Error Characteristics of Refractivity Profiles Retrieved from CHAMP Radio Occultation Data

  • A. K. Steiner
  • A. Löscher
  • G. Kirchengast

Abstract

We present results of an empirical error analysis of refractivity profiles based on CHAMP radio occultation data. We analyzed two seasons of observations, boreal winter 2002/03 and boreal summer 2003. The processing was performed with the WegCenter/CHAMPCLIM Retrieval version 2. The error statistics is based on comparison to reference profiles calculated from ECMWF analyses fields. Bias profiles and error covariance matrices are provided, the latter separated into standard deviation profiles and error correlation matrices. Since the error characteristics contain both the observational error of the retrieved data and the model error of the ECMWF analyses we performed an estimation of the ECMWF model error and separated the observation error. The relative refractivity bias of CHAMP radio occultation data with respect to ECMWF was found to oscillate around −0.4 % at 5–25 km globally. Wavelike structures apparent at high latitudes in Southern Hemisphere winter are mainly due to the representation of the polar vortex in the ECMWF analyses. The combined relative standard deviation was found to be 0.7–1 % at 5–25 km height globally, showing larger values in winter than in summer in the upper stratosphere at mid- and high latitudes. The global observation error for CHAMP refractivity was estimated to be 0.5–0.75 % at 6–30 km. The results are compared to the findings of Kuo et al. (2004) and to those of an end-to-end simulation study being the precursor of this work (Steiner and Kirchengast 2004, 2005). Based on the simulation study we provide simple observation error covariance matrix formulations for CHAMP refractivity for convenient use in retrieval algorithms and in data assimilation systems.

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References

  1. Borsche M, Gobiet A, Steiner AK, Foelsche U, Kirchengast G, Schmidt T, Wickert J (2006) Pre-operational retrieval of radio occultation based climatologies. This issueGoogle Scholar
  2. Foelsche U, Kirchengast G (2004) Sensitivity of GNSS occultation profiles to horizontal variability in the troposphere: A simulation study. In: Kirchengast G, Foelsche U, Steiner AK (eds) Occultations for Probing Atmosphere and Climate. Springer-Verlag, Berlin Heidelberg, pp 127–136Google Scholar
  3. Gobiet A, Kirchengast G (2004a) Advancement of GNSS radio occultation retrieval in the upper stratosphere. In Kirchengast G, Foelsche U, Steiner AK (eds) Occultations for Probing Atmosphere and Climate. Springer-Verlag, Berlin Heidelberg, pp 137–148Google Scholar
  4. Gobiet A, Kirchengast G (2004b) Advancements of GNSS radio occultation retrieval in the upper stratosphere for optimal climate monitoring utility. J Geophys Res 109(D24110), doi:10.1029/2004JD005117Google Scholar
  5. 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:10.1029/2005GL022617Google Scholar
  6. Healy SB, Jupp AM, Marquardt C (2005) Forecast impact experiment with GPS radio occultation measurements, Geophys Res Lett 32:L03804, doi:10.1029/2004GL020806CrossRefGoogle Scholar
  7. Hollingsworth A, Lönnberg P (1986) The statistical structure of short range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus 38A:111–136CrossRefGoogle Scholar
  8. Kuo Y-H, Sokolovskiy SV, Anthes RA, Vandenberghe F (2000) Assimilation of GPS radio occultation data for numerical weather prediction. Terr Atmos Oceanic Sci 11:157–186Google Scholar
  9. Kuo Y-H, Wee T-K, Sokolovskiy S, Rocken C, Schreiner W, Hunt D, Anthes RA (2004) Inversion and error estimation of GPS radio occultation data. J Meteorol Soc Japan 82: 507–531CrossRefGoogle Scholar
  10. Smith EK, Weintraub S (1953) The constants in the equation for atmospheric refractive index at radio frequencies. Proc of the IRE 41:1035–1037Google Scholar
  11. Steiner AK, Kirchengast G (2004) Ensemble-based analysis of errors in atmospheric profiles retrieved from GNSS occultation data. In: Kirchengast G, Foelsche U, Steiner AK (eds) Occultations for Probing Atmosphere and Climate. Springer-Verlag, Berlin Heidelberg, pp 149–160Google Scholar
  12. Steiner AK, Gobiet A, Foelsche U, Kirchengast G (2004) Radio occultation data processing advancements for optimizing climate utility. IGAM/Uni Graz Tech Rep for ASA No 3/2004Google Scholar
  13. Steiner AK, 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:10.1029/2004JD005251Google Scholar
  14. Syndergaard S, Flittner DE, Kursinski ER, Feng DD, Herman BM, Ward DM (2004) Simulating the influence of horizontal gradients on retrieved profiles from ATOMS occultation measurements — a promising approach for data assimilation. In: Kirchengast G, Foelsche U, Steiner AK (eds) Occultations for Probing Atmosphere and Climate. Springer-Verlag, Berlin Heidelberg, pp 221–232Google Scholar
  15. Syndergaard S, Kuo Y-H, Lohmann M (2006) Observation operators for the assimilation of occultation data into atmospheric models: A review. This issueGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. K. Steiner
    • 1
  • A. Löscher
    • 1
  • G. Kirchengast
    • 1
  1. 1.Wegener Center for Climate and Global Change (WegCenter) and Institute for Geophysics, Astrophysics, and Meteorology (IGAM)University of GrazAustria

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