Method for estimating and predicting annual average value of near-surface or sea surface 65m atmospheric refractive index gradient with time exceeding probability of 99% in equatorial region

Abstract

The invention discloses a method for estimating and predicting an annual average value of a near-ground or sea surface 65m atmospheric refractive index gradient with a time exceeding probability of 99% in an equatorial region, which comprises the following steps of: 1, on the basis of ERA5 reanalysis data of a mesoscale weather forecast center in European, calculating an annual average value of the near-ground or sea surface 65m atmospheric refractive index gradient; the near-ground or sea surface 65m atmospheric refractive index gradient statistical value of each grid point in the equatorial region year by year with the global time exceeding probability of 99% is completed; step 2, on the basis of each grid point statistical value year by year in the equatorial region, arithmetic averaging of all grid point statistical values along the equatorial region is carried out, and quadratic polynomial fitting is carried out according to trend change; and step 3, based on the fitted quadratic polynomial, calculating the annual average value of the atmospheric refractive index gradient. According to the method disclosed by the invention, accurate statistical parameters which can represent climate changes can be obtained, credible support can be provided for planning and design of a radio informatization system in a hot spot area, and the contradiction restricting high-reliability design of a radio system in a related frequency band is relieved.

Description

Technical Field

[0001] The invention belongs to the field of meteorological forecasting and radio technology, and particularly relates to a method for estimating and predicting the annual average value of the atmospheric refractive index gradient at 65 m near the ground or sea surface with a time probability exceeding 99% in the equatorial region. Background Art

[0002] In modern electronic information technology, the characteristics of the tropospheric radio wave environment are important factors that affect the effective performance of electronic information systems such as radar, communication, and navigation. The planning and design of radio information systems require research on the statistical characteristics of the radio wave environment. As an important part of the radio information system environment, the radio wave environment is a factor that must be considered in the planning, design, and operation guarantee of the system. The combat capabilities of the regional information system and network system should be established and improved, and the adaptive capabilities of electronic systems such as communication and detection should be established and improved. If the impact of radio wave propagation is underestimated, the system reliability will be affected at best, and the system may fail at worst. However, if the impact of radio wave propagation is overestimated, the system load and complexity will increase, which will not only cause waste, but also increase technical difficulty, and will also cause unnecessary interference to other systems.

[0003] The influence of the troposphere on radio wave propagation depends on the electrical characteristics of the troposphere itself, and atmospheric refraction is an important radio meteorological parameter that characterizes the influence of this electrical characteristic on radio wave propagation. In the ultra-short wave and microwave frequency bands, the tropospheric refractive index depends only on the dielectric constant of the air, which is a function of the atmospheric temperature, pressure and humidity. Therefore, the refractive index can be calculated by atmospheric temperature, pressure and humidity parameters. The influence of tropospheric refraction on propagation is mainly in the lower atmosphere below 1 to 2 km in altitude, and the refractive index gradient of the lower atmosphere is the main parameter used in the calculation of propagation effects. Line-of-sight propagation, especially ground or sea line-of-sight propagation, involves the refractive index gradient of 65m near the ground or sea surface. The refractive index gradient of 65km near the ground or sea surface is the main environmental influencing factor that needs to be considered in the study of ground or sea line-of-sight propagation.

[0004] Line-of-sight propagation refers to a propagation mode in which radio waves are directly transmitted from the transmitting point to the receiving point (sometimes including ground reflected waves) within the distance where the transmitting antenna and the receiving antenna can "see" each other. It is also called direct wave or space wave propagation. Line-of-sight propagation can be roughly divided into three categories according to the different spatial positions of the transmitting and receiving antennas: the first category refers to line-of-sight propagation on the ground or sea surface, such as relay communication, television, broadcasting, and mobile communication on the ground; the second category refers to line-of-sight propagation from ground (sea) to air, such as radar sounding, communication satellites, etc.; the third category refers to air-to-air line-of-sight propagation, such as radio wave propagation between aircraft and spacecraft, etc. These propagation paths are at least partially in the troposphere, which will cause propagation effects such as refraction of radio waves. Under normal atmospheric conditions, the farthest distance of direct waves between the transmitting and receiving antennas of a radio system is line-of-sight.

[0005] For the planning and demonstration of radio information systems, the study of tropospheric radio wave environment characteristics depends largely on the acquisition and statistics of tropospheric radio wave environment parameters. The atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of more than 99% is a very important statistical parameter of the atmospheric refractivity distribution with height. It represents the gradient value of the atmospheric refractivity gradient parameter at 65 m near the ground or sea surface with a time probability of 99%, representing an extreme case. This parameter requires a large number of years of atmospheric refractivity gradient statistics at 65 m near the ground or sea surface. In the past, the atmospheric refractivity gradient at 65 m near the ground or sea surface could only be detected by meteorological gradiometers, tethered airships or high-altitude sounding balloons, which consumed manpower and material resources. In addition, from the perspective of rapid response time, the observation time was too long and it was unsafe. For areas such as oceans and seas, it is impossible to regularly set up meteorological gradiometers, release tethered airships or high-altitude sounding balloons, and the observation conditions are not easy to achieve and cannot meet the requirements. Therefore, it is difficult to obtain a large number of atmospheric refractivity gradient values ​​at 65 m near the ground or sea surface for many years.

