A method for identifying double-layer jets based on a wind-measuring lidar

Abstract

The present invention discloses a method for identifying double-layer jets based on a wind-measuring lidar, comprising the following steps: (1) obtaining horizontal wind speed profile data of the wind-measuring lidar within a preset time period; (2) performing time-height two-dimensional connected component detection on the horizontal wind speed profile data to screen candidate jet connected components; (3) extracting jet core parameters, including: jet axis height, maximum wind speed, and upper boundary height of the jet; (4) calculating the maximum wind speed difference and wind speed gradient at each time point, setting a wind speed difference threshold and a gradient threshold, and screening effective jet layers; (5) dynamically grading the jet intensity based on the maximum wind speed and wind speed difference of the effective jet layers; the present invention improves the reliability of the identification result.

Description

Technical Field

[0001] The present invention relates to the technical field of lidar, and particularly to a method for identifying double jets based on a wind-measuring lidar. Background Art

[0002] As a zonal air current with a significant increase in wind speed within the atmospheric boundary layer, the low-level jet has significant vertical wind shear characteristics and a typical diurnal variation pattern. The formation and maintenance of the low-level jet are related to the coupled development of the upper and lower air circulations, and it is one of the core dynamic mechanisms triggering mesoscale severe convective systems and extreme precipitation events. Traditional research has mostly focused on the contribution of a single low-level jet (such as the synoptic-scale low-level jet SLLJ or the boundary layer jet BLJ) to heavy rain. However, recent observations have shown that the double low-level jet (i.e., the synergistic coupling of SLLJ and BLJ) can significantly amplify the intensity of heavy rain through multi-level dynamic-thermal coupling effects, but its mechanism of action has not been fully revealed, and there are significant gaps in related detection technologies.

[0003] Currently, the main observation methods for low-level jets are radiosondes and wind profilers. However, generally, radiosondes are only launched twice a day, with extremely low time resolution and unable to capture the continuous evolution characteristics and diurnal variation characteristics of the jets. Although wind profilers provide relatively high time resolution, their vertical resolution is relatively limited (usually on the order of hundreds of meters), and the data is vulnerable to interference in complex terrain and strong precipitation environments. In addition, strong gusts near the ground layer caused by afternoon thermal convection are likely to produce instantaneous wind speed maxima, and their wind field characteristics are highly similar to those of real low-level jets. Therefore, they are likely to exhibit characteristics similar to low-level jets in the observed data, leading to misjudgment. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a method for identifying double jets based on a wind-measuring lidar, which can realize the real-time extraction of the core parameters of low-level jets through a connected domain recognition algorithm and high-precision vertical wind speed profile analysis, effectively identify double low-level jets, filter out abnormal wind speed fluctuations, and reduce the false alarm rate.

[0005] Technical Solution: A method for identifying double jets based on a wind-measuring lidar according to the present invention includes the following steps:

[0006] (1) Obtain the horizontal wind speed profile data of the wind-measuring lidar within a preset time period;

[0007] (2) Perform time-height two-dimensional connected domain detection on the horizontal wind speed profile data to screen candidate jet connected domains;

[0008] (3) Extract the core parameters of the jet, including: jet axis height, maximum wind speed, and upper boundary height of the jet;

[0009] (4) Calculate the maximum wind speed difference and wind speed gradient at each time point, set the wind speed difference threshold and gradient threshold, and screen for effective jet streams;

[0010] (5) Dynamically classify the jet intensity based on the maximum wind speed and wind speed difference of the effective jet streams.

[0011] Further, in step (2), specifically as follows: Set the altitude range for low-level jet identification and the maximum wind speed threshold , traverse the time-altitude wind speed data, and mark the pixel points that meet or exceed the maximum wind speed threshold as candidate areas; Scan the candidate areas point by point, based on the time window threshold and altitude window threshold, allowing the wind speed to be discontinuous within the window, and mark the same connected domain; Filter out short-term perturbations and isolated noises through the time span threshold and pixel area threshold to obtain the final candidate jet-connected domain.

