Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains

Luo, Lifeng, Robock, Alan, Mitchell, Kenneth E., Houser, Paul R., Wood, Eric F., Schaake, John C., Lohmann, Dag, Cosgrove, Brian A., Wen, Fenghua, Sheffield, Justin, Duan, Qingyun, Higgins, R. Wayne, Pinker, Rachel T. and Tarpley, J. Dan (2003) Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains. Journal of Geophysical Research: Atmospheres, 108 (22), GCP 4-1 - GCP 4-10. (doi:10.1029/2002jd003246).

Abstract

Atmospheric forcing used by land surface models is a critical component of the North American Land Data Assimilation System (NLDAS) and its quality crucially affects the final product of NLDAS and our work on model improvement. A three-year (September 1996-September 1999) retrospective forcing data set was created from the Eta Data Assimilation System and observations and used to run the NLDAS land surface models for this period. We compared gridded NLDAS forcing with station observations obtained from networks including the Oklahoma Mesonet and Atmospheric Radiation Measurement/Cloud and Radiation Testbed at the southern Great Plains. Differences in all forcing variables except precipitation between the NLDAS forcing data set and station observations are small at all timescales. While precipitation data do not agree very well at an hourly timescale, they do agree better at longer timescales because of the way NLDAS precipitation forcing is generated. A small high bias in downward solar radiation and a low bias in downward longwave radiation exist in the retrospective forcing. To investigate the impact of these differences on land surface modeling we compared two sets of model simulations, one forced by the standard NLDAS product and one with station-observed meteorology. The differences in the resulting simulations of soil moisture and soil temperature for each model were small, much smaller than the differences between the models and between the models and observations. This indicates that NLDAS retrospective forcing provides an excellent state-of-the-art data set for land surface modeling, at least over the southern Great Plains region.

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Contributors

Author: Justin Sheffield

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