global nwp model performance in polar areas - … · global nwp model performance in polar ......

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


Medium‐range predictive skill: HRES day+6

fit ≈ 1 day/decade

12‐18 monthsdevelopment

T1279/T639T799/T399

Activity


Medium‐range predictive skill: ENS day+3, day+6

ideal

mysterious 3‐year cycle (lagged; also in SH)


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


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


Medium‐range predictability: z500

NAO (NCDC)

day 5‐6

day 3‐4

more pred

ictable/skill

ERA‐I

HRESENS CF


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


Analysis uncertainty: TIGGE multi‐model vs Ensemble Data Assimilation

TIGGE multi‐model AN spread ECMWF EDA spread

z500

q850

DJF2014


Forecast uncertainty: Ensemble spread‐error 65‐90N

spread

RMSE

RMSE (vs OBS)error std. dev. RMSE (vs AN)spread


Forecast uncertainty: Ensemble spread‐errorEnsemble forecast spread Ensemble forecast RMSE

ECMWFt850day+5

MetOt850day+5

DJF2014


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


SYNOP, AIREP, DRIBU, TEMP, PILOT

Metop AMSU‐A

Observational data coverage

SST/sea‐ice


Tropics: Sea points Arctic: Sea and sea ice points

Inital 6‐hour tendencies (DJF 1989‐2010)

Mean

Std. dev.

SPPT: Tendency perturbations

[Soumia Serrar]


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


Forecast uncertainty: Ensemble spread 0‐120 hoursENS integration in EDA‐mode (w/o SKEB and SV)

z u

v T

[Massimo Bonavita, Simon Lang]


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

global nwp model performance in polar areas - … · global nwp model performance in polar ......

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