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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

A Study of Cirrus Clouds and Aerosols in the Upper Troposphere using Models and Satellite Data

This report is the final report for the Cooperative Agreement NCC2-1213. It is a compilation of publications produced under this Cooperative Agreement and conference presentations. The tasks for the Aerosol Physical Chemistry Model for the Upper Troposphere include: Task 1: To compare APCM predictions against the SUCCESS data and other aircraft campaigns and to investigate the role of aerosol composition on cirrus cloud nucleation; Task 2: To study the seasonal evolution and spatial distribution of upper-tropospheric tropical and polar cirrus; Task 3: To investigate CLAES cirrus data with other complementary (TOGA-COARE and CEPEX) data. Tasks for Upper Tropospheric Cirrus Clouds include: Task 1: Assemble 3-hourly (or more frequent) meteorological satellite data fiom geostationary satellites to obtain a global, or nearly global, dataset of infiared brightness temperatures as a function of time for airborne experimental periods; Task 2: Explore methods to improve the cloud top altitude distributions calculated fiom meteorological satellite data. This will focus on linlung the 6.5 micron channel geostationary brightness temperatures and the 10.5 micron brightness temperatures; Task 3: Explore methods to differentiate convective fiom stratiform cloudiness; Task 4: Perform trajectory analyses using an existing trajectory modeling package that links the cloud data with air mass histories; Task 5: Apply techniques from tasks 1 through 4 to provide meteorological support to the CRYSTAL-FACE mission, both in its preparation and deployment phases. The report include four published articles and two slide presentations.

Bergstrom, Robert W.↗
DOE OSTI2023

A Comparison of Adverse Meteorology Inputs for Emergency Management and Safety Basis Dispersion Modeling Applications at SRS

The MELCOR Accident Consequence Calculation System (MACCS), used for safety basis accident analyses at SRS, implements adjustment factors to consider the increased vertical turbulence associated with local surface roughness and the increased lateral turbulence associated with time-based plume meander. The two models used for SRS emergency planning radiological consequence assessment, i.e., Hotspot and Puff/Plume, currently do not have an option to input or apply diffusion adjustments for meander or surface roughness. Adverse meteorology conditions at SRS have been determined as E stability class and 1.3 meters per second wind speed for ground level releases. This is the input used for emergency planning models. Contrastingly safety basis analyses use a different input, representing adverse meteorology values as F stability class and wind speeds of 1.3 meters per second (for a ground level release). This results in an apparent discrepancy between the two approaches. In this report we document the differing modeling assumptions, discuss the logic behind each modeling approach used to represent local roughness and plume meander conditions, and numerically compare dispersion coefficients and concentration estimates obtained from each set of model inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A machine learning pipeline for identifying infiltration managed aquifer recharge locations from satellite imagery in the San Joaquin Valley, California

This study focuses on an agricultural region in California’s Central Valley, USA, where Managed Aquifer Recharge (MAR) is widely implemented to mitigate groundwater depletion under increasing water demand and climate variability. A deep learning and machine learning framework was developed to identify infiltration-MAR locations using satellite imagery and environmental data. The framework integrates surface water detection from Sentinel-2 imagery, geospatial delineation of water bodies, spatiotemporal tracking of water body dynamics, and supervised classification using meteorological, environmental, and topographic variables. The framework was applied to a 2379 km² study area southwest of Fresno, where 765 water bodies were detected, including 139 identified MAR sites based on publicly available datasets and expert knowledge. The classification model achieved an accuracy of 0.94 and an F1 score of 0.85. Feature importance analysis indicates that cropland, normalized difference vegetation index (NDVI), and evaporation are among the most influential predictors for infiltration-MAR. Notably, the framework suggests that engineered water management in infiltration-MAR systems can disrupt or even reverse the expected positive correlation between surface water extent and precipitation. These findings provide physically interpretable insights into the characteristics of existing infiltration-MAR facilities and demonstrate the potential of the proposed framework as a reproducible, interpretable, and potentially transferable tool for data-driven infiltration-MAR identification and inventory development under growing climatic and hydrological uncertainty.

