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Developed under an SBIR contract with Goddard Space Flight Center, Jentek Sensors, Inc.'s GridStation Measurement System provides thermal spray coating thickness, porosity, and age degradation monitoring capability using comformable inductive and capacitative sensors and model-based grid measurement methods for data interpretation.
The QuadraScan Longterm Flow Monitoring System is a second generation sewer monitor developed by American Digital Systems, Inc.'s founder Peter Petroff. Petroff, a former spacecraft instrumentation designer at Marshall Space Flight Center, used expertise based on principles acquired in Apollo and other NASA programs. QuadraScan borrows even more heavily from space technology, for example in its data acquisition and memory system derived from NASA satellites. "One-time" measurements are often plagued with substantial errors due to the flow of groundwater absorbed into the system. These system sizing errors stem from a basic informational deficiency: accurate, reliable data on how much water flows through a sewer system over a long period of time is very difficult to obtain. City officials are turning to "permanent," or long-term sewer monitoring systems. QuadraScan offers many advantages to city officials such as the early warning capability to effectively plan for city growth in order to avoid the crippling economic impact of bans on new sewer connections in effect in many cities today.
Dependence on rainfed agriculture in a highly variable climate, renders crop and livestock production vulnerable to impacts of drought in Kenya. Stakeholders in the region have highlighted the need for timely and actionable detailed early warning information on drought and its implication on crop productivity. Here we apply the Regional Hydrological Extremes Assessment System (RHEAS) to estimate current and future drought conditions onset, severity, recovery, and duration) and expected productivity outlooks.
Gears used in current helicopters and turboprops are designed for light weight, high margins of safety, and high reliability. However, unexpected gear failures may occur even with adequate tooth design. To design an extremely safe system, the designer must ask and address the question, "What happens when a failure occurs?" With gear-tooth bending fatigue, tooth or rim fractures may occur. A crack that propagates through a rim will be catastrophic, leading to disengagement of the rotor or propeller, loss of an aircraft, and possible fatalities. This failure mode should be avoided. A crack that propagates through a tooth may or may not be catastrophic, depending on the design and operating conditions. Also, early warning of this failure mode may be possible because of advances in modern diagnostic systems. One concept proposed to address bending fatigue fracture from a safety aspect is a splittooth gear design. The prime objective of this design would be to control crack propagation in a desired direction such that at least half of the tooth would remain operational should a bending failure occur. A study at the NASA Lewis Research Center analytically validated the crack-propagation failsafe characteristics of a split-tooth gear. It used a specially developed three-dimensional crack analysis program that was based on boundary element modeling and principles of linear elastic fracture mechanics. Crack shapes as well as the crack-propagation life were predicted on the basis of the calculated stress intensity factors, mixed-mode crack-propagation trajectory theories, and fatigue crack-growth theories. The preceding figures show the effect of the location of initial cracks on crack propagation. Initial cracks in the fillet of the teeth produced stress intensity factors of greater magnitude (and thus, greater crack growth rates) than those in the root or groove areas of the teeth. Crack growth was simulated in a case study to evaluate crack-propagation paths. Tooth fracture was predicted from the crackgrowth simulation for an initial crack in the tooth fillet region. This was the desired failure mode for an ultrasafe design. Lastly, tooth loads on the uncracked mesh of the split-tooth design were up to five times greater than those on the cracked mesh if equal deflections of the cracked and uncracked teeth were considered. This effect needs to be considered in the design of a split-tooth configuration. This work was done in-house at Lewis in support of the National Rotorcraft Technology Center project, Ultra-Safe Gear Design, with the Boeing Defense and Space Group. The crack-propagation package was developed by the Cornell Fracture Group at Cornell University. The reported results, which are the initial findings of Lewis gear-crackpropagation research for the Rotorcraft Base Program, will be further investigated to develop generalized gear design guidelines.
The ERTS Data Collection System makes it feasible for the first time to monitor the level of activity at widely separated volcanoes and to relay these data rapidly to one central office for analysis. While prediction of specific eruptions is still an evasive goal, early warning of a reawakening of quiescent volcanoes is now a distinct possibility. A prototypical global volcano surveillance system was established under the ERTS program. Instruments were installed in cooperation with local scientists on 15 volcanoes in Alaska, Hawaii, Washington, California, Iceland, Guatemala, El Salvador and Nicaragua. The sensors include 19 seismic event counters that count four different sizes of earthquakes and six biaxial borehole tiltmeters that measure ground tilt with a resolution of 1 microradian. Only seismic and tilt data are collected because these have been shown in the past to indicate most reliably the level of volcano activity at many different volcanoes. Furthermore, these parameters can be measured relatively easily with new instrumentation.
