A Simulation Method for Remote Sensing of Wind Field by Dual-Beam Doppler Radar

Abstract

The present invention discloses a simulation method for remote sensing of wind fields by dual-beam Doppler radar, comprising the following steps: establishing a data simulation model; using the data simulation model to generate a synthetic observation data set including the radar scattering coefficient of the forward beam, the radar scattering coefficient of the backward beam, the Doppler frequency shift of the forward beam, and the Doppler frequency shift of the backward beam for a given wind vector; calculating the theoretical value of the normalized radar backscattering coefficient and calculating the theoretical value of the Doppler frequency shift based on the DopRIM model; constructing a cost function containing four items of observation data, substituting the generated observation data set into the wind vector inversion algorithm to obtain the solutions of the wind speed and relative wind direction at a height of 10 m above the sea surface; comparing the deviation and root mean square error between the given wind vector and the inverted wind vector to evaluate the variation of the inversion accuracy with the wind speed, relative wind direction, incident angle, and antenna angle; the present invention provides a simulation model for the robustness evaluation of subsequent wind vector inversion algorithms.

Description

Technical Field

[0001] The present invention relates to the technical field of wind field inversion, and particularly relates to a simulation method for remote sensing of wind field by a dual-beam Doppler radar. Background Art

[0002] As an important physical quantity of the ocean and the atmosphere, the sea surface wind field is not only the core physical parameter that regulates the momentum transfer, heat exchange, and mass transport at the ocean-atmosphere interface during the energy and material exchange process between the ocean and the atmosphere, but also the main energy source of the upper ocean mixed layer dynamics and the key driving mechanism for the formation of regional and global-scale ocean circulations. As one of the basic climate elements, the change of the sea surface wind vector is directly related to key climate factors such as ocean circulation, heat transport, sea surface temperature, and humidity distribution. Especially in the research of global climate change and extreme weather events, the accurate measurement of the wind vector is particularly important. Traditional in-situ observations of the sea surface wind field mainly rely on equipment such as buoys, ships, and shore-based wind measurement towers to provide high-precision real-time wind speed and wind direction data, which can directly obtain local details of the sea surface wind field and synchronously record relevant ocean parameters (such as waves, air temperature, etc.). However, its spatial coverage is limited, it is difficult to achieve large-scale synchronous observations, and the equipment layout and maintenance costs are relatively high, especially in deep-sea areas where the operation is difficult. In contrast, satellite remote sensing wind field monitoring technology can achieve large-scale and continuous observations of the sea surface wind field, covering remote sea areas that are difficult to reach by traditional in-situ observations, and providing global-scale wind field data with high spatio-temporal resolution. Spaceborne microwave scatterometers, with their advantages of multiple frequencies, multiple polarizations, and multiple viewing angles, are the mainstream sensors for global wind field monitoring. Their principle is based on the Bragg scattering theory, that is, the differential response of the sea surface roughness to the backscattering coefficient under different sea surface wind vectors, and then the sea surface wind field information such as wind speed and wind direction is analyzed. The development of satellite scatterometers has always been closely related to the research of geophysical model functions, wind field inversion algorithms, and ambiguity resolution methods. Based on the empirical relationship between the backscattering coefficient and the sea surface wind vector (i.e., the geophysical model function), multiple ambiguous wind vector solutions can be generated during the inversion process.

[0003] However, most of the sea surface wind field inversion relies on the background wind field. However, under extreme weather conditions, the background wind field often fails, resulting in the inability to accurately detect the sea surface wind under extreme sea conditions. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a simulation method for remote sensing of wind field by a dual-beam Doppler radar, aiming to introduce Doppler frequency shift information on the basis of the backscattering coefficient of traditional scatterometers, overcome the deficiencies of existing methods in complex wind field inversion, construct a cost function for independent inversion of wind vectors under a dual-beam system, and then simulate the influence of radar system performance indicators on the accuracy of wind field inversion, so as to solve the problems existing in the background art.

