IN41B-01 INVITED
An Unconventional Path Toward the Operational Leveraging of Research-Grade Environmental Satellites
The traditional and proper path followed in transitioning research applications to operational support entails a rigorous gamut of quality control, testing, validation, technical documentation, and software optimization. In times of dire need when observations are in high demand and resources are few, however, convention must sometimes give way to outside-of-the-box thinking. Here, considerations made for manageable compromises forge a pathway to accelerated transition of developing technologies. Such was the case in Coalition mobilizations immediately following the 9/11 attacks, when the United States Office of Naval Research issued a challenge to the environmental research and development community to expedite the delivery of any and all capabilities bearing support relevance to mission planners and executors involved in the increasingly likely military response. It was under this directive that the Naval Research Laboratory's (NRL) Satellite Meteorological Applications Section reconfigured its base research program and internal processing infrastructure to effectively transform itself into an agile operational production system for rapid transition of value-added satellite environmental characterization products centered around next-generation ‘research grade' satellite observing systems. Integral to this transformation was the coincident establishment of the Near Real-Time Processing Effort (NRTPE) coordinated among members of the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), and Department of Defense (DoD; Air Force and Navy participants) working in a ‘badgeless environment'. The NRTPE provided a portal for acquisition of NASA's MODerate resolution Imaging Spectroradiometer (MODIS) data at 2-4 hr latency worldwide. By virtue of NRTPE modifications to the Terra and Aqua satellite telemetry downlinks and transmission across the high-speed Defense Research/Engineering Network, data previously relegated to research-only pursuits suddenly became operationally viable for mission planning purposes. Through close coordination with Fleet Numerical Meteorology/Oceanography Center (FNMOC), NRL leveraged these NRTPE MODIS and other research satellite datasets to infuse unprecedented operational capabilities (e.g., dust detection, low clouds and fog, snow cover, natural color imagery)—making these and other products available on the ‘Satellite Focus' secure internet web page at precisely the time of highest need. Post deployment reports from aircraft carrier groups engaged in Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF) give testimony to the utility and impact of these resources. This paper describes the chain of events leading to an unlikely success story in the leveraging of research satellite data. Illustrative examples from Satellite Focus, as well as from the NexSat web page (public analog), are presented. The positive outcome of this activity suggests a new paradigm for future observing systems whereby operational utility is evaluated well in advance of their implementation on formal operational platforms. These demonstrations help users anticipate (and provide feedback to developers concerning) the capabilities and limitations of next-generation sensors—addressing in part the ‘missing link' between higher-risk research sensors and tried-and-true (off the shelf) technology appropriate for operational systems.
IN41B-02
Soil Moisture Data Assimilation with the Noah Land Surface Model
The Ensemble Kalman Filter (EnKF) is an essential tool in land surface data assimilation, as near-real-time land observations such as MODIS and AMSR-E land satellite products have become available, and the high- performance uncoupled Land Information System (LIS) infrastructure has become available as a test bed at NCEP. Currently the Kalman Filter data assimilation technique has been implemented in LIS and works with the Noah Land Surface Model (LSM). Our previous study has shown that temporal and spatial variability from the standard AMSR-E and modeled soil moisture alone compare poorly with in-situ observations. In this study, to use AMSR-E to provide an improved analysis of land fields that can be directly used as initial conditions for weather and climate prediction, our focus is to reduce the possible biases existing in observations and model forecasts, and investigate the efficiency and benefits of assimilating the AMSR-E data products into the Noah model. To this end using the 1-D EnKF scheme, a pair of comparison experiments for individually assimilating AMSR-E soil moisture retrievals and SCAN in-situ measurements into the Noah model have been carefully designed and will be carried out over a local domain which mostly covers Mississippi and Arkansas, United States of America. Next, for better assimilation performance we will employ several bias-correction algorithms in the data assimilation framework to reduce the observation and model biases. Lastly, the assimilation results will be evaluated with SCAN data, and the performance for each bias correction option will be compared as well. The simulation and evaluation results will be presented in the meeting.
IN41B-03
Solving Large-scale Spatial Optimization Problems in Water Resources Management through Spatial Evolutionary Algorithms
A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators to represent spatial variables in a more efficient way. The hyper-population consists of a set of populations, which correspond to the spatial distributions of the individual agents (organisms). Furthermore spatial crossover and mutation operators are designed in accordance with the tree representation and then applied to both organisms and populations. This study applies the SEA to a specific problem of water resources management- maximizing the riparian vegetation coverage in accordance with the distributed groundwater system in an arid region. The vegetation coverage is impacted greatly by the nonlinear feedbacks and interactions between vegetation and groundwater and the spatial variability of groundwater. The SEA is applied to search for an optimal vegetation configuration compatible to the groundwater flow. The results from this example demonstrate the effectiveness of the SEA. Extension of the algorithm for other water resources management problems is discussed.
