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Tellus A: Dynamic Meteorology and Oceanography Volume 61, 2009 - Issue 2
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Original Articles

Observation bias correction with an ensemble Kalman filter

Elana FertigOncology Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USACorrespondence[email protected]
,
Seung-Jong BaekInstitute for Research in Electronics and Applied Physics and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
,
Brian HuntInstitute for Physical Science and Technology and Department of Mathematics, University of Maryland, College Park, MD 20742, USA
,
Edward OttInstitute for Research in Electronics and Applied Physics, Department of Electrical and Computer Engineering and Department of Physics, University of Maryland, College Park, MD 20742, USA
,
Istvan SzunyoghDepartment of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
,
José AravéquiaDepartment of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA and Center for Weather Forecast and Climatic Studies, Brazilian Institute of Space Research, Cahoeira Paulista, San Paulo 12630, Brazil
,
Eugenia KalnayDepartment of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
,
Hong LiShanghai Typhoon Institute, Shanghai, China
&
Junjie LiuEarth and Planetary Science Department, University of California, Berkeley, CA94720, USA
 show all
Pages 210-226 | Received 21 Apr 2008, Accepted 24 Oct 2008, Published online: 15 Dec 2016

References

  • Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev. 129,2884–2903.
  • Auligne, T., McNally, A. P. and Dee, D. P. 2007. Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. R. MeteoroL Soc. 133, 631–642.
  • Baek, S.-J., Hunt, B. R., Kalnay, E., Ott, E. and Szunyogh, I. 2006. Local ensemble Kalman filtering in the presence of model bias. Tellus 58A, 293–306.
  • Cameron, J. R. N. 2003. The effectiveness of the AIRS bias correction for various air-mass predictor combinations. Technical Report, Met. Office NWP. Available at: http://www.metoffice.com/research/nwp/publications/papers/technical_reports/2003/FRTR421/FRTR421.pdf.
  • Dee, D. P. 2005. Bias and data assimilation. Quart. J. R. MeteoroL Soc. 131, 3323–3343.
  • Dee, D. P. and da Silva, A. M. 1998. Data assimilation in the presence of forecast bias. Quart. J. R. MeteoroL Soc. 124, 269–295.
  • Derber, J. C. and Wu, W.-S. 1998. The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev. 126, 2287–2299.
  • Eyre, J. R. 1992. A bias correction scheme for simulated TOVS bright-ness temperatures. Technical Memorandum 186, ECMWF, Reading, UK.
  • Fertig, E. J., Hunt, B. R., Ott, E. and Szunyogh, I. 2007. Assimilating nonlocal observations with a local ensemble Kalman filter. Tellus 59A, 719–730.
  • Friedland, B. 1969. Treatment of bias in recursive filtering. IEEE Trans. Auto. Control 14, 359–367.
  • Hamill, T., Whitaker, J. S. and Snyder, C. 2001. Distance-dependent filtering of background covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev. 129, 2776–2790.
  • Han, Y., van Delst, P., Liu, Q., Weng, E and Derber, J. C. 2005. User’s Guide to the JCSDA Community Radiative Transfer Model (Beta Ver-sion). Joint Center for Satellite Data Assimilation, Camp Springs, MD, USA.
  • Harlim, J. and Hunt, B. R. 2007. A non-Gaussian ensemble filter for assimilating infrequent noisy observations. Tellus 59A, 225–237.
  • Harris, B. A. and Kelly, G. 2001. A satellite radiance-bias correction scheme for data assimilation. Quart. J. R. MeteoroL Soc. 127, 1453–1468.
  • Harris, B. A., Cameron, J., Collard, A. and Saunders, R. 2004. Effect of air-mass predictor choice on the AIRS bias correction at the Met Office. In: Proceedings of the Thirteenth International TOVS Study Conference, International ATOVES Working Group, Sainte-Adele, Quebec, Canada,92-98.
  • Houtelcamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123–137.
  • Houtelcamer, P. L. and Mitchell, H. L. 2006. Ensemble Kalman filtering. Quart. J. R. MeteoroL Soc. 131, 3269–3289.
  • Houtelcamer, P. L., Mitchell, H. L., Pellerin, G., Buehner, M., Spacek, L. and co-author. 2005. Atmospheric data assimilation with the ensemble Kalman filter: results with real observations. Mon. Wea. Rev. 133, 604-620.
  • Hunt, B. R., Kostelich, E. J. and Szunyogh, I. 2007. Efficient data assim-ilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D. 230, 112–126.
