用深度学习预测印度季风降雨分布，精度更高更实用。
A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction
- 将气象数据当作视频输入，用CNN学习气候模式
- 基于85年数据，可预测四个月的高分辨率降雨图
- 适合农业和水资源管理，提供区域级预报
印度夏季季风是影响超过十亿人农业、经济和水资源安全的关键气候现象。传统长期预报多聚焦于单一空间平均的季节性数值，缺乏区域资源管理所需的细节。为此，我们提出一种新型深度学习框架，将格点季风预测重构为时空计算机视觉任务。将多变量前期大气与海洋场视为多通道图像序列，构建类似视频的输入张量。利用85年ERA5再分析数据作为预测因子，印度气象局（IMD）降雨数据作为目标，采用基于卷积神经网络（CNN）的架构，学习从五个月前期（1-5月）到后续季风季（6-9月）高分辨率格点降雨分布的复杂映射关系。该框架成功生成了6-9月每月及整个季节总平均的独立预测结果，展现出对季内与季节性展望的实用性。
原文摘要 · Abstract (English)
The Indian Summer Monsoon (ISM) is a critical climate phenomenon, fundamentally impacting the agriculture, economy, and water security of over a billion people. Traditional long-range forecasting, whether statistical or dynamical, has predominantly focused on predicting a single, spatially-averaged seasonal value, lacking the spatial detail essential for regional-level resource management. To address this gap, we introduce a novel deep learning framework that reframes gridded monsoon prediction as a spatio-temporal computer vision task. We treat multi-variable, pre-monsoon atmospheric and oceanic fields as a sequence of multi-channel images, effectively creating a video-like input tensor. Using 85 years of ERA5 reanalysis data for predictors and IMD rainfall data for targets, we employ a Convolutional Neural Network (CNN)-based architecture to learn the complex mapping from the five-month pre-monsoon period (January-May) to a high-resolution gridded rainfall pattern for the subsequent monsoon season. Our framework successfully produces distinct forecasts for each of the four monsoon months (June-September) as well as the total seasonal average, demonstrating its utility for both intra-seasonal and seasonal outlooks.
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