[0012] Further, in step (2), the time window threshold and altitude window threshold respectively allow the connected domain to be discontinuous for no more than 3 consecutive time points in the time dimension and no more than 5 consecutive altitude layers in the altitude dimension.

[0013] Further, in step (3), detect the number of connected domains in the vertical profile at each time point; Determine the maximum wind speed point and the corresponding jet axis altitude of each connected domain; Search for the minimum wind speed between the jet axis altitude and the search upper boundary to determine the upper boundary altitude of the jet;

[0014] Further, in step (3), determine the maximum wind speed point of each connected domain through a local extreme value detection algorithm and exclude the pseudo-extreme values caused by noise.

[0015] Further, in step (3), if there is only a single connected domain, then search for the minimum wind speed between the jet axis altitude and to determine the upper boundary altitude .

[0016] Further, in step (4), calculate the maximum wind speed difference and wind speed gradient at each vertical altitude at each time point. The calculation formulas are as follows:

[0017] ;

[0018] ;

[0019] where, is the maximum wind speed of the effective jet stream, is the corresponding altitude position; is the minimum wind speed above, is the corresponding altitude position.

[0020] Further, in step (5), the dynamic classification types of the jet intensity are as follows:

[0021] Type 1: and is a weak jet;

[0022] Type 2: and is a medium jet;

[0023] Type 3: and is a strong jet;

[0024] Type 4: and is an extremely strong jet.

[0025] An electronic device according to the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is loaded into the processor, it implements any one of the methods for identifying double-layer jets based on a wind-measuring lidar.

[0026] A storage medium according to the present invention stores a computer program, characterized in that when the computer program is executed by a processor, it implements any one of the methods for identifying double-layer jets based on a wind-measuring lidar.

[0027] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: By using the time-height two-dimensional connected domain detection method and combining the extreme wind speed analysis and the extraction of jet core parameters, the present invention can effectively identify the double-layer jet structure in the low-altitude atmosphere. Compared with the traditional method, this method can not only capture a single jet layer, but also accurately locate the double-layer low-altitude jet with complex stratification characteristics, providing more accurate data support for the research of the low-altitude wind field structure and the analysis of atmospheric dynamics; By setting the combination of spatio-temporal continuity detection conditions and wind speed gradient screening, the present invention effectively filters out short-term sudden wind speed abnormal areas, avoids misidentifying local short-term strong gusts as low-altitude jets, and improves the reliability of the identification results. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow schematic diagram of the present invention;

[0029] Figure 2 is a vertical wind speed profile diagram of the present invention;

[0030] Figure 3 is the first effect diagram of double-layer jet identification of the present invention; wherein, Figure 3 in (a) is the traditional method; Figure 3 in (b) is the method of the present invention;

[0031] Figure 4 This is the second effect diagram for identifying double-layer jets of the present invention; among them, Figure 4 in (a) is the traditional method; Figure 4 in (b) is the method of the present invention. Detailed implementation manners

[0032] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0033] As Figure 1 shown, an embodiment of the present invention provides a method for identifying low-level jets based on a wind-measuring lidar, including the following steps:

[0034] Step S1: Obtain horizontal wind speed profile data within a certain time period , where respectively represent time and height, respectively represent the corresponding time index and height index.

[0035] Step S2: Perform time-height two-dimensional connected component detection on the horizontal wind speed profile data to preliminarily screen candidate jet connected components. It includes the following steps:

[0036] S21, set the height interval for identifying low-level jets , set the maximum wind speed threshold for jets , traverse the two-dimensional wind speed data of time-height, and identify all wind speed regions that satisfy being greater than the preset maximum wind speed threshold. At the same time, create a jet marking matrix , mark the regions that satisfy being greater than the preset maximum wind speed threshold as 1, and mark the regions that do not satisfy the condition as 0, which is used to number the identified jet connected components later.

