Evaluation of Vertically Integrated Liquid Water Content in Indian Summer Monsoon Clouds Using Dual-Polarimetric Doppler Weather Radar

Accurate evaluation of cloud microphysical variables is essential for improving cloud parameterization and weather forecasting. However, obtaining high-resolution, spatially and temporally extensive observation dataset remains a challenge due to the limitations of in situ measurements. Therefore, this study addresses this gap by assessing existing equations for estimating vertically integrated liquid water content (VIL, kg/m²) from liquid water content (LWC, g/m³) using C-band dual-polarised doppler weather radar (DWR) data from IMD Jaipur station over 78 deep convective summer monsoon days in the years 2020-2022. A long-term climatological study (2003-2023) of total column cloud liquid water (TCCLW, kg/m²) from ERA5, liquid water cloud water content (LWCP, kg/m²) from MODIS and rainfall data from IMD, IMERG, and GPCP datasets is also performed. VIL is computed as the vertical integral of LWC across atmospheric layers using four reflectivity-LWC (Z-LWC) relationships and one reflectivity-differential reflectivity (Z, ZDR-LWC) relationship from existing literature. The performance of each equation is evaluated by comparing radar-derived VIL with satellite-derived parameters like MODIS cloud liquid water path (LWP, kg/m²) and TCCLW. The results show that VIL values increase with rainfall intensity and cloud vertical height, leading to higher estimation errors. Among the equations tested, the hybrid ZDR-based equation consistently demonstrated superior performance, particularly during high-intensity rainfall events, with lower root mean square error (RMSE) and mean absolute error (MAE) values which also captured more detailed spatial patterns of liquid water distribution and reduced bias, making it the most reliable estimator. Despite some limitations, such as beam blockage and slight spatial shifts due to interpolation, the study highlights the advantages of incorporating polarimetric radar products for VIL estimation. These findings provide a foundation for improving real-time precipitation forecasts and understanding cloud microphysics, with future work aimed at refining the methodology by addressing data gaps and enhancing cloud-type-specific estimators.