A prediction method for the occurrence probability of foEs at a single station in China

Abstract

The present invention discloses a prediction method for the probability of occurrence of foEs at a single station in China, comprising the following steps: Step 1, obtaining the magnetic inclination angle of the station location, and determining whether the station is in the middle latitude or the low latitude; Step 2, obtaining 11 years of foEs observation data of various ionospheric observation stations in China, and analyzing the statistical law of foEs; Step 3, calculating the probability of occurrence of foEs from 0:00 to 23:00 in spring, autumn, summer and winter; Step 4, performing polynomial fitting on the foEs value and the probability of occurrence of foEs, calculating the best fitting curves in different seasons, and calculating the probability of occurrence of any foEs value using a prediction model. The method disclosed by the present invention can utilize the ionospheric vertical measurement data in China, classify the foEs data according to the season, establish a single-station foEs probability prediction model, and further calculate the probability of occurrence of any foEs value, 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 particularly relates to a prediction method for the probability of occurrence of foEs at a single station in China 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 sporadic E layer (Es layer) is a sudden inhomogeneous structure of the E layer, named because of its irregular appearance. Usually, the Es layer often exists in the equatorial region during the day and does not have much seasonal variation; the foEs value in the mid-latitude region is relatively low and has obvious seasonal variation. It is particularly noteworthy that the southeastern region of my country is a high-incidence area of ​​the Es layer. In the summer of years with low sunspot numbers, Es often appears and the foEs value is very high.

[0007] ITU-R P.534-5 discloses the relationship between the foEs value in Asia and the percentage of time when the probability of occurrence does not exceed 50%, and gives the probability distribution diagram of the occurrence of foEs in the global summer, winter, spring and autumn when the foEs is 7MHz, where May to August is summer, January, February, November and December is winter, and March, April, September and October is spring and autumn. Due to the lack of foEs data in China, the prediction accuracy of the foEs occurrence probability is not high. In practical applications, the definition of the foEs observation value and the foEs value of a given occurrence rate are different. The foEs observation value cannot simply replace the foEs value of a given occurrence rate. The research on the changing law of the current ionospheric radio wave environment is far from meeting the requirements of Es communication calculations, and the prediction accuracy of the probability of sporadic E-layer occurrence is also difficult to meet actual needs. Summary of the invention

[0008] The technical problem to be solved by the present invention is to use the ionospheric vertical measurement data of a single station in China to statistically analyze the statistical characteristics of the probability of occurrence of foEs, and to provide a prediction method for the probability of occurrence of foEs at a single station in China.

[0009] The present invention adopts the following technical solution:

[0010] A method for predicting the probability of occurrence of foEs at a single station in China, the improvement of which is that it comprises the following steps:

[0011] Step 1: Get the magnetic inclination of the site to determine whether the site is in mid-latitude or low-latitude:

[0012] The calculation formula of magnetic inclination I is:

[0013]

[0014]

[0015] In the above formula: λ is the geographical latitude, is the associated Legendre function, and are the numerical coefficients of the Gaussian magnetic field model, θ is the degree east of the Greenwich meridian, h r To assess the height of the magnetic field;

[0016] Step 2: Obtain 11 years of foEs observation data from various ionospheric observation stations in China, preprocess the foEs, remove invalid data, and analyze the statistical laws of foEs:

[0017] Use ionospheric vertical detection technology to obtain the ionospheric vertical frequency and height diagrams of each ionospheric observation station, and read the h'Es and foEs values ​​of the vertical ionosphere;

[0018] Preprocess the historical measured meteorological data of foEs, remove invalid data, and analyze the statistical laws of foEs;

[0019] Step 3: Classify the foEs data according to different seasons and different times, and calculate the occurrence probability of foEs from 0:00 to 23:00 in spring, autumn, summer and winter:

[0020] The foEs data were classified according to seasons to form the foEs spring, autumn, summer and winter data sets;

[0021] Calculate the maximum value of foEs in each data set, and calculate the probability P of foEs occurrence from 0 to 23 o'clock according to a 1MHz step frequency starting from 1MHz:

[0022]

[0023] 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;

[0024] Step 4: Perform polynomial fitting on the foEs value and the probability of foEs occurrence, calculate the best fitting curves in different seasons, establish a single-station foEs occurrence probability prediction model, and use the prediction model to calculate the occurrence probability of any foEs value.

