Coordinated coupling assimilation method and device for ocean data and sea ice data

Abstract

The invention provides a coordinated coupling assimilation method and device for ocean data and sea ice data. Comprising the steps that ocean observation data and sea ice observation data of a target sea area at the current moment and an ocean sample set and a sea ice sample set of the target sea area are obtained, and the ocean sample set comprises ocean data of the target sea area at multiple moments within the preset duration from the current time; the sea ice sample set comprises sea ice data of the target sea area at multiple moments within a preset duration from the current time; performing data assimilation processing on the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set according to a preset data assimilation mode to obtain ocean and sea ice data assimilation results; and performing coupling processing on the assimilation results of the ocean and the sea ice data, and updating the ocean mode variable state and the sea ice mode variable state according to the coupling result so as to obtain an accurately coordinated ocean and sea ice variable analysis field.

Description

Technical Field

[0001] The present application relates to the technical field of data coupling and assimilation, and in particular to a method and device for coordinated coupling and assimilation of ocean data and sea ice data. Background Art

[0002] Coupled assimilation is an extremely critical technology for numerical simulation and prediction of multi-sphere coupled climate systems or earth systems. How to build a coupled assimilation system based on complex coupling models and assimilation methods, accurately describe the interaction of assimilated components in each sphere, and achieve coordinated analysis of multi-sphere and multi-source observation data is a challenging task in the current field of numerical climate prediction and weather forecasting. After nearly 20 years of research and business practice, coupled assimilation has been gradually applied to climate and earth system simulation and prediction, and has become the most reliable means of generating multi-sphere coupled analysis fields and initial fields.

[0003] The existing climate model or Earth system model assimilation and prediction system has not achieved the effective coupling assimilation and application of multi-source real observation data on the ocean and sea ice, resulting in obvious limitations in depicting the mutual feedback effects between the ocean and sea ice. It is impossible to achieve close coupling and synchronous updating of ocean and sea ice information, and is not suitable for situations that require more accurate analysis of the mutual impact of the ocean and sea ice. Summary of the invention

[0004] The technical problem to be solved by the embodiments of the present application is to provide a method and device for coordinated coupling assimilation of ocean data and sea ice data, so as to achieve close coupling and synchronous updating of ocean and sea ice information, solve the coupling limitations of ocean and sea ice interactions in assimilation, and improve the accuracy of multi-sphere feedback of ocean and sea ice.

[0005] In a first aspect, an embodiment of the present application provides a method for coordinated coupling assimilation of ocean data and sea ice data, the method comprising: Acquire ocean observation data and sea ice observation data of the target sea area at the current time, as well as an ocean sample set and a sea ice sample set of the target sea area, wherein the ocean sample set includes ocean data of the target sea area at multiple times within a preset time from the current time, and the sea ice sample set includes sea ice data of the target sea area at multiple times within a preset time from the current time; According to a preset data assimilation method, the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set are subjected to data assimilation processing to obtain ocean and sea ice data assimilation results; The ocean and sea ice data assimilation results are coupled and the ocean model variable states and sea ice model variable states are updated according to the coupling results.

[0006] In a second aspect, an embodiment of the present application provides a coordinated coupling assimilation device for ocean data and sea ice data, the device comprising: An ocean and sea ice data acquisition module, used to acquire ocean observation data and sea ice observation data of a target sea area at a current moment, as well as an ocean sample set and a sea ice sample set of the target sea area, wherein the ocean sample set includes ocean data of the target sea area at multiple moments within a preset time from the current time, and the sea ice sample set includes sea ice data of the target sea area at multiple moments within a preset time from the current time; A data assimilation result acquisition module, used to perform data assimilation processing on the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set according to a preset data assimilation method to obtain ocean and sea ice data assimilation results; The ocean and sea ice variable state updating module is used to couple the ocean and sea ice data assimilation results, and update the ocean model variable state and the sea ice model variable state according to the coupling results.

[0007] Compared with the prior art, the embodiments of the present application have the following advantages: In the embodiment of the present application, the ocean observation data and sea ice observation data of the target sea area at the current moment, as well as the ocean sample set and sea ice sample set of the target sea area are obtained, wherein the ocean sample set includes the ocean data of the target sea area at multiple moments within a preset time length from the current time, and the sea ice sample set includes the sea ice data of the target sea area at multiple moments within a preset time length from the current time. According to the pre-set data assimilation method, the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set are subjected to data assimilation processing to obtain the ocean and sea ice data assimilation results. The ocean and sea ice data assimilation results are coupled, and the ocean model variable state and the sea ice model variable state are updated according to the coupling results. In the embodiment of the present application, the ocean variable data and the sea ice variable data are incorporated into the assimilation process, and different data assimilation methods are used for assimilation processing, so as to realize the close coupling and synchronous update of the ocean and sea ice information, solve the coupling limitations of the ocean and sea ice interaction in assimilation, improve the accuracy of the multi-sphere feedback of the ocean and sea ice, and obtain a more coordinated and accurate ocean and sea ice variable analysis field.

