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NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration

Summary

This data set provides a Climate Data Record (CDR) of passive microwave sea ice concentration based on the recommendations from the National Research Council (NRC) (2004). It is produced from gridded brightness temperatures (T_Bs) from the Defense Meteorological Satellite Program (DMSP) series of Special Sensor Microwave Imager (SSM/I) passive microwave radiometers: F-8, F-11, and F-13. The NOAA/NSIDC CDR sea ice concentrations provide a consistent, daily time series of sea ice concentrations from 09 July 1987 through 31 December 2007.

The NOAA/NSIDC CDR sea ice concentrations are an estimate of the fraction of ocean area covered by sea ice that is produced by combining concentration estimates created using two algorithms developed at the NASA Goddard Space Flight Center (GSFC): the NASA Team algorithm (Cavalieri et al., 1984) and the Bootstrap algorithm (Comiso, 1986). The individual algorithms are processed and combined at NSIDC using brightness temperature data from Remote Sensing Systems, Inc. (RSS).

The data are gridded on the NSIDC polar stereographic grid with 25 x 25 km grid cells and are available in netCDF file format. Each daily file includes four different sea ice concentration variables: a variable with the primary CDR sea ice concentrations created by NSIDC and three variables with sea ice concentrations created by Goddard. The three Goddard-processed sea ice concentrations are Goddard NASA Team algorithm sea ice concentrations, Goddard Bootstrap sea ice concentrations, and a merged version of the Goddard NASA Team/Bootstrap algorithm sea ice concentrations. Variables containing standard deviation, quality flags, and projection information are also included in the netCDF file.

The three Goddard-produced sea ice concentrations are included in the data files for a number of reasons. The merged Goddard NASA Team/Bootstrap sea ice concentrations are an ancillary data set that is analogous to the NOAA/NSIDC CDR data but that adds late 1978 through mid 1987 data to the record. A different instrument, the Scanning Multichannel Microwave Radiometer (SMMR), was the source for the brightness temperatures from this period. Sea ice concentrations from the extended period are not part of the primary NSIDC-produced CDR record because complete documentation of the SMMR brightness temperature processing method is not available. The separate Goddard NASA Team and Bootstrap sea ice concentrations are provided for reference.

The data are available via FTP.

Citing These Data

Meier, W., F. Fetterer, M. Savoie, S. Mallory, R. Duerr, and J. Stroeve. 2011. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.

Overview Table
 

Table of Contents

1. Contacts
2. Background Information
3. Detailed Data Description
4. Data Acquisition and Processing
5. Data Access and Related Collections
   
6. References and Related Publications
7. Acknowledgements
8. Document Information

1. Contacts

Investigators

Walt Meier (PI)
Ruth Duerr (Co-I)
Florence Fetterer (Co-I)
Julienne Stroeve (Co-I)
National Snow and Ice Data Center (NSIDC)
Boulder, Colorado USA

Technical Contact

2. Background Information

This passive microwave sea ice CDR was produced to meet the standards for climate data records as put forth by U.S. and international organizations such as the NOAA CDR Program and the National Research Council (NRC, 2004).

3. Detailed Data Description

Parameter

The parameter of this data set is sea ice concentration or the fraction of ocean area covered by sea ice. 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.

Spatial Coverage and Resolution

These data cover both the Northern and Southern polar regions at a 25 x 25 km grid cell size. While resolution and grid cell size are often used interchangeably with regards to satellite data, there is an important difference. Resolution refers more properly to the instantaneous field of view (IFOV) of a particular sensor frequency. That is, resolution is the spot size on the ground that the sensor channel can resolve. The SSM/I channels used are the 19 GHz vertical, the 19 GHz horizontal, and the 37 GHz vertical. The IFOV of the 19 GHz SSM/I passive microwave channel is approximately 70 km x 45 km. See Table 7 for a complete list of IFOVs by channel.

Since these data are gridded onto a 25 x 25 km grid and the IFOV of the sensor is coarser than this, the sensor is obtaining information from up to a 3 x 2 grid cell (~75 km x 45 km) region, but because a simple drop-in-the-bucket gridding method is used, that signature is placed in a single grid cell. This results in a spatial "smearing" across several grid cells. Also, some grid cells do not coincide with the center of the sensor footprint and are thus left as missing even though there is brightness temperature information available at that region. Higher frequency channels have finer resolution, but because the sea ice concentration algorithms use data from the 19 GHz channel, the sea ice concentration estimate is affected by the makeup of the surface over an area considerably larger than the nominal 25 km resolution.

