Abstract
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.
Highlights
Snowfall is an important component in the global hydrological cycle
The results show that the discrepancy between simulated and observed brightness temperatures is the greatest for very shallow or very deep snow clouds with discrepancy values being over 10 K in the former and over 30 K in the latter case, it is generally less than 3 K when averaged over all selected
The nearsurface snow clouds are most likely to be missed by currently available space-borne radars because of the blind zone caused by the contamination of surface clutter, and their shallowness and liquid water abundance may present challenges to satellite radiometer observations
Summary
Snowfall is an important component in the global hydrological cycle. Its global distribution may be observed using satellite-based passive and active microwave sensors. The cloud radar on board the CloudSat satellite (Stephens et al, 2002; Tanelli et al, 2008) is the first spaceborne active sensor in operation that is suitable for snowfall observations It has a minimum detectability of nearly −30 dBZ near the ground, allowing us to observe the weak scattering signal from snowflakes. The snowfall rates in a Bayesian algorithm database are often retrievals from radars, and the brightness temperatures are either collocated measurements of passive microwave radiometers or simulated measurements by radiative transfer models. The radiative transfer model, which uses CloudSat radar and GMI retrievals as input, failed to account for this liquid water abundance, resulting in a large discrepancy between simulated and observed brightness temperatures. We examine how a Bayesian snowfall retrieval algorithm with GPM/GMI observations would perform for the snow clouds observed during this field experiment
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
