integer-to-float conversion on all binary reads, and generally spruced things up. Also went through the parameters one by one and fixed (hopefully) their scaling factors at each stage. What a minefield!

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The PET problem, or unwriteable numbers, was solved by this tightening of secondaries, particularly VAP, and also putting in a clause to abs() any negative values from the wind climatology. I really don't think there should be any, but there are!

Finally I'm able to get a run of all ten parameters. The results, compared to 2.10 with sidebyside3col.m, are pretty good on the whole. Not really happy with FRS (range OK but mysterious banding in Southern Hemisphere), or PET:

pet range210 = 0 573 range300 = 0 17.5000

So I've ended up with a range that doesn't scale simply to the 2.10 range. I also have no idea what the actual range ought to be. And they said PET would be easy. Next step has to be a comparison of max/min values of PET precursors vs. PET actuals for the two sources. Did that. No significant differences, except that of course the 2.10 PET was produced with uncorrected wind. When I took out the correction for 3.00, it shot up to even higher levels, so we'll just have to ignore 2.10 comparisons with PET.

Still, a top whack of 17.5 isn't too good for PET. Printed out the ranges of the
precursors:

PET precursor parameters: ranges

tm -49.40 39.20 tn -52.80 39.50 tx -45.10 59.80 vp 0.00 36.60 wn 0.00 29.00 cl 0.00 1.00

So the temps are in degs C, vapour pressure's in hPa, wind's in m/s and cloud's fractional.

Then I thought about it. 17.5mm/day is pretty good - especially as it looks to be Eastern Sahara.

As for FRS.. with those odd longitudinal stripes - I just tidied the IDL prog up and it, er..

..went away. How very comforting.

Did a complete run for 7/06 to 12/06, ran the Matlab visuals, all params looked OK (if not special).

FTP'd the program suite and reference tree to BADC, replacing the existing ones, and tried the same full run there.

Well the first thing I noticed was how slow it was! Ooops. Maybe 3x slower than uealogin1. Then, lots of error messages (see below). I had wondered whether the big endian scene was going to show,

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maybe this is it. Anyway, it finished! Here's the screen dump:

<QUOTE> date25: 0903270742 date05: 0903270742 last6190: 0901010001 Producing anomalies Producing station counts Gridding primary parameters Producing gridded binaries for synthetics gridding TMP binary anomalies for secondary support % Program caused arithmetic error: Floating illegal operand gridding DTR binary anomalies for secondary support % Program caused arithmetic error: Floating illegal operand gridding TMP anomalies at 0.5 for synthetics gridding DTR anomalies at 0.5 for synthetics gridding PRE anomalies at 0.5 for synthetics % Program caused arithmetic error: Floating illegal operand Producing secondary: VAP % Program caused arithmetic error: Floating divide by 0 % Program caused arithmetic error: Floating underflow % Program caused arithmetic error: Floating overflow % Program caused arithmetic error: Floating illegal operand gridding VAP anomalies and synthetics Producing secondary: WET Producing secondary: CLD Making synthetic CLD from DTR anomalies gridding CLD anomalies and synthetics Producing secondary: FRS Converting anomalies to absolutes Deriving PET Creating output data and station files creating final n-station tmpdtr files creating final 0-station tmpdtr files

All work completed satisfactorarily see: logs/completion/infolog.0904010108.dat and: logs/logs.0904010108/update.0904010108.log

-bash-3.00$ <END_QUOTE>

Pulled back the output files and ran the sidebyside3col Matlab script to compare with ours. Interesting. Here are the ranges:

tmp: BADC 300 m/m: -49.4 39.2, CRU 300 m/m: -49.4 39.2 tmn: BADC 300 m/m: -52.8 39.5, CRU 300 m/m: -52.8 39.5 tmx: BADC 300 m/m: -45.1 59.8, CRU 300 m/m: -45.1 59.8 dtr: BADC 300 m/m: 1 39.2, CRU 300 m/m: 1 39.2 pre: BADC 300 m/m: 0 4573, CRU 300 m/m: 0 4573 vap: BADC 300 m/m: 0 47.9, CRU 300 m/m: 0 36.3 wet: BADC 300 m/m: 0 310, CRU 300 m/m: 0 309.5 cld: BADC 300 m/m: 0 99.9, CRU 300 m/m: 0 100 frs: BADC 300 m/m: 0 310, CRU 300 m/m: 0 310 pet: BADC 300 m/m: 0 17.1, CRU 300 m/m: 0 17.5

I don't know which is more worrying - the VAP discrepancy or the fact that the minimum DTR is 1 degree (for both!), the maximum BADC CLD is 99.9%, and the maximum CRU WET is 30.95 days! Well I guess the VAP issue is the show-stopper, and must be related to those errors:

Producing secondary: VAP

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% Program caused arithmetic error: Floating divide by 0 % Program caused arithmetic error: Floating underflow % Program caused arithmetic error: Floating overflow % Program caused arithmetic error: Floating illegal operand

Now, these are IDL errors, and probably from our old pal vap_gts_anom_m.pro. So, the established procedure is to re-run just that program, with all the info turned on. Oh, my:

IDL> !path = 'programs/idl:' + !path

IDL> vap_gts_anom_m,2006,2006,dtr_prefix='interim_data/gbins/gbins.0904010108/dtr/dtr.',t mp_prefix='interim_data/gbins/gbins.0904010108/tmp/tmp.',outprefix='interim_data/syn s/syns.0904010108/vap/vap.syn.',dumpbin=1,startm=07,endm=12 % Compiled module: VAP_GTS_ANOM_M. Land,sea: 56016 68400 Calculating tmn normal Calculating synthetic vap normal Calculating synthetic anomalies 200607 vap (x,s2,<<,>>): -Inf -NaN -Inf 12.1306 200608 vap (x,s2,<<,>>): -Inf -NaN -Inf 15.4191 200609 vap (x,s2,<<,>>): -Inf -NaN -Inf 23.2317 200610 vap (x,s2,<<,>>): -Inf -NaN -Inf 22.4792 200611 vap (x,s2,<<,>>): -Inf -NaN -Inf 15.7444 200612 vap (x,s2,<<,>>): -Inf -NaN -Inf 11.2271 % Program caused arithmetic error: Floating divide by 0 % Program caused arithmetic error: Floating underflow % Program caused arithmetic error: Floating overflow % Program caused arithmetic error: Floating illegal operand

IDL>

Yes, it's back. Right back where we started with VAP at CRU, all those, er, days
ago. Well last time it was big endian stuff, wasn't it? And presumably the little Linux box at BADC is big endian. So I might try changing those rdbin calls, just to see..

..that didn't seem to help. Here's a dump of key array ranges, just before the main loop kicks in:

norgrd min/max: -68.9000 36.6000 tadj min/max: -715.433 445.602 tmpgrd min/max: -3165.30 3153.60 dtrgrd min/max: -1868.90 1612.80 vapsyn min/max: 0.01000000 Inf v min/max: 0.00000 Inf

So tmpgrd and dtrgrd look waaay too high, though could just be *100. v and vapsyn are shot.

