Multi-model cross-pollination in time

PHYSICA D-NONLINEAR PHENOMENA

卷:353

Article

Du, Hailiang^1,2  Smith, Leonard A.^1,2,3 

[1] Univ Chicago, Ctr Robust Decis Making Climate & Energy Policy, Chicago, IL 60637 USA

[2] London Sch Econ, Ctr Anal Time Series, London WC2A 2AE, England

[3] Pembroke Coll, Oxford, England

关键词: Multi-model ensemble;    Data assimilation;    Cross-pollination;    Structural model error;   

DOI  :  10.1016/j.physd.2017.06.001

来源: Elsevier

【 摘 要 】

The predictive skill of complex models is rarely uniform in model-state space; in weather forecasting models, for example, the skill of the model can be greater in the regions of most interest to a particular operational agency than it is in remote regions of the globe. Given a collection of models, a multi-model forecast system using the cross-pollination in time approach can be generalized to take advantage of instances where some models produce forecasts with more information regarding specific components of the model-state than other models, systematically. This generalization is stated and then successfully demonstrated in a moderate (similar to 40) dimensional nonlinear dynamical system, suggested by Lorenz, using four imperfect models with similar global forecast skill. Applications to weather forecasting and in economic forecasting are discussed. Given that the relative importance of different phenomena in shaping the weather changes in latitude, changes in attitude among forecast centers in terms of the resources assigned to each phenomena are to be expected. The demonstration establishes that cross-pollinating elements of forecast trajectories enriches the collection of simulations upon which the forecast is built, and given the same collection of models can yield a new forecast system with significantly more skill than the original forecast system. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free