Weather early warning method and device based on radar data and meteorological data and storage equipment

Abstract

The invention discloses a weather early warning method and device based on radar data and meteorological data and storage equipment, and the method comprises the following steps: S1, obtaining high-temporal-spatial-resolution wind field data, an inversion wind shear field and a visibility field through a high-precision high-temporal-spatial-resolution coherent laser radar; s2, detecting atmospheric parameters of an overhead area of the airport in real time to form airport auxiliary observation data; s3, based on the wind shear field, the visibility field and the airport auxiliary observation data, artificial intelligence machine learning is used for data processing, and a forecasting scheme is formed. Compared with the prior art, the method is based on the high-precision and high-temporal-spatial-resolution coherent laser radar, atmospheric parameters of the area above the airport are detected in real time, high-temporal-spatial-resolution wind field data are obtained, and the prediction accuracy is improved. And inverting a wind shear field and a visibility field, and processing by using methods such as artificial intelligence machine learning and the like to form a forecasting scheme.

Description

Technical Field

[0001] The present invention relates to the field of wind shear prediction, and in particular to a weather warning method, device and storage device based on radar data and meteorological data. Background Art

[0002] With the development of my country's aviation industry, aviation throughput is increasing, and the demand for detection and early warning of low-altitude dangerous weather is becoming stronger. There are many types of low-altitude dangerous weather, especially airport wind shear, because it is irregular and uneven in time and space, and the means of observing atmospheric wind fields are limited by the level of existing instruments.

[0003] Therefore, the current wind shear warning process has problems such as low detection accuracy, false alarms, and missed alarms. Traditional transmission-type visibility meters and forward scattering visibility meters can only measure the average visibility in a small area, and the temporal and spatial continuity is insufficient. The use of laser radar can obtain the visibility profile on the airport flight path, but its detection accuracy is limited by the accuracy of the inverted extinction coefficient.

[0004] At present, the technical means used for airport wind shear detection mainly include Doppler weather radar, wind profiler, low-altitude wind shear warning system and laser wind measurement radar.

[0005] Doppler weather radar: The detection principle is the Doppler effect. Due to the relative motion between precipitation particles and the radar, there is a Doppler frequency shift between the received signal and the transmitted signal. By calculating the speed of precipitation particles relative to the radar movement, the atmospheric wind field can be inverted. According to the distribution and change form of the wind field, the appropriate threshold is selected to distinguish low-altitude wind shear. Advantages: The detection target of Doppler weather radar is precipitation particles. Therefore, when there is sufficient water vapor conditions near the airport, it has better detection performance for low-altitude wind shear accompanied by downbursts; Disadvantages: 1. The measured wind speed is only relative to the radial velocity of the radar, and the actual wind field needs to be inverted. However, the existing wind field inversion algorithm is not perfect, and there will be errors between the obtained wind field and the actual wind field; 2. The actual range of wind field detection is small, and the radar detection accuracy gradually decreases with the increase of distance, so the wind shear of smaller scale is often ignored; 3. Due to certain restrictions on site selection when building Doppler radar, the radar cannot be directly aimed at the airport for detection, so it cannot provide real-time runway wind field conditions, and there are some difficulties in practical application; 4. Since the detection target is precipitation particles, the detection ability of Doppler weather radar under clear sky conditions will be limited, and the detection effect is not good; 5. When used for detection and analysis of low-altitude wind shear, the operation is relatively complicated and requires professional operation. Its detection accuracy is between a few meters per second and more than ten meters per second. Summary of the invention

[0006] The purpose of the present invention is to address the problems of low accuracy of existing wind shear detection, false alarms, missed alarms, etc.; the existing visibility measurement can only measure the average visibility in a small area, and the temporal and spatial continuity representation is insufficient. To address this deficiency, a weather warning method, device and storage device based on radar data and meteorological data are proposed.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: The weather warning method based on radar data and meteorological data includes the following steps: S1. High-precision and high-temporal-resolution coherent lidar, which can obtain wind field data with high temporal-spatial resolution and invert wind shear field and visibility field; S2. Real-time detection of atmospheric parameters in the area above the airport to form airport auxiliary observation data; S3. Based on the wind shear field, visibility field and airport auxiliary observation data, artificial intelligence machine learning is used to process data and form a forecast plan.

[0008] As a further preferred embodiment of the present invention, the inversion of wind shear field comprises the following steps: Collecting echo signals by high-precision and high-temporal-spatial resolution coherent laser radar; the echo signals include frequency information and intensity information; according to , where V is the radial wind speed, is the laser wavelength, Indicates the frequency change; the radial wind speed V is calculated; The coherent wind laser radar is scanned to obtain the raw data of the laser radar; the raw data includes radial wind speed data and signal-to-noise ratio data; The quality of the raw data was controlled based on the K-neighbor frequency algorithm; Based on the least squares method, the radial shear value is calculated for m range gates in the radial direction of the radar. ; Calculate the tangential shear value for n range gates in the radar tangential direction ; according to , calculate the wind shear value of the horizontal wind field .

[0009] As a further preferred embodiment of the present invention, the coherent wind measurement laser radar adopts PPI scanning mode, glide slope scanning mode or DBS scanning mode for scanning.

