The Consideration of Noise in the Direct NWP Model Output Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.

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Presentation transcript:

The Consideration of Noise in the Direct NWP Model Output Susanne Theis Andreas Hense Ulrich Damrath Volker Renner

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion The 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

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Example of Convective Precipitation 100 km

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Limits of Deterministic Predictability lead time: 48h grid size: 7 km The NWP Model LM: The DMO of the LM might contain a considerable amount of noise!

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion From the Model to the User judgment by an expert user model + autom. postprocessing

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Automatic Forecast Product Forecast Time mm Precipitation at Gridpoint xy (DMO) The uncertainty inherent in forecasters‘ judgments is not reflected – the forecast is not consistent!

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Aims of the Project detection of cases with limited predictability optimal interpretation of the DMO in such cases (automatic method!)

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion The Experimental Ensemble Perturbation of sub-grid scale processes: parametrized tendencies (ECMWF) solar radiation flux at the ground roughness length

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Statistical Postprocessing DMO of a single simulation noise-reduced QPF and PQPF

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Basic Assumption random variability = variability in space & time Forecasts within a neighbourhood in space & time constitute a sample of the forecast at grid point A

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Products of Postprocessing Mean Value and Expectation Value Quantiles (10%, 25%, 50%, 75%, 90%) Probability of Precipitation (several thresholds)

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Example of a Forecast Product Forecast Time mm Precipitation at Gridpoint xy 50%-quantile

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Example of a Forecast Product Forecast Time mm Precipitation at Gridpoint xy 75%-quantile 25%-quantile

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Example of a Forecast Product Forecast Time Probability of Precipitation > 2.0 mm at Gridpoint xy

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Verification 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

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Verification of Mean Value mean DMO

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Verification of PoP Forecasts Reliability Diagram prec. thresh.: 0.1 mm/hprec. thresh.: 2.0 mm/h

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Conclusion 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

OUTLINE Motivation Experimental Ensemble Statistical Postprocessing Conclusion Outlook make further refinements to the postprocessing method can we improve the PQPF? another postprocessing method: application of wavelets

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