Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks
arXiv:2606.18436v1 Announce Type: cross Abstract: Sparse point observations are increasingly available for precipitation nowcasting, but it is unclear how much they improve dense radar-field forecasts. We partially address this question with a multimodal graph neural network nowcasting system over the Nordic radar domain. The model predicts rain rate every five minutes up to two hours ahead and is trained with different combinations of radar history, MEPS numerical weather prediction, Netatmo surface observations, MSG satellite channels, stochastic noise, and CRPS-based ensemble losses. The st
The increasing availability of diverse geographical data and advancements in Graph Neural Networks (GNNs) are converging to refine environmental forecasting capabilities, making this research timely.
Improved precipitation nowcasting using advanced AI techniques like GNNs can significantly enhance disaster preparedness, resource management, and economic planning across various sectors.
This research contributes to understanding how different data modalities (radar, NWP, surface observations, satellite) contribute to the accuracy of short-term weather predictions, potentially optimizing data collection and model design.
- · Weather forecasting agencies
- · Insurance industry
- · Agriculture sector
- · AI/ML researchers
- · Traditional statistical models
- · Regions lacking diverse data infrastructure
More accurate short-term weather predictions lead to better emergency response and operational planning.
Optimized use of sensors and data collection strategies informed by the contribution of various data types to prediction accuracy.
Potential for development of more robust, multimodal AI systems that integrate disparate data sources for complex environmental modeling globally.
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Read at arXiv cs.LG