A cobweb warning method based on the PPI scanning mode of a lidar wind profiler

Abstract

The present invention belongs to the field of meteorological technology, and particularly relates to a cobweb warning method based on the PPI scanning mode of a lidar wind profiler, including: Step 1, collecting the wind speed and wind direction values of warning points by using the PPI scanning mode of the lidar wind profiler; Step 2, obtaining the wind speed and wind direction values around the warning points and the wind speed and wind direction values of each component based on the wind speed and wind direction values of the warning points; Step 3, calculating the difference result by using the difference method based on the warning points and the wind speed and wind direction values of each surrounding component, and comparing it with a preset threshold to determine the warning color around the warning points. The technical solution of the present invention is different from the vertical profile of the RHI mode and the top of the radar in the wind profile mode. The cone surface with the radar as the vertex is obtained by scanning in the PPI mode, which can measure a wider wind field and can detect the wind shear in the entire area during the takeoff and landing of the aircraft.

Description

Technical Field

[0001] The present invention belongs to the field of meteorological technology, and more particularly to a cobweb warning method based on the PPI scanning mode of a lidar wind profiler. Background Art

[0002] In aviation meteorology, the change of wind vector (wind direction and wind speed) in the horizontal and / or vertical distance in the air within a certain distance at a height below 600 m near the ground is called low-level wind shear, which has the characteristics of strong suddenness, short life cycle, low altitude, small scale, and large intensity. When an aircraft encounters low-level wind shear during the takeoff and landing stages with low airspeed and low altitude from the ground, the wind shear will instantly change the stable flight attitude of the aircraft, causing the aircraft to bump, sway or even crash, and it is recognized as an invisible killer in the air. Currently, the commonly used low-level wind shear detection devices at Chinese airports are ultrasonic anemometers, Doppler weather radars (TDWR), and wind profiler radars (WPR). The detection height of the ultrasonic anemometer is 10 m, and multiple units are often arranged on both sides of the runway to form a low-level wind shear warning system (LLWAS) for monitoring horizontal wind shear. Its monitoring height is limited, and its monitoring ability is easily affected by the deployment location and quantity; the tracer of the Doppler weather radar is atmospheric cloud and rain particles, which is good at detecting low-level wind shear caused by systematic weather such as thunderstorms and fronts, but has insufficient monitoring ability for local small-scale wind shear and clear-air turbulence; the tracer of the wind profiler radar is atmospheric turbulence, which can only detect the wind field in the top air region, and the low-level wind field is easily interfered by ground clutter with low credibility, which is not conducive to identifying low-level wind shear. The lidar wind profiler obtains the atmospheric wind field information by measuring the scattered echo signal of aerosol particles. As a new type of wind field detection means, it has the advantages of small volume, light weight, strong anti-interference ability, high spatio-temporal resolution of data, and can provide very fine low-level three-dimensional wind field information, which can make up for the deficiency of traditional devices in the ability to identify low-level wind shear, and is currently the most effective means to identify low-level wind shear under clear sky conditions.

[0003] Currently, the wind shear warning based on the lidar wind profiler mainly relies on the RHI mode and the wind profile mode. The RHI mode scans the vertical profile of the wind field. First, the radial data is interpolated to obtain grid data, and then the horizontal and vertical components are obtained by using the wind speed and wind direction values of each point in the grid data. The wind shear level can be judged by calculating the difference in wind speed and wind direction between a certain point in the grid data and its adjacent points. The wind profile mode obtains the change of the wind field at different heights on the top of the radar over time. The wind shear level can be judged by calculating the difference in wind speed and wind direction values between a point at a certain height and its adjacent upper and lower points, and the difference in wind speed and wind direction values between the same point at adjacent times can also be calculated to judge the wind shear level. However, there is no wind shear warning based on the PPI mode.

