FTP Data
IDC Data on FTP

Data Access

SMMR & SSM/I Sea Ice Concentration Data

Readme Contents

(Based on Cavalieri et al., 1984 & 1997, and NSIDC 1995 User's Guide)
Data Set Overview
Original Archive
Future Updates

The Data

The Files
Name and Directory Information
Companion Software

The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data

Points of Contact



Data Set Overview

Sea ice plays an important role in the global climate system. It serves as an effective insulator between the ocean and the atmosphere, restricting exchanges of heat, mass momentum, and chemical constituents. Multi-channel passive microwave radiance measurements made from number of satellites are used to map, monitor and study the Arctic and Antarctic polar sea ice covers.

The monthly averaged sea ice concentration data set presented here is generated from observations made by four different space borne microwave imagers. The data set spans over 18 years(1978-1996), starting with the Scanning Multichannel Microwave Radiometer (SMMR) on NASA Nimbus 7 in 1978 and continuing with the Defense Meteorological Satellite Program (DMSP) Special Sensor Micro-wave/Imager (SSM/I) series beginning in 1987.

The production and distribution of this data set are being funded by NASA's Earth Science enterprise. The data are not copyrighted; however, we request that when you publish data or results using these data please acknowledge as follows:

The authors wish to thank the Distributed Active Archive Center (Code 902) at Goddard Space Flight Center, Greenbelt, MD, 20771, for producing the data in its present format and distributing them. The original data products were produced by NASA Team :Drs. Cavalieri, Parkinson, Gloersen, and Zwally of the Oceans and Ice Branch (code 971), Laboratory for Hydrospheric Processes, NASA Goddard Space Flight Center in Greenbelt. Goddard DAAC's share in these activities was sponsored by NASA's Earth Science enterprise.

Original Archive
The monthly averaged sea ice concentration data was acquired from the Laboratory for Hydrospheric Processes, Ocean and Ice Branch at NASA Goddard Space Flight Center. The original data can be obtained also from the National Snow and Ice Data Center (NSIDC). The original data was on a polar stereographic projection with grid elements of approximately 25x25 km. The Goddard DAAC has resampled the data on 1x1 degree equal angle projection and combined the data sets provided separately for North and South polar regions into one file (filling the uncovered regions near the equator with the masking code) for the comaptibility with the other climate datasets in the interdisciplinay data collection.

Future Updates
The Goddard DAAC will update this data set as new data are processed and made available at NSIDC.

The Data



Multichannel Passive-Microwave Satellite Data Sets

For the purpose of providing a consistent long term ice-concentration data set, brightness temperature data from the SMMR and SSM/I sensors mapped on to a common (the SSM/I) north and south polar grids were obtained from NSIDC( NSIDC, 1995). The NASA Team Algorithm (Cavalieri et al. 1984, Gloersen and Cavalieri 1986)was used to calculate sea ice concentrations from brightness temperatures derived from Scanning Multichannel Microwave data, and a common land mask, recently updated for the SSM/I grids (Martino et al., 1995), was applied.

The four satellite data sets employed and the periods for which the data used are: the Nimbus 7 SMMR from October 25, 1978 through August 20, 1987, the DMSP-F8 SSM/I from July 9, 1987 through December 18, 1991 (with the exception of the data gap from December 3, 1987 through January 12, 1988), the DMSP-F11 SSM/I from December 3, 1991 through September 30, 1995, and the DMSP-F13 SSM/I starting from October 1995. A single channel and two other multichannel passive microwave satellite imagers flown in the 1970s, but not included here, are the Nimbus 5 ESMR, the Nimbus 6 ESMR and the SeaSat SMMR respectively. The Nimbus 5 ESMR was not used because of the lack of overlap data with the Nimbus 7 SMMR, while the Nimbus 6 ESMR was omitted because of the poor quality of the data. The SeaSat SMMR was omitted because of not providing adequate coverage of the polar regions.

Nimbus 7 SMMR

Descriptions of the SMMR instrument design, the operating characteristics, and the procedures used to obtain calibrated brightness temperatures and sea ice concentrations are given by Gloersen et al. (1992). The Scanning Multichannel Microwave Radiometer operated on NASA's Nimbus 7 satellite for more than eight years, from 24 October 1978 to 20 August 1987, transmitting data every other day. Intended to obtain ocean circulation parameters such as sea surface temperatures, low altitude winds, water vapor and cloud liquid water content on an all weather basis, the SMMR is a ten channel instrument capable of receiving both horizontally and vertically polarized radiation. The instrument could deliver orthogonally polarized antenna temperature data at five microwave wavelengths, 0.81, 1.36, 1.66, 2.8 and 4.54 cm. The antenna beam maintained a constant nadir angle of 42 degrees, resulting in an incidence angle of 50.3 degrees at Earth's surface. The antenna was forward viewing and rotated equally +/- 25 degrees about the satellite subtrack. The 50 degree scan provided a 780 km swath of the Earth's surface. Scan period was 4.096 seconds.

Conversion of the raw radiometric readings to microwave brightness temperatures involved correcting for actual antenna patterns, including side lobe effects, as well as separating out the horizontal and vertical polarization components of each of ten channels of radiometric data.

After launch, the prelaunch constants was updated by checking against earth targets of known properties - open, calm sea water with clear skies or light clouds, and consolidated first-year sea ice. The brightness temperatures were verified by comparison with brightness temperatures obtained from airborne radiometer with all SMMR channels during Nimbus 7 underflights. The underflights were particularly important, since extrapolation from the laboratory cold reference of 100 degrees Kelvin to the postlaunch value of 30 degrees K cannot be done with complete confidence. The algorithm to obtain sea ice concentration employs three of the ten channels of the SMMR instrument: vertically and horizontally polarized radiances at 18 GHz and vertically polarized radiances at 37 GHz. Before computing sea ice concentrations, isolated missing brightness temperature pixels on the daily brightness temperature maps were filled by spatial interpolation. Larger areas of missing data were filled later by temporal interpolation of the sea ice concentrations.

The corrections (Gloersen et al.1992), were made for a long term drift in the SMMR data and for errors related to ecliptic angle ( observed in the first 8.8 year data set), full orbits of bad data, individual scans of bad data, misplaced scans from the opposite node, and misplaced scans from unknown origin. These were identified by checking each daily image from both the ascending node data and the descending node data. All of the errors identified and considered to be sufficiently serious to warrant exclusion were removed in the ascending and descending node data sets separately before averaging the data from the two nodes to provide daily brightness temperature matrices. Finally, additional corrections were applied to the three channels (18 GHz H & V, 37 GHz V) of previously corrected data used in the sea ice algorithm (Gloersen et al. 1992), following a procedure similar to that described in Gloersen et al. (1992), but with higher precision. The 8.8 year drifts in these channels were reduced to values well below the instrument noise values given in Gloersen and Barath (1977) and lower than in the previously corrected data.

