Ionospheric Electron Research Articles

Investigating the prediction ability of the ionospheric continuity equation during the geomagnetic storm on May 8, 2016

Abstract Ionospheric electron density (IED) and vertical total electron content (VTEC) are two of the most important characteristics that interpret the ionosphere. Today, satellite geodesy plays an important role in providing these data. Apart from monitoring the ionosphere by means of Global Navigation Satellite System observations, predictions of these ionospheric characteristics have also been taken into consideration. The majority of prediction methods in the ionosphere are focused on VTEC prediction. The continuity equation in the ionosphere is a spatiotemporal partial differential equation that can be used to predict both IED and VTEC. In this study, we address this issue during May 8, 2016, which was a day of high geomagnetic activity. For this purpose, first, high-accuracy IED grids with a spatial resolution of 0.5° × 0.5° in longitude and latitude, 50 km in altitude, and a temporal resolution of 10 min are prepared. These IED grids are called analysis grids and are derived by assimilation of GPS-derived VTECs into the background IED grids provided by international reference ionosphere. Second, the analysis grids can be utilized as the initial and boundary values in the continuity equation to predict IED for the next 3 h with a step of 10 min. The analysis grids are constructed over the Iran region, and the performance of the continuity equation in the ionosphere prediction is investigated during the strongest geomagnetic storm of 2016. Finally, the accuracy of the predictions has been evaluated in two ways. First, analysis grids with high accuracy are available for each epoch in which the prediction is presented. On average, the root mean square (RMS) of the differences between the predicted IEDs and the analysis IEDs is 1.66 × 1010 el/m3 for 1-h predictions and 5.33 × 1010 el/m3 for 3-h predictions. Second, by numerical integration of the predicted grids in the vertical direction, VTECs are estimated and compared with VTEC values of test data that were already known. On average, the RMS of their difference was 1.29 total electron content unit (TECU) for 1-h predictions and 2.57 TECU for 3-h predictions. An acceptable accuracy for both IED and VTEC prediction by a continuity equation has been obtained up to 3 h on the most active ionospheric day.

Impact of Ionospheric Electron Density on Second-Order Ionospheric Error at L5 and S1 Frequencies Using Dual-Frequency NavIC System

Impact of Ionospheric Electron Density on Second-Order Ionospheric Error at L5 and S1 Frequencies Using Dual-Frequency NavIC System

Relationship Between TEC Perturbations and Rayleigh Waves Associated With 2023 Turkey Earthquake Doublet

AbstractAn earthquake doublet occurred in Turkey on 6 February 2023, with propagating Rayleigh waves triggering perturbations in the ionospheric total electron content (TEC) for both the M 7.8 earthquake (EQ7.8) and the M 7.5 earthquake (EQ7.5). A discrepancy between the velocities of TEC perturbations and Rayleigh waves has been noted, but its causes remain unresolved in previous studies. In this study, we calculated the velocities of TEC perturbations and the frequency‐dependent velocities of Rayleigh waves, considering their intrinsic dispersive characteristics. To retrieve TEC, we utilized ground‐based Global Navigation Satellite System (GNSS) data from geostationary Earth orbit (GEO) satellites to mitigate the effects of moving ionospheric pierce points (IPPs) from orbiting satellites. The results reveal that the velocities of TEC perturbations (2.60 km/s for EQ7.8 and 2.77 km/s for EQ7.5) do not align with the velocities of Rayleigh waves across the entire frequency band (2.4–3.0 km/s for EQ7.8 and 2.6–3.5 km/s for EQ7.5). However, they are comparable within specific periods of 10–30 s due to dispersion effects for both EQ7.8 and EQ7.5. The dispersive Rayleigh waves, which exhibit significant amplification in the 10–30 s period range, are identified as the primary source of the pronounced coseismic TEC perturbations, particularly for EQ7.5.

Ground and Space (LEO) Based GNSS Ionospheric Detection Using Chapman Function Constraint

AbstractLimited by the number and location distribution of ground stations, the ground based ionospheric detection using Global Navigation Satellite Systems (GNSS) technology suffers from low accuracy and poor reliability in some regions. To improve the performance of the global ionospheric model, this study combines the Continuously Operating Reference Station and the Low Earth Orbit (LEO) Satellite observation to conduct the ground and space‐based (G/SBased) joint ionospheric detection technique. With reference to the ionospheric radar data of the global ionospheric radio observatory, the performance of the G/SBased GNSS joint ionospheric detection technique was tested in quiet and disturbed geomagnetic environments. Results show that in the ground and space based joint mode, the distribution of ionospheric puncture points (IPPs) is uniform, thus effectively avoiding the problem of IPPs blank in the absence of ground stations. In the quiet geomagnetic environment, the ionospheric detection of the G/SBased joint mode has a remarkable performance improvement compared with the ground GNSS mode, with an average improvement of 55.51%. In the geomagnetically disturbed environment, the G/SBased joint mode still has high consistency with the ionospheric radar results, and the detection performance is better than that of the ground GNSS mode. This research shows that combining with ground GNSS and LEO satellite observation data can substantially provide the performance of GNSS ionospheric total electron content detection and can provide good data support for the construction of a global ionospheric refinement model.

