Details

Title

Cross-comparison of meteorological parameters and ZTD observations supplied by microwave radiometers, radiosondes, and GNSS services

Journal title

Geodesy and Cartography

Yearbook

2021

Volume

vol. 70

Issue

No 2

Affiliation

Trzcina, Estera : Wroclaw University of Environmental and Life Science, Wroclaw, Poland ; Tondaś, Damian : Wroclaw University of Environmental and Life Science, Wroclaw, Poland ; Rohm, Witold : Wroclaw University of Environmental and Life Science, Wroclaw, Poland

Authors

Keywords

water vapour radiometers ; zenith tropospheric delay ; GNSS meteorology

Divisions of PAS

Nauki Techniczne

Coverage

article no. e08

Publisher

Commitee on Geodesy PAS

Bibliography

Bauer, H.-S.,Wulfmeyer, V., Schwitalla, T. et al. (2011). Operational assimilation of GPS slant path delay measurements into the MM5 4DVAR system. Tellus A: Dynamic Meteorology and Oceanography, 63, 263–282. DOI: 10.1111/j.1600-0870.2010.00489.x.
Bengtsson, L. (2010). The global atmospheric water cycle. Environ. Res. Lett., 5, 202. DOI: 10.1088/1748-9326/5/2/025202.
Bennitt, G.V. and Jupp, A. (2012). Operational assimilation of GPS zenith total delay observations into the Met Office numerical weather prediction models. Mon. Weather Rev., 140, 2706–2719. DOI: 10.1175/MWR-D-11-00156.1.
Bevis, M., Businger, S., Chiswell, T.A. et al. (1994). Gps meteorology: Mapping zenith wet delays onto precipitable water. J. Appl. Meteorol. Climatol., 33 (3), 379–386. DOI: 10.1175/1520-0450(1994)0330379:GMMZWD>2.0.CO;2.
Bock, O., Bosser, P., Pacione, R. et al. (2016). A high-quality reprocessed ground-based GPS dataset for atmospheric process studies, radiosonde and model evaluation, and reanalysis of HyMeX Special Observing Period. Q. J. R. Meteorol. Soc., 142, 56–71. DOI: 10.1002/qj.2701.
Böhm, J. and Schuh, H. (2013). Atmospheric effects in space geodesy. Springer. DOI: 10.1007/978-3-642-36932-2S.
Boniface, K., Ducrocq, V., Jaubert, G. et al. (2009). Impact of high-resolution data assimilation of GPS zenith delay on Mediterranean heavy rainfall forecasting. Ann. Geophys., 27, 2739–2753. DOI: 10.5194/angeo-27-2739-2009.
Brenot, H., Rohm, W., Kaˇcmaˇrík, M. et al. (2020). Cross-Comparison and methodological improvement in GPS tomography. Remote Sens., 12(1), 30. DOI: 10.3390/rs12010030.
Buehler, S., Östman, S., Melsheimer, C. et al. (2012). A multi-instrument comparison of integrated water vapour measurements at a high latitude site. Atmospheric Chem. Phys., 12, 10925–10943. DOI: 10.1.1.662.8109.
Churnside, J.H., Stermitz, T.A., and Schroeder, J.A. (1994). Temperature Profiling with Neural Network Inversion of Microwave Radiometer Data. J. Atmos. Ocean Technol., 11(1).
Crewell, S., and Lohnert, U. (2007). Accuracy of boundary layer temperature profiles retrieved with multifrequency multiangle microwave radiometry. IEEE Trans.Geosci.Remote Sens., 45(7), 2195–2201. DOI: 10.1109/TGRS.2006.888434.
Dach, R., Lutz, S., Walser, P. et al. (2015). Bernese GNSS Software Version 5.2. User manual, Astronomical Institute. Universtiy of Bern: Bern Open Publishing.
Dai, A., Wang, J., Ware, R.H. et al. (2002). Diurnal variation in water vapor over North America and its implications for sampling errors in radiosonde humidity. J. Geophys. Ress: Atmos., 107, ACL-11. DOI: 10.1029/2001JD000642.
Dymarska, N., Rohm, W., Sierny, J. et al. (2017). An assessment of the quality of near-real time GNSS observations as a potential data source for meteorology. Meteorol. Hydro. Water Managem. Res. Operational Applications, 5, 3–13. DOI: 10.26491/mhwm/65146.
