Comparison of Cloud Top Height Observed by a Ka-band Cloud Radar and COMS

Oh, Su‐Bin; Won, Hye Young; Ha, Jong-Chul; Chung, Kwan-Young

doi:10.14191/atmos.2014.24.1.039

Cited by 5 publications

(9 citation statements)

References 26 publications

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“…To evaluate the accuracy of ARM MMCR and GMS-5 satellite data over Manus Island, Hollars et al (2004) compared the cloud top heights calculated from each piece of equipment according to the type of cloud and precipitation. Oh et al (2014) performed a comparative analysis of CTHs observed by cloud radar and sensors onboard the Communication, Ocean, and Meteorological Satellite (COMS). They established that cloud radar was useful in detecting the CTH in the absence of precipitation.…”

Section: Introductionmentioning

confidence: 99%

Verification and correction of cloud base and top height retrievals from Ka-band cloud radar in Boseong, Korea

Kim

et al. 2015

Adv. Atmos. Sci.

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In this study, cloud base height (CBH) and cloud top height (CTH) observed by the cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013 (September-November) were verified and corrected. For comparative verification, CBH and CTH were obtained using a ceilometer (CL51) and the Communication, Ocean and Meteorological Satellite (COMS). During rainfall, the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops, and this difference increased with rainfall intensity. During dry periods, however, the CBH and CTH observed by the cloud radar, ceilometer, and COMS were similar. Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS. In cases of rainfall or missing cloud radar data, the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data. These corrected cloud data were used to classify cloud types, which revealed that low clouds occurred most frequently.

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Section: Introductionmentioning

confidence: 99%

Verification and correction of cloud base and top height retrievals from Ka-band cloud radar in Boseong, Korea

Kim

et al. 2015

Adv. Atmos. Sci.

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“…Attenuation occurs under heavy rainfall owing to the characteristics of cloud radar, and such attenuation increases as the rainfall intensity increases (Oh et al, 2014). Thus, in the present study, attenuation was calculated by using the high rain-rate cases (mean DV > DV thres ) to derive the A-R relationship.…”

Section: A-r Relationship For High Rain-rate Casesmentioning

confidence: 99%

Rain‐rate estimation algorithm using signal attenuation of Ka‐band cloud radar

Kollias

Lee

et al. 2019

Meteorological Applications

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A millimetre‐wave cloud radar has limitations for observing heavy rainfall because the short wavelength leads to strong attenuation from raindrops. However, recent studies have attempted to estimate the rain rate using attenuation, which becomes greater as the rain intensity increases. The rain‐rate‐retrieval algorithm is developed in the present paper using the Ka‐band cloud radar (KaCR) installed at the Boseong Global Standard Observatory (BGSO) in the Republic of Korea. First, rain profiles were identified using the threshold of reflectivity (Z) and Doppler velocity (DV) averaged for the analysis of layer that minimized the effects of the receiver saturation and classified as low or high rain‐rate cases using the averaged DV. The reflectivity and rain‐rate (Z‐R) relationship was then derived for low rain‐rate cases that showed insignificant effects for signal attenuation. On the other hand, the attenuation and rain‐rate (A‐R) relationship was derived using high rain‐rate cases that showed the dominant effects of attenuation. The attenuation was calculated using a reflectivity gradient between the upper and lower boundaries of the attenuated layer. Finally, the rain‐rate‐retrieval algorithm was designed using the derived Z‐R and A‐R relationship and then applied back to the KaCR. The estimated rain rate in the KaCR was a similar trend to the observed rain rate in the optical rain gauge (ORG), but slightly underestimated.

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“…Oh et al . (, ) conducted a comparative analysis on the cloud‐top height, observed with KaCR and COMS in the Boseong NCIO region. When precipitation did not occur, the cloud‐top heights were similar with the KaCR and the COMS.…”

Section: Kacr Reflectivity Correctionmentioning

confidence: 99%

“…However, this method can be used even with the KaCR alone. The cloud base height was observed by reflectivity from the KaCR (Oh et al ., , ). The rain rate was also obtained using the attenuation value of the cloud radar in recent studies (Matrosov, ; Chandra et al ., ).…”

Section: Z–lwc Relationships Based On the Kacr And The Mwrmentioning

confidence: 99%

“…The data indicated that the KaCR was more effective in observing cloud‐top heights when there was no rainfall or weak rainfall. However, signal attenuation occurred during heavy rainfall (Oh et al ., ). To address these observational limitations, the cloud type information calculated through the KaCR was corrected using satellite and ceilometer data, and the cloud occurrence characteristics for the Boseong region were analysed based on the corrected data (Oh et al ., ).…”

Section: Introductionmentioning

confidence: 97%

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

Lee

Jeong

et al. 2018

Meteorological Applications

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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 . 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.

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Comparison of Cloud Top Height Observed by a Ka-band Cloud Radar and COMS

Cited by 5 publications

References 26 publications

Verification and correction of cloud base and top height retrievals from Ka-band cloud radar in Boseong, Korea

Verification and correction of cloud base and top height retrievals from Ka-band cloud radar in Boseong, Korea

Rain‐rate estimation algorithm using signal attenuation of Ka‐band cloud radar

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

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