Space Weather Data Research Articles

Identification and extraction of type II and III radio bursts based on YOLOv7

Solar radio bursts (SRBs) are extreme space weather events characterized by intense solar radio emissions that are closely related to solar flares. They represent signatures of the same underlying processes that are responsible for well-documented solar phenomena such as sunspots, solar flares, and coronal mass ejections (CMEs). The study of SRBs holds significant importance as it provides a means to monitor and predict solar flares and CMEs, enhancing our ability to forecast potential impacts on Earth’s communications and satellites. Typically, SRBs below several hundred megahertz can be categorized into five types (I–V), with type II and type III bursts being the most prevalent. This study introduces a novel approach based on the YOLOv7 model for the detection and classification of type II and type III SRBs. The proposed method effectively identifies and classifies various SRB types, achieving a mean average precision accuracy of 73.5%. A trained neural network was deployed for SRB detection in the Chashan Broadband Solar radio spectrograph at meter wavelength (CBSm) data, enabling the extraction of valuable SRB information for subsequent research. This demonstrates that even when we are dealing with extensive datasets, this method can automatically recognize outbursts and extract pertinent physical information. Although our experiments with the CBSm dataset currently rely on the daily spectrum, further advancements in CBSm backend data processing techniques are expected to enable near-real-time burst detection, which is a powerful tool for accurately assessing and analyzing SRBs, and significantly contribute to the field of space weather forecasting and protective measures. Furthermore, the applicability of this method to other stations within the Chinese Meridian Project II (e.g., Mingantu Spectral Radioheliograph and Daocheng Solar Radio Telescope) enhances the capability of space weather data fusion and model development. Therefore, this research represents a substantial contribution to the domain of space weather research, offering a valuable tool for the detection and classification of SRBs and thereby improving our ability to predict and mitigate the impacts of extreme space weather events on Earth’s technology and infrastructure.

Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning

AbstractThis study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. Our objective is to simulate monthly global PI characteristics using a multilayer 1D‐CNN model trained on 12 space weather and ionospheric parameters. In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. Our findings indicate that non‐equinox months exhibit the highest equatorial PI magnitude over the American‐African longitudinal sector, contrary to the expected higher Rayleigh‐Taylor instability growth rate during equinox months. Winter months display the most intense and widespread vertically and horizontally distributed equatorial PI patterns. We also observe double peaks across geomagnetic latitudes and longitudinally varying wavelike irregularity structures, particularly in May, August, and predominantly in September. Furthermore, north‐south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance‐related factors play a crucial role in predicting global PI variabilities. These findings emphasize the significance of these variables in controlling the strongest PI characteristics observed in the Atlantic sector, which has garnered considerable attention in PI research. The employed 1D‐CNN model demonstrates exceptional predictive capabilities, exhibiting a strong correlation of 0.98 for global PI characteristics across all months and satellites.

Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning.

Forecast verification is a crucial task for assessing the predictive power of prognostic model forecasts and it is usually implemented by checking quality-based skill scores. In this article, we propose a novel approach to realize forecast verification focusing not just on the forecast quality but rather on its value. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive nonoccurring events, and that, on the other hand, a miss of an isolated event has a worse impact than a miss of a single event, which is part of several consecutive occurrences. Relying on this idea, we introduce a notion of value-weighted skill scores giving greater importance to the value of the prediction rather than to its quality. Then, we introduce an ensemble strategy to maximize quality-based and value-weighted skill scores independently of one another. We test it on the predictions provided by deep learning methods for binary classification in the case of four applications concerned with pollution, space weather, stock price, and IoT data stream forecasting. Our experimental studies show that using the ensemble strategy for maximizing the value-weighted skill scores generally improves both the value and quality of the forecast.

