Dry micro-downburst detection and identification method and device and medium

Abstract

The invention discloses a dry micro-downburst detection and identification method and device and a medium. The method comprises the following steps: determining a threshold value of spectral width; according to the threshold value of the spectral width, marking an area of which the radar spectral width w is within the threshold value in an area scanned by the radar once as a sensitive area; using a least square method to create a synthetic wind shear segment for each radial speed data scanned by the radar as a synthetic wind shear segment of the sensitive area; and screening the synthetic wind shear segments by using a spectral width sensitive area image to obtain a synthetic wind shear cluster, and identifying and positioning the dry micro-downburst through the synthetic wind shear cluster. The method can solve the problem that the existing micro-downburst detection technology cannot detect and identify the dry micro-downburst, and has great significance in guaranteeing the flight safety of civil aircrafts.

Description

Technical Field

[0001] The present invention relates to the field of aviation meteorology in meteorology, and more specifically, to a dry microdownburst detection and identification method, device and medium. Background Art

[0002] There is little research on microburst detection technology in China, and it mainly refers to foreign research. Large airports in the United States rely on the automatic wind shear detection technology provided by the TDWR radar integrated terminal weather system (ITWS) to detect microbursts. Some other medium-sized airports use ASR-9 microburst detection technology. In addition, in 2013, the Lincoln Laboratory of the Massachusetts Institute of Technology proposed the AMDA automatic microburst detection algorithm based on the NEXRAD radar.

[0003] Existing microburst detection technologies are based on the reflectivity factors of X-band, S-band, and C-band weather radars to determine thunderstorm-sensitive areas, and then locate wind shear detection thresholds. However, due to the long wavelengths of these three radars, when a microburst occurs without precipitation, the radar reflectivity factor is very low and the thunderstorm centroid cannot be distinguished, so the thunderstorm-sensitive area cannot be detected. Summary of the invention

[0004] The present invention is provided to solve the above-mentioned problems existing in the prior art. Therefore, a dry microburst detection and identification method, device and medium are needed to solve the problem that the existing microburst detection technology cannot detect and identify dry microbursts. On the one hand, the present invention performs dry microburst detection based on laser wind radar scanning data, rather than the prior art based on X-band, S-band, and C-band radar scanning data for detection; on the other hand, the present invention detects and locates thunderstorm sensitive areas through the spectral width threshold range of the laser wind radar, rather than the prior art based on the radar reflectivity factor (echo intensity) to locate the sensitive area.

[0005] According to a first aspect of the present invention, a dry microburst detection and identification method is provided, characterized in that the method comprises:

[0006] Determine the threshold value of the spectrum width;

[0007] According to the threshold of the spectrum width, marking the area in the area scanned by the radar once where the radar spectrum width w is within the threshold as a sensitive area;

[0008] A synthetic wind shear segment is created for each radial velocity data scanned by the radar using the least square method as the synthetic wind shear segment of the sensitive area;

[0009] The synthetic wind shear segments are screened using spectral width sensitive region images to obtain synthetic wind shear clusters, and the dry microburst is identified and located through the synthetic wind shear clusters.

[0010] Furthermore, determining the threshold of the spectrum width includes:

[0011] The radial wind data scanned by the laser wind radar is read and the spectrum width is calculated. The observation test is carried out in the area where the radar is located for 1-2 years to obtain the threshold range (w1-w2) of dry wind shear with a spectrum width w.

[0012] Furthermore, a synthetic wind shear segment is created for each radial velocity data scanned by the radar using the least square method as the synthetic wind shear segment of the sensitive area, including:

[0013] Obtaining velocity data for each radial of the radar scan;

[0014] Creating segments of synthetic wind shear for each radial velocity data using a least squares method, the segments representing changes in velocity when moving radially outward;

[0015] If the radial velocity data do not contain enough valid data, or if the correlation coefficient calculated during the least squares fit is too low, the value of the synthetic shear is set to a null value;

[0016] After a segment is generated, the start of the segment is located by searching for a velocity increase greater than a threshold, and the segment splitting continues along the radial direction of the segment until one of the following conditions is met:

[0017] (1) Radial end;

[0018] (2) the missing speed value exceeds the first threshold;

[0019] (3) the resultant shear is below a second threshold;

[0020] (4) the total speed reduction measured along the segment exceeds a third threshold;

[0021] (5) The total speed increase measured along the segment exceeds the fourth threshold.

