Numerical mode power framework construction method and device for weather forecast

Abstract

The invention relates to a numerical mode power framework construction method for weather forecast. The method comprises the following steps: establishing a basic equation set and an energy conversion relational expression of atmospheric motion of a numerical mode power framework; and establishing a spatial discrete model of the power frame based on the basic equation set of the atmospheric motion of the numerical mode power frame and the energy conversion relational expression. According to the method, the energy balance property of the mode is remarkably improved, the depiction capability of the mode on a fine structure is improved, and the response capability of the mode on terrain influence is improved. The invention further relates to equipment and a storage medium.

Description

Technical Field

[0001] The present application relates to the technical field of weather forecasting, and in particular to a method and device for constructing a numerical model dynamic framework for weather forecasting. Background Art

[0002] With the continuous improvement of computer technology and the advancement of diversified and refined observation technology, the spatial and temporal resolution of numerical weather forecast models has been significantly improved, and cloud-resolution scale numerical models have been widely used in severe convective weather forecast simulations. However, the existing numerical weather forecast models still have a very low ability to predict severe convective weather. There are many reasons for this. The problem with the model's dynamic framework discretization scheme may be a fundamental reason for the low ability of the current numerical model to predict severe convective weather. The model's dynamic framework (Dynamical Core) is closely related to the model's non-adiabatic physical process, and its defects will inevitably affect the effects of other physical processes.

[0003] The occurrence and development of severe convective weather is not only affected by large-scale weather systems, but also includes the process of combination or merger of small-scale systems. Multi-scale interaction is the fundamental cause of severe convective weather. The inability of numerical models to reproduce the multi-scale interaction process, especially the effective simulation of the combination or merger of small-scale systems, is the main reason for the low level of severe convective weather forecasting at present. Therefore, improving the description of multi-scale interactions is one of the difficult problems in constructing numerical weather forecast models. Multi-scale interactions are intuitively manifested as the generation and disappearance of wind pressure fields of various scales, which essentially represents the scale changes of energy such as kinetic energy and potential energy, that is, the dispersion characteristics of energy. Summary of the invention

[0004] In view of the above-mentioned problems existing in the existing numerical weather forecast model dynamic framework, the present application provides a method and device for constructing a numerical model dynamic framework for weather forecasting.

[0005] In a first aspect, the present application provides a method for constructing a numerical model dynamic framework for weather forecasting, the method comprising:

[0006] Establish the basic equations of atmospheric motion and energy conversion relations for the dynamic framework of numerical models;

[0007] Based on the basic equations of atmospheric motion of the numerical model dynamic framework and the energy conversion relationship, a dynamic framework spatial discrete model is established.

[0008] In a second aspect, the present application also provides a device for constructing a numerical model dynamic framework for weather forecasting, the device comprising:

[0009] The first processing module is used to establish the basic equations of atmospheric motion and energy conversion relations of the dynamic framework of the numerical model;

[0010] The second processing module is used to establish a dynamic framework spatial discrete model based on the basic equations of atmospheric motion in the numerical model dynamic framework and the energy conversion relationship.

[0011] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for constructing a numerical model dynamic framework for weather forecasting described in any one of the first aspects is implemented.

[0012] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for constructing a numerical model dynamic framework for weather forecasting as described in any one of the first aspects is implemented.

[0013] The present application provides a method for constructing a numerical model dynamic framework for weather forecasting, including establishing a basic set of equations for atmospheric motion and an energy conversion relationship of the numerical model dynamic framework; and establishing a dynamic framework spatial discrete model based on the basic set of equations for atmospheric motion of the numerical model dynamic framework and the energy conversion relationship. The present invention significantly improves the energy balance properties of the model, improves the model's ability to depict fine structures, and improves the model's ability to respond to terrain influences. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0015] Figure 1 It is a flow chart of a method for constructing a dynamic framework of a numerical model for weather forecasting provided in an embodiment of the present application;

[0016] Figure 2 It is a topological schematic diagram of a mode power framework space discretization solution provided by another embodiment of the present application;

[0017] Figure 3 is a schematic diagram of the results of a 100m resolution hot bubble experiment provided in another embodiment of the present application;

[0018] Figure 4 is a schematic diagram of a potential temperature profile of a hot bubble experiment provided in another embodiment of the present application;

[0019] Figure 5 is a schematic diagram of a time variation curve of the maximum position temperature and the maximum and minimum wind speed of a hot bubble provided by another embodiment of the present application;

[0020] Figure 6It is a schematic diagram of energy curves of 100m FM and WRF hot bubble experiments provided in the embodiments of the present application;

[0021] Figure 7 It is a schematic diagram of the potential temperature plane of a cold bubble in a 50m resolution Fm and WRF experiment provided in another embodiment of the present application;

[0022] Figure 8 It is a schematic diagram of the potential temperature plane of a cold bubble based on a 100m resolution Fm and WRF experiment provided by another embodiment of the present application;

[0023] Fig. 9 It is a schematic diagram of the potential temperature plane of a cold bubble in a 50m resolution Fm and WRF experiment provided in another embodiment of the present application;

[0024] Fig.10 It is a schematic diagram of the potential temperature profile of the cold bubble experiment of FM and WRF with a resolution of 100 m provided in another embodiment of the present application;

[0025] Fig.11 It is a schematic diagram of the minimum potential temperature value and the maximum and minimum wind speed values ​​of the cold bubble experiment of FM and WRF with a resolution of 100m provided in the embodiment of the present application;

[0026] Fig.12 is a schematic diagram of energy curves of 100m FM and WRF cold-bubble experiments provided in another embodiment of the present application;

[0027] Fig.13 is a schematic diagram of a WRF and FM mountain wave experiment provided by another embodiment of the present application;

[0028] Fig.14 is a schematic diagram of energy diagnosis of a mountain wave experiment provided by another embodiment of the present application;

[0029] Fig.15 is a schematic diagram of the vertical momentum flux of mountain waves provided by another embodiment of the present application;

[0030] Fig.16 It is a patent innovative schematic diagram provided by the embodiment of this application;

[0031] Fig.17 It is a schematic diagram of a patent application scenario provided by another embodiment of the present application;

[0032] Fig.18 is a flow chart of a method for constructing a dynamic framework of a numerical model for weather forecasting provided by another embodiment of the present application;

[0033] Fig.19 It is a schematic diagram of a numerical model dynamic framework building module for weather forecasting provided in another embodiment of the present application. DETAILED DESCRIPTION

[0034] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0035] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0036] The factors that affect the forecasting effect of numerical weather forecast models can be roughly summarized into three aspects: initial data processing, physical process scheme and dynamic framework scheme. In comparison, the research on improving the simulation and forecasting level of numerical models by improving the dynamic framework scheme is slightly weak.

[0037] With the continuous improvement of computer technology and the progress of diversified and refined observation technology, the spatial and temporal resolution of numerical weather forecast models has been significantly improved, and cloud-resolution scale numerical models have been widely used in severe convective weather forecast simulations. However, the existing numerical weather forecast models still have a very low ability to predict severe convective weather. The reasons are many. The problems with the model dynamic framework discretization scheme may be a fundamental reason for the low ability of the current numerical model to predict severe convective weather. The model dynamic framework (Dynamical Core) is closely related to the non-adiabatic physical process of the model, and its defects will inevitably affect the effects of other physical processes. Constructing a model dynamic framework discretization scheme suitable for describing severe convective weather processes will improve the ability of numerical models to predict severe convective weather.

[0038] The occurrence and development of severe convective weather is not only affected by large-scale weather systems, but also includes the process of combination or merger of small-scale systems. Multi-scale effects are the fundamental cause of severe convective weather. The inability of numerical models to reproduce the multi-scale interaction process, especially the effective simulation of the combination or merger of small-scale systems, is the main reason for the low level of severe convective weather forecasting at present. Therefore, improving the description of multi-scale interactions is one of the difficult problems in constructing numerical weather forecast models. Multi-scale interactions are intuitively manifested as the generation and disappearance of wind pressure fields of various scales, which essentially represents the scale changes of energy such as kinetic energy and potential energy, that is, the dispersion characteristics of energy. Only by constructing a dynamic framework discretization scheme that maintains the characteristics of energy transmission and conversion can the ability of numerical models to describe energy dispersion processes be improved, thereby improving the level of numerical forecast simulation of severe convective weather.

[0039] Generally speaking, the discrete equations cannot completely maintain the physical properties of the original continuous form equations, which may lead to computational disorder and nonlinear computational instability in the model. Therefore, constructing a discrete scheme that can maintain the main physical properties of the continuous model directly affects the basic performance of the model dynamic framework and the model prediction ability. A good model dynamic framework discretization scheme should not only be able to maintain the overall physical properties of the original continuous form equations in the entire region, but also pay attention to maintaining the physical properties of the model in each local space (such as maintaining the conversion relationship between the total energy components within each microelement and the energy transfer relationship between adjacent microelements).

[0040] Representative numerical weather forecast models widely used at home and abroad include MM5, WRF, FV3, GRAPES, etc. Their dynamic framework space discretization schemes have their own characteristics, but none of them considers discrete construction from the perspective of maintaining the local space energy transmission and conversion characteristics. The MM5 dynamic framework discretization scheme does not consider maintaining the energy and mass properties in a non-hydrostatic sense, resulting in false sources and sinks related to the discrete destruction of the overall properties of energy and the local distribution conversion mechanism. In order to maintain computational stability, numerical smoothing and dissipation techniques are excessively used in the model. These technologies have less damage to the long-wave part of the system and more damage to the medium and short-wave parts, which directly weakens the model's ability to simulate small and medium-scale weather processes. As an updated model of MM5, the WRF model has begun to consider the maintenance of energy and mass properties. Its dynamic framework satisfies the mass conservation property better; its motion equation adopts the flux form, which better maintains the energy characteristics than the MM5 framework; because the advection term discretization adopts a high-order upwind format, the artificial viscosity dissipation term is reduced, and the dissipation intensity of short waves and various scale components is reduced. It has better medium and small scale weather simulation capabilities than MM5 and is currently widely used in numerical weather forecast simulation. However, the discrete form of the WRF dynamic framework still does not correctly reflect the local space energy conversion relationship, and there are false computational sources and sinks generated by it. FV3 is a new generation of weather and climate integrated global forecast model (GFS) dynamic framework scheme in the United States. It is characterized by the use of scalable spherical cube grids, which improves the isotropy and local adaptability of the spherical grid and has high computational efficiency. Although it constructs a dynamic framework spatial discretization scheme from the perspective of finite volume, it does not consider the energy transfer relationship between grids, and there are still related false computational sources and sinks. GRAPES is a numerical weather forecast model system independently developed by my country and is widely used in operational forecasting. Its power framework scheme adopts a semi-Lagrangian scheme, which has high computational efficiency, but it cannot maintain the spatial transmission of energy and the relationship between various energy conversions, and there are related false computational sources and sinks.

[0041] In view of the above-mentioned deficiencies in the existing dynamic framework of numerical weather forecast models, the present invention constructs a spatial discretization scheme of a non-hydrostatic model dynamic framework that maintains the local micro-element energy transmission and conversion characteristics, better characterizes the small and medium-scale structures of weather systems, suppresses the weakening of small and medium-scale weather processes caused by excessive use of numerical smoothing and dissipation techniques, and enhances the forecasting and simulation capabilities of numerical models for severe convective weather processes.

[0042] The present invention constructs a dynamic framework space discretization scheme based on the energy conversion and transformation properties of the atmospheric motion equations. The given scheme can better maintain the local micro-element energy transmission and conversion characteristics, thereby improving the model's ability to characterize the small and medium-scale structures of the weather system.

[0043] The specific implementation steps of the present invention are as follows:

[0044] (I) Establishment of the basic equations of atmospheric motion and their energy properties used in this dynamic framework scheme

[0045] like Figure 1-2 As shown in the figure, with the improvement of the model grid resolution, the role of energy cascade problem in the simulation becomes more prominent, especially the upward cascade of energy becomes a prominent problem affecting the simulation effect. Satoh (2002, 2003, NICAM) constructed a dynamic framework scheme from the perspective of mass conservation and energy conservation properties, which can meet the conservation of mass, momentum and internal energy. However, due to the use of flux form momentum equations, it is difficult to effectively describe the kinetic energy transfer conversion process and cannot meet the energy conservation property. The dynamic framework of a new energy-fidelity model proposed in this paper adopts the WRF software architecture (the WRF Advanced Software Framework (ASF)). Its purpose is to build a dynamic framework starting from satisfying the energy transfer conversion process, effectively improve the model's calculation process of the energy cascade process, and then improve the simulation effect of the high-resolution model. The new generation of model dynamic framework proposed in this paper has two main purposes:

[0046] First, the transmission of each energy component and its conversion properties are maintained on the spatial grid elements, especially reducing the kinetic energy transmission error between grids; second, the energy transmission and conversion on the grid scale is maintained, and the accuracy of energy exchange between various scales can be maintained, which is conducive to the correct description of the energy level chain process.

