Data-driven Seasonal Hydrologic Prediction Using Earth Observing Satellites

及时预测水旱灾害，大大减少了相关损失。它需要精确的降水估计作为跨时空尺度陆地过程的预测器。在本财年，我们提出了一种新的降水检索框架，其中回归和分类任务同时使用多任务学习方法进行训练。基于卫星的降水估计提供了频繁的大规模测量。最近，深度学习在提高估计精度方面显示出了巨大的潜力。在这个项目中，我们设计了一种新的网络架构和损失函数来最大化多任务学习的好处。所提出的多任务（即双任务）模型比传统的单任务模型取得了更好的性能，这可能是由于任务之间有效的知识转移。产品比对表明，我们的产品在雨率检索方面优于现有产品，并且在雨/无雨检索任务中也获得了更好的技能。

Timely prediction of flood and drought greatly minimize the related losses. It requires precise precipitation estimates as a predictor of terrestrial processes across spatiotemporal scales. In this fiscal year, we proposed a novel precipitation retrieval framework in which regression and classification tasks are simultaneously trained using multi-task learning approach. Satellite-based precipitation estimations provide frequent, large-scale measurements. Recently, deep learning has shown significant potential for improving estimation accuracy. In this project, we designed a novel network architecture and loss function to maximize the benefits of multi-task learning. The proposed multi-task (i.e., two-task) model successfully achieved a better performance than the conventional single-task model possibly due to efficient knowledge transfer between tasks. The product intercomparison showed that our product outperformed existing products in rain rate retrieval and also yielded better skills in the rain/no-rain retrieval task.