Coordinated Coupled Assimilation Method and Device for Ocean Data and Sea Ice Data

Abstract

The present application provides a method and apparatus for coordinated coupled assimilation of ocean data and sea ice data. The method includes: obtaining ocean observation data and sea ice observation data of a 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, where the ocean sample set includes ocean data of the target sea area at multiple moments within a preset time period 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 period 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 method to obtain an ocean and sea ice data assimilation result; performing coupling processing on the ocean and sea ice data assimilation result, and updating the states of ocean model variables and sea ice model variables according to the coupling result to obtain a coordinated and accurate ocean sea ice variable analysis field.

Description

Technical Field

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

[0002] For numerical simulation and prediction of multi-sphere coupled climate systems or Earth systems, coupled assimilation is an extremely crucial technology. How to construct a coupled assimilation system based on complex coupling models and assimilation methods, accurately describe the interactions of assimilation components in each sphere, and achieve coordinated analysis of multi-source observational data in multiple spheres is a challenging task in the current field of numerical climate prediction and weather forecasting. After nearly 20 years of research and operational practice, coupled assimilation has been gradually applied to climate and Earth system simulation and prediction, and has become the most reliable means for generating multi-sphere coupled analysis fields and initial fields.

[0003] Existing climate models or Earth system model assimilation and prediction systems have not effectively applied the coupled assimilation of multi-source real observational data of the ocean and sea ice, resulting in obvious limitations in depicting the mutual feedback effects between the ocean and sea ice, being unable to achieve tight coupling and synchronous update of ocean and sea ice information, and not being applicable to situations that require more precise analysis of the mutual influence between the ocean and sea ice. Summary of the Invention

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

[0005] In a first aspect, the embodiments of this application provide a method for coordinated coupling and assimilation of ocean data and sea ice data, the method including:

[0006] Obtain ocean observational data and sea ice observational data of a 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, where the ocean sample set includes ocean data of the target sea area at multiple moments within a preset time period 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 period from the current time;

[0007] Perform data assimilation processing on the ocean observational data, the ocean sample set, the sea ice observational data, and the sea ice sample set according to a preset data assimilation method to obtain an ocean and sea ice data assimilation result;

[0008] Perform coupling processing on the ocean and sea ice data assimilation result, and update the states of ocean model variables and sea ice model variables according to the coupling result.

[0009] In a second aspect, an embodiment of the present application provides a device for coordinated coupling and assimilation of ocean data and sea ice data. The device includes:

[0010] An ocean and sea ice data acquisition module, configured to acquire ocean observation data and sea ice observation data of a 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 period 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 period from the current time;

[0011] A data assimilation result acquisition module, configured 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;

[0012] An ocean and sea ice variable state update module, configured to perform coupling processing on 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.

[0013] Compared with the prior art, the embodiments of the present application have the following advantages:

[0014] In the embodiments of the present application, by acquiring ocean observation data and sea ice observation data of a 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 period 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 period from the current time. According to a preset data assimilation method, perform data assimilation processing 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. Perform coupling processing on 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. In the embodiments of the present application, by incorporating ocean variable data and sea ice variable data into the assimilation process and using different data assimilation methods for assimilation processing, the tight coupling and synchronous update of ocean and sea ice information are realized, the coupling limitation of ocean - sea ice interaction in assimilation is solved, the accuracy of multi - layer feedback of ocean and sea ice is improved, and a more coordinated and accurate ocean - sea ice variable analysis field can be obtained.

[0015] It should be understood that the above general description and subsequent detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The flowchart of steps of a method for coordinated coupled assimilation of ocean data and sea ice data provided by an embodiment of the present application;

[0017] Figure 2 The schematic diagram of a weak coupling assimilation process provided by an embodiment of the present application;

[0018] Figure 3 The schematic diagram of a strong coupling assimilation process provided by an embodiment of the present application;

[0019] Figure 4 The schematic diagram of an assimilation experiment process provided by an embodiment of the present application;

[0020] Figure 5 The schematic diagram of an experimental assimilation result provided by an embodiment of the present application;

[0021] Figure 6 The schematic diagram of another experimental assimilation result provided by an embodiment of the present application;

[0022] Figure 7 The structural schematic diagram of a device for coordinated coupled assimilation of ocean data and sea ice data provided by an embodiment of the present application. Detailed implementation manners

[0023] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0024] 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 of "a", "the", and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0025] Referring to Figure 1 , the flowchart of steps of a method for coordinated coupled assimilation of ocean data and sea ice data provided by an embodiment of the present application is shown. As Figure 1 shown, the method for coordinated coupled assimilation of ocean data and sea ice data may include: Step 101, Step 102, and Step 103.

