A method, apparatus, terminal equipment, and storage medium for parameterizing marine vertical buoyancy flux based on convergent frontal generation.

Abstract

This invention discloses a method, apparatus, terminal device, and storage medium for parameterizing marine vertical buoyancy flux based on convergence and frontogenesis. The method includes: acquiring grid point parameters through a marine simulation system; filtering vertical velocity and buoyancy to extract corresponding sub-mesoscale components and calculating the actual vertical buoyancy flux; determining target empirical coefficients through iterative optimization, including calculating horizontal divergence, horizontal buoyancy gradient, and depth correlation values, and calculating the theoretical vertical buoyancy flux; determining the target empirical coefficients when the correlation between the actual and theoretical vertical buoyancy fluxes is greater than a preset correlation threshold; otherwise, updating the empirical coefficients and recalculating; and performing sub-mesoscale sub-grid parameterization based on the theoretical vertical buoyancy flux corresponding to the target empirical coefficients. By implementing this invention, the problem of traditional vertical buoyancy flux parameterization based on a single mixed-layer baroclinic instability process is solved, improving the ability to characterize complex marine dynamic processes.

Description

Technical Field

[0001] This invention relates to the field of marine numerical simulation technology, and in particular to a method, apparatus, terminal equipment, and storage medium for parameterizing marine vertical buoyancy flux based on convergence and frontal generation. Background Technology

[0002] In low-resolution ocean models, sub-mesoscale dynamic processes cannot be directly analyzed due to spatial resolution limitations, but these motions contribute significantly to the buoyancy budget of the upper ocean. The spatiotemporal scales of sub-mesoscale processes are O(1–10) km and O(1–10) days, falling between mesoscale and dissipative scales, and can serve as a bridge for energy cascades. Dynamically, the Rossby number and equilibrium Richardson number of sub-mesoscale motions are O(1), indicating that they are not constrained by geostrophic forces and can induce vertical velocities approximately 100 times stronger than mesoscale eddies through non-geostrophic flows. Due to these dynamic characteristics, sub-mesoscale processes can generate significant buoyancy fluxes within and below the mixing layer, thus influencing the stratification of the upper ocean. Therefore, sub-mesoscale parameterization is introduced to characterize the subgrid processes.

[0003] Existing submesoscale parameterization, in order to quantify the restratification effect of submesoscale processes in the mixed layer, describes the inclination of isodensity lines from vertical to horizontal by flipping a stream function. This stream function is proportional to the product of the horizontal density gradient, the square of the mixed layer depth, and the inertial period. It has been proven that flipping the stream function can capture buoyancy variations in the simulation very well. Incorporating this parameter into global ocean models can reduce the bias in the simulation of mixed layer depth, making the simulation results more accurate.

[0004] However, traditional parameterization schemes for vertical buoyancy flux are mainly based on a single baroclinic instability process in the mixed layer, without taking into account the influence of other major sub-mesoscale generation mechanisms. Recent studies and observations have shown that convergence rather than strain in the upper ocean may play a more important role in sub-mesoscale frontogenesis, but traditional parameterization schemes have ignored the influence of convergence-dominated frontogenesis. Summary of the Invention

[0005] This invention provides a method, apparatus, terminal equipment, and storage medium for parameterizing marine vertical buoyancy flux based on convergence and frontal generation. This addresses the problem that traditional vertical buoyancy flux parameterization schemes are mainly based on a single mixed-layer baroclinic instability process, thereby improving the ability of vertical buoyancy flux parameterization to characterize complex marine dynamic processes.

[0006] One embodiment of the present invention provides a method for parameterizing marine vertical buoyancy flux based on convergent frontogenesis, comprising:

[0007] The system obtains the three-dimensional ocean velocity, temperature, depth, seawater density, and geographic latitude for each grid point output by the ocean simulation system; the three-dimensional ocean velocity includes: eastward velocity, northward velocity, and vertical velocity;

[0008] The corresponding buoyancy and mixing layer depth are determined based on the seawater density and temperature at each grid point;

[0009] The vertical velocity and buoyancy are filtered to obtain the sub-mesoscale components of the vertical velocity and buoyancy, and the actual vertical buoyancy flux is determined based on the sub-mesoscale components of the vertical velocity and buoyancy.

[0010] Initialize the current empirical coefficients and repeat the parameter optimization operation until the target empirical coefficients are obtained. The parameter optimization operation includes: calculating the horizontal divergence, horizontal buoyancy gradient, and depth correlation values ​​based on the three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude; determining the theoretical vertical buoyancy flux based on the horizontal divergence, horizontal buoyancy gradient, depth correlation values, mixed layer depth, geographic latitude, and the current empirical coefficients; calculating the correlation between the actual vertical buoyancy flux and the theoretical vertical buoyancy flux, and determining whether the correlation is greater than a preset correlation threshold. If so, the current empirical coefficients are used as the target empirical coefficients; otherwise, the empirical coefficients are reset as the current empirical coefficients for the next parameter optimization operation.

[0011] Based on the theoretical vertical buoyancy flux corresponding to the target empirical coefficient, a sub-mesoscale subgrid parameterized representation is performed.

[0012] Furthermore, based on the seawater density and temperature at each grid point, the corresponding buoyancy and mixing layer depth are determined, including:

[0013] The buoyancy is calculated based on the seawater density at each grid point:

[0014] For each grid point, the vertical temperature gradient is calculated along the depth direction. If there is a depth point where the absolute value of the vertical temperature gradient exceeds a preset gradient threshold, the depth corresponding to the first depth point that meets the condition is taken as the mixing layer depth. If there is no depth point where the absolute value of the vertical temperature gradient exceeds the preset gradient threshold, the maximum depth of that grid point is taken as the mixing layer depth.

[0015] Furthermore, the vertical velocity and buoyancy are filtered to obtain the sub-mesoscale components of the vertical velocity and buoyancy, including:

[0016] High-pass filtering is performed on the vertical velocity and buoyancy based on a preset cutoff wavelength to obtain the sub-mesoscale components of the vertical velocity and buoyancy.

