Wiki Display Wiki Display

GOES-R Rainfall Rate

Frequently Asked Questions about the GOES-R Rainfall Rate / Quantitative Precipitation Estimate (QPE) Product


1) What is this product?

The GOES-R Rain Rate product produces output as instantaneous rain rates in millimeters / hour (which is changed to inches / hour locally on AWIPS).  The range of the output data varies from 0 - 100 mm/hr (0 - 3.93 in/hr).  The resolution of this product is 2 km.  The GOES-R Rain Rate product is produced both day and night. It has an accuracy of 6 mm/hr (0.24 in/hr) at 10 mm/hr (0.39 in/hr), with higher values at higher rates.  The product can be considered quantitative out to 70 degrees local zenith angle or 60 degrees latitude (whichever is less) and qualitative beyond that.

This product is useful when trying to assess rainfall rates over data-denied areas where satellite estimates of rainfall are all that is available.  This product can also be compared to radar-based and observation-based rainfall rates to understand how well the algorithm is capturing the current rainfall rates.  If it is found that the rates are close to ground-truth, greater confidence can be placed in data from areas where satellite rainfall is all that is available.



2) How often do I receive this data?

The cadence of the GOES-R Rain Rate product is 15 minute Full Disk images, regardless of GOES-R scan mode.



3) How do I display this product in AWIPS-II?

To display this product in AWIPS-II, go to the "GOES-R" tab of the CAVE menu, then select "Derived Products."  From there, select the region of interest (GOES-E, GOES-W, or GOES Test) and then select Full Disk.  Then, select the "Rainfall Rate" product.

Alternately, use the AWIPS Product Browser.  Select "Sat", then either "GOES-16" or "GOES-17".  From there, choose "Full Disk," then select "QPE."



4) How do I interpret the color maps associated with this product?

Colormaps are still being created and evaluated for the GOES-R Rainfall Rate product.



5) What other imagery/products might I use in conjunction with this product?

Displaying the rainfall rate product over either visible or IR imagery and toggling back and forth is useful to identify which clouds are producing precipitation and which are not, especially in areas where radar coverage is unavailable.  In areas where radar coverage IS available, it might be useful to display both the radar-derived rainfall rates with the GOES-R rainfall rates (and any polar data rainfall rates available) to compare the values for each.  Any ground-truth (rain gages, etc) available can also be displayed for comparison purposes.  Using the sampling tool to display all available rain rates at once is one way to do such a comparison.



6) How is this product created?

The GOES-R Rainfall Rate utilizes the 6.2 um, 7.4 um, 8.4 um, 11.2 um, and 12.3 um channels.  In addition to the data from the individual bands, the algorithm also uses brightness temperature differences (BTD’s) between pairs of  selected bands, and also uses some spatial gradient information from the infrared (IR) window band (14).

The rain rate algorithm identifies raining pixels and derives rain rates on a pixel level in ABI  imagery. Its calibration is based on  matches of ABI data with microwave (MW)-derived rainfall rates, which are considered to be the most accurate estimates of instantaneous  rainfall rate available from satellite data. The ABI rain rate algorithm is based on the Self-Calibrating Multivariate Precipitation  Retrieval (SCaMPR) algorithm. The algorithm derives rainfall rate fields in two steps:

1.Identify pixels that are experiencing rainfall. The predictors and predictor coefficients for detecting rainfall are derived using  discriminant analysis in a calibration against MW-retrieved rainfall areas.

2.Retrieve rainfall rates for pixels where rainfall has been detected. The predictors and predictor coefficients for retrieving rainfall rate are derived using stepwise forward linear regression in a calibration against MW-retrieved rainfall rates.

The rain rate algorithm provides estimates of instantaneous rainfall rate at the same pixel resolution as the ABI. In addition to its use in estimating rainfall rates from current ABI data, the estimates are also extrapolated forward in time in the GOES-R Rainfall Potential Algorithm, and these nowcasts are in turn used as  input for the Probability of Rainfall Algorithm.

The following list briefly describes the ancillary data requited to run the Rainfall Rate Algorithm. Ancillary data is defined as data that requires information not included in the ABI  observations or geolocation data. All three of these ancillary data sets would be considered  to be non-ABI dynamic data (i.e., they are not other ABI-derived products); no static ancillary data (i.e., time-constant ancillary data such as topography or a land/sea mask) are required.


- Microwave-derived rainfall rates - Rainfall rates, presumably from MW data but also permissible from active radar, are required as a calibration target for the algorithm. These rainfall rates do not need to be available in real time, though the accuracy of the rain rate estimates tends degrade slightly as the difference between the time period covered by the training data and the time of the retrieval from the ABI becomes longer. The MW rainfall  rates will be obtained from an operational NESDIS Blended Microwave Rainfall Rate product that will combine rainfall rates from multiple  platforms (e.g., SSMIS, AMSU-B/MHS) and match their statistical distributions in order to resolve inconsistencies between the two.


- Matched microwave rain rates and ABI predictors - These MW-derived rainfall rates are matched with ABI-derived predictors that have been aggregated to the spatial resolution of the MW rain rates (nominally 15 km). Each data point is on a separate data record (the data are not necessarily on a regularly-spaced grid, though they can be).


- Retrieval coefficient table - This table contains the ID’s (from the matched file) of the selected predictors along with their calibration coefficients for both rain / no rain discrimination and rain rate calibration.



1Kuligowski, Robert J.. NOAA NESDIS Center for Satellite Applications and Research GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document for Rainfall Rate (QPE) v.2.0. 24 September 2010.

0 Attachments
Average (0 Votes)