On the afternoon of 16th August 2004, the village of Boscastle on the north coast of Cornwall was severely damaged by flooding (Golding et al., 2005). This is one example of high impact hazardous weather associated with small meso- and convective-scale weather phenomena, the prediction of which can be uncertain even a few hours ahead (Lorenz, 1969; Hohenegger and Schar, 2007). Taking advantage of the increased computer power (e.g. https://www.metoffice.gov.uk/research/technology/supercomputer) this has motivated many operational and research forecasting centres to introduce convection-permitting ensemble prediction systems (CP-EPSs), in order to give timely weather warnings of severe weather.
However, despite being an exciting new forecasting technology, CP-EPSs place a heavy burden on the computational resources of forecasting centres. They are usually run on limited areas with initial and boundary conditions provided by global lower resolution ensembles (LR-EPS). They also produce large amounts of data which needs to be rapidly digested and utilized by operational forecasters. Assessing whether the convective-scale ensemble is likely to provide useful additional information is key to successful real-time utilisation of this data. Similarly, knowing where equivalent information can be gained (even if partially) from LR-EPS using statistical/dynamical post-processing both extends lead time (due to faster production time) and also potentially provides information in regions where no convective-scale ensemble is available.
There have been many studies on the verification of CP-EPSs (Klasa et al., 2018, Hagelin et al., 2017, Barret et al., 2016, Beck et al., 2016 amongst the others), but none of them has dealt with the quantification of the skill gained by CP-EPSs in comparison with LR-EPSs, when fully exploited, for specific weather phenomena and for a long enough evaluation period.
In my PhD, I have focused on the sea-breeze phenomenon for different reasons:
- Sea breezes have an impact on air quality by advecting pollutants, on heat stress by providing a relief on hot days and also on convection by providing a trigger, especially when interacting with other mesoscale flows (see for examples figure 1 or figures 6, 7 in Golding et al., 2005).
- Sea breezes occur on small spatio-temporal scales which are properly resolved at convection-permitting resolutions, but their occurrence is still influenced by synoptic-scale conditions, which are resolved by the global LR-EPS.
Therefore this study aims to investigate whether the sea breeze is predictable by only knowing a few predictors or whether the better representation of fine-scale structures (e.g. orography, topography) by the CP-EPS implies a better sea-breeze prediction.
In order to estimate probabilistic forecasts from both the models, two different methods have been applied. A novel tracking algorithm for the identification of sea-breeze front, in the domain represented in figure 2, was applied to CP-EPSs data. A Bayesian model was used instead to estimate the probability of sea-breeze conditioned on two LR-EPSs predictors and trained on CP-EPSs data. More details can be found in Cafaro et al. (2018).
The results of the probabilistic verification are shown in figure 3. Reliability (REL) and resolution (RES) terms have been computed decomposing the Brier score (BS) and Information gain (IGN) score. Finally, scores differences (BSD and IG) have been computed to quantify any gain in the skill by the CP-EPS. Figure 3 shows that CP-EPS forecast is significantly more skilful than the Bayesian forecast. Nevertheless, the Bayesian forecast has more resolution than a climatological forecast (figure 3e,f), which has no resolution by construction.
This study shows the additional skill provided by the Met Office convection-permitting ensemble forecast for the sea-breeze prediction. The ability of CP-EPSs to resolve meso-scale dynamical features is thus proven to be important and only two large-scale predictors, relevant for the sea-breeze, are not sufficient for skilful prediction.
It is believed that both the methodologies can, in principle, be applied to other locations of the world and it is thus hoped they could be used operationally.
Barrett, A. I., Gray, S. L., Kirshbaum, D. J., Roberts, N. M., Schultz, D. M., and Fairman J. G. (2016). The utility of convection-permitting ensembles for the prediction of stationary convective bands. Monthly Weather Review, 144(3):1093–1114, doi: 10.1175/MWR-D-15-0148.1
Beck, J., Bouttier, F., Wiegand, L., Gebhardt, C., Eagle, C., and Roberts, N. (2016). Development and verification of two convection-allowing multi-model ensembles over Western europe. Quarterly Journal of the Royal Meteorological Society, 142(700):2808–2826, doi: 10.1002/qj.2870
Cafaro C., Frame T. H. A., Methven J., Roberts N. and Broecker J. (2018), The added value of convection-permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble, Quarterly Journal of the Royal Meteorological Society., under review.
Golding, B. , Clark, P. and May, B. (2005), The Boscastle flood: Meteorological analysis of the conditions leading to flooding on 16 August 2004. Weather, 60: 230-235, doi: 10.1256/wea.71.05
Hagelin, S., Son, J., Swinbank, R., McCabe, A., Roberts, N., and Tennant, W. (2017). The Met Office convective-scale ensemble, MOGREPS-UK. Quarterly Journal of the Royal Meteorological Society, 143(708):2846–2861, doi: 10.1002/qj.3135
Hohenegger, C. and Schar, C. (2007). Atmospheric predictability at synoptic versus cloud-resolving scales. Bulletin of the American Meteorological Society, 88(11):1783–1794, doi: 10.1175/BAMS-88-11-1783
Klasa, C., Arpagaus, M., Walser, A., and Wernli, H. (2018). An evaluation of the convection-permitting ensemble cosmo-e for three contrasting precipitation events in Switzerland. Quarterly Journal of the Royal Meteorological Society, 144(712):744–764, doi: 10.1002/qj.3245
Lorenz, E. N. (1969). Predictability of a flow which possesses many scales of motion.Tellus, 21:289 – 307, doi: 10.1111/j.2153-3490.1969.tb00444.x