Atmospheric correction refractive index profile calculation method under turbulence effect

Abstract

The invention relates to the field of ocean atmospheric boundary layer detection, and discloses an atmospheric correction refractive index profile calculation method under a turbulence effect, which comprises the following steps: acquiring meteorological observation data at different heights; different predicted temperature and humidity profiles are obtained through different temperature and humidity dimensionless pervasive functions; performing sensitivity analysis on the temperature and humidity dimensionless pervasive function, merging or splitting meteorological parameter intervals according to an analysis result, and selecting a temperature and humidity dimensionless pervasive function to be optimized; optimizing the coefficient by adopting a particle swarm intelligent optimization algorithm; and based on the optimized temperature and humidity dimensionless pervasive function in the meteorological parameter interval, calculating a refractive index structure constant, and finally obtaining an atmosphere correction refractive index profile under the turbulence effect. According to the method disclosed by the invention, the influence of the turbulence effect on the atmospheric correction refractive index is considered, and the optimal dimensionless pervasive function is selected for different meteorological parameter intervals, so that the purpose of improving the diagnosis precision of the atmospheric correction refractive index profile of the model is achieved.

Description

Technical Field

[0001] The invention relates to the field of ocean-atmosphere boundary layer detection, and in particular to a method for calculating an atmospheric corrected refractive index profile under turbulence effect. Background Art

[0002] In the marine environment, atmospheric duct is an important factor affecting electromagnetic propagation at sea, and it is of great significance to accurately predict the atmospheric duct near the sea surface. Due to ocean evaporation, turbulent motion near the sea surface, wave effects, and large-scale frontal sinking, the characteristics of the atmospheric duct near the sea surface will change, affecting the propagation of electromagnetic waves at sea, resulting in over-the-horizon propagation and detection blind spots.

[0003] At present, the main method for diagnosing the atmospheric duct near the sea surface is to construct an evaporation duct model using the Morning-Obukhov similarity theory to predict the atmospheric corrected refractive index profile near the sea surface by inputting the temperature, humidity, wind speed, air pressure and sea surface temperature at a certain height. Atmospheric turbulence is one of the main motion forms of the atmosphere near the sea surface. It plays a major role in the momentum transfer, heat transfer, water vapor exchange and material transfer between the sea surface and the atmosphere. It is also an important way to form the atmospheric duct. In the sea-air interface in the marine atmospheric boundary layer, turbulent motion is complex. The current calculation method of the evaporation duct does not consider the turbulence effect, which affects the diagnosis of the atmospheric corrected refractive index profile by the duct model. Therefore, it is necessary to consider the influence of turbulence effects on the atmospheric duct in order to achieve a more accurate diagnosis of the atmospheric corrected refractive index profile. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for calculating the atmospheric corrected refractive index profile under the turbulence effect. In the calculation process, the influence of the turbulence effect on the atmospheric corrected refractive index is considered, and the optimal temperature and humidity dimensionless universal function is selected for different meteorological parameter intervals to achieve the purpose of improving the diagnostic accuracy of the model atmospheric corrected refractive index profile.

[0005] To achieve the above object, the technical solution of the present invention is as follows: A method for calculating an atmospheric corrected refractive index profile under turbulence effect comprises the following steps: Step 1: Use meteorological and hydrological sensors installed at different heights on the sea surface observation platform to obtain meteorological observation data at different heights, and record the sensor installation height; Step 2: Using meteorological observation data, according to the Monin-Obukhov similarity theory and the COARE algorithm, different predicted temperature and humidity profiles are obtained through different temperature and humidity dimensionless universal functions, and the true specific humidity is calculated according to the temperature, humidity and air pressure measured by the meteorological and hydrological sensors, and the true temperature and humidity profile is obtained through nonlinear least squares fitting; Step 3, perform sensitivity analysis on the temperature and humidity dimensionless universal function, merge or split the meteorological parameter intervals according to the analysis results, and for each meteorological parameter interval, select the temperature and humidity dimensionless universal function with the smallest root mean square error between the predicted temperature and humidity profile and the actual temperature and humidity profile as the temperature and humidity dimensionless universal function to be optimized; Step 4, using a particle swarm intelligent optimization algorithm to optimize the coefficients of the temperature and humidity dimensionless universal function to obtain an optimized temperature and humidity dimensionless universal function; Step 5: Collect meteorological observation data, determine which meteorological parameter interval it belongs to, calculate the refractive index structure constant based on the optimized temperature and humidity dimensionless universal function in the meteorological parameter interval, calculate the atmospheric refractive index pulsation index in combination with the turbulence spectrum, and finally obtain the atmospheric corrected refractive index profile under the turbulence effect.

