Method and system for identifying submesoscale vortices using SWOT satellite data

Abstract

This invention discloses a method and system for identifying submesoscale eddies using SWOT satellite data. The method comprises obtaining a SWOT satellite dataset; obtaining gridded SWOT satellite data and satellite single-orbit data using interpolation filtering; interpolating a land mask dataset to the spatial resolution of the SWOT satellite gridded data, and calculating the data difference between the SWOT satellite gridded data and the interpolated land mask dataset to serve as gridded data with overland water removed; and performing eddy identification using an eddy identification function to obtain a submesoscale eddy dataset and extract eddy characteristics of the submesoscale eddies. The invention aims to fully utilize the high-resolution SWOT satellite data to accurately and completely identify submesoscale eddies, providing reliable data support for ocean dynamics research.

Description

Technical Field

[0001] The present invention belongs to the field of physical oceanography, and in particular relates to a method and system for identifying sub-mesoscale vortices using SWOT satellite data. Background Art

[0002] Ocean activities are complex, and various phenomena are subtle and detailed, from large scales to small scales. Large-scale ocean currents drive the distribution of global ocean matter and heat, while mesoscale (vortex radius of hundreds of kilometers) and submesoscale (vortex radius of tens of kilometers) eddies promote the exchange of matter and energy on a local scale. These eddies play a key role in the ocean, especially in the exchange of matter and energy between the surface and deep layers of the ocean. Submesoscale eddies in local areas promote vertical mixing, bringing nutrients from the deep to the surface, while transferring heat and momentum to deeper water layers, thus having a profound impact on marine ecosystems and climate regulation. Submesoscale eddies can also have a significant impact on marine equipment and ships, such as reduced navigation efficiency and increased equipment wear. Research on this phenomenon is particularly important for ensuring the safety of marine operations, optimizing navigation routes, and protecting marine equipment. Although the observation technology for mesoscale vortices is relatively mature, the observation of sub-mesoscale vortices still faces challenges. For example, the spatial resolution of traditional satellites is limited, making it difficult to capture their detailed changes; in situ observation methods require a lot of manpower and resources, and may not be able to fully detect the existence of vortices; there is also the widely used numerical simulation method, which uses existing data to simulate missing data. Although this method improves the resolution, it reduces the reliability of the data.

[0003] Since the launch of the first ocean satellite in 1993, after three decades of continuous development, the observation technology of mesoscale eddies has gradually matured. However, due to the resolution of satellite instruments, the observation of sub-mesoscale eddies is still greatly limited. The resolution of traditional observation methods is insufficient, and it is difficult to capture the subtle changes in eddies at smaller scales. As a result, the study of sub-mesoscale eddies mainly relies on numerical simulation and indirect inference. For example, the resolution of traditional satellites is 0.25°, so the eddies identified using them are mostly mesoscale eddies with larger radii, mainly concentrated between 50km and 100km. In December 2022, the launch of the SWOT (Surface Water and Ocean Topography) satellite marked a change in this situation. The SWOT satellite has an epoch-making high spatial resolution and can observe changes in sea surface height at scales of 1 km and smaller. Compared with the resolution of previous satellites, it has increased by an order of magnitude. The high-precision observations of the SWOT satellite can directly observe the existence and evolution of sub-mesoscale eddies for the first time. However, even so, the existing observation methods of sub-mesoscale vortices still find it difficult to fully utilize the high-resolution data of the SWOT satellite and achieve accurate and reliable sub-mesoscale vortex identification. Summary of the Invention

[0004] The technical problem to be solved by the present invention is as follows: In response to the above-mentioned problems in the prior art, a method and system for identifying sub-mesoscale vortices using SWOT satellite data are provided. The present invention aims to make full use of the high-resolution data of SWOT satellites to accurately and completely identify sub-mesoscale vortices, thereby providing reliable data support for ocean dynamics research.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for identifying submesoscale vortices using SWOT satellite data comprises the following steps:

[0007] S1, obtain the SWOT satellite dataset, including the orbital dataset of the SWOT satellite during the test operation period and the orbital dataset of the SWOT satellite during the scientific cycle;

