Consequence forecasting: A rational framework for predicting the consequences of approaching storms

As our climate continues to respond anthropogenic forcing, the magnitude and frequency of individual weather events intensity extremes associated with these, remains highly uncertain. This is a particular concern for infrastructure networks, as increasing storm-related damage these vital lifelines has significant consequences communities. Effective first response hence becoming an increasingly important part management systems. Here, we propose novel rational framework ‘consequence forecasting’ that enables probabilistic, pre-event decision-making responders effectively target resources prior extreme event thus reduce societal consequences. Our method unique in it minimises model bias by using same numerical prediction both fault attribution prediction. can predict failure rates are within 50% true value more than considered, some 24 h advance, therefore demonstrating be resilience reducing reinstatement times.