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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Performance Assessment of the Eastern Range False Cape 915-MHz Doppler Radar Wind Profiler

The United States Space Force (USSF) is responsible for space vehicle launches at its Eastern Range (ER), which includes the Cape Canaveral Space Force Station (CCSFS). Multiple systems are used to measure the atmosphere at the ER, including suites of Doppler Radar Wind Profilers (DRWPs) operating at 915 MHz that measure winds within the lowest few kilometers of the atmosphere. Observations of boundary layer winds can be used for multiple applications, including serving as input to toxic dispersion models and characterizing winds for low-level aborts. The USSF upgraded the False Cape DRWP, which collected data during the autumn of 2020. The USSF also requested NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) to evaluate wind output from this DRWP system. This report describes the system and the analyses that MSFC NE conducted to demonstrate the system’s wind accuracy relative to balloons from the Automated Meteorological Profiling System (AMPS), data availability, and effective vertical resolution (EVR).

BJ Barbre↗
NASA NTRS1963

Tiros IV Radiation Data Catalog and Users' Manual

The TIROS IV Meteorological Satellite contains a medium resolution scanning radiometer. Two of the channels of this instrument are sensitive to reflected solar radiation and the remaining two respond to emitted thermal radiation from the earth and its atmosphere. The two thermal channels are calibrated in terms of equivalent blackbody temperatures, and the reflected solar radiation channels in terms of effective radiant emittances. The calibration data, along with orbital and attitude data and the radiation data from the satellite, were incorporated in a computer program for an IBM 7090 which was used to produce, in binary form, the "Final Meteorological Radiation Tape" which is the basic repository of all radiation data. After launch, the radiometer displaye d the same degradation of response characteristics as did its predecessors in TIROS II and TIROS III. The onset of degradation results in a departure of the data from the prelaunch laboratory calibration. The cause of degradation has not been determined, and the matter is still being studied at the Goddard Space Flight Center. Before work with the TIROS IV radiation data is attempted, an understanding of the radiometer, its calibration, and the problems encountered in the experiment, especially from response degradation, is essential. The instrumentation design, development work, and the calibrations herein described were performed by the Goddard Space Flight Center Staff, whereas the computer and programming efforts were carried out jointly by the staffs of the National Weather Satellite Center, U. S. Weather Bureau, and the Goddard Space Flight Center. In this Catalog-Manual, the radiometer an d its calibration, data processing, the "Final Meteorological Radiation Tape", the observed degradation patterns, and possible corrections for degradation are discussed. The Catalog-Manual also includes, in two forms, documentation of each orbit of successfully reduced radiation data acquired by TIROS IV. One method of presentation is the Index of Final Meteorological Radiation Tapes and the other is a Subpoint Track, Summary of Available Radiation Data in diagrammatic form.

TIROS IV SATELLITE↗
NASA NTRS1976

Field support, data analysis and associated research for the acoustic grenade sounding program

Temperature and horizontal winds in the 30 to 90 km altitude range of the upper atmosphere, were determined by acoustic grenade soundings conducted at Wallops Island, Virginia and Kourou, French Guiana. Field support provided at these locations included deployment of the large area microphone system, supervision, maintenance and operation of sound ranging stations; and coordination of activities. Data analysis efforts included the analysis of field data to determine upper atmospheric meteorological parameters. Profiles for upper atmospheric temperature, wind and density are provided in plots and tables for each of the acoustic grenade soundings conducted during the contract period. Research efforts were directed toward a systematic comparison of temperature data from acoustic grenade with other meteorological sensor probes in the upper atmosphere.

Barnes, T. G.↗
DOE OSTI2025

Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches

Accurately predicting stream temperature in ungauged basins remains a critical challenge for water resource management, thermoelectric power plant cooling, and ecosystem conservation. Large-sample machine learning models trained on hundreds of well-monitored river basins have shown remarkable performance; however, such models have yet to be developed solely using forcing data that can be readily extracted to simulate stream temperatures anywhere in the contiguous United States (CONUS). In this study, we present a scalable, large-sample deep learning framework using Long Short-Term Memory (LSTM) networks to simulate daily stream temperatures in ungauged basins across the CONUS. The framework leverages both modeled reanalysis of meteorological and streamflow inputs as well as static attributes available for all 2.7 million CONUS river reaches in the National Hydrography Dataset Plus (NHDPlusV2). By generating dynamical inputs from predefined thermally relevant upstream contributing areas, rather than the entire upstream basin, the model also offers improvements in very large basins where full-basin averaging can dilute the most important influences on stream temperature. Evaluated across 300 basins, the model achieves a median Mean Absolute Error (MAE) of 1.1 °C and a Nash-Sutcliffe Efficiency (NSE) of 0.95 on temporally and spatially distinct test folds—comparable to models trained exclusively using meteorological and streamflow observational data. The flexible, high-performing framework generalizes to any unmonitored river reach without significant regulation or unnatural thermal input immediately upstream, substantially expanding predictive capabilities in data-scarce regions.

