Impact of large-scale analysis constraints on C-band radar data assimilation during the simulation of two heavy rainfall events over southern peninsular India

This study examines the impact of radar data assimilation with Large-Scale Analysis Constraints (LSAC) under different monsoon synoptic conditions, with special focus on vertical hydrometeor profiles and associated convective processes. The role of LSAC in reducing convective-scale imbalances is also evaluated. Two extreme monsoon rainfall events during August 2018 and 2019 are considered to represent contrasting synoptic environments, with the 2019 event characterized by strong localized convection. In this study, C-band radar reflectivity is assimilated indirectly, while radial wind is assimilated directly, and their combined impact on the forecast of these extreme events is assessed. The experiments are carried out with and without the application of Large-Scale Analysis Constraints, referred to as LSAC and noLSAC, respectively. It is hypothesized that the imbalances in large-scale and convective-scale processes, introduced during high-resolution radar assimilation, can be reduced by incorporating LSAC. The results show a clear improvement in minimizing convective-scale imbalances, particularly for the August 2019 event, where strong localized convection was present. Rainfall verification indicates that the inclusion of LSAC improves the location, spatial pattern, and amount of precipitation in both cases. The total mean squared error have reduced by an average of 250 mm for both the cases. Cloud-top heights are also better represented in LSAC experiments with root mean squared error reduced by an average of 2K, whereas high-resolution radar assimilation without LSAC leads to spurious cloud-top development. Analysis of hydrometeor profiles shows that radar assimilation with LSAC consistently reduces the overestimation of hydrometeor condensate throughout the vertical column for the 2018 event. For 2019, overestimated hydrometeor condensates above the melting layer are minimized. This is supported by radar reflectivity comparisons, which indicate that unrealistically strong reflectivity signatures above the melting layer are minimized in LSAC experiments for the 2019 event. More organized and consolidated convective echoes are also evident with LSAC. Thermodynamic analysis further supports these findings, showing that excessive convective potential generated during radar assimilation above the melting layer is moderated with large-scale constraints in the presence of a strong and localized convective environment. In general, incorporation of LSAC reduces biases in hydrometeor formation and improves cloud structure and precipitation forecasts.