Geomagnetic Quiet Periods Research Articles

Latitudinal Characteristics of the Post‐Sunset Enhancements in Ionospheric Electron Density During the Geomagnetic Quiet Period in May 2021 Over East‐Asian Region

AbstractThis study investigated the latitudinal variations of post‐sunset enhancements in the ionospheric electron density during the geomagnetic quiet period in May 2021 with a combination of high‐precision ionospheric parameters obtained from four ionosondes, Beidou geostationary satellite (BD‐GEO) receiver network and Sanya incoherent scatter radar (SYISR). We identified four categories of post‐sunset enhancement phenomena (Types 1–4), each with unique spatial and temporal evolutions, yet uniformly accompanied by a decrease in hmF2. Measurements of plasma drift vector velocities from SYISR and hmF2 gradients across various latitudes provided pivotal insights, confirming that the ionospheric post‐sunset enhancements can result from downward plasma motion due to westward electric field, downward field‐aligned drift, or a combination of both. For Type 1, dominated by field‐aligned drift, plasma density enhancements not only intensify at low latitudes but may also extend to mid‐latitudes, exhibiting a distinct temporal delay with increasing latitude. In contrast, Type 4, primarily driven by the westward electric field, is characterized by modest increases in plasma density confined to localized low‐latitude regions, with no observable latitudinal time delay in the peak of enhancements. Types 2 and 3, which are subject to the combined influence of the westward electric field and field‐aligned drift, exhibit plasma density increases at certain low‐latitude areas, with Type 2 presenting a delayed pattern and Type 3 showing none with rising latitude. Meanwhile, neutral winds can partially account for the observed post‐sunset enhancement from low to middle latitudes. These findings offer new insights into the factors influencing ionospheric behavior after sunset.

Impact of interplanetary shock on nitric oxide cooling emission: A superposed epoch study

The impact of interplanetary (IP) shock on Nitric Oxide (NO) 5.3 µm cooling emission is studied during geomagnetic quiet periods. The Active Magnetosphere and Planetary Electrodynamics Response Experiment measurements of field-aligned-currents intensify during IP shock with a relatively higher magnitude in southern hemisphere as compared to the northern hemispheric counterpart. The Defense Meteorological Satellite Program spacecraft observations displayed an early and strong enhancement in the precipitating particle flux of energy less than 1 keV. The particle flux of higher energy responds at later time. The NO density exhibited a significant, pre-event increase by an order of magnitude due to low-energy particle precipitation. The thermospheric temperature increased by about 100 K at 400 km. The superposed epoch analysis study revealed a linear enhancement in SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) measurements of NO cooling emission onboard the TIMED (Thermosphere Ionosphere Mesosphere Energetics Dynamics) satellite due to the prompt increase in particle precipitations and thermospheric temperature triggered by IP shock.

MAGE Model Simulation of the Pre‐Reversal Enhancement and Comparison With ICON and Jicamarca ISR Observations

AbstractUsing the latest coupled geospace model Multiscale Atmosphere‐Geospace Environment (MAGE) and observations from Jicamarca Incoherent scatter radar (ISR) and ICON ion velocity meter (IVM) instrument, we examine the pre‐reversal enhancement (PRE) during geomagnetic quiet time period. The MAGE shows comparable PRE to both the Jicamarca ISR and ICON observations. There appears to be a discrepancy between the Jicamarca ISR and ICON IVM with the later showed PRE about two times larger (∼40 m/s). This is the first time that MAGE is used to simulate the PRE. The results show that the MAGE can simulate the PRE well and are mostly consistent with observations.

Detection and Analysis of Radiation Doses in Multiple Orbital Space during Solar Minimum

