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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physics.ao-ph 4years
2026 4verdicts
UNVERDICTED 4roles
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Stratospheric polar vortex predictability is multimodal, with short-term forecasts dominated by persistence of the leading state and extended forecasts arising from higher-order stratospheric structures plus tropospheric variability.
A multi-branch β-VAE on tropical Pacific SST, OHC, and OLR fields yields a latent space that reconstructs data well and aligns with physical ENSO and longer-term coupled variability modes.
Wet-season rainfall over southeast India is increasing in amount and variability but shows potential predictability up to 10 months ahead from tropical sea surface temperature networks.
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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State-resolved multimodal contributions to stratospheric polar vortex predictability
Stratospheric polar vortex predictability is multimodal, with short-term forecasts dominated by persistence of the leading state and extended forecasts arising from higher-order stratospheric structures plus tropospheric variability.
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What's in the latent space? Exploring coupled tropical Pacific variability within a Multi-branch $\beta$-Variational Autoencoder
A multi-branch β-VAE on tropical Pacific SST, OHC, and OLR fields yields a latent space that reconstructs data well and aligns with physical ENSO and longer-term coupled variability modes.
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Prediction and Predictability of the Wet-Season Rainfall over Southeast India
Wet-season rainfall over southeast India is increasing in amount and variability but shows potential predictability up to 10 months ahead from tropical sea surface temperature networks.