Hail forecasting method and device based on single-parameter single-ice cloud micro-physical parameterization

Abstract

The invention relates to a hail forecasting method and device based on single-parameter single-ice cloud micro-physical parameterization. The method comprises the following steps: determining the spectral distribution of hail particles based on the diameters of the hail particles, and sequentially determining the relationships between the diameters of the hail particles and the mass of the hail particles and the falling speed of the hail particles; based on the relationship between the hail particle diameter and the hail particle mass, determining the slope of the distribution spectrum pattern of the hail particle spectrum; based on the relation between the hail particle diameter and the hail particle falling speed, the final falling speed of the hail particles is determined; and based on the slope-determined hail particle spectrum distribution and the final falling speed of the hail particles, simulation of a hail formation conversion process is carried out through a cloud microphysical parameterization scheme in a numerical forecasting mode at the above-zero temperature and the below-zero temperature, and hail weather forecast of the complete process is obtained. By adopting the method, accurate hail weather forecasting can be carried out through a numerical forecasting mode, and auxiliary decision-making information can be conveniently provided for meteorological disaster prevention and reduction.

Description

Technical Field

[0001] The present application relates to the field of numerical forecasting technology, and in particular to a hail forecasting method and device based on single-parameter single ice cloud microphysical parameterization. Background Art

[0002] Hail is a type of extreme weather that can develop in a short period of time. With global warming, the probability of extreme weather is gradually increasing, the probability of large hail particles forming is increasing, and the intensity and destructiveness of hail will increase, making the demand for numerical forecasting of hail weather more urgent.

[0003] In numerical forecast models, cloud microphysical parameterization schemes are mainly responsible for the mutual conversion between water vapor and various particles in the cloud, as well as the latent heat processing generated during the conversion process. They are one of the most critical parameterization schemes in numerical model precipitation simulation. However, most of the current cloud microphysical parameterization schemes fail to accurately describe the formation and evolution process of hail particles. In order to improve the simulation of hail weather in numerical forecast models, it is very necessary to develop cloud microphysical parameterization schemes with hail simulation capabilities. Summary of the invention

[0004] Based on this, it is necessary to provide a hail forecasting method and device based on single-parameter single ice cloud microphysical parameterization to address the above technical problems.

[0005] A hail forecasting method based on single-parameter single ice cloud microphysical parameterization, the method comprising: Determine the hail particle spectrum distribution based on the hail particle diameter, and determine the relationship between the hail particle diameter and the hail particle mass and the hail particle falling speed in turn; Based on the relationship between the diameter of hail particles and the mass of hail particles, the slope of the hail particle distribution spectrum is determined; Based on the relationship between the diameter of hail particles and the falling speed of hail particles, the final falling speed of hail particles is determined; Based on the determined slope of the hail particle spectrum distribution and the final falling velocity of the hail particles, the hail formation conversion process is simulated through the cloud microphysical parameterization scheme in the numerical forecast model when the temperature is less than 0℃ and greater than 0℃, and the hail weather forecast for the complete process is obtained.

[0006] In one embodiment, the hail particle spectrum distribution is expressed as ; Where D is the diameter of hail particles; is the spectral intercept, in units of ; is the slope of the hail particle distribution spectrum; Represents an exponential function.

[0007] In one embodiment, determining the relationship between the diameter of the hail particle, the mass of the hail particle, and the falling speed of the hail particle in sequence includes: The relationship between the diameter of hail particles and the mass of hail particles is expressed as ; in, is the mass of hail particles, and Control parameter representing the mass of hail particles; The relationship between the diameter of hail particles and the falling speed of hail particles is expressed as ; in, is the falling speed of hail particles, is the air density at sea level, is the air density, and A control parameter that represents the falling speed of hail particles.

[0008] In one embodiment, determining the slope of the hail particle distribution spectrum based on the relationship between the hail particle diameter and the hail particle mass includes: Based on the hail particle diameter D and hail particle mass The relationship between and is calculated using the gamma function to obtain the hail particle mixing ratio. , expressed as ; in, represents the spectral intercept, represents the gamma function, represents the parameters of the gamma function, represents the integral variable, represents the exponential function; According to the mixing ratio of hail particles Calculate and get the slope of the hail particle distribution spectrum , expressed as .

[0009] In one embodiment, determining the final falling speed of the hail particles based on the relationship between the diameter of the hail particles and the falling speed of the hail particles includes: Based on the hail particle diameter D and the falling speed of hail particles and the control parameters based on the mass of hail particles. and the slope of the hail particle distribution spectrum , using the gamma function Calculate the final falling speed of the hail particles , expressed as ; in, and represents the control parameter of the falling speed of hail particles, is the air density at sea level, is the air density.

