IN21A-0069
Improving the EOS MLS Science Data Processing
The EOS Microwave Limb Sounder (MLS) is one of 4 instruments on the NASA Aura satellite launched 15 July 2004. It includes in its measurement suite simultaneous global measurement of vertical profiles of several atmospheric chemical constituents (O3, HCl, ClO, HOCl, BrO, OH, H2O, HO2, HNO3, N2O, CO, HCN, CH3CN, volcanic SO2), cloud ice, geopotential height, and temperature. The MLS Science Investigator-led Processing System (SIPS) produces calibrated radiances (Level 1B), validated sets of the above profiles (Level 2), daily and monthly gridded maps (Level 3), and daily and monthly Zonal Means (Level 3). This paper presents an overview and latest developments in the EOS MLS data processing.
IN21A-0070
Near-Real-Time Processing Plans for Aura MLS Data for Use in Data Assimilation
The Microwave Limb Sounder (MLS) aboard the Aura satellite, launched July 15, 2004, has already obtained 3 years of daily global atmospheric data. The dataset includes temperature, geopotential height, and a number of constituents such as O3, H2O, CO, N2O, HNO3, HCl, ClO, BrO, OH, HO2, with retrievals spanning the upper troposphere to the mesosphere (or higher) in some cases. Close to 3500 profiles are retrieved (per product) each day; ice water content (IWC) and ice water path (IWP) information is also obtained. A significant amount of these 3 years of data has been reprocessed using the version 2.2 algorithms (the 2nd public release of MLS data). Several groups have already done experiments using MLS data, especially O3 and temperature, in assimilation The MLS team is working on a fast but accurate production stream for a select number of products (mainly T, O3, and H2O), in order to enable near-real-time processing and data assimilation using some of the MLS capabilities. We discuss the plans for creating such a data stream from MLS and early test results using a preliminary subset as an example. http://mls.jpl.nasa.gov
IN21A-0071
Open-source Peer-to-Peer Environment to Enable Sensor Web Architecture: Application to Geomagnetic Observations and Modeling
A flexible, dynamic, and reliable secure peer-to-peer (P2P) communication environment is under development at NASA's Goddard Space Flight Center (GSFC). Popular open-source P2P software technology provides a self- organizing, self-healing ad hoc "virtual network overlay" protocol-suite. The current effort builds a proof-of-concept geomagnetic Sensor Web upon this foundation. Our long-term objective is to enable an evolution of many types of distributed Earth system sensors and related processing/storage components into elements of an operational Sensor Web via integration into this P2P Environment. In general, the Environment distributes data communication tasks among the sensors (viewed as peers, each assigned a peer-role) and controls the flow of data. This work encompasses dynamic discovery, monitoring, control, and configuration as well as autonomous operations, real-time modeling and data processing, and secure ubiquitous communications. We currently restrict our communications to be within the secure GSFC network environment, and have integrated "simulated" (via historical data) geomagnetic sensors. Each remote sensor has operating modes to manage (from remote interfaces) and is designed to have features nearly indistinguishable from a live magnetometer. We have implemented basic identity management features (organized around GSFC identity-management practices); providing mechanisms which restrict data-serving privileges to authorized users, and which allow improved trust and accountability among users of the Environment. Data-serving peers digitally "sign" their services, and their data-browsing counterparts will only accept the products of services whose signature (and hence identity) can be verified. The current usage scenario involves modeling-peers, which operate within the same Environment as the sensors and also have operating modes to remotely manage, portraying a near-real- time global representation of geomagnetic activity from dynamic sensor-reported values. Remote "browsing" peers access these modeling-run results within the Environment, but also have the option to access the sensors directly. We expect that this preparatory work will benefit the LWS/Geospace program, as real-time geomagnetic observations are relevant to Sun-Earth Connection studies.