[0006] In the above scheme, in step 2, the dimensionless universal functions of temperature and humidity include HYQ92, SHEBA07, BH91 and CB05 under stable atmospheric conditions, and HYQ92, Edson04, Grachev00 and Fairall03 under unstable conditions; According to the block Richardson number The specific method to determine whether the current atmospheric state is stable or unstable is as follows: Block Richardson number Calculated by the following formula: ; in, is the acceleration due to gravity, The installation height of the temperature and humidity sensor. is the temperature at the installation height of the temperature and humidity sensor, is the difference between the temperature at the installation height of the temperature and humidity sensor and the sea surface temperature, is the wind speed, is the section coefficient; When the block Richardson number When , the current atmospheric state is determined to be stable, and the dimensionless universal function of temperature and humidity under stable conditions is selected; when When , the current atmospheric state is determined to be unstable, and the dimensionless universal function of temperature and humidity under unstable conditions is selected.

[0007] In the above scheme, in step 2, the predicted temperature and humidity profile includes a predicted temperature profile and a predicted specific humidity profile, and the calculation method is as follows: ; ; in, is the height, and For at a height of The predicted temperature and predicted specific humidity at is the sea surface temperature, is the specific humidity at the sea surface, is the saturated specific humidity calculated based on the sea surface temperature, is the characteristic scale of potential temperature, is the characteristic scale of specific humidity, is the temperature roughness length, is the dimensionless universal function of temperature, is the dimensionless universal function of humidity, is the stability correction factor, is the dry adiabatic lapse rate, is the Karman constant.

[0008] In the above scheme, in step 3, the Morris model is used for sensitivity analysis, and the process is as follows: S1: First, determine that when the number of input parameters is 5, namely temperature, humidity, air pressure, wind speed and sea surface temperature, select one of the 5 input parameters , the rest of the input parameters remain unchanged; S2: Set the initial range of meteorological parameters and determine the input parameter interval D; S3: Normalize all input parameters. The calculation method is as follows: ; in, For the input parameters, For the The upper limit of input parameters, For the The lower limit of the input parameters; S4: Discretize the input parameters and determine the number of horizontal points p and the disturbance factor by setting the parameter range and parameter spacing , the calculation method is as follows: ; ; Where p is the number of horizontal points; S5: Construct a single trajectory and determine the model input initial point , where each Randomly select from the discretized data Random increase or decrease , get a new set of input points, change only one parameter at a time, and change 5 Random increase or decrease After that, a total of 6 groups of input parameters are obtained together with the input initial point, and the new group of input parameters is brought into the COARE algorithm to obtain the simulated temperature profile or the simulated temperature profile as the output result; S6: Repeat step S5 times, a total of Group input parameters and output results as the total sample size for sensitivity analysis; S7: Calculate variable sensitivity discriminant coefficient : ; in, Morris model The output result of this time is Morris model Output results, The model running results after the input parameters are adjusted according to the parameter spacing. For the model After the first run, the rate of change of the input parameter relative to the initial input parameter; For the model After the first run, the rate of change of the input parameter relative to the initial input parameter; is the number of runs of the Morris model.

[0009] In the above scheme, in step 3, according to the variable sensitivity discriminant coefficient The size of the meteorological parameter interval is divided. When the temperature dimensionless universal function and the humidity dimensionless universal function are calculated in the same initial interval, If they are inconsistent, take the larger As the interval ,when When it is greater than 1, the interval is split evenly. When it is less than 0.2, the interval is merged. Between 0.2 and 1, the interval remains unchanged.

