CN117390897A - Method, system, device and storage medium for multi-scale assimilation of SWOT images - Google Patents
Method, system, device and storage medium for multi-scale assimilation of SWOT images Download PDFInfo
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Abstract
The invention relates to the field of data assimilation, in particular to a method, a system, equipment and a storage medium for multi-scale assimilation of SWOT images. The method comprises the following steps: obtaining sea surface height observation data based on SWOT satellite images; decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data; based on the large-scale observation data, obtaining a first assimilation result by assimilation of a four-dimensional assimilation model through a regional ocean numerical mode and combining the sea surface height observation data; and based on the small-medium scale observation data, the small-medium scale analysis field is assimilated by using a four-dimensional assimilation model through a regional ocean numerical mode and combining the first assimilation result, and a multi-scale assimilation result of the SWOT satellite image is obtained. According to the invention, SWOT satellite image data are decomposed and then subjected to two times of data assimilation, so that abundant information in high-resolution observation data is efficiently utilized, and the assimilation effect is greatly improved.
Description
Technical Field
The invention relates to the field of data assimilation, in particular to a method, a system, equipment and a storage medium for multi-scale assimilation of SWOT images.
Background
The assimilation of observation data is a main way for improving the simulation and forecast results of the ocean numerical values, and an initial field with higher precision is obtained by optimizing the integration results and input parameters of a model. The surface water and marine topography (Surface Water and Ocean Topography, SWOT) satellite is used as a first wide swath radar interference height measurement satellite capable of providing centimeter-level precision, can acquire marine hydrologic image data with high resolution and wide coverage range, can provide abundant marine medium-small scale process information for data assimilation, and can also put forward higher demands on an assimilation method. The traditional data assimilation method mostly takes discrete data as direct assimilation without considering medium-and-small scale signals carried in high-resolution observation, wherein the 3DVAR method usually correlates the observed data with discrete grid points of model state variables and does not fully utilize space continuity and small scale change in the observed data; the Kalman filtering method can effectively process a linear system, but does not consider correlation among observed errors, and has higher requirement on assumption of model errors; sequential data assimilation methods are suitable for large-scale nonlinear systems, but require a large number of set members to efficiently estimate uncertainty.
The adoption of the traditional data assimilation method not only can lose the high-resolution observation advantage, but also can lead the local ultrahigh-resolution information to exceed the bearing capacity of a conventional resolution model, so that the simulation capacity of the mode on the marine medium-small scale dynamic structure is difficult to effectively improve, and the optimal assimilation effect is difficult to obtain. In order to fully exert the advantages of SWOT satellite observation images such as swath width and high resolution, the development of a multi-scale assimilation method considering the spatial correlation of observation information can be considered, the spatial continuous signals of different scales in observation data can be respectively combined with ocean numerical modes, the efficient utilization of rich information in high-resolution observation is realized, the assimilation effect is greatly improved, the problem that the existing assimilation strategy is difficult to adapt to novel high-resolution observation data is effectively solved, and how to utilize SWOT satellite high-resolution elevation images and develop a high-efficiency data assimilation method by combining the applicable ocean numerical modes is a problem to be solved urgently.
Disclosure of Invention
Aiming at the inadequacy of the existing method and the requirement of practical application, an efficient data assimilation method is developed by combining the applicable marine numerical mode in order to effectively utilize the high-resolution elevation image of the SWOT satellite. In one aspect, the present invention provides a method of multi-scale assimilating a SWOT image, comprising the steps of: based on the SWOT satellite image, acquiring a sea surface height simulation result of the target sea area, and acquiring sea surface height observation data; decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data; based on the large-scale observation data, a large-scale analysis field is obtained through a regional ocean numerical mode, and the large-scale analysis field is assimilated by a four-dimensional assimilation model in combination with the sea surface height observation data to obtain a first assimilation result; based on the small-medium scale observation data, a small-medium scale analysis field is obtained through a regional ocean numerical mode, and the small-medium scale analysis field is assimilated by using a four-dimensional assimilation model in combination with the first assimilation result, so that a multi-scale assimilation result of the SWOT satellite image is obtained. According to the invention, based on regional ocean numerical mode ROMS, different-scale observation data obtained by decomposing SWOT satellite wide swath and high-resolution sea surface height observation data are respectively assimilated, so that the high-resolution observation advantage is fully utilized, the simulation capability of the regional ocean numerical mode on a marine medium-small-scale dynamic structure is effectively improved, the assimilation effect is greatly improved, and the problem that the existing assimilation strategy is difficult to adapt to novel high-resolution observation data is effectively solved.
