Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals

Area of Science:

• Hydrology and Remote Sensing
• Atmospheric Science and Meteorology
• Environmental Modeling

Background:

• Mountain precipitation is highly variable and difficult to measure accurately.
• Existing precipitation estimation methods struggle with complex orographic effects.
• Accurate snowfall data is crucial for water resource management in mountainous areas.

Purpose of the Study:

• To develop and test a novel approach for characterizing orographic snowfall in mountain regions.
• To leverage satellite-derived snow depth data to correct precipitation product biases.
• To improve estimates of snow variables and streamflow in alpine basins.

Main Methods:

• Utilized a particle batch smoother (PBS) data assimilation technique.
• Integrated Sentinel-1 satellite data for snow depth retrievals.
• Corrected multiple gridded precipitation products using the PBS and satellite information.
• Validated the approach with a simple snow model in an alpine basin (Trentino Alto Adige, Italy).

Main Results:

• The assimilation method effectively corrected snowfall biases and uncertainties.
• Resulting cumulative snowfall patterns showed improved consistency across different precipitation products.
• Demonstrated enhanced accuracy in basin-wide snow depth and snow cover area estimations.
• Led to more reliable basin streamflow predictions.

Conclusions:

• The proposed novel approach significantly improves the characterization of orographic snowfall.
• Satellite data assimilation enhances the reliability of precipitation and snow variable estimates in mountains.
• This method offers a valuable tool for improved water resource management in alpine regions.