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- ItemThe flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era(Les Ulis : EDP Sciences, 2021) Georgoulis, Manolis K.; Bloomfield, D. Shaun; Piana, Michele; Massone, Anna Maria; Soldati, Marco; Gallagher, Peter T.; Pariat, Etienne; Vilmer, Nicole; Buchlin, Eric; Baudin, Frederic; Csillaghy, Andre; Sathiapal, Hanna; Jackson, David R.; Alingery, Pablo; Benvenuto, Federico; Campi, Cristina; Florios, Konstantinos; Gontikakis, Constantinos; Guennou, Chloe; Guerra, Jordan A.; Kontogiannis, Ioannis; Latorre, Vittorio; Murray, Sophie A.; Park, Sung-Hong; Stachelski, Samuel von; Torbica, Aleksandar; Vischi, Dario; Worsfold, MarkThe European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic.
- ItemUsing Bayesian Networks to Investigate the Influence of Subseasonal Arctic Variability on Midlatitude North Atlantic Circulation(Boston, Mass. [u.a.] : AMS, 2021) Harwood, Nathanael; Hall, Richard; Di Capua, Giorgia; Russell, Andrew; Tucker, AllanRecent enhanced warming and sea ice depletion in the Arctic have been put forward as potential drivers of severe weather in the midlatitudes. Evidence of a link between Arctic warming and midlatitude atmospheric circulation is growing, but the role of Arctic processes relative to other drivers remains unknown. Arctic–midlatitude connections in the North Atlantic region are particularly complex but important due to the frequent occurrence of severe winters in recent decades. Here, dynamic Bayesian networks with hidden variables are introduced to the field to assess their suitability for teleconnection analyses. Climate networks are constructed to analyze North Atlantic circulation variability at 5-day to monthly time scales during the winter months of the years 1981–2018. The inclusion of a number of Arctic, midlatitude, and tropical variables allows for an investigation into the relative role of Arctic influence compared to internal atmospheric variability and other remote drivers. A robust covariability between regions of amplified Arctic warming and two definitions of midlatitude circulation is found to occur entirely within winter at submonthly time scales. Hidden variables incorporated in networks represent two distinct modes of stratospheric polar vortex variability, capturing a periodic shift between average conditions and slower anomalous flow. The influence of the Barents–Kara Seas region on the North Atlantic Oscillation is found to be the strongest link at 5- and 10-day averages, while the stratospheric polar vortex strongly influences jet variability on monthly time scales.
- ItemMachine learning-based calibration of the GOCE satellite platform magnetometers(Heidelberg : Springer, 2022) Styp-Rekowski, Kevin; Michaelis, Ingo; Stolle, Claudia; Baerenzung, Julien; Korte, Monika; Kao, OdejAdditional datasets from space-based observations of the Earth’s magnetic field are of high value to space physics and geomagnetism. The use of platform magnetometers from non-dedicated satellites has recently successfully provided additional spatial and temporal coverage of the magnetic field. The Gravity and steady-state Ocean Circulation Explorer (GOCE) mission was launched in March 2009 and ended in November 2013 with the purpose of measuring the Earth’s gravity field. It also carried three platform magnetometers onboard. Careful calibration of the platform magnetometers can remove artificial disturbances caused by other satellite payload systems, improving the quality of the measurements. In this work, a machine learning-based approach is presented that uses neural networks to achieve a calibration that can incorporate a variety of collected information about the satellite system. The evaluation has shown that the approach is able to significantly reduce the calibration residual with a mean absolute residual of about 6.47nT for low- and mid-latitudes. In addition, the calibrated platform magnetometer data can be used for reconstructing the lithospheric field, due to the low altitude of the mission, and also observing other magnetic phenomena such as geomagnetic storms. Furthermore, the inclusion of the calibrated platform magnetometer data also allows improvement of geomagnetic field models. The calibrated dataset is published alongside this work. Graphical Abstract: [Figure not available: see fulltext.].