This does look like scaling. Boo hoo. I *fixed* that!! These are the ranges on UEALOGIN1:

norgrd min/max: -68.9000 36.6000 tadj min/max: -46.7671 25.8468 tmpgrd min/max: -68.9000 37.6000 dtrgrd min/max: -7.40000 6.30000 vapsyn min/max: 0.0100000 33.4119 v min/max: 0.00521439 41.9604

I wonder if I need to reverse the rdbin logic? reverse_if_little_endian on the non-clim calls? Since normals are reading OK without it (CRU version has bigend=1, BADC version doesn't). Let's

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try with just the tmpgrd & dtrgrd reads.. ooh:

norgrd min/max: -68.9000 36.6000 tadj min/max: -715.433 445.602 tmpgrd min/max: -68.9000 37.6000 dtrgrd min/max: -7.40000 6.30000 vapsyn min/max: 0.01000000 59.3142 v min/max: 0.00521439 61.0593 200607 vap (x,s2,<<,>>): 0.614392 1.57785 -5.33517 10.2892 200608 vap (x,s2,<<,>>): 0.593988 1.69958 -3.55033 12.3374 200609 vap (x,s2,<<,>>): 0.448958 0.793516 -5.20586 10.4787 200610 vap (x,s2,<<,>>): 0.525755 1.15223 -2.83614 8.92979 200611 vap (x,s2,<<,>>): 0.243011 0.939122 -4.66185 21.2776 200612 vap (x,s2,<<,>>): 0.302154 0.628504 -5.05943 5.84549 % Program caused arithmetic error: Floating underflow % Program caused arithmetic error: Floating overflow

IDL>

So, just tadj to 'fix', then? Though surely I should read the 2006 tmp & dtr the
same way. Or is it that I copied the 61-90 over from here, but generated the 2006 there. Ah. Should probably regenerate the 61-90 binaries at BADC? Yes. Anyway, found the 'other' tmp/dtr reads and adjusted those, and behold:

norgrd min/max: -68.9000 36.6000 tadj min/max: -46.7671 25.8468 tmpgrd min/max: -68.9000 37.6000 dtrgrd min/max: -7.40000 6.30000 vapsyn min/max: 0.01000000 33.4119 v min/max: 0.00521439 41.9604 200607 vap (x,s2,<<,>>): 0.493936 1.15553 -6.14130 5.82037 200608 vap (x,s2,<<,>>): 0.460588 0.994776 -4.42765 5.69217 200609 vap (x,s2,<<,>>): 0.381708 0.674991 -4.56722 8.27954 200610 vap (x,s2,<<,>>): 0.506931 0.895855 -4.37382 5.13692 200611 vap (x,s2,<<,>>): 0.263565 0.656560 -3.84704 11.5171 200612 vap (x,s2,<<,>>): 0.374605 0.791715 -3.15873 6.41023

The CRU version has the same ranges, but some month stats differ:

norgrd min/max: -68.9000 36.6000 tadj min/max: -46.7671 25.8468 tmpgrd min/max: -68.9000 37.6000 dtrgrd min/max: -7.40000 6.30000 vapsyn min/max: 0.0100000 33.4119 v min/max: 0.00521439 41.9604 200607 vap (x,s2,<<,>>): 0.462426 1.11003 -6.14130 4.69486 200608 vap (x,s2,<<,>>): 0.428764 0.999707 -4.42765 6.48673 200609 vap (x,s2,<<,>>): 0.342277 0.642287 -4.56722 8.27954 200610 vap (x,s2,<<,>>): 0.485457 0.858445 -4.37382 5.13692 200611 vap (x,s2,<<,>>): 0.276767 0.685921 -3.72504 11.5171 200612 vap (x,s2,<<,>>): 0.373327 0.862642 -3.15873 15.3975

December in particular has quite a drift! No idea why, since the data going in should be the same.

So, another full run, with regeneration of binary reference grids enforced:

tmp: BADC 300 m/m: -49.4 39.2, CRU 300 m/m: -49.4 39.2 tmn: BADC 300 m/m: -52.8 39.5, CRU 300 m/m: -52.8 39.5 tmx: BADC 300 m/m: -45.1 59.8, CRU 300 m/m: -45.1 59.8 dtr: BADC 300 m/m: 1 39.2, CRU 300 m/m: 1 39.2

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pre: BADC 300 m/m: 0 4573, CRU 300 m/m: 0 4573 vap: BADC 300 m/m: 0 36.5, CRU 300 m/m: 0 36.3 wet: BADC 300 m/m: 0 309.3, CRU 300 m/m: 0 309.5 cld: BADC 300 m/m: 0 99.9, CRU 300 m/m: 0 100 frs: BADC 300 m/m: 0 310, CRU 300 m/m: 0 310 pet: BADC 300 m/m: 0 17.5, CRU 300 m/m: 0 17.5

I honestly don't think it'll get closer. So, I guess I'll clear out and reset the BADC process, and let Kevin loose on it.

Well, BADC have had it for a good while, without actually doing anything. what a surprise. It's lucky actually, as I've ironed out a few bugs (including PET being garbage). One bug is eluding me, however - I can't get a full 1901-2008 run to complete! It gets stuck after producing the final TMP files (data plus stations), it just seems to sit there indefinitely. So I tried different periods.

1901-2008 failed 1901 only worked 2008 only worked 1901-1910 worked 1901-1950 worked 1951-2008 worked 1901-2008 failed

**sigh** WHAT THE HELL'S GOING ON?! Well, time to ask the compiler. So I recompiled as follows:

g77 -o update -Wall -Wsurprising -fbounds-check programs/fortran/update.for

Then, I re-ran. This time I got an error almost immediately:

Producing anomalies Subscript out of range on file line 1011, procedure programs/fortran/update.for/MAIN. Attempt to access the 6-th element of variable dobin25[subscript-1-of-2]. Abort (core dumped)

Hurrah! In a way.. thyat bug was easy enough, I'd just forgotten to put an extra test (ipar.le.5) in the test for binary production, so as it was in a 1..8 loop, there was bound (ho ho) to be trouble. There was a second, identical, instance.

After all that - final success:

date25: 0903270742 date05: 0903270742 last6190: 0901010001 Producing anomalies Producing station counts Gridding primary parameters Producing gridded binaries for synthetics gridding TMP binary anomalies for secondary support gridding DTR binary anomalies for secondary support gridding PRE binary anomalies for secondary support gridding TMP anomalies at 0.5 for synthetics gridding DTR anomalies at 0.5 for synthetics gridding PRE anomalies at 0.5 for synthetics Producing secondary: VAP gridding VAP anomalies and synthetics Producing secondary: WET gridding WET anomalies and synthetics Producing secondary: CLD Making synthetic CLD from DTR anomalies gridding CLD anomalies and synthetics

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Producing secondary: FRS Converting anomalies to absolutes Deriving PET Creating output data and station files creating final n-station tmpdtr files creating final 0-station tmpdtr files

All work completed satisfactorarily see: logs/completion/infolog.0905070939.dat and: logs/logs.0905070939/update.0905070939.log

uealogin1[/esdata/cru/f098/update_top]

..and in terms of disk usage (um, remember it's not *that* reliable):

uealogin1[/esdata/cru/f098/update_top] du -ks * 64 anomauto 32 batchdel 64 bom2cruauto 64 climat2cruauto 32 compile_all 629856 db 32 dtr2cldauto 64 glo2absauto 16108896 gridded_finals 13822176 interim_data 18368 logs 416 makegridsauto 64 makepetauto 64 mcdw2cruauto 32 movenormsauto 32 newdata.latest.date 288 newmergedbauto 2368 programs 1101088 reference 2848 results 3008 runs 32 saved_timings_090420_1716 64 stncountsauto 64 stncountsauto_safe 704 timings 32 tmnx2dtrauto 32 tmpdtrstnsauto 352 update 638432 updates uealogin1[/esdata/cru/f098/update_top]

Meaning that a complete 1901-2008 run will need about 14gb of working data and the resulting files will need approximately 16gb. All gzipped!!