[0006] Internationally, the research mainly focuses on the planning and design of information systems, with the statistics of radio wave environment characteristics and long-term statistical models of radio wave propagation prediction as the main focus, forming radio wave environment statistical models and propagation prediction models applicable to different frequency bands and different working modes. These long-term research results are concentratedly reflected in the ITU-R Radio Bureau (ITU-R) ITU-RP.453 series of standards and other recommendations.

[0007] The sea level refractive index N0 and N in Recommendation ITU-R P.453-11 S The calculation statistics use surface sounding site data from about 1000 ground stations for a period of 5 years (1955-1959); N S The median wet term and 65m refractivity gradient calculation statistics are calculated using two years (1992-1993) of numerical weather forecasting initialization meteorological data (four times a day) from the European Centre for Medium-Range Weather Forecasts (ECMWF). The data range is from 0° to 360° in longitude and from +90° to -90° in latitude, with a resolution of 1.5°. The resolution has been improved to 0.75° in Recommendation ITU-R P.453-13. The wet term of the refractivity at locations other than the grid points can be derived by bilinear interpolation using the four nearest grid point values. The data files are available from the Radiocommunication Bureau. The 1000m and 100m refractivity gradient calculation statistics use upper-air sounding data from 99 sounding stations for 5 years (1955-1959).

[0008] In addition to the research progress represented by the International Telecommunication Union, some representative figures and their achievements in the world have also had a far-reaching impact. The formula for directly calculating the atmospheric refractive index through meteorological parameters was first proposed by BR Bean and EJ Dutton in their monograph

[0009] The formula is given in full in Radio Meteorology. For electromagnetic waves with frequencies higher than 30 GHZ, the error of the atmospheric refractive index calculated by this formula is within 0.5% within the range of air pressure, temperature and relative humidity usually encountered. Scientists further analyzed the needs of the radio information system and found that the basic factor affecting radio meteorology beyond the horizon, microwave propagation, etc. is mainly the atmospheric refractive index gradient. Therefore, subsequent work mainly focuses on related research. They found that the monthly average ground refractive index is and the refractive index gradient within the layer 1000m above the ground There is a good correlation. After that, several typical countries such as the United States, Germany, Japan, the United Kingdom and China established and The exponential relationship of .

[0010] In 2010, Abu-Almal analyzed the refractive index gradient and equivalent earth radius distribution of designated sites in the Arab region, as well as their impact on the microwave line-of-sight link system; Oluwole analyzed the impact of African meteorological parameters on atmospheric refractive index in 2013; AbouAlmal used 17 years of meteorological ground and sounding data from 1997 to 2013 in 2014 to analyze the refractive index gradient below 1 km in the subtropical region; Daniel Effiong Oku analyzed the seasonal variation of atmospheric refractive index under tropical monsoon conditions in Calabar, Africa in 2015, and gave the variation characteristics during the rainy and dry seasons.

[0011] In the design of wireless TV transmission, the equivalent earth radius is usually used to estimate the line-of-sight transmission conditions. The temporal and spatial changes in atmospheric refractive index will cause changes in the equivalent earth radius. For example, the propagation clearance and the corresponding height of the transmitting and receiving antennas, the change in the angle of arrival, and the requirements for the directional characteristics of the transmitting and receiving antennas, etc. Since Schelleng first derived the classic calculation formula of the equivalent earth radius in 1933, later generations have conducted extensive research on its calculation method and distribution characteristics. Xiong Hao et al. gave a detailed description of the connotation and expression of the equivalent earth radius. In recent years, scholars from all over the world have carried out a large number of studies on the distribution characteristics of the equivalent earth radius using measured meteorological data. For example, in 2006, Afullo TJ et al. studied the distribution characteristics of the equivalent earth radius factor in South Africa; in 2011, Naveen Kumar Chaudhary of India analyzed the impact of the Indian semi-desert area on the K factor closely related to the atmospheric refractive index gradient in radio propagation; in 2014, Adediji AT studied the meteorological characteristics of the equivalent earth radius factor in Akure, Nigeria, and the impact on the radio link. In 2016, Etokebe IJ calculated the distribution of the equivalent earth radius of Lagos, the capital of Nigeria, under clear sky conditions, and gave the distribution of the equivalent earth radius factor when the refractive index gradient is within a given range. This shows that the distribution characteristics of the equivalent earth radius obtained by using meteorological parameters at different locations can be used to analyze the propagation environment of the line-of-sight link of the ground radio system, and then evaluate the impact of atmospheric refraction on the performance of the radio system.

