Observation-only machine learning can generate multi-decade global atmospheric reanalyses with large-scale skill near ERA5 and surface errors between ERA-Interim and ERA5, in a single day of compute.
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Suárez, Ricardo Todling, Andrea Molod, Lawrence Takacs, Cynthia A
3 Pith papers cite this work, alongside 9,365 external citations. Polarity classification is still indexing.
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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.
Atmosphere functions as steam engine with global power 4.4±0.9 W/m² from water cycle, matching total atmospheric power 4.3±0.6 W/m² and explaining condensation-driven dynamics via precipitation.
citing papers explorer
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Global reanalysis from observations alone with machine learning
Observation-only machine learning can generate multi-decade global atmospheric reanalyses with large-scale skill near ERA5 and surface errors between ERA-Interim and ERA5, in a single day of compute.
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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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Atmosphere as a steam engine
Atmosphere functions as steam engine with global power 4.4±0.9 W/m² from water cycle, matching total atmospheric power 4.3±0.6 W/m² and explaining condensation-driven dynamics via precipitation.