[0005] Technical solution: A method for remote sensing simulation of wind field by dual-beam Doppler radar according to the present invention includes the following steps:

[0006] S1: According to the central limit theorem, based on the Monte Carlo simulation algorithm, for the forward and backward dual-beam radar systems, establish a radar observation data simulation model of the normalized radar backscattering coefficient and Doppler shift that follows a Gaussian distribution;

[0007] S2: Using the data simulation model, for a given wind vector, generate a synthetic observation data set including the radar backscattering coefficient of the forward beam, the radar backscattering coefficient of the backward beam, the Doppler shift of the forward beam, and the Doppler shift of the backward beam;

[0008] S3: Convert the wind speed and relative wind direction at a height of 10 m above the sea surface into the eastward and northward wind vector components in Cartesian coordinates, calculate the theoretical value of the normalized radar backscattering coefficient based on the CMOD5.N model, and calculate the theoretical value of the Doppler shift based on the DopRIM model;

[0009] S4: Construct a cost function including four items of observation data. By matching the wind vector corresponding to the minimum sum of squares in the cost function, substitute the observation data set generated in step S2 into the wind vector inversion algorithm to obtain the wind speed and relative wind direction solutions at a height of 10 m above the sea surface;

[0010] S5: Compare the deviation and root mean square error between the given wind vector and the inverted wind vector, and evaluate the variation of the inversion accuracy with the wind speed, relative wind direction, incident angle, and antenna angle.

[0011] Further, in step S1, the Monte Carlo simulation algorithm generates radar backscattering coefficient and Doppler shift observation parameters that follow a Gaussian distribution by adjusting the mean value, standard deviation, and number of samples.

[0012] Further, in step S2, the standard deviation of the normalized radar backscattering coefficient of the forward and backward beams in the synthetic observation data set is 1 dB, and the standard deviation of the Doppler shift is 8 Hz.

[0013] Further, in step S3, the conversion of the wind speed and relative wind direction is based on the conversion from polar coordinates to Cartesian coordinates, and the CMOD5.N and DopRIM models are respectively used to calculate the theoretical backscattering coefficient and Doppler shift under different wind field conditions.

[0014] Further, in step S4, the form of the cost function is:

[0015] ;

[0016] where , , , Represent the cost functions of the backscattering coefficient of the forward beam, the backscattering coefficient of the backward beam, the Doppler shift of the forward beam, and the Doppler shift term of the backward beam, respectively. , Represent the radar scattering coefficients measured by the forward and backward antennas, respectively. The subscript m represents the estimated value of the backscattering coefficient calculated by the corresponding geophysical model function under different wind vectors. , Are the Doppler shifts measured by the forward and backward radar antennas, respectively. The subscript m represents the Doppler shift estimated by the DopRIM model under different wind vectors. u and v are the eastward and northward components of the wind vector, respectively. Is the standard deviation of the radar backscattering coefficient, set to , Is the radar backscattering coefficient measured by the antenna in the corresponding direction; Is the standard deviation of the Doppler shift, set to .

[0017] Furthermore, in step S4, a two-dimensional wind vector grid covering all wind directions is established. The grid range: the horizontal and vertical coordinate ranges are both from -20 m / s to 20 m / s, and the interval is 0.2 m / s. A two-dimensional velocity wind vector grid covering all wind directions is established, and the solution corresponding to the minimum value of the cost function is obtained by traversing all wind vectors in the grid.

[0018] Furthermore, in step S4, the wind vector velocity component solutions obtained in the cost function along the eastward and northward directions are converted into the wind speed and relative wind direction at a height of 10 m above the sea surface.

[0019] Furthermore, in step S5, the calculation formulas for the deviation and root mean square error of the accuracy evaluation are:

[0020] ;

[0021] ;

[0022] Where, Is the wind vector solution obtained through the cost function inversion algorithm, y is the true value of the wind vector, and N is the number of samples of the observed data; Bias is the deviation, and RMSE is the root mean square error.

[0023] An electronic device according to the present invention includes a memory, a processor, and a computer program stored on the memory. When the processor executes the program, the steps of any one of the above methods are implemented.

[0024] A computer-readable storage medium according to the present invention stores a computer program. When the program is executed by a processor, the steps of any one of the above methods are implemented.

