Related papers: Forecasting Solar Flares Using Magnetogram-based P…

A hemispheric preference in the dominant sign of magnetic helicity has been observed in numerous features in the solar atmosphere: i.e., left-handed/right-handed helicity in the northern/southern hemisphere. The relative importance of…

The Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) filtergrams, taken at six wavelengths around the Fe I 6173.3 {\AA} line, contain information about the line-of-sight velocity over a range of heights in the solar…

We present a Python tool to generate a standard dataset from solar images that allows for user-defined selection criteria and a range of pre-processing steps. Our Python tool works with all image products from both the Solar and…

Solar Energetic Particle (SEP) events and their major subclass, Solar Proton Events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity.…

Accurate cyclone forecasting is essential for minimizing loss of life, infrastructure damage, and economic disruption. Traditional numerical weather prediction models, though effective, are computationally intensive and prone to error due…

Using neural networks as a prediction method, we attempt to demonstrate that forecasting of the Sun's sunspot time series can be extended to the spatial-temporal case. We employ this machine learning methodology to forecast not only in time…

While machine learning (ML) post-processing of convection-allowing model (CAM) output for severe weather hazards (large hail, damaging winds, and/or tornadoes) has shown promise for very short lead times (0-3 hours), its application to…

We present a concept for a machine-learning classification of hard X-ray (HXR) emissions from solar flares observed by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), identifying flares that are either occulted by the…

We discuss a prediction of the solar activity on a short time-scale applying the method based on a combination of a nonlinear mean-field dynamo model and the artificial neural network. The artificial neural network which serves as a…

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model…

Sunspots or active regions (ARs) with a delta-magnetic configuration are known to be associated with strong eruptions such as flares and mass ejections. This article investigates the relationship between delta-ARs and flares over the course…

This article discusses statistical models for solar flare interval distribution in individual active regions. We analyzed solar flare data in 55 active regions that are listed in the GOES soft X-ray flare catalog. We discuss some problems…

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately,…

Investigating the solar magnetic field is crucial to understand the physical processes in the solar interior as well as their effects on the interplanetary environment. We introduce a novel method to predict the evolution of the solar…

A test of a new Bayesian approach to solar flare prediction (Wheatland 2004a) is presented. The approach uses the past history of flaring together with phenomenological rules of flare statistics to make a prediction for the probability of…

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting using robust methods. We demonstrate a proof of concept of using a data-driven supervised…

We have recently developed the capability to make solar vector (Stokes IQUV) magnetograms using the infrared line of MgI at 12.32 microns. On 24 April 2001, we obtained a vector magnetic map of solar active region NOAA 9433, fortuitously…

A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we describe…

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural…

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather. Their prediction depends on various factors including the source solar eruptions such as flares and coronal mass ejections (CMEs). The Geostationary…