new TRends in EXtremes, prediction and validation – T-REX

Forecast is a major task of statistics in many domains of application. It often takes the form of a probabilistic forecast where the so-called predictive istribution represents the uncertainty of the future outcome given the information available today. Of particular interest is the distributional forecast of rare and extreme events, for instance environmental hazards such as flooding or heat waves, that can have major socio-economic consequences but for which current prediction are often inaccurate and unsatisfactory.

In meteorology and weather forecast, the current numerical weather prediction (NWP) models are rather skillful for non-extreme weather events but often fail to provide accurate predictions on extreme weather events. One objective is to derive new statistical post-processing methods that are tailored to the output of existing NWP models in order to improve the forecast of extreme weather. Tree-based method such as generalized random forest for extremes will be developed. Data sets required to develop and validate the new proposed methods will be provided by the Centre National de Recherches Météorologiques (CNRM).

In energy and electricity consumption forecast, balancing production and demand is a major concern and forecasting the demand and especially its peaks is crucial. Electricit\'e de France (EDF) has developed a strong expertise where one key tool is the possibility to combine different models to improve prediction. Aggregation of experts is another main direction of the T-REX project with an emphasis on specialized/sleeping experts that focus on extreme regimes. EDF will provide relevant data sets for electricity consumption forecast.

Extreme value theory (EVT) provides a theoretical framework for risk assessment and mathematically justified estimation of rare event probabilities. The T-REX project is rooted in EVT and will bridge different research fields to improve probability forecasts of extremes from complex and high dimensional systems.