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NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 1

Note: Version 1 of this data set has been replaced by Version 2. The Version 1 data is no longer available and the documentation (this document) is no longer updated as of June 2013. See the Version 2 page.

This data set provides a Climate Data Record (CDR) of sea ice concentration from passive microwave data. It provides a consistent, daily and monthly time series of sea ice concentrations from 09 July 1987 through 31 December 2007 for both the north and south polar regions on a 25 km x 25 km grid.

Table of Contents

  1. Detailed Data Description
  2. Data Access
  3. Data Acquisition and Processing
  4. References and Related Publications
  5. Contacts and Acknowledgments
  6. Document Information

Register

NSIDC encourages you to register as a user of this data product. As a registered user, you will be notified of updates and corrections. Register.

Citing These Data

The following example shows how to cite the use of this data set in a publication.

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. http://dx.doi.org/10.7265/N5B56GN3

Overview

Parameters

Sea Ice Concentration

Spatial Coverage & Resolution

North and south polar regions gridded onto a polar stereographic projection with 25 x 25 km grid cells

Temporal Coverage & Resolution

NOAA/NSIDC CDR: 09 July 1987 - 31 December 2007 at a daily and monthly resolution

Platform

Defense Meteorological Satellite Program (DMSP)

Sensor

Special Sensor Microwave Imager (SSM/I) passive microwave radiometers: F-8, F-11, and F-13

Data Format

NetCDF4 CF-1.5

Metadata Access

View Metadata Record

Version Replaced with Version 2. See the Version 2 documentation to obtain the most recent data.

Data Access

Version 1 is no longer available. See the Version 2 documentation to obtain the most recent data.

1. Detailed Data Description

Summary

The NOAA/NSIDC CDR is based on the recommendations from the National Research Council (NRC) (2004). It is produced from gridded brightness temperatures 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 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). NSIDC applies the individual algorithms to 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 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-produced sea ice concentrations are the Goddard NASA Team algorithm sea ice concentrations, the Goddard Bootstrap sea ice concentrations, and a merged version of the two sea ice concentrations. These Goddard-produced sea ice concentrations are included in the data files for a number of reasons. First, 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. Second, the separate Goddard NASA Team and Bootstrap sea ice concentrations are provided for reference.

Variables containing standard deviation, quality flags, and projection information are also included in the netCDF files.

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).

Both the daily and monthly files contain 12 variables that are described in the sections below:

Daily File Variable Description

The daily netCDF files contain 12 variables. They are described below.

seaice_conc_cdr

Description: NSIDC-produced CDR sea ice concentrations which is the fraction of ocean area covered by sea ice that span 1987 to 2007. Note that the 1978 to 1987 data files contain this variable, but they are populated with a fill value of 255. See Table 1 for a list of all flag values.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

Table 1. Flag Values for Sea Ice Concentration Variables
Flag Name Value
Northern Hemisphere pole hole (the region around the pole not imaged by the sensor) 251
Lakes 252
Coast/Land adjacent to ocean 253
Land 254
Missing/Fill 255

stdev_of_seaice_conc_cdr

Description:Standard deviation for the daily NOAA/NSIDC CDR sea ice concentration. This value is the standard deviation of a given grid cell along with its eight surrounding grid cells (for nine values total) from both the NASA Team and Bootstrap data. This means that the standard deviation is computed using a total of 18 values: nine from the intermediate NISDC NASA Team data and nine from the intermediate NSIDC Bootstrap data. Grid cells with high standard deviations indicate values with lower confidence levels.

Data Type: Float array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0.0 to 1.0

Attributes: _FillValue, valid_range, long_name, units, coordinates, missing_value, datum, grid_mapping

Fill Value: -1.0

Units: Unitless

qa_of_seaice_conc_cdr

Description: A number of different quality flags related to the daily NSDIC CDR sea ice concentration. See Table 2 for a list of the flags. Note: Grid cells that meet multiple conditions will have a value that is the sum of the values of each individual condition. For example, if the byte value for a cell is 129, both the melt_start_detected flag and BT_source_for_CDR flag have been set.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 1 to 255

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, coordinates, flag_masks, flag_meanings, missing_value, datum, grid_mapping

