Ground wind field correction method and device based on physical constraint

Abstract

The invention relates to the technical field of ground wind field correction, and discloses a ground wind field correction method and device based on physical constraint, and the method comprises the steps: obtaining high-resolution topographic data, and calculating the influence of the topography on a local wind field based on the high-resolution topographic data; obtaining numerical forecast meteorological data, and calculating a surface layer wind field by using a Monin-Obchhoff similarity theory based on the influence of the numerical forecast meteorological data and the terrain on a local wind field; and performing statistical correction on the surface layer wind field to obtain a corrected surface layer wind field. According to the method, systematic errors are eliminated, and the forecasting accuracy of the surface layer wind field is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of ground wind field correction, and in particular to a ground wind field correction method and device based on physical constraints. Background Art

[0002] The near-surface wind field plays an important role in regulating the earth's energy and water cycle because it directly affects surface evaporation and sensible heat flux. At the same time, the near-surface wind field also affects aerosol distribution, air pollution, visibility, incident solar radiation, and plant growth, and is a hot topic of common concern in many fields. More importantly, the near-surface wind field is an important sustainable renewable energy source, which is developing rapidly around the world due to its safety and cleanliness. Therefore, improving the forecasting skills of the near-surface wind field is crucial to fields such as weather forecasting and renewable energy assessment.

[0003] Reanalysis products have been widely used in the assessment of near-ground wind speed. However, due to the lack of long-term and uniform wind speed observation data on a global scale, although reanalysis products provide long-term series and high temporal and spatial resolution data, they inevitably introduce uncertainties caused by assimilation models and physical process parameterization. Therefore, there is an urgent need to invent a wind field correction method to eliminate the systematic bias of reanalysis data and thus achieve the correction of the forecast wind field. Summary of the invention

[0004] In view of this, the present invention provides a ground wind field correction method and device based on physical constraints to solve the problem of inaccurate forecast of near-surface wind field caused by systematic deviations of reanalysis data.

[0005] In a first aspect, the present invention provides a ground wind field correction method based on physical constraints, the method comprising:

[0006] Obtain high-resolution terrain data and calculate the impact of terrain on the local wind field based on the high-resolution terrain data;

[0007] Obtain numerical forecast meteorological data, and calculate the near-surface wind field using the Monin-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of terrain on the local wind field;

[0008] The near-surface layer wind field is statistically corrected to obtain the corrected near-surface layer wind field.

[0009] The present embodiment provides a ground wind field correction method based on physical constraints. The method calculates the influence of terrain on the local wind field through high-resolution terrain data, and calculates the near-ground wind field using the Morning-Obukhov similarity theory based on numerical forecast meteorological data and the influence of terrain on the local wind field. The near-ground wind field is recalculated, and the near-ground wind field forecast error caused by defects in the physical model is corrected. In addition, the near-ground stability of the model and the uncertainty of the upper atmospheric wind field will also affect the wind speed forecast. Therefore, by statistically correcting the near-ground wind field, the wind speed forecast error caused by the uncertainty of the initial conditions or the defects of the parameterization scheme is eliminated, and the near-ground wind field is further corrected to ensure the accuracy of the forecast of the near-ground wind field.

[0010] In an optional implementation, calculating the influence of terrain on the local wind field based on high-resolution terrain data includes:

[0011] Determine terrain height based on high-resolution terrain data;

[0012] The horizontal wind speed before adjustment is obtained, and based on the horizontal wind speed before adjustment and the terrain height, the influence of the terrain on the local wind field is calculated in combination with the three-dimensional divergence approximation conservation constraint.

[0013] This embodiment provides a ground wind field correction method based on physical constraints. By adjusting the front horizontal wind speed and terrain height and combining the three-dimensional divergence approximation conservation constraint conditions to calculate the impact of terrain on the local wind field, the wind speed error is reduced, and thus the systematic deviation is reduced to a certain extent, laying the foundation for the subsequent recalculation of the near-ground wind field.

[0014] In an optional implementation, based on numerical forecast meteorological data and the influence of terrain on the local wind field, the Morning-Obukhov similarity theory is used to calculate the near-surface wind field, including:

[0015] Screening of relevant element variables of near-surface wind field based on numerical forecast meteorological data;

[0016] The friction velocity and Obukhov length are calculated based on the variables of wind field related elements near the surface layer.

