A method for reconstructing the probability of occurrence of foEs

Abstract

The present invention discloses a method for reconstructing the probability of occurrence of foEs in the Chinese region, comprising the following steps: step 1, collecting the observation data of foEs in the Chinese region over the years and preprocessing the data; step 2, calculating the probability of occurrence of foEs value of a single observation station; step 3, linearly fitting the foEs value and the probability of occurrence of foEs, and establishing a prediction model of the probability of occurrence of foEs at a single observation station; step 4, calculating the probability of occurrence of foEs at any place in the Chinese region. The method disclosed in the present invention, by fitting the foEs value of each observation station and the probability of occurrence of foEs, establishes a prediction model of the probability of occurrence of foEs at a single observation station, and predicts the probability of occurrence of foEs value at any place in the Chinese region, thus providing good technical support for the design and operation and maintenance of radio systems.

Description

Technical Field

[0001] The invention belongs to the research field of ionospheric environment monitoring, and in particular relates to a foE occurrence probability reconstruction method in the field. Background Art

[0002] Ionospheric parameters description:

[0003] Es: The sudden E layer in the ionospheric vertical measurement circuit diagram, that is, the sudden inhomogeneous structure of the E region.

[0004] foEs: The top frequency of the ordinary wave component of the Es layer continuously traced in the ionospheric vertical measurement circuit diagram.

[0005] h'Es: is the lowest false height of the foEs trace.

[0006] The ionosphere Es (Sporadic-E) layer is a short-lived, localized, relatively high-electron-density irregular patch in the E region of the ionosphere that seriously affects radio wave propagation. It is named because of its irregular appearance time. The appearance of the Es layer can cause reflection and forward scattering of radio waves in the HF or VHF band, so that communications that are usually only possible within the line-of-sight range can suddenly expand to more than 2000 km, realizing beyond-line-of-sight communications.

[0007] ITU-R P.534-5 conducted a statistical analysis of the occurrence probability of the Es layer in mid- and low-latitude regions such as Europe, Africa, America, and Asia (Far East), and gave the foEs values ​​that are equal to or exceeded for different time percentages of the foEs occurrence probability in summer, spring, autumn, and winter, as well as the time percentages of the sporadic E layer (foEs) with vertical incidence equal to or exceeding 7MHz in different seasons in mid- and low-latitude regions. It is believed that summer, daytime, and the Far East are the high-incidence seasons, times, and regions of the global Es layer. ITU-R P.534-5 plotted the curve of the occurrence probability of the Es layer with the season, time, and solar activity, and found that the critical frequency foEs of the Es layer and its probability of occurrence or exceeding the time conform to the Philips "frequency dependence" rule. This rule is completely empirical and applies to the critical frequency foEs of the mid-latitude region with a time percentage of less than 30% and a frequency higher than the conventional E layer.

[0008] Due to the lack of data on FOEs in China, the prediction accuracy of the probability of FOEs occurrence is not high and cannot meet actual needs. Summary of the invention

[0009] The technical problem to be solved by the present invention is to provide a method for reconstructing the probability of foE occurrence based on the randomness and statistical laws of foE occurrence by using the ionospheric vertical measurement data in the Chinese region.

[0010] The present invention adopts the following technical solution:

[0011] A method for reconstructing the probability of occurrence of foEs, the improvement of which is that it comprises the following steps:

[0012] Step 1: Collect the historical observation data of foEs in China and preprocess the data:

[0013] Step 11, using ionospheric vertical detection technology to obtain ionospheric vertical frequency and height maps of the observation stations in Beijing, Changchun, Kunming, Xinxiang, Qingdao, Lanzhou, Urumqi, Chongqing, Guangzhou, Haikou, Xi'an and Manzhouli, and read the h'Es and foEs values ​​of the vertical ionospheric detection;

[0014] Step 12, parse the foEs data and the Es layer false high data, pre-process the foEs historical data, and remove invalid data;

[0015] Step 2: Calculate the occurrence probability of the foEs value at a single observation station:

[0016] Step 21, classify the foEs data into summer, winter, spring and autumn to form foEs data sets of different seasons;

[0017] Step 22, calculate the maximum value of foEs at each observation station, and calculate the probability of occurrence of foEs at a 1MHz step frequency starting from 1MHz;

[0018] The probability of foEs occurrence is defined as:

[0019]

[0020] In the above formula, N represents the total number of valid samples; N(foEs≥f) represents the number of times the foEs value is not less than the f value, where the f value ranges from 1MHz to the maximum value of foEs;

