  Mitigating climate change demands a transition towards renewable electricity
generation, with wind power being a particularly promising technology. Long
periods either of high or of low wind therefore essentially define the
necessary amount of storage to balance the power system. While the general
statistics of wind velocities have been studied extensively, persistence
(waiting) time statistics of wind is far from well understood. Here, we
investigate the statistics of both high- and low-wind persistence. We find
heavy tails and explain them as a superposition of different wind conditions,
requiring $q$-exponential distributions instead of exponential distributions.
Persistent wind conditions are not necessarily caused by stationary atmospheric
circulation patterns nor by recurring individual weather types but may emerge
as a combination of multiple weather types and circulation patterns.
Understanding wind persistence statistically and synoptically, may help to
ensure a reliable and economically feasible future energy system, which uses a
high share of wind generation.