[0026] Step 101: Obtain 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. Among them, the ocean sample set includes the ocean data of the target sea area at multiple moments within a preset time period 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 period from the current time.

[0027] In this embodiment, the ocean observation data can be the ocean data of the target sea area collected at the current moment, which can include: sea surface temperature observation data, sea surface height observation data, ocean temperature profile observation data, ocean salinity profile observation data, etc.

[0028] The sea ice observation data can be the sea ice data of the target sea area collected at the current moment, which can include: sea ice concentration observation data, sea ice thickness observation data, etc.

[0029] The ocean sample set can include the ocean data of the target sea area at multiple moments within a preset time period from the current time. In this example, the ocean sample set is sampled from the long-term simulation integration experiment of the ocean model, and the number of samples is variable. For example, when the value is 100, the ocean ensemble sample includes the simulated data of the multi-level ocean temperature, salinity, flow velocity, and sea surface height field at 100 time steps (the sea surface height field is an important physical quantity that can reflect the dynamic and thermodynamic processes of the ocean surface), etc.

[0030] The sea ice sample set can include the sea ice data of the target sea area at multiple moments within a preset time period from the current time. Similar to the ocean sample set, the sea ice sample set is also sampled from the long-term simulation integration experiment of the sea ice model, and the number of samples is variable. When the value is 100, it includes the simulated data of the sea ice concentration and sea ice thickness at 10 time steps, etc.

[0031] When performing the 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.

[0032] Among them, the ocean sample set and the sea ice sample set can be obtained by sampling from the long-term simulation integration experiment.

[0033] The ocean observation data and sea ice observation data of the target sea area at the current moment can be collected by in-situ observation or satellite remote sensing technology. In-situ observation is to deploy drifting or fixed observation instruments in the target sea area and use various sensors and devices on the instruments for data collection. Satellite remote sensing technology is to use the remote sensing instruments on the satellite to observe and collect data of the target sea area, and appropriate satellites and sensors can be selected according to the observation requirements. For example, for ocean observation, a satellite equipped with ocean surface temperature sensors, ocean surface salinity sensors, etc. can be selected. For sea ice observation, a satellite equipped with a microwave radiometer, visible / infrared sensors, etc. can be selected. During the on-orbit operation of the satellite, it will continuously send observation data to the ground. The ground receiving station will receive these data and perform preliminary processing and storage.

[0034] 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.

[0035] Step 102: According to the pre-set data assimilation method, perform data assimilation processing on the ocean observation data, the ocean sample set, the sea ice observation data, and the sea ice sample set to obtain the ocean and sea ice data assimilation results.

[0036] 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 (Climate System Model, CSM) or an Earth system model (Earth System Model, ESM) including components such as the ocean and sea ice can be 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 the pre-set data assimilation method to obtain the ocean and sea ice data assimilation results.

[0037] The embodiments of the present application provide two data assimilation methods, namely the weak coupling assimilation method and the strong coupling assimilation method. In a specific implementation, a read parameter control script can be set, and the system decides whether to use the weak coupling assimilation method or the strong coupling assimilation method according to the settings in the parameter control script.

[0038] Next, the data assimilation processes of the two data assimilation methods will be described in detail in combination with two specific implementation methods.

[0039] In a specific implementation manner of the present application, the above step 102 may include:

[0040] Sub-step A1: When the data assimilation method is the weak coupling assimilation method, construct an ocean perturbation matrix according to the ocean sample set, and construct a sea ice perturbation matrix according to the sea ice sample set.

[0041] In this embodiment, when the data assimilation method is the weak coupling assimilation method, an ocean perturbation matrix can be constructed according to the ocean sample set, and a sea ice perturbation matrix can be constructed according to 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 moments. When constructing the ocean perturbation matrix, the ocean data at each moment can be used as row elements, and the row elements at multiple moments can be combined to obtain the ocean perturbation matrix. Similarly, when constructing the sea ice perturbation matrix, the sea ice data at each moment can be used as row elements, and the row elements at multiple moments can be combined to obtain the sea ice perturbation matrix.

