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

Smith, A. W., Forsyth, C., Rae, Jonathan, Garton, T. M., Jackman, C. M., Bakrania, M., Shore, R. M., Richardson, G. S., Beggan, C. D., Heyns, M. J., Eastwood, J. P., Thomson, A. W. P. and Johnson, J. M. (2022) On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting. Space Weather, 20 (7). e2022SW003098. ISSN 1542-7390

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Official URL: https://doi.org/10.1029/2022sw003098


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.

Item Type: Article
Additional Information: Funding information: We acknowledge and thank the ACE and DSCOVR teams for the solar wind data and NASA GSFC's Space Physics Data Facility's CDAWeb service for data availability. The authors would like to thank Howard Singer for helpful discussions and suggestions. A. W. Smith, C. Forsyth and I. J. Rae were supported by NERC grants NE/P017150/1 and NE/V002724/1. C. Forsyth was also supported by the NERC Independent Research Fellowship NE/N014480/1. J. P. Eastwood and M. J. Heyns were supported by NERC grant NE/V003070/1. C. M. Jackman was supported by the Science Foundation Ireland Grant 18/FRL/6199. A. W. P. Thomson, C. D. Beggan and G. S. Richardson are supported by NERC award NE/V002694/1 (SWIMMR Activities in Ground Effects, SAGE). The analysis in this paper was performed using python, including the TensorFlow (Abadi et al., 2015), pandas (McKinney, 2010), numpy (Van Der Walt et al., 2011), scikit-learn (Pedregosa et al., 2011), scipy (Virtanen et al., 2020) and matplotlib (Hunter, 2007) libraries.
Uncontrolled Keywords: forecasting, geomagnetically induced currents, near real time, operational, research to operations
Subjects: F800 Physical and Terrestrial Geographical and Environmental Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 14 Jul 2022 10:51
Last Modified: 02 Sep 2022 08:30
URI: https://nrl.northumbria.ac.uk/id/eprint/49549

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