Next Article in Journal
Research on Multiscale Atmospheric Chaos Based on Infrared Remote-Sensing and Reanalysis Data
Previous Article in Journal
ANN-Based Filtering of Drone LiDAR in Coastal Salt Marshes Using Spatial–Spectral Features
Previous Article in Special Issue
Latitudinal Characteristics of Nighttime Electron Temperature in the Topside Ionosphere and Its Dependence on Solar and Geomagnetic Activities
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023

1
Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650032, China
2
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(18), 3375; https://doi.org/10.3390/rs16183375
Submission received: 12 July 2024 / Revised: 8 September 2024 / Accepted: 9 September 2024 / Published: 11 September 2024

Abstract

:
The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using ground-based Global Navigation Satellite System (GNSS) networks. This study employs a hybrid approach combining space-based and ground-based techniques to investigate the spatiotemporal characteristics of ionospheric perturbations during Typhoon Doksuri. Plane maps depict significant plasma fluctuations extending outward from the typhoon’s gale wind zone on 24 July, reaching distances of up to 1800 km from the typhoon’s center, while space weather conditions remained relatively calm. These ionospheric perturbations propagated at velocities between 173 m/s and 337 m/s, consistent with AGW features and associated propagation speeds. Vertical mapping reveals that energy originating from Typhoon Doksuri propagated upward through a 500 km layer, resulting in substantial enhancements of plasma density and temperature in the topside ionosphere. Notably, the topside horizontal density gradient was 1.5 to 2 times greater than that observed in the bottom-side ionosphere. Both modeling and observational data convincingly demonstrate that the weak background winds favored the generation of AGWs associated with Typhoon Doksuri, influencing the development of distinct MSTIDs.

1. Introduction

Ionospheric variations significantly impact human activities, affecting areas such as shortwave radio communication, satellite navigation positioning, and electric power delivery [1,2,3]. Typically, solar and geomagnetic activities serve as the primary influencers regulating ionospheric density levels. In the presence of severe space weather conditions, solar flares, geomagnetic storms, substorms, and subauroral polarization streams could instigate large-scale traveling ionospheric disturbances (LSTIDs) and medium-scale traveling ionospheric disturbances (MSTIDs), causing temporary disruptions in radio wave propagation across wavelengths ranging from extremely low frequency (ELF) to very high frequency (VHF) [4,5,6]. Moreover, recent research has unveiled that huge natural disasters could also generate powerful acoustic-gravity waves, gravity waves, or lamb waves that result in multi-scale TIDs, including strong earthquakes, volcanic eruptions, intense typhoons, etc. [7,8,9,10,11,12]. Typhoons, characterized by tropical cyclones with numerous convective cells in the eyewall and rainbands, continuously generate atmospheric gravity waves (AGWs) [9,13]. Notably, when these typhoons make landfall or pass through an island, the reconstruction of the wind field promotes the propagation of gravity waves, extending over thousands of kilometers.
It is known that the dynamic variation of the global ionosphere is very complex, including periodic variations and sudden fluctuations. Sudden ionospheric fluctuations usually contain Equatorial Plasma Bubbles (EPBs) and Traveling Ionospheric Disturbances (TIDs). EPBs are large-scale magnetic field-aligned structures characterized by plasma depletion, typically originating at the bottom-side of the ionospheric F region over post-sunset low latitudes. The generalized Rayleigh–Taylor (R-T) instability is widely accepted as the physical mechanism responsible for EPB generation [14]. TIDs are typically categorized into two types: AGW-TIDs and Electrified-TIDs. The former is associated with Atmospheric Gravity Waves, while the latter is often accompanied by large electric fields. In most cases, daytime TIDs are assumed to be AGW-TIDs, while nighttime TIDs are considered to be Electrified-TIDs, as outlined in the Heliophysics 2050 White Papers (No. 4104). Therefore, it is a great challenge to distinguish the kinds of ionospheric fluctuations and explore the possible corresponding physical mechanisms. The initial investigation into the coupling relationship between ionospheric perturbation and typhoons was conducted by Bauer [15]. The findings revealed an increase in the critical frequency of the F2 layer over Washington, D.C. with the approach of hurricanes. Since then, a growing number of scientists have turned their attention to typhoon-ionospheric perturbations, utilizing various observations derived from Digisondes and High-Frequency Doppler sounding. These studies have unveiled fundamental features of ionospheric responses to typhoons [16,17,18]. However, the sparse distribution of stations has presented challenges in capturing two-dimensional maps of ionospheric disturbances in the vicinity of typhoons and further investigating the detailed parameters of typhoon-induced Traveling Ionospheric Disturbances (TIDs), including direction, velocity, and duration.
Over the past two decades, the rapid advancement of the Global Navigation Satellite System (GNSS), encompassing GPS, GLONASS, Galileo, and Beidou, has led to a significant breakthrough in tropospheric-ionospheric applications [19,20,21,22,23,24,25]. The densely deployed GNSS stations have facilitated in-depth studies focusing on the detailed features of two-dimensional (2-D) Total Electron Content (TEC) perturbation maps generated by severe typhoons, employing both case studies and statistical analyses. A noteworthy milestone occurred when more than 2700 GPS receivers provided the first clear evidence of a severe tropospheric event causing atmospheric waves that disturbed the thermospheric system. This event followed an Enhanced Fujita (EF) scale 5 tornado in 2013, revealing concentric waves characterized by non-dispersive behavior with a period of approximately 13 min and a wavelength of about 120 km [26]. Chou et al. [9] presented a comprehensive analysis of the spatial-temporal characteristics of concentric Traveling Ionospheric Disturbances (CTIDs) during the approach of Super Typhoon Nepartak to Taiwan. These CTIDs exhibited horizontal velocities ranging from approximately 161 to 200 m/s, wavelengths spanning 160 to 270 km, and periods lasting approximately 15 to 22 min. A distinct correlation linking Typhoon Dujuan and the ionosphere was observed by Kong et al. [27]. Xiao et al. [28] explored the ionospheric couplings with 24 strong typhoons from 1987 to 1992, revealing the emergence of clear mesoscale TIDs, particularly during a typhoon’s landing or near coastal areas—a finding corroborated by Li et al. [29] and Song et al. [14]. Li et al. [10] investigated variations in Total Electron Content (TEC) and foF2 during four typhoons in Oceania, noting that all ionospheric perturbations were situated at the edge of rainbands rather than the eyewall. In a statistical analysis covering 22 typhoon events from 2013 to 2016, Peng et al. [30] identified a significant positive correlation between the propagation velocity of typhoon-induced ionospheric disturbances and the change rate of eye air pressure. However, this correlation was negative for eye air pressure before landing. Recently, researchers, including Song et al. [31], reconstructed three-dimensional structures of typhoon-related CTIDs to gain insights into the interaction between driving forces originating from the lower atmosphere and the dynamic processes of ionospheric plasma. As outlined earlier, previous studies on couplings in the tropospheric-ionospheric system predominantly relied on ground-based techniques, including the heavily ground-dependent Global Navigation Satellite System (GNSS) technique. Given that typhoons predominantly originate in the Northwestern Pacific within tropical regions, and their migratory routes traverse vast oceanic expanses with few nearby observation stations, performing a three-dimensional diagnosis for typhoon-related Traveling Ionospheric Disturbances (TIDs) becomes a significant challenge in the absence of adequate ground stations.
With the successful deployment of low-orbit satellite missions, these satellite constellations emerge as pivotal tools for partially unveiling the three-dimensional morphology of the troposphere-ionosphere coupling process. The severe Typhoon Doksuri, originating on 20 July 2023, was a potent, lethal, and destructive tropical cyclone. It caused substantial rainfall and powerful winds in the capital of China, marking it as the costliest typhoon to impact China on record. In this study, we select this event as a case study to investigate the spatial-temporal evolutions of two-dimensional (2-D) and three-dimensional (3-D) structures of Doksuri-related TIDs using multiple space-ground techniques, including GNSS, Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), and Swarm. Additionally, simulations from the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM) and the Horizontal Wind Model (HWM), along with observations from the Global Ultraviolet Imager (GUVI) of TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics Mission) satellite and magnetometers, are utilized to explore potential mechanisms of typhoon-ionosphere coupling.