[0047] The basic equations of atmospheric motion are the Lamb form of the NS equations

[0048]

[0049] Where V = (u, v, w) is the atmospheric wind field, K is the atmospheric kinetic energy (K = |V| 2 / 2), ρ is the atmospheric density, p is the atmospheric pressure, T is the atmospheric temperature, F represents various dissipative forces, Q represents the heat source, q is the water vapor specific humidity, S represents the water vapor source and sink, Ω is the angular velocity of the earth's rotation, g is the gravitational acceleration, c v is the atmospheric isochoric specific heat coefficient, and R is the atmospheric gas constant.

[0050] The above basic equations of atmospheric motion have the following energy conversion relationship:

[0051]

[0052] Where E=K+c v T+φ+Lq,c v T is internal energy, φ is gravitational potential energy Lq is the latent heat of water vapor.

[0053] When the system has no dissipation (F = 0) and only the heat change caused by phase change is considered (Q = -LS), the energy conservation relationship at any point in space can be obtained:

[0054] 1) Atmospheric motion equations in vertical coordinates following the altitude terrain

[0055] The horizontal coordinates of the atmospheric motion equations use local coordinates (x, y), and the vertical coordinates use altitude terrain following coordinates where z S =z S (x, y), is the ground height; z T is the height of the top of the atmosphere, taken as a constant.

[0056] In order to maintain the energy and mass properties, the basic prediction quantities are u, v, w, ρ, ρT; the map magnification factor m (the ratio of the grid spacing in the x direction to the actual grid spacing in the x direction), n (the ratio of the grid spacing in the y direction to the actual grid spacing in the y direction), where m and n are equal. The motion equation adopts the non-flux non-advection form, which is as follows

[0057] in

[0058]

[0059]

[0060]

[0061] Where r = a + z (a is the radius of the earth), is the latitude.

[0062] The equations under the above terrain coordinates satisfy the following energy conversion relationship:

[0063]

[0064] (II) Establishing a dynamic frame space discretization scheme

[0065] The discrete grid distribution of the forecast variables is Arakawa-C grid distribution horizontally and Lorenz distribution vertically. The horizontal uniform grid spacing Δx and Δy and the vertical non-uniform grid spacing Δσ are used.

[0066] Define the following discrete operator

[0067]

[0068] Kinetic energy is defined at the Cross point (scalar point),

[0069] The principle of constructing a spatial discrete solution is to satisfy the discrete form of the energy conversion equation, and the discrete solution satisfying the energy flux expression form is the premise for the energy conversion equation to be established. The most critical one is to satisfy the kinetic energy flux form, that is, the momentum equation (wind field) and the continuity equation (density) related discrete solution is required to satisfy the following kinetic energy flux relationship

[0070]

[0071] Based on the above constraints, the following dynamic frame space discretization scheme can be obtained:

[0072]

[0073]

[0074] 2) The dynamic frame space discretization scheme in the form of disturbance decomposes the state quantity (ρ, T, p) into basis

[0075] The amount (ρ b ,T b ,p b ) plus the disturbance (ρ', T', p'), that is

[0076] ρ=ρ b +ρ′,T=T b +T′,p=p b +p′

[0077] Assume that the basic quantity changes only with height and satisfies the static equilibrium relationship The basic temperature is given by the vertical profile of the mean temperature. The corresponding perturbation form of the atmospheric equation of motion is given by

[0078]

[0079] The corresponding discrete form

[0080] 3) Time discrete expression:

[0081] The time discretization adopts the classic semi-implicit (HE-VI) time splitting algorithm. The fast wave correlation term is integrated with a semi-implicit small time step, and the rest is integrated with a large time step. The following is a discretization scheme with a small time step (Δτ is a small time step, Δt is a large time step)

[0082]

[0083] Appendix: Vertical velocity of underlying surface

[0084]

[0085] By adopting the spatial discretization scheme of this dynamic framework and combining it with the widely used semi-implicit time splitting discretization scheme, a complete numerical weather forecast model dry framework was constructed, and a variety of numerical simulation ideal tests were carried out. By comparing with the corresponding results of the WRF model framework, significant results were achieved. It is mainly reflected in the following three aspects:

[0086] 1) Significantly improve the mode energy balance properties

[0087] 2) Improved the model’s ability to depict fine structures

[0088] 3) Improve the model's ability to respond to terrain effects

[0089] Example analysis:

[0090] Density flow experiment

[0091] The most effective way to evaluate a numerically modeled system is by comparison to cases with known results obtained through dynamical analysis or to benchmark solutions that converge under specific conditions. Commonly used analytical and / or benchmark cases include mountain wave solutions (e.g., Clark 1977; Dudhia 1993), inertial gravity waves (Skamarock and Klemp 1994), nonlinearly evolving cold pools and density currents (Straka et al. 1993), and rising warm currents (Tripoli 1992). Simulations such as these are important for a number of reasons, such as establishing the fidelity of a new numerical modeling system or testing the accuracy, efficiency, and efficacy of a new numerical technique.

[0092] The newly established conservation mode and WRF mode are used to conduct experimental comparison of density flow. Two-dimensional dry hot bubble and cold bubble ideal numerical tests are used to compare the differences in experimental results. The same initial conditions are set for the two modes, as follows:

[0093]

[0094] Table 1 Mode parameter configuration

[0095] The model was verified using a two-dimensional heat bubble experiment in a density flow experiment. In a region with a spatial resolution of 100×100m, in a closed environment with rigid boundary conditions, an air bubble with a diameter of about 50m was set at a height of 10m from the ground. The initial state of the air bubble was static, the initial wind field was zero, and the heat bubble was in an isentropic and static equilibrium state. The potential temperature was initially 300K, the initial surface pressure was 1000mb, and the bubble was in an environment slightly higher than the bubble by 0.5℃ during its development. Under the action of buoyancy, the heat bubble continued to rise and develop.

[0096] Figure 3 Shown in the figure is a comparison between the FM experiment and the WRF hot bubble experiment. The selected figure is a typical moment in the development process. The hot bubble at the initial moment has a completely consistent initial field due to the consistency of environmental conditions. During the development of the hot bubble, it continues to rise and develop under the action of buoyancy. During the rise, the hot bubble stretches, and differences have appeared at the 10th moment. The largest center of the hot bubble in the FM experiment is about 0.5K higher than the largest center of the WRF experiment, and the shapes are relatively consistent; at the 20th moment, the hot bubble develops into a semi-annular structure, and the shapes of the WRF and FM experiments are consistent. The largest center of the hot bubble in the FM experiment is about 1K higher than the largest center of the WRF experiment. There is an obvious difference in the overall potential temperature; at the last moment, the potential temperature of the hot bubble in the WRF experiment is about 300.1-300.3K, while that in the FM experiment is 300.1-301.1K. It is obvious that the potential temperature of the hot bubble in the FM experiment is higher. During the development of the entire hot bubble, the shape development of the Fm experiment and the WRF experiment is almost the same, but the Fm mode shows a lower potential temperature decay rate, indicating that the energy fidelity is better than the WRF mode.

