[HTML][HTML] Hybrid forecasting: blending climate predictions with AI models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction
B Zhang, H Yu, Z Hu, P Yue, Z Tang, H Luo… - … in Atmospheric Sciences, 2025 - Springer
Seasonal precipitation has always been a key focus of climate prediction. As a dynamic-
statistical combined method, the existing observational constraint correction establishes a …
statistical combined method, the existing observational constraint correction establishes a …
Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques
S Velivelli, GC Satyanarayana, MM Ali - Theoretical and Applied …, 2024 - Springer
Abstract Surface Air Temperature (SAT) predictions, typically generated by Global Climate
Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission …
Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission …
Evaluating the relationship between sudden stratospheric warmings and tropospheric weather regimes in the NMME phase-2 models
Abstract The Northern Annular Mode (NAM) dominates variability of the Northern
Hemisphere (NH) wintertime extratropical circulation in both the troposphere and …
Hemisphere (NH) wintertime extratropical circulation in both the troposphere and …