A cloud microphysical model optimization and numerical verification method for hail wet growth process

Abstract

The present invention discloses a cloud microphysics model optimization and numerical verification method for the wet growth process of hail. Based on the physical principle of freezing caused by the collision of hail with supercooled water droplets, a parameterization scheme for the wet growth process of hail is constructed and coupled to the Morrison cloud microphysics scheme of the CMA-MESO model for verification. By calculating the hail ventilation coefficient and combining it with the ambient temperature, air density, and diffusion coefficient, the conditions for wet growth are determined, and the changes in the hail mass mixing ratio are updated. The CMA-MESO model is used to simulate actual severe convective hail cases, and the effect of the newly added process on the forecast improvement of variables such as radar reflectivity, hail mass mixing ratio and number concentration vertical profiles, horizontally integrated vertical profiles of partial hydrometeors in the cloud, and maximum estimated hail size is evaluated. The present invention improves the model's simulation capability for the generation and development process of large hail, and enhances the accuracy of quantitative simulation of hail in severe convective weather.

Description

Technical Field

[0001] The present invention belongs to the field of cloud microphysics technology, and in particular relates to a cloud microphysics model optimization and numerical verification method for hail wet growth processes. Background Art

[0002] Hail formation is a core component of cloud microphysics schemes, and its mechanisms involve complex thermodynamic and microphysical processes. During hail formation, wet growth is crucial for forecast accuracy. Wet growth occurs when hail particles capture a large number of supercooled water droplets within the cloud, releasing a portion of the latent heat released during freezing to raise the particle temperature, forming a transparent ice layer. However, the current cloud microphysics scheme in the China Meteorological Administration-Mesoscale Model (CMA_MESO) numerical forecast model does not account for this wet growth process, resulting in significant discrepancies between hail forecasts and actual conditions. The wet growth process plays a crucial role in hail growth. Supercooled water droplets form a water film on the surface of hail particles, which then gradually freezes, causing the hail particles to rapidly increase in size, develop a dense structure, and accelerate their growth rate. This wet growth process significantly influences hail volume, structure, and growth rate, and is an essential mechanism in hail growth. Summary of the Invention

[0003] To address the above-mentioned defects, the present invention provides a cloud microphysics model optimization and numerical verification method for the hail wet growth process, optimizes the CMA_MESO model and cloud microphysics scheme, and enables the model to quantify the physical phenomenon of hail wet growth.

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

[0005] Step 1: Construct a parameterized scheme for the hail wet growth process: Based on the physical mechanism of hail freezing upon collision with supercooled water droplets, calculate the hail ventilation coefficient. Combined with ambient temperature, air density, diffusion coefficient, and Schmidt number, set trigger conditions for hail wet growth to determine whether to initiate the hail wet growth process.

[0006] Step 2: Coupled optimization of the Morrison cloud microphysics model: embed the parameterization scheme into the hail collection cloud water, rainwater and snow particle conversion process, and update the mass mixing ratio when it is determined that the hail wet growth process has begun;

[0007] Step 3, Numerical Verification: Run the optimized model from Step 2 in the CMA-MESO model. Compare the simulated results of radar reflectivity, hail mass mixing ratio, vertical profile of number concentration, temporal horizontal mass distribution of some hydrometeors in the cloud, and maximum estimated hail size with radar observations to evaluate the effectiveness of the model optimization.

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

[0009] ,

[0010] Among them, Avx and Bvx are constants, representing the ventilation empirical coefficients under laminar and turbulent states, respectively; GH32 and COMS11 are shape functions, of which GH32 is used for calculations in laminar states and COMS11 is used for calculations in turbulent states; LAM_h is the hail slope parameter, and alpha_h is the shape parameter of the hail particle spectrum distribution; SC is the Schmidt number threshold, used to distinguish between laminar and turbulent states; gam is the air density factor, A_h and B_h are both constants, and MUKin is the dynamic viscosity.

[0011] Preferably, in step 1, the triggering condition for hail wet growth is: the hail mass mixing ratio is greater than 10 -8 g / kg, the mass mixing ratio is less than the sum of the mass increments of the hailstone's collection of cloud water, rainwater, and snow conversion process, and the calculation of the hailstone entering the wet growth process when the ambient temperature TC>-40℃.

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

[0013] ,

[0014] Among them, DT is the model step size, 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 snow attached to cloud water converted to hail; PGRACS is the mass mixing ratio increment of snow attached to rain water 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.

[0015] As a preference, in step 2, the mass increment update method for the hail collection cloud water PSACWG, rain water PRACG, snow conversion PGSACW, and PGRACS process is:

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

[0017] ,

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

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

[0020] ,

[0021] Among them, QI is the initial ice crystal mass mixing ratio data.

