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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 NTRS1976

Tethered balloon-based measurements of meteorological variables and aerosols

Tethered balloon based measurements of the vertical distributions of temperature, humidity, wind speed, and aerosol concentrations were taken over a 4-hour period beginning at sunrise on June 29, 1976, at Wallops Island, Virginia. Twelve consecutive profiles of each variable were obtained from ground to about 500 meters. These measurements were in conjuction with a noise propagation study on remotely arrayed acoustic range (ROMAAR) at Wallops Flight Center. An organized listing of these vertical soundings is presented. The tethered balloon system configuration utilized for these measurements is described.

Sentell, R. J.↗
NASA NTRS2019

Assimilation of GPM Retrieved Surface Meteorology Variables with ICE-POP Case Studies

Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM has a broad global coverage ~70°S – 70°N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall.

GPM retrieval product↗
NASA NTRS1992

The response of the SSM/I to the marine environment. I - An analytic model for the atmospheric component of observed brightness temperatures

A detailed parameterization is developed for the contribution of the nonprecipitating atmosphere to the microwave brightness temperatures observed by the Special Sensor Microwave/Imager (SSM/I). The atmospheric variables considered include the viewing angle, the integrated water vapor amount and scale height, the effective tropospheric lapse rate and near-surface temperature, the total cloud liquid water, the effective cloud height, and the surface pressure. The dependence of the radiative variables on meteorological variables is determined for each of the SSM/I frequencies 19.35, 22.235, 37.0, and 85.5 GHz, based on the values computed from 16,893 maritime temperature and humidity profiles representing all latitude belts and all seasons. A comparison of the predicted brightness temperatures with brightness temperatures obtained by direct numerical integration of the radiative transfer equation for the radiosonde-profile dataset yielded rms differences well below 1 K for all four SSM/I frequencies.

Petty, Grant W.↗
NASA NTRS2016

Evaluation of Different MODIS AOD Retrieval Algorithms for PM (sub 2.5) Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP (Minority University Research and Education Project) Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns PM (sub 2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM (sub 2.5) varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target versus Deep Blue), Collections (5.1 versus 6) and spatial resolutions (10-kilometers versus 3-kilometers) for cities in the Western, Midwestern and Southeastern U.S. We developed and validated PM (sub 2.5) prediction models using remotely-sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM (sub 2.5) models, especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Our study re-establishes the connection between PM (sub 2.5) and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM (sub 2.5) data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM (sub 2.5).

Environmental Public Health↗
DOE OSTI2023

Subsetted model output for meteorology-related variables on staggered grids from a LASSO-CACTI WRF simulation

Meteorology-related model output for variables on staggered grids from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables on the raw, model eta levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quantification of the Radiative Effect of Aerosol–Cloud Interactions in Shallow Continental Cumulus Clouds

The indirect radiative effect of aerosol variability on shallow cumulus clouds is realized in nature with considerable concurrent meteorological variability. Large-eddy simulations constrained by observations at a continental site in Oklahoma are performed to represent the variability of different meteorological states on days with different aerosol conditions. The total radiative effect of this natural covariation between aerosol and other meteorological drivers of total cloud amount and albedo is quantified. The changes to these bulk quantities are used to understand the response of the cloud radiative effect to aerosol–cloud interactions (ACI) in the context of concurrent processes, as opposed to attempting to untangle the effect of individual processes on a case-by-case basis. Mutual information (MI) analysis suggests that meteorological variability masks the strength of the relationship between cloud drop number concentration and the cloud radiative effect. This is shown to be mostly due to variation in solar zenith angle and cloud field horizontal heterogeneity masking the relationship between cloud drop number and cloud albedo. By combining MI and more traditional differential analyses, a framework to identify important modes of covariation between aerosol, clouds, and meteorological conditions is developed. This shows that accounting for solar zenith angle variation and implementing an albedo bias correction increases the detectability of the radiative effects of ACI in simulations of shallow cumulus.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subsetted model output for meteorology-related variables from a LASSO-CACTI WRF simulation

Meteorology-related model output from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables on the raw, model eta levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subsetted model output on pressure levels for meteorology-related variables from a LASSO-CACTI WRF simulation

Meteorology-related model output interpolated to pressure levels from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables interpolated to pressure levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subsetted model output on height-above-ground levels for meteorology-related variables from a LASSO-CACTI WRF simulation

Meteorology-related model output interpolated to height-above-ground levels from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables interpolated to heights based on distance above ground level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subsetted model output on height-above-mean-sea-level levels for meteorology-related variables from a LASSO-CACTI WRF simulation

Meteorology-related model output interpolated to heights-above-mean-sea-level from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables interpolated to heights based on distance above mean sea level.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Estimation of Daily PM(sub 10) Concentrations in Italy (2006-2012) Using Finely Resolved Satellite Data, Land Use Variables and Meteorology

Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM10measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM10concentrations at 1-km2 grid over Italy, for the years 2006-2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM10=0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM10levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM10concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Particulate Matter/analysis↗
NASA NTRS2001

