arXiv Machine Learning July 7, 2026 By Yu Wang, Yong Cao, Kan Dai, Yue Shen, Xiaoqing Zeng, Ruixia Zhao

Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations

Read the original on arXiv Machine Learning →

arXiv:2607. 04862v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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