PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting

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.

PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting

Although the Transformer has been the dominant architecture for time series forecasting tasks in recent years, a fundamental challenge remains: the permutation-invariant self-attention mechanism within Transformers leads to a loss of temporal information. To tackle these challenges, we propose PatchMixer, a novel CNN-based model. It introduces a permutation-variant convolutional structure to preserve temporal information. Diverging from conventional CNNs in this field, which often employ multiple scales or numerous branches, our method relies exclusively on depthwise separable convolutions. This allows us to extract both local features and global correlations using a single-scale architecture. Furthermore, we employ dual forecasting heads that encompass both linear and nonlinear components to better model future curve trends and details. Our experimental results on seven time-series forecasting benchmarks indicate that compared with the state-of-the-art method and the best-performing CNN, PatchMixer yields $3.9\%$ and $21.2\%$ relative improvements, respectively, while being 2-3x faster than the most advanced method. We will release our code and model.