[0025] Further, in step 1, when -20°≤I≤20°, the station is located at low latitudes, and when 20°≤I≤60° or -60°≤I≤-20°, the station is located at mid-latitudes.

[0026] Further, in step 3, summer is from May to August, winter is January, February, November and December, and spring and autumn are March, April, September and October.

[0027] The beneficial effects of the present invention are:

[0028] The method disclosed in the present invention can utilize the ionospheric vertical measurement data in China, classify the foEs data according to season, establish a single-station foEs occurrence probability prediction model, and further calculate the occurrence probability of any foEs value, providing good technical support for the design and operation and maintenance of the radio system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0030] Figure 2 It is the ±20° magnetic inclination variation diagram;

[0031] Figure 3 It is a vertical survey map of the ionosphere;

[0032] Figure 4 It is the daily variation graph of foEs;

[0033] Figure 5 This is a graph of the number of Es occurrences in Manchuria, Beijing, Xi'an, Chongqing and Kunming in 2011;

[0034] Figure 6 This is the map of the number of Es occurrences in Manchuria, Beijing, Xi'an, Chongqing and Kunming in 2016;

[0035] Figure 7 This is a graph showing the number of Es occurrences in different months;

[0036] Figure 8 This is the probability diagram of occurrence of foEs not less than 4MHz at Manchuria station;

[0037] Fig. 9 This is the probability diagram of occurrence of foEs not less than 4MHz at Beijing station;

[0038] Fig.10This is the probability diagram of the occurrence of foEs not less than 4MHz at Guangzhou station;

[0039] Fig.11 This is a graph comparing the prediction and observation of the occurrence probability of foEs at Guangzhou Station in the spring of 1997;

[0040] Fig.12 This is a comparison chart of the prediction and observation of the probability of occurrence of foEs at Guangzhou Station in the summer of 1997;

[0041] Fig.13 This is the comparison between the prediction and observation of the occurrence probability of foEs at Guangzhou Station in the winter of 1997;

[0042] Fig.14 This is a comparison chart of the prediction and observation of the occurrence probability of foEs at Beijing Station in the spring of 1997;

[0043] Fig.15 This is a comparison chart of the prediction and observation of the occurrence probability of foEs at Beijing Station in the summer of 1997;

[0044] Fig.16 This is a comparison chart of the prediction and observation results of the foE occurrence probability at Beijing Station in the winter of 1997. DETAILED DESCRIPTION

[0045] 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.

[0046] Example 1: This example discloses a method for predicting the probability of occurrence of foEs at a single station in China. The foEs observation data of a single station for 11 years is collected. According to the statistical characteristics of the probability of occurrence of foEs, the probability of occurrence of foEs values ​​at different seasons and times at the single station is determined. The best fitting curve of the probability of occurrence of foEs is selected, and the probability of occurrence of any foEs is further calculated. Figure 1 As shown, the specific steps include:

[0047] Step 1: Get the magnetic inclination of the site to determine whether the site is in mid-latitude or low-latitude:

[0048] The magnetic inductions Fx, Fy and Fz (gauss) are the directions of geographic north, east and vertical downward;

[0049] The entire magnetic field F is defined as:

[0050] The magnetic inclination is calculated according to the position of the transmitting and receiving points. The calculation formula of the magnetic inclination I is:

[0051]

[0052] In the above formula: Co-latitude north λ is the geographic latitude (unit: °), with north as positive, -90°≤λ≤90°, is the associated Legendre function, and is the numerical coefficient of the Gaussian magnetic field model, θ is the degree east of the Greenwich meridian, that is, the geographic east longitude, 0°≤θ≤360°, R is the conversion factor determined by altitude, h r To evaluate the height of the magnetic field (300 km);

[0053] The specific method for judging whether the station is at mid-latitude or low-latitude is: when -20°≤I≤20°, the station is at low-latitude, and when 20°≤I≤60° or -60°≤I≤-20°, the station is at mid-latitude.

[0054] Figure 2 The ±20° magnetic inclination variation diagram is given, where the black line and the red dotted line are the 20° and -20° magnetic inclination variation lines respectively. It can be seen from the figure that the change of magnetic inclination is related to the geographical latitude and longitude. When calculating the Es occurrence rate, the magnetic inclination is divided into two areas, one is the magnetic inclination latitude exceeding ±20°, and the other is the magnetic inclination latitude between ±20°.