Fill Value: 0

Units: Unitless

Table 2. Daily QA Flag Values
Condition Flag Value Label in NetCDF variable Description
BT source for CDR (BT > NT) 1 BT_source_for_CDR Indicates that the value from the Bootstrap algorithm was greater than the NASA Team algorithm, thus the Bootstrap value was used for this grid cell.
NT source for CDR (NT > BT) 2 NT_source_for_CDR Indicates that the value from the NASA Team algorithm was greater than the Bootstrap algorithm, thus the NASA Team value was used for this grid cell.
Region masked by ocean climatology 4 no_ice_allowed_per_climatology Indicates that this grid cell has been designated as ocean via an ocean mask.
Grid cell near the coast 8 grid_cell_near_to_coast Indicates that this grid cell is located near the coastline, so it may be less reliable and of lower quality than cells further from the coast.
Concentration < 50% 32 concentration_below_fifty_percent Indicates that the concentration value for this grid cell is under 50%. This is important because the NASA Team and Bootstrap algorithms do not pick up low concentration ice very well and also confuse low concentration with thin ice. Therefore, these grid cells have a lower confidence level.
Start of Melt Detected (Arctic only) 128 melt_start_detected Indicates that the ice in this grid cell has shown evidence of starting to melt, so values may be less reliable.

 

goddard_merged_seaice_conc

Description: Merged Goddard NASA Team/Bootstrap daily sea ice concentrations from 1978 to 2007. This variable is merged from the goddard_nt_seaice_conc and the goddard_bt_seaice_conc variables. For a description of the algorithm used to merge these, see the CDR Algorithm section of this document. For a list of flag values for this variable, see Table 1.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

goddard_nt_seaice_conc

Description: Goddard NASA Team daily sea ice concentrations sea ice concentrations from 1978 to 2007. These values come from the daily portion of the Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data (NSIDC-0051). For a list of flag values for this variable, see Table 1.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

goddard_bt_seaice_conc

Description: Goddard Bootstrap daily sea ice concentrations sea ice concentrations from 1978 to 2007. These values come from the daily portion of the Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS (NSIDC-0079) data set. For a list of flag values for this variable, see Table 1.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

Projection

Description: Provides details about the polar stereo projection information for the data.

Data Type: Char array with dimension [304]

Attributes: grid_boundary_top_projected_y, grid_boundary_bottom_projected_y, grid_boundary_right_projected_x, grid_boundary_left_projected_x, parent_grid_cell_row_subset_start, parent_grid_cell_row_subset_end, parent_grid_cell_column_subset_start, parent_grid_cell_column_subset_end, spatial_ref, proj4text, srid, GeoTransform, grid_mapping_name, latitude_of_projection_origin, standard_parallel, straight_vertical_longitude_from_pole, longitude_of_projection_origin, scaling_factor, false_easting, false_northing , semimajor_radius, semiminor_radius, units

Fill Value: N/A

Units: Meters

time

Description: Time in days since 1601-01-01 00:00:00.

Data Type: Double

Attributes: standard_name, units, long_name, calendar, axis

Fill Value: N/A

Units: Days since 1601-01-01 00:00:00

ygrid

Description: Y-offset in meters of the projection grid centers.

Data Type: Float array with dimension [448]

Valid Range: -5.35000e+006 to 5.85000e+006

Attributes: valid_range, units, long_name, standard_name, axis

Fill Value: N/A

Units: Meters

xgrid

Description: X-offset in meters of the projection grid centers.

Data Type: Float array with dimension [304]

Valid Range: -3.85000e+006 to 3.75000e+006

Attributes: valid_range, units, long_name, standard_name, axis

Fill Value: N/A

Units: Meters

latitude

Description: Latitude in degrees north of the projection grid centers.

Data Type: Double array with dimensions [304, 448]

Valid Range: 0.0 to 90.0 for northern hemisphere files, and -90.0 to 0.0 for southern hemisphere files.

Attributes: standard_name, long_name, units, valid_range, _FillValue

Fill Value: -999.0

Units: Degrees north

longitude

Description: Longitude in degrees east of the projection grid centers.

Data Type: Double array with dimensions [304, 448]

Valid Range: -180.0 to 180.0

Attributes: standard_name, long_name, units, valid_range, _FillValue

Fill Value: -999.0

Units: Degrees east

Monthly File Variable Description

The monthly netCDF files contain 12 variables; they are described below.

seaice_conc_monthly_cdr

Description: The monthly average of the NSIDC-produced CDR sea ice concentrations (seaice_conc_cdr) that span 1987 to 2007. Note that the 1978 to 1987 data files contain this variable, but they are populated with a fill value of 255. See Table 1 for a list of flag values used in this variable.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

stdev_of_seaice_conc_monthly_cdr

Description: Standard deviation for the monthly NOAA/NSIDC CDR sea ice concentration variable (seaice_conc_monthly_cdr). This value is the standard deviation of the concentration of all daily values for the month at that grid cell.

