Sea level change trend accurate calculation method based on multi-source data fusion

Abstract

The invention relates to the technical field of marine surveying and mapping, and discloses a sea level change trend accurate calculation method based on multi-source data fusion, which comprises the following steps: acquiring multi-source satellite height measurement data, performing data grouping, establishing a plurality of average sea level models, establishing a time-varying average sea level model through the average sea level models, and calculating the sea level change trend according to the time-varying average sea level model. And performing linear regression fitting on the average sea surface height time sequence at each grid point, constructing a grid average sea level change model, and calculating a change trend at each grid point. The method can improve the separation capability of signals with different time scales in sea level changes, is not affected by extreme climate events, natural environment changes and physical environment correction, and can greatly improve the prediction accuracy of the sea level trend.

Description

Technical Field

[0001] The present invention relates to the field of ocean surveying and mapping technology, and in particular to a method for accurately calculating sea level change trends based on multi-source data fusion. Background Art

[0002] The ocean, which covers about 71% of the Earth's surface area, plays an important role in global climate change. Affected by the continuous warming of the global climate, the global sea level is continuing to rise at an accelerated rate. The consequence is that it has caused serious damage to the construction and development of the global economy, especially to coastal cities, and directly threatens human survival and development.

[0003] Sea level change is typically estimated by least squares fitting a time series of sea level changes calculated from tide gauges or altimeter satellites. This represents the average sea level change over a given observational time span. Accurate estimates of sea level change are influenced not only by the observation period of sea level height data but also by interannual variations in sea level caused by oscillations such as El Niño and the Southern Oscillation.

[0004] Traditional methods are incapable of separating signals at different time scales in sea level change. Short-term, dramatic sea level fluctuations caused by extreme climate events (such as typhoons and El Niño) are prone to being misinterpreted as trend terms or periodic signals due to their non-stationary and sudden nature, distorting trend estimates. Sea level change is driven by nonlinear processes such as glacier melt and ocean thermal expansion, and conventional linear models cannot capture the acceleration, turning points, or sudden changes in trends, causing long-term forecasts to deviate from reality. Residuals from geophysical environmental corrections (such as vertical crustal motion, atmospheric load deformation, and systematic errors in satellite altimetry) significantly interfere with trend analysis. Existing correction models are often based on global homogeneity assumptions and fail to account for the heterogeneity of the temporal and spatial distribution of errors (such as regional differences in crustal movement rates), resulting in regional biases in the corrected residuals. Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0006] With the continuous advancement of satellite altimetry technology, an increasing number of altimetry satellites are being launched, providing massive amounts of high-precision sea surface height observation data. By fusing these satellite altimetry data, high-resolution (e.g., 1′×1′) global or regional mean sea level models can be constructed. The process of constructing a mean sea level model requires eliminating the effects of temporal sea level variations within a limited time span. Therefore, the mean sea level model does not include information on sea level variations, such as interannual sea level variability. However, sea level variation information exists between mean sea level models constructed based on different time spans. Consider three mean sea level models A, B, and C, constructed based on different time spans: 1993 to 2012, 1994 to 2013, and 1995 to 2014, respectively. Clearly, the mean sea levels represented by models A, B, and C vary over time, and there is a single year of sea level variation between A and B, or between B and C. Therefore, a time series of mean sea level change can be constructed from these three models, and A, B, and C can be referred to as time-varying mean sea level models. If models A, B, and C have a sufficiently high grid resolution, such as 1′×1′, then a high-resolution mean sea level change model can be derived from the mean sea level change time series constructed from these three models. The higher the grid resolution of this model, the more detailed sea level changes can be reflected, which is helpful for studying the mechanisms of global or regional sea level change.

[0007] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for accurately calculating sea level change trends based on multi-source data fusion to solve the above problems.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for accurately calculating sea level change trends based on multi-source data fusion, comprising: Acquire multi-source satellite altimetry data and group the data; Establishing a plurality of mean sea level models based on the multi-source satellite altimetry data, and constructing a time-varying mean sea level model through the mean sea level models; Based on the time-varying mean sea level model, a linear regression fitting is performed on the mean sea level time series at each grid point to construct a grid mean sea level change model and calculate the change trend at each grid point.

[0009] As a preferred solution of the method for accurately calculating sea level change trends based on multi-source data fusion described in the present invention, obtaining multi-source satellite altimetry data and grouping the data includes: Obtaining historical sea level data based on the multi-source satellite altimetry data; A sliding window period is set, and the historical sea level data are slidingly grouped with the sliding window period as a time interval to obtain multiple groups of sea level height data sets.

