• Bengtsson, L., Hodges K. I. , and Froude L. S. R. , 2005: Global observations and forecast skill. Tellus, 57A, 515527, doi:10.1111/j.1600-0870.2005.00138.x.

    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., Arribas A. , Beare S. E. , Mylne K. R. , and Shutts G. J. , 2009: The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 767776, doi:10.1002/qj.394.

    • Search Google Scholar
    • Export Citation
  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 13, doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frame, T. H. A., Ambaum M. H. P. , Gray S. L. , and Methven J. , 2011: Ensemble prediction of transitions of the North Atlantic eddy-driven jet. Quart. J. Roy. Meteor. Soc., 137, 12881297, doi:10.1002/qj.829.

    • Search Google Scholar
    • Export Citation
  • Froude, L. S., 2010: TIGGE: Comparison of the prediction of Northern Hemisphere extratropical cyclones by different ensemble prediction systems. Wea. Forecasting, 25, 819836, doi:10.1175/2010WAF2222326.1.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2012: Verification of TIGGE multimodel and ECMWF reforecast-calibrated probabilistic precipitation forecasts over the contiguous United States. Mon. Wea. Rev., 140, 22322252, doi:10.1175/MWR-D-11-00220.1.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., and Juras J. , 2006: Measuring forecast skill: Is it real skill or is it the varying climatology? Quart. J. Roy. Meteor. Soc., 132, 29052923, doi:10.1256/qj.06.25.

    • Search Google Scholar
    • Export Citation
  • Hawcroft, M., Shaffrey L. , Hodges K. , and Dacre H. , 2012: How much Northern Hemisphere precipitation is associated with extratropical cyclones? Geophys. Res. Lett., 39, doi:10.1029/2012GL053866.

    • Search Google Scholar
    • Export Citation
  • Hewson, T. D., 1998: Objective fronts. Meteor. Appl., 5, 3765, doi:10.1017/S1350482798000553.

  • Hewson, T. D., and Titley H. A. , 2010: Objective identification, typing and tracking of the complete life-cycles of cyclonic features at high spatial resolution. Meteor. Appl., 17, 355381, doi:10.1002/met.204.

    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and Hodges K. I. , 2002: New perspectives on the Northern Hemisphere winter storm tracks. J. Atmos. Sci., 59, doi:10.1175/1520-0469(2002)059<1041:NPOTNH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jolliffe, I. T., and Stephenson D. B. , 2012: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd ed. J. Wiley and Sons, 292 pp.

  • Leith, C., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409418, doi:10.1175/1520-0493(1974)102<0409:TSOMCF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., Buizza R. , Palmer T. N. , and Petroliagis T. , 1996: The ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73119, doi:10.1002/qj.49712252905.

    • Search Google Scholar
    • Export Citation
  • Morss, R. E., Lazo J. K. , Brown B. G. , Brooks H. E. , Ganderton P. T. , and Mills B. N. , 2008: Societal and economic research and applications for weather forecasts: Priorities for the North American THORPEX program. Bull. Amer. Meteor. Soc., 89, 335346, doi:10.1175/BAMS-89-3-335.

    • Search Google Scholar
    • Export Citation
  • Neu, U., and Coauthors, 2013: IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms. Bull. Amer. Meteor. Soc., 94, 529547, doi:10.1175/BAMS-D-11-00154.1.

    • Search Google Scholar
    • Export Citation
  • Park, Y.-Y., Buizza R. , and Leutbecher M. , 2008: TIGGE: Preliminary results on comparing and combining ensembles. Quart. J. Roy. Meteor. Soc., 134, 20292050, doi:10.1002/qj.334.

    • Search Google Scholar
    • Export Citation
  • Rudeva, I., and Gulev S. K. , 2007: Climatology of cyclone size characteristics and their changes during the cyclone life cycle. Mon. Wea. Rev., 135, 25682587, doi:10.1175/MWR3420.1.

    • Search Google Scholar
    • Export Citation
  • Rudeva, I., Gulev S. K. , Simmonds I. , and Tilinina N. , 2014: The sensitivity of characteristics of cyclone activity to identification procedures in tracking algorithms. Tellus, 66A, 24961, doi:10.3402/tellusa.v66.24961.

