Cloud base height forecasting method based on multi-factor dynamic analysis

Abstract

The invention discloses a cloud base height forecasting method based on multi-factor dynamic analysis, relates to the technical field of meteorological numerical forecasting and atmospheric boundary layer analysis, and aims to predict the height of a cloud base by identifying the structural feature that the internal humidity of a mixed layer changes along with the height instead of only depending on the average relative humidity of the mixed layer in the cloud base height forecasting process. A continuous humidity increasing section is extracted, and the position of a potential condensation layer is determined, so that the initial condition of air block lifting calculation can more accurately reflect the actual water vapor distribution condition in the mixing layer. Under the condition that a local wet layer or a local high-humidity area exists, a wet layer structure closer to an actual condensation occurrence position can still be identified, so that the cloud base height calculation process is closer to a real atmospheric state; by calculating the thickness of the mixed layer, the vertical range of the mixed layer can be definitely limited, and the subsequent humidity structure analysis can be carried out aiming at the important region where the low cloud is formed, so that the cloud bottom height analysis process corresponds to the actual atmospheric boundary layer structure.

Description

Technical Field

[0001] This invention relates to the field of meteorological numerical forecasting and atmospheric boundary layer analysis technology, specifically a cloud base height forecasting method based on multi-factor dynamic analysis. Background Technology

[0002] In aviation meteorological services, low-cloud monitoring, general aviation flight support, and regional weather forecasting, cloud base height is a crucial meteorological parameter reflecting the structure of low clouds and the distribution of near-surface water vapor. The formation of cloud base height is closely related to the temperature structure, water vapor distribution, and air uplift processes within the atmospheric boundary layer. The mixing layer is a key region influencing low cloud formation. The mixing layer typically refers to the atmospheric layer extending approximately 100 hPa above the ground. Within this region, air undergoes relatively uniform heat exchange under turbulent conditions. However, in actual atmospheric environments, the mixing layer often exhibits localized moist layer structures and complex humidity distribution characteristics with altitude. These structures significantly influence the formation altitude of low clouds.

[0003] In existing cloud base height forecasting methods, the average relative humidity of the mixing layer is often used as a basic condition for determining the presence of low clouds, and the height of condensation is calculated based on this, combined with parcel uplift. However, when using average humidity to describe the water vapor condition of the mixing layer, the vertical distribution differences of humidity within the mixing layer are often ignored. When there are local humidity-increasing layers or local high-humidity layers within the mixing layer, the average humidity will smooth out these local structures, weakening the information on the wet layer structure and making it difficult to accurately identify the actual height range where condensation may occur.

[0004] Because the vertical humidity structure within the mixing layer is not fully identified, when localized wet layers or increasing humidity zones exist in the actual atmosphere, the lifting condensation height calculated using traditional methods often only reflects the overall average state and fails to reflect the influence of localized wet layers on the condensation process. This leads to cloud base height forecasts deviating from the actual cloud base position. In some cases, localized high-humidity layers within the mixing layer may become the first areas where air parcels reach saturation. However, average humidity calculations weaken this information, resulting in unreasonable initial conditions for air parcel lifting and consequently, cloud base height calculations that are either too high or too low. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a cloud base height prediction method based on multi-factor dynamic analysis, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides a cloud base height prediction method based on multi-factor dynamic analysis, comprising the following steps: S1. Read the corresponding numerical profile data of the target area, extract the data of each pressure layer within the range of 100 hPa above the ground, calculate the mixed layer thickness Dm and the humidity discrete change QD, and fit it into the mixed layer humidity vertical structure state set. S2. Based on the vertical structure state set of humidity in the mixed layer, perform vertical difference operation between adjacent layers on the humidity sequence to obtain the humidity change rate sequence Gk between each layer, and obtain the continuously increasing discriminant Lc and humidity return amount Bf. Finally, select the wet layer candidate segments that meet the continuously increasing condition. S3. Calculate the degree of humidity aggregation Cw and the average temperature-dew difference Ms for each candidate wet layer segment, and construct the potential condensation layer determination quantity Sc. Determine the location of the potential condensation layer from multiple candidate segments, and calculate the average temperature pT and average relative humidity pRH of the potential condensation layer according to the layer thickness weighting method. S4. Calculate the corresponding dew point temperature LTd based on the average temperature pT and average relative humidity pRH, and obtain the initial value of condensation height HC0 for the rise of the air parcel. At the same time, perform structural correction on the initial value of condensation height HC0 to obtain the cloud base height correction value HC1. S5. Based on the cloud base height correction value HC1 and the humidity distribution of the mixing layer below the potential condensation layer, obtain the interlayer humidity support Zs, and perform a structural consistency check on the cloud base height correction value HC1 to obtain the final cloud base height forecast result HCZ.

[0007] Preferably, S1 includes S11; S11. Read the profile data of the target area, perform normalization processing to eliminate the dimensions of the data, and extract the data of each pressure layer within the range of 100 hPa above the ground, including pressure layer temperature Tk, ground temperature Td, relative humidity RHk and geopotential height information Hk, where k represents the kth pressure layer number within the mixing layer range. Based on the order of pressure layer height, the pressure layers within the range of 100 hPa above the ground are arranged from low to high according to their geopotential height, forming a temperature sequence T={T1, T2, T3, ..., Tn} and a humidity sequence RH={RH1, RH2, RH3, ..., RHn}; where n represents the total number of pressure layers involved in the calculation within the range of 100 hPa above the ground. After obtaining the temperature sequence T and humidity sequence RH, the thickness Dm of the mixing layer is calculated by the potential height difference; The thickness Dm of the mixed layer is obtained by the difference between the geopotential height of the top pressure layer and the geopotential height of the bottom pressure layer. The overall distribution characteristics of the humidity structure inside the mixing layer are described, and the vertical distribution offset of humidity Sr is calculated. The humidity vertical distribution offset Sr is obtained using the following formula; ; In the formula, RHk+1 represents the relative humidity of the (k+1)th pressure layer.

[0008] Preferably, S1 further includes S12; S12. After obtaining the temperature sequence T and humidity sequence RH of the mixed layer, the discrete change in humidity QD is calculated based on the humidity difference and height ratio, as shown in the following formula: ; In the formula, Hk+1 represents the geopotential height information of the (k+1)th pressure layer; The humidity discrete change QD is compared with the preset humidity discrete threshold Tqd to determine the humidity distribution state of the mixing layer. When the humidity discrete change QD < the humidity discrete threshold Tqd, it indicates that the humidity distribution in the mixing layer is uniform. When the humidity discrete change QD ≥ the humidity discrete threshold Tqd, it indicates that there is a humidity fluctuation structure inside the mixing layer; To further characterize the concentration of humidity structure in the vertical direction, the humidity accumulation amount Cr is constructed, and the formula is as follows: ; The obtained humidity accumulation amount Cr, humidity discrete change amount QD, humidity vertical distribution offset Sr and mixing layer thickness Dm are fitted to obtain the humidity vertical structure state set of the mixing layer.