Hydrology↗
DOE OSTI2023

Architecture of a Data Portal for Publishing and Delivering Open Data for Atmospheric Measurement

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2021

Performance Assessment of the Eastern Range Titusville-Cocoa 915-MHz Doppler Radar Wind Profiler

The United States Space Force (USSF) is responsible for space vehicle launches at its Eastern Range (ER), which includes the Cape Canaveral Space Force Station (CCSFS). Multiple systems are used to measure the atmosphere at the ER, including suites of Doppler Radar Wind Profilers (DRWPs)that operate at 915MHz and measure winds within the lowest few kilometers of the atmosphere. Observations of boundary layer winds can be used for multiple applications, including serving as input to toxic dispersion models and characterizing winds for low-level aborts. The USSF upgraded the Titusville-Cocoa(TICO)DRWP, which collected data during the spring and summer of 2020. The USSF also requested NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) to evaluate wind output from this DRWP system. This report describes the system and the analyses that MSFC NE conducted to demonstrate the system’s wind accuracy relative to balloons from the Automated Meteorological Profiling System (AMPS), data availability, and effective vertical resolution (EVR).

B. J. Barbre↗
NASA NTRS2022

Midwest Water Resources II: Evaluating Evapotranspiration with NASA Earth Observations and In Situ Observations to Understand Water Balance in Midwest Agriculture

Seasonal water variability in the midwestern United States extensively affects the agricultural community, as it impacts irrigation schedules, growing seasons, and overall ecosystem function. Evapotranspiration (ET) is a critical climatic variable in the water cycle and is used to evaluate spatiotemporal trends in drought and flood conditions. The NASA DEVELOP team partnered with the United States Department of Agriculture (USDA) Midwest Climate Hub, the Minnesota Department of Agriculture, the Illinois State Water Survey, and Michigan State University to compare remotely sensed ET products with in situ observations from January 2001 through December 2020. Remotely sensed actual ET (aET) data were sourced from NASA’s Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and reference ET (refET) data were derived from the Gridded Surface Meteorological (gridMET) dataset. For in situ comparison, aET data were downloaded from the AmeriFlux database while refET data were collected from the Illinois Climate Network and Michigan State University’s Enviro-weather database. For a holistic assessment of ET, this project generated comparisons between remotely sensed and in situ observations, calculated descriptive statistics for validation between refET datasets, and spatially produced statistical validation maps regarding in situ sites. The temporal and spatial gaps of AmeriFlux data limited aET analysis. This comparative assessment of ET products across the Midwest can be used by project partners to assess regional water trends and guide future land management decisions.

Addison Pletcher↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Preliminary analysis of 15 GHz scintillations on an ATS-5 satellite-to-ground path

Although the ATS 5 satellite failed to de-spin, a rather intricate analysis procedure allows the extraction of scintillation information at spectral rates from 20 to 60 Hz, as well as below 0.5 Hz. The procedure has been applied to 15.3 GHz signals. Distributions and spectra were obtained for a limited amount of data, representing a variety of meteorological conditions. A definite correlation of scintillation strength and variability with rainfall is apparent. The data analysis is continuing.

Levis, C. A.↗
NASA NTRS1981

Determining economic benefits of satellite data in short-range forecasting

The chances of enhanced short term weather predictions and economic benefits from the use of GOES satellite data were examined. Results for a meteorological consulting firm before and after the introduction of GOES data were chosen as the method, and monetary benefits were selected as the measure. Services were provided for use by road and street departments, commodities dealers, and marine clients of the consulting firm. The Man-computer Interactive Data Access Program (McIDAS) was employed to furnish 1/2 hour visual or IR imagery for remote access. The commodities clients reconnected the GOES real-time imagery once the study was completed, while the consulting firm, which was personnel and not equipment intensive, did not. Further development of the flexibility of access to the GOES data and improvements in the projected grids are indicated.

Suchman, D.↗
NASA NTRS1993

A technique for deriving column-integrated water content using VAS split-window data

An algorithm is examined that uses VISSR Atmospheric Sounder (VAS) 11- and 12-micron (split-window) data to derive column-integrated water content (IWC) at mesoscale resolution. The algorithm is physically based and derives its first-guess information from radiosonde data. The procedure is applied first to a test case data set and then to the 19 June 1986 study day from the Cooperative Huntsville Meteorological Experiment (COHMEX). Ground truth data for verifying results from the technique include IWC from National Weather Service and COHMEX radiosondes, the Multispectral Atmospheric Mapping Sensor (MAMS), and a special set of VAS soundings (12 channel) using an independent retrieval method. Results from the test case show reasonable accuracy with the rms errors as low as +/- 3.8 mm. On the 19 June case study day IWC analyses depict reasonable gradients and exhibit good spatial and temporal continuity. Furthermore, they provide insight into preferred regions for cumulus cloud and thunderstorm formation. On the average, a mean absolute retrieval error of 2.4 mm (an 8.1 percent error) and a rms error of +/- 2.9 mm are obtained on the case study day. These results compare favorably with those from existing VAS IWC techniques. Overall, the findings indicate that the technique has excellent potential to depict mesoscale moisture variations.

