Quantifying the responses of AI precipitation forecast errors to reanalysis errors using analysis of covariance

Divergent responses of streamflow reanalysis errors to precipitation re-analysis errors modulated by catchment heterogeneity

Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity

Streamflow reanalysis is vital for water resources management and climate impact assessment; however, the extent to which it is affected by precipitation forcing errors remains poorly understood. Focusing on the Global Flood Awareness System driven by the European Centre for Medium-Range Weather Forecasts Reanalysis...

Global Evaluation of AI and NWP Precipitation Forecasts During Atmospheric River Events

Atmospheric rivers (ARs) produce many of the world's most extreme precipitation events and hydrometeorological hazards. Although artificial intelligence weather prediction (AIWP) models have demonstrated skill comparable to or exceeding numerical weather prediction (NWP) systems for large-scale atmospheric variables, t...

Diagnosing Forecast Error Propagation and Large‐Scale Dynamics of Weather Extremes With an AI Weather Model

A true‐state constraint (TSC) method that can diagnose error propagation by adjusting forecasts in an upstream region toward reanalysis data and tracking the responses elsewhere, and a climatology constraint method that, when combined with the TSC method, can quantify the remote impacts of evolving atmospheric circulat...

Evaluating Rainfall Forecast Skill in Numerical Weather Prediction Models and the Effects of Bias Correction on the PCJ (Piracicaba-Capivari-Jundiaí) River Basins in Brazil

Precipitation forecasts from numerical weather prediction systems can contain systematic errors in both occurrence and magnitude, limiting their usefulness for hydrological applications. This study evaluated two operational precipitation forecasting systems against two observational reference datasets across three basi...

Performance-based weighting improves forecast skill without calibration

Post-processing in Numerical Weather Prediction (NWP) is essential for transforming raw model outputs into forecasts that are both accurate and actionable. This study investigates a dynamic weighting approach for multi-model blending, in which the contribution of each model is adjusted in real time based on recent perf...