Method and system for identifying sub-mesoscale vortexes by using SWOT satellite data

Abstract

The invention discloses a method and a system for identifying sub-mesoscale vortexes by using SWOT satellite data. The method comprises the following steps: acquiring an SWOT satellite data set; obtaining gridding data of the SWOT satellite by using interpolation filtering, and obtaining satellite monoorbit data; interpolating the land mask data set to the spatial resolution of the gridding data of the SWOT satellite, and calculating the gridding data of the SWOT satellite and the data difference between the interpolated land mask data set as the gridding data for removing the water area on the land; and carrying out vortex identification by utilizing the vortex identification function to obtain a sub-mesoscale vortex data set and extracting vortex characteristics of the sub-mesoscale vortex. The invention aims to fully utilize the high-resolution data of the SWOT satellite, accurately and completely identify the sub-mesoscale vortex, and provide reliable data support for ocean dynamics research.

Description

Technical Field

[0001] The present invention belongs to the field of physical oceanography, and particularly relates to a method and system for identifying submesoscale eddies by using SWOT satellite data. Background Art

[0002] Marine activities are intricate, with various phenomena being meticulous from large scales to small scales. The large-scale ocean current movements drive the distribution of global ocean substances and heat, while mesoscale (eddy radius of several hundred kilometers) and submesoscale (eddy radius of several tens of kilometers) eddies promote the exchange of substances and energy within local areas. These eddies play a crucial role in the ocean, especially in the exchange of substances and energy between the ocean surface and deep layers. The submesoscale eddies within local areas drive vertical mixing, bringing deep nutrients to the surface and transferring heat and momentum to deeper water layers, thus having a profound impact on the marine ecosystem, climate regulation, etc. Submesoscale eddies also have a significant impact on marine equipment and ships, such as reducing navigation efficiency and increasing equipment wear. The research on this phenomenon is particularly important for ensuring the safety of marine operations, optimizing shipping routes, and protecting marine equipment. Although the observation technology for mesoscale eddies has been relatively mature, the observation of submesoscale eddies 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 large amount of manpower and resources, and may not be able to comprehensively detect the existence of eddies; there is also the widely used numerical simulation method, which uses existing data to simulate the missing data. Although the resolution has been improved, the reliability of the data has decreased.

[0003] Since the launch of the first ocean satellite in 1993, after 30 years of continuous development, the observation technology of mesoscale vortices has gradually matured. However, limited by the resolution of satellite instruments, there are still great limitations in the observation of submesoscale vortices. The resolution of traditional observation methods is insufficient to capture the subtle changes of vortices at smaller scales, making the research on submesoscale vortices mainly rely on numerical simulation and indirect inference. For example, the resolution of traditional satellites is 0.25°, so most of the vortices identified by them are mesoscale vortices with larger radii, mainly concentrated between 50 km and 100 km. The launch of the SWOT (Surface Water and Ocean Topography) satellite in December 2022 marked a change in this situation. The SWOT satellite has epoch-making high spatial resolution and can observe the sea surface height changes at a scale of 1 km and smaller. Compared with the resolution of previous satellites, it has been improved by an order of magnitude. The high-precision observation of the SWOT satellite can directly observe the existence and evolution of submesoscale vortices for the first time. However, even so, the existing observation methods of submesoscale vortices are still difficult to make full use of the high-resolution data of the SWOT satellite and achieve accurate and reliable identification of submesoscale vortices. Summary of the Invention

[0004] The technical problem to be solved by the present invention: Aiming at the above problems of the prior art, the present invention provides a method and system for identifying submesoscale vortices using SWOT satellite data. The present invention aims to make full use of the high-resolution data of the SWOT satellite to accurately and completely identify submesoscale vortices and provide reliable data support for ocean dynamics research.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is as follows: A method for identifying submesoscale vortices using SWOT satellite data, comprising the following steps: S1, obtaining the SWOT satellite data set, including the orbit data set of the SWOT satellite during the commissioning period and the orbit data set of the SWOT satellite during the scientific cycle; S2, using interpolation filtering on the SWOT satellite data set to obtain the gridded data of the SWOT satellite, and obtaining the satellite single-orbit data from the SWOT satellite data set; S3, interpolating the land mask data set to the spatial resolution of the gridded data of the SWOT satellite, and calculating the data difference between the gridded data of the SWOT satellite and the interpolated land mask data set as the gridded data for removing the water area on land; S4, respectively using the vortex identification function on the satellite single-orbit data and the gridded data for removing the water area on land to perform submesoscale vortex identification to obtain the submesoscale vortex data set; S5. Statistically obtain the vortex characteristics of the sub-mesoscale vortices in the sub-mesoscale vortex dataset for the target area.

