Cloud micro-physical model optimization and numerical verification method for hail wet growth process

Abstract

The invention discloses a cloud micro-physical model optimization and numerical verification method for a hail wet growth process, and the method comprises the steps: constructing a hail wet growth process parameterization scheme based on the physical principle that hail and supercooled water drops collide and freeze, and coupling the hail wet growth process parameterization scheme to a Morrison cloud micro-physical scheme of a CMA-MESO mode for detection. By calculating a hail ventilation coefficient and combining the environment temperature, the air density and the diffusion coefficient, the condition of wet growth is judged, and the change of the hail mass mixing ratio is updated. The CMA-MESO mode is used for carrying out actual severe convection hail reduction example simulation, and the forecast improvement effect of the newly-added process on variables such as radar reflectivity, hail mass mixing ratio and number concentration vertical section, time horizontal integral vertical profile of part of hydrogel in cloud and maximum estimated hail size is evaluated. According to the invention, the simulation capability of the mode on the generation and development process of large hail is improved, and the accuracy of quantitative simulation of hail in severe convection weather is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud microphysics, and particularly relates to a method for optimizing a cloud microphysical model and numerical verification for the wet growth process of hail. Background Art

[0002] The formation of hail is one of the core contents in the cloud microphysical scheme, and its mechanism involves complex thermodynamics and microphysical processes. The wet growth of hail during the formation process is a key process for the prediction accuracy. Wet growth refers to the process in which when hail particles capture a large number of supercooled water droplets in the cloud, the latent heat released by freezing is partially used to increase the particle temperature, forming a transparent ice layer. However, currently in the CMA_MESO (China Meteorological Administration - Mesoscale Model) numerical prediction model, the cloud microphysical scheme does not consider the wet growth process of hail, resulting in a large deviation between the prediction of hail and the actual situation. The wet growth process of hail plays an important role in the growth process of hail. It gradually freezes after forming a water film on the surface of hail particles through supercooled water droplets, enabling the hail particles to rapidly increase in volume, form a dense structure, and accelerate the growth rate of hail. The wet growth process has a significant impact on aspects such as the volume, structure, and growth rate of hail, and is an important and indispensable mechanism in the hail growth process. Summary of the Invention

[0003] Aiming at the above defects, the present invention provides a method for optimizing a cloud microphysical model and numerical verification for the wet growth process of hail, optimizing the CMA_MESO model and the cloud microphysical scheme, enabling the model to quantify the physical phenomena of hail wet growth.

[0004] A method for optimizing a cloud microphysical model and numerical verification for the wet growth process of hail includes the following steps:

[0005] Step 1, constructing a parameterization scheme for the wet growth process of hail: Based on the physical mechanism of the collision and freezing of hail and supercooled water droplets, calculating the ventilation coefficient of hail; combining the environmental temperature, air density, diffusion coefficient, and Schmidt number, setting the trigger condition for hail wet growth, and determining whether to start the hail wet growth process;

[0006] Step 2, coupling and optimizing the Morrison cloud microphysical model: Embedding the parameterization scheme into the processes of hail collecting cloud water, rainwater, and snow particle conversion, and updating the mass mixing ratio when it is determined to start the hail wet growth process;

[0007] Step 3, numerical verification: Run the optimized model in step 2 in the CMA-MESO model, compare the radar reflectivity, hail mass mixing ratio, number concentration vertical profile, the time-horizontal mass distribution of some hydrometeors in the cloud, and the simulation results of the maximum estimated hail size with the radar observation data to evaluate the model optimization effect.

[0008] Preferably, in step 1, the calculation formula for the hail ventilation coefficient Vent_h is:

[0009] ,

[0010] where Avx and Bvx are constants, representing the ventilation empirical coefficient in the laminar flow state and the ventilation empirical coefficient in the turbulent flow state respectively; GH32 and COMS11 are shape functions, where GH32 is used for the calculation in the laminar flow state and COMS11 is used for the calculation in the turbulent flow state; LAM_h is the hail slope parameter, alpha_h is the shape parameter of the hail particle size distribution; SC is the Schmidt number threshold for distinguishing the laminar flow and turbulent flow states; gam is the air density factor, both A_h and B_h are constants, and MUKin is the dynamic viscosity.

