CN117236227A

CN117236227A - Boundary layer scheme prediction method, system, equipment and medium

Info

Publication number: CN117236227A
Application number: CN202311395987.7A
Authority: CN
Inventors: 彭孝强; 刘上嘉; 叶建东; 张艳霞; 李燕平; 张思球; 邓华; 刘泓锴; 龚成; 敖健永; 黄秀秀; 张韧; 何恭耀
Original assignee: Guangdong Power Grid Co Ltd; Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2023-10-25
Filing date: 2023-10-25
Publication date: 2023-12-15

Abstract

The invention discloses a boundary layer scheme prediction method, a boundary layer scheme prediction system, boundary layer scheme prediction equipment and boundary layer scheme prediction media, and relates to the technical field of fluid dynamics. And determining a forecast equation corresponding to each variable in the boundary layer scheme by acquiring a turbulence diffusion forecast equation in the boundary layer scheme and adopting the turbulence diffusion forecast equation. And carrying out discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set. And solving the discrete differential equation set by adopting a tri-diagonal matrix, and determining the prediction data corresponding to the boundary layer scheme. The calculation errors caused by non-uniform layering are reduced by the vertical diffusion coefficient calculation formula and the three-diagonal matrix for prediction, and particularly, the calculation errors of middle and high layers with larger mode non-uniformity are reduced.

Description

Boundary layer scheme prediction method, system, equipment and medium

Technical Field

The present invention relates to the field of fluid dynamics, and in particular, to a boundary layer scheme prediction method, system, apparatus, and medium.

Background

The atmospheric boundary layer, also known as the planetary boundary layer, is defined as the layer of the atmosphere affected by the underlying surface, or the layer of the atmosphere interacting with the underlying surface. The thickness of the boundary layer is between hundred meters and kilometers, the movement form of the boundary layer is mainly turbulent flow, and the exchange of material energy such as ground air momentum, heat, water vapor and the like is carried out through the turbulent exchange in the boundary layer, so that the occurrence and the development of a weather system are directly influenced. Because the atmospheric boundary layer turbulence motion is generally much smaller than the existing small-medium scale mode horizontal lattice spacing, the current numerical mode describes the effect of sub-grid boundary layer turbulence on resolvable atmospheric motion, typically by parameterization, taking into account sub-grid scale effects.

Boundary layer parameterization schemes are divided from the closed frame perspective into 1-order closures, 1.5-order closures, and 2-order or higher-order closures. The first order closure scheme is based primarily on the method of K theory, also known as the local K scheme. The local K-scheme is simple and stable and can produce reasonable results under typical atmospheric conditions and is widely used in atmospheric numerical modes. Many pneumologists, however, indicate that there are also some disadvantages to the local K-solution, the biggest drawback of which is that mass, momentum transport in the planetary boundary layer is accomplished by large vortices, which are an overall feature of the boundary layer, rather than a local feature. To overcome this drawback, researchers have proposed several solutions, one of which is to apply a higher order closure scheme that can well represent the structure of the mixed boundary layer, but is computationally intensive, and in addition, there have been studies showing that higher order closure schemes have a strong sensitivity to local diffusion. The other is to use a non-local K-solution, which is most likely to migrate to weather forecast mode, due to its simplicity and ability to represent vortices in the mixed boundary layer, which is currently widely used in both weather and climate numerical modes.

In an unstable atmosphere, the K-profile closure describes the process of non-localized diffusion in an unstable boundary layer while taking into account the effects of anti-gradient transport caused by non-localized diffusion. In a stable atmosphere, the scheme describes a locally unstable diffusion process using a local K closure. The boundary layer scheme considers the vertical diffusion process of free atmosphere in addition to the turbulent diffusion process of the boundary layer, so the non-uniform layering of the modes has an important influence on the calculation result of the boundary scheme. However, the existing boundary layer scheme prediction method cannot predict calculation errors caused by non-uniform layering, so that the accuracy of a prediction result is low.

Disclosure of Invention

The invention provides a boundary layer scheme prediction method, a boundary layer scheme prediction system, boundary layer scheme prediction equipment and boundary layer scheme prediction media, and solves the technical problem that the accuracy of a prediction result is low because calculation errors caused by non-uniform layering cannot be predicted by the existing boundary layer scheme prediction method.

The invention provides a boundary layer scheme prediction method, which comprises the following steps:

acquiring a turbulence diffusion forecast equation in a boundary layer scheme, and determining a forecast equation corresponding to each variable in the boundary layer scheme by adopting the turbulence diffusion forecast equation;

Performing discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set;

and solving the discrete differential equation set by adopting a tri-diagonal matrix, and determining the prediction data corresponding to the boundary layer scheme.

Optionally, the predictive equations include a momentum predictive equation and a heat predictive equation; the vertical diffusion coefficient calculation formula comprises a momentum vertical diffusion coefficient calculation formula and a heat vertical diffusion coefficient calculation formula; the step of performing discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set comprises the following steps:

performing discrete difference on the heat forecast equation by adopting the heat vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation and a water vapor discrete difference equation;

the heat vertical diffusion coefficient calculation formula is as follows:

wherein K is _t1 The vertical diffusion coefficient of the heat of the mixed layer; k (K) _m1 Is the momentum vertical diffusion coefficient of the mixed layer; p (P) _r Is the prandtl number from the near stratum unstable layer to the upper stable layer along with the change of the height; pr (Pr) ₀ Is the Plandter number near the top of the formation; zz is the mid-layer height of the half-layer; epsilon is a constant, epsilon=0.1; h is the boundary layer height; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; a is a constant, a=0.4; v (V) _Sc A speed scale is rolled into the cloud top; z _b Cloud base level below top drive hybrid expansion; h is a _b Is the layer cloud top height; k (K) _t3 Is the vertical diffusion coefficient of the heat of the free layer; l is the mixing length; f (f) _t (R _i ) As a function of thermal stability; r is R _i Is the rational Charson number; v is wind speed;

performing discrete difference on the momentum forecast equation by adopting the momentum vertical diffusion coefficient calculation formula to generate a weft wind discrete difference equation and a warp wind discrete difference equation;

the momentum vertical diffusion coefficient calculation formula is as follows:

K _m2 ＝0.75*K _t2 ；

wherein K is _m1 Is the momentum vertical diffusion coefficient of the mixed layer; a is a constant; w (w) _s Is the convection scale velocity; zz is the mid-layer height of the half-layer; h is the boundary layer height; k (K) _m2 The vertical diffusion coefficient is the layer cloud top momentum; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; k (K) _m3 Is the free layer momentum vertical diffusion coefficient; l is the mixing length; f (f) _m (R _i ) As a function of momentum stability; r is R _i Is the rational Charson number; v is wind speed;

and constructing a discrete difference equation set by adopting the temperature discrete difference equation, the water vapor discrete difference equation, the weft wind discrete difference equation and the warp wind discrete difference equation.