[0006] Optionally, in step S2, obtaining the gridded data of the SWOT satellite by using interpolation filtering on the SWOT satellite dataset means obtaining the gridded data of the SWOT satellite by using interpolation filtering on the orbital dataset of the SWOT satellite during the scientific cycle; obtaining the single-orbit data of the satellite from the SWOT satellite dataset means obtaining the single-orbit data of the satellite from the orbital dataset of the SWOT satellite during the trial operation period.

[0007] Optionally, in step S2, obtaining the gridded data of the SWOT satellite by using interpolation filtering on the SWOT satellite dataset includes: S2.1. Take the average of the repeated orbital segments in the orbital data in the SWOT satellite dataset, and perform interpolation processing on the orbital areas without data in the SWOT satellite dataset to complete the preprocessing of the orbital data; S2.2. Interpolate the preprocessed orbital data to the specified spatial resolution; S2.3. Convert the orbital data interpolated to the specified spatial resolution to the specified temporal resolution; S2.4. Filter the orbital data converted to the specified temporal resolution to obtain the gridded data of the SWOT satellite.

[0008] Optionally, in step S2.2, interpolating the preprocessed orbital data to the specified spatial resolution means interpolating the preprocessed orbital data from the original spatial resolution of 0.018° to 0.02° using the natural neighbor interpolation method; in step S2.3, converting the orbital data interpolated to the specified spatial resolution to the specified temporal resolution means doubling the temporal resolution of the orbital data interpolated to the specified spatial resolution, splitting a 21-day revisit cycle into two 10.5-day revisit cycles to achieve orbital cross-selection; the filtering in step S2.4 refers to median filtering with a window size of 15 km on each side.

[0009] Optionally, in step S3, the land mask dataset includes the land mask of the SWOT satellite and the land masks of some or all of the satellites GEOSAT, TOPEX, Jason-1, Jason-2, and Jason-3.

[0010] Optionally, step S4 includes: S4.1. For the satellite single-orbit data, first remove the nadir altimeter data of the SWOT satellite therein and interpolate the intermediate blank data, and then use the vortex identification function to identify vortices to obtain the data of submesoscale vortices; for the gridded data with land waters removed, use the vortex identification function to identify vortices to obtain the data of submesoscale vortices; the data of the submesoscale vortices includes the data of subscale vortices and mesoscale vortices, where the subscale vortex refers to a vortex with a radius of dozens of kilometers, and the mesoscale vortex refers to a vortex with a radius of hundreds of kilometers; the vortex identification function uses the closed contour method for vortex identification, and the conditions set when using the closed contour method for vortex identification include that the minimum amplitude limit of the vortex is 2 cm, the vortex radius is set to 50 km, the longitude and latitude span cannot be less than 0.05°, and the maximum distance between the vortex boundary points and other points does not exceed 100 km. The amplitude limit of the vortex refers to the maximum potential difference between the vortex center position and the contour line. S4.2. Form a submesoscale vortex dataset from all the data of submesoscale vortices.

[0011] Optionally, the vortex characteristics in step S5 include some or all of the vortex radius, amplitude, and vortex center position.

[0012] In addition, the present invention also provides a system for identifying submesoscale vortices using SWOT satellite data, including a microprocessor and a memory connected to each other. The microprocessor is programmed or configured to execute the method for identifying submesoscale vortices using SWOT satellite data.

[0013] In addition, the present invention also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the method for identifying submesoscale vortices using SWOT satellite data through a processor.

[0014] In addition, the present invention also provides a computer program product, including a computer program or instruction. The computer program or instruction is programmed or configured to execute the method for identifying submesoscale vortices using SWOT satellite data through a processor.

[0015] Compared with the prior art, the present invention mainly has the following advantages: 1. The present invention includes obtaining a SWOT satellite dataset, including the orbit dataset of the SWOT satellite during the commissioning period and the orbit dataset of the SWOT satellite during the science cycle. By using two data sources to identify submesoscale vortices, the integrity of identifying submesoscale vortices is improved.

