A lightning potential forecast method fusing cloud microphysical conditions

Abstract

This invention discloses a lightning potential forecasting method integrating cloud microphysical conditions, belonging to the field of lightning potential forecasting technology. It involves acquiring numerical weather prediction data for a target area, constructing a cloud microphysical dynamic-water vapor coupling index for the target area, and determining the meteorological forecast results for the target area based on the cloud microphysical dynamic-water vapor coupling index. Based on the meteorological forecast results, convection parameters for the target area are extracted, and convection information for the target area is obtained through comprehensive analysis. Based on the convection information, mixed-phase cloud microphysical parameters for the target area are extracted. Based on the mixed-phase cloud microphysical parameters, collision intensity characterization parameters for the target area are determined. The collision intensity characterization parameters are then corrected for charge separation capability based on numerical weather prediction data to obtain an effective collision intensity index for the target area. Based on the effective collision intensity index, the lightning potential index for the target area is determined, and the lightning potential information for the target area is output, thus improving the accuracy of lightning potential forecasting.

Description

Technical Field

[0001] This invention relates to the field of lightning potential forecasting technology, specifically to a lightning potential forecasting method that integrates cloud microphysical conditions. Background Technology

[0002] In the operational monitoring and short-term forecasting of severe convective weather, lightning potential forecasting methods are mainly used for the refined characterization of convective cloud development. Its core task is to comprehensively analyze vertical motion, water vapor conditions, unstable energy, and cloud microphysical structure based on numerical model forecasts and multi-source observation data to determine whether lightning will occur and its potential intensity. Especially during the initial and rapid development stages of thunderstorms, it is necessary to identify cloud structures with discharge potential in advance, before significant electrical activity occurs within the cloud particles. This provides timely decision support for weather forecasting, power dispatching, and aviation operations.

[0003] However, in the actual evolution of convective clouds, lightning generation is not solely determined by the coexistence of microphysical components such as cloud ice and graupel, but rather depends on effective collision processes between different particles. In the mixed-phase region above the zero-degree layer, graupel and cloud ice particles move relative to each other under the influence of updrafts. Only when they collide at high frequencies under certain temperature and moisture conditions will charge separation be triggered, gradually accumulating to form an electric field structure. If only cloud ice or graupel exists, or if both exist simultaneously but are spatially separated, lack sufficient relative motion, or have a low collision probability, effective charge separation is difficult to achieve. Therefore, the key to lightning formation lies not in the static existence of particles, but in the collision frequency and interaction intensity under specific dynamic conditions.

[0004] If the aforementioned collision process is not accurately characterized, it will directly lead to a systematic bias in the determination of lightning potential. In practical applications, common scenarios include: cloud ice and graupel may coexist within the cloud, but due to weak updrafts or non-overlapping particle distributions, the collision process is insufficient, and no lightning actually occurs; conversely, driven by strong updrafts, even with moderate particle content, the significantly increased collision frequency may rapidly trigger intense electrical activity. If forecasting methods cannot distinguish these differences, it will cause false alarms or missed alarms in lightning forecasts, thereby affecting the accuracy of lightning forecasts, increasing the risk of power system tripping, and posing potential hazards to aviation safety and outdoor work safety.

[0005] Current technologies for lightning potential assessment generally only consider the occurrence conditions of strong convection and rarely employ cloud microphysical parameters. Even when using existence determination based on cloud microphysical parameters or simple threshold superposition methods, they focus on analyzing single indicators such as cloud ice content, graupel content, or supercooled water distribution, lacking dynamic modeling of the interaction processes between different particles, and especially failing to explicitly characterize the key physical quantity of collision frequency. This approach, which substitutes static conditions for dynamic mechanisms, makes it difficult for forecast models to reflect the actual charge separation process, fundamentally limiting the accuracy and reliability of lightning potential forecasts and failing to meet the operational requirements of high precision and timely response. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a lightning potential prediction method that integrates cloud microphysical conditions, which can effectively solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: a lightning potential forecasting method that integrates cloud microphysical conditions, including acquiring numerical weather forecast data of the target area, constructing a cloud microphysical dynamic-water vapor coupling index for the target area, and determining the meteorological forecast result of the target area based on the cloud microphysical dynamic-water vapor coupling index.

[0008] Based on the meteorological forecast results for the target area, the convection parameters of the target area are extracted, and the convection information of the target area is obtained through comprehensive analysis.

[0009] Based on the convection information of the target region, the microphysical parameters of the mixed-phase cloud in the target region are extracted, and the collision intensity characterization parameters of the target region are determined based on the microphysical parameters of the mixed-phase cloud.

[0010] Based on numerical weather prediction data, the collision intensity characterization parameters are corrected for charge separation capability to obtain the effective collision intensity index of the target area.

[0011] Based on the effective collision intensity index of the target area, determine the lightning potential index of the target area and output the lightning potential information of the target area.

[0012] Furthermore, the method for constructing the cloud microphysical dynamics-water vapor coupling index of the target region is as follows: The numerical weather forecast data for the target area includes the average temperature, average relative humidity, average vertical wind speed, cloud water content, cloud ice content, graupel content, and supercooled water content of the target area.

[0013] Based on the average temperature and average relative humidity of the target area, a water vapor index for the target area is constructed.

[0014] Based on the average vertical wind speed of the target area, a dynamic index for the target area is constructed.

[0015] Based on the cloud water content, cloud ice content, graupel content, and supercooled water content of the target area, cloud microphysical indicators for the target area are constructed.

