GPS Solutions

, Volume 12, Issue 4, pp 227–235 | Cite as

Variational data assimilation for deriving global climate analyses from GNSS radio occultation data

Review Article


A comprehensive global navigation satellite system (GNSS) based radio occultation (RO) data set is available for meteorology and climate applications since the start of GNSS RO measurements aboard the CHAllenging Mini-satellite Payload (CHAMP) satellite in February 2001. Global coverage, all-weather capability, long-term stability and accuracy not only makes this innovative use of GNSS signals a valuable supplement to the data set assimilated into numerical weather prediction (NWP) systems but also an excellent candidate for global climate monitoring. We present a 3D variational data assimilation (3D-Var) scheme developed to derive consistent global analysis fields of temperature, specific humidity, and surface pressure from GNSS RO data. The system is based on the assimilation of RO data within 6 h time windows into European Centre for Medium-Range Weather Forecasts (ECMWF) short-term (24 h, 30 h) forecasts, to derive climatologic monthly mean fields. July 2003 was used as a test-bed for assessing the system’s performance. The results show good agreement with climatologies derived from RO data only and recent NWP impact studies. These findings are encouraging for future developments to apply the approach for longer term climatologic analyses, validation of other data sets, and atmospheric variability studies.


GPS GNSS Radio occultation Climatology Champ Climate change Climate variability Climate monitoring Climate maps Variational optimization Assimilation 3D-Var Data fusion Recursive filters Atmospheric studies 



The authors thank U. Foelsche, A. Gobiet, and M. Borsche (WegCenter, University. of Graz) for providing processed CHAMP profiles, A.K. Steiner (WegCenter) for discussions on CHAMP error characteristics, GFZ Potsdam (Germany) for the basic CHAMP phase delay data, and M. Fisher (ECWMF Reading) for providing ECMWF analysis error characteristics. S.B. Healy (ECMWF), A. von Engeln and C. Marquardt (EUMETSAT Darmstadt), and X.-Y. Huang (NCAR Boulder) are thanked for providing important stimuli for some parts of the work. A. Löscher received financial support from the START research award of G. Kirchengast funded under Program No. Y103-N03 of the Austrian Science Fund.


