Search Results

You are looking at 1 - 5 of 5 items for :

  • NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research x
  • All content x
Clear All
Susan Frankenstein, Maria Stevens, and Constance Scott

the latest analyses or forecasts through influence of assimilated information on model state evolution) and the resolution dilemma. This is how we plan on eventually using the SMAP products. The aim of this study was to investigate the differences in mobility predictions in data-sparse and/or denied areas when using the current Noah LIS–based soil moisture climatology method of Baylot et al. (2013) versus using simulated SMAP combined soil moisture products directly. To do this, we first compare

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

promoting the use of NASA mission products and information in decision-making activities for societal benefit, often termed applications. It is commonly the case in mission planning to emphasize science and engineering in the prelaunch efforts and to delay consideration of applications until after launch. The integration of applications into prelaunch mission planning, with examples from the Soil Moisture Active Passive (SMAP) mission, is the theme of this Special Collection and the topic of this

Full access
John D. Hottenstein, Guillermo E. Ponce-Campos, Julio Moguel-Yanes, and M. Susan Moran

. 2002 ; Cox et al. 1986 ). Second, the currently orbiting Soil Moisture Ocean Salinity (SMOS) and planned Soil Moisture Active Passive (SMAP) sensors will provide global measurements of soil moisture at this depth ( Kerr et al. 2001 ; Entekhabi et al. 2010 ). 2. Methods a. Study sites and data selection Nine sites were selected across the southern United States ( Fig. 1 ), composed of seven Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations, one U

Full access
Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

; Drusch and Viterbo 2007 ). To better represent the land surface in environmental prediction and assimilation systems, the Canadian Land Data Assimilation System (CaLDAS) is being developed at EC’s Meteorological Research Division (MRD). It is planned that CaLDAS will provide the initial conditions, including soil moisture, surface temperature, snow cover, and vegetation properties to both EC’s deterministic and ensemble prediction systems. Much of the early operational development of CaLDAS focused

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

1. Introduction Because of the importance of accurate streamflow forecasts for water resources planning (e.g., Yao and Georgakakos 2001 ; Hamlet et al. 2002 ), the development of approaches for producing useful streamflow forecasts and the evaluation of these approaches over time has a rich history, going back to at least the 1930s ( Pagano et al. 2004 ). Operational streamflow forecasts generally rely on statistical techniques (e.g., Garen 1992 ). Using various quantities describing the

Full access