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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

DoD-GEIS Rift Valley Fever Monitoring and Prediction System as a Tool for Defense and US Diplomacy

Over the last 10 years the Armed Forces Health Surveillance Center's Global Emerging Infections Surveillance and Response System (GEIS) partnering with NASA'S Goddard Space Flight Center and USDA's USDA-Center for Medical, Agricultural & Veterinary Entomology established and have operated the Rift Valley fever Monitoring and Prediction System to monitor, predict and assess the risk of Rift Valley fever outbreaks and other vector-borne diseases over Africa and the Middle East. This system is built on legacy DoD basic research conducted by Walter Reed Army Institute of Research overseas laboratory (US Army Medical Research Unit-Kenya) and the operational satellite environmental monitoring by NASA GSFC. Over the last 10 years of operation the system has predicted outbreaks of Rift Valley fever in the Horn of Africa, Sudan, South Africa and Mauritania. The ability to predict an outbreak several months before it occurs provides early warning to protect deployed forces, enhance public health in concerned countries and is a valuable tool use.d by the State Department in US Diplomacy. At the international level the system has been used by the Food and Agricultural Organization (FAD) and the World Health Organization (WHO) to support their monitoring, surveillance and response programs in the livestock sector and human health. This project is a successful testament of leveraging resources of different federal agencies to achieve objectives of force health protection, health and diplomacy.

Anyamba, Assaf↗
NASA NTRS2012

Use of the Earth Observing One (EO-1) Satellite for the Namibia SensorWeb Flood Early Warning Pilot

The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with a messaging architecture and web services. Disasters are the perfect arena to use SensorWebs. One SensorWeb pilot project that has been active since 2009 is the Namibia Early Flood Warning SensorWeb pilot project. The Pilot Project was established under the auspices of the Namibian Ministry of Agriculture Water and Forestry (MAWF)/Department of Water Affairs, the Committee on Earth Observing Satellites (CEOS)/Working Group on Information Systems and Services (WGISS) and moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort began by identifying and prototyping technologies which enabled the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management and water-borne disease management. This was followed by an international collaboration to build small portions of the identified system which was prototyped during that past few years during the flood seasons which occurred in the February through May timeframe of 2010 and 2011 with further prototyping to occur in 2012. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team also began to examine the socioeconomic component to determine the impact of the SensorWeb technology and how best to assist in the infusion of this technology in lesser affluent areas with low levels of basic infrastructure. This paper provides an overview of these efforts, highlighting the EO-1 usage in this SensorWeb.

Mandl, Daniel↗
DOE OSTI2025

AI Improves the Accuracy, Reliability, and Economic Value of Continental‐Scale Flood Predictions

Accurate flood early warnings are critical to minimize damage and loss of life. Current large‐scale operational forecasting systems, however, have limited accuracy, description of uncertainty, and computational efficiency. While Artificial intelligence (AI) can address these limitations in principle, the accuracy and reliability of AI forecasts have thus far proven insufficient. Here we present a novel hybrid framework that integrates AI‐based machinery termed Errorcastnet (ECN) with the National Water Model (NWM) to showcase the potential of ensemble AI flood forecasts over the contiguous U.S. ECN boosts prediction accuracy four‐ to six‐fold across lead times of 1–10 days, while providing uncertainty quantification. It also outperforms Google's state‐of‐the‐art global AI model. ECN‐based forecasts offer superior economic value (up to four‐fold) for decision‐making as compared to those from NWM alone. ECN performs well in varied ecoregions, physiography, and land management conditions. The framework is computationally efficient, enabling national‐scale ensemble forecasts in minutes.

artificial intelligence↗
NASA NTRS2000

LIS Validation at The KSC-ER

Each year, thousands of lightning electric field disturbances are recorded and archived by the ground-based field mill (FM) network at the NASA Kennedy Space Center (KSC) and USAF Eastern Range (ER). The FM network has a range of several tens of kilometers, and a digital accuracy of 4 V/m. It has provided years of continuous lightning warning surveillance to KSC-ER space vehicle launch operations, and has undergone one major hardware upgrade since its inception in the early 1970s. Additional KSC lightning warning data is derived from a multistation radio time-of-arrival system called Lightning Detection and Ranging (LDAR). This system provides the location and space-time mapping of individual lightning channels (for both cloud and ground flashes). Additional lightning information for the KSC region is available from the National Lightning Detection Network (NLDN) and a 5-station local magnetic direction finder network. In this study, all of the above mentioned data are used to ground-validate data derived from the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM). The FM network can be used to retrieve the charges deposited in a lightning flash, provided the flash is within a few kilometers of the FM Network. Although it is rare to obtain a TRMM overpass of thunderstorms hat occur this close to the FM network, seven such storms have been found and examined in this study. We compare the times and locations of LIS optical pulses with the spatial-temporal character of the FM, LDAR, and magnetic direction finder data. We also inter-compare LIS optical pulse amplitude data with FM-derived charge magnitudes, number of LDAR radio sources, and peak current values from magnetic direction finder data. Generally speaking, LIS lightning locations and times agree favorably with the KSC ground-based systems for most cases, but little correlation appears to exist between optical pulse amplitude and any of charge, # LDAR sources, peak current), owing possibly to the effects of source complexity and/or cloud multiple scattering.

