recent trends in skill for some leading global nwp …...2019/05/07 · recent trends in skill for...
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RecentTrendsinSkillforSomeLeadingGlobalNWPCenters
RossNHoffmana,b,d,e,KrishnaKumarc,d,SidA.Boukabarad,KayoIdee,FanglinYangf,RobertAtlasa
7May2019
(a)NOAA/AtlanRcOceanographicandMeteorologicalLaboratory,Miami,Florida(b)CooperaRveInsRtuteforMarineandAtmosphericStudies,UniversityofMiami,Miami,Florida
(c)RiversideTechnologyInc.,CollegePark,Maryland(d)NOAA/NESDIS/STAR,CollegePark,Maryland
(e)UniversityofMaryland,CollegePark,CollegePark,Maryland(f)NOAA/NCEP/EnvironmentalModelingCenter,CollegePark,Maryland
IntroducRon
• AlookatthedeterminisRcforecastsofthreeleadingNWPcenters(ECMWF,NCEP,UKMO)fortheyears2015-2017.
• PAMs(primaryassessmentmetrics)suchasthe500-hPageopotenRalanomalycorrelaRon(AC)orthe250-hPawindRMSEareconvertedtoNAMs(normalizedassessmentmetrics)andthenaveragedintoSAMs(summaryassessmentmetrics).
01/10/19 TrendsinSkill 2
NWPForecasts – PAMs
ReferenceSample
X(�) NAMs Σ SAMs
Verification(Analyses)
Upgrades2015-2017Center i Date Upgrade DeltaECMWF 1 20150512 IFSCycle41r1 2.10
2 20160308 IFSCycle41r2(CubicOctahedral1280) 1.313 20161122 IFSCycle43r1 2.584 20170717 IFSCycle43r3 5.22
NCEP 1 20150114 TIN14-46(T1534) -4.122 20160511 TIN16-11(4DEnVar) 7.373 20170719 SCN17-67(NEMSIO) 0.81
UKMO 1 20161121 PS38(satelliteobs.) 4.752 20170907 PS39(10-kmresoluRon) 2.82
01/10/19 TrendsinSkill 3
01/10/19 TrendsinSkill 4
120-h500-hP
aNHX
geo
potenR
alheightR
MSE(m
)
1998 2018
45
65
Context:20yearsofforecastskill
01/10/19 TrendsinSkill 5
Jan May Sep Jan May Sep Jan May Sep
79
11
ECMWFNCEPUKMO
ECMWFNCEPUKMO
|----------2015--------|---------2016--------|--------2017--------|
Win
d R
MSE
(m s
-1)
120-h500-hPaNHXvectorwindRMSE;MA(365)andMA(31)
01/10/19 TrendsinSkill 6
ScorecardsofIFSCycle45r1versusIFSCycle43r3.
FromECM
WFNew
leferNo.156
.
01/10/19 TrendsinSkill 7
ScorecardsofIFSCycle45r1versusIFSCycle43r3.FromECMWFNewleferNo.156.ShowingHRESvs.analysisonly.
01/10/19 TrendsinSkill 8
NWPForecasts – PAMs
ReferenceSample
X(�) NAMs Σ SAMs
Verification(Analyses)
PAMstoNAMstoSAMs
NWPForecasts – PAMs
ReferenceSample
X(�) NAMs Σ SAMs
Verification(Analyses)
PAMstoNAMstoSAMs• WeogenfocusonafewkeyPAMs,butthismayignoreotherimportantaspects
offorecastskill.TheuseofSAMsincreasesstaRsRcalsignificanceandenablesexploringdifferentaspectsofforecastskill.