IN41B-04
Development of a Rapid Prototyping Capability for Hydrological Applications of Global Precipitation Measurements
The future NASA/JAXA Global Precipitation Measurement (GPM) mission includes a primary focus on water management applications that use land surface models to simulate the main variables in surface water and energy budgets. GPM is envisioned as a constellation mission, centered about a core satellite surrounded by various constellation satellites, many of which will be existing assets with passive microwave (PMW) radiometer systems (e.g, the DMSP and other operational satellites). However, owing to variable launch schedules, mission changes, etc., the configuration (number of satellites, orbit times, sensor types) of the GPM constellation will likely change before and during the mission, thereby impacting the quality of the combined-sensor precipitation estimates. This proposed RPC experiment is aimed at evaluating and characterizing the rainfall estimates from GPM for decision support needs in the context of earth science applications using land surface and hydrological models. Precipitation estimates are gathered from the existing passive/active satellite systems (DMSP, TRMM, Aqua, etc.) and are analyzed in a blended-satellite precipitation technique to simulate GPM-like data. The impact of various constellation configurations is examined against local rainguage and radar data, as well as against hydrological runoff models. The focus area is the Arkansas-Red River basin in the central United States during June-August 2007. Though these evaluations are focused around water management issues, they are still relevant for cross-cutting applications to address real-world problems, such as agricultural production, water resource management, flood prediction, and water supply. http://gpm.gsfc.nasa.gov/
IN41B-05
Remote Sensing and Ecosystem Modeling for Protected Area Management
Managers of U.S. national parks and international protected areas are under increasing pressure to monitor changes in park ecosystems resulting from climate and land use change within and adjacent to park boundaries. Despite great interest in these areas and the fact that some U.S. parks receive as many as 3.5 million visitors per year, U.S. and international protected areas are often sparsely instrumented, making it difficult for resource managers to quickly identify trends and changes in landscape conditions. Remote sensing and ecosystem modeling offer protected area managers important tools for monitoring of ecosystem conditions and scientifically based decision-making. These tools, however, can generate large data volumes and can require labor-intensive data processing making them difficult for protected area managers to use. To overcome these obstacles, the Terrestrial Observation and Prediction System (TOPS) is currently being applied to automate the production, analysis, and delivery of a suite of data products from NASA satellites and ecosystem models to assist managers of U.S. national parks. TOPS uses ecosystem models to combine satellite data with ground-based observations to produce nowcasts and forecasts of ecosystem conditions. We are utilizing TOPS to deliver data products via a browser-based interface to NPS resource managers in near real- time for use in landscape monitoring and operational decision-making. Current products include measures of vegetation condition, ecosystem productivity, soil moisture, snow cover, climate, and fire occurrence. The use of TOPS component models and technologies streamlines the data processing chain and automates the process of ingesting and synthesizing heterogeneous data inputs. In addition, we describe the use of TOPS to automate the identification of trends and anomalies in ecosystem conditions, enabling protected area managers to track park-wide conditions daily, identify significant changes, focus monitoring efforts, and improve decision making through infusion of NASA data. http://ecocast.arc.nasa.gov
IN41B-06
Investigating Pathways from the Earth Science Knowledge Base to Candidate Solutions
A principle objective of the NASA Applied Sciences Program is to support the transition of scientific research results into decisions which benefit society. One of the Solutions Network activities supporting this goal is the generation of Candidate Solutions derived from NASA Earth Science research results that have the potential to enhance future operational systems for societal benefit. In short, the program seeks to fill gaps between Earth Science results and operational needs. The Earth Science Knowledge Base (ESKB) is being developed to provide connectivity and deliver content for the research information needs of the NASA Applied Science Program and related scientific communities of practice. Data has been collected which will permit users to identify and analyze the current network of interactions between organizations within the community of practice, harvest research results fixed to those interactions, examine the individual components of that research, and assist in developing strategies for furthering research. The ESKB will include information about organizations that conduct NASA-funded Earth Science research, NASA research solicitations, principal investigators, research publications and other project reports, publication authors, inter-agency agreements like memoranda-of-understanding, and NASA assets, models, decision support tools, and data products employed in the course of or developed as a part of the research. The generation of candidate solutions is the first step in developing rigorously tested applications for operational use from the normal yet chaotic process of natural discovery. While the process of ‘idea generation' cannot be mechanized, the ESKB serves to provide a resource for testing theories about advancing research streams into the operational realm. Formulation Reports are the documents which outline a Candidate Solution. The reports outline the essential elements, most of which are detailed in the ESKB, which must be analyzed when assessing the value of the solution. Through developmental testing of the ESKB, several pathways to Candidate Solutions have been discovered.
IN41B-07
NASA Earth Science Research Results for Improved Regional Crop Yield Prediction
National agencies such as USDA Foreign Agricultural Service (FAS), Production Estimation and Crop Assessment Division (PECAD) work specifically to analyze and generate timely crop yield estimates that help define national as well as global food policies. The USDA/FAS/PECAD utilizes a Decision Support System (DSS) called CADRE (Crop Condition and Data Retrieval Evaluation) mainly through an automated database management system that integrates various meteorological datasets, crop and soil models, and remote sensing data; providing significant contribution to the national and international crop production estimates. The "Sinclair" soybean growth model has been used inside CADRE DSS as one of the crop models. This project uses Sinclair model (a semi-mechanistic crop growth model) for its potential to be effectively used in a geo-processing environment with remote-sensing-based inputs. The main objective of this proposed work is to verify, validate and benchmark current and future NASA earth science research results for the benefit in the operational decision making process of the PECAD/CADRE DSS. For this purpose, the NASA South American Land Data Assimilation System (SALDAS) meteorological dataset is tested for its applicability as a surrogate meteorological input in the Sinclair model meteorological input requirements. Similarly, NASA sensor MODIS products is tested for its applicability in the improvement of the crop yield prediction through improving precision of planting date estimation, plant vigor and growth monitoring. The project also analyzes simulated Visible/Infrared Imager/Radiometer Suite (VIIRS, a future NASA sensor) vegetation product for its applicability in crop growth prediction to accelerate the process of transition of VIIRS research results for the operational use of USDA/FAS/PECAD DSS. The research results will help in providing improved decision making capacity to the USDA/FAS/PECAD DSS through improved vegetation growth monitoring from high spatial and temporal resolution remote sensing datasets; improved time-series meteorological inputs required for crop growth models; and regional prediction capability through geo-processing-based yield modeling.