Method and system for bursty-sodium-layer identification, characterization and e-layer correlation analysis based on long-term observation at low latitude

Abstract

The application discloses a method and system for identifying, characterizing and analyzing E-layer correlation of burst sodium layer based on long-term observation at low latitude in the field of space physics and atmospheric detection technology.The method comprises the following steps: obtaining long-term observation data of a sodium laser radar and a digital altimeter at a low latitude region, preprocessing and constructing a multi-year same-month average background sodium density field; identifying burst sodium layer events according to intensity criteria, shape criteria and time criteria; synchronously identifying burst E-layer events; calculating and outputting climatological characteristics such as annual occurrence rate and monthly average occurrence rate of the burst sodium layer, and performing spatiotemporal correlation analysis on the burst E-layer.The application solves the problems that the prior art is difficult to stably process long-term non-uniform observation data, the identification standard is not unified and the physical correlation analysis is insufficient, and can automatically and accurately identify burst sodium layer events from long sequence, abnormal and missing data, thereby providing reliable statistical basis and technical means for low-latitude mesosphere-thermosphere coupling research.

Description

Technical Field

[0001] This invention relates to the field of space physics and atmospheric sounding technology, specifically to a method and system for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term low-latitude observations. Background Technology

[0002] The metallic sodium layer in the middle and upper atmosphere is an important tracer for lidar to detect atmospheric dynamics and space weather processes in the mesosphere and lower thermosphere. Among them, the sporadic sodium layer (SSL) is characterized by a transient and sharp increase in the density of neutral sodium atoms in the altitude range of about 90–100 km, and is characterized by a narrow vertical thickness (usually 1–3 km), strong intermittent occurrence time, and peak density that significantly exceeds the background value.

[0003] Currently, research on SSL, especially in low-latitude regions, largely focuses on case studies or statistics based on a limited number of observation years. Existing technologies use inconsistent criteria for SSL identification. For example, the selection of background sodium layer density often employs the "average value of non-occasional periods on the same day." This method suffers from problems such as a subjective definition of "non-occasional periods," small background sample size, and the risk of circular reasoning between events and background, leading to insufficient accuracy and comparability of long-term statistical results.

[0004] The sporadic E layer (Es) is a thin layer of dense metal ions that appears in the E region of the ionosphere. It has a potential physical coupling with the sporadic sodium layer in terms of formation mechanism, height distribution, and temporal evolution. In low-latitude regions, the sporadic sodium layer is affected by tides, gravity waves, ionospheric conditions, and meteor inputs, and exhibits significant interannual variability, weak monthly preference, and complex spatiotemporal characteristics.

[0005] Existing methods struggle to effectively address issues such as inconsistent data quality and uneven observation durations caused by instrument malfunctions and observation gaps. This hinders a comprehensive understanding of the stellar climatological characteristics and in-depth analysis of their physical correlation with the sporadic E layer (Es). Specific shortcomings are as follows:

[0006] 1. It is mostly based on single events or short-term observation statistics, making it difficult to process lidar data that spans many years, is non-uniform, and contains instrument malfunctions and observation gaps; 2. The background sodium layer is usually based on the daily average, which is susceptible to short-term fluctuations, noise and the event itself, and there is a risk of circular reasoning. The identification criteria are not uniform. 3. The lack of an outlier removal mechanism leads to poor comparability of long-term series statistical results; 4. There is a lack of a complete workflow for standardized correlation analysis of sudden sodium layers and sudden E layers over long time scales; 5. No normalized incidence rate index is provided to address the issue of uneven observation duration, making it impossible to objectively quantify interannual / intermonthly variations.

[0007] Therefore, there is an urgent need for a method and system that can stably and automatically process long-term, non-uniform observation data and achieve SSL standardization identification, multi-dimensional feature representation, and correlation analysis with the Es layer. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for identifying, characterizing, and analyzing the correlation between sudden sodium layer events and the E layer based on long-term observations in low latitudes. This invention solves the technical problem of how to accurately identify sudden sodium layer events and reveal their true physical connection with sudden E layers using long-term, imperfect observation data from low latitude regions. It enables stable identification, multi-parameter characterization, and climatological statistics of sudden sodium layers in low latitude regions, and completes long-term series correlation analysis with sudden E layers.

[0009] To achieve the above objectives, the technical solution of the present invention is as follows: This invention provides a method for identifying, characterizing, and performing E-layer correlation analysis of abrupt sodium layers based on long-term low-latitude observations, comprising the following steps: S1. Obtain sodium lidar observation data in low-latitude regions to form sodium density time-high sequence, preprocess the sodium density time-high sequence to form a standardized sodium density dataset, and at the same time obtain the raw observation data of the digital altimeter Es layer in adjacent regions, and extract the critical frequency foEs of the burst E layer and the virtual height hEs of the burst E layer. S2. Based on the standardized sodium density dataset, construct a multi-year average background sodium density field for the same month; using this background field as a benchmark, identify sudden sodium layer events according to comprehensive criteria, which include intensity criteria, morphological criteria, and temporal criteria. S3. Determine the sudden E-layer event based on the sudden E-layer critical frequency foEs in the observation data of the digital altimeter, compare the sudden E-layer false height hEs with the sudden sodium layer peak height, the time of occurrence of the sudden E-layer with the time of occurrence of the sudden sodium layer, and analyze the correlation of monthly Es. S4. Based on all identified sudden sodium layer events, calculate and output their long-term climatological characteristics, including interannual occurrence rate, monthly average occurrence rate, local time distribution, peak density distribution, peak height distribution, and duration distribution; and combine the sudden E layer events to analyze the spatiotemporal correlation between sudden sodium layers and sudden E layers in long-term series.