  1. Anthes RA, Rocken C, Kuo Y (2000) Applications of COSMIC to meteorology and climate. Terr Atmos Ocean Sci 11:115–156Google Scholar
  2. Barker DM, Huang W, Guo Y-R, Bourgeois AJ (2003) A three-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Tech. Note, NCAR/TN-453 + STR, p 68 [available from UCAR Communications, P.O. Box 3000, Boulder, CO 80307]Google Scholar
  3. Barker DM, Huang W, Guo Y-R, Bourgeois AJ, Xiao QN (2004) A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon Weather Rev 132:897–914CrossRefGoogle Scholar
  4. Bengtsson L, Hagemann S, Hodges KI (2004) Can climate trends be calculated from reanalysis data? J Geophys Res 109:D11111. doi:10.1029/2004JD004536 CrossRefGoogle Scholar
  5. Beyerle G, Schmidt T, Wickert J, Heise S, Rothacher M, König-Langlo G, Lauritsen KB (2006) Observations and simulations of receiver-induced refractivity biases in GPS radio occultation. J Geophys Res 111:D12101. doi:10.1029/2005JD006673 CrossRefGoogle Scholar
  6. Borsche M, Kirchengast G, Foelsche U (2007) Tropical tropopause climatology as observed with radio occultation measurements from CHAMP compared to ECMWF and NCEP analyses. Geophys Res Lett 34:L03702. doi:10.1029/2006GL027918 CrossRefGoogle Scholar
  7. Bouttier F, Courtier P (1999) Data assimilation concepts and methods, meteorological training course lecture series. ECMWF, ReadingGoogle Scholar
  8. Foelsche U, Gobiet A, Steiner AK, Borsche M, Wickert J, Schmidt T, Kirchengast G (2006) Global climatologies based on radio occultation data: the CHAMPCLIM project. In: Foelsche U, Kirchengast G, Steiner AK (eds) Atmosphere and climate, studies by occultation methods, Springer, Berlin, pp 303–314CrossRefGoogle Scholar
  9. Foelsche U, Borsche M, Steiner AK, Gobiet A, Pirscher B, Kirchengast G (2007) Observing upper troposphere–lower stratosphere climate with radio occultation data from the CHAMP satellite. Climate Dyn (revised)Google Scholar
  10. Gobiet A, Foelsche U, Steiner AK, Borsche M, Kirchengast G, Wickert J (2005) Climatological validation of stratospheric temperatures in ECMWF operational analyses with CHAMP radio occultation data. Geophys Res Lett 32:L12806. doi:10.1029/2005GL022617 CrossRefGoogle Scholar
  11. Gobiet A, Kirchengast G, Manney GL, Borsche M, Retscher C, Stiller G (2007) Retrieval of temperature profiles fro CHAMP for climate monitoring: intercomparison with Envisat MIPAS and GOMOS and different atmospheric analyses. Atmos Chem Phys 7:3519–3536CrossRefGoogle Scholar
  12. Gorbunov ME (2002) Canonical transform method for processing radio occultation data in the lower troposphere. Radio Sci 37:1076, doi:10.1029/2000RS002592 CrossRefGoogle Scholar
  13. Gorbunov ME, Lauritsen KB (2004) Analysis of wave fields by Fourier integral operators and their application for radio occultations. Radio Sci 39:RS4010. doi:10.1029/2003RS002971 CrossRefGoogle Scholar
  14. Hayden CM, Lorenc AC (1995) Recursive filter for objective analysis of meteorological fields, applications to NESDIS operational processing. J Appl Meteor 34:3–15CrossRefGoogle Scholar
  15. Healy SB (2007) Operational assimilation of GPS radio occultation measurements at ECMWF. ECMWF Newsl 111:6–11Google Scholar
  16. Healy SB, Thépaut J-N (2006) Assimilation experiments with CHAMP GPS radio occultation measurements. Q J R Meteorol Soc 132:605–623. doi:10.1256/qj.04.182 CrossRefGoogle Scholar
  17. Healy SB, Jupp AM, Marquardt C (2005) Forecast impact experiment with GPS radio occultation measurements. Geophys Res Lett 32:L03804. doi:10.1029/2004GL020806 CrossRefGoogle Scholar
  18. INRIA (2002) Software TAPENADE inria 2002, version 2.0, Tech. Rep. Domaine de Voluceau, Rocquencourt––BP 105, 78153 Le Chesnay Cedex, FranceGoogle Scholar
  19. IPCC (2007) Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007––the physical science basis, contribution of WG I to the fourth assessment report of the IPCC. Cambridge University Press, Cambridge and New York, p 996Google Scholar
  20. Jensen AS, Lohmann MS, Benzon H-H, Nielsen AS (2003) Full spectrum inversion of radio occultation signals. Radio Sci 38:1040. doi:10.1029/2002RS002763 CrossRefGoogle Scholar
  21. Kållberg P, Simmons A, Uppala S, Fuentes M (2004) The ERA40 archive, ERA40 project report series No. 17, ECMWF. Shinfield Park, ReadingGoogle Scholar
  22. Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, CambridgeGoogle Scholar
  23. Kirchengast G, Steiner AK, Foelsche U, Kornblueh L, Manzini E, Bengtsson L (2000) Spaceborne climate change monitoring by GNSS occultation sensors. In: Proceedings of the 11th symposium on global change studies, Amer Met Soc Annual Meeting 2000, Long Beach, CA, USA, pp 62–65Google Scholar
  24. Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP-NCAR 50-year reanalysis: CD-ROM and documentation. Bull Am Meteor Soc 82:247–268CrossRefGoogle Scholar
  25. Kozo N (Ed.) (1997) Data assimilation in meteorology and oceanography: theory and practice. Special issue of the J Meteorol Soc Japan 75,1B, Meteorological Society of JapanGoogle Scholar
  26. Kuo Y-H, Sokolovskiy SV, Anthes RA, Vandenberghe F (2000) Assimilation of GPS radio occultation data for numerical weather prediction. Terr Atmos Ocean Sci 11:157–186Google Scholar
