Problem
Research questions and friction points this paper is trying to address.
Radar nowcasting
Evidence-grounded forecasting
Operational bulletins
Post-event verification
Diagnostic evidence
WeatherDiagFlow: Evidence-Grounded Radar Nowcasting with Diagnostic Flow Refinement
🤖 AI Summary
This study addresses the lack of structured evidence in traditional radar nowcasting for operational communication and post-event verification. We propose a leakage-free controlled end-to-end framework that integrates diagnostic flow refinement, frozen-scaffold residual calibration, and multi-agent systems to transform numerical predictions into structured evidence, thereby establishing a closed-loop “nowcast–bulletin–audit” workflow. By refining predicted motion, decay, and uncertainty, this approach automatically generates interpretable operational bulletins alongside independent audits. Evaluated on the FJRADAR dataset, the proposed method achieves competitive overall performance while significantly enhancing forecasting skill for intense echo events.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Radar Nowcasting
Diagnostic Flow Refinement
Multi-agent Layer
Evidence-Grounded Forecasting
Frozen-Scaffold Residual Calibration
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