Abstract Ensemble forecasting is widely used in numerical weather prediction. However, the ensemble may not satisfy a perfect Gaussian probability distribution because of a limited number of members, with some members significantly deviating from the true atmospheric state. Such outliers (belonging to low probability events) may downgrade the accuracy of an ensemble forecast. In this study, the observed track of a tropical cyclone (TC) is used to restrict the probability distribution of samples by investigating the evolution of TCs. Unlike data assimilation, the method we employed uses observational data. By restricting the probability distribution, ensemble spread could be decreased through sample optimization. In addition, the prediction results showed that track and intensity errors could be reduced by sample optimization. When the vertical structures of TCs considered in this study were compared, different thermal structures were found. This difference may have been caused by sample optimization, which may affect intensity and track. Nevertheless, it should be noted that the replacement of a large number of inferior samples may inhibit the improvement of simulated results.

中文翻译：

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使用观测轨迹模拟热带气旋的集合预报样本优化

摘要 集合预报广泛应用于数值天气预报。然而，由于成员数量有限，该系综可能不满足完美的高斯概率分布，其中一些成员显着偏离了真实的大气状态。这种异常值（属于低概率事件）可能会降低集合预测的准确性。本研究利用热带气旋（TC）的观测轨迹，通过研究热带气旋的演化来限制样本的概率分布。与数据同化不同，我们采用的方法使用观察数据。通过限制概率分布，可以通过样本优化来减少集合传播。此外，预测结果表明，通过样本优化可以减少轨迹和强度误差。当比较本研究中考虑的 TC 的垂直结构时，发现了不同的热结构。这种差异可能是由样本优化引起的，这可能会影响强度和轨迹。尽管如此，需要注意的是，大量劣质样本的替换可能会抑制模拟结果的改进。