[0097] Figure 4 The figure shows the average profile of the potential temperature over the whole layer, which is used to observe the evolution of the heat bubble experiment with height and time. The potential temperature profiles at the initial moment are consistent. As time goes by, there are some differences between the FM mode and the WRF mode. At the 10th moment, the potential temperature of Fm is about 0.1 degrees higher than that of WRF mode. At the 20th moment, there is a large difference in potential temperature, which is mainly reflected in the height of 40-60 layers. The difference in the maximum value is about 0.5 degrees higher than that of WRF. At the last moment, the potential temperature of WRF at the height of 60-90 layers is basically consistent with that of FM, and the potential temperature of WRF at the height of 90-110 layers is higher than that of FM. The maximum value at the 120th layer is significantly different, about 1 degree. During the development of the entire heat bubble, the maximum central potential temperature of the FM experiment is always close to the potential temperature value at the initial moment, while the potential temperature value of the WRF heat bubble decreases continuously during the development process.

[0098] It is further proved that the decrease of potential temperature in the development of hot bubble in FM mode is smaller than that in WRF mode.

[0099] Figure 5 It can be clearly seen from the maximum potential temperature curve that the potential temperature of WRF decreases continuously with the passage of time, while the maximum value of FM decreases steadily and rebounds at the end. There are also obvious differences in the changes in the maximum and minimum wind speeds. The maximum and minimum wind speeds simulated by FM are both greater than those of the WRF mode.

[0100] like Figure 6 As shown, the diagnosis of various energies of the FM mode and the WRF mode is further analyzed. The figure shows the change curves of internal energy, kinetic energy, potential energy and total energy in each development process of the FM and WRF mode hot bubble experiments. From the perspective of kinetic energy, the difference between the two is very small. Affected by buoyancy, the kinetic energy is constantly increasing; from the perspective of the potential energy curve, the potential energy of WRF increases from 0 to 5 minutes, remains stable from 10 to 25 minutes, and slightly decays in the final stage, while the potential energy of the Fm experiment continues to decrease in the fluctuation; the change in internal energy is more obvious. The internal energy of the WRF experiment continues to decay rapidly during the development of the hot bubble, while the internal energy of the FM experiment is less effective and finally stabilizes in the fluctuation. From the perspective of the total energy curve, the energy line of FM is almost straight, and the total energy of WRF is decaying, which further proves the energy conservation of the FM mode.

[0101] From the perspective of energy conversion, the potential energy of FM is constantly reduced and converted into kinetic energy, and the internal energy is also reduced and converted into kinetic energy. At about 24 minutes, the reduction of potential energy exceeds the internal energy. In the WRF experiment, the internal energy is mainly converted into kinetic energy and a part of potential energy. This is also a clear difference between the two modes.

[0102] The initial conditions of the cold bubble are similar to those of the hot bubble experiment, with rigid boundary conditions on all sides, and the initial u velocity and v velocity are set to zero. Under hydrostatic equilibrium, it is initialized in a neutral atmosphere at 300K. Three groups of control experiments are set with grid resolutions of 50m, 100 and 200m in the x and y directions respectively to study the differences in the simulation of the cold bubble experiment with different resolutions. The simulation run time is 900s, or 15 minutes.

[0103] The temperature drops linearly from 300K to 290K from high altitude to the ground. Just like the reverse case of the hot bubble in the previous figure, negative buoyancy causes the cold air bubble to move downward. When the cold bubble falls to the ground, it forms a forward-propagating front. This forward motion of the cold front produces shear at the top boundary of the front, which generates Kelvin-Helmholtz waves. In the simulation, the propagation of the front and the formation of these waves can be seen at time = 450s and time = 900s ( Figure 3 ).

[0104] Figure 7As shown, the cold bubble experiment with a resolution of 50m shows a consistent initial field. At 5 minutes, the lowest center value of FM is 286, and the lowest center of WRF is 288, with a difference of about 2K. At 10 minutes, the shape of the cold bubble is quite different. The structure of the cold bubble Fm has a larger gradient than that of WRF, and the shape is also slightly different. Kelvin-Helmholtz waves appear. At the last moment, the Fm experiment shows a clear wave train structure near the ground compared with the WRF. The cold bubble of Fm has a lower cold center at the front of the wave train, while the overall value of the WRF experiment is greater than 290K, which is similar to the conclusion of the hot bubble experiment. The FM experiment has better energy fidelity properties.

[0105] Figure 8 In the cold bubble experiment with a resolution of 100m, the lowest center value of the FM experiment is 286K at t=5min, and the lowest center value of the WRF experiment is 288K, with a difference of about 2K. After t=10min, the shapes of the cold bubbles in the two experiments are different. The structural gradient of the cold bubble in the FM experiment is larger than that in the WRF experiment, and the shape is also different to a certain extent. The WRF experiment shows that there is a clear wave train structure near the ground. The cold bubble in the FM experiment has a lower cold center at the front of the wave train, while the overall temperature in the WRF experiment is >290K, which is similar to the results of the hot bubble experiment. The FM experiment has better energy fidelity than the WRF model.

[0106] Fig. 9 As shown in the figure, the conclusion of the cold bubble experiment with a resolution of 200m is similar to those with the resolutions of 50 and 100m. The cold center of the cold bubble becomes colder during the descent, and the structure of the Kelvin-Helmholtz wave is different from that of the WRF. The difference is that the shape of the cold bubble Kelvin-Helmholtz wave with a resolution of 200m is different, the wave train structure is not as clear as that with low resolution, and the Kelvin-Helmholtz wave is more broken at the last moment and the lowest center temperature in the front is lower.

[0107] Fig.10 As shown in the figure, from the perspective of the potential temperature profile of the cold bubble, the overall difference between FM and WRF is small. At the 6th moment, the potential temperature profile of the lower layer showed obvious differences, but the profiles of the upper layer were basically the same. The FM experiment showed lower potential temperature values ​​at the bottom layer than at the initial moment. As time went by, the difference between the potential temperature profiles at the 11th moment and the 16th moment was small, and the two curves basically overlapped. This shows that the potential temperature development of the cold bubble was basically the same in the subsequent time.

