Predicting tropical cyclone (TC) rapid intensification (RI) is an important yet challenging task in current operational forecast due to our incomplete understanding of TC nonlinear processes. This study examines the variability of RI onset, including the probability of RI occurrence and the timing of RI onset, using a low-order stochastic model for TC development. Defining RI onset time as the first hitting time in the model for a given subset in the TC-scale state space, we quantify the probability of the occurrence of RI onset and the distribution of the timing of RI onset for a range of initial conditions and model parameters. Based on asymptotic analysis for stochastic differential equations, our results show that RI onset occurs later, along with a larger variance of RI onset timing, for weaker vortex initial condition and stronger noise amplitude. In the small noise limit, RI onset probability approaches one and the RI onset timing has less uncertainty (i.e., a smaller variance), consistent with observation of TC development under idealized environment. Our theoretical results are verified against Monte-Carlo simulations and compared with explicit results for a general 1-dimensional system, thus providing new insights into the variability of RI onset and helping better quantify the uncertainties of RI variability for practical applications.
翻译:预测热带气旋(TC)快速强化(RI)是当前业务预测中一项重要而富有挑战性的任务,因为我们对技术合作非线性进程的理解不完全,本研究审查了可再生能源的变异性,包括RI的发生概率和RI的发生时间,使用低级随机模型进行技术合作开发,将RI的开始时间确定为技术合作规模空间中某个子集的模型中第一个点击时间,我们根据对一系列初始条件和模型参数的观察,量化了发生RI的概率和RI的开始时间分布。根据对随机差异方程式的无症状分析,我们的结果显示,在较弱的旋动初始条件和较强的噪音振荡方面,RI的发生时间以及RI的开始时间差异较大。在小型噪音限制下,RI的开始概率方法之一和RI的开始时间比较较少不确定性(即差异较小),这与在理想化环境中观察到的技术合作发展情况相一致。根据对随机偏差差异方程式的零星分析,我们核实了我们的理论结果,而后又发现RI的起始性模拟和直观性变化性,从而将更精确地显示,以便比较地分析新的系统,从而提供精确的精确度,以便比较,以便比较地分析。