Assessing the Accuracy of 3D-VAR in Supercell Thunderstorm Forecasting: A Regional Background Error Covariance Study

Abstract

Data assimilation (DA) integrates observational data with numerical weather predictions to enhance weather forecast accuracy. This study evaluates three regional background error (BE) covariance statistics for numerical weather prediction (NWP) via a variational data assimilation (VAR) scheme. The best practices in DA are highlighted, as well as the impact of BE covariance calculation in DA procedures by employing the Weather Research and Forecasting (WRF) model. Forecasts initialized at different intervals were used to compute distinct regional background error statistics utilizing three control variable (CV) methodologies over a span of 20 days. These statistics are used by the three-dimensional VAR DA process of WRF DA software, producing analysis fields that lead to forecasts for a distinct convective supercell event during the summer of 2019 over northern Greece. This high-impact convective event underscores the importance of selecting appropriate BE over complex terrain areas. The results emphasize the significance of BE usage in DA, proposing the optimal DA approach for simulations of convective systems.

Keywords

• background error statistics
• convection
• data assimilation
• numerical weather prediction
• supercell
• surface observations