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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Cloud and surface textural features in polar regions

The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.

Welch, Ronald M.↗
NASA NTRS2019

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (TB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and TB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted TB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic TB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

Kwon, Yonghwan↗
NASA NTRS2005

On Modeling Air/Space-Borne Radar Returns in the Melting Layer

The bright band is the enhanced radar echo associated with the melting of hydrometeors in stratiform rain where the melting process usually occurs below 0 C isotherm over a distance of about 500m. To simulate this radar signature, a scattering model of melting snow is proposed in which the fractional water content is prescribed as a function of the radius of a spherical mixed- phase particle consisting of air, ice and water. The model is based on the observation that melting starts at the surface of the particle and then gradually develops towards the center. To compute the scattering parameters of a non-uniform melting particle, the particle is modeled as a sphere represented by a collection of 64(exp 3) cubic cells of identical size where the probability of water at any cell is prescribed as a function of the radius. The internal field of the particle, used for deriving the effective dielectric constant, is computed by the Conjugate Gradient and Fast Fourier Transform (CGFFT) numerical methods. To make computations of the scattering parameters more efficient, a multi-layer stratified-sphere scattering model is introduced after demonstrating that the scattering parameters of the non-uniformly melting particle can be accurately reproduced by the stratified sphere. In conjunction with a melting layer model that describes the melting fractions and fall velocities of hydrometeors as a function of the distance from the 0 C isotherm, the stratified-sphere model is used to simulate the radar bright band profiles. These simulated profiles are shown to compare well with measurements from the Precipitation Radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite and a dual-wavelength airborne radar. The results suggest that the proposed model of a melting snow particle may be useful in studying the characteristics of the bright-band in particular and mixed- phase hydrometeors in general.

Liao, Liang↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
NASA NTRS2012

Incorporation of the Mass Concentration and the New Snow Albedo Schemes into the Global Forecasting Model, GEOS-5 and the Impact of the New Schemes over Himalayan Glaciers

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASA/GSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et aL including me. The observed data was obtained when I was Ph.D. candidate. The original GEOS-5during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris cover, inside refreezing process, surface flow of glacier, etc. affect glacier mass balance and the simulated results immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

Yasunari, Teppei↗
NASA NTRS1983

Microwave emission from an irregular snow layer

Emission from an irregular snow layer is modeled by a layer of Mie scatterers using the radiative transfer method. Comparisons are made with measurements showing snow wetness effects and rough air-snow boundary effects. For convenience of reference, theoretical model behavior is also illustrated.

Eom, H. J.↗
NASA NTRS1993

Observational and modeling studies of the influence of snow anomalies on the atmospheric circulation

Current research on the effects of snow covers on the atmosphere over short term, interannual, and decadal timescales is reviewed. Data obtained show that anomalies of snow cover are clearly associated with significant local anomalies of air temperature, at least in the lowest 100-200 mb. The duration of the local anomalies generally ranges from several days to several weeks and is often limited by the weekly and monthly fluctuation of the snow anomalies. The snow-hydrology-soil moisture feedback is capable of producing a meaningful response in the atmosphere during spring and summer.

Walsh, John E.↗
NASA NTRS1995

Measurement of the spectral absorption of liquid water in melting snow with an imaging spectrometer

Melting of the snowpack is a critical parameter that drives aspects of the hydrology in regions of the earth where snow accumulates seasonally. New techniques for measurement of snow melt over regional scales offer the potential to improve monitoring and modeling of snow-driven hydrological processes. We present the results of measuring the spectral absorption of liquid water in a melting snowpack with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data were acquired over Mammoth Mountain, in east central California on 21 May 1994 at 18:35 UTC. The air temperature at 2926 m on Mammoth Mountain at site A was measured at 15-minute intervals during the day preceding the AVIRIS data acquisition. At this elevation, the air temperature did not drop below freezing the night of May 20 and had risen to 6 degrees Celsius by the time of the overflight on May 21. These temperature conditions support the presence of melting snow at the surface as the AVIRIS data were acquired.

Green, Robert O.↗
NASA NTRS1995

Measurement of the Spectral Absorption of Liquid Water in Melting Snow With an Imaging Spectrometer

Melting of the snowpack is a critical parameter that drives aspects of the hydrology in regions of the Earth where snow accumulates seasonally. New techniques for measurement of snow melt over regional scales offer the potential to improve monitoring and modeling of snow-driven hydrological processes. In this paper we present the results of measuring the spectral absorption of liquid water in a melting snowpack with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data were acquired over Mammoth Mountain, in east central California on 21 May 1994 at 18:35 UTC. The air temperature at 2926 m on Mammoth Mountain at site A was measured at 15-minute intervals during the day preceding the AVIRIS data acquisition. At this elevation. the air temperature did not drop below freezing the night of the May 20 and had risen to 6 degrees Celsius by the time of the overflight on May 21. These temperature conditions support the presence of melting snow at the surface as the AVIRIS data were acquired.