[0055] Step 2: Obtain 11 years of foEs observation data from various ionospheric observation stations in China, preprocess the foEs, remove invalid data, and analyze the statistical laws of foEs:

[0056] Use ionospheric vertical detection technology to obtain the ionospheric vertical frequency and height diagrams of each ionospheric observation station, and read the h'Es and foEs values ​​of the vertical ionosphere;

[0057] The historical measured meteorological data of foEs are preprocessed to remove invalid data and ensure that the foEs data set is complete and valid.

[0058] Analyze foEs statistical laws;

[0059] The above data extraction was completed by relying on 19 ionospheric observation stations in China of the National Defense Science and Technology Industry Radio Environment Observation Station Network. According to the provided frequency-height map, the foEs data in the map was read by manual methods. Among them, the map with almost no recognizable traces was not used. In order to achieve better results, the foEs historical measured meteorological data was preprocessed to eliminate invalid data to ensure that the foEs data set is complete and valid. Figure 3 This is a vertical survey map of the ionosphere.

[0060] Figure 4The daily variation diagram of foEs is given. The daily foEs appear randomly, either during the day or at night.

[0061] Using the ionospheric vertical measurement foEs data from Manchuria, Beijing, Xi'an, Chongqing and Kunming stations in 2011 and 2016, the latitudinal variation characteristics of foEs in high and low solar activity years are analyzed. Figure 5 and Figure 6 As shown in the figure, the occurrence frequency of foEs not less than 4MHz shows a decreasing trend with increasing latitude, especially the occurrence frequency of Manzhouli station decreases significantly, which may be related to the high latitude of Manzhouli station.

[0062] like Figure 7 As shown, the number of Es occurrences varies with seasons, with the highest number in summer;

[0063] Step 3: Classify the foEs data according to different seasons and different times, and calculate the occurrence probability of foEs from 0:00 to 23:00 in spring, autumn, summer and winter:

[0064] Seasons are defined as: summer is May to August, winter is January, February, November and December, and spring and fall are March, April, September and October.

[0065] The foEs data were classified according to seasons to form the foEs spring, autumn, summer and winter data sets;

[0066] Calculate the maximum value of foEs in each data set, and calculate the probability P of foEs occurrence from 0 to 23 o'clock according to a 1MHz step frequency starting from 1MHz:

[0067]

[0068] 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 specific value f, where the value of f ranges from 1MHz to the maximum value of foEs (15MHz);

[0069] like Figure 8-10 As shown in the figure, taking Manzhouli, Beijing and Guangzhou stations as examples, the probability of occurrence of foEs in different seasons is calculated.

[0070] Step 4: Perform polynomial fitting on the foEs value and the probability of foEs occurrence, calculate the best fitting curves in different seasons, establish a single-station foEs occurrence probability prediction model, and use the prediction model to calculate the occurrence probability of any foEs value.

[0071] As an example, the foEs data of Guangzhou Station from 2008 to 2018 are used to analyze the changes of foEs occurrence probability with season and local time. The following formula is used for fitting.

[0072] lg(p)=p7*x^6+p6*x^5+p5*x^4+p4*x^3+p3*x^2+p2*x+p1

[0073] In the above formula, p is the probability of foEs occurrence, x is the foEs value, p1, p2,…, p7 are fitting coefficients, and their values ​​are shown in Tables 1 to 3.

[0074] Table 1 Fitting coefficients of foE occurrence probability in spring and autumn at Guangzhou Station