[0009] Preferably, S2 includes S21; S21. After obtaining the vertical structure state set of humidity in the mixed layer, read the humidity sequence RH and the corresponding potential height sequence H={H1, H2, ..., Hn}, and perform vertical difference operation on the humidity sequence according to the order of adjacent layers to obtain the humidity change rate sequence Gk between each layer. The humidity change rate sequence Gk is obtained by the ratio of the humidity difference between two adjacent layers to the geopotential height difference between two adjacent layers; When the humidity change rate sequence Gk > 0, it indicates that the relative humidity is increasing along the height direction; When the humidity change rate sequence Gk=0, it indicates that the humidity of the layer remains basically unchanged; When the humidity change rate sequence Gk < 0, it indicates that the relative humidity is decreasing along the height direction; The humidity change rate sequence Gk is easily affected by the fluctuations between model layers. After obtaining the humidity change rate sequence Gk, the humidity increment amplitude Dk of the adjacent layer and the interlayer change stationary quantity Pk are calculated. The humidity increment value Dk of the adjacent layer is obtained by the difference between the relative humidity of the (k+1)th pressure layer and the relative humidity of the kth pressure layer; The humidity increment amplitude Dk of adjacent layers is used to represent the absolute direction and actual amount of humidity change. The difference between it and the humidity change rate sequence Gk is that the humidity increment amplitude Dk of adjacent layers reflects the actual value of the increase or decrease in humidity between two layers, while the humidity change rate sequence Gk reflects the distribution intensity of the same change per unit height. The interlayer variation constant Pk is obtained by taking the absolute value of the difference between the humidity change rate sequence of the (k+1)th pressure layer and the humidity change rate sequence of the kth pressure layer.

[0010] When the interlayer variation constant Pk is small, it indicates that the humidity change rate of two consecutive layers is close and the humidity increase process is relatively smooth. When the interlayer variation stability Pk is large, it indicates that a sudden change has occurred in a certain layer segment, and there is a change in the humidity structure. Preferably, S2 also includes S22; S22. After obtaining the humidity increment amplitude Dk of adjacent layers and the interlayer change stability Pk, for each continuous layer segment composed of positive humidity change rate, further calculate the coherence of the internal humidity structure, and obtain the continuously increasing discriminant Lc(i,j), humidity return amount Bf(i,j), and consistency U(i,j) of adjacent gradient changes for the candidate layer segment (i,j); where i represents the starting layer number of the candidate wet layer segment, and j represents the ending layer number of the candidate wet layer segment. The formula for obtaining the continuously increasing discriminant Lc(i,j) of candidate segment (i,j) is: ; In the formula, This represents the cumulative value of all positively humidified portions within the candidate layer. This represents the absolute value of the net humidity change across the entire candidate layer. Lc(i,j) specifically represents the proportion of the positive humidification process in the overall humidity change within the layer. When Lc(i,j) is close to 1, it indicates that the layer is mainly composed of continuous humidification. When Lc(i,j) deviates from 1, it indicates that the layer contains obvious humidity drop or fluctuation and is not suitable as a continuous humidification layer. The formula for obtaining the humidity reflection amount Bf(i,j) of candidate segment (i,j) is: ; In the formula, min(Dk, 0) represents the original value when the interlayer humidity difference is negative, and 0 otherwise; Bf(i,j) represents the cumulative value of all the decline amplitudes within the entire candidate layer. The smaller the value of Bf(i,j), the closer the humidity within the layer is to a monotonically increasing trend along the height direction. The larger the value of Bf(i,j), the more obvious the backflow within the layer, making it unsuitable as a potential wet layer candidate layer. The formula for obtaining the consistency U(i,j) of adjacent gradient changes is: ; When the consistency U(i,j) of adjacent gradient changes is large, it indicates that the humidity change rate within the layer is relatively smooth; when the consistency U(i,j) of adjacent gradient changes is small, it indicates that there are many gradient abrupt changes within the layer. A unified analysis is performed on the continuously increasing discriminant Lc(i,j), humidity return amount Bf(i,j), and consistency U(i,j) of adjacent gradient changes of the obtained candidate layer segments (i,j) to construct the candidate wet layer judgment value W(i,j). The formula for obtaining the candidate wet layer determination value W(i,j) is as follows: ; Analyze the candidate wet layer determination value W(i,j) to determine whether the candidate layer segment (i,j) satisfies the condition of continuous increase of the wet layer candidate segment; When the candidate wet layer judgment value W(i,j) is less than the preset judgment threshold, it means that although the segment has local humidification, the internal structure is incomplete or has fallen back a lot, and it does not enter the wet layer candidate segment. When the candidate wet layer judgment value W(i,j) is greater than or equal to the preset judgment threshold, it indicates that the layer segment has the following characteristics: overall upward humidification; less internal drop; smooth transition of humidity change rate; and humidification is not caused by a sudden increase in a single layer. The segment is retained as a candidate segment for wet layer.

[0011] The threshold is obtained by performing vertical difference calculation on the mixed layer humidity sequence for each time step according to the methods of steps S1 and S2, and obtaining the candidate wet layer judgment value W(i,j) for each segment.

[0012] Subsequently, all the layers in the historical samples that met the condition of increasing humidity were used as the initial sample set. Based on the actual cloud base observation data or cloud cover records at the same time, the sample set was marked. The layers that could correspond to the formation of low clouds or stratiform clouds were marked as valid wet layer samples, and the remaining layers were marked as invalid samples.

[0013] After sample identification, statistical analysis is performed on the W(i,j) values ​​corresponding to all valid wet layer samples to calculate their average value, dispersion, and distribution range. An initial judgment threshold is then determined based on the lower boundary of the valid sample distribution interval. This initial threshold is then substituted into historical samples to re-perform the wet layer screening calculation. The screening results are compared with historical cloud base height or cloud cover records for consistency. The threshold is gradually fine-tuned based on the screening accuracy until the matching degree between the screening results and historical cloud base records stabilizes. The final value is used as the judgment threshold for candidate wet layer judgment values ​​W(i,j) and is used as a fixed parameter in subsequent cloud base height forecast calculations. Preferably, S3 includes S31; S31. Extract the pressure layer temperature Tk, relative humidity RHk, and geopotential height information Hk corresponding to each candidate wet layer segment. Statistically analyze the humidity distribution within the segment and calculate the humidity aggregation degree Cw, using the following formula: ; In the formula, RHmax represents the maximum relative humidity value within the mixing layer range, and Cw(i,j) represents the humidity aggregation degree of candidate segment (i,j). When the value of Cw(i,j) is large, it indicates that the humidity level inside the segment is generally high and the distribution is relatively concentrated. Calculate the dew point temperature Tdk of each layer based on temperature and humidity data, and calculate the average temperature-dew difference Ms of the candidate section by using the difference between the dew point temperature and the air temperature. Dew point temperature Tdk is obtained using the following formula: ; The average temperature-dew difference Ms is obtained as follows: The starting and ending pressure layers of the wet layer candidate segment are determined; the air temperature and corresponding dew point temperature of each pressure layer within the segment are read sequentially; then, the difference between the temperature and dew point temperature of each layer within the segment is calculated by subtracting the dew point temperature of that layer from the air temperature of that layer; after obtaining the temperature-dew difference of all pressure layers within the segment, these temperature-dew difference values ​​are accumulated layer by layer to obtain the total temperature-dew difference within the wet layer segment; subsequently, the number of pressure layers contained in the segment is calculated; finally, the total temperature-dew difference is divided by the number of layers in the segment to obtain the average temperature-dew difference Ms within the wet layer candidate segment. The obtained humidity polymerization degree Cw and average temperature-dew difference Ms are integrated to calculate the potential condensation layer determination quantity Sc, as shown in the following formula: ; In the formula, Sc(i,j) represents the potential condensation layer determination quantity of candidate segment (i,j), and Ms(i,j) represents the average temperature-dew difference of candidate segment (i,j).