[0010] In one embodiment, when the temperature is less than 0°C, the hail formation transformation process simulated by the cloud microphysical parameterization scheme in the numerical forecast model is expressed as: ; in, Indicates the transformation process of hail formation, haut It is the process of transformation from snow to hail. hf The process of rain condensing into hail. hdep The process of water vapor condensing into hail. q It is the process of hailstones colliding with cloud water particles. haci It is the process of hail particles colliding with cloud ice particles. hacr It is the process of hail particles colliding with rain particles. hacp It is the process of hail particles colliding with snow and graupel particles.

[0011] In one embodiment, when the temperature is greater than 0°C, the hail formation transformation process simulated by the cloud microphysical parameterization scheme in the numerical forecast model is expressed as ; in, Indicates the transformation process of hail formation, hevp The process of hail sublimating into water vapor. hmlt The process of hail melting into rain. heml It is the evaporation process during the melting of hail.

[0012] In one embodiment, the numerical forecasting model is a WRF model.

[0013] A hail forecasting device based on single-parameter single ice cloud microphysical parameterization, the device comprising: A data preprocessing module is used to determine the hail particle spectrum distribution based on the hail particle diameter, and to determine the relationship between the hail particle diameter and the hail particle mass and the hail particle falling speed in turn; A slope calculation module, used for determining the slope of the hail particle distribution spectrum based on the relationship between the hail particle diameter and the hail particle mass; A final falling velocity calculation module is used to determine the final falling velocity of hail particles based on the relationship between the diameter of hail particles and the falling velocity of hail particles; The numerical prediction module is used to simulate the hail formation conversion process based on the hail particle spectrum distribution with a determined slope and the final falling speed of the hail particles, when the temperature is less than 0℃ and when the temperature is greater than 0℃, through the cloud microphysical parameterization scheme in the numerical prediction model, to obtain a complete hail weather forecast.

[0014] The above-mentioned hail forecasting method and device based on single-parameter single-ice cloud microphysical parameterization simulates the hail formation conversion process through the cloud microphysical parameterization scheme in the numerical forecast model, and develops a cloud microphysical parameterization scheme with hail simulation capabilities, so that hail weather forecasts can be made, which is convenient for providing auxiliary decision-making information for meteorological disaster prevention and mitigation. In addition, in the simulation of the hail formation conversion process, this scheme can accurately simulate the formation and evolution process of hail under different temperature conditions of less than 0°C and greater than 0°C by determining the slope of the hail particle spectrum distribution and the final falling speed of the hail particles. This simulation in different temperature ranges helps to more realistically reflect the physical mechanism of hail formation, thereby improving the accuracy of the forecast. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flow chart of a hail forecasting method based on single-parameter single ice cloud microphysical parameterization in one embodiment; Figure 2 A schematic diagram of the mutual conversion process between particles in the SBU_Ylin cloud microphysical parameterization scheme with hail particles in one embodiment; Figure 3 FIG. 1 is a schematic diagram showing the comparison of hail forecast results of two cloud microphysical parameterization schemes, SBU_YLin with hail particles and original SBU_YLin, in one embodiment; wherein, Figure 3 (a) Schematic diagram of hail forecast results of the original SBU_YLin cloud microphysical parameterization scheme; Figure 3 (b) Schematic diagram of hail forecast results of the SBU_YLin cloud microphysical parameterization scheme with hail particles. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] In one embodiment, Figure 1 As shown, a hail forecasting method based on single-parameter single ice cloud microphysical parameterization is provided, comprising the following steps: Step S1, determining the hail particle spectrum distribution based on the hail particle diameter, and determining the relationship between the hail particle diameter and the hail particle mass and the hail particle falling speed in turn.

[0018] The hail particle spectrum distribution is expressed as ; Where D is the diameter of hail particles; is the spectral intercept, in units of , this embodiment specifically sets The value is a constant 4×10 4 ; is the slope of the hail particle distribution spectrum; Represents an exponential function.

[0019] The relationship between the diameter of hail particles and the mass of hail particles is expressed as ; in, is the mass of hail particles, and A control parameter representing the mass of hail particles.

[0020] Specifically, the hail density is , and The values ​​are 0.37 and 2.69 respectively, from which the mass of hail particles can be obtained It can be expressed as .

[0021] The relationship between the diameter of hail particles and the falling speed of hail particles is expressed as ; in, is the falling speed of hail particles, is the air density at sea level, is the air density, and A control parameter that represents the falling speed of hail particles.