[0042] After constructing the ocean perturbation matrix and the sea ice perturbation matrix, sub-step A2 and sub-step A3 are executed.

[0043] Sub-step A2: The ocean observation data and the ocean perturbation matrix are assimilated using a preset assimilation equation to obtain an ocean data assimilation result.

[0044] The preset assimilation equation refers to a pre-set mathematical equation for assimilating ocean and sea ice data. In this example, the preset assimilation equation can be as shown in the following formulas (1) and (2):

[0045] (1)

[0046] (2)

[0047] In the above formulas (1) and (2), is the data assimilation result, is the gain matrix, is the perturbation matrix, is the transpose matrix of the perturbation matrix, is the observation operator (representing the transformation function for converting ocean model variables or sea ice model variables from the model space to the observation space), is the number of ensemble samples, is the observation error covariance matrix, is the observation data, is the background state of the model variables.

[0048] After obtaining the ocean perturbation matrix, the ocean observation data and the ocean perturbation matrix can be assimilated using a preset assimilation equation to obtain an ocean data assimilation result. Specifically, the ocean perturbation matrix and the transpose matrix corresponding to the ocean perturbation matrix can be multiplied to obtain the ocean variable background error covariance matrix (this covariance matrix can be used to represent the error correlation between ocean variable background field data), and the ocean variable background error covariance matrix and the ocean observation data are assimilated to obtain ocean analysis increment information (such as ocean temperature, salinity field, etc.), that is, processed using the above formula (1), and the assimilation equation is solved to obtain the ocean analysis increment information, which can be used as the ocean data assimilation result.

[0049] 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. The ocean model background field refers to the simulation or forecast field of the ocean model itself. Based on the model background field, when ocean observation data is introduced for assimilation analysis and calculation, analysis increments will be generated, and the assimilation analysis field is formed by superimposing them on the model background field.

[0050] Sub-step A3: Use a preset assimilation equation to assimilate the sea ice observation data and the sea ice perturbation matrix to obtain a sea ice data assimilation result.

[0051] After obtaining the sea ice perturbation matrix, a preset assimilation equation can be used to assimilate the sea ice observation data and the sea ice perturbation matrix to obtain a sea ice data assimilation result. Specifically, the sea ice perturbation matrix can be multiplied by its corresponding transpose matrix to obtain a sea ice variable background error covariance matrix (this covariance matrix can be used to represent the error distribution and correlation between sea ice variable data), and the sea ice variable background error covariance matrix and the sea ice observation data can be assimilated to obtain sea ice analysis increment information, that is, processed using the above formulas (1) and (2) to solve the assimilation equation to obtain sea ice analysis increment information, and this sea ice analysis increment information can be used as the sea ice data assimilation result.

[0052] When calculating the sea ice analysis increment information, the sea ice model background field information can be introduced, where the sea ice model background field refers to the simulation or forecast field of the sea ice model itself. Based on the model background field, introducing sea ice observation data for assimilation analysis and calculation will generate analysis increments, and superimposing them on the model background field will form an assimilation analysis field.

[0053] Sub-step A4: Use the ocean data assimilation result and the sea ice data assimilation result as the ocean and sea ice data assimilation result.

[0054] After obtaining the ocean data assimilation result and the sea ice data assimilation result, the ocean data assimilation result and the sea ice data assimilation result can be used as the ocean and sea ice data assimilation result in the weak coupling assimilation method.

[0055] In another specific implementation manner of the present application, step 102 above may include:

[0056] Sub-step B1: In the case where the data assimilation method is a strong coupling assimilation method, construct a joint perturbation matrix according to the ocean sample set and the sea ice sample set.

[0057] In this embodiment, in the case where the data assimilation method is a strong coupling assimilation method, a joint perturbation matrix can be constructed according to 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 row elements at multiple times can be combined to obtain the joint perturbation matrix.

[0058] After constructing the joint perturbation matrix according to the ocean sample set and the sea ice sample set, execute sub-step B2.

[0059] Sub-step B2: Multiply the combined perturbation matrix and the transposed matrix corresponding to the combined perturbation matrix to obtain a combined error covariance matrix.

[0060] After obtaining the combined perturbation matrix, the combined perturbation matrix and the transposed matrix corresponding to the combined perturbation matrix can be multiplied to obtain a combined error covariance matrix. This combined error covariance matrix can be used to represent the error distribution and correlation between sea ice variable data and ocean variable data.

