CN-122018046-A - Cloud base height forecasting method based on multi-factor dynamic analysis

CN122018046ACN 122018046 ACN122018046 ACN 122018046ACN-122018046-A

Abstract

The invention discloses a cloud bottom height forecasting method based on multi-factor dynamic analysis, which relates to the technical field of weather numerical forecasting and atmospheric boundary layer analysis, and aims to more accurately reflect the actual water vapor distribution condition in a mixed layer by extracting a continuous humidity increasing section and determining the position of a potential condensation layer through identifying the structural characteristics of the internal humidity of the mixed layer along with the height change in the cloud bottom height forecasting process instead of only depending on the average relative humidity of the mixed layer. Under the condition that a local wet layer or a local high-humidity area exists, a wet layer structure which is closer to the actual condensation occurrence position can be still identified, so that the cloud bottom height calculation process is closer to the actual atmosphere state, the vertical range of the mixed layer can be definitely limited by calculating the thickness of the mixed layer, the subsequent humidity structure analysis can be carried out on an important area formed by the mixed layer, which is a low cloud, and the cloud bottom height analysis process corresponds to the actual atmosphere boundary layer structure.

Inventors

LIU XINHUA
YANG BO
ZHANG YANI
MAO XU
CAI XUEWEI
MAI ZI
LI ZHONGKUN
YAN DACHUN

Assignees

国家气象中心(中央气象台、中国气象局气象导航中心)

Dates

Publication Date: 20260512
Application Date: 20260401

Claims (10)