[0010] Furthermore, the preprocessing of the sodium density time-high sequence in S1 includes: screening of effective observation nights, unification of time and height resolution, removal of noise and missing values, and identification of abnormal years based on the annual average density profile and seasonal distribution characteristics.

[0011] Furthermore, the identification of abnormal years based on the annual average density profile and seasonal distribution characteristics specifically involves: analyzing the deviation between the annual average sodium density profile and the multi-year average profile, and combining this with the spatiotemporal distribution map of sodium density throughout the year to identify and exclude years in which data systematic shifts or abnormal principal layer heights are caused by instrument malfunctions.

[0012] Furthermore, the construction method of the multi-year monthly average background sodium density field in S2 is as follows: for each month, calculate the average value of sodium density at all effective observation days and all time points in that month in terms of vertical height; and smooth the obtained monthly-height two-dimensional average field.

[0013] Furthermore, in S2, the intensity criterion is: the peak sodium density of the sudden sodium layer event must reach at least a preset multiple of the background sodium density value at the same height, and when its peak height exceeds a preset threshold, the peak sodium density must be greater than a preset absolute density threshold at the same time. The morphological criterion is: the full width at half maximum (FWHM) of a sudden sodium layer event in the vertical direction is less than a preset width threshold; The time criterion is: the duration of a sudden sodium layer event is not less than a preset duration threshold.

[0014] Furthermore, the preset multiplier is 2 times, the preset height threshold is 100 km, and the absolute density threshold is 1000 cm. -3 The preset width threshold is 4 km, and the preset duration threshold is 20 minutes.

[0015] Furthermore, in S3, the sudden E-layer event is determined by using the sudden E-layer critical frequency foEs≥4 MHz as the threshold to determine the occurrence of a sudden E-layer.

[0016] Furthermore, the monthly average incidence rate in S4 is defined as: the ratio of the cumulative duration of all sudden sodium layer events to the total effective observation time in the target month; or the ratio of the number of effective observation time points of sudden E layer events to the total number of effective observation time points in the target month.

[0017] Furthermore, the analysis of the spatiotemporal correlation between the sudden sodium layer and the sudden E layer in S4 specifically includes: comparing the monthly average occurrence curves of the sudden sodium layer and the sudden E layer to identify the months with common peaks; and comparing the peak height distribution of the sudden sodium layer with the height distribution of the artificial height of the sudden E layer to determine their consistency in vertical space.

[0018] This invention also provides a system for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term low-latitude observations, including: The data acquisition and preprocessing module is used to acquire and process sodium lidar data and digital altimeter data. The background field construction module is used to construct the average background sodium density field for the same month over many years based on long-term, standardized sodium density data. The sudden sodium layer identification module is used to automatically identify sudden sodium layer events from standardized sodium density data based on comprehensive criteria and the background field. The sudden E-layer identification module is used to automatically identify sudden E-layer events from digital altimeter data based on the sudden E-layer critical frequency threshold. The climatological feature analysis and output module is used to calculate and output the multidimensional climatological features of the abrupt sodium layer, as well as the spatiotemporal correlation analysis results between the abrupt sodium layer and the abrupt E layer.

[0019] By adopting the above technical solution, the present invention has the following advantages: 1. This invention provides a method for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term observations at low latitudes. Based on continuous observation data spanning more than ten years, it constructs a "multi-year average background of the same month" as a benchmark, effectively overcoming the shortcomings of traditional methods such as subjective background selection, small sample size, and circular reasoning. It significantly improves the stability and accuracy of SSL event identification, and has long-term and stable identification capabilities, making it particularly suitable for low-latitude regions with significant interannual and seasonal variations.

[0020] 2. This invention provides a method for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term low-latitude observations. It uses "cumulative duration of SSL / total effective observation duration" to define the occurrence rate, which solves the statistical bias caused by the uneven observation duration in different months and years in long-term observations, and makes the statistical results of different time scales and different stations have good comparability.

[0021] 3. This invention provides a method for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term low-latitude observations. It can not only identify events but also systematically output a set of multi-dimensional SSL characteristic parameters, including interannual, intermonthly, local time, altitude, intensity, and duration, providing a rich data foundation for in-depth research on the formation mechanism and climatological significance of SSLs.

[0022] 4. This invention provides a method for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term low-latitude observations. Through an abnormal year discrimination mechanism, it can automatically identify and eliminate data contamination caused by instrument malfunctions or observational anomalies, ensuring the reliability of long-term data analysis and statistical results.

