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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Transition-state correlations for predicting thermochemistry of adsorbates and surface reactions

Estimating thermochemical properties from linear correlations may provide a pathway to circumvent expensive density functional theory (DFT) calculations for quantities such as pre-exponentials and temperature corrections to DFT energies. Here, in this work, we construct thermochemical scaling relations between C 1 –C 6 n-alkanes in the gas phase and adsorbed alkyl chains extending from several transition metal surfaces, and examine changes in the slope and fit between metals and adsorption sites. We subsequently add –OH, –NH 2 , C$=$O, and C$=$C functional groups to the C 1 –C 6 molecules and demonstrate strong linear correlations for thermochemistry across all species. We broaden the correlations to incorporate transition states of C 1 –C 6 n-alkane dehydrogenation reactions, where thermochemistry for computationally prohibitive transition-state calculations can be quickly assessed. Additionally, we rationalize the linearity of thermochemical correlations based on the composition of the homologous series and theoretical assessments. As an application of the correlations, we estimate pre-exponentials for elementary surface reactions of ethane and propane hydrogenolysis on Ru(0001), which is of relevance to plastic hydrogenolysis. Depending on kinetically important steps, entropic contributions may be necessary to include in certain reaction mechanisms; in contrasting examples, entropies are found to be relatively insignificant for ethane hydrogenolysis but pertinent for propane hydrogenolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

The Land Surface in Current and Planned MERRA Reanalysis Products

Current global atmospheric reanalysis products such as the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) provide estimates of land surface states and fluxes, including soil moisture, soil temperature, snow mass, latent and sensible heat fluxes, and runoff, that are widely used in research and applications. These land surface estimates are based on land surface process models and, depending on the reanalysis product, on precipitation observations or the assimilation of land surface observations of soil moisture, soil temperature, snow conditions, and screen-level air temperature and humidity from satellite observations and in situ measurements. In this presentation, we review the land surface modeling and data assimilation components of the suite of current and planned MERRA reanalysis products. In addition to MERRA-2, we will discuss the latest NASA reanalysis, MERRA for the 21st century (M21C), which is currently under production, as well as the development and planning of the next version of the MERRA reanalysis, tentatively labeled MERRA-3. In MERRA-2, observations-based precipitation data products are used to correct the precipitation falling on the land surface. Outside of the high-latitudes and Africa, the daily, 0.5-degree, gauge-based Climate Prediction Center (CPC) Unified (CPCU) product is used. In Africa, the pentad, 2.5-degree, satellite- and gauge-based CPC Merged Analysis of Precipitation (CMAP) product is used. Poleward of 62.5 degrees latitude, the land surface sees the precipitation generated by the atmospheric model in the cycling data assimilation system. This configuration provides improved soil moisture estimates compared to those of the original (version 1) MERRA estimates, which did not benefit from the use of precipitation observations. Moreover, the use of precipitation observations facilitates a seamless spin-up of the land surface initial conditions across the MERRA-2 production streams. The use of a gauge-only precipitation product in MERRA-2 across much of the globe, however, adversely impacts the quality of the MERRA-2 land surface estimates in regions with poor gauge coverage, including most of South America and Australia. Therefore, the forthcoming M21C reanalysis uses satellite- and gauge-based precipitation from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG). This change results in significant improvements in the quality of the M21C soil moisture estimates in the Southern Hemisphere compared to those from MERRA-2. Planning for MERRA-3 focuses on the assimilation of soil moisture observations from the Soil Moisture Active Passive (SMAP) mission and the Advanced Scatterometer (ASCAT), along with snow cover area fraction observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) to further improve the quality of the land surface estimates from the reanalysis. As a first step towards the assimilation of land surface observations in MERRA-3, the offline (land-only) M21C-Land reanalysis is currently under development as a supplemental M21C product that includes the assimilation of SMAP, ASCAT, and MODIS observations. Preliminary results from M21C and M21C-Land will be discussed in the context of MERRA-2 and plans for MERRA-3.

Rolf Reichle↗
NASA NTRS2020

Atmospheric Carbon Cycle Dynamics Over the Above Domain: an Integrated Analysis Using Aircraft Observations (Arctic-CA) and Model Simulations (GEOS)

The Arctic Carbon Atmospheric Profiles (Arctic-CAP) project conducted six airborne surveys of Alaska and northwestern Canada between April and November 2017 to capture the spatial and temporal gradients of northern high-latitude carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO) as part of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4 and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. We observed vertical gradients of CO2, CH4 and CO that vary by eco-region and duration of the sampling period, which spanned the majority of the seasonal cycle. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons with GEOS simulations of atmospheric CO2, CH4 and CO highlight the potential of these multi-species observations to inform improvements in surface flux estimates and the representation of atmospheric transport. GEOS simulations provide estimates of the near surface average CO2 and CH4 enhancements that are well correlated with aircraft observations (R=0.74 and R=0.60 respectively), suggesting that GEOS has reasonable fidelity over this complex and heterogeneous region. This model-data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broadscale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are observed. The study also provides a framework for benchmarking a global model at regional scales, which is needed to use climate models as tools to investigate high-latitude carbon-climate feedbacks.