IN21A-0072
Progress in Developing a Multiangle SpectroPolarimetric Imager (MSPI) for Aerosol Remote Sensing from Space
The National Research Council's Earth Sciences Decadal Survey identifies a multiangle, multispectral, high- accuracy polarization imager as one component of its notional Aerosol-Cloud-Ecosytem (ACE) mission. Under NASA's Instrument Incubator Program (IIP) and internal JPL funding, we have been developing a candidate instrument approach, the Multiangle SpectroPolarimetric Imager (MSPI). The MSPI architecture is conceptually similar to the Terra Multi-angle Imaging SpectroRadiometer (MISR), but the new camera design incorporates features of other aerosol instruments by extending the spectral range to the ultraviolet and shortwave infrared, increasing the image swath to achieve more rapid global coverage, and adding high-accuracy polarimetry in selected spectral bands. UV intensity observations are sensitive to aerosol absorption and height; the longer wavelengths provide improved particle size discrimination; and multiangle acquisition provides sensitivity to particle shape and helps separate aerosol backscatter and extinction from surface reflectance. The 0.5% DOLP uncertainty specification allows for the simultaneous retrieval of aerosol optical depth and particle size when combined with accurate radiance measurements, and provides sensitivity to the real part of the aerosol refractive index, thus providing unique information related to particle composition. Many factors can affect polarimetric accuracy for an imager, including polarization sensitivity of the optics, gain differences among the different detectors whose signals are combined to measure polarization, and spatial displacements on the ground of the locations where different polarization orientations are measured. The MSPI camera design deals with these issues by: (a) using a reflective optical design with optimized mirror coatings to minimize instrument-induced polarization, (b) introducing a rapid, time-variable retardance into the optical path, which has the effect of modulating the polarized component of the incoming light so that DOLP is recovered using relative measurements from each detector, and (c) accounting for scene gradients in the measurement approach. We report on experimental and theoretical results that establish detailed camera design requirements, the status of enabling technology developments, and progress in constructing and testing a prototype spectropolarimetric camera.
IN21A-0073
Framework for Producing Ecological Nowcasts
TOPS is a data and modeling software system designed to seamlessly integrate data from satellite, aircraft and ground sensors, and weather/climate models with application models to quickly and reliably produce operational nowcasts and forecasts of ecological conditions. Through automation of the data retrieval, pre-processing, integration, and modeling steps, TOPS is able to reliably provide data on current and predicted ecosystem conditions, allowing TOPS data products to be used in an operational setting for a range of applications. The core of the system is located and maintained by the Ecocast group at NASA Ames Research Center. We have develop a layered approach where at the lowest level the system interacts with the numerous data providers such as the Distributed Active Archive Centers (DAAC), the National Center for Environmental Predictions (NCEP), the National Weather Service (NWS), NOAA, and others. This subsystem is fully autonomous, gathering data through periodic query to the requesting centers sometimes as often as every 15-minutes. Subsets of our input datasets are also available by subscriptions and are being pushed to us as they become available (Oregon State University direct broadcast feed is an example of such interaction). When the data are obtained, they are archived in the database and then further processed to fit the application needs. Due to significant differences in climate and satellite data processing, we have two separate subsystems to accomplish the task. On the climate side, we merge ground station data with model data and grid these to required resolution together with generation of basic statistics and QA/QC information. The satellite processing subsystem performs spatial data tiling and subsetting, as well as composition of data sets based on the QA/QC information supplied, in order to obtain the highest quality inputs. After the climate and satellite data are processed, they are inserted into a database. The model framework contains the models that are currently integrated into the system as well as a set of utilities that access, pre-process and post-process the inputs and outputs of these models. The individual components are accessible through the Java Distributed Application Framework (JDAF), which provides a unified view of the system and its capabilities and enables easier data, model and utility integration. JDAF can also be used to build applications by combining together existing components in two main ways - using java's Remote Method Invocation (RMI), and through SOAP and Web Services mechanisms. http://ecocast.arc.nasa.gov
IN21A-0074
Prototype of an Integrated Hurricane Information System for Research: Design and Implementation of the Database and Web Portal