Optionally, the heat forecast equation includes a temperature forecast equation and a water vapor forecast equation; the step of performing discrete difference on the heat forecast equation by adopting the heat vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation and a water vapor discrete difference equation comprises the following steps:

performing discrete difference on the temperature forecast equation by adopting the heat vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation;

the temperature discrete difference equation is:

wherein Δt is the time interval of two moments; (K) _t ) _k-1 The vertical diffusion coefficient of the heat of the k-1 layer; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; t (T) _k-1 ⁿ⁺¹ The temperature of the k-1 layer at time n+1; n is a time value; />The thickness of the k half layers; (K) _t ) _k The vertical diffusion coefficient of the heat of the k layers; t (T) _k ⁿ⁺¹ The temperature of the k layer at time n+1; t (T) _k+1 ⁿ⁺¹ The temperature of the k+1 layer at time n+1; />Is a temperature gradient under adiabatic and static equilibrium; gamma ray _T Is a temperature inverse gradient term;

performing discrete difference on the water vapor forecast equation by adopting the heat vertical diffusion coefficient calculation formula to generate a water vapor discrete difference equation;

the water vapor discrete difference equation is as follows:

wherein Δt is the time interval of two moments; (K) _t ) _k-1 The vertical diffusion coefficient of the heat of the k-1 layer; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; q (Q) _k-1 ⁿ⁺¹ Is water vapor of a k-1 layer at the time of n+1; (K) _t ) _k The vertical diffusion coefficient of the heat of the k layers; />The thickness of the k half layers; q (Q) _k ⁿ⁺¹ Is the water vapor of the k-1 layer at the time n; q (Q) _k+1 ⁿ⁺¹ Water vapor of the k+1 layer at the time of n+1; gamma ray _q Is a water vapor inverse gradient term.

Optionally, the momentum forecast equation comprises a latitudinal wind forecast equation and a longitudinal wind forecast equation; the step of adopting the momentum vertical diffusion coefficient calculation formula to carry out discrete difference on the momentum forecast equation to generate a weft wind discrete difference equation and a warp wind discrete difference equation comprises the following steps:

performing discrete difference on the latitudinal wind forecast equation by adopting the momentum vertical diffusion coefficient calculation formula to generate a latitudinal wind discrete difference equation;

the latitudinal wind discrete difference equation is as follows:

wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; u (u) _k-1 ⁿ⁺¹ The weft wind of the k-1 layer at the time of n+1; n is a time value;the thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; u (u) _k ⁿ⁺¹ The weft wind of the k layers at the time of n+1; u (u) _k+1 ⁿ⁺¹ The weft wind of the layer k+1 at the time of n+1; u (u) _k ⁿ The weft wind of the k layers at the time of n;

performing discrete difference on the radial wind forecast equation by adopting the momentum vertical diffusion coefficient calculation formula to generate a radial wind discrete difference equation;

the warp wind discrete difference equation is:

wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; v _k-1 ⁿ⁺¹ The wind is the directed wind of the k-1 layer at the time of n+1; n is a time value;the thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; v _k ⁿ⁺¹ The wind is the directed wind of the k layer at the time of n+1; v _k+1 ⁿ⁺¹ The wind direction of the k+1 layer at the time of n+1; v _k ⁿ Is the wind of the k layers at time n.

Optionally, the step of solving the discrete differential equation set by using a tri-diagonal matrix to determine prediction data corresponding to the boundary layer scheme includes:

converting the temperature discrete difference equation into a tri-diagonal matrix form to generate a temperature tri-diagonal matrix;

substituting data corresponding to the boundary layer scheme into the temperature tri-diagonal matrix to solve, and generating a temperature value;

converting the water vapor discrete difference equation into a tri-diagonal matrix form to generate a water vapor tri-diagonal matrix;

Substituting data corresponding to the boundary layer scheme into the water vapor tri-diagonal matrix to solve, and generating a water vapor value;

converting the latitudinal wind discrete difference equation into a tri-diagonal matrix form to generate a latitudinal wind tri-diagonal matrix;

substituting data corresponding to the boundary layer scheme into the latitudinal wind tri-diagonal matrix to solve, and generating a latitudinal wind value;

converting the radial wind discrete difference equation into a tri-diagonal matrix form to generate a radial wind tri-diagonal matrix;

substituting data corresponding to the boundary layer scheme into the warp wind tri-diagonal matrix to solve, and generating a warp wind value;

and constructing prediction data corresponding to the boundary layer scheme by adopting the temperature value, the water vapor value, the weft wind value and the warp wind value.

The invention also provides a boundary layer scheme prediction system, which comprises:

the prediction equation determining module is used for obtaining a turbulence diffusion prediction equation in a boundary layer scheme, and determining a prediction equation corresponding to each variable in the boundary layer scheme by adopting the turbulence diffusion prediction equation;

the discrete difference equation set generation module is used for carrying out discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set;

And the prediction data determining module is used for solving the discrete difference equation set by adopting a tri-diagonal matrix to determine the prediction data corresponding to the boundary layer scheme.

Optionally, the predictive equations include a momentum predictive equation and a heat predictive equation; the vertical diffusion coefficient calculation formula comprises a momentum vertical diffusion coefficient calculation formula and a heat vertical diffusion coefficient calculation formula; the discrete differential equation set generating module includes:

the temperature discrete difference equation and water vapor discrete difference equation generating module is used for carrying out discrete difference on the heat forecast equation by adopting the heat vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation and a water vapor discrete difference equation;

the heat vertical diffusion coefficient calculation formula is as follows:

the weft wind discrete difference equation and warp wind discrete difference equation generation module is used for carrying out discrete difference on the momentum forecast equation by adopting the momentum vertical diffusion coefficient calculation formula to generate a weft wind discrete difference equation and a warp wind discrete difference equation;

the momentum vertical diffusion coefficient calculation formula is as follows:

K _m2 ＝0.75*K _t2 ；

the discrete differential equation set generation submodule is used for constructing a discrete differential equation set by adopting the temperature discrete differential equation, the water vapor discrete differential equation, the weft wind discrete differential equation and the warp wind discrete differential equation.