DMSP-F8, F11, and F13 SSM/I
The DMSP Block 5D-2 F8 spacecraft flew in a near polar sun-synchronous orbit. Launched on 18 June 1987, the satellite accomplished 14.1 revolutions per day, with the subsatellite ground track repeating approximately every 16 days. F8 coverage ended 31 December 1991. The DMSP F9 did not carry an SSM/I sensor, and the orbit of the F10 did not allow for collection of useful polar data, so the DMSP F11 was selected to provide the follow-on data stream for the passive microwave polar gridded time series. Launched on 28 November 1991, the F11 also flew in a sun-synchronous orbit.

The SSM/I is a seven channel, four frequency, linearly polarized, passive microwave radiometric system. The instrument measures atmospheric/ocean surface antenna temperatures at 19.3, 22.2, 37.0 and 85.5 GigaHertz (GHz).

The instrument consists of a 24 x 26 inch offset parabolic reflector fed by a corrugated, broad-band, seven-port horn antenna. The reflector and feed are mounted on a drum which contains the radiometers The reflector-feed drum assembly is rotated about the axis of the drum by a coaxially mounted Bearing And Power Transfer Assembly (BAPTA).

The SSM/I sensor rotates at a uniform rate making one revolution in 1.9 seconds, during which time the satellite advances 12.5 km. The antenna beams are at an angle of 45 degrees to the BAPTA rotational axis, which is normal to the earth's surface. Thus, as the antenna rotates, the beams define the surface of a cone, and, from the orbital altitude of 833 km, make an angle of incidence of the ground track aft of the satellite, resulting in a scene swath width of 1394 km. The radiometer outputs are sampled differently on alternate scans. During the scene portion of the scans (Type A) the five lower frequency channels are each sampled over 64 equal 1.6 degree intervals, and the two 85.5 GHz channels are each sampled over 128 equal 0.8 degree intervals, or approximately every 11 km along the scan. During the alternate scans (Type B), only the two 85.5 GHz channels are sampled, at 128 equal intervals. Thus, the five lower channels are sampled on an approximately 25 km grid along the scan and along the track. The two 85.5 GHz channels are sampled at 12.5 km both across and along the track.

Coverage is global except for circular sectors centered over the pole, 280 km in radius located poleward of 87 degrees North and 87 degrees South which are never measured due to orbit inclination. The measurement footprint size (effective field of view) is as follows:

		19.3 GHz  70x45 km
		22.2 GHz  60x40 km
		37.0 GHz  38x30 km
		85.5 GHz  16x14 km
Scan Geometry

SSM/I A/B Scan Geometry: Swath data consist of A/B scan pairs. Each pair includes 256 scene stations (numbered). Scene station numbers (parameter position numbers) are indicated. Large circles signify all channels, small circles stand for 85 GHz channels. Brackets indicate scene stations lost due to antenna pattern correction.

For calibration, a small mirror and a hot reference absorber are mounted on the BAPTA and do not rotate with the drum assembly. They are positioned off-axis such that they pass between the feed horn and the parabolic reflector, occulting the feed once each scan. The mirror reflects cold sky radiation into the feed, thus serving, along with the hot reference absorber, as calibration references for the SSM/I. This scheme provides an overall absolute calibration which includes the feed horn. Corrections for spillover and antenna pattern effects from the parabolic reflector are incorporated in the data processing algorithms.

The 85 GHz channels are considered experimental because a passive microwave sensor with 12.5 km resolution has never before been deployed on an orbital scanner. Therefore these channels are not used in this analysis. The SSM/I Sea Ice Algorithm Working Team (SSIAWT) decided to retain as many of these data records as possible, despite the anomalies that will be observed within the 85 GHz grids. F11 antenna temperature values are sometimes completely unphysical, ranging from 0 K to 650 K. The cause for this is unclear, but telemetry errors are suspected. Data less than 50 K and greater than 350 K are flagged and not processed.

The 4.5 year F8 19-37 GHz data were found to be free of orbit dependent (ecliptic angle) brightness temperature variations using a technique similar to what was used for the SMMR data (Gloersen et al., 1992). based on the F8 experience, the F11 SSM/I was presumed also to be free of this defect. The drift determined by the method used for the SMMR data over the 7 year SSM/I period resulted in brightness temperature changes below or at the instrument noise level for the SSM/I (see Table 1.4 in Hollinger, 1989), and was therefore considered to have no significant impact on the computed sea ice concentrations (less than 0.5%) either for consolidated sea ice or at the ice edge, and so were ignored. A comparison of instruments and of the differences in orbital parameters (Abdalati et al. 1995) between the F8 and F11 using overlapping data indicated a high degree of correlation (greater than 0.98) between the F8 and F11 data sets. Small variations were attributed to the different orbital characteristics of the two satellites, especially to the differences in data collection times.

Table 1. Sensor and spacecraft orbital characteristics of the sensors used in generating the sea ice concentrations.

ParameterNimbus-7 SMMRDMSP F8DMSP F11DMSP F13
Launch TimeOct 24, 1978 Jun 18, 1987 Nov 28, 1991Mar 24, 1995
Nominal Altitude955 km 860 km830 km830 km
Inclination Angle99.3 degrees98.8 degrees98.8 degrees 98.8 degrees
Orbital Period104 minutes102 minutes 101 minutes101 minutes
Ascending Node Equatorial Crossing
(local time)
approximately 12:00 noonapproximately 6:00 a.m.approximately 5:00 p.m.,drifted to 7 p.m. approximately 5:00 p.m.
Algorithm frequencies (GHz)18.0 & 37.0 19.4 & 37.0 19.4 & 37.019.4 & 37.
3 dB Beam width (degree) 1.6, 0.8 1.9, 1.1 1.9, 1.1 1.9, 1.1
Earth incidence angle 50.2 53.1 52.8 52.8

Based on the analysis, a set of corrections have been applied to F11 and F13 data to maximize consistency between the data sets.

The Files


This dataset contains monthly averaged global gridded sea ice concentration estimates. Data in each file progresses from North to South and from West to East beginning at 180 degrees West and 90 degrees North. Thus first point represents the grid cell centered at 89.5 degree North and 179.5 West. Grids over land are filled by masking code ( -9999).


Data Files

Name and Directory Information

Naming Convention:

The file naming convention for the Global land Precipitation Dataset is
.. (Oct 78- Aug 87)
.. (Sept 87- Dec 91)
.. (Jan 92- Sept 95)
.. (Oct 95- Dec 96) ..

smmr_n07 = Scanning Multichannel Microwave Radiometer on Nimbus-7
ssmi_f08 = Special Sensor Microwave Imager on DMSP - F08
ssmi_f11 = Special Sensor Microwave Imager on DMSP - F11
ssmi_f13 = Special Sensor Microwave Imager on DMSP - F13
seaice = Sea ice concentration
1 = number of levels
n = vertical coordinate, n = not applicable
m = temporal period, m = monthly
e = horizontal grid resolution, e = 1 x 1 degree
go = spatial coverage, go = global (ocean)
yy = year
mm = month number
ddd = file type designation, (bin=binary, ctl=GrADS control file)

Directory Path


where yyyy is year.

