Iowa DNR 2002 AnnualIowa DNR 2002 AnnualMM5 Modeling ProjectMM5 Modeling ProjectMatthew JohnsonMatthew JohnsonIowa Department of Natural ResourcesIowa Department of Natural ResourcesAir Quality BureauAir Quality BureauRegional Modeling GroupRegional Modeling Groupmatthew.johnson@dnr.state.ia.usOverviewOverview1) Basic info–Configuration review•Domain•Input data•Physics options–Computational requirements2)Briefimplementation discussion3) Evaluation–Metstat (surface statistics)–Raobplot (upper air)4) Summary & ConclusionsDomainyNational RPO specsy36 km resyProjection-97°, 40°33°, 45°yDimensionsE-W 165N-S 129Data SourcesData SourcesyFirst-guess fields (REGRID)Eta 3D and surfaceanalysisdata(ds609.2)NCEP global tropospheric data(ds083.0)–ProvidesONLYthe sea surface temperatures•Eta data uses skin temperatures, which can differby up to 12o C from observed SSTs along coastlinesyObservational enhancement (LITTLE_R)NCEP ADP surface obs(ds464.0)–Land = List E–Ship = List FNCEP ADP upper air obs(ds353.4)–List APhysics OptionsPhysics OptionsyResults from the cooperative sensitivity projectyielded the following ‘optimum’ configurationPhysics OptionSelectionconfigure.userMoistureSimple IceIMPHYS = 4(MPHYSTBL = 0)CumulusKain-FritschICUPA = 6PlanetaryBoundaryLayerPleim-Chang(ACM)IBLTYP = 7RadiationRRTMFRAD = 4Land Surface ModelPleim-XiuISOIL = 3Shallow ConvectionNoISHALLO = 0Nudging (Sfc and Grid)YesFDDAGD = 1Additional Configuration ChoicesAdditional Configuration ChoicesyInitialization at 12 Z with 5 day runtimeyVertical structure34 Levels - Exactly matched to Dennis McNally’sconfiguration for EPA 2001 runyPX LSM optionsSoil temperature and soil moisture fields continuous–Requires INTERPX–Indirectly nudged using surface conditionsyAdditional details and discussion in the protocol:–http://www.state.ia.us/epd/air/prof/progdev/RegionModel.htm–(Includes modeling system scripts)Vertical StructureVertical StructureLevelSigmaHeight(m)p(mb)Depth(m)LevelSigmaHeight(m)p(mb)Depth(m)340.000146621001841160.8201400838166330.050128221451466150.8401235856163320.100113561901228140.8601071874160310.150101272351062130.880911892158300.2009066280939120.90075391078290.2508127325843110.91067591977280.3007284370767100.92059892877270.350651741570490.93052193776260.400581246065280.94044594676250.450516050560770.95036995575240.500455355056960.96029496474230.550398459553650.97022097374220.600344864050640.98014698237210.650294268548030.98510998737200.700246273036720.9907399136190.740209576626610.9953699636180.770182879325901.000010000170.8001569820169ps= 1000 mb; Ptop= 100 mb; Ts= 275 K; A = 50 K/{ln(PA)}Temporal StructureTemporal StructureyAnnual run consists of four quarters, each quarter ends on thefirst of a monthDec 29th2001 - Apr 1st2002Mar 14th2002 - Jul 1st2002Jun 14th2002 - Oct 1st2002Sep 14th2002 - Jan 1st2003yEach quarter consists of 27 five day blocks = 108 5 day simsyQuarterly applications run sequentially (due to PX config)yQuarters run simultaneously using 4 workstationsStart BLOCK End BLOCK Start (Z) End (Z) FilenameHrs SpinStart (Z) End (Z) Start (Z) End (Z) Start (CDT) End (CDT)12/13/01 12:00 12/18/01 12:0012/13/01 12:00 12/13/01 12:00MMOUT_DOMAIN1_0012/13/01 13:00 12/14/01 12:00MMOUT_DOMAIN1_0112/14/01 0:00 12/14/01 12:0013 1112/14/01 5:00 12/14/01 12:00 12/14/01 0:00 12/14/01 7:0012/14/01 13:00 12/15/01 12:00MMOUT_DOMAIN1_0212/14/01 13:00 12/15/01 12:002412/14/01 13:00 12/15/01 12:00 12/14/01 8:00 12/15/01 7:0012/15/01 13:00 12/16/01 12:00MMOUT_DOMAIN1_0312/15/01 13:00 12/16/01 12:002412/15/01 13:00 12/16/01 12:00 12/15/01 8:00 12/16/01 7:0012/16/01 13:00 12/17/01 12:00MMOUT_DOMAIN1_0412/16/01 13:00 12/17/01 12:002412/16/01 