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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
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NWP Model COSMO-DE COSMO-DE COSMO-EU GME 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 COSMO GM – September 2010
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Plans for a COSMO-DE Ensemble
How many ensemble members? preoperational: 20 members operational: members When? preoperational: 2010 operational: COSMO GM – September 2010
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COSMO-DE-EPS production steps
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system ensemble members COSMO GM – September 2010
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COSMO-DE-EPS production steps
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system ensemble members + verification + postprocessing COSMO GM – September 2010
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COSMO-DE-EPS production steps
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system 1 ensemble members next slides: step 1, production of members COSMO GM – September 2010
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Generation of Ensemble Members
Variations in Forecast System for the Representation of Forecast Uncertainty Initial Conditions Boundaries Model Physics COSMO GM – September 2010
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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 COSMO GM – September 2010
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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” modification of COSMO-DE initial conditions by different global models “multi-model” driven by different global models COSMO GM – September 2010
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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” modification of COSMO-DE initial conditions by different global models “multi-model” driven by different global models “multi-configuration” different configurations of the COSMO-DE model COSMO GM – September 2010
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Generation of Ensemble Members
The Ensemble Chain COSMO-DE 2.8km COSMO 7km COSMO-DE mm/24h global COSMO GM – September 2010
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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 COSMO GM – September 2010
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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 ECMWF DWD COSMO GM – September 2010
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Generation of Ensemble Members
COSMO 7km Which computers are used? at ECMWF: „7 km Ensemble“ at DWD: COSMO-DE-EPS GME IFS GFS …etc… transfer of data COSMO GM – September 2010
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Generation of Ensemble Members
COSMO 7km Which computers are used? at ECMWF: „7 km Ensemble“ at DWD: COSMO-DE-EPS GME IFS GFS …etc… Status: in testing phase (so far: COSMO-SREPS) transfer of data COSMO GM – September 2010
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Generation of Ensemble Members
SREPS data in DWD database 9 8 7 6 5 4 store time minus initial time [hours] July / August 2010 transfer of data COSMO GM – September 2010
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Generation of Ensemble Members
variation of initial conditions COSMO GM – September 2010
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Generation of Ensemble Members
variation of initial conditions global forecasts COSMO GM – September 2010
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Generation of Ensemble Members
variation of initial conditions ic global forecasts COSMO 7 km COSMO-DE 2.8 km COSMO GM – September 2010
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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 COSMO GM – September 2010
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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 COSMO GM – September 2010
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Spread vs Lead Time Oct 7 – Nov 24 2009 15 days selected
(15 ensemble members) IC pert Measure of ensemble spread: interquartile range of precipitation COSMO GM – September 2010
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Spread vs Lead Time (Case Study)
18 June 2009 (15 ensemble members) IC pert convective event Measure of ensemble spread: interquartile range of precipitation COSMO GM – September 2010
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Generation of Ensemble Members
variation of „model physics“ different configurations of COSMO-DE 2.8 km: entr_sc rlam_heat q_crit tur_len 1 2 3 5 4 COSMO GM – September 2010
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Generation of Ensemble Members
variation of „model physics“ Selection of Configurations subjective, based on experts, verification Selection Criteria: 1. large effect on forecasts 2. no „inferior“ configuration different configurations of COSMO-DE 2.8 km: entr_sc rlam_heat q_crit tur_len 1 2 3 5 4 COSMO GM – September 2010
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Generation of Ensemble Members
no perturbations of soil moisture (so far) some sensitivity experiments COSMO GM – September 2010
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Soil moisture sensitivity experiments
In IC+PH+BC exp no perturbations at the surface Use COSMO-EU SO analysis fields: W_SO and/or T_SO Complete or partially replace SO from COSMO-DE with COSMO-EU fields: WSO_DE = WSO_DE ± C (WSO_EU – WSO_DE), C = Start at 06 UTC, 24 hours of forecast Currently only single runs (no ensemble mode). Verification for July 2009: Quality of forecast does not decrease COSMO GM – September 2010
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Overall structure similar. Localized differences (rel diffs ~10 %).
COSMO-DE SO exp 1 cm 6 cm Overall structure similar. Localized differences (rel diffs ~10 %). COSMO-DE does a good job by itself. COSMO GM – September 2010
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Fcast does not improve (not the point).