Then, of course (or 'at last', depending on your perspective), Tim O had a look at the data with that analytical brain thingy he's got. Oooops. Lots of wild values, even for TMP and PRE - and that's compared to the previous output!! Yes, this is comparing the automated 1901-2008 files with the 1901-June2006 files, not with CRu TS 2.1. So, you guessed it, bonnet up again.

First investigation was WET, where variance was far too low - usually indicative of

a scaling issue, and thus it was. Despite having had a drains-up on scaling, WET seems

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to have escaped completely. The initial gridding (to binary) outputs at x10, which is absolutely fine. But the PRE-to-WET converters are not so simple. The 2.5-degree converter (rd0_gts_anom_m.pro) has reasonable output values (five monthly examples):

minmax rd0 2.5 binaries: -100.000 357.327 minmax rd0 2.5 binaries: -94.2232 250.621 minmax rd0 2.5 binaries: -93.0808 512.557 minmax rd0 2.5 binaries: -100.000 623.526 minmax rd0 2.5 binaries: -95.1105 521.668

The trouble is, when written to binary, these will be rounded to integer and a degree of accuracy will be lost. They should be x10. Then there's the 0.5-degree converter (rd0_gts_anom_05_m.pro), which has indescribably awful output values:

minmax rd0 0.5 binaries: -1.00000 8.33519 minmax rd0 0.5 binaries: -0.970328 8.13772 minmax rd0 0.5 binaries: -0.951749 4.33032 minmax rd0 0.5 binaries: -1.00000 9.26219 minmax rd0 0.5 binaries: -0.960226 3.80590

These are basically 1000 times too small!!! How did this happen when I specifically had a complete review of scaling factors?! FFS.

Aha. Not so silly. The 0.5 grids are saved as .glo files (because after 1989 it's all synthetic). So they're not rounded. On the other hand, they are still 100x too low for percentage anomalies. and the 2.5 grids are sent to the gridder as 'synthfac=100'!! When currently it's 1! So.. some changes to make :P

The 2.5-degree PRE/WET path is now at x10 all the way to the final gridding. The 0.5-degree PRE/WET path is at x10 until the production of the synthetic WET, at which point it has to be x1 to line up with the pre-1990 output from the gridder (the gridder outputs .glo files as x1 only, we haven't used the 'actfac' parameter yet and we're not going to start!!).

Got all that fixed. Then onto the excessions Tim found - quite a lot that really should have triggered the 3/4 sd cutoff in anomauto.for. Wrote 'retrace.for', a proglet I've been looking for an excuse to write. It takes a country or individual cell, along with dates and a run ID, and preforms a reverse trace from final output files to database. It's not complete yet but it already gives extremely helpful information - I was able to look at the first problem (Guatemala in Autumn 1995 has a massive spike) and find that a station in Mexico has a temperature of 78 degrees in November 1995! This gave a local anomaly of 53.23 (which would have been 'lost' amongst the rest of Mexico as Tim just did country averages) and an anomaly in Guatemala of 24.08 (which gave us the spike):

7674100 1808 -9425 22 COATZACOALCOS, VER. MEXICO 1951 2009 101951 -999.00

1994 188-9999 244-9999-9999 286 281 275 274 274 262-9999 1995 237-9999-9999-9999 300-9999 281 283 272-9999 780 239

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1996 219 232 235 256 285 276 280 226 285 260 247 235

Now, this is a clear indication that the standard deviation limits are not being applied. Which is extremely bad news. So I had a drains-up on anomauto.for.. and.. yup, my awful programming strikes again. Because I copied the anomdtb.f90 process, I failed to notice an extra section where the limit was applied to the whole station - I was only applying it to the normals period (1961-90)! So I fixed that and re-ran. Here are the before and after outputs from trace.for:

Trace on Mexico, 11/1995, run #0905070939: crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre] cat retrace.Mexico.tmp.1995.11.stat 1995 11 21.41 9.80 239 145 72.80 216 173 1995 11 21.41 9.80 239 145 72.80 216 173 1995 11 18.33 8.70 239 145 23.80 216 173 1995 11 3.08 1.13 239 145 49.04 216 173 1995 11 3.23 -0.57 238 148 2232 53.23 217 172 2244 1995 11 22.39 12.80 243 148 7227000 78.00 217 172 7674100

Trace on Mexico, 11/1995, run #0907031504: crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre] cat retrace.Mexico.tmp.1995.11.stat 1995 11 19.51 9.80 239 145 28.90 216 156 1995 11 19.51 9.80 239 145 28.90 216 156 1995 11 18.33 8.70 239 145 28.50 216 156 1995 11 1.18 1.13 239 145 0.36 216 156 1995 11 0.73 -0.57 238 148 2227 1.82 227 148 2231 1995 11 22.39 12.80 243 148 7227000 78.00 217 172 7674100

The column to be looking at is this one ---------------------^

Because it's a traceability program, it works backwards in time: Row 1 Final gridded output Row 2 Gridded absolutes (should == Row 1) Row 3 Climatology Row 4 Gridded anomalies Row 5 Anomalies Row 6 Actual station values

Columns are as follows: Cols 1,2 Year, Month Col 3 Mean Cols 4-6 Min with cell indices Cols 7-9 Max with cell indices

Row 5: Cols 4-7 Min with cell indices and line # in anoms file Cols 8-11 Max with cell indices and line # in anoms file

Row 6: Cols 4-7 Min with cell indices and WMO code in database Cols 8-11 Max with cell indices and WMO code in database

In this case, the erroneous value of 78 degrees has been counted in the earlier run, giving an anomaly of 53.23. In the later run, it hasn't - the anomaly of 1.82 is from a

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different cell (227,148 instead of 217,172).

So, re-running improved matters. The extremes have vanished. But the means are still out, sometimes significantly.

Stand by, we're about to go down the rabbit-hole again!

I took the twelve 1990 anomaly files from the original 1901-2006 run (that was done with some flavour of anomdtb.f90). They were here:

/cru/cruts/version_3_0/primaries/tmp/tmptxt/*1990*

Then I modified the update 'latest databases' file to say that tmp.0705101334.dtb was the current database, and made a limited run of the update program for tmp only, killing it once it had produced the anomaly files. The run was #0908181048.

So, under /cru/cruts/version_3_0/fixing_tmp_and_pre/custom_anom_comparisons, we have

a 'manual' directory and an 'automatic' directory, each with twelve 1990 anomaly files. And how do they compare? NOT AT ALL!!!!!!!!!

Example from January:

crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre/custom_anom_comparisons] head manual/tmp.1990.01.txt 70.90 -8.70 10.0 4.20000 10010 78.30 15.50 28.0 9.10000 10080 69.70 18.90 10.0 0.90000 10250 69.70 18.90 100.0 1.30000 10260 74.50 19.00 16.0 5.40000 10280 69.50 25.50 129.0 0.30000 10650 70.40 31.10 14.0 -0.40000 10980 66.00 2.00 0.0 1.70000 11000 67.30 14.40 13.0 0.80000 11520 66.80 14.00 39.0 2.50000 11530 crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre/custom_anom_comparisons] head automatic/tmp.1990.01.txt 7.09 -0.87 10.0 2.97558 10010 7.83 1.55 28.0 7.87895 10080 6.97 1.89 10.0 0.50690 10250 6.97 1.89 100.0 0.49478 10260 7.45 1.90 16.0 3.88554 10280 6.95 2.55 129.0 -1.48960 10650 7.04 3.11 14.0 -0.46391 10980 6.60 0.20 0.0 1.63333 11000 6.73 1.44 13.0 0.12662 11520 6.68 1.40 39.0 2.03333 11530

The numbers of values in each pair are not always identical, but are within 2 or 3 so that's not too worrying. Worrying, yes, just not fatal.

