A machine learning approach for forecasting the refractive index structure parameter

Author(s):  
Joshua J. Rudiger ◽  
John S. deGrassie ◽  
Stephen Hammel ◽  
Kevin Book ◽  
Brook Baker
2015 ◽  
Vol 35 (4) ◽  
pp. 0401002
Author(s):  
吴晓军 Wu Xiaojun ◽  
王红星 Wang Hongxing ◽  
李笔锋 Li Bifeng ◽  
刘传辉 Liu Chuanhui

2015 ◽  
Vol 27 (1) ◽  
pp. 11011
Author(s):  
吕炜煜 Lü Weiyu ◽  
苑克娥 Yuan Ke’e ◽  
胡顺星 Hu Shunxing ◽  
王建国 Wang Jianguo

1989 ◽  
Vol 42 (5) ◽  
pp. 573 ◽  
Author(s):  
SBSS Sarma ◽  
PK Pasricha

The spatial and temporal variations of the radio refractive index lead to scattering of electromagnetic energy. Modern communication systems are subject to vagaries of the turbulence due to the radio refractive index fluctuations. Vertical esolution of radiosondes is insufficient to resolve the structures of clear air turbulence in the atmosphere. In a tropical ountry like India, aircraft measurements of radio refractive index fluctuations have been non-existent. refractivity, has a lower value in local winter than in summer.


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