We kindly request that you cite the use of this data set in a publication using the following citation. For more information, see our Use and Copyright Web page.
Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. Zwally. 1996, updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. [indicate subset used]. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. http://dx.doi.org/10.5067/8GQ8LZQVL0VL.
Nimbus-7, DMSP-F8, -F11, -F13, -F17
SMMR, SSM/I, SSMIS
North and south polar regions
26 October 1978 – 31 December 2014
SMMR: every other day, monthly
Sea ice concentration
Flat binary (1-byte scaled, unsigned integers)
Note: The data format information in this document represents the data in its native format as it is archived at NSIDC. If you have downloaded the data using Polaris, please consult the 00README file located in the tar file for information on the data format operations that were performed on this data set.
This data set is generated from brightness temperature data derived from the following sensors: the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the Defense Meteorological Satellite Program (DMSP) -F8, -F11 and -F13 Special Sensor Microwave/Imagers (SSM/Is), and the DMSP-F17 Special Sensor Microwave Imager/Sounder (SSMIS). The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.
This product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors. The data are generated using the NASA Team algorithm developed by the Oceans and Ice Branch, Laboratory for Hydrospheric Processes at NASA Goddard Space Flight Center (GSFC).
These data include gridded daily (every other day for SMMR data) and monthly averaged sea ice concentrations for both the north and south polar regions. The data are produced at GSFC about once per year, with roughly a one-year latency, and include data since 26 October 1978. Data are produced from SMMR brightness temperature data processed at NASA GSFC and from SSM/I and SSMIS brightness temperature data processed at the National Snow and Ice Data Center (NSIDC).
Data are scaled and stored as one-byte integers in flat binary arrays. For each data file, a corresponding PNG browse image file is provided.
The goal of this data set is to provide a long term, consistent sea ice concentration product in which sea ice extent and area differences between the sensors are reduced and could serve as a baseline for future measurements. To achieve this, it is necessary to address differences between the SMMR and the DMSP-F8, -F11, and -F13 SSM/I sensors, as well as the DMSP-F17 SSMIS sensor. This document describes the basic characteristics of the SMMR, SSM/I, and SSMIS platforms and summarizes the problems encountered when deriving sea ice concentrations from brightness temperatures measured by sensors with different frequencies, different footprint sizes, different visit times, and different calibrations. A major obstacle to resolving these differences is the lack of sufficient overlapping data from sequential sensors. The techniques employed to solve these problems, or at least reduce their impacts, include:
Basic limitations also arise from the sensor resolution, temporal coverage, and algorithm assumptions and characteristics. The NASA Team algorithm is not designed to provide ice concentration for fresh-water ice (for example, lake and river ice). The filtering used to remove land-to-ocean spillover may affect the area of some open water features within the ice pack near coasts (coastal polynyas).
Potential applications for these sea ice concentration data include:
Users should be aware that the ice concentration maps were derived from algorithms that were "tuned" to minimize the differences in ice extent and ice covered area during the overlap periods when transitioning from one instrument to the next (overlap from SMMR to DMSP-F8 SSM/I, from DMSP-F8 to -F11 SSM/I, from DMSP-F11 to -F13 SSM/I, and from DMSP-F13 SSM/I to DMSP-F17 SSMIS). This does not mean that the ice concentrations themselves are well matched. See the Data Verification by Data Center section of this document for a summary of ice extent and ice covered area differences during the overlap periods.
It is also important to know that SMMR and SSM/I-SSMIS have different data gaps at the North Pole due to orbital differences. Therefore, any time series of parameters, such as ice extent and ice covered area, need to take these differences into account. A pole mask is provided for this purpose (see the Masks and Overlays section).
Particular care is needed to interpret the sea ice concentrations during summer when melt is present, and in regions where new sea ice makes up a substantial part of the sea ice cover. Some residual errors remain due to weather effects and mixing of ocean and land area within the sensor field of view, or FOV, and due to sensor differences.
It is recommended that sea ice extent and area be computed from daily maps of ice concentrations that are then used to compute monthly averages of those parameters. Computations of sea ice extents and sea ice areas should not be made from the monthly-averaged ice concentration maps because that may result in a biased time series.
Data are scaled, unsigned flat binary with one byte per pixel, and therefore have no byte order, or endianness. Data are stored as one-byte integers representing scaled sea ice concentration values. Range section for more information. For each data file, a corresponding browse image file in PNG format is also provided.
The file format consists of a 300-byte descriptive header followed by a two-dimensional array of one-byte values containing the data. The file header is composed of:
For compatibility with ANSI C, IDL, and other languages, character strings are terminated with a NULL byte.
The file header can be accessed in a variety of ways. For example, it can be treated as a simple sequence of bytes containing ASCII character strings or as a complex data structure of arrays. Table 1 describes the file header.
|1-6||Missing data integer value|
|7-12||Number of columns in polar stereographic grid|
|13-18||Number of rows in polar stereographic grid|
|25-30||Latitude enclosed by polar stereographic grid|
|31-36||Greenwich orientation of polar stereographic grid|
|43-48||J-coordinate of the grid intersection at the pole|
|49-54||I-coordinate of the grid intersection at the pole|
|55-60||Five-character instrument descriptor (SMMR, SSM/I, SSMIS)|
|61-66||Two descriptors of two characters each that describe the data;
(for example, 07 cn = Nimbus-7 ice concentration)
|67-72||Starting Julian day of grid data|
|73-78||Starting hour of grid data (if available)|
|79-84||Starting minute of grid data (if available)|
|85-90||Ending Julian day of grid data|
|91-96||Ending hour of grid data (if available)|
|97-102||Ending minute of grid data (if available)|
|103-108||Year of grid data|
|109-114||Julian day of grid data|
|115-120||Three-digit channel descriptor (000 for ice concentrations)|
|121-126||Integer scaling factor|
|127-150||24-character file name (without file-name extension)|
|151-230||80-character image title|
|231-300||70-character data information (creation date, data source, etc.)|
The data can be read with image processing software by specifying a 300-byte header, with an image size of 304 columns x 448 rows for Arctic data and 316 columns x 332 rows for Antarctic data. In a high-level programming language or image processing software, declare a 300-byte array for the header and an array (for example, 304 x 448 for Arctic) for the image. Read the 300-byte array first, then read the image array.
Data are on the FTP site in the /pub/DATASETS/nsidc0051_gsfc_nasateam_seaice/ directory, as shown in Figure 1.