[0008] S2, using interpolation filtering to obtain gridded data of SWOT satellites, and obtaining satellite single orbit data for SWOT satellite datasets;

[0009] S3, interpolating the land mask dataset to the spatial resolution of the SWOT satellite gridded data, and calculating the data difference between the SWOT satellite gridded data and the interpolated land mask dataset as the gridded data with the land water removed;

[0010] S4, using the eddy identification function to identify sub-mesoscale eddies on the satellite single-orbit data and the gridded data without land water to obtain the sub-mesoscale eddy data set;

[0011] S5, statistically obtaining the vortex characteristics of sub-mesoscale vortices in the sub-mesoscale vortex dataset for the target area.

[0012] Optionally, in step S2, obtaining gridded data of the SWOT satellite by using interpolation filtering on the SWOT satellite data set refers to obtaining gridded data of the SWOT satellite by using interpolation filtering on the orbital data set of the SWOT satellite during the scientific cycle; obtaining satellite single orbit data from the SWOT satellite data set refers to obtaining satellite single orbit data from the orbital data set of the SWOT satellite during the trial operation period.

[0013] Optionally, in step S2, obtaining gridded data of the SWOT satellite using interpolation filtering on the SWOT satellite dataset includes:

[0014] S2.1, averaging the repeated orbital segments in the orbital data in the SWOT satellite dataset and interpolating the orbital regions without data in the orbital data in the SWOT satellite dataset to complete the orbital data preprocessing;

[0015] S2.2, interpolate the preprocessed orbital data to the specified spatial resolution;

[0016] S2.3, converting the orbital data after interpolation to the specified spatial resolution to the specified time resolution;

[0017] S2.4, filtering the orbit data converted to the specified time resolution to obtain the gridded data of the SWOT satellite.

[0018] Optionally, interpolating the preprocessed orbital data to a specified spatial resolution in step S2.2 refers to interpolating the preprocessed orbital data from an original spatial resolution of 0.018° to a spatial resolution of 0.02° using a natural neighbor interpolation method; converting the orbital data interpolated to a specified spatial resolution to a specified temporal resolution in step S2.3 refers to increasing the temporal resolution of the orbital data interpolated to the specified spatial resolution by a factor of 2, so that a revisit period of 21 days is split into two revisit periods of 10.5 days to achieve orbit cross selection; filtering in step S2.4 refers to median filtering with a window size of 15 km on each side.

[0019] Optionally, the land mask dataset in step S3 includes the land mask of the SWOT satellite and land masks from some or all of the GEOSAT, TOPEX, Jason-1, Jason-2 and Jason-3 satellites.

[0020] Optionally, step S4 includes:

[0021] S4.1, for satellite single-orbit data, first remove the lowest point altimeter data of the SWOT satellite and interpolate the blank data in the middle, then use the vortex identification function to perform vortex identification to obtain data of sub-mesoscale vortices; for gridded data without land waters, use the vortex identification function to perform vortex identification to obtain data of sub-mesoscale vortices; the sub-mesoscale vortex data includes data of subscale vortices and mesoscale vortices, wherein a subscale vortex refers to an vortex with a vortex radius of tens of kilometers, and a mesoscale vortex refers to an vortex with a vortex radius of hundreds of kilometers; the vortex identification function uses a closed contour method to perform vortex identification, and the conditions set when using the closed contour method for vortex identification include a minimum amplitude limit of 2 cm for the vortex, a vortex radius of 50 km, a latitude and longitude span of not less than 0.05°, a maximum distance between the vortex boundary point and other points not exceeding 100 km, and the amplitude limit of the vortex refers to the maximum potential difference between the vortex center position and the contour line;

[0022] S4.2, the data of all sub-mesoscale eddies are formed into a sub-mesoscale eddy dataset.

[0023] Optionally, the vortex characteristics in step S5 include part or all of the vortex radius, amplitude and vortex center position.

[0024] In addition, the present invention also provides a system for identifying sub-mesoscale vortices using SWOT satellite data, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the method for identifying sub-mesoscale vortices using SWOT satellite data.

