Numerical simulation method and device for intensity weakening of freezing rain disaster based on warm cloud catalysis, electronic equipment and storage medium

Abstract

The present disclosure relates to a numerical simulation method and device for weakening intensity of freezing rain disaster based on warm cloud catalysis, electronic equipment and storage medium, the method comprising: obtaining relevant situation data in three-dimensional space, initializing freezing rain particle group model based on relevant situation data, selecting artificial intervention warm cloud layer area according to preset condition, formulating catalysis scheme combined with preset target, and performing first simulation and simulation on catalysis scheme based on freezing rain particle group model, determining initial condition in first simulation and simulation result, performing second simulation and simulation combined with initial condition and freezing rain particle group model, calculating freezing rain particle size spectrum, freezing rain disaster intensity index and freezing rain disaster grade, determining weakening value of freezing rain disaster intensity of catalysis scheme, and adjusting catalysis scheme. The full physical process coupling numerical simulation of warm cloud catalysis-raindrop particle size regulation-freezing rain formation-disaster intensity evaluation is realized, which is helpful for quantitatively and objectively evaluating the actual weakening effect of catalysis measures on freezing rain disaster.

Description

Technical Field

[0001] This disclosure relates to the field of meteorological engineering technology, and in particular to numerical simulation methods, devices, electronic equipment and storage media for reducing the intensity of freezing rain disasters based on warm cloud catalysis. Background Technology

[0002] Currently, freezing rain disasters have become one of the high-impact meteorological disasters due to their severe consequences, such as power transmission line icing and breakage, traffic paralysis, and facility damage. The harmfulness of freezing rain mainly depends on the particle size distribution of freezing rain particles, especially the quantity and spatial concentration of large-diameter particles. Even if the total precipitation is not large, the presence of even a small amount of large-diameter freezing rain particles can be enough to cause devastating damage to lifeline projects such as power grids and transportation. This dictates that the goal of disaster mitigation is not simply to increase or decrease rainfall, but to change the form of rainfall.

[0003] Traditional weather modification primarily focuses on changes in water volume. However, in the face of freezing rain disasters, the needs of the scientific community and operational departments have evolved, requiring the ability to precisely control the phase and size distribution of precipitation particles to downgrade the disaster level.

[0004] While existing numerical models for catalysis can provide information on the effects of increasing or decreasing rainfall, they are unable to effectively simulate the refined impact of human intervention on the evolution of freezing rain particle size distribution, thus making it impossible to quantitatively assess the actual mitigation effect of catalytic measures on disaster intensity.

[0005] Therefore, there is an urgent need to develop a numerical simulation method for reducing the intensity of freezing rain disasters based on the catalytic physical mechanism of warm clouds and integrating high-precision microphysical process simulation. By constructing a particle size distribution evolution-disaster intensity correlation model, we can achieve quantitative prediction and optimization of the effect of artificial intervention schemes on disaster level reduction, and provide a scientific basis for proactive disaster prevention and control. Summary of the Invention

[0006] To address the aforementioned technical problems, embodiments of this disclosure provide a numerical simulation method, apparatus, electronic device, and storage medium for reducing the intensity of freezing rain disasters based on warm cloud catalysis.

[0007] This disclosure provides a numerical simulation method, apparatus, electronic device, and storage medium for reducing the intensity of freezing rain disasters based on warm cloud catalysis. The first simulation simulates the microphysical processes triggered by the catalytic scheme within the cloud using a freezing rain particle swarm model. The second simulation simulates the process of selected raindrops freezing and forming a freezing rain particle size distribution. An iterative optimization scheme based on the freezing rain disaster intensity index is then used to finally output an adjusted catalytic scheme that reduces the disaster intensity index and effectively downgrades the disaster level. This achieves a coupled numerical simulation of the entire physical process from warm cloud catalysis to raindrop particle size regulation, freezing rain formation, and disaster intensity assessment. It overcomes the limitations of traditional models that cannot accurately simulate the impact of human intervention on microphysical processes. Furthermore, it proposes and applies a disaster intensity index based on freezing rain particle size distribution as the core physical basis for disaster level classification, providing a scientific and quantifiable standard for quantitatively and objectively evaluating the actual mitigation effect of catalytic measures on freezing rain disasters.

[0008] This disclosure provides a numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis, including: Acquire relevant data in three-dimensional space, and initialize a freezing rain particle cluster model based on the relevant data. The relevant data includes temperature field, humidity field, wind field, and distribution of supercooled water content in clouds. Based on preset conditions, a warm cloud region for artificial intervention is selected, a catalytic scheme is formulated in combination with preset objectives, and the catalytic scheme is simulated for the first time based on the freezing rain particle swarm model to obtain the first simulation results. The initial conditions in the first simulation results are determined by combining the collision freezing efficiency. The second simulation is then performed by combining the initial conditions with the freezing rain particle group model. The freezing rain particle size spectrum is calculated based on the second simulation results. The freezing rain disaster intensity index and freezing rain disaster level are calculated based on the freezing rain particle size spectrum. The reduction value of the catalytic scheme on the freezing rain disaster intensity is determined according to the freezing rain disaster intensity index and freezing rain disaster level. The reduction value is used to adjust the catalytic scheme.

[0009] The method provided in this disclosure, wherein acquiring relevant situation data in three-dimensional space and initializing a freezing rain particle swarm model based on the relevant situation data includes: Acquire relevant situation data in three-dimensional space, and initialize the freezing rain particle group model with the relevant situation data through the particle size spectrum function; The particle size spectrum function is derived from the key physical processes of the freezing rain particle concentration and the evolution of the freezing rain particle swarm over time.

[0010] The method provided in this disclosure, wherein selecting a warm cloud region for artificial intervention based on preset conditions, formulating a catalytic scheme in conjunction with preset targets, and performing a first simulation on the catalytic scheme based on a freezing rain particle swarm model to obtain a first simulation result, includes: Compare the preset conditions with the corresponding data of relevant situations, and select the warm cloud layer area for artificial intervention based on the comparison results; Multi-dimensional diagnosis of artificially intervened warm cloud areas is conducted, and key parameters of hygroscopic catalysts are determined based on the multi-dimensional diagnosis results. A catalytic scheme is formulated by integrating all key parameters with preset targets. The physical mechanism describing the competition for water by hygroscopic nuclei is introduced into the freezing rain particle swarm model, and the first simulation of the catalytic scheme is performed on the freezing rain particle swarm model to obtain the first simulation results.

[0011] The method provided in this disclosure, which involves performing multi-dimensional diagnosis of artificially intervened warm cloud regions, determining key parameters of the hygroscopic catalyst based on the multi-dimensional diagnosis results, and formulating a catalytic scheme by integrating all key parameters with preset targets, includes: Multidimensional diagnosis was conducted on the artificially intervened warm cloud region, yielding diagnostic results in the microphysical, dynamic, and thermal dimensions. Based on the microphysical dimension diagnostic results, a preset optimal cloud droplet concentration lookup table is queried, the optimal cloud droplet concentration value for the artificially intervened warm cloud layer region is calculated, and the concentration of cloud droplets to be replenished is determined based on the optimal cloud droplet concentration value. The calibration nucleation rate of the hygroscopic catalyst was determined based on the results of the thermodynamic dimension diagnosis. The total dose of hygroscopic catalyst was calculated by combining the concentration of cloud droplets to be replenished and the calibrated nucleation rate. The cloud volume in the warm cloud region under artificial intervention is calculated based on the dynamic dimension diagnostic results, and the dissemination rate of the hygroscopic catalyst is obtained based on the cloud volume. The total dosage and dispersibility of the hygroscopic catalyst are key parameters; A catalytic scheme is developed by combining the key parameters with the preset targets.