[0010] In the above scheme, in step 4, the process of optimization using the particle swarm intelligent optimization algorithm is as follows: S1: Determine the number m of coefficients to be optimized of the temperature dimensionless universal function or the humidity dimensionless universal function in the temperature and humidity dimensionless universal function to be optimized, and randomly generate E m-dimensional particles as the initial particle group; S2: Calculate the particle fitness. For the temperature dimensionless universal function, select the predicted temperature calculated by the COARE algorithm. and real temperature The root mean square error is used as the fitness function For the dimensionless universal function of humidity, the predicted specific humidity calculated by the COARE algorithm is selected. and real specific humidity The root mean square error is used as the fitness function : ; ; in, For the The true temperature of a particle, For the The predicted temperature calculated by the COARE algorithm for each particle, is the fitness value of the temperature particle; No. The real specific humidity of each particle is For the The predicted specific humidity calculated by the COARE algorithm for each particle is is the fitness value of the specific humidity particle; S3: Determine the particle speed and particle position according to the particle fitness value. The position of a particle is , Indicates The particle in The position in dimension, The speed of a particle is , Indicates The particle in The speed in the dimension; The optimal position of a particle is , Indicates The particle in The optimal position searched in dimension, the global optimal position searched by the particle swarm is , Indicates that the particle swarm is The optimal position searched in dimension; After each iteration, the particle The position and velocity in the dimension 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 number of iterations for a particle is At the Position in dimension; No. The particle in The position when iterating to the next generation in the dimension; For the The number of iterations for a particle is At the Speed ​​in dimension; For the The particle in The speed of iterating to the next generation in dimension; For the The number of iterations for a particle is At the Optimal position in dimension; The particle swarm is At the The optimal position searched in dimension; S4: Repeat steps S2 and S3 until the preset number of iterations is reached and the first The speed and position of the particle after all iterations are obtained. The optimal position of a particle and the optimal position searched by the particle swarm , In It is the optimal coefficient of the dimensionless universal function of temperature or the dimensionless universal function of humidity.

[0011] In the above scheme, in step 5, the calculation method of the refractive index structure constant is as follows: ; ; ; in, is the temperature structure constant, is the humidity structure constant, is the temperature and humidity cross structure constant, is the height, For height The predicted temperature at is the atmospheric pressure, is the ratio of the gas constant of dry air to the gas constant of water vapor, For height Predicted specific humidity at.

[0012] In the above scheme, the temperature structure constant , humidity structure constant and the temperature-humidity cross-structure constant The calculation method is as follows: ; ; ; in, is the height, is the characteristic scale of potential temperature, is the characteristic scale of specific humidity, is the dimensionless universal function of temperature, is the dimensionless universal function of humidity, is the stability correction factor.

[0013] In the above scheme, the calculation method of the atmospheric correction refractive index profile under turbulence effect is as follows: ; ; ; in, is the height, For height The predicted temperature at For height The predicted pressure at , where is the ratio of the gas constant of dry air to the gas constant of water vapor, For height The predicted specific humidity at For height The water vapor pressure at For height The atmospheric refractive index pulsation index at For height The refractive index at is the height under turbulence effect The atmospheric corrected refractive index at .

[0014] In the above scheme, the turbulence spectrum function is used to calculate the height The atmospheric refractive index pulsation index at The turbulence spectrum function includes von Karman turbulence spectrum function, Kolmogorov turbulence spectrum function, and non-Kolmogorov turbulence spectrum function.

[0015] Through the above technical solution, the method for calculating the atmospheric corrected refractive index profile under turbulence effect provided by the present invention has the following beneficial effects: The present invention is based on the Morning-Ophoff similarity theory and the COARE algorithm. On the basis of the traditional atmospheric corrected refractive index profile calculation, the atmospheric refractive index pulsation index is introduced, so that the atmospheric corrected refractive index profile calculated by this method is closer to the real atmospheric corrected refractive index profile near the sea surface where the interaction between the air and sea interface is extremely complex.

