共查询到20条相似文献，搜索用时 15 毫秒
1.
Short-Term Solar Flare Prediction Using Predictor Teams 总被引：1，自引：0，他引：1
A short-term solar flare prediction model is built using predictor teams rather than an individual set of predictors. The information provided by the set of predictors could be redundant. So it is necessary to generate subsets of predictors which can keep the information constant. These subsets are called predictor teams. In the framework of rough set theory, predictor teams are constructed from sequences of the maximum horizontal gradient, the length of neutral line and the number of singular points extracted from SOHO/MDI longitudinal magnetograms. Because of the instability of the decision tree algorithm, prediction models generated by the C4.5 decision tree for different predictor teams are diverse. The flaring sample, which is incorrectly predicted by one model, can be correctly forecasted by another one. So these base prediction models are used to construct an ensemble prediction model of solar flares by the majority voting rule. The experimental results show that the predictor team can keep the distinguishability of the original set, and the ensemble prediction model can obtain better performance than the model based on the individual set of predictors. 相似文献
2.
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. 相似文献
3.
Application of artificial neural networks to the prediction of dust storms in Northwest China 总被引：2，自引：0，他引：2
Artificial neural networks (ANN) are non-linear mapping structures analogous to the functioning of the human brain. In this study, we take the ANN approach to model and predict the occurrence of dust storms in Northwest China, by using a combination of daily mean meteorological measurements and dust storm occurrence. The performance of the ANN model in simulating dust storm occurrences is compared with a stepwise regression model. The correlation coefficients between the observed and the estimated dust storm occurrences obtained from the neural network procedure are found to be significantly higher than those obtained from the regression model with the same input data. The prediction tests show that the ANN models used in this study have the potential of forecasting dust storm occurrence in Northwest China by using conventional meteorological variables. 相似文献
4.
针对极移复杂的时变特性, 根据混沌相空间坐标延迟重构理论, 提出一种基于Volterra自适应滤波的极移预报方法. 首先, 利用最小二乘拟合算法分离极移序列中的线性趋势项、钱德勒项和周年项, 获得线性极移、钱德勒极移和周年极移的外推值; 其次, 通过C-C关联积分法对最小二乘拟合残差序列进行相空间重构, 并利用小数据量法计算残差序列的最大Lyapunov指数验证其混沌特性, 在此基础上, 构建Volterra自适应滤波器对残差序列进行预测; 最后, 将线性极移、钱德勒极移和周年极移的外推值以及最小二乘拟合残差的预测值相加获得极移最终预报值. 利用国际地球自转服务局(International Earth Rotation and Reference Systems Service, IERS)提供的极移数据进行1--60d跨度预报, 并将预报结果分别与国际地球定向参数预报比较竞赛(Earth Orientation Parameters Prediction Comparison Campaign, EOP PCC)结果和IERS A公报发布的极移预报产品进行对比, 结果表明: 对于1--30d的短期预报, 该方法的预报精度与EOP PCC最优预报方法相当, 当预报跨度超过30d时, 该方法的预报精度低于EOP PCC最优预报方法, 优于参与EOP PCC的其他方法; 与IERS A公报相比, 该方法的短期预报效果较好, 当预报跨度增加时预报精度低于IERS A公报. 预报结果表明该方法更适合于极移短期预报. 相似文献
5.
We develop and discuss the properties of a new class of lattice-based avalanche models of solar flares. These models are readily amenable to a relatively unambiguous physical interpretation in terms of slow twisting of a coronal loop. They share similarities with other avalanche models, such as the classical stick–slip self-organized critical model of earthquakes, in that they are driven globally by a fully deterministic energy-loading process. The model design leads to a systematic deficit of small-scale avalanches. In some portions of model space, mid-size and large avalanching behavior is scale-free, being characterized by event size distributions that have the form of power-laws with index values, which, in some parameter regimes, compare favorably to those inferred from solar EUV and X-ray flare data. For models using conservative or near-conservative redistribution rules, a population of large, quasiperiodic avalanches can also appear. Although without direct counterparts in the observational global statistics of flare energy release, this latter behavior may be relevant to recurrent flaring in individual coronal loops. This class of models could provide a basis for the prediction of large solar flares. 相似文献
6.
