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How businesses use forecasts to operate more efficiently in a variable climate

In this article, Professor David Brayshaw explains how extended-range forecasts can help businesses operate more efficiently despite variable climate.
© University of Reading

In the previous Step, Doug described how weather forecast data, produced by numerical weather forecast models, is already used to inform a wide range of decisions and planning.

The aviation industry is an excellent example of this: historically, routing decisions have been made using worst-case weather scenarios which tend to be operationally restrictive. However, advanced forecasting capabilities now offer far greater accuracy and detail for flight management allowing more flexibility in route planning and scheduling for both military and commercial operations. This saves time as well as fuel, supporting efficiency, climate positive actions and financial savings.

map showing northern Africa and south-western Europe with 4 varying flight paths between Vienna and Tenerife marked by different coloured lines

Track variations between Tenerife and Vienna. This highlights the need for accurate wind forecasts. Click to expand.

In broad terms, the predictive skill of numerical weather forecasts has often been said to increase at approximately a day per decade. In other words, a forecast with a 5-day lead time (ie, for the weather 6 days from now) produced in 2020 is about as reliable as forecasts with a 4-day lead time produced in 2010. However, these forecasts can’t continue improving as there’s a limit to the predictability that can be achieved using the predictability of the atmosphere alone – often referred to colloquially as the ‘Butterfly Effect’. This limit is approximately several days.

Extended-range forecasts

Recent developments in weather and climate science are beginning to offer opportunities for skilful predictions beyond this window. These subseasonal forecasts are somewhat different to their shorter-range counterparts: the skill relates to predictions made over much larger domains or longer-periods and is almost always probabilisitic. To illustrate with an example, a subseasonal forecast might suggest that two weeks from now, the weekly average temperature over the whole UK is likely to be above normal, whereas a traditional weather forecast might give hourly temperatures over London tomorrow.

The application of these extended-range (or Subseasonal-to-Seasonal, S2S) forecasts to produce actionable climate intelligence is in it’s infancy. There is, however, widespread potential for many different applications. Recent innovations have begun to demonstrate that skilful extended-range predictions can be generated for sectors such as agriculture, renewable energy, public health and flood management1. In one recent example from the telecommunications sector2 researchers from the University of Reading demonstrated that subseasonal forecast information had the potential not only to enhance a company’s performance (meeting targets for repairing weather-related asset damage more quickly), it could even be used to achieve this goal at lower overall cost (as the need for repairs could be better anticipated, work could be better managed requiring fewer resources to meet the same performance level).

In Week 2 you’ll discover that the basis of S2S predictions is that parts of the climate system evolve more slowly (or more predictably) than the near-surface atmosphere, and we’ll show how this relates to both traditional days ahead weather-forecasting and long-term climate (Step 2.6). You’ll also see how this opportunity for longer-range prediction comes with a cost: it makes it harder to estimate the ‘current climate’. You’ll learn about the process of ‘converting’ raw climate forecasts into actionable climate information (Step 2.9) and the importance of relying on ensembles of forecasts – multiple predictions of the future (Step 2.12).

Let’s first consolidate your understanding of this case study in the next Step.

References

  1. Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions, Christopher J. White et al, Bulletin of the American Meteorological Society, E1448–E1472, June 2022.
  2. Quantifying the potential for improved management of weather risk using sub-seasonal forecasting: The case of UK telecommunications infrastructure, David Brayshaw et al, Meteorological Applications, E1849, Jan 2020.
© University of Reading
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Climate Intelligence: Using Climate Data to Improve Business Decision-Making

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