A New Extreme Weather Nowcasting Product Supporting Aviation Management at Local Scale

Abstract

Extreme weather is responsible for major delays of air traffic flow management. Due to the climate changes, the storms track and intensity will also change in Europe, increasing the horizontal flight inefficiency by about 4%. In this context, an accurate short-term forecasting of extreme weather at airport spatial scale will be very useful for the aviation mangers and controllers. This work is part of the H2020 SESAR ALARM project partially devoted to nowcast extreme weather events in the area of Milano Malpensa airport. We used ground-based weather sensors, Global Navigation Satellite System (GNSS) receivers, a C-band radar and lightning detectors distributed around Malpensa to develop a machine learning model able to nowcast the rain rate, the wind speed and the lightning occurrences. The model performances strongly depend on the dataset length, data type, data temporal resolution, data spatial resolution, and frequency of the extreme events. The wind speed nowcasting is really accurate and can be re-used in different locations. The rain rate nowcasting is largely affected by the data availability and it must be customized to the specific location. The lightning nowcasting is affected by a large false alarm rate when the event already finished. We provide here a mockup product in polygon format which could be useful in future to the aviation managers to adjust and optimize the flight trajectories in case of extreme events occurrences.

Keywords: Nowcasting, Machine learning, aviation, Extreme Weather, lightning