A multi-environment physical quantity coupled convective rainstorm probability prediction method

Abstract

The application discloses a kind of multi-environment physical quantity coupling's convection rainstorm probability prediction method, it is related to meteorological prediction technical field.This method extracts convection effective potential energy, wind vertical shear and water vapor physical quantity from numerical weather prediction model output, convection effective potential energy and wind vertical shear are configured combination discrimination according to value level and obtain matching factor, according to water vapor physical quantity, calculate water vapor condition membership degree, and according to convection effective potential energy, calculate energy membership degree, the minimum value of matching factor and water vapor condition membership degree obtains necessary condition intensity, with necessary condition intensity modulation energy membership degree obtains gain efficiency, necessary condition intensity and gain efficiency are multiplied to output convection rainstorm occurrence probability.This method makes the effective contribution of energy factor be jointly restrained by energy and dynamic configuration condition and water vapor condition, and is suitable for convection rainstorm probability prediction.

Description

Technical Field

[0001] This invention relates to the field of meteorological forecasting technology, and in particular to a method for probabilistic forecasting of convective rainstorms by coupling multiple environmental physical quantities. Background Technology

[0002] Convective rainstorms are short-duration, heavy precipitation events triggered by severe convective weather systems. Their occurrence depends on the combined effects of three types of environmental physical quantities: unstable energy, dynamic organization conditions, and water vapor supply. Numerical weather prediction models can output gridded forecast fields of these environmental physical quantities, providing a data foundation for the probabilistic forecasting of convective rainstorms. Existing probabilistic forecasting methods for convective rainstorms typically map each environmental physical quantity to a normalized degree of favorability through a membership function, and then linearly weight and fuse the membership degrees of each physical quantity with fixed weights to output the probability of convective rainstorm occurrence.

[0003] In the above linear weighted fusion method, the membership degree of each physical quantity is directly involved in the probability calculation as an independent weighting term. The contribution ratio of the energy factor is determined by a fixed weight and is not adjusted with the configuration relationship of energy and power or the change of water vapor conditions. When energy and power are mismatched or water vapor supply is insufficient, high energy still contributes an artificially high probability through the weighting term, resulting in a systematically high forecast probability and false alarms. Summary of the Invention

[0004] The purpose of this invention is to provide a probabilistic forecasting method for convective rainstorms that couples multiple environmental physical quantities, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities, comprising the following steps:

[0006] Step 100: Extract convective available potential energy, vertical wind shear, and water vapor physical quantities from the numerical weather prediction model output.

[0007] Step 200: Configure and combine the convective effective potential energy and the vertical wind shear according to the value level to obtain a matching factor. The matching factor takes a high value when the convective effective potential energy and the vertical wind shear are proportionally matched, and takes a low value when they are not proportionally matched.

[0008] Step 300: Calculate the water vapor condition membership degree based on the water vapor physical quantity, and calculate the energy membership degree based on the convective effective potential energy;

[0009] Step 400: Minimize the membership degree of the matching factor and the water vapor condition to obtain the necessary condition strength;

[0010] Step 500: Modulate the energy membership degree with the required condition strength to obtain the gain efficiency, wherein the gain efficiency decreases as the required condition strength decreases;

[0011] Step 600: Multiply the necessary condition intensity by the gain efficiency to output the probability of convective rainstorm occurrence.

[0012] Preferably, in step 100, convective effective potential energy, vertical wind shear from 0 to 6 km and water vapor physical quantities are extracted from the output of the CMA-GFS numerical weather prediction model, wherein the water vapor physical quantities are atmospheric precipitable water and 850 hPa humidity.

[0013] Preferably, step 200 includes:

[0014] Step 210: Divide the convective effective potential energy into three levels: low energy, medium energy, and high energy;

[0015] Step 220: Divide the vertical wind shear into three levels: weak shear, medium shear, and strong shear;

[0016] Step 230: Query the configuration matrix based on the combination of the level of the convective effective potential energy and the level of the wind vertical shear to obtain the matching factor. In the configuration matrix, the matching factor for a matching configuration is 1, the matching factor for a transitional configuration is 0.5, and the matching factor for a non-matching configuration is 0.2.

[0017] Preferably, in the configuration matrix, the matching configuration includes high-energy strong shear and medium-energy medium shear, the transition configuration includes high-energy medium shear, medium-energy weak shear, medium-energy strong shear and low-energy medium shear, and the mismatch configuration includes high-energy weak shear, low-energy weak shear and low-energy strong shear.

[0018] Preferably, in step 210, the low energy is when the convective effective potential energy is less than 1000 joules per kilogram, the medium energy is when the convective effective potential energy is greater than or equal to 1000 joules per kilogram and less than or equal to 2500 joules per kilogram, and the high energy is when the convective effective potential energy is greater than 2500 joules per kilogram.

[0019] In step 220, the weak shear is defined as the vertical wind shear being less than 10 meters per second, the medium shear is defined as the vertical wind shear being greater than or equal to 10 meters per second and less than or equal to 20 meters per second, and the strong shear is defined as the vertical wind shear being greater than 20 meters per second.

