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  • NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research x
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Fan Chen, Wade T. Crow, and Dongryeol Ryu

application, such as insufficient model coupling strength ( Chen et al. 2011 ) as well as biased precipitation data ( Draper et al. 2011 ), have also been discussed. In addition to prestorm soil moisture level, precipitation uncertainty is another important source of error in streamflow predictions. Because of its global coverage at high temporal frequency (as high as 3 hourly), satellite precipitation measurements have become increasingly utilized in forcing hydrologic models ( Hong et al. 2007 ; Wu et

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

Jeu et al. 2008 ; Draper et al. 2009 , 2011 , 2012 ). At several meteorological centers, including the Canadian Meteorological Centre of Environment Canada (EC), soil moisture is inferred from short-range NWP forecast errors in screen-level temperature and humidity ( Bélair et al. 2003a ; Drusch and Viterbo 2007 ; Mahfouf et al. 2009 ). Soil moisture is used as a sink variable where errors in atmospheric forcing and the land surface model can accumulate over time ( Seuffert et al. 2004

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Randal D. Koster, Gregory K. Walker, Sarith P. P. Mahanama, and Rolf H. Reichle

current state of a system (e.g., snow amount, soil moisture, and climate indices), calibrated regressions are applied that transform these quantities into streamflow forecasts. The historical use of these statistical techniques is arguably a reflection of historical limitations in our ability to model accurately the physical processes that generate streamflow—in particular our ability to provide the high-resolution forcing and boundary condition data needed to support the physical modeling. The advent

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C. Albergel, W. Dorigo, R. H. Reichle, G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, L. Isaksen, R. de Jeu, and W. Wagner

updated only every 5–10 years, hence delaying the benefit from the most recent land surface model and data assimilation advances. To update only the land surface component of the ERA-Interim reanalysis, ECMWF recently developed ERA-Interim/Land (hereafter referred to as ERA-Land) simulations, where the ERA-Interim near-surface meteorological forcing is used with the latest version of the ECMWF land surface model ( Balsamo et al. 2012 ). Similarly, an enhanced MERRA land surface data product, MERRA

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

LSM, using the NLDAS-2 forcing data ( Xia et al. 2012 ) as meteorological inputs to generate the assumed “true” state of the land surface. The forward τ – ω model described in section 2 is then used to generate truth L-band T b , p for p = [ H , V ] values. L-band T b , p observations are then generated from this simulated truth by introducing realistic retrieval errors. OL and DA integrations are conducted using the Noah LSM forced with Modern-Era Retrospective Analysis for Research

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Amy McNally, Gregory J. Husak, Molly Brown, Mark Carroll, Chris Funk, Soni Yatheendradas, Kristi Arsenault, Christa Peters-Lidard, and James P. Verdin

) and maize in Zimbabwe ( Verdin and Klaver 2002 ). Hydrologic models driven with satellite rainfall, and resulting drought metrics, will be sensitive to the errors in satellite rainfall inputs (e.g., Ramarohetra et al. 2013 ). However, ancillary land measurements can help reduce forcing and parameter uncertainty. Assimilating microwave data has already been shown to improve errors associated with satellite rainfall estimates ( Crow et al. 2011 ; Ines et al. 2013 ; Pellarin et al. 2008 ) and to

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M. Susan Moran, Bradley Doorn, Vanessa Escobar, and Molly E. Brown

precipitation forcing and assimilation of L-band brightness temperatures in the Canadian Land Data Assimilation System (Carrera et al. 2014, manuscript submitted to J. Hydrometeor. ). The Institute for Climate and Society (IRI) at Columbia University tested a data assimilation framework for forecasting yields of grain crops in Africa and reported the potential to detect irrigation applications otherwise not possible through model simulations ( Ines et al. 2013 ; Das et al. 2014, manuscript submitted to J

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Susan Frankenstein, Maria Stevens, and Constance Scott

of which are indicators of soil shear strength. The unit of measurement is pressure. Cone index is defined as the force per unit area required to push a 30° stainless steel cone penetrometer through a specified thickness of soil ( Meyer et al. 1977 ). RCI is the CI corrected by a remolding index (which ranges from 0 to 1) and is used for fine-grained soils such as clays and silts. It is considered to better represent the soil subjected to traffic. The remolding index is the ratio of the CI after

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Husayn El Sharif, Jingfeng Wang, and Aris P. Georgakakos

products. This soil moisture data product is then used to “filter” an ensemble of DSSAT-CSM runs using synthetic weather input data. In this study, stochastic forcing is introduced by adding measurement noise to daily weather inputs. The “control” scenario refers to DSSAT-CSM results using the entire ensemble of synthetic input data in DSSAT-CSM runs. The “SMAP” scenario refers to DSSAT-CSM runs in which modeled top soil moisture is consistent with the SMAP-like data. Agreement is assessed via the

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