A method for calculating and monitoring analysis of internal wave data of a specific sea area based on remote sensing data

Abstract

The application discloses a kind of based on remote sensing data's to the specific sea area's internal wave data carries out calculation monitoring analysis method, jointly uses multi-sea area monitoring, wide-range tracking, time accumulation, path data transmission method, obtains the specific correlation parameter data of wide-sea area face ocean internal wave data, by establishing the internal wave amplitude profile model of multiple parameters in monitoring sea area, and combining vibration three-dimensional wave equation model, interpolation algorithm, boundary value function, objective function and the like algorithm, the internal wave distribution of monitoring sea area is comprehensively obtained.The method disclosed in the application is stronger in applicability relative to model estimation method in limited sea area;Relative to empirical function model algorithm, in non-uniform layering environment, it is stronger in applicability, cannot be monitored internal wave related parameters due to limited sea area, also cannot cause to increase internal wave parameter calculation error due to multilayer parameter measurement error.

Description

Technical Field

[0001] This invention belongs to the field of ocean internal wave parameter calculation, and in particular to a method for forecasting ocean internal wave state based on remote sensing. Background Technology

[0002] In the ocean, internal waves are a ubiquitous wave phenomenon within the ocean. The undulations of isothermal and isodense surfaces caused by internal waves affect the propagation speed and direction of acoustic signals, thus altering the sound channel and reducing sonar effectiveness, increasing the difficulty of underwater communication and target detection. Internal waves cause rapid, large-amplitude up-and-down undulations in isodense surfaces. If underwater objects such as submarines or torpedoes are located on such isodense surfaces, they will move up and down or suddenly rise or sink with the undulations, causing torpedoes to miss their targets and making submarines difficult to maneuver. Calculating the phase velocity of internal waves using Jackson's (2009) empirical mode function relies on field measurement data. Since the launch of the ESA's ERS-1 satellite in 1991, numerous SAR Earth observation satellites have been launched internationally, including my country's GF3 satellite. This has provided technical feasibility for remote sensing-based internal wave research methods. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method for calculating, monitoring and analyzing internal wave data of a specific sea area based on remote sensing data.

[0004] The present invention adopts the following technical solution:

[0005] An improved method for calculating, monitoring, and analyzing internal wave data of a specific sea area based on remote sensing data includes the following steps:

[0006] Step 1: Obtain accurate ocean parameter data from the marine meteorological observation station equipment and read the tidal data packets parsed by the observation station equipment;

[0007] Step 2: Perform integrity checks and sea area boundary value judgments on the received tidal data. Attempt to splice incomplete message data packets. Discard data that cannot be spliced ​​into a complete message. Add complete messages and information data that has been spliced ​​into a complete message to the waiting queue. Perform linear interpolation calculations on boundary value missing data and judge it against the boundary value objective function. Discard data that cannot be interpolated. Add data that meets the conditions to the waiting queue. Step 3: Extract information from the verified tidal data, save the data content, and use backtracking positioning combined with theoretical analysis of the internal wave generation area and topographic data to determine the starting location of internal wave propagation.

[0008] Step 4: Calculate the parameters of the initial position of the internal wave, using the initial position and initial time as input to generate tidal-based initial fringe data;

[0009] Step 5: Determine the range of the propagation surface of the target sea area, determine the boundary function, and perform time accumulation calculation on the internal wave parameters of the propagation process to obtain the internal wave data at the new moment.

[0010] Step 6: Perform fast travel algorithm calculation on the initial internal wave data of the calibration, so that the internal wave data can be simulated to propagate on a two-dimensional horizontal plane. Treat the stripes generated by the internal wave on the sea surface as a horizontally moving interface, treat the internal wave phase velocity as the interface propagation velocity, and use the propagation calculation algorithm to calculate the propagation of ocean internal waves. Step 7: Input the path parameters to be calculated through the interface path parameters to obtain the latitude and longitude of the region and extract the real-time propagation speed of the internal waves on the path.

