**National Climatic Data Center**

**DATA DOCUMENTATION**

**FOR**

**DATA SET 9101**

**Global Daily Climatology Network, V1.0**

**July 22, 2002**

**By**

**Byron E. Gleason**

***National Climatic Data Center***

***151 Patton Ave.***

***Asheville, NC 28801-5001 USA***

Table of Contents

TOPIC PAGE

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

INTRODUCTION

1\. Data Set ID\...\...\...\...\...\...\...\...\...\...\...\...\...3

2\. Data Set Name\...\...\...\...\...\...\...\...\...\...\...\....3

3\. Data Set Aliases\...\...\...\...\...\...\...\...\...\...\....3

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DESCRIPTION

4\. Access Method and Sort for Archived Data\...\...\....3

5\. Access Method and Sort for Supplied Data\...\...\....5

6\. Element Names and Definitions\...\...\...\...\...\...\...5

7\. Start Date\...\...\...\...\...\...\...\...\...\...\...\...\....8

8\. Stop Date\...\...\...\...\...\...\...\...\...\...\...\...\.....8

9\. Coverage\...\...\...\...\...\...\...\...\...\...\...\...\...\...9

10\. Location\...\...\...\...\...\...\...\...\...\...\...\...\...\...9

11\. Keywords\...\...\...\...\...\...\...\...\...\...\...\...\...\...9

12\. How to Order Data\...\...\...\...\...\...\...\...\...\.....10

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

DATA CENTER

13\. Archiving Data Center\...\...\...\...\...\...\...\...\....10

14\. Technical Contact\...\...\...\...\...\...\...\...\...\.....10

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DATA QUALITY

15\. Known Uncorrected Problems\...\...\...\...\...\...\.....10

16\. Quality Statement\...\...\...\...\...\...\...\...\...\.....10

17\. Revision Date \...\...\...\...\...\...\...\...\...\...\.....15

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OTHER DATA SETS

18\. Source Data Sets\...\...\...\...\...\...\...\...\...\...\...15

19\. Essential Companion Data Sets\...\...\...\...\...\.....15

20\. Derived Data Sets\...\...\...\...\...\...\...\...\...\.....15

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SUMMARIZATION

21\. References\...\...\...\...\...\...\...\...\...\...\...\...\...16

22\. Summary\...\...\...\...\...\...\...\...\...\...\...\...\...\...16

23\. Web Site\...\...\...\...\...\...\...\...\...\...\...\...\.....16

GLOSSARY FOR THIS MANUAL\...\...\...\...\...\...\...\...\...\...17

APPENDIX A\...\...\...\...\...\...\...\...\...\...\...\...\...\.....19

APPENDIX B\...\...\...\...\...\...\...\...\...\...\...\...\...\.....21

APPENDIX C\...\...\...\...\...\...\...\...\...\...\...\...\...\.....22

APPENDIX D\...\...\...\...\...\...\...\...\...\...\...\...\...\.....24

APPENDIX E\...\...\...\...\...\...\...\...\...\...\...\...\...\.....25

APPENDIX F\...\...\...\...\...\...\...\...\...\...\...\...\...\.....26

**1. [Data Set ID]{.underline}**

9101

**2. [Data Set Name]{.underline}**

Global Daily Climatology Network, Version 1.0

**3. [Data Set Aliases]{.underline}**

GDCN

**4. [Access Method and Sort for Archived Data]{.underline}**

**[Access Method for Data Files:]{.underline}**

All elements available for a single station are stored in an ASCII
format data file. These data files can be accessed using a variety of
programming languages (e.g C, Fortran, etc.) and/or software (e.g.
spreadsheets). The data format (per one line) is as follows (in Fortran
variable notation):

Variable Type Width Start Column End Column

STNID Character 11 1 11

YEAR Integer 4 12 15

MONTH Integer 2 16 17

ELEMENT Character 4 18 21

DATA Integer 5 22 26

FLAG1 Character 1 27 27

FLAG2 Character 1 28 28

NOTE: DATA, FLAG1, and FLAG2 repeat 31 times for a single record (e.g.
one line). Therefore one record of data represents one month of daily
data.

