Sensitivity to Localization Radii for an Ensemble Filter Numerical Weather Prediction System with 30-Second Update

A sensitivity analysis for the horizontal localization scale is performed for a numerical weather prediction (NWP) system that uses a 30-s update to refresh a 500-m mesh with observations from a new-generation multiparameter phased array weather radar (MP-PAWR). Testing is performed using three case studies of convective weather events that occurred during August-September 2019, with the aim to determine the most suitable scale for short-range forecasting of precipitating convective systems and to better understand model behavior to a rapid update cycle. Results showed that while the model could provide useful skill at lead times up to 30 min, forecasts would consistently overestimate rainfall and were unable to outperform nowcasts performed with a simple advection model. Using a larger localization scale, e.g., 4 km, generated stronger convective and dynamical instability in the analyses that made conditions more favorable for spurious and intense convection to develop in forecasts. It was demonstrated that lowering the localization scale reduced the size of analysis increments during early cycling, limiting the buildup of these conditions. Improved representation of the localized convection in the initial conditions was suggested as an important step to mitigating this issue in the model.

Automated summary

A sensitivity analysis was conducted for a numerical weather prediction system that uses observations from a new-generation weather radar to update a 500m mesh with a 30-second refresh rate. The aim was to determine the optimal scale for short-range forecasting of convective systems and understand the model's behavior to rapid updates. The results showed that while the model performed well within a 30-minute lead time, it consistently overestimated rainfall and did not outperform simpler nowcast models. Using a larger localization scale generated more intense convection in the analyses and hindered forecast accuracy.