A Correction Method for Linear Convection Forecast Based on Horizontal Subgrid Flux

Abstract

The present invention discloses a method for correcting linear convection forecasts based on horizontal subgrid fluxes, comprising: obtaining forecast data from a numerical weather forecast model; determining, based on the forecast data, the region where linear convection predicted by the numerical model will occur; calculating the horizontal subgrid turbulent fluxes in the region where the linear convection occurs according to a first calculation formula and a second calculation formula; multiplying the horizontal subgrid turbulent fluxes obtained by the first calculation formula and the second calculation formula to obtain a product; and correcting the intensity and speed of the linear convection based on the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer. The present invention's method for correcting linear convection forecasts based on horizontal subgrid fluxes directly adjusts the intensity and speed of linear convection qualitatively and quantitatively by combining physical evidence, significantly improving the accuracy, real-time nature, and adaptability of convection forecasts.

Description

Technical Field

[0001] The invention belongs to the field of atmospheric science and data processing technology, and in particular relates to a linear convection forecast correction method based on horizontal sub-grid flux. Background Art

[0002] Linear convection (such as squall lines) is a widespread, intense, and long-lasting convective system often accompanied by heavy rainfall, high winds, hail, thunderstorms, and even tornadoes. Refined and accurate forecasting of linear convection is a key component of meteorological forecasting and early warning technology.

[0003] Currently, linear convection forecasts are typically made using a modified algorithm based on a numerical weather prediction model. First, the numerical model uses observational data and a physical parameterization scheme to numerically solve for atmospheric motion and changes, producing a weather forecast that includes linear convection. A convection correction algorithm is then used to adjust the model's output in a specific manner, yielding the final convection forecast.

[0004] Based on the current convective forecast process, the correction methods of convective forecasts are mainly divided into two categories:

[0005] 1. Physics-Based Corrections: This approach improves the accuracy of convection forecasts by revising the parameterization schemes of physical processes in the numerical model (e.g., convection, radiation, and the boundary layer). However, this approach requires re-calculating the numerical forecast, which is computationally expensive, and its results are unstable under varying weather conditions, making it difficult to guarantee specialized optimization for linear convection.

[0006] 2. Statistical Correction: This method adjusts convective forecasts through statistical analysis of historical forecast data and observational data. For example, the "precipitation voiding" method assumes that areas of weak precipitation in convective precipitation forecasts may be false, and thus corrects them to no precipitation. However, this method primarily focuses on adjusting the precipitation coverage and intensity during weak convective weather, lacking a deep understanding of the physical processes of strong convective weather, such as linear convection, and is unable to effectively adjust the intensity and speed of linear convection.

[0007] Furthermore, current convection correction methods do not specifically address typical weather phenomena such as linear convection, nor do they provide adjustments to the intensity and speed of linear convection, making them inadequate for severe convection forecasts. In summary, existing convection forecasting and correction methods for linear convection still have significant shortcomings in adjusting for intensity and speed, making them inadequate for actual needs. Summary of the Invention

[0008] The present invention aims to solve one of the technical problems existing in the related art at least to a certain extent.

[0009] The present invention aims to provide a linear convection forecast correction method based on horizontal sub-grid flux. Combined with physical basis, it does not need to recalculate numerical forecasts and can directly make qualitative adjustments to the intensity and speed of linear convection, thereby providing accurate auxiliary information for linear convection forecast.

[0010] In order to achieve the above-mentioned object, the present invention provides, on one hand, a method for correcting linear convection forecasts based on horizontal subgrid fluxes, comprising:

[0011] Obtain forecast data from numerical weather prediction models;

[0012] Determining, based on the forecast data, the area where the linear convection predicted by the numerical model will occur;

[0013] In the linear convection region, the horizontal subgrid turbulent fluxes are calculated according to the first and second calculation formulas respectively; the first calculation formula is:

[0014] ;

[0015] in, express Subgrid turbulent fluxes in the direction, Take 1 to represent the east-west direction, and take 2 to represent the north-south direction; Indicates potential temperature; for Direction coordinate distance; is the partial differential operator; is the turbulent diffusion coefficient, which is calculated as follows:

[0016] ;

[0017] Where, The fixed parameter is set to 0.25; is the horizontal grid resolution; is the deformation rate tensor, and the calculation formula is , For Equivalent counting signs, and Choose from 1 and 2;

[0018] The second calculation formula is:

[0019] ;

[0020] In the formula, express Wind speed in direction; and They are When 1 and 2 are taken ; is the scale adaptation coefficient, given by get;

[0021] The horizontal sub-grid turbulent fluxes obtained by the first and second calculation formulas are multiplied to obtain the product; and the intensity and speed of the linear convection are corrected based on the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer.