[0014] Sc(i,j) is used to comprehensively reflect the influence of humidity concentration, air saturation, and humidity layer thickness on condensation formation. A larger Sc(i,j) value indicates that the candidate section is closer to the actual condensation layer in terms of humidity concentration, temperature-dew difference, and layer thickness. Preferably, S3 also includes S32; S32. After obtaining the potential condensation layer determination value Sc for all candidate sections, compare the values ​​of the condensation layer determination value Sc for all candidate sections, determine the candidate section with the largest value as the potential condensation layer section, and record its starting layer number and ending layer number as iz and jz respectively, and calculate the average temperature pT and average relative humidity pRH of the potential condensation layer according to the layer thickness weighting method. The formula for obtaining the average temperature pT is: ; The formula for obtaining average relative humidity (pRH) is: ; The obtained average temperature pT and average relative humidity pRH are used as the starting condition parameters for the calculation of air parcel lifting and are input into step S4.

[0015] Preferably, S4 includes S41 and S42; S41. Based on the average temperature pT and average relative humidity pRH, calculate the dew point temperature LTd corresponding to the potential condensation layer air. The dew point temperature LTd is obtained by converting the average relative humidity into a proportional form and then performing a natural logarithmic calculation. At the same time, the temperature term is calculated based on the proportional relationship between the average temperature and the constant. The two are then combined to obtain the dew point temperature LTd. The obtained dew point temperature LTd represents the temperature value corresponding to the air reaching saturation under the current air moisture content conditions. The initial value of the condensation height of the gas parcel is calculated by combining the dew point temperature LTd with the average temperature pT, as follows: HC0 = Hiz + KP × (pT - LTd); where Hiz represents the initial height of the gas parcel rise, and KP represents the conversion coefficient between the temperature-dew difference and the condensation height.

[0016] Preferably, S4 also includes S42; S42. After obtaining the initial value of the condensation height of the gas parcel, HC0, read the relative humidity and corresponding geopotential height of each pressure layer inside the potential condensation layer, and calculate the humidity dispersion degree Dh inside the potential condensation layer. The humidity dispersion degree Dh is obtained as follows: First, the starting and ending pressure layers of the potential condensation layer are determined, and the number of pressure layers contained within the segment is counted; then, the relative humidity of each layer within the segment is read sequentially, and the difference between the relative humidity of each layer and the average relative humidity of the potential condensation layer is calculated; next, the humidity difference obtained for each layer is squared; then, the squared values ​​of the humidity differences of all pressure layers within the segment are summed; finally, the summation result is divided by the number of pressure layers contained in the segment to obtain the humidity dispersion degree within the potential condensation layer. Identify the pressure layer with the highest relative humidity inside the potential condensation layer, denoted as RHmax and the corresponding geopotential height as Hmax, and calculate the humidity structure correction Rc. The humidity structure correction Rc is obtained as follows: First, the maximum relative humidity value and the geopotential height of the corresponding pressure layer in the potential condensation layer are read, and the difference between the maximum humidity value and the average relative humidity of the potential condensation layer is calculated. At the same time, the height difference between the geopotential height of the maximum humidity layer and the geopotential height of the lifting initiation layer is calculated. Then, the humidity difference and the height difference are multiplied, and the result is divided by the remaining humidity range (100-pRH) of the air distance from saturation to obtain the humidity structure correction Rc. The obtained humidity dispersion degree Dh and humidity structure correction amount Rc are applied to the initial value of condensation height HC0 and corrected to obtain the cloud base height correction value HC1, as follows: HC1=HC0-Rc+λ×Dh; where λ represents the adjustment coefficient of the influence of humidity dispersion degree on condensation height, and the value range is 0 to 1.

[0017] Preferably, S5 includes S51 and S52; S51. Read the relative humidity and corresponding geopotential height of each pressure layer within the mixing layer range below the potential condensation layer, and perform statistical calculations on the humidity distribution of each layer below the potential condensation layer to obtain the interlayer humidity support Zs. The method for obtaining the interlayer humidity support Zs is as follows: First, read the relative humidity and corresponding interlayer height difference of each pressure layer in the mixing layer below the potential condensation layer. Then, multiply the relative humidity and the layer height difference of each layer and accumulate them layer by layer to obtain the cumulative amount of humidity and layer thickness. At the same time, accumulate all the interlayer height differences to obtain the total layer thickness. Finally, divide the cumulative humidity by the total layer thickness to obtain the interlayer humidity support Zs of the air below the mixing layer. When the value of the interlayer humidity support Zs is large, it indicates that the air in the mixing layer below the potential condensation layer is generally humid, and the humidity conditions can form a continuous water vapor transport to the upper wet layer. When the interlayer humidity support value Zs is small, it indicates that the air below is relatively dry and the upper wet layer structure may be isolated. S52. Based on the interlayer humidity support Zs, calculate the humidity connection relationship Js between the potential condensation layer and the underlying mixing layer. Combine the continuously increasing discrimination Lc and the average temperature-dew difference Ms to construct the structural verification correction Rs. Perform structural consistency verification on the cloud base height correction value HC1 to obtain the final cloud base height forecast result HCZ. The humidity linkage quantity Js is obtained as follows: First, calculate the difference between the average relative humidity of the potential condensation layer and the humidity support of the underlying mixing layer; then calculate the height difference between the bottom height of the potential condensation layer and the bottom height of the mixing layer, and add a very small constant to the height difference to avoid the denominator being zero; finally, divide the humidity difference by the height difference to obtain the humidity linkage quantity Js per unit height. The formula for obtaining the structural correction amount Rs is as follows:

[0018] In the formula, Hiz represents the initial height of the air parcel lift, and Hjz represents the final height of the air parcel lift; The formula for obtaining the final cloud base height forecast result HCZ is: HCZ=HC1-Rs.

[0019] When the potential condensation layer and the underlying mixed layer have good humidity integration and the wet layer structure is intact, the cloud base height remains close to the original correction value; when there is a significant discontinuity between the wet layer structure and the underlying air humidity distribution, the cloud base height is appropriately adjusted through structural verification to obtain a final cloud base height forecast result HCZ that is more consistent with the characteristics of the wet layer structure.

[0020] This invention provides a cloud base height prediction method based on multi-factor dynamic analysis, which has the following beneficial effects: (1) By analyzing the vertical structure of humidity within the mixing layer, the cloud base height forecast no longer relies solely on the average relative humidity of the mixing layer. Instead, it identifies the structural characteristics of humidity variation with altitude within the mixing layer, extracts continuous humidity-increasing segments, and determines the location of potential condensation layers. This allows the initial conditions for calculating parcel uplift to more accurately reflect the actual water vapor distribution within the mixing layer. In this way, even in the presence of localized wet layers or areas of high humidity, it is still possible to identify the structure of the wet layer that is closer to the actual condensation location, thus making the cloud base height calculation process more closely resemble the real atmospheric conditions.

[0021] (2) By reading numerical model profile data and uniformly processing the temperature and humidity information within the mixing layer, the original profile data can form a complete temperature and humidity sequence in height order, thus providing a stable data foundation for subsequent analysis of the humidity structure inside the mixing layer. By calculating the thickness of the mixing layer, the vertical range of the mixing layer can be clearly defined, enabling subsequent humidity structure analysis to be carried out in the mixing layer, an important region for low cloud formation, thereby making the cloud base height analysis process correspond to the actual atmospheric boundary layer structure.

