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Data driven approaches to improving space weather forecasts for the power industry

Haines, C. A. (2022) Data driven approaches to improving space weather forecasts for the power industry. PhD thesis, University of Reading

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To link to this item DOI: 10.48683/1926.00113606


Space weather impacts technological infrastructure in space and on Earth. This thesis focuses on impacts on power systems through geomagnetic activity. During heightened geomagnetic activity, currents can be induced in power lines and cause degradation of transformers. Therefore, it is useful to forecast the severity of geomagnetic storms and the resulting geomagnetically induced currents (GICs) so that mitigating action can be taken. This thesis faces this challenge through three bodies of work. The first two bodies focus on forecasting parameters of geomagnetic storms and the third provides a statistical downscaling scheme to aid forecasting of GICs. In the first body of work, the duration of geomagnetic storms is investigated. A statistical relationship is established between storm intensity and duration. A skilful and reliable forecast of storm duration (given storm peak intensity) is made, using log-normal distributions. In the second body of work, two pattern-matching approaches are taken to forecast the occurrence and intensity of geomagnetic storms in geomagnetic index data. The support vector machine and analogue ensemble are implemented for an historical dataset and evaluated using several metrics. It is found that both methods are skilful with respect to climatology and the best method is dependent on the needs of the end-user. The third body of work provides a downscaling scheme to improve the output of operational magnetospheric models such that a more realistic geoelectric field can be forecast. Using the analogue ensemble approach, a proof-of-concept study is presented which relates variability on a 1-hour timescale to a 1-minute timescale. Implemented using a perfect prognostic approach, the downscaling scheme enables a skilful estimate of geoelectric field with respect to the benchmark of no downscaling.

Item Type:Thesis (PhD)
Thesis Supervisor:Owens, M.
Thesis/Report Department:Department of Meteorology
Identification Number/DOI:
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:113606
Date on Title Page:January 2021


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