تحلیل تغییرات بخار آب بارش‌شو وردسپهری با استفاده از داده‌های اختفای رادیویی سامانه‌های ماهواره‌ای ناوبری جهانی و مشاهدات رادیوسوند (مطالعه موردی: ایران)

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه ژئودزی، دانشکده مهندسی نقشه‌برداری، دانشگاه صنعتی خواجه‌نصیرالدین‌طوسی، تهران، ایران.

چکیده

این مطالعه به بررسی تغییرات بخار آب بارش‌شو (PWV) در مناطق مختلف ایران با استفاده از داده‌های اختفای رادیویی سامانه  COSMICو مشاهدات رادیوسوند طی بازه زمانی 2007 تا 2019 می‌پردازد. هدف اصلی پژوهش، ارزیابی دقت مقادیر  PWVحاصل از COSMIC در مقایسه با داده‌های مرجع رادیوسوند و تعیین میزان خطا در ایستگاه‌های مختلف است. در این راستا، از 2398 رخداد COSMIC در شعاع 150 کیلومتری 12 ایستگاه رادیوسوند استفاده شده است. نتایج نشان داد که مقادیر PWV حاصل از  COSMICالگوهای تغییرات مشابهی با داده‌های رادیوسوند دنبال می‌کنند، اما میزان انطباق در مناطق مختلف متفاوت است. میانگین خطای مطلق (MAE)، متوسط ریشه‌میانگین‌مربعات (RMSE)، و بایاس به‌ترتیب حدود 4.48، 5.65 و 3.44 میلی‌متر محاسبه شدند. ایستگاه اهواز با  RMSEبرابر با 7.92 میلی‌متر بیشترین خطا و ایستگاه کرمانشاه با RMSE  برابر با 4.69 میلی‌متر کمترین خطا را نشان دادند. تحلیل همبستگی نشان داد که شیب خط رگرسیون در اکثر ایستگاه‌ها کمتر از 1 است، که بیانگر این است که مقادیر PWV حاصل از رادیوسوند در مقادیر بالا کمتر از COSMIC است. علاوه بر این، عرض از مبدأ مثبت در تمام معادلات نشان داد که در مقادیر پایین PWV، اندازه‌گیری‌های رادیوسوند تمایل به بیشتر بودن نسبت به مقادیر COSMIC دارند. با توجه به نتایج به‌دست آمده، داده‌های COSMIC قابلیت ارائه برآوردی از PWV در مناطق فاقد داده‌های زمینی را دارند، اما میزان دقت این داده‌ها بسته به موقعیت جغرافیایی و شرایط مختلف متغیر است و نیازمند ارزیابی موردی برای هر منطقه و کاربرد خاص می‌باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Analysis of Tropospheric Precipitable Water Vapor Variations Using GNSS Radio Occultation Data and Radiosonde Observations (Case Study: Iran)

نویسندگان [English]

  • Arash Tayfehrostami
  • Yazdan Amerian
Department of Geodesy, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
چکیده [English]

