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NASA NTRS2007

Surveillance and Control of Malaria Transmission in Thailand using Remotely Sensed Meteorological and Environmental Parameters

These slides address the use of remote sensing in a public health application. Specifically, this discussion focuses on the of remote sensing to detect larval habitats to predict current and future endemicity and identify key factors that sustain or promote transmission of malaria in a targeted geographic area (Thailand). In the Malaria Modeling and Surveillance Project, which is part of the NASA Applied Sciences Public Health Applications Program, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) identification of the potential breeding sites for major vector species; 2) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity; 3) implementation of a dynamic transmission model to identify the key factors that sustain or intensify malaria transmission. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. !> Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of the remotely sensed parameters. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Discrete event simulations are used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to the Greater Mekong Subregion. Our models have been applied to malaria in Indonesia, Korea, and other regions in the world with similar success.

Kiang, Richard K.↗
NASA NTRSDate not supplied

Explore Earth Commercial Smallsat Data Acquisition (CSDA) Program

The European Space Agency (ESA), our international partner in the ESA-NASA Earth Science & Observation Joint Program Planning Group (JPPG), is hosting the VH-RODA (Very High-resolution Radar and Optical Data Assessment Workshop) at ESA/ European Space Research Institute (ERSIN) in Frascati, Italy from November 7-10, 2022. The objective of the VH-RODA workshop is to provide an open forum (for the new space, commercial and institutional space sectors) for presenting and discussing about the current status and future developments related to Earth Observation (EO) data quality, calibration and validation of space-borne very high-resolution Synthetic Aperture Radar (SAR) and Optical sensors and data products, with a dedicated focus on commercial EO data providers and related Calibration/Validation activities, synergies between optical and SAR communities, presentation of standards and best practices for data quality. Additionally, the ESA-NASA Joint Program Planning Group (JPPG) Third Party Mission component will meet to further develop non-binding practices on evaluation, identify common guard rails for comparison of data, and coordinate future schedules to leverage shared knowledge. I serve as the Project Manager for the NASA’s Commercial Smallsat Data Acquisition (CSDA) Program and will participate in the VHRODA workshop on behalf of the program.

Commercial Remote Sensing↗
NASA NTRS2009

Integration of Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health

Initial efforts to develop a deterministic model for predicting and simulating pollen release and downwind concentration to study dependencies of phenology on meteorology will be discussed. The development of a real-time, rapid response pollen release and transport system as a component of the New Mexico Environmental Public Health Tracking System (EPHTS), is based on meteorological models, NASA Earth science results (ESR), and an in-situ network of phenology cameras. The plan is to detect pollen release verified using ground based atmospheric pollen sampling within a few hours using daily MODIS daa in nearly real-time from Direct Broadcast, similar to the MODIS Rapid Response System for fire detection. As MODIS winds down, the NPOESS-VIIRS sensor will assume daily vegetation monitoring tasks. Also, advancements in geostationary satellites will allow 1km vegetation indices at 15-30 minute intervals. The pollen module in EPHTS will be used to: (1) support public health decisions for asthma and allergy alerts in New Mexico, Texas and Oklahoma; (2) augment the Centers for Disease Control and Prevention (CDC)'s Environmental Public Health Tracking Network (EPHTN); and (3) extend surveillance services to local healthcare providers subscribing to the Syndrome Reporting Information System (SYRIS). Previous studies in NASA's public health applications portfolios provide the infrastructure for this effort. The team is confident that NASA and NOAA ESR data, combined into a verified and validated dust model will yield groundbreaking results using the modified dust model to transport pollen. The growing ESR/health infrastructure is based on results from a rapid prototype scoping effort for pollen detection and simulation carried out by the principal investigators.