[0022] Furthermore, the synthetic wind shear segments are screened using the spectral width sensitive region image to obtain synthetic wind shear clusters, and the dry microburst is identified and located by the synthetic wind shear clusters, including:

[0023] Verifying the synthetic wind shear segment using a spectral width sensitive region image;

[0024] If the spectral width w along the synthetic wind shear segment does not reach the threshold of the spectral width, the synthetic wind shear segment is discarded, and the remaining synthetic wind shear segments that meet the threshold of the spectral width are integrated into an initial cluster;

[0025] Reading continuous two-dimensional regions in the initial cluster based on a binary method and deleting isolated points;

[0026] The clusters that ultimately remain are the locations of dry microbursts.

[0027] According to a second aspect of the present invention, a dry microburst detection and identification device is provided, the device comprising:

[0028] A determination module configured to determine a threshold value of a spectrum width;

[0029] A marking module is configured to mark, according to the threshold of the spectrum width, an area in the area scanned by the radar once, where the radar spectrum width w is within the threshold as a sensitive area;

[0030] A synthesis module is configured to create a synthetic wind shear segment for each radial velocity data scanned by the radar using a least square method as a synthetic wind shear segment of the sensitive area;

[0031] The identification module is configured to screen the synthetic wind shear segments using the spectral width sensitive area image to obtain synthetic wind shear clusters, and to identify and locate dry microbursts through the synthetic wind shear clusters.

[0032] Furthermore, the determination module is further configured to read radial wind data scanned by the laser wind radar, calculate the spectral width, conduct continuous observation tests on the area where the radar is located for 1-2 years, and obtain the threshold range (w1-w2) of dry wind shear with a spectral width w.

[0033] Furthermore, the synthesis module is further configured as follows:

[0034] Obtaining velocity data for each radial of the radar scan;

[0035] Creating segments of synthetic wind shear for each radial velocity data using a least squares method, the segments representing changes in velocity when moving radially outward;

[0036] If the radial velocity data do not contain enough valid data, or if the correlation coefficient calculated during the least squares fit is too low, the value of the synthetic shear is set to a null value;

[0037] After a segment is generated, the start of the segment is located by searching for a velocity increase greater than a threshold, and the segment splitting continues along the radial direction of the segment until one of the following conditions is met:

[0038] (1) Radial end;

[0039] (2) the missing speed value exceeds the first threshold;

[0040] (3) the resultant shear is below a second threshold;

[0041] (4) the total speed reduction measured along the segment exceeds a third threshold;

[0042] (5) The total speed increase measured along the segment exceeds the fourth threshold.

[0043] Furthermore, the identification module is further configured to:

[0044] Verifying the synthetic wind shear segment using a spectral width sensitive region image;

[0045] If the spectral width w along the synthetic wind shear segment does not reach the threshold of the spectral width, the synthetic wind shear segment is discarded, and the remaining synthetic wind shear segments that meet the threshold of the spectral width are integrated into an initial cluster;

[0046] Reading continuous two-dimensional regions in the initial cluster based on a binary method and deleting isolated points;

[0047] The clusters that ultimately remain are the locations of dry microbursts.

[0048] According to a third aspect of the present invention, there is provided a readable storage medium storing one or more programs, wherein the one or more programs can be executed by one or more processors to implement the method as described above.

