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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Jul 2020

30 Jul 2020

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This preprint is currently under review for the journal ACP.

Microphysical Properties of Three Types of Snow Clouds: Implication to Satellite Snowfall Retrievals

Hwayoung Jeoung1, Guosheng Liu1, Kwonil Kim2, Gyuwon Lee2, and Eun-Kyoung Seo3 Hwayoung Jeoung et al.
  • 1Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida, USA
  • 2Department of Astronomy and Atmospheric Sciences, Center for Atmospheric REmote Sensing (CARE), Kyungpook National University, Daegu 41566, Republic of Korea
  • 3Department of Earth Science Education, Kongju National University, Kongju 314-701, Republic of Korea

Abstract. Ground-based radar and radiometer data observed during the 2017–18 winter were used to simultaneously estimate both cloud liquid water path and snowfall rate for three types of snowing clouds: near-surface, shallow and deep. Surveying all the observed data, it is found that near-surface cloud is the most frequently observed cloud type with an area fraction of over 60 %, while deep cloud contributes 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 vast majority hardly reaching to 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, liquid water path in the three types of clouds all has substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager 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 a priori database. Under 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 0.33, 0.48 and 0.74, respectively, for near-surface, shallow and deep snowing clouds over land surface; 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 increased from 0.33 to 0.54, while virtually no change in skills is found for deep clouds and only marginal improvement is found for shallow clouds. This study provides a general picture of the microphysical characteristics of the different types of snowing clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

Hwayoung Jeoung et al.

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Hwayoung Jeoung et al.

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Publications Copernicus
Short summary
Radar and radiometer observations were used to study cloud liquid and snowfall for 3 types of snow clouds. While near-surface and shallow clouds have an area fraction of 90 %, deep cloud contributes half of total snowfall volume. Deeper clouds have heavier snowfall, although cloud liquid is equally abundant in all the 3 cloud types. The skills of a GMI Bayesian algorithm are examined, and it is found that snowfall in deep clouds may be reasonably retrieved but challenging for near-surface clouds.
Radar and radiometer observations were used to study cloud liquid and snowfall for 3 types of...