Open Access
Issue |
J. Space Weather Space Clim.
Volume 9, 2019
|
|
---|---|---|
Article Number | A17 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/swsc/2019016 | |
Published online | 17 May 2019 |
- Barnes G, Leka KD, Schrijver CJ, Colak T, Qahwaji R, et al. 2016. A comparison of flare forecasting methods. I. Results from the “all-clear” workshop. Astrophys J 829: 89. DOI: 10.3847/0004-637X/829/2/89. [Google Scholar]
- Bloomfield DS, Higgins PA, James McAteer RT, Gallagher P. 2012. Toward reliable benchmarking of solar flare forecasting methods. Astrophys J 747: L41. DOI: 10.1088/2041-8205/747/L41. [Google Scholar]
- Bobra MG, Couvidat S. 2015. Solar flare prediction using SDO/HMI vector magnetic field data with a machine-learning algorithm. Astrophys J 798: 135. DOI: 10.1088/0004-637X/798/2/135. [Google Scholar]
- Bröcker J, Smith LA. 2007. Increasing the reliability of reliability diagrams. Weather Forecast 22: 651. [CrossRef] [Google Scholar]
- Crown MD. 2012. Validation of the NOAA Space Weather Prediction Center’s solar flare forecasting look-up table and forecaster-issued probabilities. Space Weather 10: S06006. DOI: 10.1029/2011SW000760. [CrossRef] [Google Scholar]
- Devos A, Verbeeck C, Robbrecht E. 2014. Verification of space weather forecasting at the Regional Warning Center in Belgium. J Space Weather Space Clim 4: A29. DOI: 10.1051/swsc/2014025. [CrossRef] [EDP Sciences] [Google Scholar]
- Falconer D, Barghouty AF, Khazanov I, Moore R. 2011. A tool for empirical forecasting of major flares, coronal mass ejections, and solar particle events from a proxy of active-region free magnetic energy. Space Weather 9: S04003. DOI: 10.1029/2009SW000537. [NASA ADS] [CrossRef] [Google Scholar]
- Gneiting T, Balabdaoui F, Raftery AE. 2007. Probabilistic forecasts, calibration and sharpness. J R Statist Soc B 69: 243. [CrossRef] [Google Scholar]
- Huang X, Wang H, Xu L, Liu J, Li R, Dai X. 2018. Deep learning based solar flare forecasting model. I. Results for line-of-sight magnetograms. Astrophys J 856: 7. DOI: 10.3847/1538-4357/aaae00. [Google Scholar]
- Jolliffe IT, Stephenson DB. 2012. Forecast verification: A practitioner’s guide in atmospheric science, 2nd edn. John Wiley and Sons Ltd., Chichester, UK. [Google Scholar]
- Kubo Y, Den M, Ishii M. 2017. Verification of operational solar flare forecast: Case of Regional Warning Center Japan. J Space Weather Space Clim 7: A20. DOI: 10.1051/swsc/2017018. [CrossRef] [Google Scholar]
- Leka KD, Barnes G, Wagner E. 2018. The NWRA classification infrastructure: Description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS). J Space Weather Space Clim 8: A25. DOI: 10.1051/swsc/2018004. [Google Scholar]
- McCloskey AE, Gallagher PT, Bloomfield DS. 2016. Flaring rates and the evolution of sunspot group mcintosh classifications. Sol Phys 291: 1711. DOI: 10.1007/s11207-016-0933-y. [CrossRef] [Google Scholar]
- Muranushi T, Shibayama T, Muranushi YH, Isobe H, Nemoto S, Komazaki K, Shibata K. 2015. UFCORIN: A fully automated predictor of solar flares in GOES X-ray flux. Space Weather 13: 778. DOI: 10.1002/2015SW001257. [CrossRef] [Google Scholar]
- Murphy AH. 1977. The value of climatological, categorical and probabilistic forecasts in the cost-loss ratio situation. Mon Weather Rev 105: 803. [CrossRef] [Google Scholar]
- Murphy AH. 1991. Forecast verification: Its complexity and dimensionality. Mon Weather Rev 119: 1590. [CrossRef] [Google Scholar]
- Murphy AH. 1993. What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather Forecast 8: 281. [CrossRef] [Google Scholar]
- Murphy AH, Winkler RL. 1987. A general framework for forecast verification. Mon Weather Rev 115: 1330. [CrossRef] [Google Scholar]
- Murray SA, Bingham S, Sharpe M, Jackson DR. 2017. Flare forecasting at the Met Office Space Weather Operations Centre. Space Weather 15: 577. DOI: 10.1002/2016SW001579. [CrossRef] [Google Scholar]
- Nishizuka N, Sugiura K, Kubo Y, Den M, Watari S, Ishii M. 2017. Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. Astrophys J 835: 156. DOI: 10.3847/1538-4357/835/2/156. [CrossRef] [Google Scholar]
- Nishizuka N, Sugiura K, Kubo Y, Den M, Ishii M. 2018. Deep Flare Net (DeFN) model for solar flare prediction. Astrophys J 858: 113. DOI: 10.3847/1538-4357/aab9a7. [NASA ADS] [CrossRef] [Google Scholar]
- Primo C, Ferro CAT, Jolliffe IT, Stephenson DB. 2009. Calibration of probabilistic forecasts of binary events. Mon Weather Rev 137: 1142. DOI: 10.1175/2008MWR2579.1. [CrossRef] [Google Scholar]
- Richardson DS. 2000. Skill and relative economic value of the ECMWF ensemble prediction system. Q J R Meteorol Soc 126: 649. [CrossRef] [Google Scholar]
- Steward G, Lobzin V, Cairns IH, Li B, Neudegg D. 2017. Automatic recognition of complex magnetic regions on the Sun in SDO magnetogram images and prediction of flares: Techniques and results for the revised flare prediction program Flarecast. Space Weather 15: 1151. DOI: 10.1002/2017SW001595. [CrossRef] [Google Scholar]
- Wheatland MS. 2005. A statistical solar flare forecast method. Space Weather 3: S07003. DOI: 10.1029/2004SW000131. [NASA ADS] [CrossRef] [Google Scholar]
- Zhu Y, Toth Z, Wobus R, Richardson D, Mylne K. 2002. The economic value of ensemble-based weather forecasting. Bull Am Meteorol Soc 83: 73. [CrossRef] [Google Scholar]
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