[0020] Preferably, step 300 includes:

[0021] Step 310: Calculate the membership degree of the half-ridge of the atmospheric precipitable water and the membership degree of the half-ridge of the 850 hPa humidity, and take the average of the two as the membership degree of the water vapor condition.

[0022] Step 320: Calculate the half-ridge membership degree of the convective effective potential energy as the energy membership degree.

[0023] Preferably, the membership degree of the ascending half-ridge is calculated by the ascending half-ridge membership function, which satisfies the following: when the input physical quantity is less than or equal to the lower parameter, the ascending half-ridge membership degree is 0; when the input physical quantity is greater than or equal to the upper parameter, the ascending half-ridge membership degree is 1; when the input physical quantity is greater than the lower parameter and less than the upper parameter, the ascending half-ridge membership degree is the difference between the input physical quantity and the lower parameter divided by the difference between the upper parameter and the lower parameter.

[0024] Preferably, in step 500, the gain efficiency satisfies ,in The strength of the necessary condition. The energy membership degree, The gain efficiency is defined as follows: when the necessary condition strength is 1, the gain efficiency is equal to the energy membership degree; when the necessary condition strength is 0, the gain efficiency is equal to 0.

[0025] This invention also provides a convective rainstorm probability forecasting system that couples multiple environmental physical quantities, comprising:

[0026] The physical quantity extraction module is used to extract convective available potential energy, vertical wind shear, and water vapor physical quantities from the output of numerical weather prediction models.

[0027] A configuration discrimination module is used to configure and combine the convective effective potential energy and the wind vertical shear according to the value level to obtain a matching factor;

[0028] The membership calculation module is used to calculate the water vapor condition membership degree based on the water vapor physical quantity and the energy membership degree based on the convective effective potential energy.

[0029] The fusion output module is used to obtain the necessary condition intensity by taking the minimum value of the matching factor and the water vapor condition membership degree, modulate the energy membership degree with the necessary condition intensity to obtain the gain efficiency, and multiply the necessary condition intensity and the gain efficiency to output the probability of convective rainstorm occurrence.

[0030] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for probabilistic forecasting of convective rainstorms involving coupling multiple environmental physical quantities.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] This invention uses convective effective potential energy and vertical wind shear to determine matching factors by configuring and combining them according to their value levels, thus explicitly involving the matching relationship between energy and dynamics in probability calculations. The minimum value of the matching factor and the membership degree of the water vapor condition is used to obtain the necessary condition strength, reflecting the bottleneck constraint between energy and dynamic configuration and water vapor supply, ensuring that no physical quantities can compensate for each other. The necessary condition strength modulates the energy membership degree to obtain gain efficiency, causing the effective contribution ratio of the energy factor to dynamically change with the necessary condition strength. In environments with mismatched configurations or insufficient water vapor, the energy factor automatically reduces its contribution, avoiding systemic false alarms caused by high energy contributing excessively in unfavorable environments. The probability is output by multiplying the necessary condition strength and gain efficiency, making the probability output simultaneously constrained by the necessary condition strength and the effective energy conversion rate, which helps improve the reliability and reference value of convective rainstorm probability forecasts. Attached Figure Description

[0033] Figure 1 The main flowchart of a probabilistic forecasting method for convective rainstorms coupled with multiple environmental physical quantities provided in this embodiment of the invention;

[0034] Figure 2 This is a schematic diagram of the structure of a convective rainstorm probability forecasting system that couples multiple environmental physical quantities, as provided in an embodiment of the present invention. Detailed Implementation

[0035] 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.

[0036] Please see Figure 1 This invention provides a method for probabilistic forecasting of convective rainstorms by coupling multiple environmental physical quantities, comprising:

[0037] Step 100: Extract convective available potential energy, vertical wind shear, and water vapor physical quantities from the numerical weather prediction model output.

[0038] Step 200: Configure and combine the convective effective potential energy and the vertical wind shear according to the value level to obtain a matching factor. The matching factor takes a high value when the convective effective potential energy and the vertical wind shear are proportionally matched, and takes a low value when they are not proportionally matched.

[0039] Step 300: Calculate the water vapor condition membership degree based on the water vapor physical quantity, and calculate the energy membership degree based on the convective effective potential energy;

[0040] Step 400: Minimize the membership degree of the matching factor and the water vapor condition to obtain the necessary condition strength;

[0041] Step 500: Modulate the energy membership degree with the required condition strength to obtain the gain efficiency, wherein the gain efficiency decreases as the required condition strength decreases;

[0042] Step 600: Multiply the necessary condition intensity by the gain efficiency to output the probability of convective rainstorm occurrence.

[0043] In this embodiment, the convective rainstorm probability forecasting method addresses the problems in existing technologies where each environmental physical quantity is independently mapped to a membership degree and then linearly weighted and fused, and where the energy factor still contributes an artificially high probability through weighting terms in environments where energy and dynamics are mismatched. The method employs a multi-step fusion logic of configuration discrimination, minimum value of necessary conditions, and gain efficiency modulation to explicitly represent the coupling relationship of the three types of environmental conditions—energy, dynamics, and water vapor—as a control factor for probability output.

