GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
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A spatio-temporal GNN model reduces storm surge water-level forecast RMSE by more than 70% for 48-hour horizons and over 50% for 72-hour horizons on U.S. Gulf Coast hurricane data.
Amplified warming over western highlands is carried downstream by westerlies, strengthening low-level inversions and thereby raising the maxima of moist heat and convection extremes in the midlatitudes.
citing papers explorer
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GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
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StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model
A spatio-temporal GNN model reduces storm surge water-level forecast RMSE by more than 70% for 48-hour horizons and over 50% for 72-hour horizons on U.S. Gulf Coast hurricane data.
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Future Amplification of Moist Weather Extremes in the Midlatitudes
Amplified warming over western highlands is carried downstream by westerlies, strengthening low-level inversions and thereby raising the maxima of moist heat and convection extremes in the midlatitudes.