A method and system for initial field inversion of non-precipitation echo

Abstract

The present invention discloses an initial field inversion method and system for non-precipitation echoes, in which the average of the water vapor content analysis value within the influence radius is used for hydrometeor inversion at each altitude layer, thereby suppressing the false convection at the current moment. The method of the present invention uses the average of the wind field analysis value within the influence radius for wind field inversion at each altitude layer, and uses the temperature determination method based on the observed combined reflectivity factor to perform hydrometeor inversion, thereby weakening the heat and momentum exchange between the future false convection area and other surrounding areas.

Description

Technical Field

[0001] The invention belongs to the technical field of radar meteorological data application, and in particular relates to an initial field inversion method and system for non-precipitation echoes. Background Art

[0002] False forecasts often occur in numerical forecasts, that is, two rain belts may appear for a fast-moving system, one of which is real and the other is false. This problem may be exacerbated in circulation assimilation. At present, false convection is mainly suppressed through large-scale analytical constraints. The main technology is to reduce water vapor and hydrometeors. This scheme can only roughly estimate the water vapor content of non-precipitation echoes and contains fixed constants that do not change with time and space. Repeated experiments are required to obtain the optimal value. However, at the initial moment, only the water vapor is slightly adjusted, but the dynamic field and thermal field are not adjusted enough. New convection will still be triggered in the false area. The water vapor in the surrounding area will be quickly replenished under the action of the dynamic field, and the convection will quickly redevelop at a later moment. To this end, the researchers adopted another scheme to set the average maximum vertical velocity in the rain-free echo area close to 0 to suppress false convection. However, this scheme can only be used to improve the position deviation of convection, but not the intensity deviation of convection. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the present invention proposes an initial field inversion method and system for non-precipitation echoes to completely suppress false convection and weaken the heat and momentum exchange between the false convection area and other surrounding areas.

[0004] The technical solution of the present invention is as follows:

[0005] A method for inverting an initial field of a non-precipitation echo, wherein the initial field includes three types of data: water vapor content, hydrometeor and wind field, including:

[0006] The radar reflectivity data is subjected to sparse processing related to the spatial grid coordinate system of the numerical weather forecast model to obtain an observation combination reflectivity factor, and then the precipitation echo area and the non-precipitation echo area are divided based on the observation combination reflectivity factor;

[0007] Equivalently accumulate the background variable information in the numerical weather forecast model to obtain the model combination reflectivity factor, and then divide the heavy precipitation area and the weak precipitation area based on the combination reflectivity factor;

[0008] The areas that belong to both the non-precipitation echo area and the strong precipitation echo area are regarded as the inversion area of ​​the non-precipitation echo, and the other areas are regarded as the non-inversion area;

[0009] Invert water vapor content, hydrometeor content and wind field in the inversion area;

[0010] The boundary area between the inversion area and the non-inversion area is smoothed in terms of water vapor content, hydrometeor and wind field.

[0011] Furthermore, the specific method of performing sparse processing on the radar reflectivity data related to the numerical weather forecast model space grid coordinate system, obtaining the observation combination reflectivity factor, and then dividing the precipitation echo area and the non-precipitation echo area based on the observation combination reflectivity factor includes:

[0012] The radar reflectivity factor with radial distance, elevation angle and azimuth angle in the radar polar coordinate system is thinned to the numerical weather forecast model space grid coordinate system to obtain the observed reflectivity factor of each grid point, and the maximum value of the observed reflectivity factor of each grid point in the vertical direction is calculated to obtain the observed combined reflectivity factor; if the observed combined reflectivity factor exceeds a first threshold, it is determined to be an observed precipitation echo area, and the rest are observed non-precipitation echo areas.