13:00 12/17/01 12:00 12/16/01 8:00 12/17/01 7:0012/17/01 13:00 12/18/01 12:00MMOUT_DOMAIN1_0512/17/01 13:00 12/17/01 23:001112/17/01 13:00 12/18/01 5:00 12/17/01 8:00 12/18/01 0:0012/17/01 12:00 12/22/01 12:0012/17/01 12:00 12/17/01 12:00MMOUT_DOMAIN1_0012/17/01 13:00 12/18/01 12:00MMOUT_DOMAIN1_0112/18/01 0:00 12/18/01 12:0013 1112/18/01 5:00 12/18/01 12:00 12/18/01 0:00 12/18/01 7:0012/18/01 13:00 12/19/01 12:00MMOUT_DOMAIN1_0212/18/01 13:00 12/19/01 12:002412/18/01 13:00 12/19/01 12:00 12/18/01 8:00 12/19/01 7:0012/19/01 13:00 12/20/01 12:00MMOUT_DOMAIN1_0312/19/01 13:00 12/20/01 12:002412/19/01 13:00 12/20/01 12:00 12/19/01 8:00 12/20/01 7:0012/20/01 13:00 12/21/01 12:00MMOUT_DOMAIN1_0412/20/01 13:00 12/21/01 12:002412/20/01 13:00 12/21/01 12:00 12/20/01 8:00 12/21/01 7:0012/21/01 13:00 12/22/01 12:00MMOUT_DOMAIN1_0512/21/01 13:00 12/21/01 23:001112/21/01 13:00 12/22/01 5:00 12/21/01 8:00 12/22/01 0:00Metstat Usable CAMx UsableComputational RequirementsComputational RequirementsyCPUEach 5 day block requires ~ 42 hours run time*4536 CPU hours (189 CPU days) required for completion–47.25 CPU days per quarteryStorageInput data: 2.9 Gb per monthMM5 output: ~ 3.4 Gb per 5 day blockIntermediate: ~ 4.0 Gb per 5 day blockTOTAL: ~ 800 GbMM5 output only: ~ 354 Gb*1 Ghz Pentium III machines, 512 Mb ram1 Ghz Pentium III machines, 512 Mb ramMethodsMethodsyAbriefoverview of the steps comprising the annual runprocessDetermine the modeling system configuration–Accomplished through the MM5 sensitivity projectOverview of subsequent tasks1)Evaluate computational requirements2)Obtain necessary input data3)Determine the structure of blocks & quarters4)Process the first-guess data via pregrid5)Build and execute the master and slave scripts6)Be patient7)Evaluate resultsMethodsMethodsyInput data:–Each month requires 12 input files(occupies 3 Gb)•3Eta files (two 3D files, one Sfc file)•1NCEP global analysis file•6Sfc ADP files•1Ship obs ADP file•1ADP upper air obs fileProcess quarterly first-guess data throughPREGRID–This process is independent of when the MM5blocks start/end–Make certain that all*.tm12 Eta files are deletedMethodsMethodsyScripting invaluable...–The master script is responsiblefor automatingthequarterly simulations•Assigns starting/stopping dates•Runs each piece of the MM5 modeling system•Archives data•Ensures correct termination of MM5 beforecontinuation•Designed to optimize use of computational resources–The MM5 preprocessors are controlled via themaster•Slight modifications made congruent with the masterMaster ScriptMaster Script...Master Script & LITTLE_R (as an example MM5 system script)Master Script & LITTLE_R (as an example MM5 system script)ResultsResultsyModel PerformanceA thorough evaluation of all meteorological data requiresboth objective and subjective evaluation of many fieldsacross the entire modeling domain–The time requirements for a thorough evaluation arestaggering, thus decisions must be made regarding thenature of evaluation effortsyFocus→The statistical evaluation produced viaMetstat, in combination with analysis of observed vspredicted soundingsStatistical Model EvaluationStatistical Model Evaluation-Sub DomainsSub Domains•12 Sub-domains•CenrapN•CenrapS•DessertSW•GL•Iowa•MidAtlantic•North•NE•OhioVal•PacificNW•SE•SW• Metstat dataprocessed & plottedover all domainsPacificNWSWNorthDessertSWCenrapSCenrapNGLSENEMidAtlanticOhioValIowaResults: CenrapN (Ws)HourlyResults: CenrapN (Ws)Observed/Predicted Windspeed02468 1/ 3 1 / 4 1 / 5 1 / 6 1 / 7 1/ 8 1/ 9 1/1 0 1/1 1 1/1 2 1/1 3 1/1 4 1 /1 5 1 /1 6 1 /1 7 1 /1 8 1/ 1 9 1/ 2 0 1/ 2 1 1/ 2 2 1/23 1/24 1/25 1/26 1/2 7 1/2 8 1/2 9 1/3 0