北斗卫星导航系统(BDS)地面跟踪站都配置有高精度的氢原子钟,并基于精密定轨数据处理与主站的时间基准进行同步.在卫星轨道机动以及机动恢复期间,通常采用几何法定轨以及单星定轨确定卫星的轨道.而在这两种定轨模式中,需要提供精确的测站钟差作为输入.为提高定轨的实时性,需要对测站钟差进行预报处理.分析了2次多项式模型、附加周期项模型、灰色模型3种模型对北斗地面跟踪站钟差短期拟合和预报的性能,并将钟差预报结果应用于单星定轨,同时还分析了不同预报钟差用于定轨的精度.试验发现,以上3种模型对6个测站钟差的平均拟合精度分别为0.14 ns、0.05 ns、0.27 ns,预报1 h的平均精度分别为1.17 ns、0.88 ns、1.28 ns,预报2 h的平均精度分别为2.72 ns、2.09 ns、2.53 ns.采用3种模型对测站钟差进行预报并用于单星定轨,采用附加周期项的钟差预报模型轨道3维误差最小,不同模型轨道径向精度差异在3 cm以内.以上结果表明,附加周期项的站钟拟合及预报模型在北斗系统机动期间的轨道恢复数据处理具有最好的效果. 相似文献
7.
《Chinese Astronomy and Astrophysics》2023,47(1):236-256
In consideration of the complex time-varying characteristics of polar motion (PM), this paper takes PM as chaotic time series. A Volterra adaptive filter is employed for predicting PM based on the state space reconstruction of delay-coordinate embedding of dynamic system. This method first uses the Least Squares (LS) technology to estimate the harmonic models for the linear trend, Annual and Chandler Wobbles (AW and CW) in PM. The selected LS deterministic models are subsequently used to extrapolate the linear trend, AW, and CW, and obtain the LS residues (the difference between the LS model and PM data themselves). Secondly, the phase space and largest Lyapunov exponent of the LS residues are reconstructed, and calculated by means of the C-C and small data-set algorithm, respectively. Further, a Volterra adaptive filter is designed for generating the extrapolations of the LS residues. The extrapolated LS residues are then added to the LS deterministic models in order to obtain the predicted PM values. The EOP C04 time series released by the International Earth Rotation and Reference Systems Service (IERS) are selected as data base to generate the PM predictions up to 60 days in the future. The results of the predictions are analyzed and compared with those obtained by the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and IERS Bulletin A. The results show that the accuracy of the predictions up to 30 days is comparable with that by the most accurate prediction techniques participating in the EOP PCC for PM, but worse than that by those most accurate techniques beyond 30 days in the future. The results also illustrate that the short-term predictions are better than those published by the IERS Bulletin A. However, the errors of the predictions rapidly increase with the prediction days. It is therefore concluded that the proposed method is a potential technology for short-term PM prediction. 相似文献
8.
Wang Ji-gang Hu Yong-huiHe Zai-min Wu Jian-feng Ma Hong-jiaoWang Kang 《Chinese Astronomy and Astrophysics》2011,35(3):318
The prediction of the clock errors of atomic clocks plays an important role in the work on time and frequency. Each of the prediction models often used at present has its own merits and shortages. A combination of the predicted results obtained by means of these models can be used to synthesize the characteristics of various kinds of prediction models. In the light of the problem which occurs when the linear combination model is used to make the prediction of clock errors, the concept of learning weight is proposed and the modified combination prediction model is made by taking advantage of various kinds of pieces of accuracy information. For verifying the efficiency of this method the clock error sequences of the IGS (International GNSS Service) of 4 GPS satellites are selected and the predicted results of the quadratic polynomial and grey model are combined. The result shows that the modified model can further improve the stability and accuracy based on the guarantee of the reliability. 相似文献
9.
