Total Sea Ice Concentration Research Articles

The AutoICE Challenge

Abstract. Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants’ submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results.

AI4SeaIce: selecting loss functions for automated SAR sea ice concentration charting

For maritime navigation in the Arctic, sea ice charts are an essential tool, which still to this day is drawn manually by professional ice analysts. The total Sea Ice Concentration (SIC) is the primary descriptor of the charts and indicates the fraction of ice in an ocean surface area. Naturally, automating the SIC chart creation is desired. However, the optimal representation of the corresponding machine-learning task is ambivalent and discussed in the community. In this study, we explore the representation with either regressional or classification objectives, each with two different (weighted) loss functions: Mean Square Error and Binary Cross-Entropy, and Categorical Cross-Entropy and the Earth Mover’s Distance, respectively. While all models achieve good results they differ as the regression-based models obtain the highest numerical similarity to the reference charts, whereas the classification-optimised models generate results more visually pleasing and consistent. Rescaling the loss functions with inverse class weights improves the performance for intermediate classes at the expense of open water and fully-covered sea ice areas.

Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada

Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison done with the ASI passive microwave product shows similar errors in total sea ice concentration when using the Canadian Ice Service regional charts as reference. Nonetheless, errors from both datasets differ and the CNN outputs show greater potential to reduce masked areas, given the better spatial resolution, enabling data classification closer to land and identify features not captured by the ASI dataset.

An improved ARTSIST sea ice algorithm based on 19 GHz modified 91 GHz

An enhanced ARTSIST Sea Ice (ASI) algorithm is presented based on a data fusion method of calculating total sea ice concentration from high-frequency microwave data. Algorithms that use low-frequency data to calculate total sea ice concentration are less affected by atmosphere, but their spatial resolutions tend to be lower. In contrast, algorithms using high-frequency data have higher spatial resolution but are significantly influenced by atmosphere. Although errors can be eliminated using weather filters, the concentration of mixed pixels cannot be modified. Here, an enhanced ASI algorithm uses the 19 GHz polarization difference to modify the 91 GHz polarization difference, which is substituted into the ASI algorithm to calculate total sea ice concentration. Arctic total sea ice concentration results are obtained based on Special Sensor Microwave Imager Sounder (SSMIS) data on January 3, from 2008 to 2017. Total sea ice area and average concentration using the enhanced ASI algorithm are compared to traditional ASI and NASA Team results. In the Marginal Ice Zone, there is a considerable difference between the enhanced and traditional ASI algorithm results, with the former much closer to the NASA Team results. The proposed algorithm effectively modifies the concentration of the mixed pixels in the marginal zone.

Low-Frequency Climate Modes and Antarctic Sea Ice Variations, 1982–2013

Abstract The NCEP–NCAR composite dataset (comprising sea level pressure, 500-hPa geopotential height, 500-hPa temperature, and meridional wind stress at 10 m above the surface) is used for compiling a set of climate indices describing the most important physical modes of variability in the Southern Hemisphere (SH): the southern annular mode (SAM), semiannual oscillation (SAO), Pacific–South American (PSA), and quasi-stationary zonal wavenumber 3 (ZW3) patterns. Compelling evidence indicates that the large increase in the SH sea ice, recorded over recent years, arises from the impact of climate modes and their long-term trends. The examination of variability ranging from seasonal to interdecadal scales, and of trends within the climate patterns and total Antarctic sea ice concentration (SIC) for the 32-yr period (1982–2013), is the key focus of this paper. The results herein indicate that a progressive cooling has affected the year-to-year climate of the sub-Antarctic since the 1990s. This feature is found in association with increased positive SAM and SAO phases detected in terms of upward annual and seasonal trends (in autumn and summer) and upward decadal trends. In addition, the SIC shows upward annual, spring, and summer trends, indicating the insulation of Antarctica from the warmer flows in the midlatitudes. This picture of variations is also found to be consistent with the upward trends detected for the PSA and ZW3 patterns on the annual scale and during the last two decades. Evidence of a more frequent occurrence of the PSA–ZW3 combination could explain, in part, the significant increase of the regional and total Antarctic sea ice coverages.

