On the considerations of using near real time data for space weather hazard forecasting

Smith, A.W.; Forsyth, C.; Rae, I.J.; Garton, T.M.; Jackman, C.M.; Bakrania, M.; Shore, R.M. ORCID: https://orcid.org/0000-0002-8386-1425; Richardson, G.S.; Beggan, C.D.; Heyns, M.J.; Eastwood, J.P.; Thomson, A.W.P.; Johnson, J.M.. 2022 On the considerations of using near real time data for space weather hazard forecasting. Space Weather, 20 (7), e2022SW003098. 20, pp. 10.1029/2022SW003098

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Abstract/Summary

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 ACE and 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, e.g. for gaps of five minutes or less, which could increase the fraction of valid input data considerably.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1029/2022SW003098
ISSN:	1542-7390
Additional Keywords:	Geomagnetically Induced Currents, Forecasting, Near Real Time, Operational, Research to Operations
Date made live:	08 Jul 2022 08:44 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532869

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