Optionally, the heat forecast equation includes a temperature forecast equation and a water vapor forecast equation; the temperature discrete difference equation and water vapor discrete difference equation generating module executes the following steps:

the temperature discrete difference equation is:

the water vapor discrete difference equation is as follows:

The invention also provides an electronic device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the prediction method implementing any of the boundary layer schemes described above.

The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed implements a boundary layer scheme prediction method as any one of the above.

From the above technical scheme, the invention has the following advantages:

according to the method, the turbulence diffusion forecast equation in the boundary layer scheme is obtained, and the forecast equation corresponding to each variable in the boundary layer scheme is determined by adopting the turbulence diffusion forecast equation. And carrying out discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set. And solving the discrete differential equation set by adopting a tri-diagonal matrix, and determining the prediction data corresponding to the boundary layer scheme. The method solves the technical problems that the accuracy of the prediction result is low because the calculation error caused by non-uniform layering cannot be predicted by the existing boundary layer scheme prediction method. The calculation errors caused by non-uniform layering are reduced by the vertical diffusion coefficient calculation formula and the three-diagonal matrix for prediction, and particularly, the calculation errors of middle and high layers with larger mode non-uniformity are reduced.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.

FIG. 1 is a flow chart of the steps of a boundary layer scheme prediction method according to a first embodiment of the present invention;

FIG. 2 is a diagram of discretized layering and variable distribution in the prior art according to a first embodiment of the present invention;

FIG. 3 is a diagram illustrating discretized layering and variable distribution in a boundary layer scheme prediction method according to an embodiment of the present invention;

FIG. 4 is a flow chart illustrating a method for predicting boundary layer scenario according to a second embodiment of the present invention;

fig. 5 is a schematic diagram of a path error change of a weak typhoon example provided in the second embodiment of the present invention;

fig. 6 is a schematic diagram of intensity variation of weak typhoons according to a second embodiment of the present invention;

fig. 7 is a schematic diagram of a path error change of a strong typhoon example provided in the second embodiment of the present invention;

Fig. 8 is a schematic diagram of intensity variation of a strong typhoon example provided in the second embodiment of the present invention;

FIG. 9 is a block diagram of a boundary layer scheme prediction system according to a third embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a boundary layer scheme prediction method, a boundary layer scheme prediction system, boundary layer scheme prediction equipment and boundary layer scheme prediction media, which are used for solving the technical problem that the accuracy of a prediction result is low because calculation errors caused by non-uniform layering cannot be predicted by the existing boundary layer scheme prediction method.

In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1, fig. 1 is a flowchart illustrating a boundary layer scheme prediction method according to an embodiment of the invention.

The first embodiment of the invention provides a boundary layer scheme prediction method, which comprises the following steps:

and 101, acquiring a turbulence diffusion forecast equation in a boundary layer scheme, and determining a forecast equation corresponding to each variable in the boundary layer scheme by adopting the turbulence diffusion forecast equation.

In the embodiment of the invention, the Charney-Philips layer-jumping arrangement is adopted in the vertical direction in the boundary layer scheme, and different variables are arranged in different layers, such as temperature, water vapor in the whole layer and wind field in the half layer. The layering mode in the vertical direction adopts non-uniform layering, wherein the thickness of middle and high layers is greatly different, and the difference of various variables on different layers can influence the error caused by the difference of the various variables. The turbulence diffusion prediction equation is:

wherein K is _c Is the vertical diffusion coefficient; gamma ray _c Is an inverse gradient term; c is the forecast variable temperature T, water vapor Q, weft wind u and warp wind v.

When solving each variable forecast equation, firstly, discrete difference in the horizontal direction and the vertical direction is carried out, and the technology is mainly described from the discrete difference in the vertical direction. The turbulence diffusion equation in the boundary layer scheme is discretized according to the grid structure of the service mode, and currently, the forecast variable of the boundary layer scheme is uniformly placed on a half layer for conveniently applying the constant flux characteristic of the near stratum. The prior art scheme is that the forecast variable is placed on a half layer to carry out discrete difference, the vertical diffusion coefficient is placed on the whole layer to carry out discrete difference, and the layered structure of the discrete difference of each variable in the boundary layer turbulence forecast equation is shown in figure 2.

As shown in fig. 2, the solid line represents the setting of the whole layer in the south China business model, i.e. the layer where the vertical diffusion coefficient discrete difference is located, and the dashed line represents the setting of the half layer, i.e. the layer where the forecast variable is located in the boundary layer scheme, and also the layer where the forecast variable discrete difference is located. Defining the whole layer height variable as z, wherein any layer is denoted by k, and the height z of a certain layer _k ，Δz _k Representing the thickness between the whole layers, i.e. Δz _k ＝＝z _k ＝-z _k-1 Increasing with increasing pattern height. The variable of the half-layer height is defined as zh, and the height of a certain layer of the half-layer is zh _k Defined as one half of the addition of two layers, zh _k ＝(z _k +z _k-1 )/2，Representing the thickness between half layers, i.e

With mixed layer momentum vertical diffusion coefficient k _m And heat vertical diffusion coefficient K _t The calculation formula for the example is:

wherein a is a constant, and a is 0.4; w (w) _s Is the convection scale velocity; z is the whole layer height; pr (Pr) ₀ Being the planchet number near the top of the formation; epsilon is a constant and 0.1 is taken; b is a constant, taking 6.5; h is boundary layer height:R _i is the number of the richardson,representing a thermodynamic similarity function, +.>) Representing the dynamic similarity function, z being the overall height, z ₀ Is roughness.

As shown in fig. 2, in the prior art, taking a temperature T prediction equation as an example, a prediction equation corresponding to water vapor Q, weft direction wind u and warp direction wind v is similar to a prediction equation corresponding to temperature, and only variables in the formula are replaced correspondingly, and the prediction equation corresponding to temperature is:

Wherein T is the temperature; t is time; z is the overall height variable; k (K) _t Is the vertical diffusion coefficient of heat; gamma ray _t Is a temperature inverse gradient term.