[0075] LT P1 P2 P3 P4 P5 P6 Start frequency 0 2.222 2.135E-1 4.811E-2 6.224E-3 1.975E-4 0 1MHz 1 2.169 1.733E-1 7.32E-2 1.026E-2 3.82E-4 0 1MHz 2 2.423 5.203E-1 3.119E-2 -1.069E-3 9.291E-6 0 1MHz 3 2.977 -1.212 0.2558 3.157E-2 1.82E-2 -3.823E-5 1MHz 4 3.816 -2.599 9.586E-1 -1.847E-1 1.616E-2 -5.171E-4 1MHz 5 2.975 -1.251 0.283 -4.48E-2 3.577E-3 -1.063E-4 1MHz 6 2.53 5.791E-1 -3.321E-2 0.023E-3 -3.619E-4 0 1MHz 7 3.734 -9.976E-1 5.032E-2 0 0 0 2MHz 8 3.432 -7.133E-1 3.451E-2 -4.778E-4 0 0 2.3MHz 9 3.056E-1 1.905 -6.801E-1 8.879E-2 -5.051E-3 1.08E-4 2MHz 10 2.215 1.415E-1 -1.196E-1 1.095E-2 -2.956E-4 0 2.5MHz 11 3.078 -4.752E-1 1.727E-2 -2.69E-4 3.146E-6 0 2.5MHz 12 3.232 -3.64E-1 -1.584E-2 1.386E-3 0 3.1MHz 13 3.568 -4.989E-1 -8.925E-3 2.959E-3 -9.401E-5 0 3.2MHz 14 3.751 -6.863E-1 3.578E-2 -6.856E-4 0 0 3MHz 15 3.774 -6.764E-1 2.61E-2 -8.882E-5 0 0 3MHz 16 4.699 -1.22 1.12E-1 -4.048E-3 0 0 3MHz 17 2.791 -3.794E-1 1.485E-4 5.016E-4 0 0 3MHz 18 3 -5.707E-1 3.435E-2 -8.932E-4 0 0 2MHz 19 2.47 -4.993E-1 8.313E-2 -1.205E-2 7.375E-4 -1.517E-5 1MHz 20 2.153 -1.108E-1 -6.658E-2 1.167E-2 -8.345E-4 2.1E-05 1MHz 21 2.074 -5.38E-2 -7.26E-2 9.962E-3 -5.418E-4 1.059E-5 1MHz 22 2.578 -7.582E-1 2.19E-1 -4.04E-2 3.173E-3 -8.672E-5 1MHz 23 2.125 -1.879E-1 -1.937E-1 1.066E-3 0 0 1MHz

[0076] Table 2 Fitting coefficients of foE occurrence probability at Guangzhou station in summer

[0077] LT P1 P2 P3 P4 P5 P6 P7 Start frequency 0 2.137 -1.199E-1 7.924E-3 1.277E-4 0 0 0 1MHz 1 2.167 -1.567E-1 -1.124E-3 -4.778E-5 0 0 0 1MHz 2 2.261 -2.317E-1 8.881E-3 -3.626E-4 0 0 0 1MHz 3 2.243 -2.328E-1 5.699E-3 -1.548E-4 0 0 0 1MHz 4 2.11 -1.032E-1 -3.948E-2 7.024E-3 -5.847E-4 2.282E-5 -3.288E-7 1MHz 5 2.461 -6.761E-1 2.635E-1 -6.137E-1 6.48E-3 -3.063E-4 5.2E-6 1MHz 6 2.282 -2.41E-1 2.695E-3 3.95E-4 -2.133E-5 0 0 1MHz 7 2.458 -2.448E-1 8.935E-3 -3.732E-4 7.392E-6 0 0 2MHz 8 2.069 5.36E-2 -4.32E-2 3.508E-3 -9.07E-5 0 0 2MHz 9 2.718 -2.645E-1 1.507E-2 -5.971E-4 5.822E-6 0 0 3MHz 10 2.734 -2.719E-1 1.634E-2 -4.652E-4 -3.361E-8 0 0 3MHz 11 2.549 -1.94E-1 7.169E-3 -1.293E-4 -2.427E-6 0 0 3MHz 12 2.595 -1.83E-1 -4.403E-3 1.635E-3 -9.918E-5 1.732E-6 0 3.3MHz 13 2.61 -1.887E-1 -1.995E-3 8.941E-4 -4.898E-5 7.969E-7 0 3.3MHz 14 2.159 -4.67E-2 -1.008E-2 2.78E-4 0 0 0 3MHz 15 2.556 -2.152E-1 3.979E-3 7.184E-3 -1.683E-4 0 0 3MHz 16 2.493 -1.866E-1 3.59E-2 -7.397E-5 0 0 0 3MHz 17 2.458 -1.866E-1 3.59E-3 -8.206E-5 0 0 0 2MHz 18 2.46 -2.263E-1 1.003E-2 -3.756E-4 0 0 0 2.3MHz 19 2.345 -1.899E-1 2.804E-3 -4.137E-5 0 0 0 2MHz 20 1.634 2.909E-1 -1.014E-1 8.684E-3 -2.484E-4 0 0 2MHz 21 1.969 3.93E-2 -4.858E-2 4.209E-2 -1.238E-4 0 0 1MHz 22 2.204 -1.985E-1 1.114E-2 -1.226E-3 3.542E-5 0 0 1MHz 23 1.877 1.53E-1 -8.79E-2 8.757E-3 -2.956E-4 0 0 1MHz