Within the final-gsfc directory are north and south directories that contain data files, and a browse directory that contains browse image PNG files. Daily and monthly data are further separated into directories named daily and monthly. For daily data, there is also one directory for each year of available data. For example, all of the north daily data for 1990 are in a directory named /nsidc0051_gsfc_nasateam_seaice/final-gsfc/north/daily/1990/.
The directory structure is illustrated in Figure 1; not all directories are shown fully expanded. The structure for each south directory matches that of the corresponding north directory. Each browse directory is divided into a structure that reflects that of the data. In this illustration, the year directories underneath final-gsfc are representative placeholders; on the FTP site, there are actually many such directories, each named for the year of data it contains, such as 1987, 2000, etc.
This section explains the file naming convention used for this product with an example.
Generic File Name: nt_YYYYMMDD_SSS_vVV_R.ext
Example File Name: nt_20140131_f17_v01_n.bin
|nt||Indicates this was created with the NASA Team algorithm|
|SSS||Sensor (n07 for Nimbus-7 SMMR; f08, f11, or f13 for DMSP-F8, -F11 or -F13 SSM/I; f17 for DMSP-F17 SSMIS)|
|VV||Data version number (for example, 01)|
|R||Region (n = north; s = south)|
|.ext||File extension (.bin = binary, .png = PNG image)|
Generic File Name:nt_YYYYMM_SSS_vVV_R.ext
Example File Name: nt_201401_f17_v01_n.bin
|nt||Indicates this was created with the NASA Team algorithm|
|SSS||Sensor (n07 for Nimbus-7 SMMR; f08, f11, or f13 for DMSP-F8, -F11 or -F13 SSM/I; f17 for DMSP-F17 SSMIS)|
|VV||Data version number (for example, 01)|
|R||Region (n = north; s = south)|
|.ext||File extension (.bin = binary, .png = PNG image)|
Data file size varies by region:
Data set coverage includes the polar regions defined by the Polar Stereographic Projections and Grids spatial coverage map.
Each of the three instruments provide global coverage except for a circular sector centered over the North Pole. These sectors are never measured due to orbit inclination of the satellite. Table 4 shows the sizes and latitudes of each of the pole holes.
Note: The SSMIS pole hole was implemented in March 2015 and applied to all data from January 2008 to present. Even though SSMIS data begin in January 2007, this product does not start using the SSMIS pole hole mask until January 2008 to allow for comparison analysis with SSM/I during the transition from SSM/I to SSMIS data in 2007.
|Pole Hole Mask Name||Pole Hole Area
|Pole Hole Radius
|SSMIS Pole Hole Mask||0.029||94||89.18° N||January 2008 to present|
|SSM/I Pole Hole Mask||0.31||311||87.2° N||July 1987 through December 2007|
|SMMR Pole Hole Mask||1.19||611||84.5° N||November 1978 through June 1987|
The spatial resolution for this data set is 25 km.
The sea ice concentration data are displayed in polar stereographic projection. For more information, see Polar Stereographic Projections and Grids. The grid size varies depending on the region, as shown in Table 5.
Data are from 26 October 1978 through the most current processing. See the Data Acquisition Methods section for dates by instrument and platform.
The SMMR instrument scanner operated only on alternate days, due to spacecraft power limitations. Therefore, SMMR data were only collected every other day. Typically, there are at least 14 days of coverage per month, although there are major data gaps in August of 1982 (04, 08, and 16 August 1982), and in August of 1984 (13 through 23 August 1984) for both polar regions.
SSM/I data were collected daily and SSMIS data continue to be collected daily. A major data gap in the SSM/I data exists from 03 December 1987 to 13 January 1988. For the latest details regarding data gaps, refer to the SSM/I-SSMIS Brightness Temperature Data Availability Web page.
Sea ice concentrations are provided for each day of data and also as monthly means. The monthly means are generated by averaging all the available daily files for each individual month, excluding pixels of missing data (see the Monthly Data Generation section of this document for more information).
Sea ice concentration represents an areal coverage of sea ice. For a given grid cell, the parameter provides an estimate of the fractional amount of sea ice covering that cell, with the remainder of the area consisting of open ocean. Land areas are coded with a land mask value.
Data sources are Nimbus-7 SMMR, DMSP-F8, -F11 and -F13 SSM/I instruments, and the DMSP-F17 SSMIS instrument.
Data are stored as one-byte integers representing sea ice concentration values. The sea ice concentration data are packed into byte format by multiplying the derived fractional sea ice concentration floating-point values (ranging from 0.0 to 1.0) by a scaling factor of 250. For example, a sea ice concentration value of 0.0 (0%) maps to a stored one-byte integer value of 0, and a sea ice concentration value of 1.0 (100%) maps to a stored one-byte integer value of 250. To convert to the fractional parameter range of 0.0 to 1.0, divide the scaled data in the file by 250. To convert to percentage values (0% to 100%), divide the scaled data in the file by 2.5.
Data files may contain integers from 0 to 255, as described in Table 6.
|0 - 250||Sea ice concentration (fractional coverage scaled by 250)|
|251||Circular mask used in the Arctic to cover the irregularly-shaped data gap around the pole (caused by the orbit inclination and instrument swath)|
|254||Superimposed land mask|
The performance of the NASA Team algorithm was assessed in numerous studies (for example, Cavalieri et al. 1992); these results apply to this data set. However, improvements in this data set that differ from previous studies include the minimization of coastal and open-ocean influences that tend to yield inaccurate sea ice concentrations. Visual data checking was used to assess the performance of these modifications.
Estimates of the accuracy of the NASA Team algorithm vary depending on sea ice conditions, methods, and locations used in individual studies. Cavalieri et al. (1992) summarizes several of these studies. In general, accuracy of total sea ice concentration is within +/- 5% of the actual sea ice concentration in winter, and +/- 15% in the Arctic during summer when melt ponds are present on the sea ice. Accuracy tends to be best within the consolidated ice pack when the sea ice is relatively thick (greater than 20 cm) and ice concentration is high. Accuracy decreases as the proportion of thin ice increases. See Cavalieri et al. (1992), Steffen et al. (1992), and other listed references for an overview of the algorithm performance.
NSIDC staff visually checks the data files and selected graphics files. This includes verification of proper file structure; comparisons to existing SMMR-, SSM/I-, and SSMIS-derived sea ice concentration grids, masks, and information files; and examination of data quality.