Luvall, Jeffrey↗
NASA NTRS2013

Collaborative Platforms Aid Emergency Decision Making

Terra. Aqua. Cloudsat. Landsat. NASA runs and partners in many missions dedicated to monitoring the Earth, and the tools used in these missions continuously return data on everything from shifts in temperature to cloud formation to pollution levels over highways. The data are of great scientific value, but they also provide information that can play a critical role in decision making during times of crisis. Real-time developments in weather, wind, ocean currents, and numerous other conditions can have a significant impact on the way disasters, both natural and human-caused, unfold. "NASA has long recognized the need to make its data from real-time sources compatible and accessible for the purposes of decision making," says Michael Goodman, who was Disasters Program manager at NASA Headquarters from 2009-2012. "There are practical applications of NASA Earth science data, and we d like to accelerate the use of those applications." One of the main obstacles standing in the way of eminently practical data is the fact that the data from different missions are collected, formatted, and stored in different ways. Combining data sets in a way that makes them useful for decision makers has proven to be a difficult task. And while the need for a collaborative platform is widely recognized, very few have successfully made it work. Dave Jones, founder and CEO of StormCenter Communications Inc., which consults with decision makers to prepare for emergencies, says that "when I talk to public authorities, they say, If I had a nickel for every time someone told me they had a common operating platform, I d be rich. But one thing we ve seen over the years is that no one has been able to give end users the ability to ingest NASA data sets and merge them with their own."

Source record↗
NASA NTRS2007

Solutions Network Formulation Report. NASA's Potential Contributions for Using Solar Ultraviolet Radiation in Conjunction with Photocatalysis for Urban Air Pollution Mitigation and Increasing Air Quality

This Candidate Solution is based on using NASA Earth science research on atmospheric ozone and aerosols data as a means to predict and evaluate the effectiveness of photocatalytically created surfaces (building materials like glass, tile and cement) for air pollution mitigation purposes. When these surfaces are exposed to near UV light, organic molecules, like air pollutants and smog precursors, will degrade into environmentally friendly compounds. U.S. EPA (Environmental Protection Agency) is responsible for forecasting daily air quality by using the Air Quality Index (AQI) that is provided by AIRNow. EPA is partnered with AIRNow and is responsible for calculating the AQI for five major air pollutants that are regulated by the Clean Air Act. In this Solution, UV irradiance data acquired from the satellite mission Aura and the OMI Surface UV algorithm will be used to help understand both the efficacy and efficiency of the photocatalytic decomposition process these surfaces facilitate, and their ability to reduce air pollutants. Prediction models that estimate photocatalytic function do not exist. NASA UV irradiance data will enable this capability, so that air quality agencies that are run by state and local officials can develop and implement programs that utilize photocatalysis for urban air pollution control and, enable them to make effective decisions about air pollution protection programs.

Underwood, Lauren↗
NASA NTRS2023

The Earth in Living Color - NASA’s Surface Biology and Geology Designated Observable

The Surface Biology and Geology (SBG) Designated Observable will transform our understanding of the global land surface, inland and coastal aquatic ecosystems through visible-to-shortwave infra-red imaging (VSWIR) spectroscopy and thermal infra-red (TIR) imaging. SBG is one of four high-priority observables recommended in the 2017 NASA Earth Science Decadal Survey t o address science questions on vegetation and aquatic ecosystem health, snow-cover dynamics, volcanic activity, and minerology. With a planned launch readiness date of 2028, SBG is currently in Pre-Phase A, with Level 1 requirements being developed for a two-spacecraft architecture, including an additional constellation pathfinder. The recommended architecture emerged from an extensive study (2018-2021) that engaged the research and applications community to consider the science questions and measurement objectives of the Decadal Survey. A Science and Applications Traceability Matrix was used as a basis for scoring candidate architectures, with inputs from four working groups that covered algorithms, applications, calibration and validation, and modeling. Two pathfinder studies, Modeling End-to-End Traceability in support of SBG (MEET-SBG) and Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) are providing pre-launch modeling tools and data for algorithm development to support science value trades. The architecture consists of one spacecraft hosting a wide-swath VSWIR imaging spectrometer providing 30-m ground-sample distance (GSD), a spectral range of 380-2500 nm (at 10 nm resolution), 16-day revisit with 400 signal-to-noise for VNIR and 250 for SWIR (at 25% reflectance). A separate spacecraft will host a wide swath thermal imager, with five to seven bands placed between 4-12 μm), with 60-m (GSD), 3- day revisit, and 0.2K noise-equivalent differential temperature (NeDT). A VNIR compact camera will be hosted on the TIR spacecraft to enable coincident TIR and VNIR observations. A constellation pathfinder will evaluate options for enabling VSWIR mission continuity using Small Sats or data buys. Partnerships with international space agencies contribute technology as well as improvements to temporal revisit. SBG, when launched, will be the first dedicated mission collecting the full spectra of the Earth’s ‘living color’ and will play a critical role in NASA’s Earth System Observatory.

