پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,252 |
| تعداد مقالات | 67,992 |
| تعداد مشاهده مقاله | 116,059,427 |
| تعداد دریافت فایل اصل مقاله | 90,824,095 |
مقایسه مدل‌های تأخیر تروپوسفری با مقادیر زمین مبنای GPS ZTD در جو ایران | ||
| فیزیک زمین و فضا | ||
| مقاله 2، دوره 50، شماره 1، اردیبهشت 1403، صفحه 23-36 اصل مقاله (1.35 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2023.353897.1007493 | ||
| نویسندگان | ||
| علی سام خانیانی* ؛ روح الله نائیجیان | ||
| گروه مهندسی نقشه بردای، دانشکده مهندسی عمران، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران. | ||
| چکیده | ||
| برای تعیین دقیق موقعیت به‌کمک GPS چندین منبع خطا وجود دارد که باید در نظر گرفته شود. یکی از این منابع خطا، تأخیر تروپوسفری سیگنال است که برآورد دقیق آن منجربه افزایش دقت تعیین موقعیت در ناوبری و همچنین محاسبه دقیق بخارآب قابل بارش برای هواشناسان و اقلیم‌شناسان می‌شود. علاوه‌بر پردازش مشاهدات GPS، استفاده از مدل‌های جهانی یا منطقه‌ای تجربی و یا مدل‌های مبتنی‌بر داده‌های هواشناسی سطحی از جمله روش‌های محاسبه تأخیر تروپوسفری زنیتی (ZTD) به حساب می‌آیند. ارزیابی دقت و صحت این مدل‌ها در هر منطقه قبل از استفاده در کاربری مورد نظر، امری ضروری است. در این مطالعه، به‌کمک یک سال از برآوردهای ZTD به‌دست‌آمده از پردازش مشاهدات GPS در 28 ایستگاه واقع در منطقه ایران، کیفیت آماری مدل‌های هاپفیلد، ساستاموینن، HGPT2 و GTrop بررسی شد. بر اساس نتایج، میانگین RMSE مقادیر یک‌سال از ZTD محاسبه شده به‌کمک مدل‌های هاپفیلد، HGPT2، Gtrop و ساستاموینن به‌ترتیب 75، 8/38، 7/31 و1/26 میلی‌متر برآورد شد. همچنین، میانگین بایاس مقادیر ZTD حاصل از مدل‌های هاپفیلد، HGPT2، GTrop و ساستاموینن در کل منطقه به‌ترتیب 8/69-، 9/18، 4/5 و 8/10- میلی‌متر به‌دست آمد. براساس مقادیر ضرایب همبستگی، مدل هاپفیلد و ساستاموینن بیشترین همخوانی را با مقادیر ZTD حاصل از پردازش مشاهدات GPS زمینی داشتند. نتایج این مطالعه، نشان داد که به‌طورکلی مدل ساستاموینن نسبت به سه مدل دیگراز لحاظ آماری کارایی بیشتری دارد اما برای رسیدن به دقت‌های مناسب نیاز است که مدلی مناسب با منطقه ایران توسعه داده شود. | ||
| کلیدواژه‌ها | ||
| تأخیر تروپوسفری زنیتی؛ GPS؛ ساستاموینن؛ GTrop | ||
| عنوان مقاله [English] | ||
| Comparison of tropospheric delay models using ground based GPS ZTD values in the atmosphere of Iran | ||
| نویسندگان [English] | ||
| Ali Sam-Khaniani؛ Rohollah Naeijian | ||
| Department of Surveying Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran. | ||
| چکیده [English] | ||
| There are several sources of error that must be considered for accurate GPS positioning. One of these sources of error is the tropospheric delay of the signal, whose accurate estimation leads to an increase in the accuracy of positioning in navigation, as well as the accurate calculation of precipitable water vapor for meteorological and climatological applications. One of the accurate methods in determining ZTD values is to estimate it along with the coordinate components of ground stations using GPS observation processing. However, it is not possible to access permanent GPS receivers in all places and it is expensive. In addition to permanent GPS station data processing, the use of atmospheric profiles obtained from the radiosonde launch at each station is one of the other conventional methods for calculating the tropospheric zenith delay. The low temporal resolution of radiosonde observations (usually twice a day) and the high cost are the main limitations of this method. Moreover, the use of global or regional empirical models or models based on surface meteorological data are among the methods of calculating Zenith Tropospheric Delay (ZTD). It is necessary to evaluate the accuracy and precision of these models in each region before using them in the intended application. Iran has diverse topography and climatic conditions, so different tropospheric delay models may have different statistical quality in Iran compared to other regions. On the other hand, until today, no comprehensive research has been done in the region of Iran to evaluate the different tropospheric delay models presented in recent years. Empirical ZTD models presented in recent years are a function of position, place and time and some models, such as Hopfield and Sastamoinen ZTD models are known as famous models based on surface meteorological data. Also, according to research conducted in other parts of the world, HGPT2 and GTrop models are among the successful global