Based on orbit detection data acquired by a positive channel Metal Oxide Semiconductor (PMOS) dose detectors on FY4-A (GEO), BD3-M15 (MEO), and YH1-01A (LEO) between November 2018 and November 2022, investigations reveal variations in total dose and the mechanism of radiation dose increase within the geostationary earth orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) during the transition from the 24th to the 25th solar cycles. It provides the radiation dose parameters for the study of the space environment from different altitude orbits, and also provides an important basis for studying the solar minimum activity and dose generation The data indicate directional disparities in radiation doses among the orbital regions, with the hierarchy being FY4-A > YH1-01A > BD3-M15. Furthermore, the results show that the total doses of FY4-A and BD3-M15 were higher than that of YH1-01A by two orders of magnitude, with BD3-M15 > FY4-A > YH1-01A. The monthly radiation dose rates of FY4-A in GEO and BD3-M15 in MEO exhibited positive correlation with their corresponding APs during the solar minimum. Notably, for FY4-A, the monthly radiation dose rate during geomagnetic disturbed periods exceeded that of the dose rate during geomagnetic quiet periods by one order of magnitude. This analysis revealed the substantial impact of geomagnetic storms and space environment disturbances on radiation doses detected by MEO and GEO orbital satellites. These perturbations, attributable to medium- and small-scale high-energy electron storms induced by reproducible coronal holes, emerged as key driving factors of the increase in radiation doses in MEO and GEO environments.

A High-Precision and Lightweight Prediction Model for Global Total Electron Content

Precise prediction of the global spatial–temporal distribution of total electron content (TEC) is a challenge in space weather. Existing models are generally able to provide rather good prediction results at the cost of a large amount of computing resources. This limits the application of the method. A lightweight and highly accurate global TEC prediction model was developed in this study. Our model is capable of forecasting the global TEC map up to 12 h in advance with a step of one hour. The predicted results during geomagnetic quiet periods were consistent with measurements, with a maximum and average mean error (ME) of 1.5 TECU and −0.04 TECU under conditions of high solar activity, respectively. Our model also performed well during geomagnetic disturbed periods, with a maximum ME of 4.5 TECU and 2.5 TECU under conditions of high and low solar activities, respectively. Our model significantly reduces the training time (47%) and basic requirement of memory (60%) relative to the model of Liu et al. (2022) with no remarkable loss of model accuracy.

The Conditional Probability of Correlating East Pacific Earthquakes with NOAA Electron Bursts

A correlation between low L-shell 30–100 keV electrons precipitating into the atmosphere and M ≥ 6 earthquakes in West Pacific was presented in past works where ionospheric events anticipated earthquakes by 1.5–3.5 h. This was a statistical result obtained from the Medium Energy Protons Electrons Detector on board the NOAA-15 satellite, which was analyzed for 16.5 years. The present analysis, utilizing the same database, translated into adiabatic coordinates during geomagnetic quiet periods, lead to another significant correlation regarding East Pacific strong earthquakes. This new correlation is still observed between high energy precipitating electrons detected by the NOAA-15 0° telescope and M ≥ 6 events of another very dangerous seismic region of the Pacific ring of fire. The particle precipitation that contributed to this correlation was characterized by electron L-shell, pitch-angle, possible disturbance altitudes, and geographical locations. This correlation occurred circa 57 h prior to the East Pacific earthquakes, according to past single cases of reports. The conditional probability corresponding to the cross-correlation peak of 0.024 per binary events reached a value of 0.011. A probability gain of 2 was calculated for earthquakes after an independent L-shell EBs detection, it is therefore applicable for future earthquake forecasting experiments. Moreover, a time-dependent probability gain approaching the correlation peak was estimated.

ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model

AbstractIn this paper, we proposed an innovative encoder‐decoder structure with a convolution long short‐term memory (ED‐ConvLSTM) network to forecast global total electron content (TEC) based on the International GNSS Service (IGS) TEC maps from 2005 to 2018 with 1‐hr time cadence. The ED‐ConvLSTM model is used to forecast TEC maps 1–7 days in advance through iterations. To investigate the model's performance, we compared the model with International Reference Ionosphere (IRI2016) model in 2014 and 2018, and compared the model with 1‐day Beijing University of Aeronautics and Astronautics (BUAA) model in 2018. The results show that our 7‐day ED‐ConvLSTM model (ED‐ConvLSTM model that forecasts 7 days in advance) outperforms IRI2016 in 2014 and 2018, and our 5‐day ED‐ConvLSTM model (ED‐ConvLSTM model that forecasts 5 days in advance) outperforms 1‐day BUAA model. Furthermore, the root mean square error (RMSE) from the 1‐day ED‐ConvLSTM model with respect to the IGS TEC maps decreases by 51.5% and 43%, respectively, in 2014 and 2018 compared with that from IRI2016 model. The RMSE from the 1‐day ED‐ConvLSTM model is 20.3% lower than that from the 1‐day BUAA model in 2018. In addition, our model has the highest RMSE in the Equatorial Ionospheric Anomaly (EIA) region, but can roughly predict the features and locations of EIA. However, the model fails to forecast localized TEC enhancement and the sudden ionospheric response to the geomagnetic storms. Overall, the model shows competitive performance in medium‐term global TEC maps prediction during geomagnetic quiet periods.

