模式中常应用水平扩散项以抑制非线性计算不稳定或阻尼虚假短波,但这会导致数值模式在截断尺度附近出现小尺度动能过度耗散。为了将被过度耗散的小尺度动能补偿回模式,将随机动能后向散射扰动方法(stochastic kinetic energy backscatte...模式中常应用水平扩散项以抑制非线性计算不稳定或阻尼虚假短波,但这会导致数值模式在截断尺度附近出现小尺度动能过度耗散。为了将被过度耗散的小尺度动能补偿回模式,将随机动能后向散射扰动方法(stochastic kinetic energy backscatter,SKEB)引入CMA-REPS区域集合预报系统。首先基于由一阶自回归随机过程在水平方向上进行球谐函数展开得到的随机型,然后计算由数值扩散方案引起的局地动能耗散率,进而构造随机流函数强迫,并将其转化为水平风速扰动,对耗散的动能进行随机补偿。开展了2018年9月、10月(选取1日、7日、13日、19日、25日)的10 d集合预报随机型时间及空间尺度敏感性试验,并对试验结果进行评估。获得如下结论:在CMA-REPS区域集合预报中应用SKEB方案,可在一定程度上补偿过度耗散的小尺度动能,进而改善了模式对实际大气动能谱的模拟能力。就集合预报技巧改进而言,SKEB方案可以显著改善区域模式水平风场U、V的离散度,同时水平风场、温度等要素连续分级概率评分(CRPS)和离群值评分均获得改善。对SKEB方案开展的6个时间尺度(失相关时间尺度τ选取1、3、6、9、12、15 h)和6个空间相关尺度(最大截断波数L_(max)选取80、100、120、160、200、240)敏感性试验结果表明,12 h失相关时间尺度和最大截断波数为240空间相关尺度的集合概率预报技巧更优。结论证明SKEB方案可以补偿在截断尺度耗散的小尺度动能,有效提高集合预报技巧。展开更多
In this paper, the simultaneous perturbation stochastic approximation (SPSA) algorithm is used for seeking optimal parameters in an adaptive filter developed for assimilating observations in the very high dimensiona...In this paper, the simultaneous perturbation stochastic approximation (SPSA) algorithm is used for seeking optimal parameters in an adaptive filter developed for assimilating observations in the very high dimensional dynamical systems. The main results show that the SPSA is capable of yielding the high filter performance similar to that produced by classical optimization algorithms, with better performance for non-linear filtering problems as more and more observations are assimilated. The advantage of the SPSA is that at each iteration it requires only two measurements of the objective function to approximate the gradient vector regardless of the dimension of the control vector (or maximally, three measurements if second-order optimization algorithms are used). The SPSA approach is thus free from the need to develop a discrete adjoint of tangent linear model as it is required up to now for solving optimization problems in very high dimensional systems. This technique offers promising perspectives on developing optimal assimilation systems encountered in the field of data assimilation in meteorology and oceanography.展开更多
文摘In this paper, the simultaneous perturbation stochastic approximation (SPSA) algorithm is used for seeking optimal parameters in an adaptive filter developed for assimilating observations in the very high dimensional dynamical systems. The main results show that the SPSA is capable of yielding the high filter performance similar to that produced by classical optimization algorithms, with better performance for non-linear filtering problems as more and more observations are assimilated. The advantage of the SPSA is that at each iteration it requires only two measurements of the objective function to approximate the gradient vector regardless of the dimension of the control vector (or maximally, three measurements if second-order optimization algorithms are used). The SPSA approach is thus free from the need to develop a discrete adjoint of tangent linear model as it is required up to now for solving optimization problems in very high dimensional systems. This technique offers promising perspectives on developing optimal assimilation systems encountered in the field of data assimilation in meteorology and oceanography.