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AMSR-E/Aqua Daily L3 12.5 km Tb, Sea Ice Conc., & Snow Depth Polar Grids

Summary

The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of land, ocean, and atmospheric variables for the investigation of water and energy cycles.

This Level-3 gridded product (AE_SI12) includes brightness temperatures (T_Bs) at 18.7 through 89.0 GHz, sea ice concentration, and snow depth over sea ice. Data are mapped to a polar stereographic grid at 12.5 km spatial resolution. Sea ice concentration and TBs include daily ascending averages, daily descending averages, and daily averages; snow depth over sea ice data is a five-day running average. Data are stored in HDF-EOS format and are available via FTP, CD-ROM, 8-mm tape, DLT, or DVD-ROM.

Citing These Data

To broaden awareness of our services, NSIDC requests that you acknowledge the use of data sets distributed by NSIDC. Please refer to the citation below for the suggested form, or contact NSIDC User Services for further information. We also request that you send us one reprint of any publication that cites the use of data received from our Center. This helps us to determine the level of use of the data we distribute. Thank you.

The following example shows how to cite the use of this data set in a publication. List the principal investigators, year of data set release, data set title and version, dates of data you used, publishers (NSIDC), and digital media.

Cavalieri, D., and J. Comiso. 2004, updated daily. AMSR-E/Aqua daily L3 12.5 km Tb, sea ice conc., & snow depth polar grids V001, March to June 2004. Boulder, CO, USA: National Snow and Ice Data Center. Digital media.

Overview Table

Table of Contents

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

1. Contacts and Acknowledgments

Investigator(s) Name and Title

Donald J. Cavalieri, Josefino C. Comiso, and Thorsten Markus
Laboratory for Hydrospheric Processes
NASA Goddard Space Flight Center

Technical Contact

2. Detailed Data Description

The full suite of AMSR-E sea ice products includes brightness temperatures at 6.25 km, 12.5 km, and 25 km resolution; sea ice concentration at 12.5 km and 25 km resolution, sea ice temperature at 25 km resolution; and snow depth over sea ice at 12.5 km resolution. This document pertains only to the 12.5 km products. Following is a summary of the parameters included in this data set:

Format

Data are stored in Hierarchical Data Format - Earth Observing System (HDF-EOS) format. See Level-3 12.5 km Sea Ice Data Fields for a list of HDF-EOS fields. Data also contain core metadata and product-specific attributes.

File Naming Convention

The file naming convention is as follows:

AMSR_E_L3_SeaIce12kmX##_yyyymmdd.hdf

where:

X = product maturity code
## = file version number
yyyy = year
mm = month
dd = day

The valid values for the product maturity code are "B", "V", and "P" for beta, validated, and preliminary, respectively. Beta product maturity indicates use of data calibrated by the Japan Aerospace Exploration Agency (JAXA, Contractor: Mitsubishi Electric Corporation) in producing Level-2A T_Bs. The product maturity is upgraded to "validated" after the science software is tested and the algorithm is validated using the official NASA calibration. Preliminary refers to non-standard, near real-time data available from NSIDC. These data are only available for a limited time, until the corresponding standard product is ingested at NSIDC.

File Size

Each daily granule is approximately 53 MB.

Spatial Coverage

Spatial Coverage Map

Northern Hemisphere

Southern Hemisphere

Spatial Resolution

Spatial resolution is 12.5 km.

Projection

T_B grids are in a polar stereographic projection, which specifies a projection plane (i.e., the grid) tangent to the earth at 70 degrees. The planar grid is designed so that the grid cells at 70 degrees latitude are 12.5 km by 12.5 km. For more information on this topic please refer to Pearson (1990) and Snyder (1987).

The polar stereographic projection often assumes that the plane (grid) is tangent to the Earth at the pole. Thus, there is a one-to-one mapping between the Earth's surface and grid (with no distortion) at the pole. Distortion in the grid increases as the latitude decreases because more of the Earth's surface falls into any given grid cell. At the edge of the northern polar grid distortion reaches 31%. The southern polar grid has a maximum distortion of 22%. To minimize the distortion, the projection is true at 70° rather than at the poles. This increases the distortion at the poles by three percent and decreases the distortion at the grid boundaries by the same amount. The latitude of 70° was selected so that little or no distortion would occur in the marginal ice zone. Another result of this assumption is that fewer grid cells will be required as the Earth's surface is more accurately represented.

