The F10.7 index is crucial for assessing solar activity, significantly impacting communication, navigation, and satellite operations. The intrinsic complexity and variability of solar activity often result in sudden perturbations in the F10.7 index, compromising the accuracy and stability of forecasts. To address this challenge, we propose a novel prediction strategy that separately forecasts fundamental trends driven by the medium-to-long-term evolution of the solar cycle and the 27 day rotational modulation, along with transient disturbances caused by solar flares and the rapid evolution of active regions. These forecasts are then integrated to enhance overall prediction accuracy. We incorporate additional features such as the soft X-ray flare index (FI _SXR ), magnetic type of the active region (new_Mag), and X-ray background flux (XBF) to enhance the understanding of the underlying physical processes of solar activity. Our experiments, conducted using advanced forecasting models on the SG-F10.7-All data set, validate the efficacy of our proposed strategy. Notably, the iTransformer model demonstrates superior performance in both short-term and medium-term forecasting scenarios. The inclusion of FI _SXR , new_Mag, and XBF significantly improves forecasting accuracy, highlighting their importance in improving the F10.7 index predictions. Our method outperforms international models from the Space Weather Prediction Center, British Geological Survey, and Collecte Localisation Satellites, exhibiting greater accuracy and adaptability across various solar activity phases. This finding provides a novel approach for precise forecasting of the F10.7 index.

Accurate prediction of solar flares is essential for space weather warnings and safeguarding technological infrastructure. This study proposes a dual-stage flare prediction framework leveraging the full-disk flare index (FI). In the first stage, FI is decomposed into long-term trend and short-term disturbance components via a “trend-disturbance” decomposition strategy. The iTransformer model independently forecasts each component, which is then fused to generate high-fidelity FI predictions. The second stage develops a regression-classification architecture that maps predicted FI values to flare intensity levels, enabling comprehensive full-disk flare forecasting. Experimental results indicate that the decomposition strategy improves performance across all flare classes, reducing the mean absolute error of FI prediction to 19.066 and achieving a TSS of 0.639 and an F1 score of 0.649 for M-class flares. Tested from the solar minimum in 2019 through the 2024 solar maximum, the framework surpasses the operational forecasting capabilities of leading space weather prediction centers such as SWPC and SEPC, successfully delivering reliable 72 hr flare warnings during 2024 May events. The dual-stage framework introduces an innovative approach to improving the accuracy and reliability of flare prediction, offering a valuable reference for forecasting extreme solar activity events.

We propose a compression and rendering method for time-varying interplanetary volumes, by reducing the redundancy in solar wind based on the coronal hole characteristics, slow change and rotation along with the sun. We regard the firsttime step as the solar wind background volume, decompose it into a mean level and a detail level, and encode the detail level by a vector quantizer. Then each time step is partitioned into two parts, CME partition and solar wind partition. The CME partition is decomposed and encoded as the background volume. The solar wind partition is matched with the background, and the rotation region and angle are computed and recorded. In the decompression and rendering process, two mean sets, two index sets and two codebooks are combined and stored in a mean texture, an index texture and a codebook texture respectively. Furthermore, the progressive rendering based on GPU is employed for interactive volume visualization. Finally, we test our algorithm on four time-varying volume data sets, along with three representative algorithms. The experiment results prove that our algorithm can improve the compression rate and the encoding speed significantly, maintain the visual quality of reconstructed volumes, and also achieve an ideal decoding and rendering speed.

Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products. With the use of natural language generation methods based on the sequence-to-sequence model, space weather forecast texts can be automatically generated. To conduct our generation tasks at a fine-grained level, a taxonomy of space weather phenomena based on descriptions is presented. Then, our MDH（Multi-Domain Hybrid） model is proposed for generating space weather summaries in two stages. This model is composed of three sequence-to-sequence-based deep neural network sub-models（one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models）. Then, to evaluate how well MDH performs, quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented. The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87, 93.50, and 92.69, respectively. The results suggest that MDH can generate space weather summaries with high accuracy and coherence, as well as suitable length, which can assist forecasters in generating high-quality space weather forecast products, despite the data being starved.

Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research. Space environment simulation can produce several correlated variables at the same time. However, existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable, not dealing with the redundant information among these variables. For space environment volume data with multi-correlated variables, based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables. The volume data associated with each variable is divided into disjoint blocks of size 4~3 initially. The blocks are represented as two levels, a mean level and a detail level. The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels, a splitting based on a principal component analysis is applied to compute initial codebooks.Then, LBG algorithm is conducted for codebook refinement and quantization. We further take advantage of progressive rendering based on GPU for real-time interactive visualization. Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data, including ＞ 5, ＞ 10, ＞ 30 and ＞ 50 Me V integrated proton flux. The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression, achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.

Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain. These include the scale-invariant feature transform algorithm, speeded-up robust features algorithm, binary robust invariant scalable keypoints algorithm, and oriented fast and rotated brief algorithm. The performance of these algorithms was estimated in terms of matching accuracy, feature point richness, and running time. The experiment result showed that no algorithm achieved high accuracy while keeping low running time, and all algorithms are not suitable for image feature extraction and matching of augmented solar images. To solve this problem, an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm. Furthermore, our method and the four representative algorithms were applied to augmented solar images. Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms. Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm, which is significantly lower than other algorithms.

We propose a compression and rendering method for time-varying space environment volumes, by incorporating compression in temporal dimension into HVQ, based on the temporal coherence and cyclical change characteristics of space environment. We incorporate partitions and weights into vector quantization to give more consideration to concerned period data. We take advantage of the codebook-retraining algorithm to speed up the quantization of sequences with similar space environment condition. Furthermore, we employ the progressive rendering based on GPU for real-time interactive visualization. The results of our experiments prove that the method proposed in this paper can improve the compression rate, reduce the cost of reconstruction quality for compression significantly, and also increase rendering speed in space environment domain.

随着卫星技术的飞速发展,保障空间技术系统的运行安全对空间环境预报业务提出了更高要求.本文根据空间环境信息服务中数据及模型资源分布式集成与共享的现状,针对空间环境预报数据智能化、自动化生成的需求,将Web服务技术及语义服务组合技术应用到空间环境信息服务中.根据空间环境领域特点,提出了空间环境领域的语义相似度计算方法,并采用基于语义相似度的方法实现空间环境信息服务的自动组合.

Visualization has been widely applied in space environment domain. However, compressed volume rendering algorithms based on VQ are concerned on fidelity and compression rate, not combined with specific application. To fulfill the specific visualization requirements for space environment volume data, an application-driven compression and rendering algorithm is proposed, which is Weight Based Hierarchical Vector Quantization (WHVQ). The volume data is initially partitioned into disjoint 43 blocks. Weights are assigned to the blocks according to their importance. The blocks are then decomposed into a three level hierarchical representation and each block is represented by a mean value and two detail vectors. To the top two levels, a splitting based on principal component analysis and weight is adopted to form their initial codebooks. Then, LBG algorithm based on weight is conducted for codebook refinement and quantization. The experimental results show that WHVQ is able to improve the quality of reconstruction in interested area on the premise of the good overall fidelity.

日冕物质抛射(Coronal Mass Ejection,CME)参数识别模型是太阳风预报过程的重要组成部分.在空间环境预报业务中,为提高太阳风预报的准确率,需要提高CME参数识别的精度.模型以计算任务串行的方式运行,运算效率低导致模型运算时间长,不能满足这种需求.CME参数识别模型的物理运算过程相互不独立,其在单节点上的运行方式不能满足并行化要求.基于MapReduce的并行计算框架,改进了CME参数识别模型的计算流程,提出CDMR (CME detection under MapReduce)方法,实现了CME参数识别模型的并行计算,并对比分析CME参数识别模型在串行计算和MapReduce并行计算下的运行时间,提高了模型的识别精度和计算效率.

空间环境监测网需要通过广域网将海量空间环境数据从地面监测台站传输到数据中心.本文研究了TCP拥塞控制机制,采用BBR拥塞控制算法解决数据传输存在的问题.针对空间环境数据传输需求,设计了空间环境数据传输的工作流模型,建立了空间环境数据传输系统.实验证明该系统适于进行广域网链路上的空间环境数据传输.

Ionospheric data assimilation is a now-casting technique to incorporate irregular ionospheric measurements into certain background model, which is an effective and efficient way to overcome the limitation of the unbalanced data distribution and to improve the accuracy of the model, so that the model and the data can be optimally combined with each other to produce a more reliable and reasonable system specification. In this study, a regional total electron content (TEC) now-casting system over China and adjacent areas (70 degrees E 140 degrees E and 15 degrees N -55 degrees N) is developed on the basis of data assimilation technique. The International Reference Ionosphere (IRI) is used here as background model, and the GNSS data are derived from both the Space Environment Monitoring Network of Chinese Academy of Sciences (SEMnet) and International GNSS Service (IGS) data. A Three-dimensional variation algorithm (3DVAR) combined with Gauss-Markov Kalman filter technique is used to implement the data assimilation. The regional gridded TEC maps and the position errors of single-frequency GPS receivers can be generated and publicized online (http://sepc.ac.cn/TEC_chn.php) in quasi-real time, which is updated for every 15 min. It is one of the ionospheric now casting systems in China based on data assimilation algorithm, which can be used not only for real-time monitoring of ionosphere environment over China and adjacent areas, but also in providing accurate and effective specification of regional ionospheric TEC and error correction for satellite navigation, radar imaging, shortwave communication, and other relevant applications.