Green, Robert O.↗
NASA NTRS2022

The Impact of Firn Models on Ultrawideband Brightness Temperatures in the Partially Coherent Model

The density profile of a polar ice sheet is an important parameter for the estimation of ice mass balance. Wave reflections caused by density variations are also a key uncertainty in the retrieval of ice sheet temperature profiles in Ultra-Wide band radiometry. In this paper, we examine different firn density profile models and analyze the subsurface reflections they cause using an analytical partially coherent approach. We also examine firn density profiles obtained from borehole measurements, from past UWBRAD modeling studies, from a community firn model, and from snow radar echo measurements. In previous studies, the ice sheet has been model as a 1D random medium with density variations in depth. However, horizontal density variations also exist, so that the ice sheet is a 3D random medium. Analyses using the partially coherent model show that in the presence of horizontal fluctuations, contributions from short scale variations vanish as the horizontal correlation length decreases due to the diffraction of waves.

Haokui Xu↗
NASA NTRS2020

Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes.

Antarctic↗
NASA NTRS2021

Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation across Complex Terrain in Pakistan, Afghanistan, and Tajikistan

An ensemble Kalman filter is used to assimilate Advanced Microwave Scanning Radiometer-2 (AMSR2) observations of passive microwave (PMW) brightness temperatures (spectral differences, ΔT b ) into land surface model estimates of snow mass over northwestern high mountain Asia (HMA). Trained support vector machines serve as the observation operator and map the geophysical modeled variables into ΔT b space within the assimilation framework. Evaluation of the assimilation routine is carried out through comparison of assimilated snow mass estimates with an in situ dataset. The assimilation framework helps improve the land surface model estimates through PMW ΔT b assimilation, particularly in terms of decreasing the domain-wide bias. The assimilation framework proved more effective during the (dry) snow accumulation season and decreased the bias and root-mean-square error (RMSE) in snow mass estimates at 76% and 58% of the comparative pixels, respectively. During the snow ablation season, the PMW brightness temperature signal contained less information related to snow mass due to the presence of other concurrent geophysical features that effectively serve as noise during the snow mass update. The utilization of PMW ΔT b for accurate snow mass estimation in complex terrain such as HMA is dependent on a multitude of factors for optimal results; however, it does add utility to the land surface model if the relevant pitfalls are taken into consideration prior to the state variable update.

Jawairia Ahmad↗
NASA NTRS2003

Modelling CLPX IOP3 Radiometric Data by Means of the Dense Media Theory: Preliminary Results for the LSOS Test Site

The capabilities of the Dense Media Radiative Transfer model using the Quasi Crystalline Approximation with Coherent Potential (QCA-CP) to reproduce measured radiometric data were tested using the University of Tokyo Ground Based Microwave Radiometer (GBMR-7) during the third Intensive Observation Period (IOP3) of the NASA Cold-land Processes Field Experiment (CLPX). The data were collected at the Local-Scale Observation Site (LSOS), a 0.8-ha study site consisting of two open meadows separated by trees. Intensive measurements were also made of snow depth and temperature, density, and grain size profiles. A DMRT model is needed to describe radiative transfer in a medium such as snow because the assumption of independent scattering used in classical radiative transfer theory (CRT) is not valid. Validation of the DMRT approach requires a relationship between measured snow grain size and the DMRT approximation of snow grain radius as spherical particles with a mean radius of the log-normal particle-size distribution. This relationship is very important for a better understanding of snow modelling and for practical applications. DMRT simulations were compared with observations of microwave brightnesses at 18.7, 36.5 and 89-GHz (V and H polarizations) collected on February-1 9-25, 2003. Observation angles ranged from 30\deg to 70\deg. Model inputs included measured snow parameters except mean grain size. The average snow temperature, fractional volume and depth were held constant, together with the ice and soil permittivities. The minimum and maximum measured mean grain sizes were used to test the capabilities of the DMRT to reproduce the brightnesses as upper and lower limits. The sensitivity to the largest and smallest measured grain size in the three classes of minimum, medium and maximum observed grain sizes was also investigated. DMRT particle sizes yielding a best-fit to the experimental data for each date were computed. Results show that the measured brightnesses fall within the range of simulated brightnesses using the smallest and largest measured grain size values. The DMRT best-fit radii are comparable to the average radii for the medium observed grain sizes.

