Transformer-Based Nowcasting of Radar Composites from Satellite Images for Severe Weather

Abstract

Abstract Weather radar data are critical for nowcasting and an integral component of numerical weather prediction models. While weather radar data provide valuable information at high resolution, their ground-based nature limits their availability, which impedes large-scale applications. In contrast, meteorological satellites cover larger domains but with coarser resolution. However, with the rapid advancements in data-driven methodologies and modern sensors aboard geostationary satellites, new opportunities are emerging to bridge the gap between ground- and space-based observations, ultimately leading to more skillful weather prediction with high accuracy. Here, we present a transformer-based model for nowcasting ground-based radar image sequences using satellite data up to 2-h lead time. Trained on a dataset reflecting severe weather conditions, the model predicts radar fields occurring under different weather phenomena and shows robustness against rapidly growing/decaying fields and complex field structures. Model interpretation reveals that the infrared channel centered at 10.3 μm (C13) contains skillful information for all weather conditions, while lightning data have the highest relative feature importance in severe weather conditions, particularly in shorter lead times. The model can support precipitation nowcasting across large domains without an explicit need for radar towers, enhance numerical weather prediction and hydrological models, and provide radar proxy for data-scarce regions. Moreover, the open-source framework facilitates progress toward operational data-driven nowcasting. Significance Statement Ground-based weather radar data are essential for nowcasting, but data availability limitations hamper usage of radar data across large domains. We present a machine learning model, rooted in transformer architecture, that performs nowcasting of radar data using high-resolution geostationary satellite retrievals, for lead times of up to 2 h. Our model captures the spatiotemporal dynamics of radar fields from satellite data and offers accurate forecasts. Analysis indicates that the infrared channel centered at 10.3 μm provides useful information for nowcasting radar fields under various weather conditions. However, lightning activity exhibits the highest forecasting skill for severe weather at short lead times. Our findings show the potential of transformer-based models for nowcasting severe weather.