Companion Software
Several software packages have been made available on the CIDC CD-ROM set. The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is currently in use worldwide for the analysis and display of earth science data. GrADS meta-data files (.ctl) have been supplied for each of the data sets. A GrADS gui interface has been created for use with the CIDC data. See the GrADS document for information on how to use the gui interface.

Decompression software for PC and Macintosh platforms have been supplied for datasets which are compressed on the CIDC CD-ROM set. For additional information on the decompression software see the aareadme file in the directory:


Sample programs in FORTRAN, C and IDL languages have also been made available to read these data. You may also acquire this software by accessing the software/read_cidc_sftwr directory on each of the CIDC CD-ROMs

The Science

Sea ice forms through the freezing of sea water over large areas of the polar oceans in both hemispheres and covers as much as 30,000,000 km^2 of the Earth's surface. This large expanse of ice greatly reduces the exchange of heat, mass, and momentum between ocean and atmosphere and decreases the amount of solar radiation absorbed at the surface. These processes depend strongly on time and location because of the high temporal and spatial variability of the sea ice cover in each hemisphere.

Sea Ice exists in regions that are dark for several months of the year and very frequently cloudy in the remaining months. The ability of microwave sensors to view the Earth's Surface under all weather conditions, day or night, provides the opportunity to obtain the required sea ice and ocean observations.

The two major types of sea ice that are known (Wilheit et al., 1972; Gloersen et al., 1973; Campbell et al., 1974) to have distinctly different microwave emissivities are first-year ice (ice that is at least 30 cm thick but not undergone a melt season) and multiyear ice (ice that has survived at least one melt season). Also new and young ice (under 30 cm thick) are known to have distinctly different microwave emissivities from first-year ice (Ramseier et al., 1975; Gloersen et al., 1975; Cavalieri et al., 1986; Grenfell and Comiso, 1986; Comiso et al., 1989). However, the presence of these additional ice types within the sensor field of view cannot be determined unambiguously and thus contributes to the error of the calculated ice concentration.

Theoretical Basis of Data

The NASA Team Algorithm (Cavalieri et al, 1984)uses three microwave channels in calculating sea ice concentration. The channels are 19.4-GHz horizontally (H) and vertically (V) polarized and the vertically polarized 37.0-GHz. This algorithm is functionally the same as the Nimbus 7 SMMR algorithm described by Cavalieri et al. (1984) and Gloersen and Cavalieri (1986). The radiances from each of the three channels are first mapped onto polar stereographic grids (the so-called SSM/I grid). The gridded radiances are then used to calculate the grid values for the two independent variables used in the algorithm. These are the polarization (PR) and spectral gradient ratios (GR) defined by

      PR = [TB(19V)-TB(19H)]/[TB(19V)+TB(19H)]                   (1)

       GR = [TB(37V)-TB(19V)]/[TB(37V)+TB(19V)]                  (2)
where TB is the observed brightness temperature at the indicated frequency and polarization. From these two parameters the first-year ice concentration (CF) and the multiyear ice concentration (CM) are calculated from the following equations:

                     CF = (a0 + a1PR + a2GR + a3PR * GR)/D	(3)
                     CM = (b0 + b1PR + b2GR + b3PR * GR)/D	(4)
                    where D = c0 + c1PR + c2GR + c3PR * GR      (5)
The total ice concentration (CT) is the sum of the first-year and multiyear concentrations

                             CT = CF + CM	                (6)
The coefficients ai, bi, and ci (i = 0, 3) are functions of a set of nine TBs. These TBs, referred to as algorithm tie points, are observed SSM/I radiances over areas of known ice-free ocean, first-year (FY) sea ice, and multiyear (MY) ice for each of the three SSM/I channels. In addition to constraining the solutions to concentrations between 0% and 100%, the algorithm also sets the total ice concentration to 0% for those SSM/I grid cells with GR values greater than preset thresholds. This serves to reduce spurious ice concentrations caused by weather-related effects over ice-free ocean. This so-called weather filter is discussed later.

SSM/I Tie Points

The selection of the SSM/I F8 tie points was based on an analysis of SSM/I TBs, PR-GR distributions, histograms of sea ice concentrations, and on comparisons with near simultaneous measurements from the Nimbus-7 SMMR during July and August 1987. The two sets of SSM/I tie points (one for the Northern Hemisphere and one for the Southern Hemisphere) represent a global set designed for mapping global ice concentrations. While this global set of tie points provides a uniform measure of sea ice concentration on the large scale, improved accuracy is obtained with the use of regionally selected tie points (Steffen and Schweiger 1991). Please note that tie points are the same for F8 and F11 data.

The ice-free (open water) tie points were chosen to be near minimum ice-free ocean TBs (corresponding to near maximum values of PR). By choosing near minimum TBs, the PR range between open water and FY ice is about an order of magnitude, permitting greater algorithm sensitivity for detecting changes in ice concentration. Although the Arctic and Antarctic open water tie points were selected independently, the TB difference for corresponding channels is no more than about 1 K .

The ice tie point selection was more difficult, since the passive-microwave ice signatures depend on region and season. This is particularly true of Arctic MY ice. Even for a given region and season there is a certain amount of random variability for a given ice type. Thus, there is generally a range of TBs that could be used as tie points. The series of SSM/I aircraft underflights helped in this regard (Cavalieri et al. 1991). Mosaic patterns covering several SSM/I image pixels were flown in the central Arctic over a two-week period in March 1988. Although the mosaicked aircraft data did not provide radiometric coverage at the SSM/I frequencies and polarizations, it did provide a constraint on the ice concentrations, which were calculated from passive and active microwave imagery. This allowed adjustment of the ice tie-points within the range of allowable values to improve the accuracy to within a few percent (relative to the aircraft data).

The need for different ice type tie points for the Arctic and Antarctic results from the very different environmental conditions of the two polar regions. Indeed, the observed physical characteristics of Antarctic sea ice are different from those in the Arctic (Ackley et al. 1980, Wadhams et al. 1987), implying a corresponding difference in microwave radiance characteristics. In the Antarctic the two ice types distinguished by the algorithm are identified as ice type A and ice type B. Significant differences are found between the Arctic first-year and Antarctic ice type A tie points and between the Arctic multiyear ice and Antarctic ice type B tie points. While ice temperature differences may explain some of the observed tie point differences for corresponding ice types, real emissivity differences are reflected in the polarization and spectral differences. In the Antarctic the radiometric distinction between first-year (seasonal) ice and multiyear (perennial) ice is lost. Unlike the Arctic, where the predominant source of negative gradient ratios is the volume scattering by the empty brine pockets in the freeboard portion of multiyear ice, in the Antarctic, the main source of volume scattering is from sources other than multiyear ice. One very likely source of volume scattering is the snow cover on the sea ice. Snow cover of sufficient depth and of sufficiently large grain size will mimic the microwave signature of multiyear ice.