David S Schimel↗
NASA NTRSDate not supplied

Validation of a Probabilistic Continuity Assessment Method for Earth Observation Portfolios

With the increased reliance on spaceborne Earth observation data among the Earth science community and other end users, it is important that efforts are made to promote data continuity for a range of parameters of interest. Continuity gaps may occur between missions measuring like parameters due to mission development delays or early termination and introduce the potential of increased uncertainty for retrieved parameters. To inform portfolio-level decisions for Earth observing missions, Ivanco et al. developed a method that enables the assessment of the probability of continuity gaps for multi-mission architectures and provides a framework to assess this probability in the context of multiple scenarios that represent possible future states of the architecture [Ivanco et al., “A Scenario-Based Approach to Assess Continuity Gaps in Earth Observations,” IEEE Aerospace Conf., 2024]. While this method was previously applied to assess continuity gaps for a specific multi-mission architecture, it had not yet been validated with historical data. This paper outlines the process of validating the probabilistic gap assessment method by utilizing data obtained from past NASA Earth Science mission formulation documents and provides a discussion of the results and takeaways from the validation. Future areas of improvement pertaining to both the modeling and simulation methodologies and underlying assumptions are identified and discussed.

Katharine Burn↗
NASA NTRS2006

Utility of MODIS Aerosol Optical Depth for Estimating PM2.5 Exposure in Environmental Public Health Surveillance

As part of the National Environmental Public Health Tracking Network (EPHTN) the National Center for Environmental Health (NCEH) at the Centers for Disease Control and Prevention (CDC) is leading a project called Health and Environment Linked for Information Exchange (HELIX-Atlanta). The goal of developing the National Environmental Public Health Tracking Network is to improve the health of communities. Currently, few systems exist at the state or national level to concurrently track many of the exposures and health effects that might be associated with environmental hazards. An additional challenge is estimating exposure to environmental hazards such as particulate matter whose aerodynamic diameter is less than or equal to 2.5 micrometers (PM(2.5)) HELIX-Atlanta's goal is to examine the feasibility of building an integrated electronic health and environmental data network in five counties of Metropolitan Atlanta, GA (Clayton, Cobb, DeKalb, Fulton, and Gwinnett counties). Under HELIX-Atlanta, pilot projects are being conducted to develop methods to characterize exposure; link health and environmental data; analyze the relationship between health and environmental factors; and communicate findings. NASA Marshall Space Flight Center (NASA/MSFC) is collaborating with CDC to combine NASA earth science satellite observations related to air quality and environmental monitoring data to model surface estimates of PM(2.5) concentrations that can be linked with clinic visits for asthma. From 1999-2000 there were over 9,400 hospitalizations per year in Georgia with asthma as the primary diagnosis. The majority of these hospitalizations occurred in medical facilities in the five most populous Metro-Atlanta counties. Hospital charges resulting from asthma in Georgia are approximately $59 million dollars annually. There is evidence in the research literature that asthmatic persons are at increased risk of developing asthma exacerbations with exposure to environmental factors, including PM(2.5). Thus, HELIX-Atlanta is focusing on methods for characterizing population exposure to PM(2.5) for the Atlanta metropolitan area that could be used in on-going surveillance. While use of the Air Quality System, (AQS) PM(2.5) data alone could meet HELIX Atlanta, specifications, there are only five AQS sites in the Atlanta area, thus the spatial coverage is not ideal. Also, the AQS ground observations are made at time intervals ranging from one hour to six days leaving some temporal gaps. NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Depth (AOD) data have the potential for estimating daily ground level PM(2.5) at 10 km resolution over the metropolitan Atlanta area supplementing the AQS ground observations and filling their spatial and temporal gaps.

