Set assimilation average field relocation method based on radar combined reflectivity

Abstract

The invention provides a repositioning method of an ensemble assimilation average field based on radar combined reflectivity, and the method continuously improves the ensemble average field according to the observed radar combined reflectivity, continuously corrects a mode forecast initial field, reduces the error accumulation, and enables the forecast result to maintain higher accuracy in a longer time range. According to the method, power spectrums of combined reflectivity are obtained through transformation based on the similarity degree of mode forecast data and observation data, the relation between the mode forecast data and the observation data is established through cross spectrums, the spatial position closeness degree is judged through the phase difference between the mode forecast data and the observation data, the smaller the phase difference is, the closer the power spectrum positions are, and the more stable the power spectrums are, the closer the power spectrums are; and finding out the ensemble member closest to the observation data in the numerical mode forecasting, namely, taking the ensemble member with the minimum spatial distance from the observation data as the optimal member, replacing the ensemble average with the optimal member, updating the ensemble member, and taking the updated ensemble member as an initial field of the numerical mode forecasting, thereby improving the accuracy of the ensemble forecasting.

Description

Technical Field

[0001] The invention relates to a repositioning method based on an integrated assimilated mean field of radar combined reflectivity, belonging to the technical field of numerical model prediction in earth science. Background Art

[0002] Ensemble assimilation is one of the important methods for assimilation of numerical model forecast data. It has been theoretically proven that if the numerical model forecast and observation errors both conform to the normal distribution, then the average field of the ensemble assimilation is the best among all ensemble members. However, since the numerical model forecast errors are difficult to estimate accurately and the observation errors do not completely conform to the normal distribution, in some cases the average field of the ensemble assimilation is not as good as that of a certain ensemble member. The purpose of the ensemble average is to integrate the information of multiple forecast members to provide a more representative and accurate forecast result. If the ensemble average is biased, the final forecast value will deviate greatly from the actual weather conditions.

[0003] In numerical model forecasts, the background field provides initial meteorological element information for ensemble members. Ensemble members are multiple forecast states with certain differences generated by specific perturbation methods based on the background field. They are used for ensemble forecasts to estimate the probability density function and forecast uncertainty of the atmospheric state. The ensemble average is calculated by averaging the corresponding meteorological elements of each ensemble to obtain the ensemble average value for each time step. It can be calculated using simple average or weighted average methods.

[0004] For areas where the background field does not match observations, for example, if observations show that there is convergence of warm and humid air in a certain area, and the water vapor and vertical motion conditions in that area in the background field are weak, the assimilation process will adjust the background field, increase the water vapor content and vertical upward motion in that area, and create favorable conditions for the formation of convection.

[0005] Due to imperfections in the initial field and physical processes, the generation and development of convection predicted by numerical models often have deviations. The data assimilation process generates convection in new areas based on observational data. When convection is suppressed in false areas, data assimilation needs to gradually generate convection in new areas by assimilating observational data at a certain frequency (such as hourly). This process may require continuous assimilation of data for several hours to achieve the desired effect, which is time-consuming. In other words, it takes several assimilated forecast fields to better resolve the actual observations, which seriously affects the effectiveness of numerical model forecasts. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a repositioning method and system based on the integrated normalized mean field of radar combined reflectivity.

[0007] In order to solve the above problems, the specific technical solutions proposed by the present invention are as follows:

[0008] A relocation method based on an integrated homogenized mean field of radar combined reflectivity, comprising:

[0009] Obtain observation data of radar reflectivity factor, convert it into pattern space coordinates through spatial transformation and obtain observation combined reflectivity of radar reflectivity factor;

[0010] According to the background field data predicted by the numerical model, the ensemble mean and the model combination reflectivity of the ensemble members are calculated; the background field data are matched with the observation data in time and space; the grid point coordinates in the background field data correspond to the grid point coordinates converted into the model space through spatial transformation.

[0011] According to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data, the ensemble member most similar to the observed data is selected as the optimal member;

[0012] The best member is used to replace the ensemble mean and the ensemble members are updated. As the initial field of the numerical model forecast, ensemble forecast is carried out.