Weather Filter

A problem in mapping the polar sea ice covers in both hemispheres has been the false indication of sea ice over the open ocean and at the ice edge. These spurious sea ice concentrations result from the presence of atmospheric water vapor, nonprecipitating cloud liquid water, rain and sea surface roughening by surface winds. While these effects are relatively minor at polar latitudes in winter, they result in serious weather contamination problems at all latitudes in summer (Cavalieri et al. 1992).

This problem was addressed for sea ice concentrations derived from the Nimbus 7 SMMR data through the development of a weather filter (Gloersen and Cavalieri 1986). The filter is based on the polarization (PR18) and spectral gradient ratio (GR37/18) distribution of ice-free and ice-covered seas. If GR(37/18) is greater than 0.07, then the sea ice concentration is set to zero. While this eliminates most of the unwanted weather effects, it also eliminates sea ice concentrations less than about 12% in FY ice regions and 8% in MY ice regions. Applying GR(37/19) filters to SSM/I-derived sea ice concentration maps is less successful because the closer proximity of the 19.35 GHz SSM/I channels to the center of the 22.2 GHz atmospheric water-vapor line makes the 19.35 GHz channels more sensitive to changes in atmospheric water vapor, resulting in greater contamination problems.

A new composite weather filter has been developed (Cavalieri et al. 1994) and implemented in the NASA Team sea ice algorithm for routine processing of the SSM/I data for generating sea ice concentration maps. The new filter is a combination of the original SSM/I GR(37/19), which effectively eliminates most of the spurious concentration resulting from wind-roughening of the ocean surface, cloud liquid water, and rainfall with another GR filter based on the 22.2 GHz and 19.35 GHz channels. The rationale for using GR(22/19) is based partly on the sensitivity of the 22.2 GHz to water vapor and partly on the need to minimize the effect of ice temperature variations at the ice edge.

This new weather filter works as follows: If GR(37/19) is greater than 0.05 and/or GR(22/19) is greater than 0.045, the sea ice concentration is set to zero. These GR thresholds effectively eliminate most of the weather contamination, except for winds greater than about 30 m/s, cloud liquid water more than 24 cm, water vapor greater than 0.2 cm, and rain rates greater than 12 mm/hour. Except for a few case studies completed during the development of this filter, the extent to which it eliminates ice-edge concentrations in different regions of the Arctic and Antarctic for different seasons is unknown. Work is currently underway to determine the overall effectiveness of the new SSM/I weather filter.

Algorithm Sensitivity

The sensitivity of the algorithm to random errors has been described previously (Swift and Cavalieri 1985) for the SMMR version of the algorithm. The sensitivity analysis was redone using the SSM/I algorithm coefficients. The results are presented in Tables 2 and 3 for the Arctic and Antarctic sets of tie points. The sensitivity coefficients given in Table.2 were calculated for regions of first year (FY) ice and multi-year (MY) ice in the Arctic at three different ice concentrations. This was repeated for the Antarctic with ice type regions labeled A and B. Each coefficient represents the uncertainty in concentration in units of percent per 1 K uncertainty in TB.

These sensitivity coefficients given in Tables 2 and 3, may be used to obtain an estimate of the error incurred by variations in the radiometric properties of the ice surface. For example, a random variation in ice emissivity of 0.01 over 100% FY ice corresponds to a variation in TB of 2.5 K (assuming a value of 250 K for the physical temperature of the radiating portion of the ice), which in turn corresponds to an error of 4.5% (0.018 x 2.5) in total ice concentration, assuming all three channels are subject to this variation.

Table 2. NASA SSM/I Algorithm Sensitivity Coefficients for First-year and Multiyear Ice Regions of the Arctic at Different Concentrations

First-year Ice
year ice

Multiyear Ice
year ice

*Each coefficient represents the uncertainty in concentration in units of percent per 1-K uncertainty in brightness temperature. The sensitivity of both the total ice concentration(CT) and the multiyear ice concentration (CMY) are given.

Table 3.NASA SSM/I Algorithm Sensitivity Coefficients' for Ice Type A and Ice Type B Regions of the Antarctic at Different Concentrations

Ice Type A Ice Type B 100% 50% 15% 100% 50% 15% dCT dCT dCT dCT dCT dCT dTB19H 1.2 0.9 0.9 1.2 0.8 0.6 dTB19V 0.3 0.1 0.5 0.3 0.1 0.4 dTB37V 0.8 0.8 0.9 0.8 0.8 0.8 Sqrt(Sum(dTB)2) 1.5 1.2 1.3 1.5 1.1 1.1
*Each coefficient represents the uncertainty in the total ice concentration(CT) in units of percent per 1-K uncertainty in brightness temperature.

The sensitivity of the calculated ice concentrations to ice temperature variations is reduced through the use of radiance ratios PR and GR (Cavalieri et al. 1984, Swift and Cavalieri 1985). Except at the onset of melt, there is no apparent correlation between PR and the increasing TBs resulting from seasonal warming. This is not the case for GR, which is correlated with the seasonal variation in TB. An estimated error of 0.005 in GR (Gloersen et al. 1992) corresponds to an uncertainty in total ice concentration of about 1%, while the error in MY ice concentration is about 9%. These estimated errors are consistent with the results obtained from previously published comparative studies (Cavalieri et al. 1991, Steffen and Schweiger 1991).

Processing Sequence and Algorithms

The DMSP F8, F11 and F13 SSM/I data were obtained from the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. Data acquisition, filtering bad data, handling geolocation errors, implementation of an antenna pattern correction, and finally the swath to grid conversion are all described in the NSIDC's User's Guide (1995).

The data grids are in the polar stereographic projection.

Spatial Coverage Map
The polar stereographic projection often assumes that the plane (i.e., the grid) is tangent to the Earth at the pole. Thus, there is a one-to-one mapping between the Earth's surface and grid (i.e., no distortion) at the pole. Distortion in the grid increases as the latitude decreases because more of the Earth's surface falls into any given grid cell, which can be quite significant at the edge of the northern SSM/I grid where distortion reaches 31%. For the South Pole, the SSM/I grid has a maximum distortion of 22%. To minimize the distortion, it has been decided that the projection will be true at 70 degrees rather than the poles. This will increase the distortion at the poles by three percent and decrease the distortion at the grid boundaries by the same amount. The latitude of 70 degrees was selected so that little or no distortion would occur in the marginal ice zone. Another result of this assumption is that fewer grid cells will be required as the Earth's surface will be more accurately represented. This will save about 100 megabytes per year in storage.

