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Propagating Subsurface Uncertainty to the Atmosphere Using Fully Coupled Stochastic Simulations
Author(s) -
John L. Williams,
R. M. Maxwell
Publication year - 2011
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/2011jhm1363.1
Subject(s) - atmosphere (unit) , latent heat , environmental science , weather research and forecasting model , wind speed , sensible heat , monte carlo method , hydrometeorology , data assimilation , hydraulic conductivity , atmospheric model , atmospheric sciences , meteorology , soil science , soil water , geology , precipitation , mathematics , physics , statistics
Feedbacks between the land surface and the atmosphere, manifested as mass and energy fluxes, are strongly correlated with soil moisture, making soil moisture an important factor in land–atmosphere interactions. It is shown that a reduction of the uncertainty in subsurface properties such as hydraulic conductivity (K) propagates into the atmosphere, resulting in a reduction in uncertainty in land–atmosphere feedbacks that yields more accurate atmospheric predictions. Using the fully coupled groundwater-to-atmosphere model ParFlow-WRF, which couples the hydrologic model ParFlow with the Weather Research and Forecasting (WRF) atmospheric model, responses in land–atmosphere feedbacks and wind patterns due to subsurface heterogeneity are simulated. Ensembles are generated by varying the spatial location of subsurface properties while maintaining the global statistics and correlation structure. This approach is common to the hydrologic sciences but uncommon in atmospheric simulations where ensemble fore...

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