A causal inference approach based on information theoretic measures can help determine the direct and combined influences of factors such as solar cycle, forecast lead time, and latitudinal separation on forecast mean absolute error (MAE). Additionally, understanding the dependencies and uncertainties in forecast accuracy is crucial. To enhance forecast availability, limited interpolation can be implemented for short data gaps, increasing the fraction of valid input data. Issues such as short-term variability, occasional anomalous values, and frequent data gaps need to be addressed to produce high-quality forecasts. However, there are concerns regarding the quality and continuity of the data available in Near-Real-Time (NRT) from spacecraft like DSCOVR. Computational models have been developed to forecast hazardous intervals, but they rely on post-processed solar wind data from upstream of the Earth. How can we predict space weather more accurately? 5 answers Accurately predicting space weather is crucial for mitigating its impact on infrastructure and technology.
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