Comparison of Scalar Magnetic Field Data of China Seismo-Electromagnetic Satellite and Swarm Bravo Satellite

Based on the in-orbit magnetic field data of the China Seismo-Electromagnetic Satellite (CSES) and Swarm satellites, some research studies on the data consistency cross comparison were carried out. The condition applied is that two satellites pass by in a relatively short period of time and through the spatial location at a relatively close range, and different spatial-temporal scale standards were set, combined with the Kp index to screen for geomagnetic quiet periods. Then, with the help of the CHAOS model, indirect analysis was realized. Furthermore, the difference between the in-orbit data and model value was visualized, and the phenomenon and possible reason for data variation with time and geomagnetic latitude variation were analyzed. These analysis results are displayed in this study, which may evaluate the reliability of the satellite magnetic field detection data and the consistency of multiple satellite detection results and provide a methodological reference for carrying out similar evaluation and analysis subsequently.

The Frequency‐Domain Characterization of Cosmic Ray Intensity Variations Before Forbush Decreases Associated With Geomagnetic Storms

AbstractNonrecurrent geomagnetic storms caused by Coronal Mass Ejection (CME) can induce serious impacts on space‐ and ground‐based equipment. However, these nonrecurrent geomagnetic storms are hard to predict since CMEs are not periodic. Previous studies have shown that the variations of Cosmic Ray Intensity (CRI) before nonrecurrent storms may forebode the coming geomagnetic storm. But it is difficult to extract the variations since the cosmic ray flux is a complex signal. In order to identify the precursory signal in CRI variations triggered by CME, an ensemble self‐adaptive time‐frequency analysis method was proposed and applied to 65 nonrecurrent geomagnetic storms occurred between 1998 and 2019. The results indicate that the precursory signals are successfully identified in 43 of 45 storms, after excluding 20 storms accompanied by ground level enhancement. In addition, it is also found that the spectral density of the precursory signal could reflect the active level of geomagnetic condition, which is lower during geomagnetic quiet period.

Daytime F Region Echoes at Equatorial Ionization Anomaly Crest During Geomagnetic Quiet Period: Observations From Multi‐Instruments

AbstractBy using Qujing very high frequency radar (25.6°N, 103.7°E, magnetic latitude 16.1°N, magnetic longitude 177.0°E), daytime F region echoes were reported at equatorial ionization anomaly crest on 26 June 2020. Radar interferometry experiment was performed during the observation period. The observed results show that the spatial distributions of daytime F region echo pattern were about 150 km, 200 km, and 180 km in the zonal, meridional, and height extents, respectively. In addition, we observed a remarkable eastward drift of F region echoes with a mean velocity of about 50 m/s. To investigate the possible generation mechanism of daytime F region echoes, simultaneous occurrences of ionospheric disturbance and atmospheric gravity wave activities were presented by combining with the observations of ionosonde, global position system stations and FY‐4A satellite. The existence of gravity wave from the deep convective activities in the lower atmosphere was confirmed by using FY‐4A satellite measurements. The gravity wave signatures were also observed in the time series of virtual height deviations of sporadic E (dhES) and total electron content near the region where the F region echoes. We surmise that ionospheric disturbance in the F region could be excited during atmospheric gravity wave activities. Then the large‐scale ionospheric disturbance could further evolve into small‐scale irregularity producing radar echoes through the non‐linear cascade process.

A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps

The rapid evolutions in Artificial Intelligence (AI) and the Machine Learning era have significantly improved accuracy for ionospheric space weather forecasting models. The ionospheric Total Electron Content (TEC) forecasting is necessary to alert Global Navigation Satellite System (GNSS) users about ionospheric space weather influences on satellite-receiver radio communications. Precise modeling and forecasting of the ionospheric TEC are critical for reliable and accurate GNSS applications. In this paper, a deep learning-based Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm is implemented for 26 GPS (Global Positioning System) stations TEC data over the Indian region. The Bi-LSTM ionospheric TEC forecasting maps are generated and compared with ANN, and LSTM models during both geomagnetic quiet and disturbed periods. The potential of Bi-LSTM networks in time sequence processing is improved by having forward and backward connections. The Bi-LSTM results are demonstrated with and without solar and geomagnetic indices as input to the network. This work's outcome would help develop an ionospheric weather alert system for GNSS users.