2. Materials and Methods

In this study, two-dimensional perturbation maps were predominantly obtained through ground-based GNSS receivers, while the three-dimensional structures of Traveling Ionospheric Disturbances (TIDs) were primarily reconstructed using COSMIC-2 and Swarm. Further details regarding these observations are provided in the subsequent section.
Total Electron Content (TEC) stands as a crucial variable for monitoring the spatial-temporal evolution of ionospheric disturbances during the progression of Typhoon Doksuri. This study employed dual-frequency pseudo-range measurements from approximately 200 GNSS receivers to estimate TIDs maps. The primary data sources included the Crustal Dynamics Data Information System (CDDIS), Hong Kong Geodetic Survey Services, and Taiwan Seismological and Geophysical Data Management System. Notably, only the dual-frequency observations of GPS were utilized to mitigate the impact of differential code biases (DCB) from multiple systems on TEC accuracy. Both slant TEC (STEC) and vertical TEC (VTEC) were employed to analyze the features of two-dimensional (2-D) maps, with the corresponding estimation algorithm referencing [32,33,34].
On 25 June 2019, the COSMIC-2 constellation, consisting of six microsatellites, was successfully launched into low-inclination orbits. Each satellite is equipped with two space weather science instruments: a Radio Frequency Beacon transmitter and an Ion Velocity Meter (IVM). This groundbreaking mission has the extraordinary capacity to acquire over 4000 high-quality profiles daily, marking a new era in advancements for weather forecasting, space weather monitoring, and climate research. The European Space Agency (ESA)-funded Swarm mission aims to comprehensively survey Earth’s geomagnetic field and its temporal evolution while concurrently monitoring the atmospheric electric field. This ambitious initiative is realized through a constellation of satellites—Swarm-A, Swarm-B, and Swarm-C—each equipped with state-of-the-art magnetometers and advanced instruments. Operating within two distinct polar orbits at varying altitudes, Swarm-A and Swarm-C are situated in an initial orbital plane at an altitude of 462 km with an inclination of 87.35°, while Swarm-B resides in an 87.75° inclination orbit at an initial altitude of 511 km. Each satellite captures 13 to 14 full orbital observations daily, providing researchers with an abundance of data for meticulous analysis. The unique configuration of the Swarm constellation provides a significant advantage in investigating vertical fluctuations in thermospheric composition, particularly over Earth’s oceans. The satellites’ payloads include a GPS receiver, accelerometer, electric field sensor, absolute scalar magnetometer, laser retro-reflector, and other sophisticated tools designed to gather invaluable data on Earth’s magnetic field. The GUVI is part of the Thermosphere, Ionosphere, Mesosphere Energetics, and Dynamics (TIMED) spacecraft, comprising four instruments. It provides valuable insights into thermospheric composition changes (O/N2) under complex space conditions. In addition, the EEJ signatures are typically estimated by taking the difference between the horizontal components measured by a pair of magnetometers—one located at the equator and the other away from the equator. In this study, the horizontal components of the geomagnetic field measured by the DLT (108.48°E, 11.95°N, Vietnam) and PHU (105.95°E, 21.03°S, Vietnam) magnetometers were used to investigate EEJ variations, with data obtained from the International Real-time Magnetic Observatory Network.
The TIE-GCM model, developed by the National Center for Atmospheric Research, exhibits notable capabilities in simulating and predicting various phenomena relevant to space weather. Acknowledged as a valuable tool, it is esteemed for its effectiveness in simulating and analyzing the global dynamics of the Earth’s upper atmosphere. The Horizontal Wind Model 2014 (HWM14) is an empirical model that provides a time-dependent global specification of the horizontal neutral wind in the upper thermosphere. It improves upon previous models by incorporating realistic observations of upper atmospheric tides and general circulation patterns [35].

