Imperial College London

DrMarianaClare

Faculty of EngineeringDepartment of Earth Science & Engineering

Visiting Researcher
 
 
 
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Contact

 

m.clare17 Website

 
 
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Location

 

480Royal School of MinesSouth Kensington Campus

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Summary

 

Summary

I am a PhD student in the Mathematics of Planet Earth CDT. 

My research focuses on developing and using advanced numerical modelling and statistical tools to improve the understanding of hazards and the quantification and minimisation of erosion and flood risk.

In particular, I am studying how using adjoint and moving mesh methods in the coastal ocean model Thetis can be used to improve both the accuracy of the model and assess its uncertainty. I am also exploring how the new technique of Multilevel Monte Carlo simulations can be combined with the industry-standard coastal model XBeach to quantify erosion/flood risk.

I was recently one of only five people awarded a visiting research fellowship by the French Embassy in London. I worked at IPSL studying how neural networks can be used to predict ocean circulation changes in a changing climate (press release on my work there). 

If you are interested in my research or articles, please feel free to contact me.

Research publications



Clare, Mariana C A, et al. Multi-scale hydro-morphodynamic modelling using mesh movement methods. GEM-International Journal on Geomathematics13(1) (2022), 1-39.

Clare, Mariana C A, et al. Combining distributionā€based neural networks to predict weather forecast probabilities. Quarterly Journal of the Royal Meteorological Society 147(241) (2021).

Clare, Mariana C A, et al. Hydro-morphodynamics 2D modelling using a discontinuous Galerkin discretisation. Computers & Geosciences 146 (2020), 104658.

Clare, Mariana C A, et al. Calibration, inversion and sensitivity analysis for hydro-morphodynamic models. (2021). arXiv preprint, URL: https://eartharxiv.org/repository/view/2599/

Clare, Mariana C A, et al. Assessing erosion and flood risk in the coastal zone through the application of the multilevel Monte Carlo method. (2020). arXiv preprint, URL: https://eartharxiv.org/repository/view/1956/

Talks available online

Deltares Software Days - Delft3D and XBeach User Day, 13th November 2019: Using Multilevel Monte Carlo Methods to assess erosion/flood risk in the coastal zone Presentation slides

Mathematics of Planet Earth Webinars, 5th June 2020: Using adjoint methods to assess uncertainty in hydro-morphodynamic models Youtube video

Published articles for general audiences

How renewable are renewables really?: about clean energy's material requirements, the effect on the energy transition and possible solutions. Co-authored with Adriaan Hilbers.

Recycling plastic bags won't stop climate change: about how the two separate issues of plastic waste and climate change are often confused. Based on a recording of the BBC Radio 4 programme "Any questions?" which I attended in the House of Commons.

Les gilets jaunes: about the protest movement in France which started due to a fuel tax, and the implications these protests have for future climate policy. Co-authored with Rozi Harsanyi.

Publications

Journals

Clare MCA, Wallwork JG, Kramer SC, et al., 2022, Multi-scale hydro-morphodynamic modelling using mesh movement methods, Gem: International Journal on Geomathematics, Vol:13, ISSN:1869-2672

Clare MCA, Leijnse TWB, McCall RT, et al., 2022, Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding, Natural Hazards and Earth System Sciences, Vol:22, ISSN:1561-8633, Pages:2491-2515

Clare MCA, Piggott MD, Cotter CJ, 2022, Assessing erosion and flood risk in the coastal zone through the application of multilevel Monte Carlo methods, Coastal Engineering, Vol:174, ISSN:0378-3839

Clare MCA, Kramer SC, Cotter CJ, et al., 2022, Calibration, inversion and sensitivity analysis for hydro-morphodynamic models through the application of adjoint methods, Computers and Geosciences, Vol:163, ISSN:0098-3004, Pages:1-13

Clare MCA, Jamil O, Morcrette CJ, 2021, Combining distributionā€based neural networks to predict weather forecast probabilities, Quarterly Journal of the Royal Meteorological Society, Vol:147, ISSN:0035-9009, Pages:4337-4357

More Publications