[0013] Furthermore, the specific method of equivalently accumulating the background variable information in the numerical weather forecast model to obtain the model combination reflectivity factor, and then dividing the model heavy precipitation area and the model weak precipitation area based on the combination reflectivity factor includes:

[0014] Based on the background variable information in the numerical weather forecast model, the equivalent reflectivity of each hydrometeor is calculated, the total equivalent reflectivity is accumulated and converted into a model reflectivity factor, and the maximum value of the model reflectivity factor in the vertical direction of each grid point in the numerical weather forecast model space grid coordinate system is calculated to obtain the model combination reflectivity factor; if the model combination reflectivity factor exceeds the second threshold, it is determined to be a heavy precipitation area, and the rest are determined to be weak precipitation areas.

[0015] Furthermore, the specific method of water vapor content inversion includes:

[0016] Determine the horizontal influence radius of each grid point at each altitude layer in the inversion area in the spatial grid coordinate system of the numerical weather forecast model, and further determine the influence area of ​​each grid point at each altitude layer;

[0017] For the water vapor content analysis value corresponding to each grid point at each altitude layer in the numerical weather forecast model (which can be obtained from the background variable information), the product of the mean of the water vapor content analysis values ​​of all grid points in the influence area of ​​each grid point and the third threshold is used as the water vapor content inversion value of the grid point; thereby, the water vapor content inversion values ​​of different altitude layers in the inversion area are obtained.

[0018] Furthermore, the specific method of wind field inversion includes:

[0019] Determine the horizontal influence radius of each grid point at each altitude layer in the inversion area in the spatial grid coordinate system of the numerical weather forecast model, and further determine the influence area of ​​each grid point at each altitude layer;

[0020] For the wind field analysis values ​​corresponding to each grid point at each altitude layer in the numerical weather forecast model, the wind field analysis values ​​include the model u component and the model v component. The average of the wind field analysis values ​​of all grid points in the influence area of ​​each grid point is taken as the wind field inversion value of the grid point; thereby, the wind field inversion values ​​of different altitude layers in the inversion area are obtained.

[0021] Furthermore, the specific method of hydrometeor inversion includes:

[0022] The judgment of whether there is hydrometeor in the inversion area is performed based on the comparison between the observed combined reflectivity factor and the fourth threshold. If the observed combined reflectivity factor is greater than the fourth threshold, the temperature judgment method is used to perform hydrometeor inversion. If there is no hydrometeor in other areas, hydrometeor inversion is not performed.

[0023] Furthermore, the specific method of smoothing the water vapor content, hydrometeors and wind field in the boundary area between the inversion area and the non-inversion area includes:

[0024] Obtain the grid points of each altitude layer in the boundary area between the inversion area and the non-inversion area in the numerical weather forecast model space grid coordinate system, and use the following formulas to smooth the water vapor content, hydrometeor and wind field of each grid point layer by layer:

[0025]

[0026] In the formula, is the water vapor content, hydrometeor or wind field inversion value of the grid point (i, j) after smoothing; X i+m,j+n It is the water vapor content, hydrometeor or wind field inversion value of the grid point (i+m, j+n).

[0027] An initial field inversion system for non-precipitation echoes, wherein the initial field includes three types of data: water vapor content, hydrometeor and wind field, and includes an observation division module, a combination division module, a determination module and an inversion module;

[0028] The observation division module is used to perform sparse processing on the radar reflectivity data related to the numerical prediction mode space grid coordinate system, obtain the observation combination reflectivity factor, and then divide the precipitation echo area and the non-precipitation echo area based on the observation combination reflectivity factor;

[0029] The combination division module is used to perform equivalent accumulation of background variable information in the numerical weather forecast model, obtain the model combination reflectivity factor, and then divide the model into a strong precipitation area and a weak precipitation area based on the combination reflectivity factor;

[0030] The determination module is used to use the area belonging to both the non-precipitation echo area and the strong precipitation echo area as the inversion area of ​​the non-precipitation echo, and other areas as the non-inversion area;

[0031] The inversion module is used to perform water vapor content inversion, hydrometeor inversion, and wind field inversion on the inversion area.

