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AMSR-E/Aqua Monthly L3 5x5 deg Rainfall Accumulations

Summary

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

This Level-3 rainfall accumulation product (AE_RnGd) consists of two 72-column by 28-row grids of monthly averaged rainfall accumulation over ocean and land. Both grids are 5° x 5° resolution. Monthly ocean rainfall accumulation (mm) is derived from the Wilheit, Kummerow, and Ferraro (1991) algorithm, using Level-2A brightness temperatures as input. Monthly land rainfall accumulation (mm) is derived from the McCollum and Ferraro (2003) algorithm, using Level-2B rainfall data as input. Data are stored in HDF-EOS format, and are available via FTP, CD-ROM, 8-mm tape, DVD-ROM, or DLT.

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.

Adler, R., T. Wilheit, Jr., C. Kummerow, and R. Ferraro. 2004, updated monthly. AMSR-E/Aqua Monthly L3 5x5 deg Rainfall Accumulations V001, March 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

Dr. Robert Adler
Mesoscale Atmospheric Processes Branch
Laboratory for Atmospheres
NASA/Goddard Space Flight Center
Greenbelt, MD, USA

Dr. Thomas Wilheit, Jr.
Department of Atmospheric Sciences
Texas A&M University
College Station, TX, USA

Dr. Christian Kummerow
Department of Atmospheric Science
Colorado State University
Fort Collins, CO, USA

Ralph Ferraro
NOAA/NESDIS
E/RA2, WWBG Room 601
Camp Spring, MD, USA

Technical Contact

2. Detailed Data Description

Format

Data are stored in Hierarchical Data Format - Earth Observing System (HDF-EOS) format. Files contain core metadata, product-specific attributes, and the following 2-byte floating-point data fields:

File Naming Convention

AMSR_E_L3_RainGrid_X##_yyyymm.hdf

where:

x = product maturity code
## = file version number
yyyy = year
mm = month

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 monthly granule is approximately 88 KB.

Spatial Coverage

Spatial Coverage Map

This data set offers coverage of all ice-free and snow-free land and ocean between 70°N and 70°S.

Spatial Resolution

Data are 5° x 5° resolution.

Temporal Coverage

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

Temporal Resolution

Rainfall accumulation is averaged monthly.

Error Sources

Quantifying errors in this data set is complicated, because it involves understanding the nature of precipitation. Uncertainties arise when the rain layer thickness is not well understood, or when inhomogenous rainfall occurs below the resolution of the satellite. Another potential source of error is the non-precipitating component of clouds, which contribute to brightness temperatures. Scattering-based retrievals over land also present many uncertainties, most notably the lack of a consistent relationship between frozen rain aloft and liquid at lower altitudes. Quantifying the scattering by ice is especially problematic. Ambiguities occur in the data because microwave radiation is scattered not only by rainfall and associated ice, but by snow cover and dry land (Wilheit, Kummerow, and Ferraro 1999).

Refer to Aqua Maneuvers for a list of manuevers 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
The investigators perform QA through visual examination of rainfall products on various temporal and spatial scales to ensure that rainfall maps are consistent with climate records, and that there are no gross errors. They also compare their rainfall estimates with those from satellite missions and ground-based radar.

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.

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.

4. Data Acquisition and Processing

Theory of Measurements

Satellite-based estimates of rain rate and rain type rely primarily on cloud temperatures and information about vertical profiles. Atmospheric transmittance windows below 20 GHz, between 30 GHz and 40 GHz, and at 90 GHz are used for rainfall monitoring. Below 20 GHz, rainfall absorption and emission are predominant, and ocean surfaces are warmer than the background radiation. Above 60 GHz, evidence of rainfall is primarily from scattering, where areas of heavy rainfall are colder than their backgrounds. Between 20 GHz and 60 GHz, a combination of absorption and scattering is present.