The following lines of Fortan code can be used to read a single data
file line:

INTEGER YEAR

INTEGER MONTH

INTEGER VALUE(1:31)

CHARACTER \*1, FLAG(1:31,1:2)

CHARACTER \*4, ELEMENT

CHARACTER \*11, STNID

READ (unit,10) STNID, YEAR, MONTH, ELEMENT, +(VALUE(i),FLAG(i,1),
FLAG(i,2), i=1,31)

10 FORMAT(A11,I4,I2,A4,31(I5,2A1))

**[Access Method for Inventory Files:]{.underline}**

Station information can be found in the ASCII format inventory file. The
format of the inventory file is as follows (in Fortran variable
notation):

Variable Type Width Start Column End Column

STNID Character 11 1 11

LATITUDE Real 7 12 18

LONGITUDE Real 8 19 26

ELEVATION Integer 5 27 31

ELEVATION

ESTIMATE

FLAG Character 1 32 32

DATA SOURCE

FLAG Character 1 34 34

STN_NAME Character 30 36 65

WMOID Integer 5 66 70

STNID2 Character 8 71 78

STNID3 Character 8 79 86

The following lines of Fortran code can be used to read a single line
from an inventory file:

INTEGER ELEVATION

INTEGER WMOID

REAL LATITUDE

REAL LONGITUDE

CHARACTER \*1, ELEVATION_ESTIMATE_FLAG

CHARACTER \*1, DATA_SOURCE_FLAG

CHARACTER \*8, STNID2

CHARACTER \*8, STNID3

CHARACTER \*11, STNID

CHARACTER \*30, STN_NAME

READ (unit,10) STNID, LATITUDE, LONGITUDE, ELEVATION,
+ELEVATION_ESTIMATE_FLAG,DATA_SOURCE_FLAG,STN_NAME,WMOID,

+STNID2,STNID3

10 FORMAT(A11,F7.2,F8.2,I5,A1,1x,A1,1X,A30,I5,A8,A8)

**5. [Access Method and Sort for Supplied Data]{.underline}**

Please see #4 above.

**6. [Element Names and Definitions]{.underline}**

**Element Names and Definitions for Data Files**

[STATION ID]{.underline}: The 11 character alphanumeric station
identification element is comprised of 2 parts \... a 3 digit country
code (see Appendix A) and an 8 digit station code. In some cases the
last 5 digits of the 8 digit station code are the 5 digit World
Meteorological Organization (WMO) number.

Many countries operate weather stations in foreign lands (e.g.
possessions, military bases, research stations, etc.) and therefore it
is possible for station IDs to have a 3 digit country code that differs
from the country where the station is physically located (e.g. Canadian
military base located in Europe).

[YEAR:]{.underline} 4 digit year

[MONTH:]{.underline} 2 digit month

[ELEMENT:]{.underline} 4 character weather element that is one of TMAX
(maximum temperature), TMIN (minimum temperature), or PRCP (total 24
hour precipitation).

[DATA(31):]{.underline} There are 31 daily data values (per record)
expressed as a 5 digit integer. The missing value is -9999 and
non-missing values are in tenths of a degree Celsius for temperature and
tenths of a millimeter for precipitation. Therefore, to obtain the whole
measurement unit divide the non-missing integer datum by 10.0 to express
value as either whole degree Celsius (to the nearest tenth) or whole
millimeters (to the nearest tenth).

Example: (1) TMAX value in 5 digit integer format A00302\" would be
expressed as 30.2 degrees Celsius. (2) PRCP value in 5 digit integer
format A01200\" would be expressed as 120.0 millimeters.

[DATA MEASUREMENT FLAG(31):]{.underline} There are 31 data measurement
flags that are expressed as 1 character. These flags occur immediately
following each 5 digit integer datum. Data measurement flags are as
follows (Note: flags can apply to Tx = Maximum Temperature, Tn = Minimum
Temperature, Pr = Total Precipitation):

\> =- Blank = no data measurement information (Tx,Tn,Pr)

A - accumulated precipitation value (Pr)

C - begin or continuing precipitation accumulation (Pr)

E - estimated value provided by source (Pr)

H - accumulated precipitation indicated by source, however

no accumulated precipitation value was provided (Pr)

J - source provided accumulated precipitation value,

however no information on accumulation period was

provided (Pr)

T - trace accumulation reported by source (Pr)