There are a number of things going on. The lats and lons are the same, just scaled (because originally the TMP coordinates were x10 not x100). We can ignore that problem. The real problem is the completely different results from the automated system - I don't understand

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this because I painstakingly chcked the anomauto.for file to ensure it was doing the right job!! The overall pattern of anomalies is roughly the same - it's just that the actual values differ. Not always lower - sometime higher. Could be a rounding error..

Got anomauto to dump the first month of the first station (10010). The clue to the problem is in the first lines - we're only getting the full-length mean (used for SD calculations) and not the 61-90 mean. nv should be <= 30.

WMO = 10010, im = 01 (nv.gt.5) sums = -384.90, nv = 86, ave = -4.48 onestn(0490,01) = -1.50 d( 1,0490,01) = 2.98

Aaaaand.. FOUND IT! What happened was this: in the original anomdtb.f90 program, there's a test for existing normals (in the header of each station). If they are present, then SD is calculated (to allow excessions to be screened out). If not, SD is calculated and then used to screen excessions, then a 61-90 normal is built provided there are enough values after the screening. However, in my version, I followed the same process - but crucially, I wasn't using the same variable to store the existing normals and the calculated ones!! So we were ending up with the 'full length' normal (n=86 in the above example) instead. All I had to add was a single line:

ave = xstnrms(im)*fac ! NEW - actually *use* existing normals!!

We then get:

onestn(0490,01) = -1.50 d( 1,0490,01) = 4.20

Which is what we want. So, a complete re-run (just tmp) for 1990, still using the old db.

Tadaa: uealogin1[/cru/cruts/version_3_0/fixing_tmp_and_pre/custom_anom_comparisons/new_auto matic] head tmp.1990.01.txt 7.09 -0.87 10.0 4.20000 10010 7.83 1.55 28.0 9.10000 10080 6.97 1.89 10.0 0.90000 10250 6.97 1.89 100.0 1.30000 10260 7.45 1.90 16.0 5.40000 10280 6.95 2.55 129.0 0.30000 10650 7.04 3.11 14.0 -0.40000 10980 6.60 0.20 0.0 1.70000 11000 6.73 1.44 13.0 0.80000 11520 6.68 1.40 39.0 2.50000 11530

Spot on! A 100% match with the previously-generated anomalies. Phew!

Of course, now we have to do a proper run with the latest db..

..that very nearly worked :(

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Mostly the same, but one noticeable exception is the hot 2003 JJA in Europe - it's much less extreme in the automated version. So I ran with the original database again. Thought I'd see if there were different station counts:

For the original anomdtb and original June 2006 db, tmp.0705101334.dtb): 1259 tmp.2003.06.txt 1216 tmp.2003.07.txt 1223 tmp.2003.08.txt

For 0909041051 (fixed anomauto and original June 2006 db, tmp.0705101334.dtb): 1210 tmp.2003.06.txt (-49) 1201 tmp.2003.07.txt (-15) 1178 tmp.2003.08.txt (-45)

For 0909021348 (the 'fixed' anomauto and the latest db, tmp.0904151410.dtb): 1246 tmp.2003.06.txt (-13) 1250 tmp.2003.07.txt (+34) 1228 tmp.2003.08.txt (+ 5)

Ran retrace (because I might as well use it!).

For 0909041051, original db: 2003 6 16.13 6.90 273 375 20.60 266 380 2003 6 16.13 6.90 273 375 20.60 266 380 2003 6 16.13 6.90 273 375 20.60 266 380 2003 6 0.00 0.00 273 375 0.00 266 380 2003 6 -999.00 -999.00 -999 -999 -999 -999.00 -999 -999 -999 2003 6 -999.00 -999.00 -999 -999 -999 -999.00 -999 -999 -999 crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre] cat retrace.France.0909041051.tmp.2003.07.stat 2003 7 18.58 9.40 273 375 23.80 265 380 2003 7 18.58 9.40 273 375 23.80 265 380 2003 7 18.58 9.40 273 375 23.80 265 380 2003 7 0.00 0.00 273 375 0.00 265 380 2003 7 -999.00 -999.00 -999 -999 -999 -999.00 -999 -999 -999 2003 7 -999.00 -999.00 -999 -999 -999 -999.00 -999 -999 -999 crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre] cat retrace.France.0909041051.tmp.2003.08.stat 2003 8 18.25 8.90 273 375 23.80 265 380 2003 8 18.25 8.90 273 375 23.80 265 380 2003 8 18.25 8.90 273 375 23.80 265 380 2003 8 0.00 0.00 273 375 0.00 265 380 2003 8 -999.00 -999.00 -999 -999 -999 -999.00 -999 -999 -999 2003 8 -999.00 -999.00 -999 -999 -999 -999.00 -999 -999 -999

For 0909021348, latest db: 2003 6 19.12 8.50 273 375 23.60 267 367 2003 6 19.12 8.50 273 375 23.60 267 367 2003 6 16.13 6.90 273 375 20.10 267 367 2003 6 2.99 1.63 273 375 3.45 267 367 2003 6 3.40 1.90 277 352 66 5.10 270 360 68 2003 6 20.94 10.60 272 375 6717000 26.10 267 371 7650000 2003 7 20.61 12.20 273 375 26.70 266 380

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2003 7 20.61 12.20 273 375 26.70 266 380 2003 7 18.58 9.40 273 375 23.80 266 380 2003 7 2.03 2.80 273 375 2.87 266 380 2003 7 2.18 1.10 275 358 69 3.20 273 373 65 2003 7 20.70 9.20 272 375 6717000 26.40 266 379 7790000 2003 8 21.04 11.90 273 375 26.60 266 380 2003 8 21.04 11.90 273 375 26.60 266 380 2003 8 18.25 8.90 273 375 23.80 266 380 2003 8 2.79 2.97 273 375 2.83 266 380 2003 8 2.90 2.80 277 352 62 3.00 281 369 61 2003 8 23.15 11.80 272 375 6717000 28.20 266 379 7790000

Well the differences certainly show up! And it looks like a database change. So.. I guess I need to look at changes in French stations. Argh. And that 'argh' was prescient, since, when I ran getcountry to extract the French stations from each database, I found:

crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre] ./getcountry Enter the database to search: ../update_top/db/tmp/tmp.0705101334.dtb

Enter the country name to extract: FRANCE 33 stations written to ../update_top/db/tmp/tmp.0705101334.dtb.FRANCE

crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre] ./getcountry Enter the database to search: ../update_top/db/tmp/tmp.0904151410.dtb

Enter the country name to extract: FRANCE 104 stations written to ../update_top/db/tmp/tmp.0904151410.dtb.FRANCE

Somehow, I've added 71 new French stations?! Surely I'd remember that. Especially as they'd have had to have arrived with the MCDW/CLIMAT bulletins. Sizes:

crua6[/cru/cruts/version_3_0/fixing_tmp_and_pre] wc -l *FRANCE 2725 tmp.0705101334.dtb.FRANCE 3700 tmp.0904151410.dtb.FRANCE

That's not so bad. Well the ratio's improved. Could be a lot of unmatched incoming stations?

Oh, ****. It's the bloody WMO codes again. **** these bloody non-standard, ambiguous, illogical systems. Amateur hour again.

