Flare forecasting at the Met Office Space Weather Operations Centre

Murray, Sophie A.; Bingham, Suzy; Sharpe, Michael; Jackson, D. R.

doi:10.1002/2016sw001579

Cited by 68 publications

(75 citation statements)

References 42 publications

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“…The data set includes 303 positive samples and 5000 randomly selected negative samples, therein 30% of the data is used as testing data. The forecasting models introduced in Murray et al (2017), Muranushi et al (2015), and this work provide a rolling forecast. However, Bobra & Couvidat (2015) limits the time for flaring samples, which is exactly 24 hr prior to the peak time of solar flares.…”

Section: Comparison With Other Forecasting Modelsmentioning

confidence: 99%

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Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Huang

Wang

et al. 2018

ApJ

179

130

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Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

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Section: Comparison With Other Forecasting Modelsmentioning

confidence: 99%

“…Taking 24 hr M flare forecasting as an example, the forecasting model introduced by Murray et al (2017) is carried out every 6 hr to output the 24 hr full-disk forecasts for the M class solar flares. The testing data is from 2015 to 2016, including 141 positive samples and 1489 negative samples.…”

Section: Comparison With Other Forecasting Modelsmentioning

confidence: 99%

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Huang

Wang

et al. 2018

ApJ

179

130

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“…Baseline probabilistic models for solar flare prediction have already met this threshold allowing clear demonstration of progress with the development of new models, as well as showing a clear picture of the many aspects of model performance in an operational environment (Murray et al, ). This then naturally brings us back to the paths of R2O and O2R, as discussed in section .…”

Section: Challenges In Numerical Modelingmentioning

confidence: 99%

Challenges and Opportunities in Magnetospheric Space Weather Prediction

Morley

2020

Space Weather

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Space Weather is the study of the dynamics of the coupled Solar‐Terrestrial environment, as these dynamics impact technological systems and human activity. This paper reviews a selection of the advances, challenges, and new opportunities for magnetospheric space weather, and while the focus is on specific phenomena, many other aspects of space weather will have similar challenges and needs. As a scientific field with direct applications, the field of space weather is partly driven by imperatives from both policy and operations. We provide an introduction to some of the context in which the field exists and discuss how this might shape future developments and norms within the space weather enterprise. We briefly examine benchmarking, as a policy and operationally driven activity, as it provides immediate societal relevance and an opportunity to stretch scientific understanding. As numerical space weather prediction now becomes routine, and exascale computing is in the near future, we identify challenges relating to computational expense and big data, capturing and accounting for uncertainties, and specification of boundary conditions. Here, as with the observations supporting numerical space weather prediction, the key challenge lies in extending the lead time of predictions. We also discuss the role of data, particularly in regard to model validation and empirical modeling. Due to the growing societal impact of space weather, we also examine the relationships between space weather and its terrestrial counterpart and look at the importance of continuous evaluation, monitoring progress in predictive capability, and communication with researchers, forecasters, and end users.

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“…The different types of perturbations (X-ray flares, SEPs, CMEs, coronal holes) find their correspondence in rather separated modeling communities (Zhao & Dryer, 2014;Luhmann et al, 2015;Barnes et al, 2016;Reiss et al, 2016;Cranmer et al, 2017;Murray et al, 2017). Further splitting of modeling activity occurs for regions closer to Earth (magnetosphere, ionosphere/thermosphere, Earth atmosphere and surface) because of traditional scientific domains, specific customer needs, as well as the physical processes involved (Lathuillère et al, 2002).…”

Section: Modeling Aspectsmentioning

confidence: 99%

Climate, weather, space weather: model development in an operational context

Folini

2018

J. Space Weather Space Clim.

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-Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must À within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.

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Flare forecasting at the Met Office Space Weather Operations Centre

Cited by 68 publications

References 42 publications

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Challenges and Opportunities in Magnetospheric Space Weather Prediction

Climate, weather, space weather: model development in an operational context

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