Mechanism learning infers active local evolution rules via prototype-anchored descriptors to achieve more robust forecasting than direct state prediction on benchmarks like Burgers, WeatherBench2, and Lorenz96.
WeatherBench 2: A benchmark for the next generation of data-driven global weather mod- els
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
ESFM is a single open foundation model that unifies heterogeneous Earth data sources and forecasts missing regions while preserving inter-variable physical relationships.
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.
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.
citing papers explorer
-
Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting
Mechanism learning infers active local evolution rules via prototype-anchored descriptors to achieve more robust forecasting than direct state prediction on benchmarks like Burgers, WeatherBench2, and Lorenz96.
-
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.