m/sObsWndSpdPrdWndSpdObserved/Predicted Windspeed051015 3 / 1 3/ 2 3/ 3 3 / 4 3 / 5 3/ 6 3/ 7 3 / 8 3 / 9 3/1 0 3 /1 1 3 / 12 3/13 3/1 4 3 /1 5 3 / 16 3/17 3/1 8 3/1 9 3 / 20 3/21 3/2 2 3/2 3 3 / 24 3/25 3/2 6 3 /2 7 3 / 28 3/29 3/3 0 3 /3 1

m/sObsWndSpdPrdWndSpdBias Windspeed-1.5-1-0.500.51 8/ 1 8/ 2 8 / 3 8/ 4 8/ 5 8/ 6 8 / 7 8/ 8 8/ 9 8 /1 0 8/11 8/12 8 /1 3

8 /1 4 8/15 8/16 8 /1 7 8 /1 8 8/19 8/20 8 /2 1 8 /2 2 8/23 8/24 8 /2 5 8 /2 6 8/27 8/28 8 /2 9 8 /3 0 8/31

m/sBiasWndSpdRMSE Windspeed0123 12/ 1 12 / 2

1 2/ 3 12/ 4 1 2/ 5 12/ 6 12 / 7 1 2/ 8 12/ 9 1 2/10 1 2/11 1 2/12 1 2/13 1 2/14 1 2/15 1 2/16 1 2/17 1 2/18 1 2/19 1 2/20 1 2/21 1 2/22 1 2/23 1 2/24 1 2/25 1 2/26 1 2/27 1 2/28 1 2/29 1 2/30 1 2/31

m/sRMSEWndSpdRMSESWndSpRMSEUWndSpResults: CenrapN (T)Results: CenrapN (T)Observed/Predicted Temperature230240250260270280290

2 / 4 2/ 5 2 / 6 2/ 7 2 / 8 2/ 9 2 / 1 0 2/ 1 1 2 / 1 2 2/1 3 2 / 1 4 2/1 5 2 / 1 6 2/1 7 2/ 1 8 2/1 9 2/ 2 0 2 / 21 2/ 2 2 2 / 2 3 2/ 2 4 2 / 2 5 2/2 6 2 / 2 7