[QUALITY CONTROL FLAG(31):]{.underline} There are 31 quality control
flags that are expressed as 1 character. These flags occur immediately
following each data measurement flag. The quality control flags are as
follows (Note: flags can apply to Tx = Maximum Temperature, Tn = Minimum
Temperature, Pr = Total Precipitation):

\> =- Blank = no quality control information (Tx,Tn,Pr)

F - Failed source quality control (Tx,Tn,Pr)

S - Suspect value identified by source(Tx,Tn,Pr)

X - Exceeds known world daily extreme for that particular

element (Krause and Flood, 1997). (Tx,Tn,Pr)

O - Temperature value is greater than or equal to 6.0

bi-weight standard deviations from the period bi- weight mean. (Tx,Tn)
See Page 11-12 for details.

5 - Temperature value is greater than or equal to 5.0

and less than 6.0 bi-weight standard deviations from the period
bi-weight mean. (Tx,Tn) See Page 11-12 for details.

4 - Temperature value is greater than or equal to 4.0

and less than 5.0 bi-weight standard deviations from the period
bi-weight mean. (Tx,Tn) See Page 11-12 for details.

3 - Temperature value is greater than or equal to 3.0

and less than 4.0 bi-weight standard deviations from the period
bi-weight mean. (Tx,Tn) See Page 11-12 for details.

K - Streak value (value occurs 10 or more days in a row)

Note: 0.0 mm precipitation values are excluded. (Tx, Tn,Pr))

E - Extreme precipitation value ( \>= 90^th^ percentile of all
observations within that month for the period of record). Example: Jan.
13, 1987, data measurement

flag = \>E=. Therefore Jan. 13^th^ 1987 is \>= 90^th^

percentile value for all January observations in the

entire station record. (Pr)

I - Internal consistency error, minimum temperature value

greater than same day maximum temperature value. (Tx,Tn)

It is possible that one or more quality control flags may apply to a
single datum. Therefore, the above listed flags are listed in order of
precedence. Thus, if a value is suspect (e.g. \>S=) and also an outlier
(e.g. \>O=) then the \>S= flag will be set for that datum.

**Element Names and Definitions for Inventory File**

[STATION ID]{.underline}: same as for data file (11 character
alphanumeric)

[LATITUDE:]{.underline} Expressed in decimal degrees to the nearest
hundredth of a degree. (7 digits)

[LONGITUDE:]{.underline} Expressed in decimal degrees to the nearest
hundredth

of a degree. (8 digits)

[ELEVATION:]{.underline} Expressed in whole meters (5 digits)

[ELEVATION_ESTIMATE_FLAG:]{.underline} If a station had incomplete
elevation information, attempts were made to estimate the elevation
based on longitude and latitude. The Global Land One-km Base Elevation
(GLOBE) data set was used to estimate the elevation (Hastings et. al.,
1997). If left blank this field indicates the elevation was provided in
the supplied metadata. If the elevation was estimated using GLOBE then
this flag is set to \>E=. (1 character)

[DATA_SOURCE_FLAG:]{.underline} A one character identifier indicating
the source data network from which the data were supplied. In GDCN 1.0
this flag is either blank or set to \>G= to indicate Global Climate
Obseriving System Network stations (GCOS) (Peterson et. al., 1997). (1
character)

[STATION NAME:]{.underline} Name of place or location of station. (30
characters)

[WMO ID]{.underline}: If supplied in the original metadata this 5 digit
integer represents the World Meteorological Organization=s 5 digit
station identifier. (5 digits)

[STATION ID #2:]{.underline} 2^nd^ supplied identifier from original
metadata. This often is a cross reference with another station
identifier.

(8 characters)

[STATION_ID\_#3:]{.underline} 3^rd^ supplied identifier from original
metadata. This often is a cross reference with another station
identifier.

(8 characters)

**7. [Start Date]{.underline}**

March 1, 1840

**8. [Stop Date]{.underline}**

November 30, 2001

**9. [Coverage]{.underline}**

![](media/image1.jpeg){width="6.497916666666667in"
height="4.513888888888889in"}

**10. [Location]{.underline}**

a\. Africa

b\. Asia

c\. Europe

d\. Global

e\. North America

f\. South America

g\. Equatorial

h\. Mid‑Latitude

i\. Polar

j\. Surface

k\. Antarctica

**11. [Keywords]{.underline}**

a\. Temperature

b\. Surface Temperature

c\. Precipitation

d\. Daily

**12. [How to Order Data]{.underline}**

Ask NCDC\'s Climate Services about the costs of obtaining this data set.