[0025] Advantages: Compared with the prior art, the present invention has the following remarkable advantages: By means of the Monte Carlo simulation algorithm, the present invention simulates radar observation data, and obtains the wind vector solution by using the cost function composed of the normalized radar backscattering coefficient and the Doppler frequency shift term. Compared with the traditional method, the present invention gets rid of the dependence on the background wind field and improves the ability of wind field inversion in extreme weather conditions. By making use of the sensitivity of Doppler information to the line-of-sight velocity component, the present invention innovatively observes the sea surface wind field based on a dual-beam Doppler scatterometer, providing a simulation model for the robustness evaluation of subsequent wind vector inversion algorithms. Description of the Drawings

[0026] Figure 1 is the flow chart of the present invention;

[0027] Figure 2 is the observation geometry schematic diagram of the dual-beam Doppler radar of the present invention;

[0028] Figure 3 is the wind vector solution accuracy distribution of the cost function inversion algorithm of the present invention under the condition of a 35° incident angle and for all wind directions, for low, medium, and high wind speeds: (a) Distribution of the wind speed solution deviation with respect to the wind speed and relative wind direction (b) Distribution of the root mean square error of the wind speed solution with respect to the wind speed and relative wind direction (c) Distribution of the relative wind direction solution deviation with respect to the wind speed and relative wind direction (d) Distribution of the root mean square error of the relative wind direction solution with respect to the wind speed and relative wind direction. Detailed Embodiment

[0029] The technical solution of the present invention will be further described below with reference to the drawings.

[0030] As Figure 1 shown, the embodiment of the present invention provides a method for remote sensing simulation of a dual-beam Doppler radar wind field, including the following steps:

[0031] Step 1: In the actual remote sensing observation of the ocean wind field, the measurement processes of the normalized radar backscattering coefficient and the Doppler frequency shift are affected by the coupling of multiple sources of errors, mainly including factors such as instrument system noise, atmospheric attenuation effects, and sea surface dynamic changes. These error sources together lead to the random fluctuations and uncertainties of the observation data. According to the central limit theorem, when the number of observation samples is large enough, the overall distribution characteristics of these random errors approach a Gaussian distribution. Based on the Monte Carlo simulation algorithm, for the forward and backward dual-beam radar systems, the present invention establishes a simulation model of radar observation data for the normalized radar backscattering coefficient and the Doppler frequency shift that follows a Gaussian distribution to simulate the uncertainties of actual observations. The above-mentioned Monte Carlo-based simulation algorithm can generate the target radar observation parameters that follow a Gaussian distribution by adjusting the mean value, standard deviation, and the number of samples.

[0032] The specific observation geometry of this system is as Figure 2As shown, the arrow indicates the direction of radar movement. The radar system adopts a forward and backward dual-antenna configuration, and the antennas in each viewing direction have the ability to independently observe the NRCS and Doppler frequency shift. All simulations of the present invention are based on this dual-beam Doppler radar, and the upwind / downwind direction relative to the forward radar viewing direction is defined as.

[0033] Step 2: Using the data simulation model described in Step 1, for a given wind vector, generate a synthetic observation dataset that follows a Gaussian distribution and contains four independent channels (forward beam radar scattering coefficient, backward beam radar scattering coefficient, forward beam Doppler frequency shift, backward beam Doppler frequency shift). The mean values of the normalized radar backscattering coefficient and Doppler frequency shift are theoretical values calculated through the geophysical model function based on the given wind vector, and their standard deviations are 1 dB and 8 Hz respectively. By controlling the mean and variance parameters of the Gaussian distribution, the observation scenarios under different signal-to-noise ratios can be accurately simulated, and this dataset provides a reliable test benchmark for the robustness evaluation of subsequent wind vector inversion algorithms.

[0034] Step 3: Convert the wind speed and relative wind direction in polar coordinates into wind vector velocity components along the due east and due north directions, and calculate the theoretical values of the normalized radar backscattering coefficient and Doppler frequency shift under different wind field conditions based on CMOD5.N and DopRIM respectively.

