Skip to main content

Use of GNSS Tropospheric Products for High-Resolution, Rapid-Update NWP and Severe Weather Forecasting (Working Group 2)

  • Conference paper
  • First Online:

Abstract

For more than a decade, GNSS-meteorology has been increasingly used operationally in Europe particularly for data assimilation in Numerical Weather Prediction (NWP) models, mainly thanks to the EIG EUMETNET GNSS Water Vapour Program (E-GVAP, 2005-today). As such, GNSS has become a well-established, mature observing technique for data assimilation applications. Over this period however, scientists and specialists in GNSS-meteorology noted the clear potential for enhancements and novelties in the domain. The work carried out by the COST Action ES1206 Working Group 2 members addressed these potential enhancements and novelties from the meteorological point of view, in collaboration with WG1. This included the establishment of discussion channels with forecasters in order to determine which GNSS products would be best suited for their day-to-day operational requirements. Particular areas of interest include engaging more operational forecasters (e.g. use of meteorological case studies), especially for non-numerical nowcasting of severe weather, and getting more meteorological agencies to assimilate GNSS products in regions of Europe where they were not yet/well exploited. It also included the development of the techniques and tools necessary to benefit from the brand new products developed by the Action WG1 and WG2 members, namely real-time GNSS tropospheric products for rapid-cycle NWP and non-numerical nowcasting, data assimilation of horizontal tropospheric gradients and tropospheric slant delays as well as tomographic products. Finally, the work carried out by the WG2 members brought operational improvements through dialog, transfer of knowledge, and standardisation (e.g. the new standardized tropo-SINEX format or the development of assimilation operators). The major WG2 outcomes are discussed in this Chapter.

Section 4.2.1 contains material that is republished with kind permission.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Parts from this section were previously published in Stoycheva et al. (2017) and Stoycheva and Guerova (2015).

References

  • Askne, J., & Nordius, H. (1987, May–June). Estimation of tropospheric delay for microwaves from surface weather data. Radio Science, 22(3), 379–386. https://doi.org/10.1029/RS022i003p00379.

    Article  Google Scholar 

  • Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., & Reinhardt, T. (2011). Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Monthly Weather Review, 139(12), 3887–3905.

    Google Scholar 

  • Bennitt, G. V., & Jupp, A. (2012). Operational assimilation of GPS Zenith total delay observations into the Met office numerical weather prediction models. Monthly Weather Review, 140, 2706–2719.

    Article  Google Scholar 

  • Bevis, M., Businger, S., & Herring, T. (1992). GPS meteorology: Remote sensing of atmospheric water vapour using the Global Positioning System. Journal of Geophysical Research: Atmospheres, 97(D14), 15787–15801. https://doi.org/10.1029/92JD01517.

    Article  Google Scholar 

  • Bevis, M., Businger, S., Chiswell, S., Herring, T., Anthes, R., Rocken, Ch., & Ware, R. (1994, March). GPS meteorology: Mapping zenith wet delays onto precipitable water. Journal of Applied Meteorology and Climatology, 33(3), 379–386. https://doi.org/10.1175/1520-0450(1994)033%3C0379:GMMZWD%3E2.0.CO;2.

    Article  Google Scholar 

  • Bruyninx, C., Habrich, H., Söhne, W., Kenyeres, A., Stangl, G., & Völksen, C. (2012), Enhancement of the EUREF permanent network services and products. In Geodesy for planet Earth, vol. 136 of IAG symposia series (pp. 27–35). Springer.

    Google Scholar 

  • Caumont, O., Ducrocq, V., Wattrelot, É., Jaubert, G., & Pradier-Vabre, S. (2010). 1D+3DVar assimilation of radar reflectivity data: A proof of concept. Tellus A, 62, 173–187.

    Article  Google Scholar 

  • Chen, G., & Herring, T. (1997, September). Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data. Journal of Geophysical Research: Solid Earth, 102(B9), 20489–20502. https://doi.org/10.1029/97JB01739.

    Article  Google Scholar 

  • Dach, R., Lutz, S., Walser, P., & Fridez, P. (Eds) (2015). Bernese GNSS Software Version 5.2. User manual. Astronomical Institute, University of Bern, Bern Open Publishing. https://doi.org/10.7892/boris.72297. isbn:978-3-906813-05-9.

  • Davis, J., Herring, T., Shapiro, I., Rogers, A., & Elgered, G. (1985, November–December). Geodesy by radio interferometry: Effects of atmospheric modelling errors on estimates of baseline length. Radio Science, 20(6), 1593–1607. https://doi.org/10.1029/RS020i006p01593.

