A method for identifying and classifying regional ionospheric storm events

Abstract

The present invention discloses a method for identifying regional ionospheric storm events, comprising the following steps: Step 1, collect and organize the ionospheric foF2 data of different observation stations in our country, and remove abnormal data and invalid data; Step 2, calculate the relative deviation of the foF2 data of each observation station relative to the reference value; Step 3, calculate the disturbance index representing a single observation point; Step 4, divide our country into two regions, the north and the south, by the geographical latitude of 35o, and select the P index calculated at a certain observation station at time t and within 3 hours before it. The grading method disclosed by the present invention utilizes the ionospheric foF2 data of ionospheric observation stations in different regions of our country, statistically analyzes the variation distribution law of the ionospheric foF2 parameters during magnetic storms, and proposes an ionospheric storm grading method applicable to our country's regions.

Description

Technical Field

[0001] The invention belongs to the field of ionospheric physics research, and particularly relates to a regional ionospheric storm event identification and classification method in the field. Background Art

[0002] The changes in the ionosphere are extremely complex, and the root cause is that there are many controlling factors. The ionization sources of the F zone of the ionosphere are solar ultraviolet, extreme ultraviolet and X-rays, so solar activity has a decisive influence on the ionosphere. In addition to being controlled by solar activity, the F zone is also affected by various dynamic processes, such as the impact of the equatorial electric current on the low-latitude F zone, the impact of the thermospheric circulation, and the impact of E×B drift. These dynamic processes are controlled by the geomagnetic field, so the geomagnetic activity also affects the size of the critical frequency (foF2) of the F2 layer of the ionosphere. In addition, the density of each component of the neutral atmosphere is also affected by the geomagnetic field, which further affects the size of the electron density of the ionosphere. Affected by many factors, in addition to the usual day and night changes, seasonal changes, and 11-year solar activity changes, the ionosphere also has significant daily changes and storm changes. The critical frequency foF2 of the F2 layer of the ionosphere is one of the key parameters of the ionosphere. The daily change of foF2 in the mid-latitude ionosphere is usually as high as about 15%. During magnetic storms, the change of foF2 will exceed 30%. These ionospheric changes will have an adverse effect on the radio system, and in severe cases may cause communication interruption. In order to improve the working performance of radio systems such as communication, positioning, radar, and navigation. Positive ionospheric exposure will increase the refraction delay and measurement error, while negative ionospheric exposure will reduce the highest available shortwave frequency, narrow the available frequency band, or even interrupt it (such as Figure 1 How to use ionospheric observation data to reasonably characterize ionospheric disturbances and provide timely and effective ionospheric disturbance warning information to relevant information systems plays a vital role in the performance of electronic information systems during ionospheric disturbances.

[0003] At present, the commonly used method for detecting ionospheric disturbances is to use the relative deviation of foF2 or total electron content (TEC) relative to the reference value (usually the mid-month value) as a measure of the degree of disturbance in the ionospheric F2 layer. However, studies have shown that this representation method includes seasonal and local time changes in the ionosphere and cannot better reflect the changes in ionospheric storms. In addition, the existing methods for representing ionospheric disturbances are mostly defined based on the data of a single observation station and considering the time continuity characteristics of ionospheric storms, while the regional distribution characteristics of ionospheric storm events are not considered, which may cause the ionospheric storm information given by these detection methods to deviate from the actual situation. Summary of the invention

[0004] The present invention aims at the shortcomings of the prior art in the identification of ionospheric storm events, such as the representation method does not filter out the regular changes in the ionosphere, the unreasonable calculation of the ionospheric disturbance intensity, and the failure to consider the regional development law of ionospheric storm events. An ionospheric storm event identification and classification method based on the ionospheric vertical detection parameter foF2 is proposed, which comprehensively considers the time duration and regional development law of ionospheric disturbances.

[0005] The present invention adopts the following technical solution:

[0006] A method for identifying regional ionospheric storm events, the improvement of which is that it comprises the following steps:

[0007] Step 1: Collect and organize the ionospheric foF2 data from different observation stations in my country and remove abnormal and invalid data:

[0008] Collect ionospheric foF2 data from different observation stations in my country, covering years with high and low solar activity, different seasons, and local time, with a data period of no less than 5 years; remove the explanatory symbols in the data and abnormal data that do not conform to the law of ionospheric change during the statistical process;

[0009] Step 2: Calculate the relative deviation of the foF2 data of each observation station relative to the reference value. The reference value is the median of the 27 days before the corresponding time. The relative deviation calculation formula is:

[0010]

[0011] In the above formula, f o represents the ionospheric foF2 observation value, f m Indicates the reference value at the corresponding moment;

[0012] Step 3, calculate the disturbance index P representing a single observation point, the calculation formula is:

[0013]

[0014] In the above formula, μ and σ represent the mean and standard deviation of df under different seasons and local time conditions, respectively. The calculation formula of standard deviation is:

[0015]

[0016] In the above formula, x i represents the df value, s represents the number of df data in each classification, Represents the average value of df in each category;

[0017] Step 4: Divide my country into two regions, the north and the south, at the geographical latitude of 35°. Select the P index calculated at time t and within the previous 3 hours at each observation station in a certain area. If the data with a P index greater than 2.5 or less than -2 reaches 1 / 3 or more of the total number of selected data, it is considered that an ionospheric storm event has occurred.

