A spatial prediction method for sea ice density based on MEOF model

Abstract

The present invention discloses a spatial prediction method of sea ice density based on the MEOF model, including calculating the lead-lag correlation coefficients between the Weddell Sea sea ice density and the global sea surface temperature, the mean sea level pressure and the surface air temperature in different lead times, so as to determine the atmospheric and oceanic prediction factors affecting the Weddell Sea SIC; respectively adding the SIC data of the previous month and 12 months ago to the prediction factors; constructing a MEOF spatial prediction model based on the prediction factors, wherein the MEOF spatial prediction model projects the variable field of the prediction factors onto the basis vector of the Weddell Sea SIC to obtain the time coefficient of the SIC basis vector at the current moment; and multiplying and adding all the basis vectors and the time coefficients respectively, thereby obtaining the spatial distribution of the SIC of the current month. The method can not only capture the complex relationship of the sea-air interaction in the global climate system, but also accurately reflect the impact of these relationships on the sea ice changes in the Weddell Sea.

Description

Technical Field

[0001] The present invention relates to the field of spatial prediction of sea ice density, and in particular to a method for spatial prediction of sea ice density based on a MEOF model. Background Art

[0002] Polar sea ice is an important part of the Earth's climate system. Due to the limitations of observational data and the highly nonlinear dynamic changes of sea ice, the changes in sea ice are regular but extremely complex, involving physical processes at multiple scales and constrained by many meteorological factors. At present, sea ice forecasts mainly rely on sea ice numerical models, statistical prediction models, and deep learning prediction models. The numerical model of sea ice is part of the Earth's climate system model, which relies on the complex thermal and dynamic characteristics of sea ice for prediction. At present, the mainstream international sea ice models include the Los Alamous Sea Ice Model (Community IceCodE, CICE).

[0003] Statistical model prediction uses statistical methods to explore the changing patterns of data itself. For processes with complex influencing factors and physical processes, it can make up for the shortcomings of numerical model prediction to a certain extent. Statistical prediction of Arctic sea ice began in the 1980s, focusing on the relationship between sea ice and the atmosphere, ocean, etc. for a single parameter. In sea ice prediction, a variety of deep learning models have been widely used. Among them, convolutional neural networks (CNN) have powerful feature extraction capabilities and can automatically learn and extract key spatial features from sea ice images or time series data, providing rich information input for prediction models. In addition, deep neural networks (DNN), artificial neural networks (ANN) and convolutional long short-term memory networks (ConvLSTM) are also often used to establish deep learning prediction models for sea ice prediction.

[0004] The change process of sea ice is extremely complex, involving physical processes at different scales and significantly affected by a variety of meteorological factors. At present, large-scale sea ice changes have been deeply analyzed and simulated in numerical models. However, the abnormal changes in polar sea ice in recent years have put higher demands on existing numerical models. These changes increase the uncertainty of the physical parameter schemes in the model and may lead to significant deviations in the simulation results. In particular, when simulating the rapid reduction of polar sea ice, the model shows large deviations and poor prediction results.

[0005] In previous sea ice predictions, most methods were limited to using local meteorological data, ignoring the impact of teleconnected sea-air signals on sea ice predictions, and thus failing to fully reveal the potential predictive contribution of these teleconnected factors to sea ice changes. In addition, although deep learning models have shown significant advantages in prediction, their prediction results depend to a large extent on the quality and quantity of training data. Most previous sea ice predictions used local meteorological elements, which ignored the impact of teleconnected sea-air signals and failed to reflect the predicted impact of teleconnected sea-air factors on sea ice. Therefore, a spatial prediction method for sea ice density based on the MEOF model is needed. Summary of the invention

[0006] The purpose of the present invention is to provide a spatial prediction method for sea ice density based on the MEOF model. By using the MEOF statistical method, utilizing limited sea ice observation data itself, and based on teleconnected atmospheric and ocean climate signals, a spatial monthly prediction model for sea ice density in the Antarctic Weddell Sea is constructed, which saves computing costs, improves the accuracy of the sea ice statistical prediction model, and especially enhances the prediction capability of Antarctic sea ice extreme events.

