Using the Fourier Neural Operator and Real-Time GOES-R Satellite Data for Precipitation Retrievals in the Southern Great Plains Journal of Geophysical Research: Machine Learning and Computation

In the U. S. Southern Great Plains (SGP) region, severe weather occurs regularly during the warm season (e.g., June–August), causing extensive property damage and loss of life. Despite advancements in observations and numerical models, estimating precipitation associated with these severe weather events remains challenging, thereby complicating accurate public warnings. Recently, machine learning (ML) models have been employed as a data-driven approach to quantify precipitation during severe storms. In this study, we evaluate the performance of a ML model which utilizes the Fourier Neural Operator (FNO) for obtaining hourly precipitation retrievals in the SGP region. The FNO-based model uses water vapor-absorbing band brightness temperatures, lightning flash counts, and lightning average flash areas from NOAA's latest generation of Geostationary Operational Environmental Satellites (known as GOES-R) as inputs to produce hourly precipitation retrievals at approximately 4-km horizontal resolution. The “ground truth” rainfall data are hourly National Centers for Environmental Prediction (NCEP) Stage IV precipitation analysis totals. Results demonstrate that the FNO-based model effectively generates accurate precipitation retrieval totals, offering improvements over the operational GOES-R Quantitative Precipitation Estimation in both estimating and detecting precipitation at hourly intervals. Grant no. NA24OARX459C0003‐T1‐01