• PAM/NAM/SAMdimension::coordinatevalues– ForecastRme::24,48,72,96,120,144,168h– Level::250,500,700,850,1000hPa– Domain::northernhemisphereextratropics(NHX),southernhemisphereextratropics(SHX),
tropics– Variable::height(Z),temperature(T),wind(V)– StaRsRc::anomalycorrelaRon(AC),rootmeansquareerror(RMSE),absolutemeanerror(AME,
theabsolutevalueofbias)– VerificaRonRme::every24hat0000UTCduring2015-2017– Center::ECMWF,NCEP,UKMO
• ReferencesamplefornormalizaRon– All::(verificaRonRme,center)– ByCenter::(verificaRonRme)
01/10/19 TrendsinSkill 9
01/10/19 TrendsinSkillWind RMSE (m s-1)2 5 10 20
0.0
0.2
0.4
0.6
0.8
1.0
PAM
NAM
24 h48 h72 h96 h120 h144 h168 hAllECMWFJan
10
NAM
/ EC
DF
500-hPaNHXvectorwindRMSEECDFnormalizaRonfuncRons
01/10/19 TrendsinSkill 11Wind RMSE (m s-1)6 7 8 9 10 11 12
0.0
0.2
0.4
0.6
0.8
1.0
PAM
NAM
8
8
8
8
8
8
9
9
9
9
9
O
O
O
O
O
O
N
N
N
N
N
D
D
D
D
D
D
1
1
1
1
1
1
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2
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7
1
D
AllECMWFNCEPUKMOJan. . .Dec
NAM
/ EC
DF
120-h500-hPaNHXvectorwindRMSEECDFnormalizaRonfuncRons
01/10/19 TrendsinSkill 12
Example
0.65 0.70 0.75 0.80 0.85 0.90 0.95
0.0
0.2
0.4
0.6
0.8
1.0
PAM
NAM
2polar3pgps
3polar
cntrl
cntrl3polar3pgps2polar
0.0 0.1 0.2 0.3 0.4 0.5 0.6
01
23
45
6
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maxavgminstep
01/10/19 TrendsinSkill 13
x = PAM
s =
n ×
NAM
• ECDFnormalizaRon– NAM=(rank(PAM)-1/2)/n– RankrelaRvetoref.sample
• Forminmax,andothernormalizaRons– NAM=aPAM+b– a,bdependonref.sample
ECDFNAMiscalculatedfromtherank
01/10/19 TrendsinSkill 14
Jan May Sep Jan May Sep Jan May Sep
0.0
0.5
1.0
ECMWFNCEPUKMO
ECMWFNCEPUKMO
|----------2015--------|---------2016--------|--------2017--------|
NAM
120-h500-hPaNHXvectorwindRMSENAMs;All;MA(365)andMA(31)
Jan May Sep Jan May Sep Jan May Sep
0.0
0.5
1.0
ECMWFNCEPUKMO
ECMWFNCEPUKMO
01/10/19 TrendsinSkill 15
|----------2015--------|---------2016--------|--------2017--------|
NAM
120-h500-hPaNHXvectorwindRMSENAMs;ByCenter;MA(365)andMA(31)
Jan May Sep Jan May Sep Jan May Sep
0.0
0.5
1.0
ECMWFNCEPUKMO
ECMWFNCEPUKMO
01/10/19 TrendsinSkill 16
|----------2015--------|---------2016--------|--------2017--------|
NAM
120-h500-hPaNHXvectorwindRMSENAMs;ByMonth;MA(365)andMA(31)
020000
60000
100000
0 0.2 0.4 0.6 0.8 1
050000
150000
250000
0 0.2 0.4 0.6 0.8 1
UKMONCEPECMWF
050000
100000
150000
−0.5 −0.1 0.3 0.7 1.1 1.5
01/10/19 TrendsinSkill 17
Cou
nts
a cb
NAM
020000
60000
100000
0 0.2 0.4 0.6 0.8 1
050000
150000
250000
0 0.2 0.4 0.6 0.8 1
UKMONCEPECMWF
050000
100000
150000
−0.5 −0.1 0.3 0.7 1.1 1.5
