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]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.