With the development of the aerospace industry,the space environment may cause more and more serious harm to the space activities. In order to protect the normal work and extend the service life of spacecraft,the space environment research needs to provide fast,comprehensive and applied space environment information. The existing space environment research mode is complicated in operation process, low in operation efficiency, scattered in quantity and independent in operation and disadvantageous in management. In this paper,an integrated platform for business-oriented spatial environment mode based on Petri nets is proposed,which can be easily integrated and united to meet the needs of business operation through code-free integration and task scheduling. The platform realizes the friendly interface integrated configuration and 7 * 24 operation,convenient and unified management of multi-mode,enhance the spatial environment mode of business level.

We present in this paper an operational solar wind prediction system. The system is an outcome of the collaborative efforts between scientists in research communities and forecasters at Space Environment Prediction Center (SEPC) in China. This system is mainly composed of three modules: (1) a photospheric magnetic field extrapolation module, along with the Wang-Sheeley-Arge (WSA) empirical method, to obtain the background solar wind speed and the magnetic field strength on the source surface; (2) a modified Hakamada-Akasofu-Fry (HAF) kinematic module for simulating the propagation of solar wind structures in the interplanetary space; and (3) a coronal mass ejection (CME) detection module, which derives CME parameters using the ice-cream cone model based on coronagraph images. By bridging the gap between fundamental science and operational requirements, our system is finally capable of predicting solar wind conditions near Earth, especially the arrival times of the co-rotating interaction regions (CIRs) and CMEs. Our test against historical solar wind data from 2007 to 2016 shows that the hit rate (HR) of the high-speed enhancements (HSEs) is 0.60 and the false alarm rate (FAR) is 0.30. The mean error (ME) and the mean absolute error (MAE) of the maximum speed for the same period are −73.9 km s−1 and 101.2 km s−1, respectively. Meanwhile, the ME and MAE of the arrival time of the maximum speed are 0.15 days and 1.27 days, respectively. There are 25 CMEs simulated and the MAE of the arrival time is 18.0 h.

Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center（SEPC） affiliated with the National Space Science Center（NSSC） has been providing space weather services and helps secure space missions. Presently, SEPC is capable to offer a variety of space weather services covering many phases of space science missions including planning, design, launch,and orbital operation. The service packages consist of space weather forecasts, warnings, and effect analysis that can be utilized to avoid potential space weather hazard or reduce the damage caused by space storms,space radiation exposure for example. Extensive solar storms that occurred over Chinese Ghost Festival（CGF）in September 2017 led to a large enhancement of the solar energetic particle flux at 1 AU, which affected the near Earth radiation environment and brought great threat to orbiting satellites. Based on the space weather service by SEPC, satellite ground support groups collaborating with the space Tracking, Telemetering and Command system（TT＆C） team were able to take immediate measures to react to the CGF solar storm event.

针对数据源复杂、实时性强、准确性高和数据类型多样的Web空间环境数据采集任务,提出了一个基于Petri网的信牌驱动式Web数据采集模型.首先,通过引入Petri网的基本要素作为模型的理论基础,研究适合于Web数据采集的建模方法;在此基础上,针对模型的具体应用验证,研究了空间环境数据采集任务服务系统(SEDGSS)的架构设计,对数据源配置子系统、任务控制子系统和任务处理子系统进行具体的实现.实验结果表明,该模型实现了自动化机制和回溯校验机制,并具有良好的易配置性、可重用性和扩展灵活性;该系统7×24小时实时抓取254个复杂的数据源任务,目前正承担着自动化、业务化的空间环境数据采集任务以服务于我国空间环境预报.

剧情是太阳风暴应急演练的驱动,控制演练的过程和发展。针对传统演练中手动设置剧情、演练剧情已知,且不能反映太阳风暴动态演化过程等问题,利用二叉树重构法建立太阳风暴剧情推理引擎,根据不同演练需求智能化地生成太阳风暴剧情方案;基于历史事件库,利用分段线性插值等方法,真实表现了难以用物理模型描述的太阳风暴动态演化过程;基于以上两项关键技术,实现了太阳风暴剧情生成系统。实验结果表明,系统生成的太阳风暴剧情,贴近实战,针对性强,可有效提升演练中各部门对突发事件的快速反应和应急处置能力。