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Wednesday 29 Nov 2023New Observations for Operational Weather Forecasting

Malcolm Kitchen - Honorary Professor at the Faculty of Environment, Science, Economy, University of Exeter

Physics building, 4th floor 14:00-15:00

Mainly through investment in satellites, numerical weather prediction models, and computer power, operational weather forecasting has taken great strides forward in recent decades. In 2015, the value of weather forecasts to the UK economy was estimated to be £1-2B p.a.; order 10 times their cost. To generate even higher benefits through more detailed forecasts and more accurate targeting of warnings, the resolution of the forecasting models relentlessly increases in step with the availability of computer power. Real-time observations are necessary to both inform the forecast models of the current atmospheric state, and to verify their output. Ideally, the density of observations should be sufficient to resolve all the significant structures represented in the forecast model. Unfortunately, over the last ~10 years, it has not proved economically or practically feasible to scale-up the conventional observing networks to keep pace with the increases in forecast model resolution. The result is that, for the first time in our history, we are now able to forecast the weather in rather greater detail than we can generally observe it. If this situation is allowed to persist, or if the ‘resolution gap’ grows larger, then the UK is unlikely to realise the full benefits of the forecasting system.
Generating the much higher volumes of observations that are needed will require innovative exploitation of more ‘opportunistic’ sources of data, new observation platforms, and the design of new lower-cost instruments that can be deployed in large numbers. Currently, the small amount of R&D in this field is taking place mainly within the operational agencies, with relatively little in industry or the academic sector. This seminar will discuss the potential for high-impact research e.g. in areas of data science and remote sensing.

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