[0032] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0033] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention proposes an initial field inversion method and system for non-precipitation echoes, in which the average of water vapor content analysis values ​​within an influence radius is used for hydrometeor inversion at each altitude layer, thereby suppressing false convection at the current moment.

[0036] The method of the present invention uses the mean of the wind field analysis values ​​within the influence radius to perform wind field inversion at each altitude layer, and simultaneously uses the temperature determination method based on the observed combined reflectivity factor to perform hydrometeor inversion, thereby weakening the heat and momentum exchange between the future pseudo-convection area and other surrounding areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of the initial field inversion method of non-precipitation echo in the embodiment;

[0038] Figure 2 It is a principle block diagram of the initial field inversion method of non-precipitation echo in the embodiment;

[0039] FIG3( a ) is an observation echo graph in an application embodiment;

[0040] FIG3( b ) is an echo diagram of the initial field before false echo suppression in the application embodiment;

[0041] FIG. 3( c ) is an echo diagram after the false echo of the initial field is suppressed in the application embodiment. DETAILED DESCRIPTION

[0042] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0043] Embodiment 1:

[0044] The present invention provides an initial field inversion method for non-precipitation echoes, wherein the initial field includes three types of data: water vapor content, hydrometeor and wind field, such as Figure 1 and Figure 2 As shown, including:

[0045] S1. Sparsely process the radar reflectivity data with respect to the spatial grid coordinate system of the numerical weather forecast model to obtain the observation combination reflectivity factor, and then divide the precipitation echo area and the non-precipitation echo area based on the observation combination reflectivity factor;

[0046] S2, equivalently accumulating the background variable information in the numerical weather forecast model, obtaining the model combination reflectivity factor, and then dividing the heavy precipitation area and the weak precipitation area based on the combination reflectivity factor;

[0047] S3, taking the area belonging to both the non-precipitation echo area and the strong precipitation echo area as the initial field inversion area of ​​the non-precipitation echo, and the other areas as the non-inversion area;

[0048] S4, performing water vapor content inversion, hydrometeor inversion, and wind field inversion on the inversion area;

[0049] S5. Smoothing the water vapor content, hydrometeor and wind field in the boundary area between the inversion area and the non-inversion area.

[0050] Embodiment 2:

[0051] This embodiment is further designed on the basis of the first embodiment in that, in this embodiment, the radar reflectivity data is subjected to sparse processing related to the numerical weather forecast model space grid coordinate system, the observation combination reflectivity factor is obtained, and then the specific method of dividing the precipitation echo area and the non-precipitation echo area based on the observation combination reflectivity factor includes:

[0052] The radar reflectivity factor with radial distance, elevation angle and azimuth angle in the radar polar coordinate system is sparsely converted to the numerical weather forecast pattern space grid coordinate system (also called pattern space), and the observation reflectivity factor Zo of each grid point (i, j, k) is obtained. The maximum value of the observation reflectivity factor of each grid point in the vertical direction is calculated to obtain the observation combination reflectivity factor CR O ; If the observed combined reflectivity factor CR OIf the first threshold is exceeded, the area is determined to be an observed precipitation echo area, and the remaining areas are observed non-precipitation echo areas.

[0053] Specifically, the specific method is further described below with an example. The example is for the radar reflectivity data (also called the observed reflectivity factor) Z read. o , using spatial transformation, the spatial position Z of the observed reflectivity factor is o (r, α, ψ) is converted to the pattern space coordinate Z o (i, j, k), calculate the maximum value CR of the reflectivity factor in the vertical direction at each position (i, j) o (i, j).

[0054] Embodiment three:

[0055] This embodiment is further designed on the basis of the first embodiment in that, in this embodiment, the background variable information in the numerical weather forecast model is equivalently accumulated to obtain the model combination reflectivity factor, and then the specific method of dividing the model heavy precipitation area and the model weak precipitation area based on the combination reflectivity factor includes:

[0056] Based on the background variable information (also called background field) in the numerical weather forecast model, the equivalent reflectivity of each hydrometeor (rain, snow, graupel) is calculated, the total equivalent reflectivity is accumulated and converted into the model reflectivity factor Zf, and the maximum value of the model reflectivity factor in the vertical direction of each grid point in the numerical weather forecast model space grid coordinate system is calculated to obtain the model combination reflectivity factor CRf;

[0057] If the model combination reflectivity factor CRf exceeds 25dBZ, the area is determined to be a heavy precipitation area based on the model, and the rest of the area is determined to be a weak precipitation area based on the model.