Al-Hamdan, Mohammad↗
NASA NTRS2006

UAVSAR: A New NASA Airborne SAR System for Science and Technology Research

NASA's Jet Propulsion Laboratory is currently building a reconfigurable, polarimetric L-band synthetic aperture radar (SAR), specifically designed to acquire airborne repeat track SAR data for differential interferometric measurements. Differentian interferometry can provide key deformation measurements, important for studies of earthquakes, volcanoes and other dynamically changing phenomena. Using precision real-time GPS and a sensor controlled flight management system, the system will be able to fly predefined paths with great precision. The expected performance of the flight control system will constrain the flight path to be within a 10 m diameter tube about the desired flight track. The radar will be designed to be operable on a UAV (Unpiloted Aria1 Vehicle) but will initially be demonstrated on a NASA Gulfstream III. The radar will be fully polarimetric, with a range bandwidth of 80 MHz (2 m range resolution), and will support a 16 km range swath. The antenna will be electronically steered along track to assure that the antenna beam can be directed independently, regardless of the wind direction and speed. Other features supported by the antenna include elevation monopulse and pulse-to-pulse re-steering capabilities that will enable some novel modes of operation. The system will nominally operate at 45,000 ft (13800 m). The program began as an Instrument Incubator Project (IIP) funded by NASA Earth Science and Technology Office (ESTO).

synthetic aperture radar (SAR)↗
NASA NTRS2019

NASA Studies the Earth from the Philippines

The Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) is a NASA/NRL mission studying clouds and aerosols with the NASA P-3B aircraft, a Learet and an ocean research vessel. The mission is based in the Philippines from late August through early October 2019. This presentation is aimed at 8-12th grade students in the Philippines for education/outreach purposes to expose students to NASA Earth and Airborne Science research, the CAMP2Ex mission, and careers in science, technology, engineering and math (STEM).The presentation starts with an overview of NASA Earth science, including discussing how and why NASA studies the earth using satellites, airplanes, and sensors on the ground. We discuss the NASA Airborne Science Program and how NASA uses airplanes to study the Earth from the equator to the poles. We discuss the CAMP2Ex mission specifically, including mission science goals and why we are based in the Philippines. We will discuss basic cloud physics and remote sensing using demonstrations and activities. Finally, we discuss careers in STEM and at NASA, giving personal examples of the career paths of scientists, engineers and pilots on the CAMP2Ex mission.

Schaller, Emily L.↗
NASA NTRSDate not supplied

TOLNet’s FAIR Journey: Yesterday, Today, and Tomorrow

The Tropospheric Ozone Lidar Network (TOLNet) has generated over a decade of ozone vertical profile data products over North America and contributed to several air quality focused field studies. The science value of the TOLNet data has been demonstrated in numerous peer-reviewed publications on air quality and ozone relevant research. As the broad scientific community has moved towards adopting FAIR Principles to make data more findable, accessible, interoperable, and (re)usable, the TOLNet team has been consistently making data more FAIR. This effort has many challenges, partially reflecting on the FAIR principles being domain agnostic while the implementation needs to be domain specific. The FAIR principles declare the dependence on the community standards, domain-relevant metadata, and rich metadata. This presentation uses the TOLNet data and data system as an example to explore the best practices to implement FAIR principle. Particularly, we will examine the metadata and the “richness” to support findability and usability as well as machine-to-machine actionability via API. Last year, as part of our FAIR journey, we launched the TOLNet website (https://tolnet.larc.nasa.gov/) and the API (https://tolnet.larc.nasa.gov/api/). Part of this process included extracting and cataloging metadata across the entire TOLNet mission timeframe. This enabled users to search through the mission by various metadata criteria, improving the findability and accessibility. And computers could connect directly to the TOLNet API to extract both metadata and data, providing a level of interoperability never present before for TOLNet data. On top of that, all new TOLNet data is now automatically validated using the API to ensure it complies with GEOMS standards, aiding in reusability. It takes both technology and scientists working together to make progress. The next step is to evaluate the current TOLNet offerings against NASA’s Practical Guide for Open, Free & FAIR NASA Earth Science Data Products (https://doi.org/10.5067/DOC/ESCO/ESDSWG-0002V1).