1. The cloud bottom height forecasting method based on multi-factor dynamic analysis is characterized by comprising the following steps of: S1, reading report section data of corresponding values of a target area, extracting data of each air pressure layer in a range from the ground to 100hPa above, calculating the thickness Dm of the mixed layer and the discrete variation QD of the humidity, and fitting the mixed layer into a state set of a vertical structure of the humidity of the mixed layer; S2, based on a mixed layer humidity vertical structure state set, performing adjacent layer vertical difference operation on a humidity sequence, obtaining a humidity change rate sequence Gk between layers, obtaining a continuous increment discriminant Lc and a humidity turning-back quantity Bf, and finally screening out a wet layer candidate section meeting a continuous increment condition; S3, calculating the humidity polymerization degree Cw and the average temperature dew difference Ms of each wet layer candidate section, constructing a potential condensation layer judgment quantity Sc, determining the position of a potential condensation layer from a plurality of candidate sections, and calculating the average temperature pT and average humidity pRH of the potential condensation layer according to a layer thickness weighting mode; s4, calculating a corresponding dew point temperature LTd according to the average temperature pT and the average humidity pRH, acquiring a condensation height initial value HC0 of air block lifting, and carrying out structural correction on the condensation height initial value HC0 to obtain a cloud base height correction value HC1; S5, based on the cloud bottom height correction value HC1, according to the humidity distribution of the mixed layer below the potential condensation layer, the interlayer humidity support amount Zs is obtained, and structural consistency check is carried out on the cloud bottom height correction value HC1, so that a final cloud bottom height forecast result HCZ is obtained.
2. The method for forecasting the cloud bottom height based on the multi-factor dynamic analysis, as set forth in claim 1, wherein S1 comprises S11; S11, reading profile data of a target area, agreeing to perform normalization processing, eliminating the dimension of the data, and extracting data of each air pressure layer in a range from the ground to the upper 100hPa, wherein the data comprise air pressure layer temperature Tk, ground temperature Td, relative humidity RHk and potential height information Hk, and k represents a kth air pressure layer serial number in a mixed layer range; According to the height sequence of the air pressure layers, arranging the air pressure layers in the range from the ground to the upper 100hPa from low to high according to the potential height to form a temperature sequence T= { T1, T2, T3, & gt, tn } and a humidity sequence RH= { RH1, RH2, RH3, & gt, RHn }, wherein n represents the total number of the air pressure layers which are calculated by the reference in the range from the ground to the upper 100 hPa; after a temperature sequence T and a humidity sequence RH are obtained, the thickness Dm of the mixed layer is obtained through potential height difference calculation; the thickness Dm of the combined layer is obtained through the difference value between the potential height of the air pressure layer at the top of the mixed layer and the potential height of the air pressure layer at the bottom of the mixed layer; Describing the overall distribution characteristics of the humidity structure in the mixed layer, and calculating and obtaining the vertical distribution offset Sr of the humidity; the humidity vertical distribution offset Sr is obtained by the following formula; ; Wherein RHk +1 represents the relative humidity of the (k+1) -th barometric layer.
3. The method for forecasting the cloud bottom height based on the multi-factor dynamic analysis, as claimed in claim 2, wherein S1 further comprises S12; s12, after the mixed layer temperature sequence T and the humidity sequence RH are obtained, calculating and obtaining a discrete humidity change QD based on a humidity difference and a height proportion, wherein the formula is as follows: ; Wherein hk+1 represents potential height information of the (k+1) -th barometric layer; Comparing the humidity discrete change QD with a preset humidity discrete threshold Tqd to judge the humidity distribution state of the mixed layer; When the humidity discrete variation QD is smaller than the humidity discrete threshold Tqd, the humidity of the mixed layer is uniformly distributed; When the humidity discrete variation QD is more than or equal to the humidity discrete threshold Tqd, a humidity fluctuation structure exists in the mixed layer; in order to further describe the concentration degree of the humidity structure in the vertical direction, a humidity concentration amount Cr is constructed, and the formula is as follows: ; fitting the acquired humidity aggregation Cr, humidity discrete variation QD, humidity vertical distribution offset Sr and mixed layer thickness Dm to acquire a mixed layer humidity vertical structure state set.
4. The method for forecasting the cloud base height based on the multi-factor dynamic analysis of claim 3, wherein S2 comprises S21; S21, after a mixed layer humidity vertical structure state set is obtained, reading a humidity sequence RH and a corresponding potential height sequence H= { H1, H2, & gt, hn }, and executing vertical differential operation on the humidity sequence according to the sequence of adjacent layers to obtain a humidity change rate sequence Gk between layers; the humidity change rate sequence Gk is obtained through the ratio of the humidity difference of two adjacent layers to the potential height difference between the two adjacent layers; The humidity change rate sequence Gk is easily affected by fluctuation among the mode layers, and after the humidity change rate sequence Gk is obtained, the humidity increment amplitude Dk of the adjacent layer and the interlayer change stable quantity Pk are calculated and obtained; the humidity increment amplitude Dk of the adjacent layer is obtained through the difference value between the relative humidity of the (k+1) th air pressure layer and the relative humidity of the (k) th air pressure layer; the interlayer change stable quantity Pk is obtained by taking the absolute value through the difference value between the humidity change rate sequence of the kth+1th air pressure layer and the humidity change rate sequence of the kth air pressure layer.