[0058] The first threshold is 15dBZ; the second threshold is 25dBZ.

[0059] Specifically, the specific method is further described below with an example, reading the mixing ratio of each hydrometeor in the background field (rainwater mixing ratio q r , snow mixture ratio q s and the graupel mixing ratio q g ), temperature T b , air density ρ, calculate the reflectivity factor Z of each grid point pattern according to formulas 1 to 3 f (i, j, k), and at the same time calculate the maximum value CR of the pattern reflectivity factor at each grid point (i, j) in the vertical direction f .

[0060] Z f =10lgZ e (1)

[0061] Z e =Z r +Z ds +Z ws +Z g (2)

[0062]

[0063] Among them, x refers to rain r, dry snow ds, wet snow ws, and graupel g, and α x is the coefficient of each hydrometeor determined by the dielectric constant, density and intercept parameters. The coefficient of rainwater is 3.63×10 9 ; For snow and graupel, the wet snow coefficient is 4.26×10 11 , the dry snow coefficient is 9.80×10 8 ; The hail coefficient is 1.09×10 9 .

[0064] Embodiment 4:

[0065] This embodiment is further designed on the basis of the first embodiment in that the specific method of water vapor content inversion in this embodiment includes:

[0066] Based on the positions of each grid point in the inversion area, determine the horizontal influence radius of each grid point at each altitude layer in the inversion area in the spatial grid coordinate system of the numerical weather forecast model, and further determine the influence area of ​​each grid point at each altitude layer;

[0067] For the water vapor content analysis value corresponding to each grid point in each altitude layer in the numerical weather forecast model (which can be obtained from the background variable information), the product of the mean of the water vapor content analysis values ​​of all grid points in the influence area of ​​each grid point and the third threshold is taken as the water vapor content inversion value of the grid point; thus, the water vapor content inversion values ​​of different altitude layers in the inversion area are obtained. In this example, the third threshold is 0.9.

[0068] Specifically, the specific method is further illustrated by an example below. For the grid point position in the inversion area, the horizontal influence radius r of each point is determined, where r is 20 km. The number of model grid points N within the influence radius r is searched, and the water vapor content of the grid points within the influence radius is calculated according to formulas 4 to 6; the average water vapor content within the influence radius is calculated according to formula 7; and according to formula 8, 90% of the average value is the inversion value q of the water vapor content at the point. v,o According to the observed values ​​of the vertical layer of the observed reflectivity factor, the three-dimensional water vapor content q in the non-precipitation echo area of ​​each layer is calculated v,o (i,j,k).

[0069] q v =q s ×rh(4)

[0070]

[0071] e=6.11×10 7.5t / (237+t) (6)

[0072]

[0073]

[0074] where q v is the water vapor content, q s is the saturated specific humidity, rh is the relative humidity, e is the saturated water vapor, p is the atmospheric pressure, and t is the temperature.

[0075] Embodiment five:

[0076] This embodiment is further designed on the basis of the first embodiment in that the specific method of wind field inversion in this embodiment includes:

[0077] Based on the positions of each grid point in the inversion area, determine the horizontal influence radius of each grid point at each altitude layer in the inversion area in the spatial grid coordinate system of the numerical weather forecast model, and further determine the influence area of ​​each grid point at each altitude layer;

[0078] For the wind field analysis values ​​corresponding to each grid point at each altitude layer in the numerical weather forecast model (which can be obtained from the background variable information), specifically including the mode u component and the mode v component, the mean of the mode u component and the mode v component of all grid points in the influence area of ​​each grid point is used as the mode u component inversion value and the mode v component inversion value of the grid point; thereby, the wind field inversion values ​​of different altitude layers in the inversion area are obtained.

