The Fog of War: A Machine Learning Approach to Forecasting Weather on Mars

Daniele Bellutta
(Submitted on 26 Jun 2017)

For over a decade, scientists at NASA’s Jet Propulsion Laboratory (JPL) have been recording measurements from the Martian surface as a part of the Mars Exploration Rovers mission. One quantity of interest has been the opacity of Mars’s atmosphere for its importance in day-to-day estimations of the amount of power available to the rover from its solar arrays. This paper proposes the use of neural networks as a method for forecasting Martian atmospheric opacity that is more effective than the current empirical model. The more accurate prediction provided by these networks would allow operators at JPL to make more accurate predictions of the amount of energy available to the rover when they plan activities for coming sols.

Subjects:    Instrumentation and Methods for Astrophysics (astro-ph.IM); Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
Cite as:    arXiv:1706.08915 [astro-ph.IM]
     (or arXiv:1706.08915v1 [astro-ph.IM] for this version)
Submission history
From: Daniele Bellutta 
[v1] Mon, 26 Jun 2017 05:05:00 GMT (1698kb)
https://arxiv.org/abs/1706.08915