[0035] Among them, CMOD5.N shows good engineering practicability in calculating the sea surface wind vector in the C-band and VV polarization, while DopRIM incorporates the Bragg scattering, specular scattering, and wave breaking effects into a unified framework to achieve more precise modeling. Among them, CMOD5.N and DopRIM have a strictly non-linear transformation law with respect to wind speed and wind direction, resulting in the inability to directly obtain the analytical solution of the wind vector from the NRCS and Doppler frequency shift. Therefore, the wind vector solution is obtained by designing a cost function.

[0036] Step 4: Considering the observation capabilities and observation geometry characteristics of the dual-beam Doppler radar, the present invention constructs a cost function containing four radar observation data based on the normalized radar backscattering coefficient and Doppler frequency shift of the forward and backward radars. Its form is:

[0037] ;

[0038] Among them, , , , respectively represent the cost functions of the backscattering coefficient of the forward beam, the backscattering coefficient of the backward beam, the Doppler frequency shift of the forward beam, and the Doppler frequency shift term of the backward beam. , represent the radar scattering coefficients measured by the forward and backward antennas respectively. The subscript m represents the estimated value of the backscattering coefficient calculated by the corresponding geophysical model function under different wind vectors. and are the Doppler frequency shifts measured by the forward and backward radar antennas respectively. The subscript m represents the Doppler frequency shift estimated by the DopRIM model under different wind vectors. u and v are the eastward and northward components of the wind vector respectively. is the standard deviation of the radar backscattering coefficient, denoted as , is the radar backscattering coefficient measured by the antenna in the corresponding direction. is the standard deviation of the Doppler frequency shift, denoted as .

[0039] In step 4, a two-dimensional velocity-wind vector grid covering the full wind direction is established with the horizontal and vertical coordinate ranges both from -20 m / s to 20 m / s and an interval of 0.2 m / s. The wind vector and wind volume of each grid are brought into the cost function. The wind vector corresponding to the minimum value in the cost function grid is the wind vector solution.

[0040] Bring the observation data set generated in step 2 into the cost function wind vector inversion algorithm described above to obtain the wind vector inversion solution corresponding to the observation data. The wind vector velocity components along the eastward and northward directions obtained from the cost function are converted into the wind speed and relative wind direction at 10 m above the sea surface.

[0041] Step 5: Accuracy evaluation. Using the given wind vector in step 2 and the wind vector solution in step 4, compare the deviations and root mean square errors of the wind speed solutions and relative wind directions of each given wind vector and the inverted wind vector to obtain the variation of the sea surface wind vector inversion accuracy with wind speed, relative wind direction, incident angle, and antenna angle.

[0042] To verify the wind vector inversion accuracy, the present invention establishes an accuracy evaluation system with the deviation and root mean square error as the core parameters. The deviation is used to reflect the offset degree of the wind vector solution from the true wind vector, and the root mean square error is used to reflect the dispersion degree of the solution. The specific form is:

[0043] ;

[0044] ;

[0045] where is the wind vector solution obtained through the cost function inversion algorithm, y is the true value of the wind vector, N is the number of samples of the observation data; Bias is the deviation, and RMSE is the root mean square error.

[0046] Based on the above accuracy evaluation system, by comparing the given wind vector described in step 2 with the wind vector inversion solution described in step 4, the accuracy distribution under different ocean dynamic parameters and radar system parameters is obtained. As Figure 3 shown, the overall wind speed deviation is controlled within ±2 m / s, showing the best unbiased characteristics in the medium wind speed range (8 - 12 m / s), and the absolute value of the deviation is generally less than 0.5 m / s. The wind speed inversion accuracy first increases and then decreases with the increase of wind speed, reaching the best (about 1.0 m / s) in the range of 8 - 12 m / s. The average wind direction deviation is mostly distributed within ±10°, but there is a phenomenon of local deviation increase at specific wind direction angles (especially the crosswind direction). The wind direction inversion accuracy is significantly correlated with the wind speed: when the wind speed > 7 m / s, the root mean square error is basically stable between 15 - 25°; while under low wind speed conditions, the root mean square error increases sharply to more than 40°. The accuracy of the wind field inversion is good, verifying the reliability of the present invention under conventional wind conditions.

