Research on GNSS positioning and applications in Poland in 2019–2022

• Autorzy: Paziewski Jacek , Hadas Tomasz , Rohm Witold , Wielgosz Paweł

Identyfikatory

DOI

10.24425/agg.2023.144595

• Języki publikacji: EN

Abstrakty

EN

This paper reviews the key studies concerning GNSS positioning and applications conducted at leading Polish research institutions from 2019 until 2022. The review also constitutes a contribution to the national report of Poland for the International Union of Geodesy and Geodynamics (IUGG) presented at the 28th General Assembly of IUGG held in 2023 in Berlin, Germany. In particular, we discuss the advances in theory and applications of relative and absolute positioning, troposphere and ionosphere sounding, smartphone and low-cost GNSS data processing, and other specific studies such as those on satellite antenna calibration and clock stability. In light of these recent advances by the Polish scientific community, continuous progress in GNSS theory and processing algorithms is thought to be maintained in the future, and GNSS applications are expected to continue to proliferate.

Słowa kluczowe

EN

GNSS; positioning; geodesy; troposphere; ionosphere

PL

pozycjonowanie; geodezja; troposfera

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Advances in Geodesy and Geoinformation
• Rocznik: 2023
• Tom: Vol. 72, no. 2
• Strony: art. no. e43, 2023
• Opis fizyczny: Bibliogr. 94 poz., fot., rys., tab., wykr.