This study investigates the variations of precipitable water vapor (PWV) in Iran using data from the COSMIC satellite mission’s radio occultation (RO) events and radiosonde observations over the period 2007–2019. PWV plays a crucial role in atmospheric energy transfer, the water cycle, and climate variability, making its accurate monitoring essential for meteorological and climatic studies. Currently, PWV is measured through ground-based systems like radiosondes, sun photometers, and microwave radiometers, as well as space-based methods such as GNSS RO, MODIS, and AIRS. While radiosondes provide reliable reference data due to their high accuracy, they suffer from limitations such as sparse spatial coverage and low temporal resolution. In contrast, space-based techniques like COSMIC offer a global coverage and high vertical resolution without being affected by clouds or precipitation, making them particularly valuable in regions with limited ground-based infrastructure. This study utilized 2,398 COSMIC RO events within a 150-kilometer radius of 12 radiosonde stations distributed across Iran, spanning latitudes of 24°N to 41°N and longitudes of 43°E to 64°E. Radiosonde data were preprocessed to remove outliers based on predefined criteria, such as excessive altitude differences between consecutive pressure levels or insufficient vertical layers. PWV values were calculated from both datasets using numerical integration of atmospheric parameters, and statistical metrics like RMSE and MAE were employed to evaluate the agreement between the two sources.
Results indicate that COSMIC-derived PWV generally follows similar trends to radiosonde measurements, but the level of agreement varies across stations. Southern stations like Ahwaz and Bandar Abbas, characterized by humid climates, exhibited higher PWV values and greater discrepancies compared to northern and central stations. The RMSE values ranged from 4.69 mm (Kermanshah) to 7.92 mm (Ahwaz), with an overall mean RMSE of 5.65 mm. Similarly, MAE values varied between 3.72 mm (Kermanshah) and 6.30 mm (Ahwaz), yielding an average MAE of 4.48 mm. Correlation analysis revealed positive relationships between the two datasets, but regression slopes were consistently below 1, indicating that radiosonde measurements tend to underestimate PWV at higher values and overestimate it at lower values compared to COSMIC. The intercepts of the regression equations were positive across all stations, further confirming this trend. Spatially, southern stations demonstrated higher errors, likely due to the complex moisture patterns and high humidity levels in these regions. Despite these discrepancies, the findings suggest that COSMIC-derived PWV can serve as a reliable alternative or supplement to radiosonde measurements, especially in regions lacking sufficient ground-based observational networks. This research highlights the potential of COSMIC data to enhance numerical weather prediction (NWP) and climate studies in underserved areas, while also emphasizing the need for further investigation into the factors influencing the accuracy of COSMIC PWV retrievals under varying atmospheric conditions. Future studies should be focused on improving retrieval algorithms and integrating multi-source data to refine PWV estimation accuracy, particularly in challenging environments like Iran. This work provides a foundation for advancing atmospheric research and operational meteorology in regions with limited access to traditional ground-based data.

کلیدواژه‌ها [English]

  • Precipitable Water Vapor
  • COSMIC
  • Radiosonde
  • GNSS Radio Occultation
  • Space Geodesy
  • Earth Atmosphere