[0010] As a preferred solution of the method for accurately calculating sea level change trends based on multi-source data fusion described in the present invention, constructing the time-varying mean sea level model includes: The multi-source satellite altimetry data are fused and interpolated by various data processing methods, and a plurality of mean sea level models are respectively established from adjacent sets of data sets among the plurality of sets of sea surface height data sets; constructing a plurality of mean sea level change time series from a plurality of the mean sea level models to obtain a time-varying mean sea level model; Among them, the various data processing methods include data preprocessing, unified reference benchmark, ocean time-varying correction, intersection adjustment and data gridding processing.

[0011] As a preferred solution of the method for accurately calculating sea level change trends based on multi-source data fusion described in the present invention, the ocean time-varying correction includes: The ocean time variation correction includes ocean time variation correction of precise repetitive mission data and geodetic mission data; The precise repeated mission data is corrected for ocean time variation by collinear adjustment; The geodetic mission data is corrected for ocean time variation by using a spatiotemporal objective analysis method or a polynomial fitting interpolation method.

[0012] As a preferred solution of the method for accurately calculating sea level change trends based on multi-source data fusion described in the present invention, wherein: the precise repeated task data is corrected for ocean time variation by collinear adjustment method, including: From all collinear trajectories, the trajectory with the most observation data is selected as the reference trajectory; Calculating the sea level height of each point on the collinear trajectory corresponding to a point on the reference trajectory by collinear analysis; The time-averaged sea surface height is obtained by averaging the sea surface heights at each point on each of the collinear trajectories.

[0013] As a preferred solution of the method for accurately calculating sea level change trends based on multi-source data fusion described in the present invention, wherein: the geodetic mission data is corrected for ocean time variation by a spatiotemporal objective analysis method, including: Preprocessing the satellite altimetry data, including removing data affected by land, sea ice, and rainfall; Use the spatiotemporal Kriging interpolation model to interpolate the data and obtain a dataset that traverses time and space; The satellite altimetry data is gridded and the data is distributed into a preset space-time grid.

[0014] As a preferred solution of the method for accurately calculating sea level change trends based on multi-source data fusion described in the present invention, constructing a grid average sea level change model includes: The time series of mean sea level height obtained according to the mean sea level model is expressed as: , ,in, The sequence number of the mean sea level time series, The mean sea level model grid points; Perform linear regression fitting on the mean sea level time series at each grid point and calculate the linear trend term, which is expressed as: ; in, Indicates the The constant term of the time series of the mean sea level height at each grid point, Indicates the The sea level trend term at each grid point is and Solve by least square method; By calculating the change trend at each grid point, the grid average sea level change model with a grid resolution of 1′×1′ is obtained.

[0015] In a second aspect, the present invention provides a system for accurately calculating sea level change trends based on multi-source data fusion, comprising: The collection module is used to obtain multi-source satellite altimetry data and group the data; A first construction module is configured to establish a plurality of mean sea level models based on the multi-source satellite altimetry data, and to construct a time-varying mean sea level model using the mean sea level models; The second construction module is used to perform linear regression fitting on the mean sea level time series at each grid point based on the time-varying mean sea level model, construct a grid mean sea level change model, and calculate the change trend at each grid point.

[0016] In a third aspect, the present invention provides an electronic device, comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for accurately calculating sea level change trends based on multi-source data fusion are implemented.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for accurately calculating sea level change trends based on multi-source data fusion.

[0018] Compared with the existing technology, the beneficial effects of the present invention are: the present invention can improve the ability to separate signals of different time scales in sea level changes, and is not affected by extreme climate events, natural environmental changes and physical environmental corrections, and can greatly improve the prediction accuracy of sea level trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 This is a schematic diagram of the overall process of a method for accurately calculating sea level change trends based on multi-source data fusion according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the data processing process for establishing a mean sea level model in a method for accurately calculating sea level change trends based on multi-source data fusion according to an embodiment of the present invention; Figure 3 A schematic diagram of multi-source satellite altimetry data for a method for accurately calculating sea level change trends based on multi-source data fusion according to an embodiment of the present invention; Figure 4 Schematic diagram of the average sea level rise rate in the near sea (red) and offshore (blue) with 20, ..., 100 km from the coastline as the dividing line, respectively, according to the method for accurately calculating sea level change trends based on multi-source data fusion according to one embodiment of the present invention; Figure 5 A schematic diagram of a global mean sea level change model for a method for accurately calculating sea level change trends based on multi-source data fusion according to an embodiment of the present invention; Figure 6 A schematic diagram of the global average sea level change model AVISO according to an embodiment of the present invention, which accurately calculates the sea level change trend based on multi-source data fusion; Figure 7 A schematic diagram of the NOAA global mean sea level change model for the accurate calculation method of sea level change trends based on multi-source data fusion according to an embodiment of the present invention; Figure 8A schematic diagram of the CSIRO global mean sea level change model for the accurate calculation method of sea level change trends based on multi-source data fusion according to an embodiment of the present invention; Figure 9 A histogram of the sea level change rate distribution of the method for accurately calculating sea level change trends based on multi-source data fusion according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the average sea level change model of China's sea areas and its adjacent sea areas according to the method for accurately calculating sea level change trends based on multi-source data fusion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0022] Example 1, with reference to Figure 1-Figure 2 , is an embodiment of the present invention, which provides a method for accurately calculating sea level change trends based on multi-source data fusion, such as Figure 1 Shown, including: S100, acquiring multi-source satellite altimetry data and grouping the data; S200, based on multi-source satellite altimetry data, establishes several mean sea level models and constructs a time-varying mean sea level model through the mean sea level models; S300, based on the time-varying mean sea level model, performs linear regression fitting on the mean sea level time series at each grid point, constructs a grid mean sea level change model, and calculates the change trend at each grid point.

