A prediction method for the marine atmospheric modified refractive index profile

Abstract

The present invention relates to the field of marine atmospheric duct environment detection, and discloses a prediction method for the marine atmospheric modified refractive index profile, which includes the following steps: obtaining meteorological observation data at different heights; calculating the predicted value of the evaporation duct height and the reference atmospheric modified refractive index profile; constructing a fusion relationship model between the potential refractive index critical gradient and the meteorological observation data; inputting the meteorological observation data into the fusion relationship model to obtain the predicted potential refractive index critical gradient, calculating the reference potential refractive index critical gradient, and using the particle swarm intelligent optimization algorithm to iteratively obtain the optimal weight coefficient of the model through iteration; inputting the meteorological observation data into the optimized fusion relationship model, calculating the optimized potential refractive index critical gradient, and finally obtaining the optimized atmospheric modified refractive index profile. The method disclosed by the present invention considers the influence of different meteorological conditions on the potential refractive index critical gradient during the calculation process of the atmospheric modified refractive index profile, and improves the prediction accuracy of the atmospheric modified refractive index profile.

Description

Technical Field

[0001] The present invention relates to the field of marine atmospheric duct environment detection, and particularly to a method for predicting the profile of the atmospheric modified refractive index over the sea. Background Art

[0002] In the marine environment, sea surface evaporation and turbulent motion can induce the formation of evaporation ducts, and factors such as sea waves, large-scale frontal subsidence, advection of dry and warm air over land and sea, and nocturnal terrestrial radiation cooling will have complex effects on the characteristics of evaporation ducts. The characteristic transformation of evaporation ducts will change the profile of the atmospheric modified refractive index, thereby directly affecting the propagation of electromagnetic waves and causing abnormal refraction of electromagnetic waves near the sea surface. When electromagnetic waves propagate in the evaporation duct atmospheric stratification, the electromagnetic waves will be trapped and propagated in a narrow area, and at the same time, a detection blind area will be formed above the duct layer. Therefore, obtaining an accurate profile of the atmospheric modified refractive index near the sea surface is crucial for radar to accurately detect distant marine targets and predict the detection blind area in advance, and to achieve over-the-horizon target detection.

[0003] In recent years, there have been many studies on the generation mechanism, detection means, diagnostic methods, and the influence law on radio systems of the profile of the atmospheric modified refractive index over the sea surface. At present, the main detection method for the profile of the atmospheric modified refractive index over the sea surface is the evaporation duct model method based on the Monin-Obukhov similarity theory. By inputting the temperature, humidity, wind speed, air pressure at a certain height above the sea surface, and sea surface temperature, and using the stability correction function and roughness parameterization method in the evaporation duct model to obtain the profiles of temperature, humidity, and air pressure, and then obtain the profile of the atmospheric modified refractive index. In the model calculation process, the critical gradient of the potential refractive index always uses the value under the condition of neutral stability, that is, 0.125 , without considering the influence of the critical gradient of the potential refractive index on the near-sea surface meteorological conditions, and the problem that the value of the critical gradient of the potential refractive index varies under different meteorological conditions in different sea areas, which affects the accurate diagnosis of the profile of the atmospheric modified refractive index over the sea surface. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for predicting the profile of the atmospheric modified refractive index over the sea, which considers the influence of different meteorological conditions on the critical gradient of the potential refractive index during the calculation of the profile of the atmospheric modified refractive index, so as to achieve the purpose of improving the diagnostic accuracy of the profile of the atmospheric modified refractive index in the target sea area.

[0005] To achieve the above purpose, the technical solution of the present invention is as follows:

[0006] A method for predicting the profile of the atmospheric modified refractive index over the sea, comprising the following steps:

[0007] Step 1, using the gradient meteorological and hydrological sensors installed on the sea surface observation platform to obtain meteorological observation data at different heights;

[0008] Step 2: Input the obtained meteorological observation data into the evaporation duct model to obtain the predicted value of the evaporation duct height. Fit the atmospheric modified refractive index values at this height obtained from the meteorological observation data at different heights to obtain the reference atmospheric modified refractive index profile.