The spatial coordinates for the Northern polar region are the following:

Southernmost Latitude: 30.98° N
Northernmost Latitude: 90° N
Westernmost Longitude: 180° W
Easternmost Longitude: 180° E

The spatial coordinates for the Southern polar region are the following:

Southernmost Latitude: 90° S
Northernmost Latitude: 39.23° S
Westernmost Longitude: 180° W
Easternmost Longitude: 180° E

Temporal Coverage and Resolution

The primary NOAA/NSIDC sea ice concentrations span 09 July 1987 to 31 December 2007 at a daily resolution. The merged Goddard NASA Team/Bootstrap sea ice concentrations that are analogous to the NOAA/NSIDC CDR sea ice concentrations adds 26 October 1978 through 08 July 1987 data to the record. The two reference Goddard NASA Team and Goddard Bootstrap sea ice concentrations are also available for that time period. The three Goddard-produced sea ice concentrations are also provided at a daily resolution from 09 July 1987 to 31 December 2007 but every other day from 1978 to 08 July 1987. Note: Data files for the 1978 to 1987 time period still contain a variable for the primary CDR data (seaice_conc_cdr), but it is populated with a fill value of 255. They also contain the standard deviation variable (stdev_of_seaice_conc_cdr) and the quality assessment variable (qa_of_seaice_conc_cdr) set to -1 and 0, respectively. See the Format section of this document for a complete description of all of the file variables.

Table 1 lists the dates that each of the different instruments were used.

Projection and Grid Description

The sea ice concentration data are displayed in a polar stereographic projection. For more information, see the NSIDC Polar Stereographic Projections and Grids Web page. Note that the polar stereographic grid is not equal area; the latitude of true scale (tangent of the planar grid) is 70 degrees. The grid size varies depending on the region, as shown in Table 2.

Format

These data are provided in netCDF4 file format and are compliant with the Climate and Forecast (CF) Metadata Convention CF-1.5. "The purpose of the CF conventions is to require conforming data sets to contain sufficient metadata that they are self-describing in the sense that each variable in the file has an associated description of what it represents, including physical units if appropriate, and that each value can be located in space (relative to earth-based coordinates) and time" (Eaton B., et al., 2010).

The netCDF files contain 12 variables; they are described in Table 3.

Table 4 lists the flag values for the four sea ice concentration variables.
 

File and Directory Structure

The NetCDF data files are organized on the FTP site into two main directories by hemisphere: north and south. Within each of these directories is a subdirectory called daily. The daily directories are further divided into subdirectories labeled by the 4-digit year (YYYY) beginning with 1978. The daily files reside within their respective year directory. Figure 1 shows an image of the directory structure.

Figure 1. Directory Structure

Sample Data Record

Figure 2 shows the variables of a sample file opened using IDL 8. It shows the variables listed in Table 3.

Figure 2. Sample Data Record Opened using IDL.
You can see the self describing nature of the netCDF file. The seaice_conc_cdr variable is highlighted and it's format is shown on the right.

File Naming Convention

The file naming convention for the daily files is listed below and as described in Table 5:

seaice_conc_daily_yyyymmdd_HH_sat_vXXrXX.nc

Where:

File Size

Northern hemisphere files are 2.5 MB - 3.5 MB each and Southern hemisphere files are 1.8 MB - 2.5 MB each.

Error Sources and Quality Assessment

Several studies over the years have assessed sea ice concentration estimates from the NASA Team and Bootstrap algorithms. These assessments have typically used coincident airborne or satellite remote sensing data from optical, thermal, or radar sensors, generally at a higher spatial resolution than the SSM/I instrument but with only local or regional coverage. Several assessments indicate an accuracy of approximately five percent during mid-winter conditions away from the coast and the ice edge (Steffen et al., 1992; Gloersen et al., 1993; Comiso et al., 1997; Meier et al., 2005; Andersen et al., 2007, Belchansky and Douglas, 2002). Other assessments suggest concentration estimates are less accurate. Kwok (2002) found that passive microwave overestimates open water by three to five times in winter. Partington et al. (2003) found a difference with operational charts that was relatively low in the winter, but rose to more than 20 percent in summer.