[0021] Step 3: Linearly fit the foEs value and the foEs occurrence probability to establish a single observation station foEs occurrence probability prediction model:

[0022] Step 31, pre-processing the foEs occurrence probability, eliminating the same foEs occurrence probability value and its foEs value;

[0023] Step 32, performing linear fitting on the foEs value and the foEs occurrence probability to calculate the fitting parameters;

[0024] Step 33, establishing a single observation station foEs occurrence probability prediction model;

[0025] Step 4: Use the kriging method to reconstruct and calculate the probability of foEs occurring anywhere in China, and generate a probability distribution map of foEs occurring in China:

[0026] Step 41, let Z(x,y) be the characteristic parameter of the ionosphere, and the value of the characteristic parameter Z(x i ,y i ), i = 1, 2, 3, ..., n, then the Kriging estimator Z of any point (x0, y0) in the region p (x0,y0) is expressed as:

[0027]

[0028] In the above formula, W i is the weight coefficient, W i satisfy:

[0029]

[0030] In the above formula, μ is the Lagrange coefficient, d i0 represents the ionospheric distance between the receiving station and (x0, y0), d ij is the ionospheric distance between two points in the ionosphere:

[0031]

[0032] In the above formula, Lon(i) represents the geographical longitude of the receiving station, Lon(j) represents the geographical longitude of the transmitting station, Lat(i) represents the geographical latitude of the receiving station, Lat(j) represents the geographical latitude of the transmitting station, SK is the longitude-related distance scaling factor, and SF is the latitude-related distance scaling factor;

[0033] Step 42, calculate the occurrence probability corresponding to the specific value of foEs in the Chinese region, reconstruct it using the kriging method, and generate a probability distribution map of foEs occurrence in the Chinese region.

[0034] Further, in step 21, summer is from May to August, winter is January, February, November and December, and spring and autumn are March, April, September and October.

[0035] The beneficial effects of the present invention are:

[0036] The method disclosed in the present invention can use the ionospheric vertical measurement data in the Chinese region, classify the foEs data according to the season, and calculate the occurrence probability of different foEs values ​​at a single observation station. By fitting the foEs values ​​and foEs occurrence probabilities of each observation station, a single observation station foEs occurrence probability prediction model is established to predict the occurrence probability of foEs values ​​at any location in the Chinese region, providing good technical support for the design and operation and maintenance of radio systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1It is a schematic flow diagram of the method of the present invention;

[0038] Figure 2 It is a vertical survey map of the ionosphere;

[0039] Figure 3 It is the daily variation graph of foEs;

[0040] Figure 4 is the probability map of foEs occurrence in summer for Beijing, Guangzhou, Urumqi and Manchuria;

[0041] Figure 5 This is a graph showing the changes in the number of Es occurrences in different months in Manchuria, Beijing, Xi'an, Chongqing and Kunming in 2011;

[0042] Figure 6 This is the probability diagram of occurrence of foEs not less than 4MHz at Manchuria station;

[0043] Figure 7 This is the probability diagram of occurrence of foEs not less than 4MHz at Beijing station;

[0044] Figure 8 It is the fitting curve diagram of the probability of occurrence of foEs in summer;

[0045] Fig. 9 It is the probability distribution diagram of occurrence of foEs not less than 7MHz in summer;

[0046] Fig.10 It is the probability distribution diagram of occurrence of foEs not less than 9MHz in summer;

[0047] Fig.11 It is the probability distribution diagram of occurrence of foEs not less than 11MHz in summer. DETAILED DESCRIPTION

[0048] 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.

[0049] Embodiment 1, this embodiment discloses a method for reconstructing the probability of occurrence of foEs, which overcomes the defects of the prior art in large-area prediction of the probability of occurrence of foEs and low accuracy. Figure 1 As shown, the following steps are included:

[0050] Step 1: Collect the historical observation data of foEs in China and preprocess the data:

[0051] Step 11, using ionospheric vertical detection technology to obtain ionospheric vertical frequency and height maps of ionospheric observation stations in Beijing, Changchun, Kunming, Xinxiang, Qingdao, Lanzhou, Urumqi, Chongqing, Guangzhou, Haikou, Xi'an and Manzhouli, and read the h'Es and foEs values ​​of the vertical ionospheric measurement;

[0052] Step 12, parse the foEs data and the Es layer false high data, pre-process the foEs historical data, remove invalid data, and ensure that the foEs data set is complete and valid;

[0053] The above data extraction is completed by relying on the National Defense Science and Technology Industry Radio Environment Observation Station Network in Beijing, Changchun, Kunming, Xinxiang, Qingdao, Lanzhou, Urumqi, Chongqing, Guangzhou, Haikou, Xi'an and Manzhouli. According to the frequency-height diagram, the foEs data in the diagram is read by manual methods. Among them, the diagrams with almost no recognizable traces are not used. In order to achieve better results, the foEs historical measured meteorological data are preprocessed to eliminate invalid data to ensure that the foEs data set is complete and valid. The ionospheric vertical survey diagram is as follows Figure 2 shown.

