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DOI	10.5194/acp-20-14491-2020
	Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals
	Jeoung H.; Liu G.; Kim K.; Lee G.; Seo E.-K.
发表日期	2020
ISSN	1680-7316
起始页码	14491
结束页码	14507
卷号	20 期号:23
英文摘要	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. © Author(s) 2020.
语种	英语
scopus关键词	atmospheric chemistry; cloud cover; cloud microphysics; ground penetrating radar; microwave radiation; numerical model; probability; snow cover; Korea
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/247330
作者单位	Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, United States; Department of Astronomy and Atmospheric Sciences, Center for Atmospheric REmote Sensing (CARE), Kyungpook National University, Daegu, 41566, South Korea; Department of Earth Science Education, Kongju National University, Kongju, 314-701, South Korea
推荐引用方式 GB/T 7714	Jeoung H.,Liu G.,Kim K.,et al. Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals[J],2020,20(23).
APA	Jeoung H.,Liu G.,Kim K.,Lee G.,&Seo E.-K..(2020).Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals.ATMOSPHERIC CHEMISTRY AND PHYSICS,20(23).
MLA	Jeoung H.,et al."Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals".ATMOSPHERIC CHEMISTRY AND PHYSICS 20.23(2020).