Calculation of Sea Ice Concentrations

The NASA Nimbus-7 SMMR Team Algorithm (Cavalieri et al. 1984, Gloersen and Cavalieri 1986) with revised tie points (Gloersen et al. 1992, p. 27) was used to calculate ice concentrations from the SMMR @ SSMI brightness temperatures. Derived ice concentration grids were stored separately for the northbound and southbound (ascending and descending) orbital nodes. This was done in view of the rapid changes known to take place in the polar ice covers, and because of the nonlinear nature of the sea ice algorithm. To be consistent with the daily-averaged brightness temperatures derived from the SMMR instrument, the northbound and southbound (ascending and descending) ice concentration data were averaged to produce a daily-averaged ice concentration map for each SMMR data day (the SMMR sensor operated on alternate days to conserve power). Be cautioned that producing ice concentrations from the daily-averaged brightness temperatures may yield slightly different results than the ice concentration maps presented in the published atlas.

Comparisons of sea ice concentrations calculated for each of the sensors during overlap periods using published algorithm tie-points reveal significant differences. These differences may result from differences in sensor and orbital characteristics, differences in observation times (and therefore tidal effects), and differences in algorithm coefficients. Sensor and orbital characteristic differences for the Nimbus 7 SMMR and DMSP SSM/I F8 include antenna beam width, channel frequency, spacecraft altitude, ascending node time, and angle of incidence. In addition, the sea ice algorithm tie-points are significantly different. The SSM/I F8 and F11 sensors also differ in ascending node time, altitude, and angle of incidence. Because the visit times of the three satellites occur during different phases of the diurnal cycle, tidal effects may result in differences in the ice distribution. It is expected that any such effects would be mitigated by the correction scheme described below. Table 1 summarizes the sensor and orbital characteristic differences. These differences are accommodated for each pair of sensors by employing a self-consistent set of algorithm tie-points determined through linear relationships between the observed brightness temperatures during the overlap periods.

Daily brightness temperature maps from the Nimbus 7 SMMR and from the DMSP SSM/I F8 during their period of overlap, July 9 - August 20, 1987, were compared for both the Arctic and Antarctic. Unfortunately, there were only 22 days of common coverage. A linear least squares best fit of the cumulative data was obtained for each of the corresponding channels. For the purpose of eliminating spurious brightness temperatures resulting from residual land spillover effects, an Arctic land mask expanded 3 to 4 pixels out from the original land mask was used in the determination of the best fit between the two data sets. The eliminated pixels represent only a very small fraction of the total number of ice concentration pixels, but eliminating them helps considerably in reducing the outliers on the scatter plots. These linear relationships were used to generate a set of SSM/I tie-points that are consistent with the original SMMR sea ice algorithm tie-points (Gloersen et al., 1992). The published SSM/I F8 tie-points (Cavalieri et al., 1992) were not used. In addition to using these transformations, the SSM/I F8 open water tie-points were subjectively tuned to help minimize the differences between the SMMR and SSM/I F8 sea ice extent and area during the overlap period. In all cases except for the Antarctic F8 values, the tuned amount is within one standard error of estimate. It is suspected that the reason for the larger tuned values results from greater weather effects during the overlap period.

The period of overlap for F8 and F11 is even shorter than that for Nimbus 7 and SSM/I F8, with only 16 days of overlap of good data, from December 3-18, 1991. The linear regression was performed for each of the corresponding channels. The SSM/I F11 open water tie-points were also tuned to help reduce differences in ice extent and area as was done with the SSM/I F8 values. A further adjustment to the Antarctic 37V ice type-B F11 tie-point was also made to reduce the ice area difference. In this case, the amount of tuning needed to reduce the ice extent and area differences between the F8 and F11 values is well within one standard error of estimate.

Land-to-Ocean Spillover Correction

The next step in preparing the data sets was the correction for land-to-ocean spillover (often referred to as "land contamination") and residual weather-related effects. Land-to- ocean spillover refers to the problem of blurring sharp contrasts in brightness temperature, such as exist between land and ocean, by the relatively coarse width of the sensor antenna pattern (Figure 1a). This problem is of concern here because it results in false sea ice signals along coastlines. (Land and ice both have much higher brightness temperatures than ocean.) The method used to reduce the spillover is an extension of the method employed for the single-channel Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR) data in Parkinson et al. (1987). The rationale behind the approach is that a minimum observed (generally in late summer) sea ice concentration in the vicinity of coastlines where no ice remains offshore is probably the result of land spillover and is thus subtracted from the image. To reduce the error of subtracting ice in areas of ice cover, the technique searches for and requires the presence of open water in the vicinity of the image pixel to be corrected.

Land-to-ocean spillover was reduced by the following three-step procedure:

(1) A matrix M was created covering the entire grid and identifying each pixel as land, shore, near-shore, off-shore, or non-coastal ocean. This matrix M is created once and then used throughout the data set.

(2) A matrix CMIN, to represent minimum ice concentrations on a pixel-by-pixel basis throughout the entire grid, was created for each instrument type. CMIN was created by first constructing a matrix P containing the minimum monthly average ice concentrations throughout a given year, then adjusting that matrix at off-shore, near-shore, and shore pixels. The CMIN matrix was created once for SMMR and once for SSM/I, then used throughout the data sets.

(3) The daily ice-concentration matrices for all three data sets were adjusted at any off-shore, near-shore, and shore pixels in the vicinity of open water. At any time when the neighborhood of an off-shore, near-shore, or shore pixel contains three or more open-water pixels (i.e., ice concentration less than 15%), then the calculated ice concentration at the off-shore, near-shore, or shore pixel is reduced by the value for that pixel in the matrix CMIN. Wherever the subtraction leads to negative ice concentrations, the concentrations are set to 0%. This land-spillover -correction algorithm is clearly a rough approximation, as the contaminated amount does not stay constant over time; but the scheme has been found to reduce substantially the spurious ice concentrations on the grids.

WeatherRelated Corrections

A correction for residual weather effects was made based on monthly climatological sea surface temperatures (SSTs) from the NOAA Ocean Atlas (Levitus and Boyer, 1994). These data, originally on a 2 by 2 degree grid, were remapped onto the SSM/I grid. Because the SST data did not extend to the SSM/I coastline, the data were extrapolated to the coastline once regridded onto the SSM/I grid. The SST maps were used as follows: In the Northern Hemisphere, in any pixel where the monthly SST is greater than 278 K, the ice concentration is set to zero throughout the month; in the Southern Hemisphere, wherever the monthly SST is greater than 275 K, the ice concentration is set to zero throughout the month. The higher threshold SST value was needed in the Northern Hemisphere because the 275 K isotherm used in the South was too close to the ice edge in the North. In a few instances, corrections to the regridded SST data were needed, because otherwise we were losing actual sea ice.

