Local modeling of weighted mean temperature in Iran and its impact on GNSS meteorology

• Autorzy: Rahimi Hassan , Asgari Jamal , Nafsi Vahab

Identyfikatory

DOI

10.1007/s11600-022-00775-x

• Języki publikacji: EN

Abstrakty

EN

Weighted mean temperature (Tm) is used to determine water vapor content, precipitable water vapor, and integrated water vapor (IWV) in GNSS. This parameter is highly correlated with climate conditions as well as the type of the region. The case study is performed in Iran which has diverse climate. ERA5 reanalysis datasets were used at a compact grid of 0.125 ×0.125 between 2007 and the end of 2019 to model the T_m. The data obtained from 12 radiosonde stations along with an IGS station located in Tehran were employed in this research. Five models were examined for T_m. Bevis model, linear grouping model (LGM), and linear nearest grid point model (LNGPM) were considered as T_m linear models, and harmonic model (HM) and GPT2w model were used as nonlinear models. In LGM method the study region was divided into smaller areas with different linear model coefficients using spatial grouping method. The local model in each radiosonde station was considered as a reference. According to the results, the accuracy of linear models (Bevis and LGM model) was between 3 and 8 K (radiosonde data as reference); also 7 out of 12 stations in the LGM had higher accuracy than the Bevis model (based on RMSE). The accuracy of the two GPT2w models and the harmonic model was higher than the previous two models, and it was between 2 and 4 K. The IWV values were obtained using zenith total delay observations of IGS station located in Tehran using 5 models and were compared with the IWV values of the radiosonde station. The accuracy of the values in three linear models, Bevis, LGM, and LNGPM, was, respectively, 0.2, 0.17, and 0.14 kg m⁻², and in the two nonlinear models, GPT2w and HM, was 0.13 kg m⁻².

Słowa kluczowe

EN

weight mean temperature; radiosonde data; Numerical Weather Model; IWV; spatial grouping; perceptible water vapor; PWV; Integrated Water Vapor

PL

średnia ważona temperatura; dane radiosondy; numeryczny model pogodowy; IWV

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 3
• Strony: 1445--1454
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Rahimi Hassan

• Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Hezar-Jarib Ave, Isfahan, Iran

autor

Asgari Jamal

• Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Hezar-Jarib Ave, Isfahan, Iran

• https://orcid.org/0000-0001-7520-6917

autor

Nafsi Vahab

• Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Hezar-Jarib Ave, Isfahan, Iran

Bibliografia

• 1. Askne J, Nordius H (1987) Estimation of tropospheric delay for microwaves from surface weather data. Radio Sci 22:379–386
• 2. 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 Geophys Res Atmos 97:15787–15801
• 3. 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 33:379–386
• 4. Böhm J, Schuh H (2013) Atmospheric effects in space geodesy, vol 5. Springer, Berlin
• 5. Böhm J, Niell A, Tregoning P, Schuh H (2006) Global mapping function (GMF): a new empirical mapping function based on numerical weather model data. Geophys Res Lett 33:L07304
• 6. 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
• 7. Boutiouta S, Lahcene A (2013) Preliminary study of GNSS meteorology techniques in Algeria. Int J Remote Sens 34:5105–5118
• 8. Chen Z, J Li, L Liu, G Luo, X Mo (2017) Model establishment and analysis of weighted mean temperature in the region of Guilin. In: China satellite navigation conference, Springer, pp 199–207
• 9. Cudjoe AP (2020) Geostatistics and spatial analysis of groundwater hydrochemistry near Leliefontein in the Northern Cape South Africa. J Ecol Eng 21:8
• 10. Davis J, Herring T, Shapiro I, Rogers A, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20:1593–1607
• 11. He C, Wu S, Wang X, Hu A, Wang Q, Zhang K (2017) A new voxel-based model for the determination of atmospheric weighted mean temperature in GPS atmospheric sounding. Atmos Meas Tech 10:2045–2060
• 12. Hofmann-Wellenhof B, Lichtenegger H, Wasle E (2008) GNSS global navigation satellite systems. SpringerWien New York, New York
• 13. Hopfield H (1969) Two-quartic tropospheric refractivity profile for correcting satellite data. J Geophys Res 74:4487–4499
• 14. Li L, Wu S, Wang X, Tian Y, He C, Zhang K (2017) Seasonal multifactor modelling of weighted-mean temperature for ground-based GNSS meteorology in Hunan, China. Adv Meteorol 2017:1–13
• 15. Liou Y-A, Teng Y-T, Van Hove T, Liljegren JC (2001) Comparison of precipitable water observations in the near tropics by GPS, microwave radiometer, and radiosondes. J Appl Meteorol 40:5–15
• 16. Liu J, Yao Y, Sang J (2018) A new weighted mean temperature model in China. Adv Space Res 61:402–412
• 17. Nafisi V, Madzak M, Böhm J, Ardalan AA, Schuh H (2012) Ray-traced tropospheric delays in VLBI analysis. Radio Sci 47:1–17
• 18. Pacione R, Vespe F (2008) Comparative studies for the assessment of the quality of near-real-time GPS-derived atmospheric parameters. J Atmos Ocean Tech 25:701–714
• 19. Rózsa S, (2014) Uncertainty considerations for the comparison of water vapour derived from radiosondes and GNSS. In: C Rizos, P Willis (eds) Earth on the edge: science for a sustainable planet: proceedings of the IAG general assembly, Melbourne, Australia, June 28-July 2, 2011, Springer Berlin Heidelberg, pp 65–78
• 20. Saastamoinen J (1972) Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. In: Henriksen SW, Mancini A, Chovitz BH (eds) The use of artificial satellites for geodesy. American Geophysical Union, Washington DC, pp 247–251
• 21. Schueler T, A Pósfay, GW Hein, R Biberger (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), pp 2476–2489
• 22. Seeber G (2008) Satellite geodesy: foundations, methods, and applications. Walter de gruyter, Germany
• 23. Sun Z, Zhang B, Yao Y (2019) An ERA5-based model for estimating tropospheric delay and weighted mean temperature over China with improved spatiotemporal resolutions. Earth Sp Sci 6:1926–1941
• 24. Venkatramanan S, Chung S, Rajesh R, Lee S, Ramkumar T, Prasanna MV (2015) Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea. Environ Sci Pollut Res 22:11209–11223
• 25. Wallace JM, Hobbs PV (2006) Atmospheric science: an introductory survey, vol 92. Academic press, Cambridge
• 26. Wang J, Zhang L, Dai A (2005) Global estimates of water-vapor-weighted mean temperature of the atmosphere for GPS applications. J Geophys Res Atmos 110:D21101
• 27. 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 121:833–852
• 28. Zhou Y, Lou Y, Zhang W, Kuang C, Liu W, Bai J (2020) Improved performance of ERA5 in global tropospheric delay retrieval. J Geodesy 94:1–14

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).