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Flying safe in a changing climate

Atmosphere  |  01 March, 2020  |  Reading time: 4 minutes

Thunderstorms pose a significant risk to aviation – resulting in damage and injuries at best and a crash in the worst case. Bad weather can have a sizeable impact on air traffic operations; it accounted for 47% of all delays in 2017. Satellite data is helping to provide real time information and also work out how climate change affects the distribution, severity and frequency of such storms, thus enabling aviation to remain as safe as possible.


Safety is the top priority for airlines around the world, but the changing climate means that aviation faces a challenging landscape: more frequent hazardous weather and increased airspace congestion. Thunderstorms are particularly serious for aviation, being responsible for turbulence that can cause injuries, hail and lightning damage and – in rare cases – aircraft crashes (such as Air France flight 447).

Oxford researchers have developed a long-term dataset of thunderstorm information derived from satellite data, enabling stakeholders to understand where and how climate change is affecting the growth of storms, and hence best plan how to tackle the inevitable changes in flight routes required to avoid severe weather. By understanding where the frequency and intensity of storms is changing, airlines become proactive rather than reactive. They are able to plan future routes better and are able to provide more relevant training to their pilots and planners.

Geostationary weather satellites such as the European Meteosat and Japanese Himawari series have relatively poor spatial resolution (typically around 2 or 3 km) but can produce data very quickly: The most recent geostationary satellites produce a new image of the Earth every 10 minutes. This is particularly useful for monitoring storms and other weather events that evolve rapidly both for understanding long term trends and for development of real time warning systems.

Storm-related climate data has applications far beyond aviation: The insurance industry can use such data to forecast better at-risk regions and farmers can understand whether they are likely to experience increases in severe rainfall and flooding, for example.

UK expertise

The data analysis and related research are carried out at University of Oxford:

  • Satellite data: The data comes from the chain of geostationary satellites: Meteosat, GOES and Himawari. It is sent to Oxford directly from the satellite operators (EUMETSAT, NOAA and JMA respectively) via either satellite link or the cloud.
  • Data processing: The ORAC (Oxford-RAL Aerosol and Cloud) algorithm is applied to the raw satellite data to transform it into estimates of storm cloud position, altitude and severity, which is then stored for later analysis. The ORAC (Oxford-RAL Aerosol and Cloud) algorithm forms the heart of the data analysis and is an optimal estimation (OE) scheme for the retrieval of cloud and/or aerosol properties from visible/IR imaging radiometers. It has been widely applied to data from both geostationary and polar orbiting satellite instruments.
  • Data access: The final products cannot be accessed online, but depending on the need can be provided to people who ask (see contacts above).
  • Outputs: See for example: Analysis of aircraft flights near convective weather over Europe, WEATHER 70 (2015) 292-296, SR Proud

Team insight

Dr Simon Proud  is Research Fellow at the University of Oxford, specialising in aviation meteorology. He tackles our lack of knowledge regarding storms and turbulence, helping airlines to fly more safely and more efficiently. Funding for this work was provided by Emirates and NERC.

More generally, he works on the European Space Agency’s Cloud and Aerosol Climate Change Initiatives (CCI): Both are international projects with a goal of producing long-term datasets that describe the properties of clouds and aerosols in our atmosphere. These datasets are intended for use by the climate community in order to better understand how the climate is affecting cloud and pollution events (and vice-versa).