[0025] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction. The computer program or instruction is programmed or configured to execute the method of identifying sub-mesoscale vortices using SWOT satellite data through a processor.

[0026] In addition, the present invention also provides a computer program product, comprising a computer program or instructions, which are programmed or configured to execute the method of identifying sub-mesoscale vortices using SWOT satellite data through a processor.

[0027] Compared with the prior art, the present invention mainly has the following advantages:

[0028] 1. The present invention includes obtaining SWOT satellite data sets, including orbital data sets of the SWOT satellite during the trial operation period and orbital data sets of the SWOT satellite during the scientific cycle, and identifying sub-mesoscale vortices through two data sources, thereby improving the completeness of identifying sub-mesoscale vortices.

[0029] 2. The present invention includes the use of interpolation filtering to obtain gridded data of the SWOT satellite. By using interpolation, filtering and other processing, it can fully utilize the extremely high spatial resolution (reaching 0.02°) of the original observation data of the SWOT satellite, improve the accuracy of identifying sub-mesoscale vortices, and maximize the reliability of identifying sub-mesoscale vortices.

[0030] 3. The present invention includes interpolating the land mask data set to the spatial resolution of the SWOT satellite's gridded data, and calculating the data difference between the SWOT satellite's gridded data and the interpolated land mask data set as the gridded data with the land water removed, thereby preventing the vortex identification function from misidentifying rivers and lakes on land as vortex centers, further improving the accuracy of identifying sub-mesoscale vortices, and maximizing the reliability of identifying sub-mesoscale vortices. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0032] Figure 2 Schematic diagram of the comparison of SWOT satellite single-track data before and after processing and vortex identification results in an embodiment of the present invention, where (a) is the original satellite data diagram, (b) is the direct interpolation data diagram, (c) is the processed interpolation data diagram, and (d) is the identification result diagram of a single-track sub-mesoscale vortex.

[0033] Figure 3 Schematic diagram of the SWOT satellite orbit in the Western Pacific region in an embodiment of the present invention.

[0034] Figure 4 Schematic diagram of vortex identification results of SWOT satellite data in the western Pacific region in an embodiment of the present invention.

[0035] Figure 5 This is a schematic diagram of the difference results between SWOT data and DUACS data in the western Pacific region and the vortex identification results in an embodiment of the present invention.

[0036] Figure 6 Schematic diagram of the results of sub-mesoscale eddy identification in the absence of a land mask dataset in an embodiment of the present invention.

[0037] Figure 7 Schematic diagram of the statistical characteristics of the number of sub-mesoscale vortices in the western Pacific region in an embodiment of the present invention.

[0038] Figure 8 Schematic diagram of the statistical characteristics of the sub-mesoscale vortex radius in the western Pacific region in an embodiment of the present invention.

[0039] Figure 9 Schematic diagram of the statistical characteristics of the sub-mesoscale vortex amplitude in the western Pacific region in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0041] like Figure 1 As shown, the method for identifying submesoscale vortices using SWOT satellite data in this embodiment includes the following steps:

[0042] S1, obtain the SWOT satellite dataset, including the orbital dataset of the SWOT satellite during the test operation period and the orbital dataset of the SWOT satellite during the scientific cycle;

[0043] S2, using interpolation filtering to obtain gridded data of SWOT satellites, and obtaining satellite single orbit data for SWOT satellite datasets;

[0044] S3, interpolating the land mask dataset to the spatial resolution of the SWOT satellite gridded data, and calculating the data difference between the SWOT satellite gridded data and the interpolated land mask dataset as the gridded data with the land water removed;

[0045] S4, using the eddy identification function to identify sub-mesoscale eddies on the satellite single-orbit data and the gridded data without land water to obtain the sub-mesoscale eddy data set;

[0046] S5, statistically obtaining the vortex characteristics of sub-mesoscale vortices in the sub-mesoscale vortex dataset for the target area.

