Meteorology and Atmospheric Physics

, Volume 91, Issue 1–4, pp 149–164 | Cite as

Assimilation of COST 716 Near-Real Time GPS data in the nonhydrostatic limited area model used at MeteoSwiss

  • G. Guerova
  • J.-M. Bettems
  • E. Brockmann
  • Ch. Matzler


Application of the GPS derived water vapor into Numerical Weather Prediction (NWP) models is one of the focuses of the COST Action 716 “Exploitation of Ground based GPS for climate and numerical weather prediction applications”. For this purpose the GPS data covering Europe have been collected within the Near-Real Time (NRT) demonstration project and provided for Observing System Experiments (OSE). For the experiments presented in this manuscript the operational NWP system of MeteoSwiss is used. The limited area nonhydrostatic aLpine Model (aLMo) of MeteoSwiss covers most of western Europe, has a horizontal resolution of 7 km, 45 layers in the vertical, and uses a data assimilation scheme based on the Newtonian relaxation (nudging) method. In total 17 days analyses and two 30 hours daily forecasts have been computed, with 100 GPS sites assimilated for three selected periods in autumn 2001, winter and summer 2002. It is to be noted that only in the last period data from 10 french sites, i.e. west of Switzerland are assimilated.

The GPS NRT data quality has been compared with the Post-Processed data. Agreement within 3 mm level Zenith Total Delay bias and 8 mm standard deviation was found, corresponding to an Integrated Water Vapor (IWV) bias below 0.5 kg/m2. Most of the NRT data over aLMo domain are available within a prescribed time window of 1 h 45 min. In the nudging process the NRT data are successfully used by the model to correct the IWV deficiencies present in the reference analysis; stronger forcing with a shorter time scale could be however recommended. Comparing the GPS derived IWV with radiosonde observations, a dry radiosonde bias has been found over northern Italy. Through GPS data assimilation the aLMo analysis bias and standard deviation in the diurnal cycle has been reduced. The negative bias of –0.64 kg/m2 in the reference analysis has been reduced to 0.34 kg/m2 in GPS analysis. However, the diurnal cycle statistic from the forecast does show the characteristic negative bias only slightly reduced starting with the GPS analysis.

The GPS IWV impact on aLMo is large in June 2002 and moderate in September 2001 OSE. January OSE is inconclusive due to inconsistent use of humidity data below the freezing point. In June 2002 OSE, a substantial IWV impact is seen up to the end of the forecast. Over Switzerland the dry bias in the reference analysis has been successfully corrected and the 2 m temperature and dew point have been slightly improved over the whole aLMo domain. The subjective verification of precipitation against radar data in autumn 2001 and summer 2002 gives mixed results. In the forecast the impact is limited to the first six hours and to strong precipitation events. A missing precipitation pattern has been recovered via GPS assimilation in June 20 2002 forecast. A negative impact on precipitation analysis on June 23 has been observed.

The future operational use of GPS will depend on data availability; European GPS networks belong mainly to the geodetic community. A further increase of GPS network density in southern Europe is welcome. The GPS derived gradient and Slant Path estimates could possibly improve efficiency of IWV assimilation via the nudging technique.