First example, the beautiful city of Lille. Here are the appropriate headers:

tmp.0705101334.dtb.FRANCE: 70150 506 31 47 LILLE FRANCE 1851 2006 101851 -999.00

tmp.0904151410.dtb.FRANCE: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0 7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

So.. just what I was secretly hoping for (not!) - a drains-up on the CLIMAT and MCDW programs, otherwise known as climat2cruauto.for and mcdw2cruauto.for, as well as the merging program, mergedbauto.for.

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Some random, manual traceability that may be useful, or not:

Considering the MCDW update run numbered 0904151410:

cat /cru/cruts/version_3_0/update_top/runs/runs.0904151410/merg.mcdw.0904151410.dat db/cld/cld.0904021239.dtb

updates/MCDW/db/db.0904151410/mcdw.cld.0904151410.dtb

updates/MCDW/db/db.0904151410/int1.cld.0904151410.dtb

blind

U

..is for cld, but indicates that the input database was tmp.0904021239.dtb.

The MCDW database was mcdw.tmp.0904151410.dtb.

The output database was, of course, tmp.0904151410.dtb.

I wonder what they all have for LILLE?

tmp.0904021239.dtb: 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

mcdw.tmp.0904151410.dtb: 0701500 5034 306 52 LILLE FRANCE 2009 2009 -999 0

tmp._.dtb: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0 7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

I'll bet this just updated the 'false' LILLE with another month or something. In fact:

conv.mcdw.0904151410.dat: 1 2009 1 2009

Before (tmp.0904021239.dtb): 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2000 42 63 76 99 147 166 159 182 164 116 78 57 2001 37-9999-9999-9999 144 158 187 193 135 145 69 30 2002 45 77 80 103 133 167 175 186 148 109 87 51 2003 30 29 87 106 137 186 195 208 158 85 81 45 2004 39 53 65 106 127 166 176 192 165 120 71 34 2005 54 30 71 105 129 179 182 170 165 144 65 37 2006 20 28 49 93 137 170 226 166 185 142 90 57 2007 74 67 79 136 142 168 173 173 145 107 69 40 2008 65 59 67 93 166 159 182 176 140 102 72 30 2009 7 37-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

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After (tmp.0904151410.dtb): 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0 6190-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 2000 42 63 76 99 147 166 159 182 164 116 78 57 2001 37-9999-9999-9999 144 158 187 193 135 145 69 30 2002 45 77 80 103 133 167 175 186 148 109 87 51 2003 30 29 87 106 137 186 195 208 158 85 81 45 2004 39 53 65 106 127 166 176 192 165 120 71 34 2005 54 30 71 105 129 179 182 170 165 144 65 37 2006 20 28 49 93 137 170 226 166 185 142 90 57 2007 74 67 79 136 142 168 173 173 145 107 69 40 2008 65 59 67 93 166 159 182 176 140 102 72 30 2009 7 37-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999

The data is i-dentical. But ooh, lookit the WMO codes! Oh, Lordy! The update has changed the code from 7-digit to 6-digit!

Let's look at all available headers for this LILLE station (ie the short modern one).

tmp.0904021106.dtb:0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 tmp.0904021239.dtb:0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 tmp.0904151410.dtb: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0

Aha - it's not the same station! ;0) An input has updated the details, istr that can happen.

Let's look at the MCDW update for Jan 2009, ssm0901.fin: 07015 LILLE 5034N 00306E 52 1007.8 1013.7 .7 5.8 7 63 75 145

And a previous update, ssm0812.fin: 07015 LILLE 5034N 00306E 52 1012.7 1018.6 3.0 6.9 7 32 1 65 140

Well that's no help. Why did it change? CLIMAT? But CLIMAT doesn't have station names! Let's try and work out how the updates happened:

MCDW is first: drwx------ 2 f098 cru 4096 Mar 5 2009 db.0903051342 drwx------ 2 f098 cru 4096 Mar 5 2009 db.0903051442 drwx------ 2 f098 cru 4096 Mar 5 2009 db.0903051448 drwx------ 2 f098 cru 4096 Mar 9 2009 db.0903091631 drwx------ 2 f098 cru 4096 Apr 2 11:25 db.0904021106 drwx------ 2 f098 cru 4096 Apr 2 12:57 db.0904021239 drwx------ 2 f098 cru 4096 Apr 15 14:16 db.0904151410

Then CLIMAT: drwx------ 2 f098 cru 4096 Mar 5 2009 db.0903051342 drwx------ 2 f098 cru 4096 Mar 5 2009 db.0903051448 drwx------ 2 f098 cru 4096 Mar 9 2009 db.0903091631 drwx------ 2 f098 cru 4096 Apr 2 11:26 db.0904021106 drwx------ 2 f098 cru 4096 Apr 2 12:59 db.0904021239

We won't bother with BOM, I don't think they stretch to Lille..

I'm hoping I didn't do any on the escluster! *checks*.. nope. Just copied.

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So this looks like the sequence:

0903051342 MCDW CLIMAT 0903051442 MCDW only 0903051448 MCDW CLIMAT 0903091631 MCDW CLIMAT 0904021106 MCDW CLIMAT 0904021239 MCDW CLIMAT 0904151410 MCDW only

Interestingly, we only seem to have the last three tmp databases, at least in terms of having the short LILLE station.

This is so hard because I cannot remember the process. Have to dig some more..

OK, I think the key update is 0903091631. Here's the CLIMAT runfile: conv.climat.0903091631.dat: 1 2000 12 2008

MCDW was even further-ranging: conv.mcdw.0903091631.dat: 9 1994 11 2008

Still not there. One issue is that for some reason I didn't give the merg runfiles individual names for each parameter! So I might mod the update program to do that. Then re-run all updates.

The problem with re-running all updates, of course, is that I also fixed WMO codes. And, (though my memory is extremely flaky), probably corrected some extreme values detected by Tim. Oh bugger.

Well, WMO code fixing is identifiable because you get a log file, ie, here's the tmp dir:

-r-------- 1 f098 cru 25936385 Aug 18 10:48 tmp.0705101334.dtb -r--r--r-- 1 f098 cru 7278548 Feb 17 2009 tmp.0705101334.dtb.gz -rw-r--r-- 1 f098 cru 25936385 Mar 8 2009 tmp.0903081416.dtb -rw-r--r-- 1 f098 cru 408 Mar 8 2009 tmp.0903081416.log -rw-r--r-- 1 f098 cru 27131064 Apr 2 11:15 tmp.0904021106.dtb -rw-r--r-- 1 f098 cru 27131064 Apr 2 12:48 tmp.0904021239.dtb -rw-r--r-- 1 f098 cru 27151999 Apr 15 14:11 tmp.0904151410.dtb

tmp.0705101334.dtb had its WMO codes fixed and became tmp.0903081416.dtb, which has the accompanying log file (tmp.0903081416.log) to prove it:

Program fixwmos was run: 0903081416 Reference WMO List: /cru/cruts/version_3_0/WMO/from_dave_lister_wmo_list Country Codes (unused): /cru/cruts/version_3_0/WMO/wmo_country_codes.dat Input database: tmp.0705101334.dtb Total stations: 5065 WMOs matched: 2263 False WMOs (<0): 0 Bad WMOs (>=0 & <1000): 0 Output Database: tmp.0903081416.dtb

Unfortunately, only tmp and pre have such log files. Here's the one for pre:

Program fixwmos was run: 0903051740 Reference WMO List: /cru/cruts/version_3_0/WMO/from_dave_lister_wmo_list

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Country Codes (unused): /cru/cruts/version_3_0/WMO/wmo_country_codes.dat Input database: pre.0803271802.dtb Total stations: 15937 WMOs matched: 4196 False WMOs (<0): 88 Bad WMOs (>=0 & <1000): 1540 Bad WMO heads written to: pre.0803271802.bad Output Database: pre.0903051740.dtb

So.. I guess I will use tmp.0903081416.dtb, pre.0903051740.dtb, and the earliest available from the other parameters. In other words:

cld.0902101409.dtb dtr.0708081052.dtb pre.0903051740.dtb tmn.0708071548.dtb tmx.0708071548.dtb tmp.0903081416.dtb vap.0804231150.dtb wet.0710161148.dtb

Well it's worth a try. Actually, let's compare those eight databases - assuming we can find at least some common stations!