[0017] Determine the lowest wind speed and lowest altitude of the model based on the impact of terrain on the local wind field;

[0018] The wind field near the surface layer is calculated using the Monin-Obukhov similarity theory based on the model's lowest layer wind speed, model's lowest layer height, Obukhov length and friction velocity.

[0019] The present embodiment provides a ground wind field correction method based on physical constraints. By screening the variables of the relevant elements of the near-ground wind field and calculating the friction velocity and the Obukhov length, the near-ground wind field is calculated based on the model's lowest layer wind speed, model's lowest layer height, the Obukhov length and friction velocity using the Morning-Obukhov similarity theory. The near-ground wind field forecast error caused by defects in the physical model is corrected, and the accurate calculation of the near-ground wind field after physical constraints is achieved.

[0020] In an optional implementation, the friction velocity and the Obukhov length are calculated based on the variables of the wind field related elements in the near-surface layer, including:

[0021] Determine wind stress, air density, sensible heat flux, latent heat flux and ground temperature based on variables related to wind field in the near-surface layer;

[0022] Calculate friction velocity based on wind stress and air density;

[0023] Calculate heat flux based on sensible heat flux, latent heat flux, ground temperature and friction velocity;

[0024] The Obukhov length is calculated based on the heat flux, ground temperature, and friction velocity.

[0025] This embodiment provides a ground wind field correction method based on physical constraints, which calculates the friction velocity and Obukhov length through variables of related elements of the near-surface wind field, laying a foundation for the subsequent calculation of the near-surface wind field using the Morning-Obukhov similarity theory.

[0026] In an optional implementation, the model lowest layer wind speed and the model lowest layer height are determined based on the influence of the terrain on the local wind field, and the near-surface layer wind field is calculated using the Monin-Obukhov similarity theory based on the model lowest layer wind speed, the model lowest layer height, the Obukhov length and the friction velocity, including:

[0027] Assuming that the wind field near the ground is 10 meters, the calculation formula of 10 wind field is as follows:

[0028]

[0029] in, represents the wind field near the ground, u bottom Indicates the wind speed at the lowest layer of the model, u * represents the friction velocity, d represents the empirical surface roughness index, z bottom The lowest layer height of the model, L represents the Obukhov length, z 10 =10,Ψ M Represents a similarity function.

[0030] In an optional implementation, statistical correction is performed on the near-surface layer wind field to obtain a corrected near-surface layer wind field, including:

[0031] Obtain the number of wind field grid points involved in statistical constraints, and construct an objective function based on the near-surface wind field, the lowest layer wind speed of the model, and the number of wind field grid points;

[0032] Solve the objective function to obtain the correction coefficient;

[0033] The corrected near-surface wind field is determined based on the correction coefficient, the near-surface wind field and the lowest layer wind speed in the model.

[0034] This embodiment provides a ground wind field correction method based on physical constraints. By constructing and solving the objective function, the correction coefficient is accurately obtained, and then the corrected near-ground wind field is determined based on the correction coefficient, the near-ground wind field and the model's lowest layer wind speed. The errors caused by the uncertainty of the near-ground stability and the upper atmospheric wind field are eliminated through the statistical constraint method, thereby improving the forecast accuracy of the near-ground wind field.

[0035] In a second aspect, the present invention provides a ground wind field correction device based on physical constraints, the device comprising:

[0036] A first calculation module is used to obtain high-resolution terrain data and calculate the influence of terrain on the local wind field based on the high-resolution terrain data;

[0037] The second calculation module is used to obtain numerical forecast meteorological data, and calculate the near-surface wind field using the Morning-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of terrain on the local wind field;

[0038] The statistical correction module is used to perform statistical correction on the near-surface wind field to obtain the corrected near-surface wind field.

[0039] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the ground wind field correction method based on physical constraints of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the ground wind field correction method based on physical constraints of the above-mentioned first aspect or any corresponding embodiment thereof.

