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A sampling-based path planning algorithm for improving observations in tropical cyclones
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  • Justice Darko,
  • Larkin Folsom,
  • Hyoshin Park,
  • Masashi Minamide,
  • Masahiro Ono,
  • Hui Su
Justice Darko
North Carolina Agricultural and Technical State University

Corresponding Author:jdarko@aggies.ncat.edu

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Larkin Folsom
North Carolina Agricultural and Technical State University
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Hyoshin Park
North Carolina Agricultural and Technical State University
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Masashi Minamide
The University of Tokyo
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Masahiro Ono
NASA Jet Propulsion Laboratory
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Hui Su
Jet Propulsion Laboratory, California Institute of Technology
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Lack of high-resolution observations at the inner-core region of tropical cyclones introduces uncertainty into the structure’s true initial state. More accurate measurements at the inner-core are essential for accurate tropical cyclone forecasts. This study seeks to improve the estimates of the inner-core structure by utilizing background information from prior assimilated conventional observations. We provide a scheme for targeted high-resolution observations for platforms such as the Coyote sUAS. In an effort to identify potential locations of high uncertainty, an exploratory investigation of the background information of the state variables pressure, temperature, wind speed, and a combined representation of the state variables given by their linear weighted average is presented. A sampling-based path planning algorithm that considers the Coyote’s energy usage then locates the regions of high uncertainties along a Coyote’s flight, allowing us to maximize the removal of uncertainties. The results of a data assimilation analysis of a typical Coyote flight mission using the proposed deployment scheme shows significant improvements in estimates of the tropical cyclone structure after the resolution of uncertainties at targeted locations.
Jan 2022Published in Earth and Space Science volume 9 issue 1. 10.1029/2020EA001498