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Proceedings Paper

Tools for predicting uncertainty and confidence intervals in radiometric data products
Author(s): Maria Cristina Sanchez; J. Robert Mahan; Edwin A. Ayala; Kory J. Priestley
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Paper Abstract

Space-based observations of atmospheric energetics, such as those provided by NASA's Clouds and the Earth's Radiant Energy System (CERES), produce data products intended to be shared with the larger scientific community and merged with other complementary data sets. Meaningful fusion of complementary data requires a well-founded common statistical basis for cited precision and accuracy. A high-level numerical model is available capable of predicting the dynamic opto- electrothermal behavior of CERES-like radiometric channels. The paper reports use of this model to explore the sensitivity of data products to variations in individual optical, thermal and electronic parameters. The optical/thermal radiative part of the model is based on the Monte-Carlo ray-trace (MCRT) method in which millions of rays are traced. Several hours of execution time on a large computer are required to simulate a single scan across the Earth's surface, thus making it impractical to run the simulation for every possible variation of each parameter. A key element of the research involves an effort to determine the minimum number of simulations required to produce statistically meaningful results.

Paper Details

Date Published: 28 December 1999
PDF: 11 pages
Proc. SPIE 3870, Sensors, Systems, and Next-Generation Satellites III, (28 December 1999); doi: 10.1117/12.373181
Show Author Affiliations
Maria Cristina Sanchez, Virginia Polytechnic Institute and State Univ. (United States)
J. Robert Mahan, Virginia Polytechnic Institute and State Univ. (United States)
Edwin A. Ayala, Virginia Polytechnic Institute and State Univ. (United States)
Kory J. Priestley, NASA Langley Research Ctr. (United States)


Published in SPIE Proceedings Vol. 3870:
Sensors, Systems, and Next-Generation Satellites III
Hiroyuki Fujisada; Joan B. Lurie, Editor(s)

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