[0023] Specifically, multi-source satellite altimetry data are obtained and sliding grouped; Establish several high-precision and high-resolution mean sea level models and construct a mean sea level change time series from these mean sea level models; A linear regression fitting is performed on the mean sea level time series at each grid point, and its linear trend term is calculated. By calculating the change trend at each grid point, the mean sea level change model with a grid resolution of 1′×1′, namely, the CSAO_SLC2020 model, can be obtained. A mean sea level change model with a grid resolution of 1′×1′, namely, the SDUST_SLC2020 model, is established for the global ocean area.

[0024] It should be noted that the present invention uses the time-varying mean sea level constructed by multi-source satellite altimetry data to establish a mean sea level change model with a grid resolution of 1′×1′. Through this model, the global or regional sea level change rate can be accurately estimated, the spatial distribution of the sea level change rate in each sea area can be obtained, and its fine sea level change characteristics can be reflected.

[0025] Preferably, obtaining multi-source satellite altimetry data and grouping the data includes: Based on multi-source satellite altimetry data, historical sea level data is obtained; Set the sliding window period, and use the sliding window period as the time interval to slide and group the historical sea level data to obtain multiple groups of sea level height data sets. The sliding window period can be set according to actual needs and can be set by month, year, etc.

[0026] Specifically, the multi-source satellite altimetry data can be obtained by calculating the average sea level change model using a variety of different multi-source satellite altimetry data or different grid resolutions (e.g., 60′×60′ or 30′×30′). The multi-source satellite altimetry data are preferably grouped with a sliding window of 1 year as the sliding time interval, so that multiple groups of sea surface height data sets can be obtained. Among the multiple groups of sea surface height data sets, there is a one-year time interval between adjacent groups of data sets, and there is also a one-year time interval between the average sea level models established by the adjacent groups of data sets. Interval, therefore, multiple mean sea level models established by multiple sets of sea surface height data sets can construct a time series of mean sea level changes. Each set of sea surface height data sets contains a variety of satellite altimetry data, and the T / P series altimetry satellite data in each set of sea surface height data sets are continuous and have very high sea surface precision. Therefore, the average sea surface height along the altimetry track obtained after collinear adjustment of these continuous sea surface height data is referred to as the T / P series altimetry satellite average sea surface height, which can be used as a reference benchmark for the mean sea level model to be established in the next step.

[0027] Preferably, constructing the time-varying mean sea level model includes: Through various data processing, the multi-source satellite altimetry data are fused and interpolated, and several mean sea level models are established from the adjacent sets of data sets in multiple sets of sea surface height data sets. From several mean sea level models, multiple mean sea level change time series are constructed to obtain a time-varying mean sea level model; Among them, various data processing include data preprocessing, unified reference benchmark, ocean time-varying correction, intersection adjustment and data gridding processing.

[0028] It should be noted that in order to obtain a high-resolution mean sea level change model, a number of high-precision and high-resolution mean sea level models must be established first. Then, a mean sea level change time series is constructed from these mean sea level models. The mean sea level model is obtained by fusing and interpolating multi-source satellite altimetry data using a variety of data processing techniques. In addition to data preprocessing, it also includes the unification of reference datums, ocean time-varying correction, intersection adjustment, and data gridding. Figure 2 It is the data processing process to build the mean sea level model.

[0029] Optionally, the sea level measured by the altimetry satellite refers to the distance between the sea level and the reference ellipsoid. Different altimetry satellites use different Earth ellipsoid parameters. Therefore, the sea level observed by different altimetry satellites is also different. Before fusing the multi-source satellite altimetry data to establish a mean sea level model, it is necessary to unify the altimetry data of each satellite under the same reference ellipsoid. The multi-source satellite altimetry data used in this embodiment are all selected from L2P (secondary product) data. Before release, this data product has completed data editing and preprocessing, correction of various geophysical and environmental errors, and has been unified to the reference ellipsoid used by T / P satellite altimetry data. The unification of the reference datum is divided into the unification of the reference ellipsoid and the reference frame. The purpose of unifying the reference frame is to eliminate the systematic differences between different satellite altimetry data.