[0011] Preferably, in step 1, the triggering condition for the wet growth of hail is: when the hail mass mixing ratio > 10 -8 g / kg, the mass mixing ratio is less than the sum of the mass increments in the processes of hail collecting cloud water, rain, and snow conversion, and the environmental temperature TC > -40 °C, the calculation of the wet growth process of hail is carried out.

[0012] Preferably, in step 2, the calculation formula for the mass mixing ratio QHW_h contributed by the wet growth of hail is:

[0013] ,

[0014] where DT is the model time step, RHO is the air density, CHLC is the latent heat of condensation; DV is the diffusion coefficient; DELqvs represents the difference between the actual mixing ratio on the current grid and the saturated mixing ratio at zero degrees Celsius; KAP is the thermal conductivity of air, TC is the air temperature; N0_h is the intercept parameter of hail, CPW is the specific heat capacity of water, which is a constant; CPI is the specific heat capacity of ice crystals, which is a constant; PGSACW is the mass mixing ratio increment of the snow attached to cloud water converted into hail; PGRACS is the mass mixing ratio increment of the snow attached to rain converted into hail; ECI is the collision collection efficiency of hail for ice crystals, and ECR is the collision collection efficiency of hail for droplets, rain, and snow particles.

[0015] Preferably, in step 2, the method for updating the mass increments in the processes of hail collecting cloud water PSACWG, rain PRACG, snow conversion PGSACW, and PGRACS is:

[0016] During the process of hail collecting cloud water, the increment of the mass mixing ratio of hail collecting cloud water PSACWG is expressed as:

[0017] ,

[0018] where ECI is the collision collection efficiency of hail and cloud droplets, and QC is the initial mass mixing ratio of cloud droplets.

[0019] During the process of snow transforming into hail, the increment of the mass mixing ratio of snow with attached cloud water transforming into hail PGSACW is expressed as:

[0020] ,

[0021] where QI is the initial mass mixing ratio data of ice crystals.

[0022] The increment of the mass mixing ratio of snow with attached rainwater transforming into hail PGRACS is expressed as:

[0023] ,

[0024] where ECR is the collision collection efficiency of hail collecting droplets, rain, and snow, and QNI is the initial mass mixing ratio of snow;

[0025] During the process of hail collecting rainwater,

[0026] The increment of the mass mixing ratio of hail collecting rainwater is expressed as:

[0027] ,

[0028] where QHW_h is the mass mixing ratio contribution during the wet growth process of hail.

[0029] Preferably, in step 3, the comparison variables for numerical verification include: the vertical distributions of radar reflectivity, hail mass mixing ratio, and number concentration; the vertical distribution of the time-horizontal integral mass of some hydrometeors in the cloud; and the error analysis of the maximum estimated hail size and radar observation data.

[0030] The beneficial effects of the present invention are:

[0031] (1) The present invention innovatively quantifies the wet growth process of hail by coupling the dynamic triggering conditions of hail wet growth and the mass growth of multiple processes.

[0032] (2) The present invention adds the hail wet growth process to the Morrison scheme, couples the improved Morrison scheme to the CMA_MESO numerical prediction model, and evaluates and tests it in combination with satellite radar and the observed data of actual hail processes. The evaluation results show that the hail wet growth process can well improve the quantitative prediction results of hail.

[0033] (3) The present invention solves the problem that in the prior art, there is a cloud microphysical scheme lacking the hail wet growth process, and there is an obvious underestimation of the actual ground hail size. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments.