The polar stereographic formulae for converting between latitude/longitude and X-Y grid coordinates are taken from Snyder (1982). This projection assumes a Hughes ellipsoid with a radius of 3443.992 nautical mi or 6378.273 km and an eccentricity (e) of 0.081816153 (or e² = 0.006693883). The structural metadata (StructMetadata.0) built into the HDF-EOS data file lists the squared eccentricity value rounded to four significant digits (0.006694).

Grid Description

Northern Hemisphere: 608 columns by 896 rows
Southern Hemisphere: 632 columns by 664 rows

The origin of each x, y grid is the pole. The grids' approximate outer boundaries are defined in the following table. "Corner" points are listed; apply values to the polar grids reading clockwise from upper left. Interim rows define boundary midpoints.

Temporal Coverage

See AMSR-E Data Versions for a summary of temporal coverage for different AMSR-E products and algorithms.

Temporal Resolution

After input Level-2A T_Bs are binned into 12.5 km grid cells (see Data Source), the ascending, descending, and daily data are averaged. The daily average is not simply an average of ascending and descending orbits, because a given pixel could have, for example, three measurements from ascending orbits and two from descending orbits. Instead, the daily average is of all the observations for that grid cell. For example, if A = ascending and B = descending:

( (A1 + A2) / 2 + (B1 + B2 + B3) / 3 ) / 2

is not equal to:

(A1 + A2 + B1 + B2 + B3) / 5

However, this biases daytime (ascending) orbits over nighttime (descending).

Snow depth over sea ice is reported as a running average of the last five days.

Parameter or Variable

Parameter Description

See Level-3 12.5 km Sea Ice Data Fields for details.

Error Sources

Ashcroft and Wentz (2000) discuss errors in the source Level-2A brightness temperatures that were binned into 12.5 km grid cells for this sea ice product.

Most sources of error in the retrieval of sea ice concentration are common to each of the algorithms, but some depend on the algorithm used. Sources of error common to all ice concentration algorithms are unresolved ice types (especially thin ice types), surface melt (melt ponds on summer ice), and unfiltered weather effects (especially at the ice edge). In addition, the Bootstrap algorithm is sensitive to sea ice temperature variations, but this is minimized in most regions through the use of the dual-polarized 37 GHz channels. With the AMSR Bootstrap algorithm (discussed in the Derivation Techniques and Algorithms section of this document), the temperature effect is further reduced through the use of the 6.9 GHz vertically polarized channel.

Sources of error in the retrieval of sea ice temperature include inherent errors in sea ice concentration and spatial and temporal variations in the ice emissivity at 6.9 GHz from different ice types and surface characteristics.

Sources of error in the retrieval of snow depth on sea ice also include inherent errors in sea ice concentration, uncertainty in the linear relationship between snow depth and the AMSR-E T_Bs, undetected snow wetness, snow grain size and snow density variability, and sensitivity to extreme weather effects.

Refer to Aqua Maneuvers for a list of maneuvers and orbital anomalies that may potentially affect the quality of data.

Quality Assessment

Each HDF-EOS file contains core metadata with QA metadata flags that are set by the Science Investigator-led Processing System (SIPS) at the Global Hydrology and Climate Center (GHCC) prior to delivery to NSIDC. A separate metadata file (.met file extension) is also delivered to NSIDC with the HDF-EOS file; it contains the same information as the core metadata. Three levels of quality assessment (QA) are conducted with the AMSR-E Level 2 and 3 products: automatic, operational, and science QA. If a product does not fail QA, it is ready to be used for higher-level processing, browse generation, active science QA, archive, and distribution. If a granule fails QA, SIPS does not send the granule to NSIDC until it is reprocessed. Level-3 products that fail QA are never delivered to NSIDC (Conway 2002).

