deutscher wetterdienst cosmo-de-eps susanne theis, christoph gebhardt, michael buchhold, zied ben...

Deutscher Wetterdienst

COSMO-DE-EPS

Susanne Theis, Christoph Gebhardt, Michael Buchhold,

Zied Ben Bouallègue, Roland Ohl, Marcus Paulat, Carlos Peralta

with support by: Helmut Frank, Thomas Hanisch, Ulrich Schättler, etc

COSMO GM – September 2010

NWP Model COSMO-DE

grid size 2.8 km

without parametrization

of deep convection

(convection-permitting)

lead time 0-21 hours

operational since April 2007

COSMO-EU

GME

COSMO-DE


Plans for a COSMO-DE Ensemble

How many ensemble members?

preoperational: 20 members

operational: 40 members

When?

preoperational: 2010

operational: 2012


COSMO-DE-EPS production steps

„variations“ withinforecast system

Ensemble products:

- mean- spread- probabilities- quantiles- ...

ensemble members




Ensemble products:


ensemble members

+ verification

+ postprocessing




Ensemble products:


ensemble members

11

next slides:step 1, generation of members

next slides:step 1, generation of members


Generation of Ensemble Members

Variations in Forecast System

for the Representation of Forecast Uncertainty

Initial Conditions Boundaries Model Physics



“multi-model”

driven by different global models






“multi-model”


“multi-model”

COSMO-DE initial conditions modified by different global models






“multi-configuration”

different configurations of COSMO-DE model




“multi-model”


“multi-model”

COSMO-DE initial conditions modified by different global models



The Ensemble Chain

COSMO-DE 2.8km

COSMO 7km

globalmm/24h

COSMO-DE



The Ensemble Chain

COSMO-DE 2.8km

COSMO 7km

globalmm/24h

COSMO-DE

plus the variations of• initial conditions• model physics



Which computers are used?

at ECMWF: „7 km Ensemble“

at DWD: COSMO-DE-EPS

COSMO-DE 2.8km

COSMO 7km

global

ECMWF DWD



GFSIFSGME

COSMO 7km

…etc…

transfer of data






GFSIFSGME

COSMO 7km

…etc…




transfer of data

Status: in testing phase

(so far: COSMO-SREPS)


variation of initial conditions




global forecasts




COSMO-DE 2.8 km

global forecasts

COSMO 7 km

ic




COSMO-DE 2.8 km

COSMO-DEassimilation

global forecasts

COSMO 7 km

IC

ic




modify initial conditions of COSMO-DE

by using differences between

the COSMO 7km initial conditions

IC´ = F (IC, ic – icref)

COSMO-DE 2.8 km

COSMO-DEassimilation

global forecasts

COSMO 7 km

IC

ic



variation of „model physics“




11

22

33

55

44

entr_sc

rlam_heat

rlam_heat

q_crit

tur_len

different configurationsof COSMO-DE 2.8 km:




selection of configurations:

subjective,

based on experts and verification

selection criteria:

1. large effect on forecasts

2. no „inferior“ configuration

11

22

33

55

44

entr_sc

rlam_heat

rlam_heat

q_crit

tur_len

different configurationsof COSMO-DE 2.8 km:



future changes

- extension to 40 members

- switch to ICON as driving ensemble

(model ICON currently under development)

- apply an Ensemble Kalman Filter for initial condition perturbations

(EnKF currently under development for data assimilation)





Ensemble products:


ensemble members

22

next slides:step 2, generating „products“

next slides:step 2, generating „products“


Generation of „Ensemble Products“

variables (list will be extended):

1h-precipitation

wind gusts

2m-temperature

ensemble „products“:

probabilities

quantiles

ensemble mean

min, max

spread

ensemble products:

- mean- spread- probabilities- quantiles- ... GRIB2

22

GRIB1


further improvement:

adding a spatial neighbourhood

adding simulations started a few hours earlier



further improvement:

adding a spatial neighbourhood

adding simulations started a few hours earlier

additional product:

probabilities

with upscaling

%

event somewherein 2.8 km Box

event somewherein 28 km Box





Ensemble products:


ensemble members

33

next slides:step 3, visualization in NinJo

next slides:step 3, visualization in NinJo


Visualization in NinJo

new development: „Ensemble Layer“

for NinJo version 1.3.6

released in 2010




Ensemble products:


ensemble members




Ensemble products:


ensemble members

+ verification

+ postprocessing


Verification Results

GEBHARDT, C., S.E. THEIS, M. PAULAT, Z. BEN BOUALLÈGUE, 2010:

Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbationsand variation of lateral boundaries. Submitted to Atmospheric Research.

very first aim:

Does the ensemble meet some basic requirements?

results:

- ensemble spread is present

- members are of similar quality

- ensemble is superior to individual forecasts


Postprocessing / Calibration


Ensemble products:


ensemble members


Motivation for Postprocessing / Calibration

Aim: improve the quality

learn from past forecast errors

derive statistical connections

apply them to real-time ensemble forecasts

Forecast Obs

real-timeforecasts

historical data


Methods for Postprocessing / Calibration

First Approach:

logistic regression



statisticalpostprocessing


Methods for Postprocessing / Calibration



statisticalpostprocessing

First Approach:

logistic regression

Plan: preoperational in 2011

for precipitation


Research for Postprocessing / Calibration

in addition:

Research at Universities, funded by DWD

- University of Bonn:

Petra Friederichs, Sabrina Bentzien

Methods: Quantile Regression, Extreme Value Statistics

- University of Heidelberg:

Tilmann Gneiting, Michael Scheuerer

Methods: Bayesian Model Averaging, Geostatistics




Ensemble products:


ensemble members

+ verification

+ postprocessing


Plans COSMO-DE-EPS

2010: start of preoperational phase

(20 members)

2010-2012: further extensions

statistical postprocessing

40 members

2012: start of operational phase

convection-permitting ensemble operation convection-permitting ensemble operation

deutscher wetterdienst cosmo-de-eps susanne theis, christoph gebhardt, michael buchhold, zied ben...

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