[0012] The method provided in this disclosure, which determines the initial conditions in the first simulation results by combining the collision freezing efficiency, performs a second simulation by combining the initial conditions with the freezing rain particle swarm model, and calculates the freezing rain particle size spectrum based on the second simulation results, includes: The particle size threshold is determined based on the collision freezing efficiency, and raindrops smaller than the particle size threshold are selected as the first initial condition from the intervention results of the first simulation results. Raindrops smaller than the particle size threshold are selected as the second initial condition from the comparison results of the first simulation results; The initial conditions include a first initial condition and a second initial condition; A second simulation of the raindrop falling process was conducted in the freezing rain particle cluster model based on the first initial condition and the second initial condition, respectively, and the intervention results and the comparison results of the second simulation results were obtained. Based on the intervention results from the second simulation and the first initial conditions, the intervention-freezing rain particle size spectrum is output. Based on the comparison results of the second simulation and the second initial conditions, the natural-freezing rain particle size spectrum is output; The freezing rain particle size spectrum includes the intervention-freezing rain particle size spectrum and the natural-freezing rain particle size spectrum.

[0013] The method provided in this disclosure for calculating freezing rain particle size distribution includes: The final velocity of each raindrop of each size is calculated based on the particle size spectrum function in the freezing rain particle group model. The diameter of freezing rain particles is determined by combining the collision freezing efficiency factor, the final velocity of the fall, and the initial raindrop diameter. The freezing rain particle size spectrum is determined based on the distribution of the freezing rain particle diameter.

[0014] The method provided in this disclosure calculates a freezing rain disaster intensity index and a freezing rain disaster level based on the freezing rain particle size distribution, determines the reduction value of a catalytic scheme for the freezing rain disaster intensity based on the freezing rain disaster intensity index and the freezing rain disaster level, and adjusts the catalytic scheme based on the reduction value, including: The product of the intervention-freezing rain particle size spectrum and the corresponding sixth power of particle size is integrated, and the horizontal area of ​​the warm cloud region under artificial intervention is combined to obtain the intervention-freezing rain disaster intensity index. Based on the intervention-freezing rain disaster intensity index, a disaster assessment is carried out by comparing it with the preset freezing rain disaster level classification standard to obtain the intervention-freezing rain disaster level. The natural-freezing rain disaster intensity index and natural-freezing rain disaster level are obtained based on the natural-freezing rain particle size spectrum. Calculate the first reduction value of the intervention-freezing rain disaster intensity index and the natural-freezing rain disaster intensity index; at the same time, calculate the second reduction value of the intervention-freezing rain disaster level and the natural-freezing rain disaster level. The effectiveness of the catalytic scheme is evaluated based on the first reduction value and the second reduction value. If the effectiveness does not meet the preset expectation, the parameters of the catalytic scheme are adjusted, and the simulation is returned to the first simulation for iterative simulation until the preset expectation is met, and the adjusted catalytic scheme is output.

[0015] The numerical simulation device for reducing the intensity of freezing rain disasters based on warm cloud catalysis provided in this embodiment includes: The acquisition module is used to acquire relevant situation data in three-dimensional space and initialize the freezing rain particle group model based on the relevant situation data; The first simulation module is used to select a warm cloud layer region for artificial intervention based on preset conditions, formulate a catalytic scheme in combination with preset targets, and perform a first simulation on the catalytic scheme based on the freezing rain particle cluster model to obtain the first simulation results. The second simulation module is used to determine the initial conditions in the first simulation results by combining the collision freezing efficiency, perform the second simulation by combining the initial conditions with the freezing rain particle group model, and calculate the freezing rain particle size spectrum based on the second simulation results. The adjustment module is used to calculate the freezing rain disaster intensity index and freezing rain disaster level based on the freezing rain particle size spectrum, and to determine the reduction value of the catalytic scheme on the freezing rain disaster intensity according to the freezing rain disaster intensity index and freezing rain disaster level. The reduction value is used to adjust the catalytic scheme.

[0016] The apparatus provided in this disclosure, wherein the acquisition module is specifically used for: Acquire relevant situation data in three-dimensional space, and initialize the freezing rain particle group model with the relevant situation data through the particle size spectrum function; The particle size spectrum function is derived from the key physical processes of the freezing rain particle concentration and the evolution of the freezing rain particle swarm over time.

[0017] The apparatus provided in this disclosure embodiment, wherein the first simulation module is specifically used for: Compare the preset conditions with the corresponding data of relevant situations, and select the warm cloud layer area for artificial intervention based on the comparison results; Multi-dimensional diagnosis of artificially intervened warm cloud areas is conducted, and key parameters of hygroscopic catalysts are determined based on the multi-dimensional diagnosis results. A catalytic scheme is formulated by integrating all key parameters with preset targets. The physical mechanism describing the competition for water by hygroscopic nuclei is introduced into the freezing rain particle swarm model, and the first simulation of the catalytic scheme is performed on the freezing rain particle swarm model to obtain the first simulation results.

[0018] The apparatus provided in this disclosure embodiment, wherein the first simulation module is specifically used for: Multidimensional diagnosis was conducted on the artificially intervened warm cloud region, yielding diagnostic results in the microphysical, dynamic, and thermal dimensions. Based on the microphysical dimension diagnostic results, a preset optimal cloud droplet concentration lookup table is queried, the optimal cloud droplet concentration value for the artificially intervened warm cloud layer region is calculated, and the concentration of cloud droplets to be replenished is determined based on the optimal cloud droplet concentration value. The calibration nucleation rate of the hygroscopic catalyst was determined based on the results of the thermodynamic dimension diagnosis. The total dose of hygroscopic catalyst was calculated by combining the concentration of cloud droplets to be replenished and the calibrated nucleation rate. The cloud volume in the warm cloud region under artificial intervention is calculated based on the dynamic dimension diagnostic results, and the dissemination rate of the hygroscopic catalyst is obtained based on the cloud volume. The total dosage and dispersibility of the hygroscopic catalyst are key parameters; A catalytic scheme is developed by combining the key parameters with the preset targets.

[0019] The apparatus provided in this disclosure, wherein the second simulation module is specifically used for: The particle size threshold is determined based on the collision freezing efficiency, and raindrops smaller than the particle size threshold are selected as the first initial condition from the intervention results of the first simulation results. Raindrops smaller than the particle size threshold are selected as the second initial condition from the comparison results of the first simulation results; The initial conditions include a first initial condition and a second initial condition; A second simulation of the raindrop falling process was conducted in the freezing rain particle cluster model based on the first initial condition and the second initial condition, respectively, and the intervention results and the comparison results of the second simulation results were obtained. Based on the intervention results from the second simulation and the first initial conditions, the intervention-freezing rain particle size spectrum is output. Based on the comparison results of the second simulation and the second initial conditions, the natural-freezing rain particle size spectrum is output; The freezing rain particle size spectrum includes the intervention-freezing rain particle size spectrum and the natural-freezing rain particle size spectrum.

[0020] The apparatus provided in this disclosure, wherein the acquisition module is specifically used for: The final velocity of each raindrop of each size is calculated based on the particle size spectrum function in the freezing rain particle group model. The diameter of freezing rain particles is determined by combining the collision freezing efficiency factor, the final velocity of the fall, and the initial raindrop diameter. The freezing rain particle size spectrum is determined based on the distribution of the freezing rain particle diameter.

[0021] The adjustment module in the apparatus provided in this disclosure is specifically used for: The product of the intervention-freezing rain particle size spectrum and the corresponding sixth power of particle size is integrated, and the horizontal area of ​​the warm cloud region under artificial intervention is combined to obtain the intervention-freezing rain disaster intensity index. Based on the intervention-freezing rain disaster intensity index, a disaster assessment is carried out by comparing it with the preset freezing rain disaster level classification standard to obtain the intervention-freezing rain disaster level. The natural-freezing rain disaster intensity index and natural-freezing rain disaster level are obtained based on the natural-freezing rain particle size spectrum. Calculate the first reduction value of the intervention-freezing rain disaster intensity index and the natural-freezing rain disaster intensity index; at the same time, calculate the second reduction value of the intervention-freezing rain disaster level and the natural-freezing rain disaster level. The effectiveness of the catalytic scheme is evaluated based on the first reduction value and the second reduction value. If the effectiveness does not meet the preset expectation, the parameters of the catalytic scheme are adjusted, and the simulation is returned to the first simulation for iterative simulation until the preset expectation is met, and the adjusted catalytic scheme is output.

[0022] An electronic device provided in this disclosure includes: a processor; and a memory for storing executable instructions; wherein the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis described above.