The prediction equation corresponding to the temperature can also be written as a whole layer of prediction equation:

in the above formula, T is in half layer, and K _t The two layers are in the whole layer, the layers are inconsistent, and as the mode is in nonuniform layering, the non-uniformity is stronger along with the increase of the mode height, and the thickness difference between the whole layer and the half layer is more obvious. Firstly, calculating related variables on the whole layer when calculating a whole layer of forecast equation; second, when the temperature forecast equation is discrete, the vertical diffusion coefficient is differentiated over the whole layer, and the second derivative term of the temperature TThe transition layer of the difference is also in the whole layer, the temperature variable difference is in the half layer, and the thickness of the mode layer used for the difference is obvious in the mode, so that the calculation error of the high layer in the forecast variable in the boundary layer scheme is obviously increased relative to the calculation error of the low layer, and the forecast precision of the mode is affected.

The discrete difference equation corresponding to the forecast equation of the dimension is:

wherein T is _k ⁿ⁺¹ Represents time n+1The temperature of the k layer; t (T) _k ⁿ The temperature of the k layer at time n+1; Δz _k Representing the thickness of the whole k layers; Represents the thickness of the k half-layer; />Represents the thickness of the k-1 layer half layer; (K) _t ) _k Representing the vertical diffusion coefficient of heat of k layers; (K) _t ) _k-1 Represents the vertical diffusion coefficient of heat of the k-1 layer; Δt represents the time interval between two moments; gamma ray _T Representing an inverse gradient term; />Representing the temperature gradient at adiabatic and static equilibrium.

As shown in fig. 3, the dashed line indicates the layer where the forecast variable temperature T is located, i.e. the half-layer position in the pattern, the dotted line being the middle layer of the half-layer, the virtual layer being specifically set for the boundary layer scheme.The thickness between the half layers is shown, which is consistent with the prior art. Defining intermediate layer variables of half layers as zz, certain layer height as zz _k I.e. zz _k ＝(zh _k +zh _k ) Intermediate layer thickness of half-layer is Δzz _k ＝＝zz _k -zz _k-1 Wherein Δzz _k And->The change trend of the mode layer is consistent as the mode layer height layer is increased. In the scheme of the invention, a half-layer middle layer is used as a newly selected layer of vertical diffusion coefficient calculation and vertical discrete difference and forecast variable second derivative vertical difference.

Taking a temperature T forecast equation as an example in the invention, the forecast equation corresponding to the water vapor Q, the weft wind u and the warp wind v is similar to the forecast equation corresponding to the temperature, and only the variables in the formula are replaced correspondingly, and the temperature forecast equation corresponding to the invention is as follows:

Wherein T is the temperature; t is time; zz is an intermediate layer variable of half layer; k (K) _t Is the vertical diffusion coefficient of heat; gamma ray _t Is a temperature inverse gradient term. And the same can be used for obtaining a prediction equation corresponding to the water vapor Q, the weft wind u and the warp wind v.

And 102, carrying out discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set.

In the embodiment of the invention, a heat vertical diffusion coefficient calculation formula is adopted to carry out discrete difference on a heat forecast equation, so as to generate a temperature discrete difference equation and a water vapor discrete difference equation. And carrying out discrete difference on the momentum forecast equation by adopting a momentum vertical diffusion coefficient calculation formula to generate a weft wind discrete difference equation and a warp wind discrete difference equation. And constructing a discrete difference equation set by adopting a temperature discrete difference equation, a water vapor discrete difference equation, a weft wind discrete difference equation and a warp wind discrete difference equation.

And 103, solving a discrete difference equation set by adopting a tri-diagonal matrix, and determining prediction data corresponding to the boundary layer scheme.

In the embodiment of the invention, a temperature discrete difference equation is converted into a tri-diagonal matrix form, and a temperature tri-diagonal matrix is generated. Substituting data corresponding to the boundary layer scheme into a temperature tri-diagonal matrix to solve, and generating a temperature value. And converting the water vapor discrete difference equation into a tri-diagonal matrix form to generate a water vapor tri-diagonal matrix. Substituting data corresponding to the boundary layer scheme into a water vapor tri-diagonal matrix to solve, and generating a water vapor value. And converting the latitudinal wind discrete difference equation into a tri-diagonal matrix form to generate a latitudinal wind tri-diagonal matrix. Substituting data corresponding to the boundary layer scheme into a latitudinal wind tri-diagonal matrix to solve, and generating a latitudinal wind value. And converting the radial wind discrete difference equation into a tri-diagonal matrix form to generate a radial wind tri-diagonal matrix. Substituting data corresponding to the boundary layer scheme into a wind-oriented tri-diagonal matrix to solve, and generating a wind-oriented value. And constructing prediction data corresponding to the boundary layer scheme by adopting the temperature value, the water vapor value, the weft wind value and the warp wind value.

In the embodiment of the invention, a turbulence diffusion forecast equation in a boundary layer scheme is obtained, and the forecast equation corresponding to each variable in the boundary layer scheme is determined by adopting the turbulence diffusion forecast equation. And carrying out discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set. And solving the discrete differential equation set by adopting a tri-diagonal matrix, and determining the prediction data corresponding to the boundary layer scheme. The method solves the technical problems that the accuracy of the prediction result is low because the calculation error caused by non-uniform layering cannot be predicted by the existing boundary layer scheme prediction method. The calculation errors caused by non-uniform layering are reduced by the vertical diffusion coefficient calculation formula and the three-diagonal matrix for prediction, and particularly, the calculation errors of middle and high layers with larger mode non-uniformity are reduced.

Referring to fig. 4, fig. 4 is a flowchart illustrating a boundary layer scheme prediction method according to a second embodiment of the present invention.

Another boundary layer scheme prediction method provided in the second embodiment of the present invention includes:

step 401, acquiring a turbulence diffusion forecast equation in a boundary layer scheme, and determining a forecast equation corresponding to each variable in the boundary layer scheme by adopting the turbulence diffusion forecast equation.

In the embodiment of the present invention, the implementation process of step 401 is similar to that of step 101, and will not be repeated here.

And step 402, carrying out discrete difference on the heat forecast equation by adopting a heat vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation and a water vapor discrete difference equation.

Further, the heat forecast equation includes a temperature forecast equation and a steam forecast equation, and step 402 may include the following sub-steps S11-S12:

s11, carrying out discrete difference on the temperature forecast equation by adopting a thermal vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation.