[0022] Specifically, , where m is the distance unit: meter, s is the time unit: second, .

[0023] Step S2, determining the slope of the hail particle distribution spectrum based on the relationship between the hail particle diameter and the hail particle mass.

[0024] Specifically, based on the hail particle diameter D and the hail particle mass The relationship between and is calculated using the gamma function to obtain the hail particle mixing ratio. , expressed as ; in, represents the spectral intercept; represents the gamma function, represents the parameters of the gamma function, represents the integral variable, represents the exponential function; According to the mixing ratio of hail particles Calculate and get the slope of the hail particle distribution spectrum , expressed as .

[0025] Further , Substituting into the above formula, we can finally get .

[0026] Step S3, determining the final falling speed of the hail particles based on the relationship between the diameter of the hail particles and the falling speed of the hail particles.

[0027] Specifically, based on the hail particle diameter D and the falling speed of the hail particles and the control parameters based on the mass of hail particles. and the slope of the hail particle distribution spectrum , using the gamma function Calculate the final falling speed of the hail particles , expressed as ; in, and represents the control parameter of the falling speed of hail particles, is the air density at sea level, is the air density.

[0028] Further , and Substituting the specific value of into , we can finally get .

[0029] Step S4, based on the determined slope of the hail particle spectrum distribution and the final falling speed of the hail particles, respectively, when the temperature is less than 0°C and when the temperature is greater than 0°C, the hail formation conversion process is simulated through the cloud microphysical parameterization scheme in the numerical forecast model to obtain a complete process hail weather forecast.

[0030] Specifically, when the temperature is less than 0°C, the hail formation conversion process simulated by the cloud microphysical parameterization scheme in the numerical forecast model is expressed as ; in, Indicates the transformation process of hail formation, haut It is the process of transformation from snow to hail. hf The process of rain condensing into hail. hdep The process of water vapor condensing into hail. q It is the process of hailstones colliding with cloud water particles. haci It is the process of hail particles colliding with cloud ice particles. hacr It is the process of hail particles colliding with rain particles. hacp It is the process of hail particles colliding with snow and graupel particles.

[0031] When the temperature is greater than 0℃, the hail formation transformation process simulated by the cloud microphysical parameterization scheme in the numerical forecast model is expressed as ; in, Indicates the transformation process of hail formation, hevp The process of hail sublimating into water vapor. hmlt The process of hail melting into rain. heml It is the evaporation process during the melting of hail.

[0032] Based on the above-mentioned single-parameter single ice cloud microphysical parameterization hail forecasting method, the hail formation conversion process can be simulated through the cloud microphysical parameterization scheme in the numerical forecast model, so that hail weather forecast can be carried out to provide auxiliary decision-making information for meteorological disaster prevention and mitigation. In addition, in the simulation of hail formation conversion process, the above-mentioned method can accurately simulate the formation and evolution process of hail under different temperature conditions of less than 0°C and greater than 0°C by determining the slope of the hail particle spectrum distribution and the final falling speed of the hail particles. This simulation of the temperature range helps to more realistically reflect the physical mechanism of hail formation, thereby improving the accuracy of the forecast.

[0033] Specifically, the numerical forecasting model used in this embodiment is the WRF (The Weather Research and Forecasting Model) model, and the cloud microphysical parameterization scheme used is SBU_Ylin. The SBU_Ylin cloud microphysical parameterization scheme is a simplified numerical model that is designed to help deal with complex cloud microphysical processes in climate or weather simulations while maintaining a certain degree of accuracy and improving computational efficiency. In this embodiment, the SBU_Ylin cloud microphysical parameterization scheme in WRF is used to simulate the hail formation conversion process, so that the obtained SBU_Ylin cloud microphysical parameterization scheme with hail particles has the ability to simulate hail. The mutual conversion process between particles in the SBU_Ylin cloud microphysical parameterization scheme with hail particles is as follows: Figure 2 The conversion process is briefly shown in Table 1. Figure 2 The solid arrows in the middle represent the original particle conversion process in the SBU_Ylin cloud microphysics parameterization scheme, and the dashed arrows represent the particle conversion process in the SBU_Ylin cloud microphysics parameterization scheme after the hail particles are added.