Oh, boy:

cld.0902101409.dtb: 7015000 5056 310 52 LILLE/LESQUIN FRANCE 1971 1996 -999 -999 cld.0902101409.dtb: 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2008 -999 0 dtr.0708081052.dtb: 701500 5057 310 52 LILLE FRANCE 1973 2006 -999 0 pre.0903051740.dtb: 0701500 5060 310 47 LILLE FRANCE 1784 2006 -999 -999.00 tmn.0708071548.dtb: 701500 5057 310 52 LILLE FRANCE 1973 2006 -999 0 tmp.0903081416.dtb: 0701500 5060 310 47 LILLE FRANCE 1851 2006 101851 -999.00 tmx.0708071548.dtb: 701500 5057 310 52 LILLE FRANCE 1973 2006 -999 0 vap.0804231150.dtb: 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2003 2007 -999 0 vap.0804231150.dtb: 1378000 6108 1048 271 LILLEHAMMER SAETHER NORWAY 1972 1994 -999 -999 vap.0804231150.dtb: 7015000 5056 310 52 LILLE/LESQUIN FRANCE 1971 2003 -999 -999 wet.0710161148.dtb: 0701500 5057 310 52 LILLE/LESQUIN FRANCE 1996 2007 -999 -999

This whole project is SUCH A MESS. No wonder I needed therapy!!

So, cld already has the problem, and it's the earliest version in the archive. Also
vap.

Well, looking back (er, up ^) we know what happened to cld - it was updated with newmergedb before it went 'auto':

'So we now have cld.0902101409.dtb, a database consisting of cld.0312181428.dtb, updated first with derived-cloud data from MCDW (1994-2008), then with derived-cloud data from CLIMAT (2000-2008).'

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And, finding cld.0312181428.dtb, does it have the 'Lille problem'? No!

70150 5056 310 52 LILLE/LESQUIN FRANCE 1971 1996 -999 -999

So cld.0312181428.dtb is our new starting point I think.

VAP - oh, dear. Again, from above:

'Discovered that WMO codes are still a pain in the arse. And that I'd forgotten to match Australian updates by BOM code (last field in header) instead of WMO code - so I had to modify newmergedbauto. Also found that running fixwmos.for was less than successful on VAP, because it's already screwed:

uealogin1[/cru/cruts/version_3_0/update_top/db/vap] grep -i 'jan mayen' vap.0804231150.dtb 0100100 7093 -867 9 JAN MAYEN(NOR-NAVY) NORWAY 2003 2007 -999 0 1001000 7093 -866 9 JAN MAYEN(NOR NAVY) NORWAY 1971 2003 -999 -999'

Ulp!

I am seriously close to giving up, again. The history of this is so complex that I can't get far enough into it before by head hurts and I have to stop. Each parameter has a tortuous history of manual and semi-automated interventions that I simply cannot just go back to early versions and run the update prog. I could be throwing away all kinds of corrections - to lat/lons, to WMOs (yes!), and

more.

So what the hell can I do about all these duplicate stations? Well, how about fixdupes.for? That would be perfect - except that I never finished it, I was diverted off to fight some other fire. Aarrgghhh.

I - need - a - database - cleaner.

What about the ones I used for the CRUTEM3 work with Phil Brohan? Can't find the bugger!! Looked everywhere, Matlab scripts aplenty but not the one that produced the plots I used in my CRU presentation in 2005. Oh, FUCK IT. Sorry. I will have to WRITE a program to find potential duplicates. It can show me pairs of headers, and correlations between the data, and I can say 'yay' or 'nay'. There is the finddupes.for program, though I think the comment for *this* program sums it up nicely:

' program postprocdupes2 c Further post-processing of the duplicates file - just to show how crap the c program that produced it was! Well - not so much that but that once it was c running, it took 2 days to finish so I couldn't really reset it to improve c things. Anyway, *this* version does the following useful stuff: c (1) Removes and squirrels away all segments where dates don't match; c (2) Marks segments >5 where dates don't match; c (3) Groups segments from the same pair of stations; c (4) Sorts based on total segment length for each station pair'

You see how messy it gets when you actually examine the problem?

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This time around, (dedupedb.for), I took as simple an approach as possible - and almost immediately hit a problem that's generic but which doesn't seem to get much attention: what's the minimum n for a reliable standard deviation?

I wrote a quick Matlab proglet, stdevtest2.m, which takes a 12-column matrix of values and, for each month, calculates standard deviations using sliding windows of increasing size - finishing with the whole vector and what's taken to be *the* standard deviation.

The results are depressing. For Paris, with 237 years, +/- 20% of the real value was possible with even 40 values. Windter months were more variable than Summer ones of course. What we really need, and I don't think it'll happen of course, is a set of metrics (by latitude band perhaps) so that we have a broad measure of the acceptable minimum value count for a given month and location. Even better, a confidence figure that allowed the actual standard deviation comparison to be made with a looseness proportional to the sample size.

All that's beyond me - statistically and in terms of time. I'm going to have to say '30'.. it's pretty good apart from DJF. For the one station I've looked at.

Back to the actual database issues - I need a day or two to think about the duplicate finder.

Let's just look at the year 2003, for all the French stations in each database! Duh.

Original db extraction: tmp.0705101334.dtb.FRANCE: 2003 30 29 87 106 137 186 195 208 158 85 81 45 2003 59 64 96 108 123 159 174 188 155 110 103 75 2003-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 x 2003-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 x 2003 10 2 83 110 162 226 210 239 159 81 67 26 2003 42 54 108 122 144 199 200 232 173 109 101 65 2003 24 34 102 120 149 221 213 252 168 99 82 45 2003 12 15 86 109 154 232 220 251 161 88 67 31 2003 20 33 102 114 137 212 200 240 162 101 90 49 2003-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 x 2003 51 58 123 142 160 227 219 252 190 129 109 77 2003 45 56 114 133 165 242 243 267 197 133 109 72 2003-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 x 2003 54 63 111 141 194 261 263 279 201 153 121 85 2003 85 79 115 137 191 248 253 274 209 156 129 96 2003 73 73 118 140 181 247 262 275 205 155 125 94 2003 86 73 105 131 184 241 251 265 207 166 136 93

New db extraction: tmp.0904151410.dtb.FRANCE: 2003 30 29 87 106 137 186 195 208 158 85 81 45 * 2003 59 64 96 108 123 159 174 188 155 110 103 75 * 2003 10 2 83 110 162 226 210 239 159 81 67 26 * 2003 42 54 108 122 144 199 200 232 173 109 101 65 * 2003 24 34 102 120 149 221 213 252 168 99 82 45 * 2003 12 15 86 109 154 232 220 251 161 88 67 31 * 2003 20 33 102 114 137 212 200 240 162 101 90 49 * 2003 51 58 123 142 160 227 219 252 190 129 109 77 * 2003 45 56 114 133 165 242 243 267 197 133 109 72 * 2003 85 79 115 137 191 248 253 274 209 156 129 96 *