[0017] Furthermore, the preset cutoff wavelength is determined in the following way:

[0018] The kinetic energy of each grid point is calculated based on the three-dimensional ocean velocity and seawater density of each grid point.

[0019] Spatial spectrum analysis of kinetic energy was performed to determine the sub-mesoscale and mesoscale cutoff wavelengths;

[0020] A preset cutoff wavelength is determined between the sub-mesoscale and mesoscale cutoff wavelengths.

[0021] Furthermore, based on the sub-mesoscale components of vertical velocity and buoyancy, the actual vertical buoyancy flux is determined, including:

[0022] Based on the preset cutoff wavelength, the product of the sub-mesoscale components of vertical velocity and buoyancy is high-pass filtered to obtain the filtered sub-mesoscale vertical buoyancy flux component.

[0023] The actual vertical buoyancy flux is obtained by subtracting the filtered submesoscale vertical buoyancy flux component from the product of the submesoscale components of vertical velocity and buoyancy.

[0024] Furthermore, based on three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude, horizontal divergence, horizontal buoyancy gradient, and depth-related values ​​are calculated, including:

[0025] High-pass filtering is performed on the eastward and northward velocities based on the preset cutoff wavelength to obtain the sub-mesoscale components of the eastward and northward velocities.

[0026] Subtracting the sub-mesoscale component of the eastward velocity from the eastward velocity yields the mesoscale component of the eastward velocity.

[0027] Subtracting the sub-mesoscale component of the northward velocity from the northward velocity yields the mesoscale component of the northward velocity.

[0028] Subtracting the sub-mesoscale component of buoyancy from the buoyancy force yields the mesoscale component of buoyancy.

[0029] The mesoscale horizontal divergence is calculated using the following formula based on the mesoscale components of the eastward and northward velocities:

[0030]

[0031] The mesoscale horizontal buoyancy gradient can be calculated using the following formula based on the mesoscale component of buoyancy:

[0032]

[0033] The depth-related value is calculated using the following formula, based on the depth and the depth of the mixing layer:

[0034]

[0035] in, Represents the horizontal divergence at the mesoscale. The mesoscale component of the eastward velocity is represented. The mesoscale component of the northward velocity is represented by x, y, and z, which represent the coordinate dimensions along the eastward, northward, and vertically downward directions, respectively. This represents the horizontal buoyancy gradient at the mesoscale. denoted by , μ(z) represents the mesoscale component of buoyancy; μ(z) represents the depth-dependent value; and H represents the depth of the mixed layer.

[0036] Furthermore, based on horizontal divergence, horizontal buoyancy gradient, depth correlation value, mixed layer depth, geographic latitude, and current empirical coefficients, the theoretical vertical buoyancy flux is determined, including:

[0037] The Coriolis parameter is calculated based on geographical latitude using the following formula:

[0038] f = 2Ωsinφ;

[0039] The theoretical vertical buoyancy flux is determined using the following formula, based on horizontal divergence, horizontal buoyancy gradient, depth correlation value, mixed layer depth, Coriolis parameter, and current empirical coefficients:

[0040]

[0041] Where f represents the Coriolis parameter, used to characterize the influence of Earth's rotation on ocean motion, Ω represents the Earth's rotational angular velocity, φ represents geographical latitude, VBF represents theoretical vertical buoyancy flux, and C1 represents the current empirical coefficient.

[0042] Based on the above method embodiments, the present invention provides corresponding device embodiments, including: an ocean parameter acquisition module, a physical quantity calculation module, a scale separation module, a parameter optimization module, and a subgrid parameterization module;

[0043] The ocean parameter acquisition module is used to acquire the three-dimensional ocean velocity, temperature, depth, seawater density, and geographic latitude of each grid point output by the ocean simulation system; the three-dimensional ocean velocity includes: eastward velocity, northward velocity, and vertical velocity;

[0044] The physical quantity calculation module is used to determine the corresponding buoyancy and mixing layer depth based on the seawater density and temperature at each grid point;

[0045] The scale separation module is used to filter the vertical velocity and buoyancy to obtain the sub-mesoscale components of the vertical velocity and buoyancy, and to determine the actual vertical buoyancy flux based on the sub-mesoscale components of the vertical velocity and buoyancy.

[0046] The parameter optimization module is used to initialize the current empirical coefficients and repeatedly perform parameter optimization operations until the target empirical coefficients are obtained. The parameter optimization operations include: calculating horizontal divergence, horizontal buoyancy gradient, and depth correlation values ​​based on three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude; determining the theoretical vertical buoyancy flux based on the horizontal divergence, horizontal buoyancy gradient, depth correlation values, mixed layer depth, geographic latitude, and the current empirical coefficients; calculating the correlation between the actual vertical buoyancy flux and the theoretical vertical buoyancy flux, and determining whether the correlation is greater than a preset correlation threshold. If so, the current empirical coefficients are used as the target empirical coefficients; otherwise, the empirical coefficients are reset as the current empirical coefficients for the next parameter optimization operation.

[0047] The subgrid parameterization module is used to perform subgrid parameterization representation at a sub-mesoscale based on the theoretical vertical buoyancy flux corresponding to the target empirical coefficients.

[0048] Based on the above method embodiments, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the marine vertical buoyancy flux parameterization method based on convergent frontalization as described in the present invention.

[0049] Based on the above method embodiments, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program that, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the marine vertical buoyancy flux parameterization method based on convergent frontal generation as described in the present invention.