[0008] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of the steps of a coordinated coupling assimilation method of ocean data and sea ice data provided in an embodiment of the present application; Figure 2A schematic diagram of a weakly coupled assimilation process provided in an embodiment of the present application; Figure 3 A schematic diagram of a strongly coupled assimilation process provided in an embodiment of the present application; Figure 4 A schematic diagram of an assimilation test process provided in an embodiment of the present application; Figure 5 A schematic diagram of an experimental assimilation result provided in an embodiment of the present application; Figure 6 A schematic diagram of another test assimilation result provided in an embodiment of the present application; Figure 7 A schematic diagram of the structure of a coordinated coupling assimilation device for ocean data and sea ice data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0011] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0012] Reference Figure 1 , shows a flow chart of steps of a coordinated coupling assimilation method of ocean data and sea ice data provided by an embodiment of the present application, such as Figure 1 As shown, the coordinated coupling assimilation method of ocean data and sea ice data may include: step 101, step 102 and step 103.

[0013] Step 101: Obtain ocean observation data and sea ice observation data of the target sea area at the current moment, as well as an ocean sample set and a sea ice sample set of the target sea area, wherein the ocean sample set includes ocean data of the target sea area at multiple moments within a preset time length from the current time, and the sea ice sample set includes sea ice data of the target sea area at multiple moments within a preset time length from the current time.

[0014] In this embodiment, the ocean observation data may be ocean data of the target sea area collected at the current moment, which may include: sea surface temperature observation data, sea surface height observation data, ocean temperature profile observation data, ocean salinity profile observation data, etc.

[0015] The sea ice observation data may be the sea ice data of the target sea area collected at the current moment, which may include: sea ice density observation data, sea ice thickness observation data, etc.

[0016] The ocean sample set may include ocean data of the target sea area at multiple times within a preset time from the current time. In this example, the ocean sample set is sampled from the long-term simulation integral test of the ocean model, and the number of samples is variable. For example, when the value is 100, the ocean sample set includes 100 times of ocean multi-level temperature, salinity, current velocity, and sea surface height field simulation data (sea surface height field is an important physical quantity that can reflect the dynamic and thermodynamic processes on the ocean surface) and other data.

[0017] The sea ice sample set can include sea ice data at multiple times within a preset time from the current time in the target sea area. Similar to the ocean sample set, the sea ice sample set is also obtained from the long-term simulation integral test of the sea ice model, and the number of samples is variable. For example, when the value is 100, it contains 10 times of sea ice density, sea ice thickness simulation data and other data.

[0018] When performing coupled assimilation of ocean-sea ice variable data, the ocean observation data and sea ice observation data of the target sea area at the current moment, as well as the ocean sample set and sea ice sample set of the target sea area can be obtained.

[0019] Among them, the ocean sample set and the sea ice sample set can be obtained through long-term simulation integral test sampling.

[0020] The ocean observation data and sea ice observation data of the target sea area at the current moment can be collected by station observation or satellite remote sensing technology. Station observation is to place drifting or fixed observation instruments in the target sea area and use various sensors and equipment on the instruments to collect data. Satellite remote sensing technology uses remote sensing instruments on satellites to observe and collect data on the target sea area. Appropriate satellites and sensors can be selected according to observation needs. For example, for ocean observation, satellites equipped with sea surface temperature sensors, sea surface salinity sensors, etc. can be selected. For sea ice observation, satellites equipped with microwave radiometers, visible light / infrared sensors, etc. can be selected. During the operation of the satellite in orbit, it will continuously send observation data to the ground. The ground receiving station will receive this data and perform preliminary processing and storage.

[0021] After obtaining the ocean observation data and sea ice observation data of the target sea area at the current moment, as well as the ocean sample set and sea ice sample set of the target sea area, step 102 is executed.

[0022] Step 102: According to a preset data assimilation method, data assimilation processing is performed on the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set to obtain ocean and sea ice data assimilation results.

[0023] After obtaining the ocean observation data and sea ice observation data of the target sea area at the current moment, as well as the ocean sample set and sea ice sample set of the target sea area, a complex climate system model (CSM) or earth system model (ESM) including ocean, sea ice and other components can be used to assimilate the ocean observation data, ocean sample set, sea ice observation data and sea ice sample set according to a pre-set data assimilation method to obtain the ocean and sea ice data assimilation results.

[0024] The embodiment of the present application provides two data assimilation modes, namely, weak coupling assimilation mode and strong coupling assimilation mode. In a specific implementation, a parameter control script can be set to read, and the system decides whether to use the weak coupling assimilation mode or the strong coupling assimilation mode according to the settings in the parameter control script.

[0025] Next, the data assimilation process of the two data assimilation methods is described in detail in combination with two specific implementation methods.

[0026] In a specific implementation of the present application, the above step 102 may include: Sub-step A1: when the data assimilation mode is a weakly coupled assimilation mode, constructing an ocean disturbance matrix according to the ocean sample set, and constructing a sea ice disturbance matrix according to the sea ice sample set.

[0027] In this embodiment, when the data assimilation method is a weakly coupled assimilation method, an ocean disturbance matrix can be constructed based on an ocean sample set, and a sea ice disturbance matrix can be constructed based on a sea ice sample set. Specifically, the ocean sample set and the sea ice sample set respectively contain ocean data and sea ice data at multiple times. When constructing an ocean disturbance matrix, the ocean data at each time can be used as a row element, and the combination of row elements at multiple times can obtain an ocean disturbance matrix. Similarly, when constructing a sea ice disturbance matrix, the sea ice data at each time can be used as a row element, and the combination of row elements at multiple times can obtain a sea ice disturbance matrix.