[0007] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0008] The present invention is based on the atmospheric and oceanic factors of the teleconnection with the Weddell Sea SIC, and uses the MEOF statistical method to establish a spatial prediction model of SIC, which includes the following steps:

[0009] The lead-lag correlation coefficients between the Weddell Sea sea ice concentration and global sea surface temperature, mean sea level pressure and surface air temperature at different lead times were calculated to determine the atmospheric and oceanic predictors that affect the Weddell Sea SIC;

[0010] Add the SIC data of the previous month and 12 months ago to the above predictors;

[0011] Constructing a MEOF spatial prediction model based on the prediction factors, wherein the MEOF spatial prediction model projects the variable field of the prediction factors onto the basis vector of the SIC of the Weddell Sea to obtain the time coefficient of the SIC basis vector at the current moment;

[0012] All basis vectors are multiplied and added with the time coefficients to obtain the spatial distribution of the current month's SIC. Further, the prediction factors include the PDO index determined by the North Pacific sea temperature with a lead time of 40 months, the Eastern Tropical Pacific Sea Temperature Index ETPO SST with a lead time of 28 months, the Western Tropical Indian Ocean Sea Temperature Index WTIO SST with a lead time of 3 months, the South Tropical Atlantic Sea Temperature Index STAO SST with a lead time of 57 months, the SLP with a lead time of 6 months, and the Weddell-Amundsen Sea AWD SAT with a lead time of 1 month, where SST represents sea surface temperature and SIC represents sea ice density.

[0013] The MEOF model prediction steps are further as follows:

[0014] Using a combined training dataset Generate spatial modes and PC sequences of the fitting period, where k is the fitting period time;

[0015] During the forecast period, using the forecast set The original data of multiple predictors are projected into the obtained spatial mode to obtain the PC sequence of the prediction period, where k is the prediction period time;

[0016] Combining the spatial modes of PC and SIC during the forecast period, the spatial distribution of SIC can be predicted.

[0017] The computational efficiency can be further improved by reducing the amount of input data including the Weddell Sea SIC and the above six factors. The SIC data is resampled to 50km×50km, with a total of 2940 grid points; the North Pacific SST is resampled to a spatial resolution of 3°×2°, with a total of 1081 grid points. The ETPO SST spatial resolution is 2°×1°, with a total of 1200 grid points; the WTIO SST spatial resolution is 1°×2°, with a total of 900 grid points. The STAO SST spatial resolution is 2°×1°, with a total of 1400 grid points; the SLP spatial resolution is 6°×1°, with a total of 1260 grid points. The AWDSAT spatial resolution is 4.5°×1°, with a total of 1296 grid points.

[0018] Furthermore, in order to keep the weights consistent within the MEOF model, each factor is assigned approximately the same number of matrix points, and SIC has the largest number of matrix points, making SIC's weight the most significant.

[0019] Furthermore, the variable field of the prediction factor is projected onto the basis vector of the Weddell Sea SIC, thereby obtaining the time coefficient of the SIC basis vector at the current moment. All basis vectors are multiplied and added with the time coefficient respectively, and the spatial distribution of the SIC of the current month can be obtained.

[0020] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0021] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0022] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0023] The beneficial effects of the present invention are:

[0024] The present invention is a spatial prediction method for sea ice density based on the MEOF model. Compared with the prior art, the present invention has the following technical effects:

[0025] (1) The present invention takes into account the atmospheric and oceanic influencing factors on a global scale and far from the Weddell Sea, and also introduces the persistence characteristics of sea ice itself, and constructs an Antarctic sea ice prediction model that integrates the physical processes of important global climate factors. This model can not only capture the complex relationship between the sea and air interactions in the global climate system, but also accurately reflect the impact of these relationships on the changes in the Weddell Sea sea ice. This innovation not only broadens the in-depth understanding of the interaction between sea ice and global climate change, but also provides an important reference and reference for the further optimization and development of future sea ice prediction methods;

[0026] (2) The present invention uses the decomposition and reconstruction of the MEOF method, decomposing the combined variables containing both the predictor and the predicted quantity during the fitting period, and then using the reconstruction technology to restore the spatial distribution of the predicted quantity during the prediction period. The MEOF method can capture the spatiotemporal synergistic changes of the predictor and the predicted quantity, extract the spatial modal characteristics and temporal persistence of the variables, and has stable and excellent prediction performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of the steps of a method for spatial prediction of sea ice density based on a MEOF model of the present invention;

[0028] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0029] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0030] The present invention provides a spatial prediction method for sea ice density based on the MEOF model, comprising the following steps:

[0031] like Figure 1 As shown, in this embodiment, the following steps are included:

[0032] (1) Identify potential influencing factors

[0033] Considering the interaction of atmosphere-ocean-sea ice system, the lead-lag correlation coefficients of Weddell Sea sea ice concentration with global sea surface temperature (SST), mean sea level pressure (SLP) and sea air temperature (SAT) were calculated at lead times of 1 to 60 months to identify the atmospheric and oceanic factors affecting Weddell Sea SIC, including the PDO index determined by North Pacific SST (lead time 40 months), the Eastern Tropical Pacific SST index ETPOSST (lead time 28 months), the Western Tropical Indian Ocean SST index WTIO SST (lead time 3 months), the South Tropical Atlantic SST index STAO SST (lead time 57 months), SLP (lead time 6 months) and the Weddell-Amundsen Dipole (AWD) SAT (lead time 1 month). These predictors showed significant correlation with Weddell Sea SIC at different lead times, indicating that they can be used to predict future SIC changes.

