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AMSR-E/Aqua Daily L3 25 km Brightness Temperature & Sea Ice Concentration Polar Grids

Summary

The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA Earth Observing System (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_SI25) includes brightness temperatures at 6.9 through 89.0 GHz and sea ice concentrations. Data are mapped to a polar stereographic grid at 25 km spatial resolution. All variables include daily ascending averages, daily descending averages, and daily averages. Data are stored in HDF-EOS format and are available from 18 June 2002 to present via FTP, CD-ROM, DLT, or DVD.

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 these data sets in a publication. List the principal investigators, year of data set release, data set title and version number, dates of the data you used (for example, March to June 2004), publisher: NSIDC, and digital media.

Cavalieri, Donald, Markus Thorsten, and Josefino Comiso. 2004, updated daily. AMSR-E/Aqua Daily L3 25 km Brightness Temperature and Sea Ice Concentration Polar Grids V002, [list the dates of the data used]. Boulder, Colorado 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

Format

Data are stored in Hierarchical Data Format - Earth Observing System (HDF-EOS) format. See Level-3 25 km Brightness Temperature & Sea Ice Concentration Data Fields for a list of HDF-EOS fields. Data also contain core metadata and product-specific attributes.

File Naming Convention

This section explains the file naming convention used for this product with an example.

Example file name: AMSR_E_L3_SeaIce25km_B06_20080207.hdf

AMSR_E_L3_SeaIce25km_X##_yyyymmdd.hdf

Refer to Table 1 for the valid values for the file name variables listed above.

File Size

Each daily granule is approximately 20 MB.

Spatial Coverage

Spatial Coverage Map

Northern Hemisphere

Southern Hemisphere

Spatial Resolution

Spatial resolution is 25 km.

Projection

Brightness temperature grids are in a polar stereographic projection, which specifies a projection plane such as the grid, tangent to the earth at 70 degrees. The planar grid is designed so that the grid cells at 70 degrees latitude are 25 km by 25 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 percent. The southern polar grid has a maximum distortion of 22 percent. To minimize the distortion, the projection is true at 70 degrees 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 degrees was selected so that little or no distortion would occur in the marginal ice zone. Another result of this assumption is that fewer grid cells will be required as the Earth's surface 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: 304 columns by 448 rows
Southern Hemisphere: 316 columns by 332 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 brightness temperatures are binned into 25 km grid cells, the ascending, descending, and daily data are averaged. Refer to the Data Source section of this guide document for more information. 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).

Parameter or Variable

Parameter Description

Refer to the Level-3 25 km Sea Ice Data Fields document for a list of parameters used in this data set.

3. Data Access and Tools

Data Access

Refer to the Ordering AMSR-E Products from NSIDC Web page for a list of order options.

Volume

Each half-orbit granule is approximately 20 MB.

Software and Tools

Refer to the NSIDC: Hierarchical Data Format - Earth Observing System (HDF-EOS) Web site for tools that work with HDF-EOS 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 are binned into 25 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 brightness temperature 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           [ Equation 1]

Where:

T_bw = brightness temperature of open water
T_bi = brightness temperature of sea ice
C_w = fraction of open water within instrument field-of-view
C_i= fraction of sea ice concentration within instrument field-of-view

Sea 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)            [Equation 2]

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

Derivation Techniques and Algorithms

Sea Ice Concentration

The 25 km sea ice concentration product is generated using the Enhanced NASA Team (NT2) algorithm described by (Markus and Cavalieri 2000) for both the Arctic and the Antarctic. However, previous AE_SI25 versions employed the AMSR Bootstrap Algorithm (ABA) described by (Comiso, Cavalieri, and Markus 2003) for the Antarctic. Now the ABA is only used in the sea ice concentration difference field between the NT2 and the ABA (NT2-ABA) for both hemispheres.

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 the horizontally polarized 89 GHz data are less affected by surface conditions than are the horizontally polarized 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 brightness temperatures 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 brightness temperatures for each channel and creates matrices of values containing all combinations of ice concentration from 0 -100 perecnt, and atmospheric conditions. Spectral Gradient Ratios (GR) and Polarization Ratios (PRs) are calculated from the observed and modeled brightness temperatures using the following two equations:

The Polarization Equation:
PR(ν) = [T_b(νV) − T_b(ν )] / [T_b(νV) + T_b(νH)]

The Spectral Gradient Ratio Equation:
GR(ν1pν2p) = T_b(ν1p) − T_b(ν2p)] / [T_b(ν1p) + T_b(ν2p)]

Where:

T_b is the brightness temperature at frequency ν for the polarized component p (vertical (V) or horizontal (H)).

The 89 GHz channels together with a forward radiative transfer model provide ice concentrations under all atmospheric conditions. Using the radiative transfer model, the algorithm computes brightness temperatures for each channel and creates matrices of values containing all combinations of ice concentration from 0 - 100 percent and atmospheric conditions.