[0012] China has conducted a lot of research on the characteristics of the tropospheric atmospheric radio wave environment, and has obtained a large number of radio wave environment statistical data, models and propagation forecast models. The 22nd Institute of China Electronics Technology Group Corporation (CETC 22) is the only professional institute in China engaged in the observation and research of radio wave environment characteristics, and has been engaged in research in the field of radio meteorology for a long time. It has a deep theoretical and experimental foundation in the research of tropospheric radio wave environment characteristics. Since the 1960s, it has collected a large amount of tropospheric radio wave environment data, conducted many tropospheric radio wave environment experiments, and conducted a large amount of data statistical analysis, established a variety of atmospheric structure models, and formed a number of national standards including "Tropospheric Radio Wave Correction Atmospheric Model" and "Radar Radio Wave Propagation, Refraction and Correction Manual". Eight research results, including tropospheric scattering propagation model, atmospheric absorption model and worst month conversion model of rainfall, have been adopted by international standards, forming or revising the International Telecommunication Union (ITU-R) standards, indicating that my country has reached the international leading level in some research fields of radio wave environment characteristics. In the 1970s and 1980s, based on ten years of historical meteorological data, statistics, analysis, collation and editing were conducted on the Atlas of Low-altitude Atmospheric Refractive Index in China, Statistical Characteristics of Low-altitude Atmospheric Ducts in Typical Areas of China, and Distribution Characteristics of Typical Low-altitude Atmospheric Refractive Index in Beijing and the Middle and Lower Reaches of the Yangtze River. Qiu Shengbai and Lin Xiuwan calculated the national distribution model and regional distribution model of low-altitude atmospheric refractive index gradient and water vapor content gradient based on six years of data from 103 meteorological sounding stations in my country. In 1998, the limiting frequency and penetration angle of atmospheric ducts in various parts of my country were calculated using sounding data from 1990 to 1992, and the occurrence probability and characteristic quantity of low-altitude atmospheric ducts were statistically analyzed. In 2002, the division of climate zones of atmospheric ducts on land in my country and the changes of atmospheric ducts with seasons and months were studied, and a distribution map of atmospheric ducts on land in China was drawn. In 1996, Liu Chengguo, Pan Zhongwei and others from the 22nd Institute of China Electronics Technology Group Corporation conducted a statistical analysis of the probability of occurrence of low-altitude atmospheric ducts and duct characteristics in China, and divided my country's duct climate zone into four duct-frequent zones and four duct-free zones. This marked the beginning of nationwide duct research in my country. In 2002, Liu Chengguo, Huang Jiying, Jiang Changyin and others conducted a one-month atmospheric duct structure detection experiment in the southeast coast of my country. By processing and analyzing the experimental data, they summarized the occurrence law of atmospheric ducts in this region, and analyzed and summarized the meteorological conditions and new weather situation of atmospheric ducts in this region. During the period from 2002 to 2011, the Key Laboratory of the China Institute of Radiowave Propagation established the statistical distribution database (digital map) of my country's tropospheric radiowave environment for the first time through systematic data processing and theoretical method research. In 2017, Hu Ranran of the institute conducted a preliminary analysis of the statistical characteristics of my country's near-ground atmospheric refractive index gradient based on the global database provided by the European Centre for Mesoscale Weather Forecasts (ECMWF), and analyzed the influence of atmospheric refraction on the propagation distance of my country's near-ground wireless television.

[0013] From the above analysis, we can see that for the statistics of atmospheric refractivity gradients at 65m near the ground or sea surface with a time probability of exceeding 99% in the international ITU or domestic and foreign studies, the data comes from the sparse meteorological sounding data of the station in time and space, or the early reanalysis data of the European Centre for Mesoscale Weather Forecasts (ECMWF), such as ERA40 and ERA-I. The horizontal resolution and accuracy of the reanalysis data need to be improved. In addition, with the warming of the global climate, the statistical values ​​of atmospheric refractivity gradients at 65m near the ground or sea surface with a time probability of exceeding 99% using early data are no longer accurate, and their values ​​will also change with climate change. In the above studies, the annual average value and predicted value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a time probability of exceeding 99% with climate and age are not given.

[0014] The atmospheric refractive index environment has important scientific significance and application value for evaluating electromagnetic wave propagation. The complex changes of atmospheric refractive environment parameters with time and space will lead to various radio wave propagation phenomena such as radio wave ray propagation delay, waveguide propagation, scattering propagation, multipath propagation, signal fading, depolarization and signal fluctuation, which directly affect the working performance of the information system such as the range, detection probability, positioning accuracy and communication quality. The atmospheric refractive index is an important factor affecting the propagation of electromagnetic waves, especially the distribution of atmospheric refractive index with height. Atmospheric waveguide is a kind of propagation under abnormal refractive index gradient, which can change the propagation path of electromagnetic waves, enabling radar to detect beyond the horizon or have detection blind spots.

[0015] The atmospheric refractivity gradient value at 65m near the ground or sea surface with a time probability of exceeding 99% is a very important statistical parameter, which requires a large number of years of statistics of atmospheric refractivity gradient at 65m near the ground or sea surface. The data collected in the past are basically discrete station data, which show that there are more continents and fewer or even no ocean data, lacking the understanding of the characteristics of the global radio wave environment, especially the ocean. Therefore, it is also difficult to obtain the atmospheric refractivity gradient value at 65m near the ground or sea surface with a time probability of exceeding 99% on a global scale, especially in the ocean region (including the equatorial region).

[0016] In recent years, the global climate has undergone significant changes, which in turn affected the radio wave propagation characteristics of the relevant frequency bands. The existing atmospheric refractive index model that supports the design of radio systems was formed based on observation data from many years ago. Its prediction results are quite different from the current radio meteorological characteristics, which restricts the high-reliability design of radio systems in the relevant frequency bands.