Model Simulations of CO2 Transport Using Assimilated Meteorological Fields

The numerical simulation of CO2 transport (and other tracers such as CO, CH4, and biomass burning tracers) in the atmosphere is required to determine the fate of anthropogenic source gases. Estimation of the CO2 exchange between the ocean surface, the terrestrial biosphere, and the atmosphere is of first-order importance to understanding the global carbon cycle and the processes that are most crucial in determining the atmospheric CO2 concentration. Forward transport simulations have been conducted using two-dimensional, time-dependent grids of average surface fluxes (from TRANSCOM) and three-dimensional wind data from a prototype data assimilation system (FV-DAS) run by the Goddard Data Assimilation Office. The objective is to better understand the contribution of meteorological variability to changes in CO2 and other constituents, By accurately accounting for meteorological variability, through use of assimilated winds, we hope to better characterize the distribution of surface sources and sinks (and chemistry where applicable). With assimilated meteorology such chemistry/transport runs provide the basic framework to analyze existing (and proposed) measurement data on a point-by-point, real-time basis. We compare with measured CO2 concentration gradients on a daily, seasonal, regional, and interhemispheric basis to examine the consistency of sources, sinks, and transport formulation. We will also examine the inter-annual variability of atmospheric CO2 due to atmospheric circulation changes using longer runs with assimilated winds.

Kaea, S. R.↗
NASA NTRS1991

Climatological perspectives, oceanographic and meteorological, on variability in the subtropical convergence zone in the northwestern Atlantic

The large-scale climatological environment of the Frontal Air-Sea Interaction Experiment (FASINEX) is described, with emphasis on the largest scales. Both long-term and annual sea surface temperature (SST) variability is discussed; a climatology of the west-central North Atlantic, derived from various sets of data obtained during the intensive phases of January-March, is presented, and the meteorological and oceanographic context for FASINEX is thus established. Surface marine observations and SST variability are discussed, and the marine meteorology of the FASINEX area is examined in terms of the surface pressure and winds, the sea-air temperature difference, and the cloud cover. Near 28 deg N in February is found to be a favorable time and place to observe large mean temperature gradients and downward Ekman pumping. The large-scale processes that set up the favorable environment for frontal activity are not limited to the winter months.

Hanson, Howard P.↗
NASA NTRS2017

Probabilistic Forecasting of Surface Ozone with a Novel Statistical Approach

The recent change in the Environmental Protection Agency's surface ozone regulation, lowering the surface ozone daily maximum 8-h average (MDA8) exceedance threshold from 75 to 70 ppbv, poses significant challenges to U.S. air quality (AQ) forecasters responsible for ozone MDA8 forecasts. The forecasters, supplied by only a few AQ model products, end up relying heavily on self-developed tools. To help U.S. AQ forecasters, this study explores a surface ozone MDA8 forecasting tool that is based solely on statistical methods and standard meteorological variables from the numerical weather prediction (NWP) models. The model combines the self-organizing map (SOM), which is a clustering technique, with a step wise weighted quadratic regression using meteorological variables as predictors for ozone MDA8. The SOM method identifies different weather regimes, to distinguish between various modes of ozone variability, and groups them according to similarity. In this way, when a regression is developed for a specific regime, data from the other regimes are also used, with weights that are based on their similarity to this specific regime. This approach, regression in SOM (REGiS), yields a distinct model for each regime taking into account both the training cases for that regime and other similar training cases. To produce probabilistic MDA8 ozone forecasts, REGiS weighs and combines all of the developed regression models on the basis of the weather patterns predicted by an NWP model. REGiS is evaluated over the San Joaquin Valley in California and the northeastern plains of Colorado. The results suggest that the model performs best when trained and adjusted separately for an individual AQ station and its corresponding meteorological site.

Ozone variables↗
NASA NTRS2015

Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011

Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas.

PM2.5↗
DOE OSTI2021

diyepw: A Python package for Do-It-Yourself EnergyPlus weather file generation

diyepw allows for quick and easy generation of a set of EnergyPlus weather (EPW) files for a given location over a given historical period. The user can obtain weather files using an open-source, automated workflow by simply specifying the location of interest using the World Meteorological Organization weather station ID number, and specifying a year or set of years for which to generate EPW files. Building energy modelers can use these auto-generated weather files in building performance simulations to represent the actual observed weather conditions in the location(s) of interest, based on meteorological observations obtained from the National Oceanic and Atmospheric Administration's Integrated Surface Database. Because observed weather data are not available for every meteorological variable specified in the EPW format, diyepw starts with a widely-used set of typical meteorological year (TMY) files, using them as the template to generate new EPW files by substituting in the observed values of selected meteorological variables that are known to affect building energy performance. Its output is a weather file or group of weather files that conform to the data standards associated with the EPW format so they can be used with any building performance simulation software employing EnergyPlus as its simulation engine.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Turbulent transport and production/destruction of ozone in a boundary layer over complex terrain

The first Intensive Observation Period (IOP) of the Swiss air pollution experiment POLLUMET took place in 1990 in the Aare River Valley between Bern and Zurich. During the IOP, fast response measurements of meteorological variables and ozone concentration were made within the boundary layer aboard a motorglider. In addition, mean values of meteorological variables and the concentrations of ozone and other trace species were measured using other aircraft, pilot balloons, tethersondes, and ground stations. Turbulent flux profiles of latent and sensible heat and ozone are calculated from the fast response data. Terms in the ozone mean concentration budget (time rate of change of mean concentration, horizontal advection, and flux divergence) are calculated for stationary time periods both before and after the passage of a cold front. The source/sink term is calculated as a residual in the budget, and its sign and magnitude are related to the measured concentrations of reactive trace species within the boundary layer. Relationships between concentration ratios of trace species and ozone concentration are determined in order to understand the influence of complex terrain on the processes that produce and destroy ozone.

Greenhut, Gary K.↗