Mathematics – Logic
Scientific paper
Dec 2003
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003agufm.h12g..03p&link_type=abstract
American Geophysical Union, Fall Meeting 2003, abstract #H12G-03
Mathematics
Logic
1829 Groundwater Hydrology, 1832 Groundwater Transport
Scientific paper
The JUPITER (Joint Universal Parameter IdenTification and Evaluation of Reliability) project builds on the technology of two widely used codes for sensitivity analysis, data assessment, calibration, and uncertainty analysis of environmental models: PEST and UCODE. These programs are universal in that they can be applied to any computer model; and both have very flexible methods for interacting with application models through ASCII files. Their combination in an Application Programming Interface (API) will yield a full-featured, well-designed, flexible, stable, modular, thoroughly documented foundation for advancing the technology incorporated in UCODE and PEST. Phase 1 of the project is development of the JUPITER API, which will include (1) conventions for program input and output and internal data production and consumption, and (2) subroutines that support commonly used calculations and manipulations, to facilitate use of the API by many researchers in the field. Phase 2 is development of the first applications of the JUPITER API, J_UCODE, J_PEST, and J_MMRI, where J_MMRI is an alternative conceptual model evaluation tool for ranking and weighting models to facilitate multi-model inference. Applications developed using the JUPITER API will provide the opportunity for users to readily: (1) experiment with a number of techniques for generating conceptual models (e.g. geostatistical methods, geologic process modeling, upscaling); (2) compare alternative parameter-estimation algorithms (for example, the algorithms in J_PEST and J_UCODE); (3) "mine" results from various conceptual models for model evaluation, ranking and multi-model inferential analysis, as well as use these results to evolve the conceptual model (e.g. unreasonable parameter-value estimates provide clues to hydrogeologic structure; residual bias provides clues to conceptual model error); and (4) assess data needs to improve the calibration in light of the predictions.
Babendreier J.
Banta E.
Doherty J.
Hill Michael
Poeter E.
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