3. Characteristics of 2-D Ionospheric Perturbations

Typhoon Doksuri wreaked havoc in the Philippines, Vietnam, and China in July 2023. Originating from the western Pacific Basin on 20 July as a low-pressure area, it began with a central pressure of approximately 1005 hPa and a wind speed of 30 knots/h. Tracking northwestward, it swiftly intensified into a formidable typhoon on 24 July with the central pressure dropping to around 940 hPa and the wind speed increasing to approximately 90 knots/h. Doksuri reached its most violent level on 25 July with a lowest pressure of 925 hPa and a peak wind speed of 100 knots/h. Following this, Doksuri steadily weakened after making landfall in the Philippines. Eventually, it moved towards Fujian, China, and dissipated early the next day. The entire trajectory of Typhoon Doksuri, along with the changes in central pressure and average speed, is illustrated in Figure 1. Additionally, the distribution of ground-based GNSS receivers for this study is also detailed.
To accurately isolate ionospheric anomalous signals related to Typhoon Doksuri, it is crucial to eliminate the potential influence of space weather conditions on ionospheric behaviors. Figure 2 illustrates the temporal variations in solar-geomagnetic activities during the passage of Doksuri, encompassing proton density, solar wind temperature and speed, Dst, and Kp. The results reveal discernible fluctuations in both solar activity and geomagnetic conditions during the periods of 20–22 July and 26–27 July corresponding to Days of the Year (DOY) 201–203 and 207–208, respectively.
In Figure 2a, the proton density of solar wind experienced a sudden increase from approximately 5.3 × 1016/cm3 at Universal Time (UT) 16, DOY 201, to about 13.7 × 1016/cm3 at 18 UT, DOY 201. Simultaneously, both the temperature and speed of solar wind reached their maxima at around 3.6 × 105 K and 416 km/s, respectively. Concurrently, geomagnetic indicators show a decrease in the Dst index to approximately −35 nT, and the Kp level raised to 4 during this initial fluctuation. In contrast, during the second fluctuation, the proton density was lower, but other indicators exhibited significantly higher intensities. For instance, the maximum proton density occurred at 23 UT, DOY 206, with a value of 9.7 × 1016/cm3, while the temperature and speed experienced a sudden surge to around 7 × 105 K and 580 km/s, respectively. Additionally, the Dst dropped to a minimum level of approximately −50 nT, and the Kp increased to the class of 5. The findings from Figure 2 suggest that the solar-terrestrial environments experienced fluctuations during the formative and decline phases of Typhoon Doksuri, with fewer severe space events observed during the mature phase.
This study specifically selects three GNSS stations located in the vicinity of Typhoon Doksuri’s trajectory to examine the ionospheric response. The station list includes PTAG (121.04°E, 14.54°N), CHEN (121.37°E, 23.10°N), and HKCL (113.91°E, 22.30°N), with their geographic positions indicated by purple triangles in Figure 1. The anomalous signals of VTEC time series from these three stations are extracted using a fifth-order Butterworth filter with cutoff periods ranging from 10 to 25 min.
Figure 3 illustrates the VTEC fluctuations measured by GNSS stations PTAG, CHEN, and HKCL. It is evident that significant ionospheric disturbances were observed by all GNSS stations during the passage of the typhoon. Notably, prominent plasma irregularities were detected on DOY 204–205, despite the relatively quiet space weather conditions, with an average solar wind speed (Vsw) and Dst index of approximately 400 km/s and −20 nT, respectively. Moreover, the timing of the appearance of plasma irregularities detected by each station may be correlated with the typhoon’s location. For example, Figure 3a illustrates notable plasma disturbances observed by PTAG on DOY 204, with a maximum amplitude of 0.15 TECu, whereas for CHEN and HKCL, the plasma irregularities dropped to ~0.05 TECu. As Typhoon Doksuri moved northwestward, the plasma irregularities over stations CHEN and HKCL increased to 0.1 TECu on DOY 205, while few disturbances were observed by PTAG.
Additionally, Figure 1 shows that Typhoon Doksuri reached its peak intensity on DOY 206. Concurrently, the solar wind speed notably increased by approximately 300 km/s on DOY 206, coinciding with a drop in the Dst index to −50 nT. Such disrupted space weather conditions may induce large-scale ionospheric fluctuations, potentially interfering with the identification of typhoon-induced plasma irregularities. Interestingly, minor TEC perturbations were observed on DOY 206. Consequently, DOY 205 is identified as a critical period for discerning the characteristics of typhoon-induced ionospheric disturbances.
Given the heightened plasma fluctuations on DOY 205, the fluctuations of slant total electron content (STEC) observed by several Global Positioning System (GPS) satellites are illustrated in Figure 4. Figure 4a depicts the trajectories of STEC rays measured by CHEN on DOY 205. These trajectories are around Typhoon Doksuri, indicating that the typhoon may have had an effect on the radio rays, inducing noticeable plasma fluctuations. Plasma irregularities are estimated using the time integral and rate of the total electron content index (IROTI). The new index proposed by Ren et al. [27] could effectively identify ionospheric irregularities based on the coupling effect of amplitude change and phase mutation. Experimental results demonstrate that IROTI can successfully detect and recognize equatorial plasma bubbles (EPBs), as well as LSTIDs and MSTIDs during both geomagnetically quiet and stormy days.
Figure 4b–e show the STEC fluctuations observed by the GPS satellites G05, G10, G18, and G31, respectively. The ionospheric fluctuation stayed at a low level with an IROTI of lower than 0.2 TECu2 during most of the period, but in some hours, it suddenly enhanced to as much as 0.5 to 0.9 TECu2. For example, in Figure 4b, the maximum IROTI measured by G05 reached ~0.8 TECu2 in the periods of 03 UT and 07–09 UT. The shape of IROTI irregularities agree well with the feature of the TIDs in Ren et al. [36]. The new IROTI index has been used to classify TIDs and EPBs from several hundred space events, and concluded the thresholds for separating the two kinds of ionospheric disturbance events [36]. EPBs are considered to occur when the IROTI is larger than 7.28 TECu2, while ionospheric disturbances with an IROTI of less than 4.91 TECu2 are determined as TIDs events. It is worth noting that ionospheric irregularities with an IROTI of less than 0.2 TECu2 are regarded as “background noise”. Though most of the IROTI values were smaller than 0.2 TECu2 in Figure 4, a few plasma irregularities enhanced with IROTI values ranging from 0.5 to 0.9 TECu2. In summary, the plasma irregularities induced by Typhoon Doksuri agree well with the characteristics of TIDs.
The time-distance variations of plasma fluctuations on DOY 205 are also illustrated in Figure 5. In this study, plasma irregularities are estimated by a fifth-order Butterworth filter with a 5-min bin size in time and a 10 km bin size in distance. Five notable plasma fluctuations with wavelike structures were observed during the passage of Typhoon Doksuri, with most irregularities occurring within approximately 1800 km from the typhoon’s center. These fluctuations are marked with a (#) symbol.
The first fluctuation emerged at around 06–07 UT on DOY 205 with a magnitude of approximately 0.2 TECu; the propagated speed reached 337 m/s. As the speed of Typhoon Doksuri increased, the amplitude of subsequent fluctuations intensified to around 0.4–0.6 TECu. All TEC fluctuations propagated from the epicenter to approximately 1400 km distance. The slope of coherent TEC irregularities in plots of the distance from the epicenter versus time is traced to characterize the wave propagation speed. For more detailed information, please refer to Themens et al. [11]. The two-dimensional wave fronts of plasma irregularities reveal that the five fluctuations on DOY 205 propagated with speeds ranging from 173 m/s to 337 m/s, consistent with the velocities of Medium-Scale Travelling Ionospheric Disturbances (MSTIDs) reported in previous studies [11,14,37].
Figure 5 reveals the temporal features of five wavelike fluctuations on DOY 205. To gain a deeper understanding of the spatial features of plasma irregularities induced by Typhoon Doksuri, the spatial maps of plasma fluctuations on DOY 205 are displayed in Figure 6. It is important to note that the region with a radius of 700 km signifies the gale wind. For more details, please refer to the basic information on Doksuri provided by the National Institute of Informatics (http://agora.ex.nii.ac.jp/digital-typhoon/summary/wnp/s/202305.html.en (accessed on 24 July 2023).
Figure 6 illustrates distinct plasma irregularities around the typhoon’s eye. From 06:20 UT, a cluster of northwestward plasma irregularities (NO. #1 TIDs) with a magnitude of approximately ~0.2 TECu accumulated at the edge of rainbands. As Typhoon Doksuri moved northwestward, these accumulated plasma irregularities gradually moved outward from the eye, intensifying in response to the increasing speed. For example, at 06:50 UT, the #1 TIDs arrived at Taiwan island, which was ~1200 km away from the typhoon’s eye. At the same moment, #2 TIDs, with the maximum magnitude of more than 0.4 TECu, were generated from the gale wind zone. Figure 6c–f shows that #3, #4, and #5 TIDs all emanated from the gale wind zone during the period of 07–08 UT, and gradually propagated to the rainband over the subsequent hours. All the TIDs presented with a concentric structure, which agrees well with the structure of the ionospheric disturbances induced by Typhoon Hato [12].
Figure 6 vividly illustrates the concentric plasma irregularities propagating outward from the gale wind zone. These plasma irregularities are primarily distributed in Southeast Asia, with the corresponding time reference at approximately GMT + 09:00. In local time, most of the plasma irregularities occurred in the afternoon. The occurrence period exhibited significant discrepancies with the temporal characteristics of EPBs. Moreover, the plasma irregularities with a circular structure align with the characteristics of concentric TIDs. In conclusion, the concentric wavelike plasma fluctuations observed in Figure 6 are likely medium-scale TIDs induced by Typhoon Doksuri, rather than EPBs associated with the R-T instability.

