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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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NASA NTRS2000

AWE: Aviation Weather Data Visualization Environment

The two official sources for aviation weather reports both provide weather information to a pilot in a textual format. A number of systems have recently become available to help pilots with the visualization task by providing much of the data graphically. However, two types of aviation weather data are still not being presented graphically. These are airport-specific current weather reports (known as meteorological observations, or METARs) and forecast weather reports (known as terminal area forecasts, or TAFs). Our system, Aviation Weather Environment (AWE), presents intuitive graphical displays for both METARs and TAFs, as well as winds aloft forecasts. We start with a computer-generated textual aviation weather briefing. We map this briefing onto a cartographic grid specific to the pilot's area of interest. The pilot is able to obtain aviation-specific weather for the entire area or for his specific route. The route, altitude, true airspeed, and proposed departure time can each be modified in AWE. Integral visual display of these three elements of weather reports makes AWE a useful planning tool, as well as a weather briefing tool.

Spirkovska, Lilly↗
NASA NTRS1992

Managing an archive of weather satellite images

The author's experiences of building and maintaining an archive of hourly weather satellite pictures at NOAO are described. This archive has proven very popular with visiting and staff astronomers - especially on windy days and cloudy nights. Given access to a source of such pictures, a suite of simple shell and IRAF CL scripts can provide a great deal of robust functionality with little effort. These pictures and associated data products such as surface analysis (radar) maps and National Weather Service forecasts are updated hourly at anonymous ftp sites on the Internet, although your local Atsmospheric Sciences Department may prove to be a more reliable source. The raw image formats are unfamiliar to most astronomers, but reading them into IRAF is straightforward. Techniques for performing this format conversion at the host computer level are described which may prove useful for other chores. Pointers are given to sources of data and of software, including a package of example tools. These tools include shell and Perl scripts for downloading pictures, maps, and forecasts, as well as IRAF scripts and host level programs for translating the images into IRAF and GIF formats and for slicing & dicing the resulting images. Hints for displaying the images and for making hardcopies are given.

Seaman, R. L.↗
NASA NTRS2002

Smooth Sailing for Weather Forecasting

Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.

Source record↗
NASA NTRS2017

Applications of Satellite Data to Support Improvements in Irrigation and Groundwater Management in California

In agricultural regions around the world, threats to water supplies from drought and groundwater depletion are driving increased demand for tools to advance agricultural water use efficiency and support sustainable groundwater management. Satellite mapping of evapotranspiration (ET) from irrigated agricultural lands can provide agricultural producers and water resource managers with information that can be used to both optimize ag water use and improve estimates of groundwater withdrawals for irrigation. We describe the development of two remote sensing-based tools for ET mapping in California, including important lessons in terms of system design, partnership development, and transition to operations. For irrigation management, the integration of satellite data and surface sensor networks to provide timely delivery of information on crop water requirements can make irrigation scheduling more practical, convenient, and accurate.Developed through a partnership between NASA and the CA Department of Water Resources, the Satellite Irrigation Management Support (SIMS) framework integrates satellite data with information from agricultural weather networks to map crop canopy development and crop water requirements at the scale of individual fields. Information is distributed to agricultural producers and water managers via a web-based interface and web data services. SIMS also provides an API that facilitates integration with other irrigation decision support tools, such as CropManage and IrriQuest. Field trials using these integrated tools have shown that they can be used to sustain yields while improving water use efficiency and nutrient management. For sustainable groundwater management, the combination of satellite-derived estimates of ET and data on surface water deliveries for irrigation can increase the accuracy of estimates of groundwater pumping. We are developing an OpenET platform to facilitate access to ET data from multiple models and accelerate operational use of ET data in support of a range of water management applications, including implementation of the Sustainable Groundwater Management Act in CA. By providing a shared basis for decision making, we anticipate that the OpenET platform will accelerate implementation of solutions for sustainable groundwater management.

