Estimation of Atmospheric Optical Turbulence at Near Surface of Chengdu with Support Vector Machine

Jie, 吕 洁 Lü; Wenyue, 朱文越 Zhu; Chun, 青春 Qing; Xiaoqing, 吴晓庆 Wu; Shasha, 陈莎莎 Chen

doi:10.3788/cjl201845.0404001

Cited by 1 publication

(1 citation statement)

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“…The micro-thermometer are balloon-borne payloads that measure the in situ temperature structure coefficient (𝐶 ) using a pair of fine wire probes separated horizontally by 1 m. In the visible and near-infrared bands, temperature changes are the main factor affecting changes in the refractive index structure. 𝐶 is a proportional constant in the inertial sub-range form of the temperature structure function 𝐷 (𝑟, ℎ) [9], defined by 𝐶 (𝑟, ℎ) = ( , ) / , 𝑙 ≤ 𝑟 ≤ 𝐿 (1) where 𝐶 is the temperature structure constant. 𝐷 (𝑟, ℎ) is defined as one of the statistics describing temperature disturbance from two micro-temperature probes with distance r, which is given by ) denote the temperature at the two points separated by 𝑟, while 𝑙 and 𝐿 are the inner and outer scales of the atmospheric turbulence.…”

Section: Balloon-borne Microthermalmentioning

confidence: 99%

Estimation of atmospheric optical turbulence profile using support vector machine based on whale optimization algorithm

liang,

gang,

liming

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical Systems

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Atmospheric optical turbulence significantly impacts the efficacy of adaptive optics and various laser systems. The parameter known as the atmospheric refractive index structure constant, or C2n, is crucial for characterizing this turbulence. In response to this challenge, we have developed a model utilizing the Whale Optimization Algorithm combined with a Support Vector Machine (WOA-SVM) designed to estimate atmospheric optical turbulence profiles along Eastern China’s coast. The WOA-SVM model uses standard atmospheric sounding data from the region to forecast turbulence profiles at different times, subsequently comparing these estimates with actual observations. Our error analysis indicates that the model’s estimates have root mean square errors (RMSE) of 0.4441, 0.3012, 0.4734, and 0.4904 for the respective times, while correlation coefficients range from 85.10% to 93.66%. The research shows that despite minor discrepancies in atmospheric optical turbulence profiles estimated by the WOA-SVM model using standard atmospheric sounding data and those measured directly, the general trend is consistent. This consistency confirms the WOA-SVM model’s practicality for estimating atmospheric optical turbulence in coastal areas. Therefore, the study made an attempt for direct estimation of turbulence profiles using routine meteorological data and paves the way for future model development in this domain.

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Section: Balloon-borne Microthermalmentioning

confidence: 99%