ANNOUNCEMENTS
Extreme precipitation events, characterized by intense and localized heavy rainfall, becoming more recurring caused by climate change, leading to significant challenges to disaster preparation and climate adaptation strategies. Accurate forecasting of these types of events is necessary for mitigating their effects on infrastructure, ecosystems, and human populations. Numerical weather prediction (NWP) models, have a vital role in simulating atmospheric processes responsible for extreme precipitation. However, uncertainties in convective parameterization and spatial resolution affect the model’s accuracy in predicting rainfall intensity and distribution.
This study evaluates the performance of NCMRWF Unified Model (NCUM) Global Model in simulating extreme precipitation events. Using the IMD’s criterion for extreme rainfall (>200mm/day), two extreme precipitation events were selected for analysis. GPM rainfall data (0.1° × 0.1° resolution) was used for validation, and NCUM model forecasts were assessed for thermodynamic stability and moisture dynamics. The study examines biases in NCUM’s precipitation simulations, focusing on errors in rainfall intensity, frequency, and spatial distribution.
Preliminary findings indicate that NCUM exhibits systematic biases, with overprediction in short-term forecasts and underprediction in long-term forecasts. Errors in moisture transport and convective parameterization contribute to these discrepancies. The study highlights areas for improvement in model resolution, cloud microphysics, and data assimilation techniques. Addressing these challenges will enhance NCUM’s solidity in forecasting extreme weather events, supporting better climate risk assessment and disaster management.
Keywords: Extreme Precipitation, NCUM Global Model, Numerical Weather Prediction, Convection, Climate Science.