Optionally, decomposing the sea surface altitude observation data by a scale decomposition method to obtain large-scale observation data and medium-scale and small-scale observation data, including the following steps: setting the resolution of a four-dimensional variation assimilation model; carrying out average processing on the sea surface height observation data in the resolution around grid points to obtain corresponding grid observation data; summarizing the grid observation data to obtain the large-scale observation data; and removing the large-scale observed data in the sea surface height observed data to obtain the medium-small-scale observed data. By setting different resolutions of the four-dimensional variation assimilation model, the method can be suitable for various high-resolution image data, and improves the usability of the method; the decomposition result obtained by the scale decomposition method is standardized, is not influenced by human factors, and improves the accuracy and reliability of the invention.
Optionally, the four-dimensional assimilation model satisfies the following formula:wherein J is n Represents the objective function value, n represents the nth scale, x n Representing the analytical field at the n-th scale, < ->Representing the background field at the nth scale, y n Represents the observed value at the nth scale, B n A background error covariance matrix representing corresponding scales, R n An observed error covariance matrix representing the corresponding scale,/>Representing the value obtained by the transfer of the analytical field at the nth scale to the observation position. The result can be obtained rapidly by using the model through a computer program, which is beneficial to improving the rapidness of the invention.
Optionally, the method includes obtaining a large-scale analysis field through a regional ocean numerical mode based on the large-scale observation data, and combining the sea surface height observation data, and assimilating the large-scale analysis field by using a four-dimensional assimilation model to obtain a first assimilation result, and the method includes the following steps: setting the sea surface height observation data as a large-scale background field; obtaining large-scale assimilation increment through the regional ocean numerical mode by utilizing the large-scale observation data; obtaining a large-scale analysis field through the large-scale background field and the large-scale assimilation increment; and combining the large-scale analysis field and the sea surface height observation data, and obtaining the first assimilation result through the four-dimensional assimilation model. The primary data assimilation is carried out on the large-scale observation data, so that the loads of the regional ocean numerical model and the four-dimensional assimilation model are reduced, and a good foundation is provided for the secondary data assimilation.
Optionally, the first assimilation result satisfies the following formula:wherein->Representing the first assimilation result,/->Representing a large scale background field,/->Representing large scale assimilation increment,/->Representing the difference between the large scale analytical field and the sea surface altitude observation, +.>Representing large scale observations, +.>Representing the value obtained by the transfer of the large-scale background field to the observation position,/->Representing the value obtained by transferring the large-scale background field to the observation position after adding the large-scale assimilation increment.
Optionally, based on the small-medium scale observation data, obtaining a small-medium scale analysis field through a regional ocean numerical mode, and combining the first assimilation result, assimilating the small-medium scale analysis field by using a four-dimensional assimilation model to obtain a multi-scale assimilation result of the SWOT satellite image, wherein the multi-scale assimilation result comprises the following steps: setting the first assimilation result as a medium-small scale background field; obtaining a medium-small scale assimilation increment through the regional ocean numerical mode by utilizing the medium-small scale observation data; obtaining a medium-small scale analysis field through the medium-small scale background field and the medium-small scale assimilation increment; and combining the small-medium-scale analysis field and the small-medium-scale background field, and obtaining a multi-scale assimilation result of the SWOT satellite image through the four-dimensional assimilation model. And the secondary data assimilation is carried out on the small-medium-scale observation data, so that the high-resolution image provided by the SWOT satellite is effectively utilized, and the accuracy and the adaptability of the method are improved.