[0023] This disclosure provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis, as described above. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0025] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating the numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis, as described in this embodiment of the present disclosure. Figure 2 This is a schematic diagram of the structure of the numerical simulation device for reducing the intensity of freezing rain disaster based on warm cloud catalysis, according to an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0027] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0028] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0029] Example 1: This disclosure provides a numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis, such as... Figure 1 As shown, it includes: Acquire relevant data in three-dimensional space, and initialize a freezing rain particle cluster model based on the relevant data. The relevant data includes temperature field, humidity field, wind field, and distribution of supercooled water content in clouds. Based on preset conditions, a warm cloud region for artificial intervention is selected, a catalytic scheme is formulated in combination with preset objectives, and the catalytic scheme is simulated for the first time based on the freezing rain particle swarm model to obtain the first simulation results. The initial conditions in the first simulation results are determined by combining the collision freezing efficiency. The second simulation is then performed by combining the initial conditions with the freezing rain particle group model. The freezing rain particle size spectrum is calculated based on the second simulation results. The freezing rain disaster intensity index and freezing rain disaster level are calculated based on the freezing rain particle size spectrum, and the reduction value of the catalytic scheme on the freezing rain disaster intensity is determined according to the freezing rain disaster intensity index and freezing rain disaster level.

[0030] Figure 1 This is a flowchart illustrating the numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis, as described in this embodiment of the present disclosure.

[0031] In this embodiment, the input to the freezing rain particle swarm model is relevant situational data and a parameterized scheme of key physical processes, and the output is the particle size distribution evolving over time. Key physical processes include condensation nucleation, water vapor condensation / sublimation growth, and collision-coalescence. The core of the freezing rain particle swarm model is the particle size distribution function, which defines the number concentration of freezing rain particles with diameters within a defined range per unit volume of air.

[0032] In this embodiment, the preset conditions are preset thresholds such as supercooled water content and temperature; the preset target is a quantitative indicator set based on disaster prevention needs and cloud physical possibilities. For example, reducing the freezing rain disaster intensity index of the downstream protected area from the predicted 0.75 to below 0.3. This target guides the formulation of the entire catalytic scheme and serves as the final standard for evaluating its effectiveness. For example, the preset target of this operation is to reduce the estimated icing thickness of the target transmission line from 20 mm to below 5 mm through catalysis.

[0033] In this embodiment, the process of formulating the catalytic scheme involves comprehensively analyzing characteristic parameters such as supercooled water content, cloud droplet concentration, vertical velocity, and temperature obtained from multi-dimensional diagnostic results. Key parameters such as the total catalyst dosage and dispersal rate are calculated, and combined with preset targets, specific operational instructions are generated, including dispersal location, dispersal height layer, total catalyst dosage, initial catalyst particle size distribution, timing, and method. For example, the key parameters are: warm cloud layer (6°C layer): warm center height 2.8 km, supercooled water content 0.8 g / m². 3 Near-surface cold layer (-4℃~0℃): thickness 600m; initial raindrop spectrum: Gamma distribution (D max =4.5mm). The corresponding catalytic scheme is set as follows: hygroscopic catalyst CaCl2 is seeded in the warm cloud layer (6℃ layer, 2.8km altitude); seeding dosage: 30kg / km. 2 (Typical operating intensity); Catalyst particle size: 5μm (aerosol mode).

[0034] In this embodiment, the first simulation refers to running the established catalytic scheme in a freezing rain particle swarm model incorporating the aforementioned physical mechanisms. The results of the first simulation are the direct response of the cloud's microphysical field, mainly including the consumption of supercooled water content, the broadening of the cloud droplet spectrum, and the resulting changes in radar polarization parameters, rather than the final ground-based disaster effects. For example, the first simulation results show that in the example above, the cloud water content decreased by 52% after catalysis, and the maximum raindrop diameter D... max The thickness was reduced from 4.5mm to 2.3mm (a decrease of 49%).

[0035] In this embodiment, the initial conditions include a first initial condition and a second initial condition; the freezing rain disaster intensity index is a quantitative physical indicator used to objectively measure the potential destructive power of freezing rain disasters.

[0036] In this embodiment, the freezing rain disaster intensity index includes the intervention-freezing rain disaster intensity index and the natural-freezing rain disaster intensity index, the freezing rain disaster level includes the intervention-freezing rain disaster level and the natural-freezing rain disaster level, and the reduction value includes the first reduction value and the second reduction value.

[0037] In this embodiment, adjusting the catalytic scheme parameters is necessary if the evaluation finds that the effectiveness does not meet the preset expectations. In such cases, the key parameters of the catalytic scheme need to be modified, and the simulation is repeated until the desired effect is achieved. For example, after the first simulation, it was found that the intensity index only decreased by 40% (below the expected 60%). The analysis indicated insufficient catalyst dosage. Therefore, the catalytic scheme parameters were adjusted, increasing the total catalyst dosage from 10 kg to 15 kg, and then returning to the first simulation to start a new simulation-evaluation process.

[0038] Working principle and beneficial effects: The microphysical processes triggered by the catalytic scheme in the cloud are simulated by a freezing rain particle swarm model (the first simulation). Then, the process of freezing and forming freezing rain particle size distribution by screening raindrops as initial conditions is simulated (the second simulation). Based on the numerical iterative optimization scheme of the freezing rain disaster intensity index, the final output is an adjusted catalytic scheme that reduces the disaster intensity index and achieves effective downgrading of disaster level. This realizes the coupled numerical simulation of the entire physical process of warm cloud catalysis, raindrop particle size regulation, freezing rain formation, and disaster intensity assessment. The disaster intensity index based on freezing rain particle size distribution is proposed and applied as the core physical basis for disaster level classification, providing a scientific and quantifiable standard for quantitatively and objectively evaluating the actual mitigation effect of catalytic measures on freezing rain disasters.

[0039] Example 2: The method of this disclosure embodiment acquires relevant situation data in three-dimensional space, initializes a freezing rain particle cluster model based on the relevant situation data, and the relevant situation data includes temperature field, humidity field, wind field, and distribution of supercooled water content in clouds, including: Acquire relevant situation data in three-dimensional space, and initialize the freezing rain particle group model with the relevant situation data through the particle size spectrum function; The particle size spectrum function is derived from the key physical processes of the freezing rain particle concentration and the evolution of the freezing rain particle swarm over time.

[0040] In this embodiment, the temperature field is the distribution of temperature in the atmosphere in three-dimensional space; the humidity field is the distribution of water vapor content in the atmosphere in three-dimensional space; the wind field is the distribution of the speed and direction of air flow in the atmosphere in three-dimensional space, including horizontal wind and vertical speed; and the distribution of supercooled water content in clouds is the content and three-dimensional distribution of water in clouds that is below 0°C but still exists in liquid form.

[0041] Working principle and beneficial effects: By using three-dimensional data such as temperature field, humidity field, wind field and supercooled water content, a freezing rain particle group model based on particle size spectrum function is initialized. By simulating key physical processes such as condensation nucleation, condensation growth, collision and merger, the dynamic evolution of freezing rain particle group is reproduced, realizing accurate mapping from macroscopic meteorological field to microscopic particle spectrum, and quantifying the entire path of freezing rain formation.

[0042] Example 3: The method of this disclosure includes selecting a warm cloud region for artificial intervention based on preset conditions, formulating a catalytic scheme in conjunction with preset targets, and performing a first simulation on the catalytic scheme based on a freezing rain particle swarm model to obtain a first simulation result, including: Compare the preset conditions with the corresponding data of relevant situations, and select the warm cloud layer area for artificial intervention based on the comparison results; Multi-dimensional diagnosis of artificially intervened warm cloud areas is conducted, and key parameters of hygroscopic catalysts are determined based on the multi-dimensional diagnosis results. A catalytic scheme is formulated by integrating all key parameters with preset targets. The physical mechanism describing the competition for water by hygroscopic nuclei is introduced into the freezing rain particle swarm model, and the first simulation of the catalytic scheme is performed on the freezing rain particle swarm model to obtain the first simulation results.

