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  • 5 - Data Assimilation of Seasonal Snow

  • from Part II - ‘Fluid’ Earth Applications: From the Surface to the Space
  • Edited by Alik Ismail-Zadeh, Karlsruhe Institute of Technology, Germany, Fabio Castelli, Università degli Studi, Florence, Dylan Jones, University of Toronto, Sabrina Sanchez, Max Planck Institute for Solar System Research, Germany
  • Book:
    Applications of Data Assimilation and Inverse Problems in the Earth Sciences
    Published online:
    20 June 2023
    Print publication:
    06 July 2023, pp 79-92

  • Summary

    Abstract: There is a fundamental need to understand and improve the errors and uncertainties associated with estimates of seasonal snow analysis and prediction. Over the past few decades, snow cover remote sensing techniques have increased in accuracy, but the retrieval of spatially and temporally continuous estimates of snow depth or snow water equivalent remains challenging tasks. Model-based snow estimates often bear significant uncertainties due to model structure and error-prone forcing data and parameter estimates. A potential method to overcome model and observational shortcomings is data assimilation. Data assimilation leverages the information content in both observations and models while minimising inherent limitations that result from uncertainty. This chapter reviews current snow models, snow remote sensing methods, and data assimilation techniques that can reduce uncertainties in the characterisation of seasonal snow.