[0017] With the continuous development of high technology and computer technology, numerical simulation has become more and more superior. Atmospheric numerical models have gradually become an important method for studying weather processes and regional tropospheric radio wave environment characteristics, especially in areas where there are no observational data on the vast ocean and land surface. Reanalysis grid data based on global numerical models and integration of multiple data sources have come into being. Because it can cover the entire world (including oceans with scarce data) in the form of different grid distances and has a daily time resolution of no more than 6 hours, it makes up for the shortcomings of uneven distribution of traditional discrete station data and lack of data in some areas. Therefore, this data has advantages that other data cannot match.

[0018] At present, there is no precedent for conducting statistical values ​​of atmospheric refractivity gradients at 65m near the ground or sea surface with a time probability exceeding 99% based on the latest reanalysis data ERA5 over many years, nor is there any research, estimation and prediction of the temporal variation of this statistical value due to global warming, nor has any relevant patent been formed. Summary of the invention

[0019] The technical problem to be solved by the present invention is to overcome the disadvantage that the average value of the atmospheric refractive index gradient at 65m near the ground or sea surface in the equatorial region with a time probability of exceeding 99% in the prior art cannot reflect global climate warming and temporal changes, resulting in serious deviations from its original average value, and provide a method for estimating and predicting the annual average value of the atmospheric refractive index gradient at 65m near the ground or sea surface in the equatorial region with a time probability of exceeding 99%.

[0020] The present invention adopts the following technical solution:

[0021] A method for estimating and predicting the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of exceeding 99% in the equatorial region, the improvement of which is that it comprises the following steps:

[0022] Step 1, based on the ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts, complete the statistical values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a global probability of exceeding 99% for each grid point in the equatorial region each year:

[0023] Based on the ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts, the atmospheric refractivity gradient parameters at 65m near the ground or sea surface at several times in several years are obtained. The horizontal resolution of the ERA5 reanalysis data is 0.25°, which means that the world is divided into 720*1440 grid points, and the equatorial region is divided into 1*1440 grid points. Each grid point contains the temperature, air pressure and humidity at different heights. The atmospheric refractivity values ​​at various heights are calculated based on the atmospheric refractivity calculation formula through the temperature, air pressure and humidity at each height of the grid point directly provided by the ERA5 reanalysis data, and then the atmospheric refractivity gradient parameters at 65m near the ground or sea surface at each grid point at each time are obtained by height interpolation.

[0024] For a single grid point, the atmospheric refractivity gradient parameters at 65 m near the ground or sea surface at each time of the year are accumulated and counted to form a statistical value with a probability of exceeding 99% of the time. Repeat this step to obtain the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% of the time for 1*1440 grid points in the equatorial region each year;

[0025] Step 2: Based on the statistical values ​​of each grid point in the equatorial region year by year, the arithmetic average of the statistical values ​​of all grid points along the equatorial region is calculated to form the annual average value of the equatorial region year by year. On this basis, a quadratic polynomial fitting is performed according to the trend change:

[0026] The fitted quadratic polynomial is as follows:

[0027] y=0.1558x 2 -0.39380x-89.9159

[0028] In the above formula, x and y are the annual average values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface at 0.125°N in the equatorial region with a probability of exceeding 99% after regularizing the center and scale of the predicted data, respectively;

[0029] The relationship between the center and proportion of the regularized predicted data, the era x, and the era x1 before processing is:

[0030] x=(x1-2000) / 11.4

[0031] Step 3, based on the fitted quadratic polynomial of the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% in the equatorial region, given the year x of interest, the annual average value y of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% in the year x of interest can be estimated or predicted.

[0032] Furthermore, the number of years in step 1 is not less than 6 years.

[0033] The beneficial effects of the present invention are:

[0034] The method disclosed in the present invention is based on the newly developed fine global reanalysis data ERA5 collected to perform a fine analysis of the temporal and spatial distribution characteristics of the global atmospheric refraction environment. The resolution and accuracy are significantly improved compared with the previous reanalysis data such as FNL, ERA-40, ERA-I, etc., and the grid spacing resolution is 0.25°. On the one hand, it can solve the problem of insufficient temporal and spatial resolution and accuracy of the data source (early reanalysis data and meteorological sounding station data) for obtaining the statistical value of the atmospheric refractive index gradient near the ground or sea surface 65m with a time probability of exceeding 99%, and no one has used this data to obtain the refractive index statistical value. On the other hand, it can estimate and predict the annual average value of the atmospheric refractive index gradient near the ground or sea surface 65m with a time probability of exceeding 99% in a certain year in the equatorial region under the background of climate warming. With the warming of the global climate, the statistical value of the atmospheric refractive index gradient near the ground or sea surface 65m with a time probability of exceeding 99% using early data is no longer accurate, and its value will also change with climate change. The statistical parameters that are more representative of climate change and more accurate obtained based on the method of the present invention can provide more reliable support for the planning and design of radio information systems in hot spots, and alleviate the contradictions that restrict the high-reliability design of radio systems in related frequency bands. It is of great significance for estimating the global radio wave propagation effect and evaluating its impact on radio information systems, mobile communication systems, navigation and positioning systems, radar systems, etc. operating in different frequency bands. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the annual average distribution map of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a 99% probability of exceeding the time in the equatorial region from 1981 to 2020;

[0036] Figure 2 It is the annual average distribution map of the atmospheric refractivity gradient at 65m near the ground or sea surface with a 99% probability of exceeding the time in the equatorial region from 1981 to 2019. DETAILED DESCRIPTION

[0037] 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.