[0023] 5. This invention provides a method for identifying, characterizing, and correlating E-layers based on long-term observations at low latitudes. By performing spatiotemporal correlation analysis between SSL features and Es layer parameters, it provides quantitative and statistical evidence to verify the physical mechanism that "the Es layer promotes SSL formation through the downward transport of metal ions" in low-latitude regions. Attached Figure Description

[0024] Figure 1 This is a map showing the geographical distribution of lidar and altimeter at the observation sites of this invention; Figure 2 This is a map showing the total annual observation duration and seasonal distribution of the sodium lidar at Haikou Station from 2012 to 2024. Figure 3 Identifying abnormal years: Comparison of sodium density in 2021 with the multi-year average; Figure 4 The output is a smoothed monthly average Na density height-month contour map; Figure 5 This is a graph showing the variation characteristics of the monthly occurrence rate of sudden sodium layer formations, the cumulative monthly duration, and the effective monthly observation time. Figure 6 This is a comparison chart of the monthly average incidence rates of sudden sodium layer and sudden E layer; Figure 7 It is a multi-parameter statistical histogram of sudden sodium layer events; Figure 8 This is a histogram showing the distribution of the sudden nighttime virtual height hEs of layer E in this embodiment of the invention; Figure 9 A four-grid diagram of a typical sudden sodium layer event, including observed density, background density, anomalous density, and density ratio; Figure 10 It is a vertical profile and full width at half maximum (FWHM) plot of the peak sodium density at a typical sudden sodium layer event. Detailed Implementation

[0025] The technical solution of the present invention will be specifically described below with reference to the accompanying drawings. It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0026] This invention provides a method for identifying, characterizing, and performing E-layer correlation analysis of abrupt sodium layers based on long-term low-latitude observations, comprising the following steps: S1: Data Acquisition and Preprocessing: Acquire sodium lidar observation data in low-latitude regions. The data includes time-altitude sequences of sodium density as a function of time and altitude. Then, the sodium density time-altitude sequences are subjected to effective observation night screening, time resolution standardization, altitude resolution standardization, noise and missing values ​​removal, and abnormal year discrimination based on annual average density profile and seasonal distribution characteristics to form a standardized sodium density dataset. At the same time, the raw observation data of the Es layer of the digital altimeter adjacent to the radar observation area are acquired, and the foEs (critical frequency of burst E layer) and hEs (false height of burst E layer) data are processed. Figure 1 This illustration displays the latitude and longitude information of raw lidar and altimeter data collected for analyzing abrupt nanosphere phenomena in the low-latitude mesosphere and thermosphere. The red circle represents the lidar station in Haikou, Hainan, and the blue triangle represents the location of a nearby Fuchs altimeter. In a specific embodiment, the raw data acquisition is detailed as follows... Figure 1 As shown, the data from Haikou Station in Hainan Province (…) is obtained. Figure 1 The red circle in the middle shows the raw Na data file (.DAT format) from 2012 to 2024. Also, the nearby Fuchs station was obtained. Figure 1 The data from the digital altimeter (blue triangle) includes the critical frequency (foEs) and virtual height (hEs) information for the Es layer.

[0027] Because each of the original Na data files from 2012 to 2024 contains sodium atomic number density profiles for multiple times within a day, the number of height layers, height starting points, and sampling intervals may not be completely consistent between different files, or even at different times within the same file. Furthermore, the files contain explicit missing data markers and physically unreasonable negative or excessively large values. Therefore, the nanodensity data needs to be preprocessed using Matlab software as follows: (1) Set the height grid parameters: unify the inconsistent height sampling in the original data onto a fixed grid. Set the target height range to 80km to 110km to cover the main distribution area of ​​the sodium layer; set the height resolution to 0.5km, that is, output a density value every 0.5km, and finally form 61 equally spaced height layers.

[0028] (2) Time grid parameter settings: In order to analyze the evolution characteristics of the sodium layer from evening to night, the daily time grid was fixed: the start time was 12:30 (UTC), the end time was 21:30 (UTC), and the time interval was 10 minutes. This resulted in 55 regular time points per day, which facilitated the comparison of daily variations and seasonal averages.

[0029] (3) Temporal and Height Gridded Interpolation: A two-step interpolation strategy is adopted to ensure that the data are located on a regular grid in both time and height. First, height-direction interpolation: at each original observation time, conformal piecewise cubic interpolation (pchip) is used to interpolate the density profile from the original height layer to the aforementioned 0.5km resolution height grid. The pchip method can maintain the profile shape and avoid overshoot, and is suitable for vertical interpolation of stratiform atmospheric elements. Second, temporal interpolation: for each fixed height layer, linear interpolation is used along the time axis to interpolate the density value from the original observation time to regular time points at 10-minute intervals. Linear interpolation makes the density transition smoothly over time between two adjacent observations, which is more reasonable than "directly taking the nearest value" and can reflect the continuous change of sodium layer density.

[0030] (4) Data quality control: To ensure the reliability of interpolation and subsequent statistics, a strict quality screening was first performed on the original density data: missing marker values ​​and all physically impossible negative or zero values ​​in the file were uniformly replaced with NaN (missing marker). For the remaining valid data, the 99.5th percentile was calculated, and twice this percentile was used as the dynamic upper limit; density values ​​exceeding this upper limit were considered extreme outliers (such as strong noise or ionospheric interference) and were also set to NaN. This adaptive thresholding method can remove abnormally large values ​​according to the actual distribution of data in each year, while avoiding over-removal or under-removal that may be caused by a fixed threshold.