Analysis of space weather and small satellite data in polar orbit using machine learning

This publication explores the dependencies between factors of space weather (geomagnetic indices Dst, Kp, and Ap, Wolf solar activity index, and solar wind parameters) and the telemetry of the small satellite Cubebel-1. The importance of maintaining the functionality of such satellites in orbit is described based on the research results of specialists in this field. A correlation analysis is presented, conducted using Python language tools. The results are presented in the form of a correlation matrix. A comparison is made between the results of this study and similar results of correlation analysis conducted with data from the satellite Siriussat-1. Suggestions for further research using the telemetry of the Cubebel-1 satellite are provided.

Performance indicator development addressing mitigation of the space weather impacts on GNSS

Within the European Space Agency's (ESA) Space Weather Service Network, the development of performance indicators for the Global Navigation Satellite System (GNSS) has been identified as essential to meet the growing needs of end users for Space Weather information in the field of navigation. This requires a targeted analysis of Space Weather-related disturbances of technical systems and services in the field of satellite-based navigation, considering the growing requirements of all the different users in this domain. The goal of the Space Weather Impact on GNSS Performance: Application Development (SWIGPAD) project is to develop GNSS performance indicators (GPI) based on Space Weather data provided by European research institutes through the ESA Space Weather Service Portal (available at https://swe.ssa.esa.int) with the aim to fulfill representative use cases derived from dedicated meeting with industry and government experts. The application that serves the GPI will provide the user with information about current and expected effects of Space Weather on positioning at their respective location. Additionally, end users in the various GNSS application domains shall be assisted with an overall numerical and graphical estimate of the positioning uncertainty resulting from ionospheric conditions and its evolution over time. The intent of this article is to present the results of the project and the capabilities of the GPI application.

Utilizing GOES-16 instruments to forecast geomagnetic storm during Solar Cycle 25

This paper focuses on the study of active region AR12891 in conjunction with geostorm from space weather data. A flare of magnitude M1.7 occurred in this active region, resulting in a geomagnetic storm of magnitude 3. By observing solar activity, x-ray flux, and radiation particle monitoring, GOES-16 instruments were used to forecast these events. A python program was used to analyze data collected from the online database of solar monitor and space weather live provided by the NOAA. Solar flare events can be studied and monitored through GOES-16 instruments to forecast geomagnetic storms. This allows less impacts towards satellite navigations and power sources if geomagnetic storm was detected early.

Abstract TP53: Geomagnetic Storms Do Not Predict The Occurrence Of LVO Stroke Clusters Necessitating Endovascular Intervention

Intro: Space weather induced geomagnetic storms have been associated with an increased risk of stroke occurrence. Data on image-proven, temporally related large vessel occlusion stroke (LVO) and geomagnetic storms are lacking. We sought to investigate this potential association to determine whether space weather forecasts can be used to predict incoming cluster events. Methods: Data from a Comprehensive Stroke Center were collected from January 1, 2017, to April 15, 2022. Space weather data were downloaded from the World Data Centre for Geomagnetism, Kyoto homepage (http://wdc.kugi.kyoto-u.ac.jp/kp/index.html#LIST) The indications for mechanical endovascular reperfusion (MER) were in accordance with the Guidelines for the Early Management of Patients With Acute Ischemic Stroke Regarding Endovascular Treatment: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Univariate binary logistic regression was performed to assess the potential associations between space weather data and LVO MER cases with 48 hour lag effect. Geomagnetic activity was defined by the National Oceanic and Atmospheric Administration’s (NOAA) Space Weather Prediction Centre (SWPC) space weather scales. Results: Of the 1931 days included in this study, there were 538 (27.86%) days with at least one LVO MER (450 (23.3%) with 1, 81 (4.19%) with 2, and 7 (0.36%) with 3. There were 1784 days without geomagnetic storm activity and 147 days with minor to severe storms. 290 MER cases occurred out 1014 days of minimal activity (28.6%) 133 MER cases out 497 days of unsettled activity (26.76%) 0.91(0.72-116), 78 MER cases out of 273 days of active activity (28.57%) 1.00(0.74-1.34), 25 MER cases out 109 days of minor storm (22.94%) 0.74(0.47-1.19) and 12 MER cases out of the 38 days of moderate to extreme storms (31.58%) 1.15(0.57-2.31) p-value 0.697. Conclusion: There was no statistically significant association with geomagnetic storms and the occurrence of large vessel stroke interventions taking into consideration a 48 hour lag effect. Comprehensive stroke center readiness is of utmost importance, but space weather forecasts are unlikely to be helpful in predicting LVO stroke cluster events.

