Uncertainty estimation for environmental multimodel predictions: The BLUECAT approach and software

Abstract

An extension of the BLUECAT approach and software for uncertainty assessment of environmental predictions is presented, allowing the application to multimodel outputs. BLUECAT operates by transforming a point prediction provided by deterministic models to a corresponding stochastic formulation, thereby allowing the estimation of a bias corrected expected value along with confidence limits. In this paper we also propose to use BLUECAT for model selection in the context of multimodel predictions, by using a measure of uncertainty as selection criterion. We emphasise here the value of BLUECAT for gaining an improved understanding of the underlying environmental systems and multimodel combination. Two examples of applications are presented, highlighting the benefits attainable through uncertainty driven integration of several prediction models. These case studies can be reproduced through the BLUECAT software, that is available in the public domain along with help facilities and instructions.