[0018] A regional ionospheric storm event classification method, the improvement of which is that after step 4 of the identification method, the data greater than 2.5 or less than -2 in the selected P index are averaged to obtain an index PI characterizing the intensity of the ionospheric storm event, and the calculation formula is:

[0019]

[0020] In the above formula, N represents the number of observation stations in a certain area;

[0021] The intensity classification rules for determining ionospheric storm events are as follows:

[0022] Ionospheric storm event intensity level Value range Positive SSE PI ≥ 5 Positive phase moderate ionospheric storm 4≤PI<5 Positive phase weak ionospheric storm 2.5≤PI<4 Calm (no ionospheric storm) -2<PI<2.5 Negative phase weak ionospheric storm -3<PI≤-2 Negative phase moderate ionospheric storm -4<PI≤-3 Negative phase severe ionospheric storm PI≤-4 .

[0023] The beneficial effects of the present invention are:

[0024] The identification method disclosed in the present invention is simple to calculate, can effectively eliminate the changes in the ionosphere itself, and can more reasonably represent the ionosphere disturbance caused by some abnormal factors. The identification result can provide timely and effective ionosphere disturbance warning information for relevant information systems, and plays an important role in the system design of electronic information systems and their performance during ionosphere disturbances.

[0025] The classification method disclosed in the present invention utilizes the foF2 data of ionospheric observation stations in different regions of my country, statistically analyzes the variation distribution law of ionospheric foF2 parameters during magnetic storms, and proposes an ionospheric storm classification method suitable for my country. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram of the narrowing of the shortwave available frequency band caused by ionospheric storms;

[0027] Figure 2 is a flow chart of the classification method disclosed in the present invention;

[0028] Figure 3 This is the ionospheric disturbance index map of my country from March 17 to 19, 2015;

[0029] Figure 4 This is the regional ionospheric disturbance index map of my country from October 7 to 9, 2015. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0031] Embodiment 1: This embodiment discloses a method for identifying regional ionospheric storm events, comprising the following steps:

[0032] Step 1: Collect and organize the ionospheric foF2 data from different observation stations in my country, and remove abnormal data, invalid data, etc.:

[0033] Collect ionospheric foF2 data from different observation stations in my country. The data covers high and low solar activity years, different seasons, and local time. The data is no less than 5 years. The locations of each observation station are shown in the following table:

[0034]

[0035] The selected foF2 data are artificial measurement data of ionosphere vertical sounding ionograms. According to the measurement rules of ionosphere vertical sounding, the measurement results contain symbols with different meanings, such as measurement symbols indicating machine failure, extended F and other phenomena. The explanatory symbols in the data and some abnormal data that do not conform to the law of ionosphere changes are removed during the statistical process.

[0036] Step 2: Calculate the relative deviation of the foF2 data of each observation station relative to the reference value. The reference value is the median of the 27 days before the corresponding time. The relative deviation calculation formula is:

[0037]

[0038] In the above formula, f o represents the ionospheric foF2 observation value, f m Indicates the reference value at the corresponding moment;

[0039] Step 3: Consider the seasonal and local time variation characteristics of the ionosphere, and classify the calculated df according to different seasons (summer, winter, spring and autumn) and local time. Calculate the disturbance index P representing a single observation point, and the calculation formula is:

[0040]

[0041] In the above formula, μ and σ represent the mean and standard deviation of df under different seasons and local time conditions, respectively. The calculation formula of standard deviation is:

[0042]

[0043] In the above formula, x i represents the df value, s represents the number of df data in each classification, Represents the average value of df in each category;

[0044] Step 4: Considering the regional distribution characteristics of ionospheric changes, my country is divided into two regions, north and south, at the geographical latitude of 35°. The P index calculated at time t and within the previous 3 hours at each observation station in a certain area is selected. If the data with a P index greater than 2.5 or less than -2 reaches 1 / 3 or more of the total number of selected data, it is considered that an ionospheric storm event has occurred.

[0045] like Figure 2 As shown, this embodiment also discloses a regional ionospheric storm event classification method. After step 4 of the identification method, the data greater than 2.5 or less than -2 in the selected P index are averaged to obtain an index PI characterizing the intensity of the ionospheric storm event. The calculation formula is:

[0046]

[0047] In the above formula, N represents the number of observation stations in a certain area;

[0048] If no ionospheric storm event is identified according to this method, the selected P data are averaged as the ionospheric storm event characterization index at that moment.

[0049] The ionospheric storm characterization index PI data during the historical magnetic storm period (24 hours before the start of the magnetic storm and 72 hours after the start of the magnetic storm, a total of 96 hours) were selected and statistically analyzed.

