Predictability of frontal waves and cyclones

Frame, T. H. A. ORCID: https://orcid.org/0000-0001-6542-2173, Methven, J. ORCID: https://orcid.org/0000-0002-7636-6872, Roberts, N. M. and Titley, H. A. (2015) Predictability of frontal waves and cyclones. Weather and Forecasting, 30 (5). pp. 1291-1302. ISSN 0882-8156 doi: 10.1175/WAF-D-15-0039.1

Abstract/Summary

The statistical properties and skill in predictions of objectively identified and tracked cyclonic features (frontal waves and cyclones) are examined in MOGREPS-15, the global 15-day version of the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The number density of cyclonic features is found to decline with increasing lead-time, with analysis fields containing weak features which 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 maximised increases with lead time from 650km at 12h to 950km 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/40942
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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