基于太阳10.7 cm射电流量的全日面耀斑预报方法

太阳耀斑是一种重要的太阳爆发活动现象,表现为近乎全波段的电磁辐射增强。统计表明,太阳活动水平越高,太阳爆发越频繁,耀斑爆发的概率越大。利用1975-2007年10.7 cm流量与耀斑爆发的统计关系,建立了一种可行的全日面爆发耀斑概率的预报方法,能够实现C,M,X三种级别的耀斑在全日面爆发的概率预报。通过2008-2016年的观测数据,对模型进行了预报性能的评估,得到模型对C,M,X级耀斑发生概率的预报误差均较小,Brier评分误差分别为0.113,0.087,0.012;模型的预报性能均比平均模型有提高,对C,M,X级耀斑发生概率预报的Brier技巧评分分别为0.250,0.106,0.012。在2008-2016年未来1天耀斑预报的模型实测中,模型的预报效果与中国科学院空间环境预报中心的预报效果相当,这说明该模型在实际的空间环境预报中切实可行。     

X级以上耀斑与地磁效应关系研究

统计研究了2010年1月至2012年12月期间所有与耀斑爆发相伴生的日冕物质抛射(CME) 引发的地磁暴事件. 结果表明, 对于CME源区其主要分布在日面 45°E-45°W, 占总数的78.95%, 且西半球比东半球多, 即源区位于西半球的CME易产生地磁效应; X级耀斑与地磁效应的关联性更高, 60.0%的 X级耀斑在其爆发后的2～3天内观测到地磁暴, 而其他级别的耀斑与地磁效应的关联性低得多, 均不足10%; 通过对此期间日面爆发的所有X级耀斑研究分析后发现, 对于源区位于日面东经45°E-45°W 的X级耀斑, 若在其爆发过程中没有大尺度日面扰动, 则无伴生CME且后续产生地磁效应的可能性很低. 由此提出一种通过分析日面观测数据进行地磁暴预报的方法.      

神经网络方法在太阳质子事件短期预报中的应用

在大量统计结果的基础上,深入研究了太阳质子事件预报机理．总结了质子事件爆发与太阳活动区面积、位置、McIntosh结构、磁结构以及前两天活动区爆发耀斑事件数目之间的关系．然后,在神经网络的基础上建立了太阳质子事件短期预报模型,并对2000年以后12个未参加训练的样本进行测试,结果对事件预报的准确率为83％．此外,我们还利用该模型对2002年1-4月发生的几次质子事件进行了预报试验,结果发现,这期间发生的6次事件都被预报．其中3次质子事件系统预报提前了3天,两次事件预报提前了2天,一次事件提前1天预报．     

基于长短期记忆神经网络的太阳耀斑短期预报

提出了一个基于长短期记忆神经网络的耀斑预报模型,利用过去24 h太阳活动区的磁场变化时序构建样本,通过长短期记忆神经网络对磁场特征时序演化进行分析,预报未来48 h内是否发生≥M级别耀斑事件。使用的数据集为2010年5月到2017年5月所有活动区样本,选取了SDO/HMI SHARP的10个磁场特征参量。在建模过程中通过XGBoost方法选取权重、增益率和覆盖率均较高的6个特征参量作为输入参数。通过测试对比,模型的虚报率和准确率与传统机器学习模型相近,报准率和临界成功指数分别为0.7483和0.7402,优于传统机器学习模型。模型总体效果优于传统机器学习模型。      

太阳10.7 cm射电流量中期预报模型研究(Ⅱ)

太阳活动指数中期预报一直是空间环境业务预报的难点之一.本文在自回归方法模型的基础上,利用太阳活动区面积、位置等参数与10.7cm辐射流量之间的定量关系,根据活动区面积衰减规律,建立了一个基于活动区参数及演化规律的改进型太阳活动指数中期预报模型.通过对预报测试实例分析发现,在日面出现较大活动区导致F_(10.7)迅速增长并超过历史数据峰值的情况,在日面活动区消亡导致指数突然出现平静期的情况,新模型的预报准确性相比自回归模型有很大提高,预报的平均相对误差下降约5%～9%.由此可见,新模型在某些特定条件下提高了原有模型的精度.该研究为提高业务型太阳10.7cm射电流量中期预报模型的预报精度奠定了基础.     