[0016] The present invention uses a sensitivity analysis method to perform sensitivity analysis under different meteorological conditions. By calculating the sensitivity discrimination coefficient, the sensitivity of different temperature and humidity dimensionless universal functions to meteorological parameters is accurately analyzed. The coefficient of the temperature and humidity dimensionless universal function is optimized through an artificial intelligence-related intelligent optimization algorithm to make it more suitable for the target sea area. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0018] Figure 1 A schematic diagram of a method for calculating an atmospheric corrected refractive index profile under turbulence effect disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram comparing the temperature profile calculated by the COARE algorithm and the actual temperature profile; Figure 3 Schematic diagram of the comparison between the specific humidity profile calculated by the COARE algorithm and the actual specific humidity profile; Figure 4 This is a flowchart of Morris global sensitivity analysis method; Figure 5 Schematic diagram of the process of optimizing the dimensionless universal function of temperature and humidity for the particle swarm intelligent optimization algorithm. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] The present invention provides a method for calculating the atmospheric corrected refractive index profile under turbulence effect, such as Figure 1 As shown, the following steps are included: Step 1: Use meteorological and hydrological sensors installed at different heights on the sea surface observation platform to obtain meteorological observation data at different heights, and record the sensor installation height.

[0021] Meteorological and hydrological sensors are installed at different heights from the sea surface observation platform, including temperature and humidity sensors installed at different heights. The temperature and humidity sensors are installed starting from the bottom of the observation platform, one every two meters. The temperature and humidity sensors are installed on the left and right sides of the observation platform in sequence. In addition, a pressure sensor, a wind speed sensor and an infrared temperature sensor are installed on the observation platform to obtain temperature respectively. , relative humidity , air pressure , wind speed and sea surface temperature Data, and record the installation height of the temperature and humidity sensor, wind speed sensor, and air pressure sensor.

[0022] When installing sensors on the observation platform, the obstruction of the observation platform itself should be considered. An infrared temperature sensor is installed on each side of the observation platform to avoid shadows on the observation platform when the sun is shining, which affects the accuracy of the data. To ensure the validity of the data, the infrared sensor is installed within 15 meters of the sea surface as much as possible. In order to obtain temperature and humidity data at different heights, the observation platform should cover 0-40 heights as much as possible, and use offshore platforms such as meteorological observation towers that meet the sensor installation conditions.

[0023] Step 2: Using meteorological observation data according to the Morning-Obukhov similarity theory and the COARE algorithm, different predicted temperature and humidity profiles are obtained through different temperature and humidity dimensionless universal functions. The true specific humidity is calculated based on the temperature, humidity and air pressure measured by meteorological and hydrological sensors, and the true temperature and humidity profile is obtained through nonlinear least squares fitting.

[0024] The dimensionless universal functions of temperature and humidity include HYQ92, SHEBA07, BH91 and CB05 under stable atmospheric conditions, and HYQ92, Edson04, Grachev00 and Fairall03 under unstable conditions.

[0025] According to the block Richardson number The specific method to determine whether the current atmospheric state is stable or unstable is as follows: Block Richardson number Calculated by the following formula: ; in, is the acceleration due to gravity, The installation height of the temperature and humidity sensor. is the temperature at the installation height of the temperature and humidity sensor, is the difference between the temperature at the installation height of the temperature and humidity sensor and the sea surface temperature, is the wind speed, is the section coefficient; When the block Richardson number When , the current atmospheric state is determined to be stable, and the dimensionless universal function of temperature and humidity under stable conditions is selected; when When , the current atmospheric state is determined to be unstable, and the dimensionless universal function of temperature and humidity under unstable conditions is selected.

[0026] The predicted temperature and humidity profile includes the predicted temperature profile and the predicted specific humidity profile. The calculation method is as follows: ; ; in, is the height, and For at a height of The predicted temperature and predicted specific humidity at is the sea surface temperature, is the specific humidity at the sea surface, is the saturated specific humidity calculated based on the sea surface temperature, is the characteristic scale of potential temperature, is the characteristic scale of specific humidity, is the temperature roughness length, is the dimensionless universal function of temperature, is the dimensionless universal function of humidity, is the stability correction factor, is the dry adiabatic lapse rate, is the Karman constant.

[0027] The actual specific humidity data is calculated through the temperature, humidity and air pressure data obtained by the sensor. The calculation method is as follows: ; ; ; in, The installation height of the temperature and humidity sensor. is the temperature at the installation height of the temperature and humidity sensor, The humidity at the installation height of the temperature and humidity sensor. is the air pressure at the installation height of the temperature and humidity sensor, is the saturated water vapor pressure at the installation height of the temperature and humidity sensor, is the water vapor pressure at the installation height of the temperature and humidity sensor, It is the actual specific humidity at the installation height of the temperature and humidity sensor.