Tedesco, Marco↗
NASA NTRS1973

A study of the break-up characteristics of Chena River Basin using ERTS imagery

The author has identified the following significant results. The Chena River Basin was selected because of the availability of ground truth data for comparison. Very good agreement for snow distribution and rates of ablation was found between the ERTS-1 imagery, the snowmelt model, and field measurements. Monitoring snowmelt rates for relatively small basins appears to be practical. The main limitation of the ERTS-1 imagery is the interval of coverage. More frequent overflights providing coverage are needed for the study of transient hydrologic events. ERTS-1 data is most useful when used in conjunction with snowmelt prediction models and existing snow course data. These results should prove very useful in preliminary assessment of hydrologic conditions in ungaged watersheds and will provide a tool for month-to-month volume forecasting.

Carlson, R. F.↗
NASA NTRS2023

Sensitivity of the MAR Regional Climate Model Snowpack to the Parameterization of the Assimilation of Satellite-Derived Wet-Snow Masks on the Antarctic Peninsula

Both regional climate models (RCMs) and remote sensing (RS) data are essential tools in understanding the response of polar regions to climate change. RCMs can simulate how certain climate variables, such as surface melt, runoff and snowfall, are likely to change in response to different climate scenarios but are subject to biases and errors. RS data can assist in reducing and quantifying model uncertainties by providing indirect observations of the modeled variables on the present climate. In this work, we improve on an existing scheme to assimilate RS wet snow occurrence data with the “Modèle Atmosphérique Régional” (MAR) RCM and investigate the sensitivity of the RCM to the parameters of the scheme. The assimilation is performed by nudging the MAR snowpack temperature to match the presence of liquid water observed by satellites. The sensitivity of the assimilation method is tested by modifying parameters such as the depth to which the MAR snowpack is warmed or cooled, the quantity of water required to qualify a MAR pixel as “wet” (0.1 % or 0.2 % of the snowpack mass being water), and assimilating different RS datasets. Data assimilation is carried out on the Antarctic Peninsula for the 2019–2021 period. The results show an increase in meltwater production (+66.7 % on average, or +95 Gt), along with a small decrease in surface mass balance (SMB) (−4.5 % on average, or −20 Gt) for the 2019–2020 melt season after assimilation. The model is sensitive to the tested parameters, albeit with varying orders of magnitude. The prescribed warming depth has a larger impact on the resulting surface melt production than the liquid water content (LWC) threshold due to strong refreezing occurring within the top layers of the snowpack. The values tested for the LWC threshold are lower than the LWC for typical melt days (approximately 1.2 %) and impact results mainly at the beginning and end of the melting period. The assimilation method will allow for the estimation of uncertainty in MAR meltwater production and will enable the identification of potential issues in modeling near-surface snowpack processes, paving the way for more accurate simulations of snow processes in model projections.

regional climate models↗
NASA NTRS2020

Winter Arctic Sea Ice Thickness from ICESat-2 Freeboards

NASA’s ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System (ATLAS), is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern Oceans. In this study we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model (NESOSIM). The coarse resolution (~100 km) snow data are redistributed onto the high resolution (~30 - 100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA’s Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter-season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from ESA’s CryoSat-2 satellite mission, with ICE 33 Sat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19th February – 21st March) 2008 ice thickness estimates using the same input assumptions, which show a ~0.40 m or ~21% thinning across an inner Arctic Ocean domain in this 11-year time period.

Alek A Petty↗
NASA NTRS2021

Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced and Surface Model

This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [ DAv1 ] and without [ DAv2 ] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and DAv2 . Comparison of SWE estimates from Sentinel-1 DAv1 against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to in situ measurements. River discharge using Sentinel-1 DAv1 improved the Nash–Sutcliffe efficiency at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.

Jongmin Park↗
NASA NTRS1983

Snow reflectance from thematic mapper

Calculations of snow reflectance in all 6 TM reflective bands (i.e., 1,2,3,4,5, and 7) using a delta Eddington model show that snow reflectance in bands 4,5, and 7 is sensitive to grain size. Efforts to interpret the surface optical grain size for the spectral extension of albedo are described. Results show the TM data include spectral channels suitable for snow/cloud discrimination and for snow albedo measurements that can be extended throughout the solar spectrum. Except for band 1, the dynamic range is large enough that saturation occurs only occasionally. The finer resolution gives much better detail on the snowcovered area and might make it possible to use textural information instead of the snowline as an index to the amount of snow melt runoff.

Dozier, J.↗