Dual-beam Doppler radar wind field remote sensing simulation method

Abstract

The invention discloses a dual-beam Doppler radar wind field remote sensing simulation method. The method comprises the following steps: establishing a data simulation model; generating a synthetic observation data set containing a forward beam radar scattering coefficient, a backward beam radar scattering coefficient, a forward beam Doppler frequency shift and a backward beam Doppler frequency shift for a given wind vector by using the data simulation model; calculating a theoretical value of a normalized radar backscattering coefficient, and calculating a theoretical value of Doppler frequency shift based on a DopRIM model; constructing a cost function containing four observation data, substituting a generated observation data set into a wind vector inversion algorithm, and obtaining a wind speed and a relative wind direction solution at the height of 10m of the sea surface; comparing the deviation and the root mean square error of the given wind vector and the inversion wind vector, and evaluating the change condition of the inversion precision along with the wind speed, the relative wind direction, the incident angle and the antenna included angle; according to the method, a simulation model is provided for robustness evaluation of a subsequent wind vector inversion algorithm.

Description

Technical Field

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

[0002] As an important physical quantity of the ocean and atmosphere, the sea surface wind field is not only the core physical parameter for regulating momentum transfer, heat exchange and mass transport at the sea-air interface in the process of energy and material exchange between the ocean and atmosphere, but also the main energy source for the upper ocean mixed layer and the key driving mechanism for the formation of regional and global scale ocean circulation. As one of the basic elements of climate, the change of 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 study of global climate change and extreme weather events, the accurate measurement of wind vector is particularly important. Traditional sea surface wind field field observation mainly relies on buoys, ships and shore-based wind towers to provide high-precision real-time wind speed and direction data, which can directly obtain local details of the sea surface wind field and simultaneously record relevant ocean parameters (such as waves, temperature, etc.). However, its spatial coverage is limited, making it difficult to achieve large-scale synchronous observation, and the equipment deployment and maintenance costs are high, especially in deep sea areas. In contrast, satellite remote sensing wind field monitoring technology can achieve large-scale, continuous observation of sea surface wind fields, covering remote sea areas that are difficult to reach with traditional field observations, and providing global-scale wind field data with high temporal and spatial resolution. With its advantages of multi-frequency, multi-polarization, and multi-perspective, satellite-borne microwave scatterometers are the mainstream sensors for global wind field monitoring. Their principle is based on the Bragg scattering theory, that is, the differential response of 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 on geophysical model functions, wind field inversion algorithms, and fuzzy solution removal methods. Based on the empirical relationship between the backscattering coefficient and the sea surface wind vector (i.e., the geophysical model function), the inversion process can generate multiple fuzzy wind vector solutions.

[0003] However, the inversion of sea surface wind field mostly relies on the background wind field, but under extremely 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] Purpose of the invention: The purpose of the present invention is to provide a dual-beam Doppler radar wind field remote sensing simulation method, which intends to introduce Doppler frequency shift information based on the backscatter coefficient of the traditional scatterometer, overcome the shortcomings of the existing methods in complex wind field inversion, and solve the problems existing in the background technology by constructing an independent inversion cost function of the wind vector under the dual-beam system, thereby simulating the influence of radar system performance indicators on the wind field inversion accuracy.

[0005] Technical solution: A dual-beam Doppler radar wind field remote sensing simulation method according to the present invention comprises the following steps: S1: According to the central limit theorem and based on the Monte Carlo simulation algorithm, a radar observation data simulation model of normalized radar backscatter coefficient and Doppler shift obeying Gaussian distribution is established for the forward and backward dual-beam radar systems; S2: using the data simulation model, for a given wind vector, generating a synthetic observation data set including a forward beam radar scattering coefficient, a backward beam radar scattering coefficient, a forward beam Doppler shift, and a backward beam Doppler shift; S3: Convert the wind speed and relative wind direction at a height of 10 m above the sea surface into the due east and due north wind vector components of rectangular coordinates, calculate the theoretical value of the normalized radar backscatter 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 containing four observation data, substitute the observation data set generated in step S2 into the wind vector inversion algorithm by matching the wind vector corresponding to the minimum sum of squares in the cost function, and obtain the wind speed and relative wind direction solution at a height of 10m above the sea surface; S5: Compare the deviation and RMS error between the given wind vector and the inverted wind vector to evaluate the inversion accuracy as the wind speed, relative wind direction, incident angle and antenna angle change.