[0061] After obtaining the combined error covariance matrix, sub-step B3 and sub-step B4 can be executed.

[0062] Sub-step B3: Use a preset assimilation equation to assimilate the ocean observation data and the combined error covariance matrix to obtain ocean-sea ice analysis increment information.

[0063] After obtaining the combined error covariance matrix, a preset assimilation equation can be used to assimilate the ocean observation data and the combined error covariance matrix to obtain ocean-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 ocean-sea ice analysis increment information.

[0064] Sub-step B4: Use a preset assimilation equation to assimilate the sea ice observation data and the combined error covariance matrix to obtain sea ice-ocean analysis increment information.

[0065] After obtaining the combined covariance matrix, a preset assimilation equation can be used to assimilate the sea ice observation data and the combined error covariance matrix to obtain sea ice-ocean analysis increment information. That is, the above formulas (1) and (2) are used for processing, and the assimilation equation is solved to obtain sea ice-ocean analysis increment information.

[0066] Sub-step B5: Combine the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information to obtain the ocean and sea ice data assimilation result.

[0067] After obtaining the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information, the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information can be combined to obtain the ocean and sea ice data assimilation result, that is, the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information are mixed together as the output ocean and sea ice data assimilation result.

[0068] Based on an autonomous and controllable high-resolution climate system model, the embodiment of the present application proposes an ocean-sea ice weak coupling and strong coupling assimilation scheme, and realizes the flexible switching of strong / weak coupling modes of ocean-sea ice coupled assimilation function through an automated script control and parameter definition method.

[0069] In the weak coupling assimilation method, a marine ensemble assimilation scheme is proposed to assimilate satellite remote sensing sea surface temperature data, sea surface height data, and temperature-salinity profile data. And a sea ice ensemble assimilation scheme is proposed to assimilate satellite remote sensing sea ice concentration data and sea ice thickness data. The ocean and sea ice are assimilated separately. The ocean assimilation only targets ocean variables, and the sea ice assimilation only targets sea ice variables. Then, they realize the cross-sphere influence of ocean assimilation and sea ice assimilation information through the coupling process of the model itself (indirectly through the coupler).

[0070] In the strong coupling assimilation method, based on the ensemble assimilation method, a strong coupling assimilation scheme for the ocean and sea ice is further constructed, that is, ocean and sea ice variables (sea surface temperature, sea ice concentration, sea ice thickness) are incorporated into the assimilation equation together, a joint error covariance matrix of ocean and sea ice multi-variables is constructed, ocean and sea ice data are assimilated simultaneously, 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 covariance of ocean and sea ice variables in the assimilation algorithm.

[0071] After obtaining the ocean and sea ice data assimilation results, step 103 is executed.

[0072] Step 103: Perform coupling processing on 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.

[0073] The ocean model variable state refers to the state of ocean variables simulated by the ocean model (such as temperature, salinity, flow velocity, etc.).

[0074] The sea ice model variable state refers to the state of sea ice variables simulated by the sea ice model (such as concentration, thickness, etc.).

[0075] 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 state and the sea ice model variable state can be updated according to the coupling results.

[0076] In the weak coupling assimilation method, the sea ice data assimilation results can be transmitted to the ocean model through the coupler, and the ocean data assimilation results can be transmitted to the sea ice model through the coupler to complete their coupling. Furthermore, 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 only exchanges results through the model coupler, rather than directly related calculations.

[0077] Such as Figure 2 and Figure 3As shown, the entire coupled model includes multiple component models such as the ocean, land surface, atmosphere, and sea ice. The ocean model and the sea ice model are two of the component models, and they are complex numerical models that incorporate dynamic and physical processes (which can be software composed of hundreds of thousands of lines of code).

[0078] In the weak coupling assimilation method, the ocean model and the sea ice model 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, as well as temperature, salinity, and density changes). This further updates the variable states of the ocean model and the sea ice model, driving the model (i.e., the entire coupled climate system model or the Earth system model) to integrate forward.

[0079] In the strong coupling assimilation method, the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information can be directly transmitted to the ocean model, and the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information can be directly transmitted to the sea ice model to update the variable states of the ocean model and the sea ice model simultaneously based on this information. In the strong coupling assimilation method, the coupler is not a key component for the cross-layer propagation and influence of assimilation information, but it can integrate the received ocean and sea ice assimilation information to ensure that the interaction relationship between the two remains consistent in subsequent model runs.

