1. Statistical Downscaling of Precipitation Multimodel Ensemble Forecasts
Nanjing University of Information Science & Technology
Statistical Downscaling of Precipitation Multimodel Ensemble Forecasts
Xiefei Zhi Yanan Wang
Nanjing University of Information Science and Technology
2018/7/30

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 information from larger-scale models toward higher resolution is generally carried out using statistical or dynamical methods (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 Observed 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, 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, 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
Linear
11.45
Spline
11.77 OK
11.68
IDW
11.26
Linear
0.51
Spline
0.49 OK
0.50
IDW
0.52

10. Downscaling for single models
The mean RMSE of the interpolated (triangle) and downscaling (square) h forecast of the precipitation from ECMWF, JMA, NCEP and UKMO over the area ( °N、 °E).

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

12. 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 ( °N、 °E) for the period from June 1 to August 27, 2007.

13. 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 ( °N、 °E) .

14. 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 ( °N、 °E) for the period from June 1 to August 27, 2007.

15. 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 ( °N、 °E) .

16. precipitation from ECMWF and Superensemble.
24h ECWMF h ECWMF h ECWMF h ECWMF
24h SUP hSUP h SUP h SUP
The mean improvement (%) of the h downscaling forecast error of the
precipitation from ECMWF and Superensemble.

17. 120h ECWMF h ECWMF h ECWMF
120h SUP h SUP h SUP

18. The mean precipitation over the Wangjiaba region（110. 125-120. 125E，30
The mean precipitation over the Wangjiaba region（ E， N）for the period from June 1 to August 27, 2007
RMSE
EC-LS
4.06
EC-D
3.98
SUP-D
3.17
The mean precipitation over the southwest region （ E， N) for the period from June 1 to August 27, 2007.
RMSE
EC-LS
5.14
EC-D
4.68
SUP-D
3.93

19. 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.

20. for different precipitation threshold.
The averaged ETS score over research area (values °N、 °E)
for different precipitation threshold.

21. 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.

22. Thank you for your attention!