J. Carley, Michael P. Matthews, M. Morris, Manuel S. F. V. De Pondeca, Jenny Colavito, Runhua Yang
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引用次数: 3
摘要
摘要/ Abstract摘要:实时中尺度分析(Real Time Mesoscale Analysis, RTMA)是一种二维变分分析算法,用于对阿拉斯加上空3 km空间分辨率的地面感测天气要素进行逐时分析。在这项工作中,我们重点分析阿拉斯加的水平能见度,这是一个容易发生与天气有关的航空事故的地区,部分原因是观测网络相对稀疏。在本研究中,我们用RTMA评估了同化来自阿拉斯加网络摄像机新网络的水平能见度估计值的影响。结果表明,基于网络摄像机的能见度估计可以捕获低能见度条件,并有可能改进低仪表飞行规则和仪表飞行规则条件下的RTMA能见度分析。
Variational assimilation of web camera-derived estimates of visibility for Alaska aviation
Abstract The Real Time Mesoscale Analysis (RTMA), a two-dimensional variational analysis algorithm, is used to provide hourly analyses of surface sensible weather elements for situational awareness at spatial resolutions of 3 km over Alaska. In this work we focus on the analysis of horizontal visibility in Alaska, which is a region prone to weather related aviation accidents that are in part due to a relatively sparse observation network. In this study we evaluate the impact of assimilating estimates of horizontal visibility derived from a novel network of web cameras in Alaska with the RTMA. Results suggest that the web camera-derived estimates of visibility can capture low visibility conditions and have the potential to improve the RTMA visibility analysis under conditions of low instrument flight rules and instrument flight rules.