[0030] Preferably, the ocean time variation correction includes: The ocean time-varying correction includes the ocean time-varying correction of the Exact Repeating Mission (ERM) data and the Geodetic Mission (GM) data; The precise repeated mission data are corrected for ocean time variations through collinear adjustment; Geodetic mission data are corrected for ocean time variations through spatiotemporal objective analysis or polynomial fitting interpolation.

[0031] Preferably, the precise repetitive mission data is corrected for ocean time variation by collinear adjustment, including: From all collinear trajectories, the trajectory with the most observation data is selected as the reference trajectory; Calculate the sea surface heights of points on the collinear trajectory corresponding to points on the reference trajectory through collinearity analysis; The time-averaged sea surface height is obtained by averaging the sea surface heights at each point on each collinear trajectory.

[0032] Specifically, collinear adjustment includes: Data preprocessing to extract the longitude, latitude and sea level height of satellite altimetry data points; Reference orbit fitting: using satellite altimetry data points of different periods in the same orbit to fit the reference orbit through a quadratic polynomial; Normal point longitude and latitude calculation, calculate the longitude and latitude of the first normal point on the reference orbit; The longitude and latitude of all normal points are calculated by using the longitude and latitude of the first normal point and the satellite along-track sampling interval to calculate the longitude and latitude of all normal points on the reference orbit; To determine the sea surface height, take each normal point as the center, obtain the satellite altimetry data points within the search radius, and use the distance weighted average method to determine the sea surface height of each normal point, thereby realizing the collinear processing of the satellite altimetry data.

[0033] It should be noted that collinear adjustment is used to reduce the temporal anomaly of sea surface height caused by large-scale ocean anomalies in a specific period (such as the El Niño / La Niña phenomenon) to obtain the average value of sea surface height along the altimetry track.

[0034] Collinear adjustment is suitable for correcting ocean time-varying data for ERM data, but not for GM data. In the multi-source satellite altimetry data used in this invention, one or more ERM satellites can always be found with the same observation time span as the GM satellites. Within the same observation time span, the ocean time-varying information contained in the sea surface height data obtained from different altimetry satellites is assumed to be identical. Therefore, the SLA of the ERM data from the same observation period as the GM data can be calculated as a reference, and then the GM data can be corrected for ocean time-varying data by matching them in time and space.

[0035] Preferably, the geodetic mission data is corrected for ocean time variations by using a spatiotemporal objective analysis method, including: Preprocessing of satellite altimetry data, including removal of data affected by land, sea ice, and rainfall; Use the spatiotemporal Kriging interpolation model to interpolate the data and obtain a dataset that traverses time and space; The satellite altimetry data is gridded and distributed into a preset space-time grid, where the preset space-time grid can be set according to the actual application scenario.

[0036] Alternatively, the polynomial fitting interpolation method may be to collect a set of known data points, select an interpolation method to construct an interpolation polynomial, and then perform interpolation calculations.

[0037] Optionally, data gridding can be performed by first preprocessing the data, including removing outliers, noise and duplicate data to ensure data accuracy and consistency; then mapping the data to the grid, dividing the geographic space into a uniform grid according to the study area and resolution requirements (determined according to the actual application scenario), and mapping the observation data to the grid points.

[0038] It should be noted that the purpose of data gridding is to interpolate irregular and discrete sea surface height data onto a regular grid, which fully considers the prior statistical information of the altimetry observation data, which is very important for accurately estimating the sea surface height at the grid points.

[0039] Preferably, constructing the grid mean sea level change model includes: The time series of mean sea level height obtained from the mean sea level model is expressed as: , ,in, The sequence number of the mean sea level time series, The mean sea level model grid points; Perform linear regression fitting on the mean sea level time series at each grid point and calculate the linear trend term, which is expressed as: ; in, Indicates the The constant term of the time series of the mean sea level height at each grid point, Indicates the The sea level trend term at each grid point is and Solve by least square method; By calculating the change trend at each grid point, the grid mean sea level change model with a grid resolution of 1′×1′ is obtained.

[0040] It should be noted that when establishing the grid mean sea level change model, in the time-varying mean sea level model obtained, each mean sea level model is based on the mean sea level height of the T / P series altimetry satellites within different observation time spans as a reference benchmark. There is one year of sea level time-varying information between adjacent models. Therefore, the model sea level heights of each mean sea level model at the same grid point can constitute a mean sea level height time series. By calculating the change trend at each grid point, the mean sea level change model with a grid resolution of 1′×1′ can be obtained, and then the CSAO_SLC2020 model and the SDUST_SLC2020 model can be established.

[0041] Optionally, the grid mean sea level change model can be evaluated and optimized using methods such as root mean square error (RMSE) and mean absolute error (MAE).

[0042] It should be noted that the present invention can improve the ability to separate signals of different time scales in sea level changes, and is not affected by extreme climate events, natural environmental changes and physical environmental corrections, and can greatly improve the prediction accuracy of sea level trends.

