The fuzzy forecast of the activity of solar active regions

In this paper, the theory and method of fuzzy mathematics are applied to forecast the activity of solar active regions. According to the correlation between flares and several solar activity indices of active regions, the membership functions are constructed to comprehensively evaluate and predict the activity of solar active regions. By means of data reduction and analysis, some comparatively accurate results of prediction have been obtained. The accuracy of predicting the activity grades of active regions is higher than 97%. This implies that the method of fuzzy forecast is a good one for solar activity prediction. This revised version was published online in July 2006 with corrections to the Cover Date.     

Predicting maximum sunspot number in solar cycle 24

A few prediction methods have been developed based on the precursor technique which is found to be successful for forecasting the solar activity. Considering the geomagnetic activity aa indices during the descending phase of the preceding solar cycle as the precursor, we predict the maximum amplitude of annual mean sunspot number in cycle 24 to be 111 ± 21. This suggests that the maximum amplitude of the upcoming cycle 24 will be less than cycles 21–22. Further, we have estimated the annual mean geomagnetic activity aa index for the solar maximum year in cycle 24 to be 20.6 ± 4.7 and the average of the annual mean sunspot number during the descending phase of cycle 24 is estimated to be 48 ± 16.8.     

控制太阳活动的混沌吸引子(英)

随着非线性科学研究的进展,可利用表征太阳活动的太阳活动指数组成的时间序列来寻找或许存在的太阳混沌吸引子,并计算其关联维数、最大Lyapunov指数以及其它特征量．文中综述了用非线性科学的某些概念来研究太阳活动的进展及其在太阳活动预报方面的一些应用．     

国内外空间天气短期预报和警报及其现状

综述了空间天气短期预报和警报所研究的内容和与之相对应的研究方法。同时，介绍了目前国内外在空间天气短期预报和警报方面的研究现状，及目前国内外从事这方面研究的主要机构或组织的概况。     

Global solar dynamo models: Simulations and predictions

Flux-transport type solar dynamos have achieved considerable success in correctly simulating many solar cycle features, and are now being used for prediction of solar cycle timing and amplitude. We first define flux-transport dynamos and demonstrate how they work. The essential added ingredient in this class of models is meridional circulation, which governs the dynamo period and also plays a crucial role in determining the Sun’s memory about its past magnetic fields. We show that flux-transport dynamo models can explain many key features of solar cycles. Then we show that a predictive tool can be built from this class of dynamo that can be used to predict mean solar cycle features by assimilating magnetic field data from previous cycles.     

BoxCox multi-output linear regression for 10.7 cm solar radio flux prediction

We consider the problem of predicting the mid-term daily 10.7 cm solar radio flux(F10.7),a widely-used solar activity index.A novel approach is proposed for this task,in which BoxCox transformation with a proper parameter is first applied to make the data satisfy the property of homoscedasticity that is a basic assumption of regression models,and then a multi-output linear regression model is used to predict future F10.7 values.The experiment shows that the BoxCox transformation significantly improves the predictive performance and our new approach works substantially better than the prediction from the US Airforce and other alternative methods like Auto-regressive Model,Multi-layer Perceptron,and Support Vector Regression.     

Online Multi-step Ahead Prediction of Time-Varying Solar and Geomagnetic Activity Indices via Adaptive Neurofuzzy Modeling and Recursive Spectral Analysis

The time-varying Sun as the main source of space weather affects the Earth??s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices.     

22周上升相日面各经度带的活动规律

本文回顾了１９８３年以来一些对太阳活动的谱分析结果。大致可分为两种规律：在太阳活动１１年周期的上升相一般呈现８０天左右的周期。下降相呈现１５０天左右的周期。这些规律均是由太阳全日面总体活动指数得到的谱分析结果。文中将第２２周上升段（１９８７．１．１—１９８８．７．３１）的太阳黑子群和Ｘ射线耀斑按经度带作了极大熵谱估计。结果表明，各经度带的活动规律不同，同一经度带内，太阳黑子群和Ｘ射线耀斑的出现规律也不尽相同。这种将事件按经度带分布得到的活动规律对事件本身的中期预报将会有实际应用价值。     

Predictions of the maximum amplitude for solar cycle 23 and its subsequent behavior using nonlinear methods

Two nonlinear methods are employed for the prediction of the maximum amplitude for solar cycle 23 and its declining behavior. First, a new heuristic method based on the second derivative of the (conveniently smoothed) sunspot data is proposed. The curvature of the smoothed sunspot data at cycle minimum appears to correlate (R 0.92) with the cycle's later-occurring maximum amplitude. Secondly, in order to predict the near-maximum and declining activity of solar cycle 23, a neural network analysis of the annual mean sunspot time series is also performed. The results of the present study are then compared with some other recent predictions.     