Many hurricane websites provide historical hurricane information and real-time storm tracking. These sites often include images from various remote-sensing satellite sensors with such atmospheric and oceanic quantities as wind, temperature, rain, and water vapor. However, it has been determined that the hurricane analysis community is lacking a web portal that provides researchers a comprehensive set of observed hurricane parameters (both graphics and data) together with large-scale and convection-resolving model output. We have developed a prototype of an integrated hurricane information system of high-resolution satellite and in- situ observations along with model outputs pertaining to: i) the thermodynamic and microphysical structure of storms; ii) the air-sea interaction processes; iii) the larger-scale environment as depicted by quantities such as SST, ocean heat content and the aerosol loading of the environment. Our goal is to provide a one-stop place to access all the available information of a specific hurricane for researchers to advance the understanding, modeling and predication of hurricane genesis and intensity changes. Our hurricane information system prototype consists of high-resolution satellite data measuring three- dimensional atmospheric and oceanic parameters that includes observations from AIRS, MISR, MODIS, CloudSAT, AMSR-E, TRMM, GOES, MLS, QuikSCAT, SeaWiFS, and COSMIC GPS, in-situ observations such as ARGO floats, large scale data assimilation products from NCEP, and high resolution hurricane model output from WRF. High-resolution satellite data are sub-setted within 2000-kilometer-square area centered at the closest storm location and large-scale environmental datasets are divided into 6 predefined geographical regions. When accessing this hurricane portal, users may browse through data by year, region, category, and hurricane. At the front page, we show the hurricane track using Google Map. Users may pan and zoom, or click on the track location placemark to see the time, location, and the intensity of the hurricane. Large scale datasets, such as SST or aerosol optical depth can be overlaid on top of the hurricane track in Google Map. In addition, available satellite and in-situ data during the hurricane period are displayed as little bars in a time line organized by datasets. When clicking a little bar, pre-generated plots for the selected dataset will be displayed in a separate window together with all other datasets co-located around the same time. The raw data in user-specified format can be downloaded for further analysis or model integration. As for the 3D model data, Live Access Server (LAS) is used to provide custom subsets and on-the-fly visualization. The site is dynamically configured using a backend relational database that is designed to let users easily browse through the website to find data and plots that are pertinent to their research. In this presentation, we will describe the current status of the integrated hurricane information system prototype, the design and the implementation of the hurricane database and portal, and future enhancements.
IN21A-0075
A Novel Hydro-information System for Improving NOAA AWIPS DSS for Disaster Management
The U.S. National Weather Service (NWS) carries out its mission of protecting life and property from water-related disasters through improving forecast skills. The Advanced Weather Interactive Processing System (AWIPS), implemented by NWS in the 1990s, is employed to support its decision making in issuing forecasts, watches, and warnings for water-related disasters. A sub-Decision Support System (DSS) of the AWIPS is the NWS River Forecast System (NWSRFS), which is used to provide forecasts of floods and droughts. In this study, we focus on developing a novel hydro-information system to improve the forecast accuracy of the NWSRFS. In particular, our system will allow the automated flow of surface soil moisture satellite data from the Goddard Earth Sciences Data Information and Services Center (GES DISC) into NWSRFS through an extension of the Hydrological Integrated Data Environment (HIDE) system. A spatial data assimilation framework, together with the NOAH model, will then be employed to assimilate improved evapotranspiration data to be inputted into the NWSRFS to thus improve the behavior of the NWSRFS in its forecasting skills, especially for droughts, and for disaster management. Initial framework and investigations from this study will be presented and discussed.
IN21A-0076
The Swath Imaging Multi-polarization Photon-counting Lidar (SIMPL): A Pathfinder for the LIDAR Surface Topography (LIST) Mission
The Swath Imaging Multi-polarization Photon-counting Lidar (SIMPL) is an airborne prototype in development to demonstrate laser altimetry measurement methods and components that enable efficient, high-resolution, swath mapping of topography and surface properties from space. This demonstration is advancing technologies that are applicable to the global elevation mapping objectives (5 m spatial resolution, 10 cm vertical precision) of the LIDAR Surface Topography (LIST) mission recommended by the National Research Council in the Earth Science Decadal Survey report to NASA and NOAA. The main focus of this instrument development, sponsored by the NASA Earth Science and Technology Office Instrument Incubator Program, is to demonstrate an approach for detailed monitoring of ice sheet, sea ice and glacier change from a spacecraft in low Earth orbit. Although it currently emphasizes polar-region cryosphere objectives, the SIMPL approach is also applicable in other applications including measuring changes in land topography, forest height and structure, and inland water and snow cover height and extent. SIMPL employs a short-pulse (1 nsec) fiber laser transmitters operating at 1064 nm and 532 nm, a beam splitter to divide the energy into four parallel beams displaced cross-track, single photon counting modules (SPCM) detectors, and high precision timing electronics to achieve < 15 cm range precision per single detected photon. Measurement of the backscatter energy with polarization parallel and perpendicular to the laser transmit pulse provides the depolarization ratio of the surface returns at 532 and 1064 nm, in order to differentiate surface types based on their scattering properties. Results of laboratory testing of a single beam breadboard and the design and implementation of the four-beam flight instrument will be described.