[0009] Step 3: Set the range of simulation meteorological parameters. Input the simulation meteorological parameters into the evaporation duct model, calculate the simulated atmospheric modified refractive index profile and the simulated value of the evaporation duct height. Input the simulated atmospheric modified refractive index profile and the simulated value of the evaporation duct height into the Gerstoft multi-parameter model to obtain the simulated value of the refractive index critical gradient. Construct a fusion relationship model between the refractive index critical gradient and the meteorological observation data.

[0010] Step 4: Input the meteorological observation data into the fusion relationship model to obtain the predicted refractive index critical gradient. Calculate the reference refractive index critical gradient by inputting the predicted value of the evaporation duct height and the reference atmospheric modified refractive index profile into the Gerstoft multi-parameter model. Use the root mean square error between the predicted refractive index critical gradient and the reference refractive index critical gradient as the fitness function to calculate the fitness. Use the particle swarm intelligent optimization algorithm to iteratively obtain the optimal weight coefficients of the fusion relationship model, and obtain the optimized fusion relationship model.

[0011] Step 5: Input the meteorological observation data into the optimized fusion relationship model to calculate the optimized refractive index critical gradient. Input the optimized refractive index critical gradient and the predicted value of the evaporation duct height into the Gerstoft multi-parameter model to obtain the optimized atmospheric modified refractive index profile.

[0012] In the above solution, the meteorological and hydrological sensor includes temperature and humidity sensors, pressure sensors, wind speed sensors and infrared temperature sensors installed at different heights, which respectively obtain temperature and humidity, pressure, wind speed and sea surface temperature data.

[0013] In the above solution, the evaporation duct model includes the PJ model, the NPS model, the BYC model, the MGB model and the pseudo-refractive index model.

[0014] In the above solution, in Step 2, the specific method for calculating the atmospheric modified refractive index values at different heights according to the meteorological observation data at different heights is as follows:

[0015] The height above the sea surface The atmospheric modified refractive index value is calculated by the following formula:

[0016] ;

[0017] ;

[0018] ;

[0019] wherein, is the sensor installation height, is the atmospheric temperature at the sensor installation height, is the air pressure at the sensor installation height, is the specific humidity at the sensor installation height, which is calculated from the atmospheric humidity at the sensor installation height ; is the ratio of the gas constant of dry air to the gas constant of water vapor, is the partial vapor pressure, is the partial refractive index.

[0020] In the above solution, in step two, the reference atmospheric modified refractive index profile formula is obtained by fitting the atmospheric modified refractive index values at different heights as follows:

[0021] ;

[0022] wherein, is the height, and a, b, and c are all fitting coefficients, is the reference atmospheric modified refractive index profile obtained by fitting at the height of .

[0023] In the above solution, in step three, the scatter plot of the critical gradient simulation value of the partial refractive index changing with each meteorological parameter is obtained by respectively performing simulation analysis on the critical gradient simulation value of the partial refractive index with temperature, humidity, wind speed, air pressure, and sea surface temperature; using the regression fitting method to construct a relationship model with this meteorological parameter as the input and the critical gradient simulation value of the partial refractive index as the output one by one, and fusing the relationship models of the critical gradient simulation value of the partial refractive index with temperature, humidity, wind speed, air pressure, and sea surface temperature by assigning weight coefficients to obtain a fused relationship model.

[0024] In the above solution, the formula of the Gerstoft multi-parameter model is as follows:

[0025] ;

[0026] wherein, is the height, is the critical gradient of the partial refractive index, is the waveguide strength of the evaporation duct or the surface waveguide without a bottom layer, is the atmospheric modified refractive index of the sea surface, is the slope of the change in the modified refractive index, is the evaporation duct height, is the aerodynamic roughness factor.

[0027] In a further technical solution, in step three, the simulated value of the critical gradient of the refractive index is obtained by taking the second partial derivative of the Gerstoft multi-parameter model, and the calculation is performed as follows:

[0028] ;

[0029] where, is the simulated value of the critical gradient of the refractive index, is the simulated value of the evaporation duct height.

[0030] In the above solution, in step three, the Monte Carlo method is used for simulation analysis to obtain a scatter plot of the simulated value of the critical gradient of the refractive index changing with various meteorological parameters; the obtained fusion relationship model is as follows:

[0031] ;

[0032] where, are all weight coefficients, is the atmospheric temperature at the sensor installation height, is the atmospheric humidity at the sensor installation height, is the wind speed at the sensor installation height, is the air pressure at the sensor installation height, is the sea surface temperature.