Some weather-related effects and land contamination are still present. The amount and spatial distribution of remaining weather effects vary with season. Also, occasional bad scan lines still appear in the data. Based on NSIDC analyses, some sensor-to-sensor differences are likely to remain in these data, particularly for marginal ice zones. See NSIDC Special Report 5: An Intercomparison of DMSP F11- and F13-derived Sea Ice Products for summaries of differences among the SSM/I sensors.
Residual weather effects and processing errors in May 1986 data result in large bands of very low ice concentrations over the open ocean in the Weddell, Bellingshausen, and Amundsen seas in the Southern Hemisphere. Although the magnitude of these false ice concentrations is less than one percent, users should be aware that such errors do occur in data for many days within that month.
Overlap periods exist when transitioning from one instrument to the next. These overlaps are from SMMR to DMSP-F8 SSM/I, from DMSP-F8 to -F11 SSM/I, from DMSP-F11 to -F13 SSM/I, and from DMSP-F13 SSM/I to DMSP-F17 SSMIS. During overlap periods, data were available from two instruments, although good data may not be available from both instruments during the entire operating overlap. Differences in ice covered area and ice extent during the overlap periods were minimized by tuning the sea ice algorithms. Wavelet analysis of the time series of ice extent and ice covered area show no significant offsets between the different satellites.
Tables 7 and 8 summarize the comparison between the ice covered areas and ice extent during the overlap periods, including mean differences and linear regression results of ice covered areas and ice extent. Mean differences are computed for SMMR minus DMSP-F8 SSM/I, DMSP-F8 SSM/I minus DMSP-F11 SSM/I, DMSP-F11 SSM/I minus DMSP-F13 SSM/I, and DMSP-F13 SSM/I minus DMSP-F17 SSMIS. Regression coefficients are computed using y = a0 + a1*x, for each (x, y) pair (x=SMMR, y=DMSP-F8 SSM/I); (x=DMSP-F8 SSM/I, y=DMSP-F11 SSM/I); (x=DMSP-F11 SSM/I, y=DMSP-F13 SSM/I); and (x=DMSP-F13 SSM/I, y=DMSP-F17 SSMIS). While this analysis shows no significant differences between the overall summaries of ice covered area and ice extent, significant regional differences in ice concentration may still be present.
|SMMR to DMSP-F8 SSM/I|
|DMSP-F8 to DMSP-F11 SSM/I|
|DMSP-F11 to DMSP-F13 SSM/I|
|DMSP-F13 to DMSP-F17 SSMIS|
|SMMR to DMSP-F8 SSM/I|
|DMSP-F8 to DMSP-F11 SSM/I|
|DMSP-F11 to DMSP-F13 SSM/I|
|DMSP-F13 to DMSP-F17 SSMIS|
Data are available via FTP.
Software and tools for reading and displaying the files are located in the tools directory on the FTP site (Fig. 2). Software includes IDL routines to ingest and read sea ice concentration data. Masks and overlays are also provided.
Table 9 lists the tools that can be used with this data set. For a comprehensive list of all polar stereographic tools and for more information, see the Polar Stereographic Data Tools Web page.
|Tool Type||Tool File Name(s) or Description|
mapll.for and mapxy.for
psn25lats_v3.dat and pss25lats_v3.dat
psn25lons_v3.dat and pss25lons_v3.dat
|Pixel-Area||psn25area_v3.dat and pss25area_v3.dat|
|Land Masks||gsfc_25n.msk and gsfc_25s.msk
coast_25n.msk and coast_25s.msk
ltln_25n.msk and ltln_25s.msk
|Region Masks||region_n.msk and region_s.msk|
|Ocean Masks||Includes monthly ocean masks and maximum extent masks for the Northern (n) and Southern (s) Hemispheres|
For instructions on importing these data into ArcGIS, see our NSIDC User Services Online Support.
The SMMR, SSM/I, and SSMIS instruments are microwave radiometers that sense emitted microwave radiation. This radiation is affected by surface and atmospheric conditions, and thus provides a range of geophysical information.
The Nimbus-7 and DMSP F-series spacecraft fly in near-polar sun-synchronous orbits; details their respective orbits are compared in Table 10.
|Nominal Altitude1||955 km||860 km||830 km||850 km||850 km|
|Inclination Angle||99.1 degrees||98.8 degrees||98.8 degrees||98.8 degrees||98.8 degrees|
|Orbital Period||104 minutes||102 minutes||101 minutes||102 minutes||102 minutes|
|Ascending Node Equatorial Crossing
|Approx. 12:00 p.m.||Approx. 6:00 a.m.||Approx. 5:00 p.m.||Approx. 5:43 p.m.||Approx. 5:31 p.m.|
|Algorithm Frequencies1||18.0, 37.0 GHz||19.3, 37.0 GHz||19.3, 37.0 GHz||19.3, 37.0 GHz||19.3, 37.0 GHz|
|Earth Incidence Angle1||50.2||53.1||52.8||53.4||53.1|
|3 dB Beam Width (Degrees)1||1.6, 0.8||1.9, 1.1||1.9, 1.1||1.9, 1.1||1.9, 1.1|
1 Indicates sensor and spacecraft orbital characteristics of the sensors used in generating the sea ice concentrations.
The SMMR is a 10-channel instrument delivering orthogonally polarized antenna temperature data at five dual-polarized (horizontal, vertical) frequencies: 6.6 GHz, 10.7 GHz, 18.0 GHz, 21.0 GHz, and 37.0 GHz. Please see the SMMR Instrument Description for more details.
The SSM/I is a seven-channel, orthogonally polarized, passive-microwave radiometric system. The instrument measures combined atmosphere and surface radiances at 19.3 GHz, 22.2 GHz, 37.0 GHz and 85.5 GHz frequencies. Please see the SSM/I Instrument Description for more details.
The SSMIS sensor is a conically-scanning passive microwave radiometer that harnesses the imaging and sounding capabilities of three previous DMSP microwave sensors, including the SSMI, the SSM/T-1 temperature sounder, and the SSMI/T-2 moisture sounder. The SSMIS sensor measures microwave energy at 24 frequencies from 19 to 183 GHz with a swath width of 1700 km. Please refer to the SSMIS Instrument Description Web page for more details. Tables 11 and 12 give the FOV of each instrument.
|19.3 GHz||70x45 km|
|22.2 GHz||60x40 km|
|37.0 GHz||38x30 km|
|6.6 GHz||148x95 km|
|10.7 GHz||91x59 km|
|18.0 GHz||55x41 km|
|21.0 GHz||46x30 km|
|37.0 GHz||27x18 km|
The combined SMMR, SSM/I, and SSMIS sea ice concentration time series is produced from brightness temperatures obtained from GSFC and NSIDC. The four sets of satellite data currently used to create this data stream, and the time periods for which the data are usable, are shown in Table 13.
|Platform and Instrument||Time Period|
|Nimbus-7 SMMR||26 October 1978 through 20 August 1987|
|DMSP-F8 SSM/I||09 July 1987 through 31 December 1991|
|DMSP-F11 SSM/I||03 December 1991 through 30 September 1995|
|DMSP-F13 SSM/I||03 May 1995 through 31 December 2007 (Note: This overlaps with F17 for one year for intercomparison purposes)|
|DMSP-F17 SSMIS||01 January 2007 through the most recent data (data acquisition is ongoing)|
Sea ice concentrations were processed by GSFC using SMMR brightness temperatures. The SMMR brightness temperatures were processed and quality checked at GSFC (Gloersen et al. 1992).