[0049] The present invention has at least the following beneficial effects:

[0050] The present application adopts a laser wind radar to detect and identify dry micro-downbursts, solving the problem that the existing technology cannot identify dry micro-downbursts. The diameter of aerosol particles is 0.0001um-100um, the diameter of liquid precipitation particles is 0.2-8mm, and the diameter of solid precipitation particles is 0.2-25mm. The wavelength of the laser wind radar is usually 1550nm±10nm. Therefore, the laser wind radar has Rayleigh scattering and Mie scattering effects on sunny days, and can obtain stronger echoes, so that effective wind speed data in the radar scanning area can be obtained even in the absence of precipitation, and then detected by the detection method of the present invention. It solves the problem that the current X, S, and C weather radars cannot obtain the echo of aerosol particles in clear skies due to their wavelengths of centimeters, and cannot obtain the wind speed in the scanning area according to the Doppler effect; it solves the problem that the current technology cannot identify dry micro-downbursts and ensure the flight safety of aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart of a method for detecting and identifying dry microburst according to an embodiment of the present invention is shown;

[0052] Figure 2 A schematic diagram of Rayleigh scattering and Mie scattering effects of a laser wind measurement radar on a clear day according to an embodiment of the present invention is shown;

[0053] Figure 3A structural diagram of a dry microdownburst detection and identification method and device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a causal relationship between each other, the order in which they are described as examples herein should not be regarded as a limitation, and those skilled in the art should know that they can be adjusted in order, as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0055] Microbursts are a fatal threat to aircraft flight safety, and it is difficult to detect dry microbursts with existing technologies. Therefore, an embodiment of the present invention provides a dry microburst detection and identification method, which can realize dry microburst detection and identification to ensure the flight safety of civil aircraft. Figure 1 As shown, it is a flow chart of the method, and the method comprises the following steps:

[0056] Step 1: Determine the threshold of the spectral width w.

[0057] The radial wind data of the PPI scan of the laser wind radar is read and the spectral width is calculated. The observation test is carried out in the area where the radar is located for 1-2 years to obtain the threshold range (w1-w2) of dry wind shear with spectral width w.

[0058] Step 2: Mark sensitive areas within the radar scanning area.

[0059] According to the threshold value obtained in step 1, the sensitive area within the radar PPI scanning area with the radar spectrum width w within this threshold value is marked.

[0060] Step 3: Generate synthetic wind shear segments for sensitive areas.

[0061] The least squares method is applied to each radial velocity data of the radar scan to create a segment of synthetic wind shear. The unit of synthetic segment is s -1 , represents the change in velocity as one moves outward in the radial direction. This can show that areas of increased velocity are associated with diverging segments. Because least squares filters are sensitive to outliers and noisy data, a median filter is applied to the input velocity data prior to processing. If the velocity data does not contain enough valid data, or the correlation coefficient calculated during the least squares fit is too low, the value of the synthetic shear is set to a null value. After a synthetic shear segment is generated, the start of the segment is located by searching for an increase in velocity greater than a threshold. The segment then continues along that radial direction until one of the following occurs:

[0062] (1) Radial end;

[0063] (2) Too many missing speed values ​​are encountered, such as when more than 5 consecutive distance library speed values ​​are missing;

[0064] (3) encountering a composite shear below the threshold (0.015);

[0065] (4) The total speed measured along the section is reduced too much, such as reaching 10m / s;

[0066] (5) Too much total speed increase is detected, such as reaching 10m / s.

[0067] (4) and (5) are used to account for noisy signals because it allows small changes in velocity to be ignored, but does not allow large decreases or increases in velocity to be ignored, thereby retaining the synthetic segments to be discarded. Segments that do not meet the length requirements or the minimum velocity loss (maximum velocity difference across the segment) are discarded. Segments that are too long are broken into smaller segments.

[0068] Step 4: Generate synthetic wind shear clusters.

[0069] After the segments are formed, the generated spectral width sensitive area image is used to "verify" each segment. If the spectral width w along the segment does not reach the corresponding threshold, the segment is discarded. The remaining segments are clustered into initial "clusters". The binary connectivity method is then used to extract the continuous two-dimensional area in the "cluster" and delete the isolated points. The "cluster" that is left in the end is the location of the dry microburst - realizing the identification and positioning of the dry microburst.