COSMO-DE SO Exp Obs. Fcast does not improve (not the point). Want to increase uncertainty range (without being blatantly wrong). COSMO GM – September 2010
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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_coeff=0.5 IFS GME GFS UM COSMO GM – September 2010
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Case Study: 18 June 2009 Lead times:
0-6 hours hours hours hours mm/6h COSMO GM – September 2010
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Impact of different initial & boundary conditions
Derived by global model: IFS (ECMWF) GME (DWD) GFS (NCEP) mm/24h COSMO GM – September 2010
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Impact of different initial & boundary conditions
Derived by global model: IFS (ECMWF) GME (DWD) GFS (NCEP) mm/24h COSMO GM – September 2010
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Impact of different model configurations
Derived by changing: entr_sc rlam_heat tur_len mm/24h COSMO GM – September 2010
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Impact of different model configurations
Derived by changing: entr_sc rlam_heat tur_len mm/24h COSMO GM – September 2010
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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) COSMO GM – September 2010
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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“ COSMO GM – September 2010
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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 COSMO GM – September 2010
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Generation of „Ensemble Products“
further improvement: adding a spatial neighbourhood adding simulations started a few hours earlier COSMO GM – September 2010
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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 % COSMO GM – September 2010
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Product Generation: Example
Probability of Event „Precipitation > 10 mm/24h“ % COSMO GM – September 2010
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Product Generation: Example
Probability of Event „Precipitation > 10 mm/24h“ as usual Derived from: Ensemble Members + Spatial Neighbourhood Defined for: area of 10x10 Grid boxes % COSMO GM – September 2010
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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 COSMO GM – September 2010
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Visualization in NinJo
New Development: „Ensemble Layer“ for NinJo Version 1.3.6 released in 2010 COSMO GM – September 2010
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Visualization in NinJo
NinJo Screenshots COSMO GM – September 2010
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Visualization in NinJo
Selection: Ensemble-Layer COSMO GM – September 2010
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Visualization in NinJo
Selection: Ensemble Prediction System, Forecast Time, Variable COSMO GM – September 2010
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Visualization in NinJo
Selection: Ensemble Prediction System, Forecast Time, Variable COSMO GM – September 2010
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Visualization in NinJo
Selection: “Ensemble Product” COSMO GM – September 2010
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Visualization in NinJo
Example: Probability of precipitation > 1 mm COSMO GM – September 2010
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Visualization in NinJo
Selection: Meteogram COSMO GM – September 2010
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Visualization in NinJo
Example: Meteogram (Quantiles) Zeit COSMO GM – September 2010
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COSMO-DE-EPS production steps
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system ensemble members COSMO GM – September 2010
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COSMO-DE-EPS production steps
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system ensemble members + verification + postprocessing COSMO GM – September 2010
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Verification of COSMO-DE-EPS
quality of single members - deterministic verification quality of ensembles - probabilistic verification of probabilities - ensemble spread COSMO GM – September 2010
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Verification Results Very first aim:
Does the ensemble meet some basic requirements? Results: ensemble spread is present members are of similar quality quality of ensemble is superior to quality of 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. COSMO GM – September 2010
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Talagrand Diagram Oct 7 – Nov 24 2009 15 days selected
(15 ensemble members) COSMO GM – September 2010
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Individual Member Verification
Oct 7 – Nov 15 days selected (15 ensemble members) ETS FBI COSMO GM – September 2010
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Probabilistic Verification of Ensemble
Oct 7 – Nov 15 days selected (15 ensemble members) Brier Skill Score 0.4 0.3 0.2 0.1 0.0 reference: deterministic COSMO-DE threshold in mm/h COSMO GM – September 2010
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Probabilistic Verification of Ensemble
PhD Andreas Röpnack (DWD, Univ of Bonn) developed new verification measure (Bayesian) applies it to COSMO-DE-EPS and COSMO-SREPS COSMO GM – September 2010
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Postprocessing / Calibration
COSMO GM – September 2010
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Postprocessing / Calibration
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system ensemble members COSMO GM – September 2010
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Postprocessing / Calibration
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system ensemble members COSMO GM – September 2010
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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 COSMO GM – September 2010
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Methods for Postprocessing / Calibration
statistical postprocessing First Approach: logistic regression ensemble „products“: - mean spread probabilities - quantiles ... COSMO GM – September 2010
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Methods for Postprocessing / Calibration
First Approach: logistic regression Forecast of Probabilities by statistical connection between Predictor Observation Yes / No 1.0 0.5 0.0 Probabilities Predictor e.g. Ensemble mean [mm] COSMO GM – September 2010
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Methods for Postprocessing / Calibration
First Approach: logistic regression Forecast of Probabilities by statistical connection between Predictor Observation Yes / No Plan: preoperational in 2011 for precipitation 1.0 0.5 0.0 Probabilities Predictor e.g. Ensemble mean [mm] COSMO GM – September 2010
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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 Heidelberg: Tilmann Gneiting, Michael Scheuerer Methods: Bayesian Model Averaging, Geostatistics COSMO GM – September 2010
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COSMO-DE-EPS production steps
Ensemble products: - mean spread probabilities - quantiles ... „variations“ within forecast system ensemble members + verification + postprocessing COSMO GM – September 2010
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convection-permitting ensemble operational
Plans COSMO-DE-EPS 2010: start of preoperational phase (20 members) : further extensions statistical postprocessing 40 members 2012: start of operational phase international viel Aufmerksamkeit convection-permitting ensemble operational COSMO GM – September 2010
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