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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. Outline. Introduction Data and methods
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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
Outline • Introduction • Data and methods • Comparative study of interpolation approaches • Downscaling of single model forecasts • Downscaling of multimodel ensemble forecasts • Conclusions
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.
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°.
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
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
Comparative study of different spatial interpolation approaches • Bilinear • Spline • Ordinary Kriging (OK ) • Inverse Distance Weighted (IDW )
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).
The ACC between the interpolated (triangle) , downscaling (square) 1-7 day forecasts and the observed data.
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.
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) .
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.
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) .
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.
120h ECWMF 144h ECWMF 168h ECWMF 120h SUP 144h SUP 168h SUP
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.
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.
The averaged ETS score over research area (values15.125-49.875°N、90.125-140.125°E) for different precipitation threshold.
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.