[0041] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the ground wind field correction method based on physical constraints of the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 is a flow chart of a ground wind field correction method based on physical constraints according to an embodiment of the present invention;

[0044] Figure 2 is a flow chart of another method for correcting a ground wind field based on physical constraints according to an embodiment of the present invention;

[0045] Figure 3 is a flow chart of another method for correcting a ground wind field based on physical constraints according to an embodiment of the present invention;

[0046] Figure 4 is a structural block diagram of a ground wind field correction device based on physical constraints according to an embodiment of the present invention;

[0047] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0049] Statistical methods are mostly used for wind field correction. However, the horizontal non-uniformity of wind fields in complex terrain areas, the uneven distribution and scarcity of stations make statistical interpolation methods, such as Kriging or principal component analysis, perform poorly in reconstructing global wind speeds. Although deep learning methods can overcome the shortcomings of traditional methods to a certain extent, the corrected wind field is too smooth in spatial distribution and cannot reproduce the extreme wind speeds in observations.

[0050] Specifically, wind field correction methods include statistical downscaling and dynamic downscaling. Among them, the disadvantages of dynamic downscaling are the lack of batch verification and excessive demand for computing resources, and are also subject to the uncertainty of model parameterization schemes. The statistical downscaling correction method is generally as follows:

[0051] Firstly, the 10m wind field product with a resolution of 3km under the model terrain (the 10m wind at the terrain height of the numerical model) is interpolated to a higher resolution and finer terrain using the inverse distance interpolation method. The resolution is improved by downscaling the terrain, and the wind speed at the high-precision terrain height obtained by downscaling is used to correct the 10m wind diagnosed by the model itself. Then, the observation location is confirmed according to the latitude, longitude and altitude information of the actual observation data, and the wind speed of the background field at the corresponding location is taken out. After eliminating the influence of terrain factors and water surface resistance, the background field value is corrected with the wind field value of the observation field, and the error of the grid field at the observation location is calculated.

[0052] That is to say, for each observation station, find a grid point that best represents the observation station and make a difference with the corresponding value of the observation station to reflect the difference ΔX between the grid point data and the observation station data. k (X is the 10-meter wind), as shown in the following formula (1):

[0053]

[0054] Among them, the superscript OBS represents the grid point that best represents the observation station, and ST represents each observation station. Using the known differences, for each grid point in the study range, the nearest n stations within a horizontal distance of 12.5 km are weighted and fused to obtain the difference between the point and the observation station data. The closer the distance to the station, the greater its weight and the higher the confidence of its difference; for a certain grid point (i, j), the distance to the kth automatic weather station is r ijk , then the wind field difference ΔX(i,j) at the (i,j)th grid point is as shown in the following formula (2):

[0055]

[0056] The wind field difference field ΔX(i,j) is compared with the wind field predicted by the model Add them together to get the corrected wind field As shown in the following formula (3):

[0057]

[0058] The statistical downscaling method mentioned above, due to the use of high-temporal and spatial resolution comprehensive meteorological observation data, can capture the fine structural information of the weather system, and has a smaller amount of calculation than the dynamic downscaling method. For example, based on numerical models and automatic weather station observation data, using multi-source data fusion technology and deviation correction technology, it is possible to achieve 10-minute updates and short-term forecasts of the wind field in the competition area at a spatial scale of 100 meters. However, the improvement of the wind field forecasting ability based on the deviation correction technology of observation data fusion and terrain downscaling is mainly reflected in the short-term forecast time. After 6 hours, the wind field forecast is basically the numerical forecast result after terrain downscaling, and there is a large systematic deviation in the simulation of near-surface wind speed in complex terrain areas.

[0059] Considering that the current numerical weather forecast model mainly predicts the near-surface wind field based on the Morning-Obukhov similarity theory, which is calculated by using the average roughness of the grid frame, however, in complex terrain, the average roughness of the grid frame lacks local representativeness, which leads to errors in wind speed forecast under complex underlying surface conditions. If the physical constraint method is not used first to reduce the wind speed error, the near-surface wind speed obtained by the statistical correction method still has a large error.

[0060] The embodiment of the present invention provides a ground wind field correction method based on physical constraints, which extracts variables related to the near-surface wind field by reading numerical forecast data; utilizes high-resolution terrain data and satellite-inverted surface types, combined with typical roughness under specific surface types; and recalculates the near-surface wind field based on the Morning-Obukhov similarity theory, thereby correcting the near-surface wind field forecast error caused by physical model defects to improve the accuracy of wind speed forecasts by numerical weather forecast models; on the other hand, the near-surface stability of the model and the uncertainty of the upper atmospheric wind field will also affect the wind speed forecast. Such errors are mostly caused by the uncertainty of initial conditions or defects in parameterization schemes, which can be eliminated through statistical constraint methods.

