Data Assimilation (DA) is the systematic combination of observations and model information to provide the best possible model evolution and its uncertainty estimate. DA is able to do this by taking into account the uncertainties present in observations, forecasts and the model itself, in order to minimise errors within the model simulations. It is an essential tool for weather forecasting, providing optimal starting points that reduce the impacts of the 'butterfly effect' in forecasts.

Data assimilation has long been under-developed in space weather applications. However, this is currently changing and experiments using data assimilation within the space weather community have been performed with encouraging results. Space Weather Data Assimilation (SWDA) is currently comprised of five main areas, which are:

1) Coronal data assimilation, where DA is used to assimilate observations of the Sun’s corona to improve initial conditions for solar wind modelling and understanding of the Corona
2) Solar wind data assimilation, which is the assimilation of in-situ and remote observations of the solar wind to improve forecasts/understanding of the solar wind;
3) The assimilation of observations during extreme solar events, to aid the forecasting of potentially damaging solar wind events, such as Coronal Mass Ejections or Solar flares;
4) Ionospheric data assimilation, which aims to infer properties of the Ionosphere by assimilating observations of the solar wind and geomagnetic field, important for improvements in GPS;
5) Solar-/Geo-magnetic field data assimilation, which uses observations of magnetic field (e.g from magnetograms) to infer properties/improve dynamo models of the Sun/Earth.

These fields of data assimilation all face different problems that are not encountered within meteorological data assimilation, such as the inclusion of magnetic fields, supersonic solar wind conditions and poorly understood generation mechanisms. Whilst each field aims to solve different problems, they are intricately linked, however, there is currently very little collaboration between these fields.