Satellite↗
NASA NTRS2016

Integrating Satellite and Surface Sensor Networks for Irrigation Management Applications in California

Satellite mapping of evapotranspiration (ET) from irrigated agricultural lands can provide agricultural producers and water managers with information that can be used to optimize agricultural water use, especially in regions with limited water supplies. The timely delivery of information on agricultural crop water requirements has the potential to make irrigation scheduling more practical, convenient, and accurate. We present a system for irrigation scheduling and management support in California and describe lessons learned from the development and implementation of the system. The Satellite Irrigation Management Support (SIMS) framework integrates satellite data with information from agricultural weather networks to map crop canopy development, basal crop coefficients (Kcb), and basal crop evapotranspiration (ETcb) at the scale of individual fields. Information is distributed to agricultural producers and water managers via a web-based irrigation management decision support system and web data services. SIMS also provides an application programming interface (API) that facilitates integration with other irrigation decision support tools, estimation of total crop evapotranspiration (ETc) and calculation of on-farm water use efficiency metrics. Accuracy assessments conducted in commercial fields for more than a dozen crop types to date have shown that SIMS seasonal ETcb estimates are within 10 mean absolute error (MAE) for well-watered crops and within 15 across all crop types studied, and closely track daily ETc and running totals of ETc measured in each field. Use of a soil water balance model to correct for soil evaporation and crop water stress reduces this error to less than 8 MAE across all crop types studied to date relative to field measurements of ETc. Results from irrigation trials conducted by the project for four vegetable crops have also demonstrated the potential for use of ET-based irrigation management strategies to reduce total applied water by 20-40 relative to grower standard practices while maintaining crop yields and quality.

Nemani, Ramakrishna R.↗
NASA NTRS1991

Combined use of visible, reflected infrared, and thermal infrared images for mapping Hawaiian lava flows

The weathering of Hawaiian basalts is accompanied by chemical and physical changes of the surfaces. These changes have been mapped using remote sensing data from the visible and reflected infrared and thermal infrared wavelength regions. They are related to the physical breakdown of surface chill coats, the development and erosion of silica coatings, the oxidation of mafic minerals, and the development of vegetation cover. These effects show systematic behavior with age and can be mapped using the image data and related to relative ages of pahoehoe and aa flows. The thermal data are sensitive to silica rind development and fine structure of the scene; the reflectance data show the degree of oxidation and differentiate vegetation from aa and cinders. Together, data from the two wavelength regions show more than either separately. The combined data potentially provide a powerful tool for mapping basalt flows in arid to semiarid volcanic environments.

Abrams, Michael↗
NASA NTRS1973

Application of SLAR for monitoring Great Lakes total ice cover

A series of X-band SLAR images is presented showing the development and disintegration of the entire ice cover on Lake Erie during the winter of 1972-1973. Simultaneous ground truth observations and ERTS-1 photography establish accurate correlations of radar responses with ice conditions. The all-weather, broad areal mapping capability of SLAR is seen to be the means for obtaining the repeated coverage needed for winter navigation on the Great Lakes.

Jirberg, R. J.↗
NASA NTRS1973

Application of SLAR for monitoring Great Lakes total ice cover

A series of X-band SLAR images is presented showing the development and disintegration of the entire ice cover on Lake Erie during the winter of 1972-1973. Simultaneous ground truth observations and ERTS-1 photography establish accurate correlations of radar responses with ice conditions. The all-weather, broad areal mapping capability of SLAR is seen to be the means for obtaining the repeated coverage needed for winter navigation on the Great Lakes.

Jirberg, R. J.↗
NASA NTRS1982

Image-data-processing for earth resources - An overview

A total remote-sensing system comprises four distinct operations: data-acquisition, archiving, production, and dissemination of individual products, and extraction of the required information. The Landsat satellite system acquires data in several spectral bands, with a ground resolution of 80 meters (currently) or 30 meters (future). Radiometric and geometric corrections are normally applied, after which the data may be enhanced for photointerpretation or analyzed by computer. The accuracy of the analysis model for either is important. The extraction of information from remotely sensed data will generally be dependent on the use of it with other data such as maps or, perhaps, weather information. Methods are being developed to use the various data together in geographic data systems. The availability of remotely sensed data has encouraged the development of new models or analysis procedures for the use of this information.

