Concept: Meteorological Phenomena and Simulations
Neural network predicts shifts in extreme weather frequency
Machine learning improves predictions of how severe storms will shift in frequency and location
Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning
Quantifying uncertainty in satellite-based climate estimates from deep learning models
ICL Characterization of Climate Foundation Models: When Can Transformers Learn Weather and Climate?
Why climate models excel at predicting weather fields but struggle with extreme events
GloMarGridding supports spatial interpolation uncertainty assessment
Python toolkit for evaluating temperature dataset uncertainty from spatial interpolation
CMIP6 models project stronger precipitation extremes in the Kosi Basin
How climate models predict worsening extreme rainfall in the Kosi Basin
Southern land evaporation linked to North China extreme rain
How evaporation from land and oceans triggers extreme rainfall across northern China
Kilometre-scale simulations improved extreme rainfall forecasts in eastern Qinghai
Higher-resolution weather models better predict intense summer storms in mountain valleys
Weather regimes affect short-term satellite solar forecast error
Weather patterns significantly impact the accuracy of satellite-based solar forecasts
Wind shear strengthens soil moisture effects on thunderstorm growth
Wind shear amplifies how soil moisture patterns trigger severe thunderstorms
Climate explains mean storm activity more than individual storm features
Climate patterns drive average storm frequency, while weather conditions shape individual storms