[0047] The SWOT satellite dataset obtained in step S1 of this embodiment includes the orbital dataset of the SWOT satellite during the trial operation period and the orbital dataset of the SWOT satellite during the scientific cycle, wherein:

[0048] The orbital data set of the SWOT satellite during the trial operation period is from March 29, 2023 to July 10, 2023, including the original data of sea surface anomaly height and the denoised data. The data time resolution of this orbit is one day, with a total of 28 orbits per day, and the width of each orbit is 120km. Although the coverage area is small, it provides an opportunity to study sub-mesoscale eddies in a small area. The data range during the trial operation period is from March 29, 2023 to July 10, 2023. After that, the SWOT satellite will be converted to a scientific period orbit and maintain a 21-day cycle. When identifying vortices in a single orbit, most of the identified vortices are sub-mesoscale vortices due to the wide orbit, which directly demonstrates the ability of the SWOT satellite in fine-scale eddy identification.

[0049] The orbital data set of the SWOT satellite during the scientific cycle includes all data after July 26, 2023, as well as the original data of abnormal sea surface height and the denoised data. The scientific cycle starts after July 26, 2023, and each cycle is 21 days. Then the orbit numbers passing through the study area are screened, and the western Pacific region is selected as the research object, and the orbital data in the area are processed. The main instruments carried by the SWOT satellite include Karin and Nadir altimeters. There are systematic deviations between these two instruments, which lead to inconsistent measured data, such as Figure 2 shown. Figure 2 Schematic diagram of the SWOT satellite single track data processing before and after comparison and vortex identification results in an embodiment of the present invention, wherein (a) is the original satellite data diagram, (b) is the direct interpolation data diagram, (c) is the interpolation data diagram after processing, and (d) is the single track sub-mesoscale vortex identification result diagram. Comparing (b) and (c) diagrams, due to the presence of Nadir data, (b) diagram has obvious anomalies in the middle and on both sides of the track, and (c) diagram is smoothed a lot after removing the Nadir data. Therefore, during data processing, the data of the Nadir altimeter are removed, and only the two 50km wide strip data measured by Karin are retained (due to the total width of the SWOT satellite is 120km, but there is a 20km blank area in the middle). In the step S2 of the present embodiment, obtaining the gridded data of the SWOT satellite using interpolation filtering for the SWOT satellite data set refers to obtaining the gridded data of the SWOT satellite using interpolation filtering for the orbital data set of the SWOT satellite in the scientific cycle; obtaining satellite single track data for the SWOT satellite data set refers to obtaining satellite single track data for the orbital data set of the SWOT satellite during the trial run. By processing the data of a single track before vortex identification of the sub-mesoscale eddies in a single track, the accuracy of the sea surface anomaly height data will be reduced if identification is performed directly without processing.

[0050] In step S2, the SWOT satellite data set is subjected to interpolation filtering to obtain gridded data of the SWOT satellite, including:

[0051] S2.1, averaging the repeated orbital segments in the orbital data in the SWOT satellite dataset and interpolating the orbital regions without data in the orbital data in the SWOT satellite dataset to complete the orbital data preprocessing;

[0052] S2.2, interpolate the preprocessed orbital data to the specified spatial resolution;

[0053] S2.3, converting the orbital data after interpolation to the specified spatial resolution to the specified time resolution;

[0054] S2.4, filtering the orbit data converted to the specified time resolution to obtain the gridded data of the SWOT satellite.

[0055] Because of the resolution, the resolution of traditional satellites is 0.25°, so the vortices identified using them are mostly mesoscale vortices with larger radius, mainly concentrated between 50km and 100km. In step S2.2 of this embodiment, interpolating the pre-processed orbital data to a specified spatial resolution refers to using the natural neighbor interpolation method (existing known interpolation method) to interpolate the pre-processed orbital data from the original 0.018° spatial resolution to a spatial resolution of 0.02°; compared with other interpolation methods, natural neighbor interpolation is relatively smooth and can better retain the spatial distribution characteristics of the data. The data resolution after interpolation is 0.02°, which can identify sub-mesoscale vortices with a diameter of about 15km, ensuring the relative accuracy of the satellite data.