[0044] This embodiment first obtains three types of physical quantities characterizing the conditions for convection occurrence from the numerical weather prediction model output in step 100. Then, in step 200, it performs configuration and combination discrimination on energy and dynamics to obtain a matching factor characterizing the degree of matching between the two. Simultaneously, in step 300, the water vapor physical quantity and the energy physical quantity are normalized into membership degrees respectively. Subsequently, in step 400, the minimum value of the matching factor and the water vapor condition membership degree is taken as the necessary condition strength. In step 500, the energy membership degree is modulated with this necessary condition strength to obtain the gain efficiency. Finally, in step 600, the probability of convective rainstorm occurrence is output by multiplying the necessary condition strength and the gain efficiency. The above-mentioned value levels refer to dividing the physical quantities into several discrete levels according to preset thresholds for configuration and combination discrimination.

[0045] In step 100, the convective available potential energy, wind vertical shear, and water vapor physical quantities extracted correspond to the energy conditions, dynamic organization conditions, and water vapor supply conditions required for the occurrence of convective rainstorms, respectively. All three conditions are indispensable and form the input basis for subsequent fusion calculations. The configuration combination judgment in step 200 is based on the following understanding: the unstable energy represented by convective available potential energy cannot be effectively released under any dynamic environment. Whether the energy can be transformed into organized updrafts and generate heavy precipitation depends on the relative configuration relationship between the energy and the wind vertical shear. When they are proportionally matched, the energy can be effectively utilized by organized updrafts, and the matching factor takes a high value; when they are disproportionately matched, the energy cannot be effectively organized or the dynamic conditions lack energy support, and the matching factor takes a low value.

[0046] Furthermore, step 300 normalizes the water vapor physical quantity and convective available potential energy into water vapor condition membership degree and energy membership degree, respectively, with values ​​between 0 and 1, through membership functions. This eliminates the influence of dimensional differences between different physical quantities, allowing the favorableness of each physical quantity to be compared and integrated on a unified scale. Step 400 takes the minimum value of the matching factor and the water vapor condition membership degree to obtain the necessary condition strength. The physical basis for this is that both the energy and dynamic configuration conditions and the water vapor conditions are necessary conditions for the occurrence of convective rainstorms. The deficiency of any one condition will limit the occurrence of rainstorms. This minimum value operation does not allow mutual compensation between physical quantities, reflecting the "weakest link" effect among necessary conditions.

[0047] Specifically, step 500 modulates the energy membership degree with the necessary condition strength to obtain the gain efficiency. The necessary condition strength plays a dual role here: first, it is the weakest link in the chain, representing the strength of all necessary conditions; second, it simultaneously serves as the modulation index for gain efficiency, controlling the proportion of energy membership degree converted into effective gain. When the necessary condition strength is high, the energy membership degree can be converted into gain efficiency at a higher proportion, and the energy factor contributes strongly to the probability; when the necessary condition strength is low, the proportion of energy membership degree converted into gain efficiency decreases accordingly, and the energy factor's contribution to the probability automatically weakens, until the necessary condition strength is zero, at which point the gain efficiency is zero, and the energy factor contributes no probability at all. This modulation mechanism makes the energy factor automatically tend to become ineffective in environments with insufficient necessary conditions, thereby avoiding systemic false alarms caused by high energy artificially inflated probability contributions under mismatched environments.

[0048] It should be noted that step 600 multiplies the necessary condition strength by the gain efficiency to output the probability of convective rainstorm occurrence. This probability is a conditional probability estimate based on environmental physical quantities, ranging from 0 to 1. A higher value indicates a higher likelihood of convective rainstorm occurrence under the current environmental physical quantity conditions. The necessary condition strength controls the overall upper limit of the probability output, while the gain efficiency controls the effective contribution ratio of the energy factor. Multiplying these two values ​​ensures that the probability output is simultaneously constrained by both the necessary condition strength and the effective energy conversion rate.

[0049] Optionally, the numerical weather prediction model may be CMA-GFS, CMA-MESO, ECMWF, or other global or regional numerical prediction models capable of outputting the above physical quantities; the water vapor physical quantity may be selected based on the climate characteristics of the target area, choosing a combination of whole-layer water vapor content and lower-layer specific humidity, or other physical quantities capable of characterizing water vapor supply conditions.

[0050] Further, in step 100, convective effective potential energy, vertical wind shear from 0 to 6 km and water vapor physical quantities are extracted from the output of the CMA-GFS numerical weather prediction model, wherein the water vapor physical quantities are atmospheric precipitable water and 850 hPa humidity.

[0051] The CMA-GFS mentioned here is the China Meteorological Administration's Global Numerical Weather Prediction System. Its output products cover the entire globe and include gridded forecast fields of various meteorological elements, including convective effective potential energy, wind field, temperature, humidity, and geopotential height, providing initial field data for probabilistic forecasts of convective rainstorms. In this embodiment, the following four environmental physical quantities are extracted from the CMA-GFS model output for the target forecast grid and the corresponding forecast lead time. These physical quantities can be directly obtained from the CMA-GFS model products or calculated.