[0108] Fig.11The minimum values ​​of cold bubble potential temperature in FM mode and WRF are quite different from those in hot bubble. The minimum value of WRF potential temperature keeps rising, while the minimum value of FM potential temperature rises slowly from time 0 to time 6, drops rapidly from time 6 to time 9, and then rises sharply. The maximum and minimum values ​​of wind speed simulated by WRF and FM are slightly different, and the difference appears from time 6 to time 12. Time 12 is the exchange point of extreme values, and the overall difference is not large.

[0109] Fig.12 As shown in the figure, the energy diagnosis of the cold bubble experiment is similar to the conclusion of the hot bubble experiment. The kinetic energy of the FM experiment and the WRF experiment is constantly increasing, and the kinetic energy tends to be stable after the cold bubble is grounded. The FM potential energy and the kinetic energy are almost in anti-phase, first decreasing and then tending to be stable, while the potential energy of the WRF experiment decreases less, the internal energy of the Fm experiment changes less, and the internal energy of the WRF cold bubble experiment changes more, exceeding the potential energy. From the perspective of total energy, the energy line of the Fm experiment tends to be flat, while the energy curve of the WRF experiment fluctuates greatly with the changing trend of the internal energy and potential energy, and is ultimately dissipated.

[0110] From the perspective of energy conversion, the FM experiment and the WRF experiment are similar. Kinetic energy is converted from potential energy and internal energy. The difference is that the internal energy of the WRF experiment changes greatly.

[0111] This application starts with satisfying the energy properties of the model, and constructs a non-static model dynamic framework space discretization scheme that maintains the local micro-element energy transmission and conversion characteristics based on the energy properties of the continuous form of the non-static primitive equation group. This scheme reconstructs the mutual conversion and restriction mechanism of energy components such as kinetic energy, gravitational potential energy, internal energy and latent heat energy inside the discrete micro-element and the energy distribution mechanism between each micro-element. By comparing the experiment with the ideal experimental density flow case in WRF, the results include:

[0112] (1) Compared with the WRF hot bubble experiment, the FM temperature decay rate and amplitude are smaller than those of the WRF experiment during the hot bubble development process, which proves that the hot bubble in the FM experiment has better energy fidelity properties.

[0113] (2) From the energy diagnosis of the hot bubble experiment, the kinetic energy changes of FM and WRF are relatively close. The potential energy of the FM experiment generally shows a trend of continuous attenuation, while the potential energy of the WRF experiment first increases and then stabilizes. From the perspective of internal energy, the change range of WRF is larger. Compared with the continuously decreasing total energy of WRF, the total energy of the FM experiment is relatively stable. The FM experiment has better energy conservation properties.

[0114] (3) The results of the three cold-brew experiments showed that the results of the cold-brew experiments were more sensitive to changes in resolution than those of the WRF experiments.

[0115] (4) The energy diagnosis of the cold bubble experiment is similar to that of the hot bubble experiment. The change of FM potential energy is greater than that of WRF, while the change of internal energy is smaller than that of WRF, and the total energy change tends to be stable.

[0116] In general, the FM density flow experiment has better overall conservation and energy fidelity properties than the WRF experiment. The experiment in this case is currently limited to the density flow experiment, and more ideal experiments are needed to verify the conservation and stability of the model.

[0117] Mountain Wave Experiment

[0118] Compared with WRF, the ideal experiment of two-dimensional mountain waves is used to explore the differences between the two dynamic frameworks in the simulation process, so as to verify the simulation effect and conservation performance of the dynamic framework. The ideal experiment of mountain waves is to simulate the propagation process of mountain waves caused by the undulating terrain of the uniform zonal wind field by giving the same initial field. The simulation parameter settings of the two experiments are detailed in Table 2. No microphysical scheme or radiation scheme is used in this ideal experiment.

[0119]

[0120]

[0121] Table 2. Mode parameter settings.

[0122] The different grid spacings Δx selected in the two experiments are 200m, 2km and 20km respectively. The model has a total of 200 grid points in the horizontal direction, and the horizontal direction is a periodic boundary. In the FM experiment, the vertical spacing is Δz=400m, the model top height is 16km, the height of the absorption layer is 10km, the initial field horizontal wind speed U=10m / s, N=10 -2 s -1 , f = 10 -4 s -1 , the function of the bell-shaped mountain structure used is:

[0123]

[0124] The mountain range is located in the center of the x-axis, and the mountain height h = 400m. a is the horizontal width parameter of the mountain range. According to the results of the influence of horizontal wavelength on amplitude obtained by JIMYDUDHIA (1991), in order to make the simulation process at three scales reach a steady-state solution, the half-width parameter of the mountain range will change with the horizontal grid spacing at different resolutions a = 5Δx, and the total simulation time T = 21.6a / U corresponds to 36min, 6h and 60h respectively.

[0125] Fig.13The sensitivity experiments of this framework and the WRF version at three scales. In Figure a, when Δx = 200m, it can be seen that the horizontal wind is most obviously affected by gravity due to the small time scale of the horizontal wind crossing the mountains, which makes the waveform more inclined downstream, and the horizontal scale of the waveform is close to the vertical scale. At the last moment of the simulation, compared with the WRF version, the decay is faster downstream. This framework has a more detailed internal structure and propagates farther to the leeward side of the wave. In Figures b and c, when Δx = 2km and 20km, the horizontal wind crosses the mountains at a larger time scale, and the energy propagates significantly above the mountains, causing the waveform to develop in the vertical direction. The waveform in the WRF mode tends to be smooth and uniform, while the waveform in the fidelity framework is more detailed in the distribution of the extreme center. It can be seen that the detailed structure of the propagation and fragmentation of the waveform on both sides of the mountain range, and the influence of the Coriolis force rotation at the large-scale 20km resolution makes the center of convergence and divergence tilt more obviously to the leeward side.

[0126] Energy spectrum characteristics

[0127] In the ideal experiment of two-dimensional mountain waves, the terrain forcing will cause the mutual conversion between different energies, which are mainly divided into kinetic energy, internal energy and potential energy. The above three sets of figures show the comparison of the evolution of different energy disturbances over time under the two versions. The left side is the WRF version, and the right side corresponds to the new design version. All energies are minus the disturbance value at the initial moment (e T -e T0 ), due to the influence of the absorption layer, the total energy in the space will gradually decrease with the simulation time, so the dispersion of the total energy in the figure will gradually increase.

