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首页> 外文期刊>Quarterly Journal of the Royal Meteorological Society >Examining relationships between cloud-resolving model parameters and total flash rates to generate lightning density maps
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Examining relationships between cloud-resolving model parameters and total flash rates to generate lightning density maps

机译:检查云解决模型参数与总闪存之间的关系,以产生闪电密度图

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摘要

Forecasting the electrical activity of a storm is a difficult task because of the complexity of cloud electrification processes and the unforeseeable characteristics of lightning flashes. Nowadays, very few models are able to simulate explicitly the entire life cycle of the electric charges in clouds, which is a result of the balance between the charge separation rates (microphysics) and the charge neutralization rates by lightning flashes (physics of streamer discharges). The main objective of this study is to investigate which particular dynamical and/or microphysical parameter can serve as the best proxy for the total lightning activity. With this aim, eight storms were simulated with Meso-NH and its cloud electrical scheme. This model-to-model approach guarantees a full consistency between the dynamics, the microphysics and the lightning activity. Two approaches are followed to analyse the results. The first one considers the entire domain of simulation, while the second one concentrates on individual convective cells. Linear regressions between predicted flash rates and recorded parameters or proxies (total graupel mass, updraught volume with vertical velocity higher than 10m/s, product of precipitating and non-precipitating ice mass flux and maximum updraught speed) are analysed to show the benefit of the cell-scale approach. Then, in order to evaluate the relationships, the HyMeX storm case of 24 September 2012 was simulated. The mass of graupel best represents the overall location of the lightning activity (RMSE of the flash rate density better than 10(-2)flmin(-1)km(-2)) and locates the peak lightning flash rate well. The three other proxies tend to cause false alarms due to the y-intercept value of the regression equation.
机译:由于云电气化过程的复杂性以及闪电闪烁的可预见特性,预测风暴的电气活动是一项艰巨的任务。如今,很少有模型能够明确模拟云中电荷的整个生命周期,这是电荷分离速率(微手术)之间的平衡结果和通过闪电(垃圾排出物理)的电荷中和速率) 。本研究的主要目的是研究哪种特定的动态和/或微药物参数可以作为总避免活动的最佳代理。通过这种目标,使用Meso-NH及其云电气方案模拟了八种风暴。这种型号到模型方法可确保动态,微专家和闪电活动之间的完全一致性。遵循两种方法来分析结果。第一个考虑整个模拟领域,而第二个一个集中在单个对流细胞上。预测闪光率和记录参数或代理之间的线性回归(总格柱质量,具有高于10m / s的垂直速度的上升量,沉淀和非沉淀冰质量磁通量和最大升高的速度)以显示出益处细胞级别方法。然后,为了评估关系,模拟了2012年9月24日的Hymex Storm案例。 Graupel的质量最能代表雷电活动的整体位置(闪光速率密度的RMSE优于10(-2)FLMIN(-1)km(-2))并定位峰值闪电速率良好。由于回归方程的Y读取值,其他三个代理倾向于引起误报。

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