WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Global NWP model performance in polar
areas
Peter BauerLinus Magnusson
Jean – Noël ThépautECMWF
Tom HamillNOAA/ESRL
• Predictive skill• Predictability• Analysis/forecast uncertainty
seams
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Medium‐range predictive skill: HRES day+6
fit ≈ 1 day/decade
12‐18 monthsdevelopment
T1279/T639T799/T399
Activity
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Medium‐range predictive skill: ENS day+3, day+6
ideal
mysterious 3‐year cycle (lagged; also in SH)
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Time
0h 24h 48h 72h 96h 120hAnalysis Analysis Analysis Analysis Analysis Analysis → < Analysis tendency >
= variability of state
< (forecasti – analysis)‐ (forecasti+1 – analysis)>
→ < Forecast error tendency >≈ forecast consistency
→There is predictability if analysis tendencies > forecast tendencies(Jung & Leutbecher (2007): true up to day‐3, data from 2001‐2006)
Medium‐range predictability
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
AN tendency day 5‐6 FC HRES day 5‐6 FC ERA‐I day 5‐6 FC ENS CF
DJF 2008NAO+
DJF 2010NAO‐
DJF 2012NAO+
Medium‐range predictability: z500
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Medium‐range predictability: z500
NAO (NCDC)
day 5‐6
day 3‐4
more pred
ictable/skill
ERA‐I
HRESENS CF
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Analysis consistency between NWP centres: z500
Mean ‘error’ vs ERA‐I Met Office
Env. CanadaNCEPECMWF
JMA
JMA
Env. Canada
Met OfficeNCEP
ECMWF
RMSE vs ERA‐I
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF8
2m temperature mean sea‐level pressure
850 hPa temperature 500 hPa geopotential height
Analysis consistency between NWP centres: TIGGE
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Analysis uncertainty: TIGGE multi‐model vs Ensemble Data Assimilation
TIGGE multi‐model AN spread ECMWF EDA spread
z500
q850
DJF2014
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Forecast uncertainty: Ensemble spread‐error 65‐90N
spread
RMSE
RMSE (vs OBS)error std. dev. RMSE (vs AN)spread
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Forecast uncertainty: Ensemble spread‐errorEnsemble forecast spread Ensemble forecast RMSE
ECMWFt850day+5
MetOt850day+5
DJF2014
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Analysis uncertainty:• Perturbed observations (few)• Perturbed SST (not sea‐ice/snow)• Perturbed physics tendencies (weak)
Forecast uncertainty:• Perturbed physics tendencies• Stochastic backscatter• Singular vectors
Representation of uncertainties in ensembles
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
SYNOP, AIREP, DRIBU, TEMP, PILOT
Metop AMSU‐A
Observational data coverage
SST/sea‐ice
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Tropics: Sea points Arctic: Sea and sea ice points
Inital 6‐hour tendencies (DJF 1989‐2010)
Mean
Std. dev.
SPPT: Tendency perturbations
[Soumia Serrar]
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
EDA‐SPREAD = f (observation errors, SST, SPPT)SPPT‐tendency perturbations = tendency x tapering x 2d horizontal random pattern
Polar areas:• Few observations• Tapering affects low‐level tendency structures (radiation & low cloud/albedo errors)• No sea‐ice/land surface perturbations
870 hPa
[Soumia Serrar, Martin Leutbecher]
SPPT: Tendency perturbations
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Forecast uncertainty: Ensemble spread 0‐120 hoursENS integration in EDA‐mode (w/o SKEB and SV)
z u
v T
[Massimo Bonavita, Simon Lang]
WWRP Open Science Conference PB 08/2014 Ⓒ ECMWF
Conclusions• Evolution of predictive skill from standard scores is consistent between high and low
latitudes• Analysis uncertainty estimation currently not satisfactory • Apparent seams between analysis and forecast uncertainties derived from ensembles• The global system (forecast + analysis) is well constrained and stable, but:
• It is tuned to produce consistent performance at large scales (metrics)• It is optimized for the medium range• It is optimized for the troposphere• It is optimized for mid‐latitudes and tropics
• Problem areas:• Model:
• Physics of polar atmospheres (boundary layer, mixed phase, snow etc.)• Sea‐ice, ocean• Stratosphere‐troposphere interaction• Representation of model uncertainty
• Analysis:• Surface/lower troposphere sensitive satellite observations• Sparse networks• Observation/model error representation• Coupling