[0034] (2) Establishing the spatial prediction model of MEOF

[0035] In atmospheric science research, empirical orthogonal function decomposition technology is used to decompose the original variable field into a linear combination of orthogonal functions to form independent typical modes, each of which contains a relative amount of information about the original field. The empirical orthogonal function decomposes the climate variable field Z containing n observations of m spatial points into p spatial eigenvectors and corresponding time weight coefficients:

[0036] Z m×n =V m×p T p×n #(5)

[0037] Where T is the time coefficient and V is the spatial eigenvector. The test method used to determine whether the spatial modes obtained by EOF decomposition are independent is to perform a significance test by calculating the error range of the eigenvalue.

[0038] The present invention integrates SIC and multiple factors that have a potential impact on it into a multi-factor combined variable, and then performs EOF decomposition, namely MEOF. It can capture the temporal continuity and spatial distribution characteristics of the data, thereby realizing the spatial prediction of SIC in the Weddell Sea. The SIC of the target prediction time and the prediction factors with a lead time are combined as a combined variable for MEOF decomposition, and the six factors in the atmosphere and ocean that have a potential impact on sea ice are used to form a combined variable for prediction, which is called Case 1 of the model. According to previous research results, the variability of sea ice is continuous, has the characteristics of interannual variation, and has significantly different variation characteristics in the ablation and freezing seasons. Therefore, the SIC of the previous month and the SIC of the same month last year, that is, the SIC of the past 12 months, were added to the prediction factors to evaluate the role of the persistence of sea ice itself, and Case 2 and Case 3 were established respectively.

[0039] Therefore, the present invention sets up the above three cases to compare the prediction ability: Case 1, in which the model contains only six basic atmospheric and oceanic factors, Case 2, in which the above six factors are superimposed on the SIC of the previous month, and Case 3, in which the above six factors are superimposed on the SIC of the previous month and 12 months ago. In this way, by comparing the prediction results of the models in different situations, the impact of different factors on the SIC of the Weddell Sea can be clearly evaluated, as shown in Table 1.

[0040] Table 1 Three combinations of predictors.

[0041]

[0042] Note: ki indicates the time when the predictor leads the SIC change in the Weddell Sea.

[0043] The MEOF model prediction steps are as follows:

[0044] For Case 1, the MEOF analysis model first uses the combined training dataset

[0045] Where k is the fitting period; the spatial modes and PC sequences of the fitting period are generated.

[0046] During the forecast period, using the forecast set The original data of multiple predictors are projected into the obtained spatial mode to obtain the PC sequence of the prediction period, where k is the prediction period time.

[0047] Afterwards, the spatial distribution of SIC after 2013 can be predicted by combining the spatial modes of PC and SIC during the forecast period.

[0048] In order to improve computational efficiency, the amount of input data including Weddell Sea SIC and the above six factors is reduced. SIC data is resampled to 50km×50km, with a total of 2940 grid points. North Pacific SST is resampled to a spatial resolution of 3°×2°, with a total of 1081 grid points. ETPO sea surface temperature has a spatial resolution of 2°×1°, with a total of 1200 grid points. WTIO sea surface temperature has a spatial resolution of 1°×2°, with a total of 900 grid points. STAO sea surface temperature has a spatial resolution of 2°×1°, with a total of 1400 grid points. SLP has a spatial resolution of 6°×1°, with a total of 1260 grid points. AWD SAT has a spatial resolution of 4.5°×1°, with a total of 1296 grid points.

[0049] In this process, in order to keep the weights consistent within the MEOF model, each factor is assigned approximately equal numbers of matrix points, and SIC has the largest number of matrix points, making the weight of SIC the most significant, thus ensuring the accuracy and precision of the model.

[0050] The MEOF analysis will conduct a MEOF analysis on the variables in the areas where the above six factors are located and the Weddell Sea SIC as a whole, and establish the spatial relationship between the six leading prediction factors and the current Weddell Sea SIC. Since the six prediction factors are ahead of the current Weddell Sea SIC, when the Weddell Sea SIC needs to be predicted at the current moment,

[0051] The six leading factors are all known variables. The variable fields of the six predictive factors are projected onto the basis vectors of the Weddell Sea SIC to obtain the time coefficient of the SIC basis vector at the current moment. All basis vectors are multiplied and added with the time coefficients to obtain the spatial distribution of the SIC for the current month.

[0052] The present invention is based on the MEOF method to establish a spatial prediction model for the Weddell Sea SIC, which can integrate distant atmospheric and ocean climate signals for sea ice prediction, and can evaluate the unique impacts of the atmosphere, ocean, and sea ice persistence on the prediction results. The prediction effect of the MEOF prediction model is strongly related to the number of modes used. The more modes used, the more concentrated the prediction values ​​and the fewer extreme values. The prediction effect is best when the first 20 modes are used, and it has obvious seasonal variation characteristics, and the prediction error is the smallest in April-June.