[0013] The present invention further includes:

[0014] Calculating the fairness skill scores of the ensemble average and the ensemble members based on the ensemble average and the mode combination reflectances of the ensemble members;

[0015] Select the set member with the highest fairness skill score as the best member;

[0016] Calculate the Bias score of the best member selected by the fair skill score, and calculate the Bias score of the best member selected based on the similarity between the numerical model forecast and the observed data. Select the two best members with Bias scores closer to 1 as the actual best members, replace the ensemble average with the actual best member, and form a new ensemble average and ensemble members.

[0017] The present invention further comprises selecting the ensemble member most similar to the observed data as the optimal member according to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data, specifically:

[0018] S31. Perform Fourier transform on the ensemble average and the mode combination reflectivity of the ensemble members and the observed combination reflectivity to obtain the power spectrum distribution function. The calculation formula is as follows:

[0019]

[0020]

[0021] In the formula, F O (u,v) represents the power spectrum distribution function of the observed combined reflectivity; F f (u,v) represents the power spectrum distribution function of the mode combination reflectivity; M and N represent the number of grid points in the x and y directions, respectively; u and v represent the amplitudes in the x and y directions of the Fourier expansion, respectively; CR f (x,y) and CR o x and y in (x,y) are equivalent to i and j in grid point (i,j) respectively;

[0022] S32, calculating the cross spectrum of the observed combined reflectivity and the simulated combined reflectivity, obtaining the phase difference between the ensemble average and the mode combined reflectivity of the ensemble members and the observed combined reflectivity, the calculation formula is as follows:

[0023] G(u,v)=F O (u,v)*F f (u,v)

[0024]

[0025] Where G(u,v) represents the cross spectrum of the observed combination reflectivity and the mode combination reflectivity; f(x,y) represents the phase difference between the mode combination reflectivity and the observed combination reflectivity; G(u,v) is the inverse Fourier transform of the spectrum distribution;

[0026] S33, respectively calculate the similarity between the ensemble average and the ensemble members' mode combination reflectance and the observed combination reflectance, that is, the spatial distance D i Minimum, calculated as follows:

[0027]

[0028]

[0029]

[0030] In the formula, i represents the set member number; f i (x,y) is the grid point of f(x,y) arranged in descending order of amplitude value, f 0 (x,y) is the maximum value, and so on; u 总 represents the sum of the x-direction amplitudes of the Fourier expansion of all grid points; and v 总 represents the sum of the y-direction amplitudes of the Fourier expansion of all grid points;

[0031] The present invention further provides a calculation formula for selecting the best member based on the fairness skill score and selecting the actual best member based on the bias score as follows:

[0032] The fair skill score of the ensemble mean and the combined reflectivity of the ensemble members is calculated as follows:

[0033] ETS i =(NA-R(a)) / (NA+NB+NC-R(a))

[0034]

[0035] In the formula, ETS i represents the fairness skill score of the i-th set member; R(a) represents the number of correct predictions that may be made under random conditions; NA represents the number of correct hits; NB represents the number of false positives; NC represents the number of missed negatives; and ND represents the number of correct rejections.

[0036] The Bias score of the best member is calculated as follows:

[0037]

[0038] The present invention further comprises obtaining the observation data of the radar reflectivity factor, converting the observation data into the pattern space coordinates through spatial transformation and obtaining the observation combined reflectivity of the radar reflectivity factor, specifically:

[0039] S11. Read the observed radar reflectivity factor and use the spatial transformation to transform the radar reflectivity factor Z of the observed space o (r, α, ψ) is converted to the radar reflectivity factor Z in the pattern space o (i,j,k); Z o (r, α, ψ) where r represents the reservoir length, α represents the azimuth, and ψ represents the polar angle; Z o In (i,j,k), i represents the coordinate in the x direction, j represents the coordinate in the y direction, and k represents the coordinate in the z direction;

[0040] S12. Calculate the maximum value of the combined reflectivity factor of the pattern space grid point (i, j) in the vertical direction to obtain the observed combined reflectivity CR of the grid point (i, j) o .