Data Type: Float array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0.0 to 1.0

Attributes: _FillValue, valid_range, long_name, units, coordinates, missing_value, datum, grid_mapping

Fill Value: -1.0

Units: Unitless

qa_of_seaice_conc_monthly_cdr

Description: A number of different quality flags related to the monthly NSDIC CDR sea ice concentration variable (seaice_conc_monthly_cdr). See Table 3 for a list of the monthly flags. Note: Grid cells that meet multiple conditions will have a value that is the sum of the values of each individual condition. For example, if the byte value for a cell is 129, both the melt_detected_greater_than_half_month flag and BT_majority_algorithm_for_monthly_CDR flag have been set.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 1 to 255

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, coordinates, flag_masks, flag_meanings, missing_value, datum, grid_mapping

Fill Value: 0

Units: Unitless

Table 3. Monthly QA Flag Values
Condition Flag Value Label in NetCDF variable Description
Number of BT > Number of NT 1 BT_majority_algorithm_for_monthly_CDR Indicates that the majority of the values used for the monthly average, at this grid cell, are from the Bootstrap algorithm.
Number of NT > Number of BT 2 NT_majority_algorithm_for_monthly_CDR Indicates that the majority of the values used for the monthly average, at this grid cell, are from the NASA Team algorithm.
Region masked by ocean climatology 4 no_ice_allowed_per_climatology Indicates that this grid cell has been designated as ocean via an ocean mask.
Grid cell near the coast 8 grid_cell_near_to_coast Indicates that this grid cell is located near the coastline, so it may be less reliable and of lower quality.
Ice present < 50% 32 ice_present_less_half_of_month Indicates that for this grid cell ice was present less than half of the month, so the value is more likely due to a temporal difference of the concentration than a spatial one.
melt detected >= 1 64 melt_detected_at_least_one_day Indicates that for this grid cell melt was detected at least one day during the month, so it may be less reliable and of lower quality.
Melt detected > 50% 128 melt_detected_greater_than_half_month Indicates that for this grid cell melt was detected at least half of the days during the month, so it may be even less reliable and of lower quality than for those with melt detected less than half the month or not at all.

 

goddard_merged_seaice_conc_monthly

Description: Merged Goddard NASA Team/Bootstrap monthly sea ice concentrations from 1978 to 2007. This variable is merged from the goddard_nt_seaice_conc_monthly and the goddard_bt_seaice_conc_monthly variables. For a description of the algorithm used to merge these, see the CDR Algorithm section of this document. For a list of flag values for this variable, see Table 1.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

goddard_nt_seaice_conc_monthly

Description: Goddard NASA Team monthly sea ice concentrations from 1978 to 2007. These values come from the monthly portion of the Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. For a list of flag values for this variable, see Table 1.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

goddard_bt_seaice_conc_monthly

Description: Goddard Bootstrap monthly sea ice concentrations from 1978 to 2007. These values come from the monthly portion of the Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS data set. For a list of flag values for this variable, see Table 1.

Data Type: Byte array with dimensions [304, 448, 1] which are the xgrid, ygrid, and time, respectively.

Valid Range: 0 to 1. Note: Byte values are actually stored in the files from 0 to 100 but are presented by most, but not all, netCDF readers as values ranging from 0 to 1 because of a scaling factor attribute (scale_factor) for this variable of .01 that is applied by most netCDF readers.

Attributes: _FillValue, valid_range, _Unsigned, long_name, standard_name, units, scale_factor, coordinates, flag_values, flag_meanings, datum, grid_mapping, reference, ancillary_variables

Fill Value: 255

Units: Unitless

Projection

Description: Provides details about the polar stereo projection information for the data.

Data Type: Char array with dimension [304]

Attributes: grid_boundary_top_projected_y, grid_boundary_bottom_projected_y, grid_boundary_right_projected_x, grid_boundary_left_projected_x, parent_grid_cell_row_subset_start, parent_grid_cell_row_subset_end, parent_grid_cell_column_subset_start, parent_grid_cell_column_subset_end, spatial_ref, proj4text, srid, GeoTransform, grid_mapping_name, latitude_of_projection_origin, standard_parallel, straight_vertical_longitude_from_pole, longitude_of_projection_origin, scaling_factor, false_easting, false_northing , semimajor_radius, semiminor_radius, units

Fill Value: N/A

Units: Meters

time

Description: Time in days since 1601-01-01 00:00:00.