National Climatic Data Center

Veach-Baley Federal Building

151 Patton Avenue

Asheville, NC 28801-5001

P:(828) 271-4800

F:(828) 271-4876

http://www.ncdc.noaa.gov/contacts.html

**13. [Archiving Data Center]{.underline}**

National Climatic Data Center

Veach-Baley Federal Building

151 Patton Avenue

Asheville, NC 28801-5001

**14. [Technical Contact]{.underline}**

NCDC.GDCN@noaa.gov

**15. [Known Uncorrected Problems]{.underline}**

A. An undetermined number of stations within Mexico have recorded some
of their temperature observations in Fahrenheit. No pattern has yet been
identified so users should pay particular attention to the temperature
quality control flags which may or may not be useful in identifying
conversion issues.

B. Stations with non-standard practices for recording accumulated
precipitation have been identified in Appendix D. Please see page 14 for
further explanation.

**16. [Quality Statement]{.underline}**

All of the GDCN data (metadata and data) have been processed through an
extensive set of quality control procedures. The quality control
consisted of two parts:

\(1\) Simple datum checks (e.g. correct format, impossible values, out
of range values, etc.).

\(2\) Statistical analysis of sets of observations to locate

and identify potential outliers and/or erroneous data.

The user is referred to Appendix C for a complete list of all quality
control procedures used in step 1 (above). More extensive explanation
for step 2 is provided below for both temperature (maximum and minimum)
and precipitation.

***[Temperature QC:]{.underline}***

The quality control of temperature identifies or removes the numerous
problems that can exist with raw temperature data. The most notable
problems with temperature data are the presence of outliers. Outliers
often provoke controversy because when removed they can affect the final
analysis of extreme weather events. However if erroneous outliers are
included in the data set they can often adversely affect statistical
computations. The general philosophy behind GDCN 1.0 is to provide the
user with a minimal set of flags to identify the position of the datum
within the overall distribution of observations. The user can then make
the decision to include the datum based upon their particular
application.

The method employed in the identification of outliers is based on robust
and resistant measures of location and scale after Lanzante, 1995. The
identification of outlier values within GDCN begins with quality control
procedure #5 (Appendix C). This procedure appropriately flags all data
values greater than or less than the known world extremes as published
in the U.S. Army Corps of Engineers (Krause, 1997). Those values that
are flagged are then excluded from future quality control calculations.
Next, for both maximum and minimum temperature for each day within the
station record, a series (*X~i~*) of daily observations is created. This
series is comprised of all non-missing data and for a specified day
includes the observation from the day before, day after, and the day
after, day of, and day before for all other years in the station record.
For example, the calculation of the series (*X~i~*) for June 6^th^, 1988
for a particular station with 3 years of data (1987 through 1989) would
consist of the following daily observations:

June 5,6,7 of 1987

June 5,7 of 1988

June 5,6,7 of 1989

This series is used to calculate the biweight mean and biweight estimate
of standard deviation. Both of these estimators are more heavily
weighted towards the center of their distributions than the tails. Thus,
they are more resistant to outlier values. These estimators provide a
more robust calculation of mean and standard deviation than traditional
statistical methods which apply equal weight throughout the
distribution. All stations for which this type of outlier identification
was performed required at least 10 years of data so there was at least a
minimum of 29 potential observations. A weight (*u~i~*) is calculated
for all n observations within (*X~i~*):

![](media/image2.jpeg){width="6.497222222222222in"
height="0.5888888888888889in"} ![](media/image3.wmf)

where *M* is the median of *X~i~* and *MAD* is the median of the
absolute deviations of the values from the median (Note: for any
*u~i~*  1.0, *u~i~*=1.0). The constant *c* represents a \>censor=
value that controls the distance from the center of the distribution at
which the weight is equivalent to zero. The censor value used in
Lanzante, 1995 of 7.5 was also used throughout all GDCN 1.0
calculations. Therefore the biweight mean from Lanzante, 1995 is:

![](media/image4.jpeg){width="6.499305555555556in"
height="0.7493055555555556in"}

![](media/image7.jpeg){width="6.495138888888889in"
height="0.9458333333333333in"}and the biweight estimate of standard
deviation is:

The resulting biweight estimators were used to determine the number of
biweight standard deviations a particular value was from the biweight
mean of the distribution. Values greater than or equal to 3 biweight
standard deviations from the biweight mean were classified using the
appropriate quality control flags. A summary of these flags is found on
pages 6-7 in this documentation. Figure 2 illustrates the identification
of these outlier values for a select GDCN 1.0 station.