مراجع

 Anthes, R. A. (2011). Exploring Earth’s atmosphere with radio occultation: Contributions to weather, climate and space weather. Atmospheric Measurement Techniques, 4(6), 1077–1103. https://doi.org/10.5194/AMT-4-1077-2011
Anthes, R. A., Bernhardt, P. A., Chen, Y., Cucurull, L., Dymond, K. F., Ector, D., Healy, S. B., Ho, S.-P., Hunt, D. C., Kuo, Y.-H., & others. (2008). The COSMIC/FORMOSAT-3 mission: Early results. Bulletin of the American Meteorological Society, 89(3), 313–334.
Anthes, R., Sjoberg, J., Feng, X., & Syndergaard, S. (2022). Comparison of COSMIC and COSMIC-2 Radio Occultation Refractivity and Bending Angle Uncertainties in August 2006 and 2021. Atmosphere, 13(5), 790. https://doi.org/10.3390/atmos13050790
Bock, O., & Nuret, M. (2009). Verification of NWP model analyses and radiosonde humidity data with GPS precipitable water vapor estimates during AMMA. Weather and Forecasting, 24(4), 1085–1101.
Fonseca, Y. B., Alexander, P., de la Torre, A., Hierro, R., LLamedo, P., & Calori, A. (2018). Comparison between GNSS ground-based and GPS radio occultation precipitable water observations over ocean-dominated regions. Atmospheric Research, 209, 115–122.
Gong, S., Hagan, D. F. T., Wu, X., & Wang, G. (2018). Spatio-temporal analysis of precipitable water vapour over northwest China utilizing MERSI/FY-3A products. International Journal of Remote Sensing, 39(10), 3094–3110. https://doi.org/10.1080/01431161.2018.1437298
Haji-Aghajany, S., Amerian, Y., Verhagen, S., Rohm, W., & Schuh, H. (2021). The effect of function-based and voxel-based tropospheric tomography techniques on the GNSS positioning accuracy. Journal of Geodesy, 95(7), 1–17. https://doi.org/10.1007/S00190-021-01528-2/METRICS
Ho, S. P., Peng, L., Mears, C., & Anthes, R. A. (2018). Comparison of global observations and trends of total precipitable water derived from microwave radiometers and COSMIC radio occultation from 2006 to 2013. Atmospheric Chemistry and Physics, 18(1), 259–274. https://doi.org/10.5194/ACP-18-259-2018
Izanlou, S., Haji-Aghajany, S., & Amerian, Y. (2024). Enhanced Troposphere Tomography: Integration of GNSS and Remote Sensing Data With Optimal Vertical Constraints. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 3701–3714. https://doi.org/10.1109/JSTARS.2024.3354884
Jacob, D. (2001). The role of water vapour in the atmosphere. A short overview from a climate modeller’s point of view. Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 26(6–8), 523–527.
Kishore, P., Venkat Ratnam, M., Namboothiri, S. P., Velicogna, I., Basha, G., Jiang, J. H., Igarashi, K., Rao, S. V. B., & Sivakumar, V. (2011). Global (50°S–50°N) distribution of water vapor observed by COSMIC GPS RO: Comparison with GPS radiosonde, NCEP, ERA-Interim, and JRA-25 reanalysis data sets. Journal of Atmospheric and Solar-Terrestrial Physics, 73(13), 1849–1860. https://doi.org/10.1016/J.JASTP.2011.04.017
Kursinski, E. R., Hajj, G. A., Schofield, J. T., Linfield, R. P., & Hardy, K. R. (1997). Observing Earth’s atmosphere with radio occultation measurements using the Global Positioning System. Journal of Geophysical Research: Atmospheres, 102(D19), 23429–23465. https://doi.org/10.1029/97JD01569
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 Letters, 41(10), 3615–3621.
Lien, T. Y., Yeh, T. K., Wang, C. S., Xu, Y., Jiang, N., & Yang, S. C. (2024). Accuracy verification of the precipitable water vapor derived from COSMIC-2 radio occultation using ground-based GNSS. Advances in Space Research, 73(9), 4597–4607. https://doi.org/10.1016/J.ASR.2024.01.041
Liu, Z., Li, M., Zhong, W., & Wong, M. S. (2013). An approach to evaluate the absolute accuracy of WVR water vapor measurements inferred from multiple water vapor techniques. Journal of Geodynamics, 72, 86–94.
Mateus, P., Mendes, V. B., & Pires, C. A. L. (2022). Global Empirical Models for Tropopause Height Determination. Remote Sensing, 14(17), 4303. https://doi.org/10.3390/rs14174303
Mears, C. A., Wang, J., Smith, D., & Wentz, F. J. (2015). Intercomparison of total precipitable water measurements made by satellite-borne microwave radiometers and ground-based GPS instruments. Journal of Geophysical Research, 120(6), 2492–2504. https://doi.org/10.1002/2014JD022694