4. Characteristics of 3-D Ionospheric Perturbations

Figure 4, Figure 5 and Figure 6 show meticulously captured 2-D maps illustrating ionospheric TEC fluctuations throughout the passage of the typhoon, utilizing data from numerous GNSS stations. These figures provide a conclusive depiction of the spatial characteristics of TIDs. It is noted that most of GNSS stations are located in the northwestern area of the typhoon’s eye, which are operated by Hong Kong Geodetic Survey Services and Taiwan Seismological and Geophysical Data Management System (please refer to Figure 1). Therefore, this technique lacks the ability to resolve the vertical changes of large-scale ionospheric particles in the vicinities of typhoon’s track. The swift progress of low-orbit satellite technology has granted the advanced radio occultation technique an unparalleled advantage in reconstructing the three-dimensional structure of the refined ionospheric response. This study examines alterations in the three-dimensional structures of ionospheric particles induced by severe typhoons, comparing them to the ionospheric conditions during quiet space periods. The observations utilized in this exploration are derived from Swarm and COSMIC-2.
Swarm-A and Swarm-B operate in different orbital planes at altitudes of 462 km and 511 km, respectively. The unique structure of the Swarm constellation provides a distinct advantage in elucidating topside ionospheric plasma changes during severe space or geophysical events. As indicated in Figure 5 and Figure 6, Typhoon Doksuri induced significant plasma fluctuations on DOY 205. Consequently, profiles obtained from Swarm-A in the period of 07–10 UT during 200–206 are employed to analyze changes in electron density and electron temperature in the topside ionosphere in Figure 7.
Figure 7a illustrates the trajectories of Swarm-A from 07–10 UT on DOY 200–206, with all profiles situated at distances of less than 2000 km from the typhoon’s epicenter. As demonstrated in Figure 5 and Figure 6, the TIDs induced by Typhoon Doksuri could propagate as far as ~1800 km. Therefore, the chosen profiles are well-suited to capture the plasma fluctuations associated with the typhoon. Figure 7b shows that the moderate geomagnetic storm triggered significant plasma depletion at the altitude of 462 km, with the plasma density dropping to a minimum value of ~1 × 105 el/cm3. Compared to the profiles on quiet days, the plasma density within a range of 5°–15°N on DOY 205 increased by ~2 × 105 el/cm3, accompanied by a positive fluctuation of ~50 K in plasma temperature.
The most severe irregularity environment is found in equatorial and high-latitude regions, thus the presence of ionospheric small-scale irregularities that may disturb the amplitude and phase of radio signals is common. These small-scale ionospheric irregularities could occur during geomagnetically quiet periods or when geomagnetic indices show no activity at all, as changes in the Rayleigh–Taylor (R-T) instability and forcing from the lower thermosphere (e.g., gravity wave seeding) are regarded as potential drivers for generating equatorial ionospheric small-scale irregularities [38].
To characterize the spatial gradient contribution to in situ density irregularities, a robust ionospheric gradient index that properly estimates horizontal density gradients with different scales was proposed by Yizengaw [39], namely the spatial in situ density GRaDient Index (GRDI). This index has a strong ability to detect multi-scale plasma bubbles over oceans with a high refreshment rate. In this study, this index is used to detect the perturbations of medium-scale horizontal density gradient near Typhoon Doksuri using COSMIC-2 plasma profiles. The sliding window is set at 30; theoretically, this index can capture irregularities at a scale size of ~210 km or more. Four plasma profiles during the period of 05–09 UT on DOY205 were detected by the GRDI, revealing changes in the horizontal density gradient in the vicinity of Typhoon Doksuri. The results are shown in Figure 8.
Figure 8a depicts the ground tracks of COSMIC-2 plasma profiles that are close to Typhoon Doksuri, indicating that ionospheric electron densities may be influenced by the tropical cyclone. Figure 8b–e show that the ionospheric electron densities surrounding Typhoon Doksuri experienced significant perturbations, and the horizontal gradient of plasma fluctuations in the topside ionosphere was evidently higher than that in the bottom-side ionosphere. For example, in Figure 8b, the horizontal density gradient under the altitudes of 350 km was approximately 20 el/cm3/km, but it abruptly increased to 50–80 el/cm3/km at altitudes of 350–500 km. This phenomenon was also observed in the other three profiles. At 05:21 UT, the maximum density gradient in the topside ionosphere occurred at altitudes of 350–400 km, as well as in the profile at 09:39 UT. However, for the profile at 07:14 UT, the layer of maximum horizontal density gradient uplifted to 450–500 km with a value of 30 el/cm3/km. Overall, the topside density gradient was 1.5 to 2 times larger than that in the bottom-side ionosphere. Results from Figure 7 show that Swarm-A detected significant plasma enhancements in the topside ionosphere (~462 km). The fluctuations of COSMIC-2 profiles detected by the GRDI index agreed well with Swarm.