Automatic QA
Weather filters are employed for the Level-3 sea ice products to eliminate spurious sea ice concentrations over open ocean resulting from varying atmospheric emission. The weather filters are based on threshold values for the spectral gradient radio and thresholds derived from T_B differences. Sea ice products are checked to see if ice concentration values fall within reasonable limits. Diagnostics are based in part on satellite sea ice climatology developed since the Scanning Multichannel Microwave Radiometer (SMMR) era in 1978.

Operational QA
AMSR-E Level-2A data arriving at GHCC are subject to operational QA prior to processing higher-level products. Operational QA varies by product, but it typically checks for the following criteria in a given file (Conway 2002):

• File is correctly named and sized
• File contains all expected elements
• File is in the expected format
• Required EOS fields of Time, Latitude, and Longitude are present and populated
• Structural metadata is correct and complete
• The file is not a duplicate
• The HDF-EOS version number is provided in the global attributes
• The correct number of input files were available and processed

Science QA
AMSR-E Level-2A data arriving at GHCC are also subject to science QA prior to processing higher-level products. If less than 50% of a granule's data is good, the science Q/A flag is marked "suspect" when the granule is delivered to NSIDC. In the SIPS environment, the science QA includes checking the maximum and minimum variable values, and percent of missing data and out-of-bounds data per variable value. At the Science Computing Facility (SCF), also at GHCC, science QA involves reviewing the operational QA files, generating browse images, and performing the following additional automated QA procedures (Conway 2002):

• Historical data comparisons
• Detection of errors in geolocation
• Verification of calibration data
• Trends in calibration data
• Detection of large scatter among data points that should be consistent

Geolocation errors are corrected during Level-2A processing to prevent processing anomalies such as extended execution times and large percentages of out-of-bounds data in the products derived from Level-2A data.

The Team Lead SIPS (TLSIPS) developed tools for use at SIPS and SCF for inspecting the data granules. These tools generate a QA browse image in Portable Network Graphics (PNG) format and a QA summary report in text format for each data granule. Each browse file shows Level-2A and Level-2B data. These are forwarded from RSS to GHCC along with associated granule information, where they are converted to HDF raster images prior to delivery to NSIDC. The QA summary reports are available on the GHCC AMSR-E Web page.

Please refer to AMSR-E Validation Data for information about data used to check the accuracy and precision of AMSR-E observations.

3. Data Access and Tools

Data Access

Please see Ordering AMSR-E Products from NSIDC for a list of order options.

Volume

Each half-orbit granule is approximately 53 MB.

Software and Tools

Please visit NSIDC's HDF-EOS Web site for tools that work with HDF-EOS data. This documentation will be updated as tools become available for AMSR-E data.

Related Data Collections

Sea Ice Products at NSIDC
This site offers a complete summary of sea ice data derived from passive microwave sensors and other sources, and is useful for users who want to compare characteristics of various sea ice products to understand their similarities and differences. This site also provides links to tools for passive microwave data and a list of other sea ice resources.

Sea Ice Trends and Climatologies from SMMR and SSM/I
NSIDC provides a suite of value-added products to aid in investigations of the variability and trends of sea ice cover. These products provide users with information about sea ice extent, total ice covered area, ice persistence, monthly climatologies of sea ice concentrations, and ocean masks.

Sea Ice Remote Sensing at NASA/Goddard Space Flight Center

4. Data Acquisition and Processing

Sensor or Instrument Description

Please refer to the AMSR-E Instrument Description document.

Data Acquisition Methods

Please refer to the AMSR-E Instrument Description document.

Data Source

AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) are binned into 12.5 km grid cells using a "drop-in-the-bucket" method where the grid cell that contains the center of the observation footprint is given the whole weight of the observation. With this procedure, the number of observations is always in whole numbers. All valid T_B observations within the extent of the polar grids are binned into grid cells, including land observations.