HEPPA III Intercomparison Experiment on Electron Precipitation Impacts: 1. Estimated Ionization Rates During a Geomagnetic Active Period in April 2010

AbstractPrecipitating auroral and radiation belt electrons are considered an important part of the natural forcing of the climate system. Recent studies suggest that this forcing is underestimated in current chemistry‐climate models. The High Energy Particle Precipitation in the Atmosphere III intercomparison experiment is a collective effort to address this point. Here, eight different estimates of medium energy electron (MEE) ionization rates are assessed during a geomagnetic active period in April 2010. The objective is to understand the potential uncertainty related to the MEE energy input. The ionization rates are all based on the Medium Energy Proton and Electron Detector (MEPED) on board the NOAA/POES and EUMETSAT/MetOp spacecraft series. However, different data handling, ionization rate calculations, and background atmospheres result in a wide range of mesospheric electron ionization rates. Although the eight data sets agree well in terms of the temporal variability, they differ by about an order of magnitude in ionization rate strength both during geomagnetic quiet and disturbed periods. The largest spread is found in the aftermath of enhanced geomagnetic activity. Furthermore, governed by different energy limits, the atmospheric penetration depth varies, and some differences related to latitudinal coverage are also evident. The mesospheric NO densities simulated with the Whole Atmospheric Community Climate Model driven by highest and lowest ionization rates differ by more than a factor of eight. In a follow‐up study, the atmospheric responses are simulated in four chemistry‐climate models (CCM) and compared to satellite observations, considering both the CCM structure and the ionization forcing.

Seasonal and Interhemispheric Effects on the Diurnal Evolution of EIA: Assessed by IGS TEC and IRI-2016 over Peruvian and Indian Sectors

The global total electron content (TEC) map in 2013, retrieved from the International Global Navigation Satellite Systems (GNSS) Service (IGS), and the International Reference Ionosphere (IRI-2016) model are used to monitor the diurnal evolution of the equatorial ionization anomaly (EIA). The statistics are conducted during geomagnetic quiet periods in the Peruvian and Indian sectors, where the equatorial electrojet (EEJ) data and reliable TEC are available. The EEJ is used as a proxy to determine whether the EIA structure is fully developed. Most of the previous studies focused on the period in which the EIA is well developed, while the period before EIA emergence is usually neglected. To characterize dynamics accounting for the full development of EIA, we defined and statistically analyzed the onset, first emergence, and the peaks of the northern crest and southern crest based on the proposed crest-to-trough difference (CTD) profiles. These time points extracted from IGS TEC show typical annual cycles in the Indian sector which can be summarized as winter hemispheric priority, i.e., the development of EIA in the winter hemisphere is ahead of that in the summer hemisphere. However, these same time points show abnormal semiannual cycles in the Peruvian sector, that is, EIA develops earlier during two equinoxes/solstices in the northern/southern hemisphere. We suggest that the onset of EIA is a consequence of the equilibrium between sunlight ionization and ambipolar diffusion. However, the latter term is not considered in modeling the topside ionosphere in IRI-2016, which results in a poor capacity in IRI to describe the diurnal evolution of EIA. Meridional neutral wind’s modulation on the ambipolar diffusion can explain the annual cycle observed in the Indian sector, while the semiannual variation seen in the Peruvian sector might be due to additional competing effects induced by the F region height changes.

Circulatory and Nervous Diseases Mortality Patterns—Comparison of Geomagnetic Storms and Quiet Periods

The aim of this paper is to statistically examine whether there are different patterns in daily numbers of deaths during the quiet periods of solar activity, in contrast to the periods of the strong solar storms. We considered three periods of solar storms (storm of 14 July 2000 Bastille Day Event, storm of 28 October 2003 Halloween Solar Storms, and storm of 17 March 2015 St. Patrick’s Day event) and three periods of continuous very low solar activity (13 September–24 October 1996, 21 July–20 August 2008, and 31 July–31 August 2009) during the Solar Cycles No. 23 and No. 24. In particular, we focus on diseases of the nervous system (group VI from ICD-10) and diseases of the circulatory system (group IX from ICD-10) separately for both sexes and two age groups (under 39 and 40+). We demonstrate that in the resulting graphical models there was a connection between the daily number of deaths and all indices of solar and geomagnetic activity in periods of low solar activity in contrast to periods of strong solar storms in some monitored groups according to age, sex, and group of diagnosis.

