Former group members
Dr David Livings (Post-doctoral research scientist)
Subseasonal prediction for energy applications. Data assimilation. Probability and meteorology.
Dr Emma Suckling (Post-doctoral research scientist)
Calibrating and evaluating subseasonal to decadal forecasts for energy and other applications. Predictability and variability in weather and climate. Quantifying uncertainty. Extracting decision-relevant information from weather/climate forecasts.
Dr Dirk Cannon (Post-doctoral research scientist)
Climatological behaviour of extreme wind power generation events and their predictability up to 10 days ahead; focusing on prolonged periods of widespread low or high generation and rapid swings in generation. Additional interests include combined extreme wind and temperature events, impacts of climate variability, wind power modelling using numerical weather prediction models and the dynamics/physics of atmospheric flow over hills and mountains.
Dr Kostas Philippopoulos (Post-doctoral research scientist)
Understanding the meteorological drivers of extreme UK wind power events using self-organising maps.
Dr Francisco Santos-Alamillos (Post-doctoral research scientist)
Optimizing wind and solar generation portfolios at continental scale.
Hazel Thornton (Met Office employee, PhD student)
Climate and energy demand, wind power, extremes, monthly to seasonal predictability. PhD title: European climate and energy balancing: variability, mechanisms, predictability and impacts.
Alan Halford (EngD student)
Impact of weather on infrastructure, particularly on the UK telecommunication network. Additional interests include decision-making with forecast information. PhD title: Forecasting weather impacts on the UK telecommunication network
Kieran Lynch (PhD student)
Understanding and modelling the impacts of weather/climate on the energy system, with a particular focus on the influence of renewable generation on the price dynamics in the wholesale day ahead and forward power markets. Additional interests include predictability of these impacts and optimal decision making under uncertainty.