Meteorological Phenomena and Simulations

Neural networks leverage climate model data to predict how extreme rainfall, hail, and winds will shift geographically as climate changes, accounting for terrain effects.

Theoretical analysis explains why climate foundation models succeed at field prediction but fail at extreme event detection through in-context learning complexity.

GloMarGridding isolates and assesses structural uncertainty from spatial interpolation in global temperature datasets using Gaussian Process Regression Modelling.

CMIP6 climate models project intensified precipitation extremes in the Kosi Basin, with 47-79% increases in rainfall by 2100, critical for flood risk management and water resource planning.

Study identifies atmospheric circulation patterns and cross-regional evaporation precursors driving extreme precipitation trends in North China using information flow analysis.

Kilometre-scale convection-permitting simulations significantly improve extreme precipitation forecasting accuracy in Qinghai's eastern valleys by better representing valley circulation patterns.

Study reveals how North Atlantic weather regimes significantly influence satellite-based solar forecast accuracy, with seasonal variations up to 20% in error magnitude affecting renewable energy.

Wind shear and soil moisture interact to enhance rapid thunderstorm growth, offering new predictability for severe convective initiation across Africa and beyond.

Machine learning reveals how seasonal climate and synoptic conditions differently control storm activity, with climate trends more strongly affecting storm heat anomalies than intensity.

Study uses cloud seeding experiments and deep learning to identify ice crystal concentration as the dominant factor controlling aggregation rates in supercooled clouds, with implications for.