[0016] 2. The present invention includes obtaining the gridded data of the SWOT satellite by using interpolation filtering. Through processing such as interpolation and filtering, the characteristics of the original observation data of the SWOT satellite with extremely high spatial resolution (reaching 0.02°) can be fully utilized, improving the accuracy of identifying sub-mesoscale vortices and maximizing the reliability of identifying sub-mesoscale vortices.

[0017] 3. The present invention includes interpolating the land mask data set to the spatial resolution of the gridded data of the SWOT satellite and calculating the data difference between the gridded data of the SWOT satellite and the interpolated land mask data set as the gridded data for removing water areas on land. Thereby, it can avoid the vortex identification function 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

[0018] Figure 1 It is a schematic diagram of the basic process of the method in the embodiment of the present invention.

[0019] Figure 2 It is a schematic diagram of the comparison before and after processing the single-track data of the SWOT satellite and the vortex identification result in the embodiment of the present invention, where (a) is the original satellite data diagram, (b) is the directly interpolated data diagram, (c) is the interpolated data diagram after processing, and (d) is the identification result diagram of the single-track sub-mesoscale vortex.

[0020] Figure 3 It is a schematic diagram of the SWOT satellite orbit in the western Pacific region in the embodiment of the present invention.

[0021] Figure 4 It is a schematic diagram of the vortex identification result of the SWOT satellite data in the western Pacific region in the embodiment of the present invention.

[0022] Figure 5 It is a schematic diagram of the difference result between the SWOT data and the DUACS data and the vortex identification result in the western Pacific region in the embodiment of the present invention.

[0023] Figure 6 It is a schematic diagram of the result of identifying sub-mesoscale vortices without the land mask data set in the embodiment of the present invention.

[0024] Figure 7 It is a schematic diagram of the statistical characteristics of the number of sub-mesoscale vortices in the western Pacific region in the embodiment of the present invention.

[0025] Figure 8 It is a schematic diagram of the statistical characteristics of the radius of sub-mesoscale vortices in the western Pacific region in the embodiment of the present invention.

[0026] Figure 9Schematic diagram of the statistical characteristics of the amplitude of submesoscale eddies in the Western Pacific region in the embodiments of the present invention. Detailed implementation manners

[0027] In order to enable those skilled in the art of the present technology to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0028] As Figure 1 shown, the method for identifying submesoscale eddies using SWOT satellite data in this embodiment includes the following steps: S1. Obtain the SWOT satellite data set, including the orbit data set of the SWOT satellite during the commissioning period and the orbit data set of the SWOT satellite during the science cycle; S2. Use interpolation filtering on the SWOT satellite data set to obtain the gridded data of the SWOT satellite, and obtain the satellite single-orbit data from the SWOT satellite data set; S3. Interpolate the land mask data set to the spatial resolution of the gridded data of the SWOT satellite, and calculate the data difference between the gridded data of the SWOT satellite and the interpolated land mask data set as the gridded data for removing the waters on land; S4. Use the vortex identification function to identify submesoscale eddies for the satellite single-orbit data and the gridded data for removing the waters on land respectively to obtain the submesoscale eddy data set; S5. Statistically obtain the vortex characteristics of the submesoscale eddies in the submesoscale eddy data set for the target area.

[0029] The SWOT satellite data set obtained in step S1 of this embodiment includes the orbit data set of the SWOT satellite during the commissioning period and the orbit data set of the SWOT satellite during the science cycle, where: The orbit data set of the SWOT satellite during the commissioning period ranges from March 29, 2023 to July 10, 2023, including the original data of the sea surface anomaly height and the denoised data. The time resolution of the data for this orbit is one day, with 28 orbits per day, and the width of each orbit is 120 km. Although the covered area is small, this provides an opportunity for the study of submesoscale eddies in small areas. The data range during the commissioning period is from March 29, 2023 to July 10, 2023. After that, the SWOT satellite will switch to the science cycle orbit and maintain a 21-day cycle operation. When identifying eddies for a single orbit, due to the width of the orbit, most of the identified eddies are submesoscale eddies, which directly proves the ability of the SWOT satellite in identifying eddies at a refined scale.