[0033] In the above solution, in step four, the method for obtaining the optimal weight coefficient of the fusion relationship model by using the particle swarm intelligent optimization algorithm through iteration is as follows:

[0034] S1: Initialization. In the fusion relationship model, the number of weight coefficients is 5. Therefore, it is determined that the number of parameters to be optimized is 5, and N 5-dimensional particles are generated as the initial particle swarm;

[0035] S2: Calculate the particle fitness:

[0036] ;

[0037] where, is the reference critical gradient value of the refractive index of the th particle, is the predicted critical gradient value of the refractive index of the th particle, is the number of particles, is the fitness;

[0038] S3: Update the particle velocity and particle position according to the particle fitness. The position of the th particle is , represents the position of the th particle in the first dimension; The The velocity of a particle is , indicating the velocity of the -th particle in the first dimension; the optimal position found by the -th particle is , indicating the optimal position of the -th particle in the first dimension; the globally optimal position found by the particle swarm is , indicating the optimal position of the particle swarm in the first dimension;

[0039] The position and velocity of the particle after each iteration are calculated by the following formula:

[0040] ;

[0041] ;

[0042] where is the individual learning factor, is the swarm learning factor, and their value ranges are both [0, 2]; is the inertia weight; is the number of iterations; and are random numbers with a value range of [0, 1], is the position of the -th particle at the -th iteration; The -th particle at the -th iteration; is the velocity of the -th particle at the -th iteration; The -th particle at the -th iteration; is the optimal position of the -th particle at the -th iteration; is the optimal position found by the particle swarm in the -th iteration;

[0043] S4: Repeat steps S2 and S3 until the maximum number of iterations is reached, and obtain the optimal positions found by the -th particle and the optimal position found by the particle swarm, where the corresponds to the 5 optimal weight coefficients of the fusion relationship model.

[0044] Through the above technical solution, a method for predicting the marine atmospheric modified refractive index profile provided by the present invention has the following beneficial effects:

[0045] Based on the Monin-Obukhov similarity theory, the present invention introduces the relationship between the critical gradient of potential refractive index and different meteorological parameters on the basis of the traditional evaporation duct model calculation. Compared with the traditional calculation method in which the critical gradient of potential refractive index is taken as a fixed value, it is more applicable to the calculation of the marine atmospheric modified refractive index profile in a complex sea surface environment. This makes the atmospheric modified refractive index profile calculated by this method closer to the real atmospheric modified refractive index profile in the near-sea area where the air-sea interface interaction is extremely complex.

[0046] In the calculation of the critical gradient of potential refractive index, the present invention considers all possible meteorological conditions in the sea area, with a comprehensive data range. The relationship between the constructed critical gradient of potential refractive index and different meteorological parameters is reliable. By using meteorological and hydrological sensors to collect real meteorological data of the target sea area and optimizing the constructed fusion relationship model through intelligent optimization algorithms related to artificial intelligence, it has strong applicability to the target sea area. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0048] Figure 1 It is a schematic flow chart of a method for predicting the marine atmospheric modified refractive index profile disclosed in the embodiments of the present invention;

[0049] Figure 2 It is a scatter plot of the critical gradient of potential refractive index changing with humidity;

[0050] Figure 3 Schematic flow chart of the particle swarm intelligent optimization algorithm. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention.

[0052] The present invention provides a method for predicting the marine atmospheric modified refractive index profile, as Figure 1 shown, including the following steps:

[0053] Step 1: Use the gradient meteorological and hydrological sensors installed on the sea surface observation platform to obtain meteorological observation data at different heights, and record the installation heights of the temperature and humidity sensors, wind speed sensors, and pressure sensors.

[0054] The meteorological and hydrological sensors include temperature and humidity sensors, barometric pressure sensors, wind speed sensors, and infrared temperature sensors installed at different heights, which respectively obtain temperature , humidity , barometric pressure , wind speed and sea surface temperature data.

[0055] The observation platforms include: offshore meteorological observation towers, ship observation platforms, and floating observation platforms, etc., which are offshore platforms that meet the sensor installation conditions.

