Adaptive niche-genetic algorithm based on backpropagation neural network for atmospheric turbulence forecasting

Changdong Su; Xiaoqing Wu; Tao Luo; Su Wu; Chun Qing

doi:10.1364/AO.388959

Applied Optics
Vol. 59,
Issue 12,
pp. 3699-3705
(2020)
•https://doi.org/10.1364/AO.388959

Adaptive niche-genetic algorithm based on backpropagation neural network for atmospheric turbulence forecasting

Changdong Su, Xiaoqing Wu, Tao Luo, Su Wu, and Chun Qing

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Changdong Su, Xiaoqing Wu, Tao Luo, Su Wu, and Chun Qing, "Adaptive niche-genetic algorithm based on backpropagation neural network for atmospheric turbulence forecasting," Appl. Opt. 59, 3699-3705 (2020)

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Abstract

Because systematic direct measurements of the refractive index structure constant ($C_n^2$) are not available for many climates and seasons, we developed an indirect method to forecast optical turbulence. The $C_n^2$ was estimated from a backpropagation neural network optimized by an adaptive niche-genetic algorithm. The estimated result was validated against the corresponding six-day $C_n^2$ data from a field campaign of the 30th Chinese National Antarctic Research Expedition. We also compared the correlation coefficient, root mean square error, and systematic error bias of the proposed model with the weather research and forecasting model. The results suggest that our model shows better correlation and reliably estimates $C_n^2$.

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Instrument	Sensor	Make and Model	Measuring Element	Resolution
Micro-thermometer	MT1	Anhui Institute of Optics and Fine Mechanics	Optical turbulence ( $C_{n}^{2}$ )	$3 E - 18 m^{- 2 / 3}$
Ultrasonic anemometer	CSAT3	Campbell Scientific	Three components of wind speed	$u_{z} \leq \pm 0.04 {m s}^{- 1}$
				$u_{y} \leq \pm 0.04 {m s}^{- 1}$
				$u_{x} \leq \pm 0.04 {m s}^{- 1}$
Temperature and humidity probe	HMP155	Vaisala	Air temperature	$\pm {0.5}^{\circ} C$
Temperature and humidity probe	HMP155	Vaisala	Relative humidity	1%
Wind speed and direction sensors	05103v	R. M. Young	Wind speed	$0.3 {m s}^{- 1}$
Wind speed and direction sensors	05103v	R. M. Young	Wind direction	$\pm 3^{\circ}$
Infrared radiometer	SI-111	ICT International	Snow surface temperature	$\pm {0.2}^{\circ} C$
Barometric pressure sensor	CS106	Campbell Scientific	Pressure	$\pm 0.15 h P a$

Model	Input	Method	Output
Origin [18]	Temperature, relative humidity Pressure, wind shear, Potential temperature gradient	BP neural network	$C_{n}^{2}$
Origin_Log	2 m temperature, 0.5 m temperature, 2 m relative humidity, 0.5 m relative humidity, 2 m wind velocity, 0.5 m wind velocity, Pressure, Snow surface temperature	BP neural network	Logarithm of $C_{n}^{2}$
Proposed	2 m temperature, 0.5 m temperature, 2 m relative humidity, 0.5 m relative humidity, 2 m wind velocity, 0.5 m wind velocity, pressure, snow surface temperature	AGA-BP neural network	Logarithm of $C_{n}^{2}$

Model	$R_{xy}$	RMSE	Bias
Origin [18]	0.7866	0.4215	−0.1323
Origin_log	0.8869	0.3001	0.0395
Proposed	0.9323	0.2367	0.0407
WRF [33]	0.7001	0.4530	0.049

Instrument	Sensor	Make and Model	Measuring Element	Resolution
Micro-thermometer	MT1	Anhui Institute of Optics and Fine Mechanics	Optical turbulence ( $C_{n}^{2}$ )	$3 E - 18 m^{- 2 / 3}$
Ultrasonic anemometer	CSAT3	Campbell Scientific	Three components of wind speed	$u_{z} \leq \pm 0.04 {m s}^{- 1}$
				$u_{y} \leq \pm 0.04 {m s}^{- 1}$
				$u_{x} \leq \pm 0.04 {m s}^{- 1}$
Temperature and humidity probe	HMP155	Vaisala	Air temperature	$\pm {0.5}^{\circ} C$
Temperature and humidity probe	HMP155	Vaisala	Relative humidity	1%
Wind speed and direction sensors	05103v	R. M. Young	Wind speed	$0.3 {m s}^{- 1}$
Wind speed and direction sensors	05103v	R. M. Young	Wind direction	$\pm 3^{\circ}$
Infrared radiometer	SI-111	ICT International	Snow surface temperature	$\pm {0.2}^{\circ} C$
Barometric pressure sensor	CS106	Campbell Scientific	Pressure	$\pm 0.15 h P a$

Model	Input	Method	Output
Origin [18]	Temperature, relative humidity Pressure, wind shear, Potential temperature gradient	BP neural network	$C_{n}^{2}$
Origin_Log	2 m temperature, 0.5 m temperature, 2 m relative humidity, 0.5 m relative humidity, 2 m wind velocity, 0.5 m wind velocity, Pressure, Snow surface temperature	BP neural network	Logarithm of $C_{n}^{2}$
Proposed	2 m temperature, 0.5 m temperature, 2 m relative humidity, 0.5 m relative humidity, 2 m wind velocity, 0.5 m wind velocity, pressure, snow surface temperature	AGA-BP neural network	Logarithm of $C_{n}^{2}$

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Applied Optics