Researchers can assess and improve a CDR by comparing it with operational products — real-time products that help ships cross the sea ice. Absolute error can be approximated via comparison to operational sea ice products, such as those produced by the U.S. National Ice Center (NIC) or the Canadian Ice Service, but it is important to keep in mind that such products have an operational focus different from the climate focus of the CDR, and the two are not expected to be consistent with each other. The documentation for the daily Multi-sensor Analyzed Sea Ice Extent (MASIE), distributed by NSIDC in cooperation with NIC, gives a summary of how satellite passive microwave CDRs differ from operational products.

Errors can come from problems with the sensor, from weather effects, and from inadequacies in the algorithm. A satellite's orbit may drift over time, for example, which may degrade the data quality of an instrument. Most SSM/I instruments were used long past their designed lifetime expectancy. Atmospheric water vapor is a weather effect that can modulate the passive microwave signature of the surface, particularly at the 19 GHz frequency, causing ice concentration to be overestimated. The emissivity of sea water is generally stable, except under strong winds that cause waves to form. The emissivity of sea ice varies considerably depending on many factors including age, thickness, and surface roughness. When one considers that algorithms must arrive at a single number for ice concentration, taking into account the varying brightness temperatures of all the different surface types that may fill the footprints of the 19 GHz and 37 GHz channels, and that those footprints differ in size and shape across the instrument swath, one can appreciate the difficulty of the problem. Microwave Remote Sensing of Sea Ice, edited F. Carsey, provides a comprehensive overview of the subject (Carsey, 1992).

Another potential sensor error results from the transition between sensors on different platforms. The brightness temperature regression and tie-point adjustment corrects for this, though small artifacts remain (Cavalieri et al., 1999; Comiso and Nishio, 2008). Comparison of ice extent estimates from sensor overlap periods indicate that the adjustments yield agreements that are on the order of 0.05% or less and about 0.5% for sea ice area (Cavalieri et al., 1999; Cavalieri et al., 2011). Short overlap periods of early sensor transitions (SMMR to F-8 and F-8 to F-11) may not account for the full seasonal variability (Meier et al., 2011b; Cavalieri et al., 2011) and differences may be higher in some cases. However, differences appear to be well below the sensitivity of the instrument, thus, providing confidence in the robustness of the intercalibrated algorithms through the time series.

When melt ponds form on the surface of ice floes in the summer, the ice concentration appears to decline when in fact the true concentration may not have changed (Fetterer and Untersteiner, 1998). Melt state is a surface effect that may in itself contain a climate trend, which could influence sea ice concentration trend estimates. This and other concentration error sources have been examined to some extent in Andersen et al. (2007), and their influence appears to be small compared to the estimated sea ice trends, but such effects should be kept in mind when using these data.

The netCDF files contain a variable called qa_of_seaice_conc_cdr to help data users assess the quality of a given data value. Table 6 gives a list of the flags, their values, and their meaning. Values less than eight indicate that conditions for high error are not present — though errors could still be high — and also denotes the algorithm source (NASA Team or Bootstrap) or area covered by a climatological ocean mask. Values greater than or equal to eight indicate that conditions exist that could increase error, with higher values generally indicating greater uncertainty and hence less confidence in the CDR concentration estimate. Note that grid cells that meet multiple conditions will have a value that is the sum of the values of each individual condition.

For a more complete description of error sources and assessments, see the Climate Algorithm Theoretical Basis Document (C-ATBD): Passive Microwave Sea Ice Concentration document (Meier, W., et al., 2011a).

4. Data Acquisition and Processing

Sensor or Instrument Description

For the NOAA/NSIDC CDR data (seaice_conc_cdr), NSIDC uses brightness temperatures from SSM/I sensors on the DMSP F-8, F-11, and F-13 platforms. See Table 7 for a description of orbital parameters of the different platforms. The rationale for using only these satellites was made to keep the equatorial crossing times as consistent as possible to minimize potential diurnal effects of data from sun-synchronous orbits of the DMSP satellites. Note that the SMMR sensor on the Nimbus-7 platform is used for the merged Goddard NASA Team/Bootstrap data (goddard_merged_seaice_conc), the Goddard NASA Team data (goddard_nt_seaice_conc), and the Goddard Bootstrap data (goddard_nt_seaice_conc) from 1978 to 1987.

*Indicates sensor and spacecraft orbital characteristics of the three sensors used in generating the sea ice concentrations.

Table 8 lists the footprint size of the SSM/I instrument.

