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.
Canonical reference
Title resolution pending
Canonical reference. 78% of citing Pith papers cite this work as background.
26
Pith papers citing it
306
external citations · OpenAlex
Background
78% of classified citations
citation-role summary
background 7
baseline 1
dataset 1
citation-polarity summary
representative citing papers
Observation-only machine learning can generate multi-decade global atmospheric reanalyses with large-scale skill near ERA5 and surface errors between ERA-Interim and ERA5, in a single day of compute.
MotifGen is the first multi-source generative model for spatiotemporal interpolation of misaligned microwave cyclone images from heterogeneous instruments at irregular intervals, achieving lower CRPS via self-supervised training and closer power spectra than deterministic baselines when combining in
TianJi-Environ is a WRF-Chem-based multi-agent AI framework for autonomous validation of atmospheric chemistry mechanisms through executable experiments and evidence assessment.
Targeted perturbations in the Aurora AI model can steer Hurricane Sandy's trajectory by more than 500 km after seven days via amplification in sensitive regions identified by FTLE and wave activity diagnostics.
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.
Mosaic is a benchmark suite evaluating 14 differentiable PDE solvers across fluids, structures, and heat transfer, showing large variations in cost and conditioning but similar convergence behavior.
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
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.
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
PRISMA introduces a plug-and-play latent generative model that improves multi-sensor precipitation estimates by learning an unconditional prior from IMERG data and constraining it with independent sensor-specific branches.
Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
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.
Extreme Weather Bench supplies standardized case studies, observational data, impact metrics, and code to evaluate weather models on high-impact hazards.
EnScale emulates high-resolution regional climate model outputs from global circulation models for multiple variables using a two-step generative process with sparse local stochastic layers and energy score optimization, including a temporally consistent variant.
A physics-constrained consistency model downscales Greenland SMB and surface temperature by a factor of 32 while preserving coarse-scale sums and outperforming interpolation on test metrics.
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.
Diffusion model improves GFS/GEFS ensemble CAPE forecasts and incorporates aerosol optical depths for additional gains.
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
Probabilistic bias correction doubles ECMWF AI subseasonal skill and improves debiased dynamical forecasts for most pressure, temperature, and precipitation targets, winning ECMWF’s 2025 real-time competition.
CycloneMAE uses a TC structure-aware masked autoencoder with discrete probabilistic gridding and pre-train/fine-tune to deliver both deterministic and probabilistic forecasts, outperforming NWP systems in pressure and wind up to 120 hours and track up to 24 hours across five basins.
A standard U-Net with MAE pre-training plus short CRPS fine-tuning and MC Dropout matches GenCast and IFS ENS probabilistic skill at 1.5° while cutting training and inference cost by over 10×.
Sampling parallelism distributes Bayesian sample evaluations across GPUs for near-perfect scaling, lower memory use, and faster convergence via per-GPU data augmentations, outperforming pure data parallelism in diversity.
Evaluates standard CP, normalized CP, and conformalized quantile regression against ensemble spread and standard deviation for uncertainty in idealized data assimilation, and tests CP perturbations in the assimilation cycle.
citing papers explorer
-
AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning
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.
-
Global reanalysis from observations alone with machine learning
Observation-only machine learning can generate multi-decade global atmospheric reanalyses with large-scale skill near ERA5 and surface errors between ERA-Interim and ERA5, in a single day of compute.
-
MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones
MotifGen is the first multi-source generative model for spatiotemporal interpolation of misaligned microwave cyclone images from heterogeneous instruments at irregular intervals, achieving lower CRPS via self-supervised training and closer power spectra than deterministic baselines when combining in
-
TianJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research
TianJi-Environ is a WRF-Chem-based multi-agent AI framework for autonomous validation of atmospheric chemistry mechanisms through executable experiments and evidence assessment.
-
Steering Tropical Cyclones Using Small Perturbations in an AI Weather Model
Targeted perturbations in the Aurora AI model can steer Hurricane Sandy's trajectory by more than 500 km after seven days via amplification in sensitive regions identified by FTLE and wave activity diagnostics.
-
Cast3: Translating numerical weather prediction principles into data-driven forecasting
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.
-
Mosaic: A Benchmark Suite for Differentiable Physics Solvers
Mosaic is a benchmark suite evaluating 14 differentiable PDE solvers across fluids, structures, and heat transfer, showing large variations in cost and conditioning but similar convergence behavior.
-
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
-
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
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.
-
Mechanisms of Misgeneralization in Physical Sequence Modeling
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
-
A plug-and-play generative framework for multi-satellite precipitation estimation
PRISMA introduces a plug-and-play latent generative model that improves multi-sensor precipitation estimates by learning an unconditional prior from IMERG data and constraining it with independent sensor-specific branches.
-
Multi-Quantile Regression for Extreme Precipitation Downscaling
Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
-
AxiomOcean: Forecasting the Three-Dimensional Structure of the Upper Ocean
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.
-
Extreme Weather Bench: A framework and benchmark for evaluation of high-impact weather
Extreme Weather Bench supplies standardized case studies, observational data, impact metrics, and code to evaluate weather models on high-impact hazards.
-
EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
EnScale emulates high-resolution regional climate model outputs from global circulation models for multiple variables using a two-step generative process with sparse local stochastic layers and energy score optimization, including a temporally consistent variant.
-
Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
A physics-constrained consistency model downscales Greenland SMB and surface temperature by a factor of 32 while preserving coarse-scale sums and outperforming interpolation on test metrics.
-
Towards Fair Comparisons of AI- and Physics-Based Weather Models for Extreme Events via the Weighted Potential 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.
-
Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
Diffusion model improves GFS/GEFS ensemble CAPE forecasts and incorporates aerosol optical depths for additional gains.
-
Heterogeneous Scientific Foundation Model Collaboration
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
-
Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction
Probabilistic bias correction doubles ECMWF AI subseasonal skill and improves debiased dynamical forecasts for most pressure, temperature, and precipitation targets, winning ECMWF’s 2025 real-time competition.
-
CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
CycloneMAE uses a TC structure-aware masked autoencoder with discrete probabilistic gridding and pre-train/fine-tune to deliver both deterministic and probabilistic forecasts, outperforming NWP systems in pressure and wind up to 120 hours and track up to 24 hours across five basins.
-
U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
A standard U-Net with MAE pre-training plus short CRPS fine-tuning and MC Dropout matches GenCast and IFS ENS probabilistic skill at 1.5° while cutting training and inference cost by over 10×.
-
Sampling Parallelism for Fast and Efficient Bayesian Learning
Sampling parallelism distributes Bayesian sample evaluations across GPUs for near-perfect scaling, lower memory use, and faster convergence via per-GPU data augmentations, outperforming pure data parallelism in diversity.
-
Uncertainty quantification via conformal prediction in data assimilation
Evaluates standard CP, normalized CP, and conformalized quantile regression against ensemble spread and standard deviation for uncertainty in idealized data assimilation, and tests CP perturbations in the assimilation cycle.
-
Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil
GraphCast shows regime-dependent skill versus ECMWF HRES in Brazil, underperforming on winter baroclinic systems in medium range but gaining in extended range and summer moisture transport.
-
Modelling convective cell occurrence in proximity to cold fronts using extreme gradient boosting
An XGBoost model reproduces convective cell frequency near cold fronts with high skill but underestimates counts at the surface front, depending most on CAPE and time of day.