[0052] In this embodiment, the convective effective potential energy characterizes the magnitude of potential unstable energy in the atmosphere, measured in joules per kilogram. A higher value indicates greater atmospheric instability and stronger convective development potential, and can be directly read from the convective parameter products of the CMA-GFS model. The 0-6 km vertical wind shear characterizes the degree of change in wind direction and speed with altitude from the ground to 6 km, measured in meters per second. It is calculated as the modulus of the wind vector difference between the two altitudes (0 km and 6 km). Specifically, the wind fields of each standard isobaric surface output by the CMA-GFS model are combined with the geopotential height field of the corresponding isobaric surface. Vertical interpolation is used to obtain the wind vectors at the 0 km and 6 km altitudes, and then the difference between the two altitude wind vectors is calculated and its modulus is taken. A higher value indicates stronger dynamic organization conditions in the environment, which is more conducive to the organization and maintenance of convective storms. The 0-6 km level of vertical wind shear is chosen because this level covers the main development depth of convective storms and can characterize the dynamic control effect of the environment on the organization and structure of convective storms. The atmospheric precipitable water content, expressed in millimeters, represents the total water vapor content in the entire atmosphere. It is obtained by vertical integration of specific humidity from the ground to the top of the atmosphere. The CMA-GFS model typically outputs this product directly, but it can also be calculated through the integration of specific humidity at each layer, reflecting the total water vapor supply for precipitation. The 850 hPa specific humidity represents the water vapor content in the lower atmosphere, expressed in grams per kilogram. It can be directly read from the 850 hPa isobaric surface humidity product of the CMA-GFS model, reflecting the water vapor transport conditions from the boundary layer to the lower layers. By using both atmospheric precipitable water content and 850 hPa specific humidity, both water vapor physical quantities, the total water vapor content in the entire atmosphere and the water vapor supply in the lower layers can be simultaneously represented. The combination of these two quantities can comprehensively reflect the degree of favorability of water vapor conditions for convective rainstorms.

[0053] Optionally, the calculation level of vertical wind shear can be adjusted to 0 to 8 km or 0 to 12 km according to the average development depth of the convective system in the target area; the level of lower-level specific humidity can be replaced with 700 hPa or 925 hPa specific humidity, the specific choice depending on the water vapor transport characteristics of the target area.

[0054] Further, step 200 includes:

[0055] Step 210: Divide the convective effective potential energy into three levels: low energy, medium energy, and high energy;

[0056] Step 220: Divide the vertical wind shear into three levels: weak shear, medium shear, and strong shear;

[0057] Step 230: Query the configuration matrix based on the combination of the level of the convective effective potential energy and the level of the wind vertical shear to obtain the matching factor. In the configuration matrix, the matching factor for a matching configuration is 1, the matching factor for a transitional configuration is 0.5, and the matching factor for a non-matching configuration is 0.2.

[0058] Step 210 categorizes convective effective potential energy into three levels: low, medium, and high energy. Step 220 categorizes vertical wind shear into three levels: weak, medium, and strong shear. This classification method is based on convective environmental parameters. The three-level division effectively distinguishes differences in convective conditions while avoiding the problems of overly granular classification leading to a sparse configuration matrix and insufficient samples for each configuration. The combination of the two physical quantities' levels forms a three-row, three-column configuration matrix with nine possible combinations. Each configuration corresponds to a matching factor value.

[0059] Further, step 230 queries the configuration matrix by level combination to obtain the matching factor. The matching factor for a matched configuration is 1, indicating a proportional match between energy and power, meaning the energy can be fully utilized by the organized updraft. The matching factor for a transitional configuration is 0.5, indicating a configuration between matching and mismatch between energy and power, meaning the energy can be partially utilized. The matching factor for a mismatched configuration is 0.2, indicating a disproportionate match between energy and power, making effective energy utilization difficult. The difference between these three values ​​reflects the difference in energy utilization efficiency under different configurations. The ratio of the matching factor between the matched and mismatched configurations is five times, which can distinguish between matched and mismatched environments in the probability output. The configuration matrix can be queried using a pre-constructed two-dimensional lookup table, with the convective effective potential energy level as the row index and the wind vertical shear level as the column index. The table stores the matching factor values ​​for the corresponding configurations; inputting two levels will retrieve the matching factor from the table.

[0060] Furthermore, the matching configurations include high-energy strong shear and medium-energy medium shear. In the case of high-energy strong shear, the combination of ample unstable energy and strong dynamic organization conditions is conducive to the development of supercells and strong mesoscale convective systems, allowing convective storms to persist for a longer period and produce heavy precipitation, resulting in high energy utilization efficiency. In the case of medium-energy medium shear, medium energy is matched with medium dynamic organization conditions, which is conducive to the organized development of multicell storms and can also produce strong convective precipitation. In both of these configurations, energy and dynamics are matched proportionally, therefore they are classified as matching configurations, with a matching factor of 1.

