A new snow density parameterization for land data initialization

Nicholas Dawson, Patrick Broxton, Xubin Zeng

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.

Original languageEnglish (US)
Pages (from-to)197-207
Number of pages11
JournalJournal of Hydrometeorology
Volume18
Issue number1
DOIs
StatePublished - 2017

Keywords

  • Data assimilation
  • Parameterization
  • Snow
  • Snowpack

ASJC Scopus subject areas

  • Atmospheric Science

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