GREMLIN CONUS3 Dataset for 2021

Geostationary Operational Environmental Satellite (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that produces composite radar reflectivity using data from the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM). GREMLIN is useful for observing severe weather and providing information during convective initialization especially over regions without ground-based radars. Previous research found good skill compared to ground-based radar products, however the analysis was done over a dataset with similar climatic and precipitation characteristics as the training dataset: warm season Eastern CONUS in 2019. This study expands the analysis to the entire contiguous United States, during all seasons, and covering the period 2020-2022. Several validation metrics including root-mean-square difference (RMSD), probability of detection (POD), and false alarm ratio (FAR) are plotted over CONUS by season, day-of-year, and time-of-day, and the regional and temporal variations are examined. GREMLIN skill is highest in summer and spring, with lower skill in winter due to cold surfaces frequently mistaken as precipitating clouds. In summer, diurnal patterns of RMSD in different longitude regions follow diurnal patterns of precipitation occurrence. GREMLIN’s accuracy is the best over the Central to Eastern United States where it has been trained. Over New England, GREMLIN POD is lower due to different brightness temperature distributions and low frequency of lightning compared to the training data. Over Florida, GREMLIN FAR is higher due to high frequency of lightning. Overall, GREMLIN has reliable skill over CONUS in spring, summer, and fall, while winter needs more improvements.