![](media/image8.jpeg){width="6.0in" height="4.670833333333333in"}

Spatial quality control was conducted for maximum and minimum
temperature by comparing monthly averaged values to an independently
created gridded data set (Legates and Wilmott, 1990). Each monthly
average was compared to the gridded monthly value and a difference
series (for the entire period of record)

was computed. The median value of this difference series was then
computed for each station series and these values were used to determine
if a station was retained or excluded from the GDCN 1.0. Based upon this
analysis no maximum or minimum temperature

stations were excluded from GDCN 1.0.

*[Precipitation QC:]{.underline}*

The quality control of precipitation is often problematic due to the
spatial variability of precipitation and inadequate metadata (lack of
information concerning observing practices and times). The precipitation
quality control consisted of the basic checks listed in Appendix C. In
addition careful initial format procedures were implemented to ensure
that the received raw precipitation metadata information were preserved
within the GDCN data measurement flags. However, this often lead to some
discrepancies within the data. The data measurement flags \>H= and \>J=
both illustrate two problems unique to accumulated precipitation (see
pg. 6). Therefore in some cases the preservation of the incomplete or
erroneous supplied metadata allows these problems to persist. Thus,
users should pay particular attention to the data measurement flag when
processing precipitation data.

Unique observing practices can lead to potentially erroneous analysis
conclusions. A careful analysis (by day of week) was performed on all
precipitation data to examine potential inconsistencies within the data
record. In particular, one non-standard precipitation observing practice
was identified. If the accumulated precipitation value was 0.0 mm then
the observer would correctly record the accumulation but in the previous
days included in the accumulation the observer would also record 0.0 mm
values. Although not physically incorrect, these observers did not
observe the same practice when the accumulation was greater than 0.0 mm.
In those instances the previous days in the accumulated period would be
set to the missing value. Furthermore this occurrence was almost
exclusive to Monday accumulations from the weekend period. Thus, a
careful analysis of the record would erroneously show that Saturday and
Sunday always had 0.0 mm of precipitation or a missing value of
precipitation, if indeed the general practice was to not observe on the
weekend throughout the station record. Fortunately the incidence of this
occurrence is small within the GDCN ( \< 100 stations) and thus Appendix
D contains a list of stations that were identified with the above
mentioned problem.

Spatial quality control was carried out in a similar manner described in
the \>Temperature QC=. No precipitation stations were excluded from the
GDCN 1.0 based upon the spatial precipitation analysis.

*[Duplicate Removal:]{.underline}*

With over 800 million days of daily data, direct comparison of all data
was not feasible due to computational and time limitations. Therefore,
duplicate identification was conducted by comparison of metadata
(geographic coordinates). Stations with exact or very similar geographic
positions were identified and then their full records were compared and
the percent of similarity was computed. Stations with exact or very high
similarities were then identified and only the station with the longest
total period of record was retained for inclusion in GDCN 1.0.

**17. [Revision Date]{.underline}**

Version 1.0

**18. [Source Data Sets]{.underline}**

National Climatic Data Center scientists collected the majority of daily
data contained within this data set through cooperative research
exchanges. In addition, many stations were collected through previously
established data networks such as the Global Climate Observing System
(Peterson, et. al., 1997). Appendix B provides a complete list of
personnel who were responsible for collecting and establishing data
exchange agreements between each country.

**19. [Essential Companion Data Sets]{.underline}**

None

**20. [Derived Data Sets]{.underline}**

None

**21. [References]{.underline}**

Hastings, David A., and P. K. Dunbar, 1997. The development of global
digital elevation data. Proceedings, 18^th^ Conference on Remote
Sensing, Kuala Lumpur. pp. J=S‑3‑1 to J=S‑3‑6.