[0028] After constructing the ocean perturbation matrix and the sea ice perturbation matrix, sub-step A2 and sub-step A3 are performed.

[0029] Sub-step A2: using a preset assimilation equation to assimilate the ocean observation data and the ocean disturbance matrix to obtain an ocean data assimilation result.

[0030] The preset assimilation equation refers to a preset mathematical equation used to assimilate ocean ice data. In this example, the preset assimilation equation can be shown as the following formulas (1) and (2): (1) (2) In the above formulas (1) and (2), is the data assimilation result, is the gain matrix, is the perturbation matrix, is the transposed matrix of the perturbation matrix, is the observation operator (representing the conversion function of ocean model variables or sea ice model variables from model space to observation space), is the number of samples in the set, is the observation error covariance matrix, is the observed data, The background state of the mode variable.

[0031] After obtaining the ocean disturbance matrix, the ocean observation data and the ocean disturbance matrix can be assimilated using a preset assimilation equation to obtain the ocean data assimilation result. Specifically, the ocean disturbance matrix and the transposed matrix corresponding to the ocean disturbance matrix can be multiplied to obtain the ocean variable background error covariance matrix (the covariance matrix can be used to represent the error correlation between the ocean variable background field data), and the ocean variable background error covariance matrix and the ocean observation data can be assimilated to obtain the ocean analysis increment information (such as ocean temperature, salinity field, etc.), that is, the above formula (1) is used for processing, and the assimilation equation is solved to obtain the ocean analysis increment information, which can be used as the ocean data assimilation result.

[0032] When calculating the ocean analysis increment information, the ocean model background field information (i.e., the background state of the model variables in the above formula) can be introduced, where the ocean model background field refers to the simulation or forecast field of the ocean model itself. On the basis of the model background field, the introduction of ocean observation data for assimilation analysis calculation will generate analysis increments, which are superimposed on the model background field to form the assimilation analysis field.

[0033] Sub-step A3: using a preset assimilation equation to assimilate the sea ice observation data and the sea ice disturbance matrix to obtain a sea ice data assimilation result.

[0034] After obtaining the sea ice perturbation matrix, the sea ice observation data and the sea ice perturbation matrix can be assimilated using the preset assimilation equation to obtain the sea ice data assimilation result. Specifically, the sea ice perturbation matrix and the transposed matrix corresponding to the sea ice perturbation matrix can be multiplied to obtain the sea ice variable background error covariance matrix (the covariance matrix can be used to represent the error distribution and correlation between the sea ice variable data), and the sea ice variable background error covariance matrix and the sea ice observation data are assimilated to obtain the sea ice analysis increment information, that is, the above formulas (1) and (2) are used for processing, and the assimilation equation is solved to obtain the sea ice analysis increment information, which can be used as the sea ice data assimilation result.

[0035] When calculating the incremental information of sea ice analysis, the background field information of the sea ice model can be introduced, where the background field of the sea ice model refers to the simulation or forecast field of the sea ice model itself. On the basis of the model background field, the introduction of sea ice observation data for assimilation analysis calculation will generate analysis increments, which are superimposed on the model background field to form the assimilation analysis field.

[0036] Sub-step A4: using the ocean data assimilation result and the sea ice data assimilation result as the ocean and sea ice data assimilation result.

[0037] After obtaining the ocean data assimilation results and the sea ice data assimilation results, the ocean data assimilation results and the sea ice data assimilation results can be used as the ocean and sea ice data assimilation results under the weak coupling assimilation mode.

[0038] In another specific implementation of the present application, the above step 102 may include: Sub-step B1: When the data assimilation mode is a strongly coupled assimilation mode, a joint perturbation matrix is ​​constructed according to the ocean sample set and the sea ice sample set.

[0039] In this embodiment, when the data assimilation mode is a strongly coupled assimilation mode, a joint perturbation matrix can be constructed based on the ocean sample set and the sea ice sample set. Specifically, the ocean sample set and the sea ice sample set respectively contain ocean data and sea ice data at multiple times. When constructing the joint perturbation matrix, the ocean data and sea ice data at each time can be used as row elements, and the combination of row elements at multiple times can obtain the joint perturbation matrix.

[0040] After constructing the joint perturbation matrix based on the ocean sample set and the sea ice sample, sub-step B2 is performed.

[0041] Sub-step B2: multiply the joint disturbance matrix and the transposed matrix corresponding to the joint disturbance matrix to obtain a joint error covariance matrix.

[0042] After obtaining the joint perturbation matrix, the joint perturbation matrix and the transposed matrix corresponding to the joint perturbation matrix can be multiplied to obtain a joint error covariance matrix. The joint error covariance matrix can be used to represent the error distribution and correlation between the sea ice variable data and the ocean variable data.

[0043] After the joint error covariance matrix is ​​obtained, sub-steps B3 and B4 may be performed.

[0044] Sub-step B3: using a preset assimilation equation to assimilate the ocean observation data and the joint error covariance matrix to obtain ocean-sea ice analysis increment information.

