At the Data Assimilation Research Centre, University of Reading (UK)
a PhD studentship on „Numerical Weather Prediction of High-Impact Weather” is available. Please see details below.

Numerical Weather Prediction of High-Impact Weather
Supervisors: Dr Stefano Migliorini, Dr Ross Bannister, Prof. Alan O`Neill, Dr Mark Dixon, and Sue Ballard

Location: Department of Meteorology, University of Reading

Improving predictions of hazardous weather is currently one of the main challenges for operational meteorological centres. The motivation is that the occurrence of �significant� weather events is expected to increase in the near future due to climate change. Such phenomena often impact on very localised regions (as in the case of the Boscastle flood in 2004) and current operational models do not have enough spatial resolution for predicting them reliably and with the required accuracy. With the advent of a nonhydrostatic version of the Met Office Unified Model there is potential for increasing the resolution of the model in a meaningful way. To this end, the Met Office is currently experimenting with 4 km and 1.5 km spatial resolution versions of the Unified Model over a limited spatial region. At such resolutions (particularly at 1.5 km) it is possible to resolve convection and avoid relying on its sub-grid scale parametrization. High resolution observations,
such as radar or geostationary satellite measurements can also be properly modelled and assimilated in the model. This can potentially lead to improvements in forecasts of severe convective storms, which may lead to hazardous events such as flooding

However, many difficulties still need to be addressed on the data assimilation front, especially with regard to the treatment of so-called forecast errors, which are responsible for spreading the observational increments in space and between model variables. For example, the balance relations that are used to model forecast error covariances in variational data assimilation for synoptic scales are not necessarily suited for mesoscale flows. A possible way forward is to adapt the system used for synoptic scale data assimilation and consider balance equations that are more suited for modelling forecast error covariances on the mesoscale, by e.g. allowing for gravity waves.

A possible alternative approach for achieving better initial conditions for high resolution forecasts is to exploit a sequential data assimilation technique such as the Ensemble Kalman Filter (EnKF). With this technique forecast errors are directly estimated at each time step, starting from some initial estimate of them, possibly provided by the synoptic model. The focus of this PhD project will be to investigate the applicability of the EnKF framework to convective-scale data assimilation for a high-resolution version of the Unified Model. The work will be done in collaboration with the Met Office Joint Centre for Mesoscale Meteorology, based in the Department of Meteorology, University of Reading.

Student profile:
This project will involve a considerable amount of mathematics and computing, and it would be suitable for students with a good degree in mathematics or physical science and familiarity with a scientific programming language…detalii click [sursa]