Daytime F Region Echoes at Equatorial Ionization Anomaly Crest during Geomagnetic Quiet Period: Observations from Multi-Instruments

Daytime F Region Echoes at Equatorial Ionization Anomaly Crest during Geomagnetic Quiet Period: Observations from Multi-Instruments

On the Cause of the Post‐Sunset Nocturnal OI 630 nm Airglow Enhancement Over Low‐Latitude Thermosphere

AbstractAirglow emissions serve as good tracers of the altitudinal regions at which they originate. OI 630 nm (red line) nocturnal emissions originate from around 250 km altitude. A total of 142 nights of data corresponding to the months of January, February, and March in the years 2013, 2014, and 2016, obtained from Mt. Abu (24.6°N, 72.7°E, 16°N Mag), Gurushikhar, India, a low‐latitude location, are investigated. These are compared with the column integrated emission rates calculated using, as inputs, the measured electron density profiles obtained from a digisonde from Ahmedabad (AMD, 23.0°N, 72.6°E, 15°N Mag), India. Following the expected monotonic decrement in the emissions after sunset, an enhancement is observed on several nights that peaks at around 20–21 local time (LT). The cause for this enhancement has been investigated in detail and it is found that the neutral winds, as obtained using digisondes at two locations, show almost a very good correlation between a poleward directed wind or cessation of equatorward wind over AMD and the observed airglow emission enhancement in the post‐sunset time. Further, the percentage enhancement in emissions also shows a decrease in magnitudes from January to March which has a broad similarity to the decrease in the model climatological meridional wind magnitudes in the same duration. Based on the data spanning over different years, it is inferred that, during geomagnetic quiet periods, almost all of the nocturnal variability in OI 630 nm emissions is due to the variations in the neutral wind.

Impact of Geomagnetic Storms on Ionosphere Variability and Precise Point Positioning Application in High Latitudes of the Northern Hemisphere

Based on the ionospheric scintillation data of the Canadian High Arctic Ionospheric Network (CHAIN), the variation characteristics of ionospheric Total Electric Content (TEC), phase scintillation and Rate of TEC Index (ROTI) were analyzed during the 26 August 2018 geomagnetic storm period. Results show that the TEC anomalies reach 20 TECU on global scale and 6 TECU over Canadian regions, respectively. The occurrence of phase scintillations is about 12.6% during the selected stormy day, which is only around 1% during geomagnetic quiet period. The occurrence of ROTI exhibits high correlation with that of phase scintillations during stormy geomagnetic conditions. The impact of ionospheric scintillations on positioning performance was analyzed by means of GPS Precise Point Positioning (PPP). It is proved that the 3D positioning root mean square errors are within 0.4 m for all test stations during the quiet geomagnetic condition. When it comes to the high geomagnetic condition, the magnitude of positioning errors significantly increase, which reaches 0.9 and 1.7 m in horizontal and vertical directions, respectively.

Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

AbstractThis paper presents a study on the possibility of predicting the regional ionosphere at midlatitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the Sami2 is Another Model of the Ionosphere (SAMI2). The NN model was constructed from the data set of Jeju ionosonde (33.43°N, 126.30°E) for the period of 1 January 2011 to 31 December 2015 by using the long‐short term memory (LSTM) algorithm. The NN model provides 24‐hr prediction of the peak density (NmF2) and peak height (hmF2) of the F2 layer over Jeju. The predicted NmF2 and hmF2 were used to compute two ionospheric drivers (total ion density and effective neutral meridional wind), which were assimilated into the SAMI2 model. The SAMI2‐LSTM model estimates the ionospheric conditions over the midlatitude region around Jeju on the same geomagnetic meridional plane. We evaluate the performance of the SAMI2‐LSTM by comparing predicted NmF2 and hmF2 values with measured values during the geomagnetic quiet and storm periods. The root‐mean‐square error values of NmF2 (hmF2) from Jeju ionosonde measurements are lower by 45% and 45% (30% and 11%) than those of the SAMI2 and IRI‐2016 models during the geomagnetic quiet periods. However, during the geomagnetic storm periods, the performance of the SAMI2‐LSTM model does not predict positive geomagnetic storms well. Comparing the quiet and storm periods for the SAMI2‐LSTM model, the root‐mean‐square error (RMSE) of the storm period was calculated to be 2.76 (3.2) times higher at Jeju (Icheon) than in the quiet period. From these results, we demonstrated that in this study, the combination of the NN‐LSTM model and physics‐based model could improve the ionosphere prediction of existing theoretical and empirical models for midlatitude regions, at least in geomagnetically quiet conditions. We strongly suggest that this attempt, which has not been reported before, could be used as one of the keys to advance the physics‐based model further.