[0070] This application uses a laser wind radar to detect and identify dry microbursts, solving the problem that the existing technology cannot identify dry microbursts. Figure 2 As shown, the diameter of aerosol particles is 0.0001um-100um, the diameter of liquid precipitation particles is 0.2-8mm, and the diameter of solid precipitation particles is 0.2-25mm. The wavelength of laser wind radar is usually 1550nm±10nm. Therefore, laser wind radar has Rayleigh scattering and Mie scattering effects on sunny days, and can obtain stronger echoes, so that effective wind speed data in the radar scanning area can be obtained even in the absence of precipitation, and then detected by the detection method of the present invention. It solves the problem that the current X, S, and C weather radars cannot obtain the echo of aerosol particles in clear skies due to their centimeter-level wavelengths, and thus cannot obtain the wind speed in the scanning area based on the Doppler effect; it solves the problem that current technology cannot identify dry micro-downbursts and ensure the flight safety of aircraft.

[0071] The embodiment of the present invention also provides a dry microburst detection and identification device, such as Figure 3 As shown, the device 300 includes:

[0072] A determination module 301 is configured to determine a threshold of a spectrum width;

[0073] The marking module 302 is configured to mark, according to the threshold of the spectrum width, an area in the area scanned by the radar once, where the radar spectrum width w is within the threshold as a sensitive area;

[0074] A synthesis module 303 is configured to create a synthetic wind shear segment for each radial velocity data scanned by the radar using a least square method as a synthetic wind shear segment of the sensitive area;

[0075] The identification module 304 is configured to screen the synthetic wind shear segments using the spectrum width sensitive region image to obtain synthetic wind shear clusters, and to identify and locate the dry microburst through the synthetic wind shear clusters.

[0076] In some embodiments, the determination module is further configured to read radial wind data scanned by a laser wind radar, calculate the spectral width, conduct observation tests on the area where the radar is located for 1-2 years, and obtain a threshold range (w1-w2) of dry wind shear with a spectral width w.

[0077] In some embodiments, the synthesis module is further configured to:

[0078] Obtaining velocity data for each radial of the radar scan;

[0079] Creating segments of synthetic wind shear for each radial velocity data using a least squares method, the segments representing changes in velocity when moving radially outward;

[0080] If the radial velocity data do not contain enough valid data, or if the correlation coefficient calculated during the least squares fit is too low, the value of the synthetic shear is set to a null value;

[0081] After a segment is generated, the start of the segment is located by searching for a velocity increase greater than a threshold, and the segment splitting continues along the radial direction of the segment until one of the following conditions is met:

[0082] (1) Radial end;

[0083] (2) the missing speed value exceeds the first threshold;

[0084] (3) the resultant shear is below a second threshold;

[0085] (4) the total speed reduction measured along the segment exceeds a third threshold;

[0086] (5) The total speed increase measured along the segment exceeds the fourth threshold.

[0087] In some embodiments, the identification module is further configured to:

[0088] Verifying the synthetic wind shear segment using a spectral width sensitive region image;

[0089] If the spectral width w along the synthetic wind shear segment does not reach the threshold of the spectral width, the synthetic wind shear segment is discarded, and the remaining synthetic wind shear segments that meet the threshold of the spectral width are integrated into an initial cluster;

[0090] Reading continuous two-dimensional regions in the initial cluster based on a binary method and deleting isolated points;

[0091] The clusters that ultimately remain are the locations of dry microbursts.

[0092] It should be noted that the various device structures described in this embodiment belong to the same technical concept as the previously described method, and achieve the same technical effect through the same principle, which will not be repeated here.

[0093] An embodiment of the present invention further provides a readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the method described in any of the above embodiments.

[0094] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims will be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the implementation of this application, and the examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0095] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present invention. This should not be interpreted as a feature of an invention that is not claimed for protection being necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of the embodiments of a specific invention. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently used as a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the full scope of the equivalent forms of the attached claims and these claims.

Claims

1. A dry microburst detection and identification method, characterized in that: The method comprises: Determine the threshold value of the spectral width; According to the threshold of the spectrum width, marking the area in the area scanned by the radar once where the radar spectrum width w is within the threshold as a sensitive area; A synthetic wind shear segment is created for each radial velocity data scanned by the radar using the least square method as the synthetic wind shear segment of the sensitive area; The synthetic wind shear segments are screened using spectral width sensitive region images to obtain synthetic wind shear clusters, and the dry microburst is identified and located through the synthetic wind shear clusters.

