Implementing spatially and temporally varying snow densities into the GlobSnow snow water equivalent retrieval

• Published in: The cryosphere Vol. 17; no. 2; pp. 719 - 736
• Main Authors: Venäläinen, Pinja, Luojus, Kari, Mortimer, Colleen, Lemmetyinen, Juha, Pulliainen, Jouni, Takala, Matias, Moisander, Mikko, Zschenderlein, Lina
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 13.02.2023; Copernicus Publications
• Subjects: Algorithms; Comparative analysis; Datasets; Density; Depth perception; Equivalence; Interpolation; Interpolation techniques; Kriging interpolation; Meteorological satellites; Northern Hemisphere; Radiometers; Retrieval; Root-mean-square errors; Sensors; Snow; Snow accumulation; Snow cover; Snow density; Snow depth; Snow-water equivalent; Specific gravity; Statistical methods; Temperature; Weather stations; Canada; Northern Hemisphere; United States > US; Finland; Russia
• ISSN: 1994-0424; 1994-0416; 1994-0424; 1994-0416
• DOI: 10.5194/tc-17-719-2023

Snow water equivalent (SWE) is a valuable characteristic of snow cover, and it can be estimated using passive spaceborne radiometer measurements. The radiometer-based GlobSnow SWE retrieval methodology, which assimilates weather station snow depth observations into the retrieval, has improved the reliability and accuracy of SWE retrieval when compared to stand-alone radiometer SWE retrievals. To further improve the GlobSnow SWE retrieval methodology, we investigate implementing spatially and temporally varying snow densities into the retrieval procedure. Thus far, the GlobSnow SWE retrieval has used a constant snow density throughout the retrieval despite differing locations, snow depth, or time of winter. This constant snow density is a known source of inaccuracy in the retrieval. Four different versions of spatially and temporally varying snow densities are tested over a 10-year period (2000–2009). These versions use two different spatial interpolation techniques: ordinary Kriging interpolation and inverse distance weighted regression (IDWR). All versions were found to improve the SWE retrieval compared to the baseline GlobSnow v3.0 product, although differences between versions are small. Overall, the best results were obtained by implementing IDWR-interpolated densities into the algorithm, which reduced RMSE (root mean square error) and MAE (mean absolute error) by about 4 mm (8 % improvement) and 5 mm (16 % improvement) when compared to the baseline GlobSnow product, respectively. Furthermore, implementing varying snow densities into the SWE retrieval improves the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product and a product post-processed with varying snow densities.