[0041] The present invention further comprises calculating the ensemble mean and the mode combination reflectivity of the ensemble members according to the background field data predicted by the numerical model, specifically:

[0042] S21. Read the background field data predicted by the numerical model and calculate the model reflectivity factor Z of the model space according to the empirical formula f (i, j, k);

[0043] S22. Calculate the maximum value of the pattern reflectivity factor at the grid point (i, j) in the vertical direction, and obtain the pattern combination reflectivity CR of the grid point (i, j) f .

[0044] The present invention further comprises:

[0045] S51. Calculate the average values ​​of all meteorological elements at each grid point to obtain the ensemble mean field. The calculation formula is as follows:

[0046]

[0047] In the formula, x i Here, i represents the number of the set member, x represents the meteorological element, which is temperature, air pressure, wind field, height or water vapor content; n represents the total number of set members;

[0048] S52. For all meteorological elements at each grid point, the difference between the ensemble members and the ensemble mean field is calculated to obtain the disturbance field. The calculation formula is as follows:

[0049]

[0050] S53. Re-assign values ​​to all meteorological elements at all grid points. The calculation formula is as follows:

[0051]

[0052] In the formula, x M The meteorological element values ​​for selecting the best members.

[0053] A relocation system based on the collective normalized mean field of radar combined reflectivity, comprising a preprocessing module, an optimal member selection module, and a collective member update module;

[0054] A preprocessing module, which obtains the observation data of the radar reflectivity factor, converts it into the pattern space coordinates through spatial transformation and obtains the observation combination reflectivity of the radar reflectivity factor; and calculates the ensemble average and the pattern combination reflectivity of the ensemble members according to the background field data predicted by the numerical model; the background field data is matched with the observation data in time and space;

[0055] An optimal member selection module selects the ensemble member most similar to the observed data as the optimal member according to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data;

[0056] The set member updating module replaces the set average with the optimal member and updates the set members.

[0057] 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 the above method.

[0058] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention provides a repositioning method based on an ensemble assimilated mean field of radar combination reflectivity. The method continuously improves the ensemble mean field according to the observed radar combination reflectivity, continuously corrects the initial field of the model forecast, reduces the accumulation of errors, and enables the forecast result to maintain a high accuracy over a long time range.

[0061] The method of the present invention is based on the similarity between model prediction data and observation data. The power spectrum of the combined reflectivity is obtained through transformation, and the connection between the two is established by using the cross spectrum. The proximity of the spatial positions is judged by the phase difference between the two. The smaller the phase difference, the closer the power spectrum positions are, and the more stable and similar the two are. The ensemble member closest to the observation data in the numerical model prediction is found, that is, the ensemble member with the smallest spatial distance from the observation data is taken as the optimal member, the ensemble average is replaced by the optimal member, and the ensemble member is updated as the initial field of the numerical model prediction, thereby improving the accuracy of the ensemble prediction.

[0062] The present invention also uses the public skill score ETS to select the optimal ensemble members of the numerical model forecast, performs Bias scoring on the optimal ensemble members selected by the public skill score ETS, and compares the Bias scores of the optimal members selected based on the similarity between the numerical model forecast and the observed data, and selects the actual optimal ensemble members with Bias scores closer to 1. The ensemble average is replaced based on the selected actual optimal ensemble members, and the new ensemble members are used as the initial fields of the numerical model forecast to improve the forecast accuracy.

[0063] This method continuously improves the ensemble mean field based on the observed radar combination reflectivity, constantly corrects the initial field predicted by the numerical model, reduces the accumulation of errors, and maintains a high accuracy of the forecast results over a longer time range.

[0064] This method combines observational data with different error characteristics with numerical model forecasts in the high-frequency ensemble assimilation of numerical model forecasts, and incorporates new observational data into numerical model forecasts, allowing the model to capture the latest changes in the atmosphere in a timely manner, thereby providing more accurate forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0066] Figure 1 It is a schematic flow chart of a relocation method based on the collective normalized average field of radar combined reflectivity in an embodiment;

[0067] Figure 2 Schematic diagram of the result of the relocation method based on the integrated assimilated mean field of radar combined reflectivity in the embodiment. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0069] Embodiment 1:

[0070] The present invention is based on a relocation method of the integrated normalized mean field of radar combined reflectivity, wherein relocation refers to replacing the original mean field with a new mean field. The method comprises the following steps:

[0071] Obtain observation data of radar reflectivity factor, convert it into pattern space coordinates through spatial transformation and obtain observation combined reflectivity of radar reflectivity factor;

[0072] According to the background field data predicted by the numerical model, the ensemble mean and the model combination reflectivity of the ensemble members are calculated; the background field data are matched with the observation data in time and space; the grid point coordinates in the background field data correspond to the grid point coordinates converted into the model space through spatial transformation.