Billingsley, F. C.↗
NASA NTRS1985

The influence of surface roughness of deserts on the July circulation - A numerical study

The effect of the low surface roughness characteristics of deserts on atmospheric circulation in July is examined using numerical simulations with the GCM of the Goddard Laboratory for Atmospheric Science (GLAS). Identical sets of simulations were carried out with the model starting from the initial state of the atmosphere on June 15, for the years 1979 and 1980. The first simulation included a surface roughness factor of 45 cm, and the second set had a surface roughness factor of 0.02 cm for desert regions, and 45 cm for all other land. A comparative analysis of the numerical data was carried out in order to study the variations for the desert regions. It is shown that rainfall in the Sahara desert was reduced significantly in the data set with the nonuniform surface roughness factor in comparison with the other data set. The inter-tropical convergence zone (ITCZ) moved southward to about 15 degrees, which was close to its observed location at about 10 degrees N. In other deserts, the North American Great Plains, Rajputana in India, and the Central Asian desert, no similar changes were observed. Detailed contour maps of the weather conditions in the different desert regions are provided.

Sud, Y. C.↗
NASA NTRS1992

Accurate Inventories Of Irrigated Land

System for taking land-use inventories overcomes two problems in estimating extent of irrigated land: only small portion of large state surveyed in given year, and aerial photographs made on 1 day out of year do not provide adequate picture of areas growing more than one crop per year. Developed for state of California as guide to controlling, protecting, conserving, and distributing water within state. Adapted to any large area in which large amounts of irrigation water needed for agriculture. Combination of satellite images, aerial photography, and ground surveys yields data for computer analysis. Analyst also consults agricultural statistics, current farm reports, weather reports, and maps. These information sources aid in interpreting patterns, colors, textures, and shapes on Landsat-images.

Wall, S.↗
NASA NTRS1994

Interactive visualization of Earth and Space Science computations

Computers have become essential tools for scientists simulating and observing nature. Simulations are formulated as mathematical models but are implemented as computer algorithms to simulate complex events. Observations are also analyzed and understood in terms of mathematical models, but the number of these observations usually dictates that we automate analyses with computer algorithms. In spite of their essential role, computers are also barriers to scientific understanding. Unlike hand calculations, automated computations are invisible and, because of the enormous numbers of individual operations in automated computations, the relation between an algorithm's input and output is often not intuitive. This problem is illustrated by the behavior of meteorologists responsible for forecasting weather. Even in this age of computers, many meteorologists manually plot weather observations on maps, then draw isolines of temperature, pressure, and other fields by hand (special pads of maps are printed for just this purpose). Similarly, radiologists use computers to collect medical data but are notoriously reluctant to apply image-processing algorithms to that data. To these scientists with life-and-death responsibilities, computer algorithms are black boxes that increase rather than reduce risk. The barrier between scientists and their computations can be bridged by techniques that make the internal workings of algorithms visible and that allow scientists to experiment with their computations. Here we describe two interactive systems developed at the University of Wisconsin-Madison Space Science and Engineering Center (SSEC) that provide these capabilities to Earth and space scientists.

Hibbard, William L.↗
NASA NTRS2002

Variability of Winter Air Temperature in Mid-Latitude Europe

The aim of this paper is to report extreme winter/early-spring air temperature (hereinafter temperature) anomalies in mid-latitude Europe, and to discuss the underlying forcing to these interannual fluctuations. Warm advection from the North Atlantic in late winter controls the surface-air temperature, as indicated by the substantial correlation between the speed of the surface southwesterlies over the eastern North Atlantic (quantified by a specific Index Ina) and the 2-meter level air temperatures (hereinafter Ts) over Europe, 45-60 deg N, in winter. In mid-March and subsequently, the correlation drops drastically (quite often it is negative). This change in the relationship between Ts and Ina marks a transition in the control of the surface-air temperature: absorption of insolation replaces the warm advection as the dominant control. This forcing by maritime-air advection in winter was demonstrated in a previous publication, and is re-examined here in conjunction with extreme fluctuations of temperatures in Europe. We analyze here the interannual variability at its extreme by comparing warm-winter/early-spring of 1989/90 with the opposite scenario in 1995/96. For these two December-to-March periods the differences in the monthly mean temperature in Warsaw and Torun, Poland, range above 10 C. Short-term (shorter than a month) fluctuations of the temperature are likewise very strong. We conduct pentad-by-pentad analysis of the surface-maximum air temperature (hereinafter Tmax), in a selected location, examining the dependence on Ina. The increased cloudiness and higher amounts of total precipitable water, corollary effects to the warm low-level advection. in the 1989/90 winter, enhance the positive temperature anomalies. The analysis of the ocean surface winds is based on the Special Sensor Microwave/Imager (SSM/I) dataset; ascent rates, and over land wind data are from the European Centre for Medium-Range Weather Forecasts (ECMWF); maps of 2-m temperature, cloud cover and precipitable water are from the National Centers for Environmental Prediction (NCEP) Reanalysis.

