用GAN模型将卫星红外数据转为4小时降雨预测，夺冠竞赛。
A conditional Generative Adversarial network model for the Weather4Cast 2024 Challenge
- 用红外通道均值输入，结合光流预测未来辐射图像
- 生成的降雨图像累计4小时，CRPS达7.5，排名第一
- 适合气象预报与深度学习交叉研究者参考
本研究探索深度学习在降水预测中的应用，以静止环境红外成像仪（SEVIRI）高频率传输（HRIT）数据为输入，以操作性天气雷达信息交换计划（OPERA）地面雷达反射率数据为真实标签。采用4个红外波段的均值作为输入，利用密集光流算法预测未来4小时的辐射图像。随后采用条件生成对抗网络（GAN）模型将预测的辐射图像转换为降雨图像，并对4小时预报结果进行累加，生成累积降水量。该模型在Weather4Cast 2024竞赛中取得约7.5的连续概率评分准则（CRPS）成绩，在核心挑战排行榜上位列第一。
原文摘要 · Abstract (English)
This study explores the application of deep learning for rainfall prediction, leveraging the Spinning Enhanced Visible and Infrared Imager (SEVIRI) High rate information transmission (HRIT) data as input and the Operational Program on the Exchange of weather RAdar information (OPERA) ground-radar reflectivity data as ground truth. We use the mean of 4 InfraRed frequency channels as the input. The radiance images are forecasted up to 4 hours into the future using a dense optical flow algorithm. A conditional generative adversarial network (GAN) model is employed to transform the predicted radiance images into rainfall images which are aggregated over the 4 hour forecast period to generate cumulative rainfall values. This model scored a value of approximately 7.5 as the Continuous Ranked Probability Score (CRPS) in the Weather4Cast 2024 competition and placed 1st on the core challenge leaderboard.
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