[0031] (5) Check time zone consistency, force all time data to UTC (Universal Time) time zone, eliminate time zone ambiguity caused by file records or system settings, ensure that the time axis of different dates and different files is strictly aligned, and then index and organize the data according to the observation date of each file by year (2012–2024) and season (spring March–May, summer June–August, autumn September–November, winter December–February) to generate a data structure divided by year and season, which is convenient for subsequent time-segmented statistics and trend analysis.

[0032] (6) Data accuracy analysis: The sodium number density data for each year are averaged by altitude layer to obtain the average density profile within the altitude range of 80–110 km for each year. By comparing the average profiles of adjacent years or multiple years, it is possible to identify whether there are systematic shifts, abnormal jumps, or large-scale missing data in a particular year. At the same time, by using the spatiotemporal distribution map of sodium density throughout the year, the overall evolution characteristics of sodium layer density with season, altitude, and time (daily variation) can be visually observed to determine whether there are any anomalies in the data for that year.

[0033] Figure 2This data, generated after preprocessing, shows the total annual observation duration and seasonal distribution of the sodium lidar at Haikou Station from 2012 to 2024. Overall, the data exhibits good temporal continuity and seasonal coverage, providing a reliable data foundation for analyzing the climatic characteristics of the sodium layer and studying transient structures such as sudden sodium layers (SSLs).

[0034] Continue with preprocessing, which can be achieved through... Figure 3 The results to be displayed: Figure 3 (a) shows that the average Na density profile in 2021 was significantly lower than the historical average from 2012 to 2024, with the difference being particularly pronounced near the peak height of the main sodium layer. Figure 3 (b) indicates that the nanodensity from March to September was much lower than normal, and the observed nanolayers were located at approximately 95-110 km above, significantly deviating from the main nanolayer height (85-100 km). This is in... Figure 3 Corresponding patterns are also observed in (a), and these anomalies may be caused by abnormalities in the number of photons received by the instrument. Therefore, the data accuracy analysis steps yielded... Figure 3 Based on the analysis, 2021 was determined to be an abnormal year for the instrument, and the nanodensity data of the abnormal year (2021) can be reasonably excluded under subsequent technical implementation.

[0035] S2. Based on the standardized sodium density dataset, construct a multi-year average background sodium density field for the same month; using this background field as a benchmark, identify sudden sodium layer events according to comprehensive criteria, including intensity criteria, morphology criteria, and time criteria; the construction method of the multi-year average background sodium density field for the same month is as follows: for each month, calculate the average sodium density at all effective observation days and all time points in that month at the vertical height; and smooth the obtained monthly-height two-dimensional average field.

[0036] The intensity criteria are as follows: the peak sodium density of a sudden sodium layer event must reach at least a preset multiple of the background sodium density at the same height, and when its peak height exceeds a preset threshold, the peak sodium density must also be greater than a preset absolute density threshold. The morphological criteria are as follows: the full width at half maximum (FWHM) of the sudden sodium layer event in the vertical direction must be less than a preset width threshold. The temporal criteria are as follows: the continuous duration of the sudden sodium layer event must not be less than a preset duration threshold. Furthermore, the preset multiple is 2 times, the preset height threshold is 100 km, and the absolute density threshold is 1000 cm. -3 The preset width threshold is 4 km, and the preset duration threshold is 20 minutes.

[0037] In a specific embodiment, for all years not excluded (2012-2020, 2022-2024), the average sodium density at each altitude layer is calculated monthly. Specifically, the average density profile for all time points of each valid observation night is calculated, and the profile is accumulated according to its corresponding month, with the sample number recorded. The accumulated value is divided by the sample number to obtain the multi-year average for each altitude layer in that month. Finally, a two-dimensional Gaussian filter is applied to the monthly-altitude two-dimensional average matrix, and a smoothing is applied around the month to obtain a smoothed monthly average Na density contour map, as shown in the specific example. Figure 4 As shown, from Figure 4 It can be seen that the peak height of the Na layer remains stable at 90-93 km throughout the year, reflecting the characteristics of low-latitude regions.

[0038] In determining the background nanolayer in the context of sudden events, existing methods typically use the average of the sodium layer profile during the non-occurring period of the day as the background. This method has several drawbacks: "non-occurring period" is difficult to define objectively; the background sample size is insufficient, leading to poor reliability; the event and background originate from the same observation night, posing a risk of circular reasoning; and it cannot distinguish between long-term trends and interannual variations. This technique uses the average background profile of the same month over many years, rather than a fixed background or the overall average of the current night. The statistical year range for the background includes all years covered by the data, excluding the anomalous year (2021). Then, the density average at each altitude for all valid nights of each month is calculated independently to obtain the corresponding background profile. The advantages of this method are: background and event are independent, avoiding circular reasoning; large sample size ensures statistical robustness; it reflects normal seasonal variations, eliminating diurnal variations and short-term fluctuations; and it supports standardized comparisons across years and stations.