[0030] The orbit dataset of the SWOT satellite during the scientific cycle includes all data after July 26, 2023, and also includes the original data of sea surface anomaly height and the denoised data. The scientific cycle starts after July 26, 2023, with a cycle of every 21 days. Then, the orbit numbers passing through the study area are screened out, and the western Pacific region is selected as the research object, and the orbit data within the region is processed. The main instruments carried by the SWOT satellite include the Karin and Nadir altimeters. There are systematic biases in these two instruments, resulting in inconsistent measured data, such as Figure 2 as shown Figure 2 Figure 4 shows the comparison of the SWOT satellite single-orbit data before and after processing and the schematic diagram of vortex identification results in the embodiments of the present invention. Among them, (a) is the original satellite data map, (b) is the directly interpolated data map, (c) is the interpolated data map after processing, and (d) is the single-orbit submesoscale vortex identification result map. Comparing the maps of (b) and (c), due to the existence of Nadir data, there are obvious anomalies in the middle and both sides of the orbit in the map of (b), and the map of (c) becomes much smoother after removing the Nadir data. Therefore, when processing the data, the data of the Nadir altimeter is removed, and only the two 50-km-wide strip data measured by Karin are retained (since the total width of the SWOT satellite is 120 km, but there is a 20-km blank area in the middle). In step S2 of this embodiment, obtaining the gridded data of the SWOT satellite by using interpolation filtering for the SWOT satellite dataset means obtaining the gridded data of the SWOT satellite by using interpolation filtering for the orbit dataset of the SWOT satellite during the scientific cycle; obtaining the single-orbit data of the satellite for the SWOT satellite dataset means obtaining the single-orbit data of the satellite for the orbit dataset of the SWOT satellite during the trial operation period. By processing the single-orbit data before identifying the submesoscale vortices in a single orbit, if not processed and directly identified, the accuracy of the sea surface anomaly height data will be reduced.

[0031] Step S2 of obtaining the gridded data of the SWOT satellite by using interpolation filtering for the SWOT satellite dataset includes: S2.1, taking the average of the repeated orbit segments in the orbit data in the SWOT satellite dataset, and performing interpolation processing on the orbit areas without data in the SWOT satellite dataset to complete the preprocessing of the orbit data; S2.2, interpolating the preprocessed orbit data to the specified spatial resolution; S2.3, converting the orbit data interpolated to the specified spatial resolution to the specified temporal resolution; S2.4, filtering the orbit data converted to the specified temporal resolution to obtain the gridded data of the SWOT satellite.

[0032] Due to resolution limitations, the resolution of traditional satellites is 0.25°. Therefore, most of the vortices identified using them are mesoscale vortices with relatively large radii, mainly concentrated between 50 km and 100 km. In step S2.2 of this embodiment, interpolating the preprocessed orbit data to a specified spatial resolution means interpolating the preprocessed orbit data from the original spatial resolution of 0.018° to 0.02° using the natural neighbor interpolation method (a well-known interpolation method in the art). Compared with other interpolation methods, natural neighbor interpolation is relatively smooth and can better preserve the spatial distribution characteristics of the data. The resolution of the interpolated data is 0.02°, which can identify submesoscale vortices with a diameter of about 15 km, ensuring the relative accuracy of satellite data.

[0033] In step S2.3 of this embodiment, converting the orbit data interpolated to the specified spatial resolution to the specified temporal resolution means doubling the temporal resolution of the orbit data interpolated to the specified spatial resolution, splitting a 21-day revisit period into two 10.5-day revisit periods to achieve orbit crossing selection. The SWOT satellite has a relatively low temporal resolution with a 21-day revisit period. Therefore, when processing the satellite orbit, the higher spatial resolution is sacrificed, and instead, the temporal resolution is doubled. Due to the characteristics of the SWOT satellite orbit, the time difference between adjacent orbits can reach about ten days, while the time difference between orbits separated by one stack 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, thus splitting the 21 days in a cycle into two 10.5 days. This not only increases the amount of data for vortex identification but also reduces the spatial discontinuity caused by the time gap, which helps to observe the temporal changes of submesoscale vortices. The choice of doubling 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 orbits of the SWOT satellite is about 10 days, such a time interval may cause significant data fluctuations. However, in this embodiment, doubling the temporal resolution of the orbit data interpolated to the specified spatial resolution, splitting a 21-day revisit period into two 10.5-day revisit periods to achieve orbit crossing selection, can reduce this fluctuation, shortening the time interval between adjacent orbits to two to three days. Although this slightly reduces the amount of spatial data, it can significantly improve the temporal resolution and increase the accuracy and reliability of the data.