[0016] Based on the water vapor index, dynamic index and cloud microphysical index of the target area, a comprehensive analysis is conducted to obtain the cloud microphysical dynamic water vapor coupling index of the target area. The cloud microphysical dynamic water vapor coupling index is used to quantify the degree of influence of numerical weather forecast data on lightning formation.

[0017] Furthermore, the method for determining the weather forecast results for the target area is as follows: The meteorological forecast results for the target area include those with and without the conditions for precipitation formation.

[0018] The cloud microphysical dynamics-water vapor coupling index of the target area is compared with the preset cloud microphysical dynamics-water vapor coupling index threshold. If the cloud microphysical dynamics-water vapor coupling index of the target area is higher than the preset cloud microphysical dynamics-water vapor coupling index threshold, the meteorological forecast result of the target area is marked as having the conditions for precipitation formation; otherwise, the meteorological forecast result of the target area is marked as not having the conditions for precipitation formation.

[0019] Furthermore, the method for extracting the convection parameters of the target region is as follows: Extract the meteorological forecast results for the target area. If the meteorological forecast results for the target area indicate that there are no conditions for precipitation formation, then extract the convective parameters for the target area. The convective parameters for the target area include the temperature difference between 850 hPa and 500 hPa and the average vertical wind speed.

[0020] If the meteorological forecast for the target area indicates that the conditions for precipitation formation are met, then the convective parameters of the target area are extracted. The convective parameters of the target area include the convective effective potential energy, convective suppression energy, temperature difference between 850 hPa and 500 hPa, lifting condensation height, and average vertical wind speed.

[0021] Furthermore, the method for obtaining convection information of the target area through comprehensive analysis is as follows: The convection information of the target area includes both convectively stable and convectively unstable conditions.

[0022] Based on the convection parameters of the target area, the convection stability characteristic value of the target area is obtained through analysis. The convection stability characteristic value is used to quantify the comprehensive contribution of the convection parameters to the atmospheric convection instability.

[0023] The convective stability feature value of the target area is compared with the preset convective stability feature threshold. If the convective stability feature value of the target area is higher than the preset convective stability feature threshold, the convective information of the target area is marked as convectively unstable; otherwise, the convective information of the target area is marked as convectively stable.

[0024] Furthermore, the method for extracting the microphysical parameters of the mixed-phase cloud in the target region is as follows: Extract convection information from the target region. If the convection information in the target region is convectionally stable, no further analysis will be performed.

[0025] If the convection information of the target region is convectively unstable, then the mixed-phase cloud microphysical parameters of the target region are extracted. The mixed-phase cloud microphysical parameters of the target region include the equivalent diameter of cloud ice particles, the equivalent diameter of graupel particles, the cloud ice particle number concentration, the graupel particle number concentration, and the relative velocity of cloud ice and graupel particles.

[0026] Furthermore, the method for determining the collision intensity characterization parameters of the target region is as follows: Based on the equivalent diameters of cloud ice particles and graupel particles, the average collision cross-sectional area of ​​cloud ice particles and graupel particles in the target area is obtained through analysis.

[0027] Based on the cloud ice particle number concentration, graupel particle number concentration, average collision cross-sectional area and relative velocity of cloud ice particles and graupel particles in the target area, the collision intensity characterization parameters of the target area are obtained through comprehensive analysis.

[0028] Furthermore, the method for correcting the charge separation capability of collision intensity characterization parameters based on numerical weather forecast data is as follows: Based on the average temperature of the target area, a baseline correction factor for the target area is obtained.

[0029] The product of the collision intensity characterization parameter of the target region and the benchmark correction coefficient is denoted as the effective collision intensity index of the target region. The effective collision intensity index of the target region is used to comprehensively quantify the potential ability of cloud ice particles and graupel particles to have an effective collision and produce charge separation.

[0030] Furthermore, the method for determining the lightning potential index of the target area is as follows: Based on the convective stability characteristics and effective collision intensity index of the target area, a comprehensive analysis is conducted to obtain the lightning potential index of the target area. The lightning potential index of the target area is used to comprehensively quantify the overall probability of lightning occurring in the target area under the current meteorological conditions.

[0031] Furthermore, the method for outputting the lightning potential information of the target area is as follows: Extract the lightning potential index of the target area, and match the lightning potential information of the target area based on the lightning potential index. The lightning potential information of the target area includes the probability of lightning occurrence and the lightning potential level of the target area.

[0032] The present invention has the following beneficial effects: This invention constructs a cloud microphysical dynamic-water vapor coupling index, unifying and collaboratively modeling water vapor conditions, dynamic conditions, and cloud microphysical conditions. This changes the existing technical approach that relies on a single meteorological factor or simple superposition for judgment, and realizes a holistic quantitative assessment of precipitation formation environment from multiple meteorological elements. It effectively solves the misjudgment problem caused by insufficient water vapor or insufficient dynamics in traditional methods, and improves the accuracy and consistency of precipitation formation condition identification at the mechanistic level. It can also perform targeted feature analysis based on two scenarios: with precipitation formation conditions and without precipitation formation conditions, thus providing a reliable prerequisite for subsequent lightning potential determination and improving the stability and reliability of the overall forecast chain.

[0033] This invention introduces a hierarchical screening mechanism of convection stability eigenvalues ​​and mixed-phase cloud microphysical parameters to decouple and reconstruct convection triggering conditions from the cloud particle evolution process. This avoids the bias problems caused by relying solely on convection parameters or cloud microphysical parameters in existing technologies. At the same time, by constructing collision intensity characterization parameters, it uniformly describes the geometric, quantitative, and relative motion characteristics of cloud ice particles and graupel particles, realizing the transformation from the presence or absence of particles to the intensity of particle interaction. This can truly reflect the physical essence of the non-inductive electrification process within the cloud, significantly enhance the physical consistency of lightning occurrence condition identification, and improve the precision and reliability of lightning potential determination.