[0050] Compared with the prior art, the beneficial effects of this embodiment are as follows:

[0051] This invention acquires the three-dimensional ocean velocity, temperature, depth, seawater density, and geographic latitude of each grid point output from an ocean model. The three-dimensional ocean velocity includes eastward velocity, northward velocity, and vertical velocity. Then, based on the seawater density and temperature of each grid point, the corresponding buoyancy and mixing layer depth are determined. The vertical velocity and buoyancy are filtered to separate sub-mesoscale signals, preventing large-scale background fields from obscuring the true sub-mesoscale processes. Based on the sub-mesoscale components of the vertical velocity and buoyancy, the actual vertical buoyancy flux is determined, providing an accurate verification benchmark for the parameterization scheme. Initialize the current empirical coefficients and repeat the parameter optimization operation until the target empirical coefficients are obtained. The parameter optimization operation includes: calculating horizontal divergence, horizontal buoyancy gradient, and depth correlation values ​​based on three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude. The horizontal divergence value characterizes the convergence and divergence of seawater in the horizontal direction, quantifying sub-mesoscale frontogenesis. The horizontal buoyancy gradient reflects frontal intensity; a larger horizontal buoyancy gradient indicates a significant density difference between the seawater on both sides of the front. The depth correlation value reflects baroclinic instability. The value reflects the density stratification differences within the mixing layer and between the mixing layer and the underlying water body. Based on horizontal divergence, horizontal buoyancy gradient, depth correlation value, mixing layer depth, geographic latitude, and current empirical coefficients, the theoretical vertical buoyancy flux is determined, thus comprehensively considering the convergent frontogenic effect and baroclinic instability in the buoyancy flux calculation. The correlation between the actual and theoretical vertical buoyancy flux is calculated, and it is determined whether the correlation is greater than a preset correlation threshold. If so, the current empirical coefficient is used as the target empirical coefficient; otherwise, the empirical coefficient is reset as the current empirical coefficient for the next parameter optimization operation. Finally, based on the target empirical coefficient, the sub-mesoscale process vertical buoyancy flux is represented by a subgrid parameterization.

[0052] In summary, this invention determines the theoretical vertical buoyancy flux by calculating horizontal divergence, horizontal buoyancy gradient, and depth correlation values. This comprehensively considers the convergence frontal effect and baroclinic instability in the buoyancy flux calculation, thereby solving the problem that traditional vertical buoyancy flux parameterization schemes are mainly based on a single mixed-layer baroclinic instability process, and improving the ability of vertical buoyancy flux parameterization to characterize complex ocean dynamic processes. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for parameterizing marine vertical buoyancy flux based on convergent frontal generation, provided in an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of the vertical buoyancy flux distribution in a certain ocean during winter, provided by an embodiment of the present invention;

[0055] Figure 3This is another flowchart illustrating a method for parameterizing marine vertical buoyancy flux based on convergent frontal generation, provided in one embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram of the structure of a marine vertical buoyancy flux parameterization device based on convergent frontal generation provided in an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0059] like Figure 1 As shown, in order to address the problem that traditional vertical buoyancy flux parameterization schemes are mainly based on a single mixed-layer baroclinic instability process, an embodiment of the present invention provides a marine vertical buoyancy flux parameterization method based on convergent frontogenesis. This method includes at least the following steps:

[0060] Step S1: Obtain the three-dimensional ocean velocity, temperature, depth, seawater density, and geographic latitude of each grid point output by the ocean simulation system; the three-dimensional ocean velocity includes: eastward velocity, northward velocity, and vertical velocity;

[0061] For step S1, obtain the three-dimensional ocean velocity, temperature t, depth z, seawater density ρ, and geographical latitude φ for each grid point output by the ocean simulation system. The three-dimensional ocean velocity includes eastward velocity u, northward velocity v, and vertical velocity w.

[0062] It should be noted that the eastward velocity u refers to the speed of ocean water flowing in the east-west direction, with positive values ​​usually indicating eastward flow and negative values ​​indicating westward flow; the northward velocity v refers to the speed of ocean water flowing in the north-south direction, with positive values ​​indicating northward flow and negative values ​​indicating southward flow; and the vertical velocity w refers to the speed of ocean water moving in the vertical direction (i.e., up and down direction), with positive values ​​indicating rising speed and negative values ​​indicating sinking speed.

[0063] Preferably, the seawater density ρ can be obtained from the ocean simulation system, or it can be based on the TEOS-10 method. Specifically, using the Gibbs function toolbox, the salinity s and pressure p output by the ocean simulation system are obtained. Based on the temperature t, salinity s, and pressure p, the seawater density is calculated using the following seawater state equation:

[0064] ρ(t,s,p)=ρ0(1+β t (t-t0)+β s (s-s0)+β p (p-p0));

[0065] Where ρ0 represents the reference density, t0 represents the reference temperature, s0 represents the reference salinity, p0 represents the reference pressure, and β... t β represents the coefficient of thermal expansion. s β represents the density change coefficient caused by salinity. p This represents the compressibility coefficient caused by pressure.

[0066] Step S2: Determine the corresponding buoyancy and mixing layer depth based on the seawater density and temperature at each grid point;

[0067] In a preferred embodiment, determining the corresponding buoyancy and mixing layer depth based on the seawater density and temperature at each grid point includes:

[0068] The buoyancy is calculated based on the seawater density at each grid point:

[0069] For each grid point, the vertical temperature gradient is calculated along the depth direction. If there is a depth point where the absolute value of the vertical temperature gradient exceeds a preset gradient threshold, the depth corresponding to the first depth point that meets the condition is taken as the mixing layer depth. If there is no depth point where the absolute value of the vertical temperature gradient exceeds the preset gradient threshold, the maximum depth of that grid point is taken as the mixing layer depth.

[0070] For step S2, after obtaining ocean data in step S1, buoyancy is calculated using density data. Specifically, the buoyancy is calculated using the following formula:

[0071]

[0072] Where b represents buoyancy, g represents gravitational acceleration, ρ represents seawater density, and ρ0 represents reference density.

[0073] Next, the mixing layer is a relatively homogeneous water layer in the upper ocean, beneath which there are often significant gradients in characteristics such as temperature and salinity. By calculating the vertical temperature gradient along the depth direction, i.e., the rate of temperature change per unit depth, when the absolute value of the vertical temperature gradient exceeds a preset gradient threshold, it means that from this depth onwards, the temperature characteristics of the seawater have changed significantly and it no longer belongs to the relatively homogeneous mixing layer. Therefore, the depth corresponding to the first depth point where the absolute value of the vertical temperature gradient exceeds the preset gradient threshold is taken as the mixing layer depth H. If the absolute value of the vertical temperature gradient does not exceed the preset gradient threshold throughout the entire depth range, the maximum depth of that grid point is taken as the mixing layer depth H.

