Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting
arXiv:2607.05658v1 Announce Type: cross Abstract: Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals. This delay, the Zenith Wet Delay (ZWD), is a direct, all-weather measure of column moisture. Although assimilated into numerical weather prediction for decades, ZWD is not yet used by leading machine learning weather models (MLWM), despite addressing a known deficiency: the underestimation of severe precipitation. Here we present the first integration of GNSS-derived ZWD into Aurora, a state-of-th
The increasing sophistication of machine learning weather models (MLWM) and the availability of direct atmospheric moisture measurements from GNSS enable this integration, addressing known model deficiencies.
Improving precipitation forecasting has significant implications for disaster preparedness, agriculture, water resource management, and climate change adaptation, making infrastructure and society more resilient.
Weather foundation models are now more accurate in predicting severe precipitation events by incorporating a direct measure of atmospheric moisture, potentially leading to better early warnings.
- · Weather forecasting agencies
- · Agricultural sector
- · Insurance companies
- · Logistics and transportation
- · None
More accurate and timely warnings for severe weather events, especially heavy precipitation.
Reduced economic losses from floods and droughts, and improved resource allocation in agriculture and water management.
Enhanced confidence in AI-driven climate models influencing policy decisions on infrastructure and environmental adaptation.
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Read at arXiv cs.LG