33WAF/29NWP Assessing the Impact of Scale-Aware Convective Parameterization Schemes on Tropical Cyclone Simulations in the South China Sea Using WRF, MPAS, and CPAS

Simulating tropical cyclones (TCs) at grey-zone resolutions (1-10 km) within high-resolution global NWP models remains challenging, though significant progress has been made in addressing these issues. The choice of Convective Parameterization Schemes (CPS) is crucial for accurately simulating these conditions. Traditional CPS often struggles to accurately represent convective processes at grey-zone resolutions, leading to inconsistencies and overestimating rainfall. However, scale-aware CPS like nTDK, MSKF, and Grell-Freitas can span multiple resolutions, ensuring consistent physical processes and improving TC precipitation predictions. Given these challenges, scale-aware CPS have been integrated into models like MPAS and WRF for grey-zone simulations, showing promising results. The ClusterTech Platform for Atmospheric Simulation (CPAS), built on MPAS, introduces innovations like Customizable Unstructured Mesh Generation (CUMG) and Hierarchical Time-Stepping (HTS) to enhance model flexibility and efficiency. As resolution increases, CPAS relies heavily on scale-aware CPS to handle unresolved cloud processes and improve rainfall simulations across variable grids.

This study evaluates the performance of WRF, MPAS, and CPAS in simulating TCs in the South China Sea by addressing the following questions: How well do different NWP models simulate historical TC events? How effective are various scale-aware CPS in improving TC rainfall simulation? How does scale-aware CPS impact TC track and intensity predictions? We simulated four TCs - Mangkhut (2018), Wanda (1962), Doksuri (2023), and Saola (2023) - using WRF (9-3 km nested domains), MPAS (60-3 km grid), and CPAS (96-48-24-12-3 km grid), with ERA5 as the initial condition.

Preliminary results suggest that CPAS, with its advanced mesh generation and time-stepping, significantly improves TC simulations in the grey zone. While initial findings are promising, further research is needed to confirm these advantages. This ongoing study highlights the potential of cloud-based NWP models in improving TC forecasts, with broader implications for disaster management, air pollution forecasting, and applying high-precision CPAS data in machine learning.