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Understanding Atmospheric Motion Vector Vertical Representativity Using a Simulation Study and First-Guess Departure Statistics

机译:通过模拟研究和首次猜测离场统计数据了解大气运动矢量的垂直表示性

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Atmospheric motion vectors (AMVs) have been produced for decades and remain an important source of wind information. Many studies have suggested that the traditional interpretation of AMVs as representative of the wind at cloud top is suboptimal and that they are more representative of the winds within the cloud. This paper investigates the vertical representativity of cloudy AMVs using both first-guess departure [observation - background (O - B)] statistics and the simulation-study technique. A state-of-the-art convection-permitting mesoscale model ("UKV") is used in conjunction with a radiative transfer model and the Nowcasting Satellite Application Facility (NWCSAF) AMV package to produce synthetic AMVs over a 1-month period. The simulated upper-level AMVs suffered from large height-assignment errors uncharacteristic of those in reality; these issues were partially alleviated by using the model cloud top instead of the assigned height. In agreement with previous studies, both the simulated and real AMVs were found to have the closest fit to a layer mean of the model winds with the majority of the layer below the estimated cloud top. However, improvements in the fit between the AMVs and the model were also found by simply lowering the assigned height. A short NWP trial hinted that height reassignment might lead to short-range forecast improvements. The results of this study indicate that the simulation technique was able to match the usefulness of O - B statistics for AMVs associated with low- and medium-level clouds (albeit at a higher computational cost); however, challenges remain in the simulation of upper-level clouds.
机译:大气运动矢量(AMV)已经产生了数十年,并且仍然是风信息的重要来源。许多研究表明,将AMV表示为云顶风的传统解释不太理想,并且它们更能代表云内的风。本文使用第一猜测偏离[观测-背景(O-B)]统计数据和模拟研究技术研究了多云AMV的垂直表示性。最新的对流允许中尺度模型(“ UKV”)与辐射传输模型和临近广播卫星应用设施(NWCSAF)AMV软件包结合使用,可在1个月内生产合成AMV。模拟的上层AMV承受着较大的高度分配误差,这是现实中没有的。通过使用模型云顶而不是分配的高度,可以部分缓解这些问题。与先前的研究一致,发现模拟和实际AMV都与模型风的层均值最接近,而大多数层都在估计的云顶以下。但是,也可以通过简单地降低分配的高度来提高AMV与模型之间的拟合度。一项简短的NWP试验表明,调高可能会改善短期预报。这项研究的结果表明,该仿真技术能够将O-B统计数据用于与中低层云相关的AMV(尽管计算成本较高);然而,在高层云的模拟中仍然存在挑战。

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