[0043] In this embodiment, the process of comparing the preset conditions with the corresponding data of relevant situations involves spatially matching the preset thresholds for supercooled water content, temperature, etc., with the three-dimensional temperature field, humidity field, wind field, and distribution of supercooled water content in the cloud provided by the data corresponding to the relevant situations. Through logical judgment, a set of grid points that simultaneously meets all preset conditions is selected, thereby delineating the most suitable three-dimensional spatial region for catalysis within the cloud, i.e., the warm cloud layer region for artificial intervention. For example, the preset condition is supercooled water content > 0.2 g / m³. 3 Furthermore, the temperature was <-5°C. By comparing the three-dimensional data field output by the model with these conditions, a cloud layer located at an altitude of 3 kilometers, rich in supercooled water, and with a temperature of -7°C was finally selected as the intervention area.

[0044] In this embodiment, the physical mechanism of hygroscopic nuclei competing for water refers to the fact that after hygroscopic catalyst particles enter a supersaturated environment, the number of cloud droplets increases, and the amount of liquid water available to support the growth of a few large clouds / raindrops decreases significantly. This leads to a narrowing of the droplet (or initial raindrop) size distribution (i.e., a reduction in the proportion of large droplets), ultimately resulting in a significant reduction in the maximum possible size of raindrops generated in the cloud. For example, in the model, the seeded hygroscopic nuclei instantly consume water vapor, causing the cloud droplet spectrum to change from being dominated by small micrometer droplets to having a large number of large droplets with radii exceeding 30 micrometers within minutes, laying the foundation for the subsequent formation of precipitation particles.

[0045] In this embodiment, the specific process of microphysical dimension diagnosis involves quantifying the state and composition of liquid water within the cloud in the artificially intervened warm cloud region, providing direct input for calculating the amount of cloud droplets needed to be replenished. Distribution data of supercooled water content and effective cloud droplet radius are extracted from the three-dimensional data volume representing the target cloud layer. Three-dimensional spatial integration or weighted averaging is performed to calculate the average supercooled water content and average effective cloud droplet radius representing the entire artificially intervened warm cloud region. Using the average supercooled water content and average effective cloud droplet radius as input, a pre-set optimal cloud droplet concentration lookup table is consulted to obtain the optimal cloud droplet concentration value that achieves the highest precipitation efficiency under the current cloud water state. The optimal cloud droplet concentration value is compared with the current cloud droplet concentration value to calculate the concentration of cloud droplets to be replenished.

[0046] In this embodiment, the specific process of thermal dimension diagnosis is to determine the temperature environment in order to select the most effective catalyst and determine its calibration nucleation rate. First, three-dimensional temperature field data of the artificially intervened warm cloud region is extracted, the average temperature of the region is calculated or its most dominant temperature range is determined, the characteristic temperature is used as a key index, the pre-stored catalyst database is queried, the most suitable hygroscopic catalyst formulation with the highest calibration nucleation rate at this temperature is selected, and the corresponding calibration nucleation rate is determined.

[0047] In this embodiment, the specific process of dynamic dimension diagnosis involves quantifying the motion state of clouds in the artificially intervened warm cloud region to determine the diffusion environment and action time of the catalyst, thereby calculating the dispersal rate. First, the three-dimensional wind field within the artificially intervened warm cloud region is analyzed, particularly the distribution and intensity of vertical velocity. Based on the three-dimensional boundary of the artificially intervened warm cloud region, its geometric volume is directly calculated. Based on the average wind speed within the cloud and the horizontal scale of the intervention area, the time required for an air micro-particle to pass through the region is estimated, i.e., the cloud transit time, which determines the effective action time of the catalyst. By analyzing the turbulent diffusion coefficient or the shear condition of the wind field, the diffusion rate after catalyst dispersal is qualitatively evaluated, thus deriving the dispersal rate.

[0048] Working principle and beneficial effects: By comparing preset conditions with relevant data such as temperature field and supercooled water content, a region of warm cloud layer for artificial intervention is selected. After determining the key parameters of the catalyst through multi-dimensional diagnosis, a catalytic scheme is formulated. The first simulation is conducted by introducing a hygroscopic nucleus competing for water physical mechanism to obtain the microphysical response results within the cloud. This achieves full-process quantification from region selection and parameter calculation to scheme simulation, overcoming the blindness of traditional experience-based catalysis.

[0049] Example 4: The method of this disclosure embodiment performs multi-dimensional diagnosis of artificially intervened warm cloud regions, determines key parameters of hygroscopic catalysts based on the multi-dimensional diagnosis results, and formulates a catalytic scheme by integrating all key parameters with preset targets, including: Multidimensional diagnosis was conducted on the artificially intervened warm cloud region, yielding diagnostic results in the microphysical, dynamic, and thermal dimensions. Based on the microphysical dimension diagnostic results, a preset optimal cloud droplet concentration lookup table is queried, the optimal cloud droplet concentration value for the artificially intervened warm cloud layer region is calculated, and the concentration of cloud droplets to be replenished is determined based on the optimal cloud droplet concentration value. The calibration nucleation rate of the hygroscopic catalyst was determined based on the results of the thermodynamic dimension diagnosis. The total dose of hygroscopic catalyst was calculated by combining the concentration of cloud droplets to be replenished and the calibrated nucleation rate. The cloud volume in the warm cloud region under artificial intervention is calculated based on the dynamic dimension diagnostic results, and the dissemination rate of the hygroscopic catalyst is obtained based on the cloud volume. The total dosage and dispersibility of the hygroscopic catalyst are key parameters; A catalytic scheme is developed by combining the key parameters with the preset targets.

[0050] In this embodiment, the microphysical dimension diagnostic results are a quantitative description of the material composition, size, and quantity of cloud and precipitation particles within the artificially intervened warm cloud region. For example, the microphysical dimension diagnostic results show that the average supercooled water content in this region is 0.25 g / m³, the natural cloud droplet number concentration is 200 droplets / cm³, and the effective cloud droplet radius is 12 micrometers. This indicates that the clouds have sufficient moisture but the cloud droplets are small and dense, resulting in low natural precipitation efficiency and high catalytic potential.

[0051] In this embodiment, the dynamic dimension diagnostic results are a quantitative description of the air movement within the warm cloud region affected by artificial intervention. The results show an updraft with an average velocity of 0.8 m / s and a cloud transit time of approximately 20 minutes. This implies that the catalyst can be effectively delivered into the cloud, but the dispersal rate needs to be calculated based on the transit time.

[0052] In this embodiment, the thermal dimension diagnostic results are a quantitative description of the temperature conditions within the artificially intervened warm cloud region. The thermal dimension diagnostic results determine that the average temperature of this region is -8°C. This temperature determines the type of hygroscopic catalyst to be selected, because different catalysts have different nucleation efficiencies at different temperatures.

[0053] In this embodiment, the pre-set optimal cloud droplet concentration lookup table is a database or functional relationship established in advance through extensive cloud numerical simulations and theoretical studies. It establishes the theoretically optimal cloud droplet concentration value that achieves the highest precipitation efficiency under different supercooled water contents and effective cloud droplet radii. For example, based on the currently diagnosed supercooled water content of 0.25 g / m³... 3 Given that the effective radius of cloud droplets is 12 μm, referring to the table, the optimal cloud droplet concentration under this cloud water condition should be 80 droplets / cubic centimeter.

[0054] In this embodiment, the optimal cloud droplet concentration value is the ideal cloud droplet concentration that maximizes the initiation of the collision-coalescence process and the generation of precipitation, obtained by querying a preset optimal cloud droplet concentration lookup table based on the current cloud moisture conditions. For example, if the optimal cloud droplet concentration value is found to be 80 droplets / cubic centimeter, it means that adjusting the cloud droplet concentration in the cloud to around this value will result in the highest precipitation efficiency.

[0055] In this embodiment, the droplet concentration to be replenished is the difference between the optimal droplet concentration and the natural droplet number concentration in the current microphysical dimension diagnostic result. For example, the natural droplet number concentration is 200 droplets / cm³, and the optimal droplet concentration is 80 droplets / cm³. In this case, the droplet concentration to be replenished is 80 - 200 = -120 droplets / cm³. This negative value means that there are already too many droplets, making it unsuitable for conventional catalysis, otherwise it would lead to over-seeding. Conversely, if the natural droplet number concentration is 50 droplets / cm³, then the droplet concentration to be replenished is 80 - 50 = 30 droplets / cm³, meaning that 30 droplets / cm³ need to be replenished.

