Pure and Applied Geophysics

, Volume 173, Issue 9, pp 3085–3102 | Cite as

Monitoring Snow Using Geostationary Satellite Retrievals During the SAAWSO Project

  • Robert M. RabinEmail author
  • Ismail Gultepe
  • Robert J. Kuligowski
  • Andrew K. Heidinger


The SAAWSO (Satellite Applications for Arctic Weather and SAR (Search And Rescue) Operations) field programs were conducted by Environment Canada near St. Johns, NL and Goose Bay, NL in the winters of 2012–13 and 2013–14, respectively. The goals of these programs were to validate satellite-based nowcasting products, including snow amount, wind intensity, and cloud physical parameters (e.g., cloud cover), over northern latitudes with potential applications to Search And Rescue (SAR) operations. Ground-based in situ sensors and remote sensing platforms were used to measure microphysical properties of precipitation, clouds and fog, radiation, temperature, moisture and wind profiles. Multi-spectral infrared observations obtained from Geostationary Operational Environmental Satellite (GOES)-13 provided estimates of cloud top temperature and height, phase (water, ice), hydrometer size, extinction, optical depth, and horizontal wind patterns at 15 min intervals. In this work, a technique developed for identifying clouds capable of producing high snowfall rates and incorporating wind information from the satellite observations is described. The cloud top physical properties retrieved from operational satellite observations are validated using measurements obtained from the ground-based in situ and remote sensing platforms collected during two precipitation events: a blizzard heavy snow storm case and a moderate snow event. The retrieved snow precipitation rates are found to be comparable to those of ground-based platform measurements in the heavy snow event.


Optical Depth Geostationary Satellite Equivalent Potential Temperature Geostationary Operational Environmental Satellite Liquid Water Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank Environment Canada for support of the data collection programs, Dr William Straka (University of Wisconsin-CIMSS) for providing the GOES cloud top property data, and the GOES-R program for support in development of the SCaMPR algorithm. The Man Computer Interactive data Analysis System (McIDAS) University of Wisconsin-Madison, Space Science and Engineering Center was used for much of the data processing and visualization. This study was funded during the 2012–2015 time period by through the National Search and Rescue Secretariat (SAR) of Canada under the Search and Rescue New Initiatives Fund (SAR NIF). The authors also would like to thank Environment Canada for technical support and additional funds for this project.


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Copyright information

© Springer (outside the USA) 2015

Authors and Affiliations

  1. 1.NOAA/National Severe Storms LaboratoryNormanUSA
  2. 2.NOAA/Cooperative Institute for Meteorological Satellite StudiesUniversity of Wisconsin-MadisonMadisonUSA
  3. 3.Environment Canada, Cloud Physics and Severe Weather Research SectionTorontoCanada
  4. 4.Center for Satellite Applications and ResearchNOAA/National Environmental Satellite, Data, and Information Service (NESDIS)College ParkUSA
  5. 5.NOAA/NESDIS/STAR/Advanced Satellite Products BranchMadisonUSA

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