[0004] As the Chinese patent application number is 201810505403.X, it discloses a lateral wind measurement device for a lidar wind measurement radar, including: a lidar wind measurement radar, the light emitted by the wind measurement laser in the lidar wind measurement radar shoots forward to detect the forward wind speed; and a reflector, the reflector is located on the light transmission path of the light emitted by the wind measurement laser, and the reflector reflects a part of the light emitted by the wind measurement laser to a direction forming an angle of β degrees with the due front, where β is in the range of 0 to 180 degrees. However, it does not propose a cobweb warning method based on the PPI scanning mode of the lidar wind measurement radar.

[0005] Again, as the Chinese patent application number is 202111642223.4, it discloses a coherent wind measurement lidar, which is applied to the technical field of lidar wind measurement. A pulse width modulator is used to modulate the laser emitted by the laser to make the laser emit a laser pulse train; the laser pulse train includes at least two kinds of laser pulses arranged in a fixed order, and the laser pulses correspond to at least two different pulse widths; a processor is used to; acquire an echo pulse train signal; the echo pulse train signal is the corresponding echo signal received after the laser transceiver system emits the laser pulse train; decode the echo pulse train signal according to the fixed order to obtain the data to be processed corresponding to the pulse width; perform an inversion operation on the data to be processed to obtain the atmospheric parameters corresponding to the distance resolution of each pulse width, and realize outputting data corresponding to multiple distance resolutions simultaneously during operation, ensuring that the coherent wind measurement lidar has a high time resolution. The invention also provides a measurement method, which also has the above beneficial effects. Similarly, it does not propose a cobweb warning method based on the PPI scanning mode of the lidar wind measurement radar. Summary of the Invention

[0006] To solve the above existing problems, the present invention proposes a cobweb warning method based on the PPI scanning mode of the lidar wind measurement radar.

[0007] The cobweb warning method based on the PPI scanning mode of the lidar wind measurement radar includes:

[0008] Step 1: Use the PPI scanning mode of the lidar wind measurement radar to collect the wind speed and wind direction values of the warning points;

[0009] Step 2: Obtain the surrounding wind speed and wind direction values and the wind speed and wind direction values of each component based on the wind speed and wind direction values of the warning points;

[0010] Step 3: Calculate the difference result by using the difference method based on the warning points and the wind speed and wind direction values of each surrounding component, and compare it with a preset threshold to determine the warning color around the warning points.

[0011] Further, the step 1 of using the PPI scanning mode of the lidar wind measurement radar to collect the wind speed and wind direction values of the warning points includes:

[0012] Step 101: Under the condition that the data detected by the PPI scanning mode is the radial velocity, taking the lidar as the detection point, divide the range from the starting azimuth angle to the ending azimuth angle into equal parts with an angular rotation value of 6° as the integer multiple of the step size each time. A total of 60 radial lines with step sizes are obtained. The intersection point of each radial line and the distance contour line is defined as the warning point S(n, m).

[0013] Step 102: When the detection distance is certain, the maximum values of n and m in the warning point S(n, m) have been determined. The smaller n is, the closer it is to the detection point. When m is 1 at 0°, as the range from the starting azimuth angle to the ending azimuth angle is divided into equal parts with an angular rotation value of 6° as the integer multiple of the step size each time, and the step size increases by 6° each time, the value of m increases by 1. Use the PPI scanning mode of the lidar to obtain the wind speed and wind direction values of the current warning point S(n, m).

[0014] Furthermore, the obtaining of the surrounding wind speed and wind direction values and the component wind speed and wind direction values based on the wind speed and wind direction values of the warning point in Step 2 includes:

[0015] Step 201: Based on the wind speed and wind direction values of the current warning point S(n, m), denoted as v1, when n is determined, use the PPI scanning mode to obtain the wind speed and wind direction values of the warning point S(n, m - 1) corresponding to the previous azimuth angle and the warning point S(n, m + 1) corresponding to the next azimuth angle, denoted as v2 and v3 respectively. Similarly, when m is determined, obtain the wind speed and wind direction values of the previous warning point S(n - 1, m) and the subsequent warning point S(n + 1, m) of the current warning point S(n, m), denoted as v4 and v5 respectively. Among them, the distances between the previous warning point S(n - 1, m), the current warning point S(n, m), and the warning point S(n + 1, m) on the same radial line are equidistant, and the distance is adjusted according to the actual situation;