[0078] Table 3 Fitting coefficients of foE occurrence probability in winter at Guangzhou station

[0079] LT P1 P2 P3 P4 P5 P6 Start frequency 0 2.534 -6.09E-2 2.49E-2 0 0 0 1MHz 1 2.417 -4.833E-1 -2.042E-2 7.533E-3 -3.732E-4 0 1MHz 2 2.672 -8.513E-1 1.25E-1 -1.455E-2 9.004E-4 -2.057E-5 1MHz 3 3.187 -1.491 3.296E-1 -2.783E-2 -1.381E-4 0 1MHz 4 2.573 -6.778E-1 3.896E-2 -1.049E-3 0 0 1MHz 5 3.076 -1.359 3.118E-1 -5.182E-2 4.341E-3 -1.337E-4 1MHz 6 2.658 -3.992E-1 -4.158E-1 1.702E-1 -2.443E-2 1.189E-3 1MHz 7 3.034 -1.077 8.997E-2 -2.434E-3 0 0 1MHz 8 4.828 -1.781 1.768E-1 -5.926E-3 0 0 2MHz 9 8.346 -3.243 0.419 -2.429E-2 5.346E-4 0 2.9MHz 10 7.668 -2.817 0.3762 -2.361E-2 5.589E-4 0 3MHz 11 2.82 2.867E-1 -2.64E-1 3.044E-2 -1.051E-3 0 3MHz 12 3.448 -3.964E-1 -4.24E-2 6.375E-3 -2.149E-4 0 3MHz 13 4.065 -9.337E-1 1.076E-1 -8.566E-3 2.664E-4 0 3MHz 14 2.645 -1.928E-2 -9.521E-2 1.004E-2 -3.029E-4 0 3MHz 15 3.191 -5.172E-1 2.703E-2 -6.165E-4 0 0 3MHz 16 4.082 -9.752E-1 8.801E-2 -3.084E-3 0 0 3MHz 17 2.518 -2.729E-1 -7.673E-3 5.86E-4 0 0 2MHz 18 1.592 4.681E-1 -2.155E-1 2.232E-2 -7.502E-4 0 2MHz 19 2.194 -1.792E-1 -2.26E-2 8.742E-4 4.373E-0 0 1MHz 20 2.162 -1.182E-1 -5.946E-2 6.901E-3 -2.275E-4 0 1MHz 21 2.597 -7.079E-1 1.472E-1 -1.973E-2 9.038E-4 0 1MHz 22 2.492 -5.513E-1 5.236E-2 -3.977E-3 1.239E-4 0 1MHz 23 3.18 -1.571 4.635E-1 -7.811E-2 6.159E-3 -1.787E-4 1MHz

[0080] Figure 11-13 The comparison results of the prediction and observation of the occurrence probability of foEs at Guangzhou Station in spring, summer and winter of 1997 are given. It can be seen that the predicted values ​​are consistent with the observed values.

[0081] The Beijing station uses the following formula for fitting.

[0082] lg(p)=p5*x^4+p4*x^3+p3*x^2+p2*x+p1

[0083] In the above formula, p is the probability of foEs occurrence, x is the foEs value, p1, p2,…, p5 are fitting coefficients, and their values ​​are shown in Tables 4 to 6.