The temperature discrete difference equation is:

wherein Δt is the time interval of two moments; (K) _t ) _k-1 The vertical diffusion coefficient of the heat of the k-1 layer; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; t (T) _k-1 ⁿ⁺¹ The temperature of the k-1 layer at time n+1; n is a time value; />The thickness of the k half layers; (K) _t ) _k The vertical diffusion coefficient of the heat of the k layers; t (T) _k ⁿ⁺¹ The temperature of the k layer at time n+1; t (T) _k+1 ⁿ⁺¹ The temperature of the k+1 layer at time n+1; />Is a temperature gradient under adiabatic and static equilibrium; gamma ray _T Is a temperature inverse gradient term.

S12, carrying out discrete difference on the water vapor forecast equation by adopting a heat vertical diffusion coefficient calculation formula to generate a water vapor discrete difference equation.

The water vapor discrete difference equation is:

In the embodiment of the invention, the forecast equation comprises a momentum forecast equation and a heat forecast equation, and the heat forecast equation comprises a temperature forecast equation and a water vapor forecast equation. The momentum forecast equation includes a latitudinal wind forecast equation and a longitudinal wind forecast equation. And carrying out discrete difference on the temperature forecast equation and the water vapor forecast equation by adopting a heat vertical diffusion coefficient calculation formula corresponding to the mixed layer, the layer cloud top and the free layer to obtain a temperature discrete difference equation and a water vapor discrete difference equation.

And 403, carrying out discrete difference on the momentum forecast equation by adopting a momentum vertical diffusion coefficient calculation formula to generate a weft wind discrete difference equation and a warp wind discrete difference equation.

Further, the momentum forecast equation includes a latitudinal wind forecast equation and a longitudinal wind forecast equation. Step 403 may comprise the following sub-steps S21-S22:

S21, carrying out discrete difference on a latitudinal wind forecast equation by adopting a momentum vertical diffusion coefficient calculation formula to generate a latitudinal wind discrete difference equation;

the latitudinal wind discrete difference equation is:

wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; u (u) _k-1 ⁿ⁺¹ The weft wind of the k-1 layer at the time of n+1; n is a time value;the thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; u (u) _k ⁿ⁺¹ The weft wind of the k layers at the time of n+1; u (u) _k+1 ⁿ⁺¹ The weft wind of the layer k+1 at the time of n+1; u (u) _k ⁿ Is the weft wind of the k layers at the time of n.

S22, carrying out discrete difference on a radial wind forecast equation by adopting a momentum vertical diffusion coefficient calculation formula to generate a radial wind discrete difference equation;

the windward discrete difference equation is:

In the embodiment of the invention, the momentum vertical diffusion coefficient K of the mixed layer _m1 And heat quantity K _t1 The calculation formula of the vertical diffusion coefficient is:

layer cloud top driving vortex momentum vertical diffusion coefficient K _m2 And heat vertical diffusion coefficient K _t2 The calculation formula of (2) is as follows:

K _m2 ＝0.75*K _t2 ；

momentum vertical diffusion coefficient K of free layer _m3 And heat vertical diffusion coefficient K _t3 The calculation formula of (2) is as follows:

wherein K is _t1 The vertical diffusion coefficient of the heat of the mixed layer; k (K) _m1 Is the momentum vertical diffusion coefficient of the mixed layer; p (P) _r Is the prandtl number from the near stratum unstable layer to the upper stable layer along with the change of the height; pr (Pr) ₀ Being the planchet number near the top of the formation; zz is the mid-layer height of the half-layer; epsilon is a constant, epsilon=0.1; h is the boundary layer height; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; a is a constant, a=0.4; v (V) _Sc A speed scale is rolled into the cloud top; z _b Cloud base level below top drive hybrid expansion; h is a _b Is the layer cloud top height; k (K) _t3 Is the vertical diffusion coefficient of the heat of the free layer; l is the mixing length; f (f) _t (R _i ) As a function of thermal stability; r is R _i Is the rational Charson number; v is wind speed; k (K) _m1 Is the momentum vertical diffusion coefficient of the mixed layer; w (w) _s Is the convection scale velocity; f (f) _m (R _i ) A momentum stability function; h is the boundary layer height; k (K) _m2 The vertical diffusion coefficient is the layer cloud top momentum; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; k (K) _m3 Is the free layer momentum vertical diffusion coefficient.

And adopting momentum vertical diffusion coefficient calculation formulas corresponding to the mixed layer, the layer cloud top and the free layer to respectively carry out discrete difference on the weft wind forecast equation and the warp wind forecast equation to obtain a weft wind discrete difference equation and a warp wind discrete difference equation. The layer where the forecast variable is located and the layer where the vertical diffusion coefficient is located have better consistency, and the thickness variation trend of the layer where the difference is located also has consistency.

And 404, constructing a discrete difference equation set by adopting a temperature discrete difference equation, a water vapor discrete difference equation, a weft wind discrete difference equation and a warp wind discrete difference equation.

In the embodiment of the invention, a temperature discrete difference equation, a water vapor discrete difference equation, a weft wind discrete difference equation and a warp wind discrete difference equation are obtained by carrying out discrete difference calculation through a momentum vertical diffusion coefficient calculation formula and a heat vertical diffusion coefficient calculation formula, and a discrete difference equation set is constructed by adopting the discrete difference equations.

And 405, solving a discrete difference equation set by adopting a tri-diagonal matrix to determine prediction data corresponding to the boundary layer scheme.

Further, step 405 may include the following substeps S31-S39:

s31, converting the temperature discrete difference equation into a tri-diagonal matrix form, and generating a temperature tri-diagonal matrix.

S32, substituting data corresponding to the boundary layer scheme into a temperature tri-diagonal matrix to solve, and generating a temperature value.

S33, converting the water vapor discrete difference equation into a tri-diagonal matrix form to generate a water vapor tri-diagonal matrix.

S34, substituting data corresponding to the boundary layer scheme into the water vapor tri-diagonal matrix to solve, and generating a water vapor value.

S35, converting the latitudinal wind discrete difference equation into a tri-diagonal matrix form, and generating a latitudinal wind tri-diagonal matrix.

S36, substituting data corresponding to the boundary layer scheme into a latitudinal wind tri-diagonal matrix to solve, and generating a latitudinal wind value.

S37, converting the radial wind discrete difference equation into a tri-diagonal matrix form to generate a radial wind tri-diagonal matrix.

