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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.

Blankenship, Clay B.↗
NASA NTRS2014

Using Satellite Remote Sensing and Household Survey Data to Assess Human Health and Nutrition Response to Environmental Change

Climate change and degradation of ecosystem services functioning may threaten the ability of current agricultural systems to keep up with demand for adequate and inexpensive food and for clean water, waste disposal and other broader ecosystem services. Human health is likely to be affected by changes occurring across multiple geographic and time scales. Impacts range from increasing transmissibility and the range of vector-borne diseases, such as malaria and yellow fever, to undermining nutrition through deleterious impacts on food production and concomitant increases in food prices. This paper uses case studies to describe methods that make use of satellite remote sensing and Demographic and Health Survey data to better understand individual-level human health and nutrition outcomes. By bringing these diverse datasets together, the connection between environmental change and human health outcomes can be described through new research and analysis.

Health↗
NASA NTRS2020

An Update on Global Satellite-Based Precipitation Products and Services at NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is home to major NASA satellite precipitation measurement missions including the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) as well as other NASA projects. Accurate and timely available global and regional precipitation products play an important role in research and applications around the world. The GES DISC provides near-real-time, near-global precipitation products (e.g.IMERG) to support a wide variety of interdisciplinary research and operational activities including flood modeling, landslides, crop monitoring and assessment, vector-borne diseases, etc. Climate data record products (e.g.GPCP3) are essential for climate research, assessment, model evaluation and applications. To facilitate data access and exploration, the GES DISC has developed data services such asGiovanni, an online visualization and analysis tool for easy access to over 2000 satellite- and model-based variables. Established in the mid 1980s, the GES DISC also distributes data in other disciplines including hydrology, atmospheric chemistry, atmospheric dynamics, etc. In this presentation, we will present an update on global precipitation products and data services including the new IMERG V06B suite, the latest version of GPCP (Version 3) and value-added data subsetting services (L34RS, L2S).

Liu, Zhong↗
NASA NTRS2020

Space-Based Precipitation Measurements in Tropical Cyclones: Past, Present, and Future

Passive and active remote sensing of precipitation from space has led to significant advances in the understanding and prediction of tropical cyclones around the globe. This presentation will highlight the role of past NASA space-based measurements of precipitation by the Tropical Rainfall Measuring Mission (TRMM, 1998-2015), ongoing measurements by the Global Precipitation Measurement (GPM) mission (2014-current), and future measurements from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS, nominal launch date in 2020) as well as a potential new mission on Aerosols, Clouds, Convection, and Precipitation (ACCP) from the 2017 NASA Earth Science Decadal Survey. TRMM, which flew the first precipitation radar in space, provided the first systematic descriptions of the radial and azimuthal variations of rainfall in tropical cyclones around the globe and their relationship to storm motion and vertical wind shear. GPM is the lynchpin of a global constellation of precipitation satellites that provides high spatial (0.1°) and temporal (30 min) resolution real-time estimates of precipitation globally, making them essential to applications related to tropical cyclone prediction, disaster response, flood and landslide monitoring, and vector-borne disease monitoring. TROPICS will be a constellation of 6 Cubesat satellites with microwave imaging and sounding channels that will provide information on temperature and humidity in the storm environment, as well as estimates of precipitation and tropical cyclone intensity. ACCP is yet to be fully defined, but is envisioned to potentially carry multi-frequency radar with possible Doppler capability.

Braun, Scott↗
NASA NTRS2019

Global Disease Outbreaks Associated with the 2015-2016 El Niño Event

Interannual climate variability patterns associated with the El Niño-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 2015–2016 El Niño event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Niño-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and flooding occurred in excess (14–81% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.5–28% higher during years with El Niño events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p < 0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p < 0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

El Nino↗
NASA NTRS1997

Radar Monitoring of Wetlands for Malaria Control

Malaria is the most important vector-borne tropical disease (Collins and Paskewitz, 1995) and there is no simple and universally applicable form of vector control. While new methods such as malaria vaccine or genetic manipulation of mosquitoes are being explored in the laboratories, the need for more field research on malaria transmission remains very strong. For the foreseeable future many malaria programs must focus on controlling the vector, the anopheline mosquito, often under the specter of shrinking budgets. Therefore information on which human populations are at the greatest risk is especially valuable when allocating scarce resources. The goal of the Radar Monitoring of Wetlands for Malaria Control Project is to demonstrate the feasibility of using Radarsat or other comparable satellite radar imaging systems to determine where and when human populations are at greatest risk for contracting malaria. The study area is northern Belize, a region with abundant wetlands and a potentially serious malaria problem. A key aspect of this study is the analysis of multi-temporal satellite imagery to track seasonal flooding of anopheline mosquito breeding sites. Radarsat images of the test site in Belize have been acquired one to three times a month over the last year, however,, to date only one processed image has been received from the Alaska SAR Facility for analysis. Therefore analysis at this stage is focussed on determining the radar backscatter characteristics of known anopheline breeding sites, with future work to be dedicated toward seasonal changes.