[0056] The installation positions of the sensors on the observation platform should avoid the occlusion of objects at the observation platform as much as possible. The barometric pressure sensor and the wind speed sensor are installed at the same height. One infrared temperature sensor is installed on each of the left and right sides of the observation platform to avoid the shadow generated by the sun shining on the observation platform, which affects the accuracy of the measurement data. And to ensure the effectiveness of the data, the infrared sensor is preferably installed at a position within 20 meters above the sea surface. The barometric pressure sensor is installed at 6m to obtain the barometric pressure, and the wind speed sensor is installed at a height of 6m to obtain the wind speed.

[0057] Step 2: Input the obtained meteorological observation data into the evaporation duct model to obtain the predicted value of the evaporation duct height, and fit the atmospheric modified refractive index value at this height obtained from the meteorological observation data at different heights to obtain the reference atmospheric modified refractive index profile.

[0058] The evaporation duct model is the main method for calculating the evaporation duct height and the atmospheric modified refractive index profile, including the PJ model, NPS model, BYC model, MGB model, and pseudo-refractive index model. The existing evaporation duct prediction models are mainly based on the Monin-Obukhov similarity theory of the near-sea surface atmosphere. In the calculation of the NPS model, through the sea surface temperature and the temperature, humidity, barometric pressure, and wind speed at a certain height above the sea surface, the profiles of the near-sea surface temperature, specific humidity, and barometric pressure changing with height are calculated. Then, according to the relationship between the atmospheric modified refractive index and the temperature, specific humidity, and barometric pressure, the predicted atmospheric modified refractive index profile is obtained, and the position of the minimum value of the predicted atmospheric modified refractive index profile is used to determine the predicted value of the evaporation duct height.

[0059] The specific method for calculating the atmospheric modified refractive index values at different heights according to the meteorological observation data at different heights is as follows:

[0060] The atmospheric modified refractive index value at a height above the sea surface is calculated by the following formula:

[0061] ;

[0062] ;

[0063] ;

[0064] Among them, is the installation height of the sensor, is the atmospheric temperature at the installation height of the sensor, is the air pressure at the installation height of the sensor, is the specific humidity at the installation height of the sensor, which is calculated from the atmospheric humidity at the installation height of the sensor and obtained through calculation; is the ratio of the gas constant of dry air to the gas constant of water vapor, is the partial water vapor pressure, is the partial refractive index.

[0065] The reference atmospheric modified refractive index profile formula is obtained by fitting the atmospheric modified refractive index values at different heights as follows:

[0066] ;

[0067] Among them, is the height, and a, b, and c are all fitting coefficients, is the reference atmospheric modified refractive index profile obtained by fitting at the height of .

[0068] Step 3: Set the range of simulation meteorological parameters, input the simulation meteorological parameters into the evaporation duct model, calculate the simulation atmospheric modified refractive index profile and the simulated value of the evaporation duct height, input the simulation atmospheric modified refractive index profile and the simulated value of the evaporation duct height into the Gerstoft multi-parameter model to obtain the simulated value of the critical gradient of the partial refractive index, and construct a fusion relationship model between the critical gradient of the partial refractive index and the meteorological observation data;

[0069] Nonlinear least squares fitting is a data fitting method used to estimate the parameters of a nonlinear model. By fitting the Gerstoft multi-parameter model and the simulation atmospheric modified refractive index profile, the simulated value of the critical gradient of the partial refractive index is obtained.

[0070] The formula of the Gerstoft multi-parameter model is as follows:

[0071] ;

[0072] Among them, is the height, is the critical gradient of the partial refractive index, is the waveguide strength of the evaporation duct or the surface waveguide without a base layer, is the atmospheric modified refractive index of the sea surface, is the slope of the change in the modified refractive index, is the evaporation duct height, is the aerodynamic roughness factor, usually assumed to be 0.00015.

[0073] The simulated value of the critical gradient of the refractive index is obtained by taking the second partial derivative of the Gerstoft multi-parameter model and is calculated as follows:

[0074] ;

[0075] where is the simulated value of the critical gradient of the refractive index, is the simulated value of the evaporation duct height.

[0076] The construction method of the fusion model is as follows: The simulated value of the critical gradient of the refractive index is respectively simulated and analyzed with temperature, humidity, wind speed, air pressure, and sea surface temperature to obtain a scatter plot of the simulated value of the critical gradient of the refractive index changing with each meteorological parameter; the regression fitting method is used to construct a relationship model with this meteorological parameter as the input and the simulated value of the critical gradient of the refractive index as the output one by one, and the relationship models of the simulated value of the critical gradient of the refractive index with temperature, humidity, wind speed, air pressure, and sea surface temperature are fused by assigning weight coefficients to obtain a fusion relationship model.