[0022] A further preferred technical solution of the present invention is that the forecast data obtained from the numerical weather forecast model is original weather forecast data or a secondary weather forecast product produced based on the original weather forecast data.

[0023] Preferably, the forecast data at least includes horizontal wind field data, vertical velocity data, potential temperature or temperature field data, and radar reflectivity factor distribution data.

[0024] Preferably, the area where the linear convection occurs predicted by the numerical model is determined based on the distribution of vertical velocity data and radar reflectivity factors;

[0025] The horizontal sub-grid turbulent flux is calculated based on the horizontal wind field data and the potential temperature or temperature field data.

[0026] Preferably, the sensitive layer is at an altitude of 2000 meters from the ground.

[0027] Preferably, the intensity of the linear convection is corrected according to the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer, specifically:

[0028] When the negative value area is located inside the strong echo area, the intensity forecast of the linear convection is enhanced; otherwise, the intensity forecast of the linear convection is weakened.

[0029] As a preference, the velocity of the linear convection is corrected according to the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer, specifically:

[0030] When the negative value area is located in front of the center of the strong echo area, that is, on the side of the moving direction, the moving speed forecast of the linear convection is enhanced; otherwise, the moving speed forecast of the linear convection is weakened.

[0031] Another aspect of the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, which enable a computer to execute the above-mentioned linear convection forecast correction method based on horizontal sub-grid flux.

[0032] Another aspect of the present invention provides an electronic device comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, and the processor calls logic instructions in the memory to execute the above-mentioned linear convection forecast correction method based on horizontal sub-grid flux.

[0033] On the other hand, the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the above-mentioned linear convection forecast correction method based on horizontal sub-grid flux.

[0034] Beneficial effects: The linear convection forecast correction method based on the horizontal sub-grid flux of the present invention directly adjusts the intensity and speed of linear convection by combining physical basis, which significantly improves the accuracy, real-time and adaptability of convection forecast.

[0035] Compared with existing correction methods, the present invention combines physical evidence and introduces the core idea of ​​horizontal sub-grid flux to conduct an in-depth analysis of the physical process of convection, ensuring that the correction method has a solid physical foundation. The present invention does not need to recalculate numerical forecasts, but directly corrects the forecast results of existing numerical models, avoiding the high computational cost of re-numerical forecasts and significantly improving computational efficiency. The present invention conducts targeted optimization of the forecast of linear convection, and performs excellently in the forecast effect of linear convection. It can provide qualitative adjustment suggestions for its intensity and speed forecast, thereby improving the accuracy of the forecast. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the linear convection forecast correction method based on horizontal sub-grid flux of the present invention;

[0037] Figure 2 The actual observation map of the linear convection radar reflectivity factor and the numerical model prediction results in Example 1; Figure 2 (a) in the middle is the actual observation of the linear convection radar reflectivity factor at 08:00 Beijing time. Figure 2 Middle (b) is the actual observation of linear convection radar reflectivity factor at 11:00 Beijing time. Figure 2 Middle (c) is the numerical model forecast result at 08:00 Beijing time. Figure 2 Middle (d) is the numerical model forecast result at 11:00 Beijing time;

[0038] Figure 3 The distribution diagrams of the horizontal sub-grid turbulent fluxes calculated by the first and second calculation formulas in Example 1, and the horizontal distribution diagram of the product of the two horizontal sub-grid turbulent fluxes; Figure 3(a) is the horizontal distribution diagram of the horizontal sub-grid turbulent flux calculated according to the first calculation formula. Figure 3 (b) is the horizontal distribution diagram of the horizontal sub-grid turbulent flux calculated according to the second calculation formula. Figure 3 (c) is the horizontal distribution diagram of the product of the turbulent fluxes of two horizontal sub-grids calculated by the two calculation formulas. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0040] The following combination Figure 1-Figure 3 The present invention describes a linear convection forecast correction method based on horizontal sub-grid flux.

[0041] Example 1: This example provides a linear convection forecast correction method based on horizontal sub-grid flux.

[0042] Taking the linear convection that occurred in a certain area on July 10, 2023 as an example, the correction algorithm is demonstrated. Figure 2 As shown, at 08:00 Beijing time on July 10, 2023, the strong echo area showed that the convection development was concentrated in a linear shape, located at Figure 2 The area shown in (a) continued to move eastward, and the convection intensity increased and the range expanded. At 11:00 Beijing time, the main body of linear convection moved into Figure 2 The area shown in (b).