KObsTempPrdTempBias Temperature-3-2-10123 6/ 1 6 / 2 6/ 3 6/ 4 6 / 5 6 / 6 6/ 7 6 / 8 6/ 9

6 / 10 6/ 11 6/12 6/ 13 6 / 14 6/ 1 5 6/ 1 6 6/ 17 6/18 6/ 19 6 / 20 6 / 21 6/ 2 2 6 / 23 6/24 6/ 25 6/ 26 6 / 27 6/ 2 8 6 / 29 6/30

KBiasTempRMSE Temperature012345 6 / 1 6/ 2 6/ 3 6 / 4 6 / 5 6 / 6 6 / 7 6 / 8 6/ 9 6 / 10 6 /1 1 6/1 2 6 / 13 6/1 4 6/1 5 6/16 6 / 17 6 /1 8 6/19 6 / 20 6/2 1 6/2 2 6/2 3 6 / 24 6/2 5 6/26 6 / 27 6 /2 8 6/2 9 6/3 0

KRMSETempRMSESTempRMSEUTempBias Temperature-3-2-10123 9/ 1 9 / 2 9/ 3 9 / 4 9/ 5 9/ 6 9/ 7 9 / 8 9/ 9 9 / 10 9 / 1 1

9 / 1 2 9 / 1 3 9/14 9 / 1 5 9/16 9 / 1 7 9 / 18 9 / 1 9 9/20 9 / 2 1 9/22 9 / 2 3 9/24 9 / 25 9/ 2 6 9 / 2 7 9/28 9 / 2 9 9/30

KBiasTempRMSE Temperature01234 9/ 1 9 / 2 9/ 3 9 / 4 9/ 5 9 / 6 9 / 7 9/ 8 9 / 9 9/10 9/1 1 9/12 9/13 9/14 9/15 9/16 9/17 9/1 8 9/19 9/20 9/21 9/22 9/23 9/24 9 / 2 5 9/26 9 / 2 7 9/28 9/29 9/30

KRMSETempRMSESTempRMSEUTempObserved/Predicted Temperature250260270280290300

1 1 / 1 1 1 / 2 1 1 / 3 1 1 / 4 1 1 / 5 1 1 / 6 1 1 / 7 1 1 / 8 1 1 / 9 1 1 /1 0 1 1 / 1 1 1 1 / 1 2 1 1 / 1 3 1 1 / 1 4 1 1 /1 5 1 1 /1 6 1 1 /1 7 1 1 / 1 8 1 1 / 1 9 1 1 / 2 0 1 1 /2 1 1 1 /2 2 1 1 / 2 3 1 1 / 2 4 1 1 / 2 5 1 1 / 2 6 1 1 /2 7 1 1 /2 8 1 1 / 2 9 1 1 / 3 0

KObsTempPrdTempObserved/Predicted Temperature240250260270280290

1 / 1 1 / 2 1 / 3 1 / 4 1 / 5 1 / 6 1 / 7 1 / 8 1 / 9 1 / 1 0 1 /1 1 1 /1 2 1 /1 3 1 / 1 4 1 /1 5 1 /1 6 1 /1 7 1 / 1 8 1 / 1 9 1 /2 0 1 /2 1 1 / 2 2 1 / 2 3 1 /2 4 1 /2 5 1 / 2 6 1 / 2 7 1 /2 8 1 /2 9 1 / 3 0 1 /3 1

KObsTempPrdTempFreezingResults: CenrapN (q)Results: CenrapN (q)Predicted/Observed Humidity012345 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1 /1 0

1 /1 1 1 /1 2 1/ 1 3 1/ 1 4 1/ 1 5 1/ 1 6 1/ 1 7 1/ 1 8 1/ 1 9 1/ 2 0 1/ 2 1 1/ 2 2 1/2 3 1/2 4 1/2 5 1/2 6 1 /2 7 1 /2 8 1 /2 9 1 /3 0

g /k gObsHumPrdHumPredicted/Observed Humidity02468101214 5/ 2 5/ 3 5/ 4 5/ 5 5/ 6 5/ 7 5/ 8 5/ 9 5 /1 0