[0080] In this embodiment, according to the selected strong coupling assimilation method or weak coupling assimilation method, the assimilation system generates updated ocean and sea ice model results, which are also input to modules such as the atmosphere model and the land surface model to drive the entire model system to complete long-term climate and environmental simulations. Weak coupling assimilation: The assimilation processes of the ocean and sea ice are independent, with a low degree of coupling. Assimilation information is simply exchanged and transmitted through the coupler, and it is suitable for simulation environments with low requirements for coupling. Strong coupling assimilation: The assimilation processes of the ocean and sea ice are combined, and the coupling of the assimilation information of the two components is directly achieved through the assimilation algorithm. On this basis, the coupler will further strengthen the mutual influence of the two components, and it is suitable for situations that require more precise analysis of the ocean-sea ice mutual influence.

[0081] For the weak coupling assimilation process of the ocean-sea ice, it can be as Figure 2As shown in the figure, 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 concentration observation data and sea ice thickness observation data, etc.) can be obtained. At the same time, an ocean sample set and a 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 can perform assimilation processing on 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 can perform assimilation processing on 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 of the two. After the coupling is completed, the ocean and sea ice models will receive feedback information from each other, and through the dynamic and physical processes of the models themselves, further update the variable states of the ocean model and the sea ice model, and drive the models to integrate forward. The assimilation system generates updated ocean and sea ice model results, and these results will also be input to modules such as the atmospheric model and the land surface model to drive the entire model system to complete long-term climate and environmental simulations.

[0082] For the strong coupling assimilation process of ocean-sea ice, it can be as Figure 3As shown in the figure, 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 concentration observation data and sea ice thickness observation data, etc.) can be obtained. At the same time, an ocean sample set and a sea ice sample set can be obtained, and the ocean-sea ice variable joint error covariance matrix can be calculated based on the ocean sample set and the sea ice sample set. Secondly, the ocean-sea ice variable joint error covariance matrix and the ocean observation data can be passed to the ocean assimilation module, and the ocean assimilation module can perform assimilation processing on the ocean-sea ice variable joint error covariance matrix and the ocean observation data. At the same time, the ocean-sea ice variable joint error covariance matrix and the sea ice observation data can be passed to the sea ice assimilation module, and the sea ice assimilation module can perform assimilation processing on the ocean-sea ice variable joint error covariance matrix and the sea ice observation data. In the strong coupling assimilation method, the ocean assimilation module will generate ocean analysis increments / sea ice analysis increments after assimilating ocean observation data, and these increment information will directly affect the ocean / sea ice model and be used to update the state of the model ocean / sea ice variables. Similarly, the sea ice assimilation module will generate sea ice analysis increments / ocean analysis increments after assimilating sea ice observation data, and these increment information will also directly affect the sea ice / ocean model and be used to update the state of the model sea ice / ocean variables. The updated ocean field and sea ice field are interrelated and matched, and the assimilation results of the two component models are updated synchronously. In the strong coupling assimilation method, the coupler is not the key component for the cross-layer propagation and influence of assimilation information, but it can integrate the received ocean and sea ice assimilation information to ensure that the interaction relationship between the two remains consistent in subsequent model runs. The coupler feeds back the results of the strong coupling assimilation to the ocean model and the sea ice model. Through further model runs, the ocean and sea ice systems evolve synchronously according to the strong coupling results. Furthermore, the assimilation system generates the updated results of the ocean and sea ice models, and these results will also be input to affect modules such as the atmospheric model and the land surface model, driving the entire model system to complete long-term climate and environmental simulations.

[0083] To verify the strong / weak coupling assimilation technical route of ocean and sea ice provided in this embodiment, a comparison and application test of three assimilation strategies, namely CTL (i.e., no assimilation), WCDA (i.e., weak coupling assimilation), and SCDA (i.e., strong coupling assimilation), are carried out. The process can be as Figure 4 shown:

[0084] 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 respectively used as input data into the strong and weak assimilation processes.

[0085] 2. Assimilation experiment processing flow:

[0086] a) No assimilation control experiment (CTL experiment):

[0087] For comparison, 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 integration simulation.

[0088] b) Weak Coupling Data Assimilation process (WCDA):

[0089] In the ocean-sea ice weak coupling data assimilation experiment (WCDA), the data input includes satellite and other observational data. These data only have an indirect impact through the model coupling process, that is, the assimilation of the ocean and sea ice is relatively independent and the influence between them 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.