Twórcy

autor

Paziewski Jacek

• University of Warmia and Mazury, Olsztyn, Poland

• https://orcid.org/0000-0002-6033-2547

autor

Hadas Tomasz

• Wroclaw University of Environmental and Life Sciences, Wroclaw, Poland

• https://orcid.org/0000-0002-4409-5078

autor

Rohm Witold

• Wroclaw University of Environmental and Life Sciences, Wroclaw, Poland

• https://orcid.org/0000-0002-2082-6366

autor

Wielgosz Paweł

• University of Warmia and Mazury, Olsztyn, Poland

• https://orcid.org/0000-0002-5542-1481

Bibliografia

• 1. Adavi, Z., Rohm, W., and Weber, R. (2020). Analyzing Different Parameterization Methods in GNSS Tomography Using the COST Benchmark Dataset. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 13, 6155–6163. DOI: 10.1109/JSTARS.2020.3027909.
• 2. Adavi, Z., Weber, R., and Rohm, W. (2022). Pre-analysis of GNSS tomography solution using the concept of spread of model resolution matrix. J. Geod., 96, 27. DOI: 10.1007/s00190-022-01620-1.
• 3. Ai, Q., Maciuk, K., Lewinska, P. et al. (2021). Characteristics of Onefold Clocks of GPS, Galileo, BeiDou and GLONASS Systems. Sensors, 21, 2396. DOI: 10.3390/s21072396.
• 4. Araszkiewicz, A., Kiliszek, D., and Podkowa, A. (2019). Height Variation Depending on the Source of Antenna Phase Centre Corrections: LEIAR25.R3 Case Study. Sensors, 19, 4010. DOI: 10.3390/s19184010.
• 5. Araszkiewicz, A., and Kiliszek, D. (2020). Impact of Using GPS L2 Receiver Antenna Corrections for the Galileo E5a Frequency on Position Estimates. Sensors, 20, 5536. DOI: 10.3390/s20195536.
• 6. Araszkiewicz, A., Kiliszek, D., Mierzwiak, M. et al. (2021). GPS-Based Multi-Temporal Variation in Precipitable Water over the Territory of Poland. Remote Sens., 13, 2960. DOI: 10.3390/rs13152960.
• 7. Baldysz, Z., and Nykiel, G. (2019). Improved Empirical Coefficients for Estimating Water Vapor Weighted Mean Temperature over Europe for GNSS Applications. Remote Sens., 11, 1995. DOI: 10.3390/rs11171995.
• 8. Baldysz, Z., Nykiel, G., Latos, B. et al. (2021). Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics. Atmosphere, 12, 1698. DOI: 10.3390/atmos12121698.
• 9. Borowski, L., Kudrys, J., Kubicki, B. et al. (2022). Phase Centre Corrections of GNSS Antennas and Their Consistency with ATX Catalogues. Remote Sens., 14, 3226. DOI: 10.3390/rs14133226.
• 10. Brenot, H., Rohm, W., Kačmařík, M. et al. (2019). Cross-Comparison and Methodological Improvement in GPS Tomography. Remote Sens., 12, 30. DOI: 10.3390/rs12010030.
• 11. Cellmer, S., Nowel, K., and Fischer, A. (2022a). A search step optimization in an ambiguity function-based GNSS precise positioning. Surv. Rev., 54, 117–124. DOI: 10.1080/00396265.2021.1885947.
• 12. Cellmer, S., Nowel, K., and Fischer, A. (2022b). Reduction as an improvement of a precise satellite positioning based on an ambiguity function. J. Appl. Geod., 16, 385–392. DOI: 10.1515/jag-2022-0005.
• 13. Chwedczuk, K., Cienkosz, D., Apollo, M. et al. (2022). Challenges related to the determination of al-titudes of mountain peaks presented on cartographic sources. Geodetski vestnik, 66, 49–59. DOI: 10.15292/geodetski-vestnik.2022.01.49-59.
• 14. Dawidowicz, K. (2019). Sub-hourly precise point positioning accuracy analysis – case study for selected ASG-EUPOS stations. Surv. Rev., 52, 341–351. DOI: 10.1080/00396265.2019.1579988.
• 15. Dawidowicz, K., Rapinski, J., Smieja, M. et al. (2021). Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility. Sensors, 21, 4639. DOI: 10.3390/s21144639.
• 16. Dawidowicz, K., Krzan, G., and Wielgosz, P., (2023). Offsets in the EPN station position time series resulting from antenna/radome changes: PCC type-dependent model analyses. GPS Solut., 27, 9. DOI: 10.1007/s10291-022-01339-8.
• 17. Ejigu, Y.G., Teferle, F.N., Klos, A. et al. (2021). Monitoring and prediction of hurricane tracks using GPS tropospheric products. GPS Solut., 25, 76. DOI: 10.1007/s10291-021-01104-3.
• 18. Fischer, A., Cellmer, S., and Nowel, K. (2022). Regularizing ill-posed problem of single-epoch precise GNSS positioning using an iterative procedure. J. Appl. Geod., 16, 247–264. DOI: 10.1515/jag-2021-0031.