[0022] (3) By constructing a potential condensation layer determination metric, multiple candidate wet layer segments are compared in a unified manner, so that the determination process of potential condensation layers has a clear basis for judgment. In this way, the segment most conducive to water vapor condensation can be screened from multiple possible wet layer structures, thereby avoiding misjudgment caused by local humidity fluctuations or single-layer humidity changes, and making the location of potential condensation layers more stably reflect the true humidity structure inside the mixed layer.

[0023] (4) By statistically analyzing the humidity distribution within the potential condensation layer, the degree of humidity dispersion is calculated, and a humidity structure correction is constructed based on the location of the maximum humidity layer within the potential condensation layer. This allows the cloud base height to further reflect the differences in water vapor distribution within the wet layer, building upon the initial condensation height. When there are local high-humidity areas or uneven humidity changes within the potential condensation layer, this processing method can correct the condensation height accordingly, thereby enabling the cloud base height result to reflect the influence of local humidity structure within the wet layer and reducing the deviation caused by calculations based solely on average temperature and humidity conditions. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the steps of the cloud base height prediction method based on multi-factor dynamic analysis of the present invention; Figure 2 This is a schematic diagram of the wet layer candidate segment acquisition and judgment process of the present invention; Figure 3 This is a schematic diagram of the wet layer candidate segment extraction process of the present invention. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] Example 1 This invention provides a cloud base height prediction method based on multi-factor dynamic analysis. Please refer to [link / reference]. Figure 1This includes the following steps: S1. Read the corresponding numerical profile data of the target area, extract the data of each pressure layer within the range of 100 hPa above the ground, calculate the mixed layer thickness Dm and the humidity discrete change QD, and fit it into the mixed layer humidity vertical structure state set. S2. Based on the vertical structure state set of humidity in the mixed layer, perform vertical difference operation between adjacent layers on the humidity sequence to obtain the humidity change rate sequence Gk between each layer, and obtain the continuously increasing discriminant Lc and humidity return amount Bf. Finally, select the wet layer candidate segments that meet the continuously increasing condition. S3. Calculate the degree of humidity aggregation Cw and the average temperature-dew difference Ms for each candidate wet layer, and construct the potential condensation layer determination quantity Sc. Determine the location of the potential condensation layer from multiple candidate segments. At the same time, calculate the average temperature pT and average relative humidity pRH of the potential condensation layer according to the layer thickness weighting method. S4. Calculate the corresponding dew point temperature LTd based on the average temperature pT and average relative humidity pRH, and obtain the initial value of condensation height HC0 for the rise of the air parcel. At the same time, perform structural correction on the initial value of condensation height HC0 to obtain the cloud base height correction value HC1. S5. Based on the cloud base height correction value HC1 and the humidity distribution of the mixing layer below the potential condensation layer, obtain the interlayer humidity support Zs, and perform a structural consistency check on the cloud base height correction value HC1 to obtain the final cloud base height forecast result HCZ.

[0027] In this embodiment, by analyzing the vertical humidity structure within the mixing layer, the cloud base height forecast no longer relies solely on the average relative humidity of the mixing layer. Instead, it identifies the structural characteristics of humidity variations with altitude within the mixing layer, extracts continuous humidity-increasing segments, and determines the location of potential condensation layers. This allows the starting conditions for parcel uplift calculations to more accurately reflect the actual water vapor distribution within the mixing layer. In this way, even in the presence of localized wet layers or areas of high humidity, it is still possible to identify wet layer structures closer to the actual condensation locations, thus making the cloud base height calculation process more closely resemble the real atmospheric conditions.

[0028] In this embodiment, after determining the potential condensation layer, the temperature, humidity, and thickness distribution within the wet layer are comprehensively calculated, and the initial value of the condensation height is obtained by combining the air parcel uplift process. Then, the condensation height is structurally corrected based on the humidity distribution characteristics within the wet layer, so that the cloud base height calculation not only considers the average temperature and humidity conditions, but also reflects the non-uniform characteristics of the humidity distribution within the wet layer. Thus, the calculated cloud base height can reflect the influence of the humidity structure within the mixed layer on the condensation process.

[0029] This embodiment, after correcting the cloud base height, further incorporates the humidity distribution of the mixing layer beneath the potential condensation layer to perform structural consistency verification on the cloud base height results. This ensures that the final cloud base height result simultaneously reflects the matching relationship between the wet layer structural characteristics and the overall humidity environment of the mixing layer. Through comprehensive analysis of the wet layer structure, the air parcel uplift process, and the humidity environment of the mixing layer, the cloud base height forecast results can maintain good stability even in the presence of local wet layer structures. This reduces information loss caused by processing the average humidity of the mixing layer and improves the adaptability of the cloud base height forecast to changes in the vertical structure of the mixing layer humidity.

[0030] Example 2 Please refer to Figure 1 Specifically: S1 includes S11; S11. Read the profile data of the target area, perform normalization processing to eliminate the dimensions of the data, and extract the data of each pressure layer within the range of 100 hPa above the ground, including pressure layer temperature Tk, ground temperature Td, relative humidity RHk and geopotential height information Hk, where k represents the kth pressure layer number within the mixing layer range. Based on the order of pressure layer height, the pressure layers within the range of 100 hPa above the ground are arranged from low to high according to their geopotential height, forming a temperature sequence T={T1, T2, T3, ..., Tn} and a humidity sequence RH={RH1, RH2, RH3, ..., RHn}; where n represents the total number of pressure layers involved in the calculation within the range of 100 hPa above the ground. After obtaining the temperature sequence T and humidity sequence RH, the thickness Dm of the mixing layer is calculated by the potential height difference; The thickness Dm of the mixed layer is obtained by the difference between the geopotential height of the top pressure layer and the geopotential height of the bottom pressure layer. The overall distribution characteristics of the humidity structure inside the mixing layer are described, and the vertical distribution offset of humidity Sr is calculated. The humidity vertical distribution offset Sr is obtained using the following formula; ; In the formula, RHk+1 represents the relative humidity of the (k+1)th pressure layer.

[0031] S1 also includes S12; S12. After obtaining the temperature sequence T and humidity sequence RH of the mixed layer, the discrete change in humidity QD is calculated based on the humidity difference and height ratio, as shown in the following formula: ; In the formula, Hk+1 represents the geopotential height information of the (k+1)th pressure layer; The humidity discrete change QD is compared with the preset humidity discrete threshold Tqd to determine the humidity distribution state of the mixing layer. When the humidity discrete change QD < the humidity discrete threshold Tqd, it indicates that the humidity distribution in the mixing layer is uniform. When the humidity discrete change QD ≥ the humidity discrete threshold Tqd, it indicates that there is a humidity fluctuation structure inside the mixing layer; To further characterize the concentration of humidity structure in the vertical direction, the humidity accumulation amount Cr is constructed, and the formula is as follows: ; The obtained humidity accumulation amount Cr, humidity discrete change amount QD, humidity vertical distribution offset Sr and mixing layer thickness Dm are fitted to obtain the humidity vertical structure state set of the mixing layer.

[0032] In this embodiment, by reading numerical model profile data and uniformly processing the temperature and humidity information within the mixing layer, the original profile data can form complete temperature and humidity sequences in altitude order, thus providing a stable data foundation for subsequent analysis of the humidity structure within the mixing layer. By calculating the mixing layer thickness, the vertical range of the mixing layer can be clearly defined, allowing subsequent humidity structure analysis to be conducted on this important region for low cloud formation, thereby ensuring that the cloud base height analysis process corresponds to the actual atmospheric boundary layer structure.