[0077] Specifically, the Monte Carlo method is used for simulation and analysis. The input ranges of each meteorological and hydrological parameter are set. The temperature parameter range is set to [20, 40], the unit is °C, the parameter interval is 2, the humidity range is set to [50, 90], the unit is %, the parameter interval is 10, the air pressure parameter range is set to [900, 1100], the unit is hPa, the parameter interval is 100, the wind speed parameter range is set to [0, 16], the unit is m / s, the parameter interval is 1, the air-sea temperature difference parameter range is set to [-5, 5], the unit is °C, the parameter interval is 1. Considering the errors in the sensors for measuring meteorological and hydrological parameters, different degrees of perturbations are added to the input meteorological and hydrological parameters. Among them, the temperature input is 0.1 °C, the humidity input is 1%, the wind speed input is 0.5 m / s, the air pressure input is 0.2 hPa, and the sea surface temperature input is 1 °C; a scatter plot of the simulated value of the critical gradient of the refractive index changing with each meteorological parameter is obtained. Taking the variation relationship of the critical gradient of the refractive index with humidity as an example, the scatter plot of the critical gradient of the refractive index changing with humidity is as Figure 2 shown, and a relationship function is constructed by fitting the function curve according to the scatter change trend.

[0078] Finally, the fusion relationship model obtained by the regression fitting method is as follows:

[0079] ;

[0080] where are all weight coefficients, is the atmospheric temperature at the sensor installation height, is the atmospheric humidity at the sensor installation height, is the wind speed at the sensor installation height, is the atmospheric pressure at the sensor installation height, is the sea surface temperature.

[0081] Step 4: Input the meteorological observation data into the fusion relationship model to obtain the predicted critical gradient of the refractive index. Calculate the reference critical gradient of the refractive index through the Gerstoft multi-parameter model using the predicted evaporation duct height value and the reference atmospheric modified refractive index profile. Use the root mean square error between the predicted critical gradient of the refractive index and the reference critical gradient of the refractive index as the fitness function to calculate the fitness. Use the particle swarm intelligence optimization algorithm to obtain the optimal weight coefficients of the fusion relationship model through iteration, and obtain the optimized fusion relationship model.

[0082] The particle swarm optimization algorithm belongs to a type of evolutionary algorithm and is a parallel computing algorithm. The algorithm starts from a random solution, searches for the optimal solution through an iterative method, evaluates the quality of the solution found through fitness, and searches for the global optimal value by following the currently searched optimal value. The algorithm rules are relatively simple and are suitable for this fusion relationship model to find multiple optimal weight coefficients.

[0083] As Figure 3 shown, the method for obtaining the optimal weight coefficients of the fusion relationship model through iteration using the particle swarm intelligence optimization algorithm is as follows:

[0084] S1: Initialization. In the fusion relationship model, the number of weight coefficients is 5. Therefore, determine that the number of parameters to be optimized is 5, and generate N 5-dimensional particles as the initial particle swarm;

[0085] S2: Calculate the particle fitness:

[0086] ;

[0087] where, is the reference critical gradient value of the refractive index of the th particle, is the predicted critical gradient value of the refractive index of the th particle, is the number of particles, is the fitness;

[0088] S3: Update the particle velocity and particle position according to the particle fitness. The position of the th particle is , represents the position of the th particle in the first dimension; the velocity of the th particle is , represents the velocity of the -th particle in the first dimension; the optimal position found by the -th particle is , which represents the optimal position of the -th particle in the first dimension; the globally optimal position found by the particle swarm is , which represents the optimal position of the particle swarm in the first dimension;

[0089] The position and velocity of the particles after each iteration are calculated by the following equations:

[0090] ;

[0091] ;

[0092] where is the individual learning factor, is the swarm learning factor, and their value ranges are both [0, 2]; is the inertia weight; is the iteration number; and are random numbers with a value range of [0, 1], is the position of the -th particle at the -th iteration; the -th particle at the -th iteration; is the velocity of the -th particle at the -th iteration; the -th particle at the -th iteration; is the optimal position of the -th particle at the -th iteration; is the optimal position found by the particle swarm in the -th iteration;

[0093] S4: Repeat steps S2 and S3 until the maximum iteration number is reached, obtaining the optimal positions found by the -th particle and the optimal position found by the particle swarm , where the

[0094] Step 5: Input the meteorological observation data into the optimized fusion relationship model, calculate the optimized critical gradient of refractive index, and input the optimized critical gradient of refractive index and the predicted value of evaporation duct height into the Gerstoft multi-parameter model to obtain the optimized atmospheric modified refractive index profile.