5 /1 1 5/ 1 2 5/ 1 3 5/ 1 4 5/ 1 5 5/ 1 6 5/ 1 7 5/ 1 8 5/ 1 9 5/ 2 0 5/ 2 1 5/2 2 5/2 3 5/2 4 5/2 5 5 /2 6 5 /2 7 5 /2 8 5 /2 9

g /k gObsHumPrdHumBias Humidity-2-1.5-1-0.500.511.52 7 / 1 7/ 2 7 / 3 7 / 4 7/ 5 7 / 6 7 / 7 7/ 8 7 / 9 7 /10 7 /11 7/ 1 2 7 /13 7 /14 7/15 7 /16 7 /17 7/18 7 /19 7 /20 7/ 2 1 7/ 2 2 7 /23 7/ 2 4 7/ 2 5 7/26 7/ 2 7 7 /2 8 7 /29 7/ 3 0 7 /3 1

g/kgBiasHumRMSE Humidity00.511.522.533.5 7/ 1 7/ 2 7 / 3 7/ 4 7/ 5 7/ 6 7/ 7 7/ 8 7/ 9 7 / 1 0 7/11 7/12 7/13 7/14 7/15 7/16 7 / 17 7/18 7/19 7/ 2 0 7/21 7/22 7/23 7/24 7/25 7/26 7 / 2 7 7/28 7/ 2 9 7/ 3 0 7/31

g/kgRMSEHumRMSESHumRMSEUHumPredicted/Observed Humidity0123456 12/ 1 1 2 / 2 12/ 3 12/ 4 12/ 5 1 2 / 6

1 2 / 7 12/ 8 1 2 / 9 12/1 0 1 2 / 11 1 2 / 12 12/13 1 2 /1 4 1 2 /1 5 12/16 12/1 7 12/1 8 1 2 /1 9 12/20 12/21 1 2 /2 2 1 2 / 23 1 2 / 24 12/2 5 12/2 6 1 2 /2 7 12/28 12/29 1 2 /3 0 1 2 / 31

g/kgObsHumPrdHumRMSE Humidity00.10.20.30.40.50.60.70.80.91 1 2/ 1 12/ 2 12 / 3 12 / 4 12/ 5 12/ 6 12 / 7 12/ 8 12/ 9 12 / 10 12/11 12/12 12/13 1 2/ 1 4 12/15 12/16 1 2/ 1 7 1 2/ 1 8 12/19 12 / 20 1 2/ 2 1 12/22 12 / 23 12 / 24 12/25 12/26 12 / 27 12/28 12/29 12 / 30 12/31

g/kgRMSEHumRMSESHumRMSEUHumCenrapN Hourly Results SummaryCenrapN Hourly Results SummaryyCenrapNWinter T prediction too cool–Lows too low, high temp prediction variableWinter moisture prediction elevatedvs obsFrom late Spring through Fall:T&q predictionsimproved–Towards December, cool bias returns→again,resulting from lows being too coldThroughout the year, apositive moisture biasemerges; errors generally within acceptable limitsWind fieldsalmost always withinacceptablelimitsthroughout the annual simulation–Worst trend: tendency to underpredict daytimemax – nighttime wind fields acceptableResults: CenrapSResults: CenrapSObserved/Predicted Wind Direction060120180240300360 1/ 1 1 / 2 1/ 3 1/ 4 1/ 5 1/ 6 1 / 7 1/ 8 1/ 9 1 /1 0 1/11 1/1 2 1/ 1 3 1/1 4 1 /1 5 1/16 1/1 7 1 /1 8 1/19 1/2 0 1/ 2 1 1/2 2 1/2 3 1/ 2 4 1/2 5 1 /2 6 1/27 1/2 8 1 /2 9 1/30 1/3 1

de gObsWndDirPrdWndDirRMSE Temperature012345 3/ 1 3 / 2 3/ 3 3/ 4 3 / 5 3 / 6 3 / 7 3/ 8 3/ 9 3 /1 0