[0056] In this embodiment, the calibrated nucleation rate refers to the number of effective ice nuclei that a unit mass of hygroscopic catalyst can activate and generate under specific temperature and humidity conditions for a particular type and formulation of hygroscopic catalyst. For example, based on the thermodynamic diagnostic results, a catalyst database shows that the calibrated nucleation rate of the selected "type XX" hygroscopic flame agent is 5 × 10⁻⁶. 12 One per gram. That is, burning 1 gram of this flammable agent at -8°C can theoretically produce 5 trillion effective nuclei.

[0057] Working principle and beneficial effects: Based on multi-dimensional diagnostic results from microphysics, dynamics, and thermodynamics, the optimal cloud droplet concentration, calibrated nucleation rate, and cloud volume are calculated respectively. Then, key parameters such as the concentration of cloud droplets to be replenished, total catalyst dosage, and seeding rate are determined. Finally, all parameters are integrated to formulate a quantitative catalytic scheme, realizing the precise coupling of catalyst parameter calculation and cloud condition diagnosis. This significantly improves the scientific nature and operational efficiency of catalyst dosage determination, seeding timing, and location selection.

[0058] Example 5: The method of this disclosure embodiment, which determines the initial conditions in the first simulation result by combining the collision freezing efficiency, performs a second simulation by combining the initial conditions with the freezing rain particle swarm model, and calculates the freezing rain particle size spectrum based on the second simulation result, includes: The particle size threshold is determined based on the collision freezing efficiency, and raindrops smaller than the particle size threshold are selected as the first initial condition from the intervention results of the first simulation results. Raindrops smaller than the particle size threshold are selected as the second initial condition from the comparison results of the first simulation results; The initial conditions include a first initial condition and a second initial condition; A second simulation of the raindrop falling process was conducted in the freezing rain particle cluster model based on the first initial condition and the second initial condition, respectively, and the intervention results and the comparison results of the second simulation results were obtained. Based on the intervention results from the second simulation and the first initial conditions, the intervention-freezing rain particle size spectrum is output. Based on the comparison results of the second simulation and the second initial conditions, the natural-freezing rain particle size spectrum is output; The freezing rain particle size spectrum includes the intervention-freezing rain particle size spectrum and the natural-freezing rain particle size spectrum.

[0059] In this embodiment, determining the particle size threshold based on the collision freezing efficiency is a process of setting a boundary standard based on a physical mechanism. The collision freezing efficiency describes the probability of a supercooled raindrop successfully freezing after colliding with an ice crystal, and typically decreases sharply as the raindrop size decreases. The particle size threshold is a set critical diameter; raindrops smaller than this value are considered to have almost no effective contribution to the freezing rain formation process. For example, theoretical studies and experimental data have determined that the collision freezing efficiency of supercooled raindrops with a diameter less than 300 micrometers (0.3 millimeters) is less than 10%. Therefore, the particle size threshold is set to 300 μm. All raindrops smaller than this value are considered to contribute very little to freezing rain in subsequent simulations.

[0060] In this embodiment, the intervention result of the first simulation refers to the cloud microphysical state obtained after implementing the catalytic scheme in the freezing rain particle swarm model. It represents the warm cloud layer condition after artificial intervention, with the core being the new particle size distribution formed after catalysis, which includes more larger cloud droplets and precipitation particles transformed from hygroscopic nuclei. For example, after the first simulation, the model outputs a data file containing the particle size distribution of all particles in the cloud after catalysis. The particle size distribution shows that the concentration of raindrops with a diameter of approximately 500 micrometers has significantly increased compared to before catalysis.

[0061] In this embodiment, the control result of the first simulation refers to the cloud microphysical state obtained under the same initial weather conditions, in the freezing rain particle swarm model without any catalytic intervention, allowing it to develop naturally. This represents the warm cloud layer condition under natural conditions, serving as a benchmark or control group for evaluating the catalytic effect. For example, a simulation without catalyst seeding is run under identical weather conditions. Its output is the control result, showing how the warm cloud layer will evolve naturally without human intervention.

[0062] In this embodiment, the first and second initial conditions are input data prepared for the second simulation (raindrop falling process), and the particle size distribution of a small raindrop population selected from the first simulation results based on a particle size threshold. The first initial condition is the raindrop spectrum derived from the intervention results. The second initial condition is the raindrop spectrum derived from the control results. For example, from the complete particle size distribution of the intervention results, all raindrops with a diameter less than 300 micrometers are extracted, and their concentration distribution is used as the first initial condition. Similarly, the raindrop spectrum less than 300 micrometers is extracted from the control results and used as the second initial condition.

[0063] In this embodiment, the second simulation is a numerical experiment simulating the process of raindrops falling from the cloud base to the ground. This simulation only concerns the state changes of selected small raindrops during their descent, without considering the complex microphysical processes within the cloud. It is run twice, using a first initial condition and a second initial condition as inputs. The first initial condition is input into the model to simulate the raindrops falling through a near-surface cold layer below 0°C, calculating how many of them will freeze into freezing rain particles. The output of this simulation is the intervention result of the second simulation.

[0064] In this embodiment, the intervention-freezing rain particle size distribution is the final frozen rain particle size distribution obtained after a second simulation based on raindrops catalyzed under the first initial conditions. The natural-freezing rain particle size distribution is the final frozen rain particle size distribution obtained after a second simulation based on the second initial conditions. For example, the natural-freezing rain particle size distribution shows that the peak concentration of frozen rain particles on the ground is at 1.5 mm. The intervention-freezing rain particle size distribution shows that the peak shifts to 1.0 mm, and the total concentration decreases by 30%. This indicates that the catalytic scheme effectively reduces the particle size and number of frozen rain particles, thereby mitigating the severity of the disaster.

[0065] In this embodiment, the raindrops fall after catalysis: initial raindrop D rain =2.3mm penetrates the near-surface cold layer (-4℃). Freezing output: Based on the collision freezing model, the ground freezing rain particle spectrum is generated: D max = 2.1mm (compared to 4.2mm in the natural group) Large particle (D>3mm) concentration: 0.01 particles / m³ 3 (Natural group: 0.25 per m) 3 ).

[0066] Working principle and beneficial effects: Based on the collision freezing efficiency, a particle size threshold is set, and small raindrops are selected from the first simulation results as the initial conditions for intervention and control, respectively. The second simulation simulates the falling and freezing process, and finally outputs comparable intervention-freezing rain particle size spectrum and natural-freezing rain particle size spectrum. This realizes the whole chain of catalytic effect evaluation from cloud microphysical response to ground disaster formation, and provides a reliable method for accurately quantifying the weakening effect of artificial intervention on freezing rain particle size distribution and disaster intensity.

[0067] Example 6: The method of this disclosure embodiment, which calculates the freezing rain particle size spectrum, includes: The final velocity of each raindrop of each size is calculated based on the particle size spectrum function in the freezing rain particle group model. The diameter of freezing rain particles is determined by combining the collision freezing efficiency factor, the final velocity of the fall, and the initial raindrop diameter. The freezing rain particle size spectrum is determined based on the distribution of the freezing rain particle diameter.

[0068] In this embodiment, ,in Indicates advection transport; Represents the divergence operator; Represents the freezing rain particle size spectrum function; Indicates the condensation nucleation term; This indicates the growth term of water vapor condensation / sublimation; Indicates the collision and union of terms; k Indicates the air drag coefficient; D Indicates the diameter of freezing rain particles; Indicates the diameter of the raindrop; Indicates the subcooling coefficient; This indicates a temperature difference below 0°C. This represents the collision freeze efficiency factor; v Indicates the speed at which raindrops fall; This represents the shape parameter, which determines the shape of the distribution curve; This represents the slope parameter, which controls the rate of distribution decay; Indicates the intercept parameter; Working principle and beneficial effects: The final velocity of raindrops of various sizes is calculated based on the particle size spectrum function. The diameter of freezing rain particles is determined by combining the collision freezing efficiency factor, the physical relationship between the final velocity and the initial raindrop diameter, and finally the freezing rain particle size spectrum is generated by statistical diameter distribution. A complete physical chain from raindrop falling dynamics to freezing rain phase transformation is established, realizing the mechanistic prediction of freezing rain particle size spectrum, and providing key scientific basis for accurately assessing the risk of icing disasters.