Data Type: Double

Attributes: standard_name, units, long_name, calendar, axis

Fill Value: N/A

Units: Days since 1601-01-01 00:00:00

ygrid

Description: Y-offset in meters of the projection grid centers.

Data Type: Float array with dimension [448]

Valid Range: -5.35000e+006 to 5.85000e+006

Attributes: valid_range, units, long_name, standard_name, axis

Fill Value: N/A

Units: Meters

xgrid

Description: X-offset in meters of the projection grid centers.

Data Type: Float array with dimension [304]

Valid Range: -3.85000e+006 to 3.75000e+006

Attributes: valid_range, units, long_name, standard_name, axis

Fill Value: N/A

Units: Meters

latitude

Description: Latitude in degrees north of the projection grid centers.

Data Type: Double array with dimensions [304, 448]

Valid Range: 0.0 to 90.0 for northern hemisphere files, and -90.0 to 0.0 for southern hemisphere files.

Attributes: standard_name, long_name, units, valid_range, _FillValue

Fill Value: -999.0

Units: Degrees north

longitude

Description: Longitude in degrees east of the projection grid centers.

Data Type: Double array with dimensions [304, 448]

Valid Range: -180.0 to 180.0

Attributes: standard_name, long_name, units, valid_range, _FillValue

Fill Value: -999.0

Units: Degrees east

File and Directory Structure

The data files are organized on the FTP site into two main directories by hemisphere: north and south. Within each of these, there are two sub-directories: daily and monthly. The daily directory is further sub-divided into directories labeled by the 4-digit year (YYYY) beginning with 1978; the daily files reside within their respective year directory. All of the monthly files reside directly in the monthly directory. Figure 1 shows an image of the directory structure.

FTP Directory Structure
Figure 1. Directory Structure

File Naming Convention

The file naming convention for the daily and monthly files is listed below and described in Table 4:

Daily: seaice_conc_daily_hh_sat_yyyymmdd_vXXrXX.nc
Monthly: seaice_conc_monthly_hh_sat_yyyymm_vXXrXX.nc

Where:

Table 4. File Naming Convention
Variable Description
seaice_conc Identifies files containing sea ice concentration data
daily Identifies files containing daily sea ice concentration
monthly Identifies files containing monthly sea ice concentration
hh Hemisphere (nh: North, sh: South)
sat Satellite the data came from (n07: Nimbus 7, f08: DMSP F8, f11: DMSP F11, f13: DMSP F13)
yyyy 4-digit year
mm 2-digit month
dd 2-digit day of month
vXXrXX Version and release number of the data file (v01r00: version 01 release 00)
.nc Identifies a netCDF file

File Size

File sizes are shown in Table 5.

Table 5. File Size
  Northern Hemisphere Southern Hemisphere
Daily 2.5 MB - 3.5 MB each 1.8 MB - 2.5 MB each
Monthly 3.4 MB each 2.6 MB - 2.7 MB

Spatial Coverage

These data cover both the Northern and Southern polar regions at a 25 x 25 km grid cell size. Note: 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 12 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: 31.10° N
Northernmost Latitude: 89.84° N
Westernmost Longitude: 180° W
Easternmost Longitude: 180° E

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

Southernmost Latitude: 89.84° S
Northernmost Latitude: 39.36° S
Westernmost Longitude: 180° W
Easternmost Longitude: 180° E

Projection and Grid Description

The sea ice concentration data are displayed in a polar stereographic projection. For more information on this projection, 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 6.

Table 6. Polar Stereo Grid Size
Region Columns Rows
North 304 448
South 316 332

Temporal Coverage

The primary NOAA/NSIDC CDR sea ice concentrations span 09 July 1987 to 31 December 2007 provided at both a daily resolution and a monthly averaged 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 for daily file, seaice_conc_cdr_monthly for monthly file), but it is populated with a fill value of 255. See Table 7 for a list of the dates that each of the different instruments were used.

Table 7. Time Period Each Instrument is Used
Platform and Instrument Time Period
Nimbus-7 SMMR 26 October 1978 - 08 July 1987
DMSP-F8 SSM/I 09 July 1987 - 02 December 1991
DMSP-F11 SSM/I 03 December 1991 - 30 September 1995
DMSP-F13 SSM/I 01 October 1995 - 31 December 2007

Parameter

The parameter of this data set is sea ice concentration which is 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.