Numerical Weather Prediction Reference Analysis Zenith Total Delay Integrate Water Vapor Data Assimilation Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bettems JM (2002) EUCOS impact study using the limited-area non-hydrostatic NWP model in operational use at MeteoSwiss. SMA-MeteoSchweiz Pub. 62. (Available from SMA-MeteoSchweiz, Kraehbuehlstrasse 58, 8044, Zurich, Switzerland)Google Scholar
  2. Bevis, M, Businger, M, Herring, TA, Rocken, C, Anthes, RA, Ware, RH 1992GPS Meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System.J Geophys Res971578715801Google Scholar
  3. Brockmann E, Guerova G, Troller M (2002) Swiss Activities in combining GPS with Meteorology. Mitteilung des Bundesamtes für Kartographie und Geodäsie (Torres JA, Hornik H, eds). EUREF Publ. Vol. 23, Frankfurt, Germany, pp 95–99Google Scholar
  4. COST-716 (2001) Format Specification for COST-716 Processed GPS Data, Prepared by: D. Offiler, Met Office, Version 1.0f ( – WG2 – Documents)
  5. COST-716 (2002) GPS Meteorology – USER REQUIREMENTS, Prepared by S. J. M. Barlag and S. De Haan, KNMI, Version 2.2, ( – WG3 – Documents)
  6. Cucurull, L, Navascues, B, Ruffini, G, Elosegui, P, Ruis, A, Vila, J 2000The use of GPS to validate NWP systems: The HIRLAM model.J Atmos Oceanic Technol17773787CrossRefGoogle Scholar
  7. Davies, H 1976A lateral boundary formulation for multilevel prediction models.Quart J Roy Meteor Soc102405418CrossRefGoogle Scholar
  8. De Pondeca, M, Zou, X 2001A case study of the variational assimilation of GPS zenith delay observations into a mesoscale model.J Appl Meteor4015591576CrossRefGoogle Scholar
  9. Doms G (2002) The LM cloud ice scheme. COSMO Newsletter No. 2 (Doms G, Schaettler U, eds), pp 128–136. (Available from Deutscher Wetterdienst, P.O. Box 100465, 63004 Offenbach, Germany)Google Scholar
  10. Doms G, Schaettler U (2001) COSMO Newsletter No 1, 114 pp. (Available from Deutscher Wetterdienst, P.O. Box 100465, 63004 Offenbach, Germany)Google Scholar
  11. Doms G, Schaettler U, Montani A (2004) Model system overview: Data assimilation. COSMO Newsletter No. 4 (Doms G, Schaettler U, Montani A, eds), pp 16–21. (Available from Deutscher Wetterdienst, P.O. Box 100465, 63004 Offenbach, Germany)Google Scholar
  12. Elgered, G 2001An overview of COST Action 716: Exploitation of ground-based GPS for climate and numerical weather prediction applications.Phys Chem Earth (A)266–8399–404Google Scholar
  13. Falvey, M, Beaven, J 2002The impact of GPS precipitable water assimilation on mesoscale model retrievals of orographic rainfall during SALPEX’96.Mon Wea Rev13028742888Google Scholar
  14. Guerova, G, Brockmann, E, Quiby, J, Schubiger, F, Matzler, Ch 2003Validation of NWP mesoscale models with Swiss GPS Network AGNES.J Appl Meteor42141150CrossRefGoogle Scholar
  15. Guerova, G, Bettems, JM, Brockmann, E, Matzler, Ch 2004Assimilation of the GPS-derived Integrated Water Vapor (IWV) in the MeteoSwiss Numerical Weather Prediction model – a first experiment.Phys Chem Earth292–3177–186Google Scholar
  16. Guerova G, Brockmann E, Schubiger F, Morland J, Matzler Ch (2005) An integrated assessment of measured and modeled IWV in Switzerland for the period 2001–2003. J Appl Meteor (accepted)Google Scholar
  17. Guo, YR, Kuo, YH, Dudhia, J, Parsons, DB, Rocken, C 2000Four-dimensional data assimilation of heterogeneous mesoscale observations for a strong convective case.Mon Wea Rev128619643CrossRefGoogle Scholar
  18. Gutman, SI, Holub, K, Sahm, SR, Stewart, JQ, Smith, TL, Benjamin, SG, Schwarr, BE 2004Rapid retrieval and assimilation of ground based GPS-Met observations at the NOAA Forecast Systems Laboratory: Impact on weather forecast.Jap Meteor Soc82351360Google Scholar
  19. Haase J, Ge M, Vedel H, Calais E (2002) Accuracy and variability of GPS Tropospheric Delay Measurements of water vapor in the Western Mediterranean. Bull Am Meteor Soc (submitted)Google Scholar
  20. Kopken, C 2001Validation of integrated water vapor from numerical models using ground-based GPS, SSM/I, and water vapor radiometer measurements.J Appl Meteor4011051117Google Scholar
  21. Kuo, YH, Zou, X, Guo, YR 1996Variational assimilation of precipitable water using a non-hydrostatic mesoscale adjoint model. Part I: Moisture retrieval and sensitivity experiments.Mon Wea Rev124122147Google Scholar
  22. Nakamura, H, Koizumi, K, Mannoji, N, Seko, H 2004Data assimilation of GPS precipitable water vapor to the JMA mesoscale numerical weather prediction model and its impact on rainfall forecast.Jap Meteor Soc J82441452Google Scholar
  23. Ohtani, R, Naito, I 2000Comparisons of GPS-derived precipitable water vapors with radiosonde observations in Japan.J Geophys Res1052691726929Google Scholar
  24. Schraff, Ch 1997Mesoscale data assimilation and prediction of low stratus in the Alpine region.Meteorol Atmos Phys642150CrossRefGoogle Scholar
  25. Smith, T, Benjamin, S, Schwartz, B, Gutman, S 2000Using GPS-IPW in a 4-D data assimilation system.Earth Planets Space52921926Google Scholar
  26. Tomassini, M, Gendt, G, Dick, G, Ramatschi, M, Schraff, Ch 2002Monitoring of integrated water vapor from ground-based GPS observations and their assimilation in a limited area model.Phys Chem Earth27341346Google Scholar
  27. Van der Marel H, Brockmann E, de Haan S, Dousa J, Johansson J, Gendt G, Kristiansen O, Offiler D, Pacione R, Rius A, Vespe F (2003) COST-716 Near Real-Time demonstration project. Jap Meteor Soc J (submitted)Google Scholar
  28. Vedel H, Huang XY, Haase J, Ge M, Calais E (2003) Impact of GPS zenith tropospheric delay data on the precipitation forecasts in Mediterranean France and Spain. Geophys Res Lett (submitted)Google Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • G. Guerova
    • 1
  • J.-M. Bettems
    • 2
  • E. Brockmann
    • 3
  • Ch. Matzler
    • 4
  1. 1.Institute of Applied Physics, University of BernSwitzerland
  2. 2.Federal Office of Meteorology and ClimatologyZurichSwitzerland
  3. 3.Swiss Federal Office of TopographyWabernSwitzerland
  4. 4.Institute of Applied PhysicsUniversity of BernSwitzerland

Personalised recommendations