A multi-parameter hail size quantitative diagnostic method based on cloud resolution mode

Abstract

The application discloses a multi-parameter hail size quantitative diagnosis method based on a cloud resolution mode, combines reanalysis data and actual observation data, establishes a comprehensive data set, analyzes three-dimensional structure characteristic features of a hail storm system, carries out a cloud resolution scale numerical simulation experiment, compares multi-source observation, evaluates simulation effects of the cloud resolution mode on the hail storm, and analyzes reproduction capabilities of the mode on microphysical field characteristics in the hail cloud. Based on the best cloud resolution mode simulation result in the evaluation result, multi-parameter hail size quantitative diagnosis results including an average median diameter Dmh, a maximum estimated hail size MESH and a maximum hail diameter D* are diagnosed. Finally, multi-parameter quantitative diagnosis results of ground hail size are visually displayed based on a post-processing display system. The application provides more accurate evaluation indexes for hail size diagnosis and provides scientific support for artificial hail prevention work.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of hail disaster prevention and reduction, and particularly relates to a multi-parameter hail size quantitative diagnosis method based on a cloud resolution mode. BACKGROUND

[0002] Hail is a major meteorological natural disaster in many parts of the world, but the observation of ground hail size at the present stage is still relatively lacking, and the understanding of the hail growth physical process is still very insufficient. There is also great uncertainty in hail quantitative diagnosis analysis and artificial hail prevention work. Therefore, it is of great significance to strengthen the understanding of the hail growth mechanism and put forward a new hail quantitative diagnosis method for disaster prevention and reduction. At present, the understanding of the hailstone source, the main height layer of hail growth, and the contribution of different microphysical processes to hail growth is still not deep. In addition, most of the previous studies are based on ideal hail simulation of cloud mode thermal bubble starting, which is not conducive to comparison and verification with actual observation. Therefore, it is necessary to start from actual cases, and to use a convection-resolvable numerical prediction mode to quantitatively diagnose and evaluate the ground hail.

[0003] At present, the hail quantitative analysis method can be mainly divided into four categories: hail diagnosis method based on single-point sounding observation, hail diagnosis method based on coupling of cloud mode and hail growth mode (HAILCAST), machine learning method, and hail quantitative diagnosis method based on complex microphysical process parameterization scheme in a convection scale numerical mode. The biggest defect of the simplest hail diagnosis method based on single-point sounding is that the sounding observation data is insufficient to give high spatiotemporal resolution hail size diagnosis. HAILCAST reverses the vertical velocity and hail growth and distribution characteristics in the cloud by coupling a steady cloud mode and a time-varying hail growth mode, but some scholars point out that HAILCAST overestimates the ground hail size. The machine learning method is limited by the results of ensemble prediction, and may cause spatiotemporal deviation of hail prediction because of the weak physical relationship between the ensemble prediction variables in the merged analysis. The convection scale numerical mode (horizontal grid resolution reaching 1-4km) coupled with multiple physical process fine parameterization schemes has become an important tool for studying the cloud microphysical process and hail growth mechanism of strong convective hail storm system. Among them, the cloud microphysical process parameterization scheme directly affects the simulation of hail cloud structure, evolution and hail growth process. The bulk cloud microphysical process parameterization scheme is widely used in business weather prediction and research at present, and according to different parameters, the bulk scheme can be divided into single-parameter, double-parameter and three-parameter schemes. Among them, the single-parameter scheme only evaluates the mass mixing ratio Q x , N 0X and α x are constants. Because the single-parameter scheme is not accurate in describing the particle spectrum, and the particle number concentration N tX and Q xMonotonic correlation, eventually leading to a more uniform particle space distribution, unable to reflect the characteristics of the low-level large particles in the storm that the particle selection mechanism exhibits. The N tX and Q x , and then λ x and N 0X are inverted. The three-parameter scheme increases the forecast radar reflectivity factor on the basis of the two-parameter scheme, so that α x can be solved jointly, and on this basis, the ground hailstone size is diagnosed and analyzed. Many previous studies have pointed out that there are great differences in the simulation of storm systems using different microphysical parameterization schemes, such as storm structure, ground cumulative precipitation field, and cold pool. With the development of numerical prediction models, the hailstone diagnosis capability has gradually improved, but the quantitative diagnosis of hailstone (such as ground hailstone size, concentration, and drop zone) is still very challenging.