• 19. Hadas, T., Kazmierski, K., and Sosnica, K. (2019). Performance of Galileo-only dual-frequency absolute positioning using the fully serviceable Galileo constellation. GPS Solut., 23. DOI: 10.1007/s10291-019-0900-9.
• 20. Hadas, T., Hobiger, T., and Hordyniec, P. (2020). Considering different recent advancements in GNSS on real-time zenith troposphere estimates. GPS Solut., 24. DOI: 10.1007/s10291-020-01014-w.
• 21. Hadas, T., and Hobiger, T. (2021). Benefits of Using Galileo for Real-Time GNSS Meteorology. IEEE Geosci. Remote Sens. Lett., 18, 1756-1760. DOI: 10.1109/LGRS.2020.3007138.
• 22. Hadas, T., Bender, M., Marut, G. et al. (2021). Real-Time GNSS Meteorology in Europe – Hurricane Lorenzo Case Study. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Presented at the IGARSS 2021–2021 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Brussels, Belgium, pp. 8321–8323. DOI: 10.1109/IGARSS47720.2021.9554690.
• 23. Haji-Aghajany, S., Amerian, Y., Verhagen, S. et al. (2020). An Optimal Troposphere Tomography Technique Using the WRF Model Outputs and Topography of the Area. Remote Sens., 12, 1442. DOI: 10.3390/rs12091442.
• 24. Haji-Aghajany, S., Amerian, Y., Verhagen, S. et al. (2021). The effect of function-based and voxel-based tropospheric tomography techniques on the GNSS positioning accuracy. J. Geod., 95, 78. DOI: 10.1007/s00190-021-01528-2.
• 25. Hanna, N., Trzcina, E., Möller, G. et al. (2019). Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator. Atmos. Meas. Tech., 12, 4829-4848. DOI: 10.5194/amt-12-4829-2019.
• 26. Hordyniec, P., Huang, C.-Y., Liu, C.-Y. et al. (2019). GNSS radio occultation profiles in the neutral atmosphere from inversion of excess phase data. Terr. Atmos. Ocean. Sci., 30, DOI: 10.3319/TAO.2018.10.12.01.
• 27. Iakovidis, D.K., Ooi, M., Kuang, Y.C. et al. (2022). Roadmap on signal processing for next generation measurement systems. Meas. Sci. Technol., 33, 012002. DOI: 10.1088/1361-6501/ac2dbd.
• 28. Ilieva, M., Rudzinski, L., Pawluszek-Filipiak, K. et al. (2020). Combined Study of a Significant Mine Collapse Based on Seismological and Geodetic Data – 29 January 2019, Rudna Mine, Poland. Remote Sens., 12, 1570. DOI: 10.3390/rs12101570.
• 29. Jarmolowski, W. (2019). On the relations between signal spectral range and noise variance in least-squares collocation and simple kriging: example of gravity reduced by EGM2008 signal. BGTA. DOI: 10.4430/bgta0265.
• 30. Jarmolowski, W., Ren, X., Wielgosz, P. et al. (2019). On the advantage of stochastic methods in the modeling of ionospheric total electron content: Southeast Asia case study. Meas. Sci. Technol., 30, 044008. DOI: 10.1088/1361-6501/ab0268.
• 31. Jarmolowski, W., Belehaki, A., Hernández Pajares, M. et al. (2021a). Combining Swarm Langmuir probe observations, LEO-POD-based and ground-based GNSS receivers and ionosondes for prompt detection of ionospheric earthquake and tsunami signatures: case study of 2015 Chile-Illapel event. J. Space Weather Space Clim., 11, 58. DOI: 10.1051/swsc/2021042.
• 32. Jarmolowski, W., Wielgosz, P., Ren, X. et al. (2021b). On the drawback of local detrending in universal kriging in conditions of heterogeneously spaced regional TEC data, low-order trends and outlier occurrences. J. Geod., 95, 2. DOI: 10.1007/s00190-020-01447-8.
• 33. Jin, Y., Clausen, L.B.N., Miloch, W.J. et al. (2022). Climatology and Modeling of Ionospheric Irregularities over Greenland Based on Empirical Orthogonal Function Method. J. Space Weather Space Clim., DOI: 10.1051/swsc/2022022.
• 34. Kazmierski, K., Hadas, T., and Sosnica, K. (2018). Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning. Remote Sens., 10, 84. DOI: 10.3390/rs10010084.
• 35. Kazmierski, K., Zajdel, R., and Sosnica, K. (2020). Evolution of orbit and clock quality for real-time multi-GNSS solutions. GPS Solut., 24. DOI: 10.1007/s10291-020-01026-6.
• 36. Kiliszek, D., and Kroszczynski, K. (2020). Performance of the precise point positioning method along with the development of GPS, GLONASS and Galileo systems. Measurement, 164, 108009. DOI: 10.1016/j.measurement.2020.108009.
