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Slide 1 © ECMWF
Diagnostics of the ensemble system for polar regions
Linus Magnusson
Slide 2 © ECMWF
RMSE error (against ERA Interim reanalysis)
2-da
y er
ror
6-da
y er
ror
Antarctic Arctic
(Cf. Magnusson and Källén, 2013, MWR, in press)
Slide 3 © ECMWF
Daily errors (2012, z500, 6-day forecasts)
Arctic
Antarctic
Slide 4 © ECMWF
August 2012 over Antarctica
Slide 5 © ECMWF
Temperature anomaly in the troposphere and stratosphere
3 Aug 13 Aug 23 Aug 2 Sept
500
200
100
50
20
10
5 hPa
Slide 6 © ECMWF
Temperature at 50 hPa
2012-08-14 2012-08-19 2012-08-21 2012-08-29
Slide 7 © ECMWF
Best and worst ensemble members
Z500 (70-90S)
T10 (70-90S) 14 19 29 24
Slide 8 © ECMWF
Concept behind ensemble forecasts
Initial Perturbations
Perturbed forecast Optimal analysis = + +
Model Perturbations
Aim: Simulate the uncertainties in the forecast
Needs all components of uncertainty to simulate the forecast uncertainty
Standard deviation of the error = Standard deviation of the ensemble
Slide 9 © ECMWF
Ensemble of data assimilations – effect of polar orbiting satellites
Slide 10 © ECMWF
EDA standard deviation – T ~500 hPa (15 Oct – 1 Nov, twice a day)
0.15 0.30 0.45 0.6 0.75 0.9
All observations No polar orbiting sat.
(Thanks to M. Bonavita)
Slide 11 © ECMWF
Ensemble forecasts – Antarctic (8 cases = small sample)
RMSE Ens. Stdev.
Slide 12 © ECMWF
Ensemble forecasts - Arctic
RMSE Ens. Stdev.
Slide 13 © ECMWF
Are the uncertainties from the ensemble system reliable in the Arctic?
Slide 14 © ECMWF
RMSE and Ens.Std for temperature at 500 hPa Arctic
Good match!
RMSE (solid) Ens.Std. (dotted)
Slide 15 © ECMWF
RMSE and Ens.Std for temperature at 850 hPa Arctic
Reasonable match.
RMSE (solid) Ens.Std. (dotted)
Slide 16 © ECMWF
RMSE and Ens.Std for 2-metre temperature, Arctic (against SYNOP)
Bad match…
RMSE (solid) Ens.Std. (dotted)
2 6 4 2 8 0
Slide 17 © ECMWF
SYNOP stations north of 65N (6-day error of each station)
Slide 18 © ECMWF
Tarfala Nikkaloukta
17 km between the stations, ENS resolution 32 km..
Slide 19 © ECMWF
Observed and forecasted temperature 2013-02-07
Tarfala
Nikkaloukta
-40
-20
0
-40
-20
0
7 8 9
7 8 9
Analysis (shade) Synop (diamonds)
North-western Scandianavia
How to represent this difference in ENS?
Slide 20 © ECMWF
Stochastic perturbed physical tendencies (SPPT)
dX/dt=Dynamical tend.+(1+r)Physical tend.(convection, radiation, cloud, diffusion, dissipation)
No perturbations in the boundary layer and the stratosphere
Palmer et al. (2009)
Slide 21 © ECMWF
Mean tendencies for temperature (Dec-Jan)
Convection scheme Radiation scheme
Cloud scheme Dynamics (advection)
1000
200
500
1000
200
500
1000
200
500
1000
200
500
90N 90S Eq.
90N 90S Eq. 90N 90S Eq. Thanks to N. Wedi and S. Lang
Slide 22 © ECMWF
Summary
• The forecasting system has improved over the years
• The ensemble system have to simulate the remaining errors
• Still many types of errors to solve or simulate (stratosphere-troposphere, surface, sea-ice, boundary layer)
• How to include sub-grid variability?
Slide 23 © ECMWF
Diagnostics of the ensemble system for polar regionsRMSE error (against ERA Interim reanalysis)Daily errors (2012, z500, 6-day forecasts)August 2012 over AntarcticaTemperature anomaly in the troposphere and stratosphereTemperature at 50 hPaBest and worst ensemble membersConcept behind ensemble forecastsEnsemble of data assimilations – effect of polar orbiting satellitesEDA standard deviation – T ~500 hPa�(15 Oct – 1 Nov, twice a day)Ensemble forecasts – Antarctic�(8 cases = small sample)Ensemble forecasts - ArcticAre the uncertainties from the ensemble system reliable in the Arctic?RMSE and Ens.Std for temperature at 500 hPa ArcticRMSE and Ens.Std for temperature at 850 hPa ArcticRMSE and Ens.Std for 2-metre temperature, Arctic�(against SYNOP)SYNOP stations north of 65N (6-day error of each station)Slide Number 18Observed and forecasted temperature 2013-02-07Stochastic perturbed physical tendencies (SPPT)Mean tendencies for temperature (Dec-Jan)SummarySlide Number 23