1. National Meteorological Center, CMA, Beijing, China.
Evaluation of Forecast Performance of Asian Summer Monsoon Low-Level Wind using TIGGE Dataset
RUOYUN NIU
National Meteorological Center, CMA, Beijing, China.
(2) The overestimation or underestimation biases of the EASM (SASM) are consistent with those of land–sea thermal contrasts in
possibly related regions, which indicate that the forecast biases of the EASM (SASM) are likely influenced by those of the land–sea thermal contrasts. The forecast errors of the EASM have direct impact on forecasts of monsoon precipitation.
show higher skill in predicting the SASM than the EASM, especially
The NCEP and UKMO. The skills of all TIGGE centers in forecasting the SASM and EASM are superior to those of forecasting their respective surges.
TABLE 3. Forecast skill days for the EASM and SASM of the TIGGE centers in the summers of 2008 to 2013*
Asian summer monsoon (ASM) is an important large-scale systems of the Northern Hemisphere for China’s precipitation. They are also deemed to be important reference points for medium–extended-range weather forecasts. Considering their importance, The synoptic variation and comparison on Asian summer monsoon (ASM) are performed based on the ensemble prediction data in summers of from four - six TIGGE centers, including ECMWF, NCEP, CMA, JMA, UKMO, and CMC. Attentions have been paid not only on the differences of forecast performance but also on the collective forecast errors of ASM among ensemble prediction systems (EPSs). The main conclusions can be summarized as follows:
FIG.4. Nash-Sutcliffe efficiency coefficients (NSEC) and correlation coefficients (R) of SASM and EASM for TIGGE centers (see legend in Fig.1) during the summers of
(5) Most TIGGE centers display an improvement in skill in forecasting the EASM during recent years, especially the ECMWF and UKMO; however, their skill in predicting the SASM shows no obvious improvement.
FIG.2. Mean errors of surface temperature forecast by the TIGGE centers for lead-times of 3, 9, and 15 days for the summers of 2008 to 2013.
(6) By conducting bias-correction, the forecast skills tend to be improved for forecasts with higher a correlation coefficient (R) between the raw forecasts series and the analysis.
(3) The EASM surge is basically overestimated by the NCEP and CMA. Conversely, it is underestimated by the CMC and JMA for all lead days and by the ECMWF and UKMO for most lead days.
1. Forecast biases
(1) the EASM is overestimated by all the TIGGE centers (except the CMC). The SASM is primarily overestimated by the ECMWF, CMA, and CMC, but underestimated by the JMA. Additionally, it is overestimated by the NCEP and UKMO for the early lead days and underestimated for longer lead days .
FIG.3. Mean errors of surface temperature forecast by the TIGGE centers for lead-times of 3, 9, and 15 days for the summers of 2008 to 2013.
FIG.1. Mean error (ME) of SASM (South ASM) and EASM (East ASM) for TIGGE centers (see legend) during the summers of
(a) SASMI and (c) EASMI is respectively measured by the zonal wind averaged over the area (0-20N, E) and by the meridional wind averaged over the area (20-35N, E) at 850hPa.
(b) SASM_SI and (d) EASM_SI is respectively denoted by the percentage of the number of grid points with westerly wind above 12 ms-1 and southerly wind above 8 ms-1 at 850hPa to the total number of grid points for the same area as that of SASMI and EASMI.
2. Forecast skills
(4) The ECMWF and UKMO exhibit the highest forecast skills in predicting the EASM and SASM and both have respective advantages. The ECMWF shows better performance in forecasting the EASM than the UKMO, whereas the UKMO is superior to the ECMWF in the prediction of the SASM. The TIGGE centers generally
FIG.6. Alpha index (AI) of the forecasts of the SASM and EASM by the TIGGE centers (see legend) for the summers of 2008 to 2013.
FIG.5. Forecast skill days of the SASM and EASM by the TIGGE centers for each summer from 2008 to 2013.