The importance of ensemble techniques for operational space weather forecasting

TL;DR

This paper discusses the adoption and benefits of ensemble techniques in operational space weather forecasting, emphasizing their role in improving accuracy and uncertainty estimation, and advocates for further integration into operational use.

Contribution

It provides a detailed overview of ensemble methods, their applications in space weather, and highlights successful examples and future directions for operational implementation.

Findings

Ensemble methods improve space weather forecast accuracy.

They provide valuable uncertainty estimates.

Successful integration into operational systems is ongoing.

Abstract

The space weather community has begun to use frontier methods such as data assimilation, machine learning, and ensemble modeling to advance current operational forecasting efforts. This was highlighted by a multi-disciplinary session at the 2017 American Geophysical Union Meeting, 'Frontier Solar-Terrestrial Science Enabled by the Combination of Data-Driven Techniques and Physics-Based Understanding', with considerable discussion surrounding ensemble techniques. Here ensemble methods are described in detail; using a set of predictions to improve on a single-model output, for example taking a simple average of multiple models, or using more complex techniques for data assimilation. They have been used extensively in fields such as numerical weather prediction and data science, for both improving model accuracy and providing a measure of model uncertainty. Researchers in the space weather community have found them to be similarly useful, and some examples of success stories are highlighted in this commentary. Future developments are also encouraged to transition these basic research efforts to operational forecasting as well as providing prediction errors to aid end-user understanding.