[0061] Specifically, the mismatched configurations include high energy with weak shear, low energy with weak shear, and low energy with strong shear. In the case of high energy with weak shear, although unstable energy is sufficient, the weak vertical wind shear prevents the updraft from being organized, resulting in energy being released in scattered, pulsed convection, making it difficult to generate sustained heavy precipitation and leading to low energy utilization efficiency. In the case of low energy with strong shear, dynamic organization conditions are strong, but unstable energy is insufficient, and convective storms lack energy support, making it difficult to develop into heavy precipitation systems. In the case of low energy with weak shear, both energy and dynamic conditions are insufficient, doubly limiting convective development. In the above three configurations, energy and dynamics are not proportionally matched, therefore they are classified as mismatched configurations, with a matching factor of 0.2.

[0062] In this embodiment, the transitional configurations include high-energy with medium shear, medium-energy with weak shear, medium-energy with strong shear, and low-energy with medium shear. High-energy with medium shear provides sufficient energy but moderate dynamic organization; updrafts can be organized to some extent, but not as much as in a strong shear environment, and energy can be partially utilized. Medium-energy with weak shear provides moderate energy and relatively weak dynamic organization; convection is mainly pulsed multi-cell, resulting in low energy release efficiency. Medium-energy with strong shear provides strong dynamic organization but moderate energy; in a strong shear environment, storm structures are prone to tilting, affecting precipitation efficiency, and energy utilization is limited. Low-energy with medium shear provides insufficient energy but moderate dynamic conditions, only supporting weak convection development, resulting in limited energy utilization efficiency. The energy-dynamic matching degree in these four configurations falls between matched and mismatched configurations, therefore they are classified as transitional configurations, with a matching factor of 0.5. The nine configurations exhaustively cover all combinations of the three-row, three-column configuration matrix, with no omissions or overlaps.

[0063] Furthermore, in step 210, low energy is defined as convective effective potential energy less than 1000 joules per kilogram, medium energy as convective effective potential energy greater than or equal to 1000 joules per kilogram and less than or equal to 2500 joules per kilogram, and high energy as convective effective potential energy greater than 2500 joules per kilogram. 1000 joules per kilogram is a reference threshold for convection development; when the convective effective potential energy is below this value, atmospheric instability energy is weak, and strong convection is generally difficult to generate. 2500 joules per kilogram is a reference threshold for strong convection; when the convective effective potential energy is above this value, atmospheric instability energy is sufficient, possessing the potential to generate strong convection. This three-level classification can distinguish the potential for convection development at different energy levels.

[0064] Furthermore, in step 220, weak shear is defined as wind vertical shear less than 10 m / s, moderate shear as wind vertical shear greater than or equal to 10 m / s and less than or equal to 20 m / s, and strong shear as wind vertical shear greater than 20 m / s. 10 m / s is the boundary between weak and moderate shear; when the wind vertical shear is below this value, the environmental dynamic organization conditions are weak, and convective storms are mostly short-lived, pulse-like systems. 20 m / s is the boundary between moderate and strong shear; when the wind vertical shear is above this value, the environmental dynamic organization conditions are strong, which is conducive to the organization and maintenance of supercells and strong mesoscale convective systems. This three-level classification can distinguish the organizational structure of convective storms under different dynamic organization conditions.

[0065] Further, step 300 includes:

[0066] Step 310: Calculate the membership degree of the half-ridge of the atmospheric precipitable water and the membership degree of the half-ridge of the 850 hPa humidity, and take the average of the two as the membership degree of the water vapor condition.

[0067] Step 320: Calculate the half-ridge membership degree of the convective effective potential energy as the energy membership degree.

[0068] In step 310, the membership degree of the ascending half-ridge for atmospheric precipitable water and the membership degree of the ascending half-ridge for 850 hPa humidity are calculated respectively, and the arithmetic mean is taken as the membership degree of water vapor conditions, that is:

[0069]

[0070] in, For water vapor condition membership degree, Here, PWAT represents the membership function of the ascending half-ridge, Q850 represents the atmospheric precipitable water content, and PWAT represents the 850 hPa humidity. The arithmetic mean of the membership degrees of these two water vapor physical quantities is used because atmospheric precipitable water content represents the total water vapor volume in the entire layer, while 850 hPa humidity represents the water vapor supply in the lower layers. These two quantities reflect water vapor conditions at different levels, and taking the arithmetic mean can synthesize water vapor information from both the entire and lower layers, avoiding the bias of relying on a single water vapor physical quantity. The membership degree of the ascending half-ridge ranges from 0 to 1; a higher value indicates that the water vapor physical quantity is more favorable for convective rainfall.

[0071] In this embodiment, step 320 calculates the half-ridge membership degree of the convective effective potential energy as the energy membership degree, i.e.:

[0072]

[0073] in, Here, CAPE stands for Convective Available Potential Energy. Convective Available Potential Energy is an important parameter characterizing atmospheric instability energy; the larger its value, the stronger the energy potential for convection development. Therefore, the ascending half-ridge membership function is used to normalize it and then use it as the energy membership degree, which can reflect the degree of favorability of energy conditions for convective rainstorms.