Optionally, the multi-scale assimilation result of the SWOT satellite image satisfies the following formula:wherein X represents the multi-scale assimilation result of the SWOT satellite image, < >>Representing the first assimilation result,/->Representing medium-small scale assimilation increment,/->Representing the difference between the small and medium scale analytical field and the small and medium scale background field,/->Representing sea level altitude observations.
In a second aspect, in order to efficiently execute the method for assimilating the SWOT images in multiple scales, the invention also provides a system for assimilating the SWOT images in multiple scales, which comprises a simulation module, a processing module and a processing module, wherein the simulation module is used for obtaining a sea surface height simulation result according to the SWOT satellite images and generating sea surface height observation data of the SWOT satellites; the scale decomposition module is used for decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data; the large-scale assimilation module is used for obtaining a large-scale analysis field through a regional ocean numerical mode by utilizing the large-scale observation data, and assimilating the large-scale analysis field by utilizing a four-dimensional assimilation model in combination with the sea surface height observation data to obtain a first assimilation result; the medium-small scale assimilation module is used for obtaining a medium-small scale analysis field through a regional ocean numerical mode by utilizing the medium-small scale observation data, and assimilating the medium-small scale analysis field by utilizing a four-dimensional assimilation model in combination with the first assimilation result to obtain a multi-scale assimilation result of the SWOT satellite image. The system for multi-scale assimilating SWOT images has compact structure and stable performance, and can stably execute the method for multi-scale assimilating SWOT images, thereby improving the overall applicability and practical application capability of the invention.
In a third aspect, the present invention also provides an apparatus for multi-scale assimilation of SWOT images, comprising: a memory for storing a computer program; a processor for implementing the steps of the method of multi-scale assimilating SWOT images when executing the computer program. The multi-scale SWOT image assimilation device is convenient to install, and the overall applicability and practical application capability of the device are further improved.
In a fourth aspect, the present invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method of multi-scale assimilating a SWOT image as described. The storage medium provided by the invention has strong reliability, is easy to carry, transmit and copy, and further improves the overall applicability and practical application capability of the invention.
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FIG. 1 is a flow chart of a method for multi-scale assimilation of SWOT images provided by an embodiment of the present invention;
FIG. 2 is a multi-scale assimilation result of SWOT images of an embodiment of the present invention;
FIG. 3 is a single scale assimilation result of SWOT images obtained by a conventional assimilation method;
FIG. 4 is a schematic diagram of a system frame for multi-scale assimilation of SWOT images according to an embodiment of the invention;
fig. 5 is a schematic diagram of a device structure for multi-scale assimilating SWOT images according to an embodiment of the present invention.
Detailed Description
Specific embodiments of the invention will be described in detail below, it being noted that the embodiments described herein are for illustration only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been described in detail in order not to obscure the invention.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "one example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale.
In order to fully utilize the high-resolution observation advantage of the SWOT satellite, effectively improve the simulation capability of the regional ocean numerical mode on the small-medium-scale power structure in the ocean, greatly improve the assimilation effect, and effectively solve the problem that the existing assimilation strategy is difficult to adapt to novel high-resolution observation data, in one embodiment, the invention provides a method for assimilating the SWOT image in a multi-scale manner, please refer to FIG. 1, FIG. 1 is a flow chart of the method for assimilating the SWOT image in a multi-scale manner provided by the embodiment of the invention, and as shown in FIG. 1, the method for assimilating the SWOT image in a multi-scale manner provided by the embodiment comprises the following steps:
s1, acquiring a sea surface height simulation result of a target sea area based on an SWOT satellite image, and acquiring sea surface height observation data.
SWOT satellites are abbreviations for surface water and marine topography (Surface Water and Ocean Topography) satellites, developed jointly by the national aerospace Agency (NASA) and the french national Space research center, french Space Agency (Centre National d' É tudes Space, CNES), and assisted by canadian Space Agency (Canadian Space Agency, CSA) and UK Space Agency (UKSA). SWOT satellites are global first-class satellites that observe almost all bodies of water on the earth's surface, and will measure the body of water in the earth's lakes, rivers, reservoirs and oceans to see how the oceans affect climate change, how global warming affects lakes, rivers and reservoirs, and how society better handles disasters such as floods. The satellite will cover the entire earth surface between 78 degrees south latitude and 78 degrees north latitude, at least once every 21 days.