[0073] According to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data, the ensemble member most similar to the observed data is selected as the optimal member;

[0074] The best member is used to replace the ensemble mean and the ensemble members are updated. As the initial field of the numerical model forecast, ensemble forecast is carried out.

[0075] Embodiment 2:

[0076] This embodiment is further designed on the basis of the first embodiment in that, according to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data, the ensemble member most similar to the observed data is selected as the optimal member, specifically:

[0077] S31. Perform Fourier transform on the ensemble average and the mode combination reflectivity of the ensemble members and the observed combination reflectivity to obtain the power spectrum distribution function. The calculation formula is as follows:

[0078]

[0079]

[0080] In the formula, F O (u,v) represents the power spectrum distribution function of the observed combined reflectivity; F f (u,v) represents the power spectrum distribution function of the mode combination reflectivity; M and N represent the number of grid points in the x-direction and the y-direction, respectively; u and v represent the vibration pairs in the x-direction and the y-direction of the Fourier expansion, respectively; CR f (x,y) and CR o x and y in (x,y) are equivalent to i and j in grid point (i,j) respectively;

[0081] S32, calculating the cross spectrum of the observed combined reflectivity and the simulated combined reflectivity, obtaining the phase difference between the ensemble average and the mode combined reflectivity of the ensemble members and the observed combined reflectivity, the calculation formula is as follows:

[0082] G(u,v)=F O (u,v)*F f (u,v)

[0083]

[0084] Where G(u,v) represents the cross spectrum of the observed combination reflectivity and the mode combination reflectivity; f(x,y) represents the phase difference between the mode combination reflectivity and the observed combination reflectivity, and G(u,v) is the inverse Fourier transform of the spectrum distribution;

[0085] S33, respectively calculate the similarity between the ensemble average and the ensemble members' mode combination reflectance and the observed combination reflectance, that is, the spatial distance D i Minimum, calculated as follows:

[0086]

[0087]

[0088]

[0089] In the formula, i represents the set member number; f i (x,y) is the grid point of f(x,y) arranged in descending order of amplitude value, f 0 (x,y) is the maximum value, and so on; u 总 represents the sum of the x-direction vibrations of the Fourier expansion of all grid points; and v 总 Represents the sum of the y-direction vibration pairs of the Fourier expansion of all grid points; in this example, the cumulative values ​​of the top 20 f(x,y) are taken respectively. Since the top 20 have more than 90% of the spectrum energy of the set members, the details are as follows:

[0090]

[0091]

[0092] Embodiment three:

[0093] This embodiment is further designed on the basis of the second embodiment in that the relocation method based on the collective normalized average field of the radar combination reflectivity in this embodiment also includes:

[0094] Calculating the fair skill scores ETS of the ensemble average and the ensemble members based on the ensemble average and the mode combination reflectances of the ensemble members;

[0095] Select the set member with the highest fairness skill score ETS as the best member;

[0096] Calculate the Bias score of the best member selected by the fair skill score ETS, and calculate the Bias score of the best member selected based on the similarity between the numerical model forecast and the observed data. Select the two best members with Bias scores closer to 1 as the actual best members, and replace the ensemble average with the actual best members.

[0097] Embodiment 4:

[0098] This embodiment is further designed on the basis of the third embodiment in that the calculation formula for selecting the best member by the fairness skill score and selecting the actual best member by the bias score is as follows:

[0099] The fair skill score of the ensemble mean and the combined reflectivity of the ensemble members is calculated as follows:

[0100] ETS i =(NA-R(a)) / (NA+NB+NC-R(a))

[0101]

[0102] In the formula, ETS i represents the fairness skill score of the i-th set member; R(a) represents the number of correct predictions that may be made under random conditions; NA represents the number of correct hits; NB represents the number of false positives; NC represents the number of missed negatives; and ND represents the number of correct rejections.

