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非线性滤波在含“开关”过程的资料同化中的应用研究 总被引：2，自引：0，他引：2
利用一个描述实际数值天气预报模式中比湿在单格线上随时间发展的偏微分方程作为控制方程,研究分析了非线性滤波方法在含开关过程的资料同化中的有效性和可行性。首先在贝叶斯理论框架下,讨论了一般情形的非线性滤波方法,然后对基于粒子滤波(PF)和基于集合卡尔曼滤波(EnKF)的两种同化方法进行对比,由于EnKF是通过对集合成员的统计分析得到的误差分布的一阶矩和二阶矩来近似真实误差分布的,所以当用高斯分布近似真实误差分布所产生的误差较大时,基于EnKF的同化方法得到的结果也会有较大的误差。最后分别从观测算子为线性和非线性、观测误差为高斯型和非高斯型4种情形进行数值试验,结果显示当观测误差为高斯型时,无论观测算子为线性还是非线性,基于PF和基于EnKF的同化方法都能克服由开关过程给资料同化带来的困难,给出满意的同化结果;而当观测误差为非高斯型时,EnKF出现滤波不稳定,产生了非理想的同化结果,但PF方法仍然能够有效地发挥作用,给出满意的同化结果。 相似文献
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Some variational data assimilation (VDA) problems of time- and space-discrete models with
on/off parameterizations can be regarded as non-smooth optimization problems. Same as the sub-gradient
type method, intelligent optimization algorithms, which are widely used in engineering optimization,
can also be adopted in VDA in virtue of their no requirement of cost functions gradient (or sub-gradient)
and their capability of global convergence. Two typical intelligent optimization algorithms, genetic
algorithm (GA) and particle swarm optimization (PSO), are introduced to VDA of modified Lorenz equations
with on-off parameterizations, then two VDA schemes are proposed, that is, GA based VDA (GA-VDA) and PSO
based VDA (PSO-VDA). After revealing the advantage of GA and PSO over conventional adjoint methods in the
ability of global searching at the existence of cost functions discontinuity induced by on-off switches,
sensitivities of GA-VDA and PSO-VDA to population size, observational noise, model error and observational
density are detailedly analyzed.
Its shown that, in the context of modified Lorenz equations, with proper population size, GA-VDA and PSO-VDA
can effectively estimate the global optimal solution, while PSO-VDA consumes much less computational time than
GA-VDA with the same population size, and requires a much lower population size with nearly the same results,
both methods are not very sensitive to observation noise and model error, while PSO-VDA shows a better
performance with observational noise than GA-VDA. It is encouraging that both methods are not sensitive to
observational density, especially PSO-VDA, using which almost the same perfect assimilation results can be
obtained with comparatively sparse observations. 相似文献
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