  Accurate air turbulence forecasting can help airlines avoid hazardous
turbulence, guide the routes that keep passengers safe, maximize efficiency,
and reduce costs. Traditional turbulence forecasting approaches heavily rely on
painstakingly customized turbulence indexes, which are less effective in
dynamic and complex weather conditions. The recent availability of
high-resolution weather data and turbulence records allows more accurate
forecasting of the turbulence in a data-driven way. However, it is a
non-trivial task for developing a machine learning based turbulence forecasting
system due to two challenges: (1) Complex spatio-temporal correlations,
turbulence is caused by air movement with complex spatio-temporal patterns, (2)
Label scarcity, very limited turbulence labels can be obtained. To this end, in
this paper, we develop a unified semi-supervised framework, T$^2$-Net, to
address the above challenges. Specifically, we first build an encoder-decoder
paradigm based on the convolutional LSTM to model the spatio-temporal
correlations. Then, to tackle the label scarcity problem, we propose a novel
Dual Label Guessing method to take advantage of massive unlabeled turbulence
data. It integrates complementary signals from the main Turbulence Forecasting
task and the auxiliary Turbulence Detection task to generate pseudo-labels,
which are dynamically utilized as additional training data. Finally, extensive
experimental results on a real-world turbulence dataset validate the
superiority of our method on turbulence forecasting.
