用于降水临近预报的深度学习：时间序列预测视角的综述
Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting
AI总结：
本文从时间序列预测视角系统综述深度学习降水临近预报，梳理预处理、目标函数、评估指标及递归与多步预测策略，并在公开基准上评估模型、指出挑战与未来方向。
摘要：
基于深度学习的时间序列预测凭借其在高分辨率数据集中估计运动流的能力，已在短期降水预报领域占据主导地位。人们对降水临近预报日益增长的兴趣为当前预报技术的进步提供了大量机会。然而，目前尚缺乏对利用深度学习进行时间序列降水预报的深入综述。因此，本文系统回顾了时间序列降水预报模型的最新进展。具体而言，我们在背景组成部分考察以下关键点，涵盖：i）预处理，ii）目标函数，以及 iii）评估指标。随后，我们根据预测模型预测未来帧的方法，将其分为 recursive（递归）和 multiple（多步）两类策略，研究使用这些策略的模型所产生的影响，并进行性能评估。最后，我们在一个公开基准上评估当前基于深度学习的降水预报模型，讨论其局限与挑战，并提出若干有前景的研究方向。我们的贡献在于为更好地理解时间序列降水预报提供洞见，并助力未来开发稳健的 AI 解决方案。
英文摘要：
Deep learning-based time series forecasting has dominated the short-term precipitation forecasting field with the help of its ability to estimate motion flow in high-resolution datasets. The growing interest in precipitation nowcasting offers substantial opportunities for the advancement of current forecasting technologies. Nevertheless, there has been a scarcity of in-depth surveys of time series precipitation forecasting using deep learning. Thus, this paper systemically reviews recent progress in time series precipitation forecasting models. Specifically, we investigate the following key points within background components, covering: i) preprocessing, ii) objective functions, and iii) evaluation metrics. We then categorize forecasting models into \textit{recursive} and \textit{multiple} strategies based on their approaches to predict future frames, investigate the impacts of models using the strategies, and performance assessments. Finally, we evaluate current deep learning-based models for precipitation forecasting on a public benchmark, discuss their limitations and challenges, and present some promising research directions. Our contribution lies in providing insights for a better understanding of time series precipitation forecasting and in aiding the development of robust AI solutions for the future.
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。
