Advanced space-weather forecasting relies on the ability to accurately predict near-Earth solar wind conditions. For this purpose, physics-based, global numerical models of the solar wind are initialized with photospheric magnetic field and coronagraph observations, but no further observation constraints are imposed between the upper corona and Earth orbit. Data assimilation (DA) of the available in situ solar wind observations into the models could potentially provide additional constraints, improving solar wind reconstructions, and forecasts. However, in order to effectively combine the model and observations, it is necessary to quantify the error introduced by assuming point measurements are representative of the model state. In particular, the range of heliographic latitudes over which in situ solar wind speed measurements are representative is of primary importance, but particularly difficult to assess from observations alone. In this study we use 40+ years of observation-driven solar wind model results to assess two related properties: the latitudinal representivity error introduced by assuming the solar wind speed measured at a given latitude is the same as that at the heliographic equator, and the range of latitudes over which a solar wind measurement should influence the model state, referred to as the observational localisation. These values are quantified for future use in solar wind DA schemes as a function of solar cycle phase, measurement latitude, and error tolerance. In general, we find that in situ solar wind speed measurements near the ecliptic plane at solar minimum are extremely localised, being similar over only 1° or 2° of latitude. In the uniform polar fast wind above approximately 40° latitude at solar minimum, the latitudinal representivity error drops. At solar maximum, the increased variability of the solar wind speed at high latitudes means that the latitudinal representivity error increases at the poles, though becomes greater in the ecliptic, as long as moderate speed errors can be tolerated. The heliospheric magnetic field and solar wind density and temperature show very similar behaviour.
Forecasting the Ambient Solar Wind with Numerical Models. II. An Adaptive Prediction System for Specifying Solar Wind Speed near the Sun