[0074] In one specific implementation, the lower parameter of the half-ridge membership function for atmospheric precipitable water is set to 20 mm and the upper parameter to 50 mm; the lower parameter of the half-ridge membership function for 850 hPa humidity is set to 8 g / kg and the upper parameter to 14 g / kg; and the lower parameter of the half-ridge membership function for convective effective potential energy is set to 500 joules / kg and the upper parameter to 2000 joules / kg. These parameters are typical design values ​​based on the summer convective environment in Liaoning. In actual applications, these parameters can be adjusted according to the climatological statistical characteristics of the target area.

[0075] Furthermore, the ascending half-ridge membership degree is calculated using an ascending half-ridge membership function, which is a monotonically increasing piecewise linear membership function. Its input is the physical quantity to be normalized, and its output is the membership degree corresponding to that physical quantity. The ascending half-ridge membership function includes two parameters: a lower parameter and an upper parameter. The lower parameter is the threshold for the physical quantity at which the membership degree begins to increase, and the upper parameter is the threshold for the physical quantity at which the membership degree reaches 1. The dimensions of the lower and upper parameters are consistent with the input physical quantity. The mathematical expression of the ascending half-ridge membership function is:

[0076]

[0077] in, For input physical quantities, For the following parameters, For the above parameters, For input physical quantities The corresponding half-ridge membership degree. When the input is less than or equal to the lower parameter, the output is 0, indicating that the physical quantity condition is completely unfavorable; when the input is greater than or equal to the upper parameter, the output is 1, indicating that the physical quantity condition is completely favorable; when the input is greater than the lower parameter and less than the upper parameter, the output is the difference between the input and the lower parameter divided by the difference between the upper parameter and the lower parameter, and the membership degree increases linearly with the input physical quantity.

[0078] Specifically, the reason for using the ascending half-ridge membership function is that the favorable influence of three physical quantities—convective available potential energy, atmospheric precipitable water, and specific humidity—on convective rainstorms increases monotonically with increasing values. There is no optimal interval or a situation where the influence decreases with increasing values. Therefore, the monotonically increasing ascending half-ridge function can characterize the favorable influence of these physical quantities. The piecewise linear form only contains four arithmetic operations, with the lower and upper parameters corresponding to the unfavorable and favorable thresholds of the physical quantities, respectively, facilitating adjustments based on the climatic characteristics of the target region.

[0079] In this embodiment, the gain efficiency in step 500 satisfies:

[0080]

[0081] in, The strength of the necessary condition is between 0 and 1; This represents the energy membership degree, with a value between 0 and 1. For gain efficiency, the value ranges from 0 to 1. In this formula, This represents the energy deficit, which is the proportion of energy deficiency relative to a fully advantageous state; the energy deficit is expressed as the intensity of necessary conditions. Performing an exponentiation on the exponent yields... Then subtract that value from 1 to get the gain efficiency. The design concept of this formula lies in: the strength of necessary conditions. Not only does it participate in the final probability multiplication as the weakest link in the output of step 400, but it also acts as an exponent to control the decay of energy deficiency, thereby modulating the energy membership conversion efficiency based on the necessary condition strength. In actual calculations, when Directly define gain efficiency No calculation required To avoid Occasionally Mathematical indeterminate forms; this convention and formula are in The limiting results are consistent when approaching 0, and do not affect the physical meaning.

[0082] When the necessary condition strength is 1, the gain efficiency equals the energy membership degree, that is:

[0083]

[0084] This indicates that when the necessary condition is sufficient, the energy membership can be completely converted into gain efficiency, and the energy factor is fully effective. When the necessary condition strength is 0, the gain efficiency is 0, meaning that when the necessary condition is completely missing, the gain efficiency is zero, and the energy factor does not contribute to the gain at all. The final probability in this case is:

[0085]

[0086] The two boundary conditions mentioned above correspond to the extreme cases of the necessary conditions being completely sufficient and completely absent, respectively, and their physical meaning is clear.

[0087] Furthermore, regarding gain efficiency Regarding the strength of necessary conditions Taking the partial derivative, we get:

[0088]

[0089] because hour , Since the gain efficiency is always 1, the gain efficiency is... With the strength of necessary conditions Monotonically non-decreasing. This means that the higher the necessary condition strength, the greater the gain efficiency and the stronger the effective contribution of the energy factor; conversely, the lower the necessary condition strength, the smaller the gain efficiency and the weaker the effective contribution of the energy factor. When the energy membership degree At that time, gain efficiency final probability This indicates that when the energy condition is completely absent, the probability is 0 regardless of the strength of the necessary condition; when the energy membership degree and At that time, gain efficiency final probability This indicates that the probability of having perfectly favorable energy is determined by the strength of the necessary condition.

[0090] It should be noted that the difference between this modulation formula and the existing weighted average fusion method is that in the existing method, energy membership is directly involved in the linear combination as an independent weighting term, and its contribution ratio is determined by a fixed weight and does not change with the conditions of other physical quantities; in this method, the effective contribution ratio of energy membership is determined by the necessary condition intensity. Dynamic modulation, The contribution of the energy factor is determined by the minimum membership degree of the matching factor and the water vapor condition; therefore, it is automatically constrained by the combined energy and dynamic configuration conditions and the water vapor condition. When configuration mismatch or insufficient water vapor leads to… When the membership degree is reduced, even if the energy membership degree is high, the proportion that translates into gain efficiency will automatically decrease, thus avoiding the problem of artificially high contribution probability due to high energy in mismatched environments. Necessary condition strength Simultaneously bearing the dual roles of the weakest link in the barrel and the gain efficiency modulation index, the fusion logic consists of four basic operations: hierarchical discrimination, membership function calculation, minimum value operation, and exponentiation operation, without any machine learning training or parameter optimization steps.

