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Products Satellite Rainfall Techniques (Background Information)

 


Operational Satellite Rainfall Estimate Text Product

This information is also available in PDF format

[map of Satellite Rainfall Estimates area of responsibility]

Purpose

The operational Quantitative Precipitation Estimate (QPE) text product is intended to provide, on an event-driven basis, a tabular depiction of satellite rainfall QPE for tropical cyclones and pre-tropical cyclone disturbances. The product is primarily intended to provide forecast centers in the Caribbean, Mexico, and Central America better satellite based estimates for significant rainfall events. In addition, decision support service (DSS) entities would have access to targeted QPE guidance that may be of assistance for distributing and directing resources to areas impacted by heavy rainfall.

Content

The NHC SRE text product provides six-hourly quantitative precipitation estimates (QPEs) derived from both the NRL-Blend SRE technique provided by the U.S. Naval Research Laboratory and the QMORPH SRE technique provided by NOAA's Climate Prediction Center. The text product also provides a rainfall forecast from a recent run of the Global Forecast (GFS) model that spans the same 6-hour period as the satellite-based estimates. The time of the GFS run is specified in the text product. Information on the GFS model can be found here. The location of the tropical cyclone or disturbance at the most recent synoptic time is provided by NHC or CPHC forecasters and used as the center point for 6° latitude by 6° longitude tables created for each of the rainfall estimate methods. A range of precipitation values is specified in millimeters for each 1° bin within each table. In addition to the tables, the text product provides the amount and location of the 6-hour and 24-hour precipitation maxima determined by each of the three methods. Differences between the estimates provided by the three methods indicate uncertainty in the amount of rain received.

Coverage

The SRE text product is issued for tropical cyclones and disturbances within the NHC and CPHC areas of responsibility in the Atlantic Ocean as well as the Eastern and Central Pacific Ocean. The product may also be issued for persistent and significant convective systems affecting land within the WMO RA-IV area that spans from the equator to 50°N between 40°W and 120°W.

Issuance/Transmission

The NHC graphical and text SRE products are available online at the NHC tropical rainfall website. For tropical cyclones and tropical cyclone invests, the text product is transmitted under World Meteorological Organization (WMO) and NOAA Weather Wire Services (NWWS) headers as shown below:

Tropical Cyclone Invests

WMO

NWWS

Eastern Caribbean

TCCA21 KNHC

MIASTDECA

Central Caribbean

TCCA22 KNHC

MIASTDCCA

Western Caribbean / Mexico

TCCA23 KNHC

MIASTDWCA

Tropical Cyclones

Atlantic

TCNT21-25 KNHC

MIASTDAT(1-5)

Eastern Pacific

TCPZ21-25 KNHC

MIASTDEP(1-5)

Central Pacific

TCDC21-25 KNHC

MIASTDEP(1-5)

The SRE text product is issued four times a day as needed around 0400, 0600 1600, and 2200 UTC, within 1 hour after the tropical cyclone advisory issuance times. The exact issuance time is based on the receipt time of the last SRE technique used.


See the NHC Tropical Rainfall page for the latest SRE Text Product


Experimental 6-hr Rainfall Estimate Graphics - Discontinued Oct 25, 2016

Experimental graphics have been discontinued as of Oct 25, 2016. Click on the link for more information.


Experimental 24-hr Rainfall Forecast Graphics - Discontinued October 25, 2016

Experimental graphics have been discontinued as of Oct 25, 2016. Click on the link for more information.


Griffith-Woodley Satellite Rainfall Estimation Technique

Purpose

The Griffith-Woodley technique employed by the NHC operational SRE text product estimates precipitation from tropical cyclones and disturbances using cloud top temperatures within the system observed by infrared satellite images 6 hours apart. The Griffith-Woodley technique is described in detail in the chapter entitled "Precipitation in Tropical Cyclones" from the book Satellite Oceanic Remote Sensing (1985).  An excerpt is provided below:

"The Griffith-Woodley satellite rain estimation technique (Griffith et al., 1978) was derived in south Florida for the estimation of rainfall from summertime convection. The SMS/GOES (Synchronous Meteorological Satellite/Geostationary Operational Environmental Satellite) thermal infrared (10-12 µm) data from the Visible and Infrared Spin Scan Radiometer (VISSR) were calibrated by data from a dense gage network (1 gage/3.4 km2) and from a Weather Service radar to produce empirical relationships that infer rainfall from cloud-top temperatures. As originally constructed, the technique requires a sequence of images to determine cloud life histories and the change in cloud area with time."