For a complete description of the SSM/I instrument see the NSIDC Special Sensor Microwave Imager (SSM/I) Web page. For a complete description of the different DMSP platforms, see the NSIDC DMSP Satellite F-8, F-11, and F-13 Web pages.

Data Acquisition Methods of Input Data

NOAA/NSIDC CDR Sea Ice Concentrations

The input gridded brightness temperatures used for creating the NOAA/NSIDC CDR sea ice concentrations come from NSIDC in the DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures (NSIDC-0001) data set. These gridded brightness temperatures are produced by NSIDC from swath data obtained from RSS. For a complete description of how NSIDC obtains and processes the input data, see the Data Acquisition Methods section of the DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures data set guide document.

Goddard Sea Ice Concentrations

The two Goddard-produced sea ice concentrations — NASA Team (NSIDC-0051) and Bootstrap (NSIDC-0079) — are provided for users of the Goddard products. These data are processed almost identically to the intermediate NSIDC-produced NASA Team and Bootstrap sea ice concentrations used in the CDR concentration estimates but are produced at NASA GSFC and include manual quality control. This hand editing removes spurious ice in grid cells that are not removed through automated filtering methods and any other bad data discovered through the manual inspection. However, the hand-editing process has not been documented and, thus, is not traceable, which means that they do not pass the traceability recommendations for CDRs from NRC (2004). In addition, older versions of the RSS brightness temperatures were used as input for some parts of the record, though these differences are small. Therefore, while these products do not have full traceability, they do result in a better overall quality record. In addition, they also include sea ice estimates from the NASA Nimbus-7 SMMR sensor, which predates DMSP and extends the total time series to late 1978 with every-other-day concentration estimates. The third sea ice concentration estimate provided is a merged version of the NASA Team/Bootstrap algorithm estimates that is merged at NSIDC. This merged version provides a longer-term analog of the NOAA/NSIDC CDR.

NASA Team

The ancillary Goddard-produced NASA Team sea ice concentrations (goddard_nt_seaice_conc) that NSIDC provides with this data set are archived and available from NSIDC as the Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data (NSIDC-0051) data set.

Bootstrap

The ancillary Goddard-produced Bootstrap sea ice concentrations (goddard_bt_seaice_conc) are available from NSIDC as the Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I (NSIDC-0079) data set.

Merged NASA Team/Bootstrap

The merged Goddard NASA Team/Bootstrap sea ice concentrations (goddard_merged_seaice_conc) are merged at NSIDC using the CDR algorithm. See the CDR Algorithm section of this document for more information.

Derivation Techniques and Algorithms

NSIDC processes the input brightness temperatures into two different intermediate sea ice concentrations using two Goddard-developed algorithms: NASA Team (Cavalieri et al., 1984) and Bootstrap (Comiso, 1986). The two algorithms are described in the NASA Team Algorithm and Bootstrap Algorithm sections of this document. These intermediate NSIDC NASA Team and Bootstrap sea ice concentrations are used in the NOAA/NSIDC CDR algorithm described in further detail in the CDR Algorithm section of this document.

The passive microwave channels employed for the sea ice concentration product are the 19 GHz, 22 GHz, and 37 GHz frequencies. Table 9 lists the channels used for each algorithm and the channels used for the weather filters. A complete description of the weather filters can be found in the C-ATBD (Meier, W. N., et al., 2011a).
 

Since this data set uses multiple sensors over time, the sea ice algorithms are intercalibrated at the product (concentration) level by NASA Goddard. Thus, the brightness temperature source is less important because the intercalibration adjustment includes any necessary changes due to differences in brightness temperature across them. Both the NASA Team and Bootstrap algorithms employ varying tie-points to account for changes in sensors and spacecraft. These tie-point adjustments are derived from regressions of brightness temperatures during overlap periods. The adjustments are made at the product level by adjusting the algorithm coefficients so that the derived sea ice fields are as consistent as possible.

The NASA Team approach uses sensor-specific hemispheric tie-points for each transition (Cavalieri et al., 1999; Cavalieri et al., 2011). Tie-points were originally derived for the SMMR sensor and subsequent transitions to the different SSM/I instruments adjusted the tie-points to be consistent with the original SMMR record. The Bootstrap algorithm uses daily varying hemispheric tie-points, derived via linear regression analysis on clusters of brightness temperature values of the relevant channels (Comiso, 2009; Comiso and Nishio, 2008). Also, in contrast to the NASA Team, Bootstrap tie-points for SMMR and SSM/I are derived from matching fields from the AMSR-E sensor, which is newer and more accurate.