[0061] The embodiment of the present invention provides a method for correcting a ground wind field based on physical constraints. It should be noted that the method for correcting a ground wind field based on physical constraints provided by the embodiment of the present invention may be executed by a device for correcting a ground wind field based on physical constraints. The device for correcting a ground wind field based on physical constraints may be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware. The electronic device may be a server or a terminal. The server in the embodiment of the present application may be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application may be a smart phone, a personal computer, a tablet computer, a wearable device, an intelligent robot, or other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0062] According to an embodiment of the present invention, an embodiment of a ground wind field correction method based on physical constraints is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0063] In this embodiment, a ground wind field correction method based on physical constraints is provided, which can be used in the above-mentioned electronic device. Figure 1 is a flow chart of a ground wind field correction method based on physical constraints according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0064] Step S101, obtaining high-resolution terrain data, and calculating the impact of terrain on the local wind field based on the high-resolution terrain data.

[0065] Step S102, obtaining numerical forecast meteorological data, and calculating the near-surface wind field using the Morning-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of the terrain on the local wind field.

[0066] Specifically, numerical forecast meteorological data include temperature, humidity, surface sensible heat flux, latent heat flux and other data.

[0067] Step S103, statistically correcting the near-surface wind field to obtain a corrected near-surface wind field.

[0068] Specifically, the near-surface wind field that is physically constrained is statistically corrected using observational data combined with the least squares method.

[0069] The present embodiment provides a ground wind field correction method based on physical constraints. The method calculates the influence of terrain on the local wind field through high-resolution terrain data, and calculates the near-ground wind field using the Morning-Obukhov similarity theory based on numerical forecast meteorological data and the influence of terrain on the local wind field. The near-ground wind field is recalculated, and the near-ground wind field forecast error caused by defects in the physical model is corrected. In addition, the near-ground stability of the model and the uncertainty of the upper atmospheric wind field will also affect the wind speed forecast. Therefore, by statistically correcting the near-ground wind field, the wind speed forecast error caused by the uncertainty of the initial conditions or the defects of the parameterization scheme is eliminated, and the near-ground wind field is further corrected to ensure the accuracy of the forecast of the near-ground wind field.

[0070] In this embodiment, a ground wind field correction method based on physical constraints is provided, which can be used in the above-mentioned electronic device. Figure 2 is a flow chart of a ground wind field correction method based on physical constraints according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0071] Step S201 : acquiring high-resolution terrain data, and calculating the impact of terrain on the local wind field based on the high-resolution terrain data.

[0072] Specifically, the above step S201 includes:

[0073] Step S2011, determining terrain height based on high-resolution terrain data.

[0074] Step S2012, obtaining the horizontal wind speed before adjustment, and calculating the influence of the terrain on the local wind field based on the horizontal wind speed before adjustment and the terrain height in combination with the three-dimensional divergence approximation conservation constraint.

[0075] Specifically, high-resolution terrain data is used to calculate the impact of terrain on the local wind field. The calculation formula is as follows:

[0076]

[0077] in, represents the horizontal gradient operator, φ represents the terrain height correction operator, h represents the terrain height, U represents the horizontal wind speed before adjustment, and V represents the horizontal wind speed after adjustment.

[0078] Furthermore, the adjusted horizontal wind speed needs to meet the three-dimensional divergence approximation conservation constraint, and the expression of this constraint is as follows:

[0079]

[0080] Among them, u represents the adjusted zonal wind speed, v represents the adjusted radial wind speed, w represents the adjusted vertical wind speed, x, y, and z represent the three-dimensional coordinate axes, and ε represents the maximum acceptable divergence, which is set to 1.e -8 .

[0081] Furthermore, an iterative algorithm can be used to solve the adjusted horizontal wind speed, and the impact of the terrain on the local wind field can be determined based on the horizontal wind speeds before and after the adjustment.

[0082] Step S202, obtain numerical forecast meteorological data, and calculate the near-surface wind field using the Morning-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of terrain on the local wind field. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0083] Step S203, statistically correct the wind field near the surface layer to obtain a corrected wind field near the surface layer. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0084] This embodiment provides a ground wind field correction method based on physical constraints. By adjusting the front horizontal wind speed and terrain height and combining the three-dimensional divergence approximation conservation constraint conditions to calculate the impact of terrain on the local wind field, the wind speed error is reduced, and thus the systematic deviation is reduced to a certain extent, laying the foundation for the subsequent recalculation of the near-ground wind field.

