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Statistical Downscaling of Precipitation Multimodel Ensemble Forecasts

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jessicaallen
22 diapositives · 264 Views · Jan 02, 2020

This study focuses on the statistical downscaling of precipitation forecasts from multiple models to enhance regional hydrological and water resource analysis. Various interpolation approaches and ensemble techniques are compared for forecasting accuracy. Results show improvements in forecast skill and reduced errors post-downscaling.

#statistical downscaling of precipitation multimodel ensemble forecasts #statistical downscaling of precipitation multimodel ensemble forecasts ppt #powerpoint #presentation
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Statistical Downscaling of Precipitation Multimodel Ensemble Forecasts
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  1. Nanjing University of Information Science & Technology Statistical Downscaling of Precipitation Multimodel Ensemble Forecasts Xiefei Zhi Yanan Wang Nanjing University of Information Science and Technology zhi@nuist.edu.cn 2020/1/2

  2. Outline • Introduction • Data and methods • Comparative study of interpolation approaches • Downscaling of single model forecasts • Downscaling of multimodel ensemble forecasts • Conclusions

  3. Introduction • High resolution precipitation forecast is of importance for the regional hydrological model, water resource analysis and management etc. • The downscaling of informationfrom larger-scale models toward higherresolutionis generally carried out using statistical or dynamicalmethods(Huth 2002; Druyan et al. 2002; Kanamitsu et al. 2007). • Krishnamurti et al. (2009) combines the multimodel superensemble and statistical downscaling to conduct the precipitation forecast.

  4. Data • Multimodel ensemble forecast data The 24h-168h forecast data of the precipitation are taken from ECMWF, JMA, NCEP and UKMO models in the TIGGE archive for the period from June 1 to August 27, 2007. The spatial resolution is 1.25°×1.25° • Observed Data TRMM /3B42RT precipitation data were used as the “observed data” for the period from June 1 to August 27, 2007. The spatial resolution is 0.25°×0.25°.

  5. Methods • Interpolation：TIGGE forecast data were interpolated on a 0.25°×0.25 ° grid for research purposes. • Multimodel ensemble：Multimodel ensemble of the interpolated forecasts of single models were conducted. a.Bias-removed Ensemble Mean b. Superensemble

  6. Correction of the interpolated forecasts： • In the regression equation below, a denotes the ratio of the observed (zi) to the modeled rains (xi) for different intensities of rain, and b denotes the intercept that conveys underestimates (or overestimates) for the overall model forecast rain depending on its positive or negative values, in other words the slope coefficient a is a measure of the multiplicative bias if the systematic bias b is removed. • Evaluation of the forecast errors Root-mean-square error (RMSE） Anomaly correlation coefficients (ACC） ETS（Equitable Threat Score (ETS)and TS

  7. Comparative study of different spatial interpolation approaches • Bilinear • Spline • Ordinary Kriging (OK ） • Inverse Distance Weighted (IDW ）

  8. ECMWF interpolated 24h-Forecast of the precipitation

  9. Downscaling for single models The mean RMSE of the interpolated (triangle) and downscaling (square) 24-168h forecast of the precipitation from ECMWF, JMA, NCEP and UKMO over the area (15.125-49.125°N、90.125-140.125°E).

  10. The ACC between the interpolated (triangle) , downscaling (square) 1-7 day forecasts and the observed data.

  11. Multimodel ensemble forecasts of single models The mean RMSE of the interpolation, bias-removed ensemble mean (BREM) and superensemble (SUP) and the bias-removed ECMWF forecasts 24h-forecast of the precipitation from ECMWF, JMA, NCEP and UKMO over the area (15.125-49.125°N、90.125-140.125°E) for the period from June 1 to August 27, 2007.

  12. The mean RMSE (a) and ACC (b) of the interpolated, bias-removed ensemble mean (BREM) and superensemble (SUP) forecast of the precipitation from ECMWF, JMA, NCEP and UKMO over the area (15.125-49.125°N、90.125-140.125°E) .

  13. Downscaling of multimodel ensemble forecasts The mean RMSE and ACC of the downscaling of single models and multimodel ensemble downscaling 24h-forecast of the precipitation from ECMWF, JMA, NCEP and UKMO over the area (15.125-49.125°N、90.125-140.125°E) for the period from June 1 to August 27, 2007.

  14. Forecast lead time(days) Forecast lead time (days) The mean RMSE (left panel) and ACC (right panel) of the single model downscaling and multimodel ensemble downscaling 1-7 day forecast of the precipitation from ECMWF, JMA, NCEP and UKMO over the area (15.125-49.125°N、90.125-140.125°E) .

  15. 24h ECWMF 48h ECWMF 72h ECWMF96h ECWMF 24h SUP 48hSUP 72h SUP96h SUP The mean improvement (%) of the 24-168h downscaling forecast error of the precipitation from ECMWF and Superensemble.

  16. 120h ECWMF 144h ECWMF 168h ECWMF 120h SUP 144h SUP 168h SUP

  17. The mean precipitation over the Wangjiaba region（110.125-120.125E，30.125-35.125N）for the period from June 1 to August 27, 2007 The mean precipitation over the southwest region （90.125-100.125E，15.125-30.125N) for the period from June 1 to August 27, 2007.

  18. The ETS of single model downscaling and superensemble downscaling 24h-forecast of the precipitation for 0.1 mm/day (upper panel) and 10mm/day (bottom) during the period from June 1 to August 27, 2007.

  19. The averaged ETS score over research area (values15.125-49.875°N、90.125-140.125°E) for different precipitation threshold.

  20. Conclusions • Statistical downscaling may significantly improve the forecast skill of single models. • After the downscaling, the root-mean-square errors (RMSE) of the forecasts are significantly reduced, and the Anomaly Correlation Coefficients (ACC) between the forecasts and the TRMM data become larger. • Multimodel ensemble downscaling has better performance than the single model downscaling.

  21. Thank you for your attention!