Open AccessEstimation of the liquid water content and Z –LWC relationship using K a‐band cloud radar and a microwave radiometer
Author(s) -
Oh SuBin,
Lee Yong Hee,
Jeong JongHoon,
Kim YeonHee,
Joo Sangwon
Publication year - 2018
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1710
Subject(s) - liquid water content , environmental science , radar , meteorology , ceilometer , remote sensing , weather radar , microwave radiometer , cloud computing , microwave , satellite , radiometer , reflectivity , atmospheric sciences , physics , aerosol , optics , geology , computer science , telecommunications , quantum mechanics , astronomy , operating system
Cloud microphysical variables are needed to evaluate and improve cloud parameterization and data assimilation in a numerical weather model. In order to obtain high spatiotemporal resolution data for the cloud, data from the Ka‐band cloud radar (KaCR), an instrument specialized for cloud observation, were used. In this study, the liquid water content (LWC) was estimated, and the reflectivity–liquid water content ( Z –LWC) relationships were derived using the vertical profiles of radar reflectivity from the KaCR and the liquid water path (LWP) from a microwave radiometer. The data were collected at the Boseong National Center for Intensive Observation of Severe Weather in the Republic of Korea during an intensive observation period in 2014 (2014‐IOP, 16 June to 15 July 2014). First, the KaCR reflectivity was corrected using the linear depolarization ratio from the KaCR. The process also involved removing reflectivity profiles that have signal attenuation, compared with the cloud‐top heights retrieved from a satellite. The LWC profiles were calculated from the LWP by dividing using the weight of the KaCR reflectivity. Different constants were derived from the Z –LWC relationship according to each respective echo type. Therefore, the KaCR echoes were categorized into non‐precipitating clouds, precipitating clouds, and raindrops, using a micro rain radar and a ceilometer. The Z –LWC relationships were finally derived for each categorized echo. The results of this study suggest that the new method should be useful for retrieving cloud microphysical variables. These relationships allowed the estimation of 3D LWC in high resolution using a single KaCR platform.