[0043] The above is a schematic diagram of a method for accurately calculating sea level trends based on multi-source data fusion in accordance with this embodiment. It should be noted that the technical solution of this system for accurately calculating sea level trends based on multi-source data fusion and the technical solution of the method for accurately calculating sea level trends based on multi-source data fusion described above share the same concept. For details not described in detail in the technical solution of the system for accurately calculating sea level trends based on multi-source data fusion in this embodiment, please refer to the description of the technical solution of the method for accurately calculating sea level trends based on multi-source data fusion described above.

[0044] The accurate calculation system for sea level change trends based on multi-source data fusion in this embodiment includes: The collection module is used to obtain multi-source satellite altimetry data and group the data; The first construction module is used to establish several mean sea level models based on multi-source satellite altimetry data, and to construct a time-varying mean sea level model through the mean sea level models; The second construction module is used to perform linear regression fitting on the mean sea level time series at each grid point based on the time-varying mean sea level model, construct a grid mean sea level change model, and calculate the change trend at each grid point.

[0045] This embodiment further provides an electronic device suitable for accurately calculating sea level change trends based on multi-source data fusion, including: Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for accurately calculating sea level change trends based on multi-source data fusion as proposed in the above embodiment.

[0046] This embodiment further provides a storage medium storing a computer program, which, when executed by a processor, implements the method for accurately calculating sea level change trends based on multi-source data fusion as proposed in the above embodiment.

[0047] The storage medium proposed in this embodiment and the method for accurately calculating the sea level change trend based on multi-source data fusion proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0048] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0049] Example 2, reference Figure 3-10 Tables 1 to 3 are an embodiment of the present invention, which provides a method for accurately calculating sea level change trends based on multi-source data fusion. In order to verify its beneficial effects, comparison results of multiple schemes are provided.

[0050] This paper first takes the Chinese sea area and its adjacent sea areas (0°N~41°N, 100°E~140°E) as the research area, and establishes a mean sea level change model with a grid resolution of 1′×1′, namely the CSAO_SLC2020 model, to provide important basic information for the development of the marine economy; then establishes a mean sea level change model with a grid resolution of 1′×1′ for the global sea area, namely the SDUST_SLC2020 model.

[0051] The data sources for multi-source satellite altimetry data can be obtained from the monthly average gridded (grid resolution of 15′×15′) sea level anomaly (SLA) data product released on the official website of the Archive, Validation and Interpretation of Satellite Oceanography (AVISO). The data is calculated from multi-source satellite altimetry data from January 1, 1993 to December 31, 2019; the average sea level change model with a grid resolution of 30′×30′ released by the Satellite Altimetry Laboratory of the National Oceanic and Atmospheric Administration of the United States, which is calculated from the T / P (Topex / Poseidon) series altimetry satellite data from December 1992 to September 2020; and the average sea level change model with a grid resolution of 60′×60′ released by the Commonwealth Scientific and Industrial Research Organization of Australia, which is calculated from the T / P series altimetry satellite data from January 1993 to December 2019.

[0052] The multi-source satellite altimetry data from January 1, 1993 to December 31, 2019 were grouped into sliding groups with a sliding window of 19 years and a sliding time interval of 1 year starting from January 1993. In this way, 9 sets of sea surface height data sets were obtained, such as Figure 2 As shown in the figure, there is a one-year time interval between adjacent sets of these nine sea level height datasets, and there is also a one-year time interval between the mean sea level models constructed from these adjacent sets of datasets. Therefore, the nine mean sea level models constructed from these nine sea level height datasets can be used to construct a mean sea level change time series.

[0053] In this embodiment, the spatiotemporal objective analysis method is used to correct the ocean time variation for GM data with latitudes between 66°S and 66°N. For GM data with latitudes higher than 66°S or 66°N, the polynomial fitting interpolation method is used to correct the ocean time variation. Figure 3 The 9 sets of sea surface height data sets shown in the figure (the content in brackets in the figure indicates the number of days of the repetition period of the corresponding satellite altimetry mission) are Figure 2 The data processing process shown establishes mean sea level models with a grid resolution of 1′×1′ respectively, resulting in nine mean sea level models. Among these nine models, there is one year of sea level time-varying information between adjacent groups of mean sea level models. Therefore, a mean sea level change time series can be constructed from these nine mean sea level models. These nine mean sea level models are also called time-varying mean sea level models.