Meng, X., Cheng, J., & Liang, S. (2017). Estimating land surface temperature from Feng Yun-3C/MERSI data using a new land surface emissivity scheme. Remote Sensing, 9(12), 1247.
Mohammadi Ahoei, M. A., & Sam-Khaniani, A. (2024). Using the multivariate linear regression method to model the 2-meter air temperature from MODIS sensor data. Journal of the Earth and Space Physics, 50(3), 803–821. https://doi.org/10.22059/JESPHYS.2024.376789.1007609
Rahimi, H., Asgari, J., & Nafisi, V. (2023). The effect of data sources on calculating mean temperature and integrated water vapor in Iran. Meteorological Applications, 30(6), e2167. https://doi.org/10.1002/met.2167
SamKhaniani, A. (2023). Evaluation of GPS RO derived precipitable water vapor against ground-based GPS observations over Iran. Iranian Journal of Geophysics, 16(4), 85-100. doi: 10.30499/ijg.2022.343984.1426
Sam-Khaniani, A., & Naeijian, R. (2024). Comparison of tropospheric delay models using ground based GPS ZTD values in the atmosphere of Iran. Journal of the Earth and Space Physics, 50(1), 23–36. https://doi.org/10.22059/JESPHYS.2023.353897.1007493
Sharifi, M. A., Azadi, M., & Khaniani, A. S. (2016). Numerical simulation of rainfall with assimilation of conventional and GPS observations over north of Iran. Annals of Geophysics, 59(3). https://doi.org/10.4401/ag-6919
Shi-Jie, F., Zang, J.-F., Peng, X.-Y., Su-Qin, W., Yan-Xiong, L., & Ke-Fei, Z. (2016). Validation of atmospheric water vapor derived from ship-borne GPS measurements in the Chinese Bohai Sea. TAO: Terrestrial, Atmospheric and Oceanic Sciences, 27(2), 2.
Shi, J., Xu, C., Guo, J., & Gao, Y. (2014). Real-time GPS precise point positioning-based precipitable water vapor estimation for rainfall monitoring and forecasting. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3452–3459.
Sun, P., Wu, S., Zhang, K., Wan, M., & Wang, R. (2021). A new global grid-based weighted mean temperature model considering vertical nonlinear variation. Atmospheric Measurement Techniques, 14(3), 2529–2542. https://doi.org/10.5194/amt-14-2529-2021
Sun, Y., Yang, F., Liu, M., Li, Z., Gong, X., & Wang, Y. (2023). Evaluation of the weighted mean temperature over China using multiple reanalysis data and radiosonde. Atmospheric Research, 285, 106664. https://doi.org/10.1016/j.atmosres.2023.106664
Wang, J., Zhang, L., Dai, A., Van Hove, T., & Van Baelen, J. (2007). A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements. Journal of Geophysical Research: Atmospheres, 112(D11).
Wang, X., Zhang, K., Wu, S., Fan, S., & Cheng, Y. (2016). Water vapor-weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend. Journal of Geophysical Research, 121(2), 833–852. https://doi.org/10.1002/2015JD024181
Ward, D. M., Kursinski, E. R., Otarola, A. C., Stovern, M., McGhee, J., Young, A., Hainsworth, J., Hagen, J., Sisk, W., & Reed, H. (2017). Retrieval of water vapor using ground-based observations from a prototype ATOMMS active cm-and mm-wavelength occultation instrument. Atmospheric Measurement Techniques Discussions, 1–36.
Wick, G. A., Kuo, Y.-H., Ralph, F. M., Wee, T.-K., & Neiman, P. J. (2008). Intercomparison of integrated water vapor retrievals from SSM/I and COSMIC. Geophysical Research Letters, 35(21).
Xia, P., Ye, S., Chen, B., Chen, D., & Xu, K. (2020). Improving the weighted mean temperature model: A case study using nine year (2007–2015) radiosonde and COSMIC data in Hong Kong. Meteorological Applications, 27(1), e1864. https://doi.org/10.1002/met.1864
Yao, Y., Shan, L., & Zhao, Q. (2017). Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application. Scientific Reports, 7(1), 12465. https://doi.org/10.1038/s41598-017-12593-z
Zhang, Q., Ye, J., Zhang, S., & Han, F. (2018). Precipitable water vapor retrieval and analysis by multiple data sources: Ground-based GNSS, radio occultation, radiosonde, microwave satellite, and NWP reanalysis data. Journal of Sensors, 2018(1), 3428303. https://doi.org/10.1155/2018/3428303
Zhang, T., Wen, J., der Velde, R., Meng, X., Li, Z., Liu, Y., & Liu, R. (2008). Estimation of the total atmospheric water vapor content and land surface temperature based on AATSR thermal data. Sensors, 8(3), 1832–1845.
 
فیزیک زمین و فضا
دوره 51، شماره 2
تاریخ انتشار الکترونیکی: 29 شهریور 1404
شهریور 1404
صفحه 499-516

فایل ها

هم رسانی

ارجاع به این مقاله

آمار