5. The method for forecasting the cloud bottom height based on the multi-factor dynamic analysis of claim 4, wherein S2 further comprises S22; s22, after acquiring the adjacent layer humidity increment amplitude Dk and the interlayer change stable quantity Pk, further calculating the continuity degree of an internal humidity structure for each continuous layer section formed by the forward humidity change rate, and acquiring the continuous increment discriminant Lc (i, j) of the candidate layer section (i, j), the humidity turn-back quantity Bf (i, j) and the consistency U (i, j) of adjacent gradient change, wherein i represents the initial layer number of the candidate wet layer section and j represents the termination layer number of the candidate wet layer section; The acquisition formula of the continuous increment discriminant Lc (i, j) of the candidate layer segment (i, j) is: ; In the formula, Representing the cumulative value of all forward humidification portions within the candidate interval, Representing absolute values of the net-humidity change over the candidate interval; The acquisition formula of the humidity return quantity Bf (i, j) of the candidate layer section (i, j) is as follows: ; wherein, min (Dk, 0) represents that the original value is taken when the interlayer humidity difference is negative, otherwise, 0 is taken; the acquisition formula of the consistency U (i, j) of adjacent gradient changes is as follows: ; Uniformly analyzing the obtained continuous incremental discriminant Lc (i, j) of the candidate layer segment (i, j), the humidity turning-back quantity Bf (i, j) and the consistency U (i, j) of adjacent gradient changes to construct a candidate wet layer judgment value W (i, j); The acquisition formula of the candidate wet layer determination value W (i, j) is as follows: ; Analyzing the candidate wet layer judgment value W (i, j) to judge whether the candidate layer section (i, j) meets the wet layer candidate section of the continuous increasing condition; When the candidate wet layer judgment value W (i, j) is smaller than a preset judgment threshold value, the fact that the layer section is partially humidified but the internal tissues are incomplete or fall back more does not enter the wet layer candidate section is indicated; When the candidate wet layer judgment value W (i, j) is larger than or equal to a preset judgment threshold value, the layer section is indicated to have the characteristics that the whole layer section is humidified upwards, the internal fallback is less, the connection of the humidity change rate is smoother, and the humidification is not caused by single-layer sudden increase; the interval is retained as a wet layer candidate segment.
6. The method for forecasting the cloud bottom height based on the multi-factor dynamic analysis of claim 5, wherein S3 comprises S31; S31, extracting air pressure layer temperature Tk, relative humidity RHk and potential height information Hk corresponding to each wet layer candidate section, counting the humidity distribution in the section, and calculating to obtain humidity polymerization degree Cw, wherein the formula is as follows: ; wherein RHmax represents the maximum relative humidity value within the mixed layer range, and Cw (i, j) represents the humidity polymerization degree of the candidate layer segment (i, j); calculating dew point temperature Tdk of each layer according to the temperature and humidity data, and calculating average temperature dew difference Ms of the candidate section according to the difference between the dew point temperature and the air temperature; dew point temperature Tdk is obtained by the following formula: ; The average temperature dew difference Ms is obtained by determining an initial air pressure layer and a final air pressure layer of a wet layer candidate section, sequentially reading the air temperature and the corresponding dew point temperature of each air pressure layer in the section, then respectively calculating the difference between the temperature and the dew point temperature of each layer in the section, namely subtracting the layer dew point temperature from the layer air temperature to obtain Wen Loucha, accumulating the temperature dew difference values layer by layer after obtaining the temperature dew difference of all the air pressure layers in the section to obtain the total temperature dew difference in the wet layer section, then calculating the number of the air pressure layers contained in the section, and finally dividing the Wen Loucha total sum by the layer number of the section to obtain the average temperature dew difference Ms in the wet layer candidate section; Integrating the acquired humidity polymerization degree Cw and the average Wen Lou difference Ms, and calculating to acquire the potential condensation layer judgment quantity Sc, wherein the formula is as follows: ; Where Sc (i, j) represents the potential condensation layer determination amount of the candidate interval (i, j), and Ms (i, j) represents the average temperature dew difference of the candidate interval (i, j).
7. The method for forecasting the cloud bottom height based on the multi-factor dynamic analysis of claim 6, wherein S3 further comprises S32; S32, after obtaining potential condensation layer judgment amounts Sc of all candidate sections, comparing the values of the condensation layer judgment amounts Sc of all candidate sections, determining the candidate section with the largest value as a potential condensation layer section, marking the initial layer number and the termination layer number as iz and jz respectively, and calculating the average temperature pT and average humidity pRH of the potential condensation layer according to a layer thickness weighting mode; The average temperature pT is obtained by the following formula: ; the average relative humidity pRH is obtained by the following formula: ; The obtained average temperature pT and average humidity pRH are inputted into step S4 as starting condition parameters for the air lock lifting calculation.
