Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
2026-07-06 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new method to improve extreme rainfall forecasts by combining predictions from six weather models using a two-step deep learning approach. They added weather station data to better guide the model in predicting both where rain falls and how heavy it is. Tested on flood season data from 2025 in China, their method much better identified heavy rain events compared to individual models and current products. It also corrected errors in the location of rain bands and improved key accuracy scores, especially for very intense storms. This shows that their approach can make weather forecasts more reliable for dangerous rainfall.
extreme precipitationnumerical weather predictionmulti-model blendingU-Netprobability classificationvalue reconstructionstation observationsThreat Scorerainstorm forecastingspatial displacement correction
Authors
Yu Wang, Yong Cao, Kan Dai, Yue Shen, Xiaoqing Zeng, Ruixia Zhao
Abstract
Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement. Traditional precipitation multi-model blending algorithms perform pixel-by-pixel blending on the forecast field based on weights, which may lead to the expansion of precipitation areas and the smoothing of extreme values. This study proposes an U-Net based two-stage framework: probability classification followed by value reconstruction, to blend forecasts from six major NWP models. A novel station-grid joint supervision mechanism is introduced by integrating observations from 2411 national meteorological stations in China into the loss function, simultaneously constraining spatial structures and peak intensities. Evaluations using independent samples from the 2025 flood season demonstrate that our model significantly outperforms both individual NWPs and current operational products. For rainstorms (>=50 mm), the Threat Score (TS) improved by 38.4% compared to the best NWP. Notably, for extreme events (>=100 mm) driven by extratropical cyclones and the subtropical high, the model successfully elevated the TS to above 0.1, transforming forecasts from having negligible reference value into those with certain operational utility. Furthermore, the model exhibits data-driven spatial correction capabilities, effectively realigning systematic rainbelt displacements with actual precipitation centers. The inclusion of station observations specifically enhanced the TS for rainstorms by 10.4% and effectively balanced the Bias. These results highlight the efficacy of multi-source joint supervision in enhancing the capture of extreme precipitation events.