• 37. Kiliszek, D., Kroszczynski, K., and Araszkiewicz, A. (2022). Analysis of Different Weighting Functions of Observations for GPS and Galileo Precise Point Positioning Performance. Remote Sens., 14, 2223. DOI: 10.3390/rs14092223.
• 38. Koziol, K., and Maciuk, K. (2020). New heights of the highest peaks of Polish mountain ranges. Remote Sens., 12, 1446. DOI: 10.3390/rs12091446.
• 39. Krasuski, K., and Wierzbicki, D. (2021). New Methodology for Computing the Aircraft’s Position Based on the PPP Method in GPS and GLONASS Systems. Energies, 14, 2525. DOI: 10.3390/en14092525.
• 40. Krzan, G., Dawidowicz, K., and Wielgosz, P. (2020). Antenna phase center correction differences from robot and chamber calibrations: the case study LEIAR25. GPS Solut., 24, 44. DOI: 10.1007/s10291-020-0957-5.
• 41. Kudlacik, I. (2019). Seismic phenomena in tke light high-rate GPS precise point positioning results. Acta Geodyn. et Geomater., 99–112. DOI: 10.13168/AGG.2019.0008.
• 42. Kudlacik, I., Kaplon, J., Lizurek, G. et al. (2021). High-rate GPS positioning for tracing anthropogenic seismic activity: The 29 January 2019 mining tremor in Legnica–Glogow Copper District, Poland. Measurement, 168, 108396. DOI: 10.1016/j.measurement.2020.108396.
• 43. Lasota, E., Rohm, W., Guerova, G. et al. (2020a). A Comparison Between Ray-Traced GFS/WRF/ERA and GNSS Slant Path Delays in Tropical Cyclone Meranti. IEEE Trans. Geosci. Remote Sens., 58, 421–435. DOI: 10.1109/TGRS.2019.2936785.
• 44. Lasota, E., Steiner, A.K., Kirchengast, G. et al. (2020b). Tropical cyclones vertical structure from GNSS radio occultation: an archive covering the period 2001-2018. Earth Syst. Sci. Data, 12, 2679–2693. DOI: 10.5194/essd-12-2679-2020.
• 45. Lasota, E. (2021). Comparison of different machine learning approaches for tropospheric profiling based on COSMIC-2 data. Earth Planets Space, 73, 221. DOI: 10.1186/s40623-021-01548-4.
• 46. Lasota, E., Slavchev, M., Guerova, G. et al. (2022). Combined Space- and Ground-Based GNSS Monitoring of Two Severe Hailstorm Cases in Bulgaria. J. Atmos. Ocean. Technol., 39, 649–665. DOI: 10.1175/JTECH-D-21-0100.1.
• 47. Le Marshall, J., Norman, R., Howard, D. et al. (2019). Using GNSS Data for Real-time Moisture Analysis and Forecasting over the Australian Region I. The System. JSHESS, 69, 1–21. DOI: 10.22499/3.6901.009.
• 48. Los, M., Smolak, K., Guerova, G. et al. (2020). GNSS-Based Machine Learning Storm Nowcasting. Remote Sens., 12, 2536. DOI: 10.3390/rs12162536.
• 49. Maciuk, K. (2019a). Satellite clock stability analysis depending on the reference clock type. Arab. J. Geosci., 12. DOI: 10.1007/s12517-018-4069-2.
• 50. Maciuk, K. (2019b). Monitoring of Galileo on-board oscillators variations, disturbances &$ nmathsemicolon$ noises. Measurement, 147, 106843. DOI: 10.1016/j.measurement.2019. 07.071.
• 51. Maciuk, K., and Lewinska, P. (2019). High-Rate Monitoring of Satellite Clocks Using Two Methods of Averaging Time. Remote Sens., 11, 2754. DOI: 10.3390/rs11232754.
• 52. Maciuk, K., Kudrys, J., Skorupa, B. et al. (2021). Testing the product quality of Galileo and GPS on-board oscillators. Measurement, 167, 108261. DOI: 10.1016/j.measurement.2020.108261.
• 53. Marut, G., Hadas, T., Kaplon, J. et al. (2022). Monitoring the Water Vapor Content at High Spatio-Temporal Resolution Using a Network of Low-Cost Multi-GNSS Receivers. IEEE Trans. Geosci. Remote Sens., 60, 1–14. DOI: 10.1109/TGRS.2022.3226631.
• 54. Monte-Moreno, E., Hernandez-Pajares, M., Yang, H. et al. (2021). Method for Forecasting Ionospheric Electron Content Fluctuations Based on the Optical Flow Algorithm. IEEE Trans. Geosci. Remote Sens., 60, 1–1. DOI: 10.1109/TGRS.2021.3126888.
• 55. Mota, G.V., Song, S., and Stepniak, K. (2019). Assessment of Integrated Water Vapor Estimates from the iGMAS and the Brazilian Network GNSS Ground-Based Receivers in Rio de Janeiro. Remote Sens., 11, 2652. DOI: 10.3390/rs11222652.
• 56. Nowel, K., Cellmer, S., and Fischer, A. (2021). Validation of GNSS baseline observation models using information criteria. Surv. Rev., 53, 402–414. DOI: 10.1080/00396265.2020.1790168.
