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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Millimeter-Wave Radiometer Imager

A 3-mm radiometer system with mechanically scanned antenna built for use on small aircraft or helicopter to produce near-real-time moderate-resolution images of ground. Main advantage of passive imaging sensor able to provide information through clouds, smoke, and dust when visual and infrared (IR) systems unusable. Used also for variety of remote-sensing applications such as measurements of surface moisture, snow cover, vegetation type and extent, mineral type and extent, surface temperature, and thermal inertia. Possible to map fires and volcanic lava flows through obscuring clouds and smoke.

Wilson, W. J.↗
NASA NTRS2000

Short-Term Retrospective Land Data Assimilation Schemes

Subsurface moisture and temperature and snow/ice stores exhibit persistence on various time scales that has important implications for the extended prediction of climatic and hydrologic extremes. Hence, to improve their specification of the land surface, many numerical weather prediction (NWP) centers have incorporated complex land surface schemes in their forecast models. However, because land storages are integrated states, errors in NWP forcing accumulates in these stores, which leads to incorrect surface water and energy partitioning. This has motivated the development of Land Data Assimilation Schemes (LDAS) that can be used to constrain NWP surface storages. An LDAS is an uncoupled land surface scheme that is forced primarily by observations, and is therefore less affected by NWP forcing biases. The implementation of an LDAS also provides the opportunity to correct the model's trajectory using remotely-sensed observations of soil temperature, soil moisture, and snow using data assimilation methods. The inclusion of data assimilation in LDAS will greatly increase its predictive capacity, as well as provide high-quality land surface assimilated data.

Houser, P. R.↗
NASA NTRS2005

Using Wind Driven Tumbleweed Rovers to Explore Martian Gully Features

Gully features have been observed on the slopes of numerous Martian crater walls, valleys, pits, and graben. Several mechanisms for gully formation have been proposed, including: liquid water aquifers (shallow and deep), melting ground ice, snow melt, CO2 aquifers, and dry debris flow. Remote sensing observations indicate that the most likely erosional agent is liquid water. Debate concerns the source of this water. Observations favor a liquid water aquifer as the primary candidate. The current strategy in the search for life on Mars is to "follow the water." A new vehicle known as a Tumbleweed rover may be able to conduct in-situ investigations in the gullies, which are currently inaccessible by conventional rovers. Deriving mobility through use of the surface winds on Mars, Tumbleweed rovers would be lightweight and relatively inexpensive thus allowing multiple rovers to be deployed in a single mission to survey areas for future exploration. NASA Langley Research Center (LaRC) is developing deployable structure Tumbleweed concepts. An extremely lightweight measurement acquisition system and sensors are proposed for the Tumbleweed rover that greatly increases the number of measurements performed while having negligible mass increase. The key to this method is the use of magnetic field response sensors designed as passive inductor-capacitor circuits that produce magnetic field responses whose attributes correspond to values of physical properties for which the sensors measure. The sensors do not need a physical connection to a power source or to data acquisition equipment resulting in additional weight reduction. Many of the sensors and interrogating antennae can be directly placed on the Tumbleweed using film deposition methods such as photolithography thus providing further weight reduction. Concepts are presented herein for methods to measure subsurface water, subsurface metals, planetary winds and environmental gases.

Antol, Jeffrey↗
NASA NTRS2004

Using Wind Driven Tumbleweed Rovers to Explore Martian Gully Features

Gully features have been observed on the slopes of numerous Martian crater walls, valleys, pits, and graben. Several mechanisms for gully formation have been proposed, including: liquid water aquifers (shallow and deep), melting ground ice, snow melt, CO2 aquifers, and dry debris flow. Remote sensing observations indicate that the most likely erosional agent is liquid water. Debate concerns the source of this water. Observations favor a liquid water aquifer as the primary candidate. The current strategy in the search for life on Mars is to "follow the water." A new vehicle known as a Tumbleweed rover may be able to conduct in-situ investigations in the gullies, which are currently inaccessible by conventional rovers. Deriving mobility through use of the surface winds on Mars, Tumbleweed rovers would be lightweight and relatively inexpensive thus allowing multiple rovers to be deployed in a single mission to survey areas for future exploration. NASA Langley Research Center (LaRC) is developing deployable structure Tumbleweed concepts. An extremely lightweight measurement acquisition system and sensors are proposed for the Tumbleweed rover that greatly increases the number of measurements performed while having negligible mass increase. The key to this method is the use of magnetic field response sensors designed as passive inductor-capacitor circuits that produce magnetic field responses whose attributes correspond to values of physical properties for which the sensors measure. The sensors do not need a physical connection to a power source or to data acquisition equipment resulting in additional weight reduction. Many of the sensors and interrogating antennae can be directly placed on the Tumbleweed using film deposition methods such as photolithography thus providing further weight reduction. Concepts are presented herein for methods to measure subsurface water, subsurface metals, planetary winds and environmental gases.

