Abstract
The determination of Precipitable Water Vapor (PWV) using GNSS data with Precise Point Processing (PPP) is a useful alternative for precisely estimating atmospheric water vapor. The use of GNSS data allows for all-weather PWV tracking 24 h a day, 7 days a week. The weighted mean temperature (Tm) is an important variable in deriving GNSS-PWV values with high accuracy, especially in tropical zones. Unfortunately, the usual method of obtaining Tm is through expensive meteorological instruments such as radiosondes which are not available in every region of Thailand. This current research derives empirical models of Tm based on two data sets: one provided by the Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua platform, and one provided in the European Centre for Medium-Range Weather Forecasts’ (ECMWF) ERA5 Reanalysis. GNSS-PWV was calculated for 76 GNSS CORS around Thailand using Tm values calculated by the developed models in conjunction with GipsyX software. Results were validated against AIRS-PWV values, and compared against other existing Tm models. Results based on the developed AIRS-Tm and ERA5-Tm models, as well as the existing Suwantong, Bevis, Mendes, Schueler, and GPT3 models, showed mean biases in PWV difference against AIRS-PWV values of 0.3, 0.2, − 0.3, 1.0, 0.8, 1.8, and 1.1 mm, respectively. These results conclude that the mean bias of GPS-PWV estimations can be reduced when a more localized countrywide Tm model is used versus a global model.
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Data availability
The AIRS-only (V7 Level 2) and ERA5 Reanalysis data used in this study are available at https://disc.gsfc.nasa.gov/datasets/AIRS2RET_7.0/summary?keywords=AIRS and https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview, respectively. Python scripts used in this study are available at https://github.com/IamSkycaptain/Tmean_AIRS_ERA5_script.
References
AIRS (2019). Aqua/AIRS L2 Standard Physical Retrieval (AIRS-only) V7.0. Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/VP1M6OG1X7M1
Li ZW, Tang CZ, Tang SH, Zhang Y (2020) Comparison of Gnss Pwv and Era5-Derived Pwv Based on Gnss Pwv in Hong Kong, China. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W10, pp 987–993. https://doi.org/10.5194/isprs-archives-XLII-3-W10-987-2020
Bevis M, Businger S, Herring TA, Rocken C, Anthes RA, Ware RH (1992) GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J Gerontol Ser A Biol Med Sci 97:15. https://doi.org/10.1029/92JD01517
Bevis M, Businger S, Chiswell S, Herring TA, Anthes RA, Rocken C, Ware RH (1994) GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol 33(3):379–386. https://doi.org/10.1175/1520-0450(1994)033%3c0379:gmmzwd%3e2.0.co;2
Charoenphon C, Satirapod C (2019) Monitoring precipitable water vapor in real-time using kinematic GPS precise point positioning in Thailand. Int J Geoinf 15(1):37–46
Charoenphon C, Satirapod C (2020) Improving the accuracy of real-time precipitable water vapour using country-wide meteorological model with precise point positioning in Thailand. J Spat Sci. https://doi.org/10.1080/14498596.2020.1758969
Chen B, Yu W, Wang W, Zhang Z, Dai W (2021) A global assessment of precipitable water vapor derived from GNSS zenith tropospheric delays with ERA5, NCEP FNL, and NCEP GFS products. Earth Space Sci. https://doi.org/10.1029/2021ea001796
Davis JL, Herring TA, Shapiro II, Rogers AEE, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20(6):1593–1607. https://doi.org/10.1029/RS020i006p01593
Ding M (2018) A neural network model for predicting weighted mean temperature. J Geodesy 92(10):1187–1198. https://doi.org/10.1007/s00190-018-1114-6
Ding, M. (2020). A second generation of the neural network model for predicting weighted mean temperature. GPS Solutions, 24(2). https://doi.org/10.1007/s10291-020-0975-3
Puttipol Durongchai, Chaiwat Promtong, Withiyaphan, S. (2012). Evaluation of EGM2008 Using GPS/Leveling Data in THAILAND. 33rd Asian Conference on Remote Sensing 2012, Pattaya, Thailand.