  27. Kursinski ER, Hajj RA, Hardy KR, Schofield JT, Linfield R (1997) Observing the Earth’s atmosphere with radio occultation measurements using the global positioning system. J Geophys Res 102:23429–23465CrossRefGoogle Scholar
  28. Leroy SS, North GR (2000) The application of COSMIC to global change research. Terr Atmos Ocean Sci 11:187–210Google Scholar
  29. Leroy SS, Anderson JG, Dykema JA (2006) Testing climate models using GPS radio occultation: a sensitivity analysis. J Geophys Res 111: D17105. doi:10.1029/2005JD006145 CrossRefGoogle Scholar
  30. Löscher A (2004) Assimilation of GNSS radio occultation data into GCM fields for global climate analysis. Wiss Ber 22:211, Institute for Geophysics, Astrophysics, and Meteorology, University of Graz, AustriaGoogle Scholar
  31. Löscher A, Foelsche U, Kirchengast G (2006) CHAMP radio occultation data assimilation into ECMWF fields for global climate analyses. Tech Rep for FFG–ALR 2/2006, Wegener Center, University of Graz, AustriaGoogle Scholar
  32. Loiselet M, Stricker N, Menard Y, Luntama J-P (2000) GRAS––MetOp’s GPS-based atmospheric sounder. ESA Bull 102:38–44Google Scholar
  33. Lorenc AC (1992) Analysis methods for numerical weather prediction. Q J R Meteorol Soc 112:1177–1194CrossRefGoogle Scholar
  34. Luntama J-P, Kirchengast G, Borsche M, Foelsche U, Steiner AK, Healy S B (2007) EPS GRAS mission for operational radio occultation measurements. Bull Am Meteorol Soc (submitted)Google Scholar
  35. Nocedal J (1996) Large Scale Unconstrained Optimization. Tech Rep Dept of Electrical Engineering and Computer Science, Northwestern University, Evanston/Chicago, IL, USAGoogle Scholar
  36. Palmer P, Barnett JJ, Eyre JR, Healy SB (2000) A non-linear optimal estimation inverse method for radio occultation measurements of temperature, humidity and surface pressure. J Geophys Res 105:17513–17526CrossRefGoogle Scholar
  37. Pirscher B, Foelsche U, Lackner BC, Kirchengast G (2007) Local time influence in single-satellite radio occultation climatologies from Sun-synchronous and non-Sun-synchronous satellites. J Geophys Res 112:D11119. doi:10.1029/2006JD007934 CrossRefGoogle Scholar
  38. Rieder M J, Kirchengast G (2001) Error analysis and characterization of atmospheric profiles retrieved from GNSS occultation data. J Geophys Res 106:31755–31770CrossRefGoogle Scholar
  39. Rocken C, Kuo Y, Schreiner WS, Hunt D, Sokolovskiy S, McCormick C (2000) COSMIC system description. Terr Atmos Ocean Sci 11:21–52Google Scholar
  40. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers J G (2005) A description of the advanced research WRF Version 2. NCAR/TN-468+STR, NCAR TECHNICAL NOTE, Mesoscale and Microscale Meteorology Division, NCAR Boulder, CO, USAGoogle Scholar
  41. Steiner A K, Kirchengast G (2005) Error analysis for GNSS radio occultation data based on ensembles of profiles from end-to-end simulations. J Geophys Res 110:D15307. doi:10.1029/2004JD005251 CrossRefGoogle Scholar
  42. Steiner AK, Kirchengast G, Foelsche U, Kornblueh L, Manzini E, Bengtsson L (2001) GNSS occultation sounding for climate monitoring. Phys Chem Earth A 26:13–124CrossRefGoogle Scholar
  43. Steiner AK, Löscher A, Kirchengast G (2006) Error characteristics of refractivity profiles from CHAMP radio occultation data. In: Foelsche U, Kirchengast G, Steiner AK (eds) Atmosphere and climate, studies by occultation methods. Springer, Berlin, 27–36CrossRefGoogle Scholar
  44. Steiner AK, Kirchengast G, Borsche M, Foelsche U, Schoengassner T (2007) A multi-year comparison of lower stratospheric temperatures from CHAMP radio occultation data with MSU/AMSU records. J Geophys Res 112. doi:10.1029/2006JD008283 (in press)
  45. Thorne PW, Parker DE, Christy JR, Mears CA (2005) Uncertainties in climate trends: lessons from upper-air temperature records. Bull Am Meteorol Soc 86:1437–1442CrossRefGoogle Scholar
  46. Wickert J, Reigber C, Beyerle G, Konig R, Marquardt C, Schmidt T, Grunwaldt L, Galas R, Meehan TK, Melbourne WG, Hocke K (2001) Atmosphere sounding by GPS radio occultation: first results from CHAMP. Geophys Res Lett 28:3263–3266CrossRefGoogle Scholar
  47. Wickert J, Schmidt T, Beyerle G, König R, Reigber C, Jakowski N (2004) The radio occultation experiment aboard CHAMP: operational data analysis and validation of vertical atmospheric profiles. J Meteorol Soc Japan 82:381–395CrossRefGoogle Scholar
  48. Wu B-H, Chu V, Chen P, King T (2005) FORMOSAT-3/COSMIC science mission update. GPS Solut 9:111–121. doi:10.1007/s10291-005-0140-z CrossRefGoogle Scholar
  49. Zhu C, Byrd RH, Lu P, Nocedal J (1995) L_BFGS_B fortran subroutines for large scale bound constrained optimization, L BFGS B_FORTRAN sub routines for large scale bound constrained optimization. Tech Rep Department of Electrical Engineering and Computer Science, Northwestern University, Evanston/Chicago, IL, USAGoogle Scholar
  50. Zupanski M (1993) A precondition algorithm for large-scale minimization problems. Tellus 45A:478–492Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.European Space Agency/European Space Research and Technology Centre (ESA/ESTEC)NoordwijkThe Netherlands
  2. 2.Wegener Center for Climate and Global Change (WegCenter), Institute for Geophysics, Astrophysics, and Meteorology (IGAM)University of GrazGrazAustria
  3. 3.EOP-SF, ESTECNoordwijkThe Netherlands

Personalised recommendations