[0085] In this embodiment, a ground wind field correction method based on physical constraints is provided, which can be used in the above-mentioned electronic device. Figure 3 is a flow chart of a ground wind field correction method based on physical constraints according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0086] Step S301, obtain high-resolution terrain data, and calculate the impact of terrain on the local wind field based on the high-resolution terrain data. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0087] Step S302, obtaining numerical forecast meteorological data, and calculating the near-surface wind field using the Morning-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of the terrain on the local wind field.

[0088] Specifically, the above step S302 includes:

[0089] Step S3021, screening the variables of the near-surface layer wind field related elements based on the numerical forecast meteorological data.

[0090] Specifically, the meteorological elements related to the near-surface wind field are screened, and the meteorological elements related to the near-surface wind field are interpolated, aligned, standardized, and missing values ​​are filled to complete the preprocessing of the forecast data; and based on the acquired forecast data, the variables of the near-surface wind field related elements are extracted, such as the 10-meter wind field, wind stress, temperature field, humidity field, surface temperature and humidity, sensible heat flux, latent heat flux and other data predicted by the model, and the above data are stored in binary DAT format (a file format widely used to store data).

[0091] Step S3022, respectively calculating the friction velocity and the Obukhov length based on the variables of the wind field related elements in the near-surface layer.

[0092] In some optional implementations, the above step S3022 includes:

[0093] Step b1, determining wind stress, air density, sensible heat flux, latent heat flux and ground temperature based on variables related to the near-ground wind field.

[0094] Step b2, calculating the friction velocity based on wind stress and air density.

[0095] Specifically, the calculation formula of friction speed is as follows:

[0096]

[0097] Among them, u * represents the friction velocity, τ x and τ y represent the zonal wind stress and the meridional wind stress respectively, and ρ represents the air density.

[0098] Step b3, calculating the heat flux based on the sensible heat flux, latent heat flux, ground temperature and friction velocity.

[0099] Specifically, the heat flux calculation formula is as follows:

[0100]

[0101] Among them, Q 0v represents the heat flux, c p represents the air specific heat coefficient, SH represents the sensible heat flux predicted by the model, T s represents the surface temperature, L v represents the latent heat coefficient, L v 2.5e 6 , LH represents the latent heat flux predicted by the model.

[0102] Step b4, calculating the Obukhov length based on the heat flux, ground temperature and friction velocity.

[0103] Specifically, the calculation formula of Obukhov length L is as follows:

[0104]

[0105] Among them, κ is the kapa constant, which is 0.4, and g is the acceleration due to gravity, which is 9.8.

[0106] Step S3023, determining the model lowest layer wind speed and the model lowest layer height based on the impact of the terrain on the local wind field.

[0107] Step S3024, based on the model lowest layer wind speed, model lowest layer height, Obukhov length and friction velocity, the Morning-Obukhov similarity theory is used to calculate the near-surface layer wind field.

[0108] Specifically, assuming that the wind field near the ground is a 10-meter wind field, the calculation formula for the 10-meter wind field is as follows:

[0109]

[0110] in, represents the wind field near the ground, u bottom Indicates the wind speed at the lowest layer of the model, u* represents the friction velocity, d represents the empirical surface roughness index, z bottom The lowest layer height of the model, L represents the Obukhov length, z 10 =10,Ψ M Represents a similarity function.

[0111] Among them, the similarity function Ψ M The definition is as follows:

[0112]

[0113] Here, ξ represents the vorticity.

[0114] Step S303, statistically correcting the near-surface wind field to obtain a corrected near-surface wind field.

[0115] Specifically, the above step S303 includes:

[0116] Step S3031, obtaining the number of wind field grid points participating in the statistical constraint, and constructing an objective function based on the near-ground layer wind field, the lowest layer wind speed of the model, and the number of wind field grid points.

[0117] Specifically, the expression of the objective function f(a,b) is as follows:

[0118]

[0119] Where N represents the number of wind field grid points involved in statistical constraints, a and b represent correction coefficients, Indicates ten-meter wind observation.

[0120] Step S3032, solve the objective function to obtain the correction coefficient.