[0004] In view of the insufficient accuracy of the quantitative diagnosis of the ground hailstone size, the three-parameter cloud microphysical scheme that best matches the observed ground hailstone distribution is adopted to develop a multi-parameter hailstone size diagnosis method, so as to better provide scientific support and theoretical basis for the development of hail prevention work. SUMMARY

[0005] The purpose of the application is to provide a multi-parameter hailstone size quantitative diagnosis method based on a cloud resolution model, which provides more accurate evaluation indicators for hailstone size diagnosis.

[0006] Technical scheme: The multi-parameter hailstone size quantitative diagnosis method based on the cloud resolution model provided by the application comprises the following steps:

[0007] (1) Obtain reanalysis data and actual observation data, establish a comprehensive data set, analyze the weather situation of the hailstone process and the three-dimensional structure characteristics of the hailstorm system;

[0008] (2) Carry out cloud resolution scale numerical simulation experiment, compare with actual observation, evaluate the simulation effect of the numerical model, and analyze the reproduction ability of the model to the microphysical field characteristics in the hail cloud;

[0009] (3) Diagnose multi-parameter hailstone size characteristic indicators based on the best cloud resolution model simulation result; the hailstone size quantitative characteristic indicators include average median diameter Dmh, maximum estimated hailstone size MESH and maximum hailstone diameter D*;

[0010] (4) Visualize the quantitative characteristic indicators of the ground hailstone size.

[0011] Further, the reanalysis data and actual observation data in step (1) include ERA5 reanalysis data, ground station sounding observation, radar network observation and satellite observation.

[0012] Further, the weather condition in step (1) includes atmospheric temperature, humidity, and wind field at different height layers.

[0013] Further, the step (2) is implemented as follows:

[0014] The horizontal grid resolution is selected as 1-3km resolution, and a multi-parameter cloud microphysical scheme is adopted, including MY double parameters, double parameters and triple parameters of the National Storm Laboratory (NSSL); based on the experimental results of high-resolution numerical simulation, combined with observation data, the simulation effect of the model on the hailstorm system is evaluated.

[0015] Further, the average median diameter Dmh in step (3) represents the diameter corresponding to the median of the mass distribution of hail particles per unit area, and Dmh describes the average size of hail particles, and the calculation formula is as follows:

[0016]

[0017] Wherein, ρ and ρ h are air density and hail density, respectively, d h is a constant 3.

[0018] Further, the maximum estimated hail size MESH in step (3) is a maximum estimated hail size algorithm for estimating the maximum possible diameter of hail appearing on the ground; the specific MESH calculation formula is as follows:

[0019] E=5×10 -6 ×10 0.084Z ×W(Z) (2)

[0020]

[0021] MESH=2.5(SHI) 0.5 (4)

[0022] Wherein:

[0023]

[0024] The hail weight coefficient W(Z) is divided by the radar reflectivity factor Z, Z L and Z U are the minimum threshold and maximum threshold of the radar reflectivity factor respectively; SHI is the strong hail index, W T (H) is a weight function based on the environmental temperature distribution, H, H0, H T , H m20 represent the model height, the zero layer height, the model top height and the-20℃ height respectively.

[0025] Further, the minimum threshold Z L and the maximum threshold Z U of the radar reflectivity factor are set as 40 and 50 dBZ, respectively.