HistogramsforallNAMsfor(a)forECDF,(b)forminmax,and(c)forrescaled-minmaxnormalizaRons
01/10/19 TrendsinSkill 18
SAM
ECMWF NCEP UKMO
0.4
0.5
0.6
ECMWFNCEPUKMO
2015 2016 20170.25
0.50
0.75
ECMWFNCEPUKMO
Center Year
24 48 72 96 120 144 168
0.25
0.50
0.75
ECMWFNCEPUKMO
01/10/19 TrendsinSkill 19
24 48 72 96 120 144 1680.25
0.50
0.75
ECMWFNCEPUKMO
201520162017
SAM
Forecast time (h)Forecast time (h)
01/10/19 TrendsinSkill 20
Z T V
0.25
0.50
0.75
ECMWFNCEPUKMO
NHX SHX Tropics0.25
0.50
0.75
ECMWFNCEPUKMO
SAM
Variable Domain
NHX SHX Tropics
0.25
0.50
0.75
ECMWFNCEPUKMO
24 48 72 96 120 144 1680.25
0.50
0.75
ECMWFNCEPUKMO
NHXSHXTropics
01/10/19 TrendsinSkill 21
SAM
Forecast time (h)Domain
AC RMSE AME0.25
0.50
0.75
ECMWFNCEPUKMO
250 500 700 850 1000
0.25
0.50
0.75
ECMWFNCEPUKMO
01/10/19 TrendsinSkill 22
SAM
Level (hPa) Statistic
ByCenternormalizaRon• ReferencesamplefornormalizaRon
– All::(verificaRonRme,center)– ByCenter::(verificaRonRme)
01/10/19 TrendsinSkill 23
NWPForecasts – PAMs
ReferenceSample
X(�) NAMs Σ SAMs
Verification(Analyses)
24 48 72 96 120 144 1680.4
0.5
0.6
ECMWFNCEPUKMO
201520162017
2015 2016 2017
0.4
0.5
0.6
ECMWFNCEPUKMO
01/10/19 TrendsinSkill 24
SAM
Forecast time (h)Year
Upgrades2015-2017Center i Date Upgrade DeltaECMWF 1 20150512 IFSCycle41r1 2.10
2 20160308 IFSCycle41r2(CubicOctahedral1280) 1.313 20161122 IFSCycle43r1 2.584 20170717 IFSCycle43r3 5.22
NCEP 1 20150114 TIN14-46(T1534) -4.122 20160511 TIN16-11(4DEnVar) 7.373 20170719 SCN17-67(NEMSIO) 0.81
UKMO 1 20161121 PS38(satelliteobs.) 4.752 20170907 PS39(10-kmresoluRon) 2.82
01/10/19 TrendsinSkill 25
day-by-day
Jan May Sep Jan May Sep Jan May Sep
0.3
0.5
0.7
1 2 3 4
01/10/19 TrendsinSkill 26
SAM
|----------2015--------|---------2016--------|--------2017--------|
ECMWF
day-by-day
Upgrades2015-2017Center i Date Upgrade DeltaECMWF 1 20150512 IFSCycle41r1 2.10
2 20160308 IFSCycle41r2(CubicOctahedral1280) 1.313 20161122 IFSCycle43r1 2.584 20170717 IFSCycle43r3 5.22
NCEP 1 20150114 TIN14-46(T1534) -4.122 20160511 TIN16-11(4DEnVar) 7.373 20170719 SCN17-67(NEMSIO) 0.81
UKMO 1 20161121 PS38(satelliteobs.) 4.752 20170907 PS39(10-kmresoluRon) 2.82
01/10/19 TrendsinSkill 27
day-by-day
Jan May Sep Jan May Sep Jan May Sep
0.3
0.5
0.7
1 2 3
01/10/19 TrendsinSkill 28
SAM
|----------2015--------|---------2016--------|--------2017--------|
NCEP
day-by-day
Upgrades2015-2017Center i Date Upgrade DeltaECMWF 1 20150512 IFSCycle41r1 2.10
2 20160308 IFSCycle41r2(CubicOctahedral1280) 1.313 20161122 IFSCycle43r1 2.584 20170717 IFSCycle43r3 5.22
NCEP 1 20150114 TIN14-46(T1534) -4.122 20160511 TIN16-11(4DEnVar) 7.373 20170719 SCN17-67(NEMSIO) 0.81