[0121] Specifically, the objective function is minimized and the correction coefficient is obtained by solving.

[0122] Step S3033, determining the corrected near-surface wind field based on the correction coefficient, the near-surface wind field and the model lowest layer wind speed.

[0123] Specifically, the revised near-surface wind field The calculation formula is as follows:

[0124]

[0125] The present embodiment provides a ground wind field correction method based on physical constraints. By screening the variables of the relevant elements of the near-ground wind field and calculating the friction velocity and the Obukhov length, the near-ground wind field is calculated based on the model's lowest layer wind speed, the model's lowest layer height, the Obukhov length and the friction velocity, and the Morning-Obukhov similarity theory is used to calculate the near-ground wind field, thereby correcting the near-ground wind field forecast error caused by the defects of the physical model and achieving accurate calculation of the near-ground wind field after physical constraints. Secondly, by constructing and solving the objective function, the correction coefficient is accurately obtained, and then the corrected near-ground wind field is determined based on the correction coefficient, the near-ground wind field and the model's lowest layer wind speed. The statistical constraint method is used to eliminate the errors caused by the uncertainty of the near-ground stability and the upper atmospheric wind field, thereby improving the forecast accuracy of the near-ground wind field.

[0126] In this embodiment, a ground wind field correction device based on physical constraints is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0127] This embodiment provides a ground wind field correction device based on physical constraints, such as Figure 4 As shown, including:

[0128] The first calculation module 401 is used to obtain high-resolution terrain data and calculate the influence of terrain on the local wind field based on the high-resolution terrain data;

[0129] The second calculation module 402 is used to obtain numerical forecast meteorological data, and calculate the near-surface wind field using the Monin-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of the terrain on the local wind field;

[0130] The statistical correction module 403 is used to perform statistical correction on the near-surface layer wind field to obtain a corrected near-surface layer wind field.

[0131] In some optional implementations, the first calculation module 401 includes:

[0132] A first determining unit, configured to determine a terrain height based on high-resolution terrain data;

[0133] The first calculation unit is used to obtain the horizontal wind speed before adjustment, and calculate the influence of the terrain on the local wind field based on the horizontal wind speed before adjustment and the terrain height in combination with the three-dimensional divergence approximation conservation constraint condition.

[0134] In some optional implementations, the second calculation module 402 includes:

[0135] A screening unit, used for screening variables of wind field related elements in the near-surface layer based on numerical forecast meteorological data;

[0136] The second calculation unit is used to calculate the friction velocity and the Obukhov length respectively based on the variables of the wind field related elements in the near-surface layer;

[0137] A second determination unit is used to determine the model lowest layer wind speed and the model lowest layer height based on the influence of the terrain on the local wind field;

[0138] The third calculation unit is used to calculate the near-surface layer wind field by using the Morning-Obukhov similarity theory based on the model lowest layer wind speed, the model lowest layer height, the Obukhov length and the friction speed.

[0139] In some optional implementations, the second computing unit includes:

[0140] Determine a subunit for determining wind stress, air density, sensible heat flux, latent heat flux and ground temperature based on variables of wind field-related elements in the near-surface layer;

[0141] A first calculation subunit, for calculating friction velocity based on wind stress and air density;

[0142] a second calculation subunit, for calculating heat flux based on sensible heat flux, latent heat flux, ground temperature and friction velocity;

[0143] The third calculation subunit is used to calculate the Obukhov length based on the heat flux, the ground temperature and the friction velocity.

[0144] In some optional implementations, the third calculation unit is specifically used to assume that the near-ground layer wind field is a 10-meter wind field, and the calculation formula of the 10-meter wind field is as follows:

[0145]

[0146] in, represents the wind field near the ground, u bottom Indicates the wind speed at the lowest layer of the model, u * represents the friction velocity, d represents the empirical surface roughness index, z bottom The lowest layer height of the model, L represents the Obukhov length, z 10 =10,Ψ M Represents a similarity function.

[0147] In some optional implementations, the statistics correction module 403 includes:

[0148] A construction unit is used to obtain the number of wind field grid points participating in the statistical constraint, and to construct an objective function based on the near-surface wind field, the lowest layer wind speed of the model, and the number of wind field grid points;

[0149] A solving unit, used for solving the objective function to obtain a correction coefficient;

[0150] The correction unit is used to determine the corrected near-surface wind field based on the correction coefficient, the near-surface wind field and the lowest layer wind speed of the model.