[0016] Step 202: Based on the above five wind speed and wind direction values, with the extended line of the runway as the x-axis and the perpendicular line of the runway as the y-axis, obtain the x-direction components corresponding to the five wind speed and wind direction values. The x-direction component corresponding to the wind speed and wind direction values of the current warning point S(n, m) obtained by using the PPI scanning mode of the lidar is The x-direction component corresponding to the wind speed and wind direction values of the warning point S(n, m - 1) corresponding to the previous azimuth angle is The x-direction component corresponding to the wind speed and wind direction values of the warning point S(n, m + 1) corresponding to the next azimuth angle is The x-direction component corresponding to the wind speed and wind direction values of the previous warning point S(n - 1, m) is v 4x , and the x-direction component corresponding to the wind speed and wind direction values of the subsequent warning point S(n + 1, m) is

[0017] Further, the step of calculating the difference result by using the difference method based on the alarm point and the wind speed and direction values of each surrounding component and comparing it with a preset threshold to determine the alarm color around the alarm point includes:

[0018] Step 301: Subtract the x-direction components of the four points around the current alarm point S(n, m) from the x-direction component of the current alarm point S(n, m) to obtain the results, as shown in the following formulas (1) to (4):

[0019]

[0020] In the formula, N n1 is the difference between the x-direction component of the wind speed and direction value of the current alarm point S(n, m) and the alarm point S(n, m - 1) corresponding to the previous azimuth angle, and N n2 is the difference between the x-direction component of the wind speed and direction value of the current alarm point S(n, m) and the alarm point S(n, m + 1) corresponding to the next azimuth angle, and M n1 is the difference between the x-direction component of the wind speed and direction value of the current alarm point S(n, m) and the alarm point S(n - 1, m) of the previous section, and M n2 is the difference between the x-direction component of the wind speed and direction value of the current alarm point S(n, m) and the alarm point S(n + 1, m) of the next section;

[0021] Step 302: When any one of N n1 , N n2 , M n1 , M n2 is greater than the threshold a, the alarm point becomes effective, and the four frames around the alarm point are filled with the alarm color corresponding to the threshold.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The method of cobweb alarm based on the PPI scanning mode of the lidar is different from the vertical profile of the RHI mode and the radar top of the wind profile mode. The cone surface with the radar as the vertex is obtained by scanning in the PPI mode, which can measure a wider wind field and can detect the wind shear in the entire area during the takeoff and landing of the aircraft.

[0024] 2. The cobweb alarm based on the PPI scanning mode of the lidar is different from the box alarm between the latter two values after the difference of the RHI mode and the wind profile mode. The PPI mode alarm adopts point alarm. When a certain point reaches the threshold, the surrounding boxes are filled with the colors corresponding to the threshold, just like a cobweb. Brief Description of the Drawings

[0025] Figure 1 It is a schematic flow chart of the cobweb alarm method based on the PPI scanning mode of the lidar;

[0026] Figure 2 It is a schematic structural diagram for dividing the results of the PPI detection mode. Specific implementation manners

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0028] As Figure 1-2 shown, the cobweb warning method based on the PPI scanning mode of the lidar includes:

[0029] Step 1: Collect the wind speed and wind direction values of the warning points by using the PPI scanning mode of the lidar.

[0030] Step 101: Under the condition that the detection data based on the PPI scanning mode is the radial velocity, taking the lidar as the detection point, equal division processing is carried out at an integer multiple of 6° step for each azimuth angle rotation from the starting azimuth angle to the ending azimuth angle, and a total of 60 radial lines with steps are divided. The intersection point of each radial line and the distance isopleth is defined as the warning point S(n, m).

[0031] Step 102: When the detection distance is certain, the maximum values of n and m in the warning point S(n, m) have been determined. The smaller n is, the closer it is to the detection point. When m is 1 at 0°, as the equal division processing is carried out at an integer multiple of 6° step for each azimuth angle rotation from the starting azimuth angle to the ending azimuth angle, and each time the 6° step is increased, the value of m is increased by 1. The wind speed and wind direction values of the current warning point S(n, m) are obtained by using the PPI scanning mode of the lidar.