This section is extracted from NASA Technical Memorandum 104647.
Sea ice concentrations for this data set were produced using a revised NASA Team algorithm that uses a different set of tie points and weather filters than the original NASA Team algorithm (see NASA Team Sea Ice Algorithm for a description of the original algorithm). NASA Technical Memorandum 104647 includes information about differences (e.g., tie points) between the original algorithm and the revised NASA Team algorithm. In addition, the NASA Team algorithm uses different channels of the SMMR and the SSM/I-SSMIS brightness temperature data:
The weather filter used for the SMMR (Gloersen and Cavalieri 1986) was found to be inadequate for the SSM/I due to the SSM/I's use of the 19.3 GHz channel (which is further up on the shoulder of the water vapor line at 22.2 GHz) rather than the 18.0 GHz channel. A different weather filter is used to reduce spurious sea ice concentrations from SSM/I that result from the presence of atmospheric water vapor, non-precipitating cloud liquid water, rain, and sea surface roughening by surface winds. This filter is a combination of the SSM/I 37.0 and 19.3 GHz channels, which effectively eliminates most of the spurious sea ice concentration measurements resulting from wind-roughening of the ocean surface, cloud liquid water, and rainfall. Another filter that is based on the 19.3 and 22.2 GHz channels is also used. The rationale behind combining the 19.3 and 22.2 GHz channels is based on the sensitivity of the 22.2 GHz to water vapor and on the need to minimize the effect of ice temperature variations at the ice edge.
Comparisons of sea ice concentrations calculated for each sensor during overlap periods using published algorithm tie points reveal significant differences. These may result from differences in sensor and orbital characteristics, differences in observation times (and therefore tidal effects), and/or differences in algorithm coefficients. Sensor and orbital characteristic differences for the Nimbus-7 SMMR and DMSP-F8 SSM/I 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 DMSP sensors also differ in ascending node time, altitude, and angle of incidence. Because the visit times of the DMSP satellites occur during different phases of the diurnal cycle, tidal effects may result in differences in the sea ice distribution. GSFC presumes that any such effects are mitigated by the correction scheme described below. The Comparison of Orbital Parameters table in the Source/Platform section summarizes sensor and orbital characteristic differences. The GSFC processing attempts to accommodate for these differences in each pair of sensors by employing a 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-F8 SSM/I during their period of overlap, 09 July to 20 August 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 that expanded three to four pixels out from the original land mask was used in the determination of the best fit between the two data streams.
The eliminated pixels represent only a very small fraction of the total number of sea ice concentration pixels, but eliminating them helps considerably in reducing the outliers on the scatter plots. These linear relations 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 DMSP-F8 SSM/I tie points (Cavalieri et al. 1992) were not used. In addition to using these transformations, the DMSP-F8 SSM/I open water tie points were subjectively tuned to help minimize the differences between the SMMR and DMSP-F8 SSM/I sea ice extent and area during the overlap period. In all cases, except for the Antarctic DMSP-F8 SSM/I values, the tuned amount is within one standard error of estimate. GSFC suspects the reason for the larger tuned values results from greater weather effects during the overlap period.
For more information on the regression coefficients and revised tie points, see the NASA Technical Memorandum 104647.
The transition period from DMSP-F8 to -F11 includes only 16 days of good data overlap, from 03 to 18 December 1991. The DMSP-F11 SSM/I open water tie points were also tuned to help reduce differences in sea ice extent and area as was done with the DMSP-F8 SSM/I values. A further adjustment to the Antarctic 37V sea ice type-B F11 tie point was also made to reduce the sea ice area difference. In this case, the amount of tuning needed to reduce the sea ice extent and area differences between the DMSP-F8 and -F11 values is well within one standard error of estimate.
The effects of changing from the DMSP-F11 to the -F13 satellite were examined for a 5-month overlap period, from 5 May 1995 through 30 September 1995. Generally, in terms of hemispheric averages of mean ice concentration, the biases introduced by the transition are slight and not statistically significant; however, in some regions relatively large and significant differences are seen. In addition, differences in sea ice extent and total ice covered area between the two platforms were found to be statistically significant. For more information, please see NSIDC Special Report 5: An Intercomparison of DMSP F11- and F13-derived Sea Ice Products.
The effects of changing from the DMSP-F13 SSM/I to the -F17 SSMIS were examined for a 12-month overlap period, from 01 January 2007 to 31 December 2007. Differences in sea ice extent and total ice covered area between the two platforms and instruments were found to be statistically significant, though fairly similar when compared with previous intersensor calibrations conducted for this time series (Cavalieri et al., 1999). Earlier intersensor calibrations, however, were limited by relatively short periods of sensor overlap (such as sixteen days) and could thus account for less agreement with this transition (Cavalieri et al. 2011). In addition, earlier agreement may be due to the subjective tuning of some tie-points that was required in past intercalibrations (Cavalieri et al., 1999).
The next step in preparing the data is the correction for land-to-ocean spillover, often referred to as "land contamination," and residual weather-related effects. While these steps eliminate much of the land-to-ocean spillover and weather effects over open ocean, these problems are not entirely removed. See the section Data Verification by Data Center for additional comments.
