Optical Turbulence Profile Forecasting and Verification in the Offshore Atmospheric Boundary Layer

• Main Authors: Manman Xu, Shiyong Shao, Qing Liu, Gang Sun, Yong Han, Ningquan Weng
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Applied Sciences
• Subjects: optical turbulence; backpropagation neural network; Holloman Spring 1999 thermosonde campaigns model; Hufnagel/Andrew/Phillips model
• Online Access: https://www.mdpi.com/2076-3417/11/18/8523

A backpropagation neural network (BPNN) approach is proposed for the forecasting and verification of optical turbulence profiles in the offshore atmospheric boundary layer. To better evaluate the performance of the BPNN approach, the Holloman Spring 1999 thermosonde campaigns (HMNSP99) model for outer scale, and the Hufnagel/Andrew/Phillips (HAP) model for a single parameter are selected here to estimate profiles. The results have shown that the agreement between the BPNN approach and the measurement is very close. Additionally, statistical operators are used to quantify the performance of the BPNN approach, and the statistical results also show that the BPNN approach and measured profiles are consistent. Furthermore, we focus our attention on the ability of the BPNN approach to rebuild integrated parameters, and calculations show that the BPNN approach is reliable. Therefore, the BPNN approach is reasonable and remarkable for reconstructing the strength of optical turbulence of the offshore atmospheric boundary layer.