C Sweeney↗
NASA NTRS1979

Estimation of mean sea surfaces in the North Atlantic, the Pacific and the Indian Ocean using GEOS-3 altimeter data

The sea surface heights above the reference ellipsoid were determined for several regions of the world's ocean using data from the radar altimeter on board the GEOS-3 satellite in conjunction with precise orbital position information derived from laser data. The resolution of the estimated sea surfaces varied from 0.25 degrees off the east coast of the United States to about 2 degrees in the Indian Ocean near Australia. The rms crossover discrepancy after adjustment varied from 30 cm to 70 cm depending on geographic location. Comparison of the altimeter derived mean sea surface in the North Atlantic with the 5 x 5 ft GEM-8 detailed gravimetric geoid indicated a relative consistency of better than one meter.

Marsh, J. G.↗
NASA NTRSDate not supplied

LOFTID Surface Heating Reconstruction

On November 10, 2022, the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) reentry vehicle launched to low-Earth orbit aboard a United Launch Alliance Atlas V rocket out of Vandenberg Air Force Base. The aeroshell, the largest Hypersonic Inflatable Aerodynamic Decelerator (HIAD) ever flown, was inflated to its full 6-meter diameter before successful re-entry into the atmosphere. The aeroshell was heavily instrumented in order to understand its behavior during entry. There were 82 thermocouples (TCs) distributed across the aeroshell, with 22 integrated into the flexible thermal protection system (FTPS) on the rigid nose, 36 in the FTPS on the deployable structure, and 24 on the inflatable structure. TCs were placed at different depths throughout the FTPS. Those nearest to the surface were located just beneath the two SiC outer fabric layers. The near-surface TCs on the rigid nose were Type R with flame spray alumina insulation, while those on the flank were Type N with mica/ceramic insulation. Additionally, a radiometer was placed at the center of the nose surrounded by four total heat flux gauges in a cruciform configuration at a radius of 0.41 m. The nose instrumentation is shown in Fig. 1 and a cross-section of the aeroshell with all TC locations is shown in Fig. 2. The objective of this work was to use the temperatures measured by the TCs during flight to estimate the surface heat rate across the aeroshell throughout the period of re-entry by inverse analysis methodology. The results were used to evaluate the fidelity of measurements from the total heat flux gauges on the nose, determine the surface heat flux at aeroshell locations where gauges were not present, and compare to pre-flight CFD-based heating predictions. Inversely estimated surface heat flux continues to be used to correlate FTPS thermal models to reconstruct in-flight thermal response.

H S Alpert↗
NASA NTRSDate not supplied

LOFTID Surface Heating Reconstruction

On November 10, 2022, the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) reentry vehicle launched to low-Earth orbit aboard a United Launch Alliance Atlas V rocket out of Vandenberg Air Force Base. The aeroshell, the largest Hypersonic Inflatable Aerodynamic Decelerator (HIAD) ever flown, was inflated to its full 6-meter diameter before successful re-entry into the atmosphere. The aeroshell was heavily instrumented in order to understand its behavior during entry. There were 82 thermocouples (TCs) distributed across the aeroshell, with 22 integrated into the flexible thermal protection system (FTPS) on the rigid nose, 36 in the FTPS on the deployable structure, and 24 on the inflatable structure. TCs were placed at different depths throughout the FTPS. Those nearest to the surface were located just beneath the two SiC outer fabric layers. The near-surface TCs on the rigid nose were Type R with flame spray alumina insulation, while those on the flank were Type N with mica/ceramic insulation. Additionally, a radiometer was placed at the center of the nose surrounded by four total heat flux gauges in a cruciform configuration at a radius of 0.41 m. The nose instrumentation is shown in Fig. 1 and a cross-section of the aeroshell with all TC locations is shown in Fig. 2. The objective of this work was to use the temperatures measured by the TCs during flight to estimate the surface heat rate across the aeroshell throughout the period of re-entry by inverse analysis methodology. The results were used to evaluate the fidelity of measurements from the total heat flux gauges on the nose, determine the surface heat flux at aeroshell locations where gauges were not present, and compare to pre-flight CFD-based heating predictions. Inversely estimated surface heat flux continues to be used to correlate FTPS thermal models to reconstruct in-flight thermal response.

LOFTID↗
NASA NTRSDate not supplied

The Land Surface in Earth System Reanalysis

Current global atmospheric reanalysis products such as ERA-5, MERRA-2, and JRA-55 provide estimates of land surface states and fluxes, including soil moisture, soil temperature, snow mass, latent and sensible heat fluxes, and runoff. These land surface estimates are based on land surface process models and, depending on the reanalysis product, on precipitation observations or the assimilation of land surface observations of soil moisture and snow conditions from satellites and in situ networks. The land models used in the current global atmospheric reanalysis systems are generally focused on water and energy balance processes, with prescribed static land cover and prescribed climatological vegetation conditions. That is, current global atmospheric reanalysis products do not reflect changes in land use or land cover, nor do they provide dynamic estimates of the land carbon cycle. These and other gaps, including the omission of anthropogenic processes such as irrigation in the land model, need to be addressed in the development of the next generation Earth system reanalysis systems. This presentation provides an overview of the state-of-the-art and future developments of land surface estimates in global reanalysis systems.