[0056] In step S2.3 of this embodiment, converting the orbital data interpolated to the specified spatial resolution to the specified temporal resolution refers to increasing the temporal resolution of the orbital data interpolated to the specified spatial resolution by a factor of 2, so that a revisit period of 21 days is split into two revisit periods of 10.5 days to achieve orbit cross-selection. The temporal resolution of the SWOT satellite is relatively low, with a revisit period of 21 days. Therefore, when processing the satellite orbit, the higher spatial resolution is discarded and the temporal resolution is increased by a factor of 2. Due to the characteristics of the SWOT satellite orbit, the time difference between adjacent orbits can be up to about ten days, while the time difference between orbits after an interpolation is only 2 to 3 days. Therefore, the original orbit is evenly divided into two parts, the first ten days and the last ten days. In this way, the 21 days in a cycle are divided into two 10.5-day periods, which not only increases the amount of data for vortex identification, but also reduces the spatial discontinuity caused by the time difference. This will help observe the temporal changes of sub-mesoscale vortices. The selection of a factor of 2 is the result of considering the characteristics of the satellite orbit and the temporal resolution. The orbit within a scientific cycle is divided into two stages, the first ten days and the last ten days. Since the time interval between adjacent SWOT satellite orbits is approximately 10 days, this can cause significant data fluctuations. However, this embodiment increases the temporal resolution of the orbital data after interpolation to the specified spatial resolution by a factor of 2, splitting a 21-day revisit cycle into two 10.5-day revisit cycles to achieve orbital crossover selection. This reduces this fluctuation and shortens the time interval between adjacent orbits to two to three days. Although this slightly reduces the amount of spatial data, it significantly improves temporal resolution, increasing data accuracy and reliability.

[0057] Due to the characteristics of the satellite's orbit, filtering is required to effectively smooth high-frequency noise in the interpolated data. As an optional implementation, taking into account the characteristics of the satellite's orbit and to smooth high-frequency noise, the filtering in step S2.4 of this embodiment involves a median filter with a window size of 15 km on each side. This results in smoother and more stable data, reduces noise interference with vortex identification, and effectively mitigates instrument-induced effects.

[0058] It's important to note that the high resolution of SWOT satellites allows for the identification of numerous rivers and lakes on land. However, due to the significant difference in elevation between land and sea, the eddy identification function may mistakenly identify rivers and lakes on land as eddy centers. Therefore, before using the eddy identification function, it's necessary to remove the water data over land. To do this, a mask is applied to all land areas, marking them as unidentified. Due to the high resolution of SWOT satellite gridded data, the mask resolution also needs to be as high as possible. The land mask dataset in this embodiment includes the DUACS (Data Unification and Altimeter Combination System) dataset and the SWOT satellite land mask. The DUACS dataset is a well-known dataset used for sea level research and applications. Its data includes land masks from satellites such as GEOSAT (Geodetic Satellite), TOPEX (Poseidon), Jason-1, Jason-2, and Jason-3. By using the DUACS and SWOT combined product as the land cover mask, this land cover mask is a derivative product after technicians attempted to combine it with the SWOT satellite. It has a resolution of 0.1, but its reliability is low, so it cannot be used as data for vortex identification. However, it can be used as a mask. Before inserting the mask, the data resolution needs to be interpolated to 0.02° to better match the SWOT data. Although this data is combined with SWOT satellite data, due to the small amount of SWOT satellite data at the time, the combined product data is only updated from March 29, 2023 to May 1, 2024, and will not be updated thereafter. Although the data resolution is 0.1°, its error is large, so it is not suitable for direct identification of sub-mesoscale vortices. Through the above processing, the high-resolution data of the SWOT satellite can be better utilized to accurately identify and analyze the characteristics of sub-mesoscale vortices, providing reliable data support for further ocean dynamics research. In this embodiment, by obtaining DUACS data that matches the SWOT satellite time, including all data after July 26, 2023. By comparing and analyzing DUACS data with SWOT data, fine-scale vortices can be more accurately identified and distinguished. When making a comparison, the DUACS data needs to be interpolated onto the grid of the SWOT satellite to ensure that the spatial resolution of the two data sets is consistent. Then, the data is differenced, and the resulting difference data set mainly contains fine-scale vortex characteristics. This not only enables us to better observe and analyze the structure and evolution of sub-mesoscale vortices, but also verifies the potential and advantages of the SWOT satellite in identifying fine-scale vortices, providing high-quality data support for further ocean dynamics research.