Koshak, William J.↗
NASA NTRS2018

Detecting Early Warning Signals of Tree Mortality in Boreal North America Using Multiscale Satellite Data

Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree-level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot-level productivity, in turn, has been related to satellite-based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI-based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse-scale imagery in the heterogeneous aspen parkland was limited. Longer-term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems.

heat↗
NASA NTRS2011

Analysing the Advantages of High Temporal Resolution Geostationary MSG SEVIRI Data Compared to Polar Operational Environmental Satellite Data for Land Surface Monitoring in Africa

Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging capability every 15 minutes which is substantially greater than any temporal resolution that can be obtained from existing polar operational environmental satellites (POES) systems currently in use for environmental monitoring. Different areas of the African continent were affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher temporal resolution cloud-free (less than 5 days) measurements of the environment as compared to existing POES systems. SEVIRI MSG 5-day continental scale composites will enable rapid assessment of environmental conditions and improved early warning of disasters for the African continent such as flooding or droughts. The high temporal resolution geostationary data will complement existing higher spatial resolution polar-orbiting satellite data for various dynamic environmental and natural resource applications of terrestrial ecosystems.

Fensholt, R.↗
DOE OSTI2023

Mixotrophic microbes create carbon tipping points under warming

Mixotrophs are ubiquitous and integral to microbial food webs, but their impacts on the dynamics and functioning of broader ecosystems are largely unresolved. Here, we show that mixotrophy produces a unique type of food web module that exhibits unusual ecological dynamics, with surprising consequences for carbon flux under warming. We develop a generalizable model of a mixotrophic food web module that incorporates dynamic switching between phototrophy and phagotrophy to assess ecological dynamics and total system CO 2 flux. We find that warming switches mixotrophic systems between alternative stable carbon states—including a phototrophy-dominant carbon sink state, a phagotrophy-dominant carbon source state and cycling between these two. Moreover, warming always shifts this mixotrophic system from a carbon sink state to a carbon source state, but a coordinated increase in nutrients can erase early warning signals of this transition and expand hysteresis. This suggests that mixotrophs can generate critical carbon tipping points under warming that will be more abrupt and less reversible when combined with increased nutrient levels, having widespread implications for ecosystem functioning in the face of rapid global change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Fire Detector

An early warning fire detection sensor developed for NASA's Space Shuttle Orbiter is being evaluated as a possible hazard prevention system for mining operations. The incipient Fire Detector represents an advancement over commercially available smoke detectors in that it senses and signals the presence of a fire condition before the appearance of flame and smoke, offering an extra margin of safety.

Source record↗
NASA NTRS2024

Multi-Agency Ensemble Forecast of Wildfire Air Quality in the United States: Toward Community Consensus of Early Warning

Wildfires pose increasing risks to human health and properties in North America. Due to large uncertainties in fire emission, transport, and chemical transformation, it remains challenging to accurately predict air quality during wildfire events, hindering our collective capability to issue effective early warnings to protect public health and welfare. Here we present a new real-time Hazardous Air Quality Ensemble System (HAQES) by leveraging various wildfire smoke forecasts from three U.S. federal agencies (NOAA, NASA, and Navy). Compared to individual models, the HAQES ensemble forecast significantly enhances forecast accuracy. To further enhance forecasting performance, a weighted ensemble forecast approach was introduced and tested. Compared to the unweighted ensemble mean, the multilinear regression weighted ensemble reduced fractional bias by 34% in the major fire regions, false alarm rate by 72%, and increased hit rate by 17%. Finally, we improved the weighted ensemble using quantile regression and weighted regression methods to enhance the forecast of extreme air quality events. The advanced weighted ensemble increased the PM2.5 exceedance hit rate by 55% compared to the ensemble mean. Our findings provide insights into the development of advanced ensemble forecast methods for wildfire air quality, offering a practical way to enhance decision-making support to protect public health.

Yunyao Li↗
NASA NTRS2007

Improving Public Health DSSs by Including Saharan Dust Forecasts Through Incorporation of NASA's GOCART Model Results

Approximately 2-3 billion metric tons of soil dust are estimated to be transported in the Earth's atmosphere each year. Global transport of desert dust is believed to play an important role in many geochemical, climatological, and environmental processes. This dust carries minerals and nutrients, but it has also been shown to carry pollutants and viable microorganisms capable of harming human, animal, plant, and ecosystem health. Saharan dust, which impacts the eastern United States (especially Florida and the southeast) and U.S. Territories in the Caribbean primarily during the summer months, has been linked to increases in respiratory illnesses in this region and has been shown to carry other human, animal, and plant pathogens. For these reasons, this candidate solution recommends integrating Saharan dust distribution and concentration forecasts from the NASA GOCART global dust cycle model into a public health DSS (decision support system), such as the CDC's (Centers for Disease Control and Prevention's) EPHTN (Environmental Public Health Tracking Network), for the eastern United States and Caribbean for early warning purposes regarding potential increases in respiratory illnesses or asthma attacks, potential disease outbreaks, or bioterrorism. This candidate solution pertains to the Public Health National Application but also has direct connections to Air Quality and Homeland Security. In addition, the GOCART model currently uses the NASA MODIS aerosol product as an input and uses meteorological forecasts from the NASA GEOS-DAS (Goddard Earth Observing System Data Assimilation System) GEOS-4 AGCM. In the future, VIIRS aerosol products and perhaps CALIOP aerosol products could be assimilated into the GOCART model to improve the results.