Land-to-ocean spillover refers to the issue of the blurring of sharp contrasts in brightness temperature, such as those that exist between land and ocean, due to the relatively coarse width of the sensor antenna pattern. This problem is of concern because it results in false sea ice signals along coastlines because both land and sea ice 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). Figure 2a illustrates the effect of the coarse resolution of the microwave antenna on a coastline resulting in false sea ice signals in the vicinity of the coast, and Figure 2b shows the seven-by-seven array used in the procedure to reduce the land-to-ocean spillover effect. The rationale behind the approach is that a minimum observed sea ice concentration in the vicinity of coastlines where no sea ice remains offshore, which is generally seen in late summer, is probably the result of land spillover; so it is subtracted from the image. To reduce the error of subtracting sea ice in areas of actual sea 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:
Residual Weather-Related Effects
Weather effects can cause the passive microwave signature of seawater to appear like that of ice (Cavalieri 1995). A correction is made for removing spurious ice resulting from residual weather effects that were missed by the automatic weather filters. These valid ice masks are based on monthly climatological sea surface temperatures (SSTs) from the NOAA Ocean Atlas (Levitus and Boyer 1994). These data, originally on a two-degree by two-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 they were mapped onto the SSM/I grid. The SST maps are used as follows:
Figure 3a shows a sample sea ice concentration field without the land-spillover and residual weather correction, and Figure 3b shows that same sample after the correction is applied.
Figure 3a. Sea ice concentration map of
Arctic for 01 August 1983 before the application
of the land-spillover and residual weather
Figure 3b. After corrections.
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 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 sea ice concentration maps. No data at all were available for the period from 02 December 1987 through 12 January 1988. This gap was not filled by temporal linear interpolation; instead it was left as missing data.
Once daily data have been processed as previously described, monthly data are generated. Monthly averaged sea ice concentration grids are produced from an average of the daily sea ice concentration grids available for each month. Monthly files for both hemispheres are provided for every month beginning October 1978. However, for October 1978, December 1987 and January 1988, the time series was incomplete: only three days of data were available during October 1978 to generate the monthly mean, only two days were available for December 1987, and only 19 days were available for January 1988. Therefore, the monthly means for these months do not represent the "true" monthly means.
In most cases, GSFC used all daily data to compute monthly averaged sea ice concentrations from a particular instrument until the data were no longer available. For example, SMMR data were used to compute monthly sea ice concentrations until the instrument stopped collecting data on 20 August 1987. Beginning 21 August 1987, SSM/I data were used. In 1991, DMSP-F8 SSM/I data were used through December 18; beginning December 19, DMSP-F11 SSM/I data were used.
Note: It is recommended that sea ice extent and area be computed from daily maps of ice concentrations that are then used to compute monthly averages of those parameters. Computations of sea ice extents and sea ice areas should not be made from the monthly-averaged ice concentration maps because that may result in a biased time series.
Table 12 describes files that have been found to contain errors and that have been corrected during the life of this data set along with the types of errors that were corrected. NSIDC recommends data users download the corrected files for these dates listed in Table 15. In addition, more detail on the updates can be found in Table 16.
|File Date||File Name||Type of Correction||Date Correction was Made|
|1984-09||nt_198409_n07_v01_n.bin||Geolocation error||July 2014|
|1984-14-09||nt_19840914_n07_v01_n.bin||Geolocation error||July 2014|
|1983-07-30||nt_19830730_n07_v01_n.bin||Weather correction||January 2013|
|1984-07-26||nt_19840726_n07_v01_n.bin||Weather correction||January 2013|
|1984-07-28||nt_19840728_n07_v01_n.bin||Weather correction||January 2013|
|1984-07-30||nt_19840730_n07_v01_n.bin||Weather correction||January 2013|
|1985-07-01||nt_19850701_n07_v01_n.bin||Coastal/weather correction||January 2013|
|1985-07||nt_198507_n07_v01_n.bin||Coastal/weather correction||January 2013|
|1987-07-21||nt_19870721_f08_v01_n.bin||Weather correction||January 2013|
|1987-12-01||nt_19871201_f08_v01_n.bin||Ambiguous; not a clear source of error||January 2013|
|1987-12-01||nt_19871201_f08_v01_s.bin||Ambiguous; not a clear source of error||January 2013|
|1995-11-02||nt_19951102_f13_v01_n.bin||Ambiguous; not a clear source of error||January 2013|
|1995-11-14||nt_19951114_f13_v01_n.bin||Ambiguous; not a clear source of error||January 2013|
|1995-11||nt_199511_f13_v01_n.bin||Ambiguous; not a clear source of error||January 2013|
|1995-12-07||nt_19951207_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-04-10||nt_19960410_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-04-23||nt_19960423_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-05-09||nt_19960509_f13_v01_s.bin||Land/coastal correction||January 2013|
|1996-05||nt_199605_f13_v01_s.bin||Land/coastal correction||January 2013|
|1996-06-12||nt_19960612_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-06-18||nt_19960618_f13_v01_n.bin||Land/coastal correction; same pixels as 1996-06-12||January 2013|
|1996-06-19||nt_19960619_f13_v01_n.bin||Ambiguous; not a clear source of error||January 2013|
|1996-06-20||nt_19960620_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-06||nt_199606_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-10-06||nt_19961006_f13_v01_s.bin||Land/coastal correction||January 2013|
|1996-10||nt_199610_f13_v01_s.bin||Land/coastal correction||January 2013|
|1996-11-01||nt_19961101_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-11-06||nt_19961106_f13_v01_n.bin||Land/coastal correction||January 2013|
|1996-11-14||nt_19961114_f13_v01_n.bin||Ambiguous; not a clear source of error||January 2013|
|1996-12-05||nt_19961205_f13_v01_n.bin||Land/coastal correction; same pixels as 1996-06-12||January 2013|
|1996-12-23||nt_19961223_f13_v01_n.bin||Land/coastal correction; same pixels as 1996-06-12||January 2013|
Table 16 outlines the processing and algorithm history for this product.
|V1||March 2015||SSMIS pole hole mask replaces SSM/I pole hole mask for all data from 01 January 2008 to present.|
|V1||July 2014||An error was found in the sea ice concentration field for 14 September 1984. Due to a geolocation error in the source data, several hundred thousand square kilometers of erroneous ice occurred in that data. The original file has been removed and replaced with an average of the two files nearest in time (September 12 and 16). The monthly September 1984 average concentration field was reprocessed using the replaced September 14 data. See Table 12 for the file names and the correction made.|
|V1||June 2014||The browse images for the entire record have been reprocessed to include a title and simplified color bar; the data were not affected.|
|V1||January 2013||NSIDC applied corrections to 29 files that showed errors in a previous release of these data. The errors occurred in files from both SMMR (1983 – 1985) and SSM/I (1995 – 1996). See Table 12 for a list of these files.|
|V1||January 1996||Original version of data.|
See the following for background information pertaining to the instruments and sensor-level products used to generate this data set. Other references, particularly for sea ice characteristics and algorithm performance, are available in journals from the NSIDC library. Also, see the Selected Bibliography: SSM/I Brightness Temperatures for the Polar Regions.
