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Summary of OPR-PPR


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NAME

     opr-ppr - A Computer Program for Assessing Data Importance
               to Model Predictions Using Linear Statistics

ABSTRACT

     The OPR-PPR program calculates the Observation-Prediction (OPR)
     and Parameter-Prediction (PPR) statistics that can be used to 
     evaluate the relative importance of various kinds of data to 
     simulated predictions. The data considered fall into three categories: 
     (1) existing observations, (2) potential observations, and (3) potential
     information about parameters. The first two are addressed by the OPR 
     statistic; the third is addressed by the PPR statistic. The statistics 
     are based on linear theory and measure the leverage of the data, which 
     depends on the location, the type, and possibly the time of the data 
     being considered. For example, in a ground-water system the type of 
     data might be a head measurement at a particular location and time. 
     As a measure of leverage, the statistics do not take into account 
     the value of the measurement. As linear measures, the OPR and PPR 
     statistics require minimal computational effort once sensitivities 
     have been calculated. Sensitivities need to be calculated for only 
     one set of parameter values; commonly these are the values estimated 
     through model calibration. OPR-PPR can calculate the OPR and PPR 
     statistics for any mathematical model that produces the necessary 
     OPR-PPR input files. In this report, OPR-PPR capabilities are presented 
     in the context of using the ground-water model MODFLOW-2000 and the 
     universal inverse program UCODE_2005.
     
     The method used to calculate the OPR and PPR statistics is based on 
     the linear equation for prediction standard deviation. Using sensitivities 
     and other information, OPR-PPR calculates (a) the percent increase in the 
     prediction standard deviation that results when one or more existing 
     observations are omitted from the calibration data set; (b) the percent 
     decrease in the prediction standard deviation that results when one or 
     more potential observations are added to the calibration data set; or 
     (c) the percent decrease in the prediction standard deviation that results 
     when potential information on one or more parameters is added.
     Capabilities (a) and (b) correspond to an analysis of the data categories 
     listed in items (1) and (2) above and are the two versions of the OPR 
     statistic. Capability (c) corresponds to an analysis of the data category 
     listed in item (3) above, and is the PPR statistic. The OPR statistic can 
     be used to identify observations that are most important to one or more model 
     prediction(s), to support the design of monitoring networks, and to guide the 
     collection of new observation data. The PPR statistic can be used to guide 
     collection of new data about model parameters or related system features. 
     OPR-PPR is intended for use on any computer operating system. 
     
     The program consists of algorithms programmed in Fortran 90/95, which 
     efficiently performs numerical calculations.  The program is constructed
     in a modular fashion using the JUPITER API programming conventions and modules. 
HISTORY


      OPR-PPR Version 1.00 8/13/2007 - Initial release.
SYSTEM REQUIREMENTS
     OPR-PPR is written in Fortran 90. 
DOCUMENTATION


  The documentation for OPR-PPR is contained in the following report:

  Tonkin Matthew J., Tiedeman Claire R., Ely D. Matthew, and Hill Mary C., 2007, 
  OPR-PPR, a Computer Program for Assessing Data Importance to Model Predictions 
  Using Linear Statistics: Reston Virginia, USGS, Techniques and Methods 
  Report TM 6E2, 115 pages.

  This report is only available online, at the following url:
  
  http://pubs.usgs.gov/tm/2007/tm6e2/
  
  The report is a Portable Document Format (PDF) file, which is readable
  and printable on various computer platforms using Acrobat Reader from Adobe.
  The Acrobat Reader is freely available from the following url:
  
      http://www.adobe.com/

CONTACTS

    Claire R. Tiedeman
    U.S. Geological Survey
    345 Middlefield Road MS496
    Menlo Park, CA  94025
    (650) 329-4583
    tiedeman@usgs.gov
    
    Matthew J. Tonkin
    S.S. Papadopulos & Assoc., Inc.
    7944 Wisconsin Avenue
    Bethesda, MD  20814
    (301) 718-8900 x258
    matt@sspa.com
    
    Mary C. Hill
    U.S. Geological Survey
    3215 Marine Street
    Boulder, CO  80303  
    (303) 541-3014
    mchill@usgs.gov

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