This study characterizes the space-time structure of soil moisture background error covariance and paves the way for the development of a soil moisture variational data assimilation system for the Noah land surface model coupled to the Weather Research and Forecasting (WRF) model. The soil moisture background error covariance over the contiguous United States exhibits strong seasonal and regional variability with the largest values occurring in the uppermost soil layer during the summer. Large background error biases were identified, particularly over the southeastern United States, caused mainly by the discrepancy between the WRF-Noah simulations and the initial conditions derived from the used operational global analysis data set. The assimilation of the Soil Moisture and Ocean Salinity (SMOS) soil moisture data notably reduces the error of soil moisture simulations. On average, data assimilation with space-time varying background error covariance results in 33% and 35% reduction in the root-mean-square error and the mean absolute error, respectively, in the simulation of hourly top 10 cm soil moisture, mainly due to implicit reductions in soil moisture biases. In terms of correlation, the improvement in soil moisture simulations is also observed but less notable, indicating the limitation of coarse-scale soil moisture data assimilation in capturing fine-scale soil moisture variation. In addition, soil moisture data assimilation improves the simulations of latent heat fluxes but shows a marginal impact on the simulations of sensible latent heat fluxes and precipitation.
|Original language||English (US)|
|Number of pages||27|
|Journal||Water Resources Research|
|State||Published - Feb 1 2017|
Bibliographical noteFunding Information:
This research is part of Liao-Fan Lin's PhD dissertation [Lin,] and sponsored by the NASA Precipitation Measurement Mission (PMM) science program through grants NNX10AG84G, NNX11AQ33G, and NNX13AH35G. The support by the K. Harrison Brown Family Chair and the USDA National Institute of Food and Agriculture (project 1008517) is also gratefully acknowledged. The NCEP FNL data were obtained from the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR), freely accessible at http://rda.ucar.edu/datasets/ds083.2/. The NCEP Stage IV data were obtained from the Earth Observing Laboratory at the NCAR, freely available at http://data.eol.ucar.edu/codiac/dss/id=21.093. The SMOS data (freely available at http://cp34-bec.cmima.csic.es) were obtained from the SMOS Barcelona Expert Centre, a joint initiative of the Spanish Research Council (CSIC) and Technical University of Catalonia (UPC), mainly funded by the Spanish National Program on Space. The SCAN data were obtained from the Natural Resources Conservation Service, freely available at http://www.wcc.nrcs.usda.gov/scan/. The WRF model was obtained from the NCAR, freely available at http://www2.mmm.ucar.edu/wrf/users/. The NLDAS data were obtained from the NASA Goddard Earth Sciences Data and Information Services Center, freely available at http://disc.sci.gsfc.nasa.gov/uui/datasets?keywords=NLDAS. Authors are grateful to these agencies for providing the models, data, and assistance. The authors would also like to thank Alejandro Flores at Boise State University for his insights and useful discussions and four anonymous reviewers for their helpful comments.
© 2017. American Geophysical Union. All Rights Reserved.
- Weather Research and Forecasting model
- background error covariance estimation
- satellite retrievals
- soil moisture
- variational data assimilation