[0033] This embodiment characterizes the overall trend of humidity variation with height within the mixing layer by calculating the vertical distribution offset of humidity. This allows the humidity distribution of the mixing layer to reflect not only the numerical state of a single layer but also the characteristics of humidity variation in the vertical direction. By calculating the discrete variation and concentration of humidity, the degree of variation and concentration of humidity in the vertical direction within the mixing layer is described, enabling the humidity distribution of the mixing layer to be characterized from both overall trends and local variations.

[0034] When there are local wet layer structures or humidity fluctuations within the mixed layer, this method can identify the uneven distribution of humidity. By constructing a set of vertical humidity structure states for the mixed layer, it can uniformly express the thickness of the mixed layer, the discrete changes in humidity, and the concentration of humidity. This provides a structured data foundation for subsequent wet layer identification and potential condensation layer analysis, enabling the cloud base height forecasting process to better reflect the true humidity distribution structure within the mixed layer.

[0035] Example 3 Please refer to Figure 2 and Figure 3 Specifically: S2 includes S21; S21. After obtaining the vertical structure state set of humidity in the mixed layer, read the humidity sequence RH and the corresponding potential height sequence H={H1, H2, ..., Hn}, and perform vertical difference operation on the humidity sequence according to the order of adjacent layers to obtain the humidity change rate sequence Gk between each layer. The humidity change rate sequence Gk is obtained by the ratio of the humidity difference between two adjacent layers to the geopotential height difference between two adjacent layers; The humidity change rate sequence Gk is easily affected by the fluctuations between model layers. After obtaining the humidity change rate sequence Gk, the humidity increment amplitude Dk of the adjacent layer and the interlayer change stationary quantity Pk are calculated. The humidity increment value Dk of the adjacent layer is obtained by the difference between the relative humidity of the (k+1)th pressure layer and the relative humidity of the kth pressure layer; The interlayer variation constant Pk is obtained by taking the absolute value of the difference between the humidity change rate sequence of the (k+1)th pressure layer and the humidity change rate sequence of the kth pressure layer.

[0036] S2 also includes S22; S22. After obtaining the humidity increment amplitude Dk of adjacent layers and the interlayer change stability Pk, for each continuous layer segment composed of positive humidity change rate, further calculate the coherence of the internal humidity structure, and obtain the continuously increasing discriminant Lc(i,j), humidity return amount Bf(i,j), and consistency U(i,j) of adjacent gradient changes for the candidate layer segment (i,j); where i represents the starting layer number of the candidate wet layer segment, and j represents the ending layer number of the candidate wet layer segment. The formula for obtaining the continuously increasing discriminant Lc(i,j) of candidate segment (i,j) is: ; In the formula, This represents the cumulative value of all positively humidified portions within the candidate layer. This represents the absolute value of the net humidity change across the entire candidate layer. The formula for obtaining the humidity reflection amount Bf(i,j) of candidate segment (i,j) is: ; In the formula, min(Dk, 0) represents the original value when the interlayer humidity difference is negative, and 0 otherwise; The formula for obtaining the consistency U(i,j) of adjacent gradient changes is: ; A unified analysis is performed on the continuously increasing discriminant Lc(i,j), humidity return amount Bf(i,j), and consistency U(i,j) of adjacent gradient changes of the obtained candidate layer segments (i,j) to construct the candidate wet layer judgment value W(i,j). The formula for obtaining the candidate wet layer determination value W(i,j) is as follows: ; Analyze the candidate wet layer determination value W(i,j) to determine whether the candidate layer segment (i,j) satisfies the condition of continuous increase of the wet layer candidate segment; When the candidate wet layer judgment value W(i,j) is less than the preset judgment threshold, it means that although the segment has local humidification, the internal structure is incomplete or has fallen back a lot, and it does not enter the wet layer candidate segment. When the candidate wet layer judgment value W(i,j) is greater than or equal to the preset judgment threshold, it indicates that the layer segment has the following characteristics: overall upward humidification; less internal drop; smooth transition of humidity change rate; and humidification is not caused by a sudden increase in a single layer. The segment is retained as a candidate segment for wet layer.

[0037] In this embodiment, after obtaining the vertical structure state set of humidity in the mixed layer, the humidity change rate information between each pressure layer is obtained by performing vertical difference operations on the humidity sequence of adjacent layers. This allows humidity changes to be described not only by single-layer values ​​but also to reflect the trend characteristics of humidity changes with altitude. By introducing the humidity increment amplitude of adjacent layers and the interlayer change stationary value, the humidity change process is further characterized, enabling the change of humidity in the vertical direction to simultaneously reflect the change amplitude and stability, thereby reducing the impact of small interlayer fluctuations in the numerical model on the judgment of humidity structure.

[0038] This embodiment further analyzes the internal humidity structure of continuous layers with positive humidity change rates. By calculating the continuously increasing discriminant, humidity return, and consistency of adjacent gradient changes, the humidity change process within candidate layers is comprehensively evaluated. This processing method can not only identify layers where humidity increases overall with altitude, but also determine whether the increasing process is continuous and stable. This avoids misidentifying local anomalous layers formed by sudden increases in humidity in individual pressure layers as effective wet layer structures, making the wet layer identification process more consistent with the actual water vapor distribution within the mixed layer.

[0039] By constructing candidate wet layer criteria and combining them with preset thresholds to conduct a unified analysis of each candidate layer, the wet layer screening process has clear criteria. When a layer simultaneously exhibits characteristics such as overall humidification, minimal internal humidification, and smooth transitions in humidity changes, that layer is identified as a candidate wet layer segment.

[0040] Example 4 Please refer to Figure 1 Specifically: S3 includes S31; S31. Extract the pressure layer temperature Tk, relative humidity RHk, and geopotential height information Hk corresponding to each candidate wet layer segment. Statistically analyze the humidity distribution within the segment and calculate the humidity aggregation degree Cw, using the following formula: ; In the formula, RHmax represents the maximum relative humidity value within the mixing layer range, and Cw(i,j) represents the humidity aggregation degree of candidate segment (i,j); Calculate the dew point temperature Tdk of each layer based on temperature and humidity data, and calculate the average temperature-dew difference Ms of the candidate section by using the difference between the dew point temperature and the air temperature. Dew point temperature Tdk is obtained using the following formula: ; The average temperature-dew difference Ms is obtained as follows: The starting and ending pressure layers of the wet layer candidate segment are determined; the air temperature and corresponding dew point temperature of each pressure layer within the segment are read sequentially; then, the difference between the temperature and dew point temperature of each layer within the segment is calculated, i.e., the layer air temperature is subtracted from the layer dew point temperature to obtain the temperature-dew difference; after obtaining the temperature-dew differences of all pressure layers within the segment, these temperature-dew difference values ​​are accumulated layer by layer to obtain the total temperature-dew difference within the wet layer segment; subsequently, the number of pressure layers contained in the segment is calculated; finally, the total temperature-dew difference is divided by the number of layers in the segment to obtain the average temperature-dew difference Ms within the wet layer candidate segment. The obtained humidity polymerization degree Cw and average temperature-dew difference Ms are integrated to calculate the potential condensation layer determination quantity Sc, as shown in the following formula: ; In the formula, Sc(i,j) represents the potential condensation layer determination quantity of candidate segment (i,j), and Ms(i,j) represents the average temperature-dew difference of candidate segment (i,j).