[0034] This invention establishes a charge separation capability correction mechanism based on average temperature, transforming collision intensity characterization parameters into effective collision intensity indices. This enables a quantitative characterization of the impact of temperature on non-inductive electrification efficiency, overcoming the shortcomings of existing technologies that neglect temperature range differences, leading to distorted assessments of charge separation capability. Simultaneously, by combining convective stability eigenvalues ​​to construct a lightning potential index, it achieves a synergistic fusion judgment of dynamic triggering conditions and electrical electrification conditions. This effectively avoids false alarms in scenarios such as high collision but low electrification efficiency or high instability but low particle interaction intensity, thereby improving the accuracy, stability, and engineering application feasibility of lightning potential prediction. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please see Figure 1 As shown, this embodiment of the invention provides a technical solution: a lightning potential forecasting method that integrates cloud microphysical conditions, including acquiring numerical weather forecast data of the target area, constructing a cloud microphysical dynamic-water vapor coupling index for the target area, and determining the meteorological forecast result of the target area based on the cloud microphysical dynamic-water vapor coupling index.

[0038] Based on the meteorological forecast results for the target area, the convection parameters of the target area are extracted, and the convection information of the target area is obtained through comprehensive analysis.

[0039] Based on the convection information of the target region, the microphysical parameters of the mixed-phase cloud in the target region are extracted, and the collision intensity characterization parameters of the target region are determined based on the microphysical parameters of the mixed-phase cloud.

[0040] Based on numerical weather prediction data, the collision intensity characterization parameters are corrected for charge separation capability to obtain the effective collision intensity index of the target area.

[0041] Based on the effective collision intensity index of the target area, determine the lightning potential index of the target area and output the lightning potential information of the target area.

[0042] Specifically, the method for constructing the cloud microphysical dynamic-water vapor coupling index for the target region is as follows: The numerical weather forecast data for the target area includes the average temperature, average relative humidity, average vertical wind speed, cloud water content, cloud ice content, graupel content, and supercooled water content of the target area.

[0043] It should be added that the average temperature refers to the average level of the air thermal state in the target area within a preset altitude range. This is obtained by outputting temperature data for each grid point from the numerical weather prediction system, selecting temperature values ​​from all grid points within the target area at the same time cross-section, summarizing the temperature values ​​of each grid point, and performing an arithmetic average. The average relative humidity refers to the overall level of water vapor saturation in the air of the target area. This is obtained by outputting relative humidity data for each grid point from the numerical weather prediction system, extracting relative humidity values ​​from each grid point within the same spatial range, summarizing the relative humidity values ​​of each grid point, and taking the average. The average vertical wind speed refers to the average level of the vertical air motion intensity in the target area. This is obtained by outputting vertical wind speed data for each grid point from the numerical weather prediction system, selecting vertical wind speed values ​​at multiple altitude levels within the target area, uniformly statistically analyzing the vertical wind speeds of each grid point, and averaging them.

[0044] The cloud water content refers to the mass content of liquid water condensate per unit mass of air in the target area. It is obtained by extracting the cloud water mixing ratio data from the cloud water mixing ratio data output by the numerical weather prediction system, averaging the ratios at each grid point within the target area. The cloud ice content refers to the mass content of solid ice crystal particles per unit mass of air in the target area. It is obtained by statistically analyzing the cloud ice mixing ratio data from the cloud ice mixing ratio data output by the numerical weather prediction system, averaging the ratios at each grid point within the target area. The graupel content refers to the mass content of graupel particles per unit mass of air in the target area. It is obtained by summarizing the graupel mixing ratio data from the graupel mixing ratio data output by the numerical weather prediction system, averaging the ratios at each grid point. The supercooled water content refers to the mass content of water droplets that remain liquid at temperatures below zero degrees Celsius. It is obtained by extracting the supercooled water mixing ratio data from the supercooled water mixing ratio data output by the numerical weather prediction system, averaging the ratios at each grid point within the target area.

[0045] Based on the average temperature and average relative humidity of the target area, a water vapor index for the target area is constructed. The water vapor index for the target area is used to comprehensively quantify the water vapor content and water vapor saturation in the atmosphere of the target area, reflecting the supporting capacity of water vapor conditions for cloud formation and development.

[0046] In this embodiment, the water vapor index of the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This indicates the water vapor index of the target area. This represents the average relative humidity of the target area. This indicates the preset relative humidity reference threshold. This represents the average temperature of the target area. This indicates the preset temperature reference threshold.

[0047] Relative humidity reflects the degree of water vapor saturation in the atmosphere, while temperature determines the saturation vapor pressure. Together, they determine the availability of water vapor. Higher relative humidity and higher temperature result in more abundant water vapor in the atmosphere, which is more conducive to cloud formation and development.

[0048] Based on the average vertical wind speed of the target area, a dynamic index for the target area is constructed. This dynamic index is used to comprehensively quantify the intensity of vertical upward motion of the atmosphere in the target area, reflecting the driving force of dynamic lifting conditions on water vapor transport and cloud development.

[0049] In this embodiment, the dynamic indicators of the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, Indicators representing the dynamics of the target region This represents the average vertical wind speed in the target area. This indicates the preset vertical wind speed reference threshold.

[0050] Vertical upward motion is the core driving force behind water vapor uplift and cloud development. The greater the vertical velocity, the stronger the uplift effect, which is more conducive to transporting lower-level water vapor to upper levels and promoting cloud development.