Modelling and forecasting of ionospheric TEC irregularities over a low latitude GNSS station

The study aims to take an advantage of the systematic combination of statistical procedures namely Principal Component analysis (PCA), Linear Model to develop a local climatological model for ionospheric Total Electron Content (TEC) irregularities. Further, Auto Regressive Moving Average (ARMA) model, Neural Network (NN) model are used to forecast the ionospheric irregularities over Bengaluru region, India. Bengaluru International GNSS Service (IGS) station dataset (geographic lat.- 13.02°N, long.77.57°E; geomagnetic latitude: 4.4°N) of an 8-year period has been used to implement the proposed algorithm. Retrieval of the main components of the times series using Principal Component Analysis, identifying and analyzing the trends and cycles in the PCA residual to model the un-modelled irregularities using Linear model and finally fitting the residual series using ARMA/NN models to forecast the irregularity es has delivered successful results. The ionospheric TEC irregularities, measured in TECU/hour, are investigated during the 24th solar cycle ascending and descending phases. The MAE and RMSE values are used for the validation of the proposed models in forecasting the ionospheric TEC irregularities during both geomagnetic quiet and storm days (12–14 October 2016). It is observed that MAE value of NN model is 0.5 TECU/hour during geomagnetic quiet period whereas it is 0.98 TECU/hour during geomagnetic disturbed period. Moreover, the RMSE value of ARMA model is 0.73 TECU/hour and 0.67 TECU/hour for NN model which reveals that NN model is comparatively good in forecasting the ionospheric TEC irregularities during geomagnetic quiet period. The proposed model can be useful to develop an ionospheric irregularity climatological tool for low latitude regions.

Density Correction of NRLMSISE-00 in the Middle Atmosphere (20–100 km) Based on TIMED/SABER Density Data

This paper describes the density correction of the NRLMSISE-00 using more than 15 years (2002–2016) of TIMED/SABER satellite atmospheric density data from the middle atmosphere (20–100 km). A bias correction factor dataset is established based on the density differences between the TIMED/SABER data and NRLMSISE-00. Seven height nodes are set in the range between 20 and 100 km. The different scale oscillations of the correction factor are separated at each height node, and the spherical harmonic function is used to fit the coefficients of the different timescale oscillations to obtain a spatiotemporal function at each height node. Cubic spline interpolation is used to obtain the correction factor at other non-node heights. The spatiotemporal correction function depends on six key parameters, including height, latitude, longitude, local time, day, and year. The evaluation results show that the spatiotemporal correction function proposed in this paper achieves a good correction effect on the atmospheric density of NRLMSISE-00. The correction effect becomes more pronounced as the height increases. After correction, the relative error of the model decreased by 40%–50% in July, especially at ±40° N in the 80–100 km region. The correction effect of the spatiotemporal correction function under different geomagnetic activity may have some potential relationships with geomagnetic activities. During geomagnetic storms, the relative errors in atmospheric density at 100, 70, and 32 km decrease from 41.21%, 22.09%, and 3.03% to −9.65%, 2.60%, and 1.44%, respectively, after correction. The relative errors in atmospheric density at 100, 70, and 32 km decrease from 68.95%, 21.02%, and 3.56% to 3.49%, 2.20%, and 1.77%, respectively, during the geomagnetic quiet period. The correction effect during the geomagnetic quiet period is better than that during geomagnetic storms at a height of 100 km. The subsequent effects of geomagnetic activity will be considered, and the atmospheric density during magnetic storms and quiet periods will be corrected separately near 100 km. The ability of the model to characterize the mid-atmosphere (20–100 km) is significantly improved compared with the pre-correction performance. As a result, the corrected NRLMSISE-00 can provide more reliable atmospheric density data for scientific researches and engineering fields, such as data analysis, instrument design, and aerospace vehicles.