[0041] S3 also includes S32; S32. After obtaining the potential condensation layer determination value Sc for all candidate sections, compare the values ​​of the condensation layer determination value Sc for all candidate sections, determine the candidate section with the largest value as the potential condensation layer section, and record its starting layer number and ending layer number as iz and jz respectively, and calculate the average temperature pT and average relative humidity pRH of the potential condensation layer according to the layer thickness weighting method. The formula for obtaining the average temperature pT is: ; The formula for obtaining average relative humidity (pRH) is: ; The obtained average temperature pT and average relative humidity pRH are used as the starting condition parameters for the calculation of air parcel lifting and are input into step S4.

[0042] In this embodiment, after identifying candidate wet layer segments, a comprehensive analysis of the temperature, humidity, and altitude distribution within each segment is performed. The degree of humidity aggregation is calculated, and the condensation conditions of the candidate segments are evaluated by combining the temperature-dew difference characteristics. This ensures that the determination of the wet layer structure considers not only humidity levels but also the degree to which the air is close to saturation. By simultaneously introducing the degree of humidity aggregation and temperature-dew difference characteristics, a unified analysis of multiple candidate wet layer segments within the mixed layer is conducted. This allows for a more accurate identification of wet layer regions that more closely approximate actual condensation conditions, making the determination of potential condensation layers more consistent with the actual distribution of water vapor and the thermal structure of the atmosphere.

[0043] This embodiment constructs a potential condensation layer determination metric to uniformly compare multiple candidate wet layer segments, providing a clear basis for determining the potential condensation layer. In this way, the segment most conducive to water vapor condensation can be selected from multiple possible wet layer structures, thus avoiding misjudgments caused by local humidity fluctuations or single-layer humidity changes. This allows the location of the potential condensation layer to more stably reflect the true humidity structure within the mixed layer.

[0044] After identifying the potential condensation layer, the average temperature and average relative humidity of this layer are calculated using a thickness-weighted method. This allows temperature and humidity information from different heights within the potential condensation layer to be included in the calculation based on their actual thickness, thus providing a more accurate reflection of the overall thermal state of the potential condensation layer. By using this average temperature and humidity as the starting parameter for calculating parcel uplift, subsequent cloud base height calculations can better reflect the overall water vapor environment of the potential condensation layer, making the cloud base height forecast results more consistent with the vertical humidity structure variation characteristics within the mixing layer.

[0045] Example 5 Please refer to Figure 2 Specifically: S4 includes S41 and S42; S41. Based on the average temperature pT and average relative humidity pRH, calculate the dew point temperature LTd corresponding to the potential condensation layer air. The dew point temperature LTd is obtained by converting the average relative humidity into a proportional form and then performing a natural logarithmic calculation. At the same time, the temperature term is calculated based on the proportional relationship between the average temperature and the constant. The two are then combined to calculate the dew point temperature LTd. The initial value of the condensation height of the gas parcel is calculated by combining the dew point temperature LTd with the average temperature pT, as follows: HC0 = Hiz + KP × (pT - LTd); where Hiz represents the initial height of the gas parcel rise, and KP represents the conversion coefficient between the temperature-dew difference and the condensation height.

[0046] S4 also includes S42; S42. After obtaining the initial value of the condensation height of the gas parcel, HC0, read the relative humidity and corresponding geopotential height of each pressure layer inside the potential condensation layer, and calculate the humidity dispersion degree Dh inside the potential condensation layer. The humidity dispersion degree Dh is obtained as follows: First, the starting and ending pressure layers of the potential condensation layer are determined, and the number of pressure layers contained within the segment is counted; then, the relative humidity of each layer within the segment is read sequentially, and the difference between the relative humidity of each layer and the average relative humidity of the potential condensation layer is calculated; next, the humidity difference obtained for each layer is squared; then, the squared values ​​of the humidity differences of all pressure layers within the segment are summed; finally, the summation result is divided by the number of pressure layers contained in the segment to obtain the humidity dispersion degree within the potential condensation layer. Identify the pressure layer with the highest relative humidity inside the potential condensation layer, denoted as RHmax and the corresponding geopotential height as Hmax, and calculate the humidity structure correction Rc. The humidity structure correction Rc is obtained as follows: First, the maximum relative humidity value and the geopotential height of the corresponding pressure layer in the potential condensation layer are read, and the difference between the maximum humidity value and the average relative humidity of the potential condensation layer is calculated. At the same time, the height difference between the geopotential height of the maximum humidity layer and the geopotential height of the lifting initiation layer is calculated. Then, the humidity difference and the height difference are multiplied, and the result is divided by the remaining humidity range (100-pRH) of the air distance from saturation to obtain the humidity structure correction Rc. The obtained humidity dispersion degree Dh and humidity structure correction amount Rc are applied to the initial value of condensation height HC0 and corrected to obtain the cloud base height correction value HC1, as follows: HC1=HC0-Rc+λ×Dh; where λ represents the adjustment coefficient of the influence of humidity dispersion degree on condensation height.

[0047] S5 includes S51 and S52; S51. Read the relative humidity and corresponding geopotential height of each pressure layer within the mixing layer range below the potential condensation layer, and perform statistical calculations on the humidity distribution of each layer below the potential condensation layer to obtain the interlayer humidity support Zs. The method for obtaining the interlayer humidity support Zs is as follows: First, read the relative humidity and corresponding interlayer height difference of each pressure layer in the mixing layer below the potential condensation layer. Then, multiply the relative humidity and the layer height difference of each layer and accumulate them layer by layer to obtain the cumulative amount of humidity and layer thickness. At the same time, accumulate all the interlayer height differences to obtain the total layer thickness. Finally, divide the cumulative humidity by the total layer thickness to obtain the interlayer humidity support Zs of the air below the mixing layer. S52. Based on the interlayer humidity support Zs, calculate the humidity connection relationship Js between the potential condensation layer and the underlying mixing layer. Combine the continuously increasing discrimination Lc and the average temperature-dew difference Ms to construct the structural verification correction Rs. Perform structural consistency verification on the cloud base height correction value HC1 to obtain the final cloud base height forecast result HCZ. The humidity linkage quantity Js is obtained as follows: First, calculate the difference between the average relative humidity of the potential condensation layer and the humidity support of the underlying mixing layer; then calculate the height difference between the bottom height of the potential condensation layer and the bottom height of the mixing layer, and add a very small constant to the height difference to avoid the denominator being zero; finally, divide the humidity difference by the height difference to obtain the humidity linkage quantity Js per unit height. The formula for obtaining the structural correction amount Rs is as follows:

[0048] In the formula, Hiz represents the initial height of the air parcel lift, and Hjz represents the final height of the air parcel lift; The formula for obtaining the final cloud base height forecast result HCZ is: HCZ=HC1-Rs.

[0049] In this embodiment, after determining the potential condensation layer, the dew point temperature is calculated based on the average temperature and average relative humidity of the potential condensation layer. An initial value of the condensation height due to parcel uplift is obtained by combining this with the initial height of the parcel uplift. This allows the cloud base height calculation to directly reflect the influence of the overall temperature and humidity conditions of the potential condensation layer on the condensation process. In this way, a correspondence is established between the thermal state of the potential condensation layer and the parcel uplift process. This means that the condensation height calculation no longer relies solely on single-layer meteorological parameters, but is based on an analysis of the overall temperature and humidity environment of the potential condensation layer. Therefore, the cloud base height estimation can more closely approximate the actual water vapor conditions within the mixed layer.