Claims

1. A simulation method for remote sensing of wind field by dual-beam Doppler radar, characterized in that, It includes the following steps: S1: According to the central limit theorem, based on the Monte Carlo simulation algorithm, for the forward and backward dual-beam radar systems, establish a radar observation data simulation model of the normalized radar backscattering coefficient and Doppler frequency shift that follows a Gaussian distribution; S2: Using the data simulation model, for a given wind vector, generate a synthetic observation data set including the forward beam radar scattering coefficient, the backward beam radar scattering coefficient, the forward beam Doppler frequency shift, and the backward beam Doppler frequency shift; S3: Convert the wind speed and relative wind direction at a height of 10 m above the sea surface into the eastward and northward wind vector components in Cartesian coordinates, calculate the theoretical value of the normalized radar backscattering coefficient based on the CMOD5.N model, and calculate the theoretical value of the Doppler frequency shift based on the DopRIM model; S4: Construct a cost function including four items of observation data. By matching the wind vector corresponding to the minimum value of the sum of squares in the cost function, substitute the observation data set generated in step S2 into the wind vector inversion algorithm to obtain the solutions of the wind speed and relative wind direction at a height of 10 m above the sea surface; S5: Compare the deviation and root mean square error between the given wind vector and the inverted wind vector, and evaluate the variation of the inversion accuracy with the wind speed, relative wind direction, incident angle, and antenna angle.

2. A dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that, In step S1, the Monte Carlo simulation algorithm generates the observation parameters of the radar backscattering coefficient and Doppler frequency shift that follow a Gaussian distribution by adjusting the mean value, standard deviation, and number of samples.

3. A method for remote sensing simulation of a dual-beam Doppler radar wind field according to claim 2, characterized in that, In step S2, the standard deviation of the normalized radar backscattering coefficient of the forward and backward beams in the synthetic observation data set is 1 dB, and the standard deviation of the Doppler frequency shift is 8 Hz.

4. A dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that, In step S3, the conversion of the wind speed and relative wind direction is based on the conversion from the polar coordinate system to the Cartesian coordinate system, and the CMOD5.N and DopRIM models are respectively used to calculate the theoretical backscattering coefficient and Doppler frequency shift under different wind field conditions.

5. A dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that In step S4, the form of the cost function is: ； Among them, , , , respectively represent the cost functions of the backscattering coefficient of the forward beam, the backscattering coefficient of the backward beam, the Doppler shift of the forward beam, and the Doppler shift term of the backward beam. , respectively represent the radar scattering coefficients measured by the forward and backward antennas. The subscript m represents the estimated value of the backscattering coefficient calculated by the corresponding geophysical mode function under different wind vectors. , are the Doppler shifts measured by the forward and backward radar antennas respectively. The subscript m represents the Doppler shift under different wind vectors estimated using the DopRIM model. u and v are the east and north components of the wind vector respectively. is the standard deviation of the radar backscattering coefficient, set as , is the radar backscattering coefficient measured by the antenna in the corresponding direction; is the standard deviation of the Doppler shift, set as .

6. A dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that, In step S4, establish a two-dimensional wind vector grid covering the full wind direction. The grid range: the horizontal and vertical coordinate ranges are both from -20 m / s to 20 m / s, and the interval is 0.2 m / s. Establish a two-dimensional velocity wind volume grid covering the full wind direction, and solve for the solution corresponding to the minimum value of the cost function by traversing all wind vectors within the grid.

7. A dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that, In step S4, convert the solutions of the wind vector velocity components along the eastward and northward directions obtained in the cost function into the wind speed and relative wind direction at a height of 10 m above the sea surface.

8. A dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that In step S5, the formulas for calculating the deviation and root mean square error of the accuracy evaluation are: ； ； Among them, is the wind vector solution obtained by the cost function inversion algorithm, y is the true value of the wind vector, and N is the number of samples of the observed data; Bias is the bias, and RMSE is the root mean square error.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the steps of the method described in any one of claims 1-8.

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