Multi-model Cross Pollination in Time

TL;DR

This paper introduces a generalized multi-model forecast system using cross pollination in time, improving predictive skill by leveraging the strengths of different models, demonstrated on a Lorenz system.

Contribution

It generalizes the cross pollination in time approach for multi-model forecasting and demonstrates its effectiveness on a nonlinear Lorenz system.

Findings

Cross pollination enhances forecast skill significantly.

The method outperforms individual models in the Lorenz system.

Potential applications in weather and economic forecasting.

Abstract

Predictive skill of complex models is often not uniform in model-state space; in weather forecasting models, for example, the skill of the model can be greater in populated regions of interest than 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 generalised to take advantage of instances where some models produce systematically more accurate forecast of some components of the model-state. This generalisation is stated and then successfully demonstrated in a moderate ~40 dimensional nonlinear dynamical system suggested by Lorenz. In this demonstration four imperfect models, each with similar global forecast skill, are used. Future applications in weather forecasting and in economic forecasting are discussed. The demonstration establishes that cross pollinating forecast trajectories to enrich the collection of simulations upon which the forecast is built can yield a new forecast system with significantly more skills than the original multi-model forecast system.