    Article  Google Scholar 

  • De Cruz, L., & Duerinckx, A.(2015, April 13–16). Assimilation of GNSS and radar data in ALARO cy38t1 at RMIB. Joint 25th ALADIN Workshop & HIRLAM All Staff Meeting, Helsingor, Denmark.

    Google Scholar 

  • De Cruz, L., Duerinckx, A., & Pottiaux, E. (2015, May 11–13). GNSS assimilation in NWP: Case studies for Belgium. COST ES1206 – GNSS4SWEC: 2nd Workshop, Thessaloniki, Greece.

    Google Scholar 

  • De Haan, S., Barlag, S., Klein Blatink, H., Debie, F., & van der Marel, H. (2004, April). Synergetic use of GPS water vapour and meteosat images for synoptic weather forecasting. Journal of Applied Meteorology and Climatology, 43(3), 514–518. https://doi.org/10.1175/1520-0450(2004)043%3C0514:SUOGWV%3E2.0.CO;2.

    Article  Google Scholar 

  • De Haan, S., Holleman, I., & Holtslag, A. (2009). Real-time water vapour maps from a GPS surface network: Construction, validation, and applications. Journal of Applied Meteorology and Climatology, 48(7), 1302–1316. https://doi.org/10.1175/2008JAMC2024.1.

    Article  Google Scholar 

  • E-GVAP PDR. (2010, December 21). Product requirements document v 1.0. Prepared by the Met Office. http://egvap.dmi.dk/support/formats/egvap_prd_v10.pdf

  • Elgered, G., Davis, J., Herring, T., & Shapiro, I. (1991, April). Geodesy by radio interferometry: Water vapour radiometry for estimation of the wet delay. Journal of Geophysical Research – Solid Earth 96(B4), 6541–6555. https://doi.org/10.1029/90JB00834.

    Article  Google Scholar 

  • Grams, C. M., Binder, H., Pfahl, S., Piaget, N., & Wernli, H. (2014). Atmospheric processes triggering the central European floods in June 2013. Natural Hazards and Earth System Sciences, 14(7), 1691–1702.

    Article  Google Scholar 

  • Guerova, G. (2013). Ground-based GNSS Meteorology: Case studies for Bulgaria/Southeast Europe. In Proceedings of the 4th International colloquium – Scientific and fundamental aspects of the Galileo programme, Prague, Czech Republic, 4–6.12.2013. http://suada.phys.uni-sofia.bg/wordpress/wp-content/uploads/2015/02/2923524_guerova.pdf

  • Heise, S., Dick, G., Gendt, G., Schmidt, T., & Wickert, J. (2009). Integrated water vapour from IGS ground-based GPS observations: Initial results from a 5-min data set. Annales de Geophysique, 27, 2851–2859. https://doi.org/10.5194/angeo-27-2851-2009.

    Article  Google Scholar 

  • Hogg, D., Guiraud, F., & Decker, M. (1981). Measurement of excess transmission length on Earth–space paths. Astronomy and Astrophysics, 95, 304–307.

    Google Scholar 

  • Holton, J. R. (1972). An introduction to dynamic meteorology (p. 319). New York: Academic.

    Google Scholar 

  • Hunt, B. R., Kostelich, E. J., & Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230(1–2), 112–126.

    Article  Google Scholar 

  • Lindskog, M., Ridal, M., Thorsteinsson, S., & Ning, T. (2017). Data assimilation of GNSS Zenith Total Delays from a Nordic processing centre. Submitted to Atmospheric Chemistry Physics.

    Google Scholar 

  • Matheron, G. (1962). Traité de géostatique appliquée. Tome I, Mémoires du Bureau de Recherche Géologiques et Minières, N°14, Edt. Technip, Paris.

    Google Scholar 

  • Matheron, G. (1963a). Principles of Geostatics. Economic Geology, 58(8), 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246.

    Article  Google Scholar 

  • Matheron, G. (1963b). Traité de géostatique appliquée. Tome II: le Krigeage, Mémoires du Bureau de Recherche Géologiques et Minières, N°24, Edt. B. R. G. M., Paris.

    Google Scholar 

  • Mazany, R., Businger, S., & Gutman, S. (2002, October). A lightning prediction index that utilizes GPS integrated precipitable water vapour. Weather and Forecasting, 17, 1034–1047.

    Google Scholar 

  • Möller, G. (2017). Reconstruction of 3D wet refractivity fields in the lower atmosphere along bended GNSS signal paths. Dissertation, TU Wien, Department of Geodesy and Geoinformation, 211p.