[0026] Further, the hail maximum diameter D* generation process in step (3) is as follows:

[0027] In the Bulk scheme, the particle spectral distribution function is usually assumed to be Gamma distribution, that is:

[0028] N(D) = N0D α e -λD (7)

[0029] where N0, a, and l are the intercept, shape, and slope parameters of the Gamma distribution, respectively, a determines the particle spectral width, and D is the diameter of the particle; when a is 0, the Gamma distribution is simplified to the Marshall-Palmer distribution; N0 and l are diagnosed based on the particle number concentration N T and the mass mixing ratio q; the hail corresponds to N 0h and l h are as follows:

[0030]

[0031] For the three-parameter cloud microphysical scheme, in order to obtain a more accurate particle spectral distribution, the radar reflectivity factor of different phase water condensates is calculated, and then the spectral shape parameter a is diagnosed; the details are as follows:

[0032]

[0033] where:

[0034]

[0035] The hail maximum diameter D* is defined as the total particle number concentration N h {D *} below that is:

[0036]

[0037] When N h {D *} below the threshold , the hail diameter D* at this time is the maximum hail diameter; where is 10 -3 m -3 .

[0038] Beneficial effects: Compared with the prior art, the beneficial effects of the present application: Based on the cloud resolution numerical model simulation results, the multi-parameter cloud microphysical scheme is adopted, the multi-parameter hail size characteristic index is obtained through the diagnosis of the spectral shape parameter, and more detailed and accurate diagnosis of the ground hail size is realized; The present application can realize the diagnosis and analysis of hail size using the three-parameter cloud microphysical scheme, including the maximum size, average size and drop zone of ground hail, and is closer to the actual ground observation, significantly improving the analysis and reproduction ability of the model to hail storm system and ground hail, and providing a scientific basis for artificial hail prevention and disaster emergency management. The hail maximum diameter D * , obtained in the ground hail size evaluation results, is more ideal, which is reflected in not only the maximum size of hail but also the drop zone of hail is more in line with the actual observation. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of the present application;

[0040] Figure 2 is the cloud top temperature simulated by different cloud microphysical schemes and observed by Kuaifah No. 8 on September 30, 2021, 16 UTC; wherein (a) is the MY two-parameter scheme; (b) is the NSSL two-parameter scheme; (c) is the cloud top temperature corresponding to the 1-km inner layer grid simulated by the NSSL three-parameter scheme; (d) is the cloud top temperature retrieved by satellite observation;

[0041] Figure 3 is the radar reflectivity factor simulated by different cloud microphysical schemes and observed by radar on September 30, 2021, 16:30 UTC; wherein (a) is the MY two-parameter scheme; (b) is the NSSL two-parameter scheme; (c) is the radar reflectivity of the 1-km inner layer grid simulated by the NSSL three-parameter scheme, (d) is the radar observation;

[0042] Figure 4 is the ground hail particle size distribution simulated by three cloud microphysical schemes on September 30, 2021, 1630 UTC. DETAILED DESCRIPTION

[0043] The present application will be further described in detail below in combination with the drawings.

[0044] As Figure 1 shown, the present application proposes a multi-parameter hail size quantitative diagnosis method based on cloud resolution model, which specifically includes the following steps:

[0045] Step 1: Hail suppression occurred on October 1, 2021 at 00:00, and reanalysis data and actual observation data were obtained, including ERA5 reanalysis data, ground station sounding observation, radar network observation, satellite observation, and establishment of a comprehensive data set. Based on the above comprehensive data set, the weather situation of different height layers during hail occurrence, such as atmospheric temperature, humidity, wind field, and the three-dimensional structure characteristics of hail storm system were analyzed.

[0046] Step 2: Cloud resolution scale numerical simulation experiment was carried out, with horizontal grid resolution of 1-3 km resolution, multi-parameter cloud microphysical scheme was adopted, including Milbrandt and Yau (MY) double parameter, double parameter and triple parameter scheme of National Storm Laboratory NSSL. Based on high resolution numerical simulation experiment, combined with observation data, the simulation effect of hail storm system was evaluated.