[0069] Example 7: The method of this disclosure includes calculating a freezing rain disaster intensity index and a freezing rain disaster level based on the freezing rain particle size distribution, determining the reduction value of a catalytic scheme on the freezing rain disaster intensity based on the freezing rain disaster intensity index and the freezing rain disaster level, and adjusting the catalytic scheme based on the reduction value, including: The product of the intervention-freezing rain particle size spectrum and the corresponding sixth power of particle size is integrated, and the horizontal area of ​​the warm cloud region under artificial intervention is combined to obtain the intervention-freezing rain disaster intensity index. Based on the intervention-freezing rain disaster intensity index, a disaster assessment is carried out by comparing it with the preset freezing rain disaster level classification standard to obtain the intervention-freezing rain disaster level. The natural-freezing rain disaster intensity index and natural-freezing rain disaster level are obtained based on the natural-freezing rain particle size spectrum. Calculate the first reduction value of the intervention-freezing rain disaster intensity index and the natural-freezing rain disaster intensity index; at the same time, calculate the second reduction value of the intervention-freezing rain disaster level and the natural-freezing rain disaster level. The effectiveness of the catalytic scheme is evaluated based on the first reduction value and the second reduction value. If the effectiveness does not meet the preset expectation, the parameters of the catalytic scheme are adjusted, and the simulation is returned to the first simulation for iterative simulation until the preset expectation is met, and the adjusted catalytic scheme is output.

[0070] In this embodiment, the formula for calculating the freezing rain disaster intensity index is: ,in, Indicates the intensity index of freezing rain disaster; A This represents the horizontal area of ​​the warm cloud layer affected by artificial intervention. T Indicates the length of the evaluation period; t Represents the time variable, with the integration range starting from the simulation start time. t= 0 to the end time t=T ; Indicates the maximum diameter of freezing rain particles; Indicates the minimum diameter of freezing rain particles; Indicates time t ,Location x The number of freezing rain particles per unit size interval in a unit volume of air.

[0071] In this embodiment, the preset freezing rain disaster level classification standard is a predefined lookup table or function relation that maps continuous freezing rain disaster intensity indices to discrete disaster levels that are easy to understand and operate operationally. It is usually jointly formulated by meteorological, power, and transportation departments and serves as the direct basis for disaster early warning and emergency response. The standard is typically set as follows: I h <10 corresponds to low risk (Level I), 10 ≤ Level I h <25 corresponds to moderate risk (Level II), 25 ≤ Level I h <50 corresponds to higher risk (Level III), I h ≥ 50 corresponds to high risk (Level IV).

[0072] In this embodiment, the intervention-freezing rain disaster intensity index is a disaster index calculated based on the intervention-freezing rain particle size distribution. The intervention-freezing rain disaster level is the disaster level obtained by substituting the intervention-freezing rain disaster intensity index into a preset freezing rain disaster level classification standard. For example, simulations show that the freezing rain particles on the ground are significantly smaller after catalysis. The calculated intervention-freezing rain disaster intensity index is 24.3. According to the level standard, 24.3 belongs to Level II (moderate).

[0073] In this embodiment, the natural-freezing rain disaster intensity index is a disaster index calculated based on the natural-freezing rain particle size distribution. The natural-freezing rain disaster level is obtained by substituting the natural-freezing rain disaster intensity index into the level standard. For example, under exactly the same weather conditions, the natural-freezing rain disaster intensity index obtained from the non-catalytic simulation is 68. According to the level standard, 68 belongs to Level IV disaster.

[0074] In this embodiment, the first reduction value refers to the decrease in the intervention-freezing rain disaster intensity index relative to the natural-freezing rain disaster intensity index, expressed as a percentage or absolute difference. The second reduction value refers to the reduction in the intervention-freezing rain disaster level relative to the natural-freezing rain disaster level. For example, the first reduction value is (68-24.3) / 68*100%=64.3%, indicating that the catalytic scheme reduced the physical intensity of the freezing rain disaster by 64.3%. The second reduction value represents a reduction from Level IV to Level II (moderate), a decrease of 2 disaster levels.

[0075] In this embodiment, the effectiveness level is the success level of the catalytic scheme jointly characterized by the first reduction value and the second reduction value; the preset expectation is a minimum success standard set before the start of the catalytic operation based on disaster prevention needs and theoretical feasibility, and is a quantified target threshold. For example, the preset expectation for this operation is that the disaster level is reduced by at least 1 level and the intensity index is reduced by no less than 60%. The effectiveness level obtained from the current simulation is a reduction of 1 level in the disaster level and a reduction of 75% in intensity, which meets or even exceeds the preset expectation.

[0076] Working principle and beneficial effects: The disaster intensity index is calculated by multiplying the particle size spectrum of natural-freezing rain by the sixth power of the particle size. The disaster level is obtained by comparing it with the level standard. The effect of the catalytic scheme is evaluated by comparing the intensity index with the first and second reduction values ​​of the level. If the expected results are not achieved, the scheme is iteratively optimized. A quantitative correlation between the freezing rain particle size spectrum and the disaster intensity is established, realizing the numerical evaluation and closed-loop optimization of the disaster reduction effect of the catalytic scheme. The phase state and size distribution of precipitation particles are precisely controlled, thereby achieving the downgrading of the disaster level.

[0077] To achieve the above embodiments, this disclosure also proposes a numerical simulation device for reducing the intensity of freezing rain disasters based on warm cloud catalysis.

[0078] Figure 2 This is a schematic diagram of the structure of a numerical simulation device for reducing the intensity of freezing rain disasters based on warm cloud catalysis, provided in an embodiment of this disclosure. This device can be implemented by software and / or hardware, and is generally integrated into an electronic device. Figure 2 As shown, the device includes: an acquisition module, a first simulation module, a second simulation module, and an adjustment module, wherein, The acquisition module is used to acquire relevant situation data in three-dimensional space and initialize the freezing rain particle group model based on the relevant situation data. The relevant situation data includes temperature field, humidity field, wind field and distribution of supercooled water content in clouds. The first simulation module is used to select a warm cloud layer region for artificial intervention based on preset conditions, formulate a catalytic scheme in combination with preset targets, and perform a first simulation on the catalytic scheme based on the freezing rain particle cluster model to obtain the first simulation results. The second simulation module is used to determine the initial conditions in the first simulation results by combining the collision freezing efficiency, perform the second simulation by combining the initial conditions with the freezing rain particle group model, and calculate the freezing rain particle size spectrum based on the second simulation results. The adjustment module is used to calculate the freezing rain disaster intensity index and freezing rain disaster level based on the freezing rain particle size spectrum, and to determine the reduction value of the catalytic scheme on the freezing rain disaster intensity according to the freezing rain disaster intensity index and freezing rain disaster level. The reduction value is used to adjust the catalytic scheme.

[0079] The apparatus provided in this disclosure, wherein the acquisition module is specifically used for: Acquire relevant situation data in three-dimensional space, and initialize the freezing rain particle group model with the relevant situation data through the particle size spectrum function; The particle size spectrum function is derived from the key physical processes of the freezing rain particle concentration and the evolution of the freezing rain particle swarm over time.

[0080] The apparatus provided in this disclosure embodiment, wherein the first simulation module is specifically used for: Compare the preset conditions with the corresponding data of relevant situations, and select the warm cloud layer area for artificial intervention based on the comparison results; Multi-dimensional diagnosis of artificially intervened warm cloud areas is conducted, and key parameters of hygroscopic catalysts are determined based on the multi-dimensional diagnosis results. A catalytic scheme is formulated by integrating all key parameters with preset targets. The physical mechanism describing the competition for water by hygroscopic nuclei is introduced into the freezing rain particle swarm model, and the first simulation of the catalytic scheme is performed on the freezing rain particle swarm model to obtain the first simulation results.

