AMSR-E/Aqua Monthly L3 5x5 Degree Rainfall Accumulations, Version 2

This Level-3 rainfall accumulation product (AE_RnGd) consists of two grids of 28 rows by 72 columns of monthly averaged rainfall accumulation over ocean and land. Both grids are 5 degree by 5 degree resolution.

Citing These Data

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Adler, R., T. Wilheit, Jr., C. Kummerow, and R. Ferraro. 2004. AMSR-E/Aqua Monthly L3 5x5 deg Rainfall Accumulations. Version 2. [indicate subset used]. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: 10.5067/AMSR-E/AE_RNGD.002.

Overview Table

Category Description
Data Format HDF-EOS
Spatial Coverage and Resolution This data set offers coverage of all areas between 70°N and 70°S. Data are 5.4 km resolution resampled to a 5° x 5° grid.
Temporal Coverage and Resolution Temporal coverage is from 19 June 2002 to 1 October 2011. Temporal resolution is monthly.

See the AMSR-E Data Versions Web page for a summary of temporal coverage for different AMSR-E products and algorithms.
Tools for Accessing Data For tools that work with AMSR-E data, see the Tools for AMSR-E Data Web page.

For general tools that work with HDF-EOS data, refer to the NSIDC: Hierarchical Data Format - Earth Observing System (HDF-EOS) Web site.
Grid Type and Size This data set contains two 5° x 5° grids with 28 rows by 72 columns: one for monthly rainfall accumulation over ocean and another over land.
File Naming Convention AMSR_E_L3_RainGrid_X##_yyyymm.hdf
File Size Each monthly granule is approximately 88 KB.
Parameter(s) Rainfall Accumulation (mm) Over Ocean
Rainfall Accumulation (mm) Over Land
Get Data FTP
Reverb | ECHO

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
E/RA2, WWBG Room 601
Camp Spring, MD, USA

Technical Contact

NSIDC User Services
National Snow and Ice Data Center
University of Colorado
Boulder, CO 80309-0449  USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services

2. Detailed Data Description


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

Table 1. Data Fields
Field Name
Data Type
Not Calculated
TbOceanRain 32-bit floating-point Monthly ocean rainfall accumulation (mm) -1
RrLandRain 32-bit floating-point Monthly land rainfall accumulation (mm) -1

File Naming Convention

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

Example file name: AMSR_E_L3_RainGrid_B05_200707.hdf


Refer to Table 2 for the values of the file name variables listed above.

Table 2. Values for the File Name Variables
Product Maturity Code (Refer to Table 3 for valid values.)
file version number
four-digit year
two-digit month
Hierarchical Data Format (HDF)

Table 3. Variable Values for the Product Maturity Code


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.
Beta - indicates a developing algorithm with updates anticipated.
Transitional - period between beta and validated where the product is past the beta stage, but not quite ready for validation. This is where the algorithm matures and stabilizes.
Validated - products are upgraded to Validated once the algorithm is verified by the algorithm team and validated by the validation teams. Validated products have an associated validation stage. Refer to Table 4 for a description of the stages.

Table 4. Validation Stages
Validation Stage

Stage 1

Product accuracy is estimated using a small number of independent measurements obtained from selected locations, time periods, and ground-truth/field program efforts.
Stage 2
Product accuracy is assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts.
Stage 3
Product accuracy is assessed, and the uncertainties in the product are well-established via independent measurements made in a systematic and statistically robust way that represents global conditions.

Table 5 provides examples of file name extensions for related files that further describe or supplement data files.

Table 5. Related File Extensions and Descriptions
Extensions for Related Files Description
.jpg Browse data
.qa Quality assurance information
.ph Product history data
.xml Metadata files

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 degree by 5 degree resolution.

Grid Description

This data set contains two 5 degree by 5 degree grids with 28 rows by 72 columns: one for monthly rainfall accumulation over ocean and another over land.

Temporal Coverage

Temporal coverage is from 19 June 2002 to 1 October 2011. 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.

Parameter or Variable

Rainfall Accumulation (mm) Over Ocean
Rainfall Accumulation (mm) Over Land

3. Data Access and Tools

Data Access

Data are available via FTP and through Reverb | ECHO, the NASA search and order tool for subsetting, reprojecting, and reformatting data.

Software and Tools

For tools that work with AMSR-E data, see the Tools for AMSR-E Data Web page.

For general tools that work with HDF-EOS data, refer to the NSIDC: HDF-EOS Web site.

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. Thus, areas of the ocean where rainfall is occurring have higher brightness temperatures than the colder clear sky ocean backgrounds. 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 brightness temperatures in this data set. The absorption and scattering coefficients, which are summarized in more detail in Wilheit, Kummerow, and Ferraro (1999) and Wilheit, Kummerow, and Ferraro (2007) 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


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 brightness temperatures result from scattering by ice particles with densities and sizes characteristic of rain (Wilheit, Kummerow, and Ferraro 1999).

At all channels, brightness temperatures 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 brightness temperatures at low frequencies are primarily a function of absorption and emission. The rain rate follows from the absorption coefficient implied by the measurements. As the rain rate increases, the absorption coefficient also increases causing warmer brightness temperatures. 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.

Derivation Techniques and Algorithms

Over oceans, the algorithm is not a simple average of the retrievals from the Level-2B rainfall product. On monthly scales, the details of the cloud structure and emission characteristics are not required as much as they are for instantaneous rainfall. A simplified emission algorithm is used to relate increases in 18.7 and 23.8 brightness temperatures to rainfall. This algorithm is illustrated in Figure 1 (Wilheit, Kummerow, and Ferraro 2003).