[0038] The technical scheme of the present invention is based on the inventor's own research results (the statistical value (annual average value) of the atmospheric refractivity gradient near the ground or sea surface 65m exceeding the time probability of 99% presents a certain annual change pattern), based on the statistical value of the atmospheric refractivity gradient near the ground or sea surface 65m exceeding the time probability of 99% for each grid point in the equatorial region each year completed by the ERA5 reanalysis data newly developed by the European Centre for Mesoscale Weather Forecasts (ECMWF), by completing the average value of the statistical values ​​of all grid points in the equatorial region each year, performing quadratic polynomial fitting according to trend changes, and estimating and predicting the annual average value of the statistical value of the atmospheric refractivity gradient near the ground or sea surface 65m exceeding the time probability of 99% in the equatorial region in the year of concern based on the fitted polynomial.

[0039] As we all know, ERA5 reanalysis data is the latest data developed, and its resolution and accuracy are significantly improved compared with the previous FNL, ERA-40, ERA-I and other reanalysis data, with a grid resolution of 0.25°. This data is a major upgrade based on its predecessor ERA-I. First of all, the temporal and spatial resolution of this data has been greatly improved. Secondly, ERA5 uses ensemble reanalysis products for the first time to assess atmospheric uncertainties. This new function is based on the data assimilation ensemble system developed by ECMWF, which can explain the errors in observation and forecast models, giving users more confidence in analyzing atmospheric parameters at different times and places. Thirdly, ERA5 uses more historical observation data, especially satellite data, into advanced data assimilation and model systems to estimate more accurate atmospheric conditions.

[0040] The present invention discloses a method for estimating and predicting the annual average value of the atmospheric refractive index gradient at 65 m near the ground or sea surface in the equatorial region with a time probability of exceeding 99%, comprising the following steps:

[0041] Step 1, based on the ERA5 reanalysis data newly developed by the European Centre for Mesoscale Weather Forecasts (ECMWF), complete the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a global exceedance probability of 99% for each grid point in the equatorial region every year:

[0042] Based on the ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts, the atmospheric refractivity gradient parameters at 65 m near the ground or sea surface at several times in several years are obtained. The horizontal resolution of the ERA5 reanalysis data is 0.25°, which means that the world is divided into 720*1440 grid points and n*1440 grid points along the equator (the value of n depends on how wide the area is in the north-south latitude, and is generally 1). Each grid point contains meteorological parameters such as temperature, air pressure and humidity at different altitudes. The atmospheric refractivity values ​​at each altitude are calculated based on the atmospheric refractivity calculation formula through the temperature, air pressure and humidity at each altitude of the grid point directly provided by the ERA5 reanalysis data, and then the atmospheric refractivity gradient parameters at 65 m near the ground or sea surface at each grid point at each time are obtained through height interpolation. The data structure, large refractivity calculation formula and interpolation formula of the ERA5 reanalysis data are well-known public models and technologies, which will not be repeated here.

[0043] After obtaining the atmospheric refractivity gradient parameters at 65 m near the ground or sea surface at each grid point at each time for many years (depending on the age of collected data; the longer the age of data, the more accurate the established binomial, generally not less than 6 years), the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% can be calculated for each grid point each year.

[0044] For a single grid point, the atmospheric refractivity gradient parameters at 65 m near the ground or sea surface at each time of the year are accumulated and statistically analyzed to form a statistical value with a time probability of exceeding 99%. By repeating these steps, the statistical values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of exceeding 99% for 1*1440 grid points in the equatorial region are calculated year by year. The specific method for calculating the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of exceeding 99% is a well-known public method and will not be repeated here.

[0045] Based on the newly developed ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts (ECMWF), the statistical value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a time probability of exceeding 99% for each grid point each year is unprecedented.

[0046] Step 2: Based on the statistical values ​​of each grid point in the equatorial region year by year, the arithmetic average of the statistical values ​​of all grid points along the equatorial region is calculated to form the annual average value of the equatorial region year by year. On this basis, a quadratic polynomial is fitted according to the trend change:

[0047] After obtaining the annual statistical values ​​of the atmospheric refractivity gradient of 65m near the ground or sea surface with a time probability of 99% for 1*1440 grid points in the equatorial region, the arithmetic average of the statistical values ​​of each grid point along the equatorial region is calculated based on the statistical values ​​of each grid point in the equatorial region each year to form the annual average value of the equatorial region each year. Because there are both oceans and land nearby, there are many observation data, which are representative, and at this location, the value of each grid point along the 360° meridian in the same year does not change much, and its average value can represent the changes in the equatorial region each year mainly caused by climate change.