[0128] Fig.14 In the comparison diagram at a resolution of 200m, the air mass center rises as the airflow passes over the mountains and is affected by the terrain lift, so both versions have obvious characteristics of internal energy conversion to potential energy in the first few moments, while the kinetic energy gradually decreases over time. At the final moment of the WRF simulation, the disturbance kinetic energy gradually decreases to -1.5×10 8Moreover, the changes in internal energy and potential energy tend to be smooth in the second half of the simulation; compared with the new framework proposed in this paper, the kinetic energy dispersion is relatively small. Overall, two periodic changes in the conversion between internal energy and potential energy can be seen. At a resolution of 2km, the development trends of each energy in the two frameworks are different. The fidelity mode has more detailed conversion characteristics than WRF, and the periodicity of the conversion between internal energy and potential energy is more obvious. The WRF version attenuates the kinetic energy more seriously. At a resolution of 20km, the results simulated by the two frameworks are more significantly different. The conversion process of internal energy and potential energy in the WRF version is obviously opposite to the previous two resolutions. It is not clear what causes this, and the total energy dispersion is relatively more serious. It is worth mentioning that in the initial stage of the simulation, the internal energy and potential energy of the WRF version show a sudden increase and decrease, while at the initial moment of the simulation in the new framework, the two energies develop regionally smoothly and the kinetic energy increases first and then decreases.

[0129] In general, the simulation results in the three resolutions under this framework are relatively good. The development of internal energy, potential energy and kinetic energy over time is relatively stable. The periodic conversion process between potential energy and internal energy can be seen more intuitively, which is conducive to the correct description of the energy cascade process.

[0130] Vertical transport characteristics of horizontal momentum:

[0131] In order to intuitively reflect the performance of different dynamic frameworks in simulating nonlinear mountain waves, another reference factor selected in the experiment is to examine the vertical transport characteristics of horizontal momentum, so as to compare the sensitivity of different dynamic frameworks to the second-order effects of horizontal and vertical velocities. The vertical momentum flux of mountain waves during propagation is defined as:

[0132]

[0133] According to the research of Miles and Huppert (1969), when a mountain height and initial velocity are given under ideal conditions, the momentum flux of the mountain wave in the vertical direction can be approximated as an analytical constant:

[0134]

[0135] Where ρ0 is the surface density, Vaisala frequency N = 10 -2 s -1 , Scorer parameter l = 0.977 × 10 -3 m -1. In the simulation process, when the actual value M(Z) at different heights is closer to the analytical value M, it means that the simulation effect under this framework is closer to the actual situation. When the grid spacing Δx = 2000m, the group velocity of the mountain wave is close to the phase velocity, the energy propagates vertically upward, the vertical wavelength is about one tenth of 2πa, and the horizontal wavelength is subject to the strongest forcing, so here we only analyze the results of the total simulation time T = 6h at the resolution of Δx = 2000m.

[0136] Fig.15 The horizontal axis is the ratio of the analytical value M(Z) of the actual simulated vertical momentum flux to the theoretical value M, and the vertical axis is selected from 0-6.4km (approximately one vertical wavelength). When the simulation reaches the first moment T=1h, the vertical momentum flux of the two versions has basically the same trend with height change, from close to 0 at the high level to gradually converging to close to the theoretical value at the low level. As the simulation time goes by, the simulation results of the entire area will gradually stabilize. In the next five moments, there are obvious differences in the vertical momentum characteristics simulated under the two frameworks. The WRF version basically reaches a stable state when the simulation reaches 2h, and the time to reach stability is relatively short. The curve characteristics of the vertical momentum at the next few moments are basically the same. Overall, the momentum decays particularly significantly with height. At the final simulation moment of about 6km, the ratio of the actual momentum to the analytical value is still less than 25%. As can be seen in the right figure, the momentum change begins to flatten out after 5 hours of simulation under the new framework, and the calculation process takes a relatively longer time to reach stability, but the simulation results are obviously closer to the analytical values. The momentum flux can be close to 60% when decaying from the bottom layer to the highest layer, which is significantly better than the calculation results of the WRF version. Although both versions have a value of more than 100% at the lowest layer, it is speculated that there is a certain error between the values ​​of some constants and the actual situation when solving the analytical value, so it is only used as an evaluation indicator to predict the simulation effect. Therefore, from the above experimental results, we can believe that the use of the discrete scheme under the new framework in this ideal experiment of simulating mountain waves can obtain more accurate simulation results, and the momentum flux calculated under this model is more consistent with the analytical value.

[0137] like Figure 16-17 In this paper, a new fully elastic non-static energy-fidelity numerical scheme is constructed. In order to test and improve its simulation performance, this paper mainly compares it with the WRF model, conducts ideal experiments on two-dimensional dry mountain waves at different resolutions, and draws the following conclusions:

[0138] (1) From the simulation comparison of the three resolutions, the vertical velocity distribution of the two versions is slightly different below 10 km. The calculation results obtained by the WRF experiment are relatively smooth and uniform. The results obtained by the two experiments are relatively close in terms of numerical value, but the FM experiment shows that the energy propagation performance of mountain waves is better and the internal structure is more detailed from the vertical velocity profile.

[0139] (2) From the results of the energy spectrum, the trend of kinetic energy in both experiments is that it gradually decreases with the simulation time. The FM experiment uses the Lamb form of momentum equation in the process of constructing the kinetic energy equation, which makes the calculation error of kinetic energy better than that of the WRF experiment. From the perspective of the process of mutual conversion between internal energy and potential energy, the periodic fluctuations of the two in the FM experiment are more detailed, the energy cascade process is more accurate, and the simulation results at different resolutions are more stable.

[0140] (3) Based on the vertical transport characteristics of horizontal momentum, the significant difference in the calculation of the two frameworks can be intuitively verified. The characteristic of the WRF mode is that it can reach computational stability in a shorter time, but the vertical momentum flux converges to the analytical value more and more poorly with height, and decays more seriously within a vertical wavelength. The time spent on reaching computational stability in the FM experiment is relatively long, but its momentum convergence performance is significantly better than that of the WRF experiment, basically exceeding 70% of the analytical value in a vertical wavelength, indicating that its energy transmission performance is better.

[0141] The present application relates to a dynamic framework with a new spatial discretization scheme, in which density, velocity and air pressure are used as predicted quantities, a new differential scheme and a momentum equation converted from the Euler form to the Gromyko-Lamb form are adopted. The spatial discretization format of the dynamic framework satisfies the mass conservation property, and the time discretization scheme adopts a semi-implicit time splitting algorithm. The momentum advection thus constructed can be expressed in flux form, and the motion equation maintains the characteristics of effective kinetic energy transfer under the discrete format of the advection term, which can effectively reduce the error caused by the kinetic energy advection calculation, thereby increasing the calculation accuracy of the mutual conversion of various energies.