[0035] Figure 1 is a flowchart of an embodiment of the present invention;

[0036] Figure 2 is a vertical cross-sectional view of radar echo of an embodiment of the present invention, Figure 2 (a) is the Morrison scheme, Figure 2 (b) is the Morrison scheme coupled with the terminal fall velocity of the MY scheme, Figure 2 (c) is Figure 2 the coupled wet growth process of (b);

[0037] Figure 3 is a vertical cross-sectional view of hail mass ratio and number concentration of an embodiment of the present invention, Figure 3 (a)- Figure 3 (c) are all vertical profiles of hail mass mixing ratio, Figure 3 (d)- Figure 3 (f) are all vertical profiles of hail number concentration, Figure 3 (a), Figure 3 (c) are the Morrison scheme, Figure 3 (b), Figure 3 (e) are the Morrison scheme coupled with the terminal fall velocity of the MY scheme, Figure 3 (c), Figure 3 (f) are respectively Figure 3 (b), Figure 3 the coupled wet growth process of (e);

[0038] Figure 4 is a vertical distribution map of the average mass of hail, rain, and cloud water in the cloud from 06-12 UTC in the range of 115°E to 116.5°E and 26°N to 27.5°N of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Embodiments of the present invention provide In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] AsFigures 1 to 4 As shown in Figures 1 to 4 , an embodiment of the present invention provides a method for optimizing and numerically verifying a cloud microphysical model for the wet growth process of hail, including the following steps:

[0041] Step 1, construct a parameterization scheme for the wet growth process of hail.

[0042] The wet growth process of hail is a key mechanism in cloud microphysics. Especially in severe convective weather, it plays a decisive role in the rapid growth and volume increase of hail. The core physical mechanism of wet growth is the collision and freezing of supercooled water droplets. Hail particles collide with supercooled water droplets, and the supercooled water droplets freeze rapidly, releasing the latent heat of condensation to form a liquid water film, which then freezes to form a transparent ice layer. The mass and diameter of the hail will definitely increase. The particles grow cyclically. Hail rises and falls repeatedly with the updraft in the cloud and passes through the supercooled water accumulation area multiple times. In each cycle, the hail continues to grow through the processes of collision, coalescence, and freezing, and finally forms large hail. Based on the principle of the wet growth process, conceive how to add to the cloud microphysical scheme. First, calculate the ventilation coefficient of hail particles after the processes of hail collecting cloud water, rain, ice crystals, and snow. This coefficient affects the efficiency of wet growth. Then, calculate the mass mixing ratio contributed by the wet growth of hail in combination with the ventilation coefficient of hail. Set the determination conditions for the wet growth of hail to determine whether to start the wet growth process. Only when the relevant conditions are met, recalculate the increase in the mass and number concentration of hail due to the wet growth of hail.

[0043] The calculation formula for the ventilation coefficient Ven_h of hail particles is as follows:

[0044] ,

[0045] where Avx and Bvx are constants, representing the empirical coefficients of the ventilation coefficient in the laminar flow state and the empirical coefficients of the ventilation coefficient in the turbulent flow state respectively. GH32 and COMS11 are shape functions. GH32 is used for the calculation in the laminar flow state. COMS11 is used for the calculation in the turbulent flow state. LAM_h is the slope parameter of hail, and alpha_h is the size distribution parameter of hail, with a value of 0 in the Morrison scheme. SC is the threshold of the Schmidt number, used to distinguish between the laminar flow and turbulent flow states. Gam is the air density factor. Both A_h and B_h are constants, and MUKin is the dynamic viscosity.

[0046] The calculation formula for the mass mixing ratio QHW_h contributed by the wet growth of hail in combination with the ventilation coefficient of hail is as follows:

[0047] ,

[0048] Among them, DT is the model step size, RHO is the air density, CHLC is the latent heat of condensation which is a constant, DV is the diffusion coefficient, DELqvs represents the difference between the actual mixing ratio on the current grid and the saturated mixing ratio at 0 °C, KAP is the thermal conductivity of air, TC is the temperature of the layer where it is located, in degrees Celsius, and N0_h is the intercept parameter of hail. CPW is the specific heat capacity of water, which is set as a constant; CPI is the specific heat capacity of ice crystals, which is set as a constant. PGSACW is the change in the mass mixing ratio of the snow of attached cloud water converted into hail, PGRACS is the change in the mass mixing ratio of the snow of attached rainwater converted into hail, ECI is the collision collection efficiency of ice droplets, and its value is set as a constant 0.7, and ECR is the collision collection efficiency of droplets, rain, and snow, and its value is set as a constant 1.0.

