Elucidating cloud vertical structures based on three-year Ka-band cloud radar observations from Beijing, China

Zhang, Yong; Zhou, Qing; Lv, Shanshan; Jia, Shuze; Fa, Tao; Chen, Dandan; Guo, Jianping

doi:10.1016/j.atmosres.2019.02.007

Cited by 34 publications

(33 citation statements)

References 63 publications

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“…Figure 14 presents the NCFADs (normalized contoured frequency by attitudes diagrams) of radar-measured Z, MV, SW, SK, KT, VT, VA, DM, NT, LWC, RR, and CBH at different altitudes. The NCFAD is a kind of diagram that can conveniently express the frequency distribution of a quantity at different altitudes and value intervals [43]. Overall, Figure 14 shows two apparent features for the vertical distribution of warm clouds and precipitation.…”

Section: Vertical Structures Of Warm Clouds and Precipitationmentioning

confidence: 99%

“…Overall, Figure 14 shows two apparent features for the vertical distribution of warm clouds and precipitation. First, the value ranges of Z, MV, SW, SK, KT, VT, VA, DM, and NT gradually became wider as the altitude decreased, indicating changes in the The NCFAD is a kind of diagram that can conveniently express the frequency distribution of a quantity at different altitudes and value intervals [43]. Overall, Figure 14 shows two apparent features for the vertical distribution of warm clouds and precipitation.…”

Section: Vertical Structures Of Warm Clouds and Precipitationmentioning

confidence: 99%

“…However, MMCR is generally deployed in conjunction with other instruments, such as with a CL and disdrometer, because it suffers from signal attenuation and is incapable of detecting the cloud base height under precipitating conditions. A CL and disdrometer can complement more reliable and sophisticated measurements of cloud base height and raindrop spectra information [43,44]. Whereas, inversely, they cannot provide measurements of hydrometeors of aloft cloud and precipitation.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of Warm Clouds and Precipitation in South China during the Pre-Flood Season Using Datasets from a Cloud Radar, a Ceilometer, and a Disdrometer

et al. 2019

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The millimeter-wave cloud radar, ceilometer, and disdrometer have been widely used to observe clouds and precipitation. However, there are some drawbacks when those three instruments are solely employed due to their own limitations, such as the fact that radars usually suffer from signal attenuation and ceilometers/disdrometers cannot provide measurements of the hydrometeors of aloft clouds and precipitation. Thus, in this paper, we developed an integrated technology by combining and utilizing the advantages of three instruments together to investigate the vertical structure and diurnal variation of warm clouds and precipitation, and the raindrop size distribution. Specifically, the technology consists of appropriate data processing, quality control, and retrieval methods. It was implemented to study the warm clouds and precipitation in South China during the pre-flood season of 2016. The results showed that the hydrometeors of warm clouds and precipitation were mainly distributed below 2.5 km and most of the rainfall events were very light with a rain rate less than 1 mm h−1, however, the stronger precipitation primarily contributed the accumulated rain amount. Furthermore, a rising trend of cloud base height from 1000 to 1900 BJT was found. The cloud top height and cloud thickness gradually increased from 1200 BJT to reach a maximum at 1600 BJT (Beijing Standard Time, UTC+8), and then decreased until 2000 BJT. Also, three periods of the apparent rainfall on the ground of the day, namely, 0400–0700 BJT, 1400–1800 BJT, and 2300–2400 BJT were observed. During three periods, the raindrops had wider size spectra, higher number concentrations, larger rain rates, and higher water contents than at other times. The hydrometeor type, size, and concentration were gradually changed in the vertical orientation. The raindrop size distributions of warm precipitation in the air and on the ground were different, which can be expressed by γ distributions N(D) = 1.49 × 104D−0.9484exp(−6.79D) in the air and N(D) = 1.875 × 103D0.862exp(−2.444D) on the ground, where D and N(D) denote the diameter and number concentration of the raindrops, respectively.

