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Abstract:
The polar and subtropical jet streams are strong upper-level winds with a crucial influence on weather
throughout the Northern Hemisphere midlatitudes. In particular, the polar jet is located between cold arctic air
to the north and warmer subtropical air to the south. Strongly meandering states therefore often lead to extreme
surface weather.
Some algorithms exist which can detect the 2-D (latitude and longitude) jets’ core around the hemisphere,
but all of them use a minimal threshold to determine the subtropical and polar jet stream. This is particularly
problematic for the polar jet stream, whose wind velocities can change rapidly from very weak to very high
values and vice versa.
We develop a network-based scheme using Dijkstra’s shortest-path algorithm to detect the polar and subtropical jet stream core. This algorithm not only considers the commonly used wind strength for core detection
but also takes wind direction and climatological latitudinal position into account. Furthermore, it distinguishes
between polar and subtropical jet, and between separate and merged jet states.
The parameter values of the detection scheme are optimized using simulated annealing and a skill function
that accounts for the zonal-mean jet stream position (Rikus, 2015). After the successful optimization process,
we apply our scheme to reanalysis data covering 1979–2015 and calculate seasonal-mean probabilistic maps and
trends in wind strength and position of jet streams.
We present longitudinally defined probability distributions of the positions for both jets for all on the Northern
Hemisphere seasons. This shows that winter is characterized by two well-separated jets over Europe and Asia
(ca. 20◦ W to 140◦ E). In contrast, summer normally has a single merged jet over the western hemisphere but can
have both merged and separated jet states in the eastern hemisphere.
With this algorithm it is possible to investigate the position of the jets’ cores around the hemisphere and it
is therefore very suitable to analyze jet stream patterns in observations and models, enabling more advanced
model-validation.