[0049] Before calculating the total mass mixing ratio and number concentration, set the determination conditions for the wet growth of hail: According to the relevant principle conditions, at the grid points where the mass mixing ratio of hail is greater than 0, when the mass mixing ratio of the wet growth change of hail is less than the sum of the mass mixing ratios of the four processes of hail collecting cloud water, rainwater, the snow of attached cloud water converted into hail, and the snow of attached rainwater converted into hail and the temperature is greater than -40 °C, hail enters the calculation of the wet growth process.

[0050] The specific judgment formula is as follows:

[0051] ,

[0052] ,

[0053] ,

[0054] Among them, QHW_h is the change in the mass mixing ratio of the wet growth of hail, PSACWG is the change in the mass mixing ratio during the process of hail collecting cloud water, PRACG is the change in the mass mixing ratio during the process of hail collecting rainwater, PGSACW is the change in the mass mixing ratio during the process of the snow of attached cloud water converted into hail, PGRACS is the change in the mass mixing ratio during the process of the snow of attached rainwater converted into hail, and TC is the actual temperature, in degrees Celsius.

[0055] Step 2, couple the wet growth process of hail into the Morrison cloud microphysical model. Mainly update the formula of the hail wet growth parameterization scheme into code form and add it to the code file of the Morrison scheme model. According to the relevant principle formula in this application, add and modify the code at the corresponding position. Embed the parameterization scheme into the processes of hail collecting cloud water, rainwater, and the transformation of snow particles. When it is judged that the wet growth condition is met, enter the update calculation of the mass mixing ratio and number concentration.

[0056] The specific update process is as follows:

[0057] 1. Update of process variables for hail collecting cloud water: Since the mass increases during the process of collecting cloud water while the number of hail embryos does not increase significantly, only the mass mixing ratio is updated, and the conversion efficiency increases at this time. The specific formula is as follows:

[0058] ,

[0059] where PSACWG is the change in the mass mixing ratio of hail collecting cloud water, ECI is the ice droplet collision collection efficiency, with a value set as a constant 0.7, and QC is the initial cloud water mass mixing ratio data.

[0060] 2. Update of process variables for snow with attached cloud water converting to hail: Although new hail embryos are generated, during the wet growth process, it is very difficult for snow to directly convert to hail due to collecting cloud water, resulting in an increase in the number concentration of hail. Therefore, this situation can be considered almost not to occur. Only the mass mixing ratio is updated in this process. The specific formula is as follows:

[0061] ,

[0062] where PGSACW is the change in the mass mixing ratio of snow with attached cloud water converting to hail, ECI is the ice droplet collision collection efficiency, with a value set as a constant 0.7, and QI is the initial ice crystal mass mixing ratio data.

[0063] 3. Update of process variables for snow with attached rain converting to hail: Although new hail embryos are generated, during the wet growth process, it is very difficult for snow to directly cause an increase in the number concentration of hail due to collecting rain. Therefore, only the mass mixing ratio is updated. The specific formula is as follows:

[0064] ,

[0065] where PGRACS is the change in the mass mixing ratio of snow with attached rain converting to hail, ECR is the droplet, rain, snow collision collection efficiency, with a value set as 1.0, and QNI is the initial snow mass mixing ratio data.

[0066] 4. Update of process variables for hail collecting rain: Since the mass increases during the process of collecting rain while the number of hail embryos does not increase significantly, only the mass mixing ratio is updated. The specific formula is as follows:

[0067] ,

[0068] where QHW_h is the change in the mass mixing ratio during the wet growth process.

[0069] Step 3, Numerical Verification: Run the optimized model from Step 2 in the CMA-MESO model. Replace the original Morrison scheme code file with the Morrison scheme code file coupled with the wet growth process, and then run it normally in the CMA-MESO model to output the running data file and visualize the relevant data.