[0034] Due to the orbital characteristics of the satellite, in order to effectively smooth the high-frequency noise in the data, it is necessary to filter the interpolated data. As an alternative implementation, considering the satellite orbital characteristics, in order to smooth the high-frequency noise, the filtering in step S2.4 of this embodiment refers to median filtering with a window size of 15 km on each side, which can make the data more smooth and stable, reduce the interference of noise on vortex identification, and effectively reduce the influence caused by the instrument.

[0035] It should be noted that due to the high resolution of the SWOT satellite, numerous rivers and lakes on land can be identified. However, there is a huge elevation difference between land and sea surface, and the vortex identification function may misidentify rivers and lakes on land as vortex centers. Therefore, before using the vortex identification function, it is necessary to remove the water area data on land. To this end, all land cover needs to be masked, and the land is set as an unrecognized area. Due to the high resolution of the SWOT satellite grid data, the resolution of the mask also needs to be as high as possible. The land mask dataset in this embodiment includes the DUACS (Data Unification and AltimeterCombination System) dataset and the land mask of the SWOT satellite. The DUACS dataset is a well-known dataset for sea level research and applications, and its data includes land masks from satellites such as GEOSAT (Geodetic Satellite), TOPEX (Poseidon), Jason-1, Jason-2, and Jason-3. By using the combined product of DUACS and SWOT as the land cover mask, this land cover mask is a derivative product after technicians tried to combine with the SWOT satellite, with a resolution of 0.1, but its reliability is low. Therefore, it cannot be used as data for vortex identification, but it can be used as a mask. Before inserting the mask, the resolution of the data needs to be interpolated to 0.02° to better match the SWOT data. Although this data combines the data of the SWOT satellite, due to the small amount of SWOT satellite data at that time, the data of the combined product was only updated from March 29, 2023, to May 1, 2024, and will not be updated thereafter. Although the resolution of this data is 0.1°, its error is large, so it is not suitable to be directly used for the identification of submesoscale 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 submesoscale vortices, providing reliable data support for further ocean dynamics research. In this embodiment, by obtaining DUACS data that matches the time of the SWOT satellite, including all data after July 26, 2023. By comparing and analyzing the DUACS data with the SWOT data, finer-scale vortices can be more accurately identified and distinguished. When making the comparison, it is necessary to first interpolate the DUACS data onto the grid of the SWOT satellite to ensure that the spatial resolutions of the two datasets are consistent. Then, perform data subtraction processing, and the resulting difference dataset mainly contains the characteristics of finer-scale vortices. This not only enables us to better observe and analyze the structure and evolution of submesoscale vortices, but also verifies the potential and advantages of the SWOT satellite in identifying finer-scale vortices, providing high-quality data support for further ocean dynamics research.