[0034] Table 1 Figure 2 Brief introduction to the mutual conversion process between particles in the

[0035] In order to further illustrate the advantages of the hail forecasting method of a single-parameter single ice cloud microphysical parameterization provided by this application over the traditional cloud microphysical parameterization scheme, a comparative test with the forecast of the traditional cloud microphysical parameterization scheme was also conducted. In the test, the simulation area is an area that encounters severe convective weather, and the numerical forecast model adopts the WRF model. The specific test steps are as follows: Step 1: Download ERA5 (European Centre for Medium-Range Weather Forecasts) reanalysis data as the initial and boundary fields for numerical prediction model runs.

[0036] Step 2: Establish a WRF simulation and forecasting system. The numerical forecast model used is WRF-ARW 3.7.1, which is a non-statically compressible model. The simulation time is from 20:00 on May 5, 2015 to 00:00 on May 7, 2015), and the simulation lasts for 36 hours, of which the first 12 hours are regarded as the model initialization time, and the remaining 24 hours are taken as the research object. The model has a total of 38 non-uniform layers, from the ground to 50hPa. The center position of the model is (34°N, 113.5°E), with two nested layers. The resolution of the first nested layer is 0.1° (about 11km), and the number of horizontal grid points is 541×281; the resolution of the second nested layer is 0.033° (about 3.7km), and the number of horizontal grid points is 1342×472.

[0037] Step 3: Set the parameterization scheme of the numerical prediction model. The parameterization schemes of the numerical prediction model include the SBU_YLin cloud microphysics parameterization scheme, the Noah land surface process, the Monin-Obukhov (Janjic Eta) surface scheme, the Mellor-Yamada-Janjic (Eta) TKE (turbulent kinetic energy) boundary layer scheme, the RRTM (rapid radiative transfer model) longwave radiation scheme and the Dudhia shortwave radiation scheme. The numerical prediction model does not use the cumulus convection parameterization scheme. Specifically, the cloud scheme selects the Xu-Randall scheme. The land surface data uses 21-class MODIS (21-class satellite remote sensing) data.

[0038] Step 4: Replace the original SBU_YLin cloud microphysical parameterization scheme with the SBU_YLin cloud microphysical parameterization scheme with hail particles obtained based on this application, and re-run the WRF simulation and forecasting system. At this time, the WRF simulation and forecasting system has the hail forecasting capability.

[0039] Step 5: Compare the two cloud microphysical parameterization schemes using SBU_YLin with hail particles and the original SBU_YLin. The comparison results are shown in the figure. Figure 3 shown. Figure 3 In (a), “NSTANT FIELD-VALUE IS .0” indicates that the constant field value is 0, indicating that the hail falling area cannot be simulated based on the original SBU_YLin cloud microphysics parameterization scheme. Figure 3 (b) It can be seen that the SBU_YLin cloud microphysical parameterization scheme with hail particles obtained in this application can accurately simulate the falling area of ​​hail, which is convenient for providing auxiliary decision-making information for meteorological disaster prevention and mitigation.

[0040] In one embodiment, a hail forecasting device based on single-parameter single ice cloud microphysical parameterization is provided, comprising: A data preprocessing module is used to determine the hail particle spectrum distribution based on the hail particle diameter, and to determine the relationship between the hail particle diameter and the hail particle mass and the hail particle falling speed in turn; A slope calculation module, used for determining the slope of the hail particle distribution spectrum based on the relationship between the hail particle diameter and the hail particle mass; A final falling velocity calculation module is used to determine the final falling velocity of hail particles based on the relationship between the diameter of hail particles and the falling velocity of hail particles; The numerical prediction module is used to simulate the hail formation conversion process based on the hail particle spectrum distribution with a determined slope and the final falling speed of the hail particles, when the temperature is less than 0℃ and when the temperature is greater than 0℃, through the cloud microphysical parameterization scheme in the numerical prediction model, to obtain a complete hail weather forecast.