[0047] Step 3: Combined with different types of hail quantitative analysis parameters, the test analysis of ground hail size diagnosis effect was carried out. The specific hail quantitative evaluation index is as follows:

[0048] (1) Average median diameter (Dmh):

[0049] Dmh represents the median diameter corresponding to the median of the mass distribution of hail particles per unit area, and Dmh can describe the average size of hail particles. The calculation formula of Dmh is as follows:

[0050]

[0051] Where, ρ and ρ h are air density and hail density, respectively, d h is a constant of 3.

[0052] (2) Maximum estimated hail size (MESH):

[0053] MESH is the maximum estimated hail size algorithm, which is used to estimate the maximum possible diameter of ground hail. MESH algorithm assumes that the high value area of radar reflectivity above zero degree layer (>40dBZ) is a possible indication of hail, and radar reflectivity above-20℃ layer exceeding 50dBZ is considered to have hail. The specific MESH calculation formula is as follows:

[0054] E=5×10 -6 ×10 0.084Z ×W(Z) (2)

[0055]

[0056] MESH=2.5(SHI) 0.5 (4)

[0057] Where:

[0058]

[0059] The hail weight coefficient W(Z) is divided by the radar reflectivity factor Z, and the minimum threshold value Z L and the maximum threshold value Z U of the radar reflectivity factor are respectively set to 40 and 50 dBZ. SHI is a strong hail index, and W T (H) is a weight function based on the environmental temperature distribution, H, H0, H T , H m20 respectively represent the model height, the zero-degree layer height, the model top height and the -20℃ height.

[0060] (3) The maximum diameter of hail (D*):

[0061] The maximum diameter of hail on the ground is very important for hailstorm evolution and ground hail size. The multi-parameter cloud microphysical scheme in the numerical model can reproduce the maximum diameter of hail to some extent through quantitative diagnosis of the hail particle spectrum distribution. For the diagnosis of the maximum diameter of hail, previous studies mainly focused on the two-parameter scheme, and the hail shape parameter was fixed as a constant, which often significantly overestimates the ground hail. In order to more accurately diagnose the ground hail, a process of dynamically diagnosing the hail shape parameter is introduced in the three-parameter cloud microphysical scheme, which improves the characteristics of the hail particle spectrum distribution, and further optimizes the calculation of the hail generation microphysical process and the characteristics of the ground hail size distribution.

[0062] The Bulk scheme is adopted, and the particle spectrum distribution function is commonly assumed to be in the form of Gamma distribution, that is:

[0063] N(D)=N0D α e -λD (7)

[0064] Where N0, α, λ are the intercept, shape and slope parameters of the Gamma distribution respectively, and α determines the width of the particle spectrum, and D is the diameter of a certain hydrometeor particle. For single or two-parameter cloud microphysical schemes, α is often fixed as a constant or diagnosed by an empirical formula related to the mass median diameter. When α is 0, the Gamma distribution is simplified to the Marshall-Palmer distribution. N0 and λ are often diagnosed and calculated based on the particle number concentration N T and the mass mixing ratio q. The corresponding N 0h and λ h of hail are as follows:

[0065]

[0066] For the three-parameter cloud microphysical scheme, in order to obtain more accurate particle spectrum distribution, the calculation of radar reflectivity factor of different phase water condensate is increased, and then the spectrum shape parameter alpha is diagnosed. Specifically as follows:

[0067]

[0068] Among them,

[0069]

[0070] The maximum diameter D* of the hail is defined as the total number concentration N of particles greater than a certain hail diameter threshold h {D *} is less than That is:

[0071]

[0072] When N h {D *} is less than the threshold The hail diameter D* when N In the present application 10 -3 m -3 .

[0073] Step 4: Based on the post-processing display system, the quantitative index of the above ground hail size is visualized.