[0074] In this embodiment, the preset gradient threshold is 0.02, and the depth at which the vertical temperature gradient exceeds 0.02 is taken as the mixing layer depth H.

[0075] Step S3: Filter the vertical velocity and buoyancy to obtain the sub-mesoscale components of the vertical velocity and buoyancy, and determine the actual vertical buoyancy flux based on the sub-mesoscale components of the vertical velocity and buoyancy.

[0076] In a preferred embodiment, the vertical velocity and buoyancy are filtered to obtain the sub-mesoscale components of the vertical velocity and buoyancy, including:

[0077] High-pass filtering is performed on the vertical velocity and buoyancy based on a preset cutoff wavelength to obtain the sub-mesoscale components of the vertical velocity and buoyancy.

[0078] For step S3, in ocean motion, vertical velocity and buoyancy contain motion information at different scales. The preset cutoff wavelength corresponds to a specific frequency. Signal components smaller than the preset cutoff wavelength are related to sub-mesoscale motion. Therefore, by high-pass filtering, the components of vertical velocity and buoyancy corresponding to sub-mesoscale motion can be separated.

[0079] It should be noted that sub-mesoscale motion has a significant impact on the buoyancy budget and ocean mixing layer characteristics of the upper ocean. However, in low-resolution ocean models, due to limitations such as spatial resolution, sub-mesoscale motion is difficult to directly and accurately resolve. Therefore, high-pass filtering is used to separate the components of vertical velocity and buoyancy corresponding to sub-mesoscale motion, facilitating the independent study of sub-mesoscale motion characteristics.

[0080] Vertical buoyancy flux is the magnitude of buoyancy transported per unit time and per unit area along the vertical direction. It is an important physical quantity for measuring the intensity of buoyancy transmission in the vertical direction in the ocean. Based on the fundamental physical relationship that vertical buoyancy flux equals the product of vertical velocity and buoyancy, where vertical velocity and buoyancy correspond to the sub-mesoscale components of vertical velocity and buoyancy, respectively, the actual vertical buoyancy flux can be determined.

[0081] In a preferred embodiment, the preset cutoff wavelength is determined in the following manner:

[0082] The kinetic energy of each grid point is calculated based on the three-dimensional ocean velocity and seawater density of each grid point.

[0083] Spatial spectrum analysis of kinetic energy was performed to determine the sub-mesoscale and mesoscale cutoff wavelengths;

[0084] A preset cutoff wavelength is determined between the sub-mesoscale and mesoscale cutoff wavelengths.

[0085] In one embodiment of the present invention, the kinetic energy of each grid point is calculated using the kinetic energy calculation formula based on the three-dimensional ocean velocity and seawater density of each grid point obtained in step S1:

[0086]

[0087] Where K represents kinetic energy, ρ represents seawater density, u represents eastward velocity, v represents northward velocity, and w represents vertical velocity.

[0088] In oceanography, mesoscale and sub-mesoscale scales exhibit distinct dynamic characteristics and scale ranges. Using the power spectral density estimation function (pwelch function) in Mtalab's Signal Processing Toolbox, spatial spectral analysis of kinetic energy is performed to identify inflection points or locations of significant energy decay in the kinetic energy spectrum, which are then defined as the cutoff wavelengths for sub-mesoscale and mesoscale components. Subsequently, a preset cutoff wavelength is determined between these two wavelengths, allowing for the separation of the sub-mesoscale and mesoscale components.

[0089] In a preferred embodiment, determining the actual vertical buoyancy flux based on the sub-mesoscale components of vertical velocity and buoyancy includes:

[0090] Based on the preset cutoff wavelength, the product of the sub-mesoscale components of vertical velocity and buoyancy is high-pass filtered to obtain the filtered sub-mesoscale vertical buoyancy flux component.

[0091] The actual vertical buoyancy flux is obtained by subtracting the filtered submesoscale vertical buoyancy flux component from the product of the submesoscale components of vertical velocity and buoyancy.

[0092] In one embodiment of the present invention, the sub-mesoscale component of vertical velocity and the sub-mesoscale component of buoyancy are multiplied. Due to the nonlinear convolution effect, large-scale fluctuations may be introduced after multiplication. In order to remove the large-scale fluctuations introduced by the multiplication operation, the product of the sub-mesoscale component of vertical velocity and the sub-mesoscale component of buoyancy is subjected to a second high-pass filter according to a preset cutoff wavelength to obtain the filtered sub-mesoscale vertical buoyancy flux component.

[0093] Next, the product of the submesoscale component of vertical velocity and the submesoscale component of buoyancy is subtracted from the filtered submesoscale vertical buoyancy flux component to obtain the actual vertical buoyancy flux:

[0094]

[0095] in, The actual vertical buoyancy flux is represented by w′b′, which represents the product of the submesoscale component of vertical velocity and the submesoscale component of buoyancy, i.e., the submesoscale vertical buoyancy flux component. w′ represents the submesoscale component of vertical velocity, b′ represents the submesoscale component of buoyancy, and (w′b′)′ represents the filtered submesoscale vertical buoyancy flux component.

[0096] It should be noted that the superscript (·)′ indicates the sub-mesoscale component after high-pass filtering. The superscript indicates the mesoscale component, obtained by subtracting its submesoscale component from the total value. Here, the superscript indicates a specific physical definition, a quantity that needs to be calculated during the computation.

[0097] Step S4: Initialize the current empirical coefficients and repeat the parameter optimization operation until the target empirical coefficients are obtained; wherein, the parameter optimization operation includes: calculating the horizontal divergence, horizontal buoyancy gradient, and depth correlation value based on the three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude; determining the theoretical vertical buoyancy flux based on the horizontal divergence, horizontal buoyancy gradient, depth correlation value, mixed layer depth, geographic latitude, and the current empirical coefficients; calculating the correlation between the actual vertical buoyancy flux and the theoretical vertical buoyancy flux, and determining whether the correlation is greater than a preset correlation threshold. If so, the current empirical coefficients are used as the target empirical coefficients; otherwise, the empirical coefficients are reset as the current empirical coefficients for the next parameter optimization operation.