• 57. Nykiel, G., Figurski, M., and Baldysz, Z. (2019). Analysis of GNSS sensed precipitable water vapour and tropospheric gradients during the derecho event in Poland of 11th August 2017. J. Atmos. Sol. Terr. Phys., 193, 105082. DOI: 10.1016/j.jastp.2019.105082.
• 58. Paziewski, J., Sieradzki, R., and Baryla, R. (2018). Multi-GNSS high-rate RTK, PPP and novel direct phase observation processing method: application to precise dynamic displacement detection. Meas. Sci. Technol., 29, 035002. DOI: 10.1088/1361-6501/aa9ec2.
• 59. Paziewski, J., Sieradzki, R., and Baryla, R. (2019a). Signal characterization and assessment of code GNSS positioning with low-power consumption smartphones. GPS Solut., 23, 98. DOI: 10.1007/s10291-019-0892-5.
• 60. Paziewski, J., Sieradzki, R., Baryla, R. (2019b). Detection of Structural Vibration with High-Rate Precise Point Positioning: Case Study Results Based on 100 Hz Multi-GNSS Observables and Shake-Table Simulation. Sensors, 19, 4832. DOI: 10.3390/s19224832.
• 61. Paziewski, J. (2020). Recent advances and perspectives for positioning and applications with smartphone GNSS observations. Meas. Sci. Technol., 31, 091001. DOI: 10.1088/1361-6501/ab8a7d.
• 62. Paziewski, J., and Sieradzki, R. (2020a). Enhanced wide-area multi-GNSS RTK and rapid static positioning in the presence of ionospheric disturbances. Earth Planets Space, 72, 110. DOI: 10.1186/s40623-020-01238-7.
• 63. Paziewski, J., and Sieradzki, R. (2020b). On the performance of selected ionospheric modelling schemes in Precise Point Positioning. In: 2020 International Conference on Localization and GNSS (ICL-GNSS). Presented at the 2020 International Conference on Localization and GNSS (ICL-GNSS), IEEE, Tampere, Finland, pp. 1–5. DOI: 10.1109/ICL-GNSS49876.2020.9115506.
• 64. Paziewski, J., Kurpinski, G., Wielgosz, P. et al. (2020). Towards Galileo ¸ GPS seismology: Validation of high-rate GNSS-based system for seismic events characterisation. Measurement, 166, 108236. DOI: 10.1016/j.measurement.2020.108236.
• 65. Paziewski, J., Fortunato, M., Mazzoni, A. et al. (2021). An analysis of multi-GNSS observations tracked by recent Android smartphones and smartphone-only relative positioning results. Measurement, 175, 109162. DOI: 10.1016/j.measurement.2021.109162.
• 66. Paziewski, J. (2022). Multi-constellation single-frequency ionospheric-free precise point positioning with low-cost receivers. GPS Solut.. 26, 23. DOI: 10.1007/s10291-021-01209-9.
• 67. Paziewski, J., Høeg, P., Sieradzki, R. et al. (2022). The implications of ionospheric disturbances for precise GNSS positioning in Greenland. J. Space Weather Space Clim., 12, 33. DOI: 10.1051/swsc/2022029.
• 68. Pelc-Mieczkowska, R. (2020). Preliminary Analysis of the Applicability of the GPS PPP Method in Geodynamic Studies. Geomat. Environ. Eng., 14, 57–68. DOI: 10.7494/geom.2020.14.4.57.
• 69. Pelc-Mieczkowska, R., and Tomaszewski, D. (2020). Space State Representation Product Evaluation in Satellite Position and Receiver Position Domain. Sensors, 20, 3791. DOI: 10.3390/s20133791.
• 70. Poniatowski, M., and Nykiel, G. (2020). Degradation of Kinematic PPP of GNSS Stations in Central Europe Caused by Medium-Scale Traveling Ionospheric Disturbances During the St. Patrick’s Day 2015 Geomagnetic Storm. Remote Sens., 12, 3582. DOI: 10.3390/rs12213582.
• 71. Prochniewicz, D., Szpunar, R., Kozuchowska, J. et al. (2020). Performance of Network-Based GNSS Positioning Services in Poland: A Case Study. J. Surv. Eng., 146, 05020006. DOI: 10.1061/(ASCE)SU.1943-5428.0000316.
• 72. Prochniewicz, D., and Grzymala, M. (2021). Analysis of the Impact of Multipath on Galileo System Measurements. Remote Sens., 13, 2295. DOI: 10.3390/rs13122295.
• 73. Prochniewicz, D., Wezka, K., and Kozuchowska, J. (2021). Empirical Stochastic Model of Multi-GNSS Measurements. Sensors, 21, 4566. DOI: 10.3390/s21134566.
• 74. Prochniewicz, D., Kudrys, J., and Maciuk, K. (2022). Noises in Double-Differenced GNSS Observations. Energies, 15, 1668. DOI: 10.3390/en15051668.