[0043] The Precision Weather Analysis and Forecasting System (PWAFS) has the ability to forecast the basic conditions of this convective process, such as Figure 2 As shown in (c), in the numerical forecast results reported from 20:00 Beijing time on July 9, 2023, the linear convection was basically predicted at 08:00 on the 10th. However, in the subsequent forecast results, the convection intensity was not maintained and the convection speed was too slow, as shown in the following figure. Figure 2 As shown in (d), the forecast of this linear convection process has a large error.

[0044] Therefore, a linear convection forecast correction method based on horizontal subgrid flux is used for correction. The specific steps are as follows: Figure 1 Shown, including:

[0045] S1. Use data from the PWAFS forecast system. The data used is at 08:00 Beijing time on July 10, 2023. The strong reflectivity echo areas are marked with gray shadows in the base map. The forecast data must at least include horizontal wind field data, vertical velocity data, potential temperature or temperature field data, and distribution data of radar reflectivity factors.

[0046] S2, according to the radar reflectivity echo simulated by the model, select Figure 3 The area shown is the range where linear convection occurs.

[0047] S3. In the linear convection region, calculate the horizontal subgrid turbulent fluxes according to the first and second calculation formulas respectively; the first calculation formula is:

[0048] ;

[0049] in, express Subgrid turbulent fluxes in the direction, Take 1 to represent the east-west direction, and take 2 to represent the north-south direction; Indicates potential temperature; for Direction coordinate distance; is the partial differential operator; is the turbulent diffusion coefficient, which is calculated as follows:

[0050] ;

[0051] Where, The fixed parameter is set to 0.25; is the horizontal grid resolution; is the deformation rate tensor, and the calculation formula is , For Equivalent counting signs, and Choose from 1 and 2;

[0052] The second calculation formula is:

[0053] ;

[0054] In the formula, express Wind speed in direction; and They are When 1 and 2 are taken ; is the scale adaptation coefficient, given by get;

[0055] Since the linear convection in this forecast is moving due east, only the east-west direction is used, i.e. . The distribution at an altitude of 2 km is as follows Figure 3 (a) and Figure 3 As shown in (b).

[0056] S4. Sub-grid turbulent flux obtained by the first and second calculation formulas Multiply them together to get the product, the distribution of which at an altitude of 2 kilometers is as follows Figure 3 As shown in (c).

[0057] S5. The above product and the radar echo obtained by numerical prediction are displayed simultaneously to compare their relative positions. The negative value area of ​​the product is located in the strong echo area. The correction suggestion for the intensity is positive, that is, the linear convection intensity predicted by the numerical model should be appropriately enhanced. In terms of quantitative relationship, the sensitive layer of this example is located in the strong echo area and the product value is less than -0.2 K. 2 m 4 s -4 The area is 540 km 2 , the recommended correction for the intensity forecast of linear convection (in terms of simulated radar reflectivity) is +5.0 dBZ.

[0058] Comparing the positional relationship between the negative product area and the center of the strong echo, it is found that the negative area is located to the east of the echo. The correction of the velocity is recommended to be positive, that is, the velocity of the linear convection predicted by the numerical model should be appropriately enhanced. In terms of quantitative relationship, the distance between the negative product area and the center of the strong echo in this example is about 34 km, and the correction of the velocity forecast of the linear convection is recommended to be +2.0 ms. -1 .

[0059] Based on the above results, this correction algorithm concludes that the intensity forecast of linear convection should be appropriately strengthened and the migration speed of linear convection should be appropriately increased. Based on this conclusion, the forecast of linear convection is revised to improve the forecast accuracy.

[0060] Embodiment 2: This embodiment provides a non-transitory computer-readable storage medium having computer instructions stored thereon. The computer instructions cause a computer to execute a method for correcting linear convection forecasts based on horizontal subgrid fluxes. The method comprises the following steps:

[0061] Obtain forecast data from numerical weather prediction models;

[0062] Determining, based on the forecast data, the area where the linear convection predicted by the numerical model will occur;

[0063] In the linear convection region, the horizontal sub-grid turbulent fluxes are calculated according to the first and second calculation formulas in Example 1;

[0064] The horizontal sub-grid turbulent fluxes obtained by the first and second calculation formulas are multiplied to obtain the product; and the intensity and speed of the linear convection are corrected based on the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer.