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2003 73 73 118 140 181 247 262 275 205 155 125 94 * 2003 86 73 105 131 184 241 251 265 207 166 136 93 * 2003 30 29 87 106 137 186 195 208 158 85 81 45 Do 2003 27 32 88 101 129 181 186 209 153 87 79 45 DA 2003 59 64 96 108 123 159 174 188 155 110 103 75 Do 2003 10 2 83 110 162 226 210 239 159 81 67 26 Do 2003 42 54 108 122 144 199 200 232 173 109 101 65 Do 2003 24 34 102 120 149 221 213 252 168 99 82 45 Do 2003 12 15 86 109 154 232 220 251 161 88 67 31 Do 2003 20 33 102 114 137 212 200 240 162 101 90 49 Do 2003 18 32 93 116 157 232 217 242 160 104 83 43 DB 2003 51 58 123 142 160 227 219 252 190 129 109 77 Do 2003 45 56 114 133 165 242 243 267 197 133 109 72 Do 2003 57 60 110 133 184 245 256 267 196 147 113 81 DC 2003 54 63 111 141 194 261 263 279 201 153 121 85 * 2003 85 79 115 137 191 248 253 274 209 156 129 96 Do 2003 73 73 118 140 181 247 262 275 205 155 125 94 Do 2003 86 73 105 131 184 241 251 265 207 166 136 93 Do 2003-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999-9999 - 2003 27 32 88 101 129 181 186 209 153 87 79 45 DA 2003 18 32 93 116 157 232 217 242 160 104 83 43 DB 2003 57 60 110 133 184 245 256 267 196 147 113 81 DC 2003 35 35 85 101 128 170 183 200 152 86 83 46 2003 73 69 88 102 122 156 174 185 169 129 116 84 2003 43 49 90 105 130 178 186 204 154 99 91 55 2003 17 18 83 100 143 195 199 218 148 83 77 41 2003 69 70 95 107 124 160 177 184 164 123 109 82 2003 45 53 106 120 142 195 198 225 174 112 101 64 2003 30 39 93 107 134 191 191 221 155 91 83 47 2003 67 75 105 116 136 179 189 210 173 125 120 90 2003 28 39 102 115 144 209 205 243 167 97 85 51 2003 10 -3 78 102 156 230 210 235 150 74 62 22 2003 61 67 113 128 151 197 207 229 188 136 120 89 2003 30 43 101 122 144 214 206 243 168 104 89 54 2003 -7 -7 52 77 125 202 188 206 127 73 54 13 2003 20 28 100 128 171 254 233 261 179 107 86 42 2003 33 40 112 129 154 233 220 255 176 111 98 60 2003 -29 -40 25 32 79 158 151 189 96 39 42 -4 2003 39 48 107 131 183 254 251 268 182 126 100 61 2003 4 6 79 99 148 215 223 229 158 90 68 31 2003 48 52 113 135 159 229 224 255 184 128 99 65 2003 43 42 105 120 146 214 209 241 178 116 92 60 2003 41 44 101 117 145 216 212 237 173 116 93 55 2003 81 71 111 133 183 244 246 272 200 149 128 101 2003 93 69 111 137 191 254 264 282 212 168 137 103 2003 17 24 88 105 146 208 207 234 158 91 81 42 2003 1 3 84 99 143 208 202 233 153 77 70 29

In the original db, I've x'd those lines missing in the new one. Just missing vals. In the new db, I've asterisked all the lines matching the old one, with duplicate matches labeled 'Do'. Any other duplicates are marked Da, DB, DC. We can see that all the original 2003 lines are included, *and replicated*. Three new lines are also replicated. A further 25 lines are apparently new (though could well have parents in the original db). This implies that very little matching is being performed!!

Had a look at the .act (Action) files. Interesting..

crua6[/cru/cruts/version_3_0/update_top] gunzip -c updates/MCDW/mergefiles/merg.mcdw.tmp.0904021106.act.gz | grep 'LILLE' Master: 701500 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00 Update: 0701500 5034 306 52 LILLE FRANCE 2001 2008 -999 0

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NewH: 701500 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00 crua6[/cru/cruts/version_3_0/update_top] gunzip -c updates/MCDW/mergefiles/merg.mcdw.tmp.0904021239.act.gz | grep 'LILLE' Master: 701500 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00 Update: 0701500 5034 306 52 LILLE FRANCE 2001 2008 -999 0 NewH: 701500 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00 crua6[/cru/cruts/version_3_0/update_top] gunzip -c updates/MCDW/mergefiles/merg.mcdw.tmp.0904151410.act.gz | grep 'LILLE' Master: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0 Update: 0701500 5034 306 52 LILLE FRANCE 2009 2009 -999 0 NewH: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0

So it worked fine until the 0904151410 run, when it went crazee. So.. what happened? Why did it behave differently? No idea. It was the same for pre though!

crua6[/cru/cruts/version_3_0/update_top] gunzip -c updates/MCDW/mergefiles/merg.mcdw.pre.0904021239.act.gz | grep 'LILLE' Master: 701500 5034 306 52 LILLE FRANCE 1784 2008 -999 -999.00 Update: 0701500 5034 306 52 LILLE FRANCE 2001 2008 -999 0 NewH: 701500 5034 306 52 LILLE FRANCE 1784 2008 -999 -999.00 crua6[/cru/cruts/version_3_0/update_top] gunzip -c updates/MCDW/mergefiles/merg.mcdw.pre.0904151410.act.gz | grep 'LILLE' Master: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0 Update: 0701500 5034 306 52 LILLE FRANCE 2009 2009 -999 0 NewH: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0

There was something very fishy about that run. Of course it was a single month - I wonder if that made a difference?

crua6[/cru/cruts/version_3_0/update_top] cat runs/runs.0904021239/conv.mcdw.0904021239.dat 9 1994 12 2008 crua6[/cru/cruts/version_3_0/update_top] cat runs/runs.0904151410/conv.mcdw.0904151410.dat 1 2009 1 2009

Also of interest - how did the program find a 2000-2009 station when the previous update was to 2008?

Aha:

crua6[/cru/cruts/version_3_0/update_top] cat runs/runs.0904021239/conv.climat.0904021239.dat 1 2000 2 2009

The CLIMAT update did it!! It's that bloody no-metadata problem!! So I should be looking at the CLIMAT process for 0904021239, not the MCDW one. Duhh. So, the merge run:

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crua6[/cru/cruts/version_3_0/update_top] cat runs/runs.0904021239/merg.climat.0904021239.dat db/tmx/tmx.0708071548.dtb

updates/CLIMAT/db/db.0904021239/climat.tmx.0904021239.dtb

updates/CLIMAT/db/db.0904021239/int2.tmx.0904021239.dtb

blind

M crua6[/cru/cruts/version_3_0/update_top]

Looking at the CLIMAT dtb conversion...

