A new method for the characterisation and verification of local spatial predictability for convective scale ensembles

Dey, S. R. A., Roberts, N. M., Plant, R. S. ORCID: https://orcid.org/0000-0001-8808-0022 and Migliorini, S. (2016) A new method for the characterisation and verification of local spatial predictability for convective scale ensembles. Quarterly Journal of the Royal Meteorological Society. ISSN 0035-9009 doi: 10.1002/qj.2792

Abstract/Summary

The use of kilometre-scale ensembles in operational forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood based method is presented for evaluating and characterising the local predictability variations from convective scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S^A) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (S^A(mm)_ij), with that between members and radar observations (S^A(mo)_ij), this approach allows the location-dependent spatial spread-skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealised experiment. To demonstrate the methods in an operational context the S^A(mm)_ij and S^A(mo)_ij are calculated for six convective cases run with the Met Office UK Ensemble Prediction System. The S^A(mm)_ij highlight predictability differences between cases, which can be linked to physical processes. Maps of S^A(mm)_ij are found to summarise the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and for model interpretation. Comparison of S^A(mm)_ij and S^A(mo)_ij demonstrates the case-by-case and temporal variability of the spatial spread-skill, which can again be linked to physical processes.

Item Type	Article
URI	https://reading-pure-test.eprints-hosting.org/id/eprint/59327
Identification Number/DOI	10.1002/qj.2792
Refereed	Yes
Divisions	Central Services Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Download/View statistics	View download statistics for this item