For an atmospheric system, there exists a limit as to how far ahead one can predict, which can be referred to as the atmospheric predictability. Within this limit, however, the weather forecast will still contain some uncertainty. Given numerical models are "perfect", the predictability of tropical cyclones (TCs) is limited by the chaotic nature of the system itself. The burning question, therefore, is how long is the TC predictability limit?
Dr. Quanjia Zhong and Professor Ruiqiang Ding, from the Institute of Atmospheric Physics, Chinese Academy of Sciences, employed the nonlinear local Lyapunov exponent (NLLE) approach to estimate the predictability limit of TCs over the whole western North Pacific (WNP) basin using observed TC best-track data. They found that the predictability limit of all TC tracks over the whole WNP basin is about 102 hours (4.25 days), which is comparable to that of TC intensity. This provides a baseline from which we can measure the forecasting skill of operational weather models. The findings of this research have been recently published in Advances in Atmospheric Sciences and Monthly Weather Review.
Moreover, the team further examined the spatial distribution characteristics of the predictability limit of the TC tracks and intensities over the whole WNP basin. They found that the predictability limit of the TC tracks over the WNP basin ranges from 48 to 120 hours, while that of the intensity ranges from 24 to 120 hours, dependent largely on the location of TC genesis. The predictability limit of TCs is highest in the eastern region of the WNP, followed by the western region and then the South China Sea (SCS).
"TCs that form in the SCS have relatively low predictability, and this may explain why achieving accurate forecasts of TCs originating from the SCS is relatively difficult," says Dr. Zhong.
Their research also reveals that the predictability limit of the TC tracks varies widely with the lifetime and intensity of TCs, which should represent an effective means to improve our understanding of the characteristics and mechanisms of TC predictability.
"On the basis of understanding TC predictability, how to further improve the forecasting skill of TCs is of vital importance, and thus ensemble predictions of TCs is worthy of further investigation," adds Professor Ruiqiang Ding.
Source: https://www.eurekalert.org/pub_releases/2018-10/ioap-plf101818.php
