323 research outputs found
Ensemble Forecasting of Major Solar Flares: Methods for Combining Models
One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5\% and 15\%) than any of the members alone. Moreover, over 90\% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20\% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs
Ensemble Forecasting of Major Solar Flares: Methods for Combining Models
One essential component of operational space weather forecasting is the
prediction of solar flares. With a multitude of flare forecasting methods now
available online it is still unclear which of these methods performs best, and
none are substantially better than climatological forecasts. Space weather
researchers are increasingly looking towards methods used by the terrestrial
weather community to improve current forecasting techniques. Ensemble
forecasting has been used in numerical weather prediction for many years as a
way to combine different predictions in order to obtain a more accurate result.
Here we construct ensemble forecasts for major solar flares by linearly
combining the full-disk probabilistic forecasts from a group of operational
forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts
from each method are weighted by a factor that accounts for the method's
ability to predict previous events, and several performance metrics (both
probabilistic and categorical) are considered. It is found that most ensembles
achieve a better skill metric (between 5\% and 15\%) than any of the members
alone. Moreover, over 90\% of ensembles perform better (as measured by forecast
attributes) than a simple equal-weights average. Finally, ensemble
uncertainties are highly dependent on the internal metric being optimized and
they are estimated to be less than 20\% for probabilities greater than 0.2.
This simple multi-model, linear ensemble technique can provide operational
space weather centres with the basis for constructing a versatile ensemble
forecasting system -- an improved starting point to their forecasts that can be
tailored to different end-user needs.Comment: Accepted for publication in the Journal of Space Weather and Space
Climat
Photospheric Shear Flows in Solar Active Regions and Their Relation to Flare Occurrence
Solar active regions (ARs) that produce major flares typically exhibit strong
plasma shear flows around photospheric magnetic polarity inversion lines
(MPILs). It is therefore important to quantitatively measure such photospheric
shear flows in ARs for a better understanding of their relation to flare
occurrence. Photospheric flow fields were determined by applying the
Differential Affine Velocity Estimator for Vector Magnetograms (DAVE4VM) method
to a large data set of 2,548 co-aligned pairs of AR vector magnetograms with
12-min separation over the period 2012-2016. From each AR flow-field map, three
shear-flow parameters were derived corresponding to the mean (), maximum
(S_max) and integral (S_sum) shear-flow speeds along strong-gradient,
strong-field MPIL segments. We calculated flaring rates within 24 hr as a
function of each shear-flow parameter, and also investigated the relation
between the parameters and the waiting time ({\tau}) until the next major flare
(class M1.0 or above) after the parameter observation. In general, it is found
that the larger S_sum an AR has, the more likely it is for the AR to produce
flares within 24 hr. It is also found that among ARs which produce major
flares, if one has a larger value of S_sum then {\tau} generally gets shorter.
These results suggest that large ARs with widespread and/or strong shear flows
along MPILs tend to not only be more flare productive, but also produce major
flares within 24 hr or less.Comment: 19 pages, 8 figures, accepted for publication in Solar Physic
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