[0070] Step 4, Comparative Analysis: Compare and analyze the simulation results of radar reflectivity, hail mass mixing ratio, number concentration vertical profile, time-horizontal average vertical distribution of some hydrometeors in the cloud, and maximum estimated hail size with the radar observation data to evaluate the model optimization effect.

[0071] CMA-MESO is a high-resolution mesoscale numerical weather prediction system independently developed by the China Meteorological Administration, aiming to meet the refined forecasting needs of local severe convective weather (such as short-term heavy precipitation, thunderstorm winds, hail, etc.). Through high-resolution assimilation, multi-source data fusion, and terrain adaptability improvement, the CMA-MESO model has significantly enhanced China's refined weather forecasting ability. It has achieved remarkable results in the application of extreme precipitation, major event guarantee, etc., but still needs to be continuously optimized in aspects such as extreme weather simulation and diurnal variation deviation. With the iteration of technology, CMA-MESO will become an important support tool for meteorological disaster prevention, mitigation, and refined services. Run the Morrison scheme before and after improvement through the CMA_MESO model and output the relevant results. In this experiment, the resolution of the CMA_MESO model is set to 3KM, the horizontal resolution is 0.3 degrees, the time resolution is hourly, and the regional scope is set from 73°E to 135°E, 4°N to 54°N.

[0072] In this embodiment, a severe convective hail weather instance is selected for evaluation and verification. The specific analysis range is from 112°E to 122°E, 24°N to 34°N. The actual situation of the selected hail severe convective weather is that on March 22, 2023, Jiangxi, Fujian, Hunan and other provinces were hit by a large-scale severe convective weather. Ground winds and short-term heavy precipitation occurred in Jiangxi and other places, and the maximum ground wind speed could reach 30 m / s. At about 16:00 in the afternoon, ground hail appeared in Ganzhou, Jiangxi, Fujian and other places, and the maximum hail diameter reached 5 cm. Compare the quantitative hail forecasting results of the Morrison scheme coupled with hail wet growth with the forecasting results of the original scheme, and verify them in combination with the actual satellite or radar observations. For example, Figures 2 - 4 They respectively correspond to the radar echo vertical profile at 09 UTC on March 22, 2023, the hail mass mixing ratio and number concentration vertical profile at 09 UTC on March 22, 2023, and the time-horizontal average vertical distribution of some hydrometeors in the cloud. Figure 2Focusing on each scheme in the strong storm center of Ganzhou, Jiangxi, after coupling the hail wet growth scheme, the radar reflectivity inside the storm clearly showed a purple area, reaching above 65dbz, indicating that the hail storm developed more violently and was more conducive to the growth of large hail. Figure 3 (a)- Figure 3 (c) The mixed mass ratio of hail has increased significantly, which is in line with the principle of wet growth of hail and collides with supercooled water droplets, resulting in an increase in the updated mass; Figure 3 (d)- Figure 3 (f) The hail number concentration does not change much, which is consistent with the principle of wet growth process. Figure 4 The vertical distribution of the average mass of hail, cloud water, and rain water in the range of 115°E to 116.5°E and 26°N to 27.5°N for six hours from 06 to 12 UTC. The red curve is the scheme after coupling with wet growth. It is obvious that the mass of hail is greater than that of the other two schemes, while the mass of cloud water and rain water is not the optimal scheme. In line with the principle of wet growth process, hail collects cloud water and rain water to increase its mass. In this embodiment, the maximum estimated size of hail increases from 19.08 mm to 22.68 mm, which intuitively shows the optimization effect of the cloud microphysics scheme after coupling with the wet growth process on the hail size. It can be seen that the addition of the hail wet growth process to the cloud microphysics scheme has a substantial improvement effect on the ground hail size and spatial distribution predicted by CMA-MESO, and it is worthy of being further promoted to actual business applications.