[0028] The true temperature profile and true specific humidity profile are obtained by fitting the temperature and true specific humidity at different heights. The calculation method is as follows: ; ; Among them, a, b, and c are fitting coefficients.

[0029] The predicted temperature profile calculated by the COARE algorithm is different from the actual temperature profile. Figure 2 As shown in Figure 2, the predicted specific humidity profile calculated by the COARE algorithm is different from the actual specific humidity profile. Figure 3 shown.

[0030] Step 3: Perform sensitivity analysis on the temperature and humidity dimensionless universal function, merge or split the meteorological parameter intervals according to the analysis results, and for each meteorological parameter interval, select the temperature and humidity dimensionless universal function with the smallest root mean square error between the predicted temperature and humidity profile and the actual temperature and humidity profile as the temperature and humidity dimensionless universal function to be optimized.

[0031] like Figure 4 As shown, Morris model is used for sensitivity analysis, and the process is as follows: S1: First, determine that when the number of input parameters is 5, namely temperature, humidity, air pressure, wind speed and sea surface temperature, select one of the 5 input parameters , the rest of the input parameters remain unchanged; S2: Set the initial range of meteorological parameters and determine the input parameter interval D; In setting the initial interval range of meteorological parameters, set the temperature parameter range to [20, 40], the unit is ℃, 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 4, and the air-sea temperature difference parameter range is set to [-5, 5], the unit is ℃, and the parameter interval is 1.

[0032] S3: Normalize all input parameters. The calculation method is as follows: ; in, For the input parameters, For the The upper limit of input parameters, For the The lower limit of the input parameters; S4: Discretize the input parameters and determine the number of horizontal points p and the disturbance factor by setting the parameter range and parameter spacing , the calculation method is as follows: ; ; Where p is the number of horizontal points; S5: Construct a single trajectory and determine the model input initial point , where each Randomly select from the discretized data Random increase or decrease , get a new set of input points, change only one parameter at a time, and change 5 Random increase or decrease After that, a total of 6 groups of input parameters are obtained together with the input initial point, and the new group of input parameters is brought into the COARE algorithm to obtain the simulated temperature profile or the simulated temperature profile as the output result; S6: Repeat step S5 times, a total of Group input parameters and output results as the total sample size for sensitivity analysis; S7: Calculate variable sensitivity discriminant coefficient : ; in, Morris model The output result of this time is Morris model Output results, The model running results after the input parameters are adjusted according to the parameter spacing. For the model After the first run, the rate of change of the input parameter relative to the initial input parameter; For the model After the first run, the rate of change of the input parameter relative to the initial input parameter; is the number of runs of the Morris model.

[0033] According to the variable sensitivity discriminant coefficient The size of the meteorological parameter interval is divided. When the temperature dimensionless universal function and the humidity dimensionless universal function are calculated in the same initial interval, If they are inconsistent, take the larger As the interval ,when When it is greater than 1, the interval is split evenly. When it is less than 0.2, the interval is merged. Between 0.2 and 1, the interval remains unchanged.

[0034] Step 4: Use a particle swarm intelligent optimization algorithm to optimize the coefficients of the temperature and humidity dimensionless universal function to obtain an optimized temperature and humidity dimensionless universal function.

