AIR: Weather information for the smart economy

The proposed work will advance scholarship on the statistical post-processing of ensemble weather forecasts. Innovations are promised in several key areas: efficient specification of the forecast problem and training dataset, the possibility of automating predictor selection, the automation of the choice between statistical models, the integration of quality norms derived from the particulars of a given decision problem, the creation of an expert system to define the underlying node structure of the multi-period decision value calculation, and an automated algorithm for analyzing the meteorological and predict and outcome training data to specify the parameters of each decision node. The overall project goal is to develop and code these algorithms, and to prepare a full technology transfer plan for the commercialization of the system.

The proposed work will lay the basis for a wide range of applications in which weather forecasts are fed directly into automated decision systems. The approach envisioned will allow the value of fully probabilistic forecasts to be exploited in scientific, data-driven approaches to risk management across a wide array of weather-sensitive sectors, including energy, insurance, tourism, transportation, aviation, construction, and public safety.