5. Discussion

Numerous studies have consistently reported that distinctive mesoscale ionospheric disturbances are more readily observed during typhoon landfall or when in proximity to coastal areas [28,40]. This phenomenon can be attributed to the reconfigured wind field created as typhoons traverse mountainous islands, fostering conditions conducive to the generation of atmospheric gravity waves (AGWs). AGWs are recognized as crucial drivers in transporting significant amounts of energy carried by typhoons to the ionosphere-thermosphere system. The terrain plays a pivotal role as a source of AGWs, with mountainous topography providing favorable conditions for the vertical propagation of these waves.
Figure 9 displays the topography along the trajectory of Typhoon Doksuri. In the morning of DOY205, as Doksuri intensified into a violent typhoon, the gale wind with a radius of 700 km reached the eastern region of the Philippines. The rugged landscapes of the Philippines, including the northern Luzon highlands and the mountains of Mindanao, rising between 2000 m and 3000 m, suggest that Typhoon Doksuri could be influenced by terrain features such as blocked airflow, underlying surface friction, and strong wind shear. These factors can directly impact vorticity/angular momentum variation and indirectly affect the free atmosphere above the friction layer. Disrupted by blocking and friction, Typhoon Doksuri is likely to generate substantial amounts of AGWs, carrying dissipated energies from the troposphere to the ionosphere system and consequently influencing ion densities [30,41]. The intrinsic frequencies of AGWs induced by typhoons are generally less than the Brunt–Väisälä frequency, typically characterized by 2.9 mHz in the lower atmosphere, and their velocities are slower than the speed of sound (343 m/s) [31]. The corresponding velocities of ionospheric fluctuations in Figure 5 ranged from 173 m/s to 337 m/s, aligning well with the characteristic features of AGWs.
It is well-established that severe geomagnetic storms can induce large-scale negative and positive ionospheric perturbations driven by various physical-chemical factors, including changes in thermospheric composition, penetration of electric fields, disturbance dynamo electric fields, thermospheric neutral winds, etc. [4,42,43]. Figure 2 illustrates two moderate storms that occurred during the formative and decline phases of Typhoon Doksuri. Therefore, a thorough investigation of the variations in these physical-chemical factors is necessary to distinguish the primary driver behind the observed plasma irregularities.
The TIE-GCM offers valuable insights into the behavior of the ionosphere-thermosphere system under complex space weather conditions, such as solar flares, geomagnetic storms, and variations in solar wind [44]. However, it is essential to recognize the limitations and drawbacks of the TIE-GCM. One primary challenge is that the model’s simulations rely on simplified assumptions and parameterizations to represent intricate physical processes. The use of insufficient input parameters may hinder the model’s ability to comprehensively capture the complexities of certain phenomena. For instance, while the TIE-GCM successfully integrated solar-geomagnetic indices (Vsw, Dst, and Kp) as inputs to simulate changes in the ionosphere-thermosphere system during two moderate storms, its failure to account for the unforeseen Typhoon Doksuri resulted in an inability to capture storm-induced ionospheric perturbations, including variations in plasma density, electric fields, thermospheric composition, and more. Therefore, a comparative analysis is conducted between physical model simulations and instrument measurements to identify potential drivers for the plasma fluctuations during Typhoon Doksuri.
Figure 10 illustrates the variations in TEC, neutral winds, equatorial electrojet, and thermospheric O/N2 ratio during DOY 202–207. Figure 10a illustrates time-latitudinal variations in Total Electron Content (TEC) simulated by the TIE-GCM along the meridian 120°E. During the formative and decline phases of Typhoon Doksuri, the plasma density maintained elevated levels. Distinct features of the equatorial ionization anomaly (EIA) with a peak TEC of approximately 65 TECu were observed on DOY 202–203, corresponding to a minimum Dst of −35 nT during this period. As Dst recovered to normal levels, the maximum value of EIA decreased to around 50 TECu during DOY 204–205. Following the eruption of the second geomagnetic storm, the maximum TEC increased to approximately 60 TECu in the following two days. However, the TEC map, interpolated from observations obtained from the Taiwan Seismological and Geophysical Data Management System and presented in Figure 10b, reveals distinct daily variations. For instance, during the morning of DOY 205, when the minimum Dst dropped to −16 nT, the TEC values in the range of 22°–25°N reached approximately 80 TECu. This is 5 to 15 TECu higher than that observed on disturbed days. During the night of DOY 205, the TEC values dropped below 20 TECu, while the TECs in other disturbed days remained at a level of 30 to 40 TECu.
Previous studies reported that the weak wind systems with speeds of less than 20 m/s were conductive to the propagation of concentric atmospheric gravity waves [31,45]. In this study, we analyze variations in background wind conditions to explain the cause of significant plasma fluctuations using the HWM14 model. Figure 10c,d present contour maps of zonal and meridional winds on DOY 205, with a sample position selected at 121°E, 23°N. The yellow shaded rectangles in each panel signify the occurrence period of plasma fluctuations on DOY 205. At mesosphere altitudes (~50–90 km), both the zonal and meridional winds remain weak, with speeds below 20 m/s. In the thermosphere altitudes (above 90 km), the magnitude of the background winds is much more remarkable than those in the mesosphere. Although partial winds with speeds exceeding 20 m/s occurred during the periods of plasma fluctuations, the magnitude of background winds covered by the yellow rectangles remained at a weaker level compared to that in other periods. This provides a favorable weak background wind condition for the survival of atmospheric gravity waves in the thermosphere.
Variations in thermospheric composition are also a significant driver for large-scale ionospheric disturbances. Figure 10e depicts the changes in the thermospheric O/N2 ratio within the latitudinal span of 0° to 30°N along the meridian line at 120°E. The simulations indicate that the O/N2 ratios during the geomagnetic storms were increased when compared to those on quiet days. Specifically, the maximum O/N2 ratios during the periods of DOY 202–203 and DOY 206–207 were approximately 0.2 larger than those during DOY 204–205. The depletion of the thermospheric O/N2 ratio was also corroborated by the daily profiles obtained from the Global Ultraviolet Imager (GUVI) aboard the Thermosphere, Ionosphere, Mesosphere Energetics, and Dynamics (TIMED) spacecraft. Both the TIE-GCM simulations and instrument observations affirm that the significant plasma fluctuations on DOY204–205 were not associated with changes in thermospheric composition.
Figure 5 illustrates that plasma fluctuations on DOY205 were predominantly observed during the periods of 06–08 UT. When translated to local time (LT), these intervals corresponded to approximately 15–17 LT (daytime). According to Tsunoda et al. [46], AGWs can generate polarization electric field perturbations when concentric wavefronts align with the geomagnetic fields. AGWs are suggested as a plausible mechanism to accelerate the Perkins instability, where the growth of instability is significantly enhanced and accelerated. Therefore, investigating variations in electric fields is crucial to understanding the potential causes for daytime TIDs.
As is known, the motion of electrons near the geomagnetic equator, driven by electric fields, can generate a substantial electric current at altitudes ranging from 90 to 130 km, known as the equatorial electrojet (EEJ). During severe events, the equatorial electrojet can be influenced by disturbed electric fields penetrating from the magnetosphere. Previous studies have reported observations of polarization electric fields induced by Atmospheric Gravity Waves (AGWs) [47]. Therefore, the EEJ serves as an efficient indicator to detect changes in the equatorial electric field when Typhoon Doksuri made landfall in the Philippines. As shown in Figure 10f, the EEJ magnitude during quiet days was generally larger than during geomagnetic storms. For this study, the focus is on the EEJ variation on DOY 205. It is observed that the EEJ remained at a weak level of approximately ~20 nT during 00–09 UT, indicating minimal electric field perturbations along with the TIDs induced by Typhoon Doksuri. Therefore, the weak EEJ is not likely to play an important role in triggering concentric TIDs.

6. Conclusions

The data collected through various techniques, such as GNSS, Swarm, COSMIC-2, GUVI/TIMED, and magnetometer, have been employed to analyze the ionospheric reactions to the impactful Typhoon Doksuri occurring between 20–28 July 2023. Additionally, simulations from TIE-GCM and HWM14, coupled with measurements from GNSS, magnetometers, and GUVI, are selected to delve into the physical mechanisms underlying the notable plasma fluctuations on 24 July (DOY 205). The primary conclusions are summarized as follows:
(1)
The 2-D plasma perturbations reveal five distinct plasma fluctuations of approximately 0.6 TECu occurring on July 24th, coinciding with a daily Dst of −16 nT. These fluctuations exhibited propagation speeds ranging from approximately 173 m/s to 337 m/s. With increasing velocity, the plasma disturbances extended outward from the eye of the typhoon, spanning distances of up to 1800 km from its eye. The spatial distribution of these intense plasma irregularities exhibits a discernible correlation with both propagation direction and distance from the epicenter.
(2)
The 3-D plasma perturbations depict a continuous energy flux emanating from Typhoon Doksuri, propagating upward and leading to the occurrence of MSTIDs not only in the bottom-side ionosphere but also in the topside ionospheric layer. Profiles obtained from Swarm-A satellite confirmed positive plasma fluctuations at an altitude of 462 km during the period of 07–10 UT, with magnitudes of 2 × 105 el/cm3 and 50 K in plasma density and temperature. The fluctuations of COSMIC-2 ion profiles estimated by the GRDI index revealed that the horizontal density gradient in topside ionosphere (>~350 km) was 1.5 to 2 times larger than that in bottom-side ionosphere.
(3)
Both models and measurements sufficiently demonstrate that the significant MSTIDs on 24 July were caused by Typhoon Doksuri, rather than geomagnetic storms. The AGWs triggered by Typhoon Doksuri, resulting from terrain blocking and underlying surface friction, were the main drivers for the Concentric TIDs. Simulations using the Horizontal Wind Model 2014 (HWM14) confirm that a weak background neutral wind was conducive to the existence of AGWs. It is believed that the daytime MSTIDs were attributed to convection-generated AGWs.

Author Contributions

Conceptualization, W.L.; Methodology, F.Y.; validation, W.L. and J.Y.; formal analysis, W.L. and R.Z.; writing—original draft preparation, W.L.; writing—review and editing, F.Y.; supervision, D.Z.; data curation, C.M.H. and J.Y.; investigation, D.Z., C.M.H. and J.L.; project administration, D.Z.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grants 42204030 and 42204037), the Yunnan Fundamental Research Projects (Grants 202201BE070001-035 and 202301AU070062), the Support Programme for Developing Yunnan Talents.