"The Griffith-Woodley technique is a totally automated scheme that uses a digital array of infrared temperatures to produce first an estimate of volumetric output of the convection, and second to infer rainfall rates. Raining convective clouds are identified by the threshold temperature of -20°C. This threshold was chosen to maximize the determination of precipitating clouds, while minimizing the inclusion of nonraining clouds. The inferred rainfall, expressed as either total volumetric output (m3) or area-averaged rain depth (mm), is calculated as a function of both areal extent of the storm at -20°C as well as the fractional coverage of the storm by colder temperatures. Rainfall rates for each satellite pixel (and subsequently isohyets) are derived by apportioning this calculated volume over the storm as a function of cloud-top temperature."

"Three assumptions have been made in applying the diagnostic Griffith-Woodley technique to the real-time estimation of hurricane rainfall.
1. The empirical relationships derived for Florida convective clouds can be applied to tropical storms.
2. It is valid to treat the tropical cyclone as one large cloud, even though it is composed of individual clouds at different stages in their life cycles.
3. The tropical storm exists at its maximum cloud size on the image of interest."

 

REFERENCES:

Girz, Celia and L.S. Fedor., 1985: Precipitation in Tropical Cyclones. Satellite Oceanic Remote Sensing, B. Saltzman, Ed., Academic Press, 394-395. ISBN: 0-12-018827-9.

Griffith, C. G., W. L. Woodley, P. G. Gube, D. W. Martin, J. Stout, and D. N. Sikdar, 1978: Rain estimates from geosynchronous satellite imagery: Visible and infrared studies. Mon. Wea. Rev., 106, 1153–1171.


NRL-Blend Satellite Rainfall Estimates from the Naval Research Laboratory

The NRL-Blend technique for estimating rainfall accumulation from satellite imagers was developed and is generated by the U.S. Naval Research Laboratory (NRL). NRL-Blend produces precipitation estimates every three hours at quarter degree resolution between 60°S and 60°N latitude world-wide using data from both geostationary (GEO) visible and infrared (VIS/IR) imagers and passive microwave (PMW) imagers.  The National Hurricane Center has received permission from NRL to incorporate NRL-Blend output for tropical disturbances and tropical cyclones into their satellite rainfall estimate text product and graphics.

The NRL-Blend technique is described in detail in the chapter entitled "The NRL-Blend High Resolution Precipitation Product and its Application to Land Surface Hydrology" from the book Satellite Rainfall Applications for Surface Hydrology (2009).  An excerpt is provided below:

“Underlying the NRL blended satellite technique is an ongoing, real-time, dynamic collection of collocated (in time and space) intersecting pixels from all GEO VIS/IR and LWO PMW imagers. The operation of the NRL-Blend is essentially described by three procedures. The first procedure involves dataset collocation and is done in the background. As new PMW datasets arrive, the PMW-derived rainrate pixels are paired with the time and space-coincident geostationary 11-µm IR brightness temperature (Tβ) data from areas of GEO satellite scan coverage, using a 15-min maximum allowed time offset between the pixel observation times.” “This collection procedure is constantly ongoing with newly arrived datasets. This background collection of collocated data is used to update global histograms of the IR Tβ and the PMW rain rate (R) in the nearest 2° latitude-longitude box, as well as the eight surrounding boxes (this overlap assures a fairly smooth transition in the histogram shape between neighboring boxes)…” “As soon as a box is refreshed with new data, a probabilistic histogram matching relationship is updated using the PMW rainrate and IR Tβ histograms, and an updated Tβ-R lookup table (LUT) is created. The global LUT update process is constantly ongoing for all satellite intersections of the 2° boxes, with operationally arriving global LEO and GEO datasets.” 

“The second procedure is initiated with newly arrived GEO datasets…” “These GEO data are mapped to a common 0.1° pixel-1 rectangular map projection (1200 lines x 3600 samples, within +60° latitude) and assigned a rainrate through bicubic interpolation of the rainrate derived from the four surrounding LUT values.” “Bicubic interpolation assures smooth transitions in rain rates across box boundaries. If any LUT is more than 24-h old relative to the GEO dataset time, that LUT is not used (if all four LUTs are bad, then a missing value is assigned for the rainrate). A final step involves the use of a numerical weather prediction (NWP) model data to account for underlying environmental conditions that are not detected (or not accounted for) in a satellite-only analysis. Using the Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast model fields (interpolated to the satellite time), the 85—hPa wind vectors, temperature, humidity, and total column precipitable water (TPW) are combined with a high-resolution topographic database. A threshold based upon the product of the humidity and TPW (Vicente et al., 1998) is used together to screen false rain identification. The TPW and terrain slope are used to apply a scaling factor in regions of likely orographic effects on both the upslope and downslope sides (Vicente et al., 2002).
At each 3-hourly synoptic time (00, 03,…21 UTC), the precipitation accumulations are updated by backwards time-integrating the instantaneous LEO and GEO datasets from the previous 24 h, and outputting an accumulations dataset at 3, 6, 12, and 24-h interval. In the accumulations procedure, each GEO instantaneous rain rate pixel is weighted according to its time proximity to the nearest PMW overpass. The PMW estimates are always fully weighted and the GEO estimate receives a smaller weight the closer is occurs to a PMW overpass…” “For accumulations intervals beyond 24-h (such as monthly or seasonally), the 3-hourly accumulations are further time integrated (for computational efficiency). Although the computations are done on the 0.1° grid, the final products are averaged to a global 0.25° grid (480 lines x 1440 samples) for size and stored in a basic binary format. Each pixel is stored as a 2-byte short integer, where the integer represents the average rainrate (mm hour-1) over the time interval scaled by 100.To get back the accumulations totals (mm), the integer value is therefore divided by 100 and multiplied  the number of hours in the accumulations interval. Although the NRL-Blend was operated intermittently beginning in 2002, official data collection of the global precipitation accumulation products began in January 2004.”