The regional ocean numerical model (Regional Ocean Model System, romis) is a numerical model widely used for simulating and researching ocean circulation, tides, ocean waves, ocean biochemical processes, ocean sediment and the like.
In one embodiment, SWOT satellite data is obtained through mechanism websites such as NASA, french national space research Center (CNES) and the like, a truth experiment is carried out based on a regional ocean numerical mode, sea surface height simulation results are obtained, and sea surface height observation data of wide swath and high resolution of the SWOT satellites is generated through a simulation technology.
S2, decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data.
In yet another embodiment, decomposing the sea surface altitude observation data to obtain large scale observation data and small scale observation data comprises the steps of:
s21, setting the resolution of the four-dimensional variation assimilation model.
In the embodiment, the resolution of the four-dimensional variation assimilation model is set to be 1/10 degrees. It should be noted that the resolution set in this embodiment is only a preferred one, and in other embodiments, the resolution may be adjusted according to the actual situation.
S22, carrying out average processing on the sea surface height observation data in the resolution around the grid points to obtain corresponding grid observation data.
In the embodiment, the observation information in the grids of 1/10 degree around each grid point is subjected to average value processing to obtain corresponding grid observation data, and the corresponding grid observation data is used as large-scale observation data of the corresponding grid points; it should be appreciated that in other embodiments, the observed information within a 1/10 grid around each grid point may also be weighted average processed according to how far from the center of the grid.
And S23, summarizing the grid observation data to obtain the large-scale observation data.
In an embodiment, the large-scale observation data of each grid point is summarized as the large-scale observation data of the SWOT satellite.
S24, removing the large-scale observed data in the sea surface height observed data to obtain the small-scale and medium-scale observed data.
In the embodiment, the value corresponding to the sea surface height observation data in each grid point is subtracted from the value corresponding to the large-scale observation data to serve as the middle-small scale observation data of the corresponding grid point, and the middle-small scale observation data of each grid point are summarized to serve as the middle-small scale observation data of the SWOT satellite.
S3, based on the large-scale observation data, obtaining a large-scale analysis field through a regional ocean numerical mode, and combining the sea surface height observation data, and assimilating the large-scale analysis field by using a four-dimensional assimilation model to obtain a first assimilation result.
Setting the sea surface height observation data as a large-scale background field; obtaining large-scale assimilation increment through the regional ocean numerical mode by utilizing the large-scale observation data; obtaining a large-scale analysis field through the large-scale background field and the large-scale assimilation increment; and inputting the four-dimensional assimilation model by combining the large-scale analysis field and the sea surface height observation data to obtain the first assimilation result.
It can be understood that the simulation capability of the regional ocean numerical mode for simulating the high-resolution information is weak, and the data information of the middle and small scales cannot be simulated, so that the sea surface height observation data can be directly used as a background field to be compared with a simulation value.
In an embodiment, the four-dimensional assimilation model satisfies the following formula:wherein J is n Represents the objective function value, n represents the nth scale, x n Representing the analytical field at the n-th scale, < ->Representing the background field at the nth scale, y n Represents the observed value at the nth scale, B n A background error covariance matrix representing corresponding scales, R n An observed error covariance matrix representing the corresponding scale,/>Representing the value obtained by the transfer of the analytical field at the nth scale to the observation position. It will be appreciated that the four-dimensional assimilation model provided in this embodiment is merely a preferred option, and in other embodiments, the optimization model may be adapted.
In yet another embodiment, the first assimilation result satisfies the following formula:wherein->Representing the first assimilation result,/->Representing a large scale background field,/->Representing large scale assimilation increment,/->Representing the difference between the large scale analytical field and the sea surface altitude observation, +.>Representing large scale observations, +.>Representing large scale background field transitions toObserving the value obtained for the location,/>Representing the value obtained by transferring the large-scale background field to the observation position after adding the large-scale assimilation increment. It will be appreciated that the requirement for the first assimilation result in this embodiment is merely a preferred option and that other embodiments may be adapted.