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

[0023] ,

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

[0025] Hailstone collects rainwater during the process.

[0026] Hail collection rainwater mass mixing ratio increment Expressed as:

[0027] ,

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

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

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

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

[0032] (2) This paper adds the wet growth process of hail to the Morrison scheme and couples the improved Morrison scheme to the CMA_MESO numerical forecast model. The scheme is then evaluated and tested using satellite radar and actual hail observation data. The results show that the wet growth process can significantly improve the quantitative hail forecast.

[0033] (3) The present invention solves the problem that the existing technology lacks a cloud microphysical solution for the hail wet growth process and significantly underestimates the actual size of hail falling on the ground. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments.

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

[0036] Figure 2 is a vertical cross-sectional view of a radar echo according to an embodiment of the present invention. Figure 2 (a) is the Morrison solution, Figure 2 (b) is the final falling velocity of the Morrison scheme coupled with the MY scheme, Figure 2 (c) Figure 2 (b) Coupled wet growth process;

[0037] Figure 3 is a vertical cross-sectional diagram of the hail mass ratio and number concentration according to an embodiment of the present invention, Figure 3 (a)- Figure 3 (c) are vertical sections of hail mass mixing ratio, Figure 3 (d)- Figure 3 (f) are vertical profiles of hail number concentration, Figure 3 (a) Figure 3 (c) is the Morrison scheme, Figure 3 (b) Figure 3 (e) is the final falling velocity of the Morrison scheme coupled with the MY scheme, Figure 3 (c) Figure 3 (f) are Figure 3 (b) Figure 3 (e) Coupled wet growth process;

[0038] Figure 4 This is a vertical distribution diagram of the average mass of hail, rain, and cloud water in clouds from 06:00 to 12:00 UTC in the range of 115°E to 116.5°E and 26°N to 27.5°N according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiments of the present invention provide a method for making the purpose, technical solutions and advantages of the present invention more clearly understood. The present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] like Figures 1 to 4 As shown, an embodiment of the present invention provides a cloud microphysical model optimization and numerical verification method for the hail wet growth process, comprising the following steps:

[0041] Step 1: Construct a parameterized scheme for the hail wet growth process.

[0042] The wet growth process of hail is a key mechanism in cloud microphysics, particularly in severe convective weather, where it plays a decisive role in its rapid growth and volume increase. The core physical mechanism of wet growth is the collision and freezing of supercooled water droplets. When hail particles collide with supercooled water droplets, the supercooled water droplets rapidly freeze, releasing latent heat of condensation and forming a liquid water film. This film then freezes, forming a transparent ice layer, which inevitably increases the mass and diameter of the hailstone. Particle growth occurs cyclically, as hailstones rise and fall repeatedly within the cloud on updrafts, passing through accumulation zones of supercooled water multiple times. During each cycle, hailstones continue to grow through collisions, coalescence, and freezing, ultimately forming large hailstones. Based on the principles of the wet growth process, a solution is devised to integrate this into cloud microphysics. First, the ventilation coefficient of hailstone particles is calculated after hailstones collect cloud water, rainwater, ice crystals, and snow. This coefficient influences the efficiency of wet growth. The mass mixing ratio contributing to the hailstone's wet growth is then calculated based on the hailstone's ventilation coefficient. Conditions for determining wet growth are set to determine whether wet growth has begun. Only when the relevant conditions are met, the increase in hail mass and number concentration due to hail wet growth is recalculated.

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

[0044] ,

[0045] Where Avx and Bvx are constants, representing the empirical coefficients for the ventilation coefficients in laminar and turbulent flow regimes, respectively. GH32 and COMS11 are shape functions; GH32 is used for laminar and turbulent flow regimes, respectively. COMS11 is used for turbulent flow regimes. LAM_h is the slope parameter for hailstones, and alpha_h is the size distribution parameter for hailstones, which is 0 in the Morrison scheme. SC is the Schmidt number threshold used to distinguish between laminar and turbulent flow regimes. gam is the air density factor. A_h and B_h are both constants, and MUKin is the dynamic viscosity.

[0046] The formula for calculating the mass mixing ratio QHW_h of hail wet growth contribution combined with the ventilation coefficient of hail is as follows:

[0047] ,

[0048] Where 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 degrees Celsius, KAP is the thermal conductivity of the air, TC is the temperature of the layer in degrees Celsius, and N0_h is the intercept parameter for 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 change from cloud-bound snow to hail, PGRACS is the mass mixing ratio change from rain-bound snow to hail, ECI is the ice droplet collision collection efficiency, which is a constant of 0.7, and ECR is the droplet, rain, and snow collision collection efficiency, which is a constant of 1.0.

