Statistical Characteristics of Cloud Occurrence and Vertical Structure Observed by a Ground-Based Ka-Band Cloud Radar in South Korea

Ye, Bo-Young; Jung, Eunsil; Shin, Seungsook; Lee, GyuWon

doi:10.3390/rs12142242

Cited by 10 publications

(7 citation statements)

References 44 publications

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“…This regime shift induced the updraft (reached 1 m s −1 ) below this layer. However, Ye et al (2020) could not explain the linkage between this regime shift and updraft below. While it is beyond the scope of this study, this phenomenon will be an interesting topic for future research on cloud microphysics in this region.…”

Section: Vertical Structuresmentioning

confidence: 84%

“…A similar feature also appeared in the long-term observations with cloud radar (see Figs. 16 and 17 of Ye et al, 2020). The shift in growth regime appeared at 8 km height (3-3.5 km above the bright band peak and corresponding to ∼ −17 • C).…”

Section: Vertical Structuresmentioning

confidence: 92%

“…In this study, the radar reflectivity Z e is converted to snow water content (SWC) and snowfall rate (S) using the Z e -SWC relation of Yin and Liu (2017) and Z e -S relation of Liu (2008a). Before performing these conversions, radar reflectivity was corrected for attenuation due to absorption by atmospheric gases and cloud liquid water and to scattering by ice particles.…”

Section: Retrieved Microphysical Variablesmentioning

confidence: 99%

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Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

et al. 2020

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Abstract. Ground-based radar and radiometer data observed during the 2017–2018 winter season over the Pyeongchang area on the east coast of the Korean Peninsula were used to simultaneously estimate both the cloud liquid water path and snowfall rate for three types of snow clouds: near-surface, shallow, and deep. Surveying all the observed data, it is found that near-surface clouds are the most frequently observed cloud type with an area fraction of over 60 %, while deep clouds contribute the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with the vast majority hardly reaching 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and the liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager (GPM/GMI) channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as an a priori database. Under an idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution, and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.32, 0.41, and 0.62, respectively, for near-surface, shallow, and deep snow clouds over land surfaces; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increasing from 0.32 to 0.52, while a smaller improvement is found for shallow and deep clouds. This study provides a general picture of the microphysical characteristics of the different types of snow clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

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Section: Vertical Structuresmentioning

confidence: 84%

Section: Vertical Structuresmentioning

confidence: 92%

Section: Retrieved Microphysical Variablesmentioning

confidence: 99%

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Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

et al. 2020

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“…Many studies have revealed the cloud vertical structure and statistical characteristics of cloud base height, top height and geometrical depth using CloudSat, CALIPSO, MODIS, AIRS, MISR, GLAS and TOGA/COARE [5][6][7]9,10,[12][13][14][15][16][17][18]. There have also been extensive studies using ground-based and in-situ measurements, such as lidar and radar [7,12,[14][15][16][19][20][21][22][23][24][25], radiosonde [10,15,21,25], ceilometer [11] and meteorological station observations [26,27]. These studies have used different means to study the distribution characteristics of clouds in different regions, each with its own advantages.…”

Section: Introductionmentioning

confidence: 99%

Statistical Characteristics of Cloud Heights over Lanzhou, China from Multiple Years of Micro-Pulse Lidar Observation

Cao

et al. 2021

Atmosphere

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The macroscopic characteristics of clouds over Lanzhou, China were investigated using micro-pulse lidar data from September 2005 to November 2011. The results show that the mean of the cloud base height, cloud peak height, cloud top height and cloud thickness during the observation was 4.03 km, 4.81 km, 5.50 km and 1.47 km, respectively; the maximum frequency of the cloud base height, cloud peak height, cloud top height and cloud thickness was 25.7% in the range of 1–2 km, 16.2% in the range of 2–3 km, 14.6% in the range of 2–3 km and 42.2% in the range of 1–2 km, respectively; the maximum frequency of cloud base height was 24.2%, 24.6%, 29.7% and 21.4% in spring, summer, autumn and winter, respectively, all in the range of 1–2 km, and middle clouds occurred most frequently at 41.4%, followed by low clouds (33.7%) and high clouds (24.9%) during the observation period; the maximum frequency of cloud peak height was 15.8% in the range of 3–4 km, 18% in the range of 4–5 km, 20% in the range of 2–3 km in autumn and 18.6% in the range of 5–6 km in winter; the maximum frequency of cloud top height was 14% in the range of 3–4 km in spring, 16% in the range of 4–5 km in summer, 20.1% in the range of 2–3 km in autumn and 17.8% in the range of 7–8 km in winter; the maximum frequency of cloud thickness was 44.9%, 35.6% and 52% in the range of 1–2 km in spring, summer and winter, respectively, while it was 44.9% in the range of 0–1 km in autumn; the cloud thickness was mostly less than 3 km; generally, the thicker of cloud, the less the frequency.

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“…The Special Issue in Remote Sensing on "Winter weather research in complex terrain during ICE-POP 2018" included some of the ICE-POP 2018 results that can be used to envision the future of the field [2][3][4][5]. Additional contributions in the "Precipitation and Water Cycle" Special Issue can also help unravel the many dimensions of precipitation science, including ground observations (rain gauges, ground radars, disdrometers), satellite estimates (radars, radiometers) and models, including numerical weather prediction (NWP) models, regional climate models (RCMs), global climate/circulation models (GCMs), Earth system models (ESMs) and variable resolution models (VRMs) [6][7][8][9][10][11].…”

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confidence: 99%

Future Directions in Precipitation Science

et al. 2021

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Precipitation science is a growing research field. It is concerned with the study of the water cycle from a broad perspective, from tropical to polar research and from solid precipitation to humidity and microphysics. It includes both modeling and observations. Drawing on the results of several meetings within the International Collaborative Experiments for the PyeongChang 2018 Olympics and Paralympic Winter Games (ICE-POP 2018), and on two Special Issues hosted by Remote Sensing starting with “Winter weather research in complex terrain during ICE-POP 2018”, this paper completes the “Precipitation and Water Cycle” Special Issue by providing a perspective on the future research directions in the field.

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Statistical Characteristics of Cloud Occurrence and Vertical Structure Observed by a Ground-Based Ka-Band Cloud Radar in South Korea

Cited by 10 publications

References 44 publications

Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

Statistical Characteristics of Cloud Heights over Lanzhou, China from Multiple Years of Micro-Pulse Lidar Observation

Future Directions in Precipitation Science

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