U.S. Geological Survey (USGS) opr-ppr(1) NOTE: Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.NAMEopr-ppr - A Computer Program for Assessing Data Importance to Model Predictions Using Linear StatisticsABSTRACTThe 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.HISTORYOPR-PPR Version 1.00 8/13/2007 - Initial release. OPR-PPR Version 1.01 2/1/2016 - Minor changes for compatability with Intel Fortran Compiler. SYSTEM REQUIREMENTS OPR-PPR is written in Fortran 90.DOCUMENTATIONThe 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/CONTACTSClaire 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

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