[0051] Based on the cloud water content, cloud ice content, graupel content, and supercooled water content of the target area, cloud microphysical indicators for the target area are constructed. These cloud microphysical indicators are used to comprehensively quantify the total content and mixed phase characteristics of water condensates within the clouds of the target area, reflecting the material basis supporting the formation of precipitation through cloud microphysical processes.

[0052] In this embodiment, the cloud microphysical indicators of the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, Indicates cloud microphysical indicators for the target area. Indicates the cloud water content of the target area. Indicates the cloud ice content in the target area. Indicates the amount of graupel in the target area. This indicates the amount of supercooled water in the target area. This indicates the preset reference threshold for hydrogel content.

[0053] Based on the water vapor index, dynamic index and cloud microphysical index of the target area, a comprehensive analysis is conducted to obtain the cloud microphysical dynamic water vapor coupling index of the target area. The cloud microphysical dynamic water vapor coupling index is used to quantify the degree of influence of numerical weather forecast data on lightning formation.

[0054] In this embodiment, the cloud microphysical dynamic-water vapor coupling index of the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, Indicators representing cloud microphysical dynamics and water vapor coupling in the target region. , This represents the weighting factor corresponding to the preset water vapor index. , This represents the weighting factor corresponding to the preset dynamic indicator. Indicates cloud microphysical indicators for the target area. This indicates the weighting factor corresponding to the preset cloud microphysical index.

[0055] It should be added that, in this embodiment, the weighting factors corresponding to the preset water vapor index, the weighting factors corresponding to the dynamic index, and the weighting factors corresponding to the cloud microphysical index are obtained from the lightning potential forecast database.

[0056] It should be explained that the weighting factors corresponding to the water vapor index, dynamic index, and cloud microphysical index are used to adjust the importance of the water vapor index, dynamic index, and cloud microphysical index of the target area in the process of analyzing and obtaining the cloud microphysical-dynamic-water vapor coupling index. In the lightning potential forecast database, there is a pre-set mapping relationship between the water vapor index, dynamic index, and cloud microphysical index of the target area and the corresponding weighting factors. By matching the water vapor index, dynamic index, and cloud microphysical index of the target area with the pre-set mapping relationship, the weighting factors corresponding to the water vapor index, dynamic index, and cloud microphysical index can be obtained.

[0057] In this implementation plan, the water vapor index, dynamic index, and cloud microphysical index of the target area are correlated and do not exist independently. For example, the water vapor index reflects the atmospheric water vapor supply capacity, while the dynamic index reflects the vertical transport and lifting capacity. When the dynamic index increases, it can transport near-surface humid air upwards, allowing the water vapor index to be reflected at high altitudes, thereby promoting an increase in cloud water content. At the same time, cloud water content is further converted into cloud ice content and supercooled water content under low temperature conditions, and forms graupel under the action of continuous upward motion. Therefore, the cloud microphysical index is jointly affected by the water vapor index and the dynamic index. Conversely, the increase of condensate in the cloud microphysical index will release latent heat and enhance the updraft, thereby creating a feedback enhancement effect on the dynamic index. At the same time, the high humidity environment is conducive to maintaining cloud development and further stabilizing the water vapor index. The comprehensive analysis of the cloud microphysical dynamic-water vapor coupling index can assess the overall synergy of the target area from water vapor transport, dynamic lifting to cloud microphysical evolution, thereby helping to improve the accuracy of precipitation formation condition determination and the reliability of lightning potential forecast.

[0058] Specifically, the method for determining the weather forecast results for the target area is as follows: The meteorological forecast results for the target area include those with and without the conditions for precipitation formation.

[0059] The cloud microphysical dynamics-water vapor coupling index of the target area is compared with a preset threshold. If the cloud microphysical dynamics-water vapor coupling index of the target area is higher than the preset threshold, the meteorological forecast result of the target area is marked as having precipitation formation conditions; otherwise, the meteorological forecast result of the target area is marked as not having precipitation formation conditions. By introducing the cloud microphysical dynamics-water vapor coupling index, water vapor conditions, dynamic conditions, and cloud condensate conditions are quantified in a unified manner, which can avoid misjudgment caused by a single meteorological factor, improve the overall accuracy of precipitation and lightning precondition identification, and use the corresponding coupling index as a pre-screening condition for subsequent analysis, thereby improving the overall analysis efficiency.

[0060] Specifically, the method for extracting convection parameters from the target region is as follows: Extract the meteorological forecast results for the target area. If the meteorological forecast results for the target area indicate that there are no conditions for precipitation formation, then extract the convective parameters for the target area. The convective parameters for the target area include the temperature difference between 850 hPa and 500 hPa and the average vertical wind speed.

[0061] If the meteorological forecast for the target area indicates that the conditions for precipitation formation are met, then the convective parameters of the target area are extracted. The convective parameters of the target area include the convective effective potential energy, convective suppression energy, temperature difference between 850 hPa and 500 hPa, lifting condensation height, and average vertical wind speed.

