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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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NASA NTRS1984

Spectral signatures of soil, snow and sea ice as observed by passive microwave and thermal infrared techniques

There have been many passive microwave observations of soil, snow, and sea ice surfaces made during the past several years. These measurements have been from tower, aircraft, and spacecraft platforms covering the wavelength range from 0.8 cm to 50 cm. Based on these data it can be concluded that the longer wavelengths (greater than 5 cm) are more effective for soil moisture observations because of a greater capability to penetrate vegetation, while the shorter wavelengths (1 to 3 cm) are best for snow and sea ice observations since the dominant process is volume scattering by the ice grains in the snow and the brine cells in sea ice. Because it is the intensity of a thermal emission process that is being measured, thermal infrared measurements are necessary to separate the emissivity and temperature effects in the microwave emission.

Schmugge, T.↗
NASA NTRS2021

Passive Microwave Signatures and Retrieval of High-Latitude Snowfall Over Open Oceans and Sea Ice: Insights From Coincidences of GPM and CloudSat Satellites

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an a priori database populated with the active–passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a k -nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mm per h.

Sajad Vahedizade↗
NASA NTRS2006

Assessment of the AMSR-E Sea Ice Concentration Product at the Ice Edge Using RADARSAT-1 and MODIS Imagery

Imagery from the C-band synthetic aperture radar (SAR) aboard RADARSAT-1 and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to evaluate the performance of the Advanced Microwave Scanning Radiometer- EOS (AMSR-E) ice concentration product near the sea ice edge in the Bering Sea for four days during March 2003, which is concurrent with the AMSR-Ice03 field/aircraft campaign. The AMSR-E products were observed to perform very well in identifying open-water and pack-ice areas, although the AMSR-E products occasionally underestimate ice concentration in areas with thin ice. The position of the ice edge determined from AMSR-E data using a 15% concentration threshold was found to be, on average, within one AMSR-E grid square (12.5 km) of the ice edge determined from the SAR data, with the AMSR-E edge tending to be outside the SAR-derived edge

Henrichs, John F.↗
NASA NTRS2013

A Long-Term and Reproducible Passive Microwave Sea Ice Concentration Data Record for Climate Studies and Monitoring

A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N5B56GN3.

monitoring↗
NASA NTRS2007

A New Sea Ice Model: Pancakes to Metrics

We are developing, testing, and validating a new sea ice dynamics model that treats the ice cover as an elastic/decohesive material in the permanent pack and includes the correct frazil/pancake behavior in the marginal zone. Two salient features of present ice dynamics models are that they do not: 1) reproduce the oriented fracture patterns of openings and closings in the pack ice, and 2) accurately model the effects of frazil/pancake ice formation in the ice margin. These poorly modeled areas account for a substantial portion of the ice growth, turbulent heat flux to the atmosphere, salt flux to the ocean, and energy dissipation due to slippage, ridging, and rafting, in the Arctic. Existing sea ice models have shown limited success in predicting the degree to which a lead will open for prescribed or observed forcing conditions. An important aspect of the new model we are developing is that the existence of cracks, along with their orientation, opening, and closing, is predicted. To put this effort in perspective a short history of ice dynamics modeling and data collection is presented. The RGPS data set is used to validate the model. As part of the testing and validation of the model, we are working on a new metric for comparing linear features (leads and ridges) in the data and model to be used in data assimilation for this model. The model framework is presented as well as some results showing the creation and development of leads in a simulation of ice dynamics in the Beaufort Sea. Other presentations by the authors will show other results from this effort.

Coon, M.↗
NASA NTRS1993

(abstract) Examining Sea Ice SAR Signatures in the Arctic

This research examines the seasonal changes of the sea ice cover in the Arctic Basin as it responds to atmospheric and oceanic conditions. Monitoring this process provides a means of determining the onset and extent of the annual seasonal stages, which is thought to be an indicator for detecting climate change in the polar regions. Much of the response of sea ice to seasonal conditions results in changes in the phase of water (both in the ice and snow cover), surface roughness, and internal properties such as air bubbles. Imagery from SAR has proven to be an important tool for revealing these changes since radar backscatter is affected by both surface roughness and dielectric properties of water and salt. The major ice types and ice features may have unique SAR backscatter signatures because of the inherent variations in surface roughness, salinity, and internal properties in each category.