Formation of a White-Light Jet Within a Quadrupolar Magnetic Configuration

The cadence and resolution of solar images have been increasing dramatically with the launch of new spacecraft such as STEREO and SDO. This increase in data volume provides new opportunities for solar researchers, but the efficient processing and analysis of these data create new challenges. We introduce a fuzzy-based solar feature-detection system in this article. The proposed system processes SDO/AIA images using fuzzy rules to detect coronal holes and active regions. This system is fast and it can handle different size images. It is tested on six months of solar data (1 October 2010 to 31 March 2011) to generate filling factors (ratio of area of solar feature to area of rest of the solar disc) for active regions and coronal holes. These filling factors are then compared to SDO/EVE/ESP irradiance measurements. The correlation between active-region filling factors and irradiance measurements is found to be very high, which has encouraged us to design a time-series prediction system using Radial Basis Function Networks to predict ESP irradiance measurements from our generated filling factors.     

High energetic solar proton flares on the declining phase of solar cycle 22

The year 1991 is a part of the declining phase of the solar cycle 22, during which high energetic flares have been produced by active regions NOAA/USAF 6659 in June. The associated solar proton events have affected the Earth environment and their proton fluxes have been measured by GOES space craft. The evaluation of solar activity during the first half of June 1991, have been carried out by applying a method for high energetic solar flares prediction on the flares of June 1991. The method depends on cumulative summation curves according to observed H-alpha flares, X-ray bursts, in the active region 6659 during one rotation when the energetic solar flares of June 1991 have occurred. It has been found that the steep trend of increased activity sets on several tens of hours prior to the occurrence of the energetic flare, which can be used, together with other methods, for forecasts of major flares. All the used data at the present work are received from NOAA, Boulder, Colorado, USA.     

第23太阳活动周发展趋势预测

利用压强改正莫斯科中子监测值,对第２３太阳活动周的未来发展趋势作了预测,推测第 ２３周太阳活动和第 ２２周相当,约在 ２００１年达到 １５１± １６的极大月平均黑子相对数．     

Input Selection Based on Information Theory for Constructing Predictor Models of Solar and Geomagnetic Activity Indices

Various phenomena with solar origin and their mutual dependence must be studied in order to predict behaviors in solar – terrestrial system. Linear statistical methods prevalent in analyzing natural systems may not be able to detect nonlinear dependencies among solar and geomagnetic processes. When relations, whether linear or nonlinear, between indices and their changes over time are revealed, better predictions can be made through appropriate modeling techniques. Selection of nonredundant input variables to build suitable models for prediction of solar and geomagnetic activity is of utmost importance. Mutual information is a tool that is capable of capturing all dependencies for detecting nonlinear relations and selecting the best subset of input variables by means of an applicable algorithm that maximizes information about the output and minimizes the shared information between inputs. High generalization power and improved interpretability of the selected inputs are the consequences of this analysis.     

Short-Term Solar Flare Prediction Using a Sequential Supervised Learning Method

Solar flares are powered by the energy stored in magnetic fields, so evolutionary information of the magnetic field is important for short-term prediction of solar flares. However, the existing solar flare prediction models only use the current information of the active region. A sequential supervised learning method is introduced to add the evolutionary information of the active region into a prediction model. The maximum horizontal gradient, the length of the neutral line, and the number of singular points extracted from SOHO/MDI longitudinal magnetograms are used in the model to describe the nonpotentiality and complexity of the photospheric magnetic field. The evolutionary characteristics of the predictors are analyzed by using autocorrelation functions and mutual information functions. The analysis results indicate that a flare is influenced by the 3-day photospheric magnetic field information before flare eruption. A sliding-window method is used to add evolutionary information of the predictors into machine learning algorithms, then C4.5 decision tree and learning vector quantization are employed to predict the flare level within 48 hours. Experimental results indicate that the performance of the short-term solar flare prediction model within the sequential supervised learning framework is significantly improved.     

Turbulence In The Solar Atmosphere: Manifestations And Diagnostics Via Solar Image Processing