Space weather and hypertensive crises at high Latitudes (in Yakutsk)

The space weather impact on the health status of people with cardiovascular diseases is quite significant and it has been shown by many researchers. To examine this relationship at high latitudes, we made a Yakutsk ambulance electronic database analysis. The significant coincidences of geomagnetic disturbance indicators (Kp-index - a global geophysical disturbance index) and the increased blood pressure emergency calls number have been determined during the high solar activity years. The blood pressure increase cases analysis shows that patients with primary essential arterial hypertension (AH) seek medical help in 57%. Furthermore, 7% of AH patients experienced a hypertensive crisis (HC) with an increase in systolic blood pressure (SBP) from 180 mm Hg, diastolic blood pressure (DBP) from 120 mm Hg. The medical data analysis and the space weather data (Kp-index) comparison demonstrate a significant coincidence, which became the main base for the conclusion that heliogeophysical disturbance effects on the cardiovascular system state of a person living at high latitudes.

Development of methods for analysing telemetry of small spacecraft using machine learning methods

This article explores the influence of some averaged planetary geomagnetic indices, as well as solar activity indexes (Dst, Kp, Ap and Wolf numbers) on small spacecraft systems. The global experience in the field of research is described, and various directions in which leading specialists work are analysed. Methods of data analysis using Python are also provided. The procedure of correlation between space weather data and telemetry data of small spacecraft at a single moment in time is carried out. At the end of the experiment, a conclusion is drawn on which specific indicators should be focused on. Suggestions for further research on this topic are also provided.

Application of Deep Learning to Solar and Space Weather Data

AbstractIn this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.

First Results and Analysis From ESA Next Generation Radiation Monitor Unit Onboard EDRS-C

The first two units of the ESA Next Generation Radiation Monitor (NGRM) sensor are flying onboard the European Data Relay System, Satellite-C (EDRS-C), and the Sentinel-6 Michael Freilich (S-6), providing critical information related to the space radiation environment. This work presents a first evaluation and analysis of the measurements of the unit hosted on EDRS-C during the geostationary transfer orbit (GTO) of the satellite. The evaluation studies include comprehensive comparisons with measurements from other radiation monitors and science-class experiments. NGRM datasets will become publicly available in real time from the space weather (SWE) data center through dedicated applications contributing to the monitoring of SWE and the characterization of the space radiation environment.

Observing the Development of Two Solar Flares by Monitoring AR 12882 which Produces a Kp6 Geomagnetic Storm in October 2021

In this paper, we study active region 12882 in conjunction with the level of geomagnetic storm based on space weather data. Several flares occurred in this region in October 2021, including M1.6 and C2.1. Data were also collected from the online solar monitor and space weather live database provided by NOAA and SDO/AIA. The study of the solar flare events was done by monitoring the sunspot region for the past four days to observed changes in the earth’s atmosphere. The M-class flare event made by AR 12882 produced a CME with a speed exceeding 500 km/sec which directed towards Earth and produced an intense geomagnetic storm with kp6 index.