    Google Scholar 

  • Möller, G., Wittmann, C., Yan, X., Umnig, E., Joldzic, N., & Weber, R. (2016). 3D ground based GNSS atmospheric tomography. Final Report GNSS-ATom project 840098, 49p.

    Google Scholar 

  • Muller, M., Homleid, M., Ivarsson, K.-I., Koltzow, M., Lindskog, M., Midtbo, K.-H., Andrae, U., Aspelien, T., Berggren, L., Bjorge, D., Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., & Vigne, O. (2017). AROME-MetCoOp: A nordic convective-scale operational weather prediction model. Weather and Forecasting, 32, 609–627. https://doi.org/10.1175/WAF-D-16-0099.1.

    Article  Google Scholar 

  • Ning, T., Elgered, G., Willén, U., & Johansson, J. (2013). Evaluation of the atmospheric water vapour content in a regional climate model using ground-based GPS measurements. Journal of Geophysical Research: Atmospheres, 118(2), 329–339. https://doi.org/10.1029/2012JD018053.

    Article  Google Scholar 

  • Poli, P., Moll, P., Rabier, F., Desroziers, G., Chapnik, B., Berre, L., Healy, S. B., Andersson, E., & El Guelai, F.-Z. (2007). Forecast impact studies of Zenith total delay data from European near real-time Gps stations in Météo France 4DVAR. Journal of Geophysical Research: Atmospheres, 112(D6). Wiley Online Library.

    Google Scholar 

  • Pottiaux, E. (2010). Sounding the Earth’s atmospheric water vapour using signals emitted by global navigation satellite systems. PhD. thesis in Sciences, 200. Université Catholique de Louvain.

    Google Scholar 

  • Rocken, C., Van Hove, T., Johnson, J., Solheim, F., Ware, R., Bevis, M., Chiswell, S., & Businger, S. (1995), GPS/STORM–GPS sensing of atmospheric water vapour for meteorology. Journal of Atmospheric and Oceanic Technology, 12, 468–478. https://doi.org/10.1175/1520-0426(1995)012<0468:GSOAWV>2.0.CO;2.

  • Saastamoinen, J. (1972). Atmospheric corrections for the troposphere and stratosphere in radio ranging of satellites. In S. W. Henriksen et al. (Eds.), The use of artificial satellites for geodesy (Monograph series) (Vol. 15, pp. 247–251). Washington DC: AGU.

    Google Scholar 

  • Sánchez Arriola, J., Lindskog, M., Thorsteinsson, S., & Bojarova, J. (2016). Variational bias correction of GNSS ZTD in the HARMONIE modelling system. Journal of Applied Meteorology and Climatology, 55, 1259–1276. https://doi.org/10.1175/JAMC-D-15-0137.1.

    Article  Google Scholar 

  • Schraff, C., Reich, H., Rhodin, A., Schomburg, A., Stephan, K., Periáñez, A., & Potthast, R. (2016). Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). Quarterly Journal of the Royal Meteorological Society, 142(696), 1453–1472.

    Google Scholar 

  • Seidel, D. J., Ao, C. O., & Li, K. (2010). Estimating climatological planetary boundary layer heights from radiosonde observations: Comparison of methods and uncertainty analysis. Journal of Geophysical Research Atmospheres, 115(D16).

    Google Scholar 

  • Smet, G., Termonia, P., & Deckmyn, A. (2012). Added economic value of limited area multi-EPS weather forecasting applications. Tellus A, 64, 18901.

    Article  Google Scholar 

  • Smith, E., & Weintraub, S. (1953, August), The constants in the equation for atmospheric refractive index at radio frequencies. Proceedings of the IRE, 41(8), 1035–1037. https://doi.org/10.1109/JRPROC.1953.274297.

    Article  Google Scholar 

  • Smith, W., & Wessel, P. (1990, March). Gridding with continuous curvature splines in tension. Geophysics, 55(3), 293–305. https://doi.org/10.1190/1.1442837.

    Article  Google Scholar 

  • Stephan, K., Klink, S., & Schraff, C. (2008). Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD. Quarterly Journal of the Royal Meteorological Society, 134(634), 1315–1326.

    Article  Google Scholar 

  • Stoev, K., & Guerova, G. (2017). Climatology of the foehn in Sofia for 1975–2014. International Journal of Climatology. In review 9/2017. http://suada.phys.uni-sofia.bg/?page_id=3801

  • Stoycheva, А., & Guerova, G. (2015). Study of fog in Bulgaria by using the GNSS tropospheric products and large scale dynamic analysis. Journal of Atmospheric and Solar-Terrestrial Physics, 133, 87–97. https://doi.org/10.1016/j.jastp.2015.08.004.