[0045] After obtaining the joint error covariance matrix, the ocean observation data and the joint error covariance matrix can be assimilated using the preset assimilation equation to obtain the incremental information of ocean-sea ice analysis. That is, the above formulas (1) and (2) are used for processing, and the assimilation equation is solved to obtain the incremental information of ocean-sea ice analysis.

[0046] Sub-step B4: using a preset assimilation equation to assimilate the sea ice observation data and the joint error covariance matrix to obtain sea ice-ocean analysis increment information.

[0047] After obtaining the joint covariance matrix, the preset assimilation equation can be used to assimilate the sea ice observation data and the joint error covariance matrix to obtain the incremental information of sea ice-ocean analysis. That is, the above formulas (1) and (2) are used for processing, and the assimilation equation is solved to obtain the incremental information of sea ice-ocean analysis.

[0048] Sub-step B5: Integrate the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information to obtain the ocean and sea ice data assimilation result.

[0049] After obtaining the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information, the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information can be integrated to obtain the ocean and sea ice data assimilation result, that is, the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information are mixed together as the output ocean and sea ice data assimilation result.

[0050] The embodiment of the present application is based on an autonomous and controllable high-resolution climate system model, and proposes ocean-sea ice weak coupling and strong coupling assimilation schemes. Through automated script control and parameter definition, the ocean-sea ice coupled assimilation function with flexible switching between strong / weak coupling modes is realized.

[0051] In the weak coupling assimilation mode, an ocean ensemble assimilation scheme is proposed to realize the assimilation of satellite remote sensing sea surface temperature data, sea surface height data, and temperature and salinity profile data. A sea ice ensemble assimilation scheme is also proposed to realize the assimilation of satellite remote sensing sea ice density data and sea ice thickness data. The ocean and sea ice are assimilated separately, the ocean assimilation is only for ocean variables, and the sea ice assimilation is only for sea ice variables. They then realize the cross-sphere influence of ocean assimilation and sea ice assimilation information through the coupling process of the model itself (indirectly realized through the coupler).

[0052] In the strong coupling assimilation mode, a strong coupling assimilation scheme for ocean and sea ice is further constructed based on the ensemble assimilation method, that is, the ocean and sea ice variables (sea surface temperature, sea ice density, sea ice thickness) are included in the assimilation equation, and the error covariance matrix of the ocean and sea ice multivariable joint is constructed. At the same time, the ocean and sea ice data are assimilated, and the analysis results of the ocean and sea ice are directly obtained using the assimilation algorithm to update the model state. In this process, the ocean and sea ice assimilation information will not only indirectly affect other spheres through the model coupling process, but more importantly, it will directly affect through the ocean and sea ice variable covariance in the assimilation algorithm.

[0053] After the ocean and sea ice data assimilation results are obtained, step 103 is executed.

[0054] Step 103: performing coupling processing on the ocean and sea ice data assimilation results, and updating the ocean model variable state and the sea ice model variable state according to the coupling results.

[0055] The ocean model variable state refers to the state of ocean variables simulated by the ocean model (such as temperature, salinity, current velocity, etc.).

[0056] The sea ice model variable state refers to the sea ice variable state (such as density, thickness, etc.) simulated by the sea ice model.

[0057] After obtaining the ocean and sea ice data assimilation results, the ocean and sea ice data assimilation results can be coupled and the ocean model variable states and sea ice model variable states can be updated according to the coupling results.

[0058] In the weak coupling assimilation mode, the sea ice data assimilation results can be passed to the ocean model through the coupler, and the ocean data assimilation results can be passed to the sea ice model through the coupler to complete the coupling between the two. Then, the ocean model variable state can be updated according to the sea ice data assimilation results, and the sea ice model variable state can be updated according to the ocean data assimilation results. The coupling in this process is loose, and the influence between the ocean and sea ice assimilation components is only exchanged through the model coupler, rather than directly related calculations.

[0059] like Figure 2 and Figure 3As shown, the entire coupled model includes multiple component models such as ocean, land surface, atmosphere, and sea ice. The ocean model and sea ice model are two of the component models. They are complex numerical models that include dynamic and physical processes (they can be software consisting of hundreds of thousands of lines of programs).

[0060] Under the weakly coupled assimilation mode, the ocean model and the sea ice model will receive feedback information from each other and combine it with their own physical processes (such as ocean currents driven by factors such as wind stress, density differences and the earth's rotation, heat transfer, mixing and diffusion involving the ocean, and changes in temperature, salinity and density, etc.) to further update the state of ocean model variables and sea ice model variables and drive the model (that is, the entire coupled climate system model or earth system model) to run forward integration.

[0061] In the strong coupling assimilation mode, the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information can be directly passed to the ocean model, and the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information can be directly passed to the sea ice model to update the ocean model variable state and the sea ice model variable state simultaneously according to this information. In the strong coupling assimilation mode, the coupler is not a key component for the cross-sphere propagation of assimilated information, but it can integrate the received ocean and sea ice assimilation information to ensure that the interaction between the two continues to be consistent in subsequent model operations.

