Research on a Clustering Forecasting Method for Short-Term Precipitation in Guangdong Based on the CMA-TRAMS Ensemble Model

Abstract

In light of the 2020–2021 flood season in Guangdong, we conducted a comprehensive assessment of short-term precipitation forecasts generated by the ensemble prediction system (EPS) based on the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). Furthermore, we applied four distinct strategies to cluster the ensemble forecast data produced by the model for precipitation, aiming to enhance our understanding of their applicability in short-term precipitation forecasting for Guangdong. Our key findings were as follows.: Precipitation during the 2020–2021 flood season in Guangdong exhibited distinct characteristics. The impacting areas of frontal and subtropical high-edge rainfall were relatively scattered, predominantly occurring in the evening and nighttime. In contrast, monsoon precipitation and return-flow precipitation were concentrated, with their impacts lasting from early morning to evening. Notably, the errors using the ensemble maximum and minimum values were large, while the errors for the ensemble mean values and medians were small. This indicated that the model’s short-term precipitation forecasts possessed a high degree of stability. The vertical shear of different types of precipitation exerted a noticeable influence on the model’s performance. The model consistently displayed a tendency to underestimate short-term precipitation in Guangdong; however, this bias decreased with longer lead times. Simultaneously, the model’s dispersion increased with longer lead times. In terms of mean absolute error (MAE) test results, there was little difference in the performance of ensemble primary forecasts under various strategies, while the “ward” strategy performed well in sub-primary cluster forecasts. This was particularly true for areas and types of precipitation where the model’s performance was poor. While the clustering approach lagged behind ensemble mean forecasts in predicting rainy conditions, it exhibited improvement in forecasting short-term heavy rainfall events. The “complete” and “single” strategies consistently delivered the most accurate forecasts for such events. Our study sheds light on the effectiveness of clustering methods in improving short-term precipitation forecasts for Guangdong, particularly in regions and conditions where the model initially struggled. These findings contribute to our understanding of precipitation forecasting during flood seasons and can inform strategies for enhancing forecast accuracy in similar contexts.