Two‐ and Three‐Dimensional Propagation Characteristics of Co‐Volcanic Ionospheric Disturbances Induced by the 2022 Tonga Volcano Eruption Using Dense GNSS Network Data

AbstractOn 15 January 2022, the submarine volcano in Hunga Tonga‐Hunga Ha’apai (hereinafter Tonga volcano) erupted at 04:14 universal time. This study investigated the two‐ and three‐dimensional co‐volcanic ionospheric disturbances (CVIDs) induced by the strong Tonga volcano. Based on dense Global Navigation Satellite System network data in Australia, the two‐dimensional detrended total electron content maps were first generated by using the Savitzky‐Golay filtering method. Using a compressed sensing‐based computerized ionospheric tomography approach, the three‐dimensional ionospheric electron densities were reconstructed. After the eruption, the distinctive CVIDs began to be mostly observed over southeastern Australia, about 4,000 km from the volcano. Two patterns of observable CVIDs, that is, fast‐mode and slow‐mode CVIDs, traveled outward from the Tonga volcano at speeds of 600–850 and 200–350 m/s, respectively. The slow‐mode CVIDs were related to the Lamb and secondary gravity waves, while the fast‐mode CVIDs were related to acoustic waves. Three‐dimensional reconstruction results demonstrated that as the CVIDs propagated upward, the electron densities fluctuated at most altitude‐longitude slices, and the wave‐like propagating patterns were clearly observed around the peak ionospheric heights of 260–340 km. At the peak ionospheric height, the ionospheric parameters derived from the two ionosondes over Australia also detected similar wave‐like features. Two‐dimensional and three‐dimensional observational evidence of the Tonga‐induced CVIDs enhances research on volcanic eruption effects over the upper ionosphere.

Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaraş earthquake sequence

On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL’s GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second Mw 7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first Mw 7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip.Plain Language Summary Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.

EclipseNB: A Network of Low‐Cost GNSS Receivers to Study the Ionosphere

AbstractThis work aims to demonstrate that dense networks of low‐cost dual‐frequency global navigation satellite systems (GNSS) receivers can be used to retrieve ionospheric electron content with almost the same level of accuracy as scientific‐grade GNSS receivers. A network of 15 GNSS receivers called EclipseNB was designed and installed in New Brunswick, Canada to study ionospheric structure and dynamic behavior, including the response of the ionosphere to the total solar eclipse in April 2024. EclipseNB observations during the solar eclipse and the extreme geomagnetic storm in May 2024 are presented. The status and the future of the network are discussed.

Day‐To‐Day Variability of Ionospheric Electron Temperature Revealed by High‐Resolution SYISR Observations During April–May 2022

AbstractThis study investigated the day‐to‐day variability of ionospheric electron temperature (Te) over Sanya using the newly built Sanya Incoherent Scatter Radar (SYISR; 18.3°N, 109.6°E, dip latitude: 12.8°N). The 24 days of observations with transmitted beams encoded by the alternating code and directed toward the zenith provided Te profiles during April–May 2022. The spatial‐temporal resolution was as precise as 4.5 km and ∼120 s. The day‐to‐day variability of Te over Sanya thus revealed, which was strong in the sunrise Te peak region (above ∼260 km, duration increases from ∼0.5 to ∼3.5 hr with altitude) and the top of the 200–300 km Te bulge (∼260 to ∼300 km, lasting from ∼09:00 LT to ∼18:30 LT). The day‐to‐day variability of Te in the two regions could reach ∼26% and ∼16% of the average Te, respectively. Although the day‐to‐day variability of Te is negatively correlated with the variation of ionospheric electron densities (Ne) in general, a positive correlation still occurred during the daytime, especially at 400–480 km from ∼17:00 LT to ∼18:00 LT. Further Thermosphere Ionosphere Electrodynamics General Circulation Model simulations reproduced the positive correlation, which may result from the internal heat exchange between electrons, ions, and neutrals. The simulations suggested that the variation of solar radiation/geomagnetic activity contributes most to the daytime/sunrise day‐to‐day variability of Te over Sanya.