[0062] The convective available potential energy (FAP) refers to the ability of air in a target area to continuously rise and release energy after being lifted. Using temperature profile data and pressure layer data output from a numerical weather prediction system, multiple vertical height layers within the target area are selected. Near-surface air is lifted layer by layer vertically, and the temperature difference between the lifted air and ambient air at each height layer is statistically analyzed. The energy contribution corresponding to the temperature differences favorable for rise in all height layers is accumulated to obtain the FAP value. The convective inhibition energy refers to the degree of obstruction to the rise of air in the target area during the initial lifting stage due to environmental inhibition. Using temperature profile data output from a numerical weather prediction system, each height layer from near-surface to the free convection height is selected. The intervals where the lifted air temperature is lower than the ambient temperature are statistically analyzed, and the obstruction energy corresponding to the temperature difference within these intervals is accumulated to obtain the convective inhibition energy value. The temperature difference between 850 hPa and 500 hPa refers to the temperature difference between 850 hPa and 500 hPa in the target area. The difference between hPa temperatures; the lifting condensation height refers to the height at which the air in the target area reaches saturation and begins to condense to form clouds during the lifting process. Using temperature and relative humidity data output by the numerical weather prediction system, the initial state of the air near the ground is selected, and the change process of air temperature and dew point temperature is calculated layer by layer along the vertical direction. The height position corresponding to the equal air temperature and dew point temperature is the lifting condensation height. The height values ​​corresponding to multiple grid points in the target area are statistically analyzed and averaged to obtain the lifting condensation height.

[0063] Specifically, the method for obtaining convection information of the target area through comprehensive analysis is as follows: The convection information of the target area includes both convectively stable and convectively unstable conditions.

[0064] Based on the convection parameters of the target area, the convection stability characteristic value of the target area is obtained through analysis. The convection stability characteristic value is used to quantify the comprehensive contribution of the convection parameters to the atmospheric convection instability.

[0065] In this embodiment, the convective stability characteristic value of the target region can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the convective stability characteristic value of the target region. This represents the convective available potential energy of the target region. This indicates the preset reference threshold for convective effective potential energy. This represents the influence factor corresponding to the preset convective effective potential energy. This represents the convection suppression energy of the target region. This indicates the preset convection suppression energy reference threshold. This represents the influence factor corresponding to the preset convection suppression energy. Indicates the height of the condensation in the target area. This indicates the preset reference threshold for the height of condensation. This represents the influencing factor corresponding to the preset height of condensation. This represents the average vertical wind speed in the target area. This indicates the preset vertical wind speed reference threshold. This represents the influencing factor corresponding to the preset vertical wind speed. This indicates the temperature difference between 850 hPa and 500 hPa in the target area. This indicates the preset reference threshold for the temperature difference between 850 hPa and 500 hPa. This indicates the influence factor corresponding to the preset temperature difference of 850 hPa and 500 hPa.

[0066] It should be added that, in this embodiment, the preset influence factors corresponding to convective effective potential energy, convective suppression energy, lifting condensation height, and vertical wind speed are obtained from the lightning potential forecast database.

[0067] It should be explained that the influencing factors corresponding to convective available potential energy, convective suppression energy, the temperature difference between 850 hPa and 500 hPa, the lifting condensation height, and the vertical wind speed are used to adjust the importance of the convective parameters in the target area in the process of analyzing and obtaining convective stability characteristic values. In the lightning potential forecast database, there is a pre-set mapping relationship between the convective parameters of the target area and the corresponding influencing factors. By matching the convective parameters of the target area with the pre-set mapping relationship, the influencing factors corresponding to convective available potential energy, convective suppression energy, lifting condensation height, and vertical wind speed can be obtained.

[0068] In this implementation scheme, the convective available potential energy (FAP), convective inhibition energy, temperature difference between 850 hPa and 500 hPa, lifting condensation height, and average vertical wind speed in the target area are correlated and not independent. For example, FAP reflects the amount of energy that can be released after air is lifted, while average vertical wind speed reflects the dynamic intensity of the actual lifting process. When the average vertical wind speed increases, it is conducive to triggering and releasing FAP. Convective inhibition energy reflects the degree of resistance that air needs to overcome for initial lifting. When the convective inhibition energy is large, even if the FAP is high, it is still difficult to trigger convection when the average vertical wind speed is low. A large temperature difference between 850 hPa and 500 hPa often corresponds to a large FAP. However, because the FAP is extremely sensitive to the accuracy of the predicted parameters, when the FAP cannot be accurately calculated, it is necessary to supplement it with the temperature difference between 850 hPa and 500 hPa. The hPa temperature difference is used to measure the degree of convective instability; the lifting condensation height reflects the starting height at which air reaches saturation and forms clouds. When the lifting condensation height is low, clouds are more likely to form under weaker lifting conditions, thus reducing the difficulty of triggering convection and indirectly enhancing the release efficiency of convective effective potential energy. At the same time, a lower lifting condensation height is also conducive to enhancing the participation of near-surface water vapor in the convection process, making the average vertical wind speed more significant in cloud development. Conversely, when the lifting condensation height is high, a stronger average vertical wind speed is required to trigger effective convection, and the influence of convection suppression energy is more prominent. Comprehensive analysis yields convective stability characteristic values, which can assess the degree of synergy between the atmospheric stratification structure and dynamic triggering conditions in the target area, as well as the convective development potential, thereby helping to improve the accuracy of convection occurrence determination and the reliability of subsequent lightning potential forecasts.

[0069] The convective stability characteristic value of the target area is compared with a preset convective stability characteristic threshold. If the convective stability characteristic value of the target area is higher than the preset threshold, the convective information of the target area is marked as convectively unstable; otherwise, the convective information of the target area is marked as convectively stable. By introducing convective parameters, the atmospheric thermal structure and dynamic triggering conditions are jointly determined, which can effectively distinguish between a real convective environment with energy that cannot be released and one with triggering conditions. This avoids false triggers in subsequent lightning determination and improves the physical consistency and logical reliability of the method.

[0070] Specifically, the method for extracting the microphysical parameters of the mixed-phase cloud in the target region is as follows: Extract convection information from the target region. If the convection information in the target region is convectionally stable, no further analysis will be performed.