Filling Data Gaps

In each of the data sets, there are instances of missing data. In some cases whole days (or weeks or months) are missing. In other cases, large swaths or wedges of missing data exist within an image, along with scattered pixels of missing data throughout the grid. The scattered pixels of missing data, resulting generally from mapping the orbital radiance data to the SSM/I grid, were filled by applying a spatial linear interpolation scheme on the brightness temperature maps. The larger areas of missing data, resulting from gaps between orbital swaths (generally at low latitudes on daily maps) or from partial coverage or missing days, were filled by temporal interpolation on the ice concentration maps. No data at all were available for the period from December 2, 1987 through January 12, 1988. This gap was not filled by temporal linear interpolation, instead being left as missing data. Table 4 lists the SSM/I dates containing bad data, which were subsequently corrected through interpolation.

Table 4. SSM/I  Days Containing Bad Orbits or Bad Scans

102/1992        103/1992        351/1992        323/1993       330/1993 
331/1993        334/1993        346/1993        352/1993       357/1993 
007/1994        009/1994        011/1994        012/1994       014/1994 
017/1994        019/1994        022/1994        023/1994       025/1994 
028/1994        031/1994        042/1994        167/1994       182/1994 
350/1994        358/1994        003/1995        039/1995       059/1995 
077/1995        081/1995        082/1995        086/1995       097/1995 
105/1995        231/1995        232/1995        

There are usually at least 14 days of coverage per month, although major data gaps occur in August: in August, 1982, the 4th, 8th, and 16th are missing for both polar regions; in August, 1984, the 13th through the 23rd are missing for both polar regions.

Resampling of data from Polar Stereo projection grid to 1x1 degree equal angle grid

The original data are provided on a rectangular grid placed over a polar stereographic projection, with the projection plane cutting the globe at a latitude of 70 degrees. On the north polar grid 31 degrees N is the lowest latitude. The north polar grid size is 304 x 448 grid cells. The pole is located at x,y = 154, 234, (referenced to x,y = 0,0 at the upper left corner), the common corner of four adjacent pixels. In the southern hemisphere, the projection is the same as the Northern Hemisphere, with a grid size of 316 x 332, and the pole located at x,y = 158, 174. The grid cells are 25 x 25 km polar stereographic projection.

For inclusion in the Interdisciplinary data collection the data are resampled onto a 1x1 degreee qual angle grid(array dimension 360x180). For both North and South hemisphere regions the locations of the grid centers are transformed into geodetic latitude and longitude using the Program Locate available in Fortran language (NSIDC,95). North and South polar region data files are merged in one file, data is binned onto 1x1 degree grids,and finally the data is reoriented such that first data points represents (89.5N,179.5W) In this process, the masking of the original data is maintained. However the coastal mask value '-10000' was assigned the land mask '-9999'. The data from pole to 85 degree North is assigned a fill value of -999.9. The scattered gaps created in Arctic and Antarctic polar region due to grid transformation are filled by spatial interpolation using neighbouring points. If in a grid, fraction of sea exceeded the land, averaged sea ice value was assigned. The ocean near equator is assigned a value '-1' for the fill value since no data was reported to differentiate with the value '0' used by the data producer for the polar sea regions which were investigated and no ice was found.

The regridded data were visually examined to ensure consistency with the original data.

Scientific Potential of Data

Passive microwave observations of polar oceans have become essential to the tracking of ice edges, for estimating sea ice concentrations, and for classifying sea ice types. Global data, immediately practical for use in shipping and petroleum development activities, have broader implications from the standpoint of adding to the meteorological foundations used in understanding and modeling climate change.

Sea ice has many roles in the global climate system. For one, it serves as an effective insulator between the ocean and the atmosphere, restricting exchanges of heat, mass momentum, and chemical constituents.

Brightness temperatures are used to derive sea ice concentrations. Among the many possible applications for sea ice concentrations, researchers use them to map ice extent, actual ice area, and the amount of open water within ice packs. The latter is used to monitor occurrences, impact and persistence of polynyas, in the calculation of heat and salinity fluxes between ocean and the atmosphere in the polar regions.

Another important role is how it effects surface albedo. The list below gives the albedo for varying sea conditions:

Ice-free ocean 10% - 15% (Lamb, 1982)
Sea ice 80% (Grenfell, 1983)
Fresh snow cover on ice 98% (Vowinkel and Orvig, 1970)
Melt ponds on ice 20% - 60% (Grenfell and Maykut, 1977)

Sea also has a direct affect on oceanic circulations by the rejection of salt to the underlying ocean during ice growth. The result of this is an increase in the density of the water directly under the ice, which produce convections that tend to deepen the mixed layer. The convection contributes to driving the thermohaline circulation of the ocean (Bryon et al., 1975).

It has been used in producing ice edge product (NSIDC,95) that could be integrated into a sea surface temperature (SST) algorithm being developed as part of the NOAA-NASA AVHRR Pathfinder (Oceans Group) activities. The ice edge product would be used as a filter, to mask known areas of sea ice from the AVHRR SST retrieval algorithm and minimize any possible contamination of an SST product by the presence of ice.

While a review of NSIDC-archived data sets determined that no satisfactory digital sea ice product existed that met those requirements, it was apparent that a monthly averaged sea ice concentration product could be generated from DMSP- F8 SSM/I daily sea ice concentration grids. Monthly averaged sea ice concentration grids would provide the necessary ice extent information and could additionally be useful for model comparisons and inputs.

Validation of Data

Errors in the derived sea ice concentrations arise from several sources. Sources of sea ice concentration error in decreasing order of importance are:

  • inability of the algorithm to discriminate among more than two radiometrically different sea ice types,
  • seasonal variations in sea ice emissivities,
  • nonseasonal variations in sea ice emissivities,
  • weather effects at ice concentrations greater than 8%-12%, and
  • random and systematic instrument error.
  • The largest source of error is the inability of the algorithm to discriminate among more than two radiometrically different sea ice types (including different surface conditions). The broad categories of radiometrically different sea ice types are new and young ice, FY ice, and MY ice types. Since the algorithm allows for both FY and MY ice types, the largest source of error in total ice concentration is caused by the presence of newly forming sea ice. New and young ice, most commonly found in leads and coastal polynyas during winter, are characterized by polarization differences intermediate between open water and thick FY ice (Cavalieri et al. 1986). PR for thin ice will vary in proportion to ice thickness (Grenfell and Comiso 1986) and will increase in proportion to the fraction of new ice filling the SSM/I field of view. For example, if it is assumed that an FOV contains 10% new ice (PR = 0.14) and 90% FY ice (PR = 0.03), then the increase in PR results in an underestimate of about 10% in total ice concentration. Larger areas of new ice within the sensor FOV will result in proportionally larger underestimates by the algorithm. Recently, a new thin ice algorithm has been developed (Cavalieri et al. 1994) which mitigates this problem in seasonal sea ice zones and also permits the mapping of new and young ice types.