[0062] In this embodiment, according to the selected strong coupling assimilation mode or weak coupling assimilation mode, the assimilation system generates updated ocean and sea ice model results, which will also be input into the atmospheric model, land surface model and other modules to drive the entire model system to complete long-term climate and environmental simulation. Weak coupling assimilation: The assimilation process of the ocean and sea ice is independent, with a low degree of coupling. The assimilation information is simply exchanged and transmitted through the coupler, which is suitable for simulation environments with low coupling requirements. Strong coupling assimilation: The assimilation process of the ocean and sea ice is joint, and the coupling of the assimilation information of the two components is directly realized through the assimilation algorithm. On this basis, the coupler will further strengthen the mutual influence of the two components, which is suitable for situations where more accurate ocean-sea ice interaction analysis is required.

[0063] The ocean-sea ice weak coupling assimilation process can be as follows Figure 2As shown, first, ocean observation data (including sea surface temperature observation data, sea surface height observation data, ocean temperature profile observation data, ocean salinity profile observation data, etc.) and sea ice observation data (including sea ice density observation data and sea ice thickness observation data, etc.) can be obtained. At the same time, the ocean sample set and the sea ice sample set can be obtained, and the ocean variable background error covariance matrix can be calculated based on the ocean sample set, and the sea ice variable background error covariance matrix can be calculated based on the sea ice sample set. Secondly, the ocean variable background error covariance matrix and the ocean observation data can be passed to the ocean assimilation module, and the ocean assimilation module assimilates the ocean variable background error covariance matrix and the ocean observation data. At the same time, the sea ice variable background error covariance matrix and the sea ice observation data can be passed to the sea ice assimilation module, and the sea ice assimilation module assimilates the sea ice variable background error covariance matrix and the sea ice observation data. Finally, the sea ice data assimilation result can be passed to the ocean model through the coupler, and the ocean data assimilation result can be passed to the sea ice model through the coupler to complete the coupling between the two. After the coupling is completed, the ocean and sea ice models will receive feedback from each other, and through the dynamic and physical processes of the model itself, further update the state of the ocean model variables and the state of the sea ice model variables, driving the model to run forward. The assimilation system generates updated ocean and sea ice model results, which will also be input into the atmospheric model, land surface model and other modules, driving the entire model system to complete long-term climate and environmental simulations.

[0064] For the strongly coupled ocean-sea ice assimilation process, Figure 3As shown, first, ocean observation data (including sea surface temperature observation data, sea surface height observation data, ocean temperature profile observation data, ocean salinity profile observation data, etc.) and sea ice observation data (including sea ice density observation data and sea ice thickness observation data, etc.) can be obtained. At the same time, the ocean sample set and the sea ice sample set can be obtained, and the joint error covariance matrix of the ocean-sea ice variables can be calculated based on the ocean sample set and the sea ice sample set. Secondly, the joint error covariance matrix of the ocean-sea ice variables and the ocean observation data can be passed to the ocean assimilation module, and the ocean assimilation module assimilates the joint error covariance matrix of the ocean-sea ice variables and the ocean observation data. At the same time, the joint error covariance matrix of the ocean-sea ice variables and the sea ice observation data can be passed to the sea ice assimilation module, and the sea ice assimilation module assimilates the joint error covariance matrix of the ocean-sea ice variables and the sea ice observation data. In the strong coupling assimilation mode, the ocean assimilation module will generate ocean analysis increment / sea ice analysis increment after assimilating the ocean observation data. These incremental information will directly affect the ocean / sea ice model and be used to update the model ocean / sea ice variable state. Similarly, the sea ice assimilation module will generate sea ice analysis increment / ocean analysis increment after assimilating the sea ice observation data. These incremental information will also directly affect the sea ice / ocean model and be used to update the model sea ice / ocean variable state. The updated ocean field and sea ice field are mutually correlated and matched, and the assimilation results of the two component models are updated synchronously. In the strong coupling assimilation mode, the coupler is not a key component for the cross-sphere transmission of assimilation information, but it can integrate the received ocean and sea ice assimilation information to ensure that the interaction between the two continues to be consistent in subsequent model operations. The coupler feeds back the results of strong coupling assimilation to the ocean model and sea ice model. Through further model operations, the ocean and sea ice systems evolve synchronously according to the strong coupling results. Furthermore, the assimilation system generates updated ocean and sea ice model results, which will also be input into modules such as the atmospheric model and land surface model, driving the entire model system to complete long-term climate and environmental simulations.

[0065] In order to verify the strong / weak coupling assimilation technology route of ocean sea ice provided in this embodiment, the three assimilation strategies of CTL (no assimilation), WCDA (weak coupling assimilation) and SCDA (strong coupling assimilation) are compared and applied. The process can be as follows Figure 4 As shown: 1. Input stage: Satellite sea surface observation data, ocean temperature and salinity data, sea ice observation data, and ocean sea ice ensemble sample data are used as input data to enter two different assimilation processes: strong and weak.

[0066] 2. Assimilation test processing flow: a) No assimilation control test (CTL test): For comparison purposes, a control experiment (CTL experiment) without any assimilation processing was set up. The unassimilated model initial field was directly input into the BCC-CSM (Beijing Climate Center Climate System Model) model for long-term integrated simulation.

