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A Coupled Surface Nudging Scheme for use in Retrospective Weather and Climate Simulations for Environmental Applications
Citation:
Alapaty, Kiran, T. Glotfelty, J. He, A. Deng, W. Wang, AND J. Herwehe. A Coupled Surface Nudging Scheme for use in Retrospective Weather and Climate Simulations for Environmental Applications. 17th Annual WRF Users' Workshop, Boulder, CO, June 27 - July 01, 2016.
Impact/Purpose:
IMPACT: A new method, FASDAS, was implemented into the WRF model to consistently improve the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as well as for high resolution regional climate predictions. This new capability has been released in WRF Version 3.8 as option grid_sfdda = 2. This new capability increases the accuracy of atmospheric inputs for use air quality, hydrology, and ecosystem modeling research improving the accuracy of end-point research outcome.
Description:
A surface analysis nudging scheme coupling atmospheric and land surface thermodynamic parameters has been implemented into WRF v3.8 (latest version) for use with retrospective weather and climate simulations, as well as for applications in air quality, hydrology, and ecosystem modeling. This scheme is known as the flux-adjusting surface data assimilation system (FASDAS) developed by Alapaty et al. (2008). This scheme provides continuous adjustments for soil moisture and temperature (via indirect nudging) and for surface air temperature and water vapor mixing ratio (via direct nudging). The simultaneous application of indirect and direct nudging maintains greater consistency between the soil temperature–moisture and the atmospheric surface layer mass-field variables. The new method, FASDAS, consistently improved the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as well as for high resolution regional climate predictions. This new capability has been released in WRF Version 3.8 as option grid_sfdda = 2. This new capability increased the accuracy of atmospheric inputs for use air quality, hydrology, and ecosystem modeling research to improve the accuracy of respective end-point research outcome.
