FindAPhD Weekly PhD Newsletter | JOIN NOW FindAPhD Weekly PhD Newsletter | JOIN NOW

Development and exploitation of a high-resolution sea level product in the coastal ocean

   School of Mathematics

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Prof Finn Lindgren, Dr C Banks, Mr Chris Burnett, Dr F M Calafat  Applications accepted all year round  Competition Funded PhD Project (UK Students Only)

About the Project

See also

Scientific background and motivation

As oceans respond to local, regional and global changes a key parameter to monitor is sea level particularly along the coasts, as this is where changes impact on populations and a better understanding of measurable values is required. Currently, tide gauges provide point measurements with high temporal resolution whereas satellite altimeters can provide synoptic information but with poor temporal resolution. The quality of measuring sea level from satellite altimeters has improved markedly over recent years in particular in the critical zone close to the coast. For example, through coastal retracking ( or the introduction of SAR altimetry (CryoSat-2, Sentinel-3 and Sentinel-6). The studentship is a SENSE CDT (see details below) collaboration between the University of Edinburgh, the National Oceanography Centre (Liverpool) and 4 Earth Intelligence, and includes the possibility of a placement at NASA JPL and/or participation in a research cruise.

Aims and objectives

In order to address some key issues in the coastal zone it is proposed to develop a methodology for a gridded coastal sea level product (for 2011 onwards). By building a statistical model linking sea level data (altimetry and tide gauges) with satellite sea surface temperature and/or ocean colour products as a proxy for changes in sea level the student will be able to create a high-resolution product for scientific analyses. Associated case studies could be global or regional in nature, with a number of key areas of interest already identified (e.g. Severn Estuary).


Conventional geostatistical methods will face several challenges when applied to reconstructing coastal sea level. One challenge is that the correlation length scales of sea level are much longer along the coast than across the shelf and so isotropic covariance functions, which are predominantly used in geostatistics, will not work well. New geostatistical models based on Gaussian processes with local anisotropy will be investigated to address this problem. The approach would incorporate multiple satellite altimeters (with different sampling so simple methods result in aliasing), tide gauges and other satellite/in situ/model parameters (e.g. surface temperature and/or ocean colour) via some form of spatio-temporal model. There is the potential in the latter part of the studentship for comparison of results with results from the innovative SWOT mission that will provide the same information we are looking to produce.


This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See

Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees, tax-free stipend at the UK research council rate ( £16,062 for 2022/23), and a research training and support grant to support national and international conference travel.
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs