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Open AccessArticle

CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities

Institute of Atmospheric Sciences and Climate (ISAC)—National Research Council (CNR), 00133 Rome, Italy
SERCO S.p.A., 00044 Frascati, Italy
Department of Geological & Mining Engineering & Sciences, Michigan Technological University, Houghton, MI 49931, USA
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1263;
Received: 12 October 2017 / Revised: 22 November 2017 / Accepted: 1 December 2017 / Published: 6 December 2017
(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)
PDF [10699 KB, uploaded 6 December 2017]


The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 5.1 kg·m−2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. View Full-Text
Keywords: snowfall detection; GPM; CloudSat; CPR; CALIPSO; high latitudes; passive microwave; remote sensing of precipitation snowfall detection; GPM; CloudSat; CPR; CALIPSO; high latitudes; passive microwave; remote sensing of precipitation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Panegrossi, G.; Rysman, J.-F.; Casella, D.; Marra, A.C.; Sanò, P.; Kulie, M.S. CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities. Remote Sens. 2017, 9, 1263.

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