Medium range ensemble sensitivity analyses of two extreme Pacific extratropical cyclones. Part I: Comparison of different forecast metrics

Extratropical cyclones often cause high-impact weather such as strong winds, heavy rainfall and severe snowfall. However, medium range (3-7days) forecasts of extratropical cyclones, especially exact locations of their centers and strength, are often quite inaccurate. These forecasts errors may be due to model errors or uncertainties in initial conditions, thus it is of value to investigate how these forecasts depend on the initial conditions. With the routine use of ensemble forecast systems, ensemble sensitivity techniques have been developed to examine how forecasts depend on the initial conditions. However, previous studies have mostly evaluated the sensitivity of short-term (1-3 days) ensemble forecasts of cyclones. In this study, we employ the ensemble sensitivity approach to analyze medium range forecasts.

A global ensemble Kalman filter data assimilation and forecasting system has been used to generate ensemble forecasts for selected cases. Medium range forecasts for two extreme Pacific cyclones are diagnosed to objectively determine the relationship between the forecast uncertainties and the initial upstream flow fields. Sensitivities to different forecast metrics related to cyclone intensity and locations have been tested and compared.

The most significant findings of this study are: 1) dominant modes of forecast cyclone variabilities are changes in cyclone intensity and shifts in cyclone location; 2) the sensitivity related to east-west shift of cyclone location appears to be most robust in both cases. The signal can be tracked back to day -7. In addition, the sensitivity pattern appears to be associated with downstream development; 3) the speed of propagation of sensitivity signal appears to be linked with the structure of the mean flow. The sensitivity signal propagates faster in more zonal flow; 4) when applied to medium range forecasts, rather than directly using cyclone central pressure or cyclone longitude/latitude as forecast metrics, certain other related metrics may generate more robust sensitivity relationships. This final point will be examined in more details in part II of this series.