[0050] According to the occurrence probability of different intervals of the characterization index and considering the objective physical laws of the occurrence of ionospheric storm events, the intensity classification rules of ionospheric storm events are determined.

[0051] The intensity classification rules for determining ionospheric storm events are as follows:

[0052]

[0053]

[0054] In order to verify the effectiveness of the present invention in identifying regional ionospheric storm events in my country, this embodiment gives the occurrence of regional ionospheric storm events in my country during the magnetic storms from 2015 to 2017, as shown in the following table. In the table, -3, -2, and -1 respectively represent strong, medium, and weak ionospheric storm events in the negative phase, and 3, 2, and 1 respectively represent strong, medium, and weak ionospheric storm events in the positive phase. In the following table, among the 86 magnetic storm events that occurred from 2015 to 2017 (Dst<-30nT), the number of weak, medium, and strong ionospheric storm events in northern my country was 45, 4, and 2, respectively, and the number of weak, medium, and strong ionospheric storm events in the southern region was 28, 3, and 3, respectively.

[0055]

[0056]

[0057] This embodiment provides the occurrence of ionospheric storm events in my country during the super strong magnetic storm from March 17 to 19, 2015. During this magnetic storm, the minimum value of the geomagnetic activity index Dst reached -224nT. Figure 3 The degree of ionospheric disturbance in different regions of my country during this magnetic storm event is given. Negative ionospheric disturbance began to appear in northern my country at 20:00 UT on the 17th, and then gradually strengthened. It began to weaken at 11:00 UT on the 18th and basically returned to a calm state at 10:00 on the 19th. The negative ionospheric disturbance in southern my country started slightly later than that in northern China, and the duration was also shorter. At 16:00 UT on the 18th, the disturbances at all stations basically returned to a calm state. The identification method of the present invention timely and effectively identified this ionospheric storm event.

[0058] This embodiment provides the occurrence of ionospheric storm events in my country during the strong magnetic storm event from October 7 to 9, 2015. The minimum value of the geomagnetic activity index Dst during this magnetic storm was -124nT. Figure 4 The degree of ionospheric disturbance in different regions of my country during the magnetic storm event is given. During the magnetic storm event, negative ionospheric disturbance occurred in the northern region of my country, while no obvious ionospheric disturbance occurred in the southern region. The ionospheric disturbance index proposed in the present invention better reflects the ionospheric state in different regions of my country and provides a reasonable identification result of ionospheric storm events.

[0059] The above embodiments illustrate that the regional ionospheric disturbance index calculated by the method of this patent can reflect the degree of ionospheric F2 disturbance in different regions caused by magnetic storms, and can identify ionospheric storm events of different levels.

Claims

1. A method for identifying regional ionospheric storm events. It is characterized in that The steps include: Step 1: Collect and organize the ionospheric foF2 data from different observation stations in my country and remove abnormal and invalid data: Collect ionospheric foF2 data from different observation stations in my country, covering years with high and low solar activity, different seasons, and local time, with data of no less than 5 years; During the statistical process, the explanatory symbols in the data and the abnormal data that do not conform to the law of ionospheric changes are removed; Step 2: Calculate the relative deviation of the foF2 data of each observation station relative to the reference value. The reference value is the median of the 27 days before the corresponding time. The relative deviation calculation formula is: In the above formula, f o represents the ionospheric foF2 observation value, f m Indicates the reference value at the corresponding moment; Step 3, calculate the disturbance index P representing a single observation point, the calculation formula is: In the above formula, μ and σ represent the mean and standard deviation of df under different seasons and local time conditions, respectively. The calculation formula of standard deviation is: In the above formula, x i represents the df value, s represents the number of df data in each category, and x represents the average value of df in each category; Step 4: Divide my country into two regions, the north and the south, at the geographical latitude of 35°. Select the P index calculated at time t and within the previous 3 hours at each observation station in a certain area. If the data with a P index greater than 2.5 or less than -2 reaches 1 / 3 or more of the total number of selected data, it is considered that an ionospheric storm event has occurred.

2. The method for identifying regional ionospheric storm events according to claim 1, Features: After step 4 of claim 1, the data of the selected P index greater than 2.5 or less than -2 are averaged to obtain an index PI characterizing the intensity of the ionospheric storm event, and the calculation formula is: In the above formula, N represents the number of observation stations in a certain area; The intensity classification rules for determining ionospheric storm events are as follows: When PI≥5, it is a positive phase strong ionospheric storm; when 4≤PI<5, it is a positive phase moderate ionospheric storm; when 2.5≤PI<4, it is a positive phase weak ionospheric storm; when -2<PI<2.5, it is calm, with no ionospheric storm; when -3<PI≤-2, it is a negative phase weak ionospheric storm; when -4<PI≤-3, it is a negative phase moderate ionospheric storm; when PI≤-4, it is a negative phase strong ionospheric storm.

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