[0071] If the convection information of the target region is convectively unstable, then the mixed-phase cloud microphysical parameters of the target region are extracted. The mixed-phase cloud microphysical parameters of the target region include the equivalent diameter of cloud ice particles, the equivalent diameter of graupel particles, the cloud ice particle number concentration, the graupel particle number concentration, and the relative velocity of cloud ice and graupel particles.

[0072] It should be added that the equivalent diameter of cloud ice particles refers to the diameter of spherical particles with the same volume, which is used to characterize the average scale characteristics of cloud ice particles. This is achieved by converting cloud ice particles of different shapes and sizes within the target area into spherical particles with the same volume. Using cloud ice mixing ratio data and cloud ice particle number concentration data output from the numerical weather prediction system, the cloud ice mixing ratio and corresponding particle number concentration are selected at each grid point within the target area. The average volume of a single particle is calculated based on the relationship between the total mass of cloud ice and the number of particles per unit mass. This volume is then converted into the diameter of a spherical particle, and the average value of the results from each grid point is calculated to obtain the equivalent diameter of cloud ice particles. Similarly, the equivalent diameter of graupel particles refers to the average scale characteristics of graupel particles within the target area. Using graupel mixing ratio data and graupel number concentration data output from the numerical weather prediction system, the relationship between the total mass of graupel particles and the number of particles per unit mass of air is calculated at each grid point to obtain the average volume of a single graupel particle. This volume is then converted into the diameter of a spherical particle, and the average value of the results from all grid points is calculated to obtain the equivalent diameter of graupel particles.

[0073] The cloud ice particle number concentration refers to the number of cloud ice particles per unit volume of air. This is obtained by averaging the cloud ice particle number concentration data at each grid point using the microphysical parameterization output of the numerical weather prediction system, and by statistically analyzing the corresponding values ​​at all grid points within the target area. The graupel number concentration refers to the number of graupel particles per unit volume of air. This is obtained by averaging the graupel number concentration data at each grid point within the target area using the numerical weather prediction system, and by summarizing the graupel number concentration at each grid point. The relative velocity between cloud ice and graupel refers to the degree of difference in vertical motion between cloud ice and graupel particles. This is obtained by calculating the difference between the rising velocity of cloud ice particles and the sinking velocity of graupel particles at each grid point using the vertical wind speed data output by the numerical weather prediction system and combining the settling velocity parameters of cloud ice and graupel particles, and by statistically averaging the calculation results at each grid point.

[0074] Specifically, the method for determining the collision intensity characterization parameters of the target region is as follows: Based on the equivalent diameters of cloud ice particles and graupel particles, the average collision cross-sectional area of ​​cloud ice particles and graupel particles in the target area is analyzed. The average collision cross-sectional area of ​​cloud ice particles and graupel particles in the target area is used to comprehensively quantify the effective interaction cross-sectional size when cloud ice particles and graupel particles meet in space, reflecting the geometric probability of physical contact between the two.

[0075] In this embodiment, the average collision cross-sectional area between cloud ice particles and graupel particles in the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the average collision cross-sectional area between cloud ice particles and graupel particles in the target area. Indicates the equivalent diameter of cloud ice particles in the target region. This represents the equivalent diameter of the graupel in the target area.

[0076] Based on the cloud ice particle number concentration, graupel particle number concentration, average collision cross-sectional area of ​​cloud ice particles and graupel particles, and relative velocity of the target region, collision intensity characterization parameters for the target region are obtained through comprehensive analysis. These parameters are used to comprehensively quantify the total frequency of collisions between cloud ice particles and graupel particles, reflecting the initial collision intensity of the non-inductive electrification mechanism within the cloud. By limiting the extraction of microphysical parameters of mixed-phase clouds to convectively unstable conditions, subsequent calculations are ensured to be based on a real convective cloud environment, avoiding interference from stratiform or non-convective clouds at the source. Furthermore, by introducing collision intensity characterization parameters, the traditional particle existence judgment is transformed into a particle interaction intensity judgment, accurately reflecting the physical basis of charge separation within the cloud.

[0077] After normalizing the cloud ice particle number concentration, graupel particle number concentration, average collision cross-sectional area between cloud ice particles and graupel particles, and relative velocity of the target area, the normalized cloud ice particle number concentration, graupel particle number concentration, average collision cross-sectional area between cloud ice particles and graupel particles, and relative velocity of the target area are obtained.

[0078] In this embodiment, the collision intensity characterization parameters of the target region can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, The collision intensity characterization parameter represents the target region. This represents the normalized cloud ice particle number concentration in the target region. This represents the normalized grenade number concentration in the target region. This represents the normalized average collision cross-sectional area between cloud ice particles and graupel particles in the target region. This represents the normalized relative velocity of cloud ice and graupel particles in the target area.

[0079] Specifically, the method for correcting the charge separation capability of collision intensity characterization parameters based on numerical weather prediction data is as follows: According to the average temperature of the target area, a reference correction coefficient for the target area is obtained by matching. The reference correction coefficient of the target area is used to comprehensively quantify the efficiency difference of the conversion of collision events into effective charge separation under different temperature conditions, and reflect the temperature dependence of the non-s感应起电机制的温度依赖性。

[0080] Match the average temperature of the target area with the reference correction coefficients corresponding to each average temperature interval stored in the lightning potential forecast database, and count the reference correction coefficient corresponding to the interval where the average temperature is located, which is denoted as the reference correction coefficient of the target area.

