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

Jeoung, Hwayoung; Liu, Guosheng; Kim, Kwonil; Lee, Gyuwon; Seo, Eun-Kyoung

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 hinline-formula−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm hinline-formula−1 for shallow, and 1 mm hinline-formula−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g minline-formula−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 inline-formulaR2 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 inline-formulaR2 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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Jeoung, Hwayoung / Liu, Guosheng / Kim, Kwonil / et al: Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals. 2020. Copernicus Publications.

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