Automated Quality Control

Automated quality control measures are implemented independently on the NASA Team and Bootstrap outputs. Two weather filters, based on ratios of channels sensitive to enhanced emission over open water, are used to filter weather effects. Separate land-spillover corrections are used for each of the algorithms to filter out much of the error due to mixed land/ocean grid cells. Finally, climatological ocean masks are applied to screen out errant retrievals of ice in regions where sea ice never occurs.

For a complete description of the automated filters and masks, see the C-ATBD (Meier, W. N., et al., 2011a)

NASA Team Algorithm

The NASA Team algorithm uses brightness temperatures from the 19 GHz V, 19 GHz H, and 37 GHz V channels. The methodology is based on two brightness temperature ratios, the polarization ratio (PR) of the 19 GHz V and H channels (Equation 1) and the spectral gradient ratio (GR) of the 19 GHz V and 37 GHz V channels (Equation 2).

Where

When PR and GR are plotted against each other, brightness temperature values tend to cluster in two locations, an open water (0% ice) point and a line representing 100% ice concentration, roughly forming a triangle. The concentration of a grid cell with a given GR and PR value is calculated by a linear interpolation between the open water point and the 100% line segment. See Figure 3.

For a detailed description of the NASA Team algorithm, please see the NASA Team Sea Ice Algorithm Web page, the NASA Technical Memorandum 104647 (Cavalieri, D. J., et al., 1997) that includes information about differences (for example, tie points) between the original algorithm and the revised NASA Team algorithm, and the NASA Team Algorithm section of the C-ATBD (Meier, W. N., et al., 2011a) for a table of tie-point values.
 

Figure 3. Sample plot of GR vs. PR with typical clustering of grid cell values (small dots) around the 0% ice (open water) point (blue star) and the 100% ice line (circled in red). Points with a mixture of ice and water (circled in green) fall between these two extremes. Adapted from Figure 10-2 of Steffen et al. (1992).

Bootstrap Algorithm

Like the NASA Team algorithm, the Bootstrap algorithm is empirically derived based on relationships of brightness temperatures at different channels. The Bootstrap method uses the fact that scatter plots of different sets of channels show distinct clusters that correspond to two pure surface types: 100% sea ice or open water.

Figure 4 shows a schematic of the general relationship between two channels. Points that fall along line segment AD represent 100% ice cover. Points that cluster around point O represent open water (0% ice). Concentration for a point B is determined by a linear interpolation along the distance from O to I where I is the intersection of segment OB and segment AD. This is described by Equation 3.
 

Where:

Figure 4. Example of the relationship of the 19V vs. 37V T_B (in Kelvin) used in the Bootstrap algorithm. Brightness temperatures typically cluster around the line segments AD (representing 100% sea ice) and OW (representing 100% open water). For points that fall below the AD-5 line (dotted line), Bootstrap uses T_B relationships for 37H vs. 37V. Adapted from Comiso and Nishio (2008).

The Bootstrap algorithm uses two such combinations, 37 GHz H versus 37 GHz V and 19 GHz V versus 37 GHz V, denoted as HV37 and V1937, respectively. Points that fall within 5 K of the AD segment in a HV37 plot, corresponding roughly to concentrations greater than 90 percent, use this approach. Points that fall below the AD-5 line, use the V1937 relationship to derive the concentration. Slope and offset values for line segment AD were originally derived for each hemisphere for different seasonal conditions (Table 2 in Comiso et al, 1997). However, a newer formulation, employed in this CDR, was developed where slope and offsets are derived for each daily field based on the clustering within the daily brightness temperatures (Comiso and Nishio, 2008). For a detailed description of the Bootstrap algorithm, please see the Bootstrap Algorithm Web page.

CDR Algorithm

The NSIDC sea ice concentration CDR algorithm uses sea ice concentrations produced with the NASA Team and Bootstrap algorithms as input and merges them into a combined single concentration estimate based on the known characteristics of the NASA Team and Bootstrap algorithms. First, the Bootstrap 10 percent concentration threshold is used as a cutoff to define the limit of the ice edge. In other words, any grid cell showing a concentration of less than 10 percent is set to open water. Second, at each sea ice grid cell, the concentration value given by the NASA Team algorithm and that given by the Bootstrap algorithm are compared; whichever value is greater is selected as the CDR value. Both algorithms tend to underestimate concentration, but the source of this bias or error differs between algorithms.

