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Applicability of Large-Scale Convection and Condensation Parameterization to Meso-γ-Scale HIRLAM: A Case Study of a Convective Event

机译:大规模对流和凝结参数化对中γ尺度HIRLAM的适用性:对流事件的案例研究

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This paper presents a case study of a single cold air outbreak event with widespread convective precipitation over southern Finland on 25 May 2001. The purpose of the study is to investigate the applicability of the convection and condensation scheme of the High-Resolution Limited Area Model (HIRLAM) on meso-γ-scales. The study concentrates on the issue of grid-size-dependent convection parameterization. An explicit approach without the convection scheme is also examined. At the same time, the performance of an experimental nonhydrostatic version of HIRLAM is evaluated. Model simulations are conducted with three different horizontal grid spacings: 11, 5.6, and 2.8 km. Model results are compared to observed radar reflectivity data utilizing a radar simulation model, which calculates radar reflectivities from three-dimensional model output. The best results are obtained using nonhydrostatic dynamics and a grid-size-dependent convection scheme with a 5.6-km grid interval. However, even the best configuration still overestimates the area of strong reflectivity (intense precipitation). All the other combinations produce even stronger reflectivity. The grid-size-dependent convection parameterization is evidently beneficial at smaller grid spacings than 5.6 km. The nonhydrostatic model clearly outperforms its hydrostatic counterpart at the 5.6- and 2.8-km grid spacings, whereas with an 11-km grid interval, both models perform equally well.
机译:本文以2001年5月25日芬兰南部的一次对流降水为例,对单个冷空气暴发事件进行了案例研究。该研究的目的是研究高分辨率有限区域模型的对流和凝结方案的适用性( HIRLAM)。该研究集中在与网格大小有关的对流参数化问题上。还研究了没有对流方案的显式方法。同时,评估了HIRLAM实验性非静液压版本的性能。使用三种不同的水平网格间距进行模型仿真:11、5.6和2.8 km。利用雷达仿真模型将模型结果与观测到的雷达反射率数据进行比较,该模型从三维模型输出中计算出雷达反射率。使用非静水动力学和网格间隔为5.6公里的依赖于网格大小的对流方案可获得最佳结果。但是,即使是最佳配置,仍会高估强反射率(强降水)的面积。所有其他组合都会产生更强的反射率。在小于5.6 km的网格间距下,依赖于网格尺寸的对流参数化显然是有利的。非静水模型在5.6 km和2.8 km的网格间距上明显优于静水模型,而在11 km的网格间距下,两个模型的性能均相同。

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