[0037] S22, preliminarily screen candidate jet regions. Starting from the initial time and the lowest height , scan all pixel points of the wind speed data point by point. When a jet point that satisfies being greater than the preset wind speed threshold and has not been marked is found, it is used as the starting point of a new connected component for searching. During the search process, set the pixel point window threshold in the time dimension of the connected component and the pixel point window threshold in the height dimension, allowing the maximum wind speed to have discontinuities within the pixel point window in the time dimension and the pixel point window in the height dimension. When the maximum wind speed does not satisfy the condition of being greater than the preset wind speed threshold within the preset time step and height step range, it can still be considered to belong to the same connected component to better adapt to the non-uniform variation characteristics of low-level jets. Finally, mark all connected pixel points with the same connected component number to complete the preliminary screening of the jet region.

[0038] S23. Screen the effective jet connection regions. To avoid the influence of short-term strong gusts, spatio-temporal continuity detection is performed on the extracted wind speed regions. The detection method is as follows: Set the connection region detection time window , and filter out the connection regions with a time span less than the preset connection region detection time window to eliminate the influence of short-term disturbances on the recognition results and initially filter out the strong winds with instantaneous wind speeds reaching the jet standard caused by strong gusts. Set the connection region area threshold , and filter out the regions with a connection region pixel area less than the preset connection region area threshold to eliminate the pseudo-connection regions formed by isolated noise points. After the above screening, the final candidate jet connection regions are obtained.

[0039] Step S3: Based on the determined candidate jet connection regions, further extract the core jet parameters, including the jet axis height , the maximum wind speed , and the upper boundary height of the jet . It includes the following steps:

[0040] S31. According to the matrix, detect the number of connection regions in the vertical profile at each time point to determine the number of connection regions in the wind speed profile at that moment. According to the number, they are respectively labeled as the first connection region, the second connection region, etc.

[0041] S32. Perform extreme value detection in the first connection region and the second connection region respectively to determine the first maximum wind speed point and the second maximum wind speed point in their respective connection regions, and define their heights as the first jet axis height and the second jet axis height .

[0042] S33. Search for the minimum wind speed between the first jet axis height and the second jet axis height , and determine the corresponding height , which is defined as the candidate first upper boundary height of the jet. Search for the minimum wind speed between the second jet axis height and the set search upper boundary , and determine the corresponding height , which is defined as the candidate second upper boundary height of the jet.

[0043] When there is only one connection region in S31, that is, the first connection region, then search for the minimum wind speed between the first jet axis height and the set search upper boundary ​ , and determine the corresponding height , which is defined as the upper boundary height of the candidate jet stream.

[0044] Step S4: Based on the jet stream parameters preliminarily extracted in Step S3, calculate the maximum wind speed difference at each vertical height for each time point and the wind speed gradient , and the calculation formulas are as follows:

[0045] ;

[0046] ;

[0047] where is the maximum wind speed of the effective jet stream layer, is the corresponding height position; is the minimum wind speed above, is the corresponding height position.

[0048] To ensure the physical rationality of the jet stream, set the wind speed difference threshold and the wind speed gradient threshold , and perform data screening: only retain the jet stream layers where the wind speed difference is greater than or equal to the preset threshold to ensure that the intensity of the jet stream meets the characteristic requirements;

[0049] Only retain the jet stream layers where the wind speed gradient is less than or equal to the preset threshold to avoid misjudgment caused by high wind speed gradients formed by interference signal points.

[0050] Finally, the jet stream connectivity regions that meet the above screening conditions are identified as effective jet stream layers and used for further analysis.

[0051] Step S5: According to the finally confirmed effective jet stream layer and its maximum wind speed and wind speed difference in Step S4, dynamically classify the jet stream intensity and determine the jet stream type; among them, the dynamic classification types of jet stream intensity are:

[0052] Type 1: and is a weak jet stream;

[0053] Type 2: and is a medium jet stream;

[0054] Type 3: and is a strong jet stream;

[0055] Type 4: and is a super-strong jet stream.