The converted CLIMAT bulletins: crua6[/cru/cruts/version_3_0/update_top] gunzip -c updates/CLIMAT/db/db.0904021239/climat.tmp.0904021239.dtb.gz |grep -i 'lille' 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0

The merged bulletins in tmp.0904021239.dtb (er, presumably) crua6[/cru/cruts/version_3_0/update_top] gunzip -c updates/CLIMAT/db/db.0904021239/int2.tmp.0904021239.dtb.gz |grep -i 'lille' 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

crua6[/cru/cruts/version_3_0/update_top] grep -i 'lille' db/tmp/tmp.0*dtb db/tmp/tmp.0705101334.dtb: 70150 506 31 47 LILLE FRANCE 1851 2006 101851 -999.00

db/tmp/tmp.0903081416.dtb:0701500 5060 310 47 LILLE FRANCE 1851 2006 101851 -999.00

db/tmp/tmp.0904021106.dtb:0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 db/tmp/tmp.0904021106.dtb:7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

db/tmp/tmp.0904021239.dtb:0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 db/tmp/tmp.0904021239.dtb:7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

db/tmp/tmp.0904151410.dtb: 701500 5034 306 52 LILLE FRANCE 2000 2009 -999 0 db/tmp/tmp.0904151410.dtb:7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

OK.. let's be absolutely clear about the process.

STEP 1 - convert MCDW bulletins (09/94 - 12/08) to produce mcdw.tmp.0904021106.dtb: 0701500 5034 306 52 LILLE FRANCE 2001 2008 -999 0

STEP 2 - Merge mcdw.tmp.0904021106.dtb into tmp.0903081416.dtb to produce int1.tmp.0904021106.dtb: 701500 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

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STEP 3 - convert CLIMAT bulletins (01/00 - 02/09) to climat.tmp.0904021106.dtb: 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0

STEP 4 - Merge climat.tmp.0904021239.dtb into int1.tmp.0904021106.dtb to produce int2.tmp.0904021106.dtb: 0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

(there's then the BOM section but it's all over by now)

So, the merging of climat.tmp.0904021106.dtb into int1.tmp.0904021106.dtb FAILED. WHY?

Well, the WMO codes are the same as for MCDW: 0701500. So it can't be that. The lat and lon are ~slightly~ different, though. Remember, the DATABASE entry was originally (tmp.0903081416.dtb):

0701500 5060 310 47 LILLE FRANCE 1851 2006 101851 -999.00

After the MCDW (09/94 - 12/08) merge, it became (int1.tmp.0904021106.dtb):

701500 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

After the CLIMAT (01/00 - 02/09) merge (int2.tmp.0904021106.dtb):

0701500 5057 310 52 LILLE/LESQUIN FRANCE 2000 2009 -999 0 7015000 5034 306 52 LILLE FRANCE 1851 2008 101851 -999.00

Now, the 'LILLE/LESQUIN' station header comes from the CLIMAT bulletins, ie, from the WMO reference file wmo.0710151633.dat. But it should have matched with the existing

LILLE - the problem looks like the latitude shift (from 50.60 to 50.34) introduced by MCDW did the damage. Obviously, if we are going to trust MCDW metadata as being valid corrections, then the WMO reference file needs to be updated at the same time!! So, we'll need:

1. A file called 'wmoref.latest.dat' that contains the name of the latest WMO reference file.

- DONE

2. A hook in newmergedbauto that flags when a header is being changed by an MCDW/BOM bulletin.

- EXTREMELY COMPLICATED

3. A routine to write a new WMO reference and to archive the old one.

- EXTREMELY COMPLICATED

4. A record of every iteration of the db/latest.versions.dat file (in db/previous.latest/).

- DONE

As part of the investigations, I found that I wasn't close()-ing off channel 10 when

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I used it in update.for. Now I'm pretty sure that F77 follows the convention that an OPEN

on an open channel initiates an initial CLOSE automatically, but who wants to take that chance with the variety of compilers we're subject to? So I went through and inserted an indecent number of close(10)s.

Point 2 - flagging changes to the metadata.

Well, the merged database is written principally from dbm*, with dbu* chipping in 'new' stations. I guess that new stations should be added to the wmo reference file? They are pan-parameter (well the MCDW ones are) but I have an eerie feeling that I won't experience joy when headers are compared between parameters :/

Wrote metacmp.for. It accepts a list of parameter databases (by default, latest.versions.dat) and compares headers when WMO codes match. If all WMO matches amongst the databases share common metadata (lat, lon, alt, name, country) then the successful header is written to a file. If, however, any one of the WMO matches fails on any metadata - even slightly! - the gaggle of disjointed headers is written to a second file. I know that leeway should be given, particularly with lats & lons, but as a first stab I just need to know how bad things are. Well, I got that:

crua6[/cru/cruts/version_3_0/update_top] ./metacmp METACMP - compare parameter database metadata

RESULTS:

Matched/unopposed: 2435 Clashed horribly: 4077

Ouch! Though actually, far, far better than expected. As for the disport of those 2435:

crua6[/cru/cruts/version_3_0/update_top] grep '^1' report.0909181759.metacmp.wmo | wc -l 1250 crua6[/cru/cruts/version_3_0/update_top] grep '^2' report.0909181759.metacmp.wmo | wc -l 279 crua6[/cru/cruts/version_3_0/update_top] grep '^3' report.0909181759.metacmp.wmo | wc -l 41 crua6[/cru/cruts/version_3_0/update_top] grep '^4' report.0909181759.metacmp.wmo | wc -l 92 crua6[/cru/cruts/version_3_0/update_top] grep '^5' report.0909181759.metacmp.wmo | wc -l 83 crua6[/cru/cruts/version_3_0/update_top] grep '^6' report.0909181759.metacmp.wmo | wc -l 9 crua6[/cru/cruts/version_3_0/update_top] grep '^7' report.0909181759.metacmp.wmo | wc -l 129

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crua6[/cru/cruts/version_3_0/update_top] grep '^8' report.0909181759.metacmp.wmo | wc -l 552

Interesting, but not astounding. Roughly half are unpaired stations, with an impressive 23% showing a perfect match across all eight databases.

Analysis of the 4000+ bad matches will be more complicated unfortunately. An initial re-run looking for lat/lon within half a degree, and/or station partial, will be useful.

No, hang on. Easier to analyse the output from metacmp! And so.. postmetacmp.for:

Stats report for: report.0909181759.metacmp.bad

Overall distribution of group sizes: 2 in group: 642 3 in group: 71 4 in group: 188 5 in group: 625 6 in group: 183 7 in group: 411 8 in group: 1957

LAT: Number of diffs within a group: 1. 0 2. 3059 3. 276 4. 15 5. 0 6. 0 7. 0 8. 0 Maximum differences: <0.1: 1233 <0.2: 726 <0.5: 1225 <1.0: 15 1.0+: 151

LON: Number of diffs within a group: 1. 0 2. 2996 3. 339 4. 30 5. 1 6. 0 7. 0 8. 0 Maximum differences: <0.1: 1195 <0.2: 722 <0.5: 1242 <1.0: 30 1.0+: 177

ALT: Number of diffs within a group: 1. 0 2. 2035

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3. 237 4. 17 5. 0 6. 0 7. 0 8. 0 Maximum differences: <50m : 1767 <100m: 75 <500m: 121 <1km : 36 1km+ : 290

STATION NAME: Number of diffs within a group: 1. 0 2. 2167 3. 365 4. 43 5. 0 6. 0 7. 0 8. 0 Worst percentage matches: <25% : 281 <50% : 385 <75% : 770 <100%: 276 100% : 863

COUNTRY NAME: Total groups with country mismatches: 1698 Number of diffs within a group: 1. 0 2. 1475 3. 182 4. 41 5. 0 6. 0 7. 0 8. 0

Hmmm.. lots of groups that could be eliminated if we incorporated the WMO reference list, because then we could allow an element of 'drift' from a reference point.

Decided to make it a bit quicker and easier as well, by removing tmn/tmx and letting dtr take the strain - they should all be identical anyway.

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