Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy

• Published in: The Cryosphere
• Main Authors: Venäläinen, Pinja, Luojus, Kari, Lemmetyinen, Juha, Pulliainen, Jouni, Moisander, Mikko, Takala, Matias
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2021
• Subjects: article; Verlagsveröffentlichung; Canada; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-15-2969-2021; https://noa.gwlb.de/receive/cop_mods_00057151; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00056801/tc-15-2969-2021.pdf; https://tc.copernicus.org/articles/15/2969/2021/tc-15-2969-2021.pdf

Snow water equivalent (SWE) is an important variable in describing global seasonal snow cover. Traditionally, SWE has been measured manually at snow transects or using observations from weather stations. However, these measurements have a poor spatial coverage, and a good alternative to in situ measurements is to use spaceborne passive microwave observations, which can provide global coverage at daily timescales. The reliability and accuracy of SWE estimates made using spaceborne microwave radiometer data can be improved by assimilating radiometer observations with weather station snow depth observations as done in the GlobSnow SWE retrieval methodology. However, one possible source of uncertainty in the GlobSnow SWE retrieval approach is the constant snow density used in modelling emission of snow. In this paper, three versions of spatially and temporally varying snow density fields were implemented using snow transect data from Eurasia and Canada and automated snow observations from the United States. Snow density fields were used to post-process the baseline GlobSnow v.3.0 SWE product. Decadal snow density information, i.e. fields where snow density for each day of the year was taken as the mean calculated for the corresponding day over 10 years, was found to produce the best results. Overall, post-processing GlobSnow SWE retrieval with dynamic snow density information improved overestimation of small SWE values and underestimation of large SWE values, though underestimation of SWE values larger than 175 mm was still significant.