[0067] b) Weakly coupled assimilation process (WCDA): In the Weakly Coupled Ocean-Sea Ice Assimilation Experiment (WCDA), data input includes satellite and other observational data, which only have an indirect impact through the model coupling process, that is, the assimilation of ocean and sea ice is relatively independent and the impact on each other is relatively loose. Through this process, the WCDA experiment assimilation output is generated and the results are then input into the BCC-CSM climate system model.

[0068] c) Strongly Coupled Assimilation (SCDA): In the strongly coupled ocean-sea ice assimilation experiment (SCDA), data input also comes from a variety of observational data, but the data here are more tightly coupled. The ocean and sea ice variables are coupled through the covariance matrix, and the direct interaction is achieved in the assimilation process to generate the SCDA experiment assimilation output, which is also input into the BCC-CSM model.

[0069] 3. Output and inspection evaluation: The BCC-CSM climate system model conducts long-term simulation experiments based on different input data, and passes the output results of the three experiments to the verification and evaluation module to evaluate the accuracy and effectiveness of each test result.

[0070] Figure 4 The entire process shown can be controlled by the corresponding master control process script. All test and evaluation results can be used to analyze the accuracy and performance of each test simulation result, compare the effects of different assimilation strategies, and then analyze the assimilation performance of the improved climate model.

[0071] Figure 5 and Figure 6 The assimilation results of Arctic sea ice density and sea surface temperature obtained from three different experimental schemes are shown. Figure 5 As shown in the figure, the vertical axis represents the root mean square error of the Arctic sea ice density, and the horizontal axis represents the month (i.e. January to December). Figure 6 As shown, the vertical axis represents the root mean square error of the Arctic sea surface temperature, and the horizontal axis represents the month (i.e. January to December). Figure 5 and Figure 6 In the table, CTL means no assimilation control experiment, WCDA means weakly coupled assimilation experiment, and SCDA means strongly coupled assimilation experiment. Figure 5 and Figure 6It can be seen that both the weak coupling assimilation method and the strong coupling assimilation method have greatly reduced the errors compared with the unassimilated simulation experiment. From weak coupling to strong coupling, there is not much difference in the simulated sea ice density and sea surface temperature errors from December to May. From June to October, the simulated sea ice density and sea surface temperature errors are significantly reduced. The experimental results show that in the polar ice season, the weak coupling assimilation method can provide reliable ocean sea ice simulation, while in the polar ice-less season (the period when sea ice changes from melting to freezing and has a rapid impact), the strong coupling assimilation method can provide more accurate ocean sea ice simulation. This also shows the necessity of flexibly supporting weak and strong coupling assimilation schemes in a climate system model, so that users can make choices that meet actual application needs and economic benefits based on factors such as technical complexity, cost-effectiveness, and result accuracy.

[0072] The coordinated coupling assimilation method of ocean data and sea ice data provided in the embodiment of the present application is obtained by obtaining the ocean observation data and sea ice observation data of the target sea area at the current moment, as well as the ocean sample set and sea ice sample set of the target sea area, wherein the ocean sample set includes the ocean data of the target sea area at multiple moments within a preset time from the current time, and the sea ice sample set includes the sea ice data of the target sea area at multiple moments within a preset time from the current time. According to the pre-set data assimilation method, the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set are subjected to data assimilation processing to obtain the ocean and sea ice data assimilation results. The ocean and sea ice data assimilation results are coupled, and the ocean model variable state and the sea ice model variable state are updated according to the coupling results. The embodiment of the present application incorporates ocean variable data and sea ice variable data into the assimilation process and adopts different data assimilation methods to perform assimilation processing, thereby achieving close coupling and synchronous updating of ocean and sea ice information, solving the coupling limitations of ocean and sea ice interactions in assimilation, and improving the accuracy of multi-sphere feedback of ocean and sea ice. At the same time, a more coordinated and accurate ocean and sea ice variable analysis field can be obtained.

[0073] Reference Figure 7 , showing a schematic diagram of the structure of a coordinated coupling assimilation device for ocean data and sea ice data provided by an embodiment of the present application, such as Figure 7 As shown, the coordinated coupling assimilation device 700 for ocean data and sea ice data may include the following modules: The ocean and sea ice data acquisition module 710 is used to acquire the ocean observation data and sea ice observation data of the target sea area at the current time, as well as the ocean sample set and sea ice sample set of the target sea area, wherein the ocean sample set includes the ocean data of the target sea area at multiple times within a preset time from the current time, and the sea ice sample set includes the sea ice data of the target sea area at multiple times within a preset time from the current time; The data assimilation result acquisition module 720 is used to perform data assimilation processing on the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set according to a preset data assimilation method to obtain ocean and sea ice data assimilation results; The ocean and sea ice variable state updating module 730 is used to couple the ocean and sea ice data assimilation results, and update the ocean model variable state and the sea ice model variable state according to the coupling results.

[0074] Optionally, the data assimilation result acquisition module includes: a disturbance matrix construction unit, configured to construct an ocean disturbance matrix according to the ocean sample set and a sea ice disturbance matrix according to the sea ice sample set when the data assimilation mode is a weakly coupled assimilation mode; An ocean assimilation result acquisition unit, used to assimilate the ocean observation data and the ocean disturbance matrix using a preset assimilation equation to obtain an ocean data assimilation result; A sea ice assimilation result acquisition unit, used to assimilate the sea ice observation data and the sea ice disturbance matrix using a preset assimilation equation to obtain a sea ice data assimilation result; The first assimilation result acquisition unit is used to use the ocean data assimilation result and the sea ice data assimilation result as the ocean and sea ice data assimilation result.