And S4, based on the small-medium scale observation data, obtaining a small-medium scale analysis field through a regional ocean numerical mode, and combining the first assimilation result, assimilating the small-medium scale analysis field by using a four-dimensional assimilation model to obtain a multi-scale assimilation result of the SWOT satellite image.
In another alternative embodiment, the first assimilation result is set to a medium-to-small scale background field; obtaining a medium-small scale assimilation increment through the regional ocean numerical mode by utilizing the medium-small scale observation data; obtaining a medium-small scale analysis field through the medium-small scale background field and the medium-small scale assimilation increment; and combining the small-medium-scale analysis field and the small-medium-scale background field, inputting the four-dimensional assimilation model, and obtaining a multi-scale assimilation result of the SWOT satellite image.
In an embodiment, the multi-scale assimilation result of the SWOT satellite image satisfies the following formula:wherein X represents the multi-scale assimilation result of the SWOT satellite image, < >>Representing the first assimilation result,/->Representing medium-small scale assimilation increment,/->Representing the difference between the small and medium scale analytical field and the small and medium scale background field,/->Representing sea level altitude observations, whereinThe derivation process is as follows:=。
referring to fig. 2, fig. 2 is a multi-scale assimilation result of a SWOT image according to an embodiment of the present invention, where color depth represents density of observation points, and is used to show how many points fall within a region; the observation number represents the number of the observed sample data, and the observation (meter) represents the sea surface height of the observation point; in the examples, the correlation coefficients of the multi-scale assimilation results of the SWOT images obtained by the present invention 2 0.998, 0.009 root mean square error, 0.93% mean absolute error, and y=0.995x+0.003;
referring to FIG. 3, FIG. 3 shows the single-scale assimilation results of SWOT images obtained by conventional assimilation methods, correlation coefficients 2 0.991, 0.02 root mean square error, 2.237 mean absolute error, and y=0.966x+0.022;
by comparing the data in fig. 2 and fig. 3, the method for assimilating the SWOT image by multiple scales has more excellent assimilation effect, and can effectively solve the problem that the existing assimilation strategy is difficult to adapt to novel high-resolution observation data.
Referring to fig. 4, fig. 4 is a schematic diagram of a system frame for multi-scale assimilating SWOT images according to an embodiment of the present invention, in an embodiment, to be able to efficiently execute a method for multi-scale assimilating SWOT images provided by the present invention, the present invention further provides a system for multi-scale assimilating SWOT images, as shown in fig. 4, a system for multi-scale assimilating SWOT images provided by the present invention includes: the simulation module is used for obtaining a sea surface height simulation result according to the SWOT satellite image and generating sea surface height observation data of the SWOT satellite; the scale decomposition module is used for decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data; the large-scale assimilation module is used for obtaining a large-scale analysis field through a regional ocean numerical mode by utilizing the large-scale observation data, and assimilating the large-scale analysis field by utilizing a four-dimensional assimilation model in combination with the sea surface height observation data to obtain a first assimilation result; the medium-small scale assimilation module is used for obtaining a medium-small scale analysis field through a regional ocean numerical mode by utilizing the medium-small scale observation data, and assimilating the medium-small scale analysis field by utilizing a four-dimensional assimilation model in combination with the first assimilation result to obtain a multi-scale assimilation result of the SWOT satellite image. The simulation module, the scale decomposition module, the large-scale assimilation module and the medium-and small-scale assimilation module are connected layer by layer, and the system for assimilating SWOT images in multiple scales has compact structure and stable performance, and can stably execute the method for assimilating SWOT images in multiple scales, thereby improving the overall applicability and practical application capability of the system.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an apparatus for multi-scale assimilating SWOT images according to an embodiment of the present invention, and in an embodiment, in order to be able to efficiently execute a method for multi-scale assimilating SWOT images according to the present invention, the present invention further provides an apparatus for multi-scale assimilating SWOT images, as shown in fig. 5, including:
a memory 10 for storing a computer program; a processor 20 for executing a computer program to implement the method of multi-scale assimilating SWOT images described above.
