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Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

机译:基于对象的时域诊断在对流允许模型中的降水追踪系统中的应用

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Meaningful verification and evaluation of convection-allowing models requires approaches that do not rely on point-to-point matches of forecast and observed fields. In this study, one such approach-a beta version of the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension [known asMODEtime-domain (MODE-TD)]-was applied to 30-h precipitation forecasts from four 4-km grid-spacing members of the 2010 Storm-Scale Ensemble Forecast system with different microphysics parameterizations. Including time in MODE-TD provides information on rainfall system evolution like lifetime, timing of initiation and dissipation, and translation. The simulations depicted the spatial distribution of time-domain precipitation objects across the United States quite well. However, all simulations overpredicted the number of objects, with the Thompson microphysics scheme overpredicting the most and theMorrison method the least. For the smallest smoothing radius and rainfall threshold used to define objects [8 km and 0.10 in. (1 in. = 2.54 cm), respectively], the most common object duration was 3 h in both models and observations. With an increased smoothing radius and rainfall threshold, the most common duration became shorter. The simulations depicted the diurnal cycle of object frequencies well, but overpredicted object frequencies uniformly across all forecast hours. The simulations had spurious maxima in initiating objects at the beginning of the forecast and a corresponding spurious maximum in dissipating objects slightly later. Examining average object velocities, a slow bias was found in the simulations, which was most pronounced in the Thompson member. These findings should aid users and developers of convection-allowing models and motivate future work utilizing time-domain methods for verifying high-resolution forecasts.
机译:对流允许模型的有意义的验证和评估需要不依赖于预测场和观测场的点对点匹配的方法。在这项研究中,将一种这样的方法(基于时间的方法(基于MODE时域(MODE-TD))并入了时间维度的基于对象的诊断评估方法(MODE)的beta版)应用于来自四个地区的30小时降水预报具有不同微物理参数设置的2010年风暴规模集合预报系统的4公里网格间距成员。将时间包括在MODE-TD中可提供有关降雨系统演变的信息,例如寿命,启动和消散的时间以及转换。这些模拟很好地描述了整个美国的时域降水对象的空间分布。但是,所有模拟都高估了对象的数量,汤普森微物理学方案高估了最多,而莫里森方法最少。对于用于定义对象的最小平滑半径和降雨阈值(分别为8 km和0.10 in。(1 in。= 2.54 cm)),在模型和观测值中,最常见的对象持续时间均为3 h。随着平滑半径和降雨阈值的增加,最常见的持续时间变短了。这些模拟很好地描述了物体频率的昼夜周期,但在所有预报小时内都均匀地高估了物体频率。这些模拟在预测开始时具有初始生成对象的虚假最大值,而在稍晚的耗散对象中具有相应的虚假最大值。在检查平均物体速度时,在模拟中发现了一个缓慢的偏差,这在汤普森成员中最为明显。这些发现将有助于对流允许模型的用户和开发人员,并利用时域方法来验证高分辨率预报,从而激发未来的工作。

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