English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Studying solar wind conditions is central to forecasting impact of spaceweather on Earth. Under the assumption that the structure of this wind isconstant in time and corotates with the Sun, solar wind and thereby spaceweather forecasts have been made quite effectively. Such corotation forecastsare well studied with decades of observations from STEREO and near-Earthspacecrafts. Forecast accuracy depends upon the latitudinal separation (oroffset $\Delta \theta$) between source and spacecraft, forecast lead time($\Delta t$) and the solar cycle via the sunspot number (SSN). The precisedependencies factoring in uncertain- ties however, are a mixture of influencesfrom each of these factors. And for high precision forecasts, it is importantto understand what drives the forecast accuracy and its uncertainty. Here wepresent a causal inference approach based on information theoretic measures todo this. Our framework can compute not only the direct (linear and non-linear)dependencies of the forecast mean absolute error (MAE) on SSN, $\Delta t$ and$\Delta t$, but also how these individual variables combine to enhance ordiminish the MAE. We provide an initial assessment of this with potential ofaiding data assimilation in the future.

Causal Analysis of Influence of the Solar Cycle and Latitudinal Solar-Wind Structure on Corotation Forecasts