TOLNet↗
NASA NTRS2014

Drought Prediction for Socio-Cultural Stability Project

The primary objective of this project is to answer the question: "Can existing, linked infrastructures be used to predict the onset of drought months in advance?" Based on our work, the answer to this question is "yes" with the qualifiers that skill depends on both lead-time and location, and especially with the associated teleconnections (e.g., ENSO, Indian Ocean Dipole) active in a given region season. As part of this work, we successfully developed a prototype drought early warning system based on existing/mature NASA Earth science components including the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) forecasting model, the Land Information System (LIS) land data assimilation software framework, the Catchment Land Surface Model (CLSM), remotely sensed terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) and remotely sensed soil moisture products from the Aqua/Advanced Microwave Scanning Radiometer - EOS (AMSR-E). We focused on a single drought year - 2011 - during which major agricultural droughts occurred with devastating impacts in the Texas-Mexico region of North America (TEXMEX) and the Horn of Africa (HOA). Our results demonstrate that GEOS-5 precipitation forecasts show skill globally at 1-month lead, and can show up to 3 months skill regionally in the TEXMEX and HOA areas. Our results also demonstrate that the CLSM soil moisture percentiles are a goof indicator of drought, as compared to the North American Drought Monitor of TEXMEX and a combination of Famine Early Warning Systems Network (FEWS NET) data and Moderate Resolution Imaging Spectrometer (MODIS)'s Normalizing Difference Vegetation Index (NDVI) anomalies over HOA. The data assimilation experiments produced mixed results. GRACE terrestrial water storage (TWS) assimilation was found to significantly improve soil moisture and evapotransportation, as well as drought monitoring via soil moisture percentiles, while AMSR-E soil moisture assimilation produced marginal benefits. We carried out 1-3 month lead-time forecast experiments using GEOS-5 forecasts as input to LIS/CLSM. Based on these forecast experiments, we find that the expected skill in GEOS-5 forecasts from 1-3 months is present in the soil moisture percentiles used to indicate drought. In the case of the HOA drought, the failure of the long rains in April appears in the February 1, March 1 and April 1 initialized forecasts, suggesting that for this case, drought forecasting would have provided some advance warning about the drought conditions observed in 2011. Three key recommendations for follow-up work include: (1) carry out a comprehensive analysis of droughts observed over the entire period of record for GEOS-5 forecasts; (2) continue to analyze the GEOS-5 forecasts in HOA stratifying by anomalies in long and short rains; and (3) continue to include GRACE TWS, Soil Moisture/Ocean Salinity (SMOS) and the upcoming NASA Soil Moisture Active/Passive (SMAP) soil moisture products in a routine activity building on this prototype to further quantify the benefits for drought assessment and prediction.

Peters-Lidard, Christa↗
NASA NTRS2003

Transition from Research to Operations: Assessing Value of Experimental Forecast Products within the NWSFO Environment

The NASA Short-term Prediction Research and Transition (SPoRT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NWS forecast operations and decision-making. To meet long-term program expectations, it is not sufficient simply to give forecasters sophisticated workstations or new forecast products without fully assessing the ways in which they will be utilized. Close communication must be established between the research and operational communities so that developers have a complete understanding of user needs. In turn, forecasters must obtain a more comprehensive knowledge of the modeling and sensing tools available to them. A major goal of the SPoRT Program is to develop metrics and conduct assessment studies with NWS forecasters to evaluate the impacts and benefits of ESE experimental products on forecast skill. At a glance the task seems relatively straightforward. However, performing assessment of experimental products in an operational environment is demanding. Given the tremendous time constraints placed on NWS forecasters, it is imperative that forecaster input be obtained in a concise unobtrusive manor. Great care must also be taken to ensure that forecasters understand their participation will eventually benefit them and WFO operations in general. Two requirements of the assessment plan developed under the SPoRT activity are that it 1) Can be implemented within the WFO environment; and 2) Provide tangible results for BOTH the research and operational communities. Supplemental numerical quantitative precipitation forecasts (QPF) were chosen as the first experimental SPoRT product to be evaluated during a Pilot Assessment Program conducted 1 May 2003 within the Huntsville AL National Weather Service Forecast Office. Forecast time periods were broken up into six- hour bins ranging from zero to twenty-four hours. Data were made available for display in AWIPS on an operational basis so they could be efficiently incorporated into the forecast process. The methodology used to assess the value of experimental QPFs compared to available operational products is best described as a three-tier approach involving both forecasters and research scientists. Tier-one is a web-based survey completed by duty forecasters on the aviation and public desks. The survey compiles information on how the experimental product was used in the forecast decision making process. Up to 6 responses per twenty-four hours can be compiled during a precipitation event. Tier-two consists of an event post mortem and experimental product assessment performed daily by the NASA/NWS Liaison. Tier-three is a detailed breakdown/analysis of specific events targeted by either the NWS SO0 or SPoRT team members. The task is performed by both NWS and NASA research scientists and may be conducted once every couple of months. The findings from the Pilot Assessment Program will be reported at the meeting.