[0049] Before calculating the total mass mixing ratio and number concentration, the judgment conditions for hail wet growth are set: according to relevant principle conditions, at the grid point where the hail mass mixing ratio is greater than 0, when the mass mixing ratio of the hail wet growth change is less than the sum of the mass mixing ratios of the four processes of hail collecting cloud water, rainwater, snow attached to cloud water and converting into hail, and snow attached to rainwater and converting into hail, and the temperature is greater than minus forty degrees Celsius, the hail enters the calculation of the wet growth process.

[0050] The specific judgment formula is as follows:

[0051] ,

[0052] ,

[0053] ,

[0054] Where QHW_h is the change in mass mixing ratio of hail wet growth, PSACWG is the change in mass mixing ratio of hail in the process of collecting cloud water, PRACG is the change in mass mixing ratio of hail in the process of collecting rain water, PGSACW is the change in mass mixing ratio of snow attached to cloud water to hail, PGRACS is the change in mass mixing ratio of snow attached to rain water to hail, and TC is the actual temperature in degrees Celsius.

[0055] Step 2: Couple the hail wet growth process to the Morrison cloud microphysics model. This involves updating the hail wet growth parameterization scheme formulas into code format and adding them to the Morrison scheme model's code file. Modify the code accordingly based on the relevant principle formulas in this application. This parameterization scheme is embedded in the hail collection and conversion process of cloud water, rainwater, and snow particles. When wet growth conditions are met, the mass mixing ratio and number concentration are updated.

[0056] The specific update process is as follows:

[0057] 1. Update the variables of the hail cloud water collection process. Since the mass of the cloud water collected will increase while the number of hail embryos will not increase significantly, only the mass mixing ratio is updated, and the conversion efficiency is increased. The specific formula is as follows:

[0058] ,

[0059] Where PSACWG is the change in the cloud water mass mixing ratio collected by hail, ECI is the ice droplet collision collection efficiency, which is set to a constant of 0.7, and QC is the initial cloud water mass mixing ratio data.

[0060] 2. Update the variables for the process of converting cloud water-attached snow into hail. Although new hail embryos may be generated, this is unlikely to occur during the wet growth process due to snow collecting cloud water and directly converting into hail, thereby increasing the hail number concentration. Therefore, this can be minimized. This process only updates the mass mixing ratio. The specific formula is as follows:

[0061] ,

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

[0063] 3. The process variables for converting rainwater-attached snow into hail are updated. Although new hail embryos may be generated, it is unlikely that the hail number concentration will increase directly due to snow collecting rainwater during the wet growth process. Therefore, only the mass mixing ratio is updated. The specific formula is as follows:

[0064] ,

[0065] Among them, PGRACS is the change in the mass mixing ratio of snow with attached rainwater converted into hail, ECR is the collision collection efficiency of droplets, rain, and snow, which is set to 1.0, and QNI is the initial snow mass mixing ratio data.

[0066] 4. Update the variables of the hail collection process. Since the quality of rainwater collected will increase but the number of hail embryos will not increase significantly, only the mass mixing ratio will be updated. The specific formula is as follows:

[0067] ,

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

[0069] Step 3, numerical verification: Run the model optimized in Step 2 in the CMA-MESO mode, replace the original Morrison scheme code file with the Morrison scheme code file for the coupled wet growth process, and then run it normally in the CMA-MESO mode, output the running data file, and visualize the relevant data.

[0070] Step 4: Compare and analyze the radar reflectivity, hail mass mixing ratio, vertical profile of number concentration, time-averaged vertical 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.

[0071] CMA-MESO is a high-resolution mesoscale numerical forecast system independently developed by the China Meteorological Administration. It is designed to meet the demand for refined forecasts of localized severe convective weather (such as short-term heavy rainfall, thunderstorms, strong winds, and hail). Through high-resolution assimilation, multi-source data fusion, and improved terrain adaptability, the CMA-MESO model has significantly enhanced my country's refined weather forecast capabilities. Its application in extreme precipitation and major event support has been significant, but continued optimization is needed in areas such as extreme weather simulation and diurnal variation. With technological iterations, CMA-MESO will become an important supporting tool for meteorological disaster prevention and mitigation and refined services. The Morrison scheme, before and after its improvements, was run on the CMA-MESO model, and relevant results were output. In this experiment, the CMA-MESO model resolution was set to 3 km, the horizontal resolution to 0.3 degrees, and the temporal resolution to hourly. The regional coverage was set from 73°E to 135°E and 4°N to 54°N.