[0048] For the grid points formed by the ERA5 reanalysis data with a horizontal resolution of 0.25°, the 0.125°N latitudinal grid point is the grid point closest to the equator. The total annual average value of the equatorial region is formed by taking the arithmetic mean of the statistical values ​​of each grid point along the equatorial region at the 0.125°N latitudinal grid point. Figure 1 It is a scatter plot of the annual average value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a time probability of exceeding 99% at 0.125°N (the data here is from 1981 to 2020, a total of 40 years), representing the annual change of the annual average value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a time probability of exceeding 99% near the equator. It can be seen from the figure that with global warming, the atmospheric refractivity gradient value at 65m near the ground or sea surface with a time probability of exceeding 99% gradually increases with the years, indicating that global climate change has a great impact on the radio wave environment, and the demonstration and evaluation of related radio wave propagation must use new time limit data. It can be seen from the figure that the annual change trend is very obvious, and the center and proportion of the data can be predicted by regularization, and fitted based on a quadratic polynomial. The fitted quadratic polynomial is in the following form:

[0049] y=0.1558x 2 -0.39380x-89.9159

[0050] In the above formula, x and y are the annual average values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface at 0.125°N in the equatorial region with a probability of exceeding 99% after regularizing the center and scale of the predicted data, respectively;

[0051] The relationship between the center and proportion of the regularized predicted data, the era x, and the era x1 before processing is:

[0052] x=(x1-2000) / 11.4

[0053] The center and ratio processing techniques for regularized prediction data are well-known techniques and will not be described in detail here.

[0054] Step 3, based on the fitted quadratic polynomial of the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% in the equatorial region, given the year x of interest, the annual average value y of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% in the year x of interest can be estimated or predicted.

[0055] Two examples are given below, where Example 1 estimates the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface in the equatorial region with a time probability of exceeding 99% in 2005, an arbitrary year of interest within the 1981-2019 period. Example 2 predicts the future year of interest outside the 1981-2019 period, 2020, and obtains the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface in the equatorial region with a time probability of exceeding 99%.

[0056] Embodiment 1:

[0057] Step 1: Based on the ERA5 reanalysis data newly developed by the European Centre for Mesoscale Weather Forecasts (ECMWF), complete the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a global exceedance probability of 99% for each grid point in the equatorial region every year;

[0058] Based on the newly developed ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts (ECMWF), the atmospheric refractivity gradient parameters at 65m near the ground or sea surface at 00, 06, 12, 18, and 24 UTC for 40 years (1981-2020) are obtained. The horizontal resolution of the ERA5 reanalysis data is 0.25°, which means that the world is divided into 720*1440 grid points, and the equatorial region can be divided into n*1440 grid points (the value of n depends on how wide the area is in the north-south latitude, generally 1). Each grid point contains meteorological parameters such as temperature, humidity, and pressure at different heights. The atmospheric refractivity gradient parameters at 65m near the ground or sea surface at each grid point at each time can be directly provided by the ERA5 reanalysis data. The atmospheric temperature, pressure, and humidity of each height layer of the grid point are calculated based on the large refractivity calculation formula, and then obtained by height interpolation. The data structure, large refractivity calculation formula and interpolation formula of the ERA5 reanalysis data are well-known public models and technologies and will not be described in detail here.

[0059] After obtaining the atmospheric refractivity gradient parameters of 65 m near the ground or sea surface for each grid point at 00, 06, 12, 18, and 24 hours of daily world time for 40 years (1981-2020), the statistical value of the atmospheric refractivity gradient of 65 m near the ground or sea surface with a time probability of exceeding 99% for each grid point each year can be obtained. For a single grid point, the atmospheric refractivity gradient parameters of 65 m near the ground or sea surface at each time throughout the year are accumulated and statistically analyzed to form a statistical value with a time probability of exceeding 99%. Repeat this step to obtain the statistical value of the atmospheric refractivity gradient of 65 m near the ground or sea surface with a time probability of exceeding 99% for 1*1440 grid points in the equatorial region each year. The specific method for obtaining the statistical value of the atmospheric refractivity gradient of 65 m near the ground or sea surface with a time probability of exceeding 99% is a well-known and public method, which will not be repeated here.

[0060] Based on the newly developed ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts (ECMWF), the statistical value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a time probability of exceeding 99% for each grid point each year is unprecedented.

[0061] Step 2: Based on the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% at each grid point in the equatorial region, the arithmetic average of the statistical value of each grid point along the equatorial region is calculated to form the total annual average value of the equatorial region. Based on the average value, a quadratic polynomial fitting is performed according to the trend change to form a polynomial.

[0062] After obtaining the annual statistical values ​​of the atmospheric refractivity gradient at 65m near the ground or sea surface with a probability of exceeding 99% for 1*1440 grid points in the equatorial region, the arithmetic average of the statistical values ​​of each grid point along the equatorial region is calculated based on the statistical values ​​of each grid point in the equatorial region each year to form the annual average value of the equatorial region each year. Because there are both oceans and land nearby, there are many observation data, which are representative, and at this location, the value of each grid point along the 360° meridian in the same year does not change much, and its average value can represent the changes in the equatorial region each year, which are mainly caused by climate change.

[0063] For the grid points formed by the ERA5 reanalysis data with a horizontal resolution of 0.25°, the 0.125°N latitudinal grid point is the grid point closest to the equator. The total annual average value of the equatorial region is formed by taking the arithmetic mean of the statistical values ​​of each grid point along the equatorial region at the 0.125°N latitudinal grid point. Figure 1This is a scatter plot of the annual distribution of the annual average statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% for 40 years from 1981 to 2020 in the equatorial region (0.125°N). It represents the year-to-year change in the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% near the equator. It can be seen from the figure that with global warming, the value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% gradually increases with the years, indicating that global climate change has a great impact on the radio wave environment, and the demonstration and evaluation of related radio wave propagation must use new time limit data. Figure 1 It can be seen that the annual trend is very obvious. The center and proportion of the regularized prediction data can be fitted based on a quadratic polynomial to fit a quadratic polynomial.