[0032] Step 2: Obtain the surrounding wind speed and wind direction values and the component wind speed and wind direction values based on the wind speed and wind direction values of the warning points.

[0033] Step 201: Based on the wind speed and wind direction values of the current warning point S(n, m), denoted as v1, when n is determined, the wind speed and wind direction values of the warning point S(n, m - 1) corresponding to the previous azimuth angle and the warning point S(n, m + 1) corresponding to the next azimuth angle are obtained by using the PPI scanning mode, and are denoted as v2 and v3 respectively. Similarly, when m is determined, the wind speed and wind direction values of the previous warning point S(n - 1, m) and the subsequent warning point S(n + 1, m) of the current warning point S(n, m) are obtained, and are denoted as v4 and v5 respectively. Among them, the distances between the previous warning point S(n - 1, m), the current warning point S(n, m), and the warning point S(n + 1, m) on the same radial line are equidistant, and the distance is adjusted according to the actual situation.

[0034] Step 202: With the extended line of the runway as the x-axis and the vertical line of the runway as the y-axis based on the above five wind speed and direction values, obtain the x-direction components corresponding to the five wind speed and direction values. Use the PPI scanning mode of the laser wind lidar to obtain the x-direction component corresponding to the wind speed and direction value of the current warning point S(n, m) as The x-direction component corresponding to the wind speed and direction value of the warning point S(n, m - 1) at the previous azimuth angle corresponding point The x-direction component corresponding to the wind speed and direction value of the warning point S(n, m + 1) at the next azimuth angle corresponding point The x-direction component corresponding to the wind speed and direction value of the front-segment warning point S(n - 1, m) is v 4x , and the x-direction component corresponding to the wind speed and direction value of the rear-segment warning point S(n + 1, m) is

[0035] Step 3: Based on the warning point and the wind speed and direction values of each surrounding component, use the difference method to calculate the difference result and compare it with a pre-set threshold to determine the warning color around the warning point.

[0036] Step 301: Subtract the x-direction components of the four surrounding points of the current warning point S(n, m) from the x-direction component of the current warning point S(n, m) respectively to obtain the results, as shown in the following formulas (1) - (4):

[0037]

[0038] In the formula, N n1 is the difference between the x-direction component of the wind speed and direction value of the current warning point S(n, m) and the x-direction component of the wind speed and direction value of the warning point S(n, m - 1) at the previous azimuth angle corresponding point, N n2 is the difference between the x-direction component of the wind speed and direction value of the current warning point S(n, m) and the x-direction component of the wind speed and direction value of the warning point S(n, m + 1) at the next azimuth angle corresponding point, M n1 is the difference between the x-direction component of the wind speed and direction value of the current warning point S(n, m) and the x-direction component of the wind speed and direction value of the front-segment warning point S(n - 1, m), M n2 is the difference between the x-direction component of the wind speed and direction value of the current warning point S(n, m) and the x-direction component of the wind speed and direction value of the rear-segment warning point S(n + 1, m);

[0039] Step 302: When any one of N n1 , N n2 , M n1 , M n2 is greater than the threshold a, the warning point becomes effective, and the four frames around the warning point are filled with the warning color corresponding to the threshold.

[0040] Similarly, the component in the y direction can also calculate the strength of the crosswind shear. It should be noted here that since the surface detected by the PPI mode is a fan-shaped area, two adjacent points can be approximately regarded as uniform and can be regarded as the wind shear on the horizontal plane. Calculating the data between two points that are far apart does not make much sense. If the air mass is not considered to be uniform, the detection data can also use the data of one point as the average wind speed of the small-angle fan-shaped surface where it is located, so as to calculate the vertical shear of the horizontal wind.