[0059] In this embodiment, step S4 includes: S4.1, for the satellite single orbit data, first remove the lowest point altimeter data of the SWOT satellite and interpolate the blank data in the middle, and then use the vortex identification function to perform vortex identification to obtain data of sub-mesoscale vortices. Due to the limitation of the orbit width, the identified vortex parameters are not restricted; for the gridded data without the land water area, the vortex identification function is used to perform vortex identification to obtain data of sub-mesoscale vortices; the data of the sub-mesoscale vortex includes data of subscale vortices and mesoscale vortices, wherein the subscale vortex refers to the vortex radius of tens of kilometers. The vortex of scale, mesoscale vortex refers to the vortex with a vortex radius of several hundred kilometers; the vortex identification function adopts the closed contour method (existing known method) to carry out vortex identification, and the conditions set when adopting the closed contour method to carry out vortex identification include the amplitude limit of the vortex being at least 2 centimeters, the vortex radius being set to 50km, the longitude and latitude span not being less than 0.05°, the maximum distance between the vortex boundary point and other points not exceeding 100km, and the amplitude limit of the vortex referring to the maximum potential difference between the vortex center position and the contour line; S4.2, the data of all sub-mesoscale vortices are formed into a sub-mesoscale vortex data set. In order to ensure that what is identified is a sub-mesoscale vortex, it is necessary to adjust the conditions of the vortex identification function, including the vortex amplitude, vortex radius, the longitude and latitude span of the vortex, and the maximum distance limit between the vortex boundary and other boundary points. Use vortex identification function to identify SWOT satellite single track data. Because of orbital characteristics, the minimum radius of vortex identification function is set to 7km, and the maximum radius is limited by the width of the track itself and will not exceed 50km. The limitation of amplitude is then greater than 3cm, and the too small setting will be affected by noise. In the present embodiment, the conditions provided when adopting closed contour method to carry out vortex identification include that the amplitude limit of vortex is at least 2 centimeters, the vortex radius is set to 50km, the longitude and latitude span cannot be less than 0.05 °, and the maximum distance between vortex boundary point and other points is no more than 100km. Because the influence of mesoscale vortex is removed, the amplitude needs to be appropriately reduced. By these restrictive conditions, it is ensured that the vortex identified is a sub-mesoscale vortex. Figure 3 Schematic diagram of the SWOT satellite orbit (satellite single orbit data) in the Western Pacific region in this embodiment. Figure 4 Schematic diagram of vortex identification results based on SWOT satellite data (satellite single orbit data) in the western Pacific region in this embodiment. Figure 5 The figure shows the difference between the SWOT data and DUACS data in the western Pacific region (grid data with the land water area removed) and the vortex identification results in this embodiment. Figure 6 This is a schematic diagram of the results of sub-mesoscale eddy identification in this embodiment without a land mask dataset. Figure 5 and Figure 6, proved the effect of adding land mask dataset on sub-mesoscale eddy identification, and achieved efficient and accurate identification of sub-mesoscale eddies.

[0060] The vortex characteristics in step S5 of this embodiment include vortex radius, vortex amplitude and vortex center position. Figure 7 : is a schematic diagram of the quantitative statistical characteristics of submesoscale vortices in the western Pacific region in this embodiment, Figure 8 : is a schematic diagram of the statistical characteristics of the sub-mesoscale vortex radius in the western Pacific region in this embodiment, Figure 9 Schematic diagram of the vortex amplitude statistical characteristics of the sub-mesoscale vortex in the western Pacific region in an embodiment of the present invention.

