Using vertical profiles of atmospheric backscatter as observed with the Met Office ceilometer network to improve high resolution weather forecasts.
Lead Research Organisation:
University of Reading
Department Name: Meteorology
Abstract
Lidar ceilometers have been used for many years to measure the height of cloud base by transmitting a series of laser pulses and observing the reflected signal, but during the EU FP6 Cloudnet project it was realised that ceilometers can provide accurate vertical backscatter profiles which can be used to evaluate the representation of clouds and aerosols in weather forecast models.
In COST Action EG-CLIMET (www.eg-climet.org) the Met Office (MO) reported the first comparisons of observed backscatter profiles with simulated profiles using cloud and aerosol predicted by the MO operational 1.5km forecast model (UKV) and demonstrated that the hundreds of ceilometers deployed in Europe could be a novel source of data for initialisation and validation of forecasts. This PhD is timely because a new COST action (tinyurl.com/toprof) involving 14 national weather services will start in October 2013 with the specific aim of providing real-time ceilometer data across Europe by addressing common calibration, retrieval algorithms and data quality issues.
By the end of 2014 the MO will have 43 ceilometers reporting observed backscatter profiles in real time from 34 sites; at 9 sites 2 different instruments will operate side by side. With CAA funding, the MO is procuring 9 more advanced instruments able to directly measure optical extinction with the express aim of monitoring ash from future volcanic eruptions.
The MO currently assimilates ceilometer cloud-base observations but over the next 2 years is developing routine monitoring and assimilation of the full backscatter profiles.
The first aim of the PhD is to study the error characteristics of the simulated backscatter and identify any problems with the simulator or the UKV input parameters. For instance, preliminary work indicates that cloud base is too low in the simulated backscatter. The student can investigate sensitivity to assumed cloud, aerosol and microphysics parametrizations as well as investigate errors in the forecasts and the observations.
The ceilometer data and its assimilation alongside other synergistic observations provide a unique opportunity to investigate aerosol and cloud interactions and to check whether the UKV predicts realistic distributions of clouds and aerosols. Rain, drizzle, ice, liquid clouds, aerosol loading and relative humidity (in terms of aerosol swelling) as seen by observed and simulated backscatter should provide important verification of those fields in UKV forecasts. Finally, the ability of the ceilometer data to measure the evolution of fog and the benefit of the assimilation of the data to fog forecasting will be investigated.
The student's work will have a direct, positive benefit of improving initialisation of high-resolution weather forecast models which in turn will improve forecasts of fog, clouds, rain, aerosol and drizzle.
The PhD work will address these topics:
Year 1: Compare performance of co-located ceilometer instruments. Define and analyse observation errors. Establish the statistics of differences between model and observation for backscatter data assimilation.
Year 2: Extend forward model to ice and super-cooled clouds and fog. Examine effect of changing the assumed particle size distributions and the assumed mixture of aerosols on the statistics of model comparisons with observations.
Year 3: Evaluate potential impact of including backscatter observations on operational data assimilation and numerical weather prediction validation and formulation.
Year 4: Synthesize results. Write thesis.
Student will spend one month each summer at the MO in Exeter with the team operating the ceilometer network to gain an in-depth understanding of:
Year 1: Lidar scattering, instrumental and calibration errors
Year 2: Data quality issues. Determination of cloud base height. Stability of calibration. Year 3: Assessment of results. Implications for parametrization and data assimilation schemes in forecast models.
In COST Action EG-CLIMET (www.eg-climet.org) the Met Office (MO) reported the first comparisons of observed backscatter profiles with simulated profiles using cloud and aerosol predicted by the MO operational 1.5km forecast model (UKV) and demonstrated that the hundreds of ceilometers deployed in Europe could be a novel source of data for initialisation and validation of forecasts. This PhD is timely because a new COST action (tinyurl.com/toprof) involving 14 national weather services will start in October 2013 with the specific aim of providing real-time ceilometer data across Europe by addressing common calibration, retrieval algorithms and data quality issues.
By the end of 2014 the MO will have 43 ceilometers reporting observed backscatter profiles in real time from 34 sites; at 9 sites 2 different instruments will operate side by side. With CAA funding, the MO is procuring 9 more advanced instruments able to directly measure optical extinction with the express aim of monitoring ash from future volcanic eruptions.
The MO currently assimilates ceilometer cloud-base observations but over the next 2 years is developing routine monitoring and assimilation of the full backscatter profiles.
The first aim of the PhD is to study the error characteristics of the simulated backscatter and identify any problems with the simulator or the UKV input parameters. For instance, preliminary work indicates that cloud base is too low in the simulated backscatter. The student can investigate sensitivity to assumed cloud, aerosol and microphysics parametrizations as well as investigate errors in the forecasts and the observations.
The ceilometer data and its assimilation alongside other synergistic observations provide a unique opportunity to investigate aerosol and cloud interactions and to check whether the UKV predicts realistic distributions of clouds and aerosols. Rain, drizzle, ice, liquid clouds, aerosol loading and relative humidity (in terms of aerosol swelling) as seen by observed and simulated backscatter should provide important verification of those fields in UKV forecasts. Finally, the ability of the ceilometer data to measure the evolution of fog and the benefit of the assimilation of the data to fog forecasting will be investigated.
The student's work will have a direct, positive benefit of improving initialisation of high-resolution weather forecast models which in turn will improve forecasts of fog, clouds, rain, aerosol and drizzle.
The PhD work will address these topics:
Year 1: Compare performance of co-located ceilometer instruments. Define and analyse observation errors. Establish the statistics of differences between model and observation for backscatter data assimilation.
Year 2: Extend forward model to ice and super-cooled clouds and fog. Examine effect of changing the assumed particle size distributions and the assumed mixture of aerosols on the statistics of model comparisons with observations.
Year 3: Evaluate potential impact of including backscatter observations on operational data assimilation and numerical weather prediction validation and formulation.
Year 4: Synthesize results. Write thesis.
Student will spend one month each summer at the MO in Exeter with the team operating the ceilometer network to gain an in-depth understanding of:
Year 1: Lidar scattering, instrumental and calibration errors
Year 2: Data quality issues. Determination of cloud base height. Stability of calibration. Year 3: Assessment of results. Implications for parametrization and data assimilation schemes in forecast models.
Organisations
People |
ORCID iD |
| Anthony Illingworth (Training Grant Holder) |