A selective up-sampling method applied upon unbalanced data for flare prediction: potential to improve model performance

The Spaceweather HMI Active Region Patch (SHARP) parameters have been widely used to develop flare prediction models. The relatively small number of strong-flare events leads to an unbalanced dataset that prediction models can be sensitive to the unbalanced data and might lead to bias and limited performance. In this study, we adopted the logistic regression algorithm to develop a flare prediction model for the next 48 h based on the SHARP parameters. The model was trained with five different inputs. The first input was the original unbalanced dataset; the second and third inputs were obtained by using two widely used sampling methods from the original dataset, while the fourth input was the original dataset but accompanied by a weighted classifier. Based on the distribution properties of strong-flare occurrences related to SHARP parameters, we established a new selective up-sampling method and applied it to the mixed-up region (referred to as the confusing distribution areas consisting of both the strong-flare events and non-strong-flare events) to pick up the flare-related samples and add small random values to them and finally create a large number of flare-related samples that are very close to the ground truth. Thus, we obtained the fifth balanced dataset aiming to 1) promote the forecast capability in the mixed-up region and 2) increase the robustness of the model. We compared the model performance and found that the selective up-sampling method has potential to improve the model performance in strong-flare prediction with its F1 score reaching 0.5501 ± 0.1200, which is approximately 22% − 33% higher than other imbalance mitigation schemes

Measurement of the vertical atmospheric density profile from the X-ray Earth occultation of the Crab Nebula with Insight-HXMT

In this paper, the X-ray Earth occultation (XEO) of the Crab Nebula is investigated by using the Hard X-ray Modulation Telescope (Insight-HXMT). The pointing observation data on the 30th September, 2018 recorded by the Low Energy X-ray telescope (LE) of Insight-HXMT are selected and analyzed. The extinction lightcurves and spectra during the X-ray Earth occultation process are extracted. A forward model for the XEO lightcurve is established and the theoretical observational signal for lightcurve is predicted. The atmospheric density model is built with a scale factor to the commonly used MSIS density profile within a certain altitude range. A Bayesian data analysis method is developed for the XEO lightcurve modeling and the atmospheric density retrieval. The posterior probability distribution of the model parameters is derived through the Markov Chain Monte Carlo (MCMC) algorithm with the NRLMSISE-00 model and the NRLMSIS 2.0 model as basis functions and the best-fit density profiles are retrieved respectively. It is found that in the altitude range of 105--200 km, the retrieved density profile is 88.8% of the density of NRLMSISE-00 and 109.7% of the density of NRLMSIS 2.0 by fitting the lightcurve in the energy range of 1.0--2.5 keV based on XEOS method. In the altitude range of 95--125 km, the retrieved density profile is 81.0% of the density of NRLMSISE-00 and 92.3% of the density of NRLMSIS 2.0 by fitting the lightcurve in the energy range of 2.5--6.0 keV based on XEOS method. In the altitude range of 85--110 km, the retrieved density profile is 87.7% of the density of NRLMSISE-00 and 101.4% of the density of NRLMSIS 2.0 by fitting the lightcurve in the energy range of 6.0--10.0 keV based on XEOS method. This study demonstrates that the XEOS from the X-ray astronomical satellite Insight-HXMT can provide an approach for the study of the upper atmosphere.Comment: 31 pages, 15 figures, 5 tables, accepted for publication in Atmospheric Measurement Technique

Two-stage Hierarchical Framework for Solar Flare Prediction

Solar flares, often accompanied by coronal mass ejections and other solar phenomena, are one of the most important sources affecting space weather. It is important to investigate the forecast approach of solar flares to mitigate their destructive effect on the Earth. Statistical analysis, associated with data from 2010 to 2017 in Space-weather HMI Active Region Patches (SHARPs) collected by the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, reveals that there is a distribution divergence between the two types of active regions (ARs) of solar flares. A two-stage hierarchical prediction framework is formulated to better utilize this intrinsic distribution information. Specially, we pick up the ARs where at least one solar flare event occurs within the next 48 hr as flaring ARs through balanced random forest and naive Bayesian methods and then predict the events from flaring ARs by a cascade module of learning models. The empirical evaluation of SHARPs data from 2016 to 2019 verifies the promising performance of our framework, e.g., 0.727 for the true skill statistic

Modeling the Relationship of ≥2 MeV Electron Fluxes at Different Longitudes in Geostationary Orbit by the Machine Learning Method

The energetic electrons in the Earth’s radiation belt, known as “killer electrons”, are one of the crucial factors for the safety of geostationary satellites. Geostationary satellites at different longitudes encounter different energetic electron environments. However, organizations of space weather prediction usually only display the real-time ≥2 MeV electron fluxes and the predictions of ≥2 MeV electron fluxes or daily fluences within the next 1–3 days by models at one location in GEO orbit. In this study, the relationship of ≥2 MeV electron fluxes at different longitudes is investigated based on observations from GOES satellites, and the relevant models are developed. Based on the observations from GOES-10 and GOES-12 after calibration verification, the ratios of the ≥2 MeV electron daily fluences at 135° W to those at 75° W are mainly in the range from 1.0 to 4.0, with an average of 1.92. The models with various combinations of two or three input parameters are developed by the fully connected neural network for the relationship between ≥2 MeV electron fluxes at 135° W and 75° W in GEO orbit. According to the prediction efficiency (PE), the model only using log10 (fluxes) and MLT from GOES-10 (135° W), whose PE can reach 0.920, has the best performance to predict ≥2 MeV electron fluxes at the locations of GOES-12 (75° W). Its PE is larger than that (0.882) of the linear model using log10 (fluxes four hours ahead) from GOES-10 (135° W). We also develop models for the relationship between ≥2 MeV electron fluxes at 75° W and at variable longitudes between 95.8° W and 114.9° W in GEO orbit by the fully connected neural network. The PE values of these models are larger than 0.90. These models realize the predictions of ≥2 MeV electron fluxes at arbitrary longitude between 95.8° W and 114.9° W in GEO orbit

An operational solar wind prediction system transitioning fundamental science to operations

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

An operational solar wind prediction system transitioning fundamental science to operations

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

An operational solar wind prediction system transitioning fundamental science to operations

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