Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
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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.
A visual analytics workbench enables scientists to explore, query, and verify embedding-based similarity searches on weather and climate data by tracing results back to physical evidence.
Releases a publicly available, collocated multi-sensor dataset of Landsat, Sentinel-1, GOES-R and microwave observations for urban heat studies across 48 cities.
citing papers explorer
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PRecover 1.0: Process Rate Recovery with Machine Learning
Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
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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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Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration
A visual analytics workbench enables scientists to explore, query, and verify embedding-based similarity searches on weather and climate data by tracing results back to physical evidence.
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Urban Heat MiniCubes: An AI-Ready dataset for urban heat research
Releases a publicly available, collocated multi-sensor dataset of Landsat, Sentinel-1, GOES-R and microwave observations for urban heat studies across 48 cities.