    Seasonal variations in sea ice emissivities can be extremely large. MY ice, for example, loses its characteristic microwave spectral signature (negative GR) during spring and summer and becomes indistinguishable from FY ice. Another condition resulting in large errors in total ice concentration is the formation of melt ponds on the ice surface, making the ponded region indistinguishable from open water. While the areal extent of ponding is not well known, unpublished data reported by Carsey (1982) show that for the summer of 1975, 20% or less of the Arctic ice pack was covered by ponds and that ponding reached maximum areal extent in early July. For an area of the Beaufort Sea (AIDJEX triangle) during August 1975, Campbell et at. (1984) report that the average ponding was 30%. The percent coverage of melt ponds varies spatially and temporally across the Arctic and the extent to which they influence summer ice concentrations remains uncertain.

    Nonseasonal variations in sea ice emissivity include local variations, resulting from fluctuations in the physical and chemical properties of sea ice, and regional variations resulting from environmental differences. Regional and hemispheric variability may be considerable, as indicated by previous studies (Comiso 1983, Ackley 1979). Differences between Arctic and Antarctic sea ice microwave signatures noted above result in different sets of algorithm tie-points for each hemisphere. Algorithm errors can be reduced by using locally and seasonally chosen algorithm tie points.

    While weather effects resulting from atmospheric water vapor, cloud liquid water, rain, and sea surface roughening by near-surface winds on the calculated sea ice concentrations are greatly reduced over open ocean at polar latitudes by the algorithm weather filter described previously, they may nevertheless contribute to the sea ice concentration error at concentrations greater than about 15%. Presuming that the atmospheric contribution is nearly zero over consolidated FY ice and that the contribution at the open water end results totally from atmospheric effects estimated to be up to 15%, then the error resulting from atmospheric effects for any intermediate concentration may be estimated by a linear interpolation. While the effects of weather on high total ice concentrations are small, there is the potential for significant reductions in multiyear ice concentrations (Maslanik 1992).

    Finally, errors in ice concentration also result from random and systematic instrument errors. Except for the 85-GHz channels, over the two years of SSM/I operation, no instrument drifts are apparent. Based on prelaunch measurements and on observed radiances over relatively stable targets where temporal and spatial geophysical variability is small, the error for each of the three SSM/I channels used in the algorithm is less than 1 K, and the absolute accuracy is estimated at 3 K (Hollinger 1989). Assuming a 1-K level of random instrument noise in each channel, an upper limit to the rss uncertainty in the calculated concentrations, which depends on surface type and concentration, ranges from about 1% to 1.8% for total ice concentration and from 4.5% to 6% for MY ice concentration.

    Comparisons of aircraft SAR and ESMR mesoscale ice concentration maps with concurrent SMMR maps have given agreements to within 10%-20% (Campbell et al., 1987). Comparison of data from the Airborne Multichannel Microwave Radiometer (AMMR) transects within SMMR footprints with SMMR data has yielded agreements within 10% for both total Sea ice and multiyear sea ice (Gloersen and Campbell, 1988).


    Points of Contact
    For information about or assistance in using any DAAC data, contact

    EOS Distributed Active Archive Center(DAAC)
    Code 902
    NASA Goddard Space Flight Center
    Greenbelt, Maryland 20771

    301-614-5224 (voice)
    301-614-5268 (fax)

    To order the original Sea Ice Concentration data set, please contact NSIDC DAAC:

    National Snow and Ice Data Center
    Campus Box 449
    University of Colorado
    Boulder, CO 80309-0449

    303-492-6199 (voice)
    303-492-2468 (fax)

    For algorithm questions related to original data,please contact the data producer:

    Dr. Donald J. Cavalieri
    Laboratory for Hydrospheric Processes
    Ocean and Ice Dynamics Branch, Code 971
    NASA Goddard Space Flight Center
    Greenbelt, Maryland 20771

    301-286-2444 (voice)
    301-286-0240 (fax)


    Abdalati, W., K. Steffen, C. Otto, and K. C. Jezek .1995. Comparison of brightness temperatures from SSMI instruments on the DMSP F8 and F11 satellites for Antarctica and the Greenland Ice Sheet. International Journal of Remote Sensing, 16(7):1223-1229.

    Ackley, S. F. 1979. Mass balance aspects of Weddell Sea pack ice. J. Glaciol. 24(90):391-406.

    Ackley, S. F., A. J. Gow, K. R. Buck, and K. M. Golden. 1980. Sea ice studies in the Weddell Sea aboard USCGC Polar Sea. Antarctic J. of U. S. 15(5):84-86.

    Bryon, K., S. Manabe, and R.C. Pacanowski. 1975. A global ocean-atmosphere climate, model. Part II. The ocean circulation, J. Phys. Oceanography. 5:30-46.

    Campbell, W.J., P. Gloersen, E.G. Josberger, O.M. Johannessen, P.S. Guest, N. Mognard, R. Shuchman, B.A. Burns, N. Lannelongue, and K.L. Davidson. 1987. Variations of mesoscale and large-scale sea ice morphology in the 1984 Marginal Ice Zone Experiment as observed by microwave remote sensing, J. Geophys. Res. 92:6805-6824.

    Campbell, W.J., Gloersen, W. Norderg, and T.T. Wilheit. 1974. Dynamics and morphology of Beaufort Sea ice determined from, aircraft, and drifting stations, in Proceedings of the Symposium on Approaches to Earth Survey Problems Through Use of Space Techniques. Akademie-Verlag, Berlin, pp. 311-327.

    Campbell, W. J., P. Gloersen, and H. J. Zwally. 1984. Aspects of Arctic sea ice observable by sequential passive-microwave observations from the Nimbus 5 satellite. In Arctic technology and policy. I. Dyer and C. Chryssostomidis, eds. New York: Hemisphere Publishing. p.197-222.

    Carsey, F. D. 1982. Arctic sea ice distribution at end of summer 1973-1976 from satellite microwave data. J. Geophys. Res. 87:5809-5835.

    Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally, 1997: Arctic and Antarctic Sea Ice Concentrations from Multichannel Passive-Microwave Satellite Data Sets: October 1978-September 1995. User's Guide. NASA TM 104647, Goddard Space Flight Center, Greenbelt, MD 20771, pp17

    Cavalieri, D. J., K. M. St. Germain, and C. T. Swift. 1994. Reduction of weather effects in the calculation of sea ice concentration with the DMSP SSM/I. J. of Glaciology, 41(139):455-464.

    Cavalieri, D. J., J. Crawford, M. Drinkwater, W. J. Emery, D. T. Eppler, L. D. Farmer, M. Goodberlet, R. Jentz, A. Milman, C. Morris, R. Onstott, A. Schweiger, R. Shuchman, K. Steffen, C. T. Swift, C. Wackerman and R. L. Weaver. 1992,NASA Sea Ice Validation Program for the DMSP SSM/I: Final Report, NASA Technical Memorandum 104559, National Aeronautics and Space Administration, Washington, D. C., pp. 126.

    Cavalieri, D. J., J. Crawford, M. R. Drinkwater, D. Eppler, L. D. Farmer, R. R. Jentz and C. C. Wackerman. 1991. Aircraft active and passive microwave validation of sea ice concentration from the DMSP SSM/I. J. Geophys. Res. 96(C12):21,989-22,009.