[0065] Embodiment 3: This embodiment provides an electronic device, which may include: a processor, a communications interface, a memory, and a communications bus, wherein the processor, the communications interface, and the memory communicate with each other via the communications bus. The processor may invoke logic instructions in the memory to execute a linear convection forecast correction method based on horizontal subgrid flux, the method comprising the following steps:

[0066] Obtain forecast data from numerical weather prediction models;

[0067] Determining, based on the forecast data, the area where the linear convection predicted by the numerical model will occur;

[0068] In the linear convection region, the horizontal sub-grid turbulent fluxes are calculated according to the first and second calculation formulas in Example 1;

[0069] The horizontal sub-grid turbulent fluxes obtained by the first and second calculation formulas are multiplied to obtain the product; and the intensity and speed of the linear convection are corrected based on the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer.

[0070] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0071] Embodiment 4: This embodiment provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform a linear convection forecast correction method based on horizontal sub-grid flux. The method includes the following steps:

[0072] Obtain forecast data from numerical weather prediction models;

[0073] Determining, based on the forecast data, the area where the linear convection predicted by the numerical model will occur;

[0074] In the linear convection region, the horizontal sub-grid turbulent fluxes are calculated according to the first and second calculation formulas in Example 1;

[0075] The horizontal sub-grid turbulent fluxes obtained by the first and second calculation formulas are multiplied to obtain the product; and the intensity and speed of the linear convection are corrected based on the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0077] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A linear convection forecast correction method based on horizontal sub-grid flux, characterized by: include: Obtain forecast data from numerical weather prediction models; Determining, based on the forecast data, the area where the linear convection predicted by the numerical model will occur; In the linear convection region, the horizontal subgrid turbulent fluxes are calculated according to the first and second calculation formulas respectively; the first calculation formula is: in, represents the subgrid turbulent flux in the i direction, i is 1 for the east-west direction and 2 for the north-south direction; θ represents the potential temperature; x i is the coordinate distance in the i direction; is the partial differential operator; K H is the turbulent diffusion coefficient, which is calculated as follows: Where C is a fixed parameter set to 0.25; Δ is the horizontal grid resolution; is the deformation rate tensor, and the calculation formula is j is a counting symbol equivalent to i, and both i and j are selected from 1 and 2; The second calculation formula is: In the formula, u i Indicates the wind speed in direction i; x and y are x when i is 1 and 2 respectively. i ; C s is the scale adaptation coefficient, C s (Δ)=0.27Δ 0.41 get; The horizontal sub-grid turbulent fluxes obtained by the first and second calculation formulas are multiplied to obtain the product. Based on the positional relationship between the negative value area of ​​the product and the strong echo area in the sensitive layer, the intensity and speed of the linear convection are corrected as follows: When the negative value area is located inside the strong echo area, the intensity forecast of linear convection is enhanced; otherwise, the intensity forecast of linear convection is weakened. When the negative value area is located in front of the center of the strong echo area, that is, on the side of the moving direction, the moving speed forecast of the linear convection is enhanced; otherwise, the moving speed forecast of the linear convection is weakened.

2. The linear convection forecast correction method based on horizontal sub-grid flux according to claim 1 is characterized in that: The forecast data obtained from the numerical weather forecast model are original weather forecast data or secondary weather forecast products produced based on the original weather forecast data.

3. The linear convection forecast correction method based on horizontal sub-grid flux according to claim 2 is characterized in that: The forecast data at least includes horizontal wind field data, vertical velocity data, potential temperature or temperature field data, and radar reflectivity factor distribution data.

4. The linear convection forecast correction method based on horizontal sub-grid flux according to claim 3 is characterized in that: Based on the distribution of vertical velocity data and radar reflectivity factors, the area where linear convection occurs predicted by the numerical model is determined; The horizontal sub-grid turbulent flux is calculated based on the horizontal wind field data and the potential temperature or temperature field data.

5. The linear convection forecast correction method based on horizontal sub-grid flux according to claim 1 is characterized in that: The sensitive layer is the layer at an altitude of 2000 meters from the ground.

6. A non-transitory computer-readable storage medium, characterized in that Computer instructions are stored thereon, and the computer instructions enable the computer to execute the linear convection forecast correction method based on horizontal sub-grid flux described in any one of claims 1-5.

7. An electronic device, characterized in that: include: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, and the processor calls logic instructions in the memory to execute the linear convection forecast correction method based on horizontal subgrid flux as described in any one of claims 1 to 5.

8. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the linear convection forecast correction method based on horizontal sub-grid flux according to any one of claims 1 to 5.

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