Publications

Sabu, A; Syed, HA; Rakshit, G; Das, S; Panda, SK; Sharma, D; Pal, J (2025). Evaluation of Vertically Integrated Liquid water content using polarimetric Doppler Weather Radar. THEORETICAL AND APPLIED CLIMATOLOGY, 156(5), 238.

Abstract
Accurate evaluation of cloud microphysical variables is essential for improving cloud parameterization and weather forecasting, yet obtaining high-resolution, spatially and temporally extensive data remains a challenge due to the limitations of in-situ measurements. The present study tries to address 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-polarized Doppler Weather Radar (DWR) data from the India Meteorological Department (IMD) Jaipur station over 78 summer monsoon days in 2020-2022. A long-term climatological analysis (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 has been 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 empirically radar derived VIL has been evaluated by comparing with satellite-derived (MODIS) cloud liquid water path (LWP, kg/m2) and TCCLW. The results show that VIL values increase with rainfall intensity, leading to higher estimation errors. Among all relations tested, the hybrid equation (which includes Z and ZDR) consistently demonstrated superior performance, particularly during high-intensity rainfall events, with lower root mean square error (RMSE) and mean absolute error (MAE) values. The method also captured more detailed spatial patterns of liquid water distribution with reduced bias, making it the most reliable estimator. Despite limitations such as beam blockage and slight spatial shifts due to interpolation, the current study may provide a foundation for improving real-time precipitation forecasts and understanding cloud microphysics by incorporating polarimetric radar products. The future work may aim at refining the methodology through enhanced cloud-type-specific estimators.

DOI:
10.1007/s00704-025-05454-7

ISSN:
1434-4483