[0142] It can be seamlessly coupled with any business system. The initial field data can be obtained from the Internet. The execution process of this method is modularly designed, with a clear logical structure, simplicity and efficiency, moderate calculation amount, small storage space occupied by data, high calculation efficiency, simple and easy system construction, convenient transplantation, and easy to apply in meteorological business and various industry departments. At the same time, the historical forecast case database can be updated and expanded in real time, and new historical forecast cases can be continuously included. The larger the sample capacity of historical forecast cases, the more accurate the selection of similar forecast members, and the more ideal the forecast effect.

[0143] This application has a wide range of applications. Meteorological business departments can use this numerical model to perform numerical weather forecasts, provide scientific support in disaster reduction, agricultural production, and ensuring the safety of people's lives and property, and have great application potential. This technology has broad application prospects in the civil aviation sector, and can accurately predict the most important aviation-related dangerous weather for flight safety, accompanied by turbulence, turbulence, ice accumulation, lightning strikes (lightning strikes), downbursts, low-altitude wind shear and other weather phenomena that endanger flight safety. This forecasting method can improve the 72-hour forecast accuracy of airports and routes, and provide a reference for taking avoidance measures and planning routes in advance.

[0144] For meteorological business departments, this forecasting method can be seamlessly linked with the numerical model forecasting system of meteorological business stations, and can be integrated with each other to provide more accurate weather forecasts for the country and society. For the agricultural sector, it can provide services for agricultural food security; for the transportation management department, it can provide strong wind and lightning forecasts to contribute to the safety of people's transportation. For emergency disaster reduction departments, it can provide ensemble forecasts of disaster weather to provide scientific support for emergency measures for disaster prevention and reduction; in summary, this method can meet the needs of agriculture, transportation, disaster prevention and reduction and other industries for severe convective weather forecasts, has broad application prospects, and has significant application value.

[0145] The following will be combined with the attached Fig.18 , a method for constructing a numerical model dynamic framework for weather forecasting provided in an embodiment of the present application comprises the following steps:

[0146] S1. Establish the basic equations of atmospheric motion and energy conversion relations of the dynamic framework of the numerical model;

[0147] S2. Based on the basic equations of atmospheric motion of the numerical model dynamic framework and the energy conversion relationship, a dynamic framework spatial discrete model is established.

[0148] Further, based on the above embodiment, the basic equations of atmospheric motion for establishing the dynamic framework of the numerical model specifically include:

[0149] The basic equations of atmospheric motion are the Lamb form of the NS equations

[0150]

[0151] Where V = (u, v, w) is the atmospheric wind field, K is the atmospheric kinetic energy K = |V 2 / 2, ρ is the atmospheric density, p is the atmospheric pressure, T is the atmospheric temperature, F represents the dissipation force, Q represents the heat source, q is the water vapor specific humidity, S represents the water vapor source and sink, Ω is the angular velocity of the earth's rotation, g is the gravitational acceleration, c v is the atmospheric isochoric specific heat coefficient, and R is the atmospheric gas constant.

[0152] Furthermore, based on the above embodiment, an energy conversion equation of atmospheric motion in a numerical model dynamic framework is established, specifically including:

[0153] The above basic equations of atmospheric motion have the following energy conversion relationship:

[0154]

[0155] Where ρ is the atmospheric density, E is the total energy, E = K + c v T+φ+Lq,c v T is internal energy, φ is gravitational potential energy Lq is the latent heat of water vapor;

[0156] When the system has no dissipation F = 0, and only the heat change caused by phase change Q = -LS is considered, the energy conservation relationship at any point in space can be obtained:

[0157] Further, based on the above embodiment, it also includes:

[0158] Establish the atmospheric motion equations in the vertical coordinates of altitude terrain following;

[0159] The horizontal coordinates of the atmospheric motion equations use local coordinates (x, y), and the vertical coordinates use altitude terrain following coordinates where z S =z S (x, y) is the ground height, z is T is the height of the top of the atmosphere, taken as a constant;

[0160] In order to maintain the energy and mass properties, the basic forecast quantities are u, v, w, ρ, ρT; where u is the zonal wind speed, v is the radial wind speed, w is the vertical speed, ρ is the atmospheric density, and ρT is the product of density and temperature;

[0161] Map magnification factors m and n, where m and n are equal, where m is the ratio of the grid spacing in the x-direction of the horizontal axis of the map to the actual grid spacing in the x-direction, and n is the ratio of the grid spacing in the y-direction of the map to the actual grid spacing in the y-direction;

[0162] The equation of motion adopts the non-flux non-advection form, which is as follows

[0163] in

[0164]

[0165] Where r = a + z, a is the radius of the earth, is the latitude.

[0166] The equations under the above terrain coordinates satisfy the following energy conversion relationship:

[0167]

[0168] Among them, F x is the dissipated force in the x direction, F y is the dissipated force in the y direction, F z is the dissipated force in the z direction.

[0169] Further, based on the above embodiment, the basic equations of atmospheric motion based on the numerical model dynamic framework and the energy conversion relationship are used to establish a dynamic framework spatial discrete model, which specifically includes:

[0170] The discrete grid distribution of the forecast variables is Arakawa-C grid distribution horizontally and Lorenz distribution vertically, with uniform grid spacing Δx and Δy horizontally and non-uniform grid spacing Δσ vertically;

[0171] Define the following discrete operator:

[0172]

[0173] Kinetic energy is defined at the Cross scalar point.

[0174] The most critical thing is to satisfy the kinetic energy flux form, that is, the momentum equation, i.e. the wind field, and the continuity equation, i.e. the density-related discrete solution, satisfy the following kinetic energy flux relationship:

[0175]

[0176] Based on the above constraints, the following dynamic frame space discretization scheme is obtained:

[0177]

[0178] in is the height terrain following disturbance, δ x is the deflection in the x direction, δ y is the y-direction deviation

[0179]

[0180] Further, based on the above embodiment, the basic equations of atmospheric motion based on the numerical model dynamic framework and the energy conversion relationship are used to establish a dynamic framework spatial discrete model, which also includes:

[0181] Establish a dynamic frame space discrete model in the form of disturbance:

[0182] Decompose the state quantity (ρ,T,p) into basic quantities (ρ b ,T b ,pb ), plus the disturbance (ρ',T',p'), that is:

[0183] ρ=ρ b +ρ′,T=T b +T′,p=p b +p′

[0184] Assume that the basic quantity changes only with height and satisfies the static equilibrium relationship The basic temperature is given by the vertical profile of the mean temperature;

[0185] The atmospheric equation of motion in the corresponding perturbation form is as follows:

[0186]

[0187] The corresponding discrete form is:

[0188] Further, based on the above embodiment, the basic equations of atmospheric motion based on the numerical model dynamic framework and the energy conversion relationship are used to establish a dynamic framework spatial discrete model, which also includes:

[0189] The time discretization adopts the classic semi-implicit HE-VI time splitting algorithm, the fast wave correlation term is integrated with a semi-implicit small time step, and the rest is integrated with a large time step;

[0190] The following is a discrete model with a small time step, Δτ is a small time step, and Δt is a large time step;

[0191]

[0192] Among them, the vertical velocity of the underlying surface is:

[0193]

[0194] The following will be combined with the attached Fig.19 , a numerical model dynamic framework construction device for weather forecasting provided in an embodiment of the present application, the device comprises:

[0195] The first processing module is used to establish the basic equations of atmospheric motion and energy conversion relations of the dynamic framework of the numerical model;

[0196] The second processing module is used to establish a dynamic framework spatial discrete model based on the basic equations of atmospheric motion in the numerical model dynamic framework and the energy conversion relationship.

[0197] In addition, an embodiment of the present application includes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method for constructing a numerical model dynamic framework for weather forecasting described in any one of the above technical solutions.

[0198] An embodiment of the present application also includes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for constructing a numerical model dynamic framework for weather forecasting described in any one of the above technical solutions is implemented.

[0199] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for constructing a dynamic framework of a numerical model for weather forecasting, characterized in that: The method comprises: Establish the basic equations of atmospheric motion and energy conversion relations for the dynamic framework of numerical models; Based on the basic equations of atmospheric motion of the numerical model dynamic framework and the energy conversion relationship, a dynamic framework spatial discrete model is established.

2. The method according to claim 1, characterized in that The basic equations of atmospheric motion for establishing the dynamic framework of the numerical model specifically include: The basic equations of atmospheric motion are the Lamb form of the NS equations Where V = (u, v, w) is the atmospheric wind field, K is the atmospheric kinetic energy K = |V 2 / 2, ρ is the atmospheric density, p is the atmospheric pressure, T is the atmospheric temperature, F represents the dissipation force, Q represents the heat source, q is the water vapor specific humidity, S represents the water vapor source and sink, Ω is the angular velocity of the earth's rotation, g is the gravitational acceleration, c v is the atmospheric isochoric specific heat coefficient, and R is the atmospheric gas constant.

3. The method according to claim 2, characterized in that The energy conversion relationship of atmospheric motion in the dynamic framework of the numerical model is established, including: The above basic equations of atmospheric motion have the following energy conversion relationship: Where ρ is the atmospheric density, E is the total energy, E = K + c v T+φ+Lq,c v T is internal energy, φ is gravitational potential energy Lq is the latent heat of water vapor; When the system has no dissipation F = 0, and only the heat change caused by phase change Q = -LS is considered, the energy conservation relationship at any point in space can be obtained:

4. The method according to claim 3, characterized in that The method further comprises: Establish the atmospheric motion equations in the vertical coordinates of altitude terrain following; The horizontal coordinates of the atmospheric motion equations use local coordinates (x, y), and the vertical coordinates use altitude terrain following coordinates where z S =z S (x, y) is the ground height, z is T is the height of the top of the atmosphere, taken as a constant; In order to maintain the energy and mass properties, the basic forecast quantities are u, v, w, ρ, ρT; among them, u is the zonal wind speed, v is the radial wind speed, w is the vertical speed, ρ is the atmospheric density, and ρT is the product of density and temperature; Map magnification factors m and n, where m and n are equal, where m is the ratio of the grid spacing in the x-direction of the horizontal axis of the map to the actual grid spacing in the x-direction, and n is the ratio of the grid spacing in the y-direction of the map to the actual grid spacing in the y-direction; The equation of motion adopts the non-flux non-advection form, which is as follows in c p =c v +R Where r = a + z, a is the radius of the earth, is latitude; The equations under the above terrain coordinates satisfy the following energy conversion relationship: where F x is the dissipated force in the x direction, F y is the dissipated force in the y direction, F z is the dissipated force in the z direction.

5. The method according to claim 4, characterized in that The basic equations of atmospheric motion based on the numerical model dynamic framework and the energy conversion relationship are used to establish a dynamic framework spatial discrete model, which specifically includes: The discrete grid distribution of the forecast variables is Arakawa-C grid distribution horizontally and Lorenz distribution vertically, with uniform grid spacing Δx and Δy horizontally and non-uniform grid spacing Δσ vertically; Define the following discrete operator: Kinetic energy is defined at the Cross scalar point. The most critical thing is to satisfy the kinetic energy flux form, that is, the momentum equation, i.e. the wind field, and the continuity equation, i.e. the density-related discrete solution, satisfy the following kinetic energy flux relationship: Based on the above constraints, the following dynamic frame space discretization scheme is obtained: in is the height terrain following disturbance, δ x is the x-direction deflection, δ y is the y-direction deviation 6. The method according to claim 5, characterized in that The basic equations of atmospheric motion based on the numerical model dynamic framework and the energy conversion relationship are used to establish a dynamic framework spatial discrete model, and further include: Establish a dynamic frame space discrete model in the form of disturbance: Decompose the state quantity (ρ,T,p) into basic quantities (ρ b ,T b ,p b ), plus the disturbance (ρ',T',p'), that is: p=p b +ρ′,T=T b +T′,p=p b +p′ Assume that the basic quantity changes only with height and satisfies the static equilibrium relationship The basic temperature is given by the vertical profile of the mean temperature; The atmospheric equation of motion in the corresponding perturbation form is as follows: The corresponding discrete form is:

7. The method according to claim 6, characterized in that The basic equations of atmospheric motion based on the numerical model dynamic framework and the energy conversion relationship are used to establish a dynamic framework spatial discrete model, and further include: The time discretization adopts the classic semi-implicit HE-VI time splitting algorithm, the fast wave correlation term is integrated with a semi-implicit small time step, and the rest is integrated with a large time step; The following is a discrete model with a small time step, Δτ is a small time step, and Δt is a large time step; Among them, the vertical velocity of the underlying surface is:

8. A numerical model dynamic framework construction device for weather forecasting, characterized in that: The device comprises: The first processing module is used to establish the basic equations of atmospheric motion and energy conversion relations of the dynamic framework of the numerical model; The second processing module is used to establish a dynamic framework spatial discrete model based on the basic equations of atmospheric motion in the numerical model dynamic framework and the energy conversion relationship.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for constructing a numerical model dynamic framework for weather forecasting according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a numerical model dynamic framework for weather forecasting according to any one of claims 1 to 7 is implemented.

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