[0095] Collect meteorological observation data through meteorological and hydrological sensors, verify the optimal weight coefficients obtained by the particle swarm optimization algorithm with the measured data, and determine the critical gradient of refractive index optimized by the particle swarm algorithm. After the fusion relationship model associated with different meteorological parameters meets the actual application requirements, calculate the critical gradient of refractive index according to the optimized fusion relationship model through the meteorological observation data collected by the sensor. Input the critical gradient of refractive index calculated by the fusion relationship model and the predicted value of evaporation duct height calculated by the evaporation duct model into the Gerstoft multi-parameter model to obtain a more accurate atmospheric modified refractive index profile.

[0096] In the embodiment, according to the actual observation data, using the method for predicting the atmospheric modified refractive index profile based on the dynamic critical gradient of refractive index proposed by the present invention, by constructing the fusion relationship model between the critical gradient of refractive index and different meteorological parameters, the critical gradient of refractive index under different meteorological conditions is obtained. Theoretically, compared with the critical gradient of refractive index as a fixed value, the calculation is more reasonable in a complex sea surface environment.

[0097] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the atmospheric corrected refractive index profile at sea, characterized in that: The steps include: Step 1: Use the gradient meteorological and hydrological sensors installed on the sea surface observation platform to obtain meteorological observation data at different heights; Step 2: Input the acquired meteorological observation data into the evaporation duct model to obtain the predicted value of the evaporation duct height, and fit the atmospheric corrected refractive index value at this height obtained from the meteorological observation data at different heights to obtain a reference atmospheric corrected refractive index profile; Step 3: Set the range of simulated meteorological parameters, input the simulated meteorological parameters into the evaporation duct model, calculate the simulated atmospheric correction refractive index profile and the simulated value of the evaporation duct height, input the simulated atmospheric correction refractive index profile and the simulated value of the evaporation duct height into the Gerstoft multi-parameter model to obtain the simulated value of the critical gradient of the potential refractive index, and construct a fusion relationship model between the critical gradient of the potential refractive index and the meteorological observation data; Step 4: input meteorological observation data into the fusion relationship model to obtain the predicted refractive index critical gradient, calculate the reference refractive index critical gradient by using the Gerstoft multi-parameter model for the predicted value of the evaporation duct height and the reference atmospheric corrected refractive index profile, calculate the fitness by taking the root mean square error between the predicted refractive index critical gradient and the reference refractive index critical gradient as the fitness function, and obtain the optimal weight coefficient of the fusion relationship model through iteration using the particle swarm intelligent optimization algorithm to obtain the optimized fusion relationship model; Step 5: Input the meteorological observation data into the optimized fusion relationship model, calculate the optimized potential refractive index critical gradient, input the optimized potential refractive index critical gradient and the evaporation duct height prediction value into the Gerstoft multi-parameter model to obtain the optimized atmospheric corrected refractive index profile.

2. The method for predicting a marine atmospheric corrected refractive index profile according to claim 1, characterized in that: The meteorological and hydrological sensors include temperature and humidity sensors, air pressure sensors, wind speed sensors and infrared temperature sensors installed at different heights to obtain temperature and humidity, air pressure, wind speed and sea surface temperature data respectively.

3. The method for predicting a marine atmospheric corrected refractive index profile according to claim 1, characterized in that: The evaporation waveguide models include PJ model, NPS model, BYC model, MGB model and pseudo-refractive index model.

4. The method for predicting a marine atmospheric corrected refractive index profile according to claim 1, characterized in that: In step 2, the specific method for calculating the atmospheric correction refractive index values ​​at different altitudes based on the meteorological observation data at different altitudes is as follows: Height above sea level The atmospheric corrected refractive index value at Calculated by the following formula: ； ； ； in, is the sensor installation height, is the atmospheric temperature at the sensor installation height, is the air pressure at the sensor installation height, is the specific humidity at the sensor installation height, which is determined by the atmospheric humidity at the sensor installation height. It is calculated; is the ratio of the gas constant of dry air to the gas constant of water vapor, is the potential water vapor pressure, is the refractive index.