[0074] The present application selects a strong hail of a certain place on September 30, 2021, and diagnoses and analyzes the ground hail size of the hail based on the method in the present application. The hail occurred in a cold vortex weather background, and the atmospheric conditions on the night of September 30 (before the development of strong convection) were suitable for the development of strong convection, with a medium to high atmospheric instability energy value, a warm and wet southwest jet stream converging with a cold vortex to form a low-level convergence zone, providing good dynamic lifting conditions for the development of hail storms; The air in the middle and lower layers is relatively dry, which is beneficial to the formation and landing of large hail. A high-resolution numerical simulation experiment was carried out on the hail process, and a variety of different cloud microphysical schemes (including MY, NSSL double-parameter and three-parameter schemes) were used. Double-layer grid nesting was set, and the inner layer grid resolution was 1km. In the vertical direction, 49 layers were taken from the ground to the 10hPa height layer. The MYNN boundary layer scheme was used for boundary layer process, the RRTMG long wave and short wave radiation scheme was used for radiation process, and the Noah land surface model was used for surface process.

[0075] Figure 2 is the cloud top temperature simulated by different cloud microphysical schemes and observed by Kuifah No. 8 on September 30, 2021, 16UTC. Figure 3 is the radar reflectivity factor simulated by different cloud microphysical schemes and observed by radar on September 30, 2021, 16:30UTC.Figure 4 are the surface hail particle size distributions simulated by different cloud microphysics schemes on September 30, 1630 UTC, 2021, where the first row, i.e., a-c, is the mean median diameter of hail particles (Dmh, mm), the second row, i.e., d-f, is the maximum estimated hail size (MESH, mm), and the third row, i.e., g-i, is the hail maximum diameter (D*, mm) calculated based on the hail particle spectrum distribution and the number concentration threshold. It is worth mentioning that the leftmost column corresponds to the results of the experiment (MY_2) using the MY two-parameter scheme, the middle column corresponds to the results of the experiment (NSSL_2) using the NSSL two-parameter scheme, and the rightmost column corresponds to the results of the experiment (NSSL_3) using the three-parameter scheme. The results show that, compared with satellite observations, the MY two-parameter scheme simulates a smaller hail storm cloud range, a higher hail cloud top temperature, and significantly underestimates the updraft height and range of strong hail storms. The NSSL scheme, including the two-parameter and three-parameter schemes, better reproduces the hail storm range, especially the NSSL three-parameter scheme better reproduces the strong convective hail storm in the Dalian region. Figure 3 The radar combined reflectivity of different sensitivity experiments and radar observations is shown, and the results show that different sensitivity experiments significantly underestimate the stratiform cloud precipitation area with echo below 30 dBZ, and the three-parameter scheme simulates the strong convective area closest to the observation. Figure 4 The three-parameter scheme shows the best diagnostic effect on the ground hailstone size, which is closest to the actual observation.

[0076] In addition, the NSSL three-parameter scheme is most consistent with the actual observation in terms of hailstone fall area and hailstone size diagnosis, while the MY two-parameter and NSSL two-parameter schemes significantly overestimate the hailstone size. The present application quantitatively diagnoses the hail maximum diameter D * , which is most consistent with the actual observed ground hail maximum diameter, reflecting its better indication of the actual hail maximum size and business application prospect.

[0077] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the present application patent should be subject to the appended claims.

