Preprints
https://doi.org/10.5194/tc-2022-227
https://doi.org/10.5194/tc-2022-227
 
29 Nov 2022
29 Nov 2022
Status: a revised version of this preprint was accepted for the journal TC and is expected to appear here in due course.

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

Pinja Venäläinen1, Kari Luojus1, Colleen Mortimer2, Juha Lemmetyinen1, Jouni Pulliainen1, Matias Takala1, Mikko Moisander1, and Lina Zschenderlein1 Pinja Venäläinen et al.
  • 1Finnish Meteorological Institute, PO Box 503, FIN-00101 Helsinki, Finland
  • 2Climate Research Division, Environment Climate Change Canada, Toronto, Canada

Abstract. 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 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. Three 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 regressing (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 and 5 mm 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.

Pinja Venäläinen et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2022-227', Alain Royer, 12 Dec 2022
    • AC3: 'Reply on CC1', Pinja Venäläinen, 31 Jan 2023
  • RC1: 'Comment on tc-2022-227', Nicolas Marchand, 27 Dec 2022
    • AC1: 'Reply on RC1', Pinja Venäläinen, 31 Jan 2023
  • RC2: 'Comment on tc-2022-227', Jennifer Jacobs, 09 Jan 2023
    • AC2: 'Reply on RC2', Pinja Venäläinen, 31 Jan 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2022-227', Alain Royer, 12 Dec 2022
    • AC3: 'Reply on CC1', Pinja Venäläinen, 31 Jan 2023
  • RC1: 'Comment on tc-2022-227', Nicolas Marchand, 27 Dec 2022
    • AC1: 'Reply on RC1', Pinja Venäläinen, 31 Jan 2023
  • RC2: 'Comment on tc-2022-227', Jennifer Jacobs, 09 Jan 2023
    • AC2: 'Reply on RC2', Pinja Venäläinen, 31 Jan 2023

Pinja Venäläinen et al.

Pinja Venäläinen et al.

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Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.