UKMO 1 20161121 PS38(satelliteobs.) 4.752 20170907 PS39(10-kmresoluRon) 2.82
01/10/19 TrendsinSkill 29
day-by-day
01/10/19 TrendsinSkill 30
Jan May Sep Jan May Sep Jan May Sep
0.3
0.5
0.7
1 2
SAM
|----------2015--------|---------2016--------|--------2017--------|
UKMO
day-by-day
Jan May Sep Jan May Sep Jan May Sep
0.3
0.5
0.7
01/10/19 TrendsinSkill 31
SAM
|----------2015--------|---------2016--------|--------2017--------|
Average
day-by-day
Jan May Sep Jan May Sep Jan May Sep
0.4
0.5
0.6
ECMWFNCEPUKMO
ECMWFNCEPUKMO
01/10/19 TrendsinSkill 32
SAM
|----------2015--------|---------2016--------|--------2017--------|
MA(365)andMA(31)
01/10/19 TrendsinSkill 33
SAM
MonthJan Mar May Jul Sep Nov
0.4
0.5
0.6
●
●
● ●
●
●●
●
●
●
●
●
● ECMWFNCEPUKMO
Summary
• Allthreecentersimproveoverthethreeyearperiod.NCEPshort-termforecastskillsubstanRallyincreasesduringtheperiod.
• SAMsindicatethatintermsofforecastskillECMWFisbeferthanNCEP,whichisbeferthanbutapproximatelythesameasUKMO.
01/10/19 TrendsinSkill 34
2015 2016 2017
0.25
0.50
0.75
ECMWFNCEPUKMO
2015 2016 2017
0.25
0.50
0.75
ECMWFNCEPUKMO
• However,theobservedimpactsarewithinthecontextofslowlyimprovingforecastskillforoperaRonalglobalNWPascomparedtoearlieryears.
• TheuseofSAMsimprovesthesignaltonoiseraRoandclearimprovementsinSAMarerelatedtotheECMWFJuly2017upgradetoIFSCycle43r3,theNCEPMay2016replacementofthe3DEnVarwiththe4DEnVar,andtheUKMONovember2016(PS38)introducRonofimproveduseofsatelliteobservaRons.
NWPForecasts – PAMs
ReferenceSample
X(�) NAMs Σ SAMs
Verification(Analyses)
Concludingremarks
• WeogenfocusonafewkeyPAMs,butthismayignoreotherimportantaspectsofforecastskill.TheuseofSAMsincreasesstaRsRcalsignificanceandenablesexploringdifferentaspectsofforecastskill.
• ClearlythesystemslaggingECMWFcanimprove,andthereisevidencefromSAMsinaddiRontothe4DEnVarexamplethatimprovementsinforecastanddataassimilaRonsystemsaresRllleadingtoforecastskillimprovements.
• Infuturework,itmightbeinteresRngtoincludeothercentersandtoaddPAMsforrelaRvehumidityandprecipitaRon,forecastvariablesforwhichthereiscurrentlymajorroomforimprovement.
01/10/19 TrendsinSkill 35
NWPForecasts – PAMs
ReferenceSample
X(�) NAMs Σ SAMs
Verification(Analyses)
more...• email:• [email protected]
• Dec2018WAFpaper:• doi:10.1175/WAF-D-18-0117.1
• AMSpresentaRon:• hfps://ams.confex.com/ams/2019Annual/meeRngapp.cgi/Paper/
350739
01/10/19 TrendsinSkill 36
2015 2016 2017
0.25
0.50
0.75
ECMWFNCEPUKMO
2015 2016 2017
0.25
0.50
0.75
ECMWFNCEPUKMO
ADetecRveStory• Webeganwith5centers:
CMC,ECMWF,FNMOC,NCEP,UKMO
• Wekeptthe3best,partlybecauseFNMOCresultsdidnotmakesense
• Colorshavechangedfromthe3centerscase.