2. Data Access and Tools

Data Access

Version 1 is no longer available. See the Version 2 documentation to obtain the most recent data.

Software and Tools

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

3. Data Acquisition and Processing

Data Acquisition Methods

NOAA/NSIDC CDR Sea Ice Concentrations

The input gridded brightness temperatures used for creating the daily NOAA/NSIDC CDR sea ice concentrations (seaice_conc_cdr) come from NSIDC in the DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures 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. The input for the monthly CDR concentration (seaice_conc_cdr_monthly) is the daily CDR variable.

Note that the NOAA/NSIDC CDR does not currently 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).

Goddard Sea Ice Concentrations

The data for the Goddard-produced sea ice concentrations are created by Goddard but are archived at NSIDC. NSIDC uses the archived products as input for these variables. The input data for the goddard_nt_seaice_conc and goddard_nt_seaice_conc_monthly variables are the Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. This data set is also called the NASA Team sea ice concentration data set because the values are computed using the NASA team algorithm. See the NASA Team Algorithm section for a description of this algorithm.

The input data for the goddard_bt_seaice_conc and goddard_bt_seaice_conc_monthly variables are the Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS data. This data set is also called the Bootstrap sea ice concentration data set because the values are computed using the Bootstrap algorithm. See the Bootstrap Algorithm section for a description of this algorithm.

Note: 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 the Goddard 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 input data for the merged Goddard sea ice concentration variables (goddard_merged_seaice_conc and goddard_merged_seaice_conc_monthly) are the NASA Team and Bootstrap variables mentioned above. For a description of the algorithm used to merge the data, see the CDR Algorithm section of this document.

Derivation Techniques and Algorithms

Overview

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, respectively. 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 8 lists the channels used for each algorithm and the channels used for the weather filters. For a complete description of the weather filters, see the Climate Algorithm Theoretical Basis Document (C-ATBD): Passive Microwave Sea Ice Concentration document (Meier, W. N., et al., 2011a).
 

Table 8. NASA Team and Bootstrap Algorithm Channels
  NASA Team Bootstrap
Algorithm Channels 19H, 19V, and 37V 37H, 37V, and 19V
Weather Filters 22V and 19V 22V

 

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 intermediate 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).

PR(19) = [TB(19V) - TB(19H))]/[TB(19V) + TB(19H)] (Equation 1)
GR(37V/19V) = [TB(37V) - TB(19V)]/[TB(37V) + TB(19V)] (Equation 2)

Where

Table 9. NASA Team Algorithm Variable Descriptions
Variable Description
PR(19) Polarization ratio of the 19 GHz vertical and horizontal channels
TB(19V) Brightness temperature at the 19 GHz vertical channel
TB(19H) Brightness temperature at the 19 GHz horizontal channel
GR(37V/19V) Gradient ratio of the 37 GHz vertical channel and the 19 GHz vertical channel
TB(37V) Brightness temperature at the 37 GHz vertical channel

 

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 2.

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.
 

NASA Team Plot
Figure 2. 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 3 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.
 

C = (TB - TO)/(TI - TO) (Equation 3)

Where:

Table 10. Bootstrap Algorithm Variable Descriptions
Variable Description
C Sea ice concentration
TB Observed brightness temperature
TO Reference brightness temperatures for open water
TI Reference brightness temperatures for sea ice

 

Bootstrap Plot
Figure 3. Example of the relationship of the 19V vs. 37V TB (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 TB 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 Steps

Daily Files

The following are the general steps NSIDC uses to produce the daily NOAA/NSIDC CDR sea ice concentration product. See Figure 4 for a diagram of the data flow.

  1. Obtain input brightness temperatures from the NSIDC DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures (NSIDC-0001) data set. See Table 8 for a list of channels used.
  2. Process the brightness temperatures into sea ice concentration using the NASA Team and Bootstrap algorithms to make an intermediate NSIDC version of the NASA Team and Bootstrap sea ice concentration data — the two orange objects in the bottom left corner of Figure 4.
  3. Merge the intermediate NSIDC NASA Team and Bootstrap data to select the final CDR value using the CDR algorithm, and compute the standard deviation and any flag values at this time.
  4. Obtain the Goddard NASA Team daily data, Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data (NSIDC-0051), used to populate the goddard_nt_seaice_conc variable.
  5. Obtain the Goddard Bootstrap daily data, Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS (NSIDC-0079), used to populate the goddard_bt_seaice_conc.
  6. Merge the Goddard NASA Team and Bootstrap sea ice concentrations using the CDR algorithm to create the merged variable (goddard_merged_seaice_conc).
  7. Populate the netCDF variables and create the files.
  8. Add melt-indicator flag to the QA variable via a post-processing step.
Daily Code Flowchart
Figure 4. Flow of Data through the Daily CDR Processing. Click for larger view.