[0054] Table 1 shows the average sea level rise rates for different sea areas calculated by the CSAO_SLC2020, AVISO, NOAA, and CSIRO models (the difference between the average sea level rise rates between two adjacent time series in the mean sea surface height). These sea areas include: China Sea and its adjacent seas (100°–140°E, 0°–41°N), Bohai Sea (117.5°–122°E, 37°–41°N), Yellow Sea (119°–123°E, 34°–38°N), East China Sea (117°–130°E, 23°–31°N), South China Sea (105°–120°E, 3°–23°N), Xisha Islands (111°–113°E, 15°–18°N), and Spratly Islands (109°–118°E, 3°–12°N). The results in Table 1 do not deduct the impact of glacial isostatic adjustment, which causes a global average sea level rise rate of approximately 0.2–0.5 mm / yr. Except for the CSIRO model results, the average sea level rise rates calculated by other models in Table 1 are all higher than the global average sea level rise rate calculated by the NOAA model (approximately 3.0 mm / yr). The results in Table 1 once again confirm that mean sea level changes vary spatially across different ocean regions.

[0055] Table 1: Average sea level rise rates for different models sea areas China's maritime areas and adjacent waters Bohai Sea Yellow Sea East China Sea South China Sea Paracel Islands Spratly Islands CSAO_SLC2020 3.42 3.96 4.29 4.02 4.45 6.26 4.52 AVISO 3.50 3.87 3.92 3.58 3.80 4.45 3.79 NOAA 3.54 4.20 3.98 3.11 3.90 4.48 3.96 CSIRO 2.55 3.95 3.40 2.61 3.33 3.50 3.65 In the Chinese seas and adjacent waters, the average sea level rise rate calculated by the CSAO_SLC2020 model is approximately 3.42 mm / yr, consistent with the results from the AVISO and U.S. National Oceanic and Atmospheric Administration (NOAA) models, and higher than the 2.55 mm / yr calculated by the Commonwealth Scientific and Industrial Research Organization (CSIRO) model. The average sea level rise rates calculated by the CSAO_SLC2020 model for the Bohai Sea, Yellow Sea, East China Sea, and South China Sea are 3.96 mm / yr, 4.29 mm / yr, 4.02 mm / yr, and 4.45 mm / yr, respectively, all slightly higher than those calculated by the AVISO, NOAA, and CSIRO models, respectively. Furthermore, in the waters around the Xisha and Nansha Islands, the average sea level rise rates calculated by the CSAO_SLC2020 model (6.26 mm / yr and 4.52 mm / yr, respectively) are significantly higher than those calculated by the AVISO, NOAA, and CSIRO models, respectively.

[0056] The distances from the coastline to the offshore and nearshore waters are 20 km, 30 km, ..., and 100 km, respectively. The average sea level rise rates of the offshore and nearshore waters determined by the different dividing lines are calculated. The calculation results are as follows: Figure 4 As shown, from Figure 4 It can be seen that the sea level changes in the nearshore area are faster than those in the offshore area. The average rate of sea level rise in the nearshore area is about 0.67 mm / yr (about 20%) higher than that in the offshore area. This result is of great significance for estimating the degree of damage caused by sea level rise to the offshore area, especially to offshore cities.

[0057] Global ocean accuracy assessment, such as Figure 5 As shown in FIG. 1 , the global mean sea level change model SDUST_SLC2020 established in this embodiment has a grid resolution of 1′×1′ and a global coverage range of 80°S~84°N. It represents the mean sea level change over a time span of 27 years from January 1993 to December 2019. Figure 6 The global mean sea level change model is calculated from the monthly mean gridded SLA data product released by AVISO from January 1993 to December 2019. The model grid resolution is 15′×15′ and the global coverage range is 80°S~84°N. Figure 7 This is the mean sea level change model released by NOAA. The model has a grid resolution of 30′×30′, a global coverage range of 66°S~66°N, and a reference time span from December 1992 to September 2020. Figure 8 The mean sea level change model released by CSIRO has a grid resolution of 60′×60′, a global coverage range of 66°S~66°N, and a reference time span from January 1993 to December 2019. The differences between the four models are shown in Table 2.

[0058] Table 2: Model difference data table Model SDUST_SLC2020 AVISO NOAA CSIRO Grid resolution 1′×1′ 15′×15′ 30′×30′ 60′×60′ Coverage 80°S~84°N 80°S~84°N 66°S~66°N 66°S~66°N Reference time span 1993.01~2019.12 1993.01~2019.12 1992.12~2020.09 1993.01~2019.12 from Figure 5 、 6 , 7, and 8 show that the sea level changes in different sea areas have different trends. Some sea areas show an upward trend, while others show a downward trend. Although the four models all show the same sea level change characteristics, Figure 5 Compared to Figure 6 、 7 and 8 have higher grid resolution and can show more detailed sea level change characteristics.

[0059] like Figure 9The following is a histogram of the sea level change rate distribution at the grid points of the four models SDUST_SLC2020, AVISO, NOAA and CSIRO. Figure 9 It can be seen that the rate of sea level change at each model grid point basically conforms to the characteristics of a Gaussian normal distribution, and is mainly distributed between -2 and 8 mm / yr, and is symmetrically distributed around approximately 3 mm / yr, which coincides with the global average sea level change rate (approximately 3.1 mm / yr). Figure 9 (a) relative to Figure 9 (b), (c) and (d) are most consistent with the Gaussian normal distribution curve, which shows that the global average sea level change rate calculated by SDUST_SLC2020 should be more reliable.

