On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

• Published in: Journal of the atmospheric sciences Vol. 73; no. 9; pp. 3739 - 3747
• Main Authors: Emanuel, Kerry, Zhang, Fuqing
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.09.2016
• Subjects: Aircraft; Cyclones; Data assimilation; Data collection; Errors; Estimates; Growth; Hurricanes; Kinematics; Mathematical models; Modelling; Perturbation; Storms; Tropical cyclone intensities; Tropical cyclones; Wind; North Atlantic region
• ISSN: 0022-4928; 1520-0469
• DOI: 10.1175/JAS-D-16-0100.1

The skill of tropical cyclone intensity forecasts has improved slowly since such forecasts became routine, even though track forecast skill has increased markedly over the same period. In deciding whether or how best to improve intensity forecasts, it is useful to estimate fundamental predictability limits as well as sources of intensity error. Toward that end, the authors estimate rates of error growth in a “perfect model” framework in which the same model is used to explore the sensitivities of tropical cyclone intensity to perturbations in the initial storm intensity and large-scale environment. These are compared to estimates made in previous studies and to intensity error growth in real-time forecasts made using the same model, in which model error also plays an important role. The authors find that error growth over approximately the first few days in the perfect model framework is dominated by errors in initial intensity, after which errors in forecasting the track and large-scale kinematic environment become more pronounced. Errors owing solely to misgauging initial intensity are particularly large for storms about to undergo rapid intensification and are systematically larger when initial intensity is underestimated compared to overestimating initial intensity by the same amount. There remains an appreciable gap between actual and realistically achievable forecast skill, which this study suggests can best be closed by improved models, better observations, and superior data assimilation techniques.