[0091] The following example illustrates the specific application of the gain efficiency formula in the overall process of this method. Assume the CMA-GFS model output for a target grid point is: Convective potential energy CAPE = 1500 joules per kilogram, vertical wind shear from 0 to 6 kilometers SHEAR = 15 meters per second, atmospheric precipitable water PWAT = 35 millimeters, and specific humidity Q850 = 11 grams per kilogram.

[0092] S1, Configuration Judgment: CAPE=1500 joules per kilogram belongs to medium energy, SHEAR=15 meters per second belongs to medium shear. Medium energy paired with medium shear is a suitable configuration. Matching factor. .

[0093] S2, Calculate the membership degree of the water vapor condition:

[0094] ;

[0095] ;

[0096] .

[0097] S3, Calculate energy membership:

[0098] .

[0099] S4, Calculate the strength of necessary conditions:

[0100] .

[0101] S5, Calculate gain efficiency:

[0102] .

[0103] S6, Output probability:

[0104] .

[0105] This example demonstrates that when the necessary condition strength At that time, gain efficiency Less than the energy membership degree This indicates that the energy factor is only partially effective when the necessary conditions are insufficient, reflecting the modulation effect of the strength of the necessary conditions on the gain efficiency.

[0106] Alternatively, the modulation function for gain efficiency can be other functions that use the necessary condition strength as the modulation parameter and satisfy... hour , hour , Follow A monotonically non-decreasing function form, such as a piecewise linear modulation function or an S-shaped modulation function, is acceptable as long as it can reflect the modulation effect of the necessary condition strength on the energy membership conversion efficiency.

[0107] In this embodiment, the present invention configures and combines convective effective potential energy and vertical wind shear according to their value levels to obtain a matching factor, so that the matching relationship between energy and power explicitly participates in the probability calculation; the minimum value of the matching factor and the membership degree of water vapor condition is taken to obtain the necessary condition strength, reflecting the bottleneck constraint between energy and power configuration and water vapor supply, and the various physical quantities are not allowed to compensate for each other; the energy membership degree is modulated by the necessary condition strength to obtain the gain efficiency, so that the effective contribution ratio of the energy factor changes dynamically with the necessary condition strength. In the environment of mismatched configuration or insufficient water vapor, the energy factor automatically reduces its contribution, avoiding the systemic false alarm caused by the high contribution probability of high energy in unfavorable environment; the probability is output by multiplying the necessary condition strength and the gain efficiency, so that the probability output is constrained by both the necessary condition strength and the effective energy conversion rate, which is conducive to improving the reliability and reference value of convective rainstorm probability forecast.

[0108] Based on the above embodiments, such as Figure 2 As shown, the present invention also provides a convective rainstorm probability forecasting system coupled with multiple environmental physical quantities, used to support the convective rainstorm probability forecasting method coupled with multiple environmental physical quantities described in the above embodiments. The convective rainstorm probability forecasting system coupled with multiple environmental physical quantities includes:

[0109] The physical quantity extraction module is used to extract convective available potential energy, vertical wind shear, and water vapor physical quantities from the output of numerical weather prediction models.

[0110] A configuration discrimination module is used to configure and combine the convective effective potential energy and the wind vertical shear according to the value level to obtain a matching factor;

[0111] The membership calculation module is used to calculate the water vapor condition membership degree based on the water vapor physical quantity and the energy membership degree based on the convective effective potential energy.

[0112] The fusion output module is used to obtain the necessary condition intensity by taking the minimum value of the matching factor and the water vapor condition membership degree, modulate the energy membership degree with the necessary condition intensity to obtain the gain efficiency, and multiply the necessary condition intensity and the gain efficiency to output the probability of convective rainstorm occurrence.

[0113] Furthermore, the aforementioned convective rainstorm probability forecasting system coupled with multiple environmental physical quantities can run the above-mentioned convective rainstorm probability forecasting method coupled with multiple environmental physical quantities. For specific implementation details, please refer to the method embodiment, which will not be repeated here.

[0114] Based on the above embodiments, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for probabilistic forecasting of convective rainstorms involving coupling multiple environmental physical quantities.

[0115] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0116] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the modules and programs described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, modules, or devices, and may be electrical, mechanical, or other forms.

[0118] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0120] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program instructions, such as USB flash drives, portable hard drives, read-only storage servers, random access storage servers, magnetic disks, or optical disks.

[0121] Furthermore, it should be noted that the combination of the various technical features in this case is not limited to the combination methods described in the claims of this case or the combination methods described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way, unless they contradict each other.

[0122] It should be noted that the above examples are merely specific embodiments of the present invention, and the present invention is obviously not limited to the above embodiments, with many similar variations. All modifications that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should fall within the protection scope of this invention.