[0079] Specifically, the specific method is further explained below with an example. For the grid point position in the inversion area, the horizontal influence radius r of each point is determined, r is 20km, the number of pattern grid points N within the influence radius r is searched, and the average value of the pattern u component within the influence radius is calculated according to formula 9 Calculate the average value of the v component of the mode within the influence radius according to formula 10 According to the vertical distribution of the observed reflectivity factor, the three-dimensional wind field distribution in the non-precipitation echo area of ​​each layer is calculated.

[0080]

[0081]

[0082] In the formula, u i is the mode u component of the ith grid point, v i is the mode v component of the i-th grid point.

[0083] Embodiment six:

[0084] This embodiment is further designed on the basis of the first embodiment in that the specific method of hydrometeor inversion in this embodiment includes:

[0085] The judgment of whether there is hydrometeor in the inversion area is performed based on the comparison between the observed combined reflectivity factor and the fourth threshold. If the observed combined reflectivity factor is greater than the fourth threshold, the temperature judgment method is used to perform hydrometeor inversion. If there is no hydrometeor in other areas, hydrometeor inversion is not performed.

[0086] Specifically, the specific steps of the temperature determination method are described below with an example. First, based on the mode temperature T b The hydrometeors are classified (Formulas 11 to 13) to obtain the weight C of each hydrometeor x .

[0087]

[0088]

[0089]

[0090] Among them, C r is the rainwater weight, C ds is the dry snow weight, C ws is the wet snow weight, C g is the hail weight, and α x is the coefficient of each hydrometeor determined by the dielectric constant, density and intercept parameter. x refers to rainwater r, dry snow ds, wet snow ws, and sleet g. The coefficient of rainwater is 3.63×10 9 ; For snow and graupel, the wet snow coefficient is 4.26×10 11 , the dry snow coefficient is 9.80×10 8 ; The hail coefficient is 1.09×10 9 .

[0091] Then, the contents of various hydrometeors are inverted according to formulas 14 to 15.

[0092] Z eo =10^(Z o *0.1) (14)

[0093]

[0094] Among them, q x The hydrometeor content inverted by temperature determination, x refers to rainwater r, dry snow ds, dry graupel dg, wet snow ws, and wet graupel wg. o is the observed reflectivity factor, Z eo Observed equivalent reflectivity, α xis the coefficient of each hydrometeor determined by the dielectric constant, density and intercept parameter, ρ is the air density, C x is the weight.

[0095] Embodiment seven:

[0096] This embodiment is further designed on the basis of the first embodiment in that the specific method for smoothing the water vapor content, hydrometeors and wind field in the boundary area between the inversion area and the non-inversion area in this embodiment includes:

[0097] Obtain the grid points of each altitude layer in the boundary area between the inversion area and the non-inversion area in the numerical weather forecast model space grid coordinate system, and use the following formulas to smooth the water vapor content, hydrometeor and wind field of each grid point layer by layer:

[0098]

[0099] In the formula, is the water vapor content, hydrometeor or wind field inversion value of the grid point (i, j) after smoothing; X i+m,j+n is the water vapor content, hydrometeor or wind field inversion value of the grid point (i+m, j+n). The wind field inversion value includes the mode u component inversion value and the mode v component inversion value.

[0100] Embodiment eight:

[0101] The present invention discloses an initial field inversion system for non-precipitation echoes, wherein the initial field includes three types of data: water vapor content, hydrometeor and wind field, and comprises an observation division module, a combination division module, a determination module and an inversion module;

[0102] The observation division module is used to perform sparse processing on the radar reflectivity data related to the spatial grid coordinate system of the numerical prediction model, obtain the observation combination reflectivity factor, and then divide the precipitation echo area and the non-precipitation echo area based on the observation combination reflectivity factor;

[0103] The combination division module is used to perform equivalent accumulation of background variable information in the numerical weather forecast model, obtain the model combination reflectivity factor, and then divide the model into heavy precipitation areas and weak precipitation areas based on the combination reflectivity factor;

[0104] A determination module, used for determining the areas belonging to both the non-precipitation echo area and the strong precipitation echo area as the inversion areas of the non-precipitation echo, and the other areas as the non-inversion areas;

[0105] The inversion module is used to invert the water vapor content, hydrometeor inversion, and wind field inversion in the inversion area.

