An ML model trained only on harmonized gridded observations achieves competitive medium-range weather forecast skill with the IFS for several upper-air and surface headline scores when verified against observations.
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Baseline reference. 55% of citing Pith papers use this work as a benchmark or comparison.
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representative citing papers
HO-FNO extends standard FNO with n-linear spectral mixing and shows improved accuracy on nonlinear PDE benchmarks, sometimes with a single layer beating deeper FNO models.
A novel observationally constrained probabilistic trigger for mesoscale convective systems improves spatiotemporal scales of tropical precipitation and ensemble spread in NWP models compared to prior MCSP schemes.
Trains ACE emulator on independent SST-CO2 variations plus energy constraint to improve accuracy in decoupled climate forcing scenarios.
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.
A multilinear operator learned on PCA coefficients maps time-since-ignition inputs to smoke outputs, matching Monte Carlo accuracy with half the model calls and outperforming prior classifiers on holdout data.
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.
Time series foundation models match the performance of specialized models for day-ahead load forecasting while providing explanations that match domain knowledge on weather and calendar effects.
SCARFACE provides a harmonized annual panel dataset of over 2,700 socio-economic, environmental, and agricultural indicators for 256 sub-regions in the Po Valley, Italy, spanning 2011 to 2024.
Relaxed unitary convolutions for GNNs on meshes balance smoothness preservation with natural smoothing in dynamics, outperforming unitary convolutions and other models on PDEs and weather tasks.
FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.
MTGNN with hybrid adjacency matrix reconstructs GRACE-like TWS anomalies to 1940, reaching basin-mean correlation 0.94 and competitive performance with fewer predictors than baselines.
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
A zero-shot pipeline with physics-informed synthetic histories lets time-series foundation models outperform baselines by 1.7-2x in cold-start PV forecasting on 440 sites across four datasets.
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.
Distributional autoencoders trained on climate model simulations model full conditional distributions of European temperature fields to enable probabilistic storyline attribution, illustrated by higher intensities and probability ratios for a 2003-like heatwave in 2028 and 2053.
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.
AxiomOcean deploys a 3D encoder-backbone-decoder architecture that jointly predicts upper-ocean variables and outperforms prior AI models by 20-35% in day-1 RMSE while preserving eddy kinetic energy and vertical consistency.
Commutativity regularization mitigates transient error amplification in autoregressive neural simulators by penalizing non-normality and non-commutativity of Jacobians, yielding stable long-horizon rollouts.
AIMIP Phase 1 sets up a common experiment and five evaluation criteria for AI atmosphere models forced by historical sea surface temperatures, finding they match conventional models on most metrics but underestimate some warming trends and diverge on out-of-sample tests.
A new optimization algorithm with double machine learning for wildfire spread estimation enables better crew assignments that reduce total area burned.
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.
ESFM is a single open foundation model that unifies heterogeneous Earth data sources and forecasts missing regions while preserving inter-variable physical relationships.
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.