Guillory, Anthony R.↗
NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data -- the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data -- to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km x 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1 deg x 1 deg latitude-longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40 C for monthly minimum temperatures, near 0.25 C for monthly mean temperatures, and near 0.10 C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near -1.1 C for rural stations to 2.4 C for stations from the largest urban areas. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Epperson, David L.↗
NASA NTRS1989

Equatorial zonal wind in the middle atmosphere derived from geopotential height and temperature data

This paper examines the feasibility of deriving the zonal wind at the equator using mean temperature and geopotential height data from satellite and radiosonde/rocketsonde measurements. Using climatological data of the stratosphere and mesosphere based on monthly mean Nimbus 5 Selective Chopper Radiometer and Nimbus 6 Pressure Modulator Radiometer measurements, and stratospheric monthly mean data from the National Meteorological Center for 1979-1986, meridional cross sections and time profiles of the zonal wind in the tropical middle atmosphere are presented. The derived zonal wind at the equator reproduces the mean climatology of the tropical middle atmosphere as well as the general characteristics of the equatorial quasi-biennial (QBO) and semiannual oscillations (SAO) observed in monthly mean radiosonde and rocketsonde data. Although the amplitude of the derived wind QBO is for the most part underestimated relative to direct-wind measurements, the amplitude of the derived wind SAO compares fairly well with rocketsonde observations.

Fleming, Eric L.↗
DOE OSTI2015

Design and application of a mobile ground-based observatory for continuous measurements of atmospheric trace gas and criteria pollutant species

Ground-based measurements of atmospheric trace gas species and criteria pollutants are essential for understanding emissions dynamics across space and time. Gas composition in the lower 50 m of the atmosphere has the greatest direct impacts on human health as well as ecosystem processes; hence data at this level are necessary for addressing carbon-cycle- and public-health-related questions. However, such surface data are generally associated with stationary measurement towers, where spatial representation is limited due to the high cost of establishing and maintaining an extensive network of measurement stations. We describe here a compact mobile laboratory equipped to provide high-precision, high-frequency, continuous, on-road synchronous measurements of CO 2 , CO, CH 4 , H 2 O, NO x , O 3 , aerosol, meteorological, and geospatial position data. The mobile laboratory has been deployed across the western USA. In addition to describing the vehicle and its capacity, we present data that illustrate the use of the laboratory as a powerful tool for investigating the spatial structure of urban trace gas emissions and criteria pollutants at spatial scales ranging from single streets to whole ecosystem and regional scales. We assess the magnitude of known point sources of CH 4 and also identify fugitive urban CH 4 emissions. We illustrate how such a mobile laboratory can be used to better understand emissions dynamics and quantify emissions ratios associated with trace gas emissions from wildfire incidents. Lastly, we discuss additional mobile laboratory applications in health and urban metabolism.

Bush, S. E.↗
DOE OSTI2022

Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework

Abstract. The flow of carbon through terrestrial ecosystems and the response to climate are critical but highly uncertain processes in the global carbon cycle. However, with a rapidly expanding array of in situ and satellite data, there is an opportunity to improve our mechanistic understanding of the carbon (C) cycle's response to land use and climate change. Uncertainty in temperature limitation on productivity poses a significant challenge to predicting the response of ecosystem carbon fluxes to a changing climate. Here we diagnose and quantitatively resolve environmental limitations on the growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000–2018) at a subalpine evergreen forest in Colorado, USA. We implement the CARbon DAta-MOdel fraMework (CARDAMOM) model–data fusion network to resolve the temperature sensitivity of spring GPP. To capture a GPP temperature limitation – a critical component of the integrated sensitivity of GPP to temperature – we introduced a cold-temperature scaling function in CARDAMOM to regulate photosynthetic productivity. We found that GPP was gradually inhibited at temperatures below 6.0 ∘C (±2.6 ∘C) and completely inhibited below −7.1 ∘C (±1.1 ∘C). The addition of this scaling factor improved the model's ability to replicate spring GPP at interannual and decadal timescales (r=0.88), relative to the nominal CARDAMOM configuration (r=0.47), and improved spring GPP model predictability outside of the data assimilation training period (r=0.88). While cold-temperature limitation has an important influence on spring GPP, it does not have a significant impact on integrated growing-season GPP, revealing that other environmental controls, such as precipitation, play a more important role in annual productivity. This study highlights growing-season onset temperature as a key limiting factor for spring growth in winter-dormant evergreen forests, which is critical in understanding future responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Assimilation of Stratospheric Meteorological and Constituent Observations: A Review

