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Susan Stillman, Jason Ninneman, Xubin Zeng, Trenton Franz, Russell L. Scott, William J. Shuttleworth, and Ken Cummins

of magnitude finer than that (one sample per ~1760 km 2 ) in the Oklahoma Mesonet, which represents one of the best networks for mesoscale meteorological measurements ( www.mesonet.org/ ). One purpose of this study is to develop a long-term summer soil moisture dataset over WGEW that can be used to characterize the spatiotemporal variability of soil moisture and to evaluate other soil moisture products [e.g., from remote sensing retrievals such as the Soil Moisture Ocean Salinity mission ( Kerr

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M. F. McCabe, H. Gao, and E. F. Wood

exist to adequately or efficiently characterize this property at large scales. The insight that is accessible through remote sensors should facilitate a greater understanding of the broader-scale patterns that are available from current platforms, such as the Advanced Microwave Sounding Radiometer (AMSR)-E ( Njoku et al. 2003 ), and future satellite missions such as those of the Soil Moisture and Ocean Salinity (SMOS) ( Kerr et al. 2001 ) and Hydrosphere State (HYDROS). However, this task has been

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Wade T. Crow, George J. Huffman, Rajat Bindlish, and Thomas J. Jackson

). This limitation is consistent with the performance of current-generation soil moisture retrievals derived from AMSR-E X-band (10.7 GHz) T B observations. However, the future availability of lower-frequency L-band (1.4 GHz) T B observations from the European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity Mission (SMOS; Kerr et al. 2001 ) should significantly enhance results in areas of moderate and dense vegetation. Relative to X-band AMSR-E retrievals, L-band sensors will also reduce

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Andrea Thorstensen, Phu Nguyen, Kuolin Hsu, and Soroosh Sorooshian

-based observations to retrievals from satellites. The European Space Agency’s Soil Moisture Ocean Salinity (SMOS) satellite mission was launched in 2009 with the purpose of measuring sea surface salinity over the world’s oceans and surface soil moisture over land ( Kerr et al. 2010 ). The recently launched Soil Moisture Active Passive (SMAP) mission from the National Aeronautics and Space Administration (NASA) utilizes a passive L-band radiometer combined with active L-band radar ( Entekhabi et al. 2010

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Sujay V. Kumar, Kenneth W. Harrison, Christa D. Peters-Lidard, Joseph A. Santanello Jr., and Dalia Kirschbaum

-band (~6 GHz) microwave radiometers. However, none of these sensors were specifically designed to measure soil moisture until the launch of the Soil Moisture Ocean Salinity (SMOS; since late 2009) from the European Space Agency (ESA), which provides global observations for soil moisture and salinity from an L-band radiometer. Compared to the X and C bands, the L-band-based measurements have reduced attenuation of the signal under moderate vegetation conditions and increased penetration depth for

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Jessica M. Erlingis, Jonathan J. Gourley, and Jeffrey B. Basara

Vizy 2010 ). Sea surface salinity anomalies in the subtropical North Atlantic, indicating enhanced evaporation and vapor flux away from the region, significantly correlate with precipitation over the Midwest ( Li et al. 2016 ) This region has also been identified as a region where nonlocal soil moisture anomalies ( DeAngelis et al. 2010 ; Kustu et al. 2011 ) and anomalous evaporative moisture in the Caribbean Sea ( Dirmeyer and Kinter 2010 ) are correlated with heavy rainfall and flood events

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M. T. Yilmaz, W. T. Crow, and D. Ryu

–60-min intervals at different locations ( Jackson et al. 2010 ). These datasets have been used previously in the validation of AMSR-E and Soil Moisture Ocean Salinity (SMOS) surface soil moisture products ( Jackson et al. 2010 , 2012 ) and verified via comparisons against gravimetric soil moisture observations ( Cosh et al. 2006 , 2008 ). Hereinafter, they will be referred to as watershed-average soil moisture (WASM) values and used as an independent benchmark target for Kalman filtering analysis

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Yi-Ching Chung, Stéphane Bélair, and Jocelyn Mailhot

conductivity of sea ice both depend on the temperature and mean salinity of the ice, to account for the presence of brine pockets in the ice ( Ebert and Curry 1993 ; Flato and Brown 1996 ). Ice salinity is parameterized as a function of ice thickness. The upper-boundary condition for sea ice temperature depends on snow coverage. During snow-free periods, the temperature at the ice surface T 1 is obtained from the surface energy budget: where F 1 is the heat conduction flux at the ice

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Sujay V. Kumar, Rolf H. Reichle, Randal D. Koster, Wade T. Crow, and Christa D. Peters-Lidard

root zone through assimilation. Understanding this transfer is key to exploiting the information content of the next generation of satellite soil moisture retrievals from the Soil Moisture and Ocean Salinity ( National Research Council 2007 ) and the Soil Moisture Active and Passive ( Kerr et al. 2001 ) satellite missions to be launched in 2009 and 2013, respectively. 2. Approach a. Land surface models This study is conducted using the Land Information System (LIS) data assimilation test bed, which

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Rolf H. Reichle, Randal D. Koster, Jiarui Dong, and Aaron A. Berg

Moisture and Ocean Salinity (SMOS) mission ( Kerr et al. 2001 ) and the Hydrosphere State (HYDROS) mission ( Entekhabi et al. 2002 )—should, if successful, provide important advances in soil moisture monitoring and associated climate and forecasting studies. For the merging of the disparate datasets into a single unified time series of soil moisture anomaly fields through data assimilation we will rely on the scaling approach suggested above. The results presented here will also aid in determining the

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