Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath...
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Transcript of Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath...
![Page 1: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/1.jpg)
Statistical Postprocessing of Statistical Postprocessing of Surface Weather ParametersSurface Weather Parameters
Susanne Theis
Andreas Hense
Ulrich Damrath
Volker Renner
![Page 2: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/2.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Example of Convective PrecipitationExample of Convective Precipitation
100 km
![Page 3: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/3.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Limits of Deterministic PredictabilityLimits of Deterministic Predictability
lead time: 48h
grid size: 7 km
The NWP Model LM:The NWP Model LM:
The DMO of the LM might containa considerable amount of noise!
![Page 4: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/4.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
From the Model to the UserFrom the Model to the User
judgment by an expert
user
model + autom. postprocessing
![Page 5: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/5.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Automatic Forecast ProductAutomatic Forecast Product
Forecast Time
mmPrecipitation at Gridpoint xy (DMO)
![Page 6: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/6.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Automatic Forecast ProductAutomatic Forecast Product
Forecast Time
mmPrecipitation at Gridpoint xy (DMO)
The uncertainty inherent in forecasters‘ judgments is not reflected – the forecast is not consistent!
![Page 7: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/7.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Aims of the ProjectAims of the Project
• detection of cases with limited predictability
• optimal interpretation of the DMO in such cases (automatic method!)
![Page 8: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/8.jpg)
OUTLINE
Motivation
Experimental Ensemble
- Method
- Results
Statistical Postprocessing
Conclusion
The Experimental EnsembleThe Experimental Ensemble
Perturbation ofsub-grid scale processes:
• parametrized tendencies (ECMWF)
• solar radiation flux at the ground
• roughness length
![Page 9: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/9.jpg)
OUTLINE
Motivation
Experimental Ensemble
- Method
- Results
Statistical Postprocessing
Conclusion
The Experimental EnsembleThe Experimental Ensemble
Perturbation of parametrized tendencies:
dttetedttete
t
t
t
t
00
;;)( )P() A(
Unperturbed simulation:
dttxtetetet
tjjjj )(r)P( A(
0
;;);)(
Ensemble member:
![Page 10: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/10.jpg)
OUTLINE
Motivation
Experimental Ensemble
-Method
-Results
Statistical Postprocessing
Conclusion
The Experimental EnsembleThe Experimental Ensemble
Structures of a few gridboxes in size are very sensitive to the perturbations
• 1-hr sum of precipitationxxxx
(conv and gsc)• cloud cover (esp. conv) xxxx• net solar radiation xxxx• 2m-temperaturexx• net thermal radiationxx• 10m-wind (gusts and mean) oo
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Statistical PostprocessingStatistical Postprocessing
DMO of a
single simulation
noise-reduced QPF
and PQPF
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Basic AssumptionBasic Assumption
random variability =
variability in space & time
Forecasts within aneighbourhood in space & timeconstitute a sample of theforecast at grid point A
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Products of PostprocessingProducts of Postprocessing
• Mean Value and Expected Value
• Quantiles (10%, 25%, 50%, 75%, 90%)
• Probability of Precipitation (several thresholds)
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Example of a Forecast ProductExample of a Forecast Product
Forecast Time
mmPrecipitation at Gridpoint xy
50%-quantile
![Page 15: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/15.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Example of a Forecast ProductExample of a Forecast Product
Forecast Time
mmPrecipitation at Gridpoint xy
75%-quantile
25%-quantile
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Example of a Forecast ProductExample of a Forecast Product
Forecast Time
Probability of Precipitation > 2.0 mm at Gridpoint xy
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Verification of Postprocessed DMOVerification of Postprocessed DMO
...has been done:
- for 1-hour sums of precipitation and 2m-temperature- for several periods in the warm season (length: 2 weeks each)- on the area of Germany
Following example: 10.7.-24.7.20021-hour sums of precipitation
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Verification of Mean ValueVerification of Mean Value
meanDMO
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
- Method
- Products
- Verification
Conclusion
Verification of PoP ForecastsVerification of PoP Forecasts
Reliability Diagram
prec. thresh.: 0.1 mm/h prec. thresh.: 2.0 mm/h
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
ConclusionConclusion
• small scales of the DMO contain a considerable amount of noise (experimental ensemble)
• postprocessing (smoothing) significantly improves the DMO in some respects
• probabilistic QPF still needs improvement
![Page 21: Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.](https://reader036.fdocuments.us/reader036/viewer/2022062600/5a4d1b1d7f8b9ab059994119/html5/thumbnails/21.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
OutlookOutlook
• make further refinements to the postprocessing method
• can we improve the PQPF?
• another postprocessing method: application of wavelets