Monthly

The following are the general steps NSIDC uses to produce the monthly NOAA/NSIDC CDR sea ice concentration product. See Figure 5 for a diagram of the data flow.

  1. Read the input data: CDR daily sea ice concentration (seaice_conc_cdr).
  2. Compute the monthly mean concentration for each grid cell for a given month from the daily values.
  3. Compute the standard deviation and quality flags.
  4. Obtain the monthly NASA Team sea ice concentrations from the Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data data set, used to fill the goddard_nt_seaice_conc_monthly variable.
  5. Obtain the monthly Bootstrap sea ice concentrations from the Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS data set, used to fill the goddard_bt_seaice_conc_monthly variable.
  6. Use the CDR algorithm to merge the Goddard NASA Team and Bootstrap data for the goddard_merged_seaice_conc_monthly variable.
  7. Populate the monthly netCDF variables and create the files.
Monthly Code Flowchart
Figure 5. Flow of Data through the Monthly CDR Processing. Click for larger view.

 

Error Sources

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: 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 C-ATBD (Meier, W., et al., 2011a).

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 11 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.

Table 11. Comparison of Orbital Parameters
Parameter Nimbus-7 DMSP-F8 DMSP-F11 DMSP-F13
Nominal Altitude* 955 km 860 km 830 km 850 km
Inclination Angle 99.1 degrees 98.8 degrees 98.8 degrees 98.8 degrees
Orbital Period 104 minutes 102 minutes 101 minutes 102 minutes
Ascending Node Equatorial Crossing
(local time)
~ 12:00 p.m. ~ 6:00 a.m. ~ 5:00 p.m. ~ 5:43 p.m.
Algorithm Frequencies* 18.0, 37.0 GHz 19.4, 37.0 GHz 19.4, 37.0 GHz 19.4, 37.0 GHz
Earth Incidence Angle* 50.2 53.1 52.8 53.4
3 dB Beam Width (degrees)* 1.6, 0.8 1.9, 1.1 1.9, 1.1 1.9, 1.1

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

Table 12 lists the footprint size of the SSM/I instrument.

Table 12. SSM/I Footprint Size
Frequency (GHz) SSM/I IFOV (km)
19.350 70 x 45
22.235 60 x 40
37.000 38 x 30

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.

4. 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.

Related NSIDC Data Collections

Related Web Sites

5. Contacts and Acknowledgments

Investigators Name and Title

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

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
e-mail: nsidc@nsidc.org

Acknowledgements

This project was supported in part by a grant NA07OAR4310056 from the NOAA NCDC 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.

6. Document Information

Acronyms and Abbreviations

Table 13 lists the acronyms used in this document.

Table 13. Acronyms
Acronym Description
AMSR-E Advanced Microwave Scanning Radiometer - Earth Observing System
C-ATBD Climate Algorithm Theoretical Basis Document
CDR Climate Data Record
CF Climate and Forecast
DMSP Defense Meteorological Satellite Program
FTP File Transfer Protocol
GSFC Goddard Space Flight Center
H Horizontal
IDL Interactive Data Language
MASIE Multi-sensor Analyzed Sea Ice Extent
NAS National Academy of Sciences
NASA National Aeronautics and Space Administration
NCDC National Climatic Data Center
NetCDF Network Common Data Format
NIC National Ice Center
NOAA National Oceanic and Atmospheric Administration
NRC National Research Council
NSIDC National Snow and Ice Data Center
RSS Remote Sensing Systems, Inc.
SMMR Scanning Multichannel Microwave Radiometer
SSM/I Special Sensor Microwave Imager
TB Brightness Temperature
URL Uniform Resource Locator
V Vertical

Document Creation Date

July 2011

Document Revision Date

May 2012: A. Windnagel added the monthly file information and put the document into the new guide doc style.

Document URL

http://nsidc.org/data/docs/noaa/g02202_ice_conc_cdr/v1/index.html