change surface roughness dielectric properties↗
NASA NTRS2000

Satellite-Derived Dynamics of Southern Ocean Sea Ice

Antarctic ERS-2, RADARSAT Synthetic Aperture Radar and ERS-1/2 Scatterometer images were analyzed with SMMI radiometer image time-series data to investigate seasonal variability in satellite-tracked sea-ice dynamics in the Southern Ocean during 1992. Supporting field data were acquired during 'in-situ' experiments including the winter 1992 Ice Station Weddell and Winter Weddell Gyre studies. A variety of surface measurements were made during these experiments including Argos-buoy deployment and GPS drift measurements. These are used in conjunction with International Program for Antarctic Buoys drift trajectories for ice-motion tracking validation. Comparisons between gridded Special Sensor Microwave Imager (SSMI) ice-motion vectors and European Center for Medium Range Weather Forecasts/National Centers for Environmental Prediction (ECMWF/NCEP) analyses indicate that large-scale drift is forced predominantly by the long-term mean, large-scale synoptic pressure field. Only sub-daily SAR sea-ice tracking can capture high-frequency fluctuations, driven by polar lows or tidal forcing. In these cases, sea-ice drift can respond rapidly to changes in forcing on semi-diurnal time scales depending on the location with respect to the coastline. Seasonality of ice drift, particularly in the Weddell and Ross Seas, is linked to ice extent and compactness, and internal ice stresses transmitted through the pack ice from the coast. Three-monthly seasonal climatologies are presented of austral winter of ice drift in the Southern Ocean. The large Weddell and Ross Sea gyres are clearly resolved along with key seasonal and spatial attributes of their cyclonic circulation. Regional time series of ice dynamics parameters are used to illustrate correlations with meteorological forcing. Persistent divergence such as that occurring in the Ronne-Filchner polynya system results in large fractions of new ice. Similarly, convergence zones produce large fractions of deformed ice and characterize the dynamics of regions where perennial ice is observed. High shear strains also help delineate the axis of the Antarctic divergence in many places in the ice cover. In these regions, the separation between the coastal 'east wind drift'and ACC-dominated drift regimes is characterized by zonally extended regions of intense shear.

Drinkwater, Mark R.↗
NASA NTRS2004

Proposing an International Collaboration on Lightweight Autonomous Vehicles to Conduct Scientific Traverses and Surveys over Antarctica and the Surrounding Sea Ice

We have continued to develop a concept for use of autonomous rovers, originally developed for use in planetary exploration, in polar science on Earth; the concept was the subject of a workshop, and this report summarizes and extends that workshop. The workshop on Antarctic Autonomous Scientific Vehicles and Traverses met at the National Geographic Society on February 14 and 15, 2001 to discuss scientific objectives and benefits of the use of autonomous rovers. The participants enthusiastically viewed rovers as being uniquely valuable for such tasks as data taking on tedious or repetitive routes, traverses in polar night, difficult or hazardous routes, extremely remote regions, routes requiring only simple instrumentation, traverses that must be conducted at low speed, augments of manned traverses, and scientific procedures not compatible with human presence or combustion engines. The workshop has concluded that instrumented autonomous vehicles, of the type being developed for planetary exploration, have the potential to contribute significantly to the way science in conducted in Antarctica while also aiding planetary technology development, and engaging the public's interest. Specific objectives can be supported in understanding ice sheet mass balance, sea ice heat and momentum exchange, and surface air chemistry processes. In the interval since the workshop, we have concluded that organized program to employ such rovers to perform scientific tasks in the Fourth International Polar Year would serve the objectives of that program well.

polar science↗
NASA NTRS2007

Antarctic Sea Ice Thickness and Snow-to-Ice Conversion from Atmospheric Reanalysis and Passive Microwave Snow Depth

Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979-200 1 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April-October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow- ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10-20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods.

Markus, Thorsten↗
NASA NTRS2003

Sea ice investigations from Seasat to the present

In this paper, we provide a short review of sea ice investigations starting from Seasat. We focus particularly on the detailed and quantitative measurements of the sea ice motion field, which were some of the earliest results from Seasat and have sugsequently been shown to be of critical value to the derivation of several key climatically important variables. Other key investigations discussed include examination of the seasonal melt cycle, ice extent and concentration, and estimates of thickness from the proxy measurements of ice type and age and more directly from freeboard. We end with a brief discussion on how thse measurements might be improved in the future.

sea ice↗
NASA NTRS1997

ERS Satellite Microwave Radar Observations of Antarctic Sea-Ice Dynamics

ERS-1 and ERS-2 scatterometer and SAR data are used to monitor and track large and small-scale sea-ice dynamics in the Southern Ocean, and in particular in the Weddell Sea, Antarctica. Sea-ice formation in the Weddell Sea regulates vertical and horizontal thermohaline circulation and influences bottom water production rates.

