Search for: All records

Several natural phenomena, such as floods, earth-quakes, volcanic eruptions, or extreme space weather events often come with severity indexes. While these indexes, whether linear or logarithmic are vital, data-driven predictive models for these events rather use a fixed threshold. In this paper, we explore encoding this ordinality to enhance the performance of data-driven models, with specific application in solar flare forecasting. The prediction of solar flares is commonly approached as a binary forecasting problem, categorizing events as either Flare (FL) or No-Flare (NF) based on a chosen threshold (e.g., >C-class, > M-class, or >X-class). However, this binary formulation overlooks the inherent ordinality between the sub-classes within each binary class (FL and NF). In this paper, we propose a novel loss function aimed at optimizing the binary flare prediction problem by embedding the intrinsic ordinal flare characteristics into the binary cross-entropy (BCE) loss function. This modification is intended to provide the model with better guidance based on the ordinal characteristics of the data and improve the overall performance of the models. For our experiments, we employ a ResNet34-based model with transfer learning to predict 2:M-class flares by utilizing the shape-based features of magnetograms of active region (AR) patches spanning from -90° to +90°of solar longitude as our input data. We use a composite skill score (CSS) as our evaluation metric, which is calculated as the geometric mean of the True Skill Score (TSS) and the Heidke Skill Score (HSS) to rank and compare our models' performance. The primary contributions of this work are as follows: (i) We introduce a novel approach to encode ordinality into a binary loss function showing an application to solar flare prediction, (ii) We enhance solar flare forecasting by enabling flare predictions for each AR across the entire solar disk, without any longitudinal restrictions, and evaluate and compare performance. (iii) Our candidate model, optimized with the proposed loss function, shows an improvement of (~17%, (~14%, and (~13% for AR patches within ±30°, ±60°, and ±90° of solar longitude, respectively in terms of CSS, when compared with standard BCE. Additionally, we demonstrate the ability to issue flare forecasts for ARs in near-limb regions (regions between ±60° to ±90°) with a CSS=0.34 (TSS=0.50 and HSS=0.23), expanding the scope of AR-based models for solar flare prediction. This advances the reliability of solar flare forecasts, leading to more effective prediction capabilities. 

Dataset Description This dataset consists of processed Line-of-Sight (LoS) magnetogram images of Active Regions (ARs) from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The images are derived from the Space-Weather HMI Active Region Patches (SHARP) data product definitive series and cover the period from May 2010 to 2018, sampled hourly. Dataset Contents: Processed Magnetogram Images: Each image represents a cropped and standardized view of an AR patch, extracted and adjusted from the original magnetograms. These images have been filtered and normalized to a size of 512×512 pixels. Processing Steps: Cropping: Magnetograms are cropped using bitmaps that define the region of interest within the AR patches. Regions smaller than 70 pixels in width are excluded. Flux Adjustment: Magnetic flux values are capped at ±256 G, with values within ±25 G set to 0 to minimize noise. Standardization: Patches are resized to 512×512 pixels using zero-padding for smaller patches or a 512×512 kernel to select regions with the maximum total unsigned flux (USFLUX) for larger patches. Normalization: Final images are scaled to fit within the range of 0-255. Data Dictionary: harp_N1_N2: These tar files contains folders where the AR patches with harp number N1 to N2 are included. complete_hourly_dataset.csv: This includes the list of hourly sampled magnetograms along with their associated goes flare class, assuming a 24 hour forecast horizon. augmentations: Five different augmentations of AR patches corresponding to GOES flare classes greater than C, assuming a 24 hour forecast horizon are listed as 5 different tar files. Look for: horizontal flip, vertical_flip, add noise, polarity change, and gaussian blur. 

Magnetic polarity inversion lines (PILs) in solar active regions are key to triggering flares and eruptions. Recently, engineered PIL features have been used for predicting solar eruptions. Derived from the original PIL dataset, using line-of-sight (LoS) magnetograms provided by the Solar Dynamics Observatory's (SDO) Helioseismic and Magnetic Imager (HMI) Active Region Patches (HARPs), we provide a publicly available comprehensive dataset in a supervised format, where each instance includes a raster of Polarity Inversion Lines (PILs), one of the polarity convex hull, and a multivariate time-series of properties related to PILs. Using SDO-GOES integrated flares historical data covering May 2010 to January 2019, we have assigned each of the instances their corresponding class of flare, FQ, C, M or X. By integrating these diverse data modalities, our approach aims to improve the accuracy of solar flare predictions. Initial findings suggest that the multimodal approach can uncover new patterns and relationships, potentially leading to breakthroughs in predictive accuracy and more effective mitigation strategies against the impacts of solar activities.