Heiko Paeth Anja Scholten Petra Friederichs Andreas Hense 《Global and Planetary Change》2008,60(3-4):265-288
The sensitivity of climate phenomena in the low latitudes to enhanced greenhouse conditions is a scientific issue of high relevance to billions of people in the poorest countries of the globe. So far, most studies dealt with individual model results. In the present analysis, we refer to 79 coupled ocean–atmosphere simulations from 12 different climate models under 6 different IPCC scenarios. The basic question is as to what extent various state-of-the-art climate models agree in predicting changes in the main features of El Niño-Southern Oscillation (ENSO) and the monsoon climates in South Asia and West Africa. The individual model runs are compared with observational data in order to judge whether the spatio-temporal characteristics of ENSO are well reproduced. The model experiments can be grouped into multi-model ensembles. Thus, climate change signals in the classical index time series, in the principal components and in the time series of interannual variability can be evaluated against the background of internal variability and model uncertainty.There are large differences between the individual model predictions until the end of the 21st century, especially in terms of monsoon rainfall and the Southern Oscillation index (SOI). The majority of the models tends to project La Niña-like anomalies in the SOI and an intensification of the summer monsoon precipitation in India and West Africa. However, the response barely exceeds the level of natural variability and the systematic intermodel variations are larger than the impact of different IPCC scenarios. Nonetheless, there is one prominent climate change signal, which stands out from model variations and internal noise: All forced model experiments agree in predicting a substantial warming in the eastern tropical Pacific. This oceanic heating does not necessarily lead to a modification of ENSO towards more frequent El Niño and/or La Niña events. It simply represents a change in the background state of ENSO. Indeed, we did not find convincing multi-model evidence for a modification of the wavelet spectra in terms of ENSO or the monsoons. Some models suggest an intensification of the annual cycle but this signal is fairly model-dependent. Thus, large model uncertainty still exists with respect to the future behaviour of climate in the low latitudes. This has to be taken into account when addressing climate change signals in individual model experiments and ensembles. 相似文献
10.
北美防空司令部(North American Aerospace Defense Command, NORAD)发布的双行根数(Two Line Element, TLE)是广大航天工作者最常用的轨道根数,与其对应的轨道模型是SGP4/SDP4 (Simplified General Perturbation Version 4/Simplified Deep-space Perturbation Version 4)解析模型.由于TLE中并没有包含相应的轨道精度信息,编目轨道的应用范围受到很大的限制.基于Space-Track网站发布的历史TLE数据和配套的SGP4/SDP4动力学模型,采用定轨标预报的方法统计并生成了大量目标轨道的预报误差,通过对预报轨道的时间区间划分给出了每个目标的预报误差随预报时间变化的拟合系数,并进一步对不同类型轨道预报误差的演化规律和特征进行了分类讨论,给出了4种轨道类型目标的轨道预报误差随时间演化的平均解析模型,为拓展双行根数的应用提供有价值的参考. 相似文献
11.
Rui-Fei Cui Ya-Guang Zhu Huan Zhang Ri-Wei Zhang Hong-Yu Zhao Zheng-Lian Li 《天文和天体物理学研究(英文版)》2021,(4):147-156
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. 相似文献
12.
The geometrical and dynamical structure of a corona consisting of streamer and interstreamer regions is examined. The present paper is an extension of previous works of this series in that energy transport processes are included in the theoretical framework of the model. Under specified conditions at some reference level above the coronal base, the structure at larger distances is determined by simultaneous integration of the continuity, momentum, and energy equations for each region subject to the condition for a lateral balance of magnetic and gas pressure at all levels. Outward thermal conduction and convection by the solar wind are assumed to be the processes contributing to the energy balance of each region, the magnetic field effectively thermally insulating one region from the other.Numerical results are presented for situations representative of the solar corona. Regions occupied by streamers are found to have higher densities than their surroundings at all distances from the sun. For a given density at the coronal base, the density at the orbit of earth is lower in both the streamer and interstreamer region than that predicted for radial flow. The density enhancement increases outward to a maximum value at a distance of several solar radii. In addition, beyond a distance of a few radii streamers are characterized by higher expansion velocities and lower temperatures than their immediate surroundings. Similar to the case of radial flow, supersonic solutions exist only for base densities below a certain value, which depends upon the specified base temperature and magnetic field distribution. The general features illustrated by these models are expected to persist in the advent of more sophisticated multi-region models.The National Center for Atmospheric Research is sponsored by the National Science Foundation. 相似文献
13.