Cavalieri et al. (1997)
NSIDC Brightness Temperature User's Guide (1992), Gloersen and Barath (1977), Gloersen et al. (1992), Hollinger (1989), Hollinger and Lo (1983), Hollinger et al. (1990), Poe and Conway (1990), Svendsen et al. (1983), and Wentz (1991, 1992, 1993).
See references above, Abdalati et al. (1995), and Goodberlet (1990).
Ackley (1979), Ackley et al. (1980), Wadhams et al. (1987), Carsey (1982), Gloersen et al. (1992). Also see the algorithm references below.
Gloersen and Cavalieri (1986), Cavalieri et al. (1992), Cavalieri et al. (1984), Cavalieri (1994), Comiso (1983), Comiso (1990), Comiso et al. (1992), Comiso et al. (1984), Emery et al. (1994), Gloersen and Cavalieri (1986), Gloersen et al. (1992), Grenfell and Comiso (1986), Hollinger et al. (1984), Maslanik (1992), Massom (1991), Steffen and Schweiger (1991), Steffen et al. (1992), Svendsen et al. (1983), Swift and Cavalieri (1985), and Swift et al. (1985).
Campbell et al. (1974, 1975a, 1975b, 1976a, 1976b, 1978, 1980a, 1980b, 1981, 1984, 1987), Carsey (1982, 1985), Cavalieri et al. (1983, 1986, 1990, 1991), Cavalieri and Parkinson (1981, 1987), Cavalieri and Martin (1985), Cavalieri and Zwally (1985), Comiso (1986, 1991), Comiso et al. (1992), Comiso and Sullivan (1986), Comiso et al. (1991), Gloersen et al. (1973, 1974a, 1974b, 1975a, 1975b, 1978, 1984, 1989, 1992), Gloersen and Campbell (1988a, 1988b, 1991a, 1991b), Maslanik et al. (1996), Massom (1991), Zwally (1984), Zwally et al. (1976, 1983a, 1983b, 1985), Zwally and Gloersen (1977), and Zwally and Walsh (1987).
Martino et al. (1995), NCSA (1993), Poe and Conway (1990), and Snyder (1982).
Abdalati, W., K. Steffen, C. Otto, and K. C. Jezek. 1995. Comparison of Brightness Temperatures from SSM/I Instruments on the DMSP-F8 and -F11 Satellites for Antarctica and the Greenland Ice Sheet. International Journal of Remote Sensing. 16(7):1223-1229.
Bonbright, D. I., J. W. Brown, J. E. Hilland, I. T. Hsu, J. A. Johnson, T. L. Kotlarek, R. A. Lassanyi, C. L. Miller, C. S. Morris, and F. J. Salamone. 1987. NASA Ocean Data System Version 3.0. User handbook. Jet Propulsion Laboratory. Document 715-66, 50 pp.
Campbell, W. J., P. Gloersen, and H. J. Zwally. 1994. Short- and Long-term Temporal Behavior of Polar Sea-Ice Covers from Satellite Passive-Microwave Observations. Geophysical Monograph 85. Editors O. M. Johannessen, R. D. Muench, and J. E. Overland. American Geophysical Union, Washington, D. C.
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. Chryslers, eds., Hemisphere Publishing, New York, 197-222.
Campbell, W. J., P. Gloersen, and R. O. Ramseier. 1975. Synoptic Ice Dynamics and Atmospheric Circulation During the Bering Sea Experiment. 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, 196-218. (Republished as USSR/USA Bering Sea Experiment by A. A. Balkema, Rotterdam, 307 pp., 1982.)
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. Journal of Geophysical Research 92:6805-6824.
Campbell, W. J., P. Gloersen, W. Nordberg, and T. T. Wilheit. 1974. Dynamics and Morphology of Beaufort Sea Ice Determined from Satellite, Aircraft, and Drifting Stations, in Proc. of the Symp. on Approaches to Earth Survey Problems Through Use of Space Techniques, Akademie-Verlag, Berlin, 311-327.
Campbell, W. J., R. O. Ramseier, H. J. Zwally, and P. Gloersen. 1981. Structure and Variability of Bering and Okhotsk Sea Ice Cover by Satellite Microwave Imagery, in Energy Resources of the Pacific, M. T. Halbouty, ed., American Association of Petroleum Geologists, Tulsa, Oklahoma, 343-354.
Campbell, W. J., P. Gloersen, H. J. Zwally, R. O. Ramseier, and C. Elachi. 1980. Simultaneous Passive and Active Microwave Observations of Near-shore Beaufort Sea Ice. Journal of Petroleum Technology 21:1105-1112.
Campbell, W. J., R. O. Ramseier, W. F. Weeks, and P. Gloersen. 1976. An Integrated Approach to the Remote Sensing of Floating Ice. Proceedings of the XXVI International Astronautical Congress, Lisbon, Portugal, L. G. Napolitano, ed. 445-487.
Campbell, W. J., J. Wayenberg, J. B. Ramseyer, R. O. Ramseier, M. R. Vant, R. Weaver, A. Redmond, L. Arsenault, P. Gloersen, H. J. Zwally, T. T. Wilheit, T. C. Chang, D. Hall, L. Gray, D. C. Meeks, M. L. Bryan, F. T. Barath, C. Elachi, F. Leberl, and T. Farr. 1978. Microwave Remote Sensing of Sea Ice in the AIDJEX Main Experiment. Boundary-Layer Meteorology 13:309-337.
Cavalieri, D. J. and S. Martin. 1994. The Contribution of Alaskan, Siberian, and Canadian Coastal Polynyas to the Cold Halocline Layer of the Arctic Ocean. Journal of Geophysical Research 99:18,343-18,362.
Cavalieri, D. J., and S. Martin. 1985. A Passive-microwave Study of Polynyas Along the Antarctic Wilkes Land coast, in Oceanology of the Antarctic Continental Shelf, S. S. Jacobs, ed., Antarctic Research Series, American Geophysical Union, Washington, D. C. vol. 43:227-252.
Cavalieri, D. J., Parkinson, C. L., DiGirolamo, N. and A. Ivanoff. 2011. Intersensor Calibration between F13 SSMI and F17 SSMIS for Global Sea Ice Data Records. Geoscience and Remote Sensing Letters, in press.