emperical models in ZTD estimation that have been proposed in recent years and operate independently of surface meteorological parameters. In this study, with the help of one year of ZTD estimates obtained from the processing of GPS observations in 28 stations located in the region of Iran, the statistical qualities of Hopfield, Sastamoinen, HGPT2 and GTrop models were investigated. Based on the results, the average RMSE values of one year of ZTD calculated with the help of Hopfield, HGPT2, Gtrop and Sastamoinen models were estimated to be 75, 38.8, 31.7 and 26.1 mm, respectively. Also, the average biases of ZTD values obtained from Hopfield, HGPT2, GTrop and Sastamoinen models in the whole region were -8.69, 18.9, 5.4 and -10.8 mm, respectively. The models of Hopfield and Sastämöinen were the most consistent with the ZTD values obtained from the processing of ground GPS observations. The results of this study showed that, in general, the Sastamoinen model is statistically more efficient than the other three models, but in order to achieve proper accuracy, it is necessary to develop a model suitable for the region of Iran. | ||
| کلیدواژه‌ها [English] | ||
| Zenith Tropospheric Delay, GPS, Saastamoinen, GTrop | ||
| مراجع | ||
|
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., & Ware, R. H. (1992). GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system. Journal of Geophysical Research: Atmospheres, 97(D14), 15787-15801. https://doi.org/10.1029/92JD01517 Black, H. D., & Eisner, A. (1984). Correcting satellite Doppler data for tropospheric effects. Journal of Geophysical Research: Atmospheres. Wiley Online Library, 89(D2), 2616–2626. Boehm, J., Heinkelmann, R. & Schuh, H. (2007). Short Note: A global model of pressure and temperature for geodetic applications. J. Geod, 81, 679–683. https://doi.org/10.1007/s00190-007-0135-3 Böhm, J., Möller, G., Schindelegger, M., Pain, G., & Weber, R. (2015). Development of an improved empirical model for slant delays in the troposphere (GPT2w). GPS Solut, 19, 433–441. https://doi.org/10.1007/s10291-014-0403-7 Chen, J., Wang, J., Wang, A., Ding, J., & Zhang, Y. (2020). SHAtropE—A regional gridded ZTD model for China and the surrounding areas. Remote Sensing, 12(1), 165. https://doi.org/10.3390/rs12010165 Collins, J. P., & Langley, R. B. (1998). The residual tropospheric propagation delay: How bad can it get?, Proceedings of the 11th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1998), Nashville, TN, September 1998, 729-738. Davis, J. L., Herring, T. A., Shapiro, I. I., Rogers, A. E. E., & Elgered, G. (1985). Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length. Radio science, 20(6), 1593 1607. Ding, M., Hu, W., Jin, X., & Yu, L. (2016). A new ZTD model based on permanent ground-based GNSS-ZTD data. Survey review, 48(351), 385-391. https://doi.org/10.1179/1752270615Y.0000000034 Duan, J., Bevis, M., Fang, P., Bock, Y., Chiswell, S., Businger, S., Rocken, C., Solheim, F., Hove, T., Ware, R., McClusky, S., Herring, T., & King, R. W. (1996). GPS meteorology: Direct estimation of the absolute value of precipitable water. Journal of Applied Meteorology and Climatology, 35, 830–838. https:// doi. org/ 10. 1175/ 1520- 0450(1996) 0352.0. CO;2. Hopfield, H. S. (1969). Two-quartic tropospheric refractivity profile for correcting satellite data. Journal of Geophysical research. Wiley Online Library, 74(18), 4487–4499. doi: 10.1029/JC074i018p04487 Katsougiannopoulos, S., Pikridas, C., Rossikopoulos, D., Ifadis, I., & Fotiou, A. (2006). Tropospheric refraction estimation using various models, radiosonde measurements and permanent GPS data. PS5. 4–GNSS Processing and Applications, 15. Landskron, D., & Böhm, J. (2018). VMF3/GPT3: refined discrete and empirical troposphere mapping functions. J. Geod, 92, 349–360. https://doi.org/10.1007/s00190-017-1066-2 Leandro, R.F., Langley, R.B. & Santos, M.C. (2008). UNB3m_pack: a neutral atmosphere delay package for radiometric space techniques. GPS Solut, 12, 65–70. https://doi.org/10.1007/s10291-007-0077-5 Li, W., Yuan, Y., Ou, J., Li, H., & Li, Z. (2012). A new global zenith tropospheric delay model IGGtrop for GNSS applications. Chin. Sci. Bull., 57, 2132–2139. https://doi.org/10.1007/s11434-012-5010-9 Li, X., Dick, G., Ge, M., Heise, S., Wickert, J., & Bender, M. (2014). Real-time GPS sensing of atmospheric water vapor: Precise point positioning with orbit, clock, and phase delay corrections. Geophysical Research Letter, 41(10), 3615–3621. https:// doi. org/ 10. 