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
Cloud cover plays a pivotal role in assessing observational conditions for astronomical site-testing.Except for the fraction of observing time,its fragmentation also wields a significant influence on the quality of ni...Cloud cover plays a pivotal role in assessing observational conditions for astronomical site-testing.Except for the fraction of observing time,its fragmentation also wields a significant influence on the quality of nighttime sky clarity.In this article,we introduce the functionΓ∈[0,1],designed to comprehensively capture both the fraction of available observing time and its continuity.Leveraging in situ measurement data gathered at the Muztagh-Ata site between 2017 and 2021,we showcase the effectiveness of our approach.The statistical result illustrates that the Muztagh-Ata site affords approximately 122 nights that were absolutely clear and 205 very good nights annually,corresponding toΓ≥0.9 andΓ≥0.36 respectively.展开更多
The clarity of nights is the major factor that should be carefully considered for optical/infrared astronomical observatories in site-testing campaigns.Cloud coverage is directly related to the amount of time availabl...The clarity of nights is the major factor that should be carefully considered for optical/infrared astronomical observatories in site-testing campaigns.Cloud coverage is directly related to the amount of time available for scientific observations at observatories.In this article,we report on the results of detailed night-time cloud statistics and continuous observing time derived from ground-based all-sky cameras(ASCs)at the Muztagh-ata site from 2017to 2021.Results obtained from acquisition data show that the proportion of the annual observing time at the Muztagh-ata site is 65%,and the best period with the least cloud coverage and longer continuous observing time is from September to February.We made a comparison of the monthly mean observing nights obtained from our ASC and CLARA data set,and results show that the discrepancy between them may depend on the cloud top heights.On average,this site can provide 175 clear nights and 169 nights with at least 4 hr of continuous observing time per year.展开更多
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.
基金supported by the Chinese Academy of Science(CAS)“Light of West China”Program(No.2022_XBQNXZ_014)the Joint Research Fund in Astronomy under a cooperative agreement between the National Natural Science Foundation of China(NSFC),the Chinese Academy of Sciences(CAS)(grant No.U2031209)+1 种基金the Xinjiang Natural Science Foundation(grant No.2022D01A357)the National Natural Science Foundation of China(NSFC,grant No.11873081)。
文摘Cloud cover plays a pivotal role in assessing observational conditions for astronomical site-testing.Except for the fraction of observing time,its fragmentation also wields a significant influence on the quality of nighttime sky clarity.In this article,we introduce the functionΓ∈[0,1],designed to comprehensively capture both the fraction of available observing time and its continuity.Leveraging in situ measurement data gathered at the Muztagh-Ata site between 2017 and 2021,we showcase the effectiveness of our approach.The statistical result illustrates that the Muztagh-Ata site affords approximately 122 nights that were absolutely clear and 205 very good nights annually,corresponding toΓ≥0.9 andΓ≥0.36 respectively.
基金supported by the Chinese Academy of Sciences (CAS) “Light of West China”Program (No.2022_XBQNXZ_014)the Xinjiang Natural Science Foundation (Grant No.2022D01A357)+2 种基金the Joint Research Fund in Astronomy under a cooperative agreement between the National Natural Science Foundation of China (NSFC)and the CAS (Grant No.U2031209)the NSFC (Grant Nos.11873081,11603065,and 12073047)resource sharing platform construction project of Xinjiang Uygur Autonomous Region (No.PT2306)。
文摘The clarity of nights is the major factor that should be carefully considered for optical/infrared astronomical observatories in site-testing campaigns.Cloud coverage is directly related to the amount of time available for scientific observations at observatories.In this article,we report on the results of detailed night-time cloud statistics and continuous observing time derived from ground-based all-sky cameras(ASCs)at the Muztagh-ata site from 2017to 2021.Results obtained from acquisition data show that the proportion of the annual observing time at the Muztagh-ata site is 65%,and the best period with the least cloud coverage and longer continuous observing time is from September to February.We made a comparison of the monthly mean observing nights obtained from our ASC and CLARA data set,and results show that the discrepancy between them may depend on the cloud top heights.On average,this site can provide 175 clear nights and 169 nights with at least 4 hr of continuous observing time per year.
