Solar Influences Data Analysis Center Research Articles

Possible Influence of Solar Activity on Lightning Occurrence: A Preliminary Study Over Nepal

One of the fundamental mysteries of lightning is its initiation mechanism. Many aspects of lightning phenomenon are well known; however, the exact processes that initiate lightning discharge are not fully understood. This study delves into a relatively underexplored domain: the relationship between solar activity specifically solar sunspots and F10.7 indices, as well as solar wind speed, on the occurrence of lightning events. The study utilizes a rich dataset spanning from 1998 to 2008, capturing an extensive period of solar activity during Solar Cycle 23. Multiple correlation analyses were conducted, comparing solar activity parameters with lightning occurrence. Lightning data was sourced from the Lightning Imaging Sensor (LIS) whereas Sunspot data was obtained from the Solar Influences Data Analysis Center (SIDC) and F10.7 as well as Solar wind data was obtained from the OMNI dataset. From this preliminary study, we observed that the correlation between solar parameters and the lightning occurrence over Nepal are found to vary at different time period from insignificant correlation to moderate correlation. Hence, further exploration is needed to shed more light to interpret the relationship between solar activity and the lightning occurrence in Nepal.

An Automatic Approach for Grouping Sunspots and Calculating Relative Sunspot Number on SDO/HMI Continuum Images

The relative sunspot number is one of the major parameters for the study of long-term solar activity. The automatic calculation of the relative sunspot number is more stable and accurate as compared to manual methods. In this paper, we propose an algorithm that can detect sunspots, and divide them into groups to automatically calculate the relative sunspot number. Mathematical morphology was adopted to detect sunspots then group them. The data set used were the continuum images from SDO/HMI. The process was carried out on the overall HMI data available on the timespan from 2022 January to 2023 May with a time cadence of one day. The experimental results indicated that the method achieved high accuracy of 85.3%. It was well fitted with the international relative sunspot number provided by Solar Influences Data Analysis Center (CC = 0.91). We calculated the conversion factor K value of SDO/HMI for calculating the relative sunspots number (K = 1.03).

Prediction of the Maximum Amplitude of Solar Cycle 25 Using the Ascending Inflection Point

In this work, we predict the maximum amplitude (using the 13-month smoothed Solar Influences Data Analysis Center (SILSO) Sunspot Number, version 2) of Solar Cycle 25 using as a predictor the slope of the inflection point during the ascending part of the cycle. After a description of the data and methodology employed in this work, we obtain a value of 131 ± 32 for the maximum amplitude of Solar Cycle 25. Finally, we discuss this result in the context of the current debate on the prediction of solar activity and compare it with other predictions of Solar Cycle 25 obtained by other methods.

Sunspot interval prediction based on fuzzy information granulation and extreme learning machine

Sunspot prediction is an important task for space weather and solar physics. Traditional point forecast may not be sufficiently satisfactory and reliable. To quantify the uncertainty of point prediction, a hybrid interval prediction model has been proposed for sunspot forecasting. Three major steps are taken: (1) the complementary ensemble empirical mode decomposition (CEEMD), to decompose the sunspot sequence into a series of modal components, (2) the fuzzy information granulation (FIG), to extract the minimum, average and maximum value of each window, and (3) the extreme learning machine (ELM), to conduct point prediction and interval prediction, superimposing the prediction values of all components as the final forecast results. The empirical study focus on the 13-month smoothed monthly sunspot number recorded by Solar Influences Data Analysis Center (SIDC) and show that the mixed model with the filtered CEEMD is more effective than the unfiltered one. It also enables us to track changes of the sunspot number with fast calculating speed and high accuracy both in point prediction and interval prediction.

Statistical analysis of Sunspot and total solar irradiance effects on the climate in kirkuk area for three solar cycles (22,23,24)