[0039] The steps for determining sudden sodium layer events are as follows: First, in terms of intensity criteria, the peak density of an SSL must be at least twice the density of the background sodium layer at the same altitude, indicating a significant enhancement characteristic relative to the normal sodium layer. When an SSL occurs at an altitude exceeding 100 km, considering the relatively low background sodium density at high altitudes, to avoid misclassifying weak disturbances as SSLs, its peak density is further required to be greater than 1000 cm³. -3 .

[0040] Secondly, in terms of morphological criteria, the full width at half maximum (FWHM) of an SSL should be less than 4 km to reflect its strong locality and thin-layer characteristics in vertical structure, thus distinguishing it from the slow thickening or overall fluctuation of a typical sodium layer.

[0041] Finally, regarding the time criterion, SSL events must last continuously for at least 20 minutes to eliminate false anomalies caused by short-term noise, transient signal fluctuations, or inversion errors. Combining these constraints on intensity, structure, and duration allows for relatively effective identification of genuine SSL events. Simultaneously, basic information about SSL events is stored: start time, peak time, end time, peak height, peak density, peak moment, and full width at half maximum (FWHM) of the peak, facilitating subsequent analysis of climatic characteristics.

[0042] Based on observational data from 736 effective observation days and a cumulative total of 5779 hours collected by the Hainan sodium lidar from 2012 to 2024, a total of 249 sudden sodium layer (SSL) events were identified. To further reveal the spatiotemporal variation characteristics of SSL activity, a statistical analysis of its occurrence rate will be conducted subsequently. This invention defines the SSL occurrence rate as the ratio of the duration of an SSL event to the effective observation time during the same period, that is, the proportion of time during which SSL occurs within the total effective observation period. This definition can uniformly quantify the level of SSL activity when the effective observation duration varies across different months, seasons, and years, thereby improving the comparability of statistical results across different time scales.

[0043] SSL climatological characteristics analysis: The total effective observation time and SSL duration were extracted from the preprocessed nano-density data and SSL event data, respectively. The occurrence rate of SSL was calculated monthly. Figure 5 ,exist Figure 5 The table shows, from top to bottom, the occurrence rate of sporadic sodium layer (SSL), the duration of SSL, and the total effective observation time. The results indicate that months with high SSL occurrence rates in low latitudes exhibit significant interannual fluctuations. For example, in some years, high occurrences mainly occur in winter or autumn, while in others they occur in spring or summer. These results suggest that SSL occurrence is not only seasonally controlled but may also be influenced by interannual background dynamic processes and changes in ionospheric conditions. This technique can be used to quantitatively assess the monthly and interannual variations of sporadic sodium layer (SSL) activity at the Haikou station, while considering the heterogeneity of observational sampling (correcting for the occurrence rate through effective observation time). The final output graph can be used to illustrate the seasonal characteristics, interannual differences, and distribution of observational data volume of SSL occurrence frequency.

[0044] S3. Determine the sudden E-layer event based on the sudden E-layer critical frequency foEs in the digital altimeter observation data, compare the sudden E-layer false height hEs with the sudden sodium layer peak height, the sudden E-layer occurrence time with the sudden sodium layer occurrence time, and analyze the correlation of monthly Es; among them, the sudden E-layer event is determined by using the sudden E-layer critical frequency foEs≥4 MHz as the threshold to determine the occurrence of a sudden E-layer.

[0045] In a specific embodiment, the processing of altimeter data is as follows: (1) Collect data from the Fuke station altimeter (location at...) Figure 1 (Marked with a blue triangle) Use SAO-Explorer software to convert the E-layer C-socer, foEs, and hEs data into TXT files.

[0046] (2) Data preprocessing was performed using Matlab software. First, all TXT files were read in a loop and the original data were merged. Then, time zone processing was performed, and Beijing time and UTC time were saved separately. Finally, the data were organized by day, year and season and stored as .mat files for further analysis.

[0047] (3) Perform Es event determination on the data in the altimeter .mat file compiled in the previous step. First, filter out the valid observation data, and then extract the information of all Es events (foEs value, height, mass) according to the Es event determination condition: foEs≥4MHZ.

[0048] The occurrence rate of Es (defined as the percentage of time points in an observed timeframe that are in an Es state) is calculated. A threshold of foEs ≥ 4 MHz is set to indicate an Es event at that time. Then, each valid time point of each day is iterated over, and the total number of valid time points and the number of Es occurrences are counted monthly to obtain the monthly average occurrence rate of Es. Figure 6 The blue line indicates that the peak occurs in June, consistent with the fact that the incidence of Es is highest in the Northern Hemisphere during the summer.

[0049] S4. Based on all identified sudden sodium layer events, calculate and output their long-term climatological characteristics, including interannual occurrence rate, monthly average occurrence rate, local time distribution, peak density distribution, peak height distribution, and duration distribution; and combine with sudden E-layer events to analyze the spatiotemporal correlation between sudden sodium layers and sudden E-layers over long time series.

[0050] The monthly average incidence rate is defined as: the ratio of the cumulative duration of all sudden sodium layer events to the total effective observation time in the target month; or the ratio of the number of effective observation time points of sudden E layer events to the total number of effective observation time points in the target month.

