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Statistical Postprocessing of Surface Weather Parameters

This study focuses on improving surface weather parameter predictions using statistical postprocessing techniques. The research delves into experimental ensembles, model limitations, user judgment, automatic forecast products, and the detection of limited predictability cases. Perturbations of sub-grid-scale processes and parametrized tendencies are analyzed to enhance forecast reliability. Verification methods and the calculation of mean values, quantiles, and probabilities of precipitation are explored.

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Statistical Postprocessing of Surface Weather Parameters

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  1. Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner

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

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

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

  5. Automatic Forecast Product • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • Conclusion Precipitation at Gridpoint xy (DMO) mm Forecast Time

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

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

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

  9. The Experimental Ensemble • OUTLINE • Motivation • Experimental Ensemble • - Method • - Results • Statistical Postprocessing • Conclusion Perturbation of parametrized tendencies: Unperturbed simulation: Ensemble member:

  10. The Experimental Ensemble • OUTLINE • Motivation • Experimental Ensemble • -Method • -Results • Statistical Postprocessing • Conclusion • Structures of a few gridboxes in size are very sensitive to the perturbations • 1-hr sum of precipitation xx • (conv and gsc) • cloud cover (esp. conv) xx • net solar radiation xx • 2m-temperature x • net thermal radiation x • 10m-wind (gusts and mean) o

  11. Statistical Postprocessing • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion DMO of a single simulation noise-reduced QPF and PQPF

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

  13. Products of Postprocessing • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion • Mean Value and Expected Value • Quantiles • (10%, 25%, 50%, 75%, 90%) • Probability of Precipitation • (several thresholds)

  14. Example of a Forecast Product • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion Precipitation at Gridpoint xy mm 50%-quantile Forecast Time

  15. Example of a Forecast Product • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion Precipitation at Gridpoint xy mm 75%-quantile 25%-quantile Forecast Time

  16. Example of a Forecast Product • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion Probability of Precipitation > 2.0 mm at Gridpoint xy Forecast Time

  17. Verification of Postprocessed DMO • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion • ...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.-24.7.2002 • 1-hour sums of precipitation

  18. Verification of Mean Value • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion mean DMO

  19. Verification of PoP Forecasts • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • - Method • - Products • - Verification • Conclusion ReliabilityDiagram prec. thresh.: 0.1 mm/h prec. thresh.: 2.0 mm/h

  20. Conclusion • OUTLINE • Motivation • Experimental Ensemble • Statistical Postprocessing • 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

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

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