[0035] like Figure 5 As shown in the figure, taking the temperature dimensionless universal function of CB05 under stable conditions as an example, the optimization process using the particle swarm intelligent optimization algorithm is as follows: S1: The dimensionless universal function of the temperature of CB05 under stable conditions is expressed as , the temperature dimensionless universal function contains two coefficients and , during initialization, the number of parameters to be optimized is determined to be 2, and E 2D particles are generated as the initial particle swarm; S2: Calculate the particle fitness and select the predicted temperature calculated by the COARE algorithm and real temperature The root mean square error is used as the fitness function , calculate the fitness value: ; in, For the The true temperature of a particle, For the The predicted temperature calculated by the COARE algorithm for each particle, is the fitness value of the temperature particle; S3: Determine the particle speed and particle position according to the particle fitness value. 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 searched by a particle in the first dimension, and the global optimal position searched by the particle swarm is , Indicates the optimal position searched by the particle swarm in the first dimension; The position and velocity of the particle in the first dimension 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 number of iterations for a particle is The position of in the first dimension; No. The position of a particle when it iterates to the next generation in the first dimension; For the The number of iterations for a particle is The speed in the first dimension when For the The speed of a particle when iterating to the next generation in the first dimension; For the The number of iterations for a particle is The optimal position in the first dimension when The particle swarm is The optimal position searched in the first dimension at the time; the calculation method of the particle's position and velocity in the second dimension after each iteration is the same as the first dimension; S4: Repeat steps S2 and S3 until the preset number of iterations is reached and the first The speed and position of the particle after all iterations are obtained. The optimal position of a particle and the optimal position searched by the particle swarm , In and that is and The optimal parameters of .

[0036] The particle swarm optimization method for other dimensionless universal functions of temperature and humidity is the same as above.

[0037] Step 5: Collect meteorological observation data, determine which meteorological parameter interval it belongs to, calculate the refractive index structure constant based on the optimized temperature and humidity dimensionless universal function in the meteorological parameter interval, calculate the atmospheric refractive index pulsation index in combination with the turbulence spectrum, and finally obtain the atmospheric corrected refractive index profile under the turbulence effect.

[0038] The first step is to calculate the temperature structure constant , humidity structure constant and the temperature-humidity cross-structure constant , the calculation method is as follows: ; ; ; in, is the height, is the characteristic scale of potential temperature, is the characteristic scale of specific humidity, is the dimensionless universal function of temperature, is the dimensionless universal function of humidity, is the stability correction factor.

[0039] The second step is to calculate the refractive index structure constant. The calculation method is as follows: ; ; ; in, is the temperature structure constant, is the humidity structure constant, is the temperature and humidity cross structure constant, is the height, For height The predicted temperature at is the atmospheric pressure, is the ratio of the gas constant of dry air to the gas constant of water vapor, For height Predicted specific humidity at.

[0040] The third step is to calculate the atmospheric refractive index pulsation index : Calculate the height using the turbulence spectrum function The atmospheric refractive index pulsation index at The turbulence spectrum function includes vonKarman turbulence spectrum function, Kolmogorov turbulence spectrum function, and non-Kolmogorov turbulence spectrum function.

[0041] Taking the von Karman turbulence spectrum function as an example, The calculation method of is as follows: ; ; ; ; in, is a random number uniformly distributed between 0 and 1. is the refractive index structure constant, is the turbulence spectrum function, represents the wave number, is the inner scale wave number parameter, is the outer scale wave number parameter, is the dividing line between small and medium scale fluctuations, It is the dividing line between medium and large scale fluctuations.

[0042] The fourth step is to calculate the atmospheric corrected refractive index profile under the turbulence effect. The calculation method is as follows: ; ; ; in, is the height, For height The predicted temperature at For height The predicted pressure at , where is the ratio of the gas constant of dry air to the gas constant of water vapor, For height The predicted specific humidity at For height The water vapor pressure at For height The atmospheric refractive index pulsation index at For height The refractive index at is the height under turbulence effect The atmospheric corrected refractive index at .

[0043] Among them, the predicted pressure profile The specific calculation method is as follows: ; in, is the height, The installation height of the temperature and humidity sensor. is the gas constant under dry conditions, is the atmospheric pressure, To be at a high and The average virtual temperature at is the acceleration due to gravity.

[0044] For the gradient meteorological data of the sea surface observation platform in the embodiment, the atmospheric corrected refractive index profile calculation method under the turbulence effect proposed in the present invention is used, and the optimal temperature and humidity dimensionless universal function is selected according to the sensitivity analysis results. After algorithm optimization, the refractive index structure constant and the atmospheric refractive index pulsation index are calculated to obtain the atmospheric corrected refractive index profile under the turbulence effect, which is theoretically more reasonable than not considering the turbulence effect.