The NASA Team algorithm, because it uses a ratio of brightness temperatures, tends to cancel out any physical temperature effects. The Bootstrap algorithm uses relationships between two brightness temperatures that are dependent on physical temperature. Thus, physical temperature changes can affect Bootstrap estimates. Errors occur primarily in regimes with very low temperatures: winter in the high Arctic and near the Antarctic coast (Comiso et al., 1997), where the Bootstrap algorithm can underestimate concentration and give a lower value than the NASA Team algorithm.

During winter conditions with more moderate temperatures, NASA Team concentrations also tend to have more of a low bias (Kwok, 2002; Meier, 2005). During melt conditions, both algorithms tend to underestimate concentration; but the effect is more pronounced in the NASA Team algorithm (Comiso et al., 1997; Meier, 2005; Andersen et. al, 2007).

While these characteristics of the algorithm are true in an overall general sense, ice conditions and algorithm performance can vary from grid cell to grid cell; and in some cases, this approach of choosing the larger value will result in an overestimation of concentration (Meier, 2005). However, using the higher concentration between the two algorithms will tend to reduce the overall underestimation of the CDR estimate. For a more in-depth discussion on the reasoning behind the algorithm, see the C-ATBD (Meier, W. N., et al., 2011a).

Data Processing

The following are the general steps NSIDC uses to produce the formal CDR sea ice concentration product:

1. Obtain input brightness temperatures from the NSIDC DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures (NSIDC-0001) data set.
2. Process the brightness temperatures into sea ice concentration using the NASA Team and Bootstrap algorithms to make an intermediary NSIDC version of these data.
3. Run the CDR algorithm on the NSIDC NASA Team and Bootstrap data to select the final value and determine any flag values at this time.
4. Populate the netCDF file with the final CDR sea ice concentration, along with the ancillary Goddard merged NASA Team and Bootstrap sea ice concentration, and the reference Goddard NASA Team sea ice concentrations and Bootstrap sea ice concentrations.
5. The melt-indicator flag is added to the QA variable via a post-processing step.

Figure 5 shows a diagram of the data flow through the CDR processing.
 

Figure 5. Flow of Data through the CDR Processing

Note that the current CDR does not include the SMMR product because full provenance and documentation of the SMMR brightness temperatures and processing methodology (for example, manual filtering of bad grid cells) cannot be assured, and thus, does not fulfill the CDR standards set forth by NRC (2004).

5. Data Access and Related Collections

Data Access

The data are available via FTP.

Software and Tools

There are a number of netCDF file readers available to read/view netCDF files. For a list of tools, please see the NetCDF Resources at NSIDC: Software and Tools Web page.

Related NSIDC Data Collections

• DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures
• Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data
• Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I
• Multi-sensor Analyzed Sea Ice Extent (MASIE)
• Sea Ice Index
• AMSR-E/Aqua Daily L3 12.5 km Brightness Temperatures, Sea Ice Concentration, & Snow Depth Polar Grids
• AMSR-E/Aqua Daily L3 25 km Brightness Temperatures & Sea Ice Concentration Polar Grids

Other Related Data Collections

• EUMETSAT Ocean & Sea Ice Satellite Application Facility

6. References and Related Publications

Andersen, S., R. Tonboe, L. Kaleschke, G. Heygster, and L. T. Pedersen. 2007. Intercomparison of Passive Microwave Sea Ice Concentration Retrievals over the High-Concentration Arctic Sea Ice. J. Geophys. Res., 112, C08004, doi:10.1029/2006JC003543.

Belchansky, G. I., and D. C. Douglas. 2002. Seasonal Comparisons of Sea Ice Concentration Estimates Derived from SSM/I, OKEAN, and RADARSAT Data. Rem. Sens. Environ., 81: 67-81.

Carsey, F. D. (Ed.). 1992. Microwave Remote Sensing of Sea Ice. American Geophysical Union, 462 pp.

Cavalieri, D. J., C. L. Parkinson. Arctic and Antarctic Sea Ice Concentrations from Multichannel Passive-Microwave Satellite Data Sets: October 1978 - September 1995 - User's Guide. NASA Technical Memorandum 104647. NASA Goddard Space Flight Center, Greenbelt, Maryland.