14.
Shao-Lan Bi Tan-Da Li Kang Liu Jie Jiang Ya-Guang Li Jing-Hua Zhang Xian-Fei Zhang Ya-Qian Wu 《天文和天体物理学研究(英文版)》2021,(4):157-164
The change of sound speed has been found at the base of the convection during the solar cycles,which can be used to constrain the solar internal magnetic field.We aim to check whether the magnetic field generated by the solar dynamo can lead to the cyclic variation of the sound speed detected through helioseismology.The basic configuration of magnetic field in the solar interior was obtained by using a Babcock-Leighton(BL) type flux transport dynamo.We reconstructed one-dimensional solar models by assimilating magnetic field generated by an established dynamo and examined their influences on the structural variables.The results show that magnetic field generated by the dynamo is able to cause noticeable change of the sound speed profile at the base of the convective zone during a solar cycle.Detailed features of this theoretical prediction are also similar to those of the helioseismic results in solar cycle 23 by adjusting the free parameters of the dynamo model. 相似文献
15.
《天文和天体物理学研究(英文版)》2021,(5)
The continuous observation of the magnetic field by the Solar Dynamics Observatory(SDO)/Helioseismic and Magnetic Imager(HMI) produces numerous image sequences in time and space.These sequences provide data support for predicting the evolution of photospheric magnetic field. Based on the spatiotemporal long short-term memory(LSTM) network, we use the preprocessed data of photospheric magnetic field in active regions to build a prediction model for magnetic field evolution. Because of the elaborate learning and memory mechanism, the trained model can characterize the inherent relationships contained in spatiotemporal features. The testing results of the prediction model indicate that(1) the prediction pattern learned by the model can be applied to predict the evolution of new magnetic field in the next 6 hours that have not been trained, and predicted results are roughly consistent with real observed magnetic field evolution in terms of large-scale structure and movement speed;(2) the performance of the model is related to the prediction time; the shorter the prediction time, the higher the accuracy of the predicted results;(3) the performance of the model is stable not only for active regions in the north and south but also for data in positive and negative regions. Detailed experimental results and discussions on magnetic flux emergence and magnetic neutral lines finally show that the proposed model could effectively predict the large-scale and short-term evolution of the photospheric magnetic field in active regions. Moreover, our study may provide a reference for the spatiotemporal prediction of other solar activities. 相似文献
16.
针对BP (Back Propagation)神经网络模型预测卫星钟差中权值和阈值的最优化问题, 提出了基于遗传算法优化的BP神经网络卫星钟差短期预报模型, 给出了遗传算法优化BP神经网络的基本思想、具体方法和实施步骤. 为验证该优化模型的有效性和可行性, 利用北斗卫星导航系统(BeiDou navigation satellite system, BDS)卫星钟差数据进行钟差预报精度分析, 并将其与灰色模型(GM(1,1))和BP神经网络模型预报的结果比较分析. 结果表明: 该模型在短期钟差预报中具有较好的精度, 优于GM(1,1)模型和BP神经网络模型. 相似文献
17.
Eric Jonas Monica Bobra Vaishaal Shankar J. Todd Hoeksema Benjamin Recht 《Solar physics》2018,293(3):48
The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, ii) combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and iii) classifies these features to predict whether a solar active region will flare within a time period of \(T\) hours, where \(T = 2 \mbox{ and }24\). Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We find that when optimizing for the True Skill Score (TSS), photospheric vector-magnetic-field data combined with flaring history yields the best performance, and when optimizing for the area under the precision–recall curve, all of the data are helpful. Our model performance yields a TSS of \(0.84 \pm0.03\) and \(0.81 \pm0.03\) in the \(T = 2\)- and 24-hour cases, respectively, and a value of \(0.13 \pm0.07\) and \(0.43 \pm0.08\) for the area under the precision–recall curve in the \(T=2\)- and 24-hour cases, respectively. These relatively high scores are competitive with previous attempts at solar prediction, but our different methodology and extreme care in task design and experimental setup provide an independent confirmation of these results. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. We believe that this is the first attempt to predict solar flares using photospheric vector-magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona, and it points the way towards greater data integration across diverse sources in future work. 相似文献
18.
