Towards a Future Forecast Process – How to better utilise human knowledge and intuition in a world of too much information

The professional journey of meteorologists working in the domain for more than ten years is likely to have included a huge increase in the quality and quantity of available data for decision making. In numerical modelling this has included the move from coarse resolution (>40km) global models to high resolution and even sub km scale convection permitting models as well as the increasing acceptance and use of ensemble systems to aid representation of uncertainty. There is also an increasing demand for forecasts of the impact, not solely occurrence, of weather and so further data such as demography, land use and event timelines must be interrogated to understand the complex combination of factors.

Whilst this increase in data and customer expectation has occurred the operational meteorologist forecast process has remained much the same in framework to that of decades ago – a transition through scales from an initial assessment of the broadscale atmospheric state to an appropriate level of reliable detail at the meso or microscale.

The result is that operational meteorologists become highly adept at weather regime recognition to understand potential hazards and uncertainties without needing to access the full range of data available. This knowledge is then used to explore what the meteorologist judges to be the most important parts of the forecast in greater detail before making final forecast and warning decisions. This process has served the community well but the increasing pressures on the profession to make ever more efficient and accurate decisions with the increasing data volumes can lead to information overload and the reliance on familiar, but not necessarily the highest value, data sources and parameters.

Machine Learning, often colloquially referred to under the umbrella term of Artificial Intelligence (AI), presents an opportunity to update the forecast process utilising the ability to effectively process large multidimensional datasets along with previous human-based decisions, expertise and downstream impacts. The combination of this data and knowledge brings the potential to assess the weather from a hazard specific point of view, allowing recommendation of forecast times, locations or tools for final human decision-making. Research is underway into this application of machine learning, using an archive of issued severe weather warnings and coarse resolution weather data. When applied to the forecast process, along with the caveat that forecast data is not perfect and so human intervention and intuition remains vital to effective decision making, this helps envision a future state where operational meteorologists focus on specific highlighted hazards leading to more efficient utilisation of time and effort. This future state would allow more efficient focus of time where it matters, allowing for more effective decision making and releasing meteorologist time to use their expertise elsewhere other than in product generation.