MAP - MAchine learning based Plasma density model In this project we will demonstrate how machine learning tools can be used to produce a real-time global empirical model of the near-Earth plasma density based on a variety of measurements.

PAGER - Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation Stakeholders - such as satellite operators and manufacturers - require space weather predictions to have long lead times and confidence levels and that they should be tailored to particular engineering systems. These requirements will be addressed in the new PAGER project.

SWAMI project aims to enhance the understanding of space weather processes and their impact on atmospheric density by: developing improved atmosphere and thermosphere models; new geomagnetic activity indices; and improving the forecast capabilities of the existing and new geomagnetic activity indices.

The goal of this project is to develop a more quantitatively accurate model of plasma density in the plasmasphere and to quantify its dynamic dependence on solar wind and geomagnetic conditions.

The goal of PROGRESS is to develop an accurate and reliable forecast system for the occurrence and severity of space weather events. Within project PROGRESS we aim to develop an European tool to forecast the solar wind parameters just upstream of the Earth's magnetosphere.