[0081] In a specific embodiment, the larger the average temperature of the target area, the reference correction coefficient obtained by matching does not change monotonically. Instead, the average temperature increases with the increase of temperature in the range of -25°C to -10°C, and decreases with the increase of temperature in the range of -10°C to -5°C. The rest of the intervals remain at a constant low value. When the average temperature T ≤ -25°C, the reference correction coefficient is constantly 0.2; when -25°C < T < -20°C, the correction coefficient linearly increases from 0.2 to 0.8; when -20°C ≤ T ≤ -10°C, the correction coefficient linearly increases from 0.8 to 1.0; when -10°C < T < -5°C, the correction coefficient linearly decreases from 1.0 to 0.2; when T ≥ -5°C, the correction coefficient is constantly 0.2.

[0082] Multiply the collision intensity characterization parameter of the target area by the reference correction coefficient, which is denoted as the effective collision intensity index of the target area. The effective collision intensity index of the target area is used to comprehensively quantify the potential ability of cloud ice particles and graupel particles to have an effective collision and generate charge separation. By introducing a temperature correction mechanism, a mapping relationship is established between the collision event and the charge separation efficiency, so that the collision intensity characterization parameter is transformed from a simple dynamic quantity into an electrical effective quantity, solving the problem that the difference in the ability of collisions to generate charges under different temperature conditions is not characterized, thereby significantly improving the accuracy of lightning determination.

[0083] Specifically, the method for determining the lightning potential index of the target area is as follows: According to the convective stability eigenvalue and the effective collision intensity index of the target area, the lightning potential index of the target area is comprehensively analyzed. The lightning potential index of the target area is used to comprehensively quantify the comprehensive possibility of lightning occurring in the target area under the current meteorological conditions.

[0084] In this embodiment, the lightning potential index of the target area can be obtained through the following analysis method. The specific analysis conditions are as follows: ; In the formula, represents the lightning potential index of the target area, represents the effective collision intensity index of the target area, 7] This represents the preset effective collision intensity reference threshold. This represents the weighting coefficient corresponding to the preset effective collision intensity index. This represents the convective stability characteristic value of the target region. This represents the weighting coefficient corresponding to the preset convective stability characteristic value.

[0085] It should be added that, in this embodiment, the weighting coefficients corresponding to the preset effective collision intensity index and the weighting coefficients corresponding to the convective stability characteristic values ​​are obtained from the lightning potential forecast database.

[0086] It should be explained that the weighting coefficients corresponding to the effective collision intensity index and the convective stability characteristic value are used to adjust the importance of the convective stability characteristic value and the effective collision intensity index of the target area in the process of analyzing and obtaining the lightning potential index. In the lightning potential forecast database, there is a pre-set mapping relationship between the convective stability characteristic value and the effective collision intensity index of the target area and the corresponding weighting coefficients. By matching the convective stability characteristic value and the effective collision intensity index of the target area with the pre-set mapping relationship, the weighting coefficients corresponding to the effective collision intensity index and the convective stability characteristic value can be obtained.

[0087] Specifically, the method for outputting the lightning potential information of the target area is as follows: Extract the lightning potential index of the target area. Based on the lightning potential index of the target area, match the lightning potential information of the target area. The lightning potential information of the target area includes the probability of lightning occurrence and the lightning potential level of the target area. The higher the lightning potential index of the target area, the higher the probability of lightning occurrence and the higher the lightning potential level of the matched lightning.

[0088] In one specific embodiment, when the Lightning Potential Index (LPI) is 1.0, the matched probability of lightning occurrence is 20%, and the lightning potential level is low risk; when the LPI is 2.5, the probability of lightning occurrence is 45%, and the lightning potential level is medium risk.

[0089] The lightning potential index of the target area is matched with the lightning occurrence probability and lightning potential level corresponding to each lightning potential index interval stored in the lightning potential forecast database. The lightning occurrence probability and lightning potential level corresponding to the interval in which the lightning potential index is located are statistically analyzed and recorded as the lightning occurrence probability and lightning potential level of the target area. The lightning potential level includes low risk, low-medium risk, medium risk, medium-high risk and high risk.

[0090] It should be noted that the lightning potential forecasting method integrating cloud microphysical conditions also includes a lightning potential forecasting database. This database stores reference thresholds for relative humidity, temperature, vertical wind speed, condensate content, water vapor index, dynamic index, cloud microphysical index, cloud microphysical dynamic-water vapor coupling index, convective effective potential energy, convective suppression energy, 850 hPa and 500 hPa temperature difference, influence factors corresponding to the 850 hPa and 500 hPa temperature difference, uplift condensation height reference threshold, influence factors corresponding to convective effective potential energy, convective suppression energy, uplift condensation height, vertical wind speed, convective stability characteristic thresholds, baseline correction coefficients for each average temperature range, weight coefficients corresponding to effective collision intensity index, weight coefficients corresponding to convective stability characteristic values, lightning occurrence probability, and lightning potential level for each lightning potential index range.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0092] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method of lightning potential forecast in fusion with cloud microphysical conditions, characterized by, include: Numerical weather forecast data for the target area is obtained, cloud microphysical dynamics-water vapor coupling index for the target area is constructed, and the meteorological forecast results for the target area are determined based on the cloud microphysical dynamics-water vapor coupling index. Based on the meteorological forecast results for the target area, the convection parameters of the target area are extracted, and the convection information of the target area is obtained through comprehensive analysis. Based on the convection information of the target area, the microphysical parameters of the mixed phase cloud in the target area are extracted, and the collision intensity characterization parameters of the target area are determined based on the microphysical parameters of the mixed phase cloud. Based on numerical weather prediction data, the collision intensity characterization parameters are corrected for charge separation capability to obtain the effective collision intensity index of the target area. Based on the effective collision intensity index of the target area, determine the lightning potential index of the target area and output the lightning potential information of the target area.