3 /1 1 3/1 2 3/1 3 3 /1 4 3 /1 5 3/1 6 3/1 7 3/1 8 3/19 3/20 3/2 1 3/2 2 3/ 2 3 3/24 3/25 3 /2 6 3/ 2 7 3/ 2 8 3/29 3 /3 0 3 /3 1

KRMSETempRMSESTempRMSEUTempBias Humidity-1-0.500.511.52 3/ 1 3/ 2 3/ 3 3/ 4 3/ 5 3/ 6 3/ 7 3/ 8 3/ 9 3/10 3/11 3/12 3/13 3/14 3/15 3/1 6 3/17 3/ 18 3/19 3/20 3/21 3/22 3/23 3/24 3/25 3/26 3/27 3/28 3/29 3/30 3/31

g/kgBiasHumBias Temperature & Humidity-4-3-2-10123 6/ 1 6 / 2 6 / 3 6 / 4 6/ 5 6 / 6 6/ 7 6 / 8 6/ 9 6/10 6 / 1 1 6/12 6 / 13 6/14 6/ 1 5 6/16 6/ 1 7 6/18 6/19 6/20 6/21 6 / 2 2 6/23 6 / 24 6/25 6/ 2 6 6/27 6/ 2 8 6/29 6/30

K & g/kgBiasTempBias Humidit yOut of PhaseObserved/Predicted Temperature285290295300305310 8/ 1 8/ 2 8/ 3 8/ 4 8/ 5 8/ 6 8/ 7 8/ 8 8/ 9 8/10 8/11 8/12 8/13 8/14 8/15 8/16 8/17 8/18 8/19 8/20 8/21 8/22 8/23 8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31

KObsTempPrdTempT too cool (both min/max)Predicted/Observed Humidity02468101214 11/ 1 11/ 2 11/ 3 11/ 4 11/ 5 11/ 6 11/ 7 11/ 8

1 1 / 9 11 / 10 11 / 11 11 / 12 11 / 13 1 1/ 1 4 11 / 1 5 11 / 16 11 / 17 1 1/ 1 8 1 1/ 1 9 1 1/ 2 0 1 1/ 2 1 11/22 1 1/23 1 1/ 2 4 1 1/ 2 5 1 1/ 2 6 11 / 27 11 / 28 11/29 11/30

g/kgObsHumPrdHumBias Windspeed-3-2-101 11 / 1 11 / 2 11 / 3 11/ 4 1 1/ 5 11 / 6 11 / 7 11 / 8 11 / 9 11 / 1 0 1 1 / 1 1 11/1 2 11 / 1 3 11/1 4 11 / 1 5 1 1 / 1 6 1 1 / 1 7 11/1 8 11 / 1 9 11/2 0 11 / 2 1 1 1 / 2 2 1 1 / 2 3 11/2 4 11 / 2 5 11/2 6 11 / 2 7 1 1 / 2 8 11/2 9 11/3 0

m /sBiasWndSpdCenrapS: Diurnal Moisture ‘Trends’CenrapS: Diurnal Moisture ‘Trends’Humidity PredictionCenrapS, July 200214.515.015.516.016.517.017.57/16/9812:00 AM7/16/9812:00 PM7/17/9812:00 AM7/17/9812:00 PM7/18/9812:00 AM7/18/9812:00 PM7/19/9812:00 AM7/19/9812:00 PM7/20/9812:00 AM

M ixing R atio g/k gObsPredHumidity PredictionCenrapS, Augst 200212.012.513.013.514.014.515.015.516.016.517.08/4/0212:00AM8/4/0212:00PM8/5/0212:00AM8/5/0212:00PM8/6/0212:00AM8/6/0212:00PM8/7/0212:00AM8/7/0212:00PM8/8/0212:00AM8/8/0212:00PM8/9/0212:00AMDate