[0006] Furthermore, in step S1, the Monte Carlo simulation algorithm generates radar backscatter coefficient and Doppler frequency shift observation parameters that obey Gaussian distribution by adjusting the mean value, standard deviation and number of samples.

[0007] Furthermore, in step S2, the standard deviation of the normalized radar backscatter coefficients of the forward and backward beams of the synthetic observation data set is 1 dB, and the standard deviation of the Doppler frequency shift is 8 Hz.

[0008] Further, in step S3, the conversion of wind speed and relative wind direction is based on the conversion from polar coordinate system to rectangular coordinate system, and the CMOD5.N and DopRIM models are used to calculate the theoretical backscattering coefficient and Doppler frequency shift under different wind field conditions, respectively.

[0009] Furthermore, in step S4, the cost function is in the form of: ; in, , , , They represent the cost functions of the backscatter coefficient of the forward beam, the backscatter 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, and 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 shifts under different wind vectors estimated by the DopRIM model. u and v are the east and north components of the wind vector, respectively. is the standard deviation of the radar backscatter coefficient, set , is the radar backscatter coefficient measured by the antenna in the corresponding direction; is the standard deviation of Doppler frequency shift, let .

[0010] Furthermore, in step S4, a two-dimensional wind vector grid covering all wind directions is established, and the grid range is: the horizontal and vertical coordinate ranges are both -20m / s to 20m / s, and the interval is 0.2m / s. A two-dimensional velocity and wind volume grid covering all wind directions is established, and the solution corresponding to the minimum value of the cost function is solved by traversing all wind vectors in the grid.

[0011] Further, in step S4, the wind vector velocity components along the due east and due north directions obtained in the cost function are converted into wind speed and relative wind direction at a height of 10 m above the sea surface.

[0012] Furthermore, in step S5, the calculation formulas for the deviation and root mean square error of the accuracy assessment are: ; ; in, is the wind vector solution obtained by the cost function inversion algorithm, y is the true value of the wind vector, N is the number of samples of the observed data; Bias is the bias, and RMSE is the root mean square error.

[0013] An electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory, and the processor implements the steps of any one of the methods when executing the program.

[0014] A computer-readable storage medium according to the present invention stores a computer program, and when the program is executed by a processor, the steps of any one of the methods are implemented.

[0015] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: The present invention simulates radar observation data through a Monte Carlo simulation algorithm, and obtains a wind vector solution using a cost function consisting of a normalized radar backscatter coefficient and a Doppler frequency shift term. Compared with traditional methods, the present invention gets rid of the dependence on the background wind field and improves the ability to invert the wind field under extreme weather conditions. The present invention utilizes the sensitivity of Doppler information to the line-of-sight velocity component, and innovatively observes the sea surface wind field based on a dual-beam Doppler scatterometer, providing a simulation model for the robustness evaluation of the subsequent wind vector inversion algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the present invention; Figure 2 Schematic diagram of observation geometry of the dual-beam Doppler radar of the present invention; Figure 3 The present invention provides the following distribution of wind vector solution accuracy at low, medium and high wind speeds based on the cost function inversion algorithm under the condition of an incident angle of 35° in all wind directions: (a) distribution of wind speed solution deviation with respect to wind speed and relative wind direction (b) distribution of wind speed solution root mean square error with respect to wind speed and relative wind direction (c) distribution of relative wind direction solution deviation with respect to wind speed and relative wind direction (d) distribution of relative wind direction solution root mean square error with respect to wind speed and relative wind direction. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0018] like Figure 1 As shown, an embodiment of the present invention provides a dual-beam Doppler radar wind field remote sensing simulation method, comprising the following steps: Step 1: In the actual remote sensing observation of the ocean wind field, the measurement process of the normalized radar backscatter coefficient and the Doppler shift is affected by the coupling of multiple source errors, mainly including factors such as instrument system noise, atmospheric attenuation effect and dynamic changes in the sea surface. These error sources together lead to random fluctuations and uncertainties in the observed data. According to the central limit theorem, when the observation sample size is large enough, the overall distribution characteristics of these random errors approach the Gaussian distribution. The present invention is based on the Monte Carlo simulation algorithm, and for the forward and backward dual-beam radar systems, a radar observation data simulation model of the normalized radar backscatter coefficient and the Doppler shift that obey the Gaussian distribution is established to simulate the uncertainty of the actual observation. The above-mentioned Monte Carlo-based simulation algorithm can generate target radar observation parameters that obey the Gaussian distribution by adjusting the mean value, standard deviation and number of samples.