01/10/19 TrendsinSkill 37
CMC ECMWF FNMOC NCEP UKMO0.25
0.50
0.75
CMCECMWFFNMOCNCEPUKMO
24 48 72 96 120 144 1680.4
0.5
0.6
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CMCECMWFFNMOCNCEPUKMO
201520162017
2015 2016 2017
0.4
0.5
0.6
CMCECMWFFNMOCNCEPUKMO
01/10/19 TrendsinSkill 38
SAM
Forecast time (h)Year
Jan May Sep Jan May Sep Jan May Sep
0.3
0.5
0.7
1 2
01/10/19 TrendsinSkill 39
SAM
|----------2015--------|---------2016--------|--------2017--------|
CMC
day-by-day
Jan May Sep Jan May Sep Jan May Sep
0.3
0.5
0.7
1 2
01/10/19 TrendsinSkill 40
SAM
|----------2015--------|---------2016--------|--------2017--------|
FNMOC
day-by-day
year
SAM
2015 2016 2017
0.4
0.5
0.6
| arraytype = sam | normalization = ecdf | refsample = allNWP | gamma = 0.2652 |SAM[year,exp], only for (var=V;lev=1000), averaged over (stat,ftime,domain,day,month)
p = 0.95 c.i. shaded in grey
CMCECMWFFNMOCNCEPUKMO
outlines are for allNWP rescaled SAM
year
SAM
2015 2016 2017
0.4
0.5
0.6
| arraytype = sam | normalization = ecdf | refsample = allNWP | gamma = 0.1507 |SAM[year,exp], without (lev=1000), only for (var=V), averaged over (stat,ftime,domain,lev,day,month)
p = 0.95 c.i. shaded in grey
CMCECMWFFNMOCNCEPUKMO
outlines are for allNWP rescaled SAM
01/10/19 TrendsinSkill 41
SAM
Year Year
Wind (250,500,700,850) Wind (1000)
Why?• Inmid2016,theNAVGEMgridssharedwithNCEPchangedfrom1
degreeto1/2degreelaRtude-longitude.• Becauseofthewaythefieldsarefiltered,thischangemakesit
seemlikeNAVGEMforecastskillisdegradinginourassessmentsusingtheVSDBstaRsRcs.– MostoftheNAVGEMfieldsusedinourassessmentsarefilteredwiththe
same2done-passShapirosmootherde-smootherappliedingridpointspaceforboth1and1/2degreefields.
– Asaresultthe1/2degreefieldshavemoreenergypresentintheinherentlyhardtopredictsmallestscales,resulRnginanapparentdropinforecastskill.
– DuringVSDBprocessingnofurtherfilteringisapplied.
01/10/19 TrendsinSkill 42
Butwhataboutlowlevelwinds?• FilteringisappliedtoallgeopotenRalheights,alltemperatures,andallwindsabove900hPa.– Thusinourstudy,only1000hPawindswerenotaffectedbythischange.
• ThankstoElizabethSaferfield/NRL-MontereyandRandalPauley/FNMOCforhelpinunravelingthispuzzle.
01/10/19 TrendsinSkill 43
NWPForecasts – PAMs
ReferenceSample
X(�) NAMs Σ SAMs
Verification(Analyses)
more...• email:• [email protected]
• Dec2018WAFpaper:• doi:10.1175/WAF-D-18-0117.1
• AMSpresentaRon:• hfps://ams.confex.com/ams/2019Annual/meeRngapp.cgi/Paper/
350739
01/10/19 TrendsinSkill 44
2015 2016 2017
0.25
0.50
0.75
ECMWFNCEPUKMO
2015 2016 2017
0.25
0.50
0.75
ECMWFNCEPUKMO
ApplicaRontoimpactexperiments• TheoriginoftheSAMworkwastosummarizeOSEresultsforadatagapimactstudy.
• WerepeatedtheimpactstudyinsimulaRon(OSSEmode)tovalidateourOSSEsystem(CGOP).
