Improving 10 day rain forecasts with AI based bias correction and detail enhancement
PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast
Machine LearningArtificial Intelligence
Summary
Medium-range rain forecasts often have errors that grow over several days and miss smaller weather details. The authors created PCSDiff, an AI tool that fixes these errors and adds fine details to make forecasts clearer and more accurate. PCSDiff uses special methods to understand how rainfall errors change over time and then improves the resolution to show local rain patterns better. Tested over China, it made forecasts more reliable and faster for practical weather use. This advance could help with better planning for floods and droughts.
Medium-range weather forecastsPrecipitation bias correctionDiffusion modelsSuper-resolutionRainfall downscalingRoot mean square error (RMSE)Accuracy (ACC)Synoptic-temporal featuresECMWF forecastsDeep learning in meteorology
Authors
Yuze Sun, Shiyi Wang, Jiancheng Pan, Die Wang, Andreas F. Prein, Wentao Luo, Linhan Jiang, Jie Wu, Quan Zhang, Xiaomeng Huang
Abstract
Medium-range precipitation forecasts are impaired by persistent systematic biases, lead-time-dependent error accumulation, and coarse spatial resolution, restricting their reliability for flood-drought risk assessment. Existing AI correction techniques lack dedicated modeling for multi-day dynamic bias evolution and proper meteorological constraints, often generating over-smoothed rainfall structures, and cannot meet operational deployment demands. This work introduces PCSDiff, a cascaded task-decoupled diffusion framework targeting 10-day precipitation bias correction and downscaling. To jointly counteract temporal error drifts and reconstruct physically plausible local precipitation details, PCSDiff integrates the Precipitation Intensity-aware Multi-branch Decoder (PIMD) module for dynamic multi-day error mitigation using synoptic-temporal features, followed by a two-phase conditional diffusion super-resolution module to restore fine-scale precipitation patterns. Evaluated against CMA-CRA observations over China after global-data training, PCSDiff cuts RMSE by 16.1% and lifts ACC by 13.9% relative to raw ECMWF forecasts at 3-10-day lead times, and consistently outperforms mainstream deep-learning baselines on both general and extreme-precipitation metrics. Benefiting from a streaming inference pipeline, our method achieves low-latency rolling forecasting for practical meteorological operations.