GREMLIN CONUS3 Dataset for 2022

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.