Uncertainty Quantification and Model Validation in Space Weather Modeling

Space Weather modeling has become a vital part of space physics. It sees use beyond research such as operational use and forecasting. There are countless models in use for all kinds of regimes, from the solar wind to the magnetosphere to the ionosphere or even the thermosphere. These models have their specific use cases and operate under some necessary underlying assumptions. These assumptions either in input or output of the model lead to uncertainties and need to be validated.

With the rise of computing power, statistical methods for forecasting, such as ensemble modeling to physics-based models and machine learning, are starting to become common-place for space weather. Model outputs' sensitivity to its inputs can be studied through Global Sensitivy Analysis. These methods lend themselves to making uncertainty quantification (UQ) easy, but have yet to be widely adopted in this field. It is widely agreed upon that model validation and studying uncertainties strengthen the future of space weather modeling and forecasting. This Research Topic is meant to be a special hub for the aforementioned methods for this growing field in space weather. We plan to combine the fields of uncertainty quantification and space weather as well as model validation.

In this Research Topic, we will be exploring uncertainty quantification with all kinds of models (e.g: empirical, physics-based, machine learning), new or old. The uncertainties can be in the inputs for example with data-driven models or the outputs. We are looking for, but are not limited to, research on:

- Global Sensitivity Analysis in space weather models
- Space weather model validation
- Ensemble modeling in space weather.

We welcome all kinds of articles such as original research papers, reviews, and perspectives article. The authors are invited to look at the details of each article type here.

Keywords: space weather, modeling, ensemble, uncertainty, sensitivity analysis, validation, solar wind, ionosphere, thermosphere, magnetosphere, reduced order, machine learning, statistical

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.