Emardson, T.R., Johansson J.M., and Elgered G. (2000). The systematic behavior of water vapor estimates using four years of GPS observations. IEEE Trans. Geosci. Remote Sens., 38, 324–329. DOI: 10.1109/36.823927.
Ferraro, R.R., Kusselson, S.J., and Colton, M. (1998). An introduction to passive microwave remote sensing and its applications to meteorological analysis and forecasting. Polarization, 1(2). DOI: 10.1.1.662.8109.
Ferreira, J.A., Liberato, M.L., and Ramos, A.M. (2016). On the relationship between atmospheric water vapour transport and extra-tropical cyclones development. Phys. Chemis. Earth, Parts A/B/C, 94, 56–65. DOI: 10.1016/j.pce.2016.01.001.
Frate, F.D. and Schiavon, G. (1998). A combined natural orthogonal functions/neural network technique for the radiometric estimation of atmospheric profiles. Radio Sci., 33, 405–410. DOI: 10.1029/97RS02219.
Gradinarsky, L., Johansson, J., Bouma, H. et al. (2002). Climate monitoring using GPS. Phys. Chemis. Earth, Parts A/B/C, 27, 335–340. DOI: 10.1016/S1474-7065(02)00009-8.
Guerova, G., Brockmann, E., Schubiger, F. et al. (2005). An integrated assessment of measured and modeled integrated water vapor in Switzerland for the period 2001-03. J. Appl. Meteorol. Climatol., 44, 1033–1044. DOI: 10.1175/JAM2255.1.
Guerova, G., Jones, J., Douša, J. et al. (2016). Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe. Atmos. Meas. Tech., 9, 5385–5406. DOI: 10.5194/amt-9-5385-2016.
Haase, J., Ge, M., Vedel, H. et al. (2003). Accuracy and variability of GPS tropospheric delay measurements of water vapor in the western Mediterranean. J. Appl. Meteorol., 42(11), 1547–1568. DOI: 10.1175/1520-0450(2003)0421547:AAVOGT>2.0.CO;2.
Hanna, N., Trzcina, E., Möller, G. et al. (2019). Assimilation of GNSS tomography products into WRF using radio occultation data assimilation operator. Atmos. Meas.Tech.s Discuss., 1–32. DOI: 10.5194/amt-12-4829-2019.
Ingram,W. (2010). A very simple model for the water vapour feedback on climate change. Quarterly J. R. Meteorol. Soc., 136, 30–40. DOI: 10.1002/qj.546.
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, 523–527. DOI: 10.1016/S1464-1895(01)00094-1.
Jung, T., Ruprecht, E., and Wagner, F. (1998). Determination of cloud liquid water path over the oceans from Special Sensor Microwave/Imager (SSM/I) data using neural networks. J. Appl. Meteorol., 37, 832–844. DOI: 10.1175/1520-0450(1998)0370832:DOCLWP>2.0.CO;2.
Karabati´c, A., Weber, R., and Haiden, T. (2011). Near real-time estimation of tropospheric water vapour content from ground based GNSS data and its potential contribution to weather now-casting in Austria. Adv. Space Res., 47, 1691–1703. DOI: 10.1016/j.asr.2010.10.028.
Kleijer, F. (2004). Troposphere modeling and filtering for precise GPS leveling. Ph.D. thesis. TU Delft: Delft University of Technology. DOI: 10.26491/mhwm/65146.
Kryza, M., Werner, M., Wałszek, K. et al. (2013). Application and evaluation of the WRF model for high-resolution forecasting of rainfall-a case study of SW Poland. Meteorologische Zeitschrift, 22, 595–601. DOI: 10.1127/0941-2948/2013/0444.
Liang, H., Cao, Y., Wan, X. et al. (2015). Meteorological applications of precipitable water vapor measurements retrieved by the national GNSS network of China. Geod. Geodyn., 6, 135–142. DOI: 10.1016/J.GEOG.2015.03.001.