[0060] Table 3: Average sea level change rates in different sea areas under different models sea areas SDUST_SLC2020 AVISO NOAA CSIRO Global Oceans 3.30 3.03 2.94 3.04 Pacific Ocean 3.29 3.12 3.42 2.99 North Pacific 3.18 3.01 3.38 2.64 Atlantic 3.27 3.15 2.68 2.99 North Atlantic 3.43 3.01 2.80 2.91 Indian Ocean 3.38 3.19 3.25 3.51 Table 3 shows the average sea level change rates for different sea areas calculated by the four models: SDUST_SLC2020, AVISO, NOAA, and CSIRO. The results in Table 3 do not deduct the impact of glacial isostatic adjustment, which causes an average sea level rise rate of about 0.2-0.5 mm / yr worldwide. The results in Table 3 show that the average sea level change rates calculated by the SDUST_SLC2020 model for the global oceans (66°S–66°N, 180°E–180°W), Pacific Ocean (66°S–66°N, 105°E–75°W), North Pacific Ocean (0°N–66°N, 105°E–75°W), Atlantic Ocean (66°S–66°N, 100°W–40°E), North Atlantic Ocean (0°N–66°N, 100°W–40°E), and Indian Ocean (66°S–30°N, 30°E–135°E) are 3.30 mm / yr, 3.29 mm / yr, 3.18 mm / yr, 3.27 mm / yr, 3.43 mm / yr, and 3.38 mm / yr, respectively. These results are basically consistent with those calculated by the AVISO, NOAA, and CSIRO models.

[0061] In addition, this embodiment establishes a mean sea level change model with a grid resolution of 1′×1′ in the Chinese sea area and its adjacent sea areas, namely the CSAO_SLC2020 model. Figure 10, (a), (b), (c), and (d) are the average sea level change models with grid resolutions of 1′×1′, 15′×15′, 30′×30′, and 60′×60′, respectively. Among them, (b) is calculated from the monthly average grid SLA data product released by AVISO from January 1993 to December 2019; (c) is released by NOAA; and (d) is released by CSIRO. Specifically, Figure 10 Mean sea level change models for the China Sea and its adjacent seas: (a) the mean sea level change model CSAO_SLC2020 established in this paper (grid resolution: 1′×1′; time span: January 1993 to December 2019); (b) calculated from the monthly mean gridded sea surface anomaly data product released by AVISO (grid resolution: 15′×15′; time span: January 1993 to December 2019); (c) released by the NOAA Satellite Altimetry Laboratory (grid resolution: 30′×30′; time span: December 1992 to September 2020); (d) released by CSIRO (grid resolution: 60′×60′; time span: January 1993 to December 2019).

[0062] from Figure 10 It can be seen that the average sea level changes in China's sea areas and its adjacent sea areas are not uniform. Some sea areas show an upward trend, while some sea areas show a downward trend. Figure 10 (a), (b), (c), and (d) show the same average sea level change characteristics overall, but the high-resolution grid average sea level change model, e.g. Figure 10 (a) reveals more detailed sea level changes. The average sea level in the northern East China Sea shows a significant upward trend, while the sea level in the coastal waters near the Yangtze River estuary shows a downward trend. The former is primarily due to the combined effects of the Kuroshio Current and climate change, which have caused significant warming of the waters in this area, while the latter is primarily due to the cooling of the coastal waters near the Yangtze River estuary. The warming and cooling of seawater causes thermal expansion and contraction, resulting in rising or falling sea level trends.

