Satellite Bias Correction in the Regional Model ALADIN/CZ: Comparison of Different VarBC Approaches

Patrik Benáček Numerical Weather Prediction Department, Czech Hydrometeorological Institute, and Atmospheric Physics Department, Charles University, Prague, Czech Republic

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Máté Mile Unit of Methodology Development, Hungarian Meteorological Service, Budapest, Hungary

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Abstract

The bias correction of satellite radiances is an essential component of data assimilation system in numerical weather prediction (NWP). The variational bias correction (VarBC) scheme is widely used by global NWP centers, but there are still open questions regarding its use in limited-area models (LAMs). We present a study of key VarBC aspects in the limited-area 3D-Var system using the state-of-the-art NWP system ALADIN. Two basic VarBC applications are tested, specifically adopting bias coefficients from the global model ARPEGE and cycling bias coefficients independently in the LAM ALADIN (VarBC-LAM). The latter application is studied using daily update of bias coefficients with regards to static and dynamic settings of the VarBC stiffness. Extensive testing shows that the VarBC-LAM methods outperform the use of global coefficients from ARPEGE providing the better quality of the model first guess (3-h forecast), in the assimilation cycle with the largest normalized impact of 2%–3% for temperature and wind components in the midtroposphere. Compared to the global coefficients, there was little forecast impact between 24 and 48 h from using the VarBC-LAM coefficients. The various VarBC-LAM methods were comparable, but the CAM method may be most useful when an unexpected bias shows up.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Patrik Benáček, patrik.benacek@chmi.cz

Abstract

The bias correction of satellite radiances is an essential component of data assimilation system in numerical weather prediction (NWP). The variational bias correction (VarBC) scheme is widely used by global NWP centers, but there are still open questions regarding its use in limited-area models (LAMs). We present a study of key VarBC aspects in the limited-area 3D-Var system using the state-of-the-art NWP system ALADIN. Two basic VarBC applications are tested, specifically adopting bias coefficients from the global model ARPEGE and cycling bias coefficients independently in the LAM ALADIN (VarBC-LAM). The latter application is studied using daily update of bias coefficients with regards to static and dynamic settings of the VarBC stiffness. Extensive testing shows that the VarBC-LAM methods outperform the use of global coefficients from ARPEGE providing the better quality of the model first guess (3-h forecast), in the assimilation cycle with the largest normalized impact of 2%–3% for temperature and wind components in the midtroposphere. Compared to the global coefficients, there was little forecast impact between 24 and 48 h from using the VarBC-LAM coefficients. The various VarBC-LAM methods were comparable, but the CAM method may be most useful when an unexpected bias shows up.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Patrik Benáček, patrik.benacek@chmi.cz
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  • Auligné, T., 2007: An objective approach to modelling biases in satellite radiances: Application to AIRS and AMSU-A. Quart. J. Roy. Meteor. Soc., 133, 17891801, https://doi.org/10.1002/qj.145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631642, https://doi.org/10.1002/qj.56.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., B. E. Schwartz, E. J. Szoke, and S. E. Koch, 2004: The value of wind profiler data in U.S. weather forecasting. Bull. Amer. Meteor. Soc., 85, 18711886, https://doi.org/10.1175/BAMS-85-12-1871.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. E. Schwartz, and T. W. Schlatter, 2010: Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Mon. Wea. Rev., 138, 13191343, https://doi.org/10.1175/2009MWR3097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouttier, F., and P. Courtier, 1999: Data assimilation concepts and methods. Meteorological Training Course Lecture Series, ECMWF, 59 pp., https://www.ecmwf.int/sites/default/files/elibrary/2002/16928-data-assimilation-concepts-and-methods.pdf.

  • Bučánek, A., A. Trojáková, and R. Brožková, 2015: The BlendVar assimilation scheme at CHMI. Meteor. Bull., 68, 180185.

  • Cameron, J., and W. Bell, 2016: The testing and planned implementation of variational bias correction (VarBC) at the Met Office. Met Office, 21 pp., https://cimss.ssec.wisc.edu/itwg/itsc/itsc20/papers/11_01_cameron_paper.pdf.

  • Campins, J., A. J. Sánchez, M. V. Diez Muyo, F. J. Calvo Sánchez, and B. Navascués, 2017: Assimilation of ATOVS and GNSS ZTD data in the HARMONIE-AROME model configuration run at AEMET. ALADIN-HIRLAM Newsletter, No. 8, 40–50, https://repositorio.aemet.es/bitstream/20.500.11765/6833/1/Assimilation_Campins.pdf.

  • Dee, D. P., 2004: Variational bias correction of radiance data in the ECMWF system. Proc. ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Reading, United Kingdom, ECMWF, Vol. 28, 97–112.

  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 33233343, https://doi.org/10.1256/qj.05.137.

  • Dee, D. P., and R. Todling, 2000: Data assimilation in the presence of forecast bias: The GEOS moisture analysis. Mon. Wea. Rev., 128, 32683282, https://doi.org/10.1175/1520-0493(2000)128<3268:DAITPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 14310 162, https://doi.org/10.1029/94JC00572.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyre, J., 1992: A bias correction scheme for simulated TOVS brightness temperatures. ECMWF Tech. Memo.186, ECMWF, 35 pp.

