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.
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