利用 1987 年以来加拿大北极群岛上空的被动微波卫星数据分析雪后降雨事件的模式和趋势

IF 3.1 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
V. Sasseville, Alexandre Langlois, Ludovic Brucker, Cheryl Ann Johnson
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引用次数: 0

摘要

气候变化对北极气象极端事件(如雪后降雨)有着深远的影响,它会影响地表状态的时空变异性。被动微波卫星图像有助于在当地气象和雪信息匮乏的极地地区探测此类事件。在本研究中,我们使用一种检测算法,利用高分辨率被动微波数据来监测 1987 年至 2019 年加拿大北极群岛上空的 ROS 时空变化。该方法利用多个气象站的数据进行了验证,并对被动微波数据集进行了大气校正。我们检测 ROS 的方法基于两种方法:1)在整个研究期间的固定时间段内(即 11 月 1 日至 5 月 31 日);2)在应用我们的 ROS 算法(即所研究的冬季长度每年不同)之前,先检测雪的存在。对每个冬季的事件发生情况进行分析,并按加拿大北极群岛的岛群加以区分。结果表明,尽管没有观察到统计意义上的显著趋势,但绝对的 ROS 发生率有所增加,主要是沿海地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patterns and trend analysis of rain-on-snow events using passive microwave satellite data over the Canadian Arctic Archipelago since 1987
Climate change has a profound effect on Arctic meteorology extreme events, such as rain-on-snow (ROS), which affects surface state variable spatial and temporal variability. Passive microwave satellite images can help detect such events in polar regions where local meteorological and snow information are scarce. In this study, we use a detection algorithm using a high-resolution passive microwave data to monitor spatial and temporal variability of ROS over the Canadian Arctic Archipelago from 1987 to 2019. The method is validated using data from several meteorological stations and atmospheric corrections have been applied to the passive microwave dataset. Our approach to detect ROS is based on two methods: 1) over a fixed time-period (i.e. November 1st to May 31st) throughout the study period and 2) using an a-prior detection for snow presence before applying our ROS algorithm (i.e. length of studied winter varies yearly). Event occurrence is analyzed for each winter and separated by island groups of the Canadian Arctic Archipelago. Results show an increase in absolute ROS occurrence, mainly along the coasts, although no statistically significant trends are observed.
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来源期刊
Journal of Hydrometeorology
Journal of Hydrometeorology 地学-气象与大气科学
CiteScore
7.40
自引率
5.30%
发文量
116
审稿时长
4-8 weeks
期刊介绍: The Journal of Hydrometeorology (JHM) (ISSN: 1525-755X; eISSN: 1525-7541) publishes research on modeling, observing, and forecasting processes related to fluxes and storage of water and energy, including interactions with the boundary layer and lower atmosphere, and processes related to precipitation, radiation, and other meteorological inputs.
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