M ixin g R atio g /k gObsPredCenrapS Hourly Results SummaryCenrapS Hourly Results SummaryyWind speeds consistentlyunderpredictedthroughout year (gross error generally < 2 m/s)yTemperatureWinter prediction improved vs CenrapNJan - May: prediction acceptable, with nopredominant biases emergingJune through Mid September:neg biasesemergeMid-Sep - Dec: neg T bias erodes, improving thetemperature predictionCenrapS Hourly Results SummaryCenrapS Hourly Results SummaryyMixing ratioJan-May mixing ratio predominantlypositivelybiasedMay-Sep: q prediction improves, but episodes ofdegraded performance occurOct-Dec mixing ratio prediction improvesWhen biases occur, T&q predominantly out ofphaseHourly and episodic performancevariabilitythedominant feature in moisture fieldsDaily Averaged DataDaily Averaged DatayWhile certain nuances and biases exist in theCenrapN and CenrapS regions, in general, thesimulation is tolerable over these regionsy...Time to examine other areas of the countryFocusing upon hourly statistics far too timeconsumingDaily statistics only partially improves theproblem–Solution: Monthly averaged statistics•(caveat...not a perfect solution)-2-1.5-1-0.500.511.52 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Wind Speed BiasCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

m/s00.511.522.53 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Wind Speed Gross ErrorCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

m/s-20-15-10-505101520 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Wind Direction BiasCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

Degrees051015202530354045505560657075 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Wind Direction Gross ErrorCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

Degrees-2.5-2-1.5-1-0.500.511.522.5 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Temperature BiasCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

o C00.511.522.533.544.55 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Temperature Gross ErrorCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

o C-2.5-2-1.5-1-0.500.511.522.5 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Mixing Ratio BiasCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

g/kg00.511.522.533.5 Jan Feb Mar Apr May June July Aug Sep Oct Nov DecMonthly Averaged Mixing Ratio Gross ErrorCenrapNCenrapSDessertSWGLIowaMidAtlanticNorthNEOhioValSEPacificNWSW

g/kgContinuous PX Soil Moisture FieldsContinuous PX Soil Moisture FieldsyDue to inclusion of ~14 day spin up periods atthe beginning of each quarter, the impact ofcontinuous soil fields can be examined...by comparing the 1st 5 day simulation withina quarter with the corresponding data from theprevious quarterly simulation–March, June, and September time periodsavailableExampleJFM AMJ24thSim:March 15th-19th1stSim:March 15th-19thContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CCenrapN-CCenrapN-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CCenrapS-CCenrapS-IC ontinuous Soil Field s v s. R e-Initialization:Tem perature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CDessertSW -CDessertSW -IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CGL-CGL-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CIowa-CIowa-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CMidAtlantic-CMidAtlantic-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CNorth-CNorth-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o COhioVal-COhioVal-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CPacificNW-CPacificNW-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CSE-CSE-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CSW-CSW-IContinuous Soil Fields vs. Re-Initialization:Temperature Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CNE-CNE-IContinuous Soil Fields vs ReContinuous Soil Fields vs Re-InitializationInitializationTemperatureTemperatureyTemperature BiasIn general, re-initialization reduces the magnitude ofthe negative and positive biases–Sign reversal occasionally occurs (Iowa:March)In June, CenrapN shows a noticeable disbenefit withre-initialization (not using continuous fields)yTemperature ErrorOverall, there isn’t a striking decrease in error withre-init; Most comparisons show similar performanceHowever, if errors differed, the errors were almostalways lower with re-initialization (except withCenrapN & Iowa)Continuous Soil Fields vs ReContinuous Soil Fields vs Re-InitializationInitializationTemperature ErrorTemperature ErrorContinuous Soil vs Re-Initialization:Temperature Gross Error00.511.522.533/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