[0106] Embodiment nine:

[0107] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0108] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0109] Application examples:

[0110] This example simulates the implementation of the initial field inversion method of non-precipitation echoes of the present invention. The simulation object of this example is the radar data at 08:00 on May 27, 2024. The observed radar echo map is shown in Figure 3(a), the echo map obtained by the existing method, that is, the echo map obtained without false echo suppression is shown in Figure 3(b), and the echo map obtained by performing the initial field inversion of non-precipitation echoes using the method of the present invention is shown in Figure 3(c).

[0111] according to Figure 3(a) to Figure 3(c) It can be seen that the method of the present invention can suppress the false convection at the current moment, especially the false echo suppression effect within the circle in Figure 3(b) is significant, and the predicted echo is closer to the observed echo.

[0112] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for inverting the initial field of non-precipitation echoes, wherein the initial field includes three types of data: water vapor content, hydrometeor and wind field, characterized in that: include: The radar reflectivity data is subjected to sparse processing related to the spatial grid coordinate system of the numerical weather forecast model to obtain an observation combination reflectivity factor, and then the precipitation echo area and the non-precipitation echo area are divided based on the observation combination reflectivity factor; Equivalently accumulating the background variable information in the numerical weather forecast model to obtain the model combination reflectivity factor, and then dividing the heavy precipitation area and the weak precipitation area based on the combination reflectivity factor; The areas that belong to both the non-precipitation echo area and the heavy precipitation area are regarded as the inversion area of ​​the non-precipitation echo, and the other areas are regarded as the non-inversion area; Invert water vapor content, hydrometeor content and wind field in the inversion area; The specific method of wind field inversion includes: Determine the horizontal influence radius of each grid point at each altitude layer in the inversion area in the spatial grid coordinate system of the numerical weather forecast model, and further determine the influence area of ​​each grid point at each altitude layer; For the wind field analysis values ​​corresponding to each grid point at each altitude layer in the numerical weather forecast model, the wind field analysis values ​​include the mode u component and the mode v component, and the mean of the wind field analysis values ​​of all grid points in the influence area of ​​each grid point is used as the wind field inversion value of the grid point; thereby obtaining the wind field inversion values ​​of different altitude layers in the inversion area; The boundary area between the inversion area and the non-inversion area is smoothed in terms of water vapor content, hydrometeor and wind field.

2. The initial field inversion method of non-precipitation echo according to claim 1, characterized in that: The specific method of performing sparse processing on the radar reflectivity data related to the numerical weather forecast model space grid coordinate system, obtaining the observation combination reflectivity factor, and then dividing the precipitation echo area and the non-precipitation echo area based on the observation combination reflectivity factor includes: The radar reflectivity factor with radial distance, elevation angle and azimuth angle in the radar polar coordinate system is thinned to the numerical weather forecast model space grid coordinate system to obtain the observed reflectivity factor of each grid point, and the maximum value of the observed reflectivity factor of each grid point in the vertical direction is calculated to obtain the observed combined reflectivity factor; if the observed combined reflectivity factor exceeds a first threshold, it is determined to be an observed precipitation echo area, and the rest are observed non-precipitation echo areas.

