880 Global Multi-Sensor Land Data Assimilation Using CLM and Dart

Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Zong-Liang Yang, Univ. of Texas at Austin, Austin, TX; and L. Zhao and P. Lin

Over the past six years, we have developed a global-scale multi-source and multi-scale land data assimilation system based on the National Center for Atmospheric Research (NCAR) Data Assimilation Research Testbed (DART) and Community Land Model version 4 (CLM4). The DART has an unprecedented large ensemble (80-member) atmospheric forcing (temperature, precipitation, winds, humidity, radiation) with a quality of typical reanalysis products, which facilitates ensemble land data assimilation. This paper will evaluate land state variables including the snow water equivalent that results from the CLM/DART assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction, Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage, Advanced Microwave Scanning Radiometer–EOS (AMSR–E) snow bright temperature, and AMSR-E soil temperature. Evaluation results and inter-comparison of open-loop and data assimilation cases suggest that 1) MODIS snow cover fraction leads to marginal improvements in mid- and high-latitude snow estimation but rarely on soil moisture; 2) lower and higher frequencies of AMSR-E brightness temperature play complementary roles in improving global soil moisture and snow estimation; 3) the assimilation of GRACE tends to degrade soil moisture estimation but poses potential in improving snow depth estimation in most high-latitude regions. Generally, the combination of MODIS, GRACE, and AMSR-E observations with regard to spatial locations holds promise to provide a robust global soil moisture and snow estimation through the multi-sensor land data assimilation system.

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