This project aims to force energy conversion models with climate simulations from high-resolution models. Different bias-correction methodologies will be tested and implemented for both historical and future projections arising from the PRIMAVERA EU Horizon 2020 project. The added value of the climate models’ increased resolution will also be assessed.
Department: Meteorology
Supervised by: Paula Gonzalez
With higher penetration of renewable energies and the effort to decarbonize power production over Europe there is a strong interest in the objective characterization of energy variables and the impacts of climate variability and change. At the typical resolution of IPCC-style climate simulations (~100km ), the models are unable to represent some of the smallscale processes that are likely to impact energy variables (e.g., topographic effects, local circulations, etc). Precise regional studies of energy variables often rely on the implementation of intermediate downscaling methodologies to climate variables before the conversion to energy. This project aims to assess whether the use of high-resolution climate simulations such as the ones developed for the PRIMAVERA EU Horizon2020 project can by-pass these downscaling stages and, through apropriate bias-correction methodologies, capture the main features of European wind power and energy demand. Additionally, the PRIMAVERA simulations allow to evaluate the added value of the higher resolution of the climate model. The student will use model data to force energy models: wind power, energy demand and potentially solar power) and will implement different bias-correction methodologies to optimize the skill of the modelled energy variables. The impact of the increased resolution of the PRIMAVERA simulations will also be assessed.
The student will be provided with sample code to read model output data and to run the energy conversion models . They will have to combine them into a conversion system that implements the different biascorrection methodologies to be tested. This stage will take around 2 weeks. Post-processing code will also have to be developed to evaluate the performance of the methodologies when compared to reanalysis (i.e., to assess the systems’ ability to represent certain features of the ‘observed’ energy variables). A staged approach will be implemented to allow for the application to as many models and resolutions as possible given the time constraints. The results will be summarized through a set of figures and/or diagrams (e.g., flow charts for the different methods, maps displaying skill metrics, etc). This stage will take 3 to 4 weeks. The student will finally develop a written description of the methodologies and the results. This stage will take around 1 week.
- Experience with a programming language such as python, MATLAB or R - some knowledge of statistics will be beneficial
- The student will gain experience working in an interdisciplinary area - They will have the opportunity to attend and potentially present at Energy- Meteorology group meetings (weekly) - The project will provide enhanced experience in programming and managing large datasets - The student will gain experience working on a linux environment - If the results allow it, the student might have the opportunity to contribute towards a publication or conference presentation
Department of Meteorology, computer lab
As a reference: 9am to 5pm , 1 hour lunch break
Monday 15 June 2020 - Friday 07 August 2020
The deadline to apply for this project is Monday 11th May at 5pm. Students should submit their CV and Cover Letter to p.gonzalez@reading.ac.uk. Successful candidates will then be invited to interview.