[0064] Based on the annual average distribution of the statistical values ​​of the data from 1981 to 2019, the quadratic polynomial is fitted and the fitted quadratic polynomial form is as follows:

[0065] y=0.1558x 2 -0.39380x-89.9159

[0066] In the above formula, x and y are the annual average values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface at 0.125°N in the equatorial region with a time probability of 99% in the decade and the decade respectively after regularizing the center and scale of the predicted data.

[0067] The relationship between the center and proportion of the regularized predicted data, the era x, and the era x1 before processing is:

[0068] x=(x1-mu) / std

[0069] In the above formula, mu and std are the mean and standard deviation of x1, and their values ​​are 2000 and 11.4 respectively. The center and proportion processing techniques for regularized prediction data are well-known techniques and will not be described here.

[0070] Step 3, based on the fitted quadratic polynomial, the estimated and predicted year of interest is given, and the estimated or predicted annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface in the equatorial region with a time probability of exceeding 99% for the year of interest is obtained.

[0071] Based on the fitted annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% in the equatorial region and the related binomial of the year, the annual average value y of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% in the equatorial region can be estimated by giving the year x of interest. For example, when x1=2005, x=0.4385 after conversion, and y=-91.6N / km is estimated according to the binomial, which is only 1.9N / km different from the actual annual average value of the parameter in 2005, -93.5N / km. It can be seen that this embodiment can estimate the change value of a certain year caused by climate change in the equatorial region, and the accuracy is also guaranteed to a certain extent.

[0072] Embodiment 2:

[0073] Step 1: Based on the ERA5 reanalysis data newly developed by the European Centre for Mesoscale Weather Forecasts (ECMWF), complete the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a global exceedance probability of 99% for each grid point in the equatorial region every year;

[0074] Based on the newly developed ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts (ECMWF), the atmospheric refractivity gradient parameters at 65 m near the ground or sea surface at 00, 06, 12, 18, and 24 UTC for 40 years (1981-2020) are obtained. The data for 2020 are obtained here, and their annual average values ​​are calculated as the true value for comparison of the predicted values ​​for 2020 in future years outside the 1981-2019 period. The horizontal resolution of ERA5 reanalysis data is 0.25°, which means that the world is divided into 720*1440 grid points, and the equator can be divided into n*1440 grid points (the value of n depends on how wide the area is in the north-south latitude, generally 1). Each grid point contains meteorological parameters such as temperature, humidity, and pressure at different altitudes. The atmospheric refractivity gradient parameters at each grid point at each time near the ground or 65m above the sea surface can be directly provided by the ERA5 reanalysis data. The atmospheric temperature, pressure, and humidity of each altitude layer of the grid point are calculated based on the large refractivity calculation formula, and then obtained by height interpolation. The data structure, large refractivity calculation formula, and interpolation formula of ERA5 reanalysis data are well-known public models and technologies, which will not be repeated here.

[0075] After obtaining the atmospheric refractivity gradient parameters of 65 m near the ground or sea surface for each grid point at 00, 06, 12, 18, and 24 hours of daily universal time for 40 years (1981-2020), the statistical value of the atmospheric refractivity gradient of 65 m near the ground or sea surface with a time probability of exceeding 99% for each grid point each year can be obtained. For a single grid point, the atmospheric refractivity gradient parameters of 65 m near the ground or sea surface at each time of the year are accumulated and statistically analyzed to form a statistical value with a time probability of exceeding 99%. Repeat this step to obtain the statistical value of the atmospheric refractivity gradient of 65 m near the ground or sea surface with a time probability of exceeding 99% for 1*1440 grid points in the equatorial region each year. The specific method for obtaining the statistical value of the atmospheric refractivity gradient of 65 m near the ground or sea surface with a time probability of exceeding 99% is a well-known method and will not be repeated here.

[0076] Based on the newly developed ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts (ECMWF), the statistical value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a time probability of exceeding 99% for each grid point each year is unprecedented.

[0077] Step 2: Based on the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% at each grid point in the equatorial region, the arithmetic average of the statistical value of each grid point along the equatorial region is calculated to form the total annual average value of the equatorial region. Based on the average value, a quadratic polynomial fitting is performed according to the trend change to form a polynomial.

[0078] After obtaining the annual statistical values ​​of the atmospheric refractivity gradient at 65m near the ground or sea surface with a probability of exceeding 99% for 1*1440 grid points in the equatorial region, the arithmetic average of the statistical values ​​of each grid point along the equatorial region is calculated based on the statistical values ​​of each grid point in the equatorial region each year to form the annual average value of the equatorial region each year. Because there are both oceans and land nearby, there are many observation data, which are representative, and at this location, the value of each grid point along the 360° meridian in the same year does not change much, and its average value can represent the changes in the equatorial region each year, which are mainly caused by climate change.

