Selecting a good site for ground-based astronomy is very important. Based on the ERA-Interim global reanalysis data, this paper studied the atmospheric conditions of the Qitai Telescope(QTT) site from the aspects of a...Selecting a good site for ground-based astronomy is very important. Based on the ERA-Interim global reanalysis data, this paper studied the atmospheric conditions of the Qitai Telescope(QTT) site from the aspects of absolute humidity, mixing ratio and precipitable water vapor(PWV). Error estimations of meteorological parameters are also analyzed. These primary results show that the QTT site has obvious advantages in terms of conditions with much less atmospheric water vapor than two well-known existing sites with 100-meter-aperture radio telescopes in the world. In addition, due to the influence of atmospheric water vapor on radio observations, the atmospheric transmittance for each frequency band of the site are simulated, and the atmospheric opacity is also calculated as well as Planck radiation brightness. Based on these results, the effective observational time of different bands is further estimated.展开更多
Accurate estimation of Zenith Tropospheric Delay(ZTD)is essential for mitigating atmospheric effects in radio astronomical observations and improving the retrieval of precipitable water vapor(PWV).In this study,we fir...Accurate estimation of Zenith Tropospheric Delay(ZTD)is essential for mitigating atmospheric effects in radio astronomical observations and improving the retrieval of precipitable water vapor(PWV).In this study,we first analyze the periodic characteristics of ZTD at the NanShan Radio Telescope site using Fourier transform,revealing its dominant seasonal variations,and then investigate the correlation between ZTD and local meteorological parameters,to better understand atmospheric influences on tropospheric delay.Based on these analyses,we propose a hybrid deep learning Gated Recurrent Units-Long Short-Term Memory model,incorporating meteorological parameters as external inputs to enhance ZTD forecasting accuracy.Experimental results demonstrate that the proposed approach achieves a Root Mean Squared Error of 7.97 mm and a correlation coefficient R of 96%,significantly outperforming traditional empirical models and standalone deep learning architectures.These findings indicate that the model effectively captures both short-term dynamics and long-term dependencies in ZTD variations.The improved ZTD predictions not only contribute to reducing atmospheric errors in radio astronomical observations but also provide a more reliable basis for PWV retrieval and forecasting.This study highlights the potential of deep learning in tropospheric delay modeling,offering advancements in both atmospheric science and geodetic applications.展开更多
文摘Selecting a good site for ground-based astronomy is very important. Based on the ERA-Interim global reanalysis data, this paper studied the atmospheric conditions of the Qitai Telescope(QTT) site from the aspects of absolute humidity, mixing ratio and precipitable water vapor(PWV). Error estimations of meteorological parameters are also analyzed. These primary results show that the QTT site has obvious advantages in terms of conditions with much less atmospheric water vapor than two well-known existing sites with 100-meter-aperture radio telescopes in the world. In addition, due to the influence of atmospheric water vapor on radio observations, the atmospheric transmittance for each frequency band of the site are simulated, and the atmospheric opacity is also calculated as well as Planck radiation brightness. Based on these results, the effective observational time of different bands is further estimated.
基金funded by the CAS“Light of West China”Program(grant Nos.2021-XBQNXZ-030 and 2021-XBQNXZ-005)the Xinjiang Key Laboratory of Radio Astrophysics(grant No.2023D04064)the National Key R&D Program of China(grant No.2024YFA1611503)。
文摘Accurate estimation of Zenith Tropospheric Delay(ZTD)is essential for mitigating atmospheric effects in radio astronomical observations and improving the retrieval of precipitable water vapor(PWV).In this study,we first analyze the periodic characteristics of ZTD at the NanShan Radio Telescope site using Fourier transform,revealing its dominant seasonal variations,and then investigate the correlation between ZTD and local meteorological parameters,to better understand atmospheric influences on tropospheric delay.Based on these analyses,we propose a hybrid deep learning Gated Recurrent Units-Long Short-Term Memory model,incorporating meteorological parameters as external inputs to enhance ZTD forecasting accuracy.Experimental results demonstrate that the proposed approach achieves a Root Mean Squared Error of 7.97 mm and a correlation coefficient R of 96%,significantly outperforming traditional empirical models and standalone deep learning architectures.These findings indicate that the model effectively captures both short-term dynamics and long-term dependencies in ZTD variations.The improved ZTD predictions not only contribute to reducing atmospheric errors in radio astronomical observations but also provide a more reliable basis for PWV retrieval and forecasting.This study highlights the potential of deep learning in tropospheric delay modeling,offering advancements in both atmospheric science and geodetic applications.
