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
 Hydrology and Remote Sensing Laboratory: Beltsville, MD
 close



    Research

                    Research Projects
                                    Publications
                                    Collaborations
                                    News
                                    Software
                People

                    Staff Listing
                                    CareersSearch small
                    Search
            
            ARS Home
                                About ARS
                                Contact Us




ARS Home Northeast Area Beltsville, Maryland (BARC) Beltsville Agricultural Research Center Hydrology and Remote Sensing Laboratory 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
- People
- Research Sites
- Pesticide Volatilization
- Watershed Modeling
- Climate Change
- Soil Moisture
- Drought and ET
- HRSL Brochure

Title:   Author  item
                                            Crow, Wadeitem
                                            YILMAZ, M.T. - Collaborator
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 6/12/2014
                                    Publication  Date: 7/5/2014
                                    Citation: Crow, W.T., Yilmaz, M.T. 2014. A Parsimonious Land Data Assimilation System for the SMAP/GPM Satellite Era [abstract]. 7th International Scientific Conference on the Global Water and Envergy Cycle. Abstract S8.T14.2.Interpretive Summary:
                                Technical Abstract: Land data assimilation systems typically require complex parameterizations in order to: define required observation operators, quantify observing/forecasting errors and calibrate a land surface assimilation model. These parameters are commonly defined in an arbitrary manner and, if poorly specified, can degrade the accuracy of state estimates provided by the system. This presentation will describe the development of a parsimonious land data assimilation which attempts to auto-calibrate all parameters necessary to simultaneously assimilate active/passive satellite-based surface soil moisture retrievals into a simple land surface model driven by uncertain precipitation forcing. The approach, coined the Auto-Tuned Land Assimilation System (ATLAS), is based on combining aspects of a triple collocation analysis with a statistical analysis of filtering innovations and is coupled with a new understanding of the proper theoretical basis for rescaling soil moisture observations prior to their assimilation into a land model. As a result, ATLAS is able to internally-derive – and adaptively pass to a Colored Kalman Filter implementation – key error statistical information that is typically either neglected or arbitrarily specified by existing land data assimilation systems. Key simplifying assumptions required by ATLAS will be justified, and preliminary ATLAS results (using existing active/passive soil moisture retrieval data sets) over the contiguous United States will be presented. Overall, results demonstrate that ATLAS provides a valuable tool for intelligently integrating uncertain remotely-sensed surface soil moisture and rainfall accumulation  information acquired during the SMAP/GPM satellite era.


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