8. The method for forecasting the cloud base height based on the multi-factor dynamic analysis of claim 7, wherein S4 comprises S41 and S42; S41, calculating and obtaining the dew point temperature LTd corresponding to the air of the potential condensation layer based on the average temperature pT and the average humidity pRH; The dew point temperature LTd is obtained by converting the average relative humidity into a proportional form, then carrying out natural logarithmic operation, simultaneously calculating a temperature item according to the proportional relation between the average temperature and a constant, and carrying out combined calculation on the average relative humidity and the constant to obtain the dew point temperature LTd; combining the dew point temperature LTd with the average temperature pT, and calculating to obtain an initial value HC0 of the lifting condensation height of the air block, wherein the formula is expressed as Hc0=Hiz+KP× (pT-LTd), wherein Hiz represents the lifting initial height of the air block, and KP represents a conversion coefficient between the temperature dew difference and the condensation height.
9. The method for forecasting the cloud base height based on the multi-factor dynamic analysis of claim 8, wherein S4 further comprises S42; S42, after an initial value HC0 of the lifting condensation height of the air block is obtained, reading the relative humidity and the corresponding potential height of each air pressure layer in the potential condensation layer, and calculating and obtaining the humidity discrete degree Dh in the potential condensation layer; The method for obtaining the humidity discrete degree Dh comprises the steps of firstly determining an initial air pressure layer and a final air pressure layer of a potential condensation layer, counting the number of the air pressure layers contained in an interval, then sequentially reading the relative humidity of each layer in the interval, calculating the difference value between the relative humidity of each layer and the average relative humidity of the potential condensation layer, then squaring the obtained humidity difference value of each layer, then accumulating and summing the humidity difference square values of all the air pressure layers in the interval, and finally dividing the accumulated result by the number of the air pressure layers contained in the interval, thereby obtaining the humidity discrete degree in the potential condensation layer; Identifying an air pressure layer with the maximum relative humidity in the potential condensation layer, marking the relative humidity as RHmax, marking the corresponding potential height as Hmax, and calculating and obtaining a humidity structure correction Rc; the method for obtaining the humidity structure correction Rc comprises the steps of firstly reading a maximum relative humidity value in a potential condensation layer and the potential height of a corresponding air pressure layer, calculating the difference value between the maximum relative humidity value and the average relative humidity of the potential condensation layer, and simultaneously calculating the height difference between the maximum potential height of the humidity layer and the potential height of a lifting starting layer; the obtained humidity discrete degree Dh and the humidity structure correction Rc are acted on the initial value HC0 for correction, and the cloud bottom height correction value HC1 is obtained, wherein the formula is expressed as HC1 = HC0-Rc + lambda x Dh, and lambda represents the adjustment coefficient of the influence of the humidity discrete degree on the condensation height.
10. The method for forecasting the cloud base height based on the multi-factor dynamic analysis, as claimed in claim 9, wherein S5 comprises S51 and S52; S51, reading the relative humidity and the corresponding potential height of each air pressure layer in the range of the mixed layer below the initial layer of the potential condensation layer, and carrying out statistical calculation on the humidity distribution of each layer below the potential condensation layer to obtain interlayer humidity support amount Zs; The interlayer humidity support amount Zs is obtained by firstly reading the relative humidity and the corresponding interlayer height difference of each air pressure layer of the mixed layer below the potential condensation layer, carrying out product calculation on the relative humidity and the layer height difference of each layer and accumulating the relative humidity and the layer height difference layer by layer to obtain the accumulated amount of humidity and layer thickness; S52, calculating and obtaining a humidity connection relation quantity Js between the potential condensation layer and the lower mixed layer based on the interlayer humidity support quantity Zs, constructing a structure checking correction quantity Rs by combining the continuous increment discriminant Lc and the average Wen Loucha Ms, and executing structure consistency check on the cloud bottom height correction value HC1 to obtain a final cloud bottom height forecast result HCZ; The method for obtaining the humidity connection relation quantity Js comprises the steps of firstly calculating a difference value between the average relative humidity of a potential condensation layer and the humidity supporting quantity of a mixed layer below, then calculating the height difference between the bottom height of the potential condensation layer and the bottom height of the mixed layer, adding an extremely small constant into the height difference to avoid zero denominator, and finally dividing the humidity difference value by the height difference to obtain the humidity connection relation quantity Js on the unit height; the acquisition formula of the structure check correction amount Rs is as follows: ; Wherein Hiz represents the air block lifting initial height, hjz represents the air block lifting final height; the final cloud base height forecast result HCZ is obtained by the formula HCZ =hc1-Rs.