[0019] The specific observation geometry of the system is as follows Figure 2As shown, the arrow points to the direction of radar movement. The radar system adopts a forward and backward dual antenna configuration, and each antenna in the viewing direction has independent NRCS and Doppler shift observation capabilities. The simulation of the present invention is based on this dual-beam Doppler radar, and the upwind direction / upwind direction relative to the forward radar viewing direction is defined as.

[0020] Step 2: Using the data simulation model described in step 1, for a given wind vector, generate a synthetic observation data set that follows a Gaussian distribution and contains four independent channels (forward beam radar scattering coefficient, back beam radar scattering coefficient, forward beam Doppler shift, and back beam Doppler shift), where the average values ​​of the normalized radar backscattering coefficient and Doppler shift are theoretical values ​​calculated based on the given wind vector through the geophysical model function, 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 ratio conditions can be accurately simulated. This data set provides a reliable test benchmark for the robustness evaluation of the subsequent wind vector inversion algorithm.

[0021] Step 3: Convert the wind speed and relative wind direction in polar coordinates into wind vector velocity components along the east and north directions, and calculate the theoretical values ​​of the normalized radar backscatter coefficient and Doppler frequency shift under different wind field conditions based on CMOD5.N and DopRIM respectively.

[0022] Among them, CMOD5.N shows good engineering practicality in the calculation of sea surface wind vector in C band and VV polarization, while DopRIM incorporates Bragg scattering, mirror scattering and wave breaking effects into a unified framework to achieve more precise modeling. Among them, CMOD5.N and DopRIM have strictly nonlinear transformation laws for wind speed and wind direction, which makes it impossible to directly obtain the analytical solution of wind vector through NRCS and Doppler frequency shift, so the wind vector solution is obtained by designing a cost function.

[0023] Step 4: In view of the observation capability 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 backscatter coefficient and Doppler shift of the forward and backward radars, and its form is: ; in, , , , They represent the cost functions of the backscatter coefficient of the forward beam, the backscatter 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, and 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 shifts under different wind vectors estimated by the DopRIM model. u and v are the east and north components of the wind vector, respectively. is the standard deviation of the radar backscatter coefficient, set , is the radar backscatter coefficient measured by the antenna in the corresponding direction; is the standard deviation of Doppler frequency shift, let .

[0024] In step 4, a two-dimensional velocity and wind volume grid covering all wind directions is established with a horizontal and vertical coordinate range of -20m / s to 20m / s and an interval of 0.2m / s. The wind vector wind volume of each grid is substituted into the cost function, and the wind vector corresponding to the minimum value in the cost function grid is the wind vector solution.

[0025] The observation data set generated in step 2 is brought into the cost function wind vector inversion algorithm to obtain the wind vector inversion solution corresponding to the observation data. The wind vector velocity component solution along the due east and due north directions obtained from the cost function is converted into the wind speed and relative wind direction at a height of 10m above the sea surface.