Data Availability Statement

The authors acknowledge the following organizations for GNSS observation files, the Crustal Dynamics Data Information System (CDDIS, https://cddis.nasa.gov/archive/gnss/data/daily/2023/ (accessed on 20 July 2023), require registered), Hong Kong Geodetic Survey Services (https://www.geodetic.gov.hk/sc/rinex/DOWNV.ASPX (accessed on 20 July 2023), and Taiwan Seismological and Geophysical Data Management System (https://gdms.cwa.gov.tw/GeophyDownload.php (accessed on 20 July 2023). Global Ionosphere Radio Observatory for sounder profiles (https://giro.uml.edu/didbase/scaled.php (accessed on 24 July 2023), University Corporation for Atmospheric Research (https://cdaac-www.cosmic.ucar.edu/cdaac/cgi_bin/fileFormats.cgi?type=ionPrf (accessed on 20 July 2023), require registered), European Space Agency for SWARM profiles (https://swarm-diss.eo.esa.int/#swarm%2FLevel2daily%2FEntire_mission_data (accessed on 19 July 2023), Goddard Space Flight Center for solar and geomagnetic indices (https://omniweb.gsfc.nasa.gov/form/dx1.html (accessed on 20 July 2023), INTERMAGNET for geomagnetic field data (https://imag-data.bgs.ac.uk/GIN_V1/GINForms2 (accessed on 21 July 2023), GUVI for thermospheric O/N2 ratio profiles (http://guvitimed.jhuapl.edu/guvi-galleryl3on2 (accessed on 22 July 2023)), and High Altitude Observatory for the TIE-GCM model (https://registration.hao.ucar.edu/hao-reg_email.php (accessed on 21 July 2023), require registered).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Klobuchar, J.A. Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users. IEEE Trans. Aerosp. Electron. Syst. 1987, 3, 325–331. [Google Scholar] [CrossRef]
  2. Prieto-Cerdeira, R.; Orús-Pérez, R.; Breeuwer, E.; Lucas-Rodriguez, R.; Falcone, M. Performance of the Galileo Single-Frequency Ionospheric Correction during in-Orbit Validation. GPS World 2014, 25, 53–58. [Google Scholar]
  3. Yuan, Y.; Wang, N.; Li, Z.; Huo, X. The BeiDou Global Broadcast Ionospheric Delay Correction Model (BDGIM) and Its Preliminary Performance Evaluation Results. Navigation 2019, 66, 55–69. [Google Scholar] [CrossRef]
  4. Li, W.; Zhao, D.; He, C.; Hancock, C.M.; Shen, Y.; Zhang, K. Spatial-Temporal Behaviors of Large-Scale Ionospheric Perturbations During Severe Geomagnetic Storms on September 7–8 2017 Using the GNSS, SWARM and TIE-GCM Techniques. J. Geophys. Res. Space Phys. 2022, 127, e2021JA029830. [Google Scholar] [CrossRef]
  5. Li, W.; Zhao, D.; Feng, J.; Wu, X.; Zhang, Z. Spatial Development of Strong Storm-Induced Ionospheric Perturbations during 25–27 August 2018. Remote Sens. 2023, 15, 2549. [Google Scholar] [CrossRef]
  6. Li, W.; Yue, J.; Yang, Y.; He, C.; Hu, A.; Zhang, K. Ionospheric and Thermospheric Responses to the Recent Strong Solar Flares on 6 September 2017. J. Geophys. Res. Space Phys. 2018, 123, 8865–8883. [Google Scholar] [CrossRef]
  7. Zhou, M.; Gao, H.; Yu, D.; Guo, J.; Zhu, L.; Yang, L.; Pan, S. Analysis of the Anomalous Environmental Response to the 2022 Tonga Volcanic Eruption Based on GNSS. Remote Sens. 2022, 14, 4847. [Google Scholar] [CrossRef]
  8. Vesnin, A.; Yasyukevich, Y.; Perevalova, N.; Şentürk, E. Ionospheric Response to the 6 February 2023 Turkey–Syria Earthquake. Remote Sens. 2023, 15, 2336. [Google Scholar] [CrossRef]
  9. Chou, M.-Y.; Lin, C.C.; Yue, J.; Chang, L.C.; Tsai, H.-F.; Chen, C.-H. Medium-Scale Traveling Ionospheric Disturbances Triggered by Super Typhoon Nepartak (2016). Geophys. Res. Lett. 2017, 44, 7569–7577. [Google Scholar] [CrossRef]
  10. Li, W.; Yue, J.; Wu, S.; Yang, Y.; Li, Z.; Bi, J.; Zhang, K. Ionospheric Responses to Typhoons in Australia during 2005–2014 Using GNSS and FORMOSAT-3/COSMIC Measurements. GPS Solut. 2018, 22, 61. [Google Scholar] [CrossRef]
  11. Themens, D.R.; Watson, C.; Žagar, N.; Vasylkevych, S.; Elvidge, S.; McCaffrey, A.; Prikryl, P.; Reid, B.; Wood, A.; Jayachandran, P.T. Global Propagation of Ionospheric Disturbances Associated With the 2022 Tonga Volcanic Eruption. Geophys. Res. Lett. 2022, 49, e2022GL098158. [Google Scholar] [CrossRef]
  12. Li, K.; Zhang, D.; Zeng, Y.; Tian, Y.; Dai, G.; Liu, Z.; Yang, G.; Sun, S.; Li, G.; Hao, Y.; et al. Revisiting the Ionospheric Disturbances over Low Latitude Region of China during Super Typhoon Hato. Space Weather 2024, 22, e2023SW003694. [Google Scholar] [CrossRef]
  13. Vadas, S.; Yue, J.; Nakamura, T. Mesospheric Concentric Gravity Waves Generated by Multiple Convective Storms over the North American Great Plain. J. Geophys. Res. Atmos. 2012, 117, D07113. [Google Scholar] [CrossRef]
  14. Song, Q.; Ding, F.; Zhang, X.; Liu, H.; Mao, T.; Zhao, X.; Wang, Y. Medium-Scale Traveling Ionospheric Disturbances Induced by Typhoon Chan-h Om Over China. J. Geophys. Res. Space Phys. 2019, 124, 2223–2237. [Google Scholar] [CrossRef]
  15. Bauer, S.J. Correlations between Tropospheric and Ionospheric Parameters. Geofis. Pura E Appl. 1958, 40, 235–240. [Google Scholar] [CrossRef]
  16. Huang, Y.-N.; Cheng, K.; Chen, S.-W. On the Detection of Acoustic-Gravity Waves Generated by Typhoon by Use of Real Time HF Doppler Frequency Shift Sounding System. Radio Sci. 1985, 20, 897–906. [Google Scholar] [CrossRef]
  17. Ke, F.; Wang, J.; Tu, M.; Wang, X.; Wang, X.; Zhao, X.; Deng, J. Morphological Characteristics and Coupling Mechanism of the Ionospheric Disturbance Caused by Super Typhoon Sarika in 2016. Adv. Space Res. 2018, 62, 1137–1145. [Google Scholar] [CrossRef]
  18. Rice, D.D.; Sojka, J.J.; Eccles, J.V.; Schunk, R.W. Typhoon Melor and Ionospheric Weather in the Asian Sector: A Case Study. Radio Sci. 2012, 47, 1–9. [Google Scholar] [CrossRef]
  19. Feng, J.; Zhang, T.; Li, W.; Zhao, Z.; Han, B.; Wang, K. A New Global TEC Empirical Model Based on Fusing Multi-Source Data. GPS Solut. 2023, 27, 20. [Google Scholar] [CrossRef]
