the consideration of noise in the direct nwp model output susanne theis andreas hense ulrich damrath...
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![Page 1: The Consideration of Noise in the Direct NWP Model Output Susanne Theis Andreas Hense Ulrich Damrath Volker Renner](https://reader037.vdocuments.mx/reader037/viewer/2022110323/56649d7a5503460f94a5df69/html5/thumbnails/1.jpg)
The Consideration of Noise in The Consideration of Noise in the Direct NWP Model Outputthe Direct NWP Model Output
Susanne Theis
Andreas Hense
Ulrich Damrath
Volker Renner
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
The NWP Model LMThe NWP Model LM
• source of forecast guidance on small-scale precipitation
• operational high-resolution model of the DWD
• horizontal gridsize: 7 km
• lead time: 48 hours
![Page 3: The Consideration of Noise in the Direct NWP Model Output Susanne Theis Andreas Hense Ulrich Damrath Volker Renner](https://reader037.vdocuments.mx/reader037/viewer/2022110323/56649d7a5503460f94a5df69/html5/thumbnails/3.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Example of Convective PrecipitationExample of Convective Precipitation
100 km
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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!
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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
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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!
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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!)
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OUTLINE
Motivation
Experimental Ensemble
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: The Consideration of Noise in the Direct NWP Model Output Susanne Theis Andreas Hense Ulrich Damrath Volker Renner](https://reader037.vdocuments.mx/reader037/viewer/2022110323/56649d7a5503460f94a5df69/html5/thumbnails/9.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Statistical PostprocessingStatistical Postprocessing
DMO of a
single simulation
noise-reduced QPF
and PQPF
![Page 10: The Consideration of Noise in the Direct NWP Model Output Susanne Theis Andreas Hense Ulrich Damrath Volker Renner](https://reader037.vdocuments.mx/reader037/viewer/2022110323/56649d7a5503460f94a5df69/html5/thumbnails/10.jpg)
OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
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
Conclusion
Products of PostprocessingProducts of Postprocessing
• Mean Value and Expectation Value
• Quantiles (10%, 25%, 50%, 75%, 90%)
• Probability of Precipitation (several thresholds)
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Example of a Forecast ProductExample of a Forecast Product
Forecast Time
mmPrecipitation at Gridpoint xy
50%-quantile
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Example of a Forecast ProductExample of a Forecast Product
Forecast Time
mm
Precipitation at Gridpoint xy
75%-quantile
25%-quantile
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
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
Conclusion
Verification of Postprocessed DMOVerification of Postprocessed DMO
...has been done:
- for 1-hour sums of precipitation
- for several periods in the warm season (length: 2 weeks each)
- on the area of Germany
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
Conclusion
Verification of Mean ValueVerification of Mean Value
meanDMO
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OUTLINE
Motivation
Experimental Ensemble
Statistical Postprocessing
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
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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