摘要
集合预报方法是解决单一数值预报不确定性问题的有效手段,而针对强天气预报的中尺度区域集合预报技术已逐渐受到国内外的重视。对于区域集合预报而言,由于其不确定性来源较为复杂,如何发展有效的扰动方法是研究的热点和难点。本文根据国内外区域集合预报的研究进展,从初值扰动、模式扰动以及侧边界扰动三个方面进行了总结和回顾,并对扰动方法的发展趋势进行了介绍。对于初值扰动,较为主流的方法有动力降尺度,沿用传统的由全球集合扰动方法发展而来的技术为区域集合产生初值,以及专门为区域集合设计的扰动方法。鉴于这些方法各有利弊,目前对于初值扰动方法的研究已经开始发展充分包含大尺度和小尺度不确定性信息的混合扰动方法。区域集合预报模式扰动的研究以物理过程扰动为主,典型方法为多物理过程组合以及随机物理过程扰动,其中多物理过程组合方法简单有效,而随机物理过程扰动方法的物理意义更为明确,是物理过程扰动的趋势。通过多模式组合进行模式扰动的方法也开展了一些相关研究,且对于台风等强天气预报均显示出相对于单模式集合较好的效果。侧边界扰动的主流方法是由大尺度集合预报场来为区域集合提供不同的侧边界,研究结果表明此种侧边界扰动方法简便易行,且有助于提高区域集合预报较长预报时效离散度和预报技巧。
The ensemble forecast technique is a practical solution to the uncertainty problem of numerical weather prediction.At present,researchers around the world tend to focus on the Regional Ensemble Forecast（ REF）, which aims at the improvement of regional high impact weather forecast.As various uncertainty resources exist for mesoscale and small-scale weather phenomena, regional model simulation is a very complicated issue, thus how to generate effective perturbations for REF is a hot topic involving many technical difficulties.In the present paper, the progress of REF research is reviewed in terms of initial condition perturbation, model perturbation and lateral boundary condition perturbation,and the trends of methods related to these aspects are also presented.The results presented show the following：for initial condition perturbation, the mainstream methods include dynamical downscaling, using traditional methods developed from Global Ensemble to generate perturbations for REF, as well as some methods specifically designed for REF.All of these methods are characterized by some advantages and some shortcomings, as downscaling a lack of small scale and other components leads to the generation of insufficient large-scale uncertainty information.In addition,research on the REF initial condition perturbation has only begun to explore more effective methods such as blending, which consider not only sufficiently small-scale uncertainty, but also sufficiently large-scale uncertainty. Finally, the inconsistency problem between initial state and lateral boundary can also be ameliorated.Model perturbation is another important aspect for REF.This technique mostly perturbs model physics, such as multi-physics combination and stochastically perturb model physics.It has been re- ported that the multi-physics combination is quite simple and can effectively improve the ensemble spread of REF, while using the stochastic method to perturb model physics has greater scientific significance,thus this type of perturbation method has become the trend of model physics perturbations. Furthermore, multi-model combination is another practical method of model perturbation. Related studies have been carried out, the results of which show that this method possesses stronger skill than a single model ensemble, especially when a multi-model ensemble is applied to meso-scale severe weather forecast,such as with a Tropical Cyclone.As REF systems are constructed based on the regional models, the uncertainties originating from lateral boundary conditions cannot be ignored.Lateral boundary condition perturbation schemes mainly use large-scale ensembles, such as Global Ensemble Forecast Products,to provide different lateral boundary conditions for REF.Studies have proven that this method can achieve the goal of perturbing the lateral boundary condition of REF, and lateral boundary condition perturbation is found to aid in amplifying the ensemble spread of REF for long-range forecast lead times.In addi- tion,the ensemble forecast skill can also benefit from lateral boundary perturbation. Although the perturbation techniques for REF have already led to some fruitful achievements, much work is still needed, and all of the methods related to initial condition perturbations, model perturbations and lateral boundary condition perturbations are still under development.It can be predicted that, as the REF perturbation methods continue to improve, the REF will become increasingly more effective, and will play a more important role in operational numerical weather prediction centers.
出处
《大气科学学报》
CSCD
北大核心
2017年第2期145-157,共13页
Transactions of Atmospheric Sciences
基金
国家自然科学基金资助项目(41605082
91437113)
北极阁开放研究基金-南京大气科学联合研究中心基金(NJCAR2016ZDXX)
公益性行业(气象)科研专项(GYHY201506005)
关键词
数值预报
区域集合预报
扰动方法
Numerical Weather Prediction
regional ensemble forecast
perturbation method