Liou, Y.-A., Teng, Y.-T., Van Hove, T. et al. (2001). Comparison of precipitable water observations in the near tropics by GPS, microwave radiometer, and radiosondes. J. Appl. Meteorol., 40, 5–15. DOI: 10.1175/1520-0450(2001)0400005:COPWOI>2.0.CO;2.
Liu, J., Sun, Z., Liang, H. et al. (2005). Precipitable water vapor on the Tibetan Plateau estimated by GPS, water vapor radiometer, radiosonde, and numerical weather prediction analysis and its impact on the radiation budget. J. Geophys. Res. Atmos., 110. DOI: 10.1029/2004JD005715.
Löhnert, U., Turner, D.D., and Crewell, S. (2009). Ground-Based Temperature and Humidity Profiling Using Spectral Infrared and Microwave Observations. Part I: Simulated Retrieval Performance in Clear-Sky Conditions. J. Appl. Meteorol. Climatol., 48(5), 1017–1032. DOI: 10.1175/2008JAMC2060.1.
Löhnert, U., and Maier, O. (2012). Operational profiling of temperature using ground-based microwave radiometry at Payerne: Prospects and challenges. Atmos. Meas. Tech., 5(5), 1121–1134. DOI: 10.5194/amt-5-1121-2012.
Lu, C., Li, X., Li, Z. et al. (2016). GNSS tropospheric gradients with high temporal resolution and their effect on precise positioning. J. Geophys. Res. Atmos., 121, 912–930. DOI: 10.1002/2015JD024255.
Mahfouf, J.-F., Ahmed, F., Moll, P. et al. (2015). Assimilation of zenith total delays in the AROME France convective scale model: a recent assessment. Tellus A: Dyn. Meteorol. Oceanogr., 67, 26106. DOI: 10.3402/tellusa.v67.26106.
Massaro, G., Stiperski, I., Pospichal, B. et al. (2015). Accuracy of retrieving temperature and humidity profiles by ground-based microwave radiometry in truly complex terrain. Atmos. Meas. Tech., 8(8), 3355–3367. DOI: 10.5194/amt-8-3355-2015.
Miloshevich, L.M., Paukkunen, A., Vömel, H. et al. (2004). Development and validation of a time-lag correction for Vaisala radiosonde humidity measurements. J. Atmos. Oceanic Tech., 21, 1305–1327. DOI: 10.1175/1520-0426(2004)0211305:DAVOAT>2.0.CO;2.
Möller, G., Wittmann, C., Yan, X. et al. (2015). 3D ground based GNSS atmospheric tomography. Final report, FFG project GNSS-ATom (ID:840098).
Morland, J., Collaud Coen, M., Hocke, K. et al. (2009). Tropospheric water vapour above Switzerland over the last 12 years. Atmos. Chemis. Phys., 9, 5975–5988, 2009. DOI: 10.5194/acp-9-5975-2009.
Ning, T. and Elgered, G. (2021). High temporal resolution wet delay gradients estimated from multi-GNSS and microwave radiometer observations. Atmos. Meas. Tech. Discuss., 1–21. DOI: 10.5194/amt-14-5593-2021.
Offiler, D. (2010). EIG EUMETNET GNSSWater Vapour Programme (E-GVAP-II) Product Requirements Document. Tech. rep. EIG EUMETNET.
Ohtani, R. and Naito, I. (2000). Comparisons of GPS-derived precipitable water vapors with radiosonde observations in Japan. J. Geophys. Res. Atmos., 105, 26917–26929. DOI: 10.1029/2000JD900362.
Pacione, R., Pace, B., Vedel, H. et al. (2011). Combination methods of tropospheric time series. Adv. Space Res. DOI: 10.1016/j.asr.2010.07.021.
Pacione, R., Araszkiewicz, A., Brockmann, E. et al. (2017). EPN-Repro2: A reference GNSS tropospheric data set over Europe. Atmos. Meas. Tech., 10, 1689–1705. DOI: 10.5194/amt-10-1689-2017.
Rocken, C., Van Hove, T., Johnson et al. (1995). GPS/STORM–GPS sensing of atmospheric water vapor for meteorology. J. Atmos. Oceanic Technol., 12, 468–478. DOI: 10.1175/1520-0426(1995)0120468:GSOAWV>2.0.CO;2.
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.