Global rainfall estimates from the NRL-Blend technique can be found here:
http://www.nrlmry.navy.mil/sat-bin/rain.cgi

 

REFERENCES:


Turk, Joseph T., 2009: The NRL-Blend High Resolution Precipitation Product and its Application to Land Surface Hydrology. Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer-Verlag, 85-104. ISBN: 978-90-481-2914-0.


Vicente, G., R.A. Scofield, and W.P. Menzel. 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 1883-1898.


Vicente, G., J.C. Davenport, and R.A. Scofield, 2002: The role of orography and parallax correction on real time high resolution satellite rainfall estimation. Int. J. Remote Sens. 23, 221-230.


QMORPH Satellite Rainfall Estimates from the Climate Prediction Center

QMORPH is a technique for determining rainfall accumulation using satellite imagery that was created by NOAA's Climate Prediction Center (CPC). It is a less complex and more timely version of the CPC MORPHing (CMORPH) technique which is first available 18 hours past real-time. QMORPH is available within 3 hours of the collection of the infrared and passive microwave imagery that is used to derive a quarter degree resolution rainfall estimate between 60°N and 60°S every 30 minutes. QMORPH is available more quickly than CMORPH because it only propagates the microwave-derived rain rates forward in time based on infrared imagery rather than both forward and backward in time like CMORPH. The National Hurricane Center has received permission from CPC to incorporate QMORPH output for tropical disturbances and tropical cyclones into their satellite rainfall estimate text product and graphics.


Provided below is a description of the CMORPH technique from the CPC CMORPH webpage:
http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph_description.html

“CMORPH produces global precipitation analyses at very high spatial and temporal resolution. This technique uses precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively, and whose features are transported via spatial propagation information that is obtained entirely from geostationary satellite IR data. At present we incorporate precipitation estimates derived from the passive microwaves aboard the DMSP 13, 14 & 15 (SSM/I), the NOAA-15, 16, 17 & 18 (AMSU-B), and AMSR-E and TMI aboard NASA's Aqua and TRMM spacecraft, respectively. These estimates are generated by algorithms of Ferraro (1997) for SSM/I, Ferraro et al. (2000) for AMSU-B and Kummerow et al. (2001) for TMI. Note that this technique is not a precipitation estimation algorithm but a means by which estimates from existing microwave rainfall algorithms can be combined. Therefore, this method is extremely flexible such that any precipitation estimates from any microwave satellite source can be incorporated.

With regard to spatial resolution, although the precipitation estimates are available on a grid with a spacing of 8 km (at the equator), the resolution of the individual satellite-derived estimates is coarser than that - more on the order of 12 x 15 km or so. The finer "resolution" is obtained via interpolation.

In effect, IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. Propagation vector matrices are produced by computing spatial lag correlations on successive images of geostationary satellite IR which are then used to propagate the microwave derived precipitation estimates. This process governs the movement of the precipitation features only. At a given location, the shape and intensity of the precipitation features in the intervening half hour periods between microwave scans are determined by performing a time-weighting interpolation between microwave-derived features that have been propagated forward in time from the previous microwave observation and those that have been propagated backward in time from the following microwave scan. We refer to this latter step as "morphing" of the features.”

 

REFERENCES:


Ferraro, R. R., 1997: SSM/I derived global rainfall estimates for climatological applications. J. Geophys. Res., 102, 16715-16735.


Ferraro, R. R., F. Weng, N. C. Grody and L. Zhao, 2000: Precipitation characteristics over land from the NOAA-15 AMSU sensor. Geophys. Res. Ltr., 27, 2669-2672.


Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution.. J. Hydromet., 5, 487-503.


Kummerow, C., Y. Hong, W. S. Olson, S. Yang, R. F. Adler, J. McCollum, R. Ferraro, G. Petty, D-B Shin, and T. T. Wilheit, 2001: Evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 1801-1820.