    • Search Google Scholar
    • Export Citation
  • Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 30793102, doi:10.1256/qj.04.106.

    • Search Google Scholar
    • Export Citation
  • Swinbank, R., and Coauthors, 2015: The THORPEX Interactive Grand Global Ensemble (TIGGE) and its achievements. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-13-00191.1, in press.

    • Search Google Scholar
    • Export Citation
  • Wang, X., and Bishop C. H. , 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 11401158, doi:10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weijs, S. V., Van Nooijen R. , and Van De Giesen N. , 2010: Kullback–Leibler divergence as a forecast skill score with classic reliability-resolution-uncertainty decomposition. Mon. Wea. Rev., 138, 33873399, doi:10.1175/2010MWR3229.1.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

  • Zappa, G., Shaffrey L. C. , and Hodges K. I. , 2013: The ability of CMIP5 models to simulate North Atlantic extratropical cyclones. J. Climate, 26, 53795396, doi:10.1175/JCLI-D-12-00501.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 217 165 34
PDF Downloads 67 34 3

Predictability of Frontal Waves and Cyclones

View More View Less
  • 1 University of Reading, Reading, United Kingdom
  • | 2 Met Office, Reading, United Kingdom
  • | 3 Met Office, Exeter, United Kingdom
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

The statistical properties and skill in predictions of objectively identified and tracked cyclonic features (frontal waves and cyclones) are examined in the 15-day version of the Met Office Global and Regional Ensemble Prediction System (MOGREPS-15). The number density of cyclonic features is found to decline with increasing lead time, with analysis fields containing weak features that are not sustained past the first day of the forecast. This loss of cyclonic features is associated with a decline in area-averaged enstrophy with increasing lead time. Both feature number density and area-averaged enstrophy saturate by around 7 days into the forecast. It is found that the feature number density and area-averaged enstrophy of forecasts produced using model versions that include stochastic energy backscatter saturate at higher values than forecasts produced without stochastic physics. The ability of MOGREPS-15 to predict the locations of cyclonic features of different strengths is evaluated at different spatial scales by examining the Brier skill (relative to the analysis climatology) of strike probability forecasts: the probability that a cyclonic feature center is located within a specified radius. The radius at which skill is maximized increases with lead time from 650 km at 12 h to 950 km at 7 days. The skill is greatest for the most intense features. Forecast skill remains above zero at these scales out to 14 days for the most intense cyclonic features, but only out to 8 days when all features are included irrespective of intensity.

Corresponding author address: Thomas H. A. Frame, Dept. of Meteorology, University of Reading, Reading RG6 6BB, United Kingdom. E-mail: t.h.a.frame@reading.ac.uk

This article is included in the Diabatic Influence on Mesoscale Structures in Extratropical Storms (DIAMET) special collection.

Abstract

The statistical properties and skill in predictions of objectively identified and tracked cyclonic features (frontal waves and cyclones) are examined in the 15-day version of the Met Office Global and Regional Ensemble Prediction System (MOGREPS-15). The number density of cyclonic features is found to decline with increasing lead time, with analysis fields containing weak features that are not sustained past the first day of the forecast. This loss of cyclonic features is associated with a decline in area-averaged enstrophy with increasing lead time. Both feature number density and area-averaged enstrophy saturate by around 7 days into the forecast. It is found that the feature number density and area-averaged enstrophy of forecasts produced using model versions that include stochastic energy backscatter saturate at higher values than forecasts produced without stochastic physics. The ability of MOGREPS-15 to predict the locations of cyclonic features of different strengths is evaluated at different spatial scales by examining the Brier skill (relative to the analysis climatology) of strike probability forecasts: the probability that a cyclonic feature center is located within a specified radius. The radius at which skill is maximized increases with lead time from 650 km at 12 h to 950 km at 7 days. The skill is greatest for the most intense features. Forecast skill remains above zero at these scales out to 14 days for the most intense cyclonic features, but only out to 8 days when all features are included irrespective of intensity.

Corresponding author address: Thomas H. A. Frame, Dept. of Meteorology, University of Reading, Reading RG6 6BB, United Kingdom. E-mail: t.h.a.frame@reading.ac.uk

This article is included in the Diabatic Influence on Mesoscale Structures in Extratropical Storms (DIAMET) special collection.

Save