Flowchart of oceanic rainfall algorithm

Figure 1. Flowchart for the AMSR-E Level-3 Oceanic Rainfall Algorithm.

The algorithm accumulates three histograms of brightness temperatures for each 5 degree by 5 degree grid box for a month. The histogram consists of the 18.7 GHz brightness temperatures, the 23.8 GHz brightness temperatures, and the linear combination of these two frequencies 2*(18V-23V). These histograms are converted into monthly total rain rates on the basis of an assumed form of the Probability Distribution Function (PDF).

Per Kedam et al. (1990), rain rates are assumed to be distributed as a Mixed Log-Normal (MLN) over each 5 degree by 5 degree grid box, as shown in the following equation:

N(r) = (1-Pr) δ(r) + Pr (2π)-1/2 r-1 exp(-0.5*(ln(r/r0)/σlr)2)


N(r) the probability of a given rain rate
Pr the probability that it is raining at all
δ(r) a delta function
r0 the logarithmic mean rain rate when it is raining
σlr the standard deviation of the logarithm of the rain rate when it is raining

The time/area average rain rate, R, is determined using the following equation:
R = r0Pr exp(σlr2/2)

Each rain rate, r, is converted into a Brightness Temperature (Tb) using an analytic approximation to radiative transfer calculations using the following equation:
Tb = T0 +(285K- T0) (1-exp(-r/rc)) – a(r)0.5


T0 the non-raining brightness temperature
rc the characteristic rain rate determined by the following equation:
rc = 28.04(mm/h)/(FL(km))1.13

FL = freezing level

a is 5.02 K(mm/h)-0.5
r rain rate

These parameters are computed by fitting this form to radiative transfer calculations for freezing levels ranging from 0.1 to 6 km. The value of T0 can also be expressed this way; however, in this case, the fitting program solves for T0 to absorb calibration and modeling errors. However, the fitted form of T0 is used in the solution for the freezing level.

The freezing level for a given box at a given month is determined by calculating the 99th percentile brightness temperatures from the 18.7 GHZ and 23.8 GHz observed histogram. This brightness temperature pair is then used to select the single freezing level height. The freezing level height is then used to select the computed brightness temperature histogram from the radiative transfer calculations. The computed histogram is then compared to the observed histogram.

The algorithm adjusts the Pr and r0 parameters of the MLN distribution of rain rate as well as T0 and NE∆T (the instrumental noise) of a computed histogram to match characteristics of the observed histogram. The algorithm fits the mean, variance, and third moment as well as the point on the low brightness temperature end where the histogram value falls to 1/10 of the peak value. Normally, the value of σlr is left at one unless the fitting routine requires unphysical values of Pr. When satisfactory convergence is obtained, the average rain rate (R) is computed per the equation above. For output purposes, it is converted to units of mm/day and corrected for rainfall inhomogeniety.

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 degree by 5 degree monthly-averaged rainfall accumulations.

If the area within a grid box is more than 50 percent 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.

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 inhomogeneous 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 water 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) and (Wilheit, Kummerow, and Ferraro 2007).

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 in XML format 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

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):

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 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):

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.

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

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

Kedam, B., L. Chiu, and G. North. 1990. Estimation of Mean Rain Rates: Applications to Satellite Observations. J. Geophys Res. 95: 1965-1972

Marshall, T. S. and W. M. Palmer. 1948. The Distribution of Raindrops with Size. J. Meteorol 5: 165-166.

Mie, G. 1908. Beiträge zur Optik Trüber Medien, speziell kolloidaler Metalösungen. Ann. Phys. 26: 597-614.

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.

Tesmer, J. R., and T. T. Wilheit. 1997. An Improved Microwave Radiative Transfer Model for Tropical Oceanic Precipitation. Journal of Atmospheric Science 55:1674-1688.

Wang, S. A. 1995. Modeling the Beam Filling Correction for Microwave Retrieval of Oceanic Rainfall. PhD dissertation. Dept of Meteorology; Texas A&M University

Wilheit, Thomas, C. Kummerow, and R. Ferraro. 2007. [Supplement] AMSR-E Monthly Level-3 Rainfall Accumulations: Algorithm Theoretical Basis Document. College Park, Texas USA: Texas A&M University. (PDF file, 243 KB)

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. EOS/AMSR Rainfall: Algorithm Theoretical Basis Document. College Park, Texas USA: Texas A&M University. (PDF file, 867 KB)

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.

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.

Table 6. Acronyms and Abbreviations
AMSR-E Advanced Microwave Scanning Radiometer - Earth Observing System
CRM Cloud Resolving Model
EOSDIS Earth Observing System Data and Information System
FTP File Transfer Protocol
GHCC Global Hydrology and Climate Center
GSFC Goddard Space Flight Center
HDF-EOS Hierarchical Data Format - EOS
NASA National Aeronautics and Space Administration
NSIDC National Snow and Ice Data Center
PNG Portable Network Graphics
QA Quality Assessment
RSS Remote Sensing Systems
SCF Science Computing Facility
SI Scattering Index
SIPS Science Investigator-led Processing System
SSM/I Special Sensor Microwave/Imager

Document Creation Date

March 2004

Document Revision Date

August 2008

Document URL