In this study Sunspot number(SSN) and Total solar irradiance(TSI w ) observation were extracted from the Solar Influences Data analysis Center (SIDC) and Earth Radiation Budget Satellite (ERBS) for solar cycles 22,23 and 24 (1988-2017) and for the same cycles the parameters of climate were taken from the meteorological data recorded in Kirkuk station, which includes annual relative humidity(RH%),annual air temperature (cº)(T), annual Rainfall (mm)(p), and annual Sunshine duration(h/day)(SH).The statistical correlation between the adopted solar and climate parameters has been achieved by using Statistical program (SPSS) version (23). From this statistical analysis shows that there is a correlation between SH with both (SSN) and (TSI) less than 0.5 that is mean (SSN) and (TSI) have little effect on SH while the correlation is weak or missing between both (SSN) and solar (TSI) with RH% through the correlation coefficient values (0.05,0.032). The effect of (TSI) on the air temperature more than the impact of (SSN) but less than 0.5. The effect of (SSN) more than (TSI) on the amount of rainfall less than 0.5 according to the correlation coefficient value .this investigation shows that the climate in Kirkuk is clearly influenced by solar activity which includes SSN and TSI.

Solar cycle characteristics and their application in the prediction of cycle 25

The Solar Influences Data Analysis Center (SIDC) issued a new version (version 2) of the sunspot number data in July 2015. The 13-month smoothed monthly sunspot number from the new version is used for the first time to research the relations among the feature parameters of solar cycles under the bimodal distribution for the modern era cycles (10–23), and, their physical implications are discussed. These relations are utilized to predict the maximum amplitude of solar cycle 25. Cycle 25 is predicted to start in October 2020 and reach its maximum amplitude of 168.5±16.3 in October 2024, thus, it should be stronger than cycle 24 but weaker than cycle 23.

THE SHAPE OF SOLAR CYCLES DESCRIBED BY A SIMPLIFIED BINARY MIXTURE OF GAUSSIAN FUNCTIONS

ABSTRACT Sunspot cycles usually present a double-peak structure. This work is devoted to using a function to describe the shape of sunspot cycles, including bimodal cycles, and we find that the shape of sunspot cycles can be described by a binary mixture of Gaussian functions with six parameters, two amplitudes, two gradients of curve, and two rising times, and the parameters could be reduced to three. The fitting result of this binary mixture of Gaussian functions is compared with some other functions used previously in the literature, and this function works pretty well, especially at cycle peaks. It is worth mentioning that the function can describe well the shape of those sunspot cycles that show double peaks, and it is superior to the binary mixture of the Laplace functions that was once utilized. The Solar Influences Data Analysis Center, on behalf of the World Data Center, recently issued a new version (version 2) of sunspot number. The characteristics of sunspot cycles are investigated, based on the function description of the new version.

Verification of space weather forecasting at the Regional Warning Center in Belgium

The Solar Influences Data analysis Center (SIDC) in Brussels at the Royal Observatory of Belgium (ROB) has been providing daily space weather forecasts for more than a decade. A verification analysis was applied to evaluate the performance of the SIDC forecasts of fundamental space weather parameters such as the F10.7 radio flux, solar flare activity, and local geomagnetic index.Strengths and weaknesses are determined compared to common numerical models. Descriptive model statistics, common verification measures, error analysis and conditional plots related to forecasts and observations are presented. The verification analysis methods have been designed such that future improvements and additions can easily be included, for example with new forecasting models.The SIDC forecast (together with the persistence model) achieves the best performance for forecasting F10.7 on day 1, but has potential for improvement for a larger lead time mainly by applying estimates from the persistence and corrected recurrence models. The persistence model is superior for the forecast of flares, though corrected recurrence models are slightly better in foreseeing M- and X-class flares and the SIDC forecast estimates B- and C-class flares very well. The SIDC forecast scores better than all models in forecasting the local K -index. It best reproduces observations in the range of K = 2–4, but underestimates larger K values. The SIDC forecast provides a distribution that best matches the observations of the K -index. The analysis presented here demonstrates the influence of solar activity on the confidence level of the forecasts, as well as the hinted influence of the forecaster on duty due to the subjective nature of forecasting. The output aids to identify the strong and weak points of the SIDC forecast as well as those of the models considered. Though the presented analysis needs further extension, it already illustrates the opportunity to regularly reevaluate space weather forecasts and to stimulate ideas for improvement and increase the reliability of space weather forecasting.

