Data-driven Seasonal Hydrologic Prediction Using Earth Observing Satellites

水旱灾害的及时预报，可以大大减少灾害损失.它需要精确的降水量估计，作为跨时空尺度的陆地过程的预测。在本财政年度，我们提出了一个新的降水检索框架，其中回归和分类任务使用多任务学习方法同时训练。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，我们设计了一个新的网络架构和损失函数，以最大限度地发挥多任务学习的优势。提出的多任务（即，双任务）模型成功地实现了比传统的单任务模型更好的性能，这可能是由于任务之间有效的知识转移。产品相互比较表明，我们的产品优于现有的产品在降雨率检索，也产生了更好的技能，在雨/无雨检索任务。

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.