[0090] c) Strong Coupling Data Assimilation process (SCDA):

[0091] In the ocean-sea ice strong coupling data assimilation experiment (SCDA), the data input also comes from a variety of observational data, but the data here has undergone closer coupling processing. The ocean and sea ice variables have a direct interaction effect during the assimilation process through the coupling relationship of the covariance matrix, generating the SCDA experiment assimilation output, and the results are also input into the BCC-CSM model.

[0092] 3. Output and verification and evaluation:

[0093] The BCC-CSM climate system model conducts long-term simulation experiments based on different input data, and the output results of the three experiments are all transmitted to the verification and evaluation module to evaluate the accuracy and effect of each experiment result.

[0094] Figure 4 The entire process shown can be controlled by the corresponding master control process script. All verification and evaluation results can be applied to analyze the accuracy and performance of the simulation results of each experiment, compare the effects of different assimilation strategies, and further analyze and improve the assimilation performance of the climate model.

[0095] Figure 5 and Figure 6 show the Arctic sea ice concentration and sea surface temperature assimilation results obtained from the experiments of three different schemes. As Figure 5 shown, the vertical axis represents the root mean square error of the Arctic sea ice concentration, and the horizontal axis represents the month (i.e., January to December). As Figure 6 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 6Among them, CTL represents the control trial without assimilation, WCDA represents the weak coupling data assimilation trial, and SCDA represents the strong coupling data assimilation trial. From Figure 5 and Figure 6 it can be seen that regardless of whether it is the weak coupling data assimilation method or the strong coupling data assimilation method, the errors are significantly reduced compared with the non-assimilated simulation trial. Switching from weak coupling to strong coupling, there is not much difference in the errors of the simulated sea ice concentration and sea surface temperature from December to May. From June to October, the errors of the simulated sea ice concentration and sea surface temperature are significantly reduced. The test results show that in the polar multi-ice season, the weak coupling data assimilation method can provide reliable ocean and sea ice simulations, while in the polar less-ice season (the period when sea ice changes from ablation to freezing state and has a rapid impact), the strong coupling data assimilation method can provide more accurate ocean and sea ice simulations. This also indicates the necessity of flexibly supporting weak and strong coupling data assimilation schemes in a climate system model, facilitating users to make choices that meet the actual application requirements and economic benefits considering factors such as technical complexity, cost economy, and result accuracy.

[0096] The coordinated coupling data assimilation method for ocean data and sea ice data provided by the embodiments 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. Among them, the ocean sample set includes ocean data of the target sea area at multiple moments within a preset time period 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 period from the current time. According to the pre-set data assimilation method, data assimilation processing is performed on the ocean observation data, ocean sample set, sea ice observation data, and sea ice sample set to obtain the ocean and sea ice data assimilation results. Coupling processing is performed on the ocean and sea ice data assimilation results, and the states of ocean model variables and sea ice model variables are updated according to the coupling results. The embodiments of the present application incorporate ocean variable data and sea ice variable data into the assimilation process and perform assimilation processing using different data assimilation methods, realizing the tight coupling and synchronous update of ocean and sea ice information, solving the coupling limitations of ocean-sea ice interactions in assimilation, improving the accuracy of multi-layer feedback of ocean and sea ice, and obtaining a more coordinated and accurate ocean-sea ice variable analysis field at the same time.

[0097] Referring to Figure 7 , a schematic structural diagram of a coordinated coupling data assimilation device for ocean data and sea ice data provided by the embodiments of the present application is shown. As Figure 7 shown, the coordinated coupling data assimilation device 700 for ocean data and sea ice data may include the following modules:

[0098] The ocean 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 moment, as well as the ocean sample set and sea ice sample set of the target sea area. Among them, the ocean sample set includes the ocean data of the target sea area at multiple moments within a preset time period 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 period from the current time;

[0099] 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 the ocean and sea ice data assimilation results;

[0100] The ocean sea ice variable state update module 730 is used to perform coupling processing on 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.

