The Impact of Weak Environmental Steering Flow on Tropical Cyclone Track Predictability

Typhoon Haiyan (2013) and Typhoon Hagupit (2014) are examples of two tropical cyclones (TCs) where, despite similarities in the track and intensity, the predictability of the TCs differed greatly. Both TCs made landfall over the Philippines having followed a similar track across the Pacific and both reached intensities in excess of 60 ms-1. Operational global ensemble forecasts showed large uncertainty in the track of Hagupit, whereas the ensemble spread for Haiyan was considerably less. Using the Met Office's Unified Model, 5-day global ensemble forecasts were produced for both storms. Consistent with the operational forecasts produced at the time of the storms, the spread of tracks is greater in the forecasts produced for Hagupit than Haiyan. Haiyan was located on the southern periphery of the subtropical high and embedded in a strong easterly flow. In contrast, the position of Hagupit between two anticyclones earlier in the forecast is key to the subsequent motion of the storm and determining if Hagupit will make landfall over the Philippines or turn to the north. Upper level winds contribute the most to the depth-averaged steering flow. Statistically significant differences in the strength of the upper level anticyclone to the east of the storm, the strength and position of the upper level ridge downstream of the storm and the location of a detached PV streamer appear between two groups of ensemble members - those which turn to the north and those which make landfall. Positional differences of the TC in different ensemble members earlier in the forecasts, particularly in the east to west direction, are correlated to larger north-east to south-west position differences later in the forecast. Ensemble sensitivity analysis suggests that this initial east-west positional variance is linked to the upper level geopotential height directly south of the storm. Accurately representing both the steering flow of Hagupit and the position of Hagupit is vital for an accurate forecast.