Time Series Analysis of Variance of Sunspots in January till September 2010 and Correlation with Sunspots Areas

This paper analyzed theoretically the short period variability of the sunspot activity beginning from January 2010 till September 2010. Sunspot data are taken fro m Solar Influences Data Analysis Center (SIDC). We focused on the number of sunspots each month and correlate it with sunspot are aparameter. Fro m the analysis, it is found that there is a litt le correlation between the amplitude of a cycle. The pattern shown an almost linear relat ionship between both parameters. It also showed that the highest sunspot number per day that has been recorded is in May. The Sun was found to be most active in August and passive in April according to the nu mber o f sunspots. Uniquely, there is also no sunspot at all in a day. This study gives some interesting and potentially important clues especially in understanding the physical processes responsible for the solar activity and the solar cycle phenomena. Based on our analysis, we found that the average relation between sunspot number, R and sunspot area, A is A = 5.179R.

A Multi-wavelength Analysis of Active Regions and Sunspots by Comparison of Automatic Detection Algorithms

Since the Solar Dynamics Observatory (SDO) began recording ≈ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH), and quiet Sun (QS). One month of data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) and SOHO/Extreme Ultraviolet Imaging Telescope (EIT) instruments during 12 May – 23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oceanic and Atmospheric Administration (NOAA) and Solar Influences Data Analysis Center (SIDC) catalogues using various feature properties such as total sunspot area, which shows good agreement, and the number of features detected, which shows poor agreement. Principal Component Analysis indicates a clear distinction between photospheric properties, which are highly correlated to the first component and account for 52.86% of variability in the data set, and coronal properties, which are moderately correlated to both the first and second principal components. Finally, case studies of NOAA 10377 and 10365 are conducted to determine algorithm stability for tracking the evolution of individual features. We find that magnetic flux and total sunspot area are the best indicators of active-region emergence. Additionally, for NOAA 10365, it is shown that the onset of flaring occurs during both periods of magnetic-flux emergence and complexity development.

Preface "The 3rd European Space Weather Week"

This volume is composed of papers presented at the 3rd European Space Weather Week. The meeting took place at the Royal Library of Belgium between 13‐17 November 2006. This event built on the advances made during the first two European Space Weather Weeks held at ESA/ESTEC in 2004 and 2005. The 2006 meeting adopted the central aim of bringing together diverse communities working on all elements of space weather. The organizers are pleased to see this reflected in the papers published in this special issue, the following publications covering a range of space weather oriented advances in both scientific research and transitioning new research into applications. The 2006 event specifically focused on new results through a cross-disciplinary “hot topics” session. The agenda also focused on a number of topical thematic issues from both scientific and applications oriented perspectives. New tools and techniques were discussed and demonstrated during the workshop with a view to aiding the transition between new scientific results and space weather services. The meeting also aimed to facilitate community development and investigation of possible routes towards the creation of a sustainable space weather activity in Europe. The meeting was attended by more than two hundred participants from almost 30 countries including strong participation from many Eastern European states, leading to many fruitful discussions. Meeting organisation was carried out jointly between the Solar Influences Data analysis Center (SIDC) at the Royal Observatory of Belgium (ROB), ESA, the SWWT, the COST 724 and COST 296 communities. The local organization was done by the SIDC. The meeting website can be found at: http://sidc.oma.be/ esww3. The chairpersons of the meeting J. Lilensten and A. Glover would like to express their thanks to all members of the Organising Committee, namely A. Belehaki, A. Hilgers, A. Bourdillon, M. Rycroft, M. Hapgood, F. Lefeuvre, P.‘Gille and R. Van der Linden together with the Local Organising Committee for their support and hard work to ensure a successful meeting.

Monitoring the Sun: A Global Space Weather Enterprise

Monitoring the Sun: A Global Space Weather Enterprise

From the Wolf number to the International Sunspot Index: 25 years of SIDC

By encompassing four centuries of solar evolution, the sunspot number provides the longest available record of solar activity. Nowadays, it is widely used as the main reference solar index on which hundreds of published studies are based, in various fields of science. In this review, we will retrace the history of this crucial solar index, from its roots at the Zürich Observatory up to the current multiple indices established and distributed by the Solar Influences Data Analysis Center (SIDC), World Data Center for the International Sunspot Index, which was founded in 1981, exactly 25 years ago. We describe the principles now in use for the statistical processing of input data coming from the worldwide observing network (∼80 stations). Among the various SIDC data products and innovations, we highlight some recent ones, including the daily Estimated International Sunspot Number. Taking a wider perspective, we show how the sunspot index stands the test of time versus more recent quantitative indices, but we also consider the prospects and possible options for a future transition from the visual sunspot index heritage towards an equivalent global activity index. Based on past historical flaws, we conclude on the key requirements involved in the maintenance of any robust long-term solar activity index.