[0054] Figure 3 This is the daily variation diagram of foEs. It can be seen that foEs appear randomly every day, either during the day or at night.

[0055] Step 2: Calculate the occurrence probability of the foEs value at a single observation station:

[0056] Step 21, classify the foEs data into summer, winter, spring and autumn to form foEs data sets of different seasons; summer is from May to August, winter is January, February, November and December, and spring and autumn are March, April, September and October.

[0057] Step 22, calculate the maximum value of foEs at each observation station, and calculate the probability of occurrence of foEs at a 1MHz step frequency starting from 1MHz;

[0058] The probability of foEs occurrence is defined as:

[0059]

[0060] In the above formula, N represents the total number of valid samples; N(foEs≥f) represents the number of times the foEs value is not less than the f value, where the f value ranges from 1MHz to the maximum value of foEs;

[0061] Define p as the probability of occurrence of foEs≥f, where f is the frequency (MHz), such as Figure 4 As shown in Figure 2, taking Beijing, Guangzhou, Urumqi and Manchuria as examples, the probability of occurrence of foEs in summer is calculated.

[0062] Taking Manzhouli, Beijing, Xi'an, Chongqing and Kunming as examples, the frequency of Es occurrence in different months in 2011 is as follows: Figure 5 shown.

[0063] Figure 6 This is the probability diagram of the occurrence of foEs not less than 4MHz at Manzhouli station. Figure 7 This is the probability diagram of the occurrence of foEs not less than 4MHz at Beijing station.

[0064] Step 3: Linearly fit the foEs value and the foEs occurrence probability to establish a single observation station foEs occurrence probability prediction model:

[0065] Step 31, pre-processing the foEs occurrence probability according to the foEs statistical law, and eliminating the same foEs occurrence probability value and its foEs value;

[0066] Step 32, performing linear fitting on the foEs value and the foEs occurrence probability to calculate the fitting parameters;

[0067] Step 33, establishing a single observation station foEs occurrence probability prediction model;

[0068] The content of Philips rules is as follows:

[0069] lgp=a+bf

[0070] In the above formula, p is the probability of occurrence of foEs≥f; f is the frequency (MHz); a and b are adjustable constants, where b is the slope.

[0071] According to the above rules, the foEs value and the probability of foEs occurrence are fitted. According to the Philips rule, the fitting coefficients are shown in the table below. Taking Beijing, Guangzhou, Urumqi and Manchuria as examples, the probability of foEs occurrence in summer is calculated. Figure 8 It is the fitting curve of the probability of occurrence of foEs in summer.

[0072] Coordinates of each station and regression coefficients

[0073] Site latitude longitude a b Beijing 40.07 116.15 2.348 -0.1756 Changchun 43.83 125.27 2.45 -0.2318 Chongqing 29.5 106.4 2.467 -0.2018 Lanzhou 36.06 103.87 2.26 -0.1442 Guangzhou 23.15 113.35 2.286 -0.1681 Hainan 20.0 110.33 2.276 -0.1364 Lhasa 29.63 91.28 2.391 -0.1569 Manchuria 49.58 117.45 2.2 -0.1822 Urumqi 43.75 87.63 3.254 -0.3381 Xinxiang 35.27 113.93 2.342 -0.1553 Qingdao 36.24 120.42 2.168 -0.1213 Kunming 25.637 103.72 2.166 -0.1192

[0074] Step 4: Use the kriging method to reconstruct and calculate the probability of foEs occurring anywhere in China, and generate a probability distribution map of foEs occurring in China:

[0075] Step 41, reconstruct using the Kriging method to calculate the ionospheric foEs occurrence probability value at any point in the Chinese region:

[0076] Assume that Z(x,y) is the characteristic parameter of the ionosphere. The values ​​of the ionosphere characteristic parameter Z(x i,y i ), i = 1, 2, 3, ..., n, then the Kriging estimator Z of any point (x0, y0) in the region p (x0,y0) is expressed as:

