FLOATBench is a tabular benchmark dataset with 582,120 fatigue labels from 19,404 OpenFAST simulations of three 22 MW FOWT towers, featuring alpha-shape regime partitioning and three evaluation protocols for surrogate models.
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WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models.Journal of Advances in Modeling Earth Systems, 16(6)
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Forward Flux Sampling applied to a 1-degree neural weather emulator resolves conditional tropical cyclogenesis rates spanning three orders of magnitude across 98 Atlantic initial conditions, with self-consistency ratio 1.03 to direct sampling and computational gains up to 140X.
Cast3 translates NWP principles into a data-driven model using cubed-sphere grids, super-ensembles, and generative nudging to achieve state-of-the-art ensemble predictions that outperform baselines.
Hybrid LSTM-ViT model using mesonet surface data and profiler vertical profiles improves HRRR forecast error prediction for precipitation, wind speed, and temperature, with roughly twofold skill gain for precipitation over baseline LSTM.
NTK-UQ produces 31-37% sharper 90% prediction intervals than split conformal prediction for extreme weather forecasts, with adaptive scaling via architecture-dependent eigenvalue truncation and ICA decomposition of last-layer features.
ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
SwAIther-Precip uses lead-time-conditioned U-Net bias correction followed by diffusion-based generative downscaling to reduce CRPS by 48% and achieve ~4 km effective resolution from 0.25° AIFS forecasts.
Extreme Weather Bench supplies standardized case studies, observational data, impact metrics, and code to evaluate weather models on high-impact hazards.
Probabilistic bias correction doubles AI subseasonal forecast skill and wins a 2025 international competition by correcting biases in ECMWF models for pressure, temperature, and precipitation.
HealDA supplies ML-based initial conditions for AI weather models that produce forecasts trailing ERA5-initialized runs by less than one day of effective lead time, with the skill gap arising mainly from initial error size.
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.
Otter Weather is a spatiotemporal model that outperforms NWP baselines by 9.6% at 24h lead with under 3.5 A100-days training and extends efficiency gains to probabilistic forecasting via CRPS.
Extends Potential CRPS with weights and IDR post-processing to enable fair comparisons of AIWP and NWP models on extreme weather, finding AI models more informative across most variables and thresholds.
An open-source tool is developed for mechanistic interpretability of AI weather models, demonstrated on GraphCast by identifying latent directions corresponding to interpretable weather features.
A multi-task Patch-cGAN with lightning-derived spatial loss weighting improves post-processed forecasts of intense precipitation and lightning occurrence over the Korean Peninsula in summer 2025.
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