태양주기별 흑점수의 프랙탈 차원과 최대흑점수의 상관관계

프랙탈 차원은 연속적으로 관측된 불규칙한 자료의 성질을 정량적으로 표현하는 파라메터이다. 본 연구에서는 프랙탈 차원을 이용하여 태양 흑점수의 불규칙한 변동을 분석하고 다가오는 태양주기의 최대흑점수를 예측하였다. 이를 위하여 우리는 SIDC(Solar Influences Data analysis Center)에서 제공하는 1850년부터 2004년까지의 일일흑점수를 이용하여 Higuchi의 방법으로 각 태양주기의 프랙탈 차원을 결정하고 같은 태양주기 동안의 최대 월평균 흑점수와 비교하였다. 그 결과 프랙탈 차원과 최대 월평균 흑점수는 강한 역비례 관계를 보였다. 이러한 관계를 이용하여 태양활동 극소기부터 활동이 증가되는 기간인 4년동안의 흑점수만을 이용하여 프랙탈 차원을 구하고 다가올 극대기의 최대 월평균 흑점수를 예측한 결과 관측된 최대흑점수와 0.89의 좋은 상관관계를 보였다. The fractal dimension is a quantitative parameter describing the characteristics of irregular time series. In this study, we use this parameter to analyze the irregular aspects of solar activity and to predict the maximum sunspot number in the following solar cycle by examining time series of the sunspot number. For this, we considered the daily sunspot number since 1850 from SIDC (Solar Influences Data analysis Center) and then estimated cycle variation of the fractal dimension by using Higuchi's method. We examined the relationship between this fractal dimension and the maximum monthly sunspot number in each solar cycle. As a result, we found that there is a strong inverse relationship between the fractal dimension and the maximum monthly sunspot number. By using this relation we predicted the maximum sunspot number in the solar cycle from the fractal dimension of the sunspot numbers during the solar activity increasing phase. The successful prediction is proven by a good correlation (r=0.89) between the observed and predicted maximum sunspot numbers in the solar cycles.

Solar activity: nowcasting and forecasting at the SIDC

Abstract. The Solar Influences Data analysis Center (SIDC) is the World Data Center for the production and the distribution of the International Sunspot Index, coordinating a network of about 80 stations worldwide. From this core activity, the SIDC has grown in recent years to a European center for nowcasting and forecasting of solar activity on all timescales. This paper reviews the services (data, forecasts, alerts, software) that the SIDC currently offers to the scientific community. The SIDC operates instruments both on the ground and in space. The USET telescope in Brussels produces daily white light and Hα images. Several members of the SIDC are co-investigators of the EIT instrument onboard SOHO and are involved in the development of the next generation of Europe's solar weather monitoring capabilities. While the SIDC is staffed only during day-time (7 days/week), the monitoring service is a 24 h activity thanks to the implementation of autonomous software for data handling and analysis and the sending of automated alerts. We will give an overview of recently developed techniques for visualization and automated analysis of solar images and detection of events significant for space weather (e.g. CMEs or EIT waves). As part of the involvement of the SIDC in the ESA Pilot Project for Space Weather Applications we have developed services dedicated to the users of the Global Positioning System (GPS). As a Regional Warning Center (RWC) of the International Space Environment Service (ISES), the SIDC produces daily forecasts of flaring probability, geomagnetic activity and 10.7 cm radio flux. The accuracy of these forecasts will be investigated through an in-depth quality analysis.

The Sidc: World Data Center for the Sunspot Index

Since January 1981, the Royal Observatory of Belgium (ROB) has operated the Sunspot Index Data Center (SIDC), the World Data Center for the Sunspot Index. From 2000, the SIDC obtained the status of Regional Warning Center (RWC) of the International Space Environment Service (ISES) and became the “Solar Influences Data analysis Center”. As a data analysis service of the Federation of Astronomical and Geophysical data analysis Services (FAGS), the SIDC collects monthly observations from worldwide stations in order to calculate the International Sunspot Number, Ri. The center broadcasts the daily, monthly, yearly sunspot numbers, with middle-range predictions (up to 12 months). Since August 1992, hemispheric sunspot numbers are also provided.