该算法使用一种新的基于高效排序的全邻预测算法，经过排序以后形成预测误差集合，可以在很低失真度的情况下嵌入数据。

This algorithm employed full-enclosing prediction based on a new high efficient sorting technique and results in a forecast error set after sorting, which could embed data with a low distortion.

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采用最优集合子集预报方式时的臭氧预报均方根误差比原确定性预报低了10%以上。

By selecting sub-ensemble with smaller error, the root mean square error of forecast is reduced by over 10%.

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试验结果表明：热带气旋定位误差影响路径预报，但扰动初始位置的集合平均预报与控制试验的预报水平相接近。

The results show that TC initial position perturbation make its track different, but ensemble mean is close to control forecast.

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