[0151] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0152] In this embodiment, a ground wind field correction device based on physical constraints is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0153] The embodiment of the present invention also provides a computer device having the above Figure 4 A ground wind field correction device based on physical constraints is shown.

[0154] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0155] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0156] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0157] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0158] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0159] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0160] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0161] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0162] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0163] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A ground wind field correction method based on physical constraints, characterized in that: The method comprises: Acquire high-resolution terrain data, and calculate the influence of terrain on the local wind field based on the high-resolution terrain data; Acquire numerical forecast meteorological data, and calculate the near-surface wind field using the Morning-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of the terrain on the local wind field; The near-surface layer wind field is statistically corrected to obtain a corrected near-surface layer wind field.

2. The method according to claim 1, characterized in that The calculating the influence of terrain on the local wind field based on the high-resolution terrain data comprises: determining terrain height based on the high-resolution terrain data; The horizontal wind speed before adjustment is obtained, and based on the horizontal wind speed before adjustment and the terrain height, the influence of the terrain on the local wind field is calculated in combination with the three-dimensional divergence approximation conservation constraint.

3. The method according to claim 1, characterized in that The method of calculating the near-surface wind field based on the numerical forecast meteorological data and the influence of the terrain on the local wind field by using the Morning-Obukhov similarity theory includes: Screening relevant element variables of the near-surface wind field based on the numerical forecast meteorological data; Based on the variables of the wind field related elements in the near-surface layer, the friction velocity and the Obukhov length are calculated respectively; Determine the model lowest layer wind speed and the model lowest layer height based on the influence of the terrain on the local wind field; Based on the model lowest layer wind speed, the model lowest layer height, the Obukhov length and the friction speed, the near-surface layer wind field is calculated using the Morning-Obukhov similarity theory.

4. The method according to claim 3, characterized in that The calculating of the friction velocity and the Obukhov length based on the variables of the wind field related elements in the near-surface layer respectively comprises: Determine wind stress, air density, sensible heat flux, latent heat flux and ground temperature based on the near-surface layer wind field related element variables; calculating the friction velocity based on the wind stress and the air density; calculating a heat flux based on the sensible heat flux, the latent heat flux, the ground temperature, and the friction velocity; The Obukhov length is calculated based on the heat flux, the ground temperature, and the friction velocity.

5. The method according to claim 3, characterized in that: The method of determining the model lowest layer wind speed and the model lowest layer height based on the influence of the terrain on the local wind field, and calculating the near-surface layer wind field using the Monin-Obukhov similarity theory based on the model lowest layer wind speed, the model lowest layer height, the Obukhov length and the friction velocity, includes: Assuming that the near-ground wind field is a 10-meter wind field, the calculation formula of the 10-meter wind field is as follows: in, represents the wind field near the ground, u bottom Indicates the wind speed at the lowest layer of the model, u * represents the friction velocity, d represents the empirical surface roughness index, z bottom The lowest layer height of the model, L represents the Obukhov length, z 10 =10,Ψ M Represents a similarity function.

6. The method according to claim 3, characterized in that The statistical correction of the near-surface layer wind field to obtain a corrected near-surface layer wind field includes: Obtaining the number of wind field grid points participating in the statistical constraint, and constructing an objective function based on the near-ground layer wind field, the lowest layer wind speed of the model, and the number of wind field grid points; Solving the objective function to obtain a correction coefficient; The corrected near-surface layer wind field is determined based on the correction coefficient, the near-surface layer wind field and the model lowest layer wind speed.

7. A ground wind field correction device based on physical constraints, characterized in that: The device comprises: A first calculation module, used to obtain high-resolution terrain data, and calculate the influence of terrain on the local wind field based on the high-resolution terrain data; A second calculation module is used to obtain numerical forecast meteorological data, and calculate the near-surface wind field using the Morning-Obukhov similarity theory based on the numerical forecast meteorological data and the influence of the terrain on the local wind field; The statistical correction module is used to perform statistical correction on the near-surface layer wind field to obtain a corrected near-surface layer wind field.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the ground wind field correction method based on physical constraints as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the ground wind field correction method based on physical constraints as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the ground wind field correction method based on physical constraints as described in any one of claims 1 to 6.

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