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Modeling martian thermal inertia in a distributed memory high performance computing environment

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https://doi.org/10.1109/BigData.2016.7840942

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Abstract

Modeling martian surface properties fusing high resolution, spatially enabled, remotely sensed data and derived thermophysical modeling is an essential tool for surface property characterization studies. In this work, we describe the development of a thermal inertia modeling tool that integrates the KRC thermal model and a nine-dimensional parameter interpolation with inputs draw from remotely sensed data. Our model is classifiable as operating in both the Big Data and Big Process domains. We utilize the KRC thermal model to generate a dense lookup table. We show that the overall size of the lookup table necessary to derive thermal inertia can be reduced, through sampling, by approximately 82% while maintaining a high level of accuracy at those regions of the parameter space where thermal inertia is most frequently derived. This level of data reduction supports the distributed, in-memory application of our model and we illustrate the computational performance through a classic scaling experiment. This work extends previous modeling efforts by allowing for pixel scale thermal inertia modeling at the highest spatial scales allowed, and enabling surface properties investigations at spatial scales relevant to addressing high-priority science and engineering questions.

Publication type Conference Paper
Publication Subtype Conference Paper
Title Modeling martian thermal inertia in a distributed memory high performance computing environment
DOI 10.1109/BigData.2016.7840942
Year Published 2016
Language English
Publisher Institute of Electrical and Electronics Engineers
Contributing office(s) Astrogeology Science Center
Description 10 p.
Larger Work Type Book
Larger Work Subtype Conference publication
Larger Work Title Proceedings 2016 IEEE international conference on big data
First page 2919
Last page 2928
Conference Title 2016 IEEE International Conference on Big Data
Conference Location Washington DC
Conference Date Dec 5-8, 2015
Other Geospatial Mars
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