Data Handling and Assimilation for Solar Event Prediction

TL;DR

This paper reviews the process of creating benchmark datasets and developing algorithms for data-driven solar event prediction, emphasizing their importance for improving forecast accuracy and robustness.

Contribution

It provides a comprehensive overview of benchmark dataset generation and prediction algorithm development for solar activity forecasting.

Findings

Benchmark datasets are crucial for advancing solar event prediction methods.

Effective use of data and algorithm selection enhances forecast robustness.

Review highlights key steps in dataset creation and algorithm development.

Abstract

The prediction of solar flares, eruptions, and high energy particle storms is of great societal importance. The data mining approach to forecasting has been shown to be very promising. Benchmark datasets are a key element in the further development of data-driven forecasting. With one or more benchmark data sets established, judicious use of both the data themselves and the selection of prediction algorithms is key to developing a high quality and robust method for the prediction of geo-effective solar activity. We review here briefly the process of generating benchmark datasets and developing prediction algorithms.