Project description DEENESFRITPL Improved forecast of heatwave frequency and intensity in Southern Europe on S2S timescales To meet the need for reliable weather predictions beyond the weekly timescale, the scientific community developed subseasonal to seasonal (S2S) forecast models. However, these models still show limitations concerning summers in Europe. Southern Europe has received much less attention even though it is vulnerable to high-impact heatwaves and sensitive to climate change. The EU-funded ISSUL project will improve the S2S prediction of heatwave frequency and intensity as well as associated weather patterns over Southern Europe. The project will use a combination of two machine learning algorithms: an optimisation algorithm to identify the best set of predictors; and a neural network to provide non-linear predictions. This is the first time such an approach has been attempted for these timescales. Show the project objective Hide the project objective Objective In the recent years, the continual improvements of weather forecasting models and the sustained need for reliable weather predictions beyond the weekly timescale resulted in the development of subseasonal to seasonal (S2S) forecast models and an intense research work from the scientific community. Despite the large number of research studies, S2S forecast models still show a limited skill in summer over Europe. In addition, southern Europe, has received much less attention, even though it is highly vulnerable to high-impact summer heatwaves, and very sensitive to climate change. The aim of this project, ISSUL, is to better understand and improve the S2S prediction of heatwave frequency and intensity and their associated weather patterns over southern Europe. To do this, a combination of two machine learning algorithms, an optimisation algorithm, to identify the best set of predictors, and a neural network, to provide non-linear predictions will be used. This approach has never been attempted before for these timescales. It is expected to perform better than standard S2S forecast models in predicting heatwave frequency and intensity and associated weather patterns and to bring larger improvements compared with traditional statistical forecasts that do not identify all the predictors and cannot represent non-linear complex interactions.ISSUL is divided into three parts. The first part aims at identifying the best set of predictors, using the optimisation algorithm, at evaluating it and understanding it is related to heatwaves over southern Europe via a dynamical analysis. The second part aims a predicting the frequency and intensity of heatwaves and associated weather patterns using a neural network. The third part aims at evaluating the performance of this combined machine learning approach compared with standard S2S forecasting model. Fields of science natural sciencesearth and related environmental sciencesatmospheric sciencesmeteorologynatural sciencescomputer and information sciencesartificial intelligencemachine learningnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2020 - Individual Fellowships Call for proposal H2020-MSCA-IF-2020 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS Net EU contribution € 160 932,48 Address CALLE SERRANO 117 28006 Madrid Spain See on map Region Comunidad de Madrid Comunidad de Madrid Madrid Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 160 932,48