[0101] Optionally, the data assimilation result acquisition module includes:

[0102] The perturbation matrix construction unit is used to construct an ocean perturbation matrix according to the ocean sample set and a sea ice perturbation matrix according to the sea ice sample set when the data assimilation method is the weak coupling assimilation method;

[0103] The ocean assimilation result acquisition unit is used to perform assimilation processing on the ocean observation data and the ocean perturbation matrix by using a preset assimilation equation to obtain the ocean data assimilation result;

[0104] The sea ice assimilation result acquisition unit is used to perform assimilation processing on the sea ice observation data and the sea ice perturbation matrix by using a preset assimilation equation to obtain the sea ice data assimilation result;

[0105] 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 results.

[0106] Optionally, the ocean assimilation result acquisition unit includes:

[0107] The ocean covariance matrix acquisition subunit is used to multiply the ocean perturbation matrix and the transposed matrix corresponding to the ocean perturbation matrix to obtain the ocean variable background error covariance matrix;

[0108] The ocean assimilation result acquisition subunit is used to perform assimilation processing on the ocean variable background error covariance matrix and the ocean observation data to obtain the ocean analysis increment information, and use the ocean analysis increment information as the ocean data assimilation result.

[0109] Optionally, the sea ice assimilation result acquisition unit includes:

[0110] A sea ice covariance matrix acquisition subunit, configured to multiply the sea ice perturbation matrix by the transposed matrix corresponding to the sea ice perturbation matrix to obtain a sea ice variable background error covariance matrix;

[0111] A sea ice assimilation result acquisition subunit, configured to perform assimilation processing on 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.

[0112] Optionally, the ocean and sea ice variable state update module includes:

[0113] An ocean variable state update unit, configured to transmit the sea ice data assimilation result to an ocean model through a model coupler to update the ocean model variable state according to the sea ice data assimilation result;

[0114] A sea ice variable state update unit, configured to transmit the ocean data assimilation result to a sea ice model through a model coupler to update the sea ice model variable state according to the ocean data assimilation result.

[0115] Optionally, the data assimilation result acquisition module includes:

[0116] A joint perturbation matrix construction unit, configured to construct a joint perturbation matrix according to the ocean sample set and the sea ice sample set when the data assimilation method is a strong coupling assimilation method;

[0117] A joint error covariance matrix acquisition unit, configured to multiply the joint perturbation matrix by the transposed matrix corresponding to the joint perturbation matrix to obtain a joint error covariance matrix;

[0118] An ocean analysis increment acquisition unit, configured to perform assimilation processing on the ocean observation data and the joint error covariance matrix by using a preset assimilation equation to obtain ocean-sea ice analysis increment information;

[0119] A sea ice analysis increment acquisition unit, configured to perform assimilation processing on the sea ice observation data and the joint error covariance matrix by using a preset assimilation equation to obtain sea ice-ocean analysis increment information;

[0120] A second assimilation result acquisition unit, configured to synthesize the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information to obtain the ocean and sea ice data assimilation result.

[0121] Optionally, the ocean and sea ice variable state update module includes:

[0122] An ocean state update unit, configured to transmit the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information to an ocean model to update the state of ocean model variables;

[0123] A sea ice state update unit, configured to transmit the ocean-sea ice analysis increment information and the sea ice-ocean analysis increment information to a sea ice model to update the state of sea ice model variables.

[0124] The coordinated coupling and assimilation device for ocean data and sea ice data provided by the embodiments of the present application obtains ocean observation data and sea ice observation data of a 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. The ocean sample set includes ocean data of the target sea area at multiple moments within a preset time period 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 period from the current time. 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 an ocean and sea ice data assimilation result. Coupling processing is performed on the ocean and sea ice data assimilation result, and the state of ocean model variables and the state of sea ice model variables are updated according to the coupling result. The embodiments of the present application incorporate ocean variable data and sea ice variable data into the assimilation process, and perform assimilation processing using different data assimilation methods, realizing the tight coupling and synchronous update of ocean and sea ice information, solving the coupling limitation of ocean-sea ice interaction in assimilation, improving the accuracy of multi-layer feedback of ocean and sea ice, and obtaining a more coordinated and accurate ocean-sea ice variable analysis field at the same time.

[0125] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0126] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or terminal including the element.

[0127] The above has introduced in detail a method and device for coordinated coupling and assimilation of ocean data and sea ice data provided by this application. Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this 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; 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; 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.

2. The method according to claim 1, characterized in that The 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 also 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 mode 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 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.

7. 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; An ocean and sea ice model state updating module, used for coupling processing 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; 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.

8. The device according to claim 7, characterized in that The data assimilation result acquisition module also 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.

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