Performance evaluation of regional weather predictions with the Model for Prediction Across Scales – Atmosphere ( MPAS ‐A) over the Maritime Continent

Abstract This study presents the first configuration and evaluation of a new convection‐permitting regional weather prediction system “SINGV_NG(MPAS)” for the Maritime Continent based on the Model for Prediction Across Scales – Atmosphere (MPAS‐A). We assess its performance across various combinations of physical parameterization options, including upper‐air and near‐surface forecasts, with a primary focus on precipitation as accurately predicting it remains a primary objective during the initial SINGV_NG(MPAS) evaluation. In general, SINGV_NG(MPAS) outperforms the global European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts for precipitation, particularly for intense rainfall events. However, the upper‐air and near‐surface forecasts in this region still reflect the biases of the ECMWF model, which typically shows dry biases aloft and wet, cold biases at the surface. A systematic analysis of forecast sensitivity to cloud microphysics, subgrid‐scale convection, and planetary boundary layer processes reveals that neither of the two default physics parameterization suites defined within MPAS‐A performs adequately in the Southeast Asia region studied. Our results show that although the default convection‐permitting suite supports the development of tropical precipitation systems more effectively than the mesoscale‐reference suite, its performance is hindered by its use of the Thompson scheme. Our findings show that integrating either the Weather Research and Forecasting Single‐Moment 6‐Class (WSM6) or National Severe Storms Laboratory (NSSL) microphysics scheme with the remaining components of the convection‐permitting suite leads to a significant improvement in precipitation forecast skill. Although at a 3‐km resolution, the choice of scale‐aware convection schemes is critical, as it can shift the balance between light and heavy rainfall. Since many regional models used in Southeast Asia were initially developed and fine‐tuned for midlatitude conditions, this study provides valuable insights to the local research community working on the development of next‐generation forecasting systems.