PhD Studentship: Assimilating satellite land surface temperature data to improve numerical weather prediction skill

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

Supervisors:  Dr Claire Bulgin, Prof Sarah Dance, Dr David Fairbairn, Dr Patricia de Rosnay

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] WMO. 2022. ECVs https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables 

[2] ESA. 2019. Land-surface temperature from Copernicus Sentinel-3. https://www.esa.int/ESA_Multimedia/Images/2019/07/Land-surface_temperature_from_Copernicus_Sentinel-3

Eligibility: 

• Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree or equivalent in physics, mathematics, engineering or a closely related subject such as environmental science with a high mathematical content.
• Due to restrictions on the funding this studentship is open to UK applicants.

Funding Details:   

• Starts 18^th September 2023
• 3-year award
• Funding covers full UK tuition fees plus stipend.

*Important notes*

1. Please quote the reference ‘GS23-012’ in the ‘Scholarships applied for’ box which appears within the Funding Section of your on-line application.
2. When you are prompted by the online application system to upload a research proposal, please omit this step as the project is already defined.

Application Deadline:  28^th April 2023

Further Enquiries:  

Please note that, where a candidate is successful in being awarded funding, this will be confirmed via a formal studentship award letter; this will be provided separately from any Offer of Admission and will be subject to standard checks for eligibility and other criteria. 

For further details please contact c.e.bulgin@reading.ac.uk.

Advert information