U.S. flagAn official website of the United States government
                    Here's how you know **Official websites use .gov**
                            
                            A **.gov** website belongs to an official government organization in the United States.
 **Secure .gov websites use HTTPS**
                            
                            A **lock** (                            
                              A locked padlock                                                      
                            
                            ) or **https://** means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.


Menu Agricultural Research Service
						
                    U.S. DEPARTMENT OF AGRICULTURE
 close



Research
                Research Home
                            National Programs
                            Research Projects
                            Scientific Manuscripts
                            International Engagement
                            Scientific Software/Models
                            Databases and Datasets
                            Office of Scientific Quality Review
            Media
                News & Features
                            Multimedia
                            Briefing Room
                            Down on the Farm
                            Press Room
                            Science in Your Shopping Cart
                            Scientific Discoveries
                            Tellus
                            Under the Microscope
            About ARS
                About ARS Home
                            Staff Directory
                            Labs and Research Centers (Map)
                            Headquarter Offices
                            Organizational Chart
                            Employee Services (REE Employees Only)
                            Office of Outreach, Diversity, and Equal Opportunity
            Work With Us
                Work With Us Home
                            ARS Vacancies at USAJOBS
                            Careers at ARS Information
                            Scientific Collaborations
                            Extramural Agreements
                            1890 Faculty Research Sabbatical ProgramSearch small
                    Search
            
            ARS Home
                                About ARS
                                Contact Us




ARS Home Research Publications at this Location





- ARS National Programs
- Search for a scientific manuscript
- Search for a research project
- ARS Software
- Programs and Projects
- Collaborations
- Subjects of Investigation
- Publications

Title:   Author  item
                                            Crow, Wade
  Submitted to: American Meteorological Society
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 1/10/2010
                                    Publication  Date: 3/1/2010
                                    Citation: Crow, W.T. 2010. Acquiring observation error covariance information for land data assimilation systems [abstract]. American Meteorological Society. 2010 CDROM.Interpretive Summary:
                                Technical Abstract: Recent work has presented the initial application of adaptive filtering techniques to land surface data assimilation systems. Such techniques are motivated by our current lack of knowledge concerning the structure of large-scale error in either land surface modeling output or remotely-sensed estimates of land surface water and energy balance variables. Most current adaptive techniques are based on classical whitening approaches in which a lack of temporal auto-correlation within filtering innovations is assumed to be a necessary and sufficient condition for optimal filter performance. However, the application of these approaches to the assimilation of remotely-sensed surface soil moisture has uncovered two serious problems. First, the iterative application of whitening approaches to land surface models leads to extremely slow convergence on optimal error parameters and is therefore not appropriate for satellite data sets with limited temporal heritage. Second, errors in available remotely-sensed soil moisture data sets are commonly too heavily auto-correlated for whitening approaches to function effectively. This presentation will illustrate these problems and develop an alternative methodology which circumvents both limitations. The approach is based on the application of a so-called “triple collocation” technique to independently estimate observational errors in remotely-sensed surface soil moisture. Such approaches estimate error magnitudes in a given geophysical variable by averaging across variations within three independently-obtained estimates of the variable. Here errors in surface soil moisture retrievals obtained from the Advanced Microwave Scanning Radiometer (AMSRE-E) are estimated via triple-collocation and used to constrain optimal modeling errors (by tuning modeling errors until normalized filtering innovations are variance unity). Real data validation results within several heavily-instrumented ground test sites reveal that the procedure leads to faster convergence to optimized error parameters and significantly enhances surface soil moisture estimates relative to existing adaptive filtering approaches.


Return to top


Connect with ARS Sign up


ARS Home
                            USDA.gov
                            Plain Writing
                            Policies & Links
                            Civil Rights Statements
                            FOIA
                            Accessibility Statement
                            Privacy Policy
                            Non-Discrimination Statement
                            Quality of Information
                            USA.gov
                            WhiteHouse.gov