[0041] The above is only the specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope disclosed by the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A cobweb warning method based on the PPI scanning mode of a lidar wind profiler, characterized in that Including: Step 1: Use the PPI scanning mode of the lidar wind profiler to collect the wind speed and direction values of the warning points, including: Step 101: Under the condition that the detection data based on the PPI scanning mode is the radial velocity, take the lidar wind profiler as the detection point and divide it equally at an integer multiple of 6° step for each azimuth angle rotation from the starting azimuth angle to the ending azimuth angle. A total of 60 radial lines with steps are obtained. The intersection point of each radial line and the distance isopleth is defined as the warning point S(n, m); Step 102: When the detection distance is determined, the maximum values of n and m in the warning point S(n, m) have been determined. The smaller n is, the closer it is to the detection point. When m is 1 at 0°, as the azimuth angle rotates at an integer multiple of 6° step from the starting azimuth angle to the ending azimuth angle for equal division processing, the m value increases by 1 each time a 6° step is added. Use the PPI scanning mode of the lidar wind profiler to obtain the wind speed and direction values of the current warning point S(n, m); Step 2: Obtain the wind speed and direction values and the component wind speed and direction values around based on the wind speed and direction values of the warning points, including: Step 201: Based on the wind speed and direction values of the current warning point S(n, m), denoted as v1, when n is determined, use the PPI scanning mode to obtain the wind speed and direction values of the warning point S(n, m - 1) corresponding to the previous azimuth angle and the warning point S(n, m + 1) corresponding to the next azimuth angle, denoted as v2 and v3 respectively. Similarly, when m is determined, obtain the wind speed and direction values of the previous warning point S(n - 1, m) and the subsequent warning point S(n + 1, m) of the current warning point S(n, m), denoted as v4 and v5 respectively. Among them, the distances between the previous warning point S(n - 1, m), the current warning point S(n, m), and the warning point S(n + 1, m) on the same radial line are equidistant, and the distance can be adjusted according to the actual situation; Step 202: Taking the extended line of the runway as the x-axis and the perpendicular line of the runway as the y-axis based on the above five wind speed and direction values, obtain the x-direction components corresponding to the five wind speed and direction values. Use the PPI scanning mode of the laser wind lidar to obtain the x-direction component corresponding to the wind speed and direction value of the current warning point S(n, m) as v 1x , the x-direction component corresponding to the wind speed and direction value of the warning point S(n, m-1) corresponding to the previous azimuth angle is v 2x , the x-direction component corresponding to the wind speed and direction value of the warning point S(n, m+1) corresponding to the next azimuth angle is v 3x , the x-direction component corresponding to the wind speed and direction value of the previous warning point S(n-1, m) is v 4x , the x-direction component corresponding to the wind speed and direction value of the subsequent warning point S(n+1, m) is v 5x ; Step 3: Calculate the difference result using the difference method based on the warning points and the component wind speed and direction values around, and compare it with the preset threshold to determine the warning color around the warning points.

2. The cobweb warning method based on the PPI scanning mode of the lidar for wind measurement according to claim 1, wherein Step 3 described as calculating the difference result using the difference method based on the warning points and the component wind speed and direction values around and comparing it with the preset threshold to determine the warning color around the warning points includes: Step 301: Take the difference between the x-direction components of the four points around the current warning point S(n, m) and the x-direction component of the current warning point S(n, m) to obtain the results, as shown in the following formulas (1) - (4): N n1 = v 2x -v 1x (1), N n2 = v 3x -v 1x (2), M n1 = v 4x -v 1x (3), M n2 = v 5x -v 1x (4), Where N n1 is the difference in the x-direction component of the wind speed and direction values between the current alarm point S(n, m) and the alarm point S(n, m - 1) corresponding to the previous azimuth angle, and N n2 is the difference in the x-direction component of the wind speed and direction values between the current alarm point S(n, m) and the alarm point S(n, m + 1) corresponding to the next azimuth angle, and M n1 is the difference in the x-direction component of the wind speed and direction values between the current alarm point S(n, m) and the alarm point S(n - 1, m) in the previous section, and M n2 is the difference in the x-direction component of the wind speed and direction values between the current alarm point S(n, m) and the alarm point S(n + 1, m) in the next section; Step 302, when any value of N n1 , N n2 , M n1 , M n2 is greater than the threshold value a, the alarm point becomes effective, and the four boxes around the alarm point are filled with the alarm color corresponding to the threshold value.

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