[0098] For step S4, the current empirical coefficient is initialized by randomly selecting a value as the current empirical coefficient. Then, the empirical coefficient is optimized. Specifically, based on the obtained three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude, the horizontal divergence, horizontal buoyancy gradient, and depth correlation values ​​are calculated. The horizontal divergence can reflect the degree of convergence or divergence of seawater horizontal movement. The horizontal buoyancy gradient is used to characterize the rate of change of buoyancy in the horizontal direction. Moreover, the larger the horizontal buoyancy gradient, the more drastic the change of buoyancy in the horizontal direction. This is closely related to the intensity of sub-mesoscale fronts. Generally, areas with large horizontal buoyancy gradients also have high sub-mesoscale frontal intensities. The depth correlation value, i.e., the correlation between depth and mixed layer depth, is used to reflect the vertical stability of the ocean.

[0099] The theoretical vertical buoyancy flux is calculated using the constructed formula and based on the above parameters and current empirical coefficients.

[0100] Among them, based on the theoretical scale analysis method, the parameterization of vertical buoyancy flux, which includes the combined effects of baroclinic instability and convergent frontogenesis in the mixed layer, is derived. This process simplifies and models complex phenomena through dimensional analysis and scale estimation of physical laws. The core is to identify the order of magnitude relationship of key physical variables at different scales. That is, the relationship between variables is first established through scale analysis, and then this relationship is quantified by coefficients. The derivation process is as follows:

[0101] First, it should be noted that in theoretical scale analysis, the subscript "sm" represents the scale identifier for sub-mesoscale physical quantities, and the subscript "m" represents the scale identifier for mesoscale physical quantities. These subscripts are scale identifiers for physical quantities and are symbols used in theoretical derivation; their specific values ​​do not need to be calculated, but are only used to distinguish physical quantities at different scales. For example, b′ represents sub-mesoscale buoyancy, b... sm The scale indicates the sub-mesoscale buoyancy scale. In theoretical scale analysis, the scale identifier is used for derivation in order to determine the relationship between variables through scale analysis.

[0102] The significance of submesoscale vertical buoyancy flux in energy theory is that it characterizes the conversion of available potential energy into kinetic energy. In the absence of a source and sink of available potential energy, the vertical buoyancy flux caused by a submesoscale front can be expressed as the time derivative of the potential energy stored per unit volume of the front, i.e.:

[0103]

[0104] Wherein, VBF represents the theoretical vertical buoyancy flux. PE represents the submesoscale vertical buoyancy flux. smΔt1 represents the sub-mesoscale potential energy scale, which is the characteristic time scale of baroclinic instability in the mixed layer, i.e., the time of potential energy release. ∝ indicates that it is proportional.

[0105] Assume that the release of submesoscale potential energy is entirely dominated by baroclinic instability in the mixed layer. Define PE. sm =

[0106] PE′=b′ 2 / N 2 Where PE′ represents submesoscale potential energy, b′ represents submesoscale buoyancy, and N represents stratification (i.e., buoyancy frequency); the scale of buoyancy of the submesoscale front is determined by the characteristic length scale L across the front. f and horizontal buoyancy gradient The scale of b′ is determined as follows: in, Denotes the horizontal gradient operator, b sm L represents the submesoscale buoyancy scale. f It can be approximated as the deformation radius of the hybrid layer, i.e., L f =NH / f, where f represents the Coriolis parameter, used to characterize the influence of the Earth's rotation on ocean motion. Rearranging the above formula, we can obtain:

[0107]

[0108] According to the theory of convergence and frontogenesis, the sharpening of a front is mainly affected by the divergence (convergence) of the flow field:

[0109]

[0110] If the timescale of zeta generation is , we can obtain:

[0111]

[0112] Combining the above formula, we get:

[0113]

[0114] Where C1 represents the empirical coefficient, δ m Δt2 represents the mesoscale convergence scale, and Δt2 represents the timescale of convergence frontogenesis.

[0115] Assuming that under quasi-equilibrium conditions, the increase in available potential energy caused by convergence can be immediately released through baroclinic instability in the mixed layer, then the timescale of convergence frontogenesis should be equal to the timescale of baroclinic instability frontigenesis, i.e., Δt2 = Δt1. Simplifying VBF, we get:

[0116]

[0117] The above equation only represents the horizontal distribution of sub-mesoscale vertical buoyancy flux. To obtain its three-dimensional structure, a vertical structure function, i.e., a depth-dependent value, is introduced to reflect the modulating effect of the mixing layer depth on the vertical buoyancy flux distribution:

[0118]

[0119] The final three-dimensional structure of the vertical buoyancy flux is obtained:

[0120]

[0121] Therefore, the submesoscale vertical buoyancy flux can be parameterized using mesoscale components.

[0122] In a preferred embodiment, horizontal divergence, horizontal buoyancy gradient, and depth-related values ​​are calculated based on three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude, including:

[0123] High-pass filtering is performed on the eastward and northward velocities based on the preset cutoff wavelength to obtain the sub-mesoscale components of the eastward and northward velocities.

[0124] Subtracting the sub-mesoscale component of the eastward velocity from the eastward velocity yields the mesoscale component of the eastward velocity.

[0125] Subtracting the sub-mesoscale component of the northward velocity from the northward velocity yields the mesoscale component of the northward velocity.

[0126] Subtracting the sub-mesoscale component of buoyancy from the buoyancy force yields the mesoscale component of buoyancy.

[0127] The mesoscale horizontal divergence is calculated using the following formula based on the mesoscale components of the eastward and northward velocities:

[0128]

[0129] The mesoscale horizontal buoyancy gradient can be calculated using the following formula based on the mesoscale component of buoyancy:

[0130]

[0131] The depth-related value is calculated using the following formula, based on the depth and the depth of the mixing layer:

[0132]

[0133] in, Represents the horizontal divergence at the mesoscale. The mesoscale component of the eastward velocity is represented. The mesoscale component of the northward velocity is represented by x, y, and z, which represent the coordinate dimensions along the eastward, northward, and vertically downward directions, respectively. This represents the horizontal buoyancy gradient at the mesoscale. denoted by , μ(z) represents the mesoscale component of buoyancy; μ(z) represents the depth-dependent value; and H represents the depth of the mixed layer.

[0134] In a preferred embodiment, the theoretical vertical buoyancy flux is determined based on horizontal divergence, horizontal buoyancy gradient, depth correlation value, mixed layer depth, geographic latitude, and current empirical coefficients, including:

[0135] The Coriolis parameter is calculated based on geographical latitude using the following formula:

[0136] f = 2Ωsinφ;

[0137] The theoretical vertical buoyancy flux is determined using the following formula, based on horizontal divergence, horizontal buoyancy gradient, depth correlation value, mixed layer depth, Coriolis parameter, and current empirical coefficients:

[0138]

[0139] Where f represents the Coriolis parameter, used to characterize the influence of Earth's rotation on ocean motion, Ω represents the Earth's rotational angular velocity, φ represents geographical latitude, VBF represents theoretical vertical buoyancy flux, and C1 represents the current empirical coefficient.

[0140] In one embodiment of the present invention, similarly, based on a preset cutoff wavelength, a high-pass filter is used to separate the sub-mesoscale components of eastward velocity, northward velocity, and buoyancy. Based on this, the mesoscale horizontal divergence (reflecting horizontal convergence and divergence of seawater), horizontal buoyancy gradient (reflecting horizontal changes in buoyancy), and depth correlation value (characterizing vertical stability) are calculated according to relevant formulas. Next, based on geographical latitude, the Coriolis parameter is calculated using the formula f = 2Ωsinφ, which characterizes the influence of the Earth's rotation on ocean motion. Then, the calculated horizontal divergence, horizontal buoyancy gradient, depth correlation value, as well as the mixing layer depth, Coriolis parameter, and current empirical coefficients, are substituted into the formula... Thus, the theoretical vertical buoyancy flux can be calculated.

[0141] After calculating the theoretical vertical buoyancy flux, the correlation coefficient (r) between the actual and theoretical vertical buoyancy flux is calculated using the Pearson correlation coefficient method. A r > 0 indicates a positive correlation, while r = 1 indicates a perfectly linear correlation. The correlation is then checked against a preset threshold. If the correlation r is greater than the threshold, the current empirical coefficient indicates that the theoretical model fits the actual situation well, and it can be designated as the target empirical coefficient. If the correlation r is less than or equal to the threshold, the empirical coefficient is reset and used as the current empirical coefficient for the next parameter optimization operation. The final target empirical coefficient means that the theoretical vertical buoyancy flux calculated based on this coefficient can better reflect the vertical buoyancy flux of sub-mesoscale motion in the actual ocean.

[0142] It should be noted that resetting the empirical coefficients involves randomly selecting values ​​that have never been selected during the iteration process, ensuring that the selected empirical coefficients are not repeated.

[0143] Step S5: Based on the theoretical vertical buoyancy flux corresponding to the target empirical coefficient, perform sub-mesoscale subgrid parameterization representation.

[0144] For step S5, due to limitations in computational resources and resolution, ocean numerical models cannot directly and accurately simulate motions at all scales. Sub-mesoscale motions are relatively small in scale and often difficult to fully analyze at the model's grid scale, existing only within the sub-grid scale. Sub-mesoscale sub-grid parameterization is achieved using the theoretical vertical buoyancy flux corresponding to the target empirical coefficients. Specifically, the theoretical vertical buoyancy flux is input into the ocean numerical model as a key physical process. During model calculations, it participates in the calculation of energy and mass transport, reflecting the impact of sub-mesoscale motions on large-scale ocean dynamics and thermodynamics. This effectively characterizes the influence of sub-mesoscale motions on large-scale ocean processes within the limited model resolution.

[0145] like Figure 2 The diagram shows the vertical buoyancy flux distribution in a certain ocean during winter. Figure 2 (a) is a schematic diagram of the vertical buoyancy flux distribution parameterized by Fox Kemper et al. (2008). Figure 2 (b) is a schematic diagram of the parameterized vertical buoyancy flux distribution by Zhang et al. (2023). Figure 2 (c) is a schematic diagram of the parameterized vertical buoyancy flux distribution of the present invention. Figure 2 (d) is a schematic diagram of the actual vertical buoyancy flux distribution. By comparing these four diagrams, the simulation effect of different parameterization schemes on the actual vertical buoyancy flux can be evaluated. This invention is closer to the actual situation in terms of numerical magnitude. From the color scale comparison, the colors of most areas are similar to the actual values. Figure 2(d) The greater similarity indicates that the scheme of the present invention simulates the vertical buoyancy flux more accurately and can more realistically reflect the actual energy transmission situation.

[0146] like Figure 3 The diagram illustrates another flowchart of the ocean vertical buoyancy flux parameterization method based on convergent frontogenesis, presenting the overall implementation process of this method. Specifically, starting with determining the sub-mesoscale cutoff wavelength through spatial spectral analysis, the initial ocean data is scale-separated to obtain sub-mesoscale and mesoscale components, and key parameters such as mixing layer depth and buoyancy are calculated. Based on this, the actual and theoretical vertical buoyancy fluxes are calculated separately. By continuously optimizing parameters such as empirical coefficients, the correlation between the two is improved to a high level, thereby constructing a novel ocean vertical buoyancy flux parameterization method with multi-mechanism synergy, used to more accurately describe relevant physical processes in the ocean.