[0026] Step 5: Accuracy assessment: using the given wind vector described in step 2 and the wind vector solution described in step 4, compare the deviation and root mean square error of each given wind vector and the inverted wind vector wind speed solution and relative wind direction to obtain the change of sea surface wind vector inversion accuracy with wind speed, relative wind direction, incident angle and antenna angle.

[0027] In order to verify the accuracy of wind vector inversion, the present invention establishes an accuracy evaluation system with deviation and root mean square error as core parameters. The deviation is used to reflect the degree of deviation between the wind vector solution and the true wind vector, and the root mean square error is used to reflect the discrete degree of the solution. The specific form is: ; ; in, is the wind vector solution obtained by the cost function inversion algorithm, y is the true value of the wind vector, N is the number of samples of the observed data; Bias is the bias, and RMSE is the root mean square error.

[0028] Based on the above accuracy evaluation system, the given wind vector described in step 2 and the wind vector inversion solution described in step 4 are compared to obtain the distribution of accuracy under different ocean dynamic parameters and radar system parameters. Figure 3As shown, the overall wind speed deviation is controlled within the range of ±2 m / s, and the optimal unbiased characteristics are shown 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 shows a trend of first increasing and then decreasing with the increase of wind speed, reaching the best in the range of 8-12 m / s (about 1.0 m / s). The average wind direction deviation is mostly distributed in the range of ±10°, but there is a phenomenon of local deviation increase at specific wind direction angles (especially crosswind direction). The wind direction inversion accuracy is significantly related to wind speed: when the wind speed is >7 m / s, the root mean square error is basically stable between 15-25°; under low wind speed conditions, the root mean square error increases sharply to more than 40°. The wind field inversion has good accuracy, which verifies the reliability of the present invention under conventional wind conditions.

Claims

1. A dual-beam Doppler radar wind field remote sensing simulation method, characterized in that: The following steps are involved: S1: According to the central limit theorem and based on the Monte Carlo simulation algorithm, a radar observation data simulation model of normalized radar backscatter coefficient and Doppler shift obeying Gaussian distribution is established for the forward and backward dual-beam radar systems; S2: using the data simulation model, for a given wind vector, generating a synthetic observation data set including a forward beam radar scattering coefficient, a backward beam radar scattering coefficient, a forward beam Doppler shift, and a backward beam Doppler shift; S3: Convert the wind speed and relative wind direction at a height of 10 m above the sea surface into the due east and due north wind vector components of rectangular coordinates, calculate the theoretical value of the normalized radar backscatter 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 containing four observation data, substitute the observation data set generated in step S2 into the wind vector inversion algorithm by matching the wind vector corresponding to the minimum sum of squares in the cost function, and obtain the wind speed and relative wind direction solution at a height of 10m above the sea surface; S5: Compare the deviation and RMS error between the given wind vector and the inverted wind vector to evaluate the variation of inversion accuracy with 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 radar backscatter coefficient and Doppler frequency shift observation parameters that obey Gaussian distribution by adjusting the mean value, standard deviation and number of samples.

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

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

5. The dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that: In step S4, the cost function is in the form of: ； in, , , , They represent the cost functions of the backscatter coefficient of the forward beam, the backscatter 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 shifts under different wind vectors estimated by the DopRIM model. u and v are the east and north components of the wind vector, respectively. is the standard deviation of the radar backscatter coefficient, set , is the radar backscatter coefficient measured by the antenna in the corresponding direction; is the standard deviation of Doppler frequency shift, let .

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

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

8. The dual-beam Doppler radar wind field remote sensing simulation method according to claim 1, characterized in that: In step S5, the calculation formulas for the deviation and root mean square error of the accuracy evaluation are: ； ； in, is the wind vector solution obtained by the cost function inversion algorithm, y is the true value of the wind vector, 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 in the memory, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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