3. The initial field inversion method of non-precipitation echo according to claim 1, characterized in that: The specific method of equivalently accumulating the background variable information in the numerical weather forecast model to obtain the model combination reflectivity factor, and then dividing the heavy precipitation area and the weak precipitation area based on the combination reflectivity factor includes: Based on the background variable information in the numerical weather forecast model, the equivalent reflectivity of each hydrometeor is calculated, the total equivalent reflectivity is accumulated and converted into a model reflectivity factor, and the maximum value of the model reflectivity factor in the vertical direction of each grid point in the numerical weather forecast model space grid coordinate system is calculated to obtain the model combination reflectivity factor; if the model combination reflectivity factor exceeds the second threshold, it is determined to be a heavy precipitation area, and the rest are determined to be weak precipitation areas.

4. The initial field inversion method of non-precipitation echo according to claim 1, characterized in that: The specific method of water vapor content inversion includes: Determine the horizontal influence radius of each grid point at each altitude layer in the inversion area in the spatial grid coordinate system of the numerical weather forecast model, and further determine the influence area of ​​each grid point at each altitude layer; For the water vapor content analysis values ​​corresponding to each grid point at each altitude layer in the numerical weather forecast model, the product of the mean of the water vapor content analysis values ​​of all grid points in the influence area of ​​each grid point and the third threshold is used as the water vapor content inversion value of the grid point; thereby, the water vapor content inversion values ​​of different altitude layers in the inversion area are obtained.

5. The initial field inversion method of non-precipitation echo according to claim 1, characterized in that: The specific method of hydrometeor inversion includes: The judgment of whether there is hydrometeor in the inversion area is performed based on the comparison between the observed combined reflectivity factor and the fourth threshold. If the observed combined reflectivity factor is greater than the fourth threshold, the temperature judgment method is used to perform hydrometeor inversion. If there is no hydrometeor in other areas, hydrometeor inversion is not performed.

6. The initial field inversion method of non-precipitation echo according to claim 1, characterized in that: The specific method of smoothing the water vapor content, hydrometeors and wind field in the boundary area between the inversion area and the non-inversion area includes: Obtain the grid points of each altitude layer in the boundary area between the inversion area and the non-inversion area in the numerical weather forecast model space grid coordinate system, and use the following formulas to smooth the water vapor content, hydrometeor and wind field of each grid point layer by layer: Where, X i,j is the water vapor content, hydrometeor or wind field inversion value of the grid point (i, j) after smoothing; X i+m,j+n It is the water vapor content, hydrometeor or wind field inversion value of the grid point (i+m, j+n).

7. An initial field inversion system for non-precipitation echoes, wherein the initial field includes three types of data: water vapor content, hydrometeor and wind field, characterized in that: It includes observation division module, combination division module, judgment module and inversion module; The observation division module is used to perform sparse processing on the radar reflectivity data related to the numerical prediction mode space grid coordinate system, obtain the observation combination reflectivity factor, and then divide the precipitation echo area and the non-precipitation echo area based on the observation combination reflectivity factor; The combination division module is used to perform equivalent accumulation of background variable information in the numerical weather forecast model, obtain the model combination reflectivity factor, and then divide the heavy precipitation area and the weak precipitation area based on the combination reflectivity factor; The determination module is used to use the area belonging to both the non-precipitation echo area and the heavy precipitation area as the inversion area of ​​the non-precipitation echo, and other areas as the non-inversion area; The inversion module is used to perform water vapor content inversion, hydrometeor inversion, and wind field inversion on the inversion area; The specific method of wind field inversion includes: Determine the horizontal influence radius of each grid point at each altitude layer in the inversion area in the spatial grid coordinate system of the numerical weather forecast model, and further determine the influence area of ​​each grid point at each altitude layer; For the wind field analysis values ​​corresponding to each grid point at each altitude layer in the numerical weather forecast model, the wind field analysis values ​​include the mode u component and the mode v component, and the mean of the wind field analysis values ​​of all grid points in the influence area of ​​each grid point is used as the wind field inversion value of the grid point; thereby obtaining the wind field inversion values ​​of different altitude layers in the inversion area; The boundary area between the inversion area and the non-inversion area is smoothed in terms of water vapor content, hydrometeor and wind field.

8. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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