Diagnostics of the ensemble system for polar regions

Diagnostics of the ensemble system for polar
regions
Linus Magnusson
Slide 1
© ECMWF
RMSE error (against ERA Interim reanalysis)
Antarctic
6-day error
2-day error
Arctic
(Cf. Magnusson and Källén, 2013, MWR, in press)
Slide 2
© ECMWF
Daily errors (2012, z500, 6-day forecasts)
Arctic
Antarctic
Slide 3
© ECMWF
August 2012 over Antarctica
Slide 4
© ECMWF
Temperature anomaly in the troposphere and stratosphere
5 hPa
10
20
50
100
200
500
3 Aug
Slide 5
13 Aug
© ECMWF
23 Aug
2 Sept
Temperature at 50 hPa
2012-08-14
Slide 6
2012-08-19
© ECMWF
2012-08-21
2012-08-29
Best and worst ensemble members
Z500 (70-90S)
14 19 24 29
T10 (70-90S)
Slide 7
© ECMWF
Concept behind ensemble forecasts
Perturbed forecast
=
Optimal
analysis
+
Initial
Perturbations
+
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 8
© ECMWF
Ensemble of data assimilations – effect of polar orbiting
satellites
Slide 9
© ECMWF
EDA standard deviation – T ~500 hPa
(15 Oct – 1 Nov, twice a day)
All observations
0.15
0.30
No polar orbiting sat.
0.45
0.6
0.75
0.9
(Thanks to M. Bonavita)
Slide 10
© ECMWF
Ensemble forecasts – Antarctic
(8 cases = small sample)
RMSE
Slide 11
Ens. Stdev.
© ECMWF
Ensemble forecasts - Arctic
RMSE
Slide 12
Ens. Stdev.
© ECMWF
Are the uncertainties from the
ensemble system reliable in the Arctic?
Slide 13
© ECMWF
RMSE and Ens.Std for temperature at 500 hPa Arctic
RMSE (solid)
Ens.Std. (dotted)
Good match!
Slide 14
© ECMWF
RMSE and Ens.Std for temperature at 850 hPa Arctic
RMSE (solid)
Ens.Std. (dotted)
Reasonable match.
Slide 15
© ECMWF
RMSE and Ens.Std for 2-metre temperature, Arctic
(against SYNOP)
RMSE (solid)
Ens.Std. (dotted)
0
Slide 16
2
4
© ECMWF
6
8
Bad match…
SYNOP stations north of 65N (6-day error of each station)
Slide 17
© ECMWF
Tarfala
Nikkaloukta
17 km between the stations, ENS resolution 32 km..
Slide 18
© ECMWF
Observed and forecasted temperature 2013-02-07
North-western Scandianavia
Tarfala
0
-20
Analysis (shade)
Synop (diamonds)
-40
7
8
9
Nikkaloukta
0
-20
How to represent this difference in ENS?
Slide 19
© ECMWF
-40
7
8
9
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 20
© ECMWF
Mean tendencies for temperature (Dec-Jan)
Radiation scheme
Convection scheme
200
200
500
500
1000
1000
90N
Eq.
Cloud scheme
90S
Dynamics (advection)
200
200
500
500
1000
90N
Slide 21
Eq.
90S
© ECMWF
1000
90N
Eq.
90S
Thanks to N. Wedi and S. Lang
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 22
© ECMWF
Slide 23
© ECMWF