  • Kalnay, E. 2003. Atmospheric Modelling, Data Assimilation and Pre-dictability. Cambridge University Press, New York, 364 pp.
  • Kalnay, E., Hunt, B. R., Ott, E. and Szunyogh, I. 2006. Ensemble fore-casting and data assimilation: two problems with the same solution? In: Predictability of Weather and Climate (eds. T. N. Palmer and R. Hagedorn). Cambridge University Press, New York.
  • Keppenne, C. L., Rienecker, M. M., Kurowski, N. P. and Adamec, D. A. 2005. Ensemble Kalman filter assimilation of temperature and altime-ter data with bias correction and application to seasonal prediction. Nonlinear Proc. Geophys. 12, 491–503.
  • Kuhl, D., Szunyogh, I., Kostelich, E. J., Patil, D. J., Gyarmati, and co-authors. 2007. Assessing predictability with a local ensemble Kalman filter. J. Atmos. Sci. 64, 1116-1140.
  • Le Marshall, J., Jung, J., Zapotocny, T., Derber, J., Treadon, R., and co-authors. 2006. The application of AIRS radiances in numerical weather prediction. AusL MeteoroL Mag. 55, 213-217.
  • Liou, K. 2002. An Introduction to Atmopsheric Radiation 2nd Edition. Academic Press, New York, 583 pp.
  • Liu, J., Fertig, E. J., Li, H., Kalnay, E., Hunt, B. R., and co-authors. 2008. Comparison between local ensemble transform Kalman filter and PSAS in the NASA finite volume GCM perfect model experi-ments. Nonlinear Proc. Geophys. 15, 645-659.
  • Miyoshi, T. 2005. Ensemble Kalman filter experiments with a primitive-equation global model. PhD Thesis, University of Maryland, College Park, MD, USA.
  • Molteni, F. 2003. Atmospheric simulations using a GCM with simplified physics parameterizations, I: model climatology and variability in multi-decadal experiments. Clim. Dyn. 20, 175–191.
  • Oczkowslci, M., Szunyogh, I. and Patil, D. J. 2005. Mechanisms for the development of locally low dimensional atmospheric dynamics. J. Atmos. Sci. 61, 1135–1156.
  • Ogata, K. 1990. Modern Control Engineering 2nd Edition. Prentice Hall, Englewood Cliffs, NJ, 960 pp.
  • Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A., Kostelich, E. J., and co-authors. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A, 415-428.
  • Patil, D. J., Hunt, B. R., Kalnay, E., Yorke, J. A. and Ott, E. 2005. Local low dimensionality of atmospheric dynamics. Phys. Rev. Lett. 86, 5878–5881.
  • Rizzi, R. and Matricardi, M. 1998. The use of TOVS clear radiances for numerical weather prediction using an updated forward model. Quart. J. R. MeteoroL Soc. 124, 1293–1312.
  • Susskind, J., Barnet, C. and Blaisdell, J. 2003. Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB under cloudy condi-tions. IEEE Trans. Geosci. Remote Sens. 41, 390–409.
  • Szunyogh, I., Kostelich, E. J., Gyarmati, G., Patil, D. J., Kalnay, E., and co-authors. 2005. Assessing a local ensemble Kalman filter: Per-fect model experiments with the National Centers for Environmental Prediction global model. Tellus 57A, 528-545.
  • Szunyogh, I., Satterfield, E. A., Aravequia, J. A., Fertig, E. J., Gyarmati, G., and co-authors. 2007. The local ensemble transform Kalman filter and its implementation on the NCEP global model at the University of Maryland. In: Proceedings of the ECMWF Workshop on Flow-dependent aspects of data assimilation, ECMWF, Reading, United Kingdom, 47-64.
  • Szunyogh, I., Kostelich, E. J., Gyarmati, G., Kalnay, E., Hunt, B. R., and co-authors. 2008. A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus 60A, 113-130.
  • Turner, D. S. 1994. HMS sensitivity to CO2 mixing ratio and a pragmatic correction term. J. AppL MeteoroL 33, 1155–1162.
  • Watts, P. D. and McNally, A. P. 2004. Identification and correction of ra-diative transfer modelling errors for atmospheric sounders: AIRS and AMSU-A. In: Proceedings of the ECMWF Workshop on Assimila-tion of high spectral resolution sounders in IVWP, ECMWF, Reading, United Kingdom, 28 June-1 July, 2004.
  • Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimila-tion without perturbed observations. Mon. Wea. Re v. 130, 1913–1924.
  • Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y. and Toth, Z. 2008. Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Re v. 463–482.
  • Zupanski, D. and Zupanslci, M. 2006. Model error estimation employing an ensemble data assimilation approach. Mon. Wea. Re v. 134, 1337–1354.

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