[0061] In summary, this embodiment includes obtaining a SWOT satellite data set, including an orbital data set of the SWOT satellite during the trial operation period and an orbital data set of the SWOT satellite during the scientific cycle, and identifying sub-mesoscale vortices through two data sources, thereby improving the completeness of identifying sub-mesoscale vortices. This embodiment includes obtaining gridded data of the SWOT satellite using interpolation filtering, and using interpolation, filtering and other processing to fully utilize the characteristics of the SWOT satellite's original observation data with extremely high spatial resolution (reaching 0.02°), thereby improving the accuracy of identifying sub-mesoscale vortices and maximizing the reliability of identifying sub-mesoscale vortices. This embodiment includes interpolating a land mask data set to the spatial resolution of the SWOT satellite's gridded data, and calculating the data difference between the SWOT satellite's gridded data and the interpolated land mask data set as gridded data to remove water areas on land, thereby preventing the vortex identification function from misidentifying rivers and lakes on land as vortex centers, further improving the accuracy of identifying sub-mesoscale vortices, and maximizing the reliability of identifying sub-mesoscale vortices. As can be seen, this embodiment can fully utilize the high-resolution data from the SWOT satellite to accurately and completely identify submesoscale eddies, providing reliable data support for ocean dynamics research. Compared to traditional submesoscale eddy identification methods, the method of this application is more reliable than numerical model methods; it saves a lot of manpower and material resources compared to in situ observation methods; and it has higher resolution and higher accuracy than traditional satellite data. In addition, due to the high resolution of the SWOT satellite, it is necessary to add a land mask during the eddy identification process to remove data from terrestrial water. If the land mask is not added, the presence of eddies will also be identified on land. Based on the high-precision submesoscale eddy dataset obtained by the method of this embodiment, scientists can more accurately capture and study these phenomena, reduce errors, and improve the reliability of analysis results. Moreover, since the SWOT satellite covers more than 90% of the world's oceans, the high-precision submesoscale eddy dataset obtained by the method of this embodiment enables scientists to systematically study submesoscale eddy phenomena in different sea areas. The wide coverage not only expands research beyond a few sampling points, but also helps scientists discover the temporal and spatial distribution patterns of eddies in different regions, thereby gaining a more comprehensive understanding of ocean dynamics. The real-time nature of satellite data is a major advantage. SWOT satellite data enables real-time monitoring of submesoscale eddies and the timely issuance of early warning information. Submesoscale eddies have a significant impact on marine ecosystems, potentially affecting nutrient cycling and plankton distribution. By using the high-precision submesoscale eddy dataset obtained using the method of this embodiment and identifying and studying these eddies using SWOT satellite data, scientists can better understand their impact on ecosystems, thereby developing effective environmental protection strategies and promoting the sustainable use of marine resources. SWOT satellite data also provides important foundational data for oceanographic and meteorological research.By analyzing the high-precision sub-mesoscale eddy datasets generated by the method in this example, scientists can delve deeper into major scientific issues such as ocean dynamics and climate change, providing scientific evidence and solutions to global warming and sea level rise. This not only promotes scientific research but also provides reliable data support for policymaking.

[0062] In addition, this embodiment also provides a system for identifying sub-mesoscale vortices using SWOT satellite data, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the method for identifying sub-mesoscale vortices using SWOT satellite data.

[0063] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction. The computer program or instruction is programmed or configured to execute the method of identifying sub-mesoscale vortices using SWOT satellite data through a processor.

[0064] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the method of identifying sub-mesoscale vortices using SWOT satellite data through a processor.

[0065] Those skilled in the art should understand that the technical solution provided by the present invention may be in the form of a method, a system, or a computer program product. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions described in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0066] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying submesoscale vortices using SWOT satellite data, characterized in that: The steps include: S1, obtain the SWOT satellite dataset, including the orbital dataset of the SWOT satellite during the test operation period and the orbital dataset of the SWOT satellite during the scientific cycle; S2, using interpolation filtering to obtain gridded data of SWOT satellites, and obtaining satellite single orbit data for SWOT satellite datasets; S3, interpolating the land mask dataset to the spatial resolution of the SWOT satellite gridded data, and calculating the data difference between the SWOT satellite gridded data and the interpolated land mask dataset as the gridded data with the land water removed; S4, using the eddy identification function to identify sub-mesoscale eddies on the satellite single-orbit data and the gridded data without land water to obtain the sub-mesoscale eddy data set; S5, statistically obtaining eddy characteristics of sub-mesoscale eddies in the sub-mesoscale eddy dataset for the target area; In step S2, the SWOT satellite data set is subjected to interpolation filtering to obtain gridded data of the SWOT satellite, including: S2.1, averaging the repeated orbital segments in the orbital data in the SWOT satellite dataset and interpolating the orbital regions without data in the orbital data in the SWOT satellite dataset to complete the orbital data preprocessing; S2.2, interpolate the preprocessed orbital data to the specified spatial resolution; S2.3, converting the orbital data after interpolation to the specified spatial resolution to the specified time resolution; S2.4, filtering the orbit data converted to the specified time resolution to obtain the gridded data of the SWOT satellite.