Sea ice climatology in the Canadian Western Arctic: thermodynamic versus dynamic controls

ABSTRACTBased on the regions in the western Canadian Arctic as outlined by the Canadian Ice Service, the normals and trends from 1981 to 2010 were analysed for the monthly surface air temperature, monthly wind speed and direction. For the month of September, the temperatures from 1981 to 2010, for all of the defined regions, increased by 2–4 °C. The monthly concentrations of sea ice and multiyear ice were analysed for normals and trends from 1981 to 2010. Although the whole Arctic has seen a large reduction in the minimum sea ice extent, in the defined region of study during September, only the Beaufort Sea region shows a statistically significant decrease in sea ice concentrations. Correlations between the climatological state variables and sea ice were investigated in this study to determine relative thermodynamic and dynamic contributions to a decline in sea ice extent in the Western Arctic. As expected, the regions of interest all showed a statistically significant correlation between the surface air temperatures and the total sea ice concentrations. However, neither the wind speed nor the direction had a strong correlation on the sea ice concentration trends. This study showed that in the regions investigated, thermodynamic forcing of sea ice is the dominant driver when compared with dynamic forcing, which is a secondary driver in the western Canadian Arctic.

An analysis of cloud observations from Vernadsky, Antarctica

AbstractThis paper presents results of a combined analysis of cloud observations made at the Antarctic base Faraday/Vernadsky between 1960 and 2005 and sea ice concentration from the HadISST1 data set.The annual total cloud cover has increased significantly during this period with the strongest and most significant positive trend found in winter, and positive tendencies observable in all seasons. This trend is associated with a decrease in sea ice concentration in the area of the Western Antarctic Peninsula. Though the observed sea ice reduction is actually larger and more significant in summer and autumn, there is actually a significant relation between total cloud cover and sea ice concentration only in winter.The increase in the total cloud cover is neither reflected in the low cloud amount nor in the number of records for low, medium or high level clouds. It is therefore thought that the increase in the total cloud cover is caused by an increase in the amount of medium and/or high level clouds. Instead, records for the low cloud amount show a redistribution from cases of extreme cloud cover (0, 1, 7 and 8 okta), which account for up to 90% of annual records, to cases of moderate cloud cover. In accordance with the decrease in sea ice, this may indicate a shift from low‐level stratiform towards convective clouds. Copyright © 2009 Royal Meteorological Society

Increasing reflectivity of the Antarctic ocean‐atmosphere system: Analysis of Total Ozone Mapping Spectrometer (TOMS) and passive microwave data for 1979–1994

Measurements of Lambert equivalent reflectance at 380 nm from the Total Ozone Mapping Spectrometer (TOMS) instrument have shown increases in reflectivity between 1979 and 1994 over much of the Southern Ocean, encompassing 280° in longitude. These trends represent a possible change in the state of the Antarctic ocean‐atmosphere system related to recent climate warming. To determine if these reflectivity trends are due to changes in cloud cover or sea ice, or both, the TOMS data were collocated with a contemporaneous passive microwave satellite data set from the scanning multichannel microwave radiometer and the Special Sensor Microwave Imager. The passive microwave data sets specify total sea ice concentration, retrieved by a uniform method for all years using the NASA Team algorithm. To first order the locations of TOMS reflectivity increases coincide with regions where sea ice concentration has increased over the past 2 decades, signifying that the TOMS trends are the result of trends in underlying sea ice and not cloud cover. However, when the TOMS reflectivity measurements are sorted into fixed sea ice concentration bins of 0.1 width, the TOMS data also show increasing reflectivity trends in regions where sea ice extent has been decreasing (Amundsen and Bellingshausen Seas and the Western Antarctic Peninsula). Over open water, TOMS reflectivity trends are less convincing and may be artifacts related to uncertainties in passive microwave sea ice identification. These results suggest that a significant component of the Southern Ocean TOMS reflectivity trends may be a gradual increase in the albedo of the underlying sea ice. This could be caused by a gradual lengthening of the sea ice season, with a concomitant increase in the persistence of dry snow on the sea ice cover.