5. The method for predicting a marine atmospheric corrected refractive index profile according to claim 1, characterized in that: In step 2, the reference atmospheric corrected refractive index profile formula is obtained by fitting the atmospheric corrected refractive index values ​​at different altitudes as follows: ； in, is the height, a, b, c are fitting coefficients, For height The reference atmospheric corrected refractive index profile obtained by fitting at .

6. The method for predicting the marine atmospheric corrected refractive index profile according to claim 2, characterized in that: In step three, the simulated value of the critical gradient of the refractive index is simulated and analyzed with temperature, humidity, wind speed, air pressure and sea surface temperature respectively to obtain a scatter plot of the simulated value of the critical gradient of the refractive index changing with the change of each meteorological parameter; the regression fitting method is used to construct a relationship model with the meteorological parameter as input and the simulated value of the critical gradient of the refractive index as output one by one, and the relationship model of the simulated value of the critical gradient of the refractive index with temperature, humidity, wind speed, air pressure and sea surface temperature is fused by assigning weight coefficients to obtain a fused relationship model.

7. The method for predicting the marine atmospheric corrected refractive index profile according to claim 1, characterized in that: The formula of the Gerstoft multi-parameter model is as follows: ； in, is the height, is the critical refractive index gradient, is the waveguide strength of the evaporated waveguide or the surface waveguide without the base layer, is the atmospheric-corrected refractive index at the sea surface, To correct the slope of the refractive index change, is the evaporation duct height, is the aerodynamic roughness factor.

8. The method for predicting the marine atmospheric corrected refractive index profile according to claim 7, characterized in that: In step 3, the critical gradient simulation value of the refractive index is obtained by taking the second-order partial derivative of the Gerstoft multi-parameter model, which is calculated as follows: ； in, is the simulated value of the critical gradient of the refractive index, is the simulated value of the evaporation waveguide height.

9. The method for predicting the marine atmospheric corrected refractive index profile according to claim 6, characterized in that: In step 3, the Monte Carlo method is used for simulation analysis to obtain a scatter plot of the simulated value of the critical gradient of the refractive index as the meteorological parameters change; the obtained fusion relationship model is as follows: ； in, are weight coefficients, is the atmospheric temperature at the sensor installation height, is the atmospheric humidity at the sensor installation height, is the wind speed at the sensor installation height, is the air pressure at the sensor installation height, is the sea surface temperature.

10. A method for predicting the marine atmospheric corrected refractive index profile according to claim 9, characterized in that: In step 4, the method of using the particle swarm intelligent optimization algorithm to iteratively obtain the optimal weight coefficient of the fusion relationship model is as follows: S1: Initialization. In the fusion relationship model, the number of weight coefficients is 5, so the number of parameters to be optimized is determined to be 5, and N 5-dimensional particles are generated as the initial particle swarm; S2: Calculate particle fitness: ； in, For the The critical gradient value of the refractive index of the reference position of each particle is For the The predicted critical refractive index gradient value for each particle, is the number of particles, For fitness; S3: Update particle speed and particle position according to particle fitness. The position of a particle is , Indicates The position of a particle in the first dimension; The speed of a particle is , Indicates The velocity of a particle in the first dimension; The optimal position of a particle is , Indicates The optimal position of a particle in the first dimension; the global optimal position searched by the particle swarm is , Indicates the optimal position of the particle swarm in the first dimension; The position and velocity of the particle after each iteration are calculated by the following formula: ； ； in, is the individual learning factor, is the group learning factor, and its value range is [0, 2]; is the inertia weight; is the number of iterations; and is a random number, ranging from [0, 1], For the The particle in The position of the iteration; No. The particle in The position of the iteration; For the The particle in The speed of iterations; No. The particle in The speed of iterations; For the The particle in The optimal position of the iteration; For the particle group The optimal position searched in the iteration; S4: Repeat steps S2 and S3 until the maximum number of iterations is reached and the The optimal position searched by the particles and the optimal position searched by the particle swarm , In The five optimal weight coefficients corresponding to the fusion relationship model.

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