[0084] Table 4. Fitting coefficients of foE occurrence probability in spring and autumn at Beijing Station

[0085]

[0086]

[0087] Table 5 Fitting coefficients of foE occurrence probability at Beijing station in summer

[0088] LT P1 P2 P3 P4 Start frequency 0 1.89 9.289E-2 -4.106E-2 1.392E-3 1MHz 1 2.044 -5.986E-2 -8.817E-3 -1.095E-3 1MHz 2 2.204 -1.992E-1 9.186E-3 -1.646E-3 1MHz 3 2.187 -1.826E-1 -3.008E-3 -4.817E-4 1MHz 4 2.489 -3.406E-1 4.642E-3 0 1MHz 5 2.465 -4.747E-1 4.757E-2 -3.127E-3 1.5MHz 6 2.137 -1.066E-1 -1.582E-2 0 1MHz 7 2.471 -1.336E-1 -1.562E-2 6.676E-4 1MHz 8 2.567 -1.676E-1 -2.871E-3 7.53E-5 3MHz 9 2.776 -2.653E-1 1.163E-2 -4.062E-4 3MHz 10 2.384 -9.45E-2 -7.889E-3 2.139E-4 3.3MHz 11 2.409 -1.158E-1 -5.061E-3 2.071E-4 3MHz 12 2.354 -9.541E-2 -8.23E-3 3.046E-4 3MHz 13 2.218 -2.067E-1 6.804E-3 -5.043E-4 3MHz 14 2.523 -1.665E-1 -3.79E-3 1.925E-4 3MHz 15 2.62 -2.159E-1 6.172E-4 4.792E-5 3MHz 16 2.704 -2.809E-1 9.85E-3 -2.787E-4 3MHz 17 2.477 -2.071E-1 -1.493E-4 1.372E-4 3MHz 18 2.498 -2.425E-1 9.728E-3 -3.428E-4 2.3MHz 19 2.275 -1.409E-1 -2.072E-3 9.72E-5 2MHz 20 2.12 -8.707E-2 -5.763E-3 1.126E-4 1MHz 21 2.121 -7.809E-2 -8.281E-3 1.972E-4 1MHz 22 2.01 7.612E-3 -2.187E-2 5.612E-4 1MHz 23 1.948 6.132E-2 -3.466E-2 1.22E-3 1MHz

[0089] Table 6 Fitting coefficients of foE occurrence probability in winter at Beijing Station

[0090]

[0091]

[0092] Figure 14-16 The comparison results of the prediction and observation of the occurrence probability of foEs at Beijing Station in spring, summer and winter of 1997 are given. It can be seen that the predicted values ​​are consistent with the observed values.

Claims

1. A method for predicting the probability of occurrence of foEs at a single station in China, characterized in that: The steps include: Step 1: Get the magnetic inclination of the site to determine whether the site is in mid-latitude or low-latitude: The calculation formula of magnetic inclination I is: In the above formula: λ is the geographical latitude, is the associated Legendre function, and are the numerical coefficients of the Gaussian magnetic field model, θ is the degree east of the Greenwich meridian, h r To assess the height of the magnetic field; Step 2: Obtain 11 years of foEs observation data from various ionospheric observation stations in China, preprocess the foEs, remove invalid data, and analyze the statistical laws of foEs: Use ionospheric vertical detection technology to obtain the ionospheric vertical frequency and height diagrams of each ionospheric observation station, and read the h'Es and foEs values ​​of the vertical ionosphere; Preprocess the historical measured meteorological data of foEs, remove invalid data, and analyze the statistical laws of foEs; Step 3: Classify the foEs data according to different seasons and different times, and calculate the occurrence probability of foEs from 0:00 to 23:00 in spring, autumn, summer and winter: The foEs data were classified according to seasons to form the foEs spring, autumn, summer and winter data sets; Calculate the maximum value of foEs in each data set, and calculate the probability P of foEs occurrence from 0 to 23 o'clock according to a 1MHz step frequency starting from 1MHz: 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 4: Perform polynomial fitting on the foEs value and the probability of foEs occurrence, calculate the best fitting curves in different seasons, establish a single-station foEs occurrence probability prediction model, and use the prediction model to calculate the occurrence probability of any foEs value.

2. The method for predicting the probability of occurrence of foEs at a single station in China according to claim 1, characterized in that: In step 1, when -20°≤I≤20°, the station is located at low latitudes, and when 20°≤I≤60° or -60°≤I≤-20°, the station is located at mid-latitudes.

3. The method for predicting the probability of occurrence of foEs at a single station in China according to claim 1, characterized in that: In step 3, summer is May to August, winter is January, February, November and December, and spring and autumn are March, April, September and October.

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