Abstract
Atmospheric radio refractivity has an obvious influence on the signal transmission path and communication group delay effect. The uncertainty of water vapor distribution is the main reason for the large error of tropospheric refractive index modeling. According to the distribution and characteristics of water vapor pressure, temperature, and pressure, which are the basic components of the refractive index, a method for retrieving atmospheric refractivity profile based on GNSS (Global Navigation Satellite System) and meteorological sensor measurement is introduced and investigated in this study. The variation of the correlation between zenith wet delay and water vapor pressure is investigated and analyzed in detail. The partial pressure profiles of water vapor are retrieved with relevance vector machine method based on tropospheric zenith wet delay calculated by single ground-based GPS (Global Positioning System) receiver. The atmospheric temperature and pressure is calculated with the least square method, which is used to fit the coefficients of the polynomial model based on a large number of historical meteorological radiosonde data of local stations. By combining the water vapor pressure profile retrieving from single ground-based GPS and temperature and pressure profile from reference model, the refractivity profile can be obtained, which is compared to radiosonde measurements. The comparison results show that results of the proposed method are consistent with the results of radiosonde. By using over ten years’ (through 2008 to 2017) historical radiosonde meteorological data of different months at China Big-Triangle Points, i.e., Qingdao, Sanya, Kashi, and Jiamusi radiosonde stations, tropospheric radio refractivity profiles are retrieved and modeled. The comparison results present that the accuracies of refractivity profile of the proposed method at Qingdao, Sanya, Kashi, and Jiamusi are about 5.48, 5.63, 3.58, and 3.78 N-unit, respectively, and the annual average relative RMSE of refractivity at these stations are about 1.66, 1.53, 1.49, and 1.23%, respectively.
Highlights
Introduction published maps and institutional affilIt is well-known that refraction and time delay occur when electromagnetic wave propagates in the atmosphere as the changing properties of the atmosphere, the speed of propagation is altered, and the propagation path is changed, which lead to a refraction error that severely disrupts the accuracy of radar detection, space tracking, satellite surveying and mapping, navigation, etc
The retrieval refractivity profile can be obtained by Equation (3) after the profile of water vapor pressure, temperature profile, and pressure profile is calculated
By analyzing the varithe correlation between zenith wet delay and water vapor pressure based on over ten ations of the correlation between zenith wet delay and water vapor pressure based on years’ radiosonde meteorological data at Qingdao, Sanya, Kashi, and Jiamusi stations with over ten years’ radiosonde meteorological data at Qingdao, Sanya, Kashi, and Jiamusi different months, we found that there is a good linear correlation between zenith wet delay stations with different months, we found that there is a good linear correlation between and water vapor pressure at different heights
Summary
Introduction published maps and institutional affilIt is well-known that refraction and time delay occur when electromagnetic wave propagates in the atmosphere as the changing properties of the atmosphere, the speed of propagation is altered, and the propagation path is changed, which lead to a refraction error that severely disrupts the accuracy of radar detection, space tracking, satellite surveying and mapping, navigation, etc. As its variation in height is much greater than the horizontal direction, the accounting of atmospheric refractivity with different height is required so as to improve target tracking and navigation accuracy [5,6,7,8]. For this reason, some different atmospheric profile models were investigated based on a large number of data and theory. Hopfield model [9] and segmented model [10], as relatively accurate atmospheric prediction iations
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