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arXiv AI By Yifang Zhang, Shengwu Xiong, Henan Wang, Wenjie Yin, Yuqiang Zhang, Chen Zhou, Hua Chen, Qile Zhao, Pengfei Duan

MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting

Read the original on arXiv AI →

MZ‑Rain is a moisture‑budget‑guided zero‑inflated sLSTM framework designed for station‑level precipitation nowcasting. It decomposes precipitation formation into moisture storage, transport, surface evaporation, and persistence pathways, each modeled by dedicated sLSTM branches, and employs an adaptive Tweedie strategy to handle the dataset’s severe zero inflation. Experiments across varied climates show MZ‑Rain outperforms strong baselines on metrics such as CSI, FAR, MSE, and MAE, especially for heavy precipitation events.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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