[0011] Step 8: Obtain wind field and temperature data for the target sea area from the medium-term weather forecast center; at the same time, extract fringe data along the internal wave propagation direction on the SAR image. Step 9: Extract tidal and warm wind data to determine their completeness and whether they belong to the target calculation time. Analyze the data types and discard Ensemble members, Ensemble mean, and Ensemble spread data, keeping only the Reanalysis data.

[0012] The Reanalysis data was analyzed to obtain specific values ​​for wind speed, wind direction, and temperature in the target area. Combined with hydrological parameters obtained from NOAA, the parameterized density profile under undisturbed conditions was calculated. Step 10: Extract the propagation velocity, wind field reanalysis data, sea surface temperature reanalysis data, and path data from the aforementioned process. Based on the path input range information, calculate the internal wave propagation data for that area. For the amplitude data at a specific location, first obtain the internal wave propagation velocity along the path obtained based on the propagation algorithm. Combine this with time-accumulated parameter monitoring and utilize the fast-travel algorithm. Target value correction at time The target value at each moment is used to correct the error in the propagation process. Then, the obtained parameter data is combined to calculate the amplitude data of the current target calculation area. Step 11, under the first-order approximation, calculate the simulated sea surface current gradient distribution and the wavy lines within the SAR image. The correlation coefficient of the distribution. This forms a correlation coefficient curve that varies with amplitude. The amplitude corresponding to the maximum correlation coefficient is the inverted amplitude.

[0013] Step 12: Perform data-to-image conversion on all the calculated parameters. The browser page parses the data grouped according to the data returned by the backend calculation, and forms a table based on the calculation request and time, and displays the data in the corresponding area of ​​the page.

[0014] 2. Furthermore, in step 12, the internal wave parameter data can be displayed as two-dimensional planar visualization data in order to show the characteristics of the internal wave data.

[0015] The beneficial effects of this invention are:

[0016] The method disclosed in this invention combines multi-sea area monitoring, wide-range tracking, time accumulation, and path data transmission to obtain specific related parameter data of ocean internal waves over a wide sea area. By establishing a multi-parameter internal wave amplitude profile model over the monitoring sea area and combining it with vibration three-dimensional wave equation model, interpolation algorithm, boundary value function, objective function, and other algorithms, the distribution of ocean internal waves in the monitoring sea area is obtained comprehensively.

[0017] Compared with model estimation methods, the method disclosed in this invention is more applicable to restricted sea areas; compared with empirical function model algorithms, it is more applicable to non-uniform stratification environments. It will not fail to monitor internal wave-related parameters due to sea area restrictions, nor will it increase the internal wave parameter calculation error due to multi-layer parameter measurement errors. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Example 1 discloses a method for calculating, monitoring, and analyzing internal wave data in a specific sea area based on remote sensing data. The calculated model and data are sent to a shipborne terminal for practical use by underwater equipment. For shore-based computing centers, this framework uses temperature, salinity, and density profile data as a foundation, tidal data as a driver, and topographic data to establish a numerical model for generating internal waves in the ocean. This model compensates for the insufficient number of internal wave generation source areas in existing internal wave satellite SAR observation data. A statistical model for internal wave generation is established using these datasets combined with observation data from the generation source areas. The internal wave amplitude inversion system is an auxiliary support system for the internal wave prediction system. Based on historical satellite SAR internal wave observation data, a large amount of internal wave amplitude data can be obtained using the internal wave amplitude inversion algorithm. A statistical model of the internal wave amplitude distribution related to tidal amplitude is constructed based on these internal wave amplitude datasets. The density field can be used to calculate the location-related linear phase velocity field of internal waves, which is then correlated with the internal wave amplitude field to construct a nonlinear phase velocity field of internal waves, thereby establishing a kinematic nonlinear propagation model of internal waves. The specific logic is as follows: Figure 1 As shown, the specific steps include the following:

[0021] Step 1: Obtain accurate ocean parameter data from the marine meteorological observation station equipment and read the tidal data packets parsed by the observation station equipment; Step 2: Perform integrity checks and sea area boundary value judgments on the received tidal data. Attempt to splice incomplete message data packets, discard data that cannot be spliced ​​into a complete message, and add complete messages and information data that has been spliced ​​into a waiting queue; perform linear interpolation calculations on boundary value missing data, and judge it against the boundary value objective function. Data that cannot be interpolated is discarded, and data that meets the conditions enters the waiting queue; Step 3: Extract information from the verified tidal data, save the data content, and use backtracking positioning combined with theoretical analysis of the internal wave generation area and topographic data to determine the starting location of internal wave propagation.

[0022] Step 4: Calculate the parameters of the initial position of the internal wave, using the initial position and initial time as input to generate tidal-based initial fringe data;

[0023] Step 5: Determine the range of the propagation surface of the target sea area, determine the boundary function, and perform time accumulation calculation on the internal wave parameters of the propagation process to obtain the internal wave data at the new moment.

[0024] Step 6: Perform fast travel algorithm calculation on the initial internal wave data of the calibration, so that the internal wave data can be simulated to propagate on a two-dimensional horizontal plane. Treat the stripes generated by the internal wave on the sea surface as a horizontally moving interface, treat the internal wave phase velocity as the interface propagation velocity, and use the propagation calculation algorithm to calculate the propagation of ocean internal waves. Step 9: Extract tidal and warm wind data to determine their completeness and whether they belong to the target calculation time. Analyze the data types and discard Ensemble members, Ensemble mean, and Ensemble spread data, keeping only the Reanalysis data.

[0025] The Reanalysis data was analyzed to obtain specific values ​​for wind speed, wind direction, and temperature in the target area. Combined with hydrological parameters obtained from NOAA, the parameterized density profile under undisturbed conditions was calculated. Step 10: Extract the propagation velocity, wind field reanalysis data, sea surface temperature reanalysis data, and path data from the aforementioned process. Based on the path input range information, calculate the internal wave propagation data for that area. For the amplitude data at a specific location, first obtain the internal wave propagation velocity along the path obtained based on the propagation algorithm. Combine this with time-accumulated parameter monitoring and utilize the fast-travel algorithm. Target value correction at time The target value at each moment is used to correct the error in the propagation process. Then, the obtained parameter data is combined to calculate the amplitude data of the current target calculation area. Step 11, under the first-order approximation, calculate the simulated sea surface current gradient distribution and the wavy lines within the SAR image. The correlation coefficient of the distribution. This forms a correlation coefficient curve that varies with amplitude. The amplitude corresponding to the maximum correlation coefficient is the inverted amplitude.

[0026] Step 12: Perform data-to-image conversion on all the calculated parameters. The browser page parses the data grouped according to the data returned by the backend calculation, and forms a table based on the calculation request and time, and displays the data in the corresponding area of ​​the page.

[0027] The NB-01 Ocean Internal Wave Forecasting Auxiliary System, targeting a specific sea area, employs an internal wave parameter calculation and inversion method based on remote sensing data to obtain the internal wave propagation distribution within that area. Then, different internal wave fringes are sampled and input into the ocean internal wave amplitude inversion system to obtain characteristic parameters such as propagation velocity and amplitude of different internal waves. These characteristic parameters are then assimilated and interpolated to obtain the ocean internal wave distribution for the calculated area. This system does not require a network connection; the shore-based internal wave data center can update the model weekly. The new model will include contributions from newly acquired internal wave observation data within the most recent week. The shipborne internal wave forecasting system updates data from the data center before departure for missions, facilitating practical use.