[0063] like Figure 10As shown in (a), (b), and (c), a symmetrical "dipole" of sea level variations was detected in the waters south of Japan, primarily due to the strengthening ocean circulation. A zonal pattern of mean sea level variations was observed in the North Pacific Subtropical Countercurrent (NSCC) between 19.5° and 22.5° latitudes, in the waters east of Taiwan Island and Luzon Island, influenced by eddies. This variation is primarily attributed to changes in mesoscale eddies. Mesoscale eddies are rotating fluids with radii ranging from tens to hundreds of kilometers in the ocean. Clockwise vortices in the Northern Hemisphere are called anticyclonic eddies. These eddies have higher sea surface temperatures near the surface and are often referred to as "warm eddies." Conversely, counterclockwise vortices are called cyclonic eddies. These eddies have lower sea surface temperatures near the surface and are often referred to as "cold eddies." Higher sea surface temperatures within anticyclonic eddies enhance the trend of mean sea level variations, while lower sea surface temperatures within cyclonic eddies weaken the trend. During the observation period of the data selected for this study (1993 to 2019), the number of anticyclonic vortices increased annually in the North Pacific Subtropical Countercurrent and the waters east of Luzon Island, while it decreased annually in the waters east of Taiwan Island. This explains why the mean sea level in the North Pacific Subtropical Countercurrent and the waters east of Luzon Island is rising, while the mean sea level trend east of Taiwan Island is almost zero or even tends to be negative. The sea level rise trend in the Xiasha Sea area in the western South China Sea is significant, with an average rate of change of 6.26 mm / yr. This may be due to the rapid changes in the South China Sea circulation system driven by climate change, which has led to the more frequent occurrence of anomalous anticyclonic vortices (warm vortices) in this waters, thereby intensifying the trend of mean sea level change.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for accurately calculating sea level change trends based on multi-source data fusion, characterized in that: include: Acquire multi-source satellite altimetry data and group the data; Establishing a plurality of mean sea level models based on the multi-source satellite altimetry data, and constructing a time-varying mean sea level model through the mean sea level models; Based on the time-varying mean sea level model, a linear regression fitting is performed on the mean sea level time series at each grid point to construct a grid mean sea level change model and calculate the change trend at each grid point.

2. The method for accurately calculating sea level change trends based on multi-source data fusion according to claim 1, characterized in that: Acquiring multi-source satellite altimetry data and grouping the data includes: Obtaining historical sea level data based on the multi-source satellite altimetry data; A sliding window period is set, and the historical sea level data are slidingly grouped with the sliding window period as a time interval to obtain multiple groups of sea level height data sets.

3. The method for accurately calculating sea level change trends based on multi-source data fusion according to claim 2, characterized in that: Constructing the time-varying mean sea level model includes: The multi-source satellite altimetry data are fused and interpolated by various data processing methods, and a plurality of mean sea level models are respectively established from adjacent sets of data sets among the plurality of sets of sea surface height data sets; constructing a plurality of mean sea level change time series from a plurality of the mean sea level models to obtain a time-varying mean sea level model; Among them, the various data processing methods include data preprocessing, unified reference benchmark, ocean time-varying correction, intersection adjustment and data gridding processing.

4. The method for accurately calculating sea level change trends based on multi-source data fusion according to claim 3 is characterized in that: The ocean time variation correction includes: The ocean time variation correction includes ocean time variation correction of precise repetitive mission data and geodetic mission data; The precise repeated mission data is corrected for ocean time variation by collinear adjustment; The geodetic mission data is corrected for ocean time variation by using a spatiotemporal objective analysis method or a polynomial fitting interpolation method.

5. The method for accurately calculating sea level change trends based on multi-source data fusion according to claim 4 is characterized in that: The precise repetitive mission data is corrected for ocean time variation by collinear adjustment method, including: From all collinear trajectories, the trajectory with the most observation data is selected as the reference trajectory; Calculating the sea level height of each point on the collinear trajectory corresponding to a point on the reference trajectory by collinear analysis; The time-averaged sea surface height is obtained by averaging the sea surface heights at each point on each of the collinear trajectories.

6. The method for accurately calculating sea level change trends based on multi-source data fusion according to claim 4, characterized in that: The geodetic mission data is corrected for ocean time variations using a spatiotemporal objective analysis method, including: Preprocessing the satellite altimetry data, including removing data affected by land, sea ice, and rainfall; Use the spatiotemporal Kriging interpolation model to interpolate the data and obtain a dataset that traverses time and space; The satellite altimetry data is gridded and the data is distributed into a preset space-time grid.

7. The method for accurately calculating sea level change trends based on multi-source data fusion according to claim 1 or 6, characterized in that: Constructing a grid mean sea level change model includes: The time series of mean sea level height obtained according to the mean sea level model is expressed as: , ,in, The sequence number of the mean sea level time series, The mean sea level model grid points; Perform linear regression fitting on the mean sea level time series at each grid point and calculate the linear trend term, which is expressed as: ； in, Indicates the The constant term of the time series of the mean sea level height at each grid point, Indicates the The sea level trend term at each grid point is and Solve by least square method; By calculating the change trend at each grid point, the grid average sea level change model with a grid resolution of 1′×1′ is obtained.

8. A precise calculation system for sea level change trends based on multi-source data fusion, characterized by: include, The collection module is used to obtain multi-source satellite altimetry data and group the data; A first construction module is configured to establish a plurality of mean sea level models based on the multi-source satellite altimetry data, and to construct a time-varying mean sea level model using the mean sea level models; The second construction module is used to perform linear regression fitting on the mean sea level time series at each grid point based on the time-varying mean sea level model, construct a grid mean sea level change model, and calculate the change trend at each grid point.

9. An electronic device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for accurately calculating sea level change trends based on multi-source data fusion as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for accurately calculating sea level change trends based on multi-source data fusion as described in any one of claims 1 to 7.

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