准确度提升400%，印度季风预测模型基于36个气象站点，实现城区尺度精细预报

Core Insights - The article discusses the development of a hyperlocal extreme rainfall prediction model for Mumbai, utilizing convolutional neural networks (CNN) and transfer learning to enhance forecasting accuracy [1][2]. Group 1: Model Development - The collaboration between the Indian Institute of Technology Bombay and the University of Maryland led to the creation of a predictive model that can forecast extreme rainfall events several days in advance [1]. - The model addresses the limitations of traditional global forecasting systems, which have a resolution of approximately 25 square kilometers, making them inadequate for capturing local weather phenomena [1][3]. Group 2: Data Utilization - The research utilized two types of datasets: model data from the National Centers for Environmental Prediction (NCEP) and observational data from automatic weather stations in Mumbai, focusing on 36 stations with high data completeness [4][5]. - The model was trained using a comprehensive dataset from 2015 to 2023, ensuring high-quality input data through various preprocessing techniques [4][5]. Group 3: Model Performance - The CNN-based model significantly improved spatial accuracy and reduced root mean square error (RMSE) compared to traditional global forecasting systems [12][13]. - The introduction of transfer learning enhanced the model's ability to identify extreme rainfall events, achieving a prediction accuracy improvement of 60% to 400% for high-intensity rainfall samples [15][18]. Group 4: Practical Application - Mumbai authorities are considering integrating this hyperlocal prediction model into their official warning systems, marking a significant advancement in urban flood forecasting capabilities in South Asia [1][2]. - The model's ability to capture regional rainfall synchronization patterns through event synchronization methods further validates its practical application in urban settings [7][18].