Low-Latitude Ionospheric Responses and Coupling to the February 2014 Multiphase Geomagnetic Storm from GNSS, Magnetometers, and Space Weather Data

The ionospheric response and the associated mechanisms to geomagnetic storms are very complex, particularly during the February 2014 multiphase geomagnetic storm. In this paper, the low-latitude ionosphere responses and their coupling mechanisms, during the February 2014 multiphase geomagnetic storm, are investigated from ground-based magnetometers and global navigation satellite system (GNSS), and space weather data. The residual disturbances between the total electron content (TEC) of the International GNSS Service (IGS) global ionospheric maps (GIMs) and empirical models are used to investigate the storm-time ionospheric responses. Three clear sudden storm commencements (SSCs) on 15, 20, and 23 February are detected, and one high speed solar wind (HSSW) event on 19 February is found with the absence of classical SSC features due to a prevalent magnetospheric convection. The IRI-2012 shows insufficient performance, with no distinction between the events and overestimating approximately 20 TEC units (TECU) with respect to the actual quiet-time TEC. Furthermore, the median average of the IGS GIMs TEC during February 2014 shows enhanced values in the southern hemisphere, whereas the IRI-2012 lacks this asymmetry. Three low-latitude profiles extracted from the IGS GIM data revealed up to 20 TECU enhancements in the differential TEC. From these profiles, longer-lasting TEC enhancements are observed at the dip equator profiles than in the profiles of the equatorial ionospheric anomaly (EIA) crests. Moreover, a gradual increase in the global electron content (GEC) shows approximately 1 GEC unit of differential intensification starting from the HSSW event, while the IGS GIM profiles lack this increasing gradient, probably located at higher latitudes. The prompt penetration electric field (PPEF) and equatorial electrojet (EEJ) indices estimated from magnetometer data show strong variability after all four events, except the EEJ’s Asian sector. The low-latitude ionosphere coupling is mainly driven by the variable PPEF, DDEF (disturbance dynamo electric fields), and Joule heating. The auroral electrojet causing eastward PPEF may control the EIA expansion in the Asian sector through the dynamo mechanism, which is also reflected in the solar-quiet current intensity variability.

The Early Results and Validation of FORMOSAT‐7/COSMIC‐2 Space Weather Products: Global Ionospheric Specification and Ne‐Aided Abel Electron Density Profile

AbstractThe FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission was launched on 25 June 2019 with six low‐Earth‐orbit satellites and can provide thousands of daily radio occultation (RO) soundings in the low‐latitude and midlatitude regions. This study shows the preliminary results of space weather data products based on F7/C2 RO sounding: global ionospheric specification (GIS) electron density and Ne‐aided Abel and Abel electron density profiles. GIS is the ionospheric data assimilation product based on the Gauss‐Markov Kalman filter, assimilating the ground‐based Global Positioning System and space‐based F7/C2 RO slant total electron content, providing continuous global three‐dimensional electron density distribution. The Ne‐aided Abel inversion implements four‐dimensional climatological electron density constructed from previous RO observations, which has the advantage of providing altitudinal information on the horizontal gradient to reduce the retrieval error due to the spherical symmetry assumption of the Abel inversion. The comparisons show that climatological structures are consistent with each other above 300 km altitude. Both the Abel electron density profiles and GIS detect electron density variations during a minor geomagnetic storm that occurred within the study period. Moreover, GIS is further capable of reconstructing the variation of equatorial ionization anomaly crests. Detailed validations of all the three products are carried out using manually scaled digisonde NmF2 (hmF2), yielding correlation coefficients of 0.885 (0.885) for both Abel inversions and 0.903 (0.862) for GIS. The results show that both GIS and Ne‐aided Abel are reliable products in studying ionosphere climatology, with the additional advantage of GIS for space weather research and day‐to‐day variations.

Multivariate time series dataset for space weather data analytics

We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either “horizontally” – by adding more prediction-specific parameters, or “vertically” – by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future.

