The EU-funded S2S4E project has launched a new weather forecasting tool for renewable energy production industries.
With floods, heatwaves, droughts and storms becoming fiercer and more frequent, there’s a dire need to find tools that help us adapt to the changes happening to the Earth’s climate.
The S2S4E project’s tool is one such example that could help emergency planners and weather-dependent industries prepare for extreme weather events.
The Decision Support Tool (DST) is a novel online climate service that combines sub-seasonal to seasonal climate predictions with renewable energy production and electricity demand.
The goal of the project is to offer more reliable and usable forecasts for weather-dependent hydropower, solar and wind production.
According to its creators, DST generates tailored climate information through energy indicators derived from climate variables such as wind speed, solar radiation, precipitation, temperature and sea-level pressure.
These indicators provide information on expected variability in hydropower, solar and wind generation, as well as electricity demand in the future.
Weekly forecasts from sub-seasonal climate predictions are provided for the following 4 calendar weeks as weekly averages, whereas monthly forecasts for seasonal climate predictions are issued for the next 3 months as monthly averages.
DST could also prove useful for emergency planning, insurance and farming – with the wine sector being a clear example, notes Barcelona Supercomputing Center knowledge transfer expert Isadora Jiménez:
“Knowing well in advance if a season is going to be particularly dry or wet can make a difference for wine producers when they decide how to trim their grape vines in order to protect them from the rain or the sun, and when making decisions on how much fertilizers to use.”
According to the project’s authors, the DST will be free to use until late 2020.
During this time, S2S4E (Sub-seasonal to Seasonal climate forecasting for Energy) will analyze the tool’s impact on a weekly basis to ensure the quality of its forecasts.
Image and content: IgorZh, Shutterstock/CORDIS