1002/ 2013G L0587 21. Mao, J., Wang, Q., Liang, Y., & Cui, T. (2021). A new simplified zenith tropospheric delay model for real-time GNSS applications. GPS Solut, 25, 43 (2021). https://doi.org/10.1007/s10291-021-01092-4 Mateus, P., Catalão, J., Mendes, V. B., & Nico, G. (2020). An ERA5-based hourly global pressure and temperature (HGPT) model. Remote Sensing, 12(7), 1098. https://doi.org/10.3390/rs12071098 Mateus, P., Mendes, V. B., & Plecha, S. M. (2021). HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity. Remote Sensing, 13(11), 2179. https://doi.org/10.3390/rs13112179 Niell, A. E., Coster, A. J., Solheim, F. S., Mendes, V. B., Toor, P. C., Langley, R. B., & Upham, C. A. (2001). Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI. Journal of Atmospheric and Oceanic Technology, 18(6), 830-850. https://doi.org/10.1175/1520-0426(2001)018%3C0830:COMOAW%3E2.0.CO;2 Penna, N., Dodson, A., & Chen, W. (2001). Assessment of EGNOS Tropospheric Correction Model. Journal of Navigation, 54(1), 37-55. doi:10.1017/S0373463300001107 Pikridas, C., Katsougiannopoulos, S. & Zinas, N. (2014). A comparative study of zenith tropospheric delay and precipitable water vapor estimates using scientific GPS processing software and web based automated PPP service. Acta Geod Geophys, 49, 177–188. https://doi.org/10.1007/s40328-014-0047-7 Saastamoinen, J. (1972). Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. The use of artificial satellites for geodesy, 15, 247-251. https://doi.org/10.1029/GM015p0247 Showman, A. P., & Dowling, T. E. (2014). Earth as a planet: Atmosphere and Oceans. In Encyclopedia of the solar system (pp. 423-444). Elsevier. https://doi.org/10.1016/B978-0-12-415845-0.00020-7 Soja, B., Nilsson, T., Karbon, M., Zus, F., Dick, G., Deng, Z., Wickert, J., Heinkelmann, R., & Schuh, H. (2015). Tropospheric delay determination by Kalman filtering VLBI data. Earth Planet Sp, 67, 144 (2015). https://doi.org/10.1186/s40623-015-0293-0 Sun, L., Chen, P., Wei, E., & Li, Q. (2017). Global model of zenith tropospheric delay proposed based on EOF analysis. Advances in Space Research, 60(1), 187-198. https://doi.org/10.1016/j.asr.2017.03.045. Sun, Z., Zhang, B., & Yao, Y. (2019). A global model for estimating tropospheric delay and weighted mean temperature developed with atmospheric reanalysis data from 1979 to 2017. Remote Sensing, 11(16), 1893. https://doi.org/10.3390/rs11161893. Teke, K., Nilsson, T., Böhm, J., Hobiger, T., Steigenberger, P., García-Espada, S., Haas, R., & Willis, P. (2013). Troposphere delays from space geodetic techniques, water vapor radiometers, and numerical weather models over a series of continuous VLBI campaigns. J. Geod, 87, 981–1001. https://doi.org/10.1007/s00190-013-0662-z Teten O. (1930). Über einige meteorologische Begriffe. Z. Geophys., 6. 297-309. Yang, L., Gao, J., Zhu, D., Zheng, N., & Li, Z. (2020). Improved zenith tropospheric delay modeling using the piecewise model of atmospheric refractivity. Remote Sensing, 12(23), 3876. https://doi.org/10.3390/rs12233876 Yao, Y. B., HE, C. Y., Zhang, B., & XU, C. Q. (2013). A new global zenith tropospheric delay model GZTD. Chinese Journal of Geophysics, 56(7), 2218-2227. Yao, Y., Hu, Y., Yu, C., Zhang, B., & Guo, J. (2016). An improved global zenith tropospheric delay model GZTD2 considering diurnal variations. Nonlinear processes in geophysics, 23(3), 127-136. Yao, Y., Xu, X., Xu, C., Peng, W., & Wan, Y. (2019). Establishment of a real-time local tropospheric fusion model. Remote Sensing, 11(11), 1321. https://doi.org/10.3390/rs11111321 Zheng, F., Lou, Y., Gu, S., Gong, X., & Shi, C. (2018). Modeling tropospheric wet delays with national GNSS reference network in China for BeiDou precise point positioning. Journal of Geodesy, 92, 545–560. https:// doi. org/ 10. 1007/ s00190- 017- 1080-4. Zumberge, J. F., Heflin, M. B., Jefferson, D. C., Watkins, M. M., & Webb, F. H. (1997). Precise point positioning for the efficient and robust analysis of GPS data from large networks. Journal of geophysical research: solid earth, 102(B3), 5005-5017. | ||
|
آمار تعداد مشاهده مقاله: 272 تعداد دریافت فایل اصل مقاله: 250 |
||