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Solar activity, ranging from the background solar wind to energetic coronalmass ejections (CMEs), is the main driver of the conditions in theinterplanetary space and in the terrestrial space environment, known as spaceweather. A better understanding of the Sun-Earth connection carries enormouspotential to mitigate negative space weather effects with economic and socialbenefits. Effective space weather forecasting relies on data and models. Inthis paper, we discuss some of the most used space weather models, and proposesuitable locations for data gathering with space weather purposes. We report onthe application of \textit{Representer analysis (RA)} and \textit{Domain ofInfluence (DOI) analysis} to three models simulating different stages of theSun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solarwind - magnetosphere interaction, and the PLUTO model, used to simulate CMEpropagation in interplanetary space. Our analysis is promising for spaceweather purposes for several reasons. First, we obtain quantitative informationabout the most useful locations of observation points, such as solar windmonitors. For example, we find that the absolute values of the DOI areextremely low in the magnetospheric plasma sheet. Since knowledge of thatparticular sub-system is crucial for space weather, enhanced monitoring of theregion would be most beneficial. Second, we are able to better characterize themodels. Although the current analysis focuses on spatial rather than temporalcorrelations, we find that time-independent models are less useful for DataAssimilation activities than time-dependent models. Third, we take the firststeps towards the ambitious goal of identifying the most relevant heliosphericparameters for modelling CME propagation in the heliosphere, their arrivaltime, and their geoeffectiveness at Earth.

Domain of Influence analysis: implications for Data Assimilation in space weather forecasting