    Cavalieri, D.J., P. Gloersen, and T.T. Wilheit. 1986. Aircraft and satellite passive-microwave observations of the Bering Sea ice cover during MIZEX West, IEEE Trans. Geoscience and Remote Sensing. GE-24:368-377.

    Cavalieri, D.J., P. Gloersen, and W.J. Campbell. 1984. Determination of sea ice parameters with the Nimbus 7 SMMR, J. Geophys. Res. 89:5355-5369.

    Comiso, J.C. 1983. Sea ice effective microwave emissivities from satellite passive microwave and infrared observations. J. Geophys. Res. 88(C12):7686-7704.

    Comiso, J.C., T.C. Grenfell, D.L. Bell, M.A. Lange, and S.F. Ackley. 1989. Passive-microwave observations of winter Weddell sea ice, J. Geophys. Res. 94:10891-1-905.

    Gloersen, P., W. J. Campbell, D. J. Cavalieri, J. C. Comiso, C. L. Parkinson and H. J. Zwally. 1992. Arctic and Antarctic sea ice, 1978-1987: Satellite Passive Microwave Observations. NASA SP-511.

    Gloersen, P., and W.J. Campbell. 1988. Satellite and aircraft passive-microwave observations during the Marginal Ice Zone Experiment in 1984, J. Geophys. Res. 93:6837-6846.

    Gloersen, P. and D. J. Cavalieri. 1986. Reduction of weather effects in the calculation of sea ice concentration from microwave radiances. Journal of Geophysical Research. 91(C3):3913-3919.

    Gloersen, P., D.J. Cavalieri, A.T.C. Chang, T.T. Wilheit, W.J. Campbell, O.M. Johannessen, K.B. Katsaros, K.F. Kunzi, D.B. Ross, D. Staelin, E.P.L. Windsor, F.T. Barath, P. Gudmandsen, E. Langham, and R.O. Ramseier. 1984. A summary of results from the first Nimbus 7 SMMR observations, J. Geophys. Res. 89:5335-5344.

    Gloersen, P. and Barath, F. T.,1977. A Scanning Multichannel Microwave Radiometer for Nimbus-G and SeaSat-A, IEEE Journal of Oceanic Engineering, OE-2, 172-178.

    Gloersen, P., R.O. Ramseier, W.J. Campbell, T.C. Chang, and T.T Wilheit. 1975. Variations of ice morphology of selected mesoscale test areas during the Bering Sea Experiment, in Proceedings of the Final Symposium on the Results of the Joint Soviet-American Expedition, K.Ya. Kondratyev, Yu.I. Rabinovich, and W. Nordberg, eds., Gidrometeoizdat, Leningrad, pp. 196-218. (Republished as USSR/USA Bering Sea Experiment by A.A. Balkema, Rotterdam, 307 pp,. 1982.)

    Gloersen, P., W. Nordberg, T.J. Schmugge, T.T. Wilheit, and W.J. Campbell. 1973. Microwave signatures of first-year and multiyear sea ice, J. Geophys. Res. 78:3564-3572.

    Grenfell, T.C., and J.C. Comiso. 1986. Multifrequency passive-microwave observations of first-year sea ice grown in a tank, IEEE Trans. Geoscience and Remote Sensing. GE-24:862-831.

    Grenfell, T.C. 1983. A theoretical model of the optical properties of sea ice in the visible and near infrared, J. Geophys. Res. 88:9723-9735.

    Grenfell, T.C., and G.A. Maykut. 1977. The optical properties of ice and snow in the Arctic Basin, J. Glaciol. 18:445-463.

    Hollinger, J. P. 1989. DMSP Special Sensor Microwave/Imager Calibration/Validation. Final Report, Volume I, Space Sensing Branch , Naval Research Labs. Washington, D.C. 20375-5000

    Lamb, H.H. 1982. The climate environment of the Arctic Ocean, in The Arctic Ocean, L. Rey, ed., John Wiley & Sons, New York, 135-161.

    Martino, M., D. J. Cavalieri, P. Gloersen, and H. J. Zwally.1995. An Improved Land Mask for the SSM/I Grid, NASA Technical Memorandum 104625, pp.9.

    Levitus, S. and Boyer, T. P. 1994. World Ocean Atlas 1994, Volume 4: Temperature, NOAA National Oceanographic Data Center, Ocean Climate Laboratory, U.S. Department of Commerce, Washington, D.C.

    Maslanik, J. A. 1992. Effects of weather on the retrieval of sea ice concentration and ice type from passive microwave data. Int. J. Remote Sensing. 13(1):37-54.

    NSIDC (National Snow and Ice Data Center). 1995. DMSP SSM/I Brightness Temperature and Ice Concentrations Grids for the Polar Regions. User's Guide. Revised Edition. NSIDC Distributed Active Archive Center, University of Colorado, Boulder.

    Parkinson, C. L., J. Comiso, H. J. Zwally, D. J. Cavalieri, P. Gloersen, W. J. Campbell,1987. Arctic Sea Ice, 1973-1976: Satellite Passive Microwave Observations, National Aeronautics and Space Administration, Special Publication 489, Washington, D.C., 296 pp.

    Ramseier, R.O., W.J. Campbell, W.F. Weeks, L. Drapier-Arsenault, and K.L. Wilson. 1975. Ice dynamics in the Canadian Archipelago and adjacent Arctic Basin as determined by ERTS-1 observations, in Canada's Continental Margins and Offshore Exploration, Canadian Society of Petroleum Geologists, Calgary, Alberta, pp. 853-877.

    Steffen, K. and A. Schwieger. 1991. NASA Team algorithm for sea ice concentration retrieval from Defense Meteorological Satellite Program Special Sensor Microwave/Imager: Comparison with Landsat satellite imagery. J. Geophys. Res. 96(C12):21,971-21,988.

    Swift, C. T., D. J. Cavalieri. 1985. Passive microwave remote sensing for sea ice research. EOS. 66(49):1210-1212.

    Vowinkel, E., and S. Orvig. 1970. The climate in the north polar basin, in Climate of the Polar Regions, Vol. 14, World Survey of Climatology, Elsevier, Amsterdam, pp. 129-252.

    Wadhams, P., M. A. Lange, and S. F. Ackley. 1987. The ice thickness distribution across the Atlantic sector of the Antarctic ocean in midwinter. J. Geophys. Res. 92(C13):14,535-14, 552.

    Wilheit, T.T., W. Nordberg, J. Blinn, W. Campbell, and A. Edgerton. 1972. Aircraft measurements of microwave emission from Arctic sea ice, Remote Sensing of Environment 2:129-139.

    NASA GSFC Goddard DAAC cidc site

    Last update:Tue Dec 2 17:41:14 EST 1997
    Page Author: Dr. Suraiya Ahmad--
    Web Curator: Dr. Daniel Ziskin --
    NASA official: Dr. Paul Chan, DAAC Manager --