[0147] like Figure 4 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0148] One embodiment of the present invention provides a marine vertical buoyancy flux parameterization device based on convergence and frontal generation, comprising: a marine parameter acquisition module, a physical quantity calculation module, a scale separation module, a parameter optimization module, and a subgrid parameterization module;

[0149] The ocean parameter acquisition module is used to acquire the three-dimensional ocean velocity, temperature, depth, seawater density, and geographic latitude of each grid point output by the ocean simulation system; the three-dimensional ocean velocity includes: eastward velocity, northward velocity, and vertical velocity;

[0150] The physical quantity calculation module is used to determine the corresponding buoyancy and mixing layer depth based on the seawater density and temperature at each grid point;

[0151] The scale separation module is used to filter the vertical velocity and buoyancy to obtain the sub-mesoscale components of the vertical velocity and buoyancy, and to determine the actual vertical buoyancy flux based on the sub-mesoscale components of the vertical velocity and buoyancy.

[0152] The parameter optimization module is used to initialize the current empirical coefficients and repeatedly perform parameter optimization operations until the target empirical coefficients are obtained. The parameter optimization operations include: calculating horizontal divergence, horizontal buoyancy gradient, and depth correlation values ​​based on three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude; determining the theoretical vertical buoyancy flux based on the horizontal divergence, horizontal buoyancy gradient, depth correlation values, mixed layer depth, geographic latitude, and the current empirical coefficients; calculating the correlation between the actual vertical buoyancy flux and the theoretical vertical buoyancy flux, and determining whether the correlation is greater than a preset correlation threshold. If so, the current empirical coefficients are used as the target empirical coefficients; otherwise, the empirical coefficients are reset as the current empirical coefficients for the next parameter optimization operation.

[0153] The subgrid parameterization module is used to perform subgrid parameterization representation at a sub-mesoscale based on the theoretical vertical buoyancy flux corresponding to the target empirical coefficients.

[0154] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the marine vertical buoyancy flux parameterization method based on convergence and frontal generation provided by any of the above-described method embodiments of the present invention.

[0155] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0156] Based on the above embodiments of the ocean vertical buoyancy flux parameterization method based on convergence and frontal generation, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the ocean vertical buoyancy flux parameterization method based on convergence and frontal generation of any embodiment of the present invention.

[0157] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0158] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0159] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0160] Based on the above-described method embodiments, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the marine vertical buoyancy flux parameterization method based on convergent frontal generation described in any of the above-described method embodiments of the present invention.

[0161] The module / unit integrated into the marine vertical buoyancy flux parameterization device / terminal equipment based on convergence and frontal generation, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0162] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for parameterizing ocean vertical buoyancy flux based on a convergent front, characterized in that, The method comprises the following steps: obtaining three-dimensional ocean speed, temperature, depth, seawater density and geographical latitude of each grid point output by the ocean simulation system; the three-dimensional ocean speed comprises eastward speed, northward speed and vertical speed; determining the corresponding buoyancy and mixed layer depth according to the seawater density and temperature of each grid point; filtering the vertical speed and the buoyancy to obtain the sub-mesoscale components of the vertical speed and the buoyancy, and determining the actual vertical buoyancy flux according to the sub-mesoscale components of the vertical speed and the buoyancy; initializing the current empirical coefficient and repeatedly performing the parameter optimization operation until the target empirical coefficient is obtained; wherein the parameter optimization operation comprises: calculating the horizontal divergence, the horizontal buoyancy gradient and the depth correlation value according to the three-dimensional ocean speed, the depth, the buoyancy, the mixed layer depth and the geographical latitude; determining the theoretical vertical buoyancy flux according to the horizontal divergence, the horizontal buoyancy gradient, the depth correlation value, the mixed layer depth, the geographical latitude and the current empirical coefficient; calculating the correlation of the actual vertical buoyancy flux and the theoretical vertical buoyancy flux, and determining whether the correlation is greater than a preset correlation threshold; if yes, the current empirical coefficient is taken as the target empirical coefficient; if no, the empirical coefficient is reset as the current empirical coefficient for performing the next parameter optimization operation; performing sub-mesoscale sub-grid parameterization representation according to the theoretical vertical buoyancy flux corresponding to the target empirical coefficient; wherein the calculation of the horizontal divergence, the horizontal buoyancy gradient and the depth correlation value according to the three-dimensional ocean speed, the depth, the buoyancy, the mixed layer depth and the geographical latitude comprises: high-pass filtering the eastward speed and the northward speed according to a preset cutoff wavelength to obtain the sub-mesoscale components of the eastward speed and the northward speed; subtracting the sub-mesoscale component of the eastward speed from the eastward speed to obtain the mesoscale component of the eastward speed; subtracting the sub-mesoscale component of the northward speed from the northward speed to obtain the mesoscale component of the northward speed; subtracting the sub-mesoscale component of the buoyancy from the buoyancy to obtain the mesoscale component of the buoyancy; calculating the mesoscale horizontal divergence according to the mesoscale component of the eastward speed and the mesoscale component of the northward speed by the following formula: ； calculating the mesoscale horizontal buoyancy gradient according to the mesoscale component of the buoyancy by the following formula: ； calculating the depth correlation value according to the depth and the mixed layer depth by the following formula: ； wherein represents the mesoscale horizontal divergence, represents the mesoscale component of the eastward velocity, represents the mesoscale component of the northward velocity, , and represent the coordinate dimension in the horizontal direction along the eastward, along the northward and vertically downward, respectively; represents the mesoscale horizontal buoyancy gradient, represents the mesoscale component of the buoyancy; represents the depth dependent value, represents the mixed layer depth.

2. The ocean vertical buoyancy flux parameterization based on the converging front method according to claim 1, wherein, determining the corresponding buoyancy and mixed layer depth according to the seawater density and temperature of each grid point comprises: calculating the corresponding buoyancy according to the seawater density of each grid point: for each grid point, calculating the temperature vertical gradient along the depth direction, and when there is a depth point where the absolute value of the temperature vertical gradient exceeds a preset gradient threshold, taking the depth corresponding to the first depth point meeting the condition as the mixed layer depth; when there is no depth point where the absolute value of the temperature vertical gradient exceeds the preset gradient threshold, taking the maximum depth of the grid point as the mixed layer depth.

