Current Opportunities
NCEO Research Associate in Carbon-Climate Science
| Job Description | Deadline | Link |
| The Carbon-Climate Science Team in the Earth Observation Science (EOS) group at University of Leicester is seeking to appoint a Research Associate based in the School of Physics and Astronomy. The EOS group has a strong foundation in leading space research, and specialises in identification, investigation, and development of new/advanced parameters for space measurements. The successful candidate will work under the supervision of Dr Robert Parker and will contribute fully to the large portfolio of research conducted by the team. |
|
|
PhD Studentship in School of Mathematical, Physical and Computational Sciences
| Job Description | Eligibility | Funding Details |
| Project title: Assimilating satellite land surface temperature data to improve numerical weather prediction skill
Department/School: Department of Meteorology / School of Mathematical, Physical and Computational Sciences at the University of Reading Supervisors: Dr Claire Bulgin, Prof Sarah Dance, Dr David Fairbairn, Dr Patricia de Rosnay Project Overview: Land surface temperature (LST) has been recently recognized by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV), which is a ‘physical, chemical or biological variable that critically contributes to the characterisation of Earth’s climate’ [1]. In response to this new classification, there has been rapid development of global LST datasets from observations made by a variety of satellite instruments. Geostationary satellites observe the global tropics and mid-latitudes as frequently as every 10 minutes, providing a wealth of information on the diurnal cycle in LST, which is closely related to the land surface cover. Recent developments in numerical weather prediction enable surface temperature datasets to be integrated in the forecasting system. So far, this has only been tested with sea surface temperature. This PhD focusses on integrating LST from satellite datasets into the forecasting system, to try and improve the land surface modelling. This should benefit the representation of the diurnal temperature cycle in the model, which is currently underestimated. One of the challenges of this project will be mapping what the satellite observes (the land surface skin temperature) to the parameters included in the model, which are soil temperature and soil moisture. Once the satellite observations have been integrated with the land surface model, the student can then evaluate the improvement in the model, by comparing the output against in-situ observations and other refence datasets. Improving the validation of the two metre air temperature and humidity fields in the model will show that this technique could be beneficial more widely within numerical weather prediction. The student will be encouraged to write up their work as scientific publications throughout the PhD. [1] World Meteorological Organization (WMO). 2022. Essential Climate Variables. https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables [2] European Space Agency. 2019. Land-surface temperature from Copernicus Sentinel-3. Accessed 10/2022 from https://www.esa.int/ESA_Multimedia/Images/2019/07/Land-surface_temperature_from_Copernicus_Sentinel-3 |
|
|