Elhaty NM, Abdelfatah MA, Mousa AE, El-Fiky GS (2019) GNSS meteorology in Egypt: modeling weighted mean temperature from radiosonde data. Alex Eng J 58(2):443–450. https://doi.org/10.1016/j.aej.2019.04.001
De Haan S (2006) National/Regional operational procedures of gps water vapour networks and agreed international procedures. WMO/TD No. 1340, KNMI, Netherlands, 20 pp
Landskron D, Bohm J (2018) VMF3/GPT3: refined discrete and empirical troposphere mapping functions. J Geod 92(4):349–360. https://doi.org/10.1007/s00190-017-1066-2
Leckner B (1978) The spectral distribution of solar radiation at the Earth’s surface—elements of a model. Sol Energy 20:143–150. https://doi.org/10.1016/0038-092X(78)90187-1
Mateus P, Catalão J, Mendes VB, Nico G (2020) An ERA5-based hourly global pressure and temperature (HGPT) model. Remote Sens 12:7. https://doi.org/10.3390/rs12071098
Mendes VB, Prates G, Santos L, Langley RB (2000) An evaluation of the accuracy of models for the determination of weighted mean temperature of the atmosphere. In: Proceedings of ION 2000 national technical meeting
Meunram P, Satirapod C (2019) Spatial variation of precipitable water vapor derived from GNSS CORS in Thailand. Geodesy Geodyn 10(2):140–145. https://doi.org/10.1016/j.geog.2019.01.003
Qin J, Yang K, Koike T, Lu H, Ma Y, Xu X (2012) Evaluation of AIRS precipitable water vapor against ground-based GPS measurements over the tibetan plateau and its surroundings. J Meteorol Soc Jpn Ser 90C:87–98. https://doi.org/10.2151/jmsj.2012-C06
Rizos C, Satirapod C (2011) Contribution of GNSS CORS infrastructure to the mission of modern geodesy and status of GNSS CORS in Thailand. Eng J 15(1):25–42. https://doi.org/10.4186/ej.2011.15.1.25
Saastamoinen J (1972) Contributions to the theory of atmospheric refraction. Bull Géod (1946-1975) 105(1):279–298. https://doi.org/10.1007/BF02521844
Satirapod C, Anonglekha S, Choi Y-S, Lee H-K (2011) Performance assessment of GPS-Sensed precipitable water vapor using IGS ultra-rapid orbits: a preliminary study in Thailand. Eng J 15(1):1–8. https://doi.org/10.4186/ej.2011.15.1.1
Schueler T, Posfay A, Hein GW, Biberger R (2001). A global analysis of the mean atmospheric temperature for GPS water vapor estimation. In: Proceedings of the 14th international technical meeting of the satellite division of the institute of navigation (ION GPS 2001), Salt Lake City, UT, pp 2476–2489
Ssenyunzi RC, Oruru B, D’ujanga FM, Realini E, Barindelli S, Tagliaferro G, von Engeln A, van de Giesen N (2020) Performance of ERA5 data in retrieving precipitable water vapour over East African tropical region. Adv Space Res 65(8):1877–1893. https://doi.org/10.1016/j.asr.2020.02.003
Sun Z, Zhang B, Yao Y (2019a) An ERA5-based model for estimating tropospheric delay and weighted mean temperature over china with improved spatiotemporal resolutions. Earth Space Sci 6(10):1926–1941. https://doi.org/10.1029/2019ea000701
Sun Z, Zhang B, Yao Y (2019b) A global model for estimating tropospheric delay and weighted mean temperature developed with atmospheric reanalysis data from 1979 to 2017. Remote Sens. https://doi.org/10.3390/rs11161893
Sun Z, Zhang B, Yao Y (2021) Improving the estimation of weighted mean temperature in china using machine learning methods. Remote Sens. https://doi.org/10.3390/rs13051016
Suwantong R, Satirapod C, Srestasathiern P, Kitpracha C (2016) Deriving the mean tropospheric temperature model using AIRS and AMSU for GNSS precipitable water vapour estimation. In: 29th International technical meeting of the satellite division of the institute of navigation (ION GNSS+ 2016), Oregon Convention Center, Portland, Oregon, Sept. 12–16