Figure 1 shows the general flow of the algorithm. First brightness temperatures are calculated for the four surface types and all weather conditions, currently 12. The response of the brightness temperatures to different weather conditions is calculated using an atmospheric radiative transfer model (Kummerow, 1993). The input data for the model consists of several things such as: the emissivities of the different surface types taken from Table 4-1 in (Eppler et al. 1992) with modifications to achieve agreement between modeled and observed ratios, different cloud properties, specifically cloud base, cloud top, and cloud liquid water, taken from (Fraser et al. 1975), and average atmospheric temperature and humidity profiles for summer and winter conditions taken from Antarctic research stations. Brightness temperatures are calculated for all possible ice concentration combinations in one percent increments and for each solution calculate the ratios PRR(19), PRR(89), and ΔGR. This creates a prism in which each element within this space contains a vector with the three ratios: PRR(19), PRR(89), and ΔGR. The subscript R refers to a rotation of axes in PR-GR space by the angle so that PRR(19) and PRR(89) are independent of ice types A and B in the Antarctic, and frst-year and multiyear ice for the Arctic. For each pixel, the observed brightness temperatures are used to create a vector with the same ratios. The ice concentration for a pixel is determined where the difference between an observed and a modeled ratio is smallest.

Figure 1a and 1b. Enhanced NASA Team Algorithm

The NT2 algorithm also corrects for the presence of thin ice by using GR(37V19V) 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(37V19V) values greater than 0.05 and GR(2219) values greater than 0.045 are set to zero.

Sea Ice Concentration Difference

The AMSR Bootstrap Algorithm (ABA) (Comiso, Cavalieriand, and Markus 2003) is used in the caluculation of sea ice concentration differences between the NT2 and the ABA (NT2-ABA) for both hemispheres. The ABA makes use of the 6.9 GHz data to reduce temperature effects on V1937 data provides very similar results to those of the BBA algorithm used in the 12.5 km sea ice product, indicating that errors associated with temperature effects on the latter are relatively minor; however, ABA allows for the calculation of ice temperature, which in itself is an important climate parameter.

Processing Steps

Figure 2 summarizes the input products and algorithms used to create gridded 25 km AMSR-E sea ice products.

Figure 2. Processing Sequence for AMSR-E 25 km Sea Ice Products

Processing History

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

Error Sources

In the sea ice concentration algorithm, the primary source of error is attributed to conditions in the surface layer such as surface glaze and layering (Comiso et al. 1997), which can significantly affect the horizontally polarized 18.7 GHz brightness temperature (Matzler et al. 1984) leading to increased PR(19) values and thus underestimates in iceconcentration.

(Ashcroft and Wentz 2000) discuss errors in the source Level-2A brightness temperatures that were binned into 25 km grid cells for this sea ice product.

Quality Assessment

Each HDF-EOS file contains core metadata with Quality Assessment (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, with a .xml file extension, is also delivered to NSIDC with the HDF-EOS file; it contains the same information as the core metadata. Three levels of 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 brightness temperature 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 percent of a granule's data is good, the science QA 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.

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., D. J. Cavalieri, C. L. Parkinson, and P. Gloersen. 1997. Passive Microwave Algorithms for Sea Ice Concentration - A Comparison of Two Techniques. Remote Sensing of the Environment 60: 357-384.

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.

Eppler, D. T. and 14 others. 1992. Passive Microwave Signatures of Sea Ice, in Microwave Remote Sensing of Ice. Geophysical Monograph Series 68: 47-71. AGU, Washington, D.C.

Fraser R. S., Gaut, N. E., Reifenstein, E. C., and H. Sievering. 1975. Interaction Mechanisms Within the Atmosphere Including the Manual of Remote Sensing. American Society of Photogrammetry 181-233. Falls Church, VA.

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

Gloersen P, W. J. Cambell, D. J. Cavalieri, J. C. Comiso, C. L. Parkinson, and H. J. Zwally. 1992. Arctic and Antarctic Sea Ice, 1978-1987: Satellite Passive Microwave Observations and Analysis. National Aeronautics and Space Administration, Special Publication 511, Washington, D.C., pp.290.

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.

Matzler, C., R. O. Ramseier, and E. Svendsen. 1984. Polarization Effects in Sea-ice Signatures. IEEE Journal of Oceanic Engineering 9: 333-338.

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.

For more information regarding related publications, see the Research Using AMSR-E Data Web page.

6. Document Information

Acronyms and Abbreviations

The following acronyms and abbreviations are used in this document.

Document Creation Date

March 2004

Document Revision Date

August 2008

Document URL

http://nsidc.org/data/docs/daac/ae_si25_25km_tb_and_sea_ice.gd.html