2. The method according to claim 1, characterized in that Determine the threshold value of the spectrum width, including: The radial wind data scanned by the laser wind radar is read and the spectrum width is calculated. The observation test is carried out in the area where the radar is located for 1-2 years to obtain the threshold range (w1-w2) of dry wind shear with a spectrum width w.

3. The method according to claim 1, characterized in that The least square method is used to create a synthetic wind shear segment for each radial velocity data scanned by the radar as the synthetic wind shear segment of the sensitive area, including: Obtaining velocity data for each radial of the radar scan; Creating segments of synthetic wind shear for each radial velocity data using a least squares method, the segments representing changes in velocity when moving radially outward; If the radial velocity data do not contain enough valid data, or if the correlation coefficient calculated during the least squares fit is too low, the value of the synthetic shear is set to a null value; After a segment is generated, the start of the segment is located by searching for a velocity increase greater than a threshold, and the segment splitting continues along the radial direction of the segment until one of the following conditions is met: (1) Radial end; (2) the missing speed value exceeds the first threshold; (3) the resultant shear is below a second threshold; (4) the total speed reduction measured along the segment exceeds a third threshold; (5) The total speed increase measured along the segment exceeds the fourth threshold.

4. The method according to claim 3, characterized in that The synthetic wind shear segments are screened by using the spectral width sensitive region image to obtain synthetic wind shear clusters, and the dry microburst is identified and located by using the synthetic wind shear clusters, including: Verifying the synthetic wind shear segment using a spectral width sensitive region image; If the spectral width w along the synthetic wind shear segment does not reach the threshold of the spectral width, the synthetic wind shear segment is discarded, and the remaining synthetic wind shear segments that meet the threshold of the spectral width are integrated into an initial cluster; Reading continuous two-dimensional regions in the initial cluster based on a binary method and deleting isolated points; The clusters that ultimately remain are the locations of dry microbursts.

5. A dry microburst detection and identification device, characterized in that: The device comprises: A determination module configured to determine a threshold value of a spectrum width; A marking module is configured to mark, according to the threshold of the spectrum width, an area in the area scanned by the radar once, where the radar spectrum width w is within the threshold as a sensitive area; A synthesis module is configured to create a synthetic wind shear segment for each radial velocity data scanned by the radar using a least square method as a synthetic wind shear segment of the sensitive area; The identification module is configured to screen the synthetic wind shear segments using the spectral width sensitive area image to obtain synthetic wind shear clusters, and to identify and locate dry microbursts through the synthetic wind shear clusters.

6. The device according to claim 5, characterized in that The determination module is further configured to read radial wind data scanned by the laser wind radar, calculate the spectrum width, conduct continuous observation tests for 1-2 years in the area where the radar is located, and obtain a threshold range (w1-w2) of dry wind shear with a spectrum width w.

7. The device according to claim 5, characterized in that The synthesis module is further configured to: Obtaining velocity data for each radial of the radar scan; Creating segments of synthetic wind shear for each radial velocity data using a least squares method, the segments representing changes in velocity when moving radially outward; If the radial velocity data do not contain enough valid data, or if the correlation coefficient calculated during the least squares fit is too low, the value of the synthetic shear is set to a null value; After a segment is generated, the start of the segment is located by searching for a velocity increase greater than a threshold, and the segment splitting continues along the radial direction of the segment until one of the following conditions is met: (1) Radial end; (2) the missing speed value exceeds the first threshold; (3) the resultant shear is below a second threshold; (4) the total speed reduction measured along the segment exceeds a third threshold; (5) The total speed increase measured along the segment exceeds the fourth threshold.

8. The method according to claim 7, characterized in that The identification module is further configured to: Verifying the synthetic wind shear segment using a spectral width sensitive region image; If the spectral width w along the synthetic wind shear segment does not reach the threshold of the spectral width, the synthetic wind shear segment is discarded, and the remaining synthetic wind shear segments that meet the threshold of the spectral width are integrated into an initial cluster; Reading continuous two-dimensional regions in the initial cluster based on a binary method and deleting isolated points; The clusters that ultimately remain are the locations of dry microbursts.

9. A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, executes the method according to any one of claims 1 to 4.

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