• 75. Robustelli, U., Paziewski, J., and Pugliano, G. (2021). Observation Quality Assessment and Performance of GNSS Standalone Positioning with Code Pseudoranges of Dual-Frequency Android Smartphones. Sensors, 21. DOI: 10.3390/s21062125.
• 76. Robustelli, U., Cutugno, M., Paziewski, J. et al. (2022). GNSS-SDR pseudorange quality and single point positioning performance assessment. Appl Geomat., DOI: 10.1007/s12518-022-00457-9.
• 77. Rohm, W., and Bosy, J. (2009). Local tomography troposphere model over mountains area. Atmos. Res., 93, 777–783. DOI: 10.1016/j.atmosres.2009.03.013.
• 78. Rohm, W., Guzikowski, J., Wilgan, K. et al. (2019). 4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF. Atmos. Meas. Tech., 12, 345–361. DOI: 10.5194/amt-12-345-2019.
• 79. Sá, A., Rohm, W., Fernandes, R.M. et al. (2021). Approach to leveraging real-time GNSS tomography usage. J. Geod., 95, 8. DOI: 10.1007/s00190-020-01464-7.
• 80. Sieradzki, R., and Paziewski, J. (2019). GNSS-based analysis of high latitude ionospheric response on a sequence of geomagnetic storms performed with ROTI and a new relative STEC indicator. J. Space Weather Space Clim., 9, A5. DOI: 10.1051/swsc/2019001.
• 81. Sieradzki, R., and Paziewski, J. (2022). Towards a reliable ionospheric polar patch climatology with ground-based GNSS. IEEE Trans. Geosci. Remote Sens., 60, 1–14. DOI: 10.1109/TGRS.2022.3149635.
• 82. Stepniak, K., Bock, O., Bosser, P. et al. (2022). Outliers and uncertainties in GNSS ZTD estimates from double-difference processing and precise point positioning. GPS Solut., 26, 74. DOI: 10.1007/s10291-022-01261-z.
• 83. Szombara, S., Lewinska, P., Zadlo, A. et al. (2020). Analyses of the Prądnik riverbed shape based on archival and contemporary data sets – old maps, LiDAR, DTMs, orthophotomaps and cross-sectional profile measurements. Remote Sens., 12, 2208. DOI: 10.3390/rs12142208.
• 84. Tondas, D., Kaplon, J., and Rohm, W. (2020). Ultra-fast near real-time estimation of troposphere parameters and coordinates from GPS data. Measurement, 162, 107849. DOI: 10.1016/j.measure ment.2020.107849.
• 85. Trzcina, E., and Rohm, W. (2019). Estimation of 3D wet refractivity by tomography, combining GNSS and NWP data: First results from assimilation of wet refractivity into NWP. Q.J.R. Meteorol. Soc., 145, 1034–1051. DOI: 10.1002/qj.3475.
• 86. Trzcina, E., Hanna, N., Kryza, M. et al. (2020). TOMOREF Operator for Assimilation of GNSS Tomography Wet Refractivity Fields in WRF DA System. J. Geophys. Res. Atmos., 125. DOI: 10.1029/2020JD032451.
• 87. Van Malderen, R., Pottiaux, E., Klos, A. et al. (2020). Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets. Earth Space Sci., 7. DOI: 10.1029/2020EA001121.
• 88. Wielgocka, N., Hadas, T., Kaczmarek, A. et al. (2021). Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying. Sensors, 21, 1956. DOI: 10.3390/s21061956.
• 89. Wielgosz, P., Milanowska, B., Krypiak-Gregorczyk, A. et al. (2021). Validation of GNSS-derived global ionosphere maps for different solar activity levels: case studies for years 2014 and 2018. GPS Solut., 25, 103. DOI: 10.1007/s10291-021-01142-x.
• 90. Wilgan, K., Hurter, F., Geiger, A. et al. (2017). Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data. J. Geod., 91, 117–134. DOI: 10.1007/s00190-016-0942-5.
• 91. Wilgan, K., and Geiger, A. (2019). High-resolution models of tropospheric delays and refractivity based on GNSS and numerical weather prediction data for alpine regions in Switzerland. J. Geod., 93, 819–835. DOI: 10.1007/s00190-018-1203-6.
• 92. Yang, H., Hernandez-Pajares, M., Jarmolowski, W. et al. (2022). Systematic Detection of Anomalous Ionospheric Perturbations Above LEOs From GNSS POD Data Including Possible Tsunami Signatures. IEEE Trans. Geosci. Remote Sens., 60, 1–23. DOI: 10.1109/TGRS.2022.3182885.
• 93. Yuan, P., Hunegnaw, A., Alshawaf et al. (2021). Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance. Remote Sens. Environ., 260, 112416. DOI: 10.1016/j.rse.2021.112416.
• 94. Zajdel, R., Kazmierski, K., and Sosnica, K. (2022). Orbital Artifacts in Multi-GNSS Precise Point Positioning Time Series. J. Geophys. Res. Solid Earth, 127. DOI: 10.1029/2021jb022994.