[0056] Example 1:

[0057] The present invention proposes a method for identifying double-layer jet streams based on a wind-measuring lidar, which analyzes horizontal wind speed observation data and includes the following steps:

[0058] Step S1: Obtain horizontal wind speed profile data within a certain time period , where represent time and height respectively, represent the corresponding time index and height index respectively.

[0059] Step S2: Perform time-height two-dimensional connected component detection on the horizontal wind speed profile data to preliminarily screen candidate jet stream connected components. It includes the following steps:

[0060] S21, set the height interval for identifying low-level jet streams , and set the maximum wind speed threshold for jet streams . In this example, the height interval for identifying low-level jet streams is set as [100m, 3500m], and the maximum wind speed threshold for jet streams is set as 10m / s. Traverse the time-height wind speed pixel points to identify the areas where the wind speed is greater than or equal to 10m / s. Create a jet stream label matrix , mark the areas that meet the condition of being greater than the preset maximum wind speed threshold as 1, and mark the areas that do not meet the condition as 0, which is used to number the identified jet stream connected components later.

[0061] S22, starting from the initial time and the lowest height , scan the wind speed data point by point to find the pixel points that meet the wind speed and have not been numbered and marked, and use them as the starting points of new connected components for retrieval. During the retrieval process, when the maximum wind speed does not meet the condition of being greater than 10m / s within the set time step threshold and height step threshold , it can still be considered to belong to the same connected component. Mark all the pixel points that meet the conditions as the same connected component and assign a unique number.

[0062] S23, to avoid the influence of short-term strong gusts, perform spatio-temporal continuity detection on the extracted wind speed areas to screen effective jet stream areas. The detection method is as follows:

[0063] In this example, set the connected component detection time window , eliminate the areas where the time span of the connected component is less than , and preliminarily filter out the strong winds with instantaneous wind speeds reaching the jet stream standard caused by short-term strong gusts.

[0064] In this embodiment, a threshold for the pixel area of the connected domain is set. , and areas where the pixel area of the connected domain is less than are filtered out to eliminate pseudo-connected domains formed by isolated noise points.

[0065] Step S3: Based on the determined candidate jet connected domains, further extract the core jet parameters, including the jet axis height , the maximum wind speed , and the upper boundary height of the jet . It includes the following steps:

[0066] S31. According to the matrix, detect the number of connected domains for the vertical profile at each time point . As shown in Figure 2 , the horizontal wind speed profile at 3:00 obtained from the vertical sounding is given. It is detected that there are 2 connected domains in the vertical direction at this time, which are respectively marked as the first connected domain and the second connected domain.

[0067] S32. Perform extreme value detection on the first connected domain and the second connected domain at the time shown in Figure 2 respectively to determine the first maximum wind speed point and the second maximum wind speed point within their respective connected domains, and define their heights as the first jet axis height and the second jet axis height . As shown in Figure 2 , the maximum wind speed point of the first connected domain is marked with an asterisk, and the maximum wind speed point of the second connected domain is marked with a solid square point.

[0068] S33. Search for the minimum wind speed between the first jet axis height and the second jet axis height , corresponding to the height Figure 2 marked by the blue dashed line , which is defined as the candidate first upper boundary height of the jet. Search for the minimum wind speed between the second jet axis height and the set search upper boundary , corresponding to the height Figure 2 marked by the red dashed line , which is defined as the candidate second upper boundary height of the jet.

[0069] Step S4: Based on the jet parameters preliminarily extracted in Step S3, calculate the maximum wind speed difference and the wind speed gradient at each vertical height and time point. The calculation formula is as follows:

[0070] ;

[0071] ;

[0072] To ensure the physical rationality of the jet stream, in this embodiment, a wind speed difference threshold and a wind speed gradient threshold are set for data screening: Only the jet stream layers with a wind speed difference greater than or equal to and a wind speed gradient less than or equal to are retained to ensure that the intensity of the jet stream meets the characteristic requirements.