Current state and perspectives of Space Weather science in Italy

Italian teams have been involved many times in Space Weather observational campaigns from space and from the ground, contributing in the advancing of our knowledge on the properties and evolution of the related phenomena. Numerous Space Weather forecasting and now-casting modeling efforts have resulted in a remarkable add-on to the overall progress in the field, at both national and international level. The Italian Space Agency has participated several times in space missions with science objectives related to Space Weather; indeed, an important field for the Italian scientific and industrial communities interested in Heliophysics and Space Weather, is the development of new instrumentation for future space missions. In this paper, we present a brief state-of-the-art in Space Weather science in Italy and we discuss some ideas on a long-term plan for the support of future scientific research in the related disciplines. In the context of the current roadmap, the Italian Space Agency aims to assess the possibility to develop a national scientific Space Weather data centre to encourage synergies between different science teams with interest in the field and to motivate innovation and new mission concept development. Alongside with the proposed recommendations, we also discuss how the Italian expertise could complement international efforts in a wider international Space Weather context.

The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility

We examine the impact of solar and space weather events on the Financial Select Sector SPDR Fund (XLF) price index volatility, spanning the period 1998-2018. Comparing MAPE and RMSFE forecasting criteria, for the ARIMA-GARCH model, augmented with exogenous variables, we find that solar and space weather variables contribute statistically significant information with regard to volatility forecasting.

A Distributed File-Based Storage System for Improving High Availability of Space Weather Data

In space science research, the Indonesia National Institute of Aeronautics and Space (LAPAN) is concerned with the development of a system that provides actual information and predictions called the Space Weather Information and Forecast Services (SWIFtS). SWIFtS is supported by a data storage system that serves data, implementing a centralized storage model. This has some problems that impact to researchers as the primary users. The single point of failure and also the delay in data updating on the server is a significant issue when researchers need the latest data, but the server is unable to provide it. To overcome these problems, we proposed a new system that utilized a decentralized model for storing data, leveraging the InterPlanetary File System (IPFS) file system. Our proposed method focused on the automated background process, and its scheme would increase the data availability and throughput by spreading it into nodes through a peer-to-peer connection. Moreover, we also included system monitoring for real-time data flow from each node and information of node status that combines active and passive approaches. For system evaluation, the experiment was performed to determine the performance of the proposed system compared to the existing system by calculating mean replication time and the mean throughput of a node. As expected, performance evaluations showed that our proposed scheme had faster file replication time and supported high throughput.

An Analysis on the Formation of Solar Radio Burst Type II, III and IV by Using e-CALLISTO, IUGONET and Space Weather Data

The study of the radio solar burst data on 30th March 2018 and 31st March 2018 are essential for the understanding of type II, type III and type IV nature. Through this paper, the relationship of these three types of solar radio burst can be studied. The necessary data were collected from the free online websites which includes the Space Weather Website, e-CALLISTO and IUGONET metadata database. From this study, the occurrence of solar radio burst type II, III, and IV showed that there was the production of solar flares and Coronal Mass Ejections (CMEs) where it contributed to the geomagnetic disturbances to the Earth. On 30th March 2018, the Sun produced C-class flares with C4 value while the next day, the sun had B1 solar flares’ event. The planetary K-index for both days were 2 (30th March 2018) and 3 (31st March 2018) respectively.

On the Generation of Probabilistic Forecasts From Deterministic Models

AbstractMost of the methods that produce space weather forecasts are based on deterministic models. In order to generate a probabilistic forecast, a model needs to be run several times sampling the input parameter space, in order to generate an ensemble from which the distribution of outputs can be inferred. However, ensemble simulations are costly and often preclude the possibility of real‐time forecasting. We introduce a simple and robust method to generate uncertainties from deterministic models, that does not require ensemble simulations. The method is based on the simple consideration that a probabilistic forecast needs to be both accurate and well calibrated (reliable). We argue that these two requirements are equally important, and we introduce the Accuracy‐Reliability cost function that quantitatively measures the trade‐off between accuracy and reliability. We then define the optimal uncertainties as the standard deviation of the Gaussian distribution that minimizes the cost function. We demonstrate that this simple strategy, implemented here by means of a deep neural network, produces accurate and well‐calibrated forecasts, showing examples both on synthetic and real‐world space weather data.