3. The ocean vertical buoyancy flux parameterization based on the converging front method according to claim 1, wherein, filtering the vertical speed and the buoyancy to obtain the sub-mesoscale components of the vertical speed and the buoyancy comprises: high-pass filtering the vertical speed and the buoyancy according to a preset cutoff wavelength to obtain the sub-mesoscale components of the vertical speed and the buoyancy.

4. The ocean vertical buoyancy flux parameterization based on the converging front method according to claim 3, wherein, The preset cutoff wavelength is determined by the following method: According to the three-dimensional ocean speed and seawater density of each grid point, the kinetic energy of each grid point is calculated; The kinetic energy is subjected to spatial spectrum analysis to determine the cutoff wavelength of the sub-mesoscale and mesoscale; The preset cutoff wavelength is determined between the cutoff wavelengths of the sub-mesoscale and mesoscale.

5. The ocean vertical buoyancy flux parameterization based on the converging front method according to claim 4, wherein, According to the sub-mesoscale components of the vertical velocity and the buoyancy, the actual vertical buoyancy flux is determined, including: The product of the sub-mesoscale components of the vertical velocity and the buoyancy is subjected to high-pass filtering according to the preset cutoff wavelength to obtain a filtered sub-mesoscale vertical buoyancy flux component; The product of the sub-mesoscale components of the vertical velocity and the buoyancy is subtracted by the filtered sub-mesoscale vertical buoyancy flux component to obtain the actual vertical buoyancy flux.

6. The ocean vertical buoyancy flux parameterization based on the converging front method according to claim 5, wherein, According to the horizontal divergence, the horizontal buoyancy gradient, the depth correlation value, the mixed layer depth, the geographical latitude, and the current empirical coefficient, the theoretical vertical buoyancy flux is determined, including: According to the geographical latitude, the Coriolis parameter is calculated by the following formula: ； According to the horizontal divergence, the horizontal buoyancy gradient, the depth correlation value, the mixed layer depth, the Coriolis parameter, and the current empirical coefficient, the theoretical vertical buoyancy flux is determined by the following formula: ； where, denotes the Coriolis parameter, which characterizes the effect of the Earth's rotation on oceanic motions, denotes the Earth's rotation angular velocity, denotes the geographic latitude, denotes the theoretical vertical buoyancy flux, denotes the current empirical coefficient.

7. A device for parameterizing ocean vertical buoyancy flux based on a radiative convective front, characterized in that, Including: The ocean parameter acquisition module, the physical quantity calculation module, the scale separation module, the parameter optimization module, and the sub-grid parameterization module; The ocean parameter acquisition module is configured to acquire the three-dimensional ocean speed, temperature, depth, seawater density, and geographical latitude of each grid point output by the ocean simulation system; The three-dimensional ocean speed includes eastward speed, northward speed, and vertical speed; The physical quantity calculation module is configured to determine the corresponding buoyancy and mixed layer depth according to the seawater density and temperature of each grid point; The scale separation module is configured to filter the vertical velocity and the buoyancy to obtain the sub-mesoscale components of the vertical velocity and the buoyancy, and determine the actual vertical buoyancy flux according to the sub-mesoscale components of the vertical velocity and the buoyancy; The parameter optimization module is configured to initialize a current experience coefficient and repeatedly perform a parameter optimization operation until a target experience coefficient is obtained. The parameter optimization operation includes: calculating a horizontal divergence, a horizontal buoyancy gradient, and a depth correlation value based on three-dimensional ocean velocity, depth, buoyancy, mixed layer depth, and geographic latitude; determining a theoretical vertical buoyancy flux based on the horizontal divergence, the horizontal buoyancy gradient, the depth correlation value, the mixed layer depth, the geographic latitude, and the current experience coefficient; calculating a correlation of an actual vertical buoyancy flux and the theoretical vertical buoyancy flux, and determining whether the correlation is greater than a preset correlation threshold. If yes, the current experience coefficient is taken as the target experience coefficient. If no, the experience coefficient is reset as the current experience coefficient for performing a next parameter optimization operation. The calculation of the horizontal divergence, the horizontal buoyancy gradient, and the depth correlation value based on the three-dimensional ocean velocity, the depth, the buoyancy, the mixed layer depth, and the geographic latitude includes: performing high-pass filtering on eastward velocity and northward velocity based on a preset cutoff wavelength to obtain a sub-mesoscale component of the eastward velocity and a sub-mesoscale component of the northward velocity; subtracting the sub-mesoscale component of the eastward velocity from the eastward velocity to obtain a mesoscale component of the eastward velocity; subtracting the sub-mesoscale component of the northward velocity from the northward velocity to obtain a mesoscale component of the northward velocity; subtracting a sub-mesoscale component of the buoyancy from the buoyancy to obtain a mesoscale component of the buoyancy; and calculating a mesoscale horizontal divergence based on the mesoscale component of the eastward velocity and the mesoscale component of the northward velocity by the following formula: ; calculating a mesoscale horizontal buoyancy gradient based on the mesoscale component of the buoyancy by the following formula: ; and calculating a depth correlation value based on the depth and the mixed layer depth by the following formula: ; wherein represents the mesoscale horizontal divergence, represents the mesoscale component of the eastward velocity, represents the mesoscale component of the northward velocity, , and represent coordinate dimensions in the horizontal direction along the eastward direction, the northward direction, and the vertical downward direction, respectively; represents the mesoscale horizontal buoyancy gradient, represents the mesoscale component of the buoyancy; represents the depth correlation value, represents the mixed layer depth. The sub-grid parameterization module is configured to perform sub-mesoscale sub-grid parameterization representation according to the theoretical vertical buoyancy flux corresponding to the target empirical coefficient.

8. A terminal device, comprising: The computer readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer readable storage medium is located is controlled to perform the ocean vertical buoyancy flux parameterization method based on the convergent front as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer readable storage medium is located is controlled to perform the ocean vertical buoyancy flux parameterization method based on the convergent front as claimed in any one of claims 1-6.

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