WeatherDiagFlow: Evidence-Grounded Radar Nowcasting with Diagnostic Flow Refinement
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Computer Science > Machine Learning
Title:WeatherDiagFlow: Evidence-Grounded Radar Nowcasting with Diagnostic Flow Refinement
Abstract:Radar nowcasting is essential for short-term warning and emergency response, yet conventional systems mainly return future radar fields and provide limited support for operational communication and post-event verification. We formulate radar nowcasting as an evidence-grounded forecast--bulletin--audit task, in which a numerical forecaster produces both future radar fields and structured diagnostic evidence. Forecast-time bulletins use only model-available evidence, whereas post-event audits incorporate future radar truth only after the forecast horizon is observed. Based on this task formulation, WeatherDiagFlow predicts motion, growth and decay, heavy-echo risk, and uncertainty to condition rolling flow refinement, while frozen-scaffold residual calibration improves long-lead strong-echo preservation. A multi-agent layer converts the structured evidence into operational bulletins and independently generates verification audits without feeding textual outputs back into the forecaster. Experiments on FJRADAR demonstrate competitive overall performance and improved strong-echo event skill. WeatherDiagFlow therefore connects numerical prediction, evidence-grounded reporting, and auditable verification under a leakage-controlled protocol.
| Comments: | 5 pages, 3 figures |
| Subjects: | Machine Learning (cs.LG); Multimedia (cs.MM) |
| Cite as: | arXiv:2609.29772 [cs.LG] |
| (or arXiv:2609.29772v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29772
arXiv-issued DOI via DataCite (pending registration)
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