| 基于观测路径的集合预报样本优选对热带气旋的模拟研究 |
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| 引用本文： | 李霁杭,高郁东,万齐林.基于观测路径的集合预报样本优选对热带气旋的模拟研究[J].热带气象学报,2019,35(2):197-209. |
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| 作者姓名： | 李霁杭 高郁东 万齐林 |
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| 作者单位： | 中国气象局广州热带海洋气象研究所/广东省区域数值天气预报重点实验室，广东 广州 510640 |
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| 基金项目： | 国家重点基础研究发展规划(973计划)2015CB452802国家自然科学基金41475102国家自然科学基金41675099国家自然科学基金41475061广东省科技计划项目2017B020218003广东省科技计划项目2017B030314140广东省科技计划项目2015B02021700广东省科技计划项目2017B02024400广东省自然科学基金2016A030313140广东省自然科学基金2017A030313225广东省气象局科研项目GRMC2017Q01 |
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| 摘 要： | 集合预报是从一定误差范围内的一组初值出发，这组初值(样本)代表了大气状态的概率分布，集合预报中集合样本的好坏严重影响分析质量。质量较差样本进入集合预报中难免会降低集合预报的整体质量。由于集合样本是模拟大气可能状态的概率分布，因此样本的优选是提高分析质量的关键。通过对集合样本优胜劣汰来分析样本优选对模拟效果的影响。由于台风预报中台风路径的模拟至关重要，因此样本优选的方案为将样本模拟的路径信息与观测的台风报文路径相比较后，保留误差较低的样本，剔除误差较高的样本，从而提升样本的整体质量。但过多的样本被替换将导致集合离散度的大幅下降，因此替换样本的数量要适度。研究结果表明样本优选极可能有利于热带气旋路径和强度模拟的改进，其中对“妮妲”路径误差的改进为4% ~13%，对“鲇鱼”路径误差的改进为11%~28%，对“妮妲”的强度误差改进为5%~37%，“鲇鱼”的强度误差改进为1%~27%。
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| 关 键 词： | 热带气旋 集合预报 样本优选 观测路径 |
| 收稿时间： | 2018-03-19 |
SAMPLE OPTIMIZATION OF ENSEMBLE FORECAST IN SIMULATING TROPICAL CYCLONES BASED ON OBSERVED TRACKS |
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| LI Ji-hang,GAO Yu-dong and WAN Qi-lin.SAMPLE OPTIMIZATION OF ENSEMBLE FORECAST IN SIMULATING TROPICAL CYCLONES BASED ON OBSERVED TRACKS[J].Journal of Tropical Meteorology,2019,35(2):197-209. |
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| Authors: | LI Ji-hang GAO Yu-dong and WAN Qi-lin |
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| Institution: | Guangdong Province Key Laboratory of Regional Numerical Weather Prediction, Institute of Tropical and Marine Meteorology, CMA, Guangzhou 510640, China |
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| Abstract: | Ensemble forecasting is widely used in numerical weather prediction (NWP). However, the ensemble may not satisfy perfect Gaussian probability distribution owing to limited members, and some members significantly deviate from the true atmospheric state. Such impossible samples (belonging to small probability events) may downgrade the accuracy of ensemble forecast. In this study, the observed tropical cyclone (TC) track is used to restrict the probability distribution of samples by investigating the evolution of TCs. The method employed uses observation data rather than data assimilation. 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. Comparing vertical structures of TCs considered in this study, different thermal structures was found to exist. This difference may have been caused by sample optimization, and it 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 the simulated results. |
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| Keywords: | ensemble forecast sample optimization tropical cyclone observed track |
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