Description

Cloud base height forecasting method based on multi-factor dynamic analysis Technical Field The invention relates to the technical field of weather numerical forecasting and atmospheric boundary layer analysis, in particular to a cloud base height forecasting method based on multi-factor dynamic analysis. Background In aviation weather service, low cloud monitoring, general aviation flight guarantee and regional weather forecast business, cloud bottom height is an important weather parameter reflecting the low cloud structure and the water vapor distribution state of the near stratum. The formation of cloud floor height is closely related to the temperature structure within the atmospheric boundary layer, the water vapor distribution, and the air lifting process, where the mixed layer is an important area affecting low cloud formation. The mixed layer is usually referred to as the atmosphere in the range of about 100hPa above the ground, and air in this region forms a relatively uniform heat exchange under the action of turbulence, but in a practical atmospheric environment, there are often complex distribution features of local wet layer structures and humidity changes with height inside the mixed layer, and these structures have an important influence on the low cloud formation height. In the existing cloud bottom height forecasting method, the average relative humidity of a mixed layer is often adopted as a basic condition for judging whether low cloud exists or not, and the lifting condensation height is obtained by combining air block lifting calculation on the basis. However, when describing the moisture condition of the mixed layer using the average humidity, the difference in the distribution of the humidity inside the mixed layer in the vertical direction is often ignored. When there is a local humidity increasing layer or a local high humidity layer inside the mixed layer, the average humidity will smooth these local structures, so that the wet layer structure information is weakened, making it difficult to accurately identify the height section where condensation may actually occur. Because the vertical structure of the humidity inside the mixed layer is not fully identified, when a local wet layer or a humidity increasing section exists in the actual atmosphere, the lifting condensation height obtained by the traditional method is always only reflected in the overall average state, but cannot reflect the influence of the local wet layer on the condensation process, so that the cloud base height forecasting result deviates from the actual cloud base position. In some cases, a local high-humidity layer inside the mixed layer becomes a region where the air block first reaches a saturated state, but the average humidity calculation weakens the information, so that the setting of the air block lifting starting condition is unreasonable, and further the cloud bottom height calculation result is higher or lower. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a cloud base height forecasting method based on multi-factor dynamic analysis, and solves the problems in the background art. The cloud bottom height forecasting method based on multi-factor dynamic analysis comprises the following steps: S1, reading report section data of corresponding values of a target area, extracting data of each air pressure layer in a range from the ground to 100hPa above, calculating the thickness Dm of the mixed layer and the discrete variation QD of the humidity, and fitting the mixed layer into a state set of a vertical structure of the humidity of the mixed layer; S2, based on a mixed layer humidity vertical structure state set, performing adjacent layer vertical difference operation on a humidity sequence, obtaining a humidity change rate sequence Gk between layers, obtaining a continuous increment discriminant Lc and a humidity turning-back quantity Bf, and finally screening out a wet layer candidate section meeting a continuous increment condition; S3, calculating the humidity polymerization degree Cw and the average temperature dew difference Ms of each wet layer candidate section, constructing a potential condensation layer judgment quantity Sc, determining the position of a potential condensation layer from a plurality of candidate sections, and calculating the average temperature pT and average humidity pRH of the potential condensation layer according to a layer thickness weighting mode; s4, calculating a corresponding dew point temperature LTd according to the average temperature pT and the average humidity pRH, acquiring a condensation height initial value HC0 of air block lifting, and carrying out structural correction on the condensation height initial value HC0 to obtain a cloud base height correction value HC1; S5, based on the cloud bottom height correction value HC1, according to the humidity distribution of the mixed layer below the potential condensation layer, t