[0075] Optionally, the ocean assimilation result acquisition unit includes: An ocean covariance matrix acquisition subunit is used to multiply the ocean disturbance matrix and the transposed matrix corresponding to the ocean disturbance matrix to obtain an ocean variable background error covariance matrix; The ocean assimilation result acquisition subunit is used to assimilate the ocean variable background error covariance matrix and the ocean observation data to obtain ocean analysis increment information, and use the ocean analysis increment information as the ocean data assimilation result.

[0076] Optionally, the sea ice assimilation result acquisition unit includes: A sea ice covariance matrix acquisition subunit, used for multiplying the sea ice disturbance matrix and the transposed matrix corresponding to the sea ice disturbance matrix to obtain a sea ice variable background error covariance matrix; The sea ice assimilation result acquisition subunit is used to assimilate the sea ice variable background error covariance matrix and the sea ice observation data to obtain sea ice analysis increment information, and use the sea ice analysis increment information as the sea ice data assimilation result.

[0077] Optionally, the ocean sea ice variable state updating module includes: An ocean variable state updating unit, used for transmitting the sea ice data assimilation result to the ocean model through a model coupler, so as to update the ocean model variable state according to the sea ice data assimilation result; The sea ice variable state updating unit is used to transmit the ocean data assimilation result to the sea ice model through the mode coupler, so as to update the sea ice model variable state according to the ocean data assimilation result.

[0078] Optionally, the data assimilation result acquisition module includes: A joint perturbation matrix construction unit, used for constructing a joint perturbation matrix according to the ocean sample set and the sea ice sample set when the data assimilation mode is a strong coupling assimilation mode; A joint error covariance matrix acquisition unit, used for multiplying the joint disturbance matrix and the transposed matrix corresponding to the joint disturbance matrix to obtain a joint error covariance matrix; An ocean analysis increment acquisition unit, used for assimilating the ocean observation data and the joint error covariance matrix using a preset assimilation equation to obtain ocean-sea ice analysis increment information; A sea ice analysis increment acquisition unit, configured to assimilate the sea ice observation data and the joint error covariance matrix using a preset assimilation equation to obtain sea ice-ocean analysis increment information; The second assimilation result acquisition unit is used to synthesize the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information to obtain the ocean and sea ice data assimilation result.

[0079] Optionally, the ocean sea ice variable state updating module includes: an ocean state updating unit, configured to transmit the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information to an ocean model to update the ocean model variable state; The sea ice state updating unit is used to transmit the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information to the sea ice model to update the sea ice model variable state.

[0080] The coordinated coupling assimilation device of ocean data and sea ice data provided in the embodiment of the present application obtains the ocean observation data and sea ice observation data of the target sea area at the current moment, as well as the ocean sample set and sea ice sample set of the target sea area, wherein the ocean sample set includes the ocean data of the target sea area at multiple moments within a preset time from the current time, and the sea ice sample set includes the sea ice data of the target sea area at multiple moments within a preset time from the current time. According to the pre-set data assimilation method, the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set are subjected to data assimilation processing to obtain the ocean and sea ice data assimilation results. The ocean and sea ice data assimilation results are coupled, and the ocean model variable state and the sea ice model variable state are updated according to the coupling results. The embodiment of the present application incorporates ocean variable data and sea ice variable data into the assimilation process and adopts different data assimilation methods to perform assimilation processing, thereby achieving close coupling and synchronous updating of ocean and sea ice information, solving the coupling limitations of ocean and sea ice interactions in assimilation, and improving the accuracy of multi-sphere feedback of ocean and sea ice. At the same time, a more coordinated and accurate ocean and sea ice variable analysis field can be obtained.

[0081] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0082] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or terminal including the elements.

[0083] The above is a detailed introduction to a coordinated coupling assimilation method and device for ocean data and sea ice data provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A coordinated coupling assimilation method for ocean data and sea ice data, characterized in that: The method comprises: Acquire ocean observation data and sea ice observation data of the target sea area at the current time, as well as an ocean sample set and a sea ice sample set of the target sea area, wherein the ocean sample set includes ocean data of the target sea area at multiple times within a preset time from the current time, and the sea ice sample set includes sea ice data of the target sea area at multiple times within a preset time from the current time; According to a preset data assimilation method, the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set are subjected to data assimilation processing to obtain ocean and sea ice data assimilation results; The ocean and sea ice data assimilation results are coupled and the ocean model variable states and sea ice model variable states are updated according to the coupling results.

2. The method according to claim 1, characterized in that The method of performing data assimilation processing on the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set according to a preset data assimilation method to obtain ocean and sea ice data assimilation results includes: When the data assimilation mode is a weakly coupled assimilation mode, constructing an ocean disturbance matrix according to the ocean sample set, and constructing a sea ice disturbance matrix according to the sea ice sample set; Using a preset assimilation equation to assimilate the ocean observation data and the ocean disturbance matrix to obtain an ocean data assimilation result; Assimilation processing is performed on the sea ice observation data and the sea ice disturbance matrix using a preset assimilation equation to obtain a sea ice data assimilation result; The ocean data assimilation result and the sea ice data assimilation result are used as the ocean and sea ice data assimilation result.