Theory of Measurements

Sea ice concentration products are used to understand the spatial characterization of sea ice cover and to calculate sea ice extent and area for time series analyses and process studies in the Arctic and Antarctic. Passive microwave data are particularly useful for sea ice studies because of the relatively high contrast in emissivities between open water and sea ice. This contrast is frequency-dependent; contrast increases with decreasing channel frequency. In most algorithms, atmospheric effects are assumed constant. The satellite-received radiation, expressed as a brightness temperature (T_B), is as follows (Cavalieri and Comiso 2000):

T_B = T_BWC_W + T_BIC_I            [1]

Where:

T_BW = T_B of open water
T_BI = T_B of sea ice
C_W = fraction of open water within instrument field-of-view
C_I = fraction of sea ice (ice concentration) within instrument field-of-view

Ice concentration (C_I), corresponding to an observed T_B over a sea ice-covered region (T_B), is derived as follows:

C_I = (T_B - T_BW) / (T_BI - T_BW)            [2]

The AMSR-E sea ice algorithms use this equation, but the channels and methods to derive C_I are different. Values of T_BW, T_BI, and T_B all include contributions from the intervening atmosphere. T_BI varies spatially because of spatial changes in emissivity and temperature of ice, while T_BW is constant for open water within the ice pack.

Derivation Techniques and Algorithms

The 12.5 km sea ice concentration product is generated using the Enhanced NASA Team (NT2) algorithm (Markus and Cavalieri 2000) for both the Arctic and the Antarctic. Previous product versions employed the Bootstrap Basic Algorithm (BBA, Comiso 1995) for the Antarctic; now the BBA is used in the sea ice concentration difference (NT2-BBA) field for both hemispheres.

Sea Ice Concentration from the Enhanced NASA Team (NT2) Algorithm

The original NASA Team algorithm is based on techniques described in Cavalieri et al. (1984) and Gloersen and Cavalieri (1986). The NT2 algorithm (Markus and Cavalieri 2000) was developed to reduce ice concentration biases resulting from surface glaze and layering in the snow cover and from thin ice types. The NT2 algorithm alleviates this problem by using the 89 GHz channels, because 89 GHz data are less affected by surface conditions than are the 19 GHz data. The thin-ice bias is reduced through the use of a third ice type. The algorithm also quantifies atmospheric effects by calculating T_Bs for each channel using a forward atmospheric radiative transfer model (Kummerow 1993) for first-year ice, multiyear ice, total ice concentration, and open water. Using the radiative transfer model, the algorithm computes T_Bs for each channel and creates matrices of values containing all combinations of ice concentration (0-100%) and atmospheric conditions. Spectral gradient and polarization ratios (GRs and PRs) are calculated from the observed and modeled T_Bs. The ice concentration for a pixel is determined where the difference between an observed and a modeled ratio is smallest:

The NT2 algorithm also corrects for the presence of thin ice by using GR(37V/19V) and only two ice types. The algorithm eliminates spurious sea ice concentrations over open ocean by employing weather filters previously used for SSM/I (Cavalieri et al. 1995). Ice concentrations for image pixels with GR(37/19) values greater than 0.05 and GR(22/19) values greater than 0.045 are set to zero.

Sea Ice Concentration from the BBA Algorithm

The Basic Bootstrap Algorithm (BBA) is based on a technique described in Comiso (1995). Comiso calculated tie points for consolidated ice (TBI) and open water (TBW) with two sets of channels: 19V GHz and 37V GHz (V1937), and 37H GHz and 37V GHz (HV37). Using these channels for the retrieval of sea ice concentration ensures consistency with historical sea ice products, beginning with SMMR.

Snow Depth Over Sea Ice

The method for deriving snow depth from SSM/I data is described in Markus and Cavalieri (1998). The first step in deriving snow depth from AMSR-E is to correct input AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) for ice concentration variations:

T_B = T_BiC + T_Bw(1-C)

Where:

T_Bi = T_B of sea ice
T_Bw = mean T_B of open water (assumed constant)
C = sea ice concentration computed from the NT2 algorithm

Values of T_BI at 37V GHz and 19V GHz are then used in the following equation to calculate snow depth (SD) in cm:

SD = -2.34 - 771(T_{Bi 37V} - T_{Bi 19V}) / (T_{Bi 37V} + T_{Bi 19V})

The retrieval of Arctic snow depth is complicated by the presence of multiyear ice, which has a signature similar to snow cover on first-year ice. Both multiyear ice and deep snow on top of first-year ice results in increasingly negative values for the spectral gradient ratio (GR); therefore, the algorithm only retrieves snow depth in the seasonal sea ice zones and in regions where the value of GR(37V/19V) is greater than -0.02. This threshold corresponds to multiyear ice concentration of less than 20%. Where GR(37V/19V) is less than -0.02, the algorithm flags pixels as multiyear ice. Because of the higher sensitivity of snow depth retrievals to ice concentration less than 20%, the algorithm limits snow depth retrievals to ice concentration between 20-100%. Ice concentrations less than 20% appear almost exclusively near the ice edge.