• IdeallyeachOSSEcomponentmustberealisRc,howevertheseOSSEsused– LowerresoluRonGDAS/GFS– NoobservaRonerrors
01/10/19 TrendsinSkill 45
ObservingSystemSimulaRonExperiments
• OSSEshavebeenusedsincethe1950sto– Evaluateobservingsystemsintermsofaccuracyandcoverage(e.g.,inplanningFGGE)
– GuidedecisionmakerstoallocateresourcestomiRgatecostsandleadRmeinreality
– Conducttradestudiesofinstrumentsandsystems,and– DesignandtestnewDAmethods
01/10/19 TrendsinSkill 46
OSSE System
NR Simulate obs.
DA Forecasts
Future OSSEs 23 April 22, 2015
Revised: October 23, 2014
CalibrationVerification
ExperimentalSetup:DataGapScenario• Inter-comparison:OSSEvsOSEforControl,3Polar,2Polar
[OriginalOSEworkbyBoukabaraetal(2016b)]
Period 7Julyto7August,2014
Obssystemconfig.
2014:allconvenRonal+satellitedatagapscenarios
OSSEonly:perfectobs
DAS 3DEnVarwithT670/T254n=80ensemble
Forecast 0000UTCdailyupto168h
VerificaRon OSSE/OSE:owncntrlanalysis
OSSEsystemvalidaRon/calibraRon• BeforeapplyanOSSEsystemtoanewproposedsensor,wewanttocheckiftheresultsforadatadenialexperimentmatch
• Ifnot(andogenOSSEresultsareoverlyopRmisRc)wemustcalibrateourOSSEsystemby– TuningtheobservaRonerrors;and/or– ChangingsomeparameterizaRonsintheforecastmodel;or– AdjusRngtheOSSEresultsagerthefact
• ForthesimulaRonexperimentswith“perfect”observaRons,theOSSEwasoverlyopRmisRc,butnotintermsofSAMs!
01/10/19 TrendsinSkill 48
Results:ForecastSkill(PAM-AC&RMSE)
01/10/19 TrendsinSkill 49
• Alldatagapscenariosresultinpoorforecastskills• TendencyofimpactmostlyasexpectedalthoughtherearebitofvariabiliResinOSSEvsOSEinter-comparisonresults
00 24 48 72 96 120 144 168
02
46
810
●
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cntrl−OSSEcntrl−OSE3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
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00 24 48 72 96 120 144 168
0.0
0.5
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1.5
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2.5
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3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
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00 24 48 72 96 120 144 168
0.5
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cntrl−OSSEcntrl−OSE3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
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00 24 48 72 96 120 144 168
−0.04
0.00
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3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
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Forecast time (h)
V 250 hPa Tropics RMSE (m s-1)Z 500 hPa NHX AC
Forecast time (h)
Results:SAMsglobalandvs.forecastRme
01/10/19 TrendsinSkill 50
cntrl−OSSE 3polar−OSSE 2polar−OSSE
0.3
0.5
0.7
cntrl−OSSEcntrl−OSE3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
00 24 48 72 96 120 144 1680.1
0.3
0.5
0.7
0.9
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cntrl−OSSEcntrl−OSE3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
a bSA
M
Forecast time (h)Experiment
cntrl-OSSE
cntrl-OSE
3polar-OSE
2polar-OSE
3polar-OSSE
2polar-OSSE
Results:SAMsbylevelanddomain
01/10/19 TrendsinSkill 51
250 500 700 850 1000
0.3
0.5
0.7
cntrl−OSSEcntrl−OSE3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
NHX SHX Tropics
0.3
0.5
0.7
cntrl−OSSEcntrl−OSE3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
a bSA
M
DomainLevel (hPa)
more...• email:• [email protected]
• Oct2018JTECHpaper:• doi:10.1175/JTECH-D-18-0061.1
• AMSpresentaRon:• hfps://ams.confex.com/ams/2019Annual/meeRngapp.cgi/Paper/
353842
01/10/19 TrendsinSkill 52
cntrl−OSSE 3polar−OSSE 2polar−OSSE
0.3
0.5
0.7
cntrl−OSSEcntrl−OSE3polar−OSSE3polar−OSE2polar−OSSE2polar−OSE
Experiment
cntrl-OSSE
cntrl-OSE
3polar-OSE
2polar-OSE
3polar-OSSE
2polar-OSSE
Control3Polar2Polar