[0050] By statistically analyzing the humidity distribution within the potential condensation layer and calculating the degree of humidity dispersion, a humidity structure correction factor is constructed based on the location of the maximum humidity layer within the potential condensation layer. This allows the cloud base height to further reflect the differences in water vapor distribution within the moist layer, building upon the initial condensation height. When localized high-humidity areas or uneven humidity variations exist within the potential condensation layer, this method can correct the condensation height accordingly, ensuring that the cloud base height result reflects the influence of local humidity structure within the moist layer and reducing biases arising from calculations based solely on average temperature and humidity conditions.

[0051] After obtaining the cloud base height correction value, this embodiment further combines the humidity distribution of the mixing layer below the potential condensation layer with the calculation of interlayer humidity support and the humidity connection relationship between the potential condensation layer and the underlying mixing layer to perform structural consistency verification of the cloud base height. By incorporating structural information such as the degree of wet layer continuity, temperature-dew difference, and wet layer thickness into the verification process, the final cloud base height result can simultaneously reflect the matching relationship between the structural characteristics of the potential condensation layer and the humidity environment of the underlying mixing layer. This allows for relatively stable cloud base height forecast results even when there are local wet layers or changes in humidity structure within the mixing layer, improving the adaptability of cloud base height forecasts to changes in the vertical structure of humidity in the mixing layer.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended technical solutions and their equivalents.

Claims

1. A cloud base height prediction method based on multi-factor dynamic analysis, characterized in that: Includes the following steps: S1. Read the corresponding numerical profile data of the target area, extract the data of each pressure layer within the range of 100 hPa above the ground, calculate the mixed layer thickness Dm and the humidity discrete change QD, and fit it into the mixed layer humidity vertical structure state set. S2. Based on the vertical structure state set of humidity in the mixed layer, perform vertical difference operation between adjacent layers on the humidity sequence to obtain the humidity change rate sequence Gk between each layer, and obtain the continuously increasing discriminant Lc and humidity return amount Bf. Finally, select the wet layer candidate segments that meet the continuously increasing condition. S3. Calculate the degree of humidity aggregation Cw and the average temperature-dew difference Ms for each candidate wet layer, and construct the potential condensation layer determination quantity Sc. Determine the location of the potential condensation layer from multiple candidate segments. At the same time, calculate the average temperature pT and average relative humidity pRH of the potential condensation layer according to the layer thickness weighting method. S4. Calculate the corresponding dew point temperature LTd based on the average temperature pT and average relative humidity pRH, and obtain the initial value of condensation height HC0 for the rise of the air parcel. At the same time, perform structural correction on the initial value of condensation height HC0 to obtain the cloud base height correction value HC1. S5. Based on the cloud base height correction value HC1 and the humidity distribution of the mixing layer below the potential condensation layer, obtain the interlayer humidity support Zs, and perform a structural consistency check on the cloud base height correction value HC1 to obtain the final cloud base height forecast result HCZ.

2. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 1, characterized in that: S1 includes S11; S11. Read the profile data of the target area, perform normalization processing to eliminate the dimensions of the data, and extract the data of each pressure layer within the range of 100 hPa above the ground, including pressure layer temperature Tk, ground temperature Td, relative humidity RHk and geopotential height information Hk, where k represents the kth pressure layer number within the mixing layer range. Based on the order of pressure layer height, the pressure layers within the range of 100 hPa above the ground are arranged from low to high according to their geopotential height, forming a temperature sequence T={T1, T2, T3, ..., Tn} and a humidity sequence RH={RH1, RH2, RH3, ..., RHn}; where n represents the total number of pressure layers involved in the calculation within the range of 100 hPa above the ground. After obtaining the temperature sequence T and humidity sequence RH, the thickness Dm of the mixing layer is calculated by the potential height difference; The thickness Dm of the mixed layer is obtained by the difference between the geopotential height of the top pressure layer and the geopotential height of the bottom pressure layer. The overall distribution characteristics of the humidity structure inside the mixing layer are described, and the vertical distribution offset of humidity Sr is calculated. The humidity vertical distribution offset Sr is obtained using the following formula; ； In the formula, RHk+1 represents the relative humidity of the (k+1)th pressure layer.

3. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 2, characterized in that: S1 also includes S12; S12. After obtaining the temperature sequence T and humidity sequence RH of the mixed layer, the discrete change in humidity QD is calculated based on the humidity difference and height ratio, as shown in the following formula: ； In the formula, Hk+1 represents the geopotential height information of the (k+1)th pressure layer; The humidity discrete change QD is compared with the preset humidity discrete threshold Tqd to determine the humidity distribution state of the mixing layer. When the humidity discrete change QD < the humidity discrete threshold Tqd, it indicates that the humidity distribution in the mixing layer is uniform. When the humidity discrete change QD ≥ the humidity discrete threshold Tqd, it indicates that there is a humidity fluctuation structure inside the mixing layer; To further characterize the concentration of humidity structure in the vertical direction, the humidity accumulation amount Cr is constructed, and the formula is as follows: ； The obtained humidity accumulation amount Cr, humidity discrete change amount QD, humidity vertical distribution offset Sr and mixing layer thickness Dm are fitted to obtain the humidity vertical structure state set of the mixing layer.

4. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 3, characterized in that: S2 includes S21; S21. After obtaining the vertical structure state set of humidity in the mixed layer, read the humidity sequence RH and the corresponding potential height sequence H={H1, H2, ..., Hn}, and perform vertical difference operation on the humidity sequence according to the order of adjacent layers to obtain the humidity change rate sequence Gk between each layer. The humidity change rate sequence Gk is obtained by the ratio of the humidity difference between two adjacent layers to the geopotential height difference between two adjacent layers; The humidity change rate sequence Gk is easily affected by the fluctuations between model layers. After obtaining the humidity change rate sequence Gk, the humidity increment amplitude Dk of the adjacent layer and the interlayer change stationary amount Pk are calculated. The humidity increment value Dk of the adjacent layer is obtained by the difference between the relative humidity of the (k+1)th pressure layer and the relative humidity of the kth pressure layer; The interlayer variation constant Pk is obtained by taking the absolute value of the difference between the humidity change rate sequence of the (k+1)th pressure layer and the humidity change rate sequence of the kth pressure layer.

5. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 4, characterized in that: S2 also includes S22; S22. After obtaining the humidity increment amplitude Dk of adjacent layers and the interlayer change stability Pk, for each continuous layer segment composed of positive humidity change rate, further calculate the coherence of the internal humidity structure, and obtain the continuously increasing discriminant Lc(i,j), humidity return amount Bf(i,j), and consistency U(i,j) of adjacent gradient changes for the candidate layer segment (i,j); where i represents the starting layer number of the candidate wet layer segment, and j represents the ending layer number of the candidate wet layer segment. The formula for obtaining the continuously increasing discriminant Lc(i,j) of candidate segment (i,j) is: ； In the formula, This represents the cumulative value of all positive humidification components within the candidate layer. This represents the absolute value of the net humidity change across the entire candidate layer. The formula for obtaining the humidity reflection amount Bf(i,j) of candidate segment (i,j) is: ； In the formula, min(Dk, 0) represents the original value when the interlayer humidity difference is negative, and 0 otherwise; The formula for obtaining the consistency U(i,j) of adjacent gradient changes is: ； A unified analysis is performed on the continuously increasing discriminant Lc(i,j), humidity return amount Bf(i,j), and consistency U(i,j) of adjacent gradient changes of the obtained candidate layer segments (i,j) to construct the candidate wet layer judgment value W(i,j). The formula for obtaining the candidate wet layer determination value W(i,j) is as follows: ； Analyze the candidate wet layer determination value W(i,j) to determine whether the candidate layer segment (i,j) satisfies the condition of continuous increase of the wet layer candidate segment; When the candidate wet layer judgment value W(i,j) is less than the preset judgment threshold, it means that although the segment has local humidification, the internal structure is incomplete or has fallen back a lot, and it does not enter the wet layer candidate segment. When the candidate wet layer judgment value W(i,j) is greater than or equal to the preset judgment threshold, it indicates that the layer segment has the following characteristics: overall upward humidification; less internal drop; smooth transition of humidity change rate; and humidification is not caused by a sudden increase in a single layer. The segment is retained as a candidate segment for wet layer.

6. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 5, characterized in that: S3 includes S31; S31. Extract the pressure layer temperature Tk, relative humidity RHk, and geopotential height information Hk corresponding to each candidate wet layer segment. Statistically analyze the humidity distribution within the segment and calculate the humidity aggregation degree Cw, using the following formula: ； In the formula, RHmax represents the maximum relative humidity value within the mixing layer range, and Cw(i,j) represents the humidity aggregation degree of candidate segment (i,j); Calculate the dew point temperature Tdk of each layer based on temperature and humidity data, and calculate the average temperature-dew difference Ms of the candidate section by using the difference between the dew point temperature and the air temperature. Dew point temperature Tdk is obtained using the following formula: ； The average temperature-dew difference Ms is obtained as follows: The starting and ending pressure layers of the wet layer candidate segment are determined; the air temperature and corresponding dew point temperature of each pressure layer within the segment are read sequentially; then, the difference between the temperature and dew point temperature of each layer within the segment is calculated, i.e., the layer air temperature is subtracted from the layer dew point temperature to obtain the temperature-dew difference; after obtaining the temperature-dew differences of all pressure layers within the segment, these temperature-dew difference values ​​are accumulated layer by layer to obtain the total temperature-dew difference within the wet layer segment; subsequently, the number of pressure layers contained in the segment is calculated; finally, the total temperature-dew difference is divided by the number of layers in the segment to obtain the average temperature-dew difference Ms within the wet layer candidate segment. The obtained humidity polymerization degree Cw and average temperature-dew difference Ms are integrated to calculate the potential condensation layer determination quantity Sc, as shown in the following formula: ； In the formula, Sc(i,j) represents the potential condensation layer determination quantity of candidate segment (i,j), and Ms(i,j) represents the average temperature-dew difference of candidate segment (i,j).

7. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 6, characterized in that: S3 also includes S32; S32. After obtaining the potential condensation layer determination value Sc for all candidate sections, compare the values ​​of the condensation layer determination value Sc for all candidate sections, determine the candidate section with the largest value as the potential condensation layer section, and record its starting layer number and ending layer number as iz and jz respectively, and calculate the average temperature pT and average relative humidity pRH of the potential condensation layer according to the layer thickness weighting method. The formula for obtaining the average temperature pT is: ； The formula for obtaining average relative humidity (pRH) is: ； The obtained average temperature pT and average relative humidity pRH are used as the starting condition parameters for the calculation of air parcel lifting and are input into step S4.

8. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 7, characterized in that: S4 includes S41 and S42; S41. Based on the average temperature pT and average relative humidity pRH, calculate the dew point temperature LTd corresponding to the potential condensation layer air. The dew point temperature LTd is obtained by converting the average relative humidity into a proportional form and then performing a natural logarithmic calculation. At the same time, the temperature term is calculated based on the proportional relationship between the average temperature and the constant. The two are then combined to calculate the dew point temperature LTd. The initial value of the condensation height of the gas parcel is calculated by combining the dew point temperature LTd with the average temperature pT, as follows: HC0 = Hiz + KP × (pT - LTd); where Hiz represents the initial height of the gas parcel rise, and KP represents the conversion coefficient between the temperature-dew difference and the condensation height.

9. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 8, characterized in that: S4 also includes S42; S42. After obtaining the initial value of the condensation height of the gas parcel, HC0, read the relative humidity and corresponding geopotential height of each pressure layer inside the potential condensation layer, and calculate the humidity dispersion degree Dh inside the potential condensation layer. The humidity dispersion degree Dh is obtained as follows: First, the starting and ending pressure layers of the potential condensation layer are determined, and the number of pressure layers contained within the segment is counted; then, the relative humidity of each layer within the segment is read sequentially, and the difference between the relative humidity of each layer and the average relative humidity of the potential condensation layer is calculated; next, the humidity difference obtained for each layer is squared; then, the squared values ​​of the humidity differences of all pressure layers within the segment are summed; finally, the summation result is divided by the number of pressure layers contained in the segment to obtain the humidity dispersion degree within the potential condensation layer. Identify the pressure layer with the highest relative humidity inside the potential condensation layer, denoted as RHmax and the corresponding geopotential height as Hmax, and calculate the humidity structure correction Rc. The humidity structure correction Rc is obtained as follows: First, the maximum relative humidity value and the geopotential height of the corresponding pressure layer in the potential condensation layer are read, and the difference between the maximum humidity value and the average relative humidity of the potential condensation layer is calculated. At the same time, the height difference between the geopotential height of the maximum humidity layer and the geopotential height of the lifting initiation layer is calculated. Then, the humidity difference and the height difference are multiplied, and the result is divided by the remaining humidity range (100-pRH) of the air distance from saturation to obtain the humidity structure correction Rc. The obtained humidity dispersion degree Dh and humidity structure correction amount Rc are applied to the initial value of condensation height HC0 and corrected to obtain the cloud base height correction value HC1, as follows: HC1=HC0-Rc+λ×Dh; where λ represents the adjustment coefficient of the influence of humidity dispersion degree on condensation height.

10. The cloud base height prediction method based on multi-factor dynamic analysis according to claim 9, characterized in that: S5 includes S51 and S52; S51. Read the relative humidity and corresponding geopotential height of each pressure layer within the mixing layer range below the potential condensation layer, and perform statistical calculations on the humidity distribution of each layer below the potential condensation layer to obtain the interlayer humidity support Zs. The method for obtaining the interlayer humidity support Zs is as follows: First, read the relative humidity and corresponding interlayer height difference of each pressure layer in the mixing layer below the potential condensation layer. Then, multiply the relative humidity and the layer height difference of each layer and accumulate them layer by layer to obtain the cumulative amount of humidity and layer thickness. At the same time, accumulate all the interlayer height differences to obtain the total layer thickness. Finally, divide the cumulative humidity by the total layer thickness to obtain the interlayer humidity support Zs of the air below the mixing layer. S52. Based on the interlayer humidity support Zs, calculate the humidity connection relationship Js between the potential condensation layer and the underlying mixing layer. Combine the continuously increasing discrimination Lc and the average temperature-dew difference Ms to construct the structural verification correction Rs. Perform structural consistency verification on the cloud base height correction value HC1 to obtain the final cloud base height forecast result HCZ. The humidity linkage quantity Js is obtained as follows: First, calculate the difference between the average relative humidity of the potential condensation layer and the humidity support of the underlying mixing layer; then calculate the height difference between the bottom height of the potential condensation layer and the bottom height of the mixing layer, and add a very small constant to the height difference to avoid the denominator being zero; finally, divide the humidity difference by the height difference to obtain the humidity linkage quantity Js per unit height. The formula for obtaining the structural correction amount Rs is as follows: ; In the formula, Hiz represents the initial height of the air parcel lift, and Hjz represents the final height of the air parcel lift; The formula for obtaining the final cloud base height forecast result HCZ is: HCZ=HC1-Rs.

Images