用行星引潮力预报太阳耀斑的注记

太阳活动与空间坏境紧密相关,大耀斑会引起空间环境的剧烈扰动．太阳活动预报便成空间环境预报的基本依据．太阳预报水平长期以来提高缓慢,太阳物理学家皆有共识,寄希望于物理预报的进展,但举步维艰．近来,“太阳活动的行星潮汐效应”的研究取得了新进展1)[1,2],引潮力可以触发耀斑,从而,利用这类效应发展物理预报技术,呈现良好前景．“太阳耀斑发生率按行星引潮力的分布”已有几个具体结果,表面看来,其间似乎有出人需予澄清．1972年,董土仑和林柏森发现1958－1968年94个质子耀斑的发生率在其目面经度处（活动经度上）技引潮力…     

基于SDO/HMI全日面实时磁图的活动区自动识别

太阳活动区是太阳活动的主要发生源区，活动区的形态、结构、特征是预报太阳爆发的主要依据.因此，活动区的识别是实现太阳爆发预报的前提.SDO/HMI能够提供连续、高时空精度的全日面光球观测图像.参照文献[1]SOHO/MDI综合磁图中活动区的自动识别方法，利用实时可得的HMI全日面磁图，通过阈值法、数学形态法和区域增长法相结合的方式，开展了活动区的快速自动识别研究.将2010—2018年的自动识别结果与NOAA/SWPC每日发布的活动区结果进行比较发现:通过磁图自动识别的活动区数目与SWPC活动区数目整体变化趋势基本一致，两者的相关系数为0.87；从活动区整体标识的数目上来看，通过磁图自动识别的活动区数目少于SWPC发布的结果.未被自动标识的活动区主要为面积小、磁场弱且磁类型简单的活动区，引发太阳爆发的可能性极小，因此不会对太阳爆发的实际预报产生影响.本文的研究方法和结果能够为太阳活动预报提供实时的活动区数据，加速太阳爆发预报模型的实际应用.      

数据同化在空间天气学中的应用

由太阳活动引起的耀斑和日冕物质抛射等短时间尺度变化的空间天气事件会影响并危害地球磁层、电离层、中高层大气、卫星运行安全以及人类健康,因此对这些空间天气事件的预测显得尤为重要。数据同化在稀疏观测和异步采集的情况下能够增加模型的预测能力,对模型变量进行自洽分析。在数值预报中引入数据同化方法,能够提高预测可信度。本文从数据同化方法的角度出发,主要分析了数据同化目前在大气、电离层、磁层、太阳及其他行星科学研究中的应用,并初步讨论了数据同化未来在空间天气方面的应用。      

基于集成学习的太阳质子事件短期预报方法

太阳质子事件是一种由太阳活动爆发时喷射并传播到近地空间的高能粒子引起的空间天气现象。这些高能粒子会对航天器和宇航员产生严重危害,对太阳质子事件进行准确的短期预报是航天活动灾害预防的重要内容。针对当前主要预报模型中普遍存在的高虚报率问题,提出了一种基于集成学习的太阳质子事件短期预报方法,利用第23个太阳活动周数据,建立了一种集成8种机器学习模型的太阳质子事件短期预报系统。实验结果表明,本文方法在取得了80.95%的报准率的同时,将虚报率降低至19.05%,相比现有的预报系统具有较为明显的优势。      

太阳活动对电离层TEC变化影响分析ormalsize

为研究太阳活动对电离层TEC变化的影响,从整体到局部分析了2000—2016年的太阳黑子数、太阳射电流量F_10.7指数日均值与电离层TEC的关系,并重点分析了2017年9月6日太阳爆发X9.3级特大耀斑前后15天太阳活动与电离层TEC变化的相关性.结果表明:由2000—2016年的数据整体看来,太阳黑子数、太阳F_10.7指数、TEC两两之间具有很强的整体相关性,但局部相关性强弱不均;此次耀斑爆发前后太阳黑子数、太阳F_10.7指数和TEC具有很强的正相关特性,太阳活动对TEC的影响时延约为2天;太阳活动对全球电离层TEC的影响不同步,从高纬至低纬约有1天的延迟,且对低纬度的影响远大于中高纬度.太阳活动是影响电离层TEC变化的主要原因,但局部也可能存在其他重要影响因素.      

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

Prediction and evaluation of solar particle events based on precursor information.

Protection from the radiation effects of solar particle events for deep space mission crews requires a warning system to observe solar flares and predict subsequent charged particle fluxes. Such a system relates precursor information observed in each flare to the intensity, delay, and duration of the subsequent Solar Particle Event (SPE) at other locations in the solar system. A warning system of this type is now in operation at the NOAA Space Environment Services Center in Boulder, Colorado for support of space missions. It has been used to predict flare particle fluxes at the earth for flares of Solar Cycle 22. The flare parameters used and the effectiveness of the current warning system, based on Solar Cycle 22 experience, are presented, with an examination of the shortcomings. Needed improvements to the system include more complete observations of solar activity, especially information on the occurrences of solar mass ejections; and consideration of the effects of propagation conditions in the solar corona and interplanetary medium. Requirements for solar observations and forecasting systems on board the spacecraft are discussed.     