[0051] In addition, the analysis of the spatiotemporal correlation between the sudden sodium layer and the sudden E layer in long-term series specifically includes: comparing the monthly average occurrence curves of the sudden sodium layer and the sudden E layer to identify the months with common peaks; and comparing the peak height distribution of the sudden sodium layer with the height distribution of the artificial height of the sudden E layer to determine their consistency in vertical space.

[0052] In a specific embodiment, using the previously compiled SSL event data, the climatological distribution of the monthly average occurrence rate of sudden sodium layer (SSL) events over Hainan from 2012 to 2024 was obtained by parsing the event table, calculating the daily effective observation time, calculating the daily total SSL duration, merging and filtering, and performing monthly statistics. Figure 6 The values ​​are indicated by orange lines. The results show that there were three main high-value areas throughout the year, located in January, June, and October, with October reaching the maximum value of approximately 0.12 or higher, followed by January. In contrast, April and August had the lowest values ​​of the year, with the incidence rate dropping to approximately 0.02-0.03.

[0053] Figure 6 Simultaneously, the monthly average occurrence rates of SSL and Es were displayed. By analyzing the common characteristics of the monthly average occurrence rates of these two sudden phenomena, it can be observed whether the occurrence rate of ionospheric Es and the occurrence rate of atmospheric lower sodium layer SSL at the Hainan station exhibit similar seasonal variation patterns. Ultimately, a clear physical coupling background was found between Es and SSL over Hainan, especially the common peak around June, which supports the judgment that Es participates in the SSL formation process. That is, Es, through convergence and downward transmission of metal ions, contributes to Na+ formation. + This provides the conditions for rapid neutralization, thereby significantly increasing the probability of SSL occurring.

[0054] Figure 7 This is a statistical analysis of SSL events in Haikou, Hainan Province over the years, including histograms of SSL start time, peak time, duration, peak density, and peak height. The specific implementation steps are as follows: For the SSL event list from the Haikou station and the preprocessed LiDAR gridded density data, a corresponding observation night is matched for each event, and key parameters such as peak density, peak height, and peak occurrence time are extracted. Based on these extraction results, the overall statistical characteristics of the events are calculated (including average duration, peak density, peak height, and circular averages of start / peak times), and multi-subplot statistical histograms are generated. This can be used to analyze the morphological characteristics and nighttime activity patterns of SSL events.

[0055] from Figure 7 (a) and Figure 7 (b) It can be seen that the start time of SSL is mainly concentrated between 21:00 and 00:00 LT, while the peak time is mainly distributed between 22:00 and 01:00 LT, indicating that most events will rapidly intensify after generation and reach their strongest point around midnight. Figure 7 (c) and Figure 7 (d) It can be seen that Hainan SSL events are mainly short-duration and medium-intensity events, with most events lasting from tens of minutes to over 100 minutes, and only a few events lasting for several hours and developing into strong events. From Figure 7 (e) Further analysis shows that the peak altitude of SSL is mainly concentrated at 94-96 km, with the vast majority of events occurring in the 90-99 km range, indicating a relatively clear preferred formation altitude. Overall, SSL over Hainan (low latitude region) mainly manifests as a transient enhancement layer structure that occurs during the first half of the night to around midnight, with a short duration and a relatively concentrated peak altitude. This suggests that low-latitude SSL is a transient thin-layer structure that occurs during the first half of the night to around midnight.

[0056] SSL and Es Correlation: Based on the results of altimeter data processing in step S3, the nighttime distribution characteristics of the virtual height (hEs) of sporadic E-layer (Es) in the ionosphere at Hainan Station were further analyzed. The specific steps are as follows: First, the processed altimeter data was loaded, and foEs ≥ 4 MHz was set as the criterion for Es occurrence. The nighttime period from 20:30 local time to 05:30 the next day (spanning midnight) was selected. Then, hEs values ​​meeting the above conditions were extracted from the daily data, and invalid points were removed. Finally, a histogram of hEs was plotted at 2 km intervals to show the statistical distribution of the virtual height when the Es layer occurs at night. Figure 8 .from Figure 8 This provides a visual understanding of the main altitude ranges of the Es layer at night, offering a reference for studying its formation mechanism and seasonal variations. The study found that the peak altitude of the SSL layer is strongly concentrated around 95 kilometers, consistent with the nighttime distribution altitude of the hEs layer. This indicates a clear coupling between the SSL layer and the Es layer in Hainan (low latitude) in terms of frequency of occurrence and altitude.

[0057] Case Study Analysis of Typical Events: To further reveal the morphological characteristics and evolution of sudden sodium layer (SSL) over Hainan, a case study analysis of typical SSL events can be conducted. The specific steps are as follows: First, preprocessed sodium layer data from the Haikou station is loaded, and the observation density field for June 21, 2019 (time resolution 10 minutes, altitude 80–110 km, interval 0.5 km) is extracted. Next, a multi-year average background field is constructed using all historical nights of the same month (June) as the target date (excluding years with anomalous data). Subsequently, based on observations and the background, the anomalous field (observations minus background), the ratio field (observations minus background, with regions having a ratio < 2 set to NaN to highlight the SSL enhancement area), and the temporal rate of change are calculated. Finally, a four-grid diagram is used to illustrate the process. Figure 9 As shown, the observation density, background density, anomaly field, and ratio field are displayed to intuitively identify whether an occasional sodium layer (SSL) event occurred on that day and to assess its intensity, duration, and vertical extent.