2. The method for identifying submesoscale vortices using SWOT satellite data according to claim 1, wherein: In step S2, using interpolation filtering on the SWOT satellite data set to obtain gridded data of the SWOT satellite refers to using interpolation filtering on the orbital data set of the SWOT satellite during the scientific cycle to obtain gridded data of the SWOT satellite; and obtaining satellite single orbit data on the SWOT satellite data set refers to obtaining satellite single orbit data on the orbital data set of the SWOT satellite during the trial operation period.

3. The method for identifying submesoscale vortices using SWOT satellite data according to claim 1, wherein: Interpolating the preprocessed orbital data to the specified spatial resolution in step S2.2 refers to interpolating the preprocessed orbital data from the original spatial resolution of 0.018° to the spatial resolution of 0.02° using the natural neighbor interpolation method; converting the orbital data interpolated to the specified spatial resolution to the specified temporal resolution in step S2.3 refers to increasing the temporal resolution of the orbital data interpolated to the specified spatial resolution by a factor of 2, so that a revisit period of 21 days is split into two revisit periods of 10.5 days to achieve orbit cross selection; filtering in step S2.4 refers to median filtering with a window size of 15 km on each side.

4. The method for identifying submesoscale vortices using SWOT satellite data according to claim 1, wherein: The land mask dataset in step S3 includes the land mask of the SWOT satellite and land masks from some or all of the GEOSAT, TOPEX, Jason-1, Jason-2, and Jason-3 satellites.

5. The method for identifying submesoscale vortices using SWOT satellite data according to claim 1, wherein: Step S4 includes: S4.1, for satellite single-orbit data, first remove the lowest point altimeter data of the SWOT satellite and interpolate the blank data in the middle, then use the vortex identification function to perform vortex identification to obtain data of sub-mesoscale vortices; for gridded data without land waters, use the vortex identification function to perform vortex identification to obtain data of sub-mesoscale vortices; the sub-mesoscale vortex data includes data of subscale vortices and mesoscale vortices, wherein a subscale vortex refers to an vortex with a vortex radius of tens of kilometers, and a mesoscale vortex refers to an vortex with a vortex radius of hundreds of kilometers; the vortex identification function uses a closed contour method to perform vortex identification, and the conditions set when using the closed contour method for vortex identification include a minimum amplitude limit of 2 cm for the vortex, a vortex radius of 50 km, a latitude and longitude span of not less than 0.05°, a maximum distance between the vortex boundary point and other points not exceeding 100 km, and the amplitude limit of the vortex refers to the maximum potential difference between the vortex center position and the contour line; S4.2, all sub-mesoscale eddy data are formed into a sub-mesoscale eddy dataset.

6. The method for identifying submesoscale vortices using SWOT satellite data according to claim 1, wherein: The vortex characteristics in step S5 include part or all of the vortex radius, amplitude and vortex center position.

7. A system for identifying submesoscale vortices using SWOT satellite data, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the method for identifying sub-mesoscale vortices using SWOT satellite data as claimed in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instructions are programmed or configured to execute, through a processor, the method for identifying sub-mesoscale vortices using SWOT satellite data as recited in any one of claims 1 to 6.

9. A computer program product comprising a computer program or instructions, characterized in that The computer program or instructions are programmed or configured to execute, through a processor, the method for identifying sub-mesoscale vortices using SWOT satellite data as recited in any one of claims 1 to 6.

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