[0123] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for probabilistic forecasting of convective rainstorms involving the coupling of multiple environmental physical quantities, characterized in that, Includes the following steps: Step 100: Extract convective available potential energy, vertical wind shear, and water vapor physical quantities from the numerical weather prediction model output. Step 200: Configure and combine the convective effective potential energy and the vertical wind shear according to the value level to obtain a matching factor. The matching factor takes a high value when the convective effective potential energy and the vertical wind shear are proportionally matched, and takes a low value when they are not proportionally matched. Step 300: Calculate the water vapor condition membership degree based on the water vapor physical quantity, and calculate the energy membership degree based on the convective effective potential energy; Step 400: Minimize the membership degree of the matching factor and the water vapor condition to obtain the necessary condition strength; Step 500: Modulate the energy membership degree with the required condition strength to obtain the gain efficiency, wherein the gain efficiency decreases as the required condition strength decreases; Step 600: Multiply the necessary condition intensity by the gain efficiency to output the probability of convective rainstorm occurrence.

2. The method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities according to claim 1, characterized in that, In step 100, convective effective potential energy, vertical wind shear from 0 to 6 km and water vapor physical quantities are extracted from the output of the CMA-GFS numerical weather prediction model, wherein the water vapor physical quantities are atmospheric precipitable water and 850 hPa humidity.

3. The method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities according to claim 1, characterized in that, Step 200 includes: Step 210: Divide the convective effective potential energy into three levels: low energy, medium energy, and high energy; Step 220: Divide the vertical wind shear into three levels: weak shear, medium shear, and strong shear; Step 230: Query the configuration matrix based on the combination of the level of the convective effective potential energy and the level of the wind vertical shear to obtain the matching factor. In the configuration matrix, the matching factor for a matching configuration is 1, the matching factor for a transitional configuration is 0.5, and the matching factor for a non-matching configuration is 0.

2.

4. The method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities according to claim 3, characterized in that, In the configuration matrix, the matching configuration includes high-energy strong shear and medium-energy medium shear, the transition configuration includes high-energy medium shear, medium-energy weak shear, medium-energy strong shear and low-energy medium shear, and the mismatch configuration includes high-energy weak shear, low-energy weak shear and low-energy strong shear.

5. The method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities according to claim 3, characterized in that, In step 210, the low energy is when the convective effective potential energy is less than 1000 joules per kilogram, the medium energy is when the convective effective potential energy is greater than or equal to 1000 joules per kilogram and less than or equal to 2500 joules per kilogram, and the high energy is when the convective effective potential energy is greater than 2500 joules per kilogram. In step 220, the weak shear is defined as the vertical wind shear being less than 10 meters per second, the medium shear is defined as the vertical wind shear being greater than or equal to 10 meters per second and less than or equal to 20 meters per second, and the strong shear is defined as the vertical wind shear being greater than 20 meters per second.

6. The method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities according to claim 2, characterized in that, Step 300 includes: Step 310: Calculate the membership degree of the half-ridge of the atmospheric precipitable water and the membership degree of the half-ridge of the 850 hPa humidity, and take the average of the two as the membership degree of the water vapor condition. Step 320: Calculate the half-ridge membership degree of the convective effective potential energy as the energy membership degree.

7. The method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities according to claim 6, characterized in that, The membership degree of the ascending half-ridge is calculated by the ascending half-ridge membership function, which satisfies the following: when the input physical quantity is less than or equal to the lower parameter, the ascending half-ridge membership degree is 0; when the input physical quantity is greater than or equal to the upper parameter, the ascending half-ridge membership degree is 1; when the input physical quantity is greater than the lower parameter and less than the upper parameter, the ascending half-ridge membership degree is the difference between the input physical quantity and the lower parameter divided by the difference between the upper parameter and the lower parameter.

8. The method for probabilistic forecasting of convective rainstorms based on the coupling of multiple environmental physical quantities according to claim 1, characterized in that, In step 500, the gain efficiency satisfies ,in The strength of the necessary condition. The energy membership degree, The gain efficiency is defined as follows: when the necessary condition strength is 1, the gain efficiency is equal to the energy membership degree; when the necessary condition strength is 0, the gain efficiency is equal to 0.

9. A probabilistic forecasting system for convective rainstorms coupled with multiple environmental physical quantities, characterized in that, include: The physical quantity extraction module is used to extract convective available potential energy, vertical wind shear, and water vapor physical quantities from the output of numerical weather prediction models. A configuration discrimination module is used to configure and combine the convective effective potential energy and the wind vertical shear according to the value level to obtain a matching factor; The membership calculation module is used to calculate the water vapor condition membership degree based on the water vapor physical quantity and the energy membership degree based on the convective effective potential energy. The fusion output module is used to obtain the necessary condition intensity by taking the minimum value of the matching factor and the water vapor condition membership degree, modulate the energy membership degree with the necessary condition intensity to obtain the gain efficiency, and multiply the necessary condition intensity and the gain efficiency to output the probability of convective rainstorm occurrence.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a method for probabilistic forecasting of convective rainstorms that couples multiple environmental physical quantities as described in any one of claims 1 to 8.

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