Pope, Kevin O.↗
NASA NTRS2012

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Disasters Caused by Large Scale Rift Valley Fever Outbreaks

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever are directly influenced by large scale flooding associated with the El Ni o/Southern Oscillation. This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential Rift Valley fever outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.

Anyamba, Assaf↗
NASA NTRS2003

Dust Emissions, Transport, and Deposition Simulated with the NASA Finite-Volume General Circulation Model

Mineral dust aerosols have radiative impacts on Earth's atmosphere, have been implicated in local and regional air quality issues, and have been identified as vectors for transporting disease pathogens and bringing mineral nutrients to terrestrial and oceanic ecosystems. We present for the first time dust simulations using online transport and meteorological analysis in the NASA Finite-Volume General Circulation Model (FVGCM). Our dust formulation follows the formulation in the offline Georgia Institute of Technology-Goddard Global Ozone Chemistry Aerosol Radiation and Transport Model (GOCART) using a topographical source for dust emissions. We compare results of the FVGCM simulations with GOCART, as well as with in situ and remotely sensed observations. Additionally, we estimate budgets of dust emission and transport into various regions.

Colarco, Peter↗
NASA NTRS2010

Human Plague Risk: Spatial-Temporal Models

This chpater reviews the use of spatial-temporal models in identifying potential risks of plague outbreaks into the human population. Using earth observations by satellites remote sensing there has been a systematic analysis and mapping of the close coupling between the vectors of the disease and climate variability. The overall result is that incidence of plague is correlated to positive El Nino/Southem Oscillation (ENSO).

Pinzon, Jorge E.↗
NASA NTRS2014

Environmental and Socio-Demographic Determinants of Dengue Fever in Colombo City, Sri Lanka

Dengue fever has increased exponentially in Sri Lanka, from 24.4 cases per 100,000 in 2003 to 165.3 per 100,000 population in 2013. Although early warning systems using predictor models have been previously developed in other settings, it is important to develop such models in each local setting. Further, the ability of these models to be applicable at smaller geographic units will enhance current vector control and disease surveillance measures. The aim of this paper was to identify environmental and socio-economic status (SES) risk factors that may predict dengue fever at the Gram Niladhari Divisions (GND) level (smallest administrative unit) in Colombo city, Sri Lanka. These factors included landcover classes, amount of vegetation, population density, water access and neighborhood SES as determined by roof type. A geographically weighted regression (GWR) was used to develop the prediction model. A total 55 GND units covering an area of 37 sq km were investigated. We found that GND units with decreased vegetation, higher built-up area, higher population density and poor access to tap-water supply were associated with high risk of dengue; the pertinent GND units were concentrated in the center of the city. This is the first study in Sri Lanka to include both environmental and socio-demographic factors in prediction models for dengue fever. The methodology may be useful in enhancing ongoing dengue fever control measures in the country, and to be extended to other countries in the region that have an increasing incidence of dengue fever.

Tipre, Meghan↗
NASA NTRS1972

The use of remote sensors to relate biological and physical indicators to environmental and public health problems

Relationships between biological, ecological and botanical structures, and disease organisms and their vectors which might be detected and measured by remote sensing are determined. In addition to the use of trees as indicators of disease or potential disease, an attempt is made to identify environmental factors such as soil moisture and soil and water temperatures as they relate to disease or health problems and may be detected by remote sensing. The following three diseases and one major health problem are examined: Malaria, Rocky Mountain spotted fever, Encephalitis and Red Tide. It is shown that no single species of vascular plant nor any one environmental factor can be used as the indicator of disease or health problems. Entire vegetation types, successional stages and combinations of factors must be used.

Source record↗
NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗
NASA NTRS2000

Forecasting disease risk for increased epidemic preparedness in public health

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Disease Outbreaks↗
NASA NTRS2003

Satellites as Sentinels for Climate and Health

Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring, risk mapping, and surveillance of parameters useful to such problems as vector- borne and infectious diseases, air and water quality,. harmful algal blooms, W radiation, contaminant and pathogen transport in air and water, and thermal stress. Remote sensing, geographic information systems (GIs), global positioning systems (GPS), improved computation capabilities, and interdisciplinary research between the Earth and health science communities, together with local knowledge, are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global climate and health issues. These collaborative efforts are enabling increased understanding of the relationships among changes in temperature, rainfall, wind, soil moisture, solar radiation, vegetation, and the patterns of extreme weather events and health issues. This increased understanding and improved information and data sharing, in turn, empowers local health and environmental decision-makers to better predict climate-related health problems, decrease vulnerability, take preventive measures, and improve response actions. This paper provides a number of recent examples of how satellites - from their unique vantage point in space - can serve as sentinels for climate and health.

Maynard, Nancy G.↗
NASA NTRS1976

Dejection and/or prevention of human diseases through remote sensing

The use of remote sensors for the detection of probable areas of disease infestation, and possibly as a tool in the control of these diseases, is discussed. Particular attention is given to malaria, encephalitis, and Rocky Mountain Spotted Fever. The vector ecology, epidemiology, and pathogenesis of these diseases are examined. The use of remote sensors to detect the presence of Red Tide is also discussed.

Edmisten, J. A.↗