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Hazardous Weather
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In our changing climate, hazardous weather events such as storms, floods, wind, snow and ice, and fog events are expected to become more extreme. Our ability to manage these hazards is dependent on timely and accurate weather forecasts.
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Weather forecast accuracy is strongly constrained by estimates of the current state of the atmosphere, used as initial data to drive the forecast. Observations are combined with dynamical model information in a process called data assimilation to create such initial data. NCEO works closely with operational weather centres to co-create mathematical data assimilation methods and better approaches for the treatment of new and existing observations that will improve the weather forecasts of the future.
Ongoing projects
Some of our ongoing projects include
- A collaboration with the UK Met Office, developing new metrics to quantify the influence and impact of specific observation-types on data assimilation for convection-permitting regional weather prediction systems, especially useful for predicting intense summer rainfall and flooding. Such metrics can be used to help optimize the use of current observations and to define the observing systems of the future.
- A collaboration with the European Centre for Medium-range Weather Forecasting (ECMWF) and the Met Office, addressing improvements needed for global km-scale forecasting. In the future, global numerical weather prediction models will simulate at km-scale resolutions, allowing for improvements to the representation of orography, and explicit representation of convection, which are particularly important for better forecasts of hazardous weather. To fully realize these improvements, models must be initialized with high-resolution observation data, providing detail on appropriate scales. Large volumes of high-resolution satellite data are already available, but only 5-10% of these data are used in data assimilation, due to a lack of 1) knowledge of observation uncertainty structures and 2) appropriate fast numerical techniques. We are working together on this grand challenge.
We also have a number of ongoing PhD projects. Projects on land surface assimilation and coupled ocean-atmosphere prediction are closely linked.
References
- Hu, G., Dance, S. L., Bannister, R. N., Chipilski, H. G., Guillet, O., Macpherson, B., Weissmann, M., & Yussouf, N. (2023). Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021. Atmospheric Science Letters, 24(1), e1130. https://doi.org/10.1002/asl.1130
- Simonin D, Waller JA, Ballard SP, Dance SL, Nichols NK. A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds. Q J R Meteorol Soc. 2019; 145: 2772–2790. https://doi.org/10.1002/qj.3592
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