Otterman, J.↗
NASA NTRS2005

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

Kocurek, Michael J.↗
NASA NTRSDate not supplied

Advanced Weather Hazard Perception Capabilities for Remotely Piloted Aircraft in Wildland Fire Operations

Weather, specifically winds, are the most variable and least predictable of the weather-related elements that affect wildfire behavior.1 This leads to aviation operations which must deal with increased risks due to dynamic weather factors. Remotely piloted aircraft provide the opportunity to remove human loss of life from these risk consequences and mitigate the overall risks for air operations for fire suppression and dropping of retardant. UAS also allows the opportunity to continue fire suppression with nighttime operations. However, with the use of new technologies in aviation operations comes additional risk factors and the need for remote sensing capabilities to identify and permit navigation around extreme weather conditions, with turbulence being the most unforeseeable. This paper presents advanced concepts for weather hazard perception capabilities for remote pilot operators to include data and display techniques intended for dynamic environments and low altitude operations. This work can support the potential of weather sensing capabilities to create hazard maps to track high-risk weather patterns in mountainous regions. Advanced sensing may provide opportunity to locate extreme turbulence hazards for uninhabited aerial systems (UAS) and decrease the likelihood of flying into unknown weather conditions to enable safe operations to assist in wildfire air support.

Tyler L Willhite↗
NASA NTRS1980

A preliminary look at AVE-SESAME 3 conducted on 25-26 April 1979

General weather conditions, including synoptic maps, radar reports, satellite photographs, precipitation areas and amounts, and a summary of severe weather reports are presented. These data provide researchers a preliminary look at conditions during the AVE-SESAME 3 period.

Williams, S. F.↗
NASA NTRS2012

The Use of the North Alabama Lightning Mapping Array (NALMA) in the Real-Time Operational Warning Environment During the March 2nd, 2012 Severe Weather Outbreak in Northern Alabama

The North Alabama Lightning Mapping Array (NALMA) is a three-dimensional very high frequency (VHF) detection network consisting of 11 sensors spread across north central Alabama and two sensors located in the Atlanta, Georgia region. The primary advantage of this network is that it detects total lightning, or the combination of both cloud-to-ground and intra-cloud lightning, instead of cloud-to-ground lightning alone. This helps to build a complete picture of storm evolution and development, and can serve as a proxy for storm updraft strength, particularly since intra-cloud lightning makes up the majority of all lightning in a typical thunderstorm. While the NALMA data do not directly indicate severe weather, they can indirectly indicate when a storm is strengthening (weakening) due to increases (decreases) in updraft strength, as the updraft is responsible for charging mechanisms within the storm. Data output are VHF radiation sources, which are produced during lightning breakdown processes. These sources are made into 2x2 km source density grids and are ported into the Advanced Weather Interactive Processing System (AWIPS) for National Weather Service (NWS) offices in Huntsville, AL, Nashville, TN, Morristown, TN, and Birmingham, AL, in near real-time. An increase in sources, or source densities, correlates to increased lightning activity and trends in updraft magnitude as long as the storm is within about 125 km of the center of the LMA network. Operationally, these data have been used at the Huntsville NWS office since early 2003 through a collaborative effort with NASA s Short-term Prediction Research and Transition (SPoRT) Center. Since then, total lightning observations have become an essential tool for forecasters during real-time warning operations. One of the operational advantages of the NALMA is the two-minute temporal resolution of the data. This provides forecasters with two to three updates during a typical volume scan of the WSR-88D radar.

White, Kristopher↗