Linescan camera evaluation of SSM/I 85.5 GHz sea ice retrieval

Retrievals of total sea ice concentration from four algorithms using the 85.5 GHz vertically and horizontally polarized channels of the Special Sensor Microwave Imager (SSM/I) over the marginal ice zone in the Barents and Greenland Seas are compared with retrievals of total sea ice concentration from helicopter-borne linescan camera observations made during a cruise of the R/V Polarstern during May–June 1997. The goals are to evaluate (1) SSM/I 85.5 GHz retrievals of total sea ice concentration for climatological purposes, and (2) the ability of 85.5 GHz data to show the sea ice edge through cloud cover, for operational purposes. The SSM/I 85.5 GHz channels offer a spatial resolution of 12.5 km, which is sufficient to resolve ice edge features and small polynyas; however, there is generally more atmospheric contamination of the sea ice signal at 85.5 GHz than at the lower frequencies (19 and 37 GHz) traditionally used for sea ice remote sensing. A self-adjusting algorithm that performs a nonlinear correction for atmospheric moisture, without explicit atmospheric input data, yields the best accuracy over total sea ice concentrations greater than 30%. However, this algorithm can misclassify clouds over open water as sea ice, and is therefore unreliable for locating the sea ice edge. The best algorithm for locating the sea ice edge is found to be the SEA LION algorithm, which explicitly uses meteorological reanalysis data to correct for atmospheric contamination. For total sea ice concentrations in the range 20–70%, empirical 85.5 GHz hybrids of lower-frequency algorithms developed at the NASA Goddard Space Flight Center can improve the accuracy of these algorithms.

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms.

Classification Methods for Monitoring Arctic Sea Ice Using OKEAN Passive/Active Two-Channel Microwave Data

This paper presents methods for classifying Arctic sea ice using both passive and active (2-channel) microwave imagery acquired by the Russian OKEAN 01 polar-orbiting satellite series. Methods and results are compared to sea ice classifications derived from nearly coincident Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) image data of the Barents, Kara, and Laptev Seas. The Russian OKEAN 01 satellite data were collected over weekly intervals during October 1995 through December 1997. Methods are presented for calibrating, georeferencing and classifying the raw active radar and passive microwave OKEAN 01 data, and for correcting the OKEAN 01 microwave radiometer calibration wedge based on concurrent 37 GHz horizontal polarization SSM/I brightness temperature data. Sea ice type and ice concentration algorithms utilized OKEAN's two-channel radar and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter, together with a priori knowledge about the scattering parameters and natural emissivities of basic sea ice types. OKEAN 01 data and algorithms tended to classify lower concentrations of young or first-year sea ice when concentrations were less than 60%, and to produce higher concentrations of multi-year sea ice when concentrations were greater than 40%, when compared to estimates produced from SSM/I data. Overall, total sea ice concentration maps derived independently from OKEAN 01, SSM/I, and AVHRR satellite imagery were all highly correlated, with uniform biases, and mean differences in total ice concentration of less than four percent ( sd<15%).

A combined radiative transfer model for sea ice, open ocean, and atmosphere

A radiative transfer model to compute brightness temperatures in the microwave frequency range for polar regions including sea ice, open ocean, and atmosphere has been developed and applied to sensitivity studies and retrieval algorithm development. The radiative transfer within sea ice is incorporated according to the “many layer strong fluctuation theory” of Stogryn [1986, 1987] and T. Grenfell [Winebrenner et al., 1992]. The reflectivity of the open water is computed with the three‐scale model of Schrader [1995]. Both surface models supply the bistatic scattering coefficients, which define the lower boundary for the atmospheric model. The atmospheric model computes the gaseous absorption by the Liebe et al. [1993] model. Scattering by hydrometeors is determined by Mie or Rayleigh theory. Simulated brightness temperatures have been compared with special sensor microwave imager (SSM/I) observations. The comparison exhibits shortcomings of the ice model for 37 GHz. Applying a simple ad hoc correction at this frequency gives consistent comparison results within the range of observational accuracy. The simulated brightness temperatures show the strong influence of clouds and variations of wind speed over the open ocean, which will affect the sea ice retrieval even for an ice‐covered ocean. Simulated brightness temperatures have been used to train a neural network algorithm for the total sea ice concentration, which accounts for these effects. Sea ice concentrations sensed from the SSM/I data using the network and the NASA sea ice algorithm show systematic differences in dependence on cloudiness.

