In comparison with other distributed parameter models, the calculation result of this model has showed that it has a quality of higher precision with numerical stability even at a big time step size.
与其他分布参数模型的处理方法相比较，该文的仿真方法不仅具有较高的运算精度，而且在较大的时间步长下也有非常好的数值稳定性。
According to the different step size it is fount that field calculation results is no different from the results without the use of cloud model when grid step size too rough.
只有网格步长足够小也就是环境参数很详细时，云模型和数据场计算的结合才能充分发挥特色。
During the minimizing process of variational assimilation, calculation efficiency is very important and the optimal step size greatly influences the algorithm efficiency.
在变分同化的最小化过程中，计算效率无疑是最重要的，而优化步长的计算又直接关系到算法效率的成败。
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