[0053] Compared with the previous sea ice statistical model, the MEOF prediction model established by the present invention has improved the prediction effect in the entire Weddell Sea, especially after incorporating the past SIC data, the prediction effect is further enhanced. The MEOF prediction model can not only predict the trend of sea ice increase in the 21st century and the maximum value in 2014, but also reduce the spring predictability obstacles of sea ice prediction based on limited fitting data, effectively improve the level of sea ice prediction in spring, and reduce the calculation cost and improve the calculation efficiency.

[0054] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0055] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0056] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0057] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a mine restoration effect prediction device based on soil reconstruction at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned mine restoration effect prediction methods.

[0058] The above application Figure 1The spatial prediction method of sea ice density based on the MEOF model disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0059] The electronic device may also perform Figure 1 A spatial prediction method for sea ice density based on the MEOF model was developed and implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0060] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, they execute any of the aforementioned sea ice density spatial prediction methods based on the MEOF model.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0066] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0067] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0069] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A spatial prediction method for sea ice density based on the MEOF model, characterized in that: Based on the atmospheric and oceanic factors teleconnected with the Weddell Sea SIC, the spatial prediction model of SIC was established using the MEOF statistical method, which includes the following steps: The lead-lag correlation coefficients between the Weddell Sea sea ice concentration and global sea surface temperature, mean sea level pressure and surface air temperature at different lead times were calculated to determine the atmospheric and oceanic predictors that affect the Weddell Sea SIC; Add the SIC data of the previous month and 12 months ago to the predictors; Constructing a MEOF spatial prediction model based on the prediction factors, wherein the MEOF spatial prediction model projects the variable field of the prediction factors onto the basis vector of the SIC of the Weddell Sea to obtain the time coefficient of the SIC basis vector at the current moment; All basis vectors are multiplied and added with the time coefficients to obtain the spatial distribution of the SIC for the current month.

2. The method for spatial prediction of sea ice density based on the MEOF model according to claim 1, characterized in that: The prediction factors include the PDO index determined by the North Pacific sea temperature with a lead time of 40 months, the eastern tropical Pacific sea temperature index ETPO SST with a lead time of 28 months, the western tropical Indian Ocean sea temperature index WTIO SST with a lead time of 3 months, the South tropical Atlantic sea temperature index STAO SST with a lead time of 57 months, SLP with a lead time of 6 months and the Weddell-Amundsen Sea AWD SAT with a lead time of 1 month, where SST represents sea surface temperature and SIC represents sea ice concentration.

3. The method for spatial prediction of sea ice density based on the MEOF model according to claim 1, characterized in that: The prediction steps of the MEOF model are as follows: Using a combined training dataset Generate spatial modes and PC series of the fitting period, where k is the time series of the fitting period; During the forecast period, using the forecast set The original data of multiple predictors are projected into the obtained spatial mode to obtain the PC series of the forecast period, where t is the time series of the forecast period; Combining the spatial modes of PC and SIC during the forecast period, the spatial distribution of SIC can be predicted.

4. The method for spatial prediction of sea ice density based on the MEOF model according to claim 1, characterized in that: The computational efficiency can be improved by reducing the amount of input data including the Weddell Sea SIC and the prediction factors. The SIC data is resampled to 50 km × 50 km, with a total of 2940 grid points; the North Pacific SST is resampled to a spatial resolution of 3° × 2°, with a total of 1081 grid points, the ETPO SST spatial resolution is 2° × 1°, with a total of 1200 grid points; the WTIO SST spatial resolution is 1° × 2°, with a total of 900 grid points, the STAO SST spatial resolution is 2° × 1°, with a total of 1400 grid points; the SLP spatial resolution is 6° × 1°, with a total of 1260 grid points, and the AWD SAT spatial resolution is 4.5° × 1°, with a total of 1296 grid points.

5. The method for spatial prediction of sea ice density based on the MEOF model according to claim 1, characterized in that: In order to keep the weights consistent within the MEOF model, each factor is assigned approximately the same number of matrix points, and SIC has the largest number of matrix points, making SIC's weight the most significant.

6. The method for spatial prediction of sea ice density based on the MEOF model according to claim 1, characterized in that: The variable field of the prediction factor is projected onto the basis vector of the Weddell Sea SIC, thereby obtaining the time coefficient of the SIC basis vector at the current moment. All basis vectors are multiplied and added with the time coefficient to obtain the spatial distribution of the SIC for the current month.

7. An electronic device comprising: processor; and a memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 6.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute any one of the methods of claims 1 to 6.

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