A prototype, real-time, hourly, multi-sensor National Precipitation Analysis (NPA) has been developed at the National Centers for Environmental Prediction (NCEP) in cooperation with the Office of Hydrology (OH). This analysis merges two data sources that are currently being collected in real-time by OH and NCEP. Approximately 3000 automated, hourly raingage observations are available over the contiguous 48 states from ASOS and via the GOES Data Collection Platform (DCP). In addition, hourly digital precipitation (HDP) radar estimates are obtained as compressed digital files via the AFOS network. The HDP estimates are created by the WSR-88D Radar Product Generator on a 131 x 131 4-km grid centered over each radar site. The data analysis routines, including a bias adjustment of the radar estimates using the gage data, have been adapted by NCEP on a national 4-km grid from algorithms developed by OH ("Stage II") and executed regionally at NWS River Forecast Centers (RFC).
There are current 2 types of radar-only estimates, raw HDP and gage-adjusted. The radar bias adjustment algorithm follows Smith and Krajewski (1991). These gage-adjusted radar estimates are available after an ~6h delay, to allow gage data necessary to estimate the biases to arrive.
Please refer to Fulton et al (1998) for more information on HDP data.
The 24h "rfc" analysis uses the same gage-only algorithm as the hourly analyses, except with the RFC database. There are many more stations (~6000) than the hourly data.
Please refer to Fulton et al (1998) for more information on gage-only and multi-sensor analysis techniques.
The rainfall rate estimates are experimental products available both to outside users through the internet and for internal use in the validation and calibration process. The purpose of the research is to demonstrates the viability of an operational, automated satellite rainfall estimation technique to provide real time heavy precipitation estimates for flash flood watches and warning for a spatial and time resolutions of 4 by 4 Km and 15 to 30 minutes interval, respectively.
An experimental version of the technique is currently running at the Atmospheric Research and Applications Division at NOAA / NESDIS, Camp Springs, MD, providing real time rainfall estimates for both the USA and northern half of South America including coastal areas. The algorithm consists of 2 steps: (1) GOES-8/9 10.7 mm channel is used as the primary source of information to compute instantaneous rainfall rates every 15 minutes. These rainfall rates are computed from a previous derived power law regression curve between cloud top temperature and radar derived rainfall estimates; (2) instantaneous rainfall rate estimates for each of the 4 by 4 Km pixels are adjusted based on cloud top temperature gradient, growth rate and a moisture correction. Computation of these parameters employ two consecutive images and moisture (precipitable water and relative humidity) derived from the NCEP (National Center for Environmental Prediction) ETA model. Hourly rainfall rates are computed through a weighted average of three consecutive instantaneous images and the 3, 6 and 24 hour accumulations are available by adding the hourly rainfall rates. During day time, the GOES-8/9 visible channel is used in the location of the precipitation cores.
Please refer to Vicente et al (1998) for more information on the GOES rainfall auto-estimator.
Gilberto Alves Vicente
NOAA/NESDIS/ORA - WWB, Camp Springs, MD
Ph.: (301) 763 8251 Ex. 32, Fax: (301) 763 8580
In the initial stages, the NPA will omit manual quality control steps that are a hallmark of the RFC "Stage III" analyses. However, some initial quality control steps have been implemented into the current NPA system. A list of consistently bad raingages has been created, so that observations will be ignored from these reporting stations in the analyses. This list was subjectively determined by examination of numerous cases where the gages reported heavy rainfall for several hours while nearby gage and radar reports contained zero rainfall. As of this writing, a total of six gage stations have been omitted from the analysis, four in California, one in northeast Kansas, and one in New York. The only other quality control step currently in use involves a gross check on gage data, making sure no reports greater than 5in/hr get into the analysis system.
For the 4km grid:
To find the grid coordinate of a location given by (rlat,rlon): call w3fb06(rlat,rlon,alat1,alon1,dx,alonv,xi,xj) where alat1=22.7736 alon1=239.624 (=360.-120.376) dx=4762.5 alonv=255. The return argument, (xi,xj), gives the location of point (rlat,rlon) on the 4km HRAP grid. (nint(xi),nint(xj)) is the grid point closest to (rlat,rlon). To find the lat/lon location of a grid point (i,j): xi=float(i) xj=float(j) call w3fb07(xi,xj,alat1,alon1,dx,alonv,alat,alon) (alat,alon) is the lat/lon of the grid point (i,j)For more information, please see the documentation block of these two subroutines.
1) GRIB Documentation
Acquire the NCEP specific guide to GRIB packing at (/pub/nws/nmc/docs/gribed1) NCEP, by a BINARY get of all six pdf (or wordperfect) files in this directory (these six files are named by section number).
2) W3LIB Software
Once you acquire the README file in /pub/nws/nmc/codes/grib.wafs, change to the subdirectory corresponding to the computer platform you will be working on, e.g. for an SGI workstation use, cd to /pub/nws/nmc/codes/grib.wafs/gribsgi. Once in your chosen platform subdirectory, acquire and study the readme file there. Briefly, the latter file will instruct you how to acquire, compile, and execute the GRIB unpacking codes that reside in that directory.
3) wgrib software
wgrib, an excellent GRIB decoding, inventory, and manipulation package that is easy to compile on nearly any platform can be found at this site.
For GEMPAK:
You will need to run nagrib, version 5.3 or higher I believe. The 4km grid is over 1,000,000 points, so in order to use nagrib on these grids, a recompile is necessary to expand the size of the maximum allowable grid. The 15km grid is about 110,000 points, and should be decodable with nagrib.
You will need to subset the 15km grids so that other GEMPAK programs (GDPLOT, etc.) will be able to handle them. In nagrib:
For NCARGRAPHICS:
I have made up a sample decoding and plotting code using NCARGRAPHICS and W3LIB GRIB decoding routines. It is available for downloading here. This code requires the W3LIB GRIB decoding routines described earlier. This code will plot the 4km and 15km grids and produces a map similar to those found on the web page.
For GrADS:
Look at these instructions, provided by Jamie Kousky of OM.
A text dump from wgrib will denote missing points as 9.999e+20.
Yes, there is an archive of these analyses. We began producing these analyses back in May 1996.
The tape archive has been transferred to NCAR. You can access this archive at this site. It is recommended that users access this archive for requesting large amounts of data.
Web access to this archive has also been established. This should only be used if accessing a few days worth of data. It can be reached here.
Crosson, W.L., C.E. Duchon, R. Raghavan, S.J. Goodman, 1996: Assesment of rainfall estimates using a standard Z-R relationship and the probability matching method applied to composite radar data in central Florida. J. Appl. Meteor., 35, 1203-1219.
Crum, T.D, R.L. Alberty, and D.W. Burgess, 1993: Recording, Archiving, and Using WSR-88D Data. Bull. Amer. Meteor. Soc.,74, 645-653.
Crum, T.D, and R.L. Alberty, 1993: The WSR-88D and the WSR-88D Operational Support Facility. Bull. Amer. Meteor. Soc.,74, 1669-1687.
Klazura, G.E., and D.A. Imy, 1993: A description of the initial set of analysis products available from the NEXRAD WSR-88D System. Bull. Amer. Meteor. Soc.,74, 1293-1311.
Fulton, R.A., J.P. Breidenbach, D.J. Seo, D.A. Miller, and T. O'Bannon, 1998: The WSR-88D rainfall algorithm. Wea. and Fore.,13, 377-395.
Seo, D.J., 1998: Real-time estimation of rainfall fields using rain gauge data under fractional coverage conditions. J. of Hydrol., 208, 25-36.
Seo, D.J., 1998: Real-time estimation of rainfall fields using radar rainfall and rain gauge data. J. of Hydrol., 208, 37-52.
Seo, D.J., J.P. Breidenbach, and E.R. Johnson, 1999: Real-time estimation of mean field bias in radar rainfall data. J. of Hydrol., 209, 131-147.
Smith, J.A., and W.F. Krajewski, 1991: Estimation of mean field bias of radar rainfall estimates. J. Appl. Meteor., 30, 397-412.
Vicente, G.A., R.A. Scofield, and W.P. Menzel, 1998: The Operational GOES Infrared Rainfall Estimation Technique. Bull. Amer. Meteor. Soc.,79, 1883-1898.
Send your comments to: Mike.Baldwin@noaa.gov