[0073] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing and numerically verifying a cloud microphysical model for the wet growth process of hail, characterized in that It includes the following steps: Step 1, constructing a parameterization scheme for the hail wet growth process: Based on the physical mechanism of hail colliding and freezing with supercooled water droplets, calculate the hail ventilation coefficient; combining the environmental temperature, air density, diffusion coefficient, and Schmidt number, set the triggering condition for hail wet growth to determine whether to start the hail wet growth process; Step 2, coupling and optimizing the Morrison cloud microphysical model: Embed the parameterization scheme into the processes of hail collecting cloud water, rainwater, and snow particle conversion. When it is determined that the hail wet growth process starts, update the mass mixing ratio and number concentration; Step 3, numerical verification: Run the optimized model in Step 2 in the CMA-MESO model, compare the simulated results of radar reflectivity, vertical profiles of hail mass mixing ratio and number concentration, time-horizontal integral vertical distribution of some hydrometeors in the cloud, and maximum estimated hail size variables, and evaluate the model optimization effect.

2. The verification method according to claim 1, characterized in that In Step 1, the calculation formula for the hail ventilation coefficient Vent_h is: ， where Avx and Bvx are constants, representing the ventilation empirical coefficient in the laminar state and the ventilation empirical coefficient in the turbulent state respectively; GH32 and COMS11 are shape functions, where GH32 is used for calculations in the laminar state and COMS11 is used for calculations in the turbulent state; LAM_h is the hail slope parameter, alpha_h is the shape parameter of the hail particle spectrum distribution; SC is the Schmidt number threshold for distinguishing the laminar and turbulent states; gam is the air density factor, both A_h and B_h are constants, and MUKin is the dynamic viscosity.

3. The verification method according to claim 1, wherein In step 1, the triggering conditions for the wet growth of hail are as follows: when the hail mass mixing ratio > 10 -8 g / kg, the mass mixing ratio is less than the sum of the mass increments in the processes of hail collecting cloud water, rainwater, and snow conversion, and when the environmental temperature TC > -40°C, the calculation of the wet growth process of hail begins.

4. The verification method according to any one of claims 1 to 3, characterized in that In Step 2, the calculation formula for the mass mixing ratio QHW_h contributed by hail wet growth is: ， where DT is the model time step, RHO is the air density, CHLC is the latent heat of condensation; DV is the diffusion coefficient; DELqvs represents the difference between the actual mixing ratio on the current grid and the saturated mixing ratio at zero degrees Celsius; KAP is the thermal conductivity of air, TC is the air temperature; N0_h is the intercept parameter of hail, CPW is the specific heat capacity of water; CPI is the specific heat capacity of ice crystals; PGSACW is the mass mixing ratio increment of snow attached to cloud water converted to hail; PGRACS is the mass mixing ratio increment of snow attached to rainwater converted to hail; ECI is the collision collection efficiency of hail on ice crystals, and ECR is the collision collection efficiency of hail on droplets, rain, and snow particles.

5. The verification method according to claim 4, characterized in that, In Step 2, the method for updating the mass increment of the processes of hail collecting cloud water PSACWG, rainwater PRACG, snow conversion PGSACW, and PGRACS is: During the process of hail collecting cloud water, The mass mixing ratio increment PSACWG of hail collecting cloud water is expressed as: ， where ECI is the collision collection efficiency of hail and cloud droplets, and QC is the initial cloud droplet mass mixing ratio; During the process of snow converting to hail, The mass mixing ratio increment PGSACW of snow attached to cloud water converted to hail is expressed as: ， where QI is the initial ice crystal mass mixing ratio data; The mass mixing ratio increment PGRACS of snow attached to rainwater converted to hail is expressed as: ， where ECR is the collision collection efficiency of hail collecting droplets, rain, and snow collisions, and QNI is the initial snow mass mixing ratio; During the process of hail collecting rainwater, Increment of mass mixing ratio of hail to collected rainwater Expressed as: ， Among them, QHW_h is the mass mixing ratio contribution in the wet growth process of hail.

6. The verification method according to claim 4, wherein In step 3, the comparison variables for numerical verification include: the vertical distributions of radar reflectivity, hail mass mixing ratio and number concentration; the vertical distribution of the time-horizontal integral of some hydrometeors in the cloud; and the error analysis of the maximum estimated hail size and radar observation data.

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