SS-POD augments standard POD-Galerkin with a spectral-subspace partition and local POD to achieve lower out-of-sample error than either plain POD or pure spectral-Galerkin when only a handful of snapshots are available.
Jolliffe, and David B
5 Pith papers cite this work, alongside 1,219 external citations. Polarity classification is still indexing.
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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.
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Diffusion models recover known ENSO variability structure from synthetic LIM data when given enough samples, but require pre-training on CMIP6 plus fine-tuning to match observations with the ~700 samples available in ERSSTv5.
ArchesWeather and ArchesWeatherGen, when conditioned on monthly SST and SIC, produce stable multi-decadal climate simulations that reproduce ERA5 climatology, large-scale circulations, interannual variability, and distribution tails.
citing papers explorer
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A spectral-subspace-augmented POD-Galerkin method for parametrized PDEs with limited snapshot data
SS-POD augments standard POD-Galerkin with a spectral-subspace partition and local POD to achieve lower out-of-sample error than either plain POD or pure spectral-Galerkin when only a handful of snapshots are available.
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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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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
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Learning Climate Variability from Scarce Data with Diffusion Models: A Test Case for ENSO
Diffusion models recover known ENSO variability structure from synthetic LIM data when given enough samples, but require pre-training on CMIP6 plus fine-tuning to match observations with the ~700 samples available in ERSSTv5.
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Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations
ArchesWeather and ArchesWeatherGen, when conditioned on monthly SST and SIC, produce stable multi-decadal climate simulations that reproduce ERA5 climatology, large-scale circulations, interannual variability, and distribution tails.