Total sea ice concentration retrieval from the SSM/I 85.5 GHz channels during the arctic summer

During the 1994 Arctic Ocean Section, a joint voyage across the Arctic Ocean by the U.S. Coast Guard Cutter Polar Sea and the Canadian Coast Guard Ship Louis S. St.-Laurent, telemetry from the Defense Meteorological Satellite Program (DMSP) polar orbiters was tracked by a shipboard antenna. Special Sensor Microwave Imager (SSM/I) data was used to generate maps of total sea ice concentration, using the NASA Team algorithm with the 19 GHz and 37 GHz channels, and using a polarization-based algorithm with the 85.5 GHz channels. When compared with shipboard ice observations, the total sea ice concentration estimates from the 85.5 GHz algorithm are at least as accurate as those from the algorithm that uses only the lower SSM/I frequencies, despite the potential for greater difficulty in dealing with cloud liquid water contamination in the 85.5 GHz signal during the Arctic summer. Near the edge of the ice pack, the 85.5 GHz algorithm often provided more accurate estimates of total ice concentration when compared with surface observations, most likely because of the finer grid spacing at 85.5 GHz (12.5 km vs. 25 km for 37 GHz). However, when using the 85.5 GHz algorithm over regions of lower ice concentration, the reference polarizations in a given image must be chosen with care because over lower sea ice concentration the polarization-based algorithm is more sensitive to cloud opacity and can easily and substantially underestimate the ice concentration. The 85.5 GHz total sea ice retrievals are compared with in situ snow wetness measurements. This comparison suggests that, despite the higher atmospheric opacity at 85.5 GHz, information about sea ice surface properties that affect emissivity can be obtained from these SSM/I channels.

Atmospheric signatures in sea-ice concentration estimates from passive microwaves: Modelled and observed

The effects of weather systems on sea-ice concentration retrieval are investigated using an advanced radiative transfer model with input data from 155radiosonde ascents together with satellite and ground based observations in the Weddell Sea in 1992. The results of the model study indicate that, using the SSM/I NASA Team algorithm, cloud liquid water increases estimates of total sea-ice concentration by the same magnitude as water vapour, i.e., up to 10 per cent, depending on surface type (open ocean, first-year ice, multiyear ice), and actual concentration. Estimates of the multiyear ice concentration are reduced by up to 80 per cent by cloud liquid water whereas the water-vapour effect is smaller (up to 6 per cent). The combined effect is less than the sum of the two. Calculations using the SMMR sea-ice algorithm were made for comparison with previous estimates by Pedersen and Maslanik. In this case study, estimates of the multiyear fraction show a smaller reduction by water vapour and a larger reduction by cloud liquid water, whereas the total concentration change is in between the two previous results.The algorithm for the SSM/I radiometer exhibits stronger effects on total ice concentration due to water vapour and cloud liquid water than that for SMMR, and atmospheric effects using the future MIMR radiometer sea-ice algorithm will be in between those from SMMR and SSM/I. Different calculated ice-concentration changes for the SSM/I due to different sets of tiepoints (emissivities) can be of the same order of magnitude as the atmospheric effect of cloud liquid water. Comparison between these modelled effects and satellite-derived concentrations from SSM/I shows good geographical and quantitative agreement in areas with extensive frontal water clouds.

