33,702 research outputs found
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
Estimating snow cover from publicly available images
In this paper we study the problem of estimating snow cover in mountainous
regions, that is, the spatial extent of the earth surface covered by snow. We
argue that publicly available visual content, in the form of user generated
photographs and image feeds from outdoor webcams, can both be leveraged as
additional measurement sources, complementing existing ground, satellite and
airborne sensor data. To this end, we describe two content acquisition and
processing pipelines that are tailored to such sources, addressing the specific
challenges posed by each of them, e.g., identifying the mountain peaks,
filtering out images taken in bad weather conditions, handling varying
illumination conditions. The final outcome is summarized in a snow cover index,
which indicates for a specific mountain and day of the year, the fraction of
visible area covered by snow, possibly at different elevations. We created a
manually labelled dataset to assess the accuracy of the image snow covered area
estimation, achieving 90.0% precision at 91.1% recall. In addition, we show
that seasonal trends related to air temperature are captured by the snow cover
index.Comment: submitted to IEEE Transactions on Multimedi
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