  In this paper, we explore the possibility of detecting polar lows in C-band
SAR images by means of deep learning. Specifically, we introduce a novel
dataset consisting of Sentinel-1 images divided into two classes, representing
the presence and absence of a maritime mesocyclone, respectively. The dataset
is constructed using the ERA5 dataset as baseline and it consists of 2004
annotated images. To our knowledge, this is the first dataset of its kind to be
publicly released. The dataset is used to train a deep learning model to
classify the labeled images. Evaluated on an independent test set, the model
yields an F-1 score of 0.95, indicating that polar lows can be consistently
detected from SAR images. Interpretability techniques applied to the deep
learning model reveal that atmospheric fronts and cyclonic eyes are key
features in the classification. Moreover, experimental results show that the
model is accurate even if: (i) such features are significantly cropped due to
the limited swath width of the SAR, (ii) the features are partly covered by sea
ice and (iii) land is covering significant parts of the images. By evaluating
the model performance on multiple input image resolutions (pixel sizes of 500m,
1km and 2km), it is found that higher resolution yield the best performance.
This emphasises the potential of using high resolution sensors like SAR for
detecting polar lows, as compared to conventionally used sensors such as
scatterometers.
