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首页> 外文期刊>Pure and Applied Geophysics >Impact of Representing Model Error in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclones over the Bay of Bengal
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Impact of Representing Model Error in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclones over the Bay of Bengal

机译：在孟加拉湾的热带旋风轨道预测中代表模型误差的影响

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Abstract Uncertainties in the numerical weather prediction models are generally not well-represented in ensemble-based data assimilation (DA) systems. The performance of an ensemble-based DA system becomes suboptimal, if the sources of error are undersampled in the forecast system. The present study examines the effect of accounting for model error treatments in the hybrid ensemble transform Kalman filter—three-dimensional variational (3DVAR) DA system (hybrid) in the track forecast of two tropical cyclones viz. Hudhud and Thane, formed over the Bay of Bengal, using Advanced Research Weather Research and Forecasting (ARW-WRF) model. We investigated the effect of two types of model error treatment schemes and their combination on the hybrid DA system; (i) multiphysics approach, which uses different combination of cumulus, microphysics and planetary boundary layer schemes, (ii) stochastic kinetic energy backscatter (SKEB) scheme, which perturbs the horizontal wind and potential temperature tendencies, (iii) a combination of both multiphysics and SKEB scheme. Substantial improvements are noticed in the track positions of both the cyclones, when flow-dependent ensemble covariance is used in 3DVAR framework. Explicit model error representation is found to be beneficial in treating the underdispersive ensembles. Among the model error schemes used in this study, a combination of multiphysics and SKEB schemes has outperformed the other two schemes with improved track forecast for both the tropical cyclones.
机译：摘要数值天气预报模型中的不确定性通常在基于集合的数据同化（DA）系统中尚未良好。基于组合的DA系统的性能变为次优，如果错误在预测系统中缺乏采样源。本研究探讨了核对混合集合转换卡尔曼滤波器三维变分（3DVAR）DA系统（Hybrid）在两个热带气旋Ziz的轨道预测中的模型误差处理的效果。 Hudhud和Thane在孟加拉湾形成，采用先进的研究天气研究和预测（ARW-WRF）模型。我们调查了两种类型的模型误差处理方案及其对混合动力DA系统的影响; （i）多体验方法，使用积云，微孔和行星边界层方案的不同组合，（ii）随机动能反向散射（SKEB）方案，其渗透水平风和潜在的温度倾向，（iii）两种多体学的组合和串联计划。当在3DVAR框架中使用流量依赖的集合协方差时，在旋风器的轨道位置中注意到了大量改进。发现显式模型错误表示有利于处理未分散的合奏。在本研究中使用的模型误差方案中，多体验和SKEB方案的组合具有卓越的另外两种方案，具有改进的热带气旋的轨道预测。

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