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Section: Vertical Structures Of Warm Clouds and Precipitationmentioning

confidence: 99%

Section: Vertical Structures Of Warm Clouds and Precipitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characteristics of Warm Clouds and Precipitation in South China during the Pre-Flood Season Using Datasets from a Cloud Radar, a Ceilometer, and a Disdrometer

et al. 2019

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“…CLW path (CLWP), that is, vertically integrated CLWC in atmosphere, is a key parameter in determining the radiative effects of clouds (Chen et al, 2007; Considine, 1999; Gaussiat et al, 2007). However, CLWC is not measured operationally, and little information has been acquired by aircraft or ground‐ and satellite‐based remote sensing techniques in recent years (Calheiros & Machado, 2014; Zhang et al, 2019). Aircraft can provide reliable measurements of CLWC with the onboard probes, but this is expensive and can only provide instantaneous data that cover short periods of time and particular locations (Aydin & Singh, 2004).…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Cloud Liquid Water and Rain Liquid Water Obtained From Microwave Radiometer and Micro Rain Radar Observations Over Central China During the Monsoon

zhang

et al. 2020

JGR Atmospheres

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We investigated the cloud liquid water (CLW) and rain liquid water (RLW) during weak precipitations (rain rate below 12 mm/h) using microwave radiometer and microrain radar measurements collected by the Integrative Monsoon Frontal Rainfall Experiment over central China in 2018. The CLW path increased sharply from 0.6 to 4.1 mm for precipitation clouds. RLW path presented a similar trend, although it had a larger correlation coefficient with rain rate. Precipitation efficiency reached up tõ 50% and then clearly decreased as precipitation weakened. Because weak precipitation is mostly formed in stable nimbostratus, CLW content (CLWC) during precipitation tends to has a quasi-normal distribution with mode at 0.38 g/m 3 , whereas RLW content (RLWC) shows a positively skewed distribution with mode at 0.06 g/m 3. Normalized CLWC initially increases then decreases with height in nonprecipitation clouds but varies slightly in precipitation clouds due to relatively monodispersed droplets in the weaker convective motion. CLWC derived from millimeter-wave cloud radar (MMCR) shows similar vertical distribution but with larger values. The mean normalized CLWCs are 0.06 and 0.38 g/m 3 for nonprecipitation and precipitation clouds, respectively. RLWC varies slightly with height with a mean of 0.22 g/m 3 because both the collision and breakup of raindrops are weak. A case study showed different distributions and vertical structures of CLWC and RLWC in various stages of precipitation. Thicker clouds result in larger CLWC and RLWC, which will cause greater rain rate. This qualitatively explains relationships among cloud thickness, CLW, RLW, and rain rate in precipitation during the monsoon.

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“…The model is trained using as predictors the observed radiances (GOES-16) and ancillary predictions from the High-Resolution Rapid Refresh (HRRR, [ 18 ]) NWP model, and CBH observations from meteorological aerodrome reports (METAR) across the contiguous U.S. (CONUS). The network of METAR stations is dense in the USA and provides higher spatial coverage than other CBH retrievals such us rawinsondes [ 19 ] or cloud radars [ 20 , 21 ]. Our results highlight the potential of using geostationary satellites to provide high spatio-temporal monitoring of the CBH field.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series

Jiménez

McCandless

2021

Remote Sensing

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Although cloud base height is a relevant variable for many applications, including aviation, it is not routinely monitored by current geostationary satellites. This is probably a consequence of the difficulty of providing reliable estimations of the cloud base height from visible and infrared radiances from current imagers. We hypothesize that existing algorithms suffer from the accumulation of errors from upstream retrievals necessary to estimate the cloud base height, and that this hampers higher predictability in the retrievals to be achieved. To test this hypothesis, we trained a statistical model based on the random forest algorithm to retrieve the cloud base height, using as predictors the radiances from Geostationary Operational Environmental Satellites (GOES-16) and variables from a numerical weather prediction model. The predictand data consisted of cloud base height observations recorded at meteorological aerodrome report (METAR) stations over an extended region covering the contiguous USA. Our results indicate the potential of the proposed methodology. In particular, the performance of the cloud base height retrievals appears to be superior to the state-of-the-science algorithms, which suffer from the accumulation of errors from upstream retrievals. We also find a direct relationship between the errors and the mean cloud base height predicted over the region, which allowed us to obtain estimations of both the cloud base height and its error.

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Elucidating cloud vertical structures based on three-year Ka-band cloud radar observations from Beijing, China

Cited by 34 publications

References 63 publications

Characteristics of Warm Clouds and Precipitation in South China during the Pre-Flood Season Using Datasets from a Cloud Radar, a Ceilometer, and a Disdrometer

Characteristics of Warm Clouds and Precipitation in South China during the Pre-Flood Season Using Datasets from a Cloud Radar, a Ceilometer, and a Disdrometer

Comparative Study of Cloud Liquid Water and Rain Liquid Water Obtained From Microwave Radiometer and Micro Rain Radar Observations Over Central China During the Monsoon

Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series

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