Quantifying the skill of convection-permitting ensemble forecasts for the sea-breeze occurrence
By Carlo Cafaro, PhD Student, Maths of Planet Earth CDT, University of Reading

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 focussed on the sea-breeze phenomenon for different reasons:

1. Sea breeze 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).
2. Sea breeze occurs on small spatio-temporal scales which are properly resolved at convection-permitting resolutions, but its 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.

Figures below:
Figure 1 MODIS visible of the southeast of Italy on 6th June 2018, 1020 UTC. This shows thunderstorms occurring in the middle of the peninsula, probably triggered by sea-breezes. Source: worldview.earthdata.nasa.gov

Figure 2 A map showing the orography over the south UK domain. Orography data are from MOGREPS-UK. The solid box encloses the sub-domain used in this study with red dots indicating the location of synoptic weather stations. Source: Cafaro et al. (2018)

Figure 3(a)-(d) Reliability and resolution terms calculated for both the forecasts (green for the CP-EPS forecast and blue for LR-EPSs). (e) and (f) represent the Brier score difference (BSD) and Information gain (IG) respectively. Positive values of BSD and IG indicate that CP-EPS forecast is more skilful. Source: Cafaro et al. (2018)