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提出了一种采用预报涡旋的初始化方案,用预报涡旋代替bogus模型参与构建模式初始场,采用权重形式合成预报涡旋和分析涡旋获取台风初始涡旋。针对2015年“莲花”和“灿鸿”台风,基于该初始化方案设计了一系列对比试验进行数值模拟,并对结果进行分析。结果表明:(1)该方案得到的台风初始涡旋结构比bogus模型合理;(2)预报涡旋权重不宜取太大;(3)从长时效预报效果看,采用24 h内预报涡旋比采用长时效预报涡旋台风的路径和强度误差减小;(4)采用同一权重方案对“莲花”、“灿鸿”预报的改进效果不同,其原因与预报涡旋和分析涡旋的协调程度有关。多台风情形下可在初步评估的基础上采用不同时效的预报涡旋和不同权重方案。   相似文献   
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集合卡尔曼滤波(EnKF)目前在资料同化的科研和业务中已得到广泛应用,可为集合预报提供较好的初始场,其影响半径的选取对同化结果影响显著。2017年5月7日在珠三角(珠江三角洲)一带出现极强降水,尤以广州的花都、黄埔、增城区为盛,甚至出现了极为罕见的特大暴雨。以本次极强降水过程为例,分析影响半径对EnKF同化效果的影响。结果发现利用EnKF方法同化观测站的10 m风和2 m温度资料后,可以较好地模拟出此次强降水过程,但仍存在着位置偏南,强度偏大,局地虚报和过报的现象。当水平影响半径取值过大时,大量虚假信息引入,产生过犹不及的效果,使得强降水过程南移较快,最终导致降水落区显著偏南偏东。且水平影响半径对模拟效果极为重要,因此取值要适当。   相似文献   
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利用ECMWF新推出的0.125 °高分辨率再分析资料对2015年出现在西北太平洋海域的所有被编号的热带气旋按强度进行分类分析,通过与Bogus对比发现该资料模拟的热带气旋整体表现良好,无论是对强台风以上级别、台风还是强热带风暴级以下热带气旋的温度场、高度场、湿度场和风场的刻画均比较符合热带气旋的实际发展状态,且该再分析资料既可获得热带气旋的非对称、精细结构,并且分辨率又与模式相匹配,有利于在未来的预报和同化分析中起到积极作用,以便最终应用于热带气旋的数值预报领域,进而取代Bogus模型。   相似文献   
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Extreme rainfall is common from May to October in south China. This study investigates the key deviation ofinitial fields on ensemble forecast of a persistent heavy rainfall event from May 20 to 22, 2020 in Guangdong Province, south China by comparing ensemble members with different performances. Based on the rainfall distribution and pattern, two types are selected for analysis compared with the observed precipitation. Through the comparison of the thermal and dynamic fields in the middle and lower layers, it can be found that the thermal difference between the middle and lower layers was an important factor which led to the deviation of precipitation distribution. The dynamic factors also have some effects on the precipitation area although they were not as important as the thermal factors in this case. Correlating accumulated precipitation with atmospheric state variables further corroborates the above conclusion. This study suggests that the uncertainty of the thermal and dynamic factors in the numerical model can have a strong impact on the quantitative skills of heavy rainfall forecasts.  相似文献   
5.
Nowadays, ensemble forecasting is popular in numerical weather prediction (NWP). However, an ensemble may not produce a perfect Gaussian probability distribution owing to limited members and the fact that some members significantly deviate from the true atmospheric state. Therefore, event samples with small probabilities may downgrade the accuracy of an ensemble forecast. In this study, the evolution of tropical storms (weak typhoon) was investigated and an observed tropical storm track was used to limit the probability distribution of samples. The ensemble forecast method used pure observation data instead of assimilated data. In addition, the prediction results for three tropical storm systems, Merbok, Mawar, and Guchol, showed that track and intensity errors could be reduced through sample optimization. In the research, the vertical structures of these tropical storms were compared, and the existence of different thermal structures was discovered. One possible reason for structural differences is sample optimization, and it may affect storm intensity and track.  相似文献   
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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%。   相似文献   
7.
In a limited number of ensembles, some samples do not adequately reflect the true atmospheric state and can in turn affect forecast performance. This study explored the feasibility of sample optimization using the ensemble Kalman filter (EnKF) for a simulation of the 2014 Super Typhoon Rammasun, which made landfall in southern China in July 2014. Under the premise of sufficient ensemble spread, keeping samples with a good fit to observations and eliminating those with poor fit can affect the performance of EnKF. In the sample optimization, states were selected based on the sample spatial correlation between the ensemble state and observations. The method discarded ensemble states that were less representative and, to maintain the overall ensemble size, generated new ensemble states by reproducing them from ensemble states with a good fit by adding random noise. Sample selection was performed based on radar echo data. Results showed that applying EnKF with optimized samples improved the estimated track, intensity, precipitation distribution, and inner-core structure of Typhoon Rammasun. Therefore, the authors proposed that distinguishing between samples with good and poor fits is vital for ensemble prediction, suggesting that sample optimization is necessary to the effective use of EnKF.  相似文献   
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