一种用于高分辨率格点季风预测的时空深度学习方法
A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction
AI总结：
本文提出了一种基于深度学习的时空方法，用于高分辨率格点季风预测，利用多变量数据和CNN架构提升预测精度和实用性。
摘要：
印度夏季季风（ISM）是一种关键的气候现象，对其影响超过十亿人的农业、经济和水安全具有根本性影响。传统长期预测，无论是统计还是动力学的，主要集中在预测单一的、空间平均的季节值，缺乏对区域级资源管理至关重要的空间细节。为了解决这一差距，我们引入了一种新的深度学习框架，将格点季风预测重新定义为时空计算机视觉任务。我们将季风前的多变量大气和海洋场视为多通道图像序列，有效地创建了一个视频般的输入张量。使用85年的ERA5再分析数据作为预测器和IMD降雨数据作为目标，我们采用基于卷积神经网络（CNN）的架构来学习从五个月季风前期（1月-5月）到随后季风季节高分辨率格点降雨模式的复杂映射。我们的框架能够为四个季风月份（6月至9月）以及总季节平均值生成不同的预测，证明了其在季内和季节展望中的实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