IN21A-0077
Enabling the dynamic coupling between sensor web and Earth system models - The Self-Adaptive Earth Predictive Systems (SEPS) framework
The self-adaptation concept is the central piece of the control theory widely and successfully used in engineering and military systems. Such a system contains a predictor and a measurer. The predictor takes initial condition and makes an initial prediction and the measurer then measures the state of a real world phenomenon. A feedback mechanism is built in that automatically feeds the measurement back to the predictor. The predictor takes the measurement against the prediction to calculate the prediction error and adjust its internal state based on the error. Thus, the predictor learns from the error and makes a more accurate prediction in the next step. By adopting the self-adaptation concept, we proposed the Self-adaptive Earth Predictive System (SEPS) concept for enabling the dynamic coupling between the sensor web and the Earth system models. The concept treats Earth System Models (ESM) and Earth Observations (EO) as integral components of the SEPS coupled by the SEPS framework. EO measures the Earth system state while ESM predicts the evolution of the state. A feedback mechanism processes EO measurements and feeds them into ESM during model runs or as initial conditions. A feed-forward mechanism analyzes the ESM predictions against science goals for scheduling optimized/targeted observations. The SEPS framework automates the Feedback and Feed-forward mechanisms (the FF-loop). Based on open consensus-based standards, a general SEPS framework can be developed for supporting the dynamic, interoperable coupling between ESMs and EO. Such a framework can support the plug-in-and-play capability of both ESMs and diverse sensors and data systems as long as they support the standard interfaces. This presentation discusses the SEPS concept, the service-oriented architecture (SOA) of SEPS framework, standards of choices for the framework, and the implementation. The presentation also presents examples of SEPS to demonstrate dynamic, interoperable, and live coupling of ESM with the sensor web enabled by the framework.
IN21A-0078
Detection of regional events using streaming GPS
In recent years, GPS measurements of surface displacement have proved critical to understanding earthquake fault systems. As well as measuring the deformation resulting from tectonic processes and seismic events, there has been increasing evidence that GPS sensors are capable of detecting signals associated with "slow earthquakes" and stress transfer between faults. Our hypothesis is that there may be subtle signals of these processes that have remained undetected at the level of individual sensors but can be detected by using the aggregated information from multiple sensors. To this end, we present a method for detecting regional events that combines statistical analysis of individual sensor measurements to detect signals within a region of interest covered by a GPS network or sub-network. This method is applicable not only to archived data but also to real-time streaming data, and so offers the possibility of being used in alert-type service. In our approach, time series from individual sensors is first segmented into discrete modes according to statistical properties of the data. This is accomplished using an algorithm for robust, unconstrained fitting of hidden Markov models (HMMs); tests indicate that this significantly method outperforms standard approaches for a variety of data types, including GPS time series. The resulting segmented time series from individual stations are then compared to one another and incidences of correlated mode changes between sensors are tallied. A significant signal at a particular time is indicated by a high number of correlated mode changes between network members. This technique has been integrated into a web portal/web services environment that facilitates the simultaneous analysis of data from multiple GPS stations, provides a web-baed interface to the method, and direct access to streaming real-time as well as archived GPS data sources. We demonstrate our approach using GPS position data provided by the Scripps Orbit and Permanent Array Center (SOPAC). A map-based visualization interface allows for use as a interactive analysis tool.
IN21A-0079
Aluminum Manganese Transition Edge Sensors for use in Earth and Solar Sciences.
By working at extremely low (< 0.5K) temperatures, Transition Edge Sensors (TES) achieve unparalleled sensitivity as X-ray detectors. They may also be used extremely sensitive bolometers at infrared, submm or terahertz wavelengths. We have developed new TES technology which should reduce their noise even further and simplify their operation and fabrication, providing a robust instrument for solar physics, astronomy, and earth sciences.
IN21A-0080
Development of a Transportable Gravity Gradiometer Based on Atom Interferometry
JPL is developing a transportable gravity gradiometer based on light-pulse atom interferometers for NASA's Earth Science Technology Office's Instrument Incubator Program. The inertial sensors in this instrument employ a quantum interference measurement technique, analogous to the precise phase measurements in atomic clocks, which offers increased sensitivity and improved long-term stability over traditional mechanical devices. We report on the implementation of this technique in JPL's gravity gradiometer, and on the current performance of the mobile instrument. We also discuss the prospects for satellite-based gravity field mapping, including high-resolution monitoring of time-varying fields from a single satellite platform and multi-component measurements of the gravitational gradient tensor, using atom interferometer-based instruments.