RPG: Operation Principles and Software Description for RPG standard single polarization radiometers (G5series), Radiometer Physics GmbH,Werner-von-Siemens-Str. 4, 53340 Meckenheim, Germany, 12 edn., https://www.radiometer-physics.de, 2017.
Sá, A., Rohm, W., Fernandes, R.M. et al. (2021). Approach to leveraging real-time GNSS tomography usage. J. Geod., 95(1), 1–21. DOI: 10.1007/s00190-020-01464-7.
Shangguan, M., Heise, S., Bender, M. et al. (2015). Validation of GPS atmospheric water vapor with WVR data in satellite tracking mode. Annal. Geophys., 33, 55–61. DOI: 10.5194/angeo-33-55-2015.
Skamarock, W.C., Klemp, J.B., Dudhia, J. et al. (2008). A description of the Advanced Research WRF version 3. NCAR Technical note-475+ STR. DOI: 10.5065/D68S4MVH.
Solheim, F., Godwin, J.R., Westwater, E. et al. (1998). Radiometric profiling of temperature, water vapor and cloud liquid water using various inversion methods. Radio Sci., 33, 393–404. DOI: 10.1029/97RS03656.
Thayer, G.D. (1974). An improved equation for the radio refractive index of air. Radio Sci., 9, 803–807. DOI: 10.1029/RS009i010p00803.
Tonda´s, D., Kapłon, J., and Rohm, W. (2020). Ultra-fast near real-time estimation of troposphere parameters and coordinates from GPS data. Measurement, 107849. DOI: 10.1016/j.measurement.2020.107849.
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.
Trzcina, E., Hanna, N., Kryza, M. et al. (2020). TOMOREF Operator for Assimilation of GNSS TomographyWet Refractivity Fields in WRF DA System. J. Geophys. Res. Atmos., 125, e2020JD032, 451. DOI: 10.1029/2020JD032451.
Ulaby, F., Moore, R., and Fung, A. (1986). An improved equation for the radio refractive index of air. Artech House, I–II. DOI: 10.1029/RS009i010p00803.
Van Baelen, J., Aubagnac, J.-P., and Dabas, A. (2005). Comparison of near–real time estimates of integrated water vapor derived with GPS, radiosondes, and microwave radiometer. J. Atmos. Oceanic Tech., 22, 201–210. DOI: 10.1175/JTECH-1697.1.
Van Baelen, J., Reverdy, M., Tridon, F. et al. (2011). On the relationship between water vapour field evolution and the life cycle of precipitation systems. Q. J. R. Meteorol. Soc., 137, 204–223. DOI: 10.1002/qj.785.
Vey, S., Dietrich, R., Rülke, A. et al. (2010). Validation of precipitable water vapor within the NCEP/DOE reanalysis using global GPS observations from one decade. J. Clim., 23, 1675–1695. DOI: 10.1175/2009JCLI2787.1.
Wang, J. and Liu, Z. (2019). Improving GNSS PPP accuracy through WVR PWV augmentation. J. Geod., 93, 1685–1705. DOI: 10.1007/s00190-019-01278-2.
Wilgan, K., Rohm, W., and Bosy, J. (2015). Multi-observation meteorological and GNSS data comparison with Numerical Weather Prediction model. Atmos. Res., 156, 29–42. DOI: 10.1016/j.atmosres.2014.12.011.
Zhao, Q., Yao, Y., Yao, W. et al. (2019a). GNSS-derived PWV and comparison with radiosonde and ECMWF ERA-Interim data over mainland China. J. Atmos. and Sol.-Terr. Phys., 182, 85–92. DOI: 10.1016/j.jastp.2018.11.004.
Zhao, Y., Xu, X., Zhao, T. et al. (2019b). Effects of the Tibetan Plateau and its second staircase terrain on rainstorms over North China: From the perspective of water vapour transport. Intern. J. Climat., 39, 3121–3133. DOI: 10.1002/joc.6000.
Zus, F., Wickert, J., Bauer, H.S. et al. (2011). Experiments of GPS slant path data assimilation with an advanced MM5 4DVAR system. Meteorologische Zeitschrift, 173–184. DOI: 10.1127/0941-2948/2011/0232.

Date

2021.10.25

Type

Article

Identifier

DOI: 10.24425/gac.2021.136683
×