期刊名称：Advances in Statistical Climatology, Meteorology and Oceanography
印刷版ISSN：2364-3579
电子版ISSN：2364-3587
出版年度：2019
卷号：5
期号：2
页码：147-160
DOI：10.5194/ascmo-5-147-2019
出版社：Copernicus Publications
摘要：Abstract. Deep learning (DL) methods were used to develop an algorithm to automatically detect weather fronts in fields of atmospheric surface variables. An algorithm (DL-FRONT) for the automatic detection of fronts was developed by training a two-dimensional convolutional neural network (2-D CNN) with 5 years (2003–2007) of manually analyzed fronts and surface fields of five atmospheric variables: temperature, specific humidity, mean sea level pressure, and the two components of the wind vector. An analysis of the period 2008–2015 indicates that DL-FRONT detects nearly 90 % of the manually analyzed fronts over North America and adjacent coastal ocean areas. An analysis of fronts associated with extreme precipitation events shows that the detection rate may be substantially higher for important weather-producing fronts. Since DL-FRONT was trained on a North American dataset, its extensibility to other parts of the globe has not been tested, but the basic frontal structure of extratropical cyclones has been applied to global daily weather maps for decades. On that basis, we expect that DL-FRONT will detect most fronts, and certainly most fronts with significant weather. However, where complex terrain plays a role in frontal orientation or other characteristics, it might be less successful.