Classification↗
DOE OSTI2024

Modeled estimates of permafrost hydrology and related fields for pan-Arctic region over the period 1980-2100

This dataset contains gridded estimates of total runoff (mm/day); subsurface runoff (mm/day); snow water equivalent (SWE, mm/day); vertical upward water flux of combined surface evaporation, evapotranspiration, and snow sublimation (mm/day); and soil temperature (C) across the pan-Arctic basin from simulations of the Permafrost Water Balance Model (PWBM v4). These data were generated to allow researchers to advance understanding of the changes unfolding in the Arctic's terrestrial water cycle under the influences of climate warming, hydrological cycle intensification, and permafrost thaw. The dataset will enable researchers to address important questions spanning multiple disciplines. For example, how will rising river flows exported to the coast impact critical zone ecosystems, near-shore ice dynamics, and ocean biogeochemical processes. Spatial resolution is 25x25 km with N=39926 grid cells spanning the domain. Three forcing datasets were used to drive simulations at an implicit daily time step over the period 1980-2100. Daily air temperature, precipitation, and wind speed data from the WATCH Forcing Data methodology applied to ERA5 reanalysis (W5E5) were used for meteorological forcings over the period 1980-2019. Simulations for the period 1980-2100 used meteorological data from two global climate models; IPSL-CM6A-LR and MPI-ESM1-2-HR. The PWBM hydrological outputs are organized by grid and month in gzipped ASCII text files within each archived tar file. Soil temperature is organized by grid cell and day of year from DOY 205 to DOY 260. No special software is required to work with these data. The package contains spatial domain and river basin information files. These data are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and soil thermal dynamics. A manuscript describing the model simulations and associated analysis has been accepted for publication in The Cryosphere (Rawlins and Karmalkar, 2024).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Passive microwave remote sensing of the earth from space - A review

A brief discussion of the historical development of microwave radiometry from space is provided, followed by a review of radiometer system concepts as applied to spacecraft platforms. One of the earliest meteorological applications of spaceborne radiometry was in global atmospheric temperature sounding. Surface-sensing applications can be divided into three main categories, including ocean, ice, and land. A radiometer system description is presented, and spacecraft considerations are discussed. Spaceborne radiometric sensors are examined, taking into account the Nimbus-5 Microwave Spectrometer, the Electrically Scanning Microwave Radiometer, the Skylab microwave radiometer/scatterometer and altimeter instrument, S-194 on Skylab, the Scanning Microwave Spectrometer on Nimbus 6, the Scanning Multichannel Microwave Radiometer on Seasat and Nimbus 7, Soviet sensors, and an Indian microwave radiometer.

Njoku, E. G.↗
NASA NTRS1991

Oceanic transports of mass, heat, and salt in the western North Pacific

An optimum orbit of the satellite will be presented for confirmation of estimates of tides and interpolation errors. Hydrographic surveys complementary to the routine surveys will be planned in Phase 1 by using research vessels belonging to Japanese universities. Direction measurements of oceanic transports with moored current meters and shipborne acoustic Doppler velocimeters will be planned for selected sections as an activity of the Japan-WOCE (World Ocean Circulation Experiment) Project. In the operational phase of TOPEX/POSEIDON, the delivered altimeter data will be used for the computation of surface geostrophic velocities to be applied in the hydrographic sections of the Japan Meteorological Agency, Hydrographic Department, Japan Fisheries Agency, and the universities. The estimates of transports are made by the co-investigators of each agency. The composite of the transports in the circulations will be made at the Japan Oceanographic Data Center. The direct measurements of current velocity and transports will be made mainly by the principal investigator.

Taira, K.↗
NASA NTRS1992

Synergistic use of active and passive microwave in soil moisture estimation

Data gathered during the MACHYDRO experiment in central Pennsylvania in July 1990 have been utilized to study the synergistic use of active and passive microwave systems for estimating soil moisture. These data sets were obtained during an eleven-day period with NASA's Airborne Synthetic Aperture Radar (AIRSAR) and Push-Broom Microwave Radiometer (PBMR) over an instrumented watershed which included agricultural fields with a number of different crop covers. Simultaneous ground truth measurements were also made in order to characterize the state of vegetation and soil moisture under a variety of meteorological conditions. A combination algorithm is presented as applied to a representative corn field in the MACHYDRO watershed.

O'Neill, P.↗
NASA NTRS2008

Near-Real Time Cloud Retrievals from Operational and Research Meteorological Satellites

A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES- 10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms. Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have potential for use in many other applications.

