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During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance.
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Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals.

Manuela Girotto1, Giuseppe Formetta2, Shima Azimi2

  • 1Environmental Science and Policy Management, University of California, Berkeley, CA, USA.

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Summary
This summary is machine-generated.

Accurate mountain precipitation is challenging. This study uses satellite data and a novel particle batch smoother to improve snowfall estimates, leading to better snow and streamflow predictions in alpine regions.

Keywords:
Data assimilationMountain hydrologySentinel-1Snow depthSnowfall correctionSnowfall gradients

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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.