Lapenta, William M.↗
NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet­ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi­ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗
NASA NTRS1989

A segmented mirror antenna for radiometers

An antenna is designed for the radiometer application of the planned NASA Earth Science Geostationary Platforms in the 1990's. The antenna consists of two parts: a regular parabolic dish of 5 meters in diameter which converts the radiation from feeds into a collimated beam, and a movable mirror that redirects the beam to a prescribed scan direction. The mirror is composed of 28 segmented planar conducting plates, mostly one square meter in size. The secondary pattern of the antenna was analyzed based on a physical optics analysis. For frequencies between 50 and 230 GHz, and for a scan range of + or -8 deg (270 beamwidths scan at 230 GHz), the worst calculated beam efficiency is 95 percent. To cover such a wide frequency and scan range, each of the 28 plates is individually controlled for a tilting less than 4 deg, and for a sliding less than 0.5 cm. The sliding is done at discrete steps. At 230 GHz, a step size of 2 mil is sufficient. The plate positions must be reset for each frequency and for each scan direction. Once the position is set, the frequency bandwidth of the antenna is very narrow.

Lee, S. W.↗
NASA NTRS1991

Need for expanded environmental measurement capabilities in geosynchronous Earth orbit

The proliferation of environmental satellites in low altitude earth orbit (LEO) has demonstrated the usefulness of earth remote sensing from space. As use of the technology grows, the limitations of LEO missions become more apparent. Many inadequacies can be met by remote sensing from geosynchronous earth orbits (GEO) that can provide high temporal resolution, consistent viewing of specific earth targets, long sensing dwell times with varying sun angles, stereoscopic coverage, and correlative measurements with ground and LEO observations. An environmental platform in GEO is being studied by NASA. Small research satellite missions in GEO were studied (1990) at GSFC. Some recent independent assessments of NASA Earth Science Programs recommend accelerating the earlier deployment of smaller missions.

Mercanti, Enrico P.↗
NASA NTRS1992

Need for expanded environmental measurement capabilities in geosynchronous earth orbit

The proliferation of environmental satellites in low altitude earth orbit (LEO) has demonstrated the usefulness of earth remote sensing from space. As use of the technology grows, the limitations of LEO missions become more apparent. Many inadequacies can be met by remote sensing from geosynchronous earth orbits (GEO) that can provide high temporal resolution, consistent viewing of specific earth targets, long sensing dwell times with varying sun angles, stereoscopic coverage, and correlative measurements with ground and LEO observations. An environmental platform in GEO is being studied by NASA. Small research satellite missions in GEO were studied (1990) at GSFC. Some recent independent assessments of NASA Earth Science Programs recommend accelerating the earlier deployment of smaller missions.

Mercanti, Enrico P.↗
NASA NTRS1994

A statistical inference approach for the retrieval of the atmospheric ozone profile from simulated satellite measurements of solar backscattered ultraviolet radiation

NASA's Mission to Planet Earth (MTPE) will address important interdisciplinary and environmental issues such as global warming, ozone depletion, deforestation, acid rain, and the like with its long term satellite observations of the Earth and with its comprehensive Data and Information System. Extensive sets of satellite observations supporting MTPE will be provided by the Earth Observing System (EOS), while more specific process related observations will be provided by smaller Earth Probes. MTPE will use data from ground and airborne scientific investigations to supplement and validate the global observations obtained from satellite imagery, while the EOS satellites will support interdisciplinary research and model development. This is important for understanding the processes that control the global environment and for improving the prediction of events. In this paper we illustrate the potential for powerful artificial intelligence (AI) techniques when used in the analysis of the formidable problems that exist in the NASA Earth Science programs and of those to be encountered in the future MTPE and EOS programs. These techniques, based on the logical and probabilistic reasoning aspects of plausible inference, strongly emphasize the synergetic relation between data and information. As such, they are ideally suited for the analysis of the massive data streams to be provided by both MTPE and EOS. To demonstrate this, we address both the satellite imagery and model enhancement issues for the problem of ozone profile retrieval through a method based on plausible scientific inferencing. Since in the retrieval problem, the atmospheric ozone profile that is consistent with a given set of measured radiances may not be unique, an optimum statistical method is used to estimate a 'best' profile solution from the radiances and from additional a priori information.

Bonavito, N. L.↗