Intermittent magnetohydrodynamical turbulence is most likely at work in the magnetized solar atmosphere. As a result, an array of scaling and multi-scaling image-processing techniques can be used to measure the expected self-organization of solar magnetic fields. While these techniques advance our understanding of the physical system at work, it is unclear whether they can be used to predict solar eruptions, thus obtaining a practical significance for space weather. We address part of this problem by focusing on solar active regions and by investigating the usefulness of scaling and multi-scaling image-processing techniques in solar flare prediction. Since solar flares exhibit spatial and temporal intermittency, we suggest that they are the products of instabilities subject to a critical threshold in a turbulent magnetic configuration. The identification of this threshold in scaling and multi-scaling spectra would then contribute meaningfully to the prediction of solar flares. We find that the fractal dimension of solar magnetic fields and their multi-fractal spectrum of generalized correlation dimensions do not have significant predictive ability. The respective multi-fractal structure functions and their inertial-range scaling exponents, however, probably provide some statistical distinguishing features between flaring and non-flaring active regions. More importantly, the temporal evolution of the above scaling exponents in flaring active regions probably shows a distinct behavior starting a few hours prior to a flare and therefore this temporal behavior may be practically useful in flare prediction. The results of this study need to be validated by more comprehensive works over a large number of solar active regions. Sufficient statistics may also establish critical thresholds in the values of the multi-fractal structure functions and/or their scaling exponents above which a flare may be predicted with a high level of confidence. Based on the author's contributed talk “Manifestations and Diagnostics of Turbulence in the Solar Atmosphere”, presented at the Solar Image Processing Workshop II, Annapolis, Maryland, USA, 3–5 November 2004.     

Dynamo-based scheme for forecasting the magnitude of solar activity cycles

In this paper we present a general framework for forecasting the smoothed maximum level of solar activity in a given cycle, based on a simple understanding of the solar dynamo. This type of forecasting requires knowledge of the Sun's polar magnetic field strength at the preceeding activity minimum. Because direct measurements of this quantity are difficult to obtain, we evaluate the quality of a number of proxy indicators already used by other authors which are physically related to the Sun's polar field. We subject these indicators to a rigorous statistical analysis, and specify in detail the analysis technique for each indicator in order to simplify and systematize reanalysis for future use. We find that several of these proxies are in fact poorly correlated or uncorrelated with solar activity, and thus are of little value for predicting activity maxima.We also present a scheme in which the predictions of the individual proxies are combined via an appropriately weighted mean to produce a compound prediction. We then apply the scheme to the current cycle 22, and estimate a maximum smoothed International sunspot number of 171 ± 26, which can be expressed alternatively as a smoothed 2800 MHz radio flux (F _10.7) of 211 ± 23 × (10^–22 Wm^–2Hz^–1), or as a smoothed sunspot area of 2660 ± 430 millionths of a solar disk. Once the actual maximum for cycle 22 has been established, we will have both additional statistics for all the proxy indicators, and a clearer indication of how accurately the present scheme can predict solar activity levels.     

Study on The Prediction Method of Characteristic Parameters of Solar X-ray Flares

Solar ?ares are important events for the space weather. The predic- tion of relevant parameters of solar ?ares has practical signi?cance for evaluating the effect of sudden ionospheric disturbance (SID). The data of soft X-ray ?ux observed by the GOES-8 satellite in the 23th solar cycle are used to predict the peak intensities and ending times of X-class ?ares with the method of data ?t- ting. Using this method to analyze the X-class ?ares in the 23th solar cycle, it is possible to predict the peak ?ux of an X-class ?are 17 minutes in advance at most. And the ending time of an X-class ?are may be predicted about 60 minutes in advance at most. The predicted results indicate that the prediction method has certain effectiveness and applicability.     

Remote sensing of the heliospheric solar wind using radio astronomy methods and numerical simulations

The ground-based radio astronomy method of interplanetary scintillations (IPS) and spacecraft observations have shown, in the past 25 years, that while coronal holes give rise to stable, reclining high speed solar wind streams during the minimum of the solar activity cycle, the slow speed wind seen more during the solar maximum activity is better associated with the closed field regions, which also give rise to solar flares and coronal mass ejections (CME’s). The latter events increase significantly, as the cycle maximum takes place. We have recently shown that in the case of energetic flares one may be able to track the associated disturbances almost on a one to one basis from a distance of 0.2 to 1 AU using IPS methods. Time dependent 3D MHD models which are constrained by IPS observations are being developed. These models are able to simulate general features of the solar-generated disturbances. Advances in this direction may lead to prediction of heliospheric propagation of these disturbances throughout the solar system.     

A study of the regularities in solar magnetic activity by singular spectral analysis

The method of singular spectral analysis (SSA) is described and used to analyze the series of Wolf numbers that characterizes solar activity from 1748 until 1996. Since this method is relatively new, we detail its algorithm as applied to the data under study. We examine the advantages and disadvantages of the SSA method and the conditions for its applicability to an analysis of the solar-activity data. Certain regularities have been found in the dynamics of this series. Both short and long (80–100-year) periodicities have been revealed in the sunspot dynamics. We predict the solar activity until 2014.     

Predicting monthly sunspot numbers of solar cycle 23 by the method of “similar cycles”

We briefly describe the concept and method of “similar cycles” to be used in sunspot prediction. We have checked on the reliability of this method and made the comparison of the predictions and observations for the 23rd solar activity cycle.