Krause, Paul F. and Kathleen Flood. (1997). Weather and Climate Extremes
(TEN-0099). Fort Belvoir, VA: U.S. Army Engineer Topographic Engineering
Center).

Lanzante, John R. 1996. Resistant, robust, and nonparametric techniques
for the analysis of climate data. Theory and examples, including
applications to historical radiosonde station data. Int. J Climatol.,
16, 1197-1226.

Legates, David R., and Cort J. Willmott. 1990. \"Mean seasonal and
spatial variability in gaugencorrected global

precipitation.\" International Journal of Climatology, vol. 10. pp.
111n127.

Legates, David R. and Cort J. Willmott. 1990. \"Mean seasonal and
spatial variability in global surface air temperature.\"

Theoretical and Applied Climatology, vol. 41, pp. 11n21.

Peterson, Thomas, Harald Daan, Philip Jones, 1997: Initial Selection of
a GCOS Surface Network. Bulletin of the American Meteorological Society:
Vol. 78, No. 10, pp. 2145B2152.

**22. [Summary]{.underline}**

The Global Daily Climatology Network (GDCN) represents a compilation of
global daily timescale data into a single and consistent format. The
data set will serve the needs of researchers, weather-sensitive
businesses, agriculture, and policy makers whose are dependent upon
complete and accurate analysis of daily temperature and precipitation.
Data within the GDCN have been extensively checked through a series of
quality control procedures to ensure erroneous values have been removed
and/or identified. The GDCN currently has over 800 million days of
weather data and that number is expected to increase as the GDCN expands
in the future.

**23. [Web Site]{.underline}**

www.ncdc.noaa.gov/gdcn.html

GLOSSARY

Access Method and Sort for Archived Data ‑‑ How a digital data

set can be accessed in its archived condition. If the data

are sorted, how.

Access Method and Sort for Supplied Data ‑‑ How the user can access data
in its most commonly supplied form. If the data

are sorted, how.

Archiving Data Center ‑‑ Name, organizational acronym and address

of institutions that archive and distribute the data.

Coverage ‑‑ The smallest latitude‑longitude \"box\" on the earth\'s

> surface within which all data measurements occurred. Northernmost and
> southernmost latitudes, and westernmost and easternmost longitude, are
> required for coverage.

Data Set Aliases ‑‑ Other names or Ids associated with the data set, now
or in the past.

Data Set ID ‑‑ A unique code number or acronym identifying the

data set. Normally, it is the code number used by the data

center at the data set is archived.

Data Set Name ‑‑ A descriptive name for the data set that provides a
thumbnail description.

Elements ‑‑ The basic units of data that need to be described. Usually,
elements are simply data variables such as \"station id\",
\"precipitation\", \"ocean depth\", \"daily maximum temperature\".

Essential Companion Data Sets ‑‑ Other data sets, if any, that

must be obtained in order to use this data set.

Derived Data Sets \-- Other data sets, if any, that originated, in whole
or in part, from this data set.

How to Order Data ‑‑ Describes how to find the approximate costs

> that are associated with obtaining the data set, and how to order the
> data.

Keyword ‑‑ Words or short phrases that suggest the data set. Actual
element names should not be used as keywords.

Known Uncorrected Problems ‑‑ Known data difficulties, if any, for which
no corrections have been made.

Location ‑‑ A collection of standard phrases that name the major

geographic areas, and the atmospheric layers, to which the data refer.

Quality Statement ‑‑ A brief generalization of how good the data

are.

Reference ‑‑ Any important bibliographic references pertaining to

the data set.

Revision Date ‑‑ The date that this documentation is created, or

the date of its latest revision.

Start Date ‑‑ The date of the earliest data in the data set.

Stop Date ‑‑ The date of the latest data in the data set.

Summary ‑‑ A concise abstract that is used to capture a few of the most
important facts about the data set, most of which

should have appeared in more detail under individual topic

discussions.

Technical Contact ‑‑ The person who can be contacted to obtain

> and provide information about the data.

