# The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

## TL;DR

This paper reviews the current and future role of machine learning in space weather forecasting, emphasizing probabilistic approaches and hybrid physics-ML models, while highlighting open challenges for the community.

## Contribution

It provides a comprehensive overview of ML applications in space weather, introduces the community to ML concepts, and identifies key open challenges for future research.

## Key findings

- ML has been used for forecasting geomagnetic indices, solar flares, and CME propagation.
- Probabilistic forecasting and uncertainty assessment are crucial for reliable predictions.
- Integration of physics-based models with ML (gray-box) approaches is a promising direction.

## Abstract

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.