Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
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arXiv:2202.07575 [physics]
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WeatherSyn is the first instruction-tuned MLLM for weather forecasting report generation, outperforming closed-source models on a new dataset of 31 US cities across 8 weather aspects.
Diffusion model climate emulators provide probability density estimates that allow likelihood calculations and odds-ratio-based importance sampling for extreme events such as tropical cyclones.
A GraphCast-based ocean emulator achieves skillful 10-15 day forecasts, with a Mahalanobis loss that accounts for variable correlations improving performance over MSE and acting as a statistical-dynamical regularizer.
Image-to-image networks estimate parameters of non-stationary SAR models faster and more accurately than traditional methods by framing fields and parameters as images.
A conditional diffusion model downscales global atmospheric forecasts from 100 km to 30 km resolution while improving probabilistic skill, matching power spectra, and preserving physical relationships.
STCast introduces Spatial-Aligned Attention and Temporal Mixture-of-Experts modules to adaptively refine regional boundaries in data-driven weather forecasting and reports better performance than prior methods on global, regional, extreme-event, and ensemble tasks.
The work introduces WaLeF/FIDLAr for flood forecasting, CoDiCast for probabilistic weather, and Hypercube-RAG for explainable environmental QA, claiming superior accuracy, efficiency, and interpretability over baselines.
DroughtFormer predicts soil moisture, vegetation health, and related variables in Africa with skill out to 90 days that matches or exceeds climatology for most targets, but shows lower accuracy for precipitation and flash drought indices.
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets
citing papers explorer
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Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
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WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation
WeatherSyn is the first instruction-tuned MLLM for weather forecasting report generation, outperforming closed-source models on a new dataset of 31 US cities across 8 weather aspects.
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Towards accurate extreme event likelihoods from diffusion model climate emulators
Diffusion model climate emulators provide probability density estimates that allow likelihood calculations and odds-ratio-based importance sampling for extreme events such as tropical cyclones.
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Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss
A GraphCast-based ocean emulator achieves skillful 10-15 day forecasts, with a Mahalanobis loss that accounts for variable correlations improving performance over MSE and acting as a statistical-dynamical regularizer.
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LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
Image-to-image networks estimate parameters of non-stationary SAR models faster and more accurately than traditional methods by framing fields and parameters as images.
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Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models
A conditional diffusion model downscales global atmospheric forecasts from 100 km to 30 km resolution while improving probabilistic skill, matching power spectra, and preserving physical relationships.
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STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
STCast introduces Spatial-Aligned Attention and Temporal Mixture-of-Experts modules to adaptively refine regional boundaries in data-driven weather forecasting and reports better performance than prior methods on global, regional, extreme-event, and ensemble tasks.
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Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems
The work introduces WaLeF/FIDLAr for flood forecasting, CoDiCast for probabilistic weather, and Hypercube-RAG for explainable environmental QA, claiming superior accuracy, efficiency, and interpretability over baselines.
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Prediction of Drought and Flash Drought in Africa at the Seasonal-to-Subseasonal Scale using the Community Research Earth Digital Intelligence Twin Framework
DroughtFormer predicts soil moisture, vegetation health, and related variables in Africa with skill out to 90 days that matches or exceeds climatology for most targets, but shows lower accuracy for precipitation and flash drought indices.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets
- U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster