华南区域高分辨率数值模式前汛期预报初步评估

Based on hourly observational data from automatic stations in south China, traditional site scoring and neighborhood methods are used to evaluate the forecast skill of GRAPES Guangzhou Regional Modeling System (including GRAPES_GZ_R 1 km model and GRAPES_GZ 3 km model) in forecasting precipitation, surface temperature, and wind fields. The analysis of the results shows that the precipitation forecasting skills of the GRAPES_GZ_R 1 km model are better than those of the GRAPES_GZ 3 km model, and the deviation of GRAPES_GZ_R 1 km forecasts from observations is mainly positive. The Threat Score (TS) of rainfall forecast by GRAPES_GZ_R 1 km is significantly improved at all thresholds and is more than twice that of GRAPES_GZ 3 km. But GRAPES_GZ_R 1 km only has the highest score in the first 3 hours, while its TS decreases gradually with the increase of integration time and precipitation threshold. The Fraction Skill Scores (FSS) of GRAPES_GZ_R 1 km show improvement in both 6 h and 24 h accumulated precipitation forecast. Meanwhile, GRAPES_GZ_R 1 km can achieve the lowest forecast skill scale for rainfall above 0.1 mm, 1 mm and 5 mm, while GRAPES_GZ 3 km usually fails to reach the lowest forecast skill scale. The forecast of location and intensity of rainfall has an overall improvement. Daily time evolution of root mean square errors for 2 m temperature forecast are similar, while the amount predicted by GRAPES_GZ_R 1 km is less than that by GRAPES_GZ 3 km. The rainfall and 2 m temperature forecast have made remarkable progresses. However, it is apparent that GRAPES_GZ 3 km performs better in forecasting 10 m wind fields, as mean bias and root mean square errors of 10 m wind field forecast are less than those by GRAPES_GZ_R 1 km. Mean errors are reduced by about 1～2 m/s. In addition, the bias of 2 m temperature and 10m wind speed forecasts by GRAPES_GZ_R 1 km fluctuates significantly with the precipitation process. The 2 m temperature forecast is generally lower and the 10 m wind speed is stronger than observations. In general, forecast products of GRAPES_GZ_R 1 km have good reference value.