Enhancing Precipitation Predictions in the WRF Model: The Role of Convection Schemes and Increased Spatial Resolution in the Convective Gray Zone

The Numerical Weather Prediction (NWP) gray zone (GZ) represents a critical challenge in modeling, occurring at spatial resolutions typically ranging from approximately 500 m to 5 km, depending on factors such as the modeling framework, the prevailing atmospheric conditions, and the geographical context where neither full parameterization nor explicit simulation of physical processes is feasible. Within this range, convection parameterizations often become unreliable, particularly for cumulus clouds and turbulence, leading to uncertainties in weather forecasts. High-resolution models (below 4 km) assume explicitly resolved convection, yet this approach does not consistently improve prediction accuracy. Recent advancements in scale-aware parameterizations offer a promising solution, enabling a gradual transition from parameterized to resolved convection, enhancing model performance and reducing biases within the GZ. To explore these challenges, the Weather Research and Forecasting (WRF) model was employed to simulate eight precipitation events across Schleswig-Holstein and Baden-Württemberg in Germany, all exceeding the severe weather threshold of 40 mm/h (warning level 3) set by the German Weather Service. A comprehensive suite of 1,440 simulations was conducted, combining 10 microphysics schemes, 6 cumulus schemes, 8 event cases, and 3 spatial setups. The model setups included a single domain with a 9 km grid size and two two-way nesting configurations with spatial resolutions of 9 km and 3 km. To investigate the role of convection schemes in the convective GZ and the benefits of higher spatial resolution, simulations at 3 km resolution were run both with and without active convection schemes. Initial and boundary conditions were provided by the ERA5 dataset at a spatial resolution of 0.25°. A detailed performance analysis was carried out using pairwise comparisons and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which ranked the parameterization combinations based on multiple criteria. Results revealed that non-convection-permitting setups performed better during summer precipitation events, where convection is more localized and intense. On the other hand, winter events, influenced by larger-scale processes, showed similar accuracy between convection-permitting and non-convection-permitting configurations. Interestingly, increasing resolution from 9 km to 3 km did not consistently improve model performance. Furthermore, the best-performing parameterizations at 9 km resolution outperformed those at 3 km across all configurations, challenging the common assumption that higher resolution inherently improves model accuracy. These findings emphasize the need to carefully balance resolution and parameterization choices in severe weather forecasting, particularly for convective systems. The study underscores the critical influence of model physics and nesting configurations on simulation outcomes, offering valuable insights for future research and operational modeling efforts.