[0081] The apparatus provided in this disclosure embodiment, wherein the first simulation module is specifically used for: Multidimensional diagnosis was conducted on the artificially intervened warm cloud region, yielding diagnostic results in the microphysical, dynamic, and thermal dimensions. Based on the microphysical dimension diagnostic results, a preset optimal cloud droplet concentration lookup table is queried, the optimal cloud droplet concentration value for the artificially intervened warm cloud layer region is calculated, and the concentration of cloud droplets to be replenished is determined based on the optimal cloud droplet concentration value. The calibration nucleation rate of the hygroscopic catalyst was determined based on the results of the thermodynamic dimension diagnosis. The total dose of hygroscopic catalyst was calculated by combining the concentration of cloud droplets to be replenished and the calibrated nucleation rate. The cloud volume in the warm cloud region under artificial intervention is calculated based on the dynamic dimension diagnostic results, and the dissemination rate of the hygroscopic catalyst is obtained based on the cloud volume. The total dosage and dispersibility of the hygroscopic catalyst are key parameters; A catalytic scheme is developed by combining the key parameters with the preset targets.

[0082] The apparatus provided in this disclosure, wherein the second simulation module is specifically used for: The particle size threshold is determined based on the collision freezing efficiency, and raindrops smaller than the particle size threshold are selected as the first initial condition from the intervention results of the first simulation results. Raindrops smaller than the particle size threshold are selected as the second initial condition from the comparison results of the first simulation results; The initial conditions include a first initial condition and a second initial condition; A second simulation of the raindrop falling process was conducted in the freezing rain particle cluster model based on the first initial condition and the second initial condition, respectively, and the intervention results and the comparison results of the second simulation results were obtained. Based on the intervention results from the second simulation and the first initial conditions, the intervention-freezing rain particle size spectrum is output. Based on the comparison results of the second simulation and the second initial conditions, the natural-freezing rain particle size spectrum is output; The freezing rain particle size spectrum includes the intervention-freezing rain particle size spectrum and the natural-freezing rain particle size spectrum.

[0083] The apparatus provided in this disclosure, wherein the acquisition module is specifically used for: The final velocity of each raindrop of each size is calculated based on the particle size spectrum function in the freezing rain particle group model. The diameter of freezing rain particles is determined by combining the collision freezing efficiency factor, the final velocity of the fall, and the initial raindrop diameter. The freezing rain particle size spectrum is determined based on the distribution of the freezing rain particle diameter.

[0084] The adjustment module in the apparatus provided in this disclosure is specifically used for: The product of the intervention-freezing rain particle size spectrum and the corresponding sixth power of particle size is integrated, and the horizontal area of ​​the warm cloud region under artificial intervention is combined to obtain the intervention-freezing rain disaster intensity index. Based on the intervention-freezing rain disaster intensity index, a disaster assessment is carried out by comparing it with the preset freezing rain disaster level classification standard to obtain the intervention-freezing rain disaster level. The natural-freezing rain disaster intensity index and natural-freezing rain disaster level are obtained based on the natural-freezing rain particle size spectrum. Calculate the first reduction value of the intervention-freezing rain disaster intensity index and the natural-freezing rain disaster intensity index; at the same time, calculate the second reduction value of the intervention-freezing rain disaster level and the natural-freezing rain disaster level. The effectiveness of the catalytic scheme is evaluated based on the first reduction value and the second reduction value. If the effectiveness does not meet the preset expectation, the parameters of the catalytic scheme are adjusted, and the simulation is returned to the first simulation for iterative simulation until the preset expectation is met, and the adjusted catalytic scheme is output.

[0085] The numerical simulation device for reducing the intensity of freezing rain disasters based on warm cloud catalysis provided in this disclosure can execute the numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method.

[0086] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program / instruction, which, when executed by a processor, implements the numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis in the above embodiments.

[0087] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.

[0088] The following is a detailed reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing the electronic device 300 in the embodiments of this disclosure. The electronic device 300 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0089] like Figure 3 As shown, the electronic device 300 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a memory 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0090] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0091] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis according to embodiments of this disclosure.

[0092] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0093] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0094] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0095] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the aforementioned numerical simulation method for reducing the intensity of freezing rain disasters based on warm cloud catalysis.

[0096] Electronic devices can be programmed with computer program code in one or more programming languages ​​or combinations thereof to perform the operations of this disclosure. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0098] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0099] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0100] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0101] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0102] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0103] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A numerical simulation method for weakening the intensity of freezing rain disaster based on warm cloud catalysis, characterized in that, include: Acquire relevant situation data in three-dimensional space, and initialize freezing rain particle group model based on the relevant situation data. The relevant situation data includes temperature field, humidity field, wind field and distribution of supercooled water content in clouds. Based on preset conditions, a warm cloud layer region for artificial intervention is selected, a catalytic scheme is formulated in combination with preset objectives, and the catalytic scheme is simulated for the first time based on the freezing rain particle cluster model to obtain the first simulation results. The initial conditions in the first simulation results are determined by combining the collision freezing efficiency. The second simulation is then performed by combining the initial conditions with the freezing rain particle group model. The freezing rain particle size spectrum is calculated based on the second simulation results. The freezing rain disaster intensity index and freezing rain disaster level are calculated based on the freezing rain particle size spectrum. The reduction value of the catalytic scheme on the freezing rain disaster intensity is determined according to the freezing rain disaster intensity index and freezing rain disaster level. The reduction value is used to adjust the catalytic scheme. The initial conditions in the first simulation results are determined by combining the collision freezing efficiency. A second simulation is then performed using these initial conditions and the freezing rain particle swarm model. The freezing rain particle size spectrum is calculated based on the second simulation results, including: The particle size threshold is determined based on the collision freezing efficiency, and raindrops smaller than the particle size threshold are selected as the first initial condition from the intervention results of the first simulation results. Raindrops smaller than the particle size threshold are selected as the second initial condition from the comparison results of the first simulation results; The initial conditions include a first initial condition and a second initial condition; A second simulation of the raindrop falling process was conducted in the freezing rain particle cluster model based on the first initial condition and the second initial condition, respectively, and the intervention results and the comparison results of the second simulation results were obtained. Based on the intervention results from the second simulation and the first initial conditions, the intervention-freezing rain particle size spectrum is output. Based on the comparison results of the second simulation and the second initial conditions, the natural-freezing rain particle size spectrum is output; The freezing rain particle size spectrum includes the intervention-freezing rain particle size spectrum and the natural-freezing rain particle size spectrum.

2. The method according to claim 1, characterized in that, The step of acquiring relevant situation data in three-dimensional space and initializing the freezing rain particle group model based on the relevant situation data includes: Acquire relevant situation data in three-dimensional space, and initialize the freezing rain particle group model with the relevant situation data through the particle size spectrum function; The particle size spectrum function is derived from the key physical processes of the freezing rain particle concentration and the evolution of the freezing rain particle swarm over time.

3. The method according to claim 2, characterized in that, The process involves selecting a warm cloud region for artificial intervention based on preset conditions, formulating a catalytic scheme in conjunction with preset objectives, and conducting a first simulation of the catalytic scheme based on a freezing rain particle swarm model to obtain the first simulation results, including: Compare the preset conditions with the corresponding data of relevant situations, and select the warm cloud layer area for artificial intervention based on the comparison results; Multi-dimensional diagnosis of artificially intervened warm cloud areas is conducted, and key parameters of hygroscopic catalysts are determined based on the multi-dimensional diagnosis results. A catalytic scheme is formulated by integrating all key parameters with preset targets. The physical mechanism describing the competition for water by hygroscopic nuclei is introduced into the freezing rain particle swarm model, and the catalytic scheme is simulated based on the freezing rain particle swarm model to obtain the first simulation results.

4. The method according to claim 3, characterized in that, The process involves multi-dimensional diagnosis of the artificially intervened warm cloud region, determining key parameters of the hygroscopic catalyst based on the diagnostic results, and formulating a catalytic scheme by integrating all key parameters with preset targets, including: Multidimensional diagnosis was conducted on the artificially intervened warm cloud region, yielding diagnostic results in the microphysical, dynamic, and thermal dimensions. Based on the microphysical dimension diagnostic results, a preset optimal cloud droplet concentration lookup table is queried, the optimal cloud droplet concentration value for the artificially intervened warm cloud layer region is calculated, and the concentration of cloud droplets to be replenished is determined based on the optimal cloud droplet concentration value. The calibration nucleation rate of the hygroscopic catalyst was determined based on the results of the thermodynamic dimension diagnosis. The total dosage of the hygroscopic catalyst was calculated by combining the concentration of the cloud droplets to be replenished and the calibrated nucleation rate. The cloud volume in the warm cloud region under artificial intervention is calculated based on the dynamic dimension diagnostic results, and the dissemination rate of the hygroscopic catalyst is obtained based on the cloud volume. The total dosage and dissemination rate of the hygroscopic catalyst were used as key parameters. A catalytic scheme is developed by combining the key parameters with the preset targets.

