Quantifying the Added Value of Snow Cover Area Observations in Passive Microwave Snow Depth Data Assimilation

Sujay V. Kumar * Science Applications International Corporation, McLean, Virginia
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Christa D. Peters-Lidard Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Kristi R. Arsenault * Science Applications International Corporation, McLean, Virginia
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Augusto Getirana Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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David Mocko * Science Applications International Corporation, McLean, Virginia
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Yuqiong Liu Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Abstract

Accurate determination of snow conditions is important for several water management applications, partly because of the significant influence of snowmelt on seasonal streamflow prediction. This article examines an approach using snow cover area (SCA) observations as snow detection constraints during the assimilation of snow depth retrievals from passive microwave sensors. Two different SCA products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS)] are employed jointly with the snow depth retrievals from a variety of sensors for data assimilation in the Noah land surface model. The results indicate that the use of MODIS data is effective in obtaining added improvements (up to 6% improvement in aggregate RMSE) in snow depth fields compared to assimilating passive microwave data alone, whereas the impact of IMS data is small. The improvements in snow depth fields are also found to translate to small yet systematic improvements in streamflow estimates, especially over the western United States, the upper Missouri River, and parts of the Northeast and upper Mississippi River. This study thus demonstrates a simple approach for exploiting the information from SCA observations in data assimilation.

Corresponding author address: Sujay Kumar, Hydrological Sciences Laboratory, NASA GSFC, Code 617, Greenbelt, MD 20771. E-mail: sujay.v.kumar@nasa.gov

Abstract

Accurate determination of snow conditions is important for several water management applications, partly because of the significant influence of snowmelt on seasonal streamflow prediction. This article examines an approach using snow cover area (SCA) observations as snow detection constraints during the assimilation of snow depth retrievals from passive microwave sensors. Two different SCA products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS)] are employed jointly with the snow depth retrievals from a variety of sensors for data assimilation in the Noah land surface model. The results indicate that the use of MODIS data is effective in obtaining added improvements (up to 6% improvement in aggregate RMSE) in snow depth fields compared to assimilating passive microwave data alone, whereas the impact of IMS data is small. The improvements in snow depth fields are also found to translate to small yet systematic improvements in streamflow estimates, especially over the western United States, the upper Missouri River, and parts of the Northeast and upper Mississippi River. This study thus demonstrates a simple approach for exploiting the information from SCA observations in data assimilation.

Corresponding author address: Sujay Kumar, Hydrological Sciences Laboratory, NASA GSFC, Code 617, Greenbelt, MD 20771. E-mail: sujay.v.kumar@nasa.gov
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