# WeatherDiagFlow: Evidence-Grounded Radar Nowcasting with Diagnostic Flow Refinement

arXiv: https://arxiv.org/abs/2609.29772 · HTML: https://mlsanity.com/paper/2609.29772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting](https://mlsanity.com/paper/2312.06734/) (2023)
- [CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling](https://mlsanity.com/paper/2402.04290/) (2024)
- [WaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval](https://mlsanity.com/paper/2511.17558/) (2025)
- [Extreme Weather Nowcasting via Local Precipitation Pattern Prediction](https://mlsanity.com/paper/2602.05204/) (2026)
- [EWE: An Agentic Framework for Extreme Weather Analysis](https://mlsanity.com/paper/2511.21444/) (2025)
- [HVR-Met: A Hypothesis-Verification-Replanning Agentic System for Extreme Weather Diagnosis](https://mlsanity.com/paper/2603.01121/) (2026)
- [SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents](https://mlsanity.com/paper/2607.24588/) (2026)
- [WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation](https://mlsanity.com/paper/2605.07522/) (2026)
- [PixelFlowCast: Latent-Free Precipitation Nowcasting via Pixel Mean Flows](https://mlsanity.com/paper/2605.10046/) (2026)
- [VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting](https://mlsanity.com/paper/2608.09286/) (2026)

## Abstract (arXiv)

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.