Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices
Abstract The flux of > 2 MeV electrons at geosynchronous orbit is used by space weather forecasters as a key indicator of enhanced risk of damage to spacecraft in low, medium, or geosynchronous Earth orbits. We present a methodology that uses the amount of time a single input data set (solar wind...
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| Format: | Article |
| Language: | English |
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Wiley
2020-08-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2019SW002416 |
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| author | C. Forsyth C. E. J. Watt M. K. Mooney I. J. Rae S. D. Walton R. B. Horne |
| author_facet | C. Forsyth C. E. J. Watt M. K. Mooney I. J. Rae S. D. Walton R. B. Horne |
| author_sort | C. Forsyth |
| collection | DOAJ |
| description | Abstract The flux of > 2 MeV electrons at geosynchronous orbit is used by space weather forecasters as a key indicator of enhanced risk of damage to spacecraft in low, medium, or geosynchronous Earth orbits. We present a methodology that uses the amount of time a single input data set (solar wind data or geomagnetic indices) exceeds a given threshold to produce deterministic and probabilistic forecasts of the >2 MeV flux at GEO exceeding 1,000 or 10,000 cm−2 s−1 sr−1 within up to 10 days. By comparing our forecasts with measured fluxes from GOES 15 between 2014 and 2016, we determine the optimum forecast thresholds for deterministic and probabilistic forecasts by maximizing the receiver‐operating characteristic (ROC) and Brier skill scores, respectively. The training data set gives peak ROC scores of 0.71 to 0.87 and peak Brier skill scores of −0.03 to 0.32. Forecasts from AL give the highest skill scores for forecasts of up to 6 days. AL, solar wind pressure, or SYM‐H give the highest skill scores over 7–10 days. Hit rates range over 56–89% with false alarm rates of 11–53%. Applied to 2012, 2013, and 2017, our best forecasts have hit rates of 56–83% and false alarm rates of 10–20%. Further tuning of the forecasts may improve these. Our hit rates are comparable to those from operational fluence forecasts, that incorporate fluence measurements, but our false alarm rates are higher. This proof‐of‐concept shows that the geosynchronous electron flux can be forecast with a degree of success without incorporating a persistence element into the forecasts. |
| format | Article |
| id | doaj-art-5ac44a11defd4cc9a86ab3c4bf9f4c5b |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-5ac44a11defd4cc9a86ab3c4bf9f4c5b2025-01-14T16:27:11ZengWileySpace Weather1542-73902020-08-01188n/an/a10.1029/2019SW002416Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic IndicesC. Forsyth0C. E. J. Watt1M. K. Mooney2I. J. Rae3S. D. Walton4R. B. Horne5Department of Space and Climate Physics Mullard Space Science Laboratory, University College London London UKDepartment of Meteorology University of Reading Reading UKDepartment of Space and Climate Physics Mullard Space Science Laboratory, University College London London UKDepartment of Space and Climate Physics Mullard Space Science Laboratory, University College London London UKDepartment of Space and Climate Physics Mullard Space Science Laboratory, University College London London UKBritish Antarctic Survey Cambridge UKAbstract The flux of > 2 MeV electrons at geosynchronous orbit is used by space weather forecasters as a key indicator of enhanced risk of damage to spacecraft in low, medium, or geosynchronous Earth orbits. We present a methodology that uses the amount of time a single input data set (solar wind data or geomagnetic indices) exceeds a given threshold to produce deterministic and probabilistic forecasts of the >2 MeV flux at GEO exceeding 1,000 or 10,000 cm−2 s−1 sr−1 within up to 10 days. By comparing our forecasts with measured fluxes from GOES 15 between 2014 and 2016, we determine the optimum forecast thresholds for deterministic and probabilistic forecasts by maximizing the receiver‐operating characteristic (ROC) and Brier skill scores, respectively. The training data set gives peak ROC scores of 0.71 to 0.87 and peak Brier skill scores of −0.03 to 0.32. Forecasts from AL give the highest skill scores for forecasts of up to 6 days. AL, solar wind pressure, or SYM‐H give the highest skill scores over 7–10 days. Hit rates range over 56–89% with false alarm rates of 11–53%. Applied to 2012, 2013, and 2017, our best forecasts have hit rates of 56–83% and false alarm rates of 10–20%. Further tuning of the forecasts may improve these. Our hit rates are comparable to those from operational fluence forecasts, that incorporate fluence measurements, but our false alarm rates are higher. This proof‐of‐concept shows that the geosynchronous electron flux can be forecast with a degree of success without incorporating a persistence element into the forecasts.https://doi.org/10.1029/2019SW002416radiation beltgeosynchronous orbitspace radiationforecastsprobabilisticdeterministic |
| spellingShingle | C. Forsyth C. E. J. Watt M. K. Mooney I. J. Rae S. D. Walton R. B. Horne Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices Space Weather radiation belt geosynchronous orbit space radiation forecasts probabilistic deterministic |
| title | Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices |
| title_full | Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices |
| title_fullStr | Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices |
| title_full_unstemmed | Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices |
| title_short | Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices |
| title_sort | forecasting goes 15 2 mev electron fluxes from solar wind data and geomagnetic indices |
| topic | radiation belt geosynchronous orbit space radiation forecasts probabilistic deterministic |
| url | https://doi.org/10.1029/2019SW002416 |
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