A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

• Published in: Remote Sensing of Environment
• Main Authors: Biancamaria, Sylvain, Mognard, Nelly, Boone, Aaron, Grippa, Manuela, Josberger, Edward
• Other Authors: Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS), US Geological Survey Tacoma, United States Geological Survey Reston (USGS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2008
• Subjects: Taiga; Lakes; SSM/I; GSWP2; Snow depth; High latitude regions; ALGORITHM; CLIMATOLOGY; PARAMETERS; COVER; LAND-SURFACE; WESTERN CANADA; NORTHERN GREAT-PLAINS; PASSIVE-MICROWAVE DATA; SIMULATIONS; Tundra; [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology; [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; Arctic; Canada; taiga; Siberia
• Online Access: https://hal.archives-ouvertes.fr/hal-00284672; https://doi.org/10.1016/j.rse.2007.12.002

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia. The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987–1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is −0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient −0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to ...