**[Appendix A]{.underline}**

3-digit country codes (used as the first 3 digits of the 11 character
station ID) Note: A digital file of the following list is available at
the GDCN web site - [www.ncdc.noaa.gov/gdcn.html]{.underline} under
\>Supplementary Files=:

101 - Algeria

115 - Egypt

141 - South Africa

148 - Sudan

205 - China

207 - India

210 - Japan

213 - Kyrgyzstan

215 - Mongolia

221 - Korea

222 - Russia

223 - Saudi Arabia

227 - Tajikistan

228 - Thailand

229 - Turkmenistan

231 - Uzbekistan

301 - Argentina

303 - Brazil

306 - Ecuador

308 - Paraguay

313 - Uruguay

314 - Venezuela

403 - Canada

405 - Costa Rica

408 - El Salvador

410 - Guatemala

412 - Honduras

414 - Mexico

415 - Nicaragua

416 - Panama

425 - United States of America

501 - Australia

505 - Malaysia

514 - French Polynesia

531 - New Caledonia

602 - Armenia

603 - Austria

604 - Azerbaijan

605 - Belarus

606 - Belgium

607 - Bosnia

612 - Denmark

613 - Estonia

614 - Finland

615 - France

616 - Georgia

617 - Germany

618 - Greece

620 - Iceland

621 - Ireland

622 - Israel

623 - Italy

625 - Kazakhstan

626 - Latvia

628 - Lithuania

629 - Luxembourg

631 - Moldova

633 - Netherlands

634 - Norway

635 - Poland

636 - Portugal

641 - Slovakia

642 - Slovenia

643 - Spain

645 - Sweden

646 - Switzerland

647 - Syria

650 - Ukraine

651 - United Kingdom

**[Appendix B]{.underline}**

The National Climatic Data Center staff listed below were responsible
for obtaining GDCN data. All data contained within the GDCN are believed
to be available for re-distribution through the GDCN in compliance with
WMO resolution 40. If you have further questions about the National
Climatic Data Center=s right to re-distribute data through the GDCN,
please email your questions and/or concerns to:

[NCDC.GDCN@noaa.gov]{.underline}

Dr. Pavel Ya. Groisman Dr. Thomas C. Peterson

Visiting UCAR Scientist Physical Scientist

National Climatic Data Center National Climatic Data Center

Asheville, NC 28801-5001 Asheville, NC 28801-5001

Dr. David R. Easterling Mr. Russell S. Vose

Principal Scientist Chief, Climate Analysis Branch National Climatic
Data Center National Climatic Data Center

Asheville, NC 28801-5001 Asheville, NC 28801-5001

![](media/image9.jpeg){width="4.080555555555556in"
height="3.2729166666666667in"}

**[Appendix C]{.underline}**

Data and station information format quality control:

1\. Check if station identification in station file matches the station
identification used in the station file name.

2\. Check all non-existent days for missing data. (Example: February,
30^th^ should always equal the missing value)

3\. Check for proper sort of station data and inventory file.

4\. Trace precipitation (Flag \>T=) must equal 0.0.

5\. Check that all record elements are one of PRCP (total
precipitation), TMAX (maximum temperature), or TMIN (minimum
temperature)

6\. Check if the 4-digit year element within the station data file is:
1800 \<= YEAR \<= 2001.

7\. Check that month field is 1 \<= MONTH \<= 12.

8\. Check both the data measurement and quality control flags to ensure
they only contain documented flags.

9\. Check each datum to ensure they are integer values.

10\. Check for duplicate stations within the entire data set.

11\. Check for station -90.0 \<= Latitude \<= 90.0

-180.0 \<= Longitude \<= 180.0

12\. Check that the number of stations within the inventory file matches
the number of station data files.

13\. Check for precipitation \< 0.0 mm.

14\. Check for maximum temperature \< minimum temperature for the same
day.

15\. Check for days with the same value repeated 10 or more consecutive
times (e.g. quality control flag = \>K=). Note: 0.0 mm

precipitation values were excluded.

16\. Check for daily element value exceeding known world extreme
according to the U.S. Army Corps of Engineers World Extremes (Krause and
Flood, 1997).

World Extremes used for this data set:

highest temperature: El Azizia, Libya 57.8 deg C

September 13, 1922

lowest temperature: Vostok, Antarctica -89.4 deg C

July 21, 1983

greatest 24-hr rainfall: Foc-Foc, Réunion Island 1828.8 mm

January 7^th^ to 8^th^, 1966

17\. If source country supplied quality control information (generally
failed or suspect quality control) for a datum then the quality control
flag was set as either \>F= for failed quality control or \>S= for
suspect datum.

