Weather-R1 is a multimodal reasoning model for meteorology that uses logical consistency rewards during reinforcement fine-tuning to cut self-contradictory outputs and raises benchmark accuracy by 9.8 points over baselines.
Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead
11 Pith papers cite this work. Polarity classification is still indexing.
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Introduces WILDFIRE-FM and a fixed-contract evaluation framework demonstrating that wildfire model transfer conclusions depend strongly on evaluation design and task formulation.
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.
Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
Baguan-solar integrates Baguan weather foundation model forecasts with geostationary satellite data via a decoupled two-stage multimodal framework to deliver kilometer-scale 24-hour solar irradiance predictions, cutting RMSE by 16% versus baselines over East Asia.
QuantWeather is an end-to-end dual-head neural network that produces calibrated quantile-based probabilistic forecasts for subseasonal precipitation, achieving higher skill and lower inference cost than ensemble methods requiring post-hoc calibration.
PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
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.
LangRetrieval is a conditional flow matching framework with semantic warm-up and GRPO-based self-evolving optimization using CSI rewards to improve satellite-to-radar precipitation retrieval.
The paper reviews data sources, physical models, downstream applications, and AI techniques to outline considerations for building a foundation model for the Martian atmosphere.
A review of Earth science foundation models covering capability evolution from perception to discovery, applications across atmosphere/hydrosphere/lithosphere/biosphere/anthroposphere/cryosphere, over 200 datasets, and key challenges.
citing papers explorer
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Weather-R1: Logically Consistent Reinforcement Fine-Tuning for Multimodal Reasoning in Meteorology
Weather-R1 is a multimodal reasoning model for meteorology that uses logical consistency rewards during reinforcement fine-tuning to cut self-contradictory outputs and raises benchmark accuracy by 9.8 points over baselines.
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Does Your Wildfire Prediction Model Actually Work, or Just Score Well?
Introduces WILDFIRE-FM and a fixed-contract evaluation framework demonstrating that wildfire model transfer conclusions depend strongly on evaluation design and task formulation.
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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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Earth-o1: A Grid-free Observation-native Atmospheric World Model
Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
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Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting
Baguan-solar integrates Baguan weather foundation model forecasts with geostationary satellite data via a decoupled two-stage multimodal framework to deliver kilometer-scale 24-hour solar irradiance predictions, cutting RMSE by 16% versus baselines over East Asia.
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QuantWeather: Quantile-Aware Probabilistic Forecasting for Subseasonal Precipitation
QuantWeather is an end-to-end dual-head neural network that produces calibrated quantile-based probabilistic forecasts for subseasonal precipitation, achieving higher skill and lower inference cost than ensemble methods requiring post-hoc calibration.
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PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
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LangRetrieval: Language-Guided Self-Evolving Satellite-to-Radar Retrieval via CSI-Driven Reward
LangRetrieval is a conditional flow matching framework with semantic warm-up and GRPO-based self-evolving optimization using CSI rewards to improve satellite-to-radar precipitation retrieval.
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Towards a Foundation Model for the Martian Atmosphere
The paper reviews data sources, physical models, downstream applications, and AI techniques to outline considerations for building a foundation model for the Martian atmosphere.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
A review of Earth science foundation models covering capability evolution from perception to discovery, applications across atmosphere/hydrosphere/lithosphere/biosphere/anthroposphere/cryosphere, over 200 datasets, and key challenges.