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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Intercomparison of Sea-surface Temperature Anomaly Fields

Sea-surface temperature anomaly fields produced from analysis of HIRS2/MSU data at GLAS for the months November 1979, December 1981, March 1982, and July 1982 were compared to those produced from data from ships, AVHRR, and SMMR in a series of NASA sponsored sea-surface temperature workshops. The methods used by GLAS to analyze the HIRS2/MSU data were made to the algorithm used to retrieve sea and land surface temperatures and in the cloud filtering algorithm. One of the improvements involved generating weights for a given sounding, which decreased with increasing cloudiness and increasing humidity. These weights should be used in generating monthly mean fields. The workshop generated statistics for the weighted HIRS2/MSU field only for the month of July 1982.

Susskind, J.↗
NASA NTRS1998

Using the Space-Borne NASA Scatterometer (NSCAT) to Determine The Frozen and Thawed Seasons of a Boreal Landscape

We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any water near the surface, should make space-borne radar observations a powerful tool for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length.

NSCAT remote sensing radar BOREAS freeze thaw grow↗
DOE OSTI2022

Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2021

Simulation of Radon-222 with the GEOS-Chem Global Model: Emissions, Seasonality, and Convective Transport

Radon-222 (Rn-222) is a short-lived radioactive gas naturally emitted from land surfaces and has long been used to assess convective transport in atmospheric models. In this study, we simulate Rn-222 using the GEOS-Chem chemical transport model to improve our understanding of Rn-222 emissions and surface concentration seasonality and characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, the Modern-Era Retrospective analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We evaluate four global Rn-222 emission scenarios by comparing model results with observations at 51 surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with surface observations globally (68.9 % of data within a factor of 2) and a large underestimate of winter surface Rn-222 concentrations at Northern Hemisphere midlatitudes and high latitudes due to an oversimplified formulation of Rn-222 emission fluxes (1 atom cm−2 s−1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global Rn-222 emission scenario based on Zhang et al. (2011) and demonstrate its potential to improve simulated surface Rn-222 concentrations and seasonality. The regional components of this scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors in determining Rn-222 emission flux rates. However, large model underestimates of surface Rn-222 concentrations still exist in Asia, suggesting unusually high regional Rn-222 emissions. We therefore propose a conservative upscaling factor of 1.2 for Rn-222 emission fluxes in China, which was also constrained by observed deposition fluxes of 210Pb (a progeny of Rn-222). With this modification, the model shows better agreement with observations in Europe and North America (> 80 % of data within a factor of 2) and reasonable agreement in Asia (close to 70 %). Further constraints on Rn-222 emissions would require additional concentration and emission flux observations in the central United States, Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed Rn-222 vertical profiles in northern midlatitude summer and from three short-term airborne campaigns. While simulations with both GEOS products are able to capture the observed vertical gradient of Rn-222 concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared with GEOS-FP, MERRA leads to stronger convective transport of Rn-222, which is partially compensated for by its weaker large-scale vertical advection, resulting in similar global vertical distributions of Rn-222 concentrations between the two simulations. This has important implications for using chemical transport models to interpret the transport of other trace species when these GEOS products are used as driving meteorology.

Bo Zhang↗
NASA NTRS2015

Soil Moisture Active Passive (SMAP) Mission Level 4 Carbon (L4_C) Product Specification Document

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project.

L4-SM↗
NASA NTRS2018

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project.

Soil Moisture↗
DOE OSTI2025

Monthly averages of ED2 model simulations initialized with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon

Deforestation and forest degradation (selective logging, fires, fragmentation) have impacted nearly 40% of the original extent of the Brazilian Amazon, and have markedly impacted forest structure across the region. To date, few studies analysed how shifts in forest structure from degradation influence the forest sensitivity to climate extremes, because of the complex interactions between forest structure and micro-environmental conditions. To address this knowledge gap, we carried out a series of simulations across the Brazilian Amazon using the Ecosystem Demography Model (ED2), using observed forest structure derived from 541 airborne lidar transects (375 ha each) and two scenarios representing forest recovery and expansion of degradation to investigate how shifts in forest structure impact ecosystem function under near-average and extreme climate conditions, as part of the manuscript Longo et al 2025 "Degradation and Deforestation Increase the Sensitivity of the Amazon Forest to Climate Extremes". This dataset provides the output results from the ED2 model simulations for the three simulations at monthly time scales, in NetCDF format. For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.We also provide file ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc, which classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

A Realization of Bias Correction Method in the GMAO Coupled System

Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of the climate system. The cold or warm sea surface temperature (SST) bias in the tropics is still a problem common to most coupled ocean atmosphere general circulation models (CGCMs). The precipitation biases in CGCMs are also accompanied by SST and surface wind biases. The deficiencies and biases over the equatorial oceans through their influence on the Walker circulation likely contribute the precipitation biases over land surfaces. In this study, we introduce an approach in the CGCM modeling to correct model biases. This approach utilizes the history of the model's short-term forecasting errors and their seasonal dependence to modify model's tendency term and to minimize its climate drift. The study shows that such an approach removes most of model climate biases. A number of other aspects of the model simulation (e.g. extratropical transient activities) are also improved considerably due to the imposed pre-processed initial 3-hour model drift corrections. Because many regional biases in the GEOS-5 CGCM are common amongst other current models, our approaches and findings are applicable to these other models as well.

Chang, Yehui↗
NASA NTRS2021

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Jianxiu Qiu↗
NASA NTRS1995

An airborne study of microwave surface sensing and boundary layer heat and moisture fluxes for FIFE

The objectives of this work were to perform imaging radar and scatterometer measurements over the Konza Prairie as a part of the First International land surface climatology project Field Experiments (EIFE) and to develop an mm-wave radiometer and the data acquisition system for this radiometer. We collected imaging radar data with the University of Kansas Side-Looking Airborne Radar (SLAR) operating at 9.375 GHz and scatterometer data with a helicopter-mounted scatterometer at 5.3 and 9.6 GHz. We also developed a 35-GHz null-balancing radiometer and data acquisition system. Although radar images showed good delineation of various features of the FIFE site, the data were not useful for quantitative analysis for extracting soil moisture information because of day-to-day changes in the system transfer characteristics. Our scatterometer results show that both C and X bands are sensitive to soil moisture variations over grass-covered soils. Scattering coefficients near vertical are about 4 dB lower for unburned areas because of the presence of a thatch layer, in comparison with those for burned areas. The results of the research have been documented in reports, oral presentations, and published papers.

Gogineni, S. P.↗
NASA NTRS1964

Kinetic-energy absorber employs frictional force between mating cylinders

A kinetic energy absorbing device uses a series of coaxial, mating cylindrical surfaces. These surfaces have high frictional resistance to relative motion when axial impact forces are applied. The device is designed for safe deceleration of vehicles impacting on landing surfaces.

Conrad, E. W.↗
NASA NTRS2007

Aerosol Absorption and Radiative Forcing

We present a comprehensive examination of aerosol absorption with a focus on evaluating the sensitivity of the global distribution of aerosol absorption to key uncertainties in the process representation. For this purpose we extended the comprehensive aerosol-climate model ECHAM5-HAM by effective medium approximations for the calculation of aerosol effective refractive indices, updated black carbon refractive indices, new cloud radiative properties considering the effect of aerosol inclusions, as well as by modules for the calculation of long-wave aerosol radiative properties and instantaneous aerosol forcing. The evaluation of the simulated aerosol absorption optical depth with the AERONET sun-photometer network shows a good agreement in the large scale global patterns. On a regional basis it becomes evident that the update of the BC refractive indices to Bond and Bergstrom (2006) significantly improves the previous underestimation of the aerosol absorption optical depth. In the global annual-mean, absorption acts to reduce the shortwave anthropogenic aerosol top-of-atmosphere (TOA) radiative forcing clear-sky from -0.79 to -0.53 W m(sup -2) (33%) and all-sky from -0.47 to -0.13W m(sup -2 (72%). Our results confirm that basic assumptions about the BC refractive index play a key role for aerosol absorption and radiative forcing. The effect of the usage of more accurate effective medium approximations is comparably small. We demonstrate that the diversity in the AeroCom land-surface albedo fields contributes to the uncertainty in the simulated anthropogenic aerosol radiative forcings: the usage of an upper versus lower bound of the AeroCom land albedos introduces a global annual-mean TOA forcing range of 0.19W m(sup -2) (36%) clear-sky and of 0.12W m(sup -2) (92%) all-sky. The consideration of black carbon inclusions on cloud radiative properties results in a small global annual-mean all-sky absorption of 0.05W m(sup -2) and a positive TOA forcing perturbation of 0.02W m(sup -2). The long-wave aerosol radiative effects are small for anthropogenic aerosols but become of relevance for the larger natural dust and sea-salt aerosols.

Stier, Philip↗
DOE OSTI2026

The U.S. DOE ARM User Facility Establishes a New Site for Studies of Land–Aerosol–Cloud Interactions in the Southeastern United States

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility has established a new site in the Bankhead National Forest (BNF) in northern Alabama that will gather data on how clouds, the land surface, and aerosols interact at a hierarchy of scales important to understanding and simulating the Earth system. Starting its operations in October 2024, the BNF site provides a multi-year opportunity for scientists to unravel complex land-atmosphere interactions. A suite of ground-based sensors, elevated tower-based instrumentation, and aerial facilities will enable scientists to investigate those interactions from within the canopy to the clouds. The Southeastern United States was recommended by the DOE ARM and its collaborators in the broader community as an important region to address their common scientific questions given the region’s abundant surface-forced convective clouds and mesoscale convective systems that pose ongoing challenges in Earth system models. The region is also home to significant terrain complexity and land-use heterogeneity that will unleash new understanding of anthropogenic and biogenic aerosol processes, boundary-layer aerosol-cloud interactions, and the interactions between the terrestrial ecosystem and coupled aerosol-cloud-radiation processes.

Aerosols↗
NASA NTRS2011

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets will be developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be disseminated to end-users for decision making through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system.

Al-Hamdan, Mohammad↗
NASA NTRS2012

Assimilation of Smos Observations to Generate a Prototype SMAP Level 4 Surface and Root-Zone Soil Moisture Product

The Soil Moisture Active and Passive (SMAP; [1]) mission is being implemented by NASA for launch in October 2014. The primary science objectives of SMAP are to enhance understanding of land surface controls on the water, energy and carbon cycles, and to determine their linkages. Moreover, the high-resolution soil moisture mapping provided by SMAP has practical applications in weather and seasonal climate prediction, agriculture, human health, drought and flood decision support. The Soil Moisture and Ocean Salinity (SMOS; [2]) mission was launched by ESA in November 2009 and has since been observing L-band (1.4 GHz) upwelling passive microwaves. In this paper we describe our use of SMOS brightness temperature observations to generate a prototype of the planned SMAP Level 4 Surface and Root-zone Soil Moisture (L4_SM) product [5].

Reichle, Rolf H.↗
NASA NTRS2012

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

The overall goal of this study is to address issues of environmental health and enhance public health decision making by using NASA remotely sensed data and products. This study is a collaboration between NASA Marshall Space Flight Center, Universities Space Research Association (USRA), the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) Office of Surveillance, Epidemiology and Laboratory Services. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the environmental data sets and associated public health analyses to local, state and federal end ]user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) on a 10-km grid using US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of MODIS Land Surface Temperature (LST); and (3) a 12-km grid of daily incoming solar radiation and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) data. These environmental datasets were linked with public health data from the UAB REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental national datasets will also be made available to public health professionals, researchers and the general public via the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system, where they can be aggregated to the county-level, state-level, or regional-level as per users f need and downloaded in tabular, graphical, and map formats. This provides a significant addition to the CDC WONDER online system, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER. It also substantially expands public access to NASA data, making their use by a wide range of decisionmakers feasible.

Al-Hamdan, Mohammad↗
DOE OSTI2020

Evaluating three evapotranspiration estimates from model of different complexity over China using the ILAMB benchmarking system

The land surface models range in complexity of terrestrial evapotranspiration, yet it is unknown how model complexity translates to accuracy of modeled evapotranspiration estimates. Here, we use the International Land Model Benchmarking system to assess ET estimates from three models of varying complexity driven by the same forcing datasets: an earth system model, a terrestrial biosphere model, and a stand-alone ET model. The performance assessment includes both temporal and spatial evaluation, and different plant functional types across China. Our results indicate that the most complex model, an earth system model, performed best against the benchmarking datasets and metrics. Terrestrial biosphere model performed best in simulating inter-annual variability of ET, while earth system model performed best in simulating the seasonal cycle. The more complex models (earth system model and terrestrial biosphere model) perform better in forest, shrub and crop ecosystems, while the simpler model (stand-alone ET model) perform better in grass ecosystems. Our study demonstrates the impact of model complexity on ET estimates and highlights directions for future ET model improvements.

54 ENVIRONMENTAL SCIENCES↗