[0041] For the specific definition of the hail forecasting device based on the single-parameter single ice cloud microphysical parameterization, please refer to the definition of the hail forecasting method based on the single-parameter single ice cloud microphysical parameterization mentioned above, which will not be repeated here. Each module in the above-mentioned hail forecasting device based on the single-parameter single ice cloud microphysical parameterization can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0042] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A hail forecasting method based on single-parameter single ice cloud microphysical parameterization, characterized in that: The method comprises: Determine the hail particle spectrum distribution based on the hail particle diameter, and determine the relationship between the hail particle diameter and the hail particle mass and the hail particle falling speed in turn; Based on the relationship between the diameter of hail particles and the mass of hail particles, the slope of the hail particle distribution spectrum is determined; Based on the relationship between the diameter of hail particles and the falling speed of hail particles, the final falling speed of hail particles is determined; Based on the determined slope of the hail particle spectrum distribution and the final falling speed of the hail particles, the hail formation conversion process is simulated through the cloud microphysical parameterization scheme in the numerical forecast model when the temperature is less than 0℃ and when the temperature is greater than 0℃, and the hail weather forecast of the complete process is obtained. Wherein, the hail particle spectrum distribution is expressed as ； Where D is the diameter of hail particles; is the spectral intercept, in units of ; is the slope of the hail particle distribution spectrum; represents the exponential function; Among them, the relationship between the diameter of hail particles, the mass of hail particles and the falling speed of hail particles is determined in turn, including: The relationship between the diameter of hail particles and the mass of hail particles is expressed as ； in, is the mass of hail particles, and Control parameter representing the mass of hail particles; The relationship between the diameter of hail particles and the falling speed of hail particles is expressed as ； in, is the falling speed of hail particles, is the air density at sea level, is the air density, and A control parameter that represents the falling speed of hail particles.

2. The method according to claim 1, characterized in that Based on the relationship between the diameter of hail particles and the mass of hail particles, the slope of the hail particle distribution spectrum is determined, including: Based on the hail particle diameter D and hail particle mass The relationship between and is calculated using the gamma function to obtain the hail particle mixing ratio. , expressed as ； in, represents the spectral intercept, represents the gamma function, represents the parameters of the gamma function, represents the integral variable, represents the exponential function; According to the mixing ratio of hail particles Calculate and get the slope of the hail particle distribution spectrum , expressed as 。 3. The method according to claim 2, characterized in that Based on the relationship between the diameter of hail particles and the falling speed of hail particles, the final falling speed of hail particles is determined, including: Based on the hail particle diameter D and the falling speed of hail particles and the control parameters based on the mass of hail particles. and the slope of the hail particle distribution spectrum , using the gamma function Calculate the final falling speed of the hail particles , expressed as ； in, and represents the control parameter of the falling speed of hail particles, is the air density at sea level, is the air density.

4. The method according to claim 1, characterized in that: When the temperature is less than 0℃, the hail formation transformation process simulated by the cloud microphysical parameterization scheme in the numerical forecast model is expressed as ； in, Indicates the transformation process of hail formation, haut It is the process of transformation from snow to hail. hf The process of rain condensing into hail. hdep The process of water vapor condensing into hail. q It is the process of hailstones colliding with cloud water particles. haci It is the process of hail particles colliding with cloud ice particles. hacr It is the process of hail particles colliding with rain particles. hacp It is the process of hail particles colliding with snow and graupel particles.

5. The method according to claim 1, characterized in that When the temperature is greater than 0℃, the hail formation transformation process simulated by the cloud microphysical parameterization scheme in the numerical forecast model is expressed as ； in, Indicates the transformation process of hail formation, hevp The process of hail sublimating into water vapor. hmlt The process of hail melting into rain. heml It is the evaporation process during the melting of hail.

6. The method according to claim 1, 4 or 5, characterized in that: The numerical forecast model is the WRF model.

7. A hail forecasting device based on single-parameter single ice cloud microphysical parameterization, characterized in that: The device comprises: A data preprocessing module is used to determine the hail particle spectrum distribution based on the hail particle diameter, and to determine the relationship between the hail particle diameter and the hail particle mass and the hail particle falling speed in turn; A slope calculation module, used for determining the slope of the hail particle distribution spectrum based on the relationship between the hail particle diameter and the hail particle mass; A final falling velocity calculation module is used to determine the final falling velocity of hail particles based on the relationship between the diameter of hail particles and the falling velocity of hail particles; The numerical prediction module is used to simulate the hail formation conversion process through the cloud microphysical parameterization scheme in the numerical prediction model based on the determined slope of the hail particle spectrum distribution and the final falling speed of the hail particles when the temperature is less than 0℃ and when the temperature is greater than 0℃, and obtain the hail weather forecast of the complete process; Wherein, the hail particle spectrum distribution is expressed as ； Where D is the diameter of hail particles; is the spectral intercept, in units of ; is the slope of the hail particle distribution spectrum; represents the exponential function; Among them, the relationship between the diameter of hail particles, the mass of hail particles and the falling speed of hail particles is determined in turn, including: The relationship between the diameter of hail particles and the mass of hail particles is expressed as ； in, is the mass of hail particles, and Control parameter representing the mass of hail particles; The relationship between the diameter of hail particles and the falling speed of hail particles is expressed as ； in, is the falling speed of hail particles, is the air density at sea level, is the air density, and A control parameter that represents the falling speed of hail particles.

Images