IN31C-01 INVITED
NASA Applied Sciences Program Rapid Prototyping Results and Conclusions
NASA's Applied Sciences Program seeks to expand the use of Earth science research results to benefit current and future operational systems tasked with making policy and management decisions. The Earth Science Division within the Science Mission Directorate sponsors over 1000 research projects annually to answer the fundamental research question: How is the Earth changing and what are the consequences for life on Earth? As research results become available, largely from satellite observations and Earth system model outputs, the Applied Sciences Program works diligently with scientists and researchers (internal and external to NASA) , and other government agency officials (USDA, EPA, CDC, DOE, US Forest Service, US Fish and Wildlife Service, DHS, USAID) to determine useful applications for these results in decision-making, ultimately benefiting society. The complexity of Earth science research results and the breadth of the Applied Sciences Program national priority areas dictate a broad scope and multiple approaches available to implement their use in decision-making. Over the past five years, the Applied Sciences Program has examined scientific and engineering practices and solicited the community for methods and steps that can lead to the enhancement of operational systems (Decision Support Systems – DSS) required for decision-making. In November 2006, the Applied Sciences Program launched an initiative aimed at demonstrating the applicability of NASA data (satellite observations, models, geophysical parameters from data archive centers) being incorporated into decision support systems and their related environments at a low cost and quick turnaround of results., i.e. designed rapid prototyping. Conceptually, an understanding of Earth science research (and results) coupled with decision-making requirements and needs leads to a demonstration (experiment) depicting enhancements or improvements to an operational decisions process through the use of NASA data. Five NASA centers (GSFC, LaRC, SSC, MSFC, ARC) participated and are currently conducting fifteen prototyping experiments covering eight of the twelve national priority applications - Energy, Coastal, Carbon, and Disaster Management; Agricultural Efficiency, Aviation, Air Quality, and Ecological Forecasting. Results from six experiments will be discussed highlighting purpose, expected results, enhancement to the decision-making process achieved, and the potential plans for future collaboration and sustainable projects.
IN31C-02
Potential of VIIRS Data for Regional Monitoring of Gypsy Moth Defoliation: Implications for Forest Threat Early Warning System
A NASA RPC (Rapid Prototyping Capability) experiment was conducted to assess the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) data for monitoring non-native gypsy moth (Lymantria dispar) defoliation of forests. This experiment compares defoliation detection products computed from simulated VIIRS and from MODIS (Moderate Resolution Imaging Spectroradiometer) time series products as potential inputs to a forest threat EWS (Early Warning System) being developed for the USFS (USDA Forest Service). Gypsy moth causes extensive defoliation of broadleaved forests in the United States and is specifically identified in the Healthy Forest Restoration Act (HFRA) of 2003. The HFRA mandates development of a national forest threat EWS. This system is being built by the USFS, and NASA is aiding integration of needed satellite data products into this system, including MODIS products. This RPC experiment enabled the MODIS follow-on, VIIRS, to be evaluated as a data source for EWS forest monitoring products. The experiment included 1) assessment of MODIS-simulated VIIRS NDVI products, and 2) evaluation of gypsy moth defoliation mapping products from MODIS-simulated VIIRS and from MODIS NDVI time series data. This experiment employed MODIS data collected over the approx. 15 million acre mid-Appalachian Highlands during the annual peak defoliation time frame (June 10 through July 27) during 2000-2006. NASA Stennis Application Research Toolbox software was used to produce MODIS-simulated VIIRS data and NASA Stennis Time Series Product Tool software was employed to process MODIS and MODIS-simulated VIIRS time series data scaled to planetary reflectance. MODIS-simulated VIIRS data was assessed through comparison to Hyperion-simulated VIIRS data using data collected during gypsy moth defoliation. Hyperion- simulated MODIS data showed a high correlation with actual MODIS data. MODIS-simulated VIIRS data for the same date showed moderately high correlation with Hyperion-simulated VIIRS data, even though the datasets were collected about a half an hour apart during changing weather conditions. MODIS products (MOD02, MOD09, and MOD13) and MOD02-simulated VIIRS time series data were used to generate defoliation mapping products based on image classification and image differencing change detection techniques. Accuracy of final defoliation mapping products was assessed by image interpretation of over 170 randomly sampled locations found on Landsat and ASTER data in conjunction with defoliation map data from the USFS. The MOD02-simulated VIIRS 400-m NDVI classification produced a similar overall accuracy to the MOD02 250-m NDVI classification. MOD02 and MOD02-simulated VIIRS data both showed promise as data sources for regional monitoring of forest disturbance due to insect defoliation.