Long-term Clock Bias Prediction Based on An ARMA Model 总被引：1，自引：0，他引：1
XI Chao CAI Cheng-lin LI Si-min LI Xiao-hui LI Zhi-bin DENG Ke-qun 《Chinese Astronomy and Astrophysics》2014
The long-term and reliable prediction of satellite clock bias (SCB) is an important prerequisite for realizing the satellite autonomous navigation and orbit determination. Considering the shortcomings of the quadratic polynomial model (PM) and gray system model (GM) in the long-term prediction of SCB, a new prediction method of SCB based on an ARMA (Auto-Regressive Moving Average) model is proposed to represent the variation characteristics of SCB more accurately. In this paper, a careful precision analysis of the 90-day SCB prediction is made to verify the feasibility and validity of this proposed method by using the IGS (International GNSS Service) clock data. According to the variation characteristics of each satellite clock, the pattern recognition, modeling and prediction of SCB are conducted, and the detailed comparison is made with the other three models at the same time. The results show that adopting the ARMA model can effectively improve the accuracy of long-term SCB prediction. 相似文献
19.
L. Benacchio 《Experimental Astronomy》1997,7(4):391-397
Next generation telescopes will be all digital instruments. This fact will allow the full integration of archives into telescopes operation. This is a main step if one wants to fulfill the requirement of usefulness of both scientific and technical data contained in the archives. A great change with respect to the past is required involving the modes of scheduling, observing, calibrating etc. New models of approaching the whole scientific data flow starting from the archiving problem are studied in several Institutions. These new models of observation, whose consequences in terms of human factor seem to be as important as the technological implications, promise to ensure a high and constant quality level of the data to be archived. The current status of this topic is reviewed. Some thoughts on the data model presently used in astronomy are also briefly given. 相似文献
20.
Lida A. M. Nejad 《Astrophysics and Space Science》2005,299(1):1-29
The time dependent chemical rate equations arising from astrochemical kinetics problems are described by a system of stiff ordinary differential equations (ODEs). In this paper, using three astrochemical models of varying physical and computational complexity, and hence different degrees of stiffness, we present a comprehensive performance survey of a set of well-established ODE solver packages from the ODEPACK collection, namely LSODE, LSODES, VODE and VODPK. For completeness, we include results from the GEAR package in one of the test models. The results demonstrate that significant performance improvements can be obtained over GEAR which is still being used by many astrochemists by default. We show that a simple appropriate ordering of the species set results in a substantial improvement in the performance of the tested ODE solvers. The sparsity of the associated Jacobian matrix can be exploited and results using the sparse direct solver routine LSODES show an extensive reduction in CPU time without any loss in accuracy. We compare the performance and the computed abundances of one model with a 175 species set and a reduced set of 88 species, keeping all physical and chemical parameters identical with both sets.We found that the calculated abundances using two different size models agree quite well. However, with no extra computational effort and more reliable results, it is possible for the computation to be many times faster with the larger species set than the reduced set, depending on the use of solvers, the ordering and the chosen options. It is also shown that though a particular solver with certain chosen parameters may have severe difficulty or even fail to complete a run over the required integration time, another solver can easily complete the run with a wider range of control parameters and options. As a result of the superior performance of LSODES for the solution of astrochemical kinetics systems, we have tailor-made a sparse version of the VODE solver by replacing the full numerical matrix linear algebra component of the standard VODE solver with sparse matrix solver routines. The preliminary tests from the preconditioned iterative solver package VODPK indicate very good results for one of our test models, but not for all of the models. 相似文献