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, Wadeitem
                                            REICHLE, ROLF - NASA GSFC
  Submitted to: American Geophysical Union
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 11/30/2007
                                    Publication  Date: 12/12/2007
                                    Citation: Crow, W.T., Reichle, R.H., 2007. Towards the development of on-line model error identification system for land surface models [abstract]. American Geophysical Union Fall Meeting, December 10-14, 2007, San Francisco, California. 88(52). 2007 CDROM.Interpretive Summary:
                                Technical Abstract: Due to the complexity of potential error sources in land surface models, the accurate specification of model error parameters has emerged as a major challenge in the development of effective land data assimilation systems for hydrologic and hydro-climatic applications. While several on-line procedures for estimating model error parameters - based on the statistical analysis of filtering innovations - have been introduced for geophysical models, such procedures have not been widely applied to land surface models.  Consequently, little is currently known about their relative merits with regards to land surface data assimilation applications. 
Using a series of synthetic twin experiments and an Ensemble Kalman filter, this paper will inter-compare the performance of a number of existing adaptive filtering approaches when applied to an observation and modeling error estimation problem within a land data assimilation system.  These comparisons will highlight the suitability of classical adaptive filtering approaches (designed for purely linear systems) for nonlinear land surface models.  Special emphasis will also be placed on identifying the suitability of various approaches with regards to the unique characteristics of the land surface data assimilation problem (relative to similar problems in ocean and atmosphere modeling).  These attributes include the high degree of land surface spatial heterogeneity which precludes the use of ergodic techniques for sampling innovation statistics and the nonlinear, yet fundamentally dissipative, structure of land surface processes.  Preliminary real data results based on the assimilation of remotely-sensed surface soil moisture retrievals into a land surface model forced by satellite-based precipitation will be used to demonstrate the potential value of these approaches in an operational setting.


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