IN31C-03
Cyberinfrastructure for Rapid Prototyping Capability
The overall goal of the NASA Rapid Prototyping Capability is to speed the evaluation of potential uses of NASA research products and technologies to improve future operational systems by reducing the time to access, configure, and assess the effectiveness of NASA products and technologies. The infrastructure to support the RPC is thus expected to provide the capability to rapidly evaluate innovative methods of linking science observations. The RPC infrastructure supports two major categories of experiments (and subsequent analysis): comparing results of a particular model as fed with data coming from different sources, and comparing different models using the data coming from the same source. In spite of being conceptually simple, two use cases in fact entail a significant technical challenge. Enabling RPC experiments requires thus a radical simplification of access to both actual and simulated data, as well as tools for data pre- and post-processing. The tools must be interoperable, allowing the user to create computational workflows with the data seamlessly transferred as needed, including third-party transfers to high-performance computing platforms. In addition, the provenance of the data must be preserved in order to document results of different what-if scenarios and to enable collaboration and data sharing between users. The functionality of the RPC splits into several independent modules such as interactive Web site, data server, tool's interfaces, or monitoring service. Each such module is implemented as an independent portlet. The RPC Portal aggregates the different contents provided by the portlets into a single interface employing a popular GridSphere portlet container. The RPC data access is based on Unidata's THREDDS Data server (TDS) extended to support, among others, interactive creation of containers for new data collections and uploading new data sets, downloading the data either to the user desktop or transferring it to a remote location using gridFTP, displaying the provenance of datasets, and invoking tools for the selected files. To enable performing experiments, RPC supports three types of tools integrated with TDS: (1) Standalone tools capable of connecting to the RPC data server to browse datasets, but otherwise performing all operations independently of the RPC infrastructure; (2) Transformations that take a dataset or a collection as an input, and output the transformed files, such as HEG, MRT, ART, and TSPT; (3) The data viewers and statistical analysis tools which do not produce new datasets.
IN31C-04
Rapid prototyping of soil moisture estimates using the NASA Land Information System
The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.
IN31C-05
Mercury- Distributed Metadata Management, Data Discovery and Access System
Mercury is a federated metadata harvesting, search and retrieval tool based on both open source and ORNL- developed software. It was originally developed for NASA, and the Mercury development consortium now includes funding from NASA, USGS, and DOE. Mercury supports various metadata standards including XML, Z39.50, FGDC, Dublin-Core, Darwin-Core, EML, and ISO-19115 (under development). Mercury provides a single portal to information contained in disparate data management systems. It collects metadata and key data from contributing project servers distributed around the world and builds a centralized index. The Mercury search interfaces then allow the users to perform simple, fielded, spatial and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury supports various projects including: ORNL DAAC, NBII, DADDI, LBA, NARSTO, CDIAC, OCEAN, I3N, IAI, ESIP and ARM. The new Mercury system is based on a Service Oriented Architecture and supports various services such as Thesaurus Service, Gazetteer Web Service and UDDI Directory Services. This system also provides various search services including: RSS, Geo-RSS, OpenSearch, Web Services and Portlets. Other features include: Filtering and dynamic sorting of search results, book-markable search results, save, retrieve, and modify search criteria. http://mercury.ornl.gov
IN31C-06
Towards More Usable and Extendable Watershed Model: an Experience to Integrate RHESSys for HydroMet Forecasting System
HydroMet is a project of RENCI (Renaissance Computing Institute) to develop a new hydrologic modeling and forecasting system. It aims to provide accurate predictions for geophysical hazards including flash floods, droughts, and fire hazard at a fine spatial resolution. For this purpose, the system needs to integrate multi- disciplinary models including weather forecasting models (WRF, Weather Research and Forecasting and LDAS, Land Data Assimilation Systems), and a distributed watershed model (RHESSys, Regional Hydro-Ecologic Simulation System). As a core component to simulate full cycles of water, carbon, and nutrients in watersheds, RHESSys is required to be re-engineered to have a highly usable and extendable architecture. To build the architecture, we restructure RHESSys as a dynamically loadable package for Python, a scripting language for rapid prototyping of new algorithms and seamless integration of external programs. The entire internal structure of RHESSys is exposed to external programming environment, which enables users and external programs to closely inspect the model's states and flexibly control its behavior. The output file format is also redesigned as platform-independent and fully-annotated binary files with intuitive data access interface. Furthermore, packages for the direct access to GIS database and uncertainty estimation on parallel computing resources are newly developed. These reconstruction efforts extend the functional capability of RHESSys as a versatile model able to assimilate and produce massive spatiotemporal data, which is a critical feature required for building the operational nowcasting and forecasting system.
IN31C-07
Supporting exploration and collaboration in scientific workflow systems
As the amount of observation data captured everyday increases, running scientific workflows will soon become a fundamental step of scientific inquiry. Current scientific workflow systems offer ways to link together data, software and computational resources, but often accomplish this by requiring a deep understanding of the system with a steep learning curve. Thus, there is a need to lower user adoption barriers for workflow systems and improve the plug-and-play functionality of these systems. We created a system that allows the user to easily create and share workflows, data and algorithms. Our goal of lowering user adoption barriers is to support discoveries and to provide means for conducting research more efficiently. Current paradigms for workflow creation focus on the visual programming using a graph based metaphor. This can be a powerful metaphor in the hands of expert users, but can become daunting when graphs become large, the steps in the graph include engineering level steps such as loading and visualizing data, and the users are not very familiar with all the possible tools available. We present a different method of workflow creation that co- exists with the standard graph based editors. The method builds on exploratory interface using a macro- recording style, and focuses on the data being analyzed during the step by step creation of the workflow. Instead of storing data in system specific data structures, the use of more flexible open standards that are platform independent would create systems that are easier to extend and that provide a simple interface for external applications to query and analyze the data and metadata produced. We have explored and implemented a system that stores workflows and related metadata using the Resource Description Framework (RDF) metadata model and that is build on top of the Tupelo data and metadata archiving system. The scientific workflow system connects to shared content repositories, where users can easily share data, workflows, algorithms and annotations. Examples of the above methodologies will be illustrated using a prototype workflow solution called Cyberintegrator and a use case scenario being developed by the Corpus Christi Bay WATERS Network test bed (a group of collaborating domain scientists from Texas and Illinois) involving monitoring, predicting and understanding of the hypoxia problem in Corpus Christi Bay.