    Article  Google Scholar 

  • Stoycheva, A., Manafov, I., Vassileva, K., & Guerova, G. (2017). Study of persistent fog in Bulgaria with Sofi a Stability Index, GNSS tropospheric products and WRF simulations. Journal of Atmospheric and Solar-Terrestrial Physics, 161, 160–169. https://doi.org/10.1016/j.jastp.2017.06.011.

    Article  Google Scholar 

  • Stull, R. (1995). Meteorology today for scientists and engineers (385 p). Minneapolis: West Publishing.

    Google Scholar 

  • Van Malderen, R., Brenot, H., Pottiaux, E., Beirle, S., Hermans, C., De Mazière, M., Wagner, T., De Backer, H., & Bruyninx, C. (2014). A multi-site techniques intercomparison of integrated water vapour observations for climate change analysis. Atmospheric Measurement Techniques Discussions, 7, 1075–1151. https://doi.org/10.5194/amtd-7-1075-2014.

    Article  Google Scholar 

  • Vedel, H., Huang, X. Y., Haase, J., Ge, M., & Calais, E. (2004). Impact of GPS Zenith tropospheric delay data on precipitation forecasts in Mediterranean France and Spain. Geophysical Research Letters, 31(2), 2004.

    Article  Google Scholar 

  • Vey, S., Dietrich, R., Fritsche, M., Rülke, A., Steigenberger, P., & Rothacher, M. (2009, May). On the homogeneity and interpretation of precipitable water time series derived from global GPS observations. Journal of Geophysical Research Atmospheres, 114(D10). https://doi.org/10.1029/2008JD010415.

  • Wang, J., Zhang, L., & Dai, A. (2005). Global estimates of water-vapour-weighted mean temperature of the atmosphere for GPS applications. Journal Geophysical Research, 110, D21101. https://doi.org/10.1029/2005JD006215.

    Article  Google Scholar 

  • Wang, J., Zhang, L., Dai, A., van Hove, T., & van Baelen, J. (2007, June). A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements. Journal of Geophysical Research Atmospheres, 112(D11). https://doi.org/10.1029/2006JD007529.

  • Wang, X., Barker, Snyder, C., & Hamill, T. M. (2008). A hybrid ETKF–3DVAR data assimilation scheme for the WRF Model. Part I: Observing system simulation experiment. Monthly Weather Review, 136, 5116–5131.

    Article  Google Scholar 

  • Watson, D. (1982). Acord: Automatic contouring of raw data. Computers and Geosciences, 8(1), 97–101. https://doi.org/10.1016/0098-3004(82)90039-5.

    Article  Google Scholar 

  • Wattrelot, E., Caumont, O., & Mahfouf, J. F. (2014). Operational implementation of the 1D+ 3D-Var assimilation method of radar reflectivity data in the AROME model. Monthly Weather Review, 142(5), 1852–1873.

    Article  Google Scholar 

  • Yan, X., Ducrocq, V., Poli, P., Jaubert, G., & Walpersdorf, A. (2008). Mesoscale GPS Zenith delay assimilation during a Mediterranean heavy precipitation event. Advances in Geosciences, 17, 71–77.

    Article  Google Scholar 

  • Yan, X., Ducrocq, V., Poli, P., Hakam, M., Jaubert, G., & Walpersdorf, A. (2009). Impact of GPS zenith delay assimilation on convective-scale prediction of Mediterranean heavy rainfall. Journal of Geophysical Research, 114(D03104), 2009.

    Google Scholar 

  • Zus, F., Bender, M., Deng, Z., Dick, G., Heise, S., Shang-Guan, M., & Wickert, J. (2012). A methodology to compute GPS slant total delays in a numerical weather model. Radio Science, 47, RS2018.

    Google Scholar 

  • Zus, F., Dick, G., Heise, S., & Wickert, J. (2014). A forward operator and its adjoint for GPS slant total delays. Radio Science, 50(5), 393–405.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to E. Pottiaux or K. Stoev .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

de Haan, S. et al. (2020). Use of GNSS Tropospheric Products for High-Resolution, Rapid-Update NWP and Severe Weather Forecasting (Working Group 2). In: Jones, J., et al. Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate. Springer, Cham. https://doi.org/10.1007/978-3-030-13901-8_4

Download citation

Publish with us

Policies and ethics