Memory 10, processor 20, communication interface 31, and communication bus 32. The memory 10, the processor 20, and the communication interface 31 all communicate with each other via a communication bus 32.
In the embodiment of the present invention, the memory 10 is used to store one or more programs, and the programs may include program codes, where the program codes include computer operation instructions, and in the embodiment of the present application, the memory 10 may store programs for implementing the following functions:
based on the SWOT satellite image, acquiring a sea surface height simulation result of the target sea area, and acquiring sea surface height observation data;
decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data;
based on the large-scale observation data, a large-scale analysis field is obtained through a regional ocean numerical mode, and the large-scale analysis field is assimilated by a four-dimensional assimilation model in combination with the sea surface height observation data to obtain a first assimilation result;
based on the small-medium scale observation data, a small-medium scale analysis field is obtained through a regional ocean numerical mode, and the small-medium scale analysis field is assimilated by using a four-dimensional assimilation model in combination with the first assimilation result, so that a multi-scale assimilation result of the SWOT satellite image is obtained.
In one possible implementation, the memory 10 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, and at least one application program required for functions, etc.; the storage data area may store data created during use.
In addition, memory 10 may include read only memory and random access memory and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, where the operating instructions may include various operating instructions for performing various operations. The operating system may include various system programs for implementing various basic tasks as well as handling hardware-based tasks.
The processor 20 may be a central processing unit (Central Processing Unit, CPU), an asic, a dsp, a fpga or other programmable logic device, and the processor 20 may be a microprocessor or any conventional processor. The processor 20 may call a program stored in the memory 10.
The communication interface 31 may be an interface of a communication module for connecting with other devices or systems.
Of course, it should be noted that the structure shown in fig. 5 does not limit the apparatus for multi-scale assimilating SWOT images in the embodiments of the present application, and the apparatus for multi-scale assimilating SWOT images may include more or fewer components than those shown in fig. 5 or may combine some components in practical applications.
The invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of multi-scale assimilating a SWOT image as described above.
The storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In conclusion, the invention fully utilizes the high-resolution observation advantage, effectively improves the simulation capability of the regional ocean numerical mode on the ocean mesoscale dynamic structure, greatly improves the assimilation effect, and effectively solves the problem that the existing assimilation strategy is difficult to adapt to novel high-resolution observation data. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. A method of multi-scale assimilating SWOT images, comprising the steps of:
based on the SWOT satellite image, acquiring a sea surface height simulation result of the target sea area, and acquiring sea surface height observation data;
decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data;
based on the large-scale observation data, a large-scale analysis field is obtained through a regional ocean numerical mode, and the large-scale analysis field is assimilated by a four-dimensional assimilation model in combination with the sea surface height observation data to obtain a first assimilation result;
based on the small-medium scale observation data, a small-medium scale analysis field is obtained through a regional ocean numerical mode, and the small-medium scale analysis field is assimilated by using a four-dimensional assimilation model in combination with the first assimilation result, so that a multi-scale assimilation result of the SWOT satellite image is obtained.
2. The method of multi-scale assimilation of SWOT images according to claim 1, wherein said decomposing said sea surface altitude observation data to obtain a large scale observation data and a small scale observation data comprises the steps of:
setting the resolution of a four-dimensional variation assimilation model;
carrying out average processing on the sea surface height observation data in the resolution around grid points to obtain corresponding grid observation data;
summarizing the grid observation data to obtain the large-scale observation data;
and removing the large-scale observed data in the sea surface height observed data to obtain the medium-small-scale observed data.
3. The method of multi-scale assimilation of SWOT images according to claim 1, wherein said four-dimensional assimilation model satisfies the following formula:wherein J is n Represents the objective function value, n represents the nth scale, x n Representing the analytical field at the n-th scale, < ->Representing the background field at the nth scale, y n Represents the observed value at the nth scale, B n A background error covariance matrix representing corresponding scales, R n An observed error covariance matrix representing the corresponding scale,/>Representing the value obtained by the transfer of the analytical field at the nth scale to the observation position.