Cavalieri, D. J., P. Gloersen, and W. J. Campbell. 1984. Determination of Sea Ice Parameters with the NIMBUS-7 SMMR. J. Geophys. Res., 89(D4): 5355-5369.

Comiso, J. C., and F. Nishio. 2008. Trends in the Sea Ice Cover Using Enhanced and Compatible AMSR-E, SSM/I, and SMMR Data. J. of Geophys. Res., 113, C02S07, doi:10.1029/2007JC0043257.

Comiso, J. C., D. Cavalieri, C. Parkinson, and P. Gloersen. 1997. Passive Microwave Algorithms for Sea Ice Concentrations: A Comparison of Two Techniques. Rem. Sens. of the Environ., 60(3):357-384.

Comiso, J. C. 1986. Characteristics of Arctic Winter Sea Ice from Satellite Multispectral Microwave Observations. J. Geophys. Res., 91(C1): 975-994.

Eaton B., J. Gregory, H. Centre, B. Drach, K. Taylor, and S. Hankin. 2010. NetCDF Climate and Forecast (CF) Metadata Conventions Version 1.5. Programs for Climate Model Diagnosis and Intercomparison. 81 pp. http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.5/cf-conventions.pdf. Accessed Sep. 2011.

Fetterer, F., and N. Untersteiner. 1998. Observations of Melt Ponds on Arctic Sea Ice. J. Geophys. Res., 103(C11): 24, 821-24, 835.

Gloersen, P., W. J. Campbell, D. J. Cavalieri, J. C. Comiso, C. L. Parkinson, and H. J. Zwally. 1993. Arctic and Antarctic Sea Ice, 1978-1987: Satellite Passive-Microwave Observations and Analysis. NASA Spec. Publ. 511, 290 pp.

Kwok, R. 2002. Sea Ice Concentration Estimates from Satellite Passive Microwave Radiometry and Openings from SAR Ice Motion. Geophys. Res. Lett., 29(9), 1311, doi:10.1029/2002GL014787.

Meier, W. N., M. Savoie, and S. Mallory. 2011a. CDR Climate Algorithm and Theoretical Basis Document: Passive Microwave Sea Ice Concentration. NOAA NCDC CDR Program.

Meier, W. N., and S. J. S. Khalsa. 2011b. Intersensor Calibration between F-13 SSM/I and F-17 SSMIS Near-Real-Time Sea Ice Estimates. Geoscience and Remote Sensing 49(9): 3343-3349.

Meier, W. N. 2005. Comparison of Passive Microwave Ice Concentration Algorithm Retrievals with AVHRR Imagery in Arctic Peripheral Seas. IEEE Trans. Geosci. Remote Sens., 43(6): 1324-1337.

Partington, K., T. Flynn, D. Lamb, C. Bertoia, and K. Dedrick. 2003. Late Twentieth Century Northern Hemisphere Sea-Ice Record from U.S. National Ice Center Ice Charts. J. Geophys. Res. 108(C11): 3343. doi:10.1029/2002JC001623.

Steffen, K., J. Key, D. J. Cavalieri, J. Comiso, P. Gloersen, K. St. Germain, and I. Rubinstein. 1992. The Estimation of Geophysical Parameters using Passive Microwave Algorithms, in "Microwave Remote Sensing of Sea Ice." F.D. Carsey, ed., American Geophysical Union Monograph 68, Washington, DC:201-231.

National Research Council of the National Academies. 2004. Climate Data Records from Environmental Satellites: Interim Report. National Academies Press, Washington, D.C., 150 pp.

7. Acknowledgements

This project was supported in part by a grant NA07OAR4310056 from the NOAA Climate Data Record Program. Production of original NASA Team and Bootstrap algorithm estimates supported by the NASA Polar Distributed Active Archive Center. The sea ice concentration algorithms were developed by Donald J. Cavalieri, Josefino C. Comiso, Claire L. Parkinson, and others at the NASA Goddard Space Flight Center in Greenbelt, Maryland, USA.

The production of the NOAA/NSIDC sea ice CDR was managed by Donna Scott at NSIDC.

8. Document Information

Acronyms

Table 12 lists the acronyms used in this document.

Document Author

This document was created by A. Windnagel through correspondence with W. Meier, M. Savoie, S. Mallory, and D. Scott and from information in the C-ATBD (Meier, W. N., et al., 2001a).

Document Creation Date

July 2011

Document URL

http://nsidc.org/data/docs/noaa/g02202_ice_conc_cdr/index.html