Extreme Forecast Index Research Articles

Early warnings of extreme winds using the ECMWF Extreme Forecast Index

The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked to very intense surface cyclonic circulation systems being advected mainly by southwest and northwest flow regimes. Overall, it becomes clear that the first indications of an extreme wind event might come from the ECMWF deterministic and/or probabilistic components capturing very intense weather systems (possible windstorms) in the medium term. For early warnings, all available EPS Extreme Forecast Index (EFI) formulations were used, by linking daily maximum wind speeds to EFI values for different forecast horizons. From all possible EFI schemes deployed for issuing early warnings, the highest skill was found for the Gust Factor formulation (EFI-10FGI). Using EFI-10FGI, the corresponding 99% threshold could provide an early warning for a considerable portion of the > 99% wind extremes, but not for all. By lowering this threshold the number of hits is increased until all extremes are captured (zero misses), although by doing so the number of false alarms increased significantly. Consequently, an optimal trade-off between hits and false alarms has to be made when setting different (critical) EFI thresholds. Copyright © 2012 Royal Meteorological Society

Early detection of abnormal weather conditions using a probabilistic extreme forecast index

A new method to extract information related to unusual forecast distributions from probabilistic (e.g. ensemble) prediction systems is presented. It consists of an extreme forecast index (EFI) that ranks the departure between the forecast and the model climate between −1 (forecast given 100% probability that record-breaking low values will be reached) and +1 (record-breaking high values). First the new index is derived, it is related to other measures of statistical significance, and a few properties are given. It is then demonstrated how the accumulation of ensemble forecasts every day allows the quick build-up of a model pseudo-climate that is representative enough to detect large departures from normal conditions while being representative of the latest developments in terms of resolution or physical parametrizations. The EFI is then subjected to several severe weather events that have happened in Europe over the last few years, and it is shown to be potentially useful in alerting forecasters to the risk of severe weather up to three or four days in advance. Finally, objective verification over five months in 2001–02 is presented. Although the results confirm that the model pseudo-climate is good enough to set up thresholds for severe weather evenly throughout Europe, the false-alarm rates are much larger than usually expected by forecasters or users. It is argued, however, that operating characteristics in the early medium range for severe weather have largely been ignored in the past. Although the signal is weak in the 3 to five-day range, it is undoubtedly associated with a forecast skill that might be used for setting up pre-alerts to be used either internally by meteorological services, or externally by advanced users aware of its error characteristics. Copyright © 2003 Royal Meteorological Society

Early detection of abnormal weather conditions using a probabilistic extreme forecast index

AbstractA new method to extract information related to unusual forecast distributions from probabilistic (e.g. ensemble) prediction systems is presented. It consists of an extreme forecast index (EFI) that ranks the departure between the forecast and the model climate between −1 (forecast given 100% probability that record‐breaking low values will be reached) and +1 (record‐breaking high values). First the new index is derived, it is related to other measures of statistical significance, and a few properties are given. It is then demonstrated how the accumulation of ensemble forecasts every day allows the quick build‐up of a model pseudo‐climate that is representative enough to detect large departures from normal conditions while being representative of the latest developments in terms of resolution or physical parametrizations. The EFI is then subjected to several severe weather events that have happened in Europe over the last few years, and it is shown to be potentially useful in alerting forecasters to the risk of severe weather up to three or four days in advance. Finally, objective verification over five months in 2001–02 is presented. Although the results confirm that the model pseudo‐climate is good enough to set up thresholds for severe weather evenly throughout Europe, the false‐alarm rates are much larger than usually expected by forecasters or users. It is argued, however, that operating characteristics in the early medium range for severe weather have largely been ignored in the past. Although the signal is weak in the 3 to five‐day range, it is undoubtedly associated with a forecast skill that might be used for setting up pre‐alerts to be used either internally by meteorological services, or externally by advanced users aware of its error characteristics. Copyright © 2003 Royal Meteorological Society