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.

近期基于深度学习的时序降水预测模型研究进展与性能评估综述

降水预测的深度学习：从时间序列预测的角度看调查

Deep learning for precipitation nowcasting: A survey from the  perspective of time series forecasting

Diffusion models are widely used in image generation because they can
generate high-quality and realistic samples. This is in contrast to generative
adversarial networks (GANs) and variational autoencoders (VAEs), which have
some limitations in terms of image quality.We introduce the diffusion model to
the precipitation forecasting task and propose a short-term precipitation
nowcasting with condition diffusion model based on historical observational
data, which is referred to as SRNDiff. By incorporating an additional
conditional decoder module in the denoising process, SRNDiff achieves
end-to-end conditional rainfall prediction. SRNDiff is composed of two
networks: a denoising network and a conditional Encoder network. The
conditional network is composed of multiple independent UNet networks. These
networks extract conditional feature maps at different resolutions, providing
accurate conditional information that guides the diffusion model for
conditional generation.SRNDiff surpasses GANs in terms of prediction accuracy,
although it requires more computational resources.The SRNDiff model exhibits
higher stability and efficiency during training than GANs-based approaches, and
generates high-quality precipitation distribution samples that better reflect
future actual precipitation conditions. This fully validates the advantages and
potential of diffusion models in precipitation forecasting, providing new
insights for enhancing rainfall prediction.

通过引入扩散模型于降水预测任务，提出了一种基于历史观测数据的短时降水预报条件扩散模型（SRNDiff）。SRNDiff 通过在去噪过程中加入额外的条件解码器模块，实现了端到端的降水预测。该模型在条件生成方面超越了 GANs，虽然需要更多计算资源，但在训练过程中表现出更高的稳定性和效率，并生成更能反映未来降水情况的高质量降水分布样本。这充分验证了扩散模型在降水预测中的优势和潜力，并为提升降水预测提供了新的见解。

SRNDiff: 基于条件扩散模型的短期降雨预测

SRNDiff: Short-term Rainfall Nowcasting with Condition Diffusion Model

Accurate precipitation forecasting is indispensable for informed
decision-making across various industries. However, the computational demands
of current models raise environmental concerns. We address the critical need
for accurate precipitation forecasting while considering the environmental
impact of computational resources and propose a minimalist U-Net architecture
to be used as a baseline for future weather forecasting initiatives.

精确的降水预测对各行各业的决策是不可或缺的，然而目前模型的计算需求引起了环境问题。本文针对精确的降水预测的关键需求，同时考虑了计算资源的环境影响，并提出了一种用于未来天气预测的基准架构 —— 简约 U-Net 架构。

Weather4cast 2023 中定量降水预报的高效基准

Efficient Baseline for Quantitative Precipitation Forecasting in  Weather4cast 2023

Accurate precipitation forecasting is a vital challenge of both scientific
and societal importance. Data-driven approaches have emerged as a widely used
solution for addressing this challenge. However, solely relying on data-driven
approaches has limitations in modeling the underlying physics, making accurate
predictions difficult. Coupling AI-based post-processing techniques with
traditional Numerical Weather Prediction (NWP) methods offers a more effective
solution for improving forecasting accuracy. Despite previous post-processing
efforts, accurately predicting heavy rainfall remains challenging due to the
imbalanced precipitation data across locations and complex relationships
between multiple meteorological variables. To address these limitations, we
introduce the PostRainBench, a comprehensive multi-variable NWP post-processing
benchmark consisting of three datasets for NWP post-processing-based
precipitation forecasting. We propose CAMT, a simple yet effective Channel
Attention Enhanced Multi-task Learning framework with a specially designed
weighted loss function. Its flexible design allows for easy plug-and-play
integration with various backbones. Extensive experimental results on the
proposed benchmark show that our method outperforms state-of-the-art methods by
6.3%, 4.7%, and 26.8% in rain CSI on the three datasets respectively. Most
notably, our model is the first deep learning-based method to outperform
traditional Numerical Weather Prediction (NWP) approaches in extreme
precipitation conditions. It shows improvements of 15.6%, 17.4%, and 31.8% over
NWP predictions in heavy rain CSI on respective datasets. These results
highlight the potential impact of our model in reducing the severe consequences
of extreme weather events.

通过提出 PostRainBench 多变量 NWP 后处理基准，我们介绍了一种简单但有效的渠道注意增强多任务学习框架 CAMT，它能在极端降水条件下优于传统的数值天气预报方法，进而减轻极端天气事件的严重后果。