Moist processes in NCUM global forecasts during the boreal summer monsoon

Abstract

The objective of this study is to evaluate the performance of the National Center for Medium-Range Weather Forecasting Unified model (NCUM) global version in representing the moist processes during active and break monsoon periods over the Indian region. We employed the moist static energy (MSE) equation, a process-oriented diagnostic (POD), to examine the relationship between small-scale convection and large-scale circulation patterns in the forecast for eight monsoon seasons spanning from June to September 2015 to 2022. Results are validated by utilizing the European Center for Medium-Range Weather Forecasting Reanalysis (ERA5) data products. By employing PODs, we aimed to identify the influence of various MSE terms on rainfall evolution, particularly over the Central Indian (CI) region. We also investigated the systematic errors in certain processes that contribute to rainfall biases. Additionally, the study explored the moisture-convection relationship and compared the sensitivity of free tropospheric moisture to deep convection in the NCUM forecasts against the reanalysis data. The findings indicate weaknesses in the model's sensitivity to moisture and its ability to represent the rainfall-column water vapor (CWV) relationship highlighting areas where further improvements may be necessary to enhance the model's performance in monsoon forecasting. Despite the presence of systematic biases in the model's mean state of rainfall, such as dry biases over the Bay of Bengal and wet biases over the eastern Indian Ocean (EIO), the results obtained from the MSE diagnostics aligned with the existing understanding of monsoon dynamics. Specifically, the study emphasized the significance of horizontal advection of MSE as a leading term preceding the peak active/break conditions over the CI region. Hence, these findings not only showcase the usefulness of PODs in examining the strengths and weaknesses of numerical weather prediction (NWP) models but also serve as a verification tool for assessing model skills, leading to improved understanding and development of NWP models.