This talk reviews the assimilation of meteorological and constituent observations of the stratosphere. The first efforts to assimilate observations into stratospheric models were during the early 1980s, and a number of research studies followed during the next decade. Since the launch of the Upper Atmospheric Research Satellite (UARS) in 1991, model-assimilated data sets of the stratospheric meteorological state have been routinely available. These assimilated data sets were critical in bringing together observations from the different instruments on UARS as well as linking UARS observations to measurements from other platforms. Using trajectory-mapping techniques, meteorological assimilation analyses are, now, widely used in the analysis of constituent observations and have increased the level of quantitative study of stratospheric chemistry and transport. During the 1990s the use of winds and temperatures from assimilated data sets became standard for offline chemistry and transport modeling. variability in middle latitudes. The transport experiments, however, reveal a set of shortcomings that become obvious as systematic errors are integrated over time. Generally, the tropics are not well represented, mixing between the tropics and middle latitudes is overestimated, and the residual circulation is not accurate. These shortcomings reveal underlying fundamental challenges related to bias and noise. Current studies using model simulation and data assimilation in controlled experimentation are highlighting the issues that must be addressed if assimilated data sets are to be convincingly used to study interannual variability and decadal change. observations. The primary focus has been on stratospheric ozone, but there are efforts that investigate a suite of reactive chemical constituents. Recent progress in ozone assimilation shows the potential of assimilation to contribute to the validation of ozone observations and, ultimately, the retrieval of ozone profiles from space-based radiance measurements. Assimilated data sets provide accurate analyses of synoptic and planetary Scale At the same time, stratospheric assimilation is evolving to include constituent

Rood, Richard B.↗
DOE OSTI2017

Spatial Analysis of Stover Moisture Content During Harvest Season in the U.S.

The moisture content of a maturing crop varies as the harvest season progresses. For crop residues such as corn stover, moisture content at the time of harvest can be as high as 75% (wet mass basis) to less than 20% depending on the geographic location (climate conditions) and stage of harvest. Biomass moisture content is critical for baling and extended storage. It is therefore essential to have an estimate of the quantities of corn stover available as wet or dry for various parts of the U.S. To this end, we analyzed hourly weather data (temperature, humidity, and rainfall) from the Typical Meteorological Year v.3 (TMY3) dataset developed by the National Renewable Energy Laboratory. A recently published set of equations for calculating the moisture content of stover as a function of hourly temperature, humidity, and rainfall were used. The annual start and end of corn grain harvest along with annual grain production (in bushels) for each state were extracted from USDA National Agricultural Statistics Service reports. Using these datasets and moisture sorption equations, the percentage of corn stover tonnage with moisture content less than 20%, between 20% and 40%, or greater than 40% was estimated from the length of time that the biomass was in these moisture content ranges. These calculations were carried out for several locations within each of the states for which TMY data were available. It was concluded that about 37.2% of corn stover is dry (<20% moisture content), whereas 36.5% is wet (>40% moisture content) nationwide. The remaining 27.0% of corn stover is between 20% and 40% moisture content. Keywords: Corn stover, Equilibrium moisture content, Field drying, Moisture content, Stover harvest, Typical Meteorological Year data.

09 BIOMASS FUELS↗
DOE OSTI2015

The Initial Atmospheric Transport (IAT) Code: Description and Validation

The Initial Atmospheric Transport (IAT) computer code was developed at Sandia National Laboratories as part of their nuclear launch accident consequences analysis suite of computer codes. The purpose of IAT is to predict the initial puff/plume rise resulting from either a solid rocket propellant or liquid rocket fuel fire. The code generates initial conditions for subsequent atmospheric transport calculations. The Initial Atmospheric Transfer (IAT) code has been compared to two data sets which are appropriate to the design space of space launch accident analyses. The primary model uncertainties are the entrainment coefficients for the extended Taylor model. The Titan 34D accident (1986) was used to calibrate these entrainment settings for a prototypic liquid propellant accident while the recent Johns Hopkins University Applied Physics Laboratory (JHU/APL, or simply APL) large propellant block tests (2012) were used to calibrate the entrainment settings for prototypic solid propellant accidents. North American Meteorology (NAM )formatted weather data profiles are used by IAT to determine the local buoyancy force balance. The IAT comparisons for the APL solid propellant tests illustrate the sensitivity of the plume elevation to the weather profiles; that is, the weather profile is a dominant factor in determining the plume elevation. The IAT code performed remarkably well and is considered validated for neutral weather conditions.

97 MATHEMATICS AND COMPUTING↗