Degrees CCenrapN-CCenrapN-ICenrapS-CCenrapS-IMidAtlantic-CMidAtlantic-INE-CNE-IRegions not shown did not display significant changesMoistureMoisture–Impact Not as NoticeableImpact Not as NoticeableContinuous Soil Fields vs. Re-Initialization:Moisture Bias-2.5-1.5-0.50.51.52.5 3 /15 3 /16 3 /17 3/18 6 /15 6/1 6 6 /17 6 /18 9 /15 9 /16 9/17 9 /18

o CCenrapN-CCenrapN-IContinuous Soil Fields vs. Re-Initialization:Moisture Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CDessertSW-CDessertSW-IContinuous Soil Fields vs. Re-Initialization:Moisture Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CGL-CGL-IContinuous Soil Fields vs. Re-Initialization:Moisture Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CSE-CSE-IContinuous Soil Fields vs. Re-Initialization:Moisture Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CNE-CNE-IContinuous Soil Fields vs. Re-Initialization:Moisture Bias-2.5-2-1.5-1-0.500.511.522.53/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o CIowa-CIowa-IContinuous Soil Fields vs ReContinuous Soil Fields vs Re-InitializationInitializationMixing RatioMixing RatioyLittle change overallBias tends to beslightlyworse with re-initializationWorst impact occurred in the CenrapN region, with biasincreasing by up to ~0.5 g/kg (total error still less than 2g/kg)yMajority of gross error differences < 0.2 g/kgUpper Air EvaluationUpper Air EvaluationyReprocessing 360 Gb of output for upper air evaluationnot practicalyPartial solutionDevelopedRAOBPLOT(freely available)–Post-processing software tool designed to produce observedvs predicted soundings, based on FSL archived data•Acknowledgements:–Dennis McNally(borrowed portion of code from MAPSwhich reads TDL datasets)–Theauthors of GRAPH(borrowed SKEW-T code)–Mark Stoelinga(borrowed code from RIP)–(I did actually write new code)–Bret Anderson– beta tester/accessory improvementsTremendous amount of information producible (e.g.~18,000 soundings created examining only 23 stations)ResultsResultsyPreliminaryfindings: (finite number of soundings examined)Wind field prediction throughout the troposphere subjectivelycategorized as accurateThe cool temperature bias & positive moisture bias identifiedthrough Metstat evaluation identifiable in the soundings–The depth through which this trendsoccur is highly variable (from25 mb to 200+ mb above the surface)–Results highly variable, as expectedExamining DVN (Davenport, Iowa) summertime PBL’s: mixinglayer appears slightly shallow–Typically shifted by 50 mb or lessExample RAOBPLOTExample RAOBPLOTyDavenport, IAy2002-07-15_00ZyAn example of thecold bias/highmoisture bias, andPBL depthinaccuracies (at 7PM...)yNote: No singlesounding isrepresentativeSummarySummaryOverall, investigation of: wind fields, temperatures, and humiditygross errors found to be withinacceptable limits. However...Bias trends apparent–Temperatures underpredicted (cause=location and time dependent (e.g.Feb PacificNW, the entire diurnal cycle too cold; not just the peaktemperatures being underpredicted)–Mixing ratio positively biasedthroughout most of the annualsimulation: true for most regions–Daily peak wind speeds fall short of the observed values, but theassociated error is generally smallUpper Air–Wind field prediction acceptable, throughout the troposphere–Preliminary... Highly variable with mixed results, no grievous errors ortrends yet discoveredConclusionsConclusionsyCenrapN: overall, performance acceptableyOther regions: Biases apparent, but associated errorstolerable (must consider the course grid resolution)ySIPquality simulation?There is room for improvement...The ultimate answer will depend upon a performanceevaluation comparison involving additional annual simulationsyImplementation of continuous soil fields within PXIs this option favorable?–Generally: appears beneficial forCenrapN & Iowa only–Performance measures do not indicate the generation ofcontinuous soil fields is an advantageous practice•Caveat: only 12 days available for comparison–Couple the above statements with operationalrestrictions•...the computational burden incurred with thesequential run requirementdifficult to justify