[0058] Figure 9 This demonstrates a typical SSL incident that occurred on June 21, 2019. Figure 9 (a) gives the time-altitude distribution of the actual Na density on that day, from... Figure 9 (a) It can be seen that a distinctly enhanced narrow layer structure appears in the altitude range of approximately 15:30–18:30 UTC and 93–97 km. This enhanced layer superimposed on the normal Na background has clear local peaks and a limited vertical scale. To distinguish whether this enhancement significantly deviates from the background state, Figure 9 (b) The multi-year average background Na density distribution for the corresponding month is given. Figure 9 (c) shows the anomalous distribution of the actual Na density minus the background value. The above results indicate that a significant positive anomaly region exists around 94–96 km during the event period, and the anomaly center gradually moves to lower altitudes over time. Figure 9 (d) Further, the distribution of the ratio of actual to background Na density is given. It can be seen that the ratio in the main region of the enhancement layer reaches or exceeds 2, indicating that the event has a significant enhancement feature relative to the climatic mean background, which is consistent with the morphological characteristics of typical SSL.

[0059] Simultaneously, it can calculate the half-width at half-maximum (FWHM) of the sodium density vertical profile at Haikou station on a specific date and time, and provide a comparison between the instantaneous profile of the day and two background profiles (daily average and multi-year average for the same month) to further demonstrate the characteristics and accuracy of SSL events. The specific steps are as follows: First, based on the input date and time, find the corresponding observation night and the closest time point, extract the density profile at that time, and simultaneously calculate the average profile (daily average) for all times of that night and the average background profile (monthly average) for the same month over many years (excluding years with outlier data). Then, calculate the half-width at half-maximum (FWHM) by finding a local peak from the global highest peak or within a specified height (the threshold can be set by the user). After finding the peak, use half the peak density as a threshold and linearly interpolate to the left and right to obtain the height corresponding to the half-width; the difference between the two is the FWHM. Finally, plot the current profile (solid black line), daily average profile (dashed black line), and monthly average profile (dashed blue line) on the same graph, and label the peak point (red dot), half-height line (dashed red line), half-height position (blue square), and FWHM value. The output image is as follows: Figure 10 As shown, it can be used to quickly assess the vertical scale of sporadic sodium layers and help determine whether an event meets the SSL criterion (FWHM < 4km).

[0060] Figure 10 The corresponding Figure 9 Vertical profile characteristics of sodium density peak in China on June 21, 2019 (17:30 UTC). From Figure 10 It can be seen that the Na density exhibits a significant peak at an altitude of approximately 94 km, reaching about 8450 cm⁻¹. -3The background intensity was significantly higher than the multi-year average and the daily average background intensity, indicating that the event did not simply reflect the overall uplift of the background Na layer, but rather formed a prominent enhancement layer within a local height range.

[0061] The above results indicate that the SSL over Hainan has obvious localization, narrowing, and transient enhancement characteristics, suggesting that its formation process is different from the smooth changes in the background sodium layer, and is more likely to correspond to a short-term rapid disturbance process in the mesotope region.

[0062] In summary, this invention proposes a complete data processing and analysis method for the sudden sodium layer (SSL) phenomenon in low-latitude regions (Haikou, Hainan). First, rigorous spatiotemporal gridding interpolation (height pchip, time linearity) and quality control were performed on the raw lidar data from 2012 to 2024. After excluding the abnormal year (2021), a multi-year average background for the same month was constructed, overcoming the shortcomings of traditional daily background methods. Based on the background, multi-criteria SSL detection rules were established (peak density ≥ 2 times the background, FWHM < 4km, duration ≥ 20min), identifying a total of 249 events. Monthly statistical analysis of the defined occurrence rate revealed a common peak in the SSL occurrence rate and the ionospheric Es occurrence rate in June, and the SSL peak height (94-96km) was consistent with the nighttime distribution height of the Es virtual height, confirming that Es participates in the physical coupling of SSL formation through metal ion transport. The four-grid diagram and vertical profile FWHM calculation of a typical case (June 21, 2019) further verified the narrow layer and transient enhancement characteristics of SSL. This invention provides reliable technical means and statistical basis for the study of coupling between the low-latitude intermediate layer and the low-thermal layer.

[0063] Furthermore, this invention also provides a system for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term low-latitude observations, including: The data acquisition and preprocessing module is used to acquire and process sodium lidar data and digital altimeter data. The background field construction module is used to construct the average background sodium density field for the same month over many years based on long-term, standardized sodium density data. The sudden sodium layer identification module is used to automatically identify sudden sodium layer events from standardized sodium density data based on comprehensive criteria and the background field. The sudden E-layer identification module is used to automatically identify sudden E-layer events from digital altimeter data based on the sudden E-layer critical frequency threshold. The climatological feature analysis and output module is used to calculate and output the multidimensional climatological features of the abrupt sodium layer, as well as the spatiotemporal correlation analysis results between the abrupt sodium layer and the abrupt E layer.