Output consists of snow depth over sea ice with a running average of the last five days. For example, if a file name shows 01 March 2004, data from 26 February through 01 March were averaged. This method is consistent with other Aqua snow depth on land products. The following flowchart summarizes the snow depth algorithm:

Processing Steps

The following flowchart summarizes input products and algorithms used to create gridded AMSR-E sea ice products.

Processing History

See AMSR-E Data Versions for a summary of algorithm changes since the start of mission.

Calculated Variables

This data set contains the following gridded variables:

• Vertical and horizontal T_Bs for the following channels. Separate HDF-EOS fields are provided for ascending, descending, and daily averages.

  • 18.7 GHz
  • 23.8 GHz
  • 36.5 GHz
  • 89.0 GHz

• Arctic sea ice concentration using the NT2 algorithm. Separate HDF-EOS fields are provided for ascending, descending, and daily averages.

• Antarctic sea ice concentration using the NT2 algorithm. Separate HDF-EOS fields are provided for ascending, descending, and daily averages.

• Arctic and Antarctic sea ice concentration differences between NT2 and BBA. Separate HDF-EOS fields are provided for ascending, descending, and daily averages.

• Arctic and Antarctic five-day snow depth over sea ice, excluding Arctic perennial ice regions.

5. References and Related Publications

Cavalieri, D. and J. Comiso. 2000. Algorithm Theoretical Basis Document for the AMSR-E Sea Ice Algorithm, Revised December 1. Landover, MD, USA: Goddard Space Flight Center.  (view pdf)

Cavalieri, D.J., K.M. St. Germain, and C.T. Swift. 1995. Reduction of weather effects in the calculation of sea ice concentration with the DMSP SSM/I. Journal of Glaciology 41(139): 455-464.

Cavalieri, D.J., P. Gloersen, and W.J. Campbell. 1984. Determination of sea ice parameters with the NIMBUS-7 SMMR. Journal of Geophysical Research 89(D4):5355-5369.

Comiso, J., D. Cavalieri, and T. Markus. 2003. Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Transactions on Geoscience and Remote Sensing 41(2): 243-252.

Comiso, J., and K. Steffen. 2001. Studies of Antarctic sea ice concentrations from satellite data and their applications. Journal of Geophysical Research 106(C12): 31,361-31,385.

Comiso, J.C. 1995. SSM/I ice concentrations using the Bootstrap Algorithm. NASA RP 1380, 50 pp.

Conway, D. 2002. Advanced Microwave Scanning Radiometer - EOS Quality Assurance Plan. Huntsville, AL: Global Hydrology and Climate Center.

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

Kummerow, C. 1993. On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies. Journal of Geophysical Research 98: 2757-2765.

Markus, T. and D. Cavalieri. 1998. Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data. Antarctic Sea Ice: Physical Processes, Interactions, and Variability. Antarctic Research Series 74:19-39. Washington, DC, USA: American Geophysical Union.

Markus, T., and D. Cavalieri. 2000. An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing 38: 1387-1398.

Pearson, F. 1990. Map projections: Theory and applications. CRC Press. Boca Raton, Florida. 372 pages.

Snyder, J.P. 1987. Map projections - a working manual. U.S. Geological Survey Professional Paper 1395. U.S. Government Printing Office. Washington, D.C. 383 pages.

Snyder, J. P. 1982. Map Projections Used by the U.S. Geological Survey. U.S. Geological Survey Bulletin 1532.

6. Document Information

Acronyms and Abbreviations

The following acronyms and abbreviations are used in this document.

Document Creation Date

March 2004

Document Revision Date

January 2006

Document Review Date

March 2004

Document URL

http://nsidc.org/data/docs/daac/ae_si12_12km_tb_sea_ice_and_snow.gd.html