On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

Space weather represents a severe threat to ground‐based infrastructure, satellites and communications. Accurately forecasting when such threats are likely (e.g., when we may see large induced currents) will help to mitigate the societal and financial costs. In recent years computational models have been created that can forecast hazardous intervals, however they generally use post‐processed “science” solar wind data from upstream of the Earth. In this work we investigate the quality and continuity of the data that are available in Near‐Real‐Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft. In general, the data available in NRT corresponds well with post‐processed data, however there are three main areas of concern: greater short‐term variability in the NRT data, occasional anomalous values and frequent data gaps. Some space weather models are able to compensate for these issues if they are also present in the data used to fit (or train) the model, while others will require extra checks to be implemented in order to produce high quality forecasts. We find that the DSCOVR NRT data are generally more continuous, though they have been available for small fraction of a solar cycle and therefore DSCOVR has experienced a limited range of solar wind conditions. We find that short gaps are the most common, and are most frequently found in the plasma data. To maximize forecast availability we suggest the implementation of limited interpolation if possible, for example, for gaps of 5 min or less, which could increase the fraction of valid input data considerably. Plain Language Summary The variable plasma conditions in near‐Earth space can create hazards for modern society. These include the generation of anomalous ground currents that pose a threat to the operation of infrastructure such as high voltage power grids. Forecasts of intervals when we are likely to be at risk generally use solar wind measurements gathered by satellites from upstream of the Earth. Various computational models have shown skill in predicting risk intervals; however, they are generally created using scientific quality data which are not available in near‐real‐time (NRT). To prepare for transitioning such models to operational use we assess the similarities and differences between the scientific quality and NRT data. We assess the properties and frequency of data gaps in the NRT data, to build an understanding of how to maximize the tim