Cryospheric products from the DMSP-SSM/I: status and research applications

The DMSP Special Sensor Microwave Imager (SSM/I) was launched on 19 June 1987. This first operational passive microwave radiometer operates at four wavelengths (0.35–1.55 cm) with vertical and horizontal polarization for three of the channels. The orbit provides global coverage except for a small polar gap and 12 hour repeat coverage in high latitudes. A Cryospheric Data Management System (CDMS) for snow and ice products developed by the NASA Ocean Data System (NODS) and the National Snow and Ice Data Center (NSIDC) for NASA Polar Oceans Program extracts polar SSM/I data and makes them available in suitable formats for scientific users. The design of the CDMS at NSIDC has evolved since the initial planning in 1984 to one relying on CD-ROM technology for data distribution. The SSM/I data, and the earlier 7-year SMMR data (re-binned to the SSM/I grid), are available as brightness temperatures averaged daily for each channel/polarization in a polar stereographic 25 km grid for both polar regions and as gridded 3-day average total sea ice concentrations and multiyear ice fractions based on the NASA Team algorithm. The availability of a passive microwave record of sea ice parameters since 1978 in a directly comparable format greatly facilitates their research use. The polar regions are of considerable importance in studies of global climate change in view of snow and ice/albedo feedback processes that contribute to the high latitude amplification of such changes. Related climate-cryosphere research includes studies to validate the algorithm employed to prepare the ice products, on ice/climate processes, and on temporal trends in sea ice.

Satellite and aircraft passive microwave observations during the Marginal Ice Zone Experiment in 1984

During the Marginal Ice Zone Experiment in the Fram Strait in June–July 1984, a number of aircraft with microwave sensors and the scanning multichannel microwave radiometer (SMMR) on board the Nimbus 7 satellite were used to acquire large‐scale and mesoscale ice‐ocean observations in conjunction with local surface measurements made by experimenters based on helicopter‐equipped ice‐strengthened vessels. An analysis of the data acquired during six flights of one such aircraft, the NASA CV‐990 airborne laboratory, is discussed in this paper. Included in the instrument complement of the CV‐990 were two passive microwave imagers operating at wavelengths of 0.33 and 1.55 cm and the airborne multichannel microwave radiometer (AMMR) operating at wavelengths of 0.81, 1.4, and 1.7 cm for both horizontal and vertical polarizations. Total and multiyear sea ice concentrations calculated from the AMMR data were found to agree with similar calculations using SMMR data. This is the first check of the performance of the SMMR Team ice algorithm for near‐melting point conditions. The temperature dependence of the multiyear sea ice concentration determination near the melting point was found to be the same for both airborne and spacecraft instrument data and to be correlated with presence or absence of clouds. Finally, it was found that a spectral gradient ratio using the data from both the 0.33‐ and 1.55‐cm radiometers provides more reliable distinctions between low total ice concentrations and open water storm effects near the ice edge than does either singly.

A model for retrieving total sea ice concentration from a spaceborne dual-polarized passive microwave instrument operating near 90 GHz

Abstract An algorithm has been developed for estimating total ice concentration from spaceborne high-frequency passive microwave instrumentation. The algorithm is intended for use with the coming Special Sensor Microwave/Imager (SSM/I) data giving a spatial resolution of 12 km. It is based on radiation physics and detailed millimetre wave surface signature measurements and can therefore be applied to other similar data. However, due to large effects on the signals caused by time varying atmospheric conditions and radiation properties of the ice, the algorithm is made self-adjusting. The atmospheric effects are implicitly treated as a smooth function of the ice concentration with tie points over open ocean and 100 per cent ice for each orbit. This means that the main errors are due to patches of heavy clouds and ice floes with atypical radiation properties. An error analysis indicates possible errors of the order of 5 percent for concentrations representative for the Arctic Basin, increasing with decreasing concentration.

Norwegian Remote Sensing Experiment: Evaluation of the Nimbus 7 scanning multichannel microwave radiometer for sea ice research

An algorithm has been developed for estimating total and multiyear sea ice concentration from passive microwave and surface air temperature measurements. The algorithm was made for use with Nimbus 7 scanning multichannel microwave radiometer (SMMR) data. It is based on radiation physics and may thus easily be modified to suit other passive microwave instruments. A comparison between Nimbus 7 SMMR and aircraft microwave measurements indicates that estimates of total ice concentration are accurate to ±3% and those of multiyear ice concentration to ±10%. These accuracies are not valid during the melt season when the snow on the ice is wet. For the first time such a comparison of independent estimates has been performed to validate the capability of measuring sea ice coverage from space. The ability of the SMMR to follow moving patches of multiyear ice and of rain/wet snow areas has been demonstrated. From the concentration estimates the sharpness of the ice edge can be estimated. The need for accurate concentration estimates for reliable heat budget estimates in the Arctic is also discussed.