Оценка электронного содержания плазмосферы и высоты перехода O⁺/H⁺ во время геомагнитной бури в феврале 2022 г. по данным Иркутского радара НР

We study the topside ionosphere above the NmF2 ionization maximum and the transition region between the ionosphere and the plasmasphere. We analyze the interaction between the topside ionosphere and the plasmasphere during a strong geomagnetic storm in February 2022, using data from the Irkutsk Incoherent Scatter Radar (IISR) and total electron content data from global navigation satellite systems. To determine the ionosphere and plasmasphere electron contents, an original technique is employed to calculate the integral content of ion density from IISR data, which takes into account the two-component composition of ionospheric plasma. We compare different functions of approximation of the topside ionosphere. The original technique was adjusted for use with IISR Ne data fitted based on the β-Chapman profile. We compare the plasmasphere electron content during quiet and magnetically disturbed days, as well as the dynamics of the O⁺/H⁺ transition height, which is the upper boundary of the ionosphere and the lower boundary of the plasmasphere.

Estimated plasmasphere electron content and O⁺/H⁺ transition height during the February 2022 geomagnetic storm from Irkutsk IS radar data

We study the topside ionosphere above the NmF2 ionization maximum and the transition region between the ionosphere and the plasmasphere. We analyze the interaction between the topside ionosphere and the plasmasphere during a strong geomagnetic storm in February 2022, using data from the Irkutsk Incoherent Scatter Radar (IISR) and total electron content data from global navigation satellite systems. To determine the ionosphere and plasmasphere electron contents, an original technique is employed to calculate the integral content of ion density from IISR data, which takes into account the two-component composition of ionospheric plasma. We compare different functions of approximation of the topside ionosphere. The original technique was adjusted for use with IISR Ne data fitted based on the β-Chapman profile. We compare the plasmasphere electron content during quiet and magnetically disturbed days, as well as the dynamics of the O⁺/H⁺ transition height, which is the upper boundary of the ionosphere and the lower boundary of the plasmasphere.

Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts.

Impact of different solar extreme ultraviolet (EUV) proxies and Ap index on hmF2 trend analysis

Abstract. Long-term trend estimation in the peak height of the F2 layer, hmF2, needs the previous filtering of much stronger natural variations such as those linked to the diurnal, seasonal, and solar activity cycles. If not filtered, they need to be included in the model used to estimate the trend. The same happens with the maximum ionospheric electron density that occurs in this layer, NmF2, which is usually analyzed through the F2 layer critical frequency, foF2. While diurnal and seasonal variations can be easily managed, filtering the effects of solar activity presents more challenges, as does the influence of geomagnetic activity. However, recent decades have shown that geomagnetic activity may not significantly impact trend assessments. On the other hand, the choice of solar activity proxies for filtering has been shown to influence trend values in foF2, potentially altering even the trend's sign. This study examines the impact of different solar activity proxies on hmF2 trend estimations using data updated to 2022, including the ascending phase of solar cycle 25, and explores the effect of including the Ap index as a filtering factor. The results obtained based on two mid-latitude stations are also comparatively analyzed to those obtained for foF2. The main findings indicate that the squared correlation coefficient, r2, between hmF2 and solar proxies, regardless of the model used or the inclusion of the Ap index, is consistently lower than in the corresponding foF2 cases. This lower r2 value in hmF2 suggests a greater amount of unexplained variance, indicating that there is significant room for improvement in these models. However, in terms of trend values, foF2 shows greater variability depending on the proxy used, whereas the inclusion or exclusion of the Ap index does not significantly affect these trends. This suggests that foF2 trends are more sensitive to the choice of solar activity proxy. In contrast, hmF2 trends, while generally negative, exhibit greater stability than foF2 trends.

Ionospheric total electron content and electron density response induced by the 8 April 2024 total solar eclipse

Ionospheric total electron content and electron density response induced by the 8 April 2024 total solar eclipse

Super-Intense Geomagnetic Storm on 10-11 May 2024: Possible Mechanisms and Impacts.