[0079] For the grid points formed by the ERA5 reanalysis data with a horizontal resolution of 0.25°, the 0.125°N latitudinal grid point is the grid point closest to the equator. The total annual average value of the equatorial region is formed by taking the arithmetic mean of the statistical values ​​of each grid point along the equatorial region at the 0.125°N latitudinal grid point. Figure 2This is the annual distribution scatter plot of the annual average statistical value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a probability of exceeding 99% for 39 years from 1981 to 2019 in the equatorial region (0.125°N). It represents the year-to-year change in the annual average value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a probability of exceeding 99% near the equator. It can be seen from the figure that with global warming, the value of the atmospheric refractivity gradient at 65m near the ground or sea surface with a probability of exceeding 99% gradually increases with the years, indicating that global climate change has a great impact on the radio wave environment, and the demonstration and evaluation of related radio wave propagation must use new time limit data. Figure 2 It can be seen that the annual trend is very obvious. The center and proportion of the regularized prediction data can be fitted based on a quadratic polynomial to fit a quadratic polynomial.

[0080] Based on the annual average distribution of the statistical values ​​of the data from 1981 to 2019, the quadratic polynomial is fitted and the fitted quadratic polynomial form is as follows:

[0081] y=0.1558x 2 -0.39380x-89.9159

[0082] In the above formula, x and y are the annual average values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface at 0.125°N in the equatorial region with a time probability of 99% in the decade and the decade respectively after regularizing the center and scale of the predicted data.

[0083] The relationship between the center and proportion of the regularized predicted data, the era x, and the era x1 before processing is:

[0084] x=(x1-mu) / std

[0085] In the above formula, mu and std are the mean and standard deviation of x1, and their values ​​are 2000 and 11.4 respectively.

[0086] The annual average of the data statistics for 2020 is calculated as the true value for comparison of the forecast values ​​for future years outside the 1981-2019 period. The center and proportion processing techniques for regularized forecast data are well-known techniques and will not be described in detail here.

[0087] Step 3, based on the fitted quadratic polynomial, give the predicted future year of concern, 2020 (for this embodiment, the selected future year is 2020), and obtain the annual average value of the atmospheric refractive index gradient at 65m near the ground or sea surface in the equatorial region with a time probability of exceeding 99% for the predicted year 2020 of the year of concern.

[0088] Based on the fitted binomial related to the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of exceeding 99% in the equatorial region and the year, given the year of concern 2020, the annual average value y of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of exceeding 99% in the future year of concern x can be predicted.

[0089] For example, when x1=2020, the converted value is x=1.7541. According to the binomial, y=-96.3N / km is predicted, which is only 4.8N / km different from the actual annual average value of the parameter in 2020, -101.1N / km. It can be seen that this embodiment can not only predict the change value of a certain year caused by climate change in the equatorial region, but also has a certain guarantee in accuracy.

Claims

1. A method for estimating and predicting the annual average value of the atmospheric refractive index gradient at 65 m near the ground or sea surface with a time probability of exceeding 99% in the equatorial region, characterized in that: The steps include: Step 1, based on the ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts, complete the statistical values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a global probability of exceeding 99% for each grid point in the equatorial region each year: Based on the ERA5 reanalysis data of the European Centre for Mesoscale Weather Forecasts, the atmospheric refractivity gradient parameters at 65m near the ground or sea surface at several times in several years are obtained. The horizontal resolution of the ERA5 reanalysis data is 0.25°, which means that the world is divided into 720*1440 grid points, and the equatorial region is divided into 1*1440 grid points. Each grid point contains the temperature, air pressure and humidity at different heights. The atmospheric refractivity values ​​at various heights are calculated based on the atmospheric refractivity calculation formula through the temperature, air pressure and humidity at each height of the grid point directly provided by the ERA5 reanalysis data, and then the atmospheric refractivity gradient parameters at 65m near the ground or sea surface at each grid point at each time are obtained by height interpolation. For a single grid point, the atmospheric refractivity gradient parameters at 65 m near the ground or sea surface at each time of the year are accumulated and counted to form a statistical value with a probability of exceeding 99% of the time. Repeat this step to obtain the statistical value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a probability of exceeding 99% of the time for 1*1440 grid points in the equatorial region each year; Step 2: Based on the statistical values ​​of each grid point in the equatorial region year by year, the arithmetic average of the statistical values ​​of all grid points along the equatorial region is calculated to form the annual average value of the equatorial region year by year. On this basis, a quadratic polynomial fitting is performed according to the trend change: The fitted quadratic polynomial is as follows: <h2 style=";text-align:left;direction:ltr">y = 0.1558x<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -0.39380x-89.9159 In the above formula, x and y are the annual average values ​​of the atmospheric refractivity gradient at 65 m near the ground or sea surface at 0.125°N in the equatorial region with a probability of exceeding 99% after regularizing the center and scale of the predicted data, respectively; The relationship between the center and proportion of the regularized predicted data, the era x, and the era x1 before processing is: x=(x1-2000) / 11.4 Step 3, based on the fitted quadratic polynomial of the annual average value of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% in the equatorial region, given the year x of interest, the annual average value y of the atmospheric refractivity gradient at 65 m near the ground or sea surface with a time probability of 99% in the year x of interest can be estimated or predicted.

2. The method for estimating and predicting the annual average value of the atmospheric refractive index gradient at 65 m near the ground or sea surface with a time probability of exceeding 99% in the equatorial region according to claim 1, characterized in that: The number of years in step 1 shall not be less than 6 years.

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