Claims

1. A method for calculating and monitoring analysis of internal wave data in a specific sea area based on remote sensing data, characterized in that, It comprises the following steps: Step 1, obtaining marine accurate parameter data from marine weather observation station equipment, reading tide data packet parsed by observation station equipment; Step 2, integrity detection and sea area boundary value judgment are performed on the received tide data, incomplete message data packets are spliced, data that cannot be spliced into complete messages are discarded, complete messages and information data that become complete through splicing are added to the processing queue, linear interpolation calculation is performed on boundary value missing data, and the boundary value target function is judged, data that cannot be interpolated is discarded, and data that meets the conditions is entered into the processing queue; wherein is the horizontal coordinate value, is the vertical coordinate value, linear interpolation is performed for missing data; Step 3, extracting information from the verified tide data, saving data content, using backtracking positioning combined with theoretical analysis of internal wave generation area and topographic data to determine the starting position of internal wave propagation; Step 4, calculating the parameters of the internal wave starting position, taking the initial position and initial time as the calculation input, generating starting stripe data based on tides; Step 5, judging the range of the propagation surface of the target sea area, determining the boundary function, and performing time accumulation calculation on the internal wave parameters in the propagation process to obtain the internal wave data at the new time; Step 6, calculating the starting internal wave data through the fast marching algorithm, so that the internal wave data can be propagated on the two-dimensional horizontal plane, the stripes generated by the internal wave on the sea surface are regarded as the interface moving in the horizontal direction, and the internal wave phase velocity is regarded as the interface propagation velocity. The propagation calculation algorithm is used to calculate the propagation of the internal wave; wherein is a function of the propagation speed, is the target time, is the start computation 0 time; Step 7, input the path parameters to be calculated through the interface path parameters, obtain the latitude and longitude of the region, and extract the real-time propagation speed of the internal wave on the path; Step 8, obtaining the wind field and temperature data of the target sea area from the medium-term weather forecast center; at the same time, extracting the stripe data on the SAR image along the direction of internal wave propagation; wherein, leader wave data extracted for a soliton in the SAR image; Step 9, taking out the tide data and temperature data to judge the integrity and whether it belongs to the target calculation time, analyzing the data type, discarding Ensemble members, Ensemble mean, and Ensemble spread data, and only keeping Reanalysis data; Analyzing the Reanalysis data to obtain the specific values of wind speed, wind direction, and temperature in the target area, combining the hydrological parameters obtained by NOAA, and calculating the undisturbed parameterized density profile: wherein, is the depth of the sea pycnocline, is the thickness of the pycnocline, is the maximum value of the buoyancy frequency, is the water depth of the target sea area obtained, is the gravitational acceleration of the area, is the directly obtained density value; Step 10, extract the propagation speed, wind field reanalysis data, sea surface temperature reanalysis data, path data in the foregoing process, calculate the internal wave propagation data in the range of the region according to the path input range information, and obtain the amplitude data of the specific position, first obtain the internal wave propagation speed on the path based on the propagation algorithm, jointly use the time accumulation parameter monitoring, use the fast marching algorithm, and use target value correction at the moment target value at the moment, so as to correct the error in the propagation process, and then jointly obtain the parameter data, and calculate the amplitude data of the current target calculation region; When The stream function of the internal wave is wherein, is the assumed internal wave amplitude value, is the internal wave propagation speed; wherein, radar normalized backscatter cross section, is the gradient of the sea surface current field; Step 11: Under the first-order approximation, calculate the simulated sea surface current gradient distribution and the wavy lines in the SAR image. The correlation coefficient of the distribution forms a correlation coefficient curve that varies with the amplitude. The amplitude corresponding to the maximum correlation coefficient is the inverted amplitude. Step 12, converting all the calculated parameters into data-image, the browser page returns data according to the background calculation, the data is grouped and parsed, and the data is grouped and displayed in the corresponding area of the page according to the calculation request and time.

2. The method for calculating and monitoring analysis of internal wave data in a specific sea area based on remote sensing data according to claim 1, characterized in that: In step 12, the internal wave parameter data can be displayed as two-dimensional plane visualized data to show the characteristics of the internal wave data.

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