SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
This work enhances short-term convective-scale weather forecasts and data assimilation for numerical weather prediction over the United States, representing an incremental advance in domain-specific applications.
The authors tackled the problem of generating high-resolution synthetic radar reflectivity from satellite imagery to improve weather forecasting, achieving improved sharpness and accuracy compared to convolutional methods.
We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims to enhance short-term convective-scale forecasts of high-impact weather events and aid in data assimilation for numerical weather prediction over the United States. Compared to convolutional approaches, which have limited receptive fields, our results show improved sharpness and higher accuracy across various composite reflectivity thresholds. Additional case studies over specific atmospheric phenomena support our quantitative findings, while a novel attribution method is introduced to guide domain experts in understanding model outputs.