S38, substituting data corresponding to the boundary layer scheme into the wind-oriented tri-diagonal matrix to solve, and generating a wind-oriented value.

S39, adopting a temperature value, a water vapor value, a weft wind value and a warp wind value to construct prediction data corresponding to the boundary layer scheme.

In the embodiment of the invention, a boundary layer turbulence discretization equation needs to be solved by adopting a tri-diagonal matrix, and the form is as follows:

a _l (k)X _k-1 +a _d (k)X _k +a _u (k)X _k+1 ＝R _k ；

I.e. in the form of a matrix:

wherein N represents the order of the matrix, a _l (k)、a _d (k) And a _u (k) Coefficients respectively representing tri-diagonal matrices, R _k Representing the right-hand term of the matrix, the two parts being known terms, X _k Representing the solved variables; k is the layer type.

The solving process is as follows:

wherein the coefficients are:

will first coefficient c _k ^* And a second coefficient d _k ^* Substitution of x _k Solving for variable X _k 。

Therefore, the temperature discrete difference equation is converted into a tri-diagonal matrix form, and the obtained temperature tri-diagonal moment is:

wherein Δt is the time interval of two moments; (K) _t ) _k-1 The vertical diffusion coefficient of the heat of the k-1 layer; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; t (T) _k-1 ⁿ⁺¹ The temperature of the k-1 layer at time n+1; n is a time value; />The thickness of the k half layers; (K) _t ) _k The vertical diffusion coefficient of the heat of the k layers; t (T) _k ⁿ The temperature of the k layer at time n; />Is a temperature gradient under adiabatic and static equilibrium; gamma ray _T Is a temperature inverse gradient term; />A variable of the k-1 layer at time n+1, herein referred to as a temperature value; />A variable of the k layer at time n+1, here the temperature value; />The variable of the k+1 layer at time n+1 is referred to herein as the temperature value.

Converting the water vapor discrete difference equation into a tri-diagonal matrix form, wherein the obtained water vapor tri-diagonal matrix is as follows:

wherein Δt is the time interval of two moments; (K) _t ) _k-1 The vertical diffusion coefficient of the heat of the k-1 layer; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; q (Q) _k ⁿ Is the water vapor of the k layer at the time n; (K) _t ) _k The vertical diffusion coefficient of the heat of the k layers; />The thickness of the k half layers; gamma ray _q Is a water vapor inverse gradient item; />The variable of the k-1 layer at time n+1, here the water vapor value; />The variable of the k layer at time n+1, here the water vapor value; />The variable of the k+1 layer at time n+1 is referred to herein as the moisture value.

Converting a latitudinal wind discrete difference equation into a tri-diagonal matrix form, wherein the obtained latitudinal wind tri-diagonal matrix is as follows:

wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; u (u) ⁿ⁺¹ The weft direction wind at the time of n+1; n is a time value; />The thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; u (u) _k ⁿ The weft wind of the k layers at the time of n; />The weft wind value is indicated here as the variable of the k-1 layer at time n+1; />The variation of the k layer at time n+1, here the weft wind value; />The variation of the layer k+1 at time n+1 is referred to herein as the weft wind value.

Converting the warp wind discrete difference equation into a tri-diagonal matrix form, wherein the obtained warp wind tri-diagonal matrix is as follows:

Wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; v ⁿ⁺¹ Is the warp direction wind at time n+1; n is a time value; />The thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; v _k ⁿ The wind is the directed wind of the k layers at the time of n; />The variable of the k-1 layer at time n+1, here the warp wind value; />The variable of the k layer at time n+1 is referred to as the warp wind value; />The variable of the layer k+1 at time n+1 is referred to herein as the warp wind value.

Substituting data corresponding to the boundary layer scheme into a temperature tri-diagonal matrix, a water vapor tri-diagonal matrix, a weft tri-diagonal matrix and a warp tri-diagonal matrix, respectively solving to obtain a temperature value, a water vapor value, a weft wind value and a warp wind value, and constructing prediction data corresponding to the boundary layer scheme by adopting the temperature value, the water vapor value, the weft wind value and the warp wind value.

In the embodiment of the invention, two typhoons are tested based on the south China business model. The prior art scheme is the original scheme, and the prior art scheme is the new scheme. As shown in fig. 5 and 6, from the path error of the weak typhoon case, the path error of the new scheme is obviously reduced after 60 hours, for example, the path error of the new scheme is reduced by nearly 70km compared with the original scheme in 96 hours; whereas before 60 hours the errors of the two schemes were comparable. From the point of view of intensity change, the two schemes were closer to and slightly stronger than the observations before 72 hours; but after 72 hours the forecast has a significant trend of enhancement, especially the new proposal is more significant, which may have a close relationship with other physical processes of the pattern, such as the convection parameterization process, requiring further in-depth analysis. From the path change, the two forecasted typhoons were comparable to the observed speed before 48 hours, but were significantly faster after 48 hours, with the original protocol being slightly faster than the new protocol. In addition, typhoons predicted by both schemes had a southerly bias after 48 hours, with the original scheme being southerly more and more error between 72 and 96 hours.

As shown in fig. 7 and 8, from the comparison of the path errors of the strong typhoons, the path errors after 24 hours of the new scheme are smaller than those of the original scheme, for example, the path errors of 60 hours are reduced by about 40km, and the path errors of 96 hours are reduced by about 60 km. The two schemes show synchronism to the intensity pre-report of the strong typhoons, and the difference between the two schemes and the observation is obvious. The typhoon intensity predicted by both schemes was gradually increased between 6 and 60 hours, with a decreasing trend after 60 hours, while observations remained substantially between 950-960hPa, showing no significant trend of increasing to decreasing. From the path change, typhoons forecasted by both schemes are faster than observed, wherein typhoons forecasted by the original scheme move faster, and the moving path is biased to the west and the south, and the moving speed of the new scheme is relatively slower from 48 hours later, and the moving path is closer to the observed.

Referring to fig. 9, fig. 9 is a block diagram of a boundary layer scheme prediction system according to a third embodiment of the present invention.

The boundary layer scheme prediction system provided in the third embodiment of the present invention includes:

the forecast equation determining module 901 is configured to obtain a turbulence diffusion forecast equation in the boundary layer scheme, and determine a forecast equation corresponding to each variable in the boundary layer scheme by using the turbulence diffusion forecast equation.