[0036] In this embodiment, step S4 includes: S4.1. For the satellite single-orbit data, first remove the nadir altimeter data of the SWOT satellite therein and interpolate the intermediate blank data, and then use the vortex identification function to identify vortices to obtain the data of submesoscale vortices. Due to the limitation of the orbital width, no restrictions are imposed on the identified vortex parameters. For the gridded data with land waters removed, use the vortex identification function to identify vortices to obtain the data of submesoscale vortices. The data of the submesoscale vortices includes the data of subscale vortices and mesoscale vortices, where the subscale vortices refer to vortices with a radius of dozens of kilometers, and the mesoscale vortices refer to vortices with a radius of hundreds of kilometers. The vortex identification function uses the closed contour method (a well-known method in the art) for vortex identification, and the conditions set when using the closed contour method for vortex identification include that the minimum amplitude limit of the vortex is 2 cm, the vortex radius is set to 50 km, the longitude and latitude span cannot be less than 0.05°, and the maximum distance between the vortex boundary point and other points does not exceed 100 km. The amplitude limit of the vortex refers to the maximum potential difference between the vortex center position and the contour line. S4.2. Form a submesoscale vortex dataset from all the data of the submesoscale vortices. To ensure that the identified vortices are submesoscale vortices, it is necessary to adjust the conditions of the vortex identification function, including the vortex amplitude, vortex radius, longitude and latitude span of the vortex, and the maximum distance limit between the vortex boundary and other boundary points. Use the vortex identification function to identify the SWOT satellite single-orbit data. Due to the orbital characteristics, the minimum radius of the vortex identification function is set to 7 km, and the maximum radius is limited by the width of the orbit itself and does not exceed 50 km. The amplitude limit is greater than 3 cm. If it is set too small, it will be affected by noise. In this embodiment, the conditions set when using the closed contour method for vortex identification include that the minimum amplitude limit of the vortex is 2 cm, the vortex radius is set to 50 km, the longitude and latitude span cannot be less than 0.05°, and the maximum distance between the vortex boundary point and other points does not exceed 100 km. Since the influence of mesoscale vortices is removed, the amplitude needs to be appropriately reduced. Through these limiting conditions, it is ensured that the identified vortices are submesoscale vortices. 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 the vortex identification result of the SWOT satellite data (satellite single-orbit data) in the western Pacific region in this embodiment Figure 5 Schematic diagram of the difference result between the SWOT data and the DUACS data (gridded data with land waters removed) in the western Pacific region in this embodiment and the vortex identification result Figure 6 Schematic diagram of the result of submesoscale vortex identification in the case of no land mask dataset in this embodiment. Comparison Figure 5 and Figure 6, which proves the effect of adding the land mask dataset on the identification of submesoscale eddies and realizes the efficient and accurate identification of submesoscale eddies.

[0037] The vortex features in step S5 of this embodiment include vortex radius, vortex amplitude, and vortex center position. Figure 7 It is a schematic diagram of the statistical characteristics of the number of submesoscale eddies in the western Pacific region of this embodiment. Figure 8 It is a schematic diagram of the statistical characteristics of the vortex radius of submesoscale eddies in the western Pacific region of this embodiment. Figure 9 It is a schematic diagram of the statistical characteristics of the vortex amplitude of submesoscale eddies in the western Pacific region in the embodiment of the present invention.

[0038] In summary, this embodiment includes obtaining SWOT satellite datasets, including the orbit datasets of the SWOT satellite during the commissioning period and the orbit datasets of the SWOT satellite during the science cycle. By using two data sources to identify submesoscale eddies, the integrity of identifying submesoscale eddies is improved. This embodiment includes obtaining the gridded data of the SWOT satellite by using interpolation filtering. Through processing such as interpolation and filtering, the characteristics of the original observation data of the SWOT satellite with extremely high spatial resolution (reaching 0.02°) can be fully utilized, improving the accuracy of identifying submesoscale eddies and maximizing the reliability of the identified submesoscale eddies. This embodiment includes interpolating the land mask dataset to the spatial resolution of the gridded data of the SWOT satellite and calculating the data difference between the gridded data of the SWOT satellite and the interpolated land mask dataset as the gridded data for removing waters on land, so as to avoid the vortex identification function misidentifying rivers and lakes on land as vortex centers, and further improving the accuracy of identifying submesoscale eddies and maximizing the reliability of the identified submesoscale eddies. It can be seen that this embodiment can fully utilize the high-resolution data of the SWOT satellite to accurately and completely identify submesoscale eddies, providing reliable data support for ocean dynamics research. Compared with traditional submesoscale eddy identification methods, the method of this application is more reliable than the numerical model method; it saves a large amount of manpower and material resources compared with the in-situ observation method; it has higher resolution and higher accuracy than traditional satellite data. In addition, due to the relatively high resolution of the SWOT satellite, it is necessary to add a land mask during the vortex identification process to remove the data of land water. If the land mask is not added, vortices 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 the 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 range not only makes the research no longer limited to a few sampling points, but also helps scientists discover the spatio-temporal distribution laws of eddies in different regions, so as to more comprehensively understand ocean dynamics; the real-time nature of satellite data is one of its major advantages. By using SWOT satellite data, real-time monitoring of submesoscale eddies can be carried out, and early warning information can be issued in a timely manner; submesoscale eddies have an important impact on the marine ecosystem and may affect nutrient cycling, plankton distribution, etc. Based on the high-precision submesoscale eddy dataset obtained by the method of this embodiment, by using SWOT satellite data to identify and study these eddies, scientists can better understand their effects on the ecosystem, so as to formulate effective environmental protection strategies and promote the sustainable utilization of marine resources; SWOT satellite data provides important basic data for oceanography and meteorology research.Based on the high-precision submesoscale eddy dataset obtained by the method of this embodiment, by analyzing these data, scientists can deeply study major scientific issues such as ocean dynamics and climate change, providing scientific basis and countermeasures for global warming, sea-level rise, etc. This not only promotes the progress of scientific research but also provides reliable data support for policy-making.