Rolf Reichle↗
DOE OSTI2026

Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)

This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
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 NTRS2003

Estimates of the Lunar Surface Composition with Clementine Images and LSCC Data

There have been several approaches to develop remote sensing technique to estimate composition of the lunar surface using multispectral observations. We present a new approach based on statistical analysis of spectral and compositional data for lunar samples by the Lunar Soil Characterization Consortium (LSCC) and using the UVVIS Clementine data with 1 km resolution. This technique allows us to estimate and map the abundance of TiO2, SiO2, and FeO, pyroxene and plagioclase content, and maturity degree (Is/FeO).

Shkuratov, Yu.↗
DOE OSTI2020

Observed Sensitivity of Low-Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans

Understanding how marine low clouds and their radiative effects respond to changing meteorological conditions is crucial to constrain low-cloud feedbacks to greenhouse warming and internal climate variability. In this paper, we use observations to quantify the low-cloud radiative response to meteorological perturbations over the global oceans to shed light on physical processes governing low-cloud and planetary radiation budget variability in different climate regimes. We assess the independent effect of perturbations in sea surface temperature, estimated inversion strength, horizontal surface temperature advection, 700-hPa relative humidity, 700-hPa vertical velocity, and near-surface wind speed. Stronger inversions and stronger cold advection greatly enhance low-level cloudiness and planetary albedo in eastern ocean stratocumulus and midlatitude regimes. Warming of the sea surface drives pronounced reductions of eastern ocean stratocumulus cloud amount and optical depth, and hence reflectivity, but has a weaker and more variable impact on low clouds in the tropics and middle latitudes. By reducing entrainment drying, higher free-tropospheric relative humidity enhances low-level cloudiness. At low latitudes, where cold advection destabilizes the boundary layer, stronger winds enhance low-level cloudiness; by contrast, wind speed variations have weak influence at midlatitudes where warm advection frequently stabilizes the marine boundary layer, thus inhibiting vertical mixing. These observational constraints provide a framework for understanding and evaluating marine low-cloud feedbacks and their simulation by models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Variation of the low level winds during the passage of a thunderstorm gust front

Three time histories of wind profiles in thunderstorm gust fronts at Cape Kennedy and three at Oklahoma City are analyzed. Wind profiles at maximum wind strength below 100 m follow logarithmic laws, so that winds above the surface layer can be estimated from surface winds once the roughness length is known. A statistical analysis of 81 cases of surface winds during thunderstorms at Tampa revealed no predictor with skill to predict the time of maximum gust. Some 34% of the variance of the strength of the gust is accounted for by a stability index and surface wind prior to the gust; the regression equations for these variables are given. The coherence between microscale wind speed variations at the different levels has the same proportions as in non-thunderstorm cases.

Sinclair, R. W.↗
NASA NTRS1973

Comparison between photogrammetric and bistatic-radar slope frequency distributions

A comparison was made of the results for lunar surface roughness estimates and slope frequency distributions obtained by using photogrammetric techniques with those obtained by using bistatic radar. Three topics were discussed: (1) the form of the slope frequency distributions, (2) the change of roughness estimates of the lunar surface with scale, and (3) the magnitude of surface roughness.

Moore, H. J.↗
NASA NTRS1989

On the temperature of surfaces

The concept of the temperature of a surface is introduced from the viewpoint of the physical chemistry of surfaces. The surface, near surface and microlayer regions of the interface are defined. Most methods measure the temperature of the microlayer or at best the near surface region and may err in representing the surface temperature. Methods based on capillary ripples actually measure the surface temperature since surface tension (or surface tension tensor when a monolayer has been spread or absorbed at the interface) is the main restoring force that controls their propagation. Light scattering methods are described for determining the elevation of very small amplitude capillary waves through the computation of various correlation functions from which the surface tension can be estimated. Procedures for estimating the surface temperature are described.

Mann, J. Adin, Jr.↗
NASA NTRS1989

Results of a study on polarization mix selection for the NSCAT scatterometer

The NASA scatterometer (NSCAT) is an instrument designed to measure the radar backscatter of the ocean's surface for estimating the near-surface wind velocity. A given resolution element is observed from several different azimuth angles. From these measurements the near-surface vector wind over the ocean may be inferred using a geophysical model function relating the normalized radar backscatter coefficient (sigma0) to the near-surface wind. The results of a study to select a polarization mix for NSCAT using an end-to-end simulation of the NSCAT scatterometer and ground processing of the sigma0 measurements into unambiguous wind fields using a median-filter-based ambiguity-removal algorithm are presented. The system simulation was used to compare the wind measurement accuracy and ambiguity removal skill over a set of realistic mesoscale wind fields for various polarization mixes. Considerations in the analysis and simulation are discussed, and a recommended polarization mix is given.

Long, David G.↗