3. The method according to claim 2, characterized in that The assimilation processing of the ocean observation data and the ocean disturbance matrix to obtain an ocean data assimilation result includes: Multiplying the ocean disturbance matrix and the transposed matrix corresponding to the ocean disturbance matrix to obtain an ocean variable background error covariance matrix; The ocean variable background error covariance matrix and the ocean observation data are assimilated to obtain ocean analysis increment information, and the ocean analysis increment information is used as the ocean data assimilation result.

4. The method according to claim 3, characterized in that The assimilation processing of the sea ice observation data and the sea ice disturbance matrix to obtain the sea ice data assimilation result includes: Multiplying the sea ice disturbance matrix and the transposed matrix corresponding to the sea ice disturbance matrix to obtain a sea ice variable background error covariance matrix; The sea ice variable background error covariance matrix and the sea ice observation data are assimilated to obtain sea ice analysis increment information, and the sea ice analysis increment information is used as the sea ice data assimilation result.

5. The method according to claim 2, characterized in that: The coupling processing of the ocean and sea ice data assimilation results and updating the ocean model variable state and the sea ice model variable state according to the coupling results include: The sea ice data assimilation result is transmitted to the ocean model through a model coupler to update the ocean model variable state according to the sea ice data assimilation result; The ocean data assimilation result is transmitted to the sea ice model through a model coupler to update the sea ice model variable state according to the ocean data assimilation result.

6. The method according to claim 1, characterized in that The method of performing data assimilation processing on the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set according to a preset data assimilation method to obtain ocean and sea ice data assimilation results includes: When the data assimilation mode is a strongly coupled assimilation mode, constructing a joint perturbation matrix according to the ocean sample set and the sea ice sample set; Multiplying the joint disturbance matrix and the transposed matrix corresponding to the joint disturbance matrix to obtain a joint error covariance matrix; Assimilating the ocean observation data and the joint error covariance matrix using a preset assimilation equation to obtain ocean-sea ice analysis increment information; Assimilating the sea ice observation data and the joint error covariance matrix using a preset assimilation equation to obtain sea ice-ocean analysis increment information; The ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information are integrated to obtain the ocean and sea ice data assimilation result.

7. The method according to claim 6, characterized in that The coupling processing of the ocean and sea ice data assimilation results and updating the ocean model variable state and the sea ice model variable state according to the coupling results include: passing the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information to an ocean model to update the ocean model variable state; The ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information are passed to the sea ice model to update the sea ice model variable status.

8. A coordinated coupling assimilation device for ocean data and sea ice data, characterized in that: The device comprises: An ocean and sea ice data acquisition module, used to acquire ocean observation data and sea ice observation data of a target sea area at a current moment, as well as an ocean sample set and a sea ice sample set of the target sea area, wherein the ocean sample set includes ocean data of the target sea area at multiple moments within a preset time from the current time, and the sea ice sample set includes sea ice data of the target sea area at multiple moments within a preset time from the current time; A data assimilation result acquisition module, used to perform data assimilation processing on the ocean observation data, the ocean sample set, the sea ice observation data and the sea ice sample set according to a preset data assimilation method to obtain ocean and sea ice data assimilation results; The ocean and sea ice model state update module is used to couple the ocean and sea ice data assimilation results, and update the ocean model variable state and the sea ice model variable state according to the coupling results.

9. The device according to claim 8, characterized in that The data assimilation result acquisition module includes: a disturbance matrix construction unit, configured to construct an ocean disturbance matrix according to the ocean sample set and a sea ice disturbance matrix according to the sea ice sample set when the data assimilation mode is a weakly coupled assimilation mode; An ocean assimilation result acquisition unit, used to assimilate the ocean observation data and the ocean disturbance matrix using a preset assimilation equation to obtain an ocean data assimilation result; A sea ice assimilation result acquisition unit, used to assimilate the sea ice observation data and the sea ice disturbance matrix using a preset assimilation equation to obtain a sea ice data assimilation result; The first assimilation result acquisition unit is used to use the ocean data assimilation result and the sea ice data assimilation result as the ocean and sea ice data assimilation result.

10. The device according to claim 8, characterized in that The data assimilation result acquisition module includes: A joint perturbation matrix construction unit, used for constructing a joint perturbation matrix according to the ocean sample set and the sea ice sample set when the data assimilation mode is a strong coupling assimilation mode; a joint error covariance matrix acquisition unit, configured to multiply the joint disturbance matrix by a transposed matrix corresponding to the joint disturbance matrix to obtain a joint error covariance matrix; An ocean analysis increment acquisition unit, used for assimilating the ocean observation data and the joint error covariance matrix using a preset assimilation equation to obtain ocean-sea ice analysis increment information; A sea ice analysis increment acquisition unit, configured to assimilate the sea ice observation data and the joint error covariance matrix using a preset assimilation equation to obtain sea ice-ocean analysis increment information; The second assimilation result acquisition unit is used to synthesize the ocean-sea ice analysis incremental information and the sea ice-ocean analysis incremental information to obtain the ocean and sea ice data assimilation result.

Images