18\. Compute monthly sums and/or averages for each station and compare
to the global gridded climatology by Legates and Wilmott, 1990.

**[Appendix D]{.underline}**

Stations with potentially inconsistent accumulated precipitation
observing practices. See page 14 for further explanation. (Note: A
digital file containing a list of the following stations is available at
the GDCN web site ([www.ncdc.noaa.gov/gdcn.html)]{.underline} under
\>Supplementary Files=:

14100174520

14100284150

14100315070

14100322090

14101274850

14101516040

14101521900

14101524820

14102090390

14102400730

14102985120

14103044460

14103688310

14103728520

14103734850

14104002030

14104753700

14104757610

14105116720

14105140100

14105558780

14105945390

14105945900

14105948060

20704171200

3030C5‑0500

3030E3‑0530

31300001220

31300001440

31300001558

31300001709

31300001734

31300001751

31300001784

40301191440

40302204000

40304013400

40306044903

40306060773

40307022280

40307063400

40307074240

40308100500

40308202300

40308206450

42500055507

42500058064

42500067432

42500105708

42500113595

42500244328

42500300870

42500301265

42500301580

42500304207

42500304944

42500305171

42500305679

42500318778

42500336786

42500338366

42500340394

42500358780

42500360785

42500361033

42500367978

42500455387

42500456909

42500510145

42500510211

42500510470

42500511075

42500511339

42500511665

42500511887

42500512751

42500513734

42500513982

42500514318

42500514634

42500514742

42500515286

42500515710

42500515864

42500516134

42500516588

42500518760

42500518815

42500518941

42500519955

42500660040

42500660152

42500661345

42500662801

42500663904

42500665020

42500667843

42500669466

42500669608

42500914000

50100094380

50100094570

50100094693

50100094784

50100094869

50100094910

**[Appendix E]{.underline}**

**[Global Daily Climatology Network 1.0 Use and Distribution
LICENSE]{.underline}**

The data and metadata within the Global Daily Climatology Network 1.0
are governed by:

> WMO Resolution 40

NOAA Policy

The following data and products may have conditions placed on their
international commercial use. They can be used within the U.S. or for
non‑commercial international activities without restriction.
Re‑distribution of these data by others must provide this same
notification. For details, please consult:

[www.wmo.ch]{.underline}

**[Appendix F]{.underline}**

**[Acknowledgments:]{.underline}**

Below is a list (not necessarily complete) of people and/or institutions
who assisted in the construction of this data set by either directly or
indirectly providing data and/or advice.

Mr. Red Ezell Dr. Thomas C. Peterson

Computer Programmer Physical Scientist

National Climatic Data Center National Climatic Data Center

Asheville, NC 28801-5001 Asheville, NC 28801-5001

Dr. David R. Easterling Dr. Pavel Ya. Groisman

Principal Scientist Visiting UCAR Scientist

National Climatic Data Center National Climatic Data Center

Asheville, NC 28801-5001 Asheville, NC 28801-5001

Mr. Russell S. Vose Mr. Roy Jenne

Chief, Climate Analysis Branch Manager, SCD Data Support Sec.

National Climatic Data Center NCAR

Asheville, NC 28801-5001 Boulder, CO 80307-3000

Mr. Brant Liebmann Dr. Arthur V. Douglas

Research Scientist Dept. of Atmospheric Science

Climate Diagnostics Center Creighton University

Boulder, CO 80305‑3328 Omaha, NE 68178

Dr. Mark Morrissey KNMI (Koninklijk Nederlands

Environmental Verification and Meteorologisch Instituut)

Analysis Center (EVAC) www.knmi.nl/samenw/eca

Norman, OK 73069 The Netherlands

ANEEL (Agência Nacional De National Climate Center

Energia Elétrica) - Brazil China Meteorological Admin.

www.aneel.gov.br Beijing, China

Mr. Robert Morris Mr. Johan Koch

Manager, Information Svcs. Div. South African Weather Service

Meteorological Service of Canada Pretoria, South Africa

Environment Canada

Toronto, Ontario

M3H 5T4