  • Fertig, E., and Coauthors, 2009: Observation bias correction with an ensemble Kalman filter. Tellus, 61A, 210226, https://doi.org/10.1111/j.1600-0870.2008.00378.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, C., T. Montmerle, L. Berre, L. Auger, and S. E. Ştefănescu, 2005: An overview of the variational assimilation in the ALADIN/France numerical weather-prediction system. Quart. J. Roy. Meteor. Soc., 131, 34773492, https://doi.org/10.1256/qj.05.115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geer, A. J., 2016: Significance of changes in medium-range forecast scores. Tellus, 68A, 30229, https://doi.org/10.3402/tellusa.v68.30229.

    • Search Google Scholar
    • Export Citation
  • Gérard, E., F. Rabier, D. Lacroix, and Z. Sahlaoui, 2003: Use of ATOVS raw radiances in the operational assimilation system at Météo-France. Proc. ITSC XIII, Ste. Adéle, Canada, Météo-France,1829.

  • Guidard, V., N. Fourrié, P. Brousseau, and F. Rabier, 2011: Impact of IASI assimilation at global and convective scales and challenges for the assimilation of cloudy scenes. Quart. J. Roy. Meteor. Soc., 137, 19751987, https://doi.org/10.1002/qj.928.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gustafsson, N., and Coauthors, 2018: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., 144, 12181256, https://doi.org/10.1002/qj.3179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 14531468, https://doi.org/10.1002/qj.49712757418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Järvinen, H., and P. Undén, 1997: Observation screening and background quality control in the ECMWF 3D-Var data assimilation system. ECMWF Tech. Memo. 236, ECMWF, 34 pp.

  • Karbou, F., É. Gérard, and F. Rabier, 2006: Microwave land emissivity and skin temperature for AMSU-A and -B assimilation over land. Quart. J. Roy. Meteor. Soc., 132, 23332355, https://doi.org/10.1256/qj.05.216.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kazumori, M., 2014: Satellite radiance assimilation in the JMA operational mesoscale 4DVAR system. Mon. Wea. Rev., 142, 13611381, https://doi.org/10.1175/MWR-D-13-00135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lahoz, W., B. Khattatov, and R. Menard, 2010: Data Assimilation: Making Sense of Observations. Springer-Verlag, 718 pp.

    • Crossref
    • Export Citation
  • Lin, H., S. S. Weygandt, S. G. Benjamin, and M. Hu, 2017: Satellite radiance data assimilation within the hourly updated rapid refresh. Wea. Forecasting, 32, 12731287, https://doi.org/10.1175/WAF-D-16-0215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindskog, L., M. Dahlbom, S. Thorsteinsson, P. Dahlgren, R. Randriamampianina, and J. Bojarova, 2012: ATOVS processing and usage in the HARMONIE reference system. HIRLAM Newsletter, Vol. 59, HIRLAM, 33–43.

  • Liu, Z., C. S. Schwartz, C. Snyder, and S. Ha, 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon. Wea. Rev., 140, 40174034, https://doi.org/10.1175/MWR-D-12-00083.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montmerle, T., F. Rabier, and C. Fischer, 2007: Relative impact of polar-orbiting and geostationary satellite radiances in the Aladin/France numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 655671, https://doi.org/10.1002/qj.34.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randriamampianina, R., 2005: Radiance-bias correction for a limited area model. Quart. J. Hungarian Meteor. Service, 109 (3), 143155.

    • Search Google Scholar
    • Export Citation
  • Randriamampianina, R., T. Iversen, and A. Storto, 2011: Exploring the assimilation of IASI radiances in forecasting polar lows. Quart. J. Roy. Meteor. Soc., 137, 17001715, https://doi.org/10.1002/qj.838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romine, G. S., C. S. Schwartz, C. Snyder, J. L. Anderson, and M. L. Weisman, 2013: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts. Mon. Wea. Rev., 141, 12631284, https://doi.org/10.1175/MWR-D-12-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 14071425, https://doi.org/10.1002/qj.1999.49712555615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of typhoon morakot. Wea. Forecasting, 27, 424437, https://doi.org/10.1175/WAF-D-11-00033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strajnar, B., N. Žagar, and L. Berre, 2015: Impact of new aircraft observations Mode-S MRAR in a mesoscale NWP model. J. Geophys. Res. Atmos., 120, 39203938, https://doi.org/10.1002/2014JD022654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Termonia, P., and Coauthors, 2018: The ALADIN system and its canonical model configurations AROME CY41T1 and ALARO CY40T1. Geosci. Model Dev., 11, 257–281, https://doi.org/10.5194/gmd-11-257-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. A. Davis, 2012: The influence of shallow convection on tropical cyclone track forecasts. Mon. Wea. Rev., 140, 21882197, https://doi.org/10.1175/MWR-D-11-00246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

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