Estimation of the liquid water content and <scp><i>Z</i>–LWC</scp> relationship using <scp>K</scp>a‐band cloud radar and a microwave radiometer

Oh, Su‐Bin; Lee, Yong Hee; Jeong, Jong‐Hoon; Kim, Yeon-Hee; Joo, Sangwon

doi:10.1002/met.1710

Cited by 11 publications

(14 citation statements)

References 30 publications

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“…The data obtained from October 2014 to November 2017 at the BSWO are analyzed in this study, following detailed specifications and operation schedules introduced by Oh et al. (2018).…”

Section: Methodsmentioning

confidence: 99%

Climatology of Melting Layer Heights Estimated From Cloud Radar Observations at Various Locations

et al. 2021

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A melting layer (ML) detection algorithm for cloud radar with polarimetric capability was developed and applied to the cloud radar data collected from five different sites around the world for several years. The retrieved melting layer top height (MLH) showed a very good correspondence with the ECMWF Reanalysis 5 zero degree level data for all five sites. The ML characteristics were distinctively different for different sites, revealing climatological characteristics of ML forming clouds in different regions. Generally, ML tended to occur more frequently in summer than in winter except for a maritime site, where low stratiform clouds formed frequently in summer, the top of which might be lower than the freezing level. In contrast, at two Arctic sites, ML occurred almost exclusively in summer because it was too cold to have an ML in the other seasons. The MLH also varied significantly from site to site but generally was higher during warmer seasons. Based on MLH, two new indices, bulk temperature lapse rate (BLR) and relative depth (RD) of liquid cloud below MLH (RD) were developed, which were useful to explain the environmental characteristics of the five sites. BLR generally increased with the surface temperature at all sites except at the marine site that showed an opposite trend, where a unique synoptic pattern in winter generated high BLR in this cold season. These findings confirm that studies on thermodynamic structures using cloud radars can be broadened, taking advantage of BLR and RD information, as these indices can represent environmental thermodynamic characteristics of the clouds that have ML.

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“…The data obtained from October 2014 to November 2017 at the BSWO are analyzed in this study, following detailed specifications and operation schedules introduced by Oh et al. (2018).…”

Section: Methodsmentioning

confidence: 99%

Climatology of Melting Layer Heights Estimated From Cloud Radar Observations at Various Locations

et al. 2021

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“…during daytime in the warm season at the SACOL site, which is close to or even higher than melting layer height. In order to avoid wrongly identifying the melting layer with high LDR as clutter, the melting layer is recognized by analyzing the gradient of reflectivity and velocity that has a large value associated with the melting layer (Baldini and Gorgucci, 2006;Matrosov et al, 2007;Perry et al, 2017). The peak of |reflectivity | × |velocity | (Fig.…”

Section: Removing Noise and Non-cloud Meteorological Targetmentioning

confidence: 99%

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

et al. 2021

Atmos. Meas. Tech.

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Abstract. Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The accurate long-term observation of low-level clouds is essential for understanding their climate effect and model constraints. Both ground-based and spaceborne millimeter-wavelength cloud radars can penetrate clouds but the detected low-level clouds are always contaminated by clutter, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutter for ground-based cloud radar using multi-dimensional probability distribution functions along with the Bayesian method. The radar reflectivity, linear depolarization ratio, spectral width, and their dependence on the time of the day, height, and season are used as the discriminants. A low-pass spatial filter is applied to the Bayesian undecided classification mask by considering the spatial correlation difference between clouds and clutter. The final feature mask result has a good agreement with lidar detection, showing a high probability of detection rate (98.45 %) and a low false alarm rate (0.37 %). This algorithm will be used to reliably detect low-level clouds at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the study of their climate effect and the interaction with local abundant dust aerosol in semi-arid regions.

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“…Borque et al (2014) showed the advantages of the ARM Kaband radar for observing clouds without precipitation. The Korean Meteorological Administration also operates a scanning Ka-band radar in combination with microwave radiometers to study cloud water content estimation (Oh et al 2018). A scanning Ka-band radar has also been developed in Japan (Hamazu et al 2003) to observe fog (Uematsu et al 2005) and the development of cumulonimbus clouds (Sakurai et al 2012;Nishiwaki et al 2013;Misumi et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Convective Echoes and Their Development Using a Ka-Band Radar Network

Yoshida

Misumi

Maesaka

2021

Weather and Forecasting

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Cumulonimbus clouds, which cause local heavy rainfall and urban floods, can develop within 20 minutes after being detected by operational centimeter-wavelength (X-, C-, or S-band) weather radars. To detect such clouds with greater lead times, Ka-band radars at a wavelength of 8.6 mm together with operational X-band radars were used in this study. The vertically averaged radar reflectivity (VAR) of convective echoes detected by the Ka-band and X-band radars were defined as mesoscale cloud echoes (MCEs) and mesoscale precipitation echoes (MPEs), respectively. The time series of each echo was analyzed by an echo tracking algorithm. On average, MCEs that developed into MPEs (denoted as developed MCEs) were detected 17 minutes earlier than the MPEs and 33 minutes earlier than the peak time of the area-averaged VAR (VARa) for MPEs. Some MCEs dissipated without developing into MPEs (denoted as non-developed MCEs). There were statistically significant differences between the developed and non-developed MCEs in terms of the maximum VARa values, maximum MCEs areas, and increase amounts of the VARa values and MCE areas for the first 6 to 12 minutes after their detection. Among these indicators, the maximum VARa for the first 9 minutes showed the most significant differences. Therefore, an algorithm for predicting MCE development using this indicator is discussed.

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Estimation of the liquid water content and Z–LWC relationship using Ka‐band cloud radar and a microwave radiometer

Cited by 11 publications

References 30 publications

Climatology of Melting Layer Heights Estimated From Cloud Radar Observations at Various Locations

Climatology of Melting Layer Heights Estimated From Cloud Radar Observations at Various Locations

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

Early Detection of Convective Echoes and Their Development Using a Ka-Band Radar Network

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