[0103] The Bias score of the best member is calculated as follows:

[0104]

[0105] Embodiment five:

[0106] This embodiment is further designed on the basis of the fourth embodiment in that in this embodiment, the observation data of the radar reflectivity factor is obtained, converted into the pattern space coordinates through spatial transformation, and the observed combined reflectivity of the radar reflectivity factor is obtained, specifically:

[0107] S11. Read the observed radar reflectivity factor and use the spatial transformation to transform the radar reflectivity factor Z of the observed space o (r, α, ψ) is converted to the radar reflectivity factor Z in the pattern space o (i,j,k); Z o (r, α, ψ) where r represents the reservoir length, α represents the azimuth, and ψ represents the polar angle; Z o In (i,j,k), i represents the coordinate in the x direction, j represents the coordinate in the y direction, and k represents the coordinate in the z direction;

[0108] S12. Calculate the maximum value of the combined reflectivity factor of the pattern space grid point (i, j) in the vertical direction to obtain the observed combined reflectivity CR of the grid point (i, j) o (i,j).

[0109] According to the background field data predicted by the numerical model, the ensemble mean and the model combination reflectivity of the ensemble members are calculated, specifically:

[0110] S21, read the various hydrometeoric mixing ratios (rain mixing ratio qr, snow mixing ratio qs and graupel mixing ratio qg) and temperature T of the background field predicted by the numerical model b , air density ρ, calculate the reflectivity factor Zf(i,j,k) of each grid point pattern according to formulas 1 to 3;

[0111] S22. Calculate the maximum value of the pattern reflectivity factor at the grid point (i, j) in the vertical direction, and obtain the pattern combination reflectivity CR of the grid point (i, j) f :

[0112] Z f =10lgZ e (1)

[0113] Z e =Z r +Z ds +Z ws +Z g (2)

[0114]

[0115] 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 .

[0116] Embodiment six:

[0117] This embodiment is further designed on the basis of the fifth embodiment in that, in this embodiment, after replacing the set average with the optimal member, the specific process of updating the set members is as follows:

[0118] S51. Calculate the average values ​​of all meteorological elements at each grid point to obtain the ensemble mean field. The calculation formula is as follows:

[0119]

[0120] In the formula, x i Here, i represents the number of the set member, x represents the meteorological element, which is temperature, air pressure, wind field, height or water vapor content; n represents the total number of set members;

[0121] S52. For all meteorological elements at each grid point, the difference between the ensemble members and the ensemble mean field is calculated to obtain the disturbance field. The calculation formula is as follows:

[0122]

[0123] S53. Re-assign values ​​to all meteorological elements at all grid points. The calculation formula is as follows:

[0124]

[0125] In the formula, x M The meteorological element values ​​for selecting the best members.

[0126] Embodiment seven:

[0127] The present invention discloses a relocation system based on the collective normalized mean field of radar combined reflectivity, comprising a preprocessing module, an optimal member selection module, and a collective member update module;

[0128] A preprocessing module, which obtains the observation data of the radar reflectivity factor, converts it into the pattern space coordinates through spatial transformation and obtains the observation combination reflectivity of the radar reflectivity factor; and calculates the ensemble average and the pattern combination reflectivity of the ensemble members according to the background field data predicted by the numerical model; the background field data is matched with the observation data in time and space;

[0129] An optimal member selection module selects the ensemble member most similar to the observed data as the optimal member according to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data;

[0130] The set member updating module replaces the set average with the optimal member and updates the set members.

[0131] Embodiment eight:

[0132] An electronic device of the present invention includes 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 the method described in any of the above embodiments.

[0133] A computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0134] Test example 1:

[0135] This example adopts the repositioning method based on the ensemble normalized average field of radar combination reflectivity of the present invention, and compares the radar combination reflectivity results 1h, 3h and 5h before and after the repositioning reported from 18:00 on June 13, 2022, where OBS represents the observed radar combination reflectivity, CNTL represents the forecast combination reflectivity before repositioning, and RCTR represents the forecast combination reflectivity after repositioning.