[0072] In this embodiment, an example of severe convective hail weather was selected for evaluation and testing, and the specific analysis range was 112°E to 122°E, 24°N to 34°N. The selected severe convective hail weather situation was that on March 22, 2023, Jiangxi, Fujian, Hunan and other provinces suffered a large-scale severe convective weather attack. Strong winds appeared on the ground in Jiangxi and other places, accompanied by short-term heavy rainfall. The maximum wind speed on the ground could reach 30 m / s. At around 16:00 in the afternoon, ground hail appeared in Ganzhou, Jiangxi, Fujian and other places, with the maximum hail diameter reaching 5 cm. The quantitative hail forecast results of the Morrison scheme coupled with hail wet growth were compared with the forecast results of the original scheme, and verified in combination with actual observations such as actual satellites or radars. For example, Figure 2-Figure 4 They correspond to the vertical profile of radar echo at 09UTC on March 22, 2023, the vertical profile of hail mass mixing ratio and number concentration at 09UTC on March 22, 2023, and the time-averaged vertical distribution of some hydrometeors in the cloud. Figure 2Focusing on the various schemes in the strong center of the storm in 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 intensely and was more conducive to the growth of large hail. Figure 3 (a)- Figure 3 (c) The hailstone mixing mass ratio has increased significantly, which is consistent with the principle of wet growth of hailstones. The collision with supercooled water droplets leads to 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 06-12 UTC is shown. 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 accordance 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 19.08mm to 22.68mm, which intuitively shows the optimization effect of the cloud microphysics scheme after coupling with the wet growth process on hail size. It can be seen that adding the hail wet growth process to the cloud microphysics scheme has a practical 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 descriptions are merely preferred embodiments of the present invention and are 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 will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A cloud microphysical model optimization and numerical verification method for hail wet growth process, characterized by: The following steps are involved: Step 1: Construct a parameterized scheme for the wet growth process of hailstones: Based on the physical mechanism of the freezing of hailstones by collision with supercooled water droplets, calculate the hailstone ventilation coefficient Vent_h. ， Among them, Avx and Bvx are constants, representing the ventilation empirical coefficients in laminar and turbulent states, respectively; GH32 and COMS11 are shape functions, of which GH32 is used for calculations in laminar states and COMS11 is used for calculations in turbulent states; LAM_h is the hail slope parameter, and alpha_h is the shape parameter of the hail particle spectrum distribution; SC is the Schmidt number threshold, used to distinguish between laminar and turbulent states; gam is the air density factor, A_h and B_h are both constants, and MUKin is the dynamic viscosity; Combined with the ventilation coefficient of hail, the mass mixing ratio QHW_h contributing to the wet growth of hail is calculated: ， Wherein, DT is the model step size, 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 with attached cloud water converted to hail; PGRACS is the mass mixing ratio increment of snow with attached rain water 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. Combined with ambient temperature, air density, diffusion coefficient and Schmidt number, the trigger conditions for hail wet growth are set to determine whether the hail wet growth process begins; Step 2: Coupled optimization of the Morrison cloud microphysics model: embed the parameterization scheme into the hail collection cloud water, rainwater and snow particle conversion process, and update the mass mixing ratio and number concentration when it is determined that the hail wet growth process has begun; Step 3, numerical verification: Run the model optimized in Step 2 in the CMA-MESO model and compare the simulation results of radar reflectivity, hail mass mixing ratio and number concentration vertical profiles, time-integrated vertical distribution of some hydrometeors in the cloud, and maximum estimated hail size variables to evaluate the model optimization effect.

2. The verification method according to claim 1, wherein: In step 1, the triggering condition for hail wet growth is: the hail mass mixing ratio is greater than 10 -8 g / kg, 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 in which hail collects cloud water, rain water, snow attached to cloud water is converted into hail, and snow attached to rain water is converted into hail, as well as the calculation of the hail entering the wet growth process when the ambient temperature TC>-40℃.

3. The verification method according to claim 2, wherein: In step 2, the incremental update method of the mass mixing ratio in the conversion process of hail-collected cloud water, rainwater, and snow particles is: During the process of hailstones collecting cloud water, The hail collection cloud water mass mixing ratio increment PSACWG is expressed as: ， Where ECI is the collision collection efficiency of hail and cloud droplets, QC is the initial cloud droplet mass mixing ratio; During the transformation of snow into hail, The mass mixing ratio increment PGSACW of snow with attached cloud water converted into hail is expressed as: ， Among them, QI is the initial ice crystal mass mixing ratio data; The mass mixing ratio increment PGRACS of snow with attached rainwater converted into hail is expressed as: ， Where ECR is the collection efficiency of hail droplets, rain, and snow collisions, and QNI is the mass mixing ratio of the initial snow; Hailstone collects rainwater during the process. Hail collection rainwater mass mixing ratio increment Expressed as: ， Where QHW_h is the mass mixing ratio contribution of the hail wet growth process.

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

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