基于U-net的紫外极光观测极光卵形态提取

极光卵形态提取是极光研究的重要手段.如何提高强干扰背景下的紫外极光图像极光卵形态提取精度,目前仍是一个难题.本文提出一种基于深度学习语义分割模型U-net的方法,实现了对极光卵形态的高精度提取.在Polar卫星紫外极光观测数据的实验结果表明,该方法相比于已有算法精度更高,对完整型极光卵和缺口型极光卵图像均能得到更加精确的提取结果,特别是针对强日辉干扰、灰度不均匀和对比度低情况下的紫外极光图像时,该方法显示了明显优势.      

PPS-87: a new event oriented solar proton prediction model.

A new event-oriented solar proton prediction model has been developed and implemented at the USAF Space Environment forecast facility. This new model generates predicted solar proton time-intensity profiles for a number of user adjustable energy ranges and is also capable of making predictions for the heavy ion flux. The computer program is designed so a forecaster can select inputs based on the data available in near real-time at the forecast center as the solar flare is occurring. The predicted event amplitude is based on the electromagnetic emission parameters of the solar flare (either microwave or soft X-ray emission) and the solar flare position on the sun. The model also has an update capability where the forecaster can normalize the prediction to actual spacecraft observations of spectral slope and particle flux as the event is occurring in order to more accurately predict the future time-intensity profile of the solar particle flux. Besides containing improvements in the accuracy of the predicted energetic particle event onset time and magnitude, the new model converts the predicted solar particle flux into an expected radiation dose that might be experienced by an astronaut during EVA activities or inside the space shuttle.     

太阳X射线耀斑特征参数预报方法研究

太阳耀斑是重要的空间天气事件, 有关太阳耀斑参数的预报对于电离层突然骚扰(SID)影响的评估具有实用意义. 本文采用GOES-8卫星上第23太阳周软X射线通量的数据, 通过数值拟合的方法对X级耀斑强度的峰值以及X级耀斑的结束时间进行预测. 利用这种方法对第23太阳周中的X级耀斑进行分析, 最多可以提前17min预测出X级耀斑的峰值, 在预测X级耀斑结束时间时, 预测的X级耀斑结束时间最多可以提前60min左右, 从预报结果来看, 预报方法具有一定的有效性和实用性.      

Solar activity prediction studies and services in NAOC

Solar activity prediction services started in 1960’s in National Astronomical Observatories, Chinese Academy of Sciences (NAOC). As one of the members of the International Space Environment Service (ISES), Regional Warning Center of China (RWC-China) was set up in 1990’s. Solar Activity Prediction Center (SAPC), as one of the four sub-centers of RWC-China, is located in NAOC. Solar activity prediction studies and services in NAOC cover short-term, medium-term, and long-term forecast of solar activities. Nowadays, certain prediction models, such as solar X-ray flare model, solar proton event model, solar 10 cm radio flux model, have been established for the practical prediction services. Recently, more and more physical analyses are introduced in the studies of solar activity prediction, such as the magnetic properties of solar active regions and magnetic structure of solar atmosphere. Besides traditional statistics algorithms, Machine Learning and Artificial Intelligence techniques, such as Support Vector Machine (SVM) method, are employed in the establishment of forecast models. A Web-based integrated platform for solar activity data sharing and forecast distribution is under construction.     

太阳质子事件警报

采用人工神经网络预报方法,利用太阳耀斑的日面位置、X射线辐射的峰值流量及其上升时间、2695MHz和8800MHz微波辐射的半积分流量等5个物理参量,提出了一个新的太阳质子事件警报方案,预报太阳质子事件的发生及其流量和时间．该方案在本文检验中达到93．75％的预报准确率．     

Flux emergence,flux imbalance,magnetic free energy and solar flares

Emergence of complex magnetic flux in the solar active regions lead to several observational effects such as a change in sunspot area and flux embalance in photospheric magnetograms. The flux emergence also results in twisted magnetic field lines that add to free energy content. The magnetic field configuration of these active regions relax to near potential-field configuration after energy release through solar flares and coronal mass ejections. In this paper, we study the relation of flare productivity of active regions with their evolution of magnetic flux emergence, flux imbalance and free energy content. We use the sunspot area and number for flux emergence study as they contain most of the concentrated magnetic flux in the active region. The magnetic flux imbalance and the free energy are estimated using the HMI/SDO magnetograms and Virial theorem method. We find that the active regions that undergo large changes in sunspot area are most flare productive. The active regions become flary when the free energy content exceeds 50% of the total energy. Although, the flary active regions show magnetic flux imbalance, it is hard to predict flare activity based on this parameter alone.     

太阳极紫外光谱仪无缝成像技术研究

传统极紫外成像光谱仪无法实时观测大范围的太阳活动.无缝成像技术使得光谱仪能够获得大视场范围内的太阳空间信息和光谱信息.通过无缝成像光谱仪成果分析,提出了一种改进的光学设计思路,并通过模拟数据重建证明其能够大幅提高多普勒速度反演的准确性,从而极大提高了观测数据的可信度.