4. The method for multi-scale assimilating SWOT images according to claim 1, wherein said obtaining a large scale analysis field by a regional ocean numerical model based on said large scale observation data, and assimilating said large scale analysis field with a four-dimensional assimilation model in combination with said sea surface altitude observation data, obtaining a first assimilation result, comprises the steps of:
setting the sea surface height observation data as a large-scale background field;
obtaining large-scale assimilation increment through the regional ocean numerical mode by utilizing the large-scale observation data;
obtaining a large-scale analysis field through the large-scale background field and the large-scale assimilation increment;
and combining the large-scale analysis field and the sea surface height observation data, and obtaining the first assimilation result through the four-dimensional assimilation model.
5. The method of multi-scale assimilation of a SWOT image according to claim 4, wherein said first assimilation result satisfies the following formula:wherein->Representing the first assimilation result,/->Representing a large scale backgroundField (F)>Representing large scale assimilation increment,/->Representing the difference between the large scale analytical field and the sea surface altitude observation, +.>Representing large scale observations, +.>Representing the value obtained by the transfer of the large-scale background field to the observation position,/->Representing the value obtained by transferring the large-scale background field to the observation position after adding the large-scale assimilation increment.
6. The method for multi-scale assimilating a SWOT image according to claim 1, wherein said obtaining a mid-small scale analysis field by a regional ocean numerical model based on said mid-small scale observation data, and said assimilating said mid-small scale analysis field with said first assimilating result using a four-dimensional assimilating model, obtaining a multi-scale assimilating result of a SWOT satellite image, comprises the steps of:
setting the first assimilation result as a medium-small scale background field;
obtaining a medium-small scale assimilation increment through the regional ocean numerical mode by utilizing the medium-small scale observation data;
obtaining a medium-small scale analysis field through the medium-small scale background field and the medium-small scale assimilation increment;
and combining the small-medium-scale analysis field and the small-medium-scale background field, and obtaining a multi-scale assimilation result of the SWOT satellite image through the four-dimensional assimilation model.
7. According to claimThe method for multi-scale assimilation of a SWOT image according to claim 6, wherein the multi-scale assimilation result of the SWOT satellite image satisfies the following formula:wherein X represents the multi-scale assimilation result of the SWOT satellite image, < >>Representing the first assimilation result,/->Representing medium-small scale assimilation increment,/->Representing the difference between the small and medium scale analytical field and the small and medium scale background field,/->Representing sea level altitude observations.
8. A system for multi-scale assimilation of SWOT images, wherein the system for multi-scale assimilation of SWOT images is adapted for use in the method for multi-scale assimilation of SWOT images of any of claims 1-7, the system for multi-scale assimilation of SWOT images comprising:
the simulation module is used for obtaining a sea surface height simulation result according to the SWOT satellite image and generating sea surface height observation data of the SWOT satellite;
the scale decomposition module is used for decomposing the sea surface height observation data to obtain large-scale observation data and medium-scale and small-scale observation data;
the large-scale assimilation module is used for obtaining a large-scale analysis field through a regional ocean numerical mode by utilizing the large-scale observation data, and assimilating the large-scale analysis field by utilizing a four-dimensional assimilation model in combination with the sea surface height observation data to obtain a first assimilation result;
the medium-small scale assimilation module is used for obtaining a medium-small scale analysis field through a regional ocean numerical mode by utilizing the medium-small scale observation data, and assimilating the medium-small scale analysis field by utilizing a four-dimensional assimilation model in combination with the first assimilation result to obtain a multi-scale assimilation result of the SWOT satellite image.
9. A device for multi-scale assimilation of SWOT images, characterized in that the device for multi-scale assimilation of SWOT images comprises:
a memory for storing a computer program;
a processor for implementing the steps of the method of multi-scale assimilating SWOT images according to any of claims 1-7 when executing said computer program.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of multi-scale assimilating SWOT images according to any of claims 1-7.
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