A radiative transfer equation that includes absorption and scattering coefficients is the basis for deriving rain rate from T_Bs in this data set. The absorption and scattering coefficients, which are summarized in more detail in (Wilheit, Kummerow, and Ferraro 1999), are expressed as an integral over the range of rain drop sizes. Excluding updrafts and downdrafts, the rain rate is expressed as:

R = V(D)(πD³ / 6)N(D)dD

where:

V(D) = fall speed of the drops as a function of diameter, D
N(D) = number density of drops with diameters between D and D+dD
πD³ / 6 = volume of a drop of diameter, D

The large size of rain drops, compared with other water droplets within clouds, increases their absorption per unit mass and causes enough scattering that it must be considered in the retrieval. The introduction of ice above the freezing level greatly increases the importance of scattering. For wavelengths of a few mm or less, very low T_Bs result from scattering by ice particles with densities and sizes characteristic of rain (Wilheit, Kummerow, and Ferraro 1999).

At all channels, T_Bs increase toward a maximum and then drop off as rainfall rates increase further. The main difference between channels is the range of rainfall rates for which the curve increases in the emission region and decreases in the scattering region (Wilheit, Kummerow, and Ferraro 1999) The T_B at low frequencies is primarily a function of absorption. The rain rate follows from the absorption coefficient implied by the measurements. Ice and snow are efficient scatterers of microwave radiation compared with rain. Since land background has a high emissivity, rainfall rate over land must be inferred from the ice-scattering signature, instead of relying on the emission signal from rain drops.

Sensor or Instrument Description

Please refer to the AMSR-E Instrument Description document.

Data Source

The following data sets were used as input to this product.

• AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb)
• AMSR-E/Aqua L2B Global Swath Rain Rate/Type GSFC Profiling Algorithm

Derivation Techniques and Algorithms

Over ocean, an emission algorithm is used to relate increases in 18 GHz and 23 GHz T_Bs to rainfall. The algorithm assumes that the rainfall for the month in each grid box can be represented by a mixed log-normal distribution. The algorithm accumulates three histograms of T_Bs for each 5° x 5° grid box for a month. The 99th percentile T_Bs for the 18V and 23V histograms are used to compute a single freezing level for the month for each grid box. The freezing level and the histogram of 2*(18V-23V) are used to calculate the log normal parameters from which the monthly rain accumulation can be computed. This algorithm is not a simple average of retrievals from Level-2A T_Bs. The algorithm is described in more detail in Wilheit, Kummerow, and Ferraro (1991). On monthly scales, the details of cloud structure and emission characteristics are not required as much as they are for instantaneous rainfall.

Over land, Level-3 products are generated directly from the Level-2B rainfall products using the McCollum and Ferraro (2003) algorithm. Level-3 land products are created by simply summing rainfall rates into 5° x 5° monthly-averaged rainfall accumulations.

If the area within a grid box is more than 50% ocean, that grid box is considered to be an ocean grid box. More rain retrieval details can be found in Wilheit, Kummerow, and Ferraro (1999) and Wilheit, Kummerow, and Ferraro (2003).

Processing History

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

5. References and Related Publications

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

McCollum, J., and R. Ferraro. 2003. Next generation of NOAA/NESDIS TMI, SSM/I, and AMSR-E microwave land rainfall algorithms. Journal of Geophysical Research - Atmospheres 108(D8): art. no. 8382.

Wilheit, T., C. Kummerow, and R. Ferraro. 2003. Rainfall algorithms for AMSR-E. IEEE Transactions on Geosciences and Remote Sensing 41(2): 204-214.

Wilheit, T., C. Kummerow, and R. Ferraro. 1999. Algorithm Theoretical Basis Document for EOS/AMSR Rainfall. College Park, TX, USA: Texas A&M University. (View PDF)

Wilheit, T.T., A.T.C. Chang, and L.S. Chiu. 1991. Retrieval of monthly rainfall indices from microwave radiometric measurement using probability distribution functions. Journal of Atmospheric Oceanic Technology 8: 118-136.

6. Document Information

Acronyms and Abbreviations

The following acronyms and abbreviations are used in this document.

Document Creation Date

March 2004

Document Revision Date

02 November 2004

Document Review Date

March 2004

Document URL

http://nsidc.org/data/docs/daac/ae_rngd_l3_rain.gd.html