Cavalieri, D. J., C. l. Parkinson, P. Gloersen, J. C. Comiso, and H. J. Zwally. 1999. Deriving Long-term Time Series of Sea Ice Cover from Satellite Passive-Microwave Multisensor Data Sets. Journal of Geophysical Research 104(7): 15,803-15,814.
Cavalieri, D. J., and C. L. Parkinson. 1987. On the Relationship Between Atmospheric Circulation and the Fluctuations in the Sea Ice Extents of the Bering and Okhotsk Seas. Journal of Geophysical Research 92: 7141-7162.
Cavalieri, D. J., B. a. Burns, and R. G. Onstott. 1990. Investigation of the Effects of Summer Melt on the Calculation of Sea Ice Concentration Using Active and Passive-microwave Data. Journal of Geophysical Research 95:5359-5369.
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 Transactions on Geosciences and Remote Sensing GE-24: 368-377.
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. 126 pages.
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. Journal of Geophysical Research 96(C12):21,989-22,009.
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 to December 1996, User's Guide. NASA Technical Memorandum 104647. 17 pages.
Comiso, J. C., T. C. Grenfell, M. Lange, A. Lohanick, R. Moore, and P. Wadhams. 1992. Microwave Remote Sensing of the Southern Ocean Ice Cover. Chapt. 12 In: Microwave remote sensing of sea ice. Frank Carsey, editor. American Geophysical Union. Washington, D. C. 243-259.
Emery, W. J., C. Fowler, J. A. Maslanik. 1994. Arctic Sea Ice Concentrations from SSM/I and AVHRR Satellite Data. Journal of Geophysical Research 99(C9):18,329-18,342.
Eppler, D. T., L. D. Farmer, A. W. Lohanick, M. A. Anderson, D. J. Cavalieri, J. C. Comiso, P. Gloersen, C. Garrity, T. C. Grenfell, M. Hallikainen, J. A. Maslanik, C. Matzler, R. A. Melloh, I. Rubenstein, C. T. Swift. 1992. Passive Microwave Signatures of Sea Ice. In Microwave Remote Sensing of Sea Ice, ed. F. Carsey, Geophysical Monograph 68 (AGU).
Gloersen, P., and W. J. Campbell. 1991a. Variations of Extent, Area, and Open Water of the Polar Sea Ice Covers: 1978-1987, Proc. of the Int. Conf. on the Role of the Polar Regions in Global Change, G. Weller, C. L. Wilson, and B. A. B. Severin, eds., Geophysical Institute, University of Fairbanks, Alaska. 778 pages.
Gloersen, P., and W. J. Campbell. 1988a. Variations in the Arctic, Antarctic, and Global Sea Ice Covers During 1978-1987 as Observed with the Nimbus-7 Scanning Multichannel Microwave Radiometer. Journal of Geophysical Research 93:10,666-10,674.
Gloersen, P, and A. Mernicky. 1997. Oscillatory Behavior in Antarctic Sea Ice Concentrations. In AGU Antarctic Research Series: Antarctic Sea Ice Physical Properties and Processes. Editor M. O. Jeffries. In press.
Gloersen, P., E. Mollo-Christensen, and P. Hubanks. 1989. Observations of Arctic Polar Lows with the Nimbus-7 Scanning Multichannel Microwave Radiometer, in Polar and Arctic Lows, P. F. Twitchell, E. A. Rasmussen, and K. L. Davidson, eds., A. Deepak, Hampton, Virginia, 359-371.
Gloersen, P., C. l. Parkinson, D. J. Cavalieri, J. C. Comiso, and H. J. Zwally. 1999. Spatial Distribution of Trends and Seasonality in the Hemispheric Sea Ice Covers: 1978-1996. Journal of Geophysical Research 104(9): 20,827-20,835.
Gloersen, P., W. J. Campbell, D. J. Cavalieri, J. C. Comiso, C. l. Parkinson, and H. J. Zwally. 1993. Satellite Passive Microwave Observations and Analysis of Arctic and Antarctic Sea Ice, 1978-1987. Annals of Glaciology 17:149-154.
Gloersen P., W. J. Campbell, D. J. Cavalieri, J. C. Comiso, C. L. Parkinson, H. J. Zwally. 1992. Arctic and Antarctic Sea Ice, 1978-1987: Satellite Passive Microwave Observations and Analysis. NASA Special Publication 511.
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. Journal of Geophysical Research 89:5335-5344.
Gloersen, P., T. C. Chang, T. T. Wilheit, and W. J. Campbell. 1974. Polar Sea Ice Observations by Means of Microwave Radiometry, in Advanced Concepts and Techniques in the Study of Snow and Ice, H. S. Santeford and J. L. Smith, eds., National Academy of Science 541-550.
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, 196-218. (Republished as USSR/USA Bering Sea Experiment by A. A. Balkema, Rotterdam, 307 pages. 1982.)
Gloersen, P., R. Ramseier, W. J. Campbell, P. M. Kuhn, and W. J. Webster, Jr. 1975. Ice Thickness Distribution as Inferred from Infrared and Microwave Remote Sensing 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, 282-293. (Republished as USSR/USA Bering Sea Experiment by A. A. Balkema, Rotterdam, 307 pages. 1982.)
Gloersen, P., H. J. Zwally, A. T. C. Chang, D. K. Hall, W. J. Campbell, and R. O. Ramseier. 1978. Time-dependence of Sea Ice Concentration and Multiyear Ice Fraction in the Arctic Basin. Boundary-Layer Meteorology 13:339-359.
Hollinger, J. P., B. E. Troy, R. O. Ramseier, K. W. Asmus, M. F. Hartman, and C. A. Luther. 1984. Microwave Emission from High Arctic Sea Ice During Freeze-up. Journal of Geophysical Research 89(C5):8104-8122.
Hughes Aircraft Company. 1980. Special Sensor Microwave Imager (SSM/I), Computer Program Product Specification (Specification for FNMOC). Vol. II, Sensor Data Processing, Computer Program Component (SMISDP).
Johannessen, O. M., W. J. Campbell, R. Shuchman, S. Sandven, P. Gloersen, E. G. Jospberger, J. A. Johannessen, and P. M. Haugan. 1992. Microwave Study Programs of Air-Ice-Ocean Interactive Processes in the Seasonal Ice Zone of the Greenland and Barents Seas. In Microwave Remote Sensing of Sea Ice, ed. F. Carsey. Geophysical Monograph 68 (AGU).
Josberger, E. G., W. J. Campbell, P. Gloersen, A. T. C. Chang, and A. Rango. 1993. A Hydrology of the Upper Colorado River Basin Derived from Satellite Passive-microwave Observation. Annals of Glaciology 17:322-331.