[0077]

[0078] In the above formula, W i is the weight coefficient, W i satisfy:

[0079]

[0080] In the above formula, μ is the Lagrange coefficient, d i0 represents the ionospheric distance between the receiving station and (x0, y0), d ij is the ionospheric distance between two points in the ionosphere:

[0081]

[0082] In the above formula, Lon(i) represents the geographical longitude of the receiving station, Lon(j) represents the geographical longitude of the transmitting station, Lat(i) represents the geographical latitude of the receiving station, Lat(j) represents the geographical latitude of the transmitting station, SK is the longitude-related distance scaling factor, and SF is the latitude-related distance scaling factor;

[0083] Solve the above n+1 order linear equations and find the weight coefficient W i and the Lagrange coefficient μ;

[0084] Step 42, calculate the occurrence probability corresponding to the specific value of foEs in the Chinese region (for example, 7 MHz), reconstruct it using the kriging method, and generate a probability distribution map of foEs occurrence in the Chinese region.

[0085] The probability of foEs occurrence at twelve stations in China, including Beijing, Changchun and Kunming, is calculated. The Kriging method is used for reconstruction, and the probability of foEs occurrence in the ionosphere at any point in China can be calculated.

[0086] For example, when the summer foEs are 7MHz, 9MHz and 11MHz, the probability of occurrence of foEs at each station in summer is calculated and reconstructed using the kriging method to generate Fig. 9 —11 shows the probability distribution map of foE occurrence in China.

Claims

1. A method for reconstructing the probability of occurrence of foEs, characterized in that: The steps include: Step 1: Collect the historical observation data of foEs in China and preprocess the data: Step 11, using ionospheric vertical detection technology to obtain ionospheric vertical frequency and height maps of the observation stations in Beijing, Changchun, Kunming, Xinxiang, Qingdao, Lanzhou, Urumqi, Chongqing, Guangzhou, Haikou, Xi'an and Manzhouli, and read the h'Es and foEs values ​​of the vertical ionospheric detection; Step 12, parse the foEs data and the Es layer false high data, pre-process the foEs historical data, and remove invalid data; Step 2: Calculate the occurrence probability of the foEs value at a single observation station: Step 21, classify the foEs data into summer, winter, spring and autumn to form foEs data sets of different seasons; Step 22, calculate the maximum value of foEs at each observation station, and calculate the probability of occurrence of foEs at a 1MHz step frequency starting from 1MHz; The probability of foEs occurrence is defined as: In the above formula, N represents the total number of valid samples; N(foEs≥f) represents the number of times the foEs value is not less than the f value, where the f value ranges from 1MHz to the maximum value of foEs; Step 3: Linearly fit the foEs value and the foEs occurrence probability to establish a single observation station foEs occurrence probability prediction model: Step 31, pre-processing the foEs occurrence probability, eliminating the same foEs occurrence probability value and its foEs value; Step 32, performing linear fitting on the foEs value and the foEs occurrence probability to calculate the fitting parameters; Step 33, establishing a single observation station foEs occurrence probability prediction model; Step 4: Use the kriging method to reconstruct and calculate the probability of foEs occurring anywhere in China, and generate a probability distribution map of foEs occurring in China: Step 41, let Z(x,y) be the characteristic parameter of the ionosphere, and the value of the ionosphere characteristic parameter Z(x i ,y i ), i = 1, 2, 3, ..., n, then the Kriging estimator Z of any point (x0, y0) in the region p (x0,y0) is expressed as: In the above formula, W i is the weight coefficient, W i satisfy: In the above formula, μ is the Lagrange coefficient, d i0 represents the ionospheric distance between the receiving station and (x0, y0), d ij is the ionospheric distance between two points in the ionosphere: In the above formula, Lon(i) represents the geographical longitude of the receiving station, Lon(j) represents the geographical longitude of the transmitting station, Lat(i) represents the geographical latitude of the receiving station, Lat(j) represents the geographical latitude of the transmitting station, SK is the longitude-related distance scaling factor, and SF is the latitude-related distance scaling factor; Step 42, calculate the occurrence probability corresponding to the specific value of foEs in the Chinese region, reconstruct it using the kriging method, and generate a probability distribution map of foEs occurrence in the Chinese region.

2. The method for reconstructing the probability of occurrence of foEs according to claim 1, characterized in that: In step 21, summer is from May to August, winter is January, February, November and December, and spring and autumn are March, April, September and October.

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