Thrastarson ETOHT (2020) AIRS V7 Level 2 Standard Pressure Levels (E. T. O. H. T. Thrastarson, Ed.). https://docserver.gesdisc.eosdis.nasa.gov/public/project/AIRS/V7_L2_Standard_Pressure_Levels.pdf
Trakolkul C, Satirapod C (2020a) Analysis of PWV derived from the GNSS CORS stations for determining the onset of the Southwest monsoon in Thailand. Int J Geoinf 16(2):71–78. https://doi.org/10.52939/ijg.v16i2.1821
Trakolkul C, Satirapod C (2020b) Variations of precipitable water vapor using GNSS CORS in Thailand. Surv Rev. https://doi.org/10.1080/00396265.2020.1713611
Trakolkul C, Charoenphon C, Satirapod C (2022) Impact of El Niño–Southern oscillation (ENSO) on the precipitable water vapor in Thailand from long term GPS observation. Int J Geoinf. https://doi.org/10.52939/ijg.v18i3.2197
Wallace JM, Hobbs PV (2006). Atmospheric Science 2nd Edition (vol 92 (p. 80))
Wang J, Zhang L, Dai A (2005) Global estimates of water-vapor-weighted mean temperature of the atmosphere for GPS applications. J Geophys Res. https://doi.org/10.1029/2005jd006215
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. J Geophys Res Atmos 121(2):833–852. https://doi.org/10.1002/2015jd024181
Wang S, Xu T, Nie W, Jiang C, Yang Y, Fang Z, Li M, Zhang Z (2020) Evaluation of precipitable water vapor from five reanalysis products with ground-based GNSS observations. Remote Sens. https://doi.org/10.3390/rs12111817
Yang F, Guo J, Meng X, Shi J, Xu Y, Zhang D (2019) Determination of weighted mean temperature (Tm) lapse rate and assessment of its impact on Tm calculation. IEEE Access 7:155028–155037. https://doi.org/10.1109/access.2019.2946916
Yang F, Guo J, Meng X, Shi J, Zhang D, Zhao Y (2020) An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate. GPS Solut. https://doi.org/10.1007/s10291-020-0953-9
Yao Y, Zhang B, Xu C, Yan F (2013) Improved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology. J Geodesy 88(3):273–282. https://doi.org/10.1007/s00190-013-0684-6
Yao Y, Xu C, Zhang B, Cao N (2014) GTm-III: a new global empirical model for mapping zenith wet delays onto precipitable water vapour. Geophys J Int 197(1):202–212. https://doi.org/10.1093/gji/ggu008
Yao Y, Shan L, Zhao Q (2017) Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application. Sci Rep 7(1):12465. https://doi.org/10.1038/s41598-017-12593-z
Yue L (2020) AIRS Version 7 Level 2 Performance Test and Validation Report.
Zhou C, Wang J, Dai A, Thorne PW (2021) A New Approach to Homogenize Global Subdaily Radiosonde Temperature Data from 1958 to 2018. J Clim 34(3):1163–1183. https://doi.org/10.1175/jcli-d-20-0352.1
Acknowledgements
The author would like to acknowledge the anonymous public departments such as the Department of Lands, Royal Thai Survey Department, Department of Public Works and Town & Country Planning, and Hydro–Informatics Institute (Public Organization) for providing GNSS observation data. This study was supported by grants for the development of new faculty staff, Ratchadapiseksomphot Fund, Chulalongkorn University (DNS 64082210061) and the NRCT-NSFC collaboration under the Climate Change & Climate Variability Research in Monsoon Asia (CMON3), National Research Council of Thailand.
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Charoenphon, C., Trakolkul, C. & Satirapod, C. Performance assessment of weighted mean temperature models derived from AIRS and ERA5 reanalysis for calculating GPS precipitable water vapor in the thailand region. Acta Geod Geophys 57, 661–675 (2022). https://doi.org/10.1007/s40328-022-00397-1
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DOI: https://doi.org/10.1007/s40328-022-00397-1