Classifying and bounding geomagnetic storms based on the SYM-H and ASY-H indices

Collado-Villaverde, Armando; Muñoz, Pablo; Cid, Consuelo

doi:10.1007/s11069-023-06241-1

Cited by 5 publications

(7 citation statements)

References 38 publications

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“…The vertical colored lines mark the separation of the different intensities categories as classified in Collado‐Villaverde et al. (2023a). The green shaded histogram depicts the number of samples in each bin, on a logarithmic scale.…”

Section: Case Study and Discussionmentioning

confidence: 99%

“…In order to systematically partition the available geomagnetic storms into distinct sets for our analysis, we performed a multistep approach. Initially, we categorized the storms into various categories based on the classification framework established in Collado-Villaverde et al (2023a), thereby ensuring that each storm was grouped into four categories according to the index peak: superintense, intense, moderate and low. Subsequently, we further considered the solar cycle during which each storm occurred, recognizing the potential impact of solar cycle variations on geomagnetic storm behavior.…”

Section: Storms Setsmentioning

confidence: 99%

“…Particularly, the number assigned to the storm, the start and end dates of the storm (the selected time is from 00:00 of the start date until the start of the end date at 00:00), the SYM‐H peak, the category of the storm according to the SYM‐H peak following Collado‐Villaverde et al. (2023a) criteria, the solar cycle of the storm and whether the disturbance was caused by a CME or HSS, according to the CME catalog in Larrodera and Temmer (2023).…”

Section: Storms Setsmentioning

confidence: 99%

“…The vertical colored lines mark the separation of the different intensities categories as classified in Collado‐Villaverde et al. (2023a).…”

Section: Binned Forecasting Error Metricmentioning

confidence: 99%

“…In an effort to overcome this limitation and expand the models' capacity to generalize across a broader spectrum of geomagnetic conditions, we have incorporated additional geomagnetic storms identified in the study conducted in Collado‐Villaverde et al. (2023a). By doing so, we have substantially increased the number of storms available for model training and validation, enhancing the models' learning capabilities.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Evaluating Geomagnetic Indices Forecasting Models

Collado‐Villaverde,

Muñoz,

Cid

2024

Space Weather

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The use of Deep Learning models to forecast geomagnetic storms is achieving great results. However, the evaluation of these models is mainly supported on generic regression metrics (such as the Root Mean Squared Error or the Coefficient of Determination), which are not able to properly capture the specific particularities of geomagnetic storms forecasting. Particularly, they do not provide insights during the high activity periods. To overcome this issue, we introduce the Binned Forecasting Error to provide a more accurate assessment of models' performance across the different intensity levels of a geomagnetic storm. This metric facilitates a robust comparison of different forecasting models, presenting a true representation of a model's predictive capabilities while being resilient to different storms duration. In this direction, for enabling fair comparison among models, it is important to standardize the sets of geomagnetic storms for model training, validation and testing. To do this, we have started from the current sets used in the literature for forecasting the SYM‐H, enriching them with newer storms not considered previously, focusing not only on disturbances caused by Coronal Mass Ejections but also addressing High‐Speed Streams. To operationalize the evaluation framework, a comparative study is conducted between a baseline neural network model and a persistence model, showcasing the effectiveness of the new metric in evaluating forecasting performance during intense geomagnetic storms. Finally, we propose the use of preliminary measurements from ACE to evaluate the model performance in settings closer to an operational real‐time scenario, where the forecasting models are expected to operate.

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Section: Case Study and Discussionmentioning

confidence: 99%

Section: Storms Setsmentioning

confidence: 99%