The impact of weak environmental steering flow on tropical cyclone track predictability

Abstract

AbstractTyphoonsHaiyan(2013) andHagupit(2014) are examples of two tropical cyclones (TCs) for which, despite similarities in the track and intensity, the predictabilities 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 , whereas the ensemble spread for 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 than . The latter was located on the southern periphery of the subtropical high and embedded in a strong easterly flow. In contrast, the position of between two anticyclones earlier in the forecast is key to the subsequent motion of the storm in determining whether would make landfall over the Philippines or turn to the north. Upper‐level winds contributed 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 potential vorticity (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 northeast to southwest 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 and the position of is vital for an accurate forecast.