The Barcelona Supercomputing Center has produced the first global dataset containing tall tower wind information to boost the use of these data and help deliver trustful seasonal climate predictions to the energy sector.
The risk of blackouts is usually highest on very cold and sunny winter days when power demand for heating soars. However, this will change as the share of renewables in the electricity system increases, a new study by the S2S4E project shows.
In a recent study, S2S4E researchers examined atmospheric patterns, known as teleconnections, that affect annual variations in the climate. Better understanding these phenomena can help improve seasonal forecasts.
Luz Calvo, UX & Visualization Researcher at Barcelona Supercomputing Center, analyses how User Experience techniques allow to display complex forecasting data in an understandable way.
S2S4E encourages children to learn about renewable energy through the Clean Energy Drawing Challenge launched during the COVID-19 pandemic.
The European Green Deal calls for a carbon-neutral Europe by 2050. This is only possible with a rapid transition to renewable energy, and giving up our fossil fuel dependence. The young generation will have to lead this transition in the near future. Teaching children about renewable energy is thus more urgent than ever.
Several researchers have been involved in improving the forecasts within the S2S4E Decision Support Tool. This work has included combining the forecasts with observational data and analysing past forecasts and how the weather impacts power production and demand.
“Climate change is expected to lead to more extreme weather and to more frequent extreme weather events, and it is important to take this into account when making predictions about the weather to come over the coming weeks and months,” explains climatologist Irene Cionni.
Temperature and wind speed multi-model predictions based on four forecasting systems are now available in the S2S4E Decision Support Tool for 3 months ahead (seasonal forecasts).
The operational tool of the S2S4E project, the Decision Support Tool (DST), offers seasonal forecasts for the next 3 months aimed at the renewable energy sector. Currently, the SEAS5 ECMWF seasonal system is used to generate these predictions.
A cold spell in France in January 2017 led to a jump in electricity demand and power prices. Sub-seasonal forecasts could have helped French power producers better prepare for this situation, research by S2S4E shows.
The quality of seasonal forecasts for flow in rivers in Europe is generally good, but varies depending on local conditions, a recent study by the Swedish Meteorological and Hydrological Institute (SMHI) has found.
The study was done as part of the EU-funded S2S4E project, and was published in scientific journal Water Resources Research in May.
Ahead of eight selected extreme weather events, the S2S4E team figured out how more reliable sub-seasonal to seasonal (S2S) forecasts can prevent economic losses for the energy sector, and improve both climate modellers' and traders' decision-making strategies.