Claims

1. A multi-parameter quantitative diagnosis method for hail size based on cloud resolution patterns, characterized in that, Includes the following steps: (1) Obtain reanalysis data and actual observation data, establish a comprehensive dataset, and analyze the weather patterns of storm system occurrence and the three-dimensional structural characteristics of hail storm system; (2) Conduct cloud resolution scale numerical simulation experiments, compare with actual observations, evaluate the simulation effect of numerical models on storm systems, and assess the ability of sub-models to reproduce the microphysical field characteristics within hail clouds. (3) Based on the simulation results of the cloud resolution model with the best evaluation results, diagnose multiple quantitative characteristic parameters of hail size; The quantitative characteristic parameters of hail size include the median hail diameter Dmh, the maximum estimated hail size MESH, and the maximum hail diameter D*. (4) Visualize multiple dimensional parameters of ground hailfall; The diagnostic process for the maximum hail diameter D* in step (3) is as follows: In the Bulk scheme, the hail particle spectral distribution function is often assumed to be a Gamma distribution, i.e.: （7） in, , , These represent the intercept, shape, and slope parameters of the Gamma distribution, respectively. The spectral width of the particle is determined by D, where D is the particle diameter; when When the Gamma value is 0, the Gamma distribution is simplified to the Marshall-Palmer distribution; and Based on particle number concentration and mass mixing ratio Perform diagnostic calculations; Hail corresponds to and The specific calculations are as follows: （8） （9） For the three-parameter cloud microphysics scheme, in order to obtain a more accurate particle spectrum distribution, the radar reflectivity factor of hydrocondensates in different phases was calculated, thereby diagnosing the spectral parameters. The details are as follows: （10） in: （11） The maximum hail diameter D* is defined as the concentration of the total number of particles greater than a certain hail diameter threshold. Below ,Right now: （12） when Below the threshold The hail diameter D* corresponding to the time is the maximum hail diameter.

2. The multi-parameter hail size quantitative diagnosis method based on cloud resolution pattern according to claim 1, characterized in that, The reanalysis data and actual observation data mentioned in step (1) include ERA5 reanalysis data, ground station radiosonde observations, radar network observations and satellite observations.

3. The multi-parameter hail size quantitative diagnosis method based on cloud resolution pattern according to claim 1, characterized in that, The weather conditions described in step (1) include atmospheric temperature, humidity, and wind field at different altitudes.

4. The multi-parameter hail size quantitative diagnosis method based on cloud resolution pattern according to claim 1, characterized in that, The implementation process of step (2) is as follows: The horizontal grid resolution was selected as 1-3km, and a multi-parameter cloud microphysics scheme was adopted, including the MY dual-parameter scheme, the dual-parameter and three-parameter scheme of the strong storm laboratory. Based on the results of high-resolution numerical simulation experiments and combined with observation data, the simulation effect of the evaluation model on hail storm systems was evaluated.

5. The multi-parameter hail size quantitative diagnosis method based on cloud resolution pattern according to claim 1, characterized in that, The average median diameter Dmh mentioned in step (3) represents the diameter corresponding to the median of the mass distribution of hail particles per unit area. Dmh describes the average size of hail particles, and the calculation formula is as follows: （1） in, , and These are air density and hail density, respectively. It is a constant of 3.

6. The multi-parameter hail size quantitative diagnosis method based on cloud resolution pattern according to claim 1, characterized in that, The maximum estimated hail size MESH mentioned in step (3) is a maximum estimated hail size algorithm used to estimate the maximum possible diameter of hailstones appearing on the ground; the specific MESH calculation formula is as follows: （2） （3） （4） in: （5） （6） The hail weighting coefficient W(Z) is divided using the radar reflectivity factor Z. and These are the minimum and maximum thresholds for the radar reflectivity factor, respectively; SHI is the severe hail index. It is a weighting function based on the distribution of ambient temperature. , , , These represent the mode altitude, zero-degree layer altitude, mode top altitude, and -20°C altitude, respectively.

7. The multi-parameter hail size quantitative diagnosis method based on cloud resolution pattern according to claim 6, characterized in that, The minimum threshold of the radar reflectivity factor and maximum threshold Set them to 40 and 50 dBZ respectively.

8. The multi-parameter hail size quantitative diagnosis method based on cloud resolution pattern according to claim 1, characterized in that, The 10 -3 m -3 .

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