Probabilistic subseasonal weather forecasts for the energy & agricultural sectors

The proposed research addresses Climate Adaptation and Mitigation: Probability Forecasts of Business Impact Variables from CFS2 Ensembles. Climate Forecast Applications Network (CFAN) develops innovative weather and climate forecast tools that support decision-oriented solutions for our clients. The focus of this proposal is on business-relevant subseasonal forecasts for the energy and agricultural sectors, including applications to renewable energy. Analysis of the reforecast library against observation and analyses enables predictability assessment of business-relevant variables by region, initial and target month, and enables predictability assessment and recent forecast errors to correct for model bias error to improve the shape of the ensemble distribution. A multi-model prediction system using the CFSv2 and ECMWF forecasts will be developed to exploit the advantages of each model using ensemble clustering techniques. A strategy for assessing confidence of each forecast is based on a comprehensive forecast evaluation, predictability assessment, and ensemble characteristics. A web-based dashboard system is designed to display and deliver the forecast information in a flexible manner to aid decision support integration. Commercial applications of the forecast products will be targeted at the energy and agricultural sectors in the U.S. and Asia.