2. The method of claim 1, wherein the cloud microphysical conditions are fused with the lightning potential forecast. The method for constructing the cloud microphysical dynamic-water vapor coupling index of the target region is as follows: The numerical weather forecast data for the target area includes the average temperature, average relative humidity, average vertical wind speed, cloud water content, cloud ice content, graupel content, and supercooled water content of the target area. Based on the average temperature and average relative humidity of the target area, construct the water vapor index for the target area; Based on the average vertical wind speed of the target area, construct the dynamic indicators of the target area; Based on the cloud water content, cloud ice content, graupel content, and supercooled water content of the target area, cloud microphysical indicators for the target area are constructed. Based on the water vapor index, dynamic index and cloud microphysical index of the target area, a comprehensive analysis is conducted to obtain the cloud microphysical dynamic water vapor coupling index of the target area. The cloud microphysical dynamic water vapor coupling index is used to quantify the degree of influence of numerical weather forecast data on lightning formation.

3. The method of claim 2, wherein the cloud microphysical conditions are fused with the lightning potential forecast. The method for determining the meteorological forecast results for the target area is as follows: The meteorological forecast results for the target area include those with and without conditions for precipitation formation; The cloud microphysical dynamics-water vapor coupling index of the target area is compared with the preset cloud microphysical dynamics-water vapor coupling index threshold. If the cloud microphysical dynamics-water vapor coupling index of the target area is higher than the preset cloud microphysical dynamics-water vapor coupling index threshold, the meteorological forecast result of the target area is marked as having the conditions for precipitation formation; otherwise, the meteorological forecast result of the target area is marked as not having the conditions for precipitation formation.

4. The method of claim 3, wherein the cloud microphysical conditions are fused with the lightning potential forecast. The method for extracting convection parameters of the target region is as follows: Extract the meteorological forecast results for the target area. If the meteorological forecast results for the target area indicate that there are no conditions for precipitation formation, then extract the convective parameters for the target area. The convective parameters for the target area include the temperature difference between 850 hPa and 500 hPa and the average vertical wind speed in the target area. If the meteorological forecast for the target area indicates that the conditions for precipitation formation are met, then the convective parameters of the target area are extracted. The convective parameters of the target area include the convective effective potential energy, convective suppression energy, temperature difference between 850 hPa and 500 hPa, lifting condensation height, and average vertical wind speed.

5. The lightning potential prediction method based on cloud microphysical conditions according to claim 1, characterized in that: The method for obtaining convection information of the target area through comprehensive analysis is as follows: The convection information of the target area includes both stable and unstable convection. Based on the convection parameters of the target area, the convection stability characteristic value of the target area is obtained through analysis. The convection stability characteristic value is used to quantify the comprehensive contribution of the convection parameters to the atmospheric convection instability. The convective stability feature value of the target area is compared with the preset convective stability feature threshold. If the convective stability feature value of the target area is higher than the preset convective stability feature threshold, the convective information of the target area is marked as convectively unstable; otherwise, the convective information of the target area is marked as convectively stable.

6. The lightning potential prediction method based on cloud microphysical conditions according to claim 5, characterized in that: The method for extracting the microphysical parameters of the mixed-phase cloud in the target region is as follows: Extract convection information from the target region. If the convection information in the target region is convectionally stable, no further analysis will be performed. If the convection information of the target region is convectively unstable, then the mixed-phase cloud microphysical parameters of the target region are extracted. The mixed-phase cloud microphysical parameters of the target region include the equivalent diameter of cloud ice particles, the equivalent diameter of graupel particles, the cloud ice particle number concentration, the graupel particle number concentration, and the relative velocity of cloud ice and graupel particles.

7. The lightning potential prediction method based on cloud microphysical conditions according to claim 6, characterized in that: The method for determining the collision intensity characterization parameters of the target region is as follows: Based on the equivalent diameters of cloud ice particles and graupel particles, the average collision cross-sectional area of ​​cloud ice particles and graupel particles in the target area is obtained through analysis. Based on the cloud ice particle number concentration, graupel particle number concentration, average collision cross-sectional area and relative velocity of cloud ice particles and graupel particles in the target area, the collision intensity characterization parameters of the target area are obtained through comprehensive analysis.

8. The lightning potential prediction method based on cloud microphysical conditions according to claim 7, characterized in that: The method for correcting the charge separation capability of collision intensity characterization parameters based on numerical weather prediction data is as follows: Based on the average temperature of the target area, a baseline correction coefficient for the target area is obtained. The product of the collision intensity characterization parameter of the target region and the benchmark correction coefficient is denoted as the effective collision intensity index of the target region. The effective collision intensity index of the target region is used to comprehensively quantify the potential ability of cloud ice particles and graupel particles to have an effective collision and produce charge separation.

9. The lightning potential prediction method based on cloud microphysical conditions according to claim 1, characterized in that: The method for determining the lightning potential index of the target area is as follows: Based on the convective stability characteristics and effective collision intensity index of the target area, a comprehensive analysis is conducted to obtain the lightning potential index of the target area. The lightning potential index of the target area is used to comprehensively quantify the overall probability of lightning occurring in the target area under the current meteorological conditions.

10. The lightning potential prediction method based on cloud microphysical conditions according to claim 9, characterized in that: The method for outputting the lightning potential information of the target area is as follows: Extract the lightning potential index of the target area, and match the lightning potential information of the target area based on the lightning potential index. The lightning potential information of the target area includes the probability of lightning occurrence and the lightning potential level of the target area.

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