Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System

Abstract

A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective featuretracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.

Can we trust climate models to realistically represent severe European windstorms?

“…Williams and Brooks 2008). Froude (2009) for example found a larger error in the predicted amplitudes of intense storms than of weaker storms in the operational ECMWF EPS system consistent with many climate models (Lambert and Fyfe 2006). This general perception led to the concept of "seamless prediction", which is based on the idea of using only one modelling system in different configurations to predict the state of the atmosphere over a large range of spatial and temporal scales (e.g.…”

“…Other studies, however, report a good correspondence (Bengtsson et al 2006) or even a higher number of intense systems in the model's control climate than in analysis data (Bengtsson et al 2009). Such biases also occur in operational weather forecasts as documented by Froude (2009) for the ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) over the North Atlantic. A fundamental understanding of the exact reasons for such deviations is of great importance for assessing sources of uncertainty in climate projections.…”

Objective identification, typing and tracking of the complete life-cycles of cyclonic features at high spatial resolution

“…Froude (2009) provides extensive results of this type obtained using the Hodges' methodology. Oneoff deterministic or ensemble forecasts of high profile cyclones can also be verified in a similar way, and viewed in the context of any overall model bias.…”

“…Figure 11), using for example the Brier Skill Score, intrinsically gives more weight to cross-track errors than to along-track errors; this can be particularly useful for ensemble verification. For further discussion of some verification issues see Froude (2009) and references.…”

“…in Bengtsson et al (2009) and Froude (2009) data are upscaled to ∼450 km resolution for detection and tracking purposes. This is because higher resolution data can present problems, as these and other authors have recognized.…”