COSMO-DE-EPS

1
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

2
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-DE
COSMO-EU
GME
3
Plans for a COSMO-DE Ensemble

• How many ensemble members?
• preoperational 20 members
• operational 40 members
• When?
• preoperational 2010
• operational 2012

4
COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
5
COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
verification postprocessing
6
COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
1
ensemble members
next slides step 1, generation of members
7
Generation of Ensemble Members
Variations in Forecast System for the
Representation of Forecast Uncertainty
Initial Conditions Boundaries Model Physics
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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
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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 COSMO-DE initial conditions
modified by different global models
10
Generation of Ensemble Members
Variations in Forecast System for the
Representation of Forecast Uncertainty
Initial Conditions Boundaries Model Physics
multi-configuration different configurations
of COSMO-DE model
multi-model driven by different global models
multi-model COSMO-DE initial conditions
modified by different global models
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Generation of Ensemble Members
The Ensemble Chain
COSMO-DE 2.8km
COSMO 7km
COSMO-DE
mm/24h
global
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Generation of Ensemble Members

• plus the variations of
• initial conditions
• model physics

The Ensemble Chain
COSMO-DE 2.8km
COSMO 7km
COSMO-DE
mm/24h
global
13
Generation of Ensemble Members

• Which computers are used?
• at ECMWF 7 km Ensemble
• at DWD COSMO-DE-EPS

COSMO-DE 2.8km
COSMO 7km
global
DWD
ECMWF
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Generation of Ensemble Members
COSMO 7km

• Which computers are used?
• at ECMWF 7 km Ensemble
• at DWD COSMO-DE-EPS

GFS
IFS
GME
etc
transfer of data
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Generation of Ensemble Members
COSMO 7km

• Which computers are used?
• at ECMWF 7 km Ensemble
• at DWD COSMO-DE-EPS

GFS
IFS
GME
etc

• Status in testing phase
• (so far COSMO-SREPS)

transfer of data
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Generation of Ensemble Members

• variation of initial conditions

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Generation of Ensemble Members

• variation of initial conditions

global forecasts
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Generation of Ensemble Members

• variation of initial conditions

ic
global forecasts
COSMO 7 km
COSMO-DE 2.8 km
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Generation of Ensemble Members
COSMO-DE assimilation

• variation of initial conditions

IC
ic
global forecasts
COSMO 7 km
COSMO-DE 2.8 km
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Generation of Ensemble Members
COSMO-DE assimilation

• variation of initial conditions
• modify initial conditions of COSMO-DE
• by using differences between
• the COSMO 7km initial conditions
• IC F (IC, ic icref)

IC
ic
global forecasts
COSMO 7 km
COSMO-DE 2.8 km
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Generation of Ensemble Members

• variation of model physics

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Generation of Ensemble Members

• variation of model physics

different configurations of COSMO-DE 2.8 km
entr_sc rlam_heat rlam_heat q_crit tur_len
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Generation of Ensemble Members

• variation of model physics
• selection of configurations
• subjective,
• based on experts and verification
• selection criteria
• 1. large effect on forecasts
• 2. no inferior configuration

different configurations of COSMO-DE 2.8 km
entr_sc rlam_heat rlam_heat q_crit tur_len
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Generation of Ensemble Members
Variations within Forecast System 20 Ensemble
Members
Model Physics
(here still with COSMO-SREPS) (this design will
be modified)
1
2
3
4
5
Initial Conditions Boundaries
Lhn_coeff0.5
IFS
GME
GFS
UM
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Generation of Ensemble Members

• 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)

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COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
2
ensemble members
next slides step 2, generating products
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Generation of Ensemble Products

• variables (list will be extended)
• 1h-precipitation
• wind gusts
• 2m-temperature
• ensemble products
• probabilities
• quantiles
• ensemble mean
• min, max
• spread

2
GRIB1

• ensemble products
• mean
• spread
• probabilities
• - quantiles
• ... GRIB2

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Generation of Ensemble Products

• further improvement
• adding a spatial neighbourhood
• adding simulations started a few hours earlier

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Generation of Ensemble Products

• further improvement
• adding a spatial neighbourhood
• adding simulations started a few hours earlier
• additional product
• probabilities
• with upscaling

event somewhere in 2.8 km Box
event somewhere in 28 km Box

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COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
3
next slides step 3, visualization in NinJo
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Visualization in NinJo

• new development Ensemble Layer
• for NinJo version 1.3.6
• released in 2010

32
COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
33
COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
verification postprocessing
34
Verification Results

• 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

GEBHARDT, C., S.E. THEIS, M. PAULAT, Z. BEN
BOUALLÈGUE, 2010 Uncertainties in COSMO-DE
precipitation forecasts introduced by model
perturbations and variation of lateral
boundaries. Submitted to Atmospheric Research.
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Postprocessing / Calibration

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
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Postprocessing / Calibration

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
37
Motivation for Postprocessing / Calibration

• Aim improve the quality
• learn from past forecast errors
• derive statistical connections
• apply them to real-time ensemble forecasts

historical data
Forecast
Obs
real-time forecasts
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Methods for Postprocessing / Calibration
statistical postprocessing

• First Approach
• logistic regression

• ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

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Methods for Postprocessing / Calibration
statistical postprocessing

• First Approach
• logistic regression
• Plan preoperational in 2011
• for precipitation

• ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

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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

41
COSMO-DE-EPS production steps

• Ensemble products
• - mean
• spread
• probabilities
• - quantiles
• ...

variations within forecast system
ensemble members
verification postprocessing
42
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