[0045] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may 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 rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating the atmospheric corrected refractive index profile under turbulence effect, characterized in that: The steps include: Step 1: Use meteorological and hydrological sensors installed at different heights on the sea surface observation platform to obtain meteorological observation data at different heights, and record the sensor installation height; Step 2: Using meteorological observation data, according to the Monin-Obukhov similarity theory and the COARE algorithm, different predicted temperature and humidity profiles are obtained through different temperature and humidity dimensionless universal functions, and the true specific humidity is calculated according to the temperature, humidity and air pressure measured by the meteorological and hydrological sensors, and the true temperature and humidity profile is obtained through nonlinear least squares fitting; Step 3, perform sensitivity analysis on the temperature and humidity dimensionless universal function, merge or split the meteorological parameter intervals according to the analysis results, and for each meteorological parameter interval, select the temperature and humidity dimensionless universal function with the smallest root mean square error between the predicted temperature and humidity profile and the actual temperature and humidity profile as the temperature and humidity dimensionless universal function to be optimized; Step 4, using a particle swarm intelligent optimization algorithm to optimize the coefficients of the temperature and humidity dimensionless universal function to obtain an optimized temperature and humidity dimensionless universal function; Step 5: Collect meteorological observation data, determine which meteorological parameter interval it belongs to, calculate the refractive index structure constant based on the optimized temperature and humidity dimensionless universal function in the meteorological parameter interval, calculate the atmospheric refractive index pulsation index in combination with the turbulence spectrum, and finally obtain the atmospheric corrected refractive index profile under the turbulence effect.

2. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 1, characterized in that: In step 2, the temperature and humidity dimensionless universal functions include HYQ92, SHEBA07, BH91 and CB05 under stable atmospheric conditions, and HYQ92, Edson04, Grachev00 and Fairall03 under unstable conditions; According to the block Richardson number The specific method to determine whether the current atmospheric state is stable or unstable is as follows: Block Richardson number Calculated by the following formula: ； in, is the acceleration due to gravity, The installation height of the temperature and humidity sensor. is the temperature at the installation height of the temperature and humidity sensor, is the difference between the temperature at the installation height of the temperature and humidity sensor and the sea surface temperature, is the wind speed, is the section coefficient; When the block Richardson number When , the current atmospheric state is determined to be stable, and the dimensionless universal function of temperature and humidity under stable conditions is selected; when When , the current atmospheric state is determined to be unstable, and the dimensionless universal function of temperature and humidity under unstable conditions is selected.

3. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 1, characterized in that: In step 2, the predicted temperature and humidity profile includes a predicted temperature profile and a predicted specific humidity profile, and the calculation method is as follows: ； ； in, is the height, and For at a height of The predicted temperature and predicted specific humidity at is the sea surface temperature, is the specific humidity at the sea surface, is the saturated specific humidity calculated based on the sea surface temperature, is the characteristic scale of potential temperature, is the characteristic scale of specific humidity, is the temperature roughness length, is the dimensionless universal function of temperature, is the dimensionless universal function of humidity, is the stability correction factor, is the dry adiabatic lapse rate, is the Karman constant.

4. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 1, characterized in that: In step 3, the Morris model is used for sensitivity analysis, and the process is as follows: S1: First, determine that when the number of input parameters is 5, namely temperature, humidity, air pressure, wind speed and sea surface temperature, select one of the 5 input parameters , the rest of the input parameters remain unchanged; S2: Set the initial range of meteorological parameters and determine the input parameter interval D; S3: Normalize all input parameters. The calculation method is as follows: ； in, For the input parameters, For the The upper limit of input parameters, For the The lower limit of the input parameters; S4: Discretize the input parameters, and determine the number of horizontal points p and the disturbance factor by setting the parameter range and parameter spacing , the calculation method is as follows: ； ； Where p is the number of horizontal points; S5: Construct a single trajectory and determine the model input initial point , where each Randomly select from the discretized data Random increase or decrease , get a new set of input points, change only one parameter at a time, and change 5 Random increase or decrease After that, a total of 6 groups of input parameters are obtained together with the input initial point, and the new group of input parameters is brought into the COARE algorithm to obtain the simulated temperature profile or the simulated temperature profile as the output result; S6: Repeat step S5 times, a total of Group input parameters and output results as the total sample size for sensitivity analysis; S7: Calculate variable sensitivity discriminant coefficient : ； in, Morris model The output result of this time is Morris model Output results, The model running results after the input parameters are adjusted according to the parameter spacing. For the model After the first run, the rate of change of the input parameter relative to the initial input parameter; For the model After the first run, the rate of change of the input parameter relative to the initial input parameter; is the number of runs of the Morris model.

5. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 4, characterized in that: In step 3, according to the variable sensitivity discriminant coefficient The size of the meteorological parameter interval is divided. When the temperature dimensionless universal function and the humidity dimensionless universal function are calculated in the same initial interval, If they are inconsistent, take the larger As the interval ,when When it is greater than 1, the interval is split evenly. When it is less than 0.2, the interval is merged. Between 0.2 and 1, the interval remains unchanged.

6. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 1, characterized in that: In step 4, the optimization process using the particle swarm intelligent optimization algorithm is as follows: S1: Determine the number m of coefficients to be optimized of the temperature dimensionless universal function or the humidity dimensionless universal function in the temperature and humidity dimensionless universal function to be optimized, and randomly generate E m-dimensional particles as the initial particle group; S2: Calculate the particle fitness. For the temperature dimensionless universal function, select the predicted temperature calculated by the COARE algorithm. and real temperature The root mean square error is used as the fitness function For the dimensionless universal function of humidity, the predicted specific humidity calculated by the COARE algorithm is selected. and real specific humidity The root mean square error is used as the fitness function : ； ； in, For the The true temperature of a particle, For the The predicted temperature calculated by the COARE algorithm for each particle, is the fitness value of the temperature particle; No. The real specific humidity of each particle is For the The predicted specific humidity calculated by the COARE algorithm for each particle is is the fitness value of the specific humidity particle; S3: Determine the particle speed and particle position according to the particle fitness value. The position of a particle is , Indicates The particle in The position in dimension, The speed of a particle is , Indicates The particle in The speed in the dimension; The optimal position of a particle is , Indicates The particle in The optimal position searched in dimension, the global optimal position searched by the particle swarm is , Indicates that the particle swarm is The optimal position searched in dimension; After each iteration, the particle The position and velocity in the dimension 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 number of iterations for a particle is At the Position in dimension; No. The particle in The position when iterating to the next generation in the dimension; For the The number of iterations for a particle is At the Speed ​​in dimension; For the The particle in The speed of iterating to the next generation in dimension; For the The number of iterations for a particle is At the Optimal position in dimension; The particle swarm is At the The optimal position searched in dimension; S4: Repeat steps S2 and S3 until the preset number of iterations is reached and the first The speed and position of the particle after all iterations are obtained. The optimal position of a particle and the optimal position searched by the particle swarm , In It is the optimal coefficient of the dimensionless universal function of temperature or the dimensionless universal function of humidity.

7. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 1, characterized in that: In step 5, the refractive index structure constant is calculated as follows: ； ； ； in, is the temperature structure constant, is the humidity structure constant, is the temperature and humidity cross structure constant, is the height, For height The predicted temperature at is the atmospheric pressure, is the ratio of the gas constant of dry air to the gas constant of water vapor, For height Predicted specific humidity at.

8. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 7, characterized in that: Temperature structure constant , humidity structure constant and the temperature-humidity cross-structure constant The calculation method is as follows: ； ； ； in, is the height, is the characteristic scale of potential temperature, is the characteristic scale of specific humidity, is the dimensionless universal function of temperature, is the dimensionless universal function of humidity, is the stability correction factor.

9. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 7, characterized in that: The calculation method of the atmospheric corrected refractive index profile under turbulence effect is as follows: ； ； ； in, is the height, For height The predicted temperature at For height The predicted pressure at , where is the ratio of the gas constant of dry air to the gas constant of water vapor, For height The predicted specific humidity at For height The water vapor pressure at For height The atmospheric refractive index pulsation index at For height The refractive index at is the height under turbulence effect The atmospheric corrected refractive index at .

10. The method for calculating the atmospheric corrected refractive index profile under turbulence effect according to claim 9, characterized in that: Calculate the height using the turbulence spectrum function The atmospheric refractive index pulsation index at The turbulence spectrum function includes vonKarman turbulence spectrum function, Kolmogorov turbulence spectrum function, and non-Kolmogorov turbulence spectrum function.

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