Josberger, E. G., P. Gloersen, A. T. C. Chang, A. Rango. 1996. The Effects of Snowpack Grain Size on the Passive Microwave Signatures from the Upper Colorado River Basin Snowpack. Journal of Geophysical Research 101:6679-6688.
Maslanik, J. A., M. C. Serreze, and R. G. Barry. 1996. Recent Decreases in Arctic Summer Sea Ice Cover and Linkages to Atmospheric Circulation Anomalies. Geophysical Research Letters 23(13):1677-1680.
Parkinson, C. l., and R. A. Bindschadler. 1984. Response of Antarctic Sea Ice to Uniform Atmospheric Temperature Increases, in Climate Processes and Climate Sensitivity, J. E. Hansen and T. Takahashi, eds., Maurice Ewing Series, Vol. 5, American Geophysical Union, Washington, D. C., pp. 254-264.
Parkinson, C. l., and P. Gloersen. 1993. Global Sea Ice Coverage. In Atlas of Satellite Observations Relate to Global Change. Editors R. Gurney, J. Foster, and C. Parkinson. Cambridge University Press.
Parkinson, C. l., J. C. Comiso, H. J. Zwally, D. J. Cavalieri, P. Gloersen, and W. J. Campbell. 1987. Arctic Sea Ice, 1973-1976: Satellite Passive-Microwave Observations, NASA SP-489, National Aeronautics and Space Administration, Washington, D. C. 296 pages.
Shuchman, R. A., W. J. Campbell, B. Burns, E. Ellingsen, B. Farrelly, P. Gloersen, T. Grenfell, J. Hollinger, D. Horn, J. Johannessen, O. Johannessen, E. Josberger, C. Livingstone, C. Luther, T. Manley, R. Markson, C. Mätzler, E. Mollo-Christensen, R. Onstott, D. Ross, S. Sandven, C. Schgoun, A. Stiffey, E. Svendsen, G. Simmonds, and Z. Top. 1984. Remote Sensing of the Marginal Ice Zone Experiment, in Proceedings of the IGARSS'84 Symposium, Strasbourg, France, European Space Agency, ESA SP-215, 404-409.
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. Journal of Geophysical Research 96(C12):21,971-21,988.
Steffen, K., D. J. Cavalieri, J. C. Comiso, K. St. Germain, P. Gloersen, J. Key, and I. Rubinstein. 1992. The Estimation of Geophysical Parameters Using Passive Microwave Algorithms. Chapt 10 In Microwave remote sensing of sea ice. Frank Carsey, editor. American Geophysical Union. Washington, D. C. 243-259.
Sullivan, C. W., C. R. McClain, J. C. Comiso, and W. O. Smith, Jr. 1988. Phytoplankton Standing Crops within an Antarctic Ice Edge Assessed by Satellite Remote Sensing. Journal of Geophysical Research 93:12,487-12,498.
Svendsen, E., K. Kloster, B. Farrelly, O. M. Johannessen, J. A. Johannessen, W. J. Campbell, P. Gloersen, D. Cavalieri, and C. Matzler. 1983. Norwegian Remote Sensing Experiment: Evaluation of the Nimbus-7 Scanning Multichannel Microwave Radiometer for Sea Ice Research. Journal of Geophysical Research 88(C5):2781-2791.
Wadhams, P., M. A. Lange, and S. F. Ackley. 1987. The Ice Thickness Distribution across the Atlantic Sector of the Antarctic Ocean in Midwinter. Journal of Geophysical Research 92(C13):14,535-14, 552.
Zwally, H. J., and P. Gloersen. 1993. Variability of the Arctic Perennial Ice Pack. In Proceedings of the International Symposium on ISY Polar Ice Extent, February 1993. Editor F. Nishio. 127-132. National Space Development Agency, Mombetsu, Japan.
Zwally, H. J., J. C. Comiso, and A. l. Gordon. 1985. Antarctic Offshore Open Water within the Pack and Oceanographic Effects, in Oceanology of the Antarctic Continental Shelf, S. S. Jacobs, ed., Antarctic Research Series vol. 43, American Geophysical Union, Washington, D. C. 203-226.
Zwally, H. J., J. C. Comiso, C. l. Parkinson, W. J. Campbell, F. D. Carsey, and P. Gloersen. 1983. Antarctic Sea Ice, 1973-1976: Satellite Passive-microwave Observations, NASA SP-459, National Aeronautics and Space Administration, Washington, D. C. 206 pages.
Zwally, H. J., T. T. Wilheit, P. Gloersen, and J. l. Mueller. 1976. Characteristics of Antarctic Sea Ice as Determined by Satellite-borne Microwave Imagers, in Proceedings of the Symposium on Meteorological Observations from Space: Their Contribution to the First GARP Global Experiment, Committee on Space Research of the International Council of Scientific Unions, Philadelphia, 94-97.
Donald J. Cavalieri, Claire L. Parkinson, Per Gloersen, and H. Jay Zwally
NASA Goddard Space Flight Center (GSFC)
Greenbelt, Maryland USA
Walt Meier, Florence Fetterer, Ken Knowles,
Matt Savoie, Mary Jo Brodzik
National Snow and Ice Data Center (NSIDC)
Boulder, Colorado USA
NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449 USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
The acronyms used in this document are listed in Table 17.
|ANSI||American National Standards Institute|
|ASCII||American Standard Code for Information Interchange|
|CIRES||Cooperative Institute for Research in Environmental Sciences|
|DMSP||Defense Meteorological Satellite Program|
|DOS||Disk Operating System|
|ESMR||Electrically Scanning Microwave Radiometer|
|FNMOC||Fleet Numerical Meteorology and Oceanography Center|
|FTP||File Transfer Protocol|
|GIF||Graphical Interchange Format|
|GSFC||Goddard Space Flight Center|
|IDL||Interactive Data Language|
|NASA||National Aeronautics and Space Administration|
|NEMS||Nimbus-E Microwave Spectrometer|
|NOAA||National Oceanic and Atmospheric Administration|
|NSIDC||National Snow and Ice Data Center|
|PNG||Portable Network Graphics|
|RSS||Remote Sensing Systems, Inc.|
|SCAMS||Scanning Microwave Spectrometer|
|SMMR||Scanning Multichannel Microwave Radiometer|
|SSM/I||Special Sensor Microwave/Imager|
|SST||Sea surface temperature|
|URL||Uniform Resource Locator|