UK: PhD Studentship on Numerical Weather Prediction of High-Impact Weather, University of Reading.
UK: PhD Studentship on Numerical Weather Prediction of High-Impact Weather, University of Reading.
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.
Funding particulars:
This project is proposed as a ”Co-operative Awards in Sciences of the
Environment” (CASE) award between the Natural Environment Research
Council and the UK Met Office. Full funding is generally restricted to UK
students studying in England, Scotland or Wales. Students from other EU
countries may be eligible for a fees-only award or a full award, if
they have lived in the UK for the past three years.
For further details about the project please see
http://www.met.rdg.ac.uk/phd/topics/descriptions/darc2.pdf
and then contact Dr Stefano Migliorini (s.migliorini@reading.ac.uk) if
you have any further questions.
For details of how to apply for the PhD project go to
http://www.met.rdg.ac.uk/phd/topics/
-------------------------------------------------
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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.
Funding particulars:
This project is proposed as a ”Co-operative Awards in Sciences of the
Environment” (CASE) award between the Natural Environment Research
Council and the UK Met Office. Full funding is generally restricted to UK
students studying in England, Scotland or Wales. Students from other EU
countries may be eligible for a fees-only award or a full award, if
they have lived in the UK for the past three years.
For further details about the project please see
http://www.met.rdg.ac.uk/phd/topics/descriptions/darc2.pdf
and then contact Dr Stefano Migliorini (s.migliorini@reading.ac.uk) if
you have any further questions.
For details of how to apply for the PhD project go to
http://www.met.rdg.ac.uk/phd/topics/
-------------------------------------------------
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