Experimental Determination of Forecast Sensitivity and the Degradation of Forecasts through the Assimilation of Good Quality Data

Adrian Semple Met Office, Exeter, United Kingdom

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Michael Thurlow Met Office, Exeter, United Kingdom

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Sean Milton Met Office, Exeter, United Kingdom

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Abstract

The case of a small vigorous cyclone crossing the United Kingdom on 1 November 2009 is investigated. Met Office Global Model forecasts at the time displayed a marked change in solutions at a forecast range of 72 h, with those at longer ranges being more representative of the correct solution and those at shorter ranges only gradually migrating toward it. The strong bimodal nature of the Global Model forecasts is enough to overwhelmingly dominate the solutions from the Met Office Global Ensemble on which it is based. An investigation into the case is used as a vehicle for developing an experimental method determining the critical location of assimilated data leading to the largest impact on forecast consistency and the origins of the bimodal solutions. It allows the identification of one global positioning system radio occultation (GPSRO) and three surface observations located around the developing low that have conclusively led to the degradation in forecast skill. An assessment of these observations concludes that they are of relatively good quality and correctly assimilated. The case is suggested to be an example of forecast degradation as a result of the addition of growing errors by the data assimilation scheme.

Corresponding author address: Adrian Semple, Met Office, Fitzroy Road, Exeter EX1 3PB, United Kingdom. E-mail: adrian.semple@metoffice.gov.uk

Abstract

The case of a small vigorous cyclone crossing the United Kingdom on 1 November 2009 is investigated. Met Office Global Model forecasts at the time displayed a marked change in solutions at a forecast range of 72 h, with those at longer ranges being more representative of the correct solution and those at shorter ranges only gradually migrating toward it. The strong bimodal nature of the Global Model forecasts is enough to overwhelmingly dominate the solutions from the Met Office Global Ensemble on which it is based. An investigation into the case is used as a vehicle for developing an experimental method determining the critical location of assimilated data leading to the largest impact on forecast consistency and the origins of the bimodal solutions. It allows the identification of one global positioning system radio occultation (GPSRO) and three surface observations located around the developing low that have conclusively led to the degradation in forecast skill. An assessment of these observations concludes that they are of relatively good quality and correctly assimilated. The case is suggested to be an example of forecast degradation as a result of the addition of growing errors by the data assimilation scheme.

Corresponding author address: Adrian Semple, Met Office, Fitzroy Road, Exeter EX1 3PB, United Kingdom. E-mail: adrian.semple@metoffice.gov.uk
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  • Bowler, N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703722.

    • Search Google Scholar
    • Export Citation
  • Cullen, M., 1993: The Unified Forecast Climate Model. Meteor. Mag., 122, 8194.

  • Davies, T., M. Cullen, A. Malcolm, M. Mawson, A. Staniforth, A. White, and N. Wood, 2005: A new dynamical core for the Met Office’s global and regional modelling of the atmosphere. Quart. J. Roy. Meteor. Soc., 131, 17591782.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter–3D variational analysis scheme. Mon. Wea. Rev., 128, 29052919.

  • Hoskins, B. J., M. E. McIntyre, and A. W. Robertson, 1985: On the use and significance of isentropic potential vorticity maps. Quart. J. Roy. Meteor. Soc., 111, 877946.

    • Search Google Scholar
    • Export Citation
  • Jensen, A. S., M. S. Lohmann, H. H. Benzon, and A. S. Nielsen, 2003: Full spectrum inversion of radio occultation signals. Radio Sci., 38, 1040, doi:10.1029/2002RS002763.

    • Search Google Scholar
    • Export Citation
  • Johnson, C., B. J. Hoskins, N. K. Nichols, and S. P. Ballard, 2006: A singular vector perspective of 4DVAR: The spatial structure and evolution of baroclinic weather systems. Mon. Wea. Rev., 134, 34363455.

    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and N. L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56A, 189201.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 11771194.

  • Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP—A comparison with 4D-VAR. Quart. J. Roy. Meteor. Soc., 126, 29913012.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., M. A. Ringer, V. D. Pope, A. Jones, C. Dearden, and T. J. Hinton, 2005: The physical properties of the atmosphere in the new Hadley Centre Global Environment Model (HadGEM1). Part I: Model description and global climatology. J. Climate, 19, 12741301.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73119.

    • Search Google Scholar
    • Export Citation
  • Morss, R. E., and K. A. Emanuel, 2002: Influence of added observations on analysis and forecast errors: Results from idealized systems. Quart. J. Roy. Meteor. Soc., 128, 285322.

    • Search Google Scholar
    • Export Citation
  • Persson, A., 2000: Synoptic-dynamic diagnosis of medium range weather forecast systems. Proc. Seminars on Diagnosis of Models and Data Assimilation Systems, Reading, United Kingdom, ECMWF, 123–137.

  • Rawlins, F., S. P. Ballard, K. J. Bovis, A. M. Clayton, D. Li, G. W. Inverarity, A. C. Lorenc, and T. J. Payne, 2007: The Met Office global four-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 133, 347362.

    • Search Google Scholar
    • Export Citation
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