Effective gear designs balance strength, durability, reliability, size, weight, and cost. However, unexpected gear failures may occur even with adequate gear tooth design. To design an extremely safe system, the designer must ask and address the question "What happens when a failure occurs?" With regard to gear-tooth bending fatigue, tooth or rim fractures may occur. For aircraft, a crack that propagated through a rim would be catastrophic, leading to the disengagement of a rotor or propeller, the loss of an aircraft, and possible fatalities. This failure mode should be avoided. However, a crack that propagated through a tooth might or might not be catastrophic, depending on the design and operating conditions. Also, early warning of this failure mode might be possible because of advances in modern diagnostic systems. An analysis was performed at the NASA Glenn Research Center to develop design guidelines to prevent catastrophic rim fracture failure modes in the event of gear-tooth bending fatigue. The finite element method was used with principles of linear elastic fracture mechanics. Crack propagation paths were predicted for a variety of gear tooth and rim configurations. The effects of rim and web thicknesses, initial crack locations, and gear-tooth geometry factors such as diametral pitch, number of teeth, pitch radius, and tooth pressure angle were considered. Design maps of tooth and rim fracture modes, including the effects of gear geometry, applied load, crack size, and material properties were developed. The occurrence of rim fractures significantly increased as the backup ratio (rim thickness divided by tooth height) decreased. The occurrence of rim fractures also increased as the initial crack location was moved down the root of the tooth. Increased rim and web compliance increased the occurrence of rim fractures. For gears with constant-pitch radii, coarser-pitch teeth increased the occurrence of tooth fractures over rim fractures. Also, 25 degree pressure angle teeth increased the occurrence of tooth fractures over rim fractures in comparison to 20 pressure angle teeth. For gears with a constant number of teeth or for gears with constant diametral pitch, varying size had little or no effect on crack propagation paths.
The development of radiative transfer simulators for radar and microwave signals, spanning a range of frequencies from 10 to 800 GHz, is paramount for enhancing weather forecasting accuracy, particularly for severe weather events. These tools facilitate the assimilation of microwave and radar observations into numerical weather prediction models, thereby improving accuracy of weather forecasts. Additionally, they directly simulate radar signals crucial for autonomous vehicle operation, with frequencies commonly used in radar systems such as 24, 74, 77, and 79 GHz. However, these frequencies are susceptible to weather phenomena like severe rain and snow, which can significantly impact vehicle safety and performance. These simulation tools, including radiative transfer simulators, serve a dual purpose. Firstly, they enhance forecasts for severe weather events, contributing to autonomous vehicle safety by providing early warnings and risk assessments. Secondly, they enable the direct simulation of radar signals in autonomous vehicle driving systems, allowing researchers and engineers to evaluate radar system performance under various weather conditions. In addition to the radar signal simulator, this abstract discusses the incorporation of advanced scattering properties developed using the discrete dipole approximation (DDA). The DDA technique enhances scattering calculations for frozen hydrometeors at microwave frequencies, thereby improving the accuracy of radar signal simulations and enabling more accurate assessments of radar system performance in adverse weather conditions. In summary, this abstract explores the development and utilization of comprehensive simulation tools, emphasizing their significance in simulating microwave and radar signals and improving weather forecasts. Special attention is given to the simulation of radar signals at critical frequencies for autonomous vehicle sensing and navigation, addressing challenges posed by severe weather phenomena and their effects on signal propagation and detection
Food insecurity continues to grow in Sub-Saharan Africa (SSA). In 2019, chronically malnourished people numbered nearly 240 million, or 20% of the population in SSA. Globally, numerous efforts have been made to anticipate potential droughts, crop conditions, and food shortages to foster improved food insecurity early warning and risk management. To support this goal, we develop an Earth Observation (EO) and machine-learning-based operational, subnational maize yield forecast system and evaluate its out-of-sample forecast skills during the growing seasons for Kenya, Somalia, Malawi, and Burkina Faso. In general, forecast skills improve substantially during the vegetative growth period (VP) and gradually during the reproductive development period (RP). Thus, mid-season assessment can provide effective early warning months before harvest. Skillful forecasts (Nash Sutcliffe Efficiency (NSE) > 0.6 and Mean Absolute Percentage Error (MAPE) < 20%) appear approximately two dekads after the VP; for example, they appear in May in Kenya and Somalia, January in Malawi, and July in Burkina Faso.
Satellites as Sentinels for Environment & Health Remotely-sensed data and observations are providing powerful new tools for addressing human and ecosystem health by enabling improved understanding of the relationships and linkages between health-related environmental parameters and society as well as techniques for early warning of potential health problems. NASA Office of Earth Science Applications Program has established a new initiative to utilize its data, expertise, and observations of the Earth for public health applications. In this initiative, lead by Goddard Space Flight Center, remote sensing, geographic information systems, 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. This presentation provides a number of recent examples of applications of advanced remote sensing and other technologies to health.and security issues related to the following: infectious and vector-borne diseases; urban, regional and global air pollution; African and Asian airborne dust; heat stress; UV radiation; water-borne disease; extreme weather; contaminant pathways (ocean, atmosphere, ice)
The major goal of the Namibia SensorWeb Pilot Project is a scientifically sound, operational trans-boundary flood management decision support system for Southern African region to provide useful flood and waterborne disease forecasting tools for local decision makers. The Pilot Project established under the auspices of: Namibian Ministry of Agriculture Water and Forestry (MAWF), Department of Water Affairs; 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 consists of identifying and prototyping technology which enables 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.
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.
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.
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.
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.
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.
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.
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.
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.