  20. Guo, J.; Qi, L.; Liu, X.; Chang, X.; Ji, B.; Zhang, F. High-Order Ionospheric Delay Correction of GNSS Data for Precise Reduced-Dynamic Determination of LEO Satellite Orbits: Cases of GOCE, GRACE, and SWARM. GPS Solut. 2023, 27, 13. [Google Scholar] [CrossRef]
  21. Li, R.; Lei, J.; Kusche, J.; Dang, T.; Huang, F.; Luan, X.; Zhang, S.-R.; Yan, M.; Yang, Z.; Liu, F.; et al. Large-Scale Disturbances in the Upper Thermosphere Induced by the 2022 Tonga Volcanic Eruption. Geophys. Res. Lett. 2023, 50, e2022GL102265. [Google Scholar] [CrossRef]
  22. Li, W.; Zhao, D.; He, C.; Shen, Y.; Hu, A.; Zhang, K. Application of a Multi-Layer Artificial Neural Network in a 3-D Global Electron Density Model Using the Long-Term Observations of COSMIC, Fengyun-3C, and Digisonde. Space Weather 2021, 19, e2020SW002605. [Google Scholar] [CrossRef]
  23. Ren, X.; Zhang, J.; Chen, J.; Zhang, X. Global Ionospheric Modeling Using Multi-GNSS and Upcoming LEO Constellations: Two Methods and Comparison. IEEE Trans. Geosci. Remote Sens. 2021, 60, 5800215. [Google Scholar] [CrossRef]
  24. Zhao, D.; Li, W.; Li, C.; Tang, X.; Wang, Q.; Hancock, C.M.; Roberts, G.W.; Zhang, K. Ionospheric Phase Scintillation Index Estimation Based on 1 Hz Geodetic GNSS Receiver Measurements by Using Continuous Wavelet Transform. Space Weather 2022, 20, e2021SW003015. [Google Scholar] [CrossRef]
  25. Zheng, Y.; Chernogor, L.; Garmash, K.; Guo, Q.; Rozumenko, V.; Luo, Y. Disturbances in the Ionosphere and Distortion of Radio Wave Characteristics That Accompanied the Super Typhoon Lekima Event of 4–12 August 2019. J. Geophys. Res. Space Phys. 2022, 127, e2022JA030553. [Google Scholar] [CrossRef]
  26. Nishioka, M.; Tsugawa, T.; Kubota, M.; Ishii, M. Concentric Waves and Short-Period Oscillations Observed in the Ionosphere after the 2013 Moore EF5 Tornado. Geophys. Res. Lett. 2013, 40, 5581–5586. [Google Scholar] [CrossRef]
  27. Kong, J.; Yao, Y.; Xu, Y.; Kuo, C.; Zhang, L.; Liu, L.; Zhai, C. A Clear Link Connecting the Troposphere and Ionosphere: Ionospheric Reponses to the 2015 Typhoon Dujuan. J. Geod. 2017, 91, 1087–1097. [Google Scholar] [CrossRef]
  28. Xiao, Z.; Xiao, S.; Hao, Y.; Zhang, D. Morphological Features of Ionospheric Response to Typhoon. J. Geophys. Res. Space Phys. 2007, 112. [Google Scholar] [CrossRef]
  29. Li, W.; Yue, J.; Yang, Y.; Li, Z.; Guo, J.; Pan, Y.; Zhang, K. Analysis of Ionospheric Disturbances Associated with Powerful Cyclones in East Asia and North America. J. Atmos. Sol. Terr. Phys. 2017, 161, 43–54. [Google Scholar] [CrossRef]
  30. Peng, H.; Yao, Y.; Kong, J.; Zhou, C.; Kuo, C. GNSS-Based Statistical Analysis of Ionospheric Anomalies During Typhoon Landings in Taiwan/Japan. IEEE Trans. Geosci. Remote Sens. 2020, 59, 5272–5279. [Google Scholar] [CrossRef]
  31. Song, R.; Hattori, K.; Zhang, X.; Liu, J.-Y. The Two-Dimensional and Three-Dimensional Structures Concerning the Traveling Ionospheric Disturbances Over Japan Caused by Typhoon Faxai. J. Geophys. Res. Space Phys. 2022, 127, e2022JA030606. [Google Scholar] [CrossRef]
  32. Mannucci, A.; Wilson, B.; Yuan, D.; Ho, C.; Lindqwister, U.; Runge, T. A Global Mapping Technique for GPS-Derived Ionospheric Total Electron Content Measurements. Radio Sci. 1998, 33, 565–582. [Google Scholar] [CrossRef]
  33. Schaer, S. Mapping and Predicting the Earth’s Ionosphere Using the Global Positioning System. Ph.D. Thesis, University of Bern, Bern, Switzerland, 1999. [Google Scholar]
  34. Yuan, Y.; Tscherning, C.; Knudsen, P.; Xu, G.; Ou, J. The Ionospheric Eclipse Factor Method (IEFM) and Its Application to Determining the Ionospheric Delay for GPS. J. Geod. 2008, 82, 1–8. [Google Scholar] [CrossRef]
  35. Drob, D.P.; Emmert, J.T.; Meriwether, J.W.; Makela, J.J.; Doornbos, E.; Conde, M.; Hernandez, G.; Noto, J.; Zawdie, K.A.; McDonald, S.E.; et al. An Update to the Horizontal Wind Model (HWM): The Quiet Time Thermosphere. Earth Space Sci. 2015, 2, 301–319. [Google Scholar] [CrossRef]
  36. Ren, X.; Le, X.; Mei, D.; Liu, H.; Zhang, X. IROTI: A New Index to Detect and Identify Traveling Ionospheric Disturbances and Equatorial Plasma Bubbles. GPS Solut. 2024, 28, 7. [Google Scholar] [CrossRef]
  37. Li, W.; Zhu, H.; Feng, J.; Wu, X.; Tang, J.; Zhang, Z.; Chen, J. Asymmetric Ionospheric Fluctuations Over the Circum-Pacific Regions Following the January 2022 Tonga Volcanic Eruption. Space Weather 2023, 21, e2022SW003213. [Google Scholar] [CrossRef]
  38. Yizengaw, E.; Groves, K. Forcing from Lower Thermosphere and Quiet Time Scintillation Longitudinal Dependence. Space Weather 2020, 18, e2020SW002610. [Google Scholar] [CrossRef]
  39. Yizengaw, E. New Index to Characterize Ionospheric Irregularity Distribution. Space Weather 2023, 21, e2023SW003469. [Google Scholar] [CrossRef]
  40. Mao, T.; Wang, J.; Yang, G.; Yu, T.; Ping, J.; Suo, Y. Effects of Typhoon Matsa on Ionospheric TEC. Chin. Sci. Bull. 2010, 55, 712–717. [Google Scholar] [CrossRef]
  41. Vadas, S.L.; Liu, H. Generation of Large-Scale Gravity Waves and Neutral Winds in the Thermosphere from the Dissipation of Convectively Generated Gravity Waves. J. Geophys. Res. Space Phys. 2009, 114, A10310. [Google Scholar] [CrossRef]
  42. Astafyeva, E.; Zakharenkova, I.; Förster, M. Ionospheric Response to the 2015 St. Patrick’s Day Storm: A Global Multi-Instrumental Overview. J. Geophys. Res. Space Phys. 2015, 120, 9023–9037. [Google Scholar] [CrossRef]
  43. Ngwira, C.M.; Habarulema, J.-B.; Astafyeva, E.; Yizengaw, E.; Jonah, O.F.; Crowley, G.; Gisler, A.; Coffey, V. Dynamic Response of Ionospheric Plasma Density to the Geomagnetic Storm of 22–23 June 2015. J. Geophys. Res. Space Phys. 2019, 124, 7123–7139. [Google Scholar] [CrossRef]
  44. Qian, L.; Burns, A.G.; Emery, B.A.; Foster, B.; Lu, G.; Maute, A.; Richmond, A.D.; Roble, R.G.; Solomon, S.C.; Wang, W. The NCAR TIE-GCM: A Community Model of the Coupled Thermosphere/Ionosphere System. Model. Ionos. Thermosphere Syst. 2014, 201, 73–83. [Google Scholar]
