On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

Abstract

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.

Dynamics and Predictability of the Rapid Intensification of Super Typhoon Usagi (2013)

“…Our analysis of the correlation between the initial vortex intensity and the RI onset timing demonstrates that a strong initial vortex is more likely to undergo RI early, which is consistent with previous studies (Emanuel & Zhang, ; Miyamoto & Takemi, ; Munsell et al, ). The spread of initial vortex strengths can partially explain the variability of the RI onset time.…”

“…Previous studies show that a stronger initial vortex is more likely to intensify quickly, regardless certain unfavorable environmental conditions (Emanuel & Zhang, ; Munsell et al, , ; Torn & Cook, ; Zhang & Emanuel, ). This was also the case in the ensemble simulations of Usagi (Figure b).…”

“…With respect to real‐world cases, Munsell et al () found that a strong initial vortex can undergo RI regardless of unfavorable environmental conditions, such as when there is a strong vertical wind shear. Recent studies demonstrate that forecasting errors of TC intensity (including RI) are dominated by errors of the initial vortex intensity and inner‐core moisture rather than environmental factors for the first 3–5 forecasting days (Emanuel & Zhang, , ).…”

Improvements to the snow melting process in a partially double moment microphysics parameterization

“…Although both operational track and intensity forecasts of TCs have exhibited improvement over the past decades, improvements in track prediction have far outpaced the improvement in intensity prediction [e.g., DeMaria et al ., ; Emanuel and Zhang , ]. The discrepancy in skill has been attributed to a lack of understanding and accurate representation of the multiscale interactions within and around TCs which strongly affect the intensity.…”

A fast intensity simulator for tropical cyclone risk analysis

“…Here we substitute the intensity simulator for the CHIPS model in the event risk model described in detail in Emanuel et al (2006Emanuel et al ( , 2008. A summary of the technique is also provided in Emanuel and Zhang (2016). Broadly, monthly mean winds and their variances and covariances, all generated from reanalyses or global climate models, are used to generate synthetic time series of winds that have the correct monthly means, variances, and covariances and have power spectra that fall off as frequency cubed, similar to observed flows at synoptic and planetary scales.…”