One of the most intense geomagnetic storms of recent times occurred on 10-11 May 2024. With a peak negative excursion of Sym-H below -500nT, this storm is the second largest of the space era. Solar wind energy transferred through radiation and mass coupling affected the entire Geospace. Our study revealed that the dayside magnetopause was compressed below the geostationary orbit (6.6 RE) for continuously ∼6hr due to strong Solar Wind Dynamic Pressure (SWDP). Tremendous compression pushed the bow-shock also to below the geostationary orbit for a few minutes. Magnetohydrodynamic models suggest that the magnetopause location could be as low as 3.3RE. We show that a unique combination of high SWDP (≥15nPa) with an intense eastward interplanetary electric field (IEFY≥2.5mV/m) within a super-dense Interplanetary Coronal Mass Ejection lasted for 409min-is the key factor that led to the strong ring current at much closer to the Earth causing such an intense storm. Severe electrodynamic disturbances led to a strong positive ionospheric storm with more than 100% increase in dayside ionospheric Total Electron Content (TEC), affecting GPS positioning/navigation. Further, an HF radio blackout was found to occur in the 2-12MHz frequency band due to strong D- and E-region ionization resulting from a solar flare prior to this storm.

PyIRTAM: A New Module of PyIRI for IRTAM Coefficients

AbstractA novel model called PyIRI was recently developed. It constructs the ionospheric electron density for the entire day and on the entire global grid in one computation, which has a very low computational overhead. PyIRI introduced a novel approach to the computation of the global and diurnal functions and their matrix multiplication with Consultative Committee on International Radio (CCIR) coefficients or the International Union of Radio Science (URSI) coefficients, that enabled this global approach for the density specification. Since the International Reference Ionosphere‐based Real‐Time Assimilative Model (IRTAM) produces coefficients in a similar format as CCIR/URSI coefficients, the PyIRI computational approach was extended to work with IRTAM coefficients. This technical note describes the PyIRTAM software and provides usage examples. The PyIRTAM tool is made publicly available through PyPI and GitHub.

Unusual Sunrise and Sunset Terminator Variations in the Behavior of Sub-Ionospheric VLF Phase and Amplitude Signals Prior to the Mw7.8 Turkey Syria Earthquake of 6 February 2023

We report on the recent earthquakes (EQs) that occurred, with the main shock on 6 February 2023, principally in the central southern part of Turkey and northwestern Syria. This region is predisposed to earthquakes because of the tectonic plate movements between Anatolian, Arabian, and African plates. The seismic epicenter was localized at 37.08°E and 37.17°N with depth in the order of 10 km and magnitude Mw7.8. We use Graz’s very-low-frequency VLF facility (15.43°E, 47.06°N) to investigate the amplitude variation in the Denizköy VLF transmitter, localized in the Didim district of Aydin Province in the western part of the Anatolian region in Turkey. Denizköy VLF transmitter is known as Bafa transmitter (27.31°E, 37.40°N), radiating at a frequency of 26.7 kHz under the callsign TBB. This signal is detected daily by the Graz facility with an appropriate signal-to-noise ratio, predominantly during night observations. We study in this analysis the variations of TBB amplitude and phase signals as detected by the Graz facility two weeks before the earthquake occurrence. It is essential to note that the TBB VLF transmitter station and the Graz facility are included in the preparation seismic area, as derived from the Dobrovolsky relationship. We have applied the multi-terminators method (MTM), revealing anomalies occurring at sunset and sunrise terminator occasions and derived from the amplitude and the phase. Minima and maxima of the TBB signal are linked to three terminators, i.e., Graz facility, TBB transmitter, and EQ epicenter, by considering the MTM method. We show that the significant anomalies are those linked to the EQ epicenter. This leads us to make evident the precursor seismic anomaly, which appears more than one week (i.e., 27 January 2023) before EQ occurrence. They can be considered the trace, the sign, and the residue of the sub-ionospheric propagation of the TBB transmitter signal disturbed along its ray path above the preparation EQ zone. We find that the sunrise–sunset anomalies are associated with tectonic regions. One is associated with the Arabian–African tectonic plates with latitudinal stresses in the south–north direction, and the second with the African–Anatolian tectonic plates with longitudinal stresses in the east–west direction. The terminator time shift anomalies prior to EQ are probably due to the lowering (i.e., minima) and raising (i.e., maxima) of the ionospheric electron density generated by atmospheric gravity waves.