Berglund, Judith↗
NASA NTRS2020

Evaluating Drought Indices for Early Warning in East and Southern Africa

Sparsely populated regions of east and southern Africa often have little ground based data to monitor drought, crops, and water resources. The Regional Hydrologic Extremes Assessment System (RHEAS) is a NASA supported data assimilation framework that combines both a hydrologic and crop model, which provide another monitoring approach. RHEAS can also be run in forecast mode, providing outlook on drought and crop yield.

Miller, Sara↗
NASA NTRS2014

Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

Roberts, J. Brent↗
NASA NTRS2014

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

computational performance↗
DOE OSTI2024

Combined Pre-supernova Alert System with KamLAND and Super-Kamiokande

Preceding a core-collapse supernova (CCSN), various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande (SK) via inverse beta decay interactions. Once these pre-supernova (pre-SN) neutrinos are observed, an early warning of the upcoming CCSN can be provided. In light of this, KamLAND and SK, both located in the Kamioka mine in Japan, have been monitoring pre-SN neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and SK on pre-SN neutrino detection. A pre-SN alert system combining the KamLAND detector and the SK detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-SN neutrino signal from a 15 M ⊙ star within 510 pc of the Earth at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hr in advance.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1991

Real-time earthquake monitoring: Early warning and rapid response

A panel was established to investigate the subject of real-time earthquake monitoring (RTEM) and suggest recommendations on the feasibility of using a real-time earthquake warning system to mitigate earthquake damage in regions of the United States. The findings of the investigation and the related recommendations are described in this report. A brief review of existing real-time seismic systems is presented with particular emphasis given to the current California seismic networks. Specific applications of a real-time monitoring system are discussed along with issues related to system deployment and technical feasibility. In addition, several non-technical considerations are addressed including cost-benefit analysis, public perceptions, safety, and liability.

Source record↗
NASA NTRS2002

Satellites as Shared Resources for Caribbean Climate and Health Studies

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, UV (ultraviolet) radiation, contaminant and pathogen transport in air and water, and thermal stress. Remote sensing, geographic information systems (GIS), global positioning systems (GPS), improved computational capabilities, and interdisciplinary research between the Earth and health science communities are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global health issues. Collaborative efforts among scientists from health and Earth sciences together with local decision-makers are enabling increased understanding of the relationships between changes in temperature, rainfall, wind, soil moisture, solar radiation, vegetation, and the patterns of extreme weather events and the occurrence and patterns of diseases (especially, infectious and vector-borne diseases) and other health problems. This increased understanding through improved information and data sharing, in turn, empowers local health and environmental officials to better predict health problems, take preventive measure, and improve response actions. This paper summarizes the remote sensing systems most useful for climate, environment and health studies of the Caribbean region and provides several examples of interdisciplinary research projects in the Caribbean currently using remote sensing technologies. These summaries include the use of remote sensing of algal blooms, pollution transport, coral reef monitoring, vectorborne disease studies, and potential health effects of African dust on Trinidad and Barbados.

Maynard, Nancy G.↗
DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗
NASA NTRS2024

New Release Products: Combining the Relativistic Electron Alert System for Exploration With Other Early Indicators of Solar Energetic Particle Events

The Relativistic Electron Alert System for Exploration (“REleASE”) exploits the prompt arrival of near-relativistic electrons in the Earth/moon system to rapidly warn human explorers of the imminent arrival of hazardous energetic ions from solar energetic particle (SEP) events. Since 2008, the REleASE system has been implemented in near-real-time, initially with electron observations from SOHO, and since 2017 with additional observations from ACE as “HESPERIA REleASE”: upcoming opportunities will utilize IMAP and SWFO observations. REleASE is undergoing performance validation as part of the NASA/CCMC SEP scoreboarding process (https://ccmc.gsfc.nasa.gov/scoreboards/sep/). The ongoing return of STEREO-A to the vicinity of Earth provides observations of radio waves in near-real-time and allows REleASE to be combined with the identification of type-III radio bursts, which provide additional evidence of particle escape from the Sun (the use of type-III bursts for SEP forecasting was pioneered by “ESPERTA” developed by Laurenza et al., 2009). The aim of this planned “REleASE+” system is to further reduce the rate of false alarms. Another opportunity, facilitated by the CLEAR space weather center (PI L. Zhao, Univ. Michigan) will combine REleASE with “SEPSTER” (Richardson et al., 2018) to reduce the high rate of false alarms produced by SEPSTER, which is triggered by reports of CMEs, the vast majority of which do not result in detected SEP ion events. This presentation will give a status update on these objectives.

solar particle events↗