  45. Suzuki, S.; Shiokawa, K.; Otsuka, Y.; Ogawa, T.; Nakamura, K.; Nakamura, T. A Concentric Gravity Wave Structure in the Mesospheric Airglow Images. J. Geophys. Res. Atmos. 2007, 112, D02102. [Google Scholar] [CrossRef]
  46. Tsunoda, R.T. On Seeding Equatorial Spread F: Circular Gravity Waves. Geophys. Res. Lett. 2010, 37, L10104. [Google Scholar] [CrossRef]
  47. Varney, R.H.; Kelley, M.C.; Kudeki, E. Observations of Electric Fields Associated with Internal Gravity Waves. J. Geophys. Res. Space Phys. 2009, 114, A02304. [Google Scholar] [CrossRef]
Figure 1. The path of Typhoon Doksuri from July 20 to 28, 2023 (a), along with variations in central pressure (b) and average speed (c). The triangle denotes the location of GNSS receivers.
Figure 1. The path of Typhoon Doksuri from July 20 to 28, 2023 (a), along with variations in central pressure (b) and average speed (c). The triangle denotes the location of GNSS receivers.
Remotesensing 16 03375 g001
Figure 2. Fluctuations in proton density (a), temperature (b), and speed (c) of solar wind, as well as Dst (d) and Kp (e), throughout the progression of Typhoon Doksuri.
Figure 2. Fluctuations in proton density (a), temperature (b), and speed (c) of solar wind, as well as Dst (d) and Kp (e), throughout the progression of Typhoon Doksuri.
Remotesensing 16 03375 g002
Figure 3. Plasma irregularities over the stations PTAG, CHEN, and HKCL in the period of DOY 203–208.
Figure 3. Plasma irregularities over the stations PTAG, CHEN, and HKCL in the period of DOY 203–208.
Remotesensing 16 03375 g003
Figure 4. Plasma irregularities calculated by GPS data from CHEN on DOY 205.
Figure 4. Plasma irregularities calculated by GPS data from CHEN on DOY 205.
Remotesensing 16 03375 g004
Figure 5. Temporal-distance profile of STEC fluctuations within a 1800 km radius from the typhoon’s center on DOY 205.
Figure 5. Temporal-distance profile of STEC fluctuations within a 1800 km radius from the typhoon’s center on DOY 205.
Remotesensing 16 03375 g005
Figure 6. Spatial dynamic maps of plasma irregularities on DOY 205.
Figure 6. Spatial dynamic maps of plasma irregularities on DOY 205.
Remotesensing 16 03375 g006
Figure 7. Changes in electron density and electron temperature, derived from Swarm-A on DOY 200–206, 2023. The dashed circles signify distances of 700 km and 2000 km away from the typhoon’s eye, and the stars signify the typhoon’s eye at 09 UT on DOY 202–206.
Figure 7. Changes in electron density and electron temperature, derived from Swarm-A on DOY 200–206, 2023. The dashed circles signify distances of 700 km and 2000 km away from the typhoon’s eye, and the stars signify the typhoon’s eye at 09 UT on DOY 202–206.
Remotesensing 16 03375 g007
Figure 8. Ground tracks of plasma profiles from COSMIC-2 on DOY 205 and the corresponding changes of plasma density gradient.
Figure 8. Ground tracks of plasma profiles from COSMIC-2 on DOY 205 and the corresponding changes of plasma density gradient.
Remotesensing 16 03375 g008
Figure 9. Topography along the trajectory of Typhoon Doksuri on DOY 205, where the red region with a radius of 700 km indicates the influence of gale-force winds.
Figure 9. Topography along the trajectory of Typhoon Doksuri on DOY 205, where the red region with a radius of 700 km indicates the influence of gale-force winds.
Remotesensing 16 03375 g009
Figure 10. Time-latitudinal variations of (a) TEC and (e) thermospheric O/N2 ratio during DOY 202–207 simulated by the Thermosphere-Ionosphere-Electrodynamics General Circulation Model, along with observational data, including (b) Global Navigation Satellite System-TEC, (c,d) Zonal and Meridional winds (121°E, 23°N) simulated by the Horizontal Wind Model 2014 empirical model on DOY 205, and (f) temporal variation of the equatorial electrojet estimated by the difference between DLH and PHU. Additionally, (gl) showcase changes in the thermospheric O/N2 ratio within a longitudinal range of 110–140°E, as derived from the Global Ultraviolet Imager (GUVI) on TIMED satellite.
Figure 10. Time-latitudinal variations of (a) TEC and (e) thermospheric O/N2 ratio during DOY 202–207 simulated by the Thermosphere-Ionosphere-Electrodynamics General Circulation Model, along with observational data, including (b) Global Navigation Satellite System-TEC, (c,d) Zonal and Meridional winds (121°E, 23°N) simulated by the Horizontal Wind Model 2014 empirical model on DOY 205, and (f) temporal variation of the equatorial electrojet estimated by the difference between DLH and PHU. Additionally, (gl) showcase changes in the thermospheric O/N2 ratio within a longitudinal range of 110–140°E, as derived from the Global Ultraviolet Imager (GUVI) on TIMED satellite.
Remotesensing 16 03375 g010
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, W.; Yang, F.; Yang, J.; Zhang, R.; Lin, J.; Zhao, D.; Hancock, C.M. Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023. Remote Sens. 2024, 16, 3375. https://doi.org/10.3390/rs16183375

AMA Style

Li W, Yang F, Yang J, Zhang R, Lin J, Zhao D, Hancock CM. Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023. Remote Sensing. 2024; 16(18):3375. https://doi.org/10.3390/rs16183375

Chicago/Turabian Style

Li, Wang, Fangsong Yang, Jiayi Yang, Renzhong Zhang, Juan Lin, Dongsheng Zhao, and Craig M. Hancock. 2024. "Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023" Remote Sensing 16, no. 18: 3375. https://doi.org/10.3390/rs16183375

APA Style

Li, W., Yang, F., Yang, J., Zhang, R., Lin, J., Zhao, D., & Hancock, C. M. (2024). Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023. Remote Sensing, 16(18), 3375. https://doi.org/10.3390/rs16183375

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop