EMC: National Precipitation Analysis FAQ

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INTRODUCTION

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).

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HOW ARE THE RADAR-ONLY PRECIP ESTIMATES DERIVED?

The first product with a completed prototype was the national mosaic of radar precipitation HDP estimates. This radar-only product consists of nearly 100 WSR-88D radars which report to NCEP in real-time via AFOS. Each individual radar estimate is merged together on the national Hydrologic Rainfall Analysis Project (HRAP) grid and bins which contain more than one radar estimate are averaged together using a simple inverse-distance weighted average. Currently, there is no quality control of the HDP estimates, such as removal of anomalous propagation.

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.

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HOW ARE THE GAGE-ONLY AND MULTI-SENSOR FIELDS DERIVED?

In contrast to the simple radar-only mosaic technique, the analysis schemes used in the gage-only and multi-sensor analyses utilize optimal estimation theory. These were developed by Seo (1998). The schemes are fundamentally similar, and optimally estimate rainfall fields using raingage and radar data under partial data coverage conditions. This is preferred over previous statistically-based techniques because it takes into account the variability due to fractional coverage of rainfall, as well as within-storm variability. By objectively taking the spatial coverage into account, more accurate estimates of the rain versus no-rain area are obtained. Accurate delineation of this area is as important as accurate estimation of rainfall within the rain area. One of the underlying assumptions in the radar-gage analysis scheme is that the radar estimates are unbiased. Currently, radar biases are adjusted prior to the multi-sensor analysis by the technique developed by Smith and Krajewski (1991). This method attempts to remove the mean bias but does not attempt to remove range-dependent biases.

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.

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HOW ARE THE SATELLITE PRECIP ESTIMATES DERIVED?

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

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WHAT TYPE OF QUALITY CONTROL STEPS ARE TAKEN?

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.

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WHAT IS THE GRID INFORMATION?

The format of the files is GRIB. The files are compressed using the UNIX "compress" command and "uncompress" must be used before decoding.

For the 4km grid:

For the 15km grid:

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HOW DO I FIND THE LAT/LON LOCATION OF A GRID POINT (AND VICE-VERSA)?

Two subroutines, w3fb06.f and w3fb07.f, can be used to find the nearest grid-coordinate for a given lat/lon location, and to find the lat/lon of a given grid point, respectively. Here are two examples of how to use the subroutines:

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.

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HOW CAN I DECODE THESE GRIDS?

NCEP has made a set of GRIB decoders available via anonymous ftp. The site is ftp.ncep.noaa.gov /pub/nws/nmc/codes/grib.wafs Here, you can obtain documentation and code for decoding GRIB messages.

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.

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HOW CAN I PLOT THESE GRIDS?

There are numerous grid plotting packages, many of which are "GRIB-friendly". The two that I am most familiar with are GEMPAK and NCARGRAPHICS.

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:

where GAREA is the location of the subset you are interesting in, it can be nearly as large as the entire grid, but must be less than 100,000 points.

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.

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HOW IS THE GRIB BITMAP USED TO DENOTE MISSING REGIONS?

Each type of analysis uses the GRIB bitmap feature to denote the area of the domain that has enough data to provide an analyzed value. In other words, the bitmap tells you if you are inside the data domain or not. In the W3FI63 unpacker, a logical array is returned that is .TRUE. where the bitmap is turned on, and .FALSE. where the bitmap is turned off. The gribplot code takes advantage of this feature to denote the extent of the data domain. The gage-only analysis is considered to be inside the data domain within approximately 50km of the nearest gage report. The radar-only analysis is considered inside of the data domain within approximately 200km of each successfully decoded radar report. The multi-sensor analysis will use the gage-only value if no radar data are available, and the radar-only value if no gage data are available.

A text dump from wgrib will denote missing points as 9.999e+20.

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IS THERE AN ARCHIVE? HOW DO I OBTAIN ARCHIVED ANALYSES?

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.

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WHAT ABOUT PLANS FOR THE FUTURE?

Near-future (0-12 month timeframe) NCEP improvements will focus on automated quality control procedures that utilize a host of NCEP and NESDIS national meteorological databases as filtering tools for such things as anomalous propagation, and overlays of regional Stage III analyses as they are obtained from the RFC's. The complete set of WSR-88D radar messages will be included, as well as 1h gage data from ASOS sites. In addition, the radar bias algorithm will likely be modified to more closely follow the sample bias given the current hour's radar and gage estimates [ Seo et al (1997)]. Operational implementation on the Cray is also planned for 1998. Eventually, the NPA will incorporate an automated, hourly, 4-km GOES-8 and GOES-9 satellite-derived precipitation estimate recently developed by NESDIS, which is available over both sea and land. A so-called "final" 24h accumulation analysis is also planned, which will include the higher resolution 24h gage data available from the RFCs. Upgrades to the Stage II algorithms from OH/HRL will be included as they are produced. Longer term plans are to develop a "true" multi-sensor analysis, integrating satellite, radar, and rain gage data, along with information from NWP analyses. Four-dimensional data assimilation will allow information on the state of the atmosphere and physical processes to be utilized while incorporating raw data from a wide variety of sources.

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REFERENCES RELATED TO STAGE IV PROCESSING

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.

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Send your comments to: Mike.Baldwin@noaa.gov