An ensemble based turbulence forecasting system

Meteorological Applications

2019

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Turbulence remains one of the leading causes of aviation incidents. Climate change is predicted to increase the occurrence of clear‐air turbulence and therefore forecasting turbulence will become more important in the future. Currently, the two World Area Forecast Centres (WAFCs) use deterministic numerical weather prediction models to predict clear‐air turbulence operationally; it has been shown that ensemble forecasts improve the forecast skill of traditional meteorological variables. This study applies multi‐model ensemble forecasting to aviation turbulence for the first time. It is shown in a 12‐month global trial from May 2016 to April 2017 that combining two different ensembles yields a similar forecast skill to a single model ensemble and yields an improvement in forecast value at low cost/loss ratios. This finding is consistent with previous work showing that the use of ensembles in turbulence forecasting is beneficial. Using a multi‐model approach is an effective way to improve the forecast skill and provide pilots and flight planners with more information about the forecast confidence, allowing them to make a more informed decision about what action needs to be taken, such as diverting around the turbulence or requiring passengers and flight attendants to fasten their seatbelts. The multi‐model ensemble approach is intended to be made operational by both WAFCs in the near future and this study lays the foundations to make this possible.

Section: Resultsmentioning

confidence: 84%

Multi‐model ensemble predictions of aviation turbulence

Meteorological Applications

2019

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“…4.1, 4.2, and 4.3 is that they do not convey uncertainty. To resolve this issue, Gill and Buchanan (2014) and Buchanan (2016) trialled the use of ensemble forecasting for aviation turbulence. An ensemble is a collection of forecast runs, each of which is considered to be equally likely.…”

Section: Ensemble Forecastingmentioning

confidence: 99%

“…The studies by Buchanan (2014) andBuchanan (2016) then combined the predictors in a manner similar to Sharman et al (2006), using an iterative scheme to maximise the forecast skill. Again, the studies showed that the ensemble forecast was more skillful than a single model deterministic forecast.…”

Section: Ensemble Forecastingmentioning

confidence: 99%

Aviation Turbulence: Dynamics, Forecasting, and Response to Climate Change

2018

Pure Appl. Geophys.

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Abstract-Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviationaffecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed-ideally in collaboration with the aviation industry-to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future.

“…These forecasts are derived from the output of deterministic models at a resolution of 1.25° with seven vertical levels (ICAO, ). However, previous studies have shown the improved performance in turbulence forecasting of probabilistic forecasts derived from ensemble models (Gill and Buchanan, ; Storer et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Multi‐diagnostic multi‐model ensemble forecasts of aviation turbulence

Meteorological Applications

2020

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Turbulence is one of the major weather hazards to aviation. Studies have shown that clear‐air turbulence may well occur more frequently with future climate change. Currently the two World Area Forecast Centres use deterministic models to generate forecasts of turbulence. It has been shown that the use of multi‐model ensembles can lead to more skilful turbulence forecasts. It has also been shown that the combination of turbulence diagnostics can also produce more skilful forecasts using deterministic models. This study puts the two approaches together to expand the range of diagnostics to include predictors of both convective and mountain wave turbulence, in addition to clear‐air turbulence, using two ensemble model systems. Results from a 12 month global trial from September 2016 to August 2017 show the increased skill and economic value of including a wider range of diagnostics in a multi‐diagnostic multi‐model ensemble.