The discrete difference equation set generating module 902 is configured to perform discrete difference on the forecast equation by using a vertical diffusion coefficient calculation formula, so as to generate a discrete difference equation set.

The prediction data determining module 903 is configured to solve the discrete differential equation set by using a tri-diagonal matrix, and determine prediction data corresponding to the boundary layer scheme.

Optionally, the predictive equations include a momentum predictive equation and a heat predictive equation; the vertical diffusion coefficient calculation formula comprises a momentum vertical diffusion coefficient calculation formula and a heat vertical diffusion coefficient calculation formula; the discrete difference equation set generating module includes:

the temperature discrete difference equation and water vapor discrete difference equation generation module is used for carrying out discrete difference on the heat forecast equation by adopting a heat vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation and a water vapor discrete difference equation.

The heat vertical diffusion coefficient calculation formula is:

wherein K is _t1 The vertical diffusion coefficient of the heat of the mixed layer; k (K) _m1 Is the momentum vertical diffusion coefficient of the mixed layer; p (P) _r Is the prandtl number from the near stratum unstable layer to the upper stable layer along with the change of the height; pr (Pr) ₀ Being the planchet number near the top of the formation; zz is the mid-layer height of the half-layer; epsilon is a constant, epsilon=0.1; h is the boundary layer height; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; a is a constant, a=0.4; v (V) _Sc A speed scale is rolled into the cloud top; z _b Cloud base level below top drive hybrid expansion; h is a _b Is the layer cloud top height; k (K) _t3 Is the vertical diffusion coefficient of the heat of the free layer; l is the mixing length; f (f) _t (R _i ) As a function of thermal stability; r is R _i Is the rational Charson number; v is wind speed.

And the weft wind discrete difference equation and warp wind discrete difference equation generating module is used for carrying out discrete difference on the momentum forecast equation by adopting a momentum vertical diffusion coefficient calculation formula to generate a weft wind discrete difference equation and a warp wind discrete difference equation.

The momentum vertical diffusion coefficient calculation formula is:

K _m2 ＝0.75*K _t2 ；

wherein K is _m1 Is the momentum vertical diffusion coefficient of the mixed layer; a is a constant; w (w) _s Is the convection scale velocity; zz is the mid-layer height of the half-layer; h is the boundary layer height; k (K) _m2 The vertical diffusion coefficient is the layer cloud top momentum; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; k (K) _m3 Is the free layer momentum vertical diffusion coefficient; l is the mixing length; f (f) _m (R _i ) As a function of momentum stability; r is R _i Is the rational Charson number; v is wind speed.

The discrete differential equation set generation submodule is used for constructing a discrete differential equation set by adopting a temperature discrete differential equation, a water vapor discrete differential equation, a weft wind discrete differential equation and a warp wind discrete differential equation.

Optionally, the heat forecast equation includes a temperature forecast equation and a moisture forecast equation. The temperature discrete differential equation and the water vapor discrete differential equation generating module may perform the steps of:

the temperature discrete difference equation and water vapor discrete difference equation generating module executes the following steps:

carrying out discrete difference on the temperature forecast equation by adopting a thermal vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation;

the temperature discrete difference equation is:

carrying out discrete difference on the water vapor forecast equation by adopting a heat vertical diffusion coefficient calculation formula to generate a water vapor discrete difference equation;

the water vapor discrete difference equation is:

Optionally, the momentum forecast equation includes a latitudinal wind forecast equation and a longitudinal wind forecast equation. The weft wind discrete difference equation and the warp wind discrete difference equation generation module may perform the steps of:

carrying out discrete difference on the latitudinal wind forecast equation by adopting a momentum vertical diffusion coefficient calculation formula to generate a latitudinal wind discrete difference equation;

the latitudinal wind discrete difference equation is:

wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; ΔZ _k The thickness of the whole k layers is; u (u) _k-1 ⁿ⁺¹ The weft wind of the k-1 layer at the time of n+1; n is a time value; />The thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; u (u) _k ⁿ⁺¹ The weft wind of the k layers at the time of n+1; u (u) _k+1 ⁿ⁺¹ The weft wind of the layer k+1 at the time of n+1; u (u) _k ⁿ The weft wind of the k layers at the time of n;

carrying out discrete difference on the radial wind forecast equation by adopting a momentum vertical diffusion coefficient calculation formula to generate a radial wind discrete difference equation;

The windward discrete difference equation is:

Optionally, the prediction data determination module 903 includes:

the temperature tri-diagonal matrix generation module is used for converting the temperature discrete difference equation into a tri-diagonal matrix form to generate a temperature tri-diagonal matrix.

And the temperature value generation module is used for substituting data corresponding to the boundary layer scheme into the temperature tri-diagonal matrix to solve, so as to generate a temperature value.

The water vapor tri-diagonal matrix generation module is used for converting the water vapor discrete difference equation into a tri-diagonal matrix form to generate a water vapor tri-diagonal matrix.

And the water vapor value generation module is used for substituting data corresponding to the boundary layer scheme into the water vapor tri-diagonal matrix to solve, so as to generate a water vapor value.

And the weft wind tri-diagonal matrix generation module is used for converting the weft wind discrete difference equation into a tri-diagonal matrix form to generate a weft wind tri-diagonal matrix.

And the weft wind value generation module is used for substituting data corresponding to the boundary layer scheme into the weft wind tri-diagonal matrix to solve and generate a weft wind value.

And the warp wind tri-diagonal matrix generation module is used for converting the warp wind discrete difference equation into a tri-diagonal matrix form to generate a warp wind tri-diagonal matrix.

And the wind-oriented value generation module is used for substituting data corresponding to the boundary layer scheme into a wind-oriented tri-diagonal matrix to solve, so as to generate a wind-oriented value.

The prediction data determination submodule is used for constructing prediction data corresponding to a boundary layer scheme by adopting a temperature value, a water vapor value, a weft wind value and a warp wind value.

The embodiment of the invention also provides electronic equipment, which comprises: a memory and a processor, the memory storing a computer program; the computer program, when executed by a processor, causes the processor to perform the boundary layer scheme prediction method of any of the embodiments described above.

The memory may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory has memory space for program code to perform any of the method steps described above. For example, the memory space for the program code may include individual program code for implementing the various steps in the above method, respectively. The program code can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. The program code may be compressed, for example, in a suitable form. The codes, when executed by a computing processing device, cause the computing processing device to perform the steps in the boundary layer scheme prediction method described above.

The embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a boundary layer scheme prediction method according to any of the embodiments described above.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of boundary layer scheme prediction, comprising:

2. The method of claim 1, wherein the prediction equations comprise a momentum prediction equation and a heat prediction equation; the vertical diffusion coefficient calculation formula comprises a momentum vertical diffusion coefficient calculation formula and a heat vertical diffusion coefficient calculation formula; the step of performing discrete difference on the forecast equation by adopting a vertical diffusion coefficient calculation formula to generate a discrete difference equation set comprises the following steps:

the heat vertical diffusion coefficient calculation formula is as follows:

wherein K is _t1 The vertical diffusion coefficient of the heat of the mixed layer; k (K) _m1 Is the momentum vertical diffusion coefficient of the mixed layer; p (P) _r Is the prandtl number from the near stratum unstable layer to the upper stable layer along with the change of the height; pr (Pr) ₀ Being the planchet number near the top of the formation; zz is the mid-layer height of the half-layer; epsilon is a constant, epsilon=0.1; h is the boundary layer height; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; a is a constant, a=0.4; v (V) _Sc A speed scale is rolled into the cloud top; z _b Cloud base level below top drive hybrid expansion; h is a _b Is the layer cloud top height; k (K) _t3 Is the vertical diffusion coefficient of the heat of the free layer; l is the mixing length; f (f) _t (R _i ) As a function of thermal stability; r is R _i Is the rational Charson number; v is wind speed;

the momentum vertical diffusion coefficient calculation formula is as follows:

K _m2 ＝0.75*K _t2 ；

wherein K is _m1 Is the momentum vertical diffusion coefficient of the mixed layer; a is a constant; w (w) _s Is the convection scale velocity; zz is the mid-layer height of the half-layer; h is the boundary layer height; k (K) _m2 The vertical diffusion coefficient is the layer cloud top momentum; k (K) _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; k (K) _m3 Is the free layer momentum vertical diffusion coefficient; l is the mixing length; f (f) _t (R _i ) As a function of momentum stability; r is R _i Is the rational Charson number; v is wind speed;

3. The method of predicting boundary layer protocols according to claim 2, characterized in that the heat prediction equations include a temperature prediction equation and a moisture prediction equation; the step of performing discrete difference on the heat forecast equation by adopting the heat vertical diffusion coefficient calculation formula to generate a temperature discrete difference equation and a water vapor discrete difference equation comprises the following steps:

the temperature discrete difference equation is:

the water vapor discrete difference equation is as follows:

4. The method of predicting boundary layer protocols according to claim 2, characterized in that said momentum forecast equations include a weft wind forecast equation and a warp wind forecast equation; the step of adopting the momentum vertical diffusion coefficient calculation formula to carry out discrete difference on the momentum forecast equation to generate a weft wind discrete difference equation and a warp wind discrete difference equation comprises the following steps:

the latitudinal wind discrete difference equation is as follows:

wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; u (u) _k-1 ⁿ⁺¹ The weft wind of the k-1 layer at the time of n+1; n is a time value; />The thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; u (u) _k ⁿ⁺¹ The weft wind of the k layers at the time of n+1; u (u) _k+1 ⁿ⁺¹ The weft wind of the layer k+1 at the time of n+1; u (u) _k ⁿ The weft wind of the k layers at the time of n;

the warp wind discrete difference equation is:

wherein Δt is the time interval of two moments; (K) _m ) _k-1 A momentum vertical diffusion coefficient of k-1 layers; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; v _k-1 ⁿ⁺¹ The wind is the directed wind of the k-1 layer at the time of n+1; n is a time value; />The thickness of the k half layers; (K) _m ) _k Is the momentum vertical diffusion coefficient of k layers; v _k ⁿ⁺¹ The wind is the directed wind of the k layer at the time of n+1; v _k+1 ⁿ⁺¹ The wind direction of the k+1 layer at the time of n+1; v _k ⁿ Is the wind of the k layers at time n.

5. The method of claim 2, wherein the step of solving the set of discrete differential equations using a tri-diagonal matrix to determine the prediction data corresponding to the boundary layer scheme comprises:

6. A boundary layer scheme prediction system, comprising:

7. The boundary layer scheme prediction system of claim 6, wherein the prediction equations include a momentum prediction equation and a heat prediction equation; the vertical diffusion coefficient calculation formula comprises a momentum vertical diffusion coefficient calculation formula and a heat vertical diffusion coefficient calculation formula; the discrete differential equation set generating module includes:

The heat vertical diffusion coefficient calculation formula is as follows:

the momentum vertical diffusion coefficient calculation formula is as follows:

K _m2 ＝0.75*K _t2 ；

wherein K is _m1 Is the momentum vertical diffusion coefficient of the mixed layer; a is a constant; w (w) _s Is the convection scale velocity; zz is the mid-layer height of the half-layer; h is the boundary layer height; k (K) _m2 Vertical diffusion coefficient for layer cloud top momentum；K _t2 The vertical diffusion coefficient of the heat of the cloud top of the layer cloud is set; k (K) _m3 Is the free layer momentum vertical diffusion coefficient; l is the mixing length; f (f) _m (R _i ) As a function of momentum stability; r is R _i Is the rational Charson number; v is wind speed;

8. The boundary layer scheme prediction system of claim 7, wherein the heat prediction equation includes a temperature prediction equation and a moisture prediction equation; the temperature discrete difference equation and water vapor discrete difference equation generating module executes the following steps:

the temperature discrete difference equation is:

wherein Δt is the time interval of two moments; (K) _t ) _k-1 The vertical diffusion coefficient of the heat of the k-1 layer; ΔZZ _k The middle k layer, which is half layer thick;a half layer thickness of the k-1 layer; t (T) _k-1 ⁿ⁺¹ The temperature of the k-1 layer at time n+1; n is a time value; / >The thickness of the k half layers; (K) _t ) _k The vertical diffusion coefficient of the heat of the k layers; t (T) _k ⁿ⁺¹ The temperature of the k layer at time n+1; t (T) _k+1 ⁿ⁺¹ The temperature of the k+1 layer at time n+1; />Is a temperature gradient under adiabatic and static equilibrium; gamma ray _T Is a temperature inverse gradient term;

the water vapor discrete difference equation is as follows:

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the boundary layer scheme prediction method of any one of claims 1 to 5.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed, implements the boundary layer scheme prediction method according to any one of claims 1 to 5.