Minnis, Patrick↗
NASA NTRS2016

The Quality Control Algorithms Used in the Process of Creating the NASA Kennedy Space Center Lightning Protection System Towers Meteorological Database

The methodology and the results of the quality control (QC) process of the meteorological data from the Lightning Protection System (LPS) towers located at Kennedy Space Center (KSC) launch complex 39B (LC-39B) are documented in this paper. Meteorological data are used to design a launch vehicle, determine operational constraints, and to apply defined constraints on day-of-launch (DOL). In order to properly accomplish these tasks, a representative climatological database of meteorological records is needed because the database needs to represent the climate the vehicle will encounter. Numerous meteorological measurement towers exist at KSC; however, the engineering tasks need measurements at specific heights, some of which can only be provided by a few towers. Other than the LPS towers, Tower 313 is the only tower that provides observations up to 150 m. This tower is located approximately 3.5 km from LC-39B. In addition, data need to be QC'ed to remove erroneous reports that could pollute the results of an engineering analysis, mislead the development of operational constraints, or provide a false image of the atmosphere at the tower's location.

Orcutt, John M.↗
NASA NTRS2009

Applications of Meteorological Tower Data at Kennedy Space Center

Members of the National Aeronautics and Space Administration (NASA) design and operation communities rely on meteorological information collected at Kennedy Space Center (KSC), located near Cape Canaveral, Florida, to correctly apply the ambient environment to various tasks. The Natural Environments Branch/EV44, located at Marshall Space Flight Center (MSFC) in Huntsville, Alabama, is responsible for providing its NASA customers with meteorological data using various climatological data sources including balloons, surface stations, aircraft, hindcast models, and meteorological towers. Of the many resources available within the KSC region, meteorological towers are preferred for near-surface applications because they record data at regular, frequent intervals over an extensive period of record at a single location. This paper discusses the uses of data measured at several different meteorological towers for a common period of record and how the data can be applied to various engineering decisions for the new Constellation Program Ares and Orion space vehicles.

Altino, Karen M.↗
NASA NTRS1982

A numerical model simulation of the regional air pollution meteorology of the greater Chesapeake Bay area - Summer day case study

The mesoscale numerical model of the University of Virginia (UVMM), has been applied to the greater Chesapeake Bay area in order to provide a detailed description of the air pollution meteorology during a typical summer day. This model provides state of the art simulations for land-sea thermally induced circulations. The model-predicted results agree favorably with available observed data. The effects of synoptic flow and sea breeze coupling on air pollution meteorological characteristics in this region, are demonstrated by a spatial and temporal presentation of various model predicted fields. A transport analysis based on predicted wind velocities indicated possible recirculation of pollutants back onto the Atlantic coast due to the sea breeze circulation.

Segal, M.↗
NASA NTRS1971

Application of conservative finite-difference methods to advection problems

The possibility of applying the most recent conservative and widely applied numerical techniques in gas dynamics to problems encountered in meteorological computations is investigated. Solutions of the advection equation are obtained using conservative differencing methods. These results are compared to those obtained by differencing the advection equation directly. The comparison shows that better results are obtained when the conservative form of governing equation is used. In addition, better results are obtained with lower order conservative methods as compared with higher order differencing applied to the advection equation.

Fattahi, B.↗
DOE OSTI2022

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

An Overview of Atmospheric Chemistry and Air Quality Modeling

This presentation will include my personal research experience and an overview of atmospheric chemistry and air quality modeling to the participants of the NASA Student Airborne Research Program (SARP 2017). The presentation will also provide examples on ways to apply airborne observations for chemical transport (CTM) and air quality (AQ) model evaluation. CTM and AQ models are important tools in understanding tropospheric-stratospheric composition, atmospheric chemistry processes, meteorology, and air quality. This presentation will focus on how NASA scientist currently apply CTM and AQ models to better understand these topics. Finally, the importance of airborne observation in evaluating these topics and how in situ and remote sensing observations can be used to evaluate and improve CTM and AQ model predictions will be highlighted.