[0073] As shown in (b) of Figure 3 and (b) of Figure 4 , the height of the jet axis of the finally screened effective jet stream layers is marked with black dotted lines. Compared with Figure 3 (a) of Figure 4 (a) of the traditional method shown, the embodiment of the present invention can not only accurately capture a single jet stream layer, but also accurately identify a double-layer low-level jet stream with complex stratification characteristics. In addition, for the wind speed fluctuations caused by short-term gusts, the present invention effectively eliminates the wind speed fluctuations caused by afternoon short-term gusts through time window screening and wind speed gradient filtering, improving the accuracy and reliability of jet stream identification.

Claims

1. A double-layer jet stream identification method based on wind laser radar, characterized in that: The following steps are involved: (1) Obtain horizontal wind speed profile data from the wind laser radar within a preset time period; (2) Perform a two-dimensional time-height connected domain detection on the horizontal wind speed profile data to screen the candidate jet stream connected domains; the details are as follows: Set the height interval for low-altitude jet stream identification and maximum wind speed threshold , traverse the time-height wind speed data, mark the pixels that meet the maximum wind speed threshold as candidate areas; scan the candidate areas point by point, based on the time window threshold and the height window threshold, allow the wind speed to be discontinuous in the window, and mark the same connected domain; The short-term disturbance and isolated noise are filtered out by the time span threshold and pixel area threshold to obtain the final candidate rapids connection domain; (3) Extract the core parameters of the jet stream, including the jet stream axis height, maximum wind speed, and jet stream upper boundary height; (4) Calculate the maximum wind speed difference and wind speed gradient at each time point, set the wind speed difference threshold and gradient threshold, and screen the effective jet layer; (5) Dynamically classify the jet stream intensity based on the maximum wind speed and wind speed difference of the effective jet layer.

2. A double-layer jet stream identification method based on wind laser radar according to claim 1, characterized in that: In step (2), the time window threshold and the height window threshold allow the connected domain to be interrupted by no more than 3 consecutive time points in the time dimension and no more than 5 consecutive height layers in the height dimension, respectively.

3. The double-layer jet stream identification method based on wind laser radar according to claim 1 is characterized in that: In step (3), the number of connected domains of the vertical section at each time point is detected; the maximum wind speed point of each connected domain and the corresponding jet axis height are determined; the minimum wind speed value is found between the jet axis height and the search upper boundary, and the jet upper boundary height is determined.

4. The double-layer jet stream identification method based on wind laser radar according to claim 3 is characterized in that: In step (3), the maximum wind speed point of each connected domain is determined by the local extreme value detection algorithm, and the pseudo extreme values ​​caused by noise are eliminated.

5. The double-layer jet stream identification method based on wind laser radar according to claim 3 is characterized in that: In step (3), if there is only a single connected domain, then at the jet axis height to Find the minimum wind speed to determine the upper boundary height .

6. The double-layer jet stream identification method based on wind laser radar according to claim 1 is characterized in that: In step (4), calculate the maximum wind speed difference at each time point in vertical height With wind speed gradient , the calculation formula is as follows: ； ； in, is the maximum wind speed in the effective jet layer, is the corresponding height position; is the minimum wind speed above, is the corresponding height position.

7. The double-layer jet stream identification method based on wind laser radar according to claim 1 is characterized in that: In step (5), the dynamic classification type of rapids intensity is: Type 1: and It is a weak rapid; Type 2: and It is a moderate rapid; Type 3: and It is a strong rapids; Type 4: and It is a super rapid.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into a processor, a double-layer jet stream identification method based on wind laser radar according to any one of claims 1 to 7 is implemented.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a double-layer jet stream identification method based on wind laser radar according to any one of claims 1 to 7 is implemented.

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