Evaluation of Multi-model Integrated Forecast Method for Precipitation in Rainy Seasons of Guangdong-Hong Kong-Macao Greater Bay Area.

This study evaluated the precipitation forecasts in the Guangdong-Hong Kong-Macao Greater Bay Area during the 2018 South China rainy seasons (April to September) using data from the THORPEX Interactive Grand Global Ensemble dataset. The dataset comprised deterministic forecasts, ensemble forecasts, and ground precipitation observations from models of five organizations: the European Centre for Medium-Range Weather Forecasts, the China Meteorological Administration, the Japan Meteorological Agency, the National Centers for Environmental Prediction of the United States, and the UK Meteorological Office. Three multi-model integrated forecast methods, namely the multi-model ensemble average (EMN), the bias-removed ensemble average (BREM), and the sliding training period superensemble method (R_SUP), were employed for the evaluation. The results showed that, in general, the root mean square error of precipitation forecasts in the first rainy season of the Guangdong-Hong Kong- Macao Greater Bay Area was higher than that in the second rainy season, with an average difference of 2 mm. The forecasting ability of the multi-mode integrated forecasting method showed a continuous and stable decline trend in the first rainy season as the forecast lead time increased. In contrast, in the second rainy season, it showed a stable decline in the short term (24~72 hours) and remained stable in the medium term (72~168 hours). EMN, which has a relatively simple mathematical principle, showed the best comprehensive performance in the precipitation forecast of the two rainy seasons. BREM and R_SUP achieved slightly lower spatial average scores, but they still demonstrated good forecasting skills in predicting precipitation areas. [ABSTRACT FROM AUTHOR]