Antarctic↗
NASA NTRS2006

ARISE (Antarctic Remote Ice Sensing Experiment) in the East 2003: Validation of Satellite-derived Sea-ice Data Product

Preliminary results are presented from the first validation of geophysical data products (ice concentration, snow thickness on sea ice (h(sub s) and ice temperature (T(sub i))fr om the NASA EOS Aqua AMSR-E sensor, in East Antarctica (in September-October 2003). The challenge of collecting sufficient measurements with which to validate the coarse-resolution AMSR-E data products adequately was addressed by means of a hierarchical approach, using detailed in situ measurements, digital aerial photography and other satellite data. Initial results from a circumnavigation of the experimental site indicate that, at least under cold conditions with a dry snow cover, there is a reasonably close agreement between satellite- and aerial-photo-derived ice concentrations, i.e. 97.2+/-.6% for NT2 and 96.5+/-2.5% for BBA algorithms vs 94.3% for the aerial photos. In general, the AMSR-E concentration represents a slight overestimate of the actual concentration, with the largest discrepancies occurring in regions containing a relatively high proportion of thin ice. The AMSR-E concentrations from the NT2 and BBA algorithms are similar on average, although differences of up to 5% occur in places, again related to thin-ice distribution. The AMSR-E ice temperature (T(sub i)) product agrees with coincident surface measurements to approximately 0.5 C in the limited dataset analyzed. Regarding snow thickness, the AMSR h(sub s) retrieval is a significant underestimate compared to in situ measurements weighted by the percentage of thin ice (and open water) present. For the case study analyzed, the underestimate was 46% for the overall average, but 23% compared to smooth-ice measurements. The spatial distribution of the AMSR-E h(sub s) product follows an expected and consistent spatial pattern, suggesting that the observed difference may be an offset (at least under freezing conditions). Areas of discrepancy are identified, and the need for future work using the more extensive dataset is highlighted.

Massom, Robert A.↗
NASA NTRS1987

Snow melt on sea ice surfaces as determined from passive microwave satellite data

SMMR data for the year 1979, 1980 and 1984 have been analyzed to determine the variability in the onset of melt for the Arctic seasonal sea ice zone. The results show melt commencing in either the Kara/Barents Seas or Chukchi Sea and progressing zonally towards the central Asian coast (Laptev Sea). Individual regions had interannual variations in melt onset in the 10-20 day range. To determine whether daily changes occur in the sea ice surface melt, the SMMR 18 and 37 GHz brightness temperature data are analyzed at day/night/twilight periods. Brightness temperatures illustrate diurnal variations in most regions during melt. In the East Siberian Sea, however, daily variations are observed in 1979, throughout the analysis period, well before any melt would usually have commenced. Understanding microwave responses to changing surface conditions during melt will perhaps give additional information about energy budgets during the winter to summer transition of sea ice.

Anderson, Mark R.↗
NASA NTRS2003

Sea Ice Surface Temperature Product from the Moderate Resolution Imaging Spectroradiometer (MODIS)

Global sea ice products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) on board both the Terra and Aqua satellites. Daily sea ice extent and ice-surface temperature (IST) products are available at 1- and 4-km resolution. Validation activities have been undertaken to assess the accuracy of the MODIS IST product at the South Pole station in Antarctica and in the Arctic Ocean using near-surface air-temperature data from a meteorological station and drifting buoys. Results from the study areas show that under clear skies, the MODIS ISTs are very close to those of the near-surface air temperatures with a bias of -1.1 and -1.2 K, and an uncertainty of 1.6 and 1.7 K, respectively. It is shown that the uncertainties would be reduced if the actual temperature of the ice surface were reported instead of the near-surface air temperature. It is not possible to get an accurate IST from MODIS in the presence of even very thin clouds or fog, however using both the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and the MODIS on the Aqua satellite, it may be possible to develop a relationship between MODIS-derived IST and ice temperature derived from the AMSR-E. Since the AMSR-E measurements are generally unaffected by cloud cover, they may be used to complement the MODIS IST measurements.

Hall, Dorothy K.↗
NASA NTRS1987

Satellite-derived ice data sets no. 2: Arctic monthly average microwave brightness temperatures and sea ice concentrations, 1973-1976

A summary data set for four years (mid 70's) of Arctic sea ice conditions is available on magnetic tape. The data include monthly and yearly averaged Nimbus 5 electrically scanning microwave radiometer (ESMR) brightness temperatures, an ice concentration parameter derived from the brightness temperatures, monthly climatological surface air temperatures, and monthly climatological sea level pressures. All data matrices are applied to 293 by 293 grids that cover a polar stereographic map enclosing the 50 deg N latitude circle. The grid size varies from about 32 X 32 km at the poles to about 28 X 28 km at 50 deg N. The ice concentration parameter is calculated assuming that the field of view contains only open water and first-year ice with an ice emissivity of 0.92. To account for the presence of multiyear ice, a nomogram is provided relating the ice concentration parameter, the total ice concentration, and the fraction of the ice cover which is multiyear ice.

Parkinson, C. L.↗