Atmospheric↗
DOE OSTI2021

The Wasatch Environmental Observatory: A mountain to urban research network in the semi‐arid western US

Abstract The 2085 km 2 Jordan River Basin, and its seven sub‐catchments draining the Central Wasatch Range immediately east of Salt Lake City, UT, are home to an array of hydrologic, atmospheric, climatic and chemical research infrastructure that collectively forms the Wasatch Environmental Observatory (WEO). WEO is geographically nested within a wildland to urban land‐use gradient and built upon a strong foundation of over a century of discharge and climate records. A 2200 m gradient in elevation results in variable precipitation, temperature and vegetation patterns. Soil and subsurface structure reflect systematic variation in geology from granitic, intrusive to mixed sedimentary clastic across headwater catchments, all draining to the alluvial or colluvial sediments of the former Lake Bonneville. Winter snowfall and spring snowmelt control annual hydroclimate, rapid population growth dominates geographic change in lower elevations and urban gas and particle emissions contribute to episodes of severe air pollution in this closed‐basin. Long‐term hydroclimate observations across this diverse landscape provide the foundation for an expanding network of infrastructure in both montane and urban landscapes. Current infrastructure supports both basic and applied research in atmospheric chemistry, biogeochemistry, climate, ecology, hydrology, meteorology, resource management and urban redesign that is augmented through strong partnerships with cooperating agencies. These features allow WEO to serve as a unique natural laboratory for addressing research questions facing seasonally snow‐covered, semi‐arid regions in a rapidly changing world and an excellent facility for providing student education and research training.

Follstad Shah, Jennifer J.↗
NASA NTRS1986

Cloud cover estimation using bispectral satellite measurements

An algorithm has been developed for cloud cover estimation using bispectral satellite measurements. Based on the distribution of pixels in albedo-brightness temperature space, a number of threshold values are applied to identify those pixels which are most likely totally cloud filled. Mean cloudy-column albedo in a region much larger than a single pixel is then estimated and cloud cover computed. The algorithm has been applied to the International Satellite Cloud Climatology Project Geostationary Meteorological Satellite B2 data. Locations of tropical convective cells and the passage of fronts in higher latitudes are identified. Since these features represent the states of large-scale atmospheric circulations, it is concluded that the algorithm can yield consistent cloud data sets useful for climate studies.

Chou, M.-D.↗
NASA NTRS1993

Rugged, no-moving-parts windspeed and static pressure probe designs for measurements in planetary atmospheres

Instruments developed for making meteorological observations under adverse conditions on Earth can be applied to systems designed for other planetary atmospheres. Specifically, a wind sensor developed for making measurements within tornados is capable of detecting induced pressure differences proportional to wind speed. Adding strain gauges to the sensor would provide wind direction. The device can be constructed in a rugged form for measuring high wind speeds in the presence of blowing dust that would clog bearings and plug passages of conventional wind speed sensors. Sensing static pressure in the lower boundary layer required development of an omnidirectional, tilt-insensitive static pressure probe. The probe provides pressure inputs to a sensor with minimum error and is inherently weather-protected. The wind sensor and static pressure probes have been used in a variety of field programs and can be adapted for use in different planetary atmospheres.

Bedard, A. J., Jr.↗
NASA NTRS1998

Application of Multilayer Feedforward Neural Networks to Precipitation Cell-Top Altitude Estimation

The use of passive 118-GHz O2 observations of rain cells for precipitation cell-top altitude estimation is demonstrated by using a multilayer feed forward neural network retrieval system. Rain cell observations at 118 GHz were compared with estimates of the cell-top altitude obtained by optical stereoscopy. The observations were made with 2 4 km horizontal spatial resolution by using the Millimeter-wave Temperature Sounder (MTS) scanning spectrometer aboard the NASA ER-2 research aircraft during the Genesis of Atlantic Lows Experiment (GALE) and the COoperative Huntsville Meteorological EXperiment (COHMEX) in 1986. The neural network estimator applied to MTS spectral differences between clouds, and nearby clear air yielded an rms discrepancy of 1.76 km for a combined cumulus, mature, and dissipating cell set and 1.44 km for the cumulus-only set. An improvement in rms discrepancy to 1.36 km was achieved by including additional MTS information on the absolute atmospheric temperature profile. An incremental method for training neural networks was developed that yielded robust results, despite the use of as few as 56 training spectra. Comparison of these results with a nonlinear statistical estimator shows that superior results can be obtained with a neural network retrieval system. Imagery of estimated cell-top altitudes was created from 118-GHz spectral imagery gathered from CAMEX, September through October 1993, and from cyclone Oliver, February 7, 1993.

Spina, Michelle S.↗