[0039] In addition, this embodiment also provides a system for identifying submesoscale eddies using SWOT satellite data, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the method for identifying submesoscale eddies using SWOT satellite data.

[0040] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored, and the computer program or instruction is programmed or configured to execute the method for identifying submesoscale eddies using SWOT satellite data through a processor.

[0041] In addition, this embodiment also provides a computer program product, including a computer program or instruction, and the computer program or instruction is programmed or configured to execute the method for identifying submesoscale eddies using SWOT satellite data through a processor.

[0042] Those skilled in the art should understand that the technical solution provided by the present invention can be in the form of a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The present invention is described with reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the function specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the processFigure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks. 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 generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 the steps of the functions specified in one block or multiple blocks.

[0043] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as the protection scope 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 for the SWOT satellite dataset, and obtaining satellite single orbit data for the SWOT satellite dataset; S3, interpolating the land mask dataset to the spatial resolution of the gridded data of the SWOT satellite, and calculating the data difference between the gridded data of the SWOT satellite and the interpolated land mask dataset as the gridded data with the water area on land removed; S4, using the satellite single track data and the gridded data without land water to identify sub-mesoscale eddies using the eddy identification function to obtain the sub-mesoscale eddy data set; S5, statistically obtaining the eddy characteristics of the sub-mesoscale eddies in the sub-mesoscale eddy data set for the target area.

2. The method for identifying submesoscale vortices using SWOT satellite data according to claim 1, characterized in that: In step S2, using interpolation filtering to obtain gridded data of the SWOT satellite for the SWOT satellite data set refers to using interpolation filtering to obtain gridded data of the SWOT satellite for the orbital data set of the SWOT satellite during the scientific cycle; and obtaining satellite single orbit data for the SWOT satellite data set refers to obtaining satellite single orbit data for 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, characterized in that: In step S2, the SWOT satellite data set is subjected to interpolation filtering to obtain gridded data of the SWOT satellite, including: S2.1, taking the average value of repeated orbital segments in the orbital data in the SWOT satellite data set, interpolating the orbital regions without data in the orbital data in the SWOT satellite data set, and completing the preprocessing of the orbital data; S2.2, interpolating the preprocessed orbital data to a specified spatial resolution; S2.3, converting the orbital data interpolated to a specified spatial resolution to a specified temporal resolution; S2.4, filtering the orbit data converted to the specified time resolution to obtain the gridded data of the SWOT satellite.

4. The method for identifying submesoscale vortices using SWOT satellite data according to claim 3, characterized in that: 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 time resolution in step S2.3 refers to increasing the time resolution of the orbital data interpolated to the specified spatial resolution by 2 times, 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.

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

6. The method for identifying submesoscale vortices using SWOT satellite data according to claim 1, characterized in that: 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, and then use the vortex identification function to perform vortex identification to obtain data of sub-mesoscale vortices; for gridded data of land waters removed, use the vortex identification function to perform vortex identification to obtain data of sub-mesoscale vortices; the data of sub-mesoscale vortices include data of sub-scale vortices and mesoscale vortices, wherein sub-scale vortices refer to vortices with a vortex radius of tens of kilometers, and mesoscale vortices refer to vortices with a vortex radius of hundreds of kilometers; the vortex identification function uses the closed contour method to identify vortices, and the conditions set when using the closed contour method for vortex identification include that the amplitude limit of the vortex is at least 2 cm, the vortex radius is set to 50km, the longitude and latitude span cannot be less than 0.05°, the maximum distance between the vortex boundary point and other points does not exceed 100km, 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 combined to form a sub-mesoscale eddy data set.

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

8. 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 described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction 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 7 through a processor.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction 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 7 through a processor.

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