[0136] Depend on Figure 2 In the figure, (a1), (a2), and (a3) ​​are the radar combined reflectivity maps observed at 19:00, 21:00, and 23:00 on June 13, 2022;

[0137] (b1), (b2), and (b3) are respectively the combined reflectivities of the future 1 h, 3 h, and 5 h predicted at 18:00 on June 13, 2022 after repositioning using the method of the present invention, that is, the combined reflectivities at 19:00, 21:00, and 23:00 on June 13, 2022;

[0138] (c1), (c2), and (c3) are the forecast combined reflectivities of 1h, 3h, and 5h predicted at 18:00 on June 13, 2022 without using the relocation technology, that is, the combined reflectivities at 19:00, 21:00, and 23:00 on June 13, 2022.

[0139] From the analysis of the impact time, the RCTR test is significantly better than the CNTL test without ensemble mean repositioning. For the forecast at 19:00 on the 13th, the observed radar echo has affected the central and northern parts of Bozhou, while the CNTL is biased to the north as a whole and only affects the northernmost part of Bozhou, with a time lag. The RCTR impact time is basically consistent with the observation. By 21:00, the convective system has affected the northern part of Bengbu, while the CNTL is still time-lagged, and the convection has not yet moved into Bengbu. As can be seen from the figure, the method of the present invention can significantly improve the forecast of severe convection in the next 3 hours.

Claims

1. A relocation method based on the collective normalized mean field of radar combined reflectivity, characterized in that: include: Obtain observation data of radar reflectivity factor, convert it into pattern space coordinates through spatial transformation and obtain observation combined reflectivity of radar reflectivity factor; Calculating the ensemble mean and the model combination reflectivity of the ensemble members based on the background field data predicted by the numerical model; the background field data is spatially and temporally matched with the observation data; According to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data, the ensemble member most similar to the observed data is selected as the optimal member; Replace the ensemble average with the best member and update the ensemble members.

2. The relocation method based on the integrated normalized mean field of radar combined reflectivity according to claim 1 is characterized in that: Also includes, Calculating the fairness skill scores of the ensemble average and the ensemble members based on the ensemble average and the mode combination reflectances of the ensemble members; Select the set member with the highest fairness skill score as the best member; Calculate the Bias score of the best member selected by the fair skill score, and calculate the Bias score of the best member selected based on the similarity between the numerical model forecast and the observed data. Select the two best members with Bias scores closer to 1 as the actual best members, and replace the ensemble average with the actual best members.

3. The relocation method based on the integrated normalized mean field of radar combined reflectivity according to claim 1 or 2, characterized in that: The method of selecting the ensemble member most similar to the observed data as the optimal member according to the ensemble average and the model combined reflectivity of the ensemble members and the observed combined reflectivity, and according to the similarity between the numerical model forecast and the observed data, is specifically as follows: S31. Perform Fourier transform on the ensemble average and the mode combination reflectivity of the ensemble members and the observed combination reflectivity to obtain the power spectrum distribution function. The calculation formula is as follows: In the formula, F O (u,v) represents the power spectrum distribution function of the observed combined reflectivity; F f (u,v) represents the power spectrum distribution function of the mode combination reflectivity; M and N represent the number of grid points taken in the x-direction and y-direction, respectively; u and v represent the amplitudes in the x-direction and y-direction of the Fourier expansion, respectively; S32, calculating the cross spectrum of the observed combined reflectivity and the simulated combined reflectivity, obtaining the phase difference between the ensemble average and the mode combined reflectivity of the ensemble members and the observed combined reflectivity, the calculation formula is as follows: G(u,v)=F O (u,v)*F f (u,v) Where G(u,v) represents the cross spectrum of the observed combination reflectivity and the mode combination reflectivity; f(x,y) represents the phase difference between the mode combination reflectivity and the observed combination reflectivity; S33, respectively calculate the similarity between the ensemble average and the ensemble members' mode combination reflectance and the observed combination reflectance, that is, the spatial distance D i Minimum, calculated as follows: In the formula, i represents the set member number; f i (x,y) is the grid point of f(x,y) arranged in descending order of amplitude value, f0(x,y) is the maximum value, and so on; u 总 represents the sum of the x-direction amplitudes of the Fourier expansion of all grid points; and v 总 Represents the sum of the y-direction amplitudes of the Fourier expansion of all grid points.

4. The relocation method based on the integrated normalized mean field of radar combined reflectivity according to claim 3 is characterized in that: The calculation formula for selecting the best member by fair skill score and the actual best member by bias score is as follows: The fair skill score of the ensemble mean and the combined reflectivity of the ensemble members is calculated as follows: ETS i =(NA-R(a)) / (NA+NB+NC-R(a)) In the formula, ETS i represents the fair skill score of the i-th set member; R(a) represents the number of correct predictions that may be made under random conditions; NA represents the number of correct hits; NB represents the number of false positives; NC represents the number of missed negatives; ND represents the number of correct rejections; The Bias score of the best member is calculated as follows:

5. The relocation method based on the integrated normalized mean field of radar combined reflectivity according to claim 4 is characterized in that: The observation data of the radar reflectivity factor is converted into pattern space coordinates through spatial transformation to obtain the observed combined reflectivity of the radar reflectivity factor, specifically: S11. Read the observed radar reflectivity factor and use the spatial transformation to transform the radar reflectivity factor Z of the observed space o (r, α, ψ) is converted to the radar reflectivity factor Z in the pattern space o (i,j,k); Z o (r, α, ψ) where r represents the reservoir length, α represents the azimuth, and ψ represents the polar angle; Z o In (i,j,k), i represents the coordinate in the x direction, j represents the coordinate in the y direction, and k represents the coordinate in the z direction; S12. Calculate the maximum value of the combined reflectivity factor of the pattern space grid point (i, j) in the vertical direction to obtain the observed combined reflectivity CR of the grid point (i, j) o .

6. The relocation method based on the integrated normalized mean field of radar combined reflectivity according to claim 5 is characterized in that: The calculation of the ensemble mean and the mode combination reflectivity of the ensemble members based on the background field data predicted by the numerical model is specifically as follows: S21. Read the background field data predicted by the numerical model and calculate the model reflectivity factor Z of the model space according to the empirical formula f (i, j, k); S22. Calculate the maximum value of the pattern reflectivity factor at the grid point (i, j) in the vertical direction, and obtain the pattern combination reflectivity CR of the grid point (i, j) f .

7. The relocation method based on the integrated assimilated mean field of radar combined reflectivity according to claim 1, characterized in that: The members of the group are specifically: S51. Calculate the average values ​​of all meteorological elements at each grid point to obtain the ensemble mean field. The calculation formula is as follows: In the formula, x i Here, i represents the number of the set member, x represents the meteorological element, which is temperature, air pressure, wind field, height or water vapor content; n represents the total number of set members; S52. For all meteorological elements at each grid point, the difference between the ensemble members and the ensemble mean field is calculated to obtain the disturbance field. The calculation formula is as follows: S53. Re-assign values ​​to all meteorological elements at all grid points. The calculation formula is as follows: In the formula, x M The meteorological element values ​​for selecting the best members.

8. A relocation system based on the collective normalized mean field of radar combined reflectivity, characterized in that: It includes pre-processing module, optimal member selection module and set member update module; A preprocessing module, which obtains the observation data of the radar reflectivity factor, converts it into the pattern space coordinates through spatial transformation and obtains the observation combination reflectivity of the radar reflectivity factor; and calculates the ensemble average and the pattern combination reflectivity of the ensemble members according to the background field data predicted by the numerical model; the background field data is matched with the observation data in time and space; An optimal member selection module selects the ensemble member most similar to the observed data as the optimal member according to the ensemble average and the model combination reflectivity of the ensemble members and the observed combination reflectivity, and according to the similarity between the numerical model forecast and the observed data; The set member updating module replaces the set average with the optimal member and updates the set members.

9. 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 7.

10. 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 as claimed in any one of claims 1 to 7 are implemented.

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