[0064] As can be seen from the above embodiments, the method and system for identifying, characterizing, and analyzing the E-layer based on long-term low-latitude observations provided by the present invention can stably and efficiently process long-term, non-uniform low-latitude observation data, realize the automated identification of SSL events, multi-dimensional feature quantification, and physical correlation analysis with the Es layer, solve many drawbacks of the prior art, and have significant technological progress.

[0065] Finally, it should be noted that although the present invention has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Various equivalent changes or substitutions can be made without departing from the concept of the present invention. Therefore, any changes or modifications to the above embodiments within the essential spirit of the present invention will fall within the scope of the claims of the present invention.

Claims

1. A method for identifying, characterizing, and analyzing E-layer correlations of sudden sodium layers based on long-term low-latitude observations, characterized in that: Includes the following steps: S1. Obtain sodium lidar observation data in low-latitude regions to form sodium density time-high sequence, preprocess the sodium density time-high sequence to form a standardized sodium density dataset, and at the same time obtain the raw observation data of the digital altimeter Es layer in adjacent regions, and extract the critical frequency foEs of the burst E layer and the virtual height hEs of the burst E layer. S2. Based on the standardized sodium density dataset, construct a multi-year average background sodium density field for the same month; using this background field as a benchmark, identify sudden sodium layer events according to comprehensive criteria, which include intensity criteria, morphological criteria, and temporal criteria. S3. Determine the sudden E-layer event based on the sudden E-layer critical frequency foEs in the observation data of the digital altimeter, compare the sudden E-layer false height hEs with the sudden sodium layer peak height, the time of occurrence of the sudden E-layer with the time of occurrence of the sudden sodium layer, and analyze the correlation of monthly Es. S4. Based on all identified sudden sodium layer events, calculate and output their long-term climatological characteristics, including interannual occurrence rate, monthly average occurrence rate, local time distribution, peak density, peak height distribution, and duration distribution; and combine the sudden E-layer events to analyze the spatiotemporal correlation between sudden sodium layers and sudden E-layers over long time series.

2. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 1, characterized in that, The preprocessing of the sodium density time-high sequence in S1 includes: screening of effective observation nights, unification of time and height resolution, removal of noise and missing values, and identification of abnormal years based on the annual average density profile and seasonal distribution characteristics.

3. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 2, characterized in that, The specific method for identifying abnormal years based on the annual average density profile and seasonal distribution characteristics is as follows: analyze the deviation between the annual average sodium density profile and the multi-year average profile, and combine the annual sodium density spatiotemporal distribution map to identify and exclude years in which data systematic shifts or abnormal main layer heights are caused by instrument malfunctions.

4. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 1, characterized in that, The construction method of the multi-year monthly average background sodium density field in S2 is as follows: for each month, calculate the average value of sodium density at all effective observation days and all time points in that month in terms of vertical height; and smooth the obtained monthly-height two-dimensional average field.

5. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 1, characterized in that, In S2, the intensity criterion is: the peak sodium density of a sudden sodium layer event must reach at least a preset multiple of the background sodium density value at the same height, and when its peak height exceeds a preset threshold, the peak sodium density must be greater than a preset absolute density threshold at the same time. The morphological criterion is: the full width at half maximum (FWHM) of a sudden sodium layer event in the vertical direction is less than a preset width threshold; The time criterion is: the duration of a sudden sodium layer event is not less than a preset duration threshold.

6. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 5, characterized in that, The preset multiplier is 2 times, the preset height threshold is 100 km, and the absolute density threshold is 1000 cm. -3 The preset width threshold is 4 km, and the preset duration threshold is 20 minutes.

7. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 1, characterized in that, The method for determining a sudden E-layer event in S3 is as follows: the threshold value of the sudden E-layer critical frequency foEs≥4 MHz is used to determine the occurrence of a sudden E-layer.

8. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 1, characterized in that, The monthly average incidence rate in S4 is defined as: the ratio of the cumulative duration of all sudden sodium layer events to the total effective observation time in the target month; or the ratio of the number of effective observation time points of sudden E layer events to the total number of effective observation time points in the target month.

9. The method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations according to claim 1, characterized in that, The analysis of the spatiotemporal correlation between the sudden sodium layer and the sudden E layer in the S4 section specifically includes: comparing the monthly average occurrence curves of the sudden sodium layer and the sudden E layer to identify the months with common peak values; and comparing the peak height distribution of the sudden sodium layer with the height distribution of the artificial height of the sudden E layer to determine their consistency in vertical space.

10. A system for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations, used to implement the method for identifying, characterizing, and performing E-layer correlation analysis of sudden sodium layers based on long-term low-latitude observations as described in any one of claims 1 to 9, characterized in that, include: The data acquisition and preprocessing module is used to acquire and process sodium lidar data and digital altimeter data. The background field construction module is used to construct the average background sodium density field for the same month over many years based on long-term, standardized sodium density data. The sudden sodium layer identification module is used to automatically identify sudden sodium layer events from standardized sodium density data based on comprehensive criteria and the background field. The sudden E-layer identification module is used to automatically identify sudden E-layer events from digital altimeter data based on the sudden E-layer critical frequency threshold. The climatological feature analysis and output module is used to calculate and output the multidimensional climatological features of the abrupt sodium layer, as well as the spatiotemporal correlation analysis results between the abrupt sodium layer and the abrupt E layer.

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