Antol, Jeffrey↗
NASA NTRS2011

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

Kumar, Sujay V.↗
NASA NTRS2017

Polycrystal Snowfakes

Building on the previous success of a mono-crystal snowflake growth model, a team of NASA researchers has invented and implemented a poly-crystal variant of the model to simulate simultaneous crystalline growth along lattices of different orientations. Inferring snowfall intensity and amount using remote sensing relies on knowing the scattering properties of snowing particles. However, these scattering properties depend strongly on the geometric shapes of the snowing particles. Poly-crystal snow particles, such as bullet rosettes, appear frequently in nature. Simulating the growth of both mono-crystal and poly-crystal particles and obtaining accurate scattering properties are paramount for furthering accurate quantitative estimates of snowfalls.

Clune, Tom↗
NASA NTRS2021

NASA's Surface Biology and Geology Designated Observable: A Perspective on Surface Imaging Algorithms

The 2017–2027 National Academies’ Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algo-rithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Aquatic↗
NASA NTRS2021

Reanalysis Surface Mass Balance of the Greenland Ice Sheet along K-transect (2000-2014)

Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea-level rise. Given the limitations of in-situ measurement, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS surface mass balance (SMB). This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow model Crocus and a 16-day albedo product derived from satellite remote sensing data. Comparison of the results against in-situ SMB measurements shows that the assimilation of the albedo product reduces the root mean square error (RMSE) of the posterior estimates of SMB by 51% and reduces bias by 95%.

Greenland Ice Sheet↗
NASA NTRSDate not supplied

Single-Scattering Properties of Melting Precipitation for GPM Passive Microwave and Radar Remote Sensing Applications

Over the past two decades, detailed computational simulations of the intricate three dimensional structures of ice-phase crystals and aggregates of those crystals have been developed. The microwave single-scattering properties of these simulated ice particles have been computed and used to improve quantitative estimates of snow rates and to better deRne the vertical structure of snow water contents in deep convective systems, as derived from satellite-borne passive microwave and/or radar remote sensing measurements. The same icephase particles have more recently been used as the starting point for simulations of melting precipitation using computational melting methods. In the current study, a heuristic melting method, as well as a physically-based melting procedure based on smoothed-particle hydrodynamics, are applied to ice particle models to describe the full evolution of the particles from dry snow to liquid drops. The discrete dipole approximation is utilized to calculate the single-scattering properties of the mixed-phase particles throughout the melting process. Then, the properties of the particles are “mapped” into simpliRed microphysical simulations of particle size spectra in the melting layers of stratiform, precipitating clouds. The bulk single-scattering properties of the melting layers and the sensitivity of their properties to modeling assumptions are explored, and the implications for combined radar-radiometer precipitation remote sensing from GPM are discussed.

William S Olson↗
NASA NTRS1982

Review of Hydrologic Models for Evaluating Use of Remote Sensing Capabilities

Hydrologic models most commonly used by federal agencies for hydrologic forecasting are reviewed. Six catchment models and one snow accumulation and ablation model are reviewed. Information on the structure, parameters, states, and required inputs is presented in schematic diagrams and in tables. The primary and secondary roles of parameters and state variables with respect to their function in the models are identified. The information will be used to evaluate the usefulness of remote sensing capabilities in the operational use of hydrologic models.

Peck, E. L.↗
NASA NTRS2005

Space Technologies for Enhancing the Resilience and Sustainability of Indigenous Reindeer Husbandry in the Russian Arctic

To adapt successfully to the major changes - climate, environment, economic, social and industrial - which have taken place across the Arctic. in recent years, indigenous communities such as reindeer herders must become increasingly empowered with the best available technologies to add to their storehouse of traditional knowledge. Remotely-sensed data and observations are providing increased capabilities for monitoring, risk mapping, and surveillance of parameters critical to the characterization of pasture quality and migratory routes, such as vegetation distribution, snow cover, infrastructure development, and pasture damages due to fires. This paper describes a series of remote sensing capabilities, which are useful to reindeer husbandry, and gives the results of the first year of a project, "Reindeer Mapper", which is a remote sensing and GIs-based system to bring together space technologies with indigenous knowledge for sustainable reindeer husbandry in the Russian Arctic. In this project, reindeer herders and scientists are joining together to utilize technologies to create a system for collecting and sharing space-based and indigenous knowledge in the Russian Arctic. The "Reindeer Mapper" system will help make technologies more readily available to the herder community for observing, data collection and analysis, monitoring, sharing, communications, and dissemination of information - to be integrated with traditional, local knowledge. This paper describes some of the technologies which comprise the system including an intranet system to enable to the team members to work together and share information electronically, remote sensing data for monitoring environmental parameters important to reindeer husbandry (e.g., SAR, Landsat, AVHRR, MODIS), indigenous knowledge about important environmental parameters, acquisition of ground- based measurements, and the integration of all useful data sets for more informed decision-making.

Maynard, Nancy G.↗
NASA NTRS2000

BOREAS HYD-4 Areal Snow Course Data

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 team focused on collecting data during the 1994 winter focused field campaign (FFCW) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the hydrology research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Airborne remote sensing data (gamma, passive microwave) were acquired along a series of flight lines established in the vicinity of the BOREAS study areas. Ground snow surveys were conducted along selected sections of these aircraft flight lines. These calibration segments were typically 10-20 km in length, and ground data were collected at one to two kilometer intervals. The data are provided in tabular ASCII files. The HYD-04 areal snow course data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Hall, Forrest G.↗
NASA NTRS2004

Comparison of Local Scale Measured and Modeled Brightness Temperatures and Snow Parameters from the CLPX 2003 by Means of a Dense Medium Radiative Transfer Theory Model

Microwave remote sensing offers distinct advantages for observing the cryosphere. Solar illumination is not required, and spatial and temporal coverage are excellent from polar-orbiting satellites. Passive microwave measurements are sensitive to the two most useful physical quantities for many hydrological applications: physical temperature and water content/state. Sensitivity to the latter is a direct result of the microwave sensitivity to the dielectric properties of natural media, including snow, ice, soil (frozen or thawed), and vegetation. These considerations are factors motivating the development of future cryospheric satellite remote sensing missions, continuing and improving on a 26-year microwave measurement legacy. Perhaps the biggest issues regarding the use of such satellite measurements involve how to relate parameter values at spatial scales as small as a hectare to observations with sensor footprints that may be up to 25 x 25 km. The NASA Cold-land Processes Field Experiment (CLPX) generated a dataset designed to enhance understanding of such scaling issues. CLPX observations were made in February (dry snow) and March (wet snow), 2003 in Colorado, USA, at scales ranging from plot scale to 25 x 25 km satellite footprints. Of interest here are passive microwave observations from ground-based, airborne, and satellite sensors, as well as meteorological and snowpack measurements that will enable studies of the effects of spatial heterogeneity of surface conditions on the observations. Prior to performing such scaling studies, an evaluation of snowpack forward modelling at the plot scale (least heterogeneous scale) is in order. This is the focus of this paper. Many forward models of snow signatures (brightness temperatures) have been developed over the years. It is now recognized that a dense medium radiative transfer (DMRT) treatment represents a high degree of physical fidelity for snow modeling, yet dense medium models are particularly sensitive to snowpack structural parameters such as grain size, density, and depth---parameters that may vary substantially within a snowpack. Microwave radiometric data and snow pit measurements collected at the Local-Scale Observation Site (LSOS) during the third Intensive Observation Period (IOP3) of the CLPX have been used to test the capabilities of a DMRT model using the Quasi Crystalline Approximation with Coherent Potential (QCA-CP). The radiometric measurements were made by the University of Tokyo s Ground Based Microwave Radiometer-7 (GBMR-7) system. We evaluate the degree to which a DMRT-based model can accurately reproduce the GBMR-7 brightness temperatures at different frequencies and incidence angles.

Tedescol, Marco↗
NASA NTRS2006

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. , Over five years of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA s Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface s radiative characteristics. However, roughly 30% of the global land surface, on an annual equal-angle basis, is obscured due to persistent and transient cloud cover, while another 207% is obscured due to ephemeral and seasonal snow effects. This precludes the MOD43B3 albedo products from being directly used in some remote sensing and ground-based applications, climate models, and global change research projects. To provide researchers with the requisite spatially complete global snow-free land surface albedo dataset, an ecosystem-dependent temporal interpolation technique was developed to fill missing or lower quality data and snow covered values from the official MOD43B3 dataset with geophysically realistic values. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data.

King, Michael D.↗
NASA NTRS2016

The Darkening of the Greenland Ice Sheet: Trends, Drivers and Projections (1981-2100)

The surface energy balance and meltwater production of the Greenland ice sheet (GrIS) are modulated by snow and ice albedo through the amount of absorbed solar radiation. Here we show, using space-borne multispectral data collected during the 3 decades from 1981 to 2012, that summertime surface albedo over the GrIS decreased at a statistically significant (99 %) rate of 0.02 decade(sup -1) between 1996 and 2012. Over the same period, albedo modelled by the Modele Atmospherique Regionale (MAR) also shows a decrease, though at a lower rate (approximately -0.01 decade(sup -1)) than that obtained from space-borne data. We suggest that the discrepancy between modelled and measured albedo trends can be explained by the absence in the model of processes associated with the presence of light-absorbing impurities. The negative trend in observed albedo is confined to the regions of the GrIS that undergo melting in summer, with the dry snow zone showing no trend. The period 1981-1996 also showed no statistically significant trend over the whole GrIS. Analysis of MAR outputs indicates that the observed albedo decrease is attributable to the combined effects of increased near-surface air temperatures, which enhanced melt and promoted growth in snow grain size and the expansion of bare ice areas, and to trends in light-absorbing impurities (LAI) on the snow and ice surfaces. Neither aerosol models nor in situ and remote sensing observations indicate increasing trends in LAI in the atmosphere over Greenland. Similarly, an analysis of the number of fires and BC emissions from fires points to the absence of trends for such quantities. This suggests that the apparent increase of LAI in snow and ice might be related to the exposure of a "dark band" of dirty ice and to increased consolidation of LAI at the surface with melt, not to increased aerosol deposition. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening, with albedo anomalies averaged over the whole ice sheet lower by 0.08 in 2100 than in 2000, driven solely by a warming climate. Future darkening is likely underestimated because of known underestimates in modelled melting (as seen in hindcasts) and because the model albedo scheme does not currently include the effects of LAI, which have a positive feedback on albedo decline through increased melting, grain growth, and darkening.

surface energy↗
NASA NTRS2007

Remote Sensing of Terrestrial Water Storage and Application to Drought Monitoring

Terrestrial water storage (TWS) consists of groundwater, soil moisture and permafrost, surface water, snow and ice, and wet biomass. TWS variability tends to be dominated by snow and ice in polar and alpine regions, by soil moisture in mid-latitudes, and by surface water in wet, tropical regions such as the Amazon (Rodell and Famiglietti, 2001; Bates et al., 2007). Drought may be defined as a period of abnormally dry weather long enough to cause significant deficits in one or more of the TWS components. Thus, along with observations of the agricultural and socioeconomic impacts, measurements of TWS and its components enable quantification of drought severity. Each of the TWS components exhibits significant spatial variability, while installation and maintenance of sufficiently dense monitoring networks is costly and labor-intensive. Thus satellite remote sensing is an appealing alternative to traditional measurement techniques. Several current remote sensing instruments are able to detect variations in one or more TWS variables, including the Advanced Microwave Scanning Radiometer (AMSR) on NASA's Aqua satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua. Future satellite missions have been proposed to improve this capability, including the European Space Agency's Soil Moisture Ocean Salinity mission (SMOS) and the Soil Moisture Active Passive (SMAP), Surface Water Ocean Topography (SWOT), and Snow and Cold Land Processes (SCLP) missions recommended by the US National Academy of Science's Decadal Survey for Earth Science (NRC, 2007). However, only one remote sensing technology is able to monitor changes in TWS from the land surface to the base of the deepest aquifer: satellite gravimetry. This paper focuses on NASA's Gravity Recovery and Climate Experiment mission (GRACE; http://www.csr.utexas.edu/grace/) and its potential as a tool for drought monitoring.

Rodell, Matt↗
NASA NTRS2018

Comparisons of Bin and Bulk Microphysics Schemes in Simulations of Topographic Winter Precipitation with Radar and Radiometer Measurements

The Hebrew University Cloud Model (HUCM) bin scheme and the Thompson bulk scheme in the Weather Research and Forecasting (WRF) model are compared to assess biases often found in simulated brightness temperature and radar reflectivity. Compared to our preceding study that evaluated several bulk schemes in the WRF model, the current study obtains a reduction of the bias from excessive microwave scattering by precipitation ice for both HUCM bin and the Thompson bulk microphysics schemes for a topographic winter precipitation event associated with an atmospheric river. The Thompson particle size distributions (PSDs) and snow particle density assumption are implemented into the Goddard Satellite Data Simulator Unit (G‐SDSU) and have produced improvements. Despite the greater sophistication of the bin scheme in representing cloud and precipitation processes, the simulation with the Thompson bulk scheme is generally in better agreement with observations for this winter event. The explicitly resolved hydrometeor PSDs in HUCM enable analysis of mass spectra variations in response to changes in microphysics assumptions. Two HUCM sensitivity runs tested the enhancement of snow particle breakup and the influence of ice nuclei (IN) concentration. Higher IN concentration resulted in increased snow mass and broadened the spectrum toward small‐size particles. Modified snow mass spectra and resultant changes in graupel contributed to modifications in scattering and reflectivity simulations. The article demonstrates the bin scheme's capability to provide a new means to improve our understanding of uncertainties in mesoscale weather models and radiative transfer models.

microwave remote sensing↗