Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters

Huazheng Du, Guoye Chen, Xuegang Hu, Na Xia and Biaodian Xu

International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 12, 1550147718815848

Abstract: Global positioning system–based meteorological parameters sensing has become a hot topic in the field of satellite navigation application. The major research content is global positioning system radio occultation observation, which utilizes the delay and bending of global positioning system signal to compute the meteorological parameters (temperature, pressure, and water vapor), so as to improve the accuracy of numerical weather prediction. In this article, the atmospheric parameters computing algorithm based on simultaneous perturbation stochastic approximation is proposed. Perturbation effect is used to obtain the approximate gradient of cost function, which can guide the searching to achieve the optimal solution gradually. The proposed algorithm avoids the complicated derivative computing for the cost function, and without designing the tangent linear and adjoint operators. The algorithm can converge to the optimal or approximately optimal solution quickly. The validity and superiority of this method has been proved by extensive comparative experiment results.

Keywords: Sensing of atmospheric parameters; global positioning system; radio occultation; variational data assimilation; simultaneous perturbation stochastic approximation (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:12:p:1550147718815848

DOI: 10.1177/1550147718815848

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