5. The method according to claim 2, characterized in that, Calculating the freezing rain particle size spectrum includes: The final velocity of each raindrop of each size is calculated based on the particle size spectrum function in the freezing rain particle swarm model. The diameter of freezing rain particles is determined by combining the collision freezing efficiency factor, the final velocity of the fall, and the initial raindrop diameter. The freezing rain particle size spectrum is determined based on the distribution of the freezing rain particle diameter.

6. The method according to claim 1, characterized in that, The process of calculating the freezing rain disaster intensity index and freezing rain disaster level based on the freezing rain particle size spectrum, and determining the reduction value of the catalytic scheme on the freezing rain disaster intensity based on the freezing rain disaster intensity index and freezing rain disaster level, includes: The product of the intervention-freezing rain particle size spectrum and the corresponding sixth power of particle size is integrated, and the horizontal area of ​​the warm cloud region under artificial intervention is combined to obtain the intervention-freezing rain disaster intensity index. Based on the intervention-freezing rain disaster intensity index, a disaster assessment is carried out by comparing it with the preset freezing rain disaster level classification standard to obtain the intervention-freezing rain disaster level. The natural-freezing rain disaster intensity index and natural-freezing rain disaster level are obtained based on the natural-freezing rain particle size spectrum. Calculate the first reduction value of the intervention-freezing rain disaster intensity index and the natural-freezing rain disaster intensity index; at the same time, calculate the second reduction value of the intervention-freezing rain disaster level and the natural-freezing rain disaster level. The effectiveness of the catalytic scheme is evaluated based on the first reduction value and the second reduction value. If the effectiveness does not meet the preset expectation, the parameters of the catalytic scheme are adjusted, and the simulation is returned to the first simulation for iterative simulation until the preset expectation is met, and the adjusted catalytic scheme is output.

7. A numerical simulation device for reducing the intensity of freezing rain disasters based on warm cloud catalysis, characterized in that, The device includes: The acquisition module is used to acquire relevant situation data in three-dimensional space and initialize the freezing rain particle group model based on the relevant situation data. The relevant situation data includes temperature field, humidity field, wind field and distribution of supercooled water content in clouds. The first simulation module is used to select a warm cloud layer region for artificial intervention based on preset conditions, formulate a catalytic scheme in combination with preset targets, and perform a first simulation on the catalytic scheme based on the freezing rain particle cluster model to obtain the first simulation results. The second simulation module is used to determine the initial conditions in the first simulation results by combining the collision freezing efficiency, perform the second simulation by combining the initial conditions with the freezing rain particle group model, and calculate the freezing rain particle size spectrum based on the second simulation results. The adjustment module is used to calculate the freezing rain disaster intensity index and freezing rain disaster level based on the freezing rain particle size spectrum, and to determine the reduction value of the catalytic scheme on the freezing rain disaster intensity according to the freezing rain disaster intensity index and freezing rain disaster level. The reduction value is used to adjust the catalytic scheme. The second simulation module is specifically used for: The particle size threshold is determined based on the collision freezing efficiency, and raindrops smaller than the particle size threshold are selected as the first initial condition from the intervention results of the first simulation results. Raindrops smaller than the particle size threshold are selected as the second initial condition from the comparison results of the first simulation results; The initial conditions include a first initial condition and a second initial condition; A second simulation of the raindrop falling process was conducted in the freezing rain particle cluster model based on the first initial condition and the second initial condition, respectively, and the intervention results and the comparison results of the second simulation results were obtained. Based on the intervention results from the second simulation and the first initial conditions, the intervention-freezing rain particle size spectrum is output. Based on the comparison results of the second simulation and the second initial conditions, the natural-freezing rain particle size spectrum is output; The freezing rain particle size spectrum includes the intervention-freezing rain particle size spectrum and the natural-freezing rain particle size spectrum.

8. The apparatus according to claim 7, characterized in that, The acquisition module is specifically used for: Acquire relevant situation data in three-dimensional space, and initialize the freezing rain particle group model with the relevant situation data through the particle size spectrum function; The particle size spectrum function is derived from the key physical processes of the freezing rain particle concentration and the evolution of the freezing rain particle swarm over time.

9. The apparatus according to claim 8, characterized in that, The first simulation module is specifically used for: Compare the preset conditions with the corresponding data of relevant situations, and select the warm cloud layer area for artificial intervention based on the comparison results; Multi-dimensional diagnosis of artificially intervened warm cloud areas is conducted, and key parameters of hygroscopic catalysts are determined based on the multi-dimensional diagnosis results. A catalytic scheme is formulated by integrating all key parameters with preset targets. The physical mechanism describing the competition for water by hygroscopic nuclei is introduced into the freezing rain particle swarm model, and the catalytic scheme is simulated based on the freezing rain particle swarm model to obtain the first simulation results.

10. The apparatus according to claim 9, characterized in that, The first simulation module is specifically used for: Multidimensional diagnosis was conducted on the artificially intervened warm cloud region, yielding diagnostic results in the microphysical, dynamic, and thermal dimensions. Based on the microphysical dimension diagnostic results, a preset optimal cloud droplet concentration lookup table is queried, the optimal cloud droplet concentration value for the artificially intervened warm cloud layer region is calculated, and the concentration of cloud droplets to be replenished is determined based on the optimal cloud droplet concentration value. The calibration nucleation rate of the hygroscopic catalyst was determined based on the results of the thermodynamic dimension diagnosis. The total dosage of the hygroscopic catalyst was calculated by combining the concentration of the cloud droplets to be replenished and the calibrated nucleation rate. The cloud volume in the warm cloud region under artificial intervention is calculated based on the dynamic dimension diagnostic results, and the dissemination rate of the hygroscopic catalyst is obtained based on the cloud volume. The total dosage and dissemination rate of the hygroscopic catalyst were used as key parameters. A catalytic scheme is developed by combining the key parameters with the preset targets.

11. The apparatus according to claim 8, characterized in that, The acquisition module is specifically used for: The final velocity of each raindrop of each size is calculated based on the particle size spectrum function in the freezing rain particle swarm model. The diameter of freezing rain particles is determined by combining the collision freezing efficiency factor, the final velocity of the fall, and the initial raindrop diameter. The freezing rain particle size spectrum is determined based on the distribution of the freezing rain particle diameter.

12. The apparatus according to claim 7, characterized in that, The adjustment module is specifically used for: The product of the intervention-freezing rain particle size spectrum and the corresponding sixth power of particle size is integrated, and the horizontal area of ​​the warm cloud region under artificial intervention is combined to obtain the intervention-freezing rain disaster intensity index. Based on the intervention-freezing rain disaster intensity index, a disaster assessment is carried out by comparing it with the preset freezing rain disaster level classification standard to obtain the intervention-freezing rain disaster level. The natural-freezing rain disaster intensity index and natural-freezing rain disaster level are obtained based on the natural-freezing rain particle size spectrum. Calculate the first reduction value of the intervention-freezing rain disaster intensity index and the natural-freezing rain disaster intensity index; at the same time, calculate the second reduction value of the intervention-freezing rain disaster level and the natural-freezing rain disaster level. The effectiveness of the catalytic scheme is evaluated based on the first reduction value and the second reduction value. If the effectiveness does not meet the preset expectation, the parameters of the catalytic scheme are adjusted, and the simulation is returned to the first simulation for iterative simulation until the preset expectation is met, and the adjusted catalytic scheme is output.

13. An electronic device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the numerical simulation method for reducing the intensity of freezing rain disaster based on warm cloud catalysis as described in any one of claims 1-6.

14. A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the numerical simulation method for reducing the intensity of freezing rain disaster based on warm cloud catalysis as described in any one of claims 1-6.

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