Disturbances of Ionospheric Total Electron Content during Great and Severe Geomagnetic Storms above Iraq and Surrounding Areas

Several studies have been made to understand the behavior of Total Electron Content (TEC) in the topside of the ionosphere during two types of geomagnetic storms. The aim of this research is to examine the ionospheric disturbances using the TEC parameter during the great and severe geomagnetic storms that occurred during the period (28-30 October 2003). Theexamination was conducted for the region,includingIraq and surrounding areas that cover the arealatitude 27-39oN; longitude 27-54oE). TEC data were obtained fromthe Defense Meteorological Satellite Program (DMSP), to determine the type of geomagnetic storm, the Disturbance storm time (Dst) index was gathered for the selected days from the website Koyota, Japan WDC.Fromthe results,it wasfound that there was a good relationship between Dst-indexand TEC parameter values. There is a discrepancy in observations recorded about TEC values during the period of geomagnetic storms.There were significant enhancements in the values of TEC during storms; however, there was an anomaly when the storm continued for several hours during the day, there was a highly broad increase in TEC from sunrise to sunset. Moreover, when two types of storms occurred, two peaks or more appeared, and they remained for one event, or even for more than one day. Comparisons between the observed TEC and the predicted values were applied in order to check the validity of the IRI-20 ionospheric model during the storm time above the mid-latitude studiedareas;it found that there is a non-linear correlation between them withi24 hours and three days, so the model wouldneed some modification in the future to be suitable for the Iraq region during storm time.

Mapping the ionosphere with millions of phones

The ionosphere is a layer of weakly ionized plasma bathed in Earth’s geomagnetic field extending about 50–1,500 kilometres above Earth1. The ionospheric total electron content varies in response to Earth’s space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting in one of the largest sources of error for position, navigation and timing services2. Networks of high-quality ground-based GNSS stations provide maps of ionospheric total electron content to correct these errors, but large spatiotemporal gaps in data from these stations mean that these maps may contain errors3. Here we demonstrate that a distributed network of noisy sensors—in the form of millions of Android phones—can fill in many of these gaps and double the measurement coverage, providing an accurate picture of the ionosphere in areas of the world underserved by conventional infrastructure. Using smartphone measurements, we resolve features such as plasma bubbles over India and South America, solar-storm-enhanced density over North America and a mid-latitude ionospheric trough over Europe. We also show that the resulting ionosphere maps can improve location accuracy, which is our primary aim. This work demonstrates the potential of using a large distributed network of smartphones as a powerful scientific instrument for monitoring Earth.

A Lightweight Prediction Model for Global Ionospheric Total Electron Content Based on Attention-BiLSTM

The ionospheric total electron content (TEC) is a critical parameter for space weather and Global Navigation Satellite System (GNSS) applications. A Bidirectional Long Short-Term Memory neural network model with an added attention mechanism (BiLSTM-Attention) was developed in this study to predict 256 spherical harmonic coefficients (SHC). Input data for the forecasting model includes F10.7, Dst, and other feature parameters, along with a lightweight historical time series of SHC from the past day. The model output is the next day’s SHC. Then, we compare the results of SHC with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The correlation coefficient form model TEC with respect to the CODE TEC are 0.93 and 0.96 in 2018 and 2022, respectively, while the correlation coefficient of the 1-day CODE prediction model are 0.91 and 0.94. The results illustrate established model both in high and low solar activity years, and exhibiting enhanced robustness during geomagnetic storms. Furthermore, typical ionospheric structures such as Equatorial Ionization Anomaly (EIA) is well reproduced in the TEC prediction maps. Compared to C1PG, the proposed model offers a lighter computational load while maintaining competitive performance in global TEC prediction.

Martian Ionosphere‐Thermosphere Coupling in Longitude Structures: Statistical Results for the Main Ionization Peak Height

AbstractThe Martian ionosphere‐thermosphere (I‐T) coupling is variable due to complex variations of the driving factors such as atmospheric tides and crustal magnetic fields. In this study, variability of the I‐T coupling in longitude structures was investigated using a series of data segments of the MGS ionospheric measurements. Measurements in each data segment can cover different longitudes, and the solar forcing and local solar time just change a little. Ionospheric and thermospheric longitude variations are statistically correlated. Ionospheric peak electron density (NmM2) decreases while ionospheric main peak height (hmM2) increases with increasing neutral scale height (Hn) along longitudes. These correlated longitude variations are consistent with the photochemical coupling that Hn longitude disturbances induce ionospheric longitude structure through photochemical processes. Statistically, NmM2 is a better indicator than hmM2 for the Hn disturbances in the lower thermosphere. Hn longitude variation intensity is a crucial factor affecting the photochemical I‐T coupling in longitude structures; it is closely related to NmM2 longitude variation intensity and tends to decline with increasing altitudes. The I‐T coupling in longitude structures tends to decline near the terminator, which is in line with the declining longitude variation of Hn with increasing altitudes since hmM2 significantly increases near the terminator. Moreover, it tends to enhance at high solar activity level due to increased photoionization rate. The I‐T coupling in longitude structures also shows seasonal dependence, as seasonal variation of hmM2 can affect the Hn longitude variation intensity nearby the ionospheric main peak.