[0010] As a further preference of the present invention, according to , calculate the radial shear value of m range gates in the radial direction of the radar ; according to , calculate the tangential shear value of n range gates in the tangential direction of the radar ; in, Indicates starting from the radial calculation point The radial velocity of the range gate, Indicates The distance from the range gate to the center of the radar, is the interval between the range gates, Indicates the length of the unit distance gate; represents the radial velocity of the jth range gate starting from the tangential calculation point, represents the azimuth of the jth range gate starting from the tangential calculation point, is the radar azimuth step length, is the angular resolution of the radar, Indicates the radar beam transmission distance.

[0011] As a further preferred embodiment of the present invention, the inversion of visibility field comprises the following steps: Determine whether the laser wavelength used deviates from the wavelength of 550nm; If the laser wavelength does not deviate from 550nm, based on the visibility inversion algorithm, , combined with the intensity information of the echo signal, the visibility is calculated ;in, For visibility, is the extinction coefficient, To identify the target contrast threshold, ; If the laser wavelength deviates from 550nm, the extinction coefficient make corrections; After correction, according to , calculate the visibility , in, , D is the distance from the starting point of the local detection light source; Set t as the time statistical period, calculate the average visibility at different altitudes, and obtain the visibility profile of visibility changing with altitude within time t.

[0012] As a further preferred embodiment of the present invention, S2 comprises the following steps: Through routine civil aviation meteorological observations, the near-surface wind speed, near-surface visibility value, precipitation, temperature, humidity and pressure data of the current airport are obtained to form a corresponding data set; Based on the percentile method, the noise is filtered out in different areas to obtain effective airport auxiliary observation data.

[0013] As a further preferred embodiment of the present invention, S3 comprises the following steps: Preprocess the wind shear field data, visibility field data and airport auxiliary observation data, and perform sample screening with the preprocessed data; Divide the filtered data into a training sample set and a test sample set; Construct a prediction model based on the generalized regression neural network method; By inputting the wind shear field data, visibility field data and airport auxiliary observation data at historical moments, the visibility value and wind shear value at future moments can be calculated; An early warning report is generated based on the predicted visibility and wind shear values ​​at future times.

[0014] As a further preferred embodiment of the present invention, the warning report includes a wind shear warning report and a visibility warning report; The wind shear warning report includes: When the wind shear value is less than 0.03, it is considered mild; When 0.03≤wind shear value<0.2, it is considered strong; When 0.2 ≤ wind shear value, it is considered severe; The visibility warning report includes: When visibility is ≤1km, visibility level is 1; When 1km<visibility≤10km, visibility level is 2; When visibility is less than 10km, the visibility level is 3.

[0015] A weather warning device based on radar data and meteorological data, the device comprising one or more processors and a memory, the memory being used to store one or more programs; when the one or more programs are executed by the processor, the processor is enabled to implement a weather warning method based on radar data and meteorological data.

[0016] A computer storage device stores at least one program instruction, and the at least one program instruction is used to be loaded and executed by a processor to implement a weather warning method based on radar data and meteorological data.

[0017] Compared with the prior art, the weather warning method, device and storage device based on radar data and meteorological data proposed in the present invention have the following beneficial effects: 1. The present invention is based on high-precision and high-temporal-resolution coherent laser radar, which detects the atmospheric parameters in the area above the airport in real time, obtains wind field data with high temporal-spatial resolution, inverts wind shear field and visibility field, and uses artificial intelligence machine learning and other methods to process and form a forecasting plan; 2. The present invention uses high-precision and high-temporal-space resolution coherent laser radar, combined with conventional meteorological data resources, to obtain high-precision, high-recognition, high-resolution wind shear and visibility fields (temporal-spatial distribution) within a small and medium scale range, thereby improving the airport's meteorological safety assurance capabilities; 3. The present invention increases the detection of visibility, which can clarify the measurement boundary of the laser radar. When the visibility is low, the confidence of the laser radar measurement data is reduced, and the confidence of the ground-assisted measurement data is improved; when the visibility is high, the confidence of the laser radar measurement data is improved, and the confidence of the ground-assisted measurement data is reduced, which provides data guarantee for all-day and all-weather airport meteorological safety monitoring; 4. The present invention uses three scanning modes and a combination of the three scanning modes to obtain a more accurate wind shear value, providing a reliable data source for wind shear warnings at airports; 5. The present invention performs partition noise filtering with airport ground data, which can effectively eliminate abnormal measurement values ​​of airport ground auxiliary data and provide a stable and effective auxiliary data source for the prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is the flow chart of wind shear inversion; Figure 3 It is a schematic diagram of radial wind speed quality control; Figure 4 It is a flowchart of the prediction algorithm based on machine learning. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Embodiment 1: Combination Figure 1-4 , a weather warning method based on radar data and meteorological data, comprising the following steps: S1. High-precision and high-temporal-resolution coherent lidar, obtains wind field data with high-temporal-resolution and high-resolution, and inverts wind shear field and visibility field.

[0021] The inversion of the wind shear field, i.e. wind shear inversion, comprises the following steps: The echo signal is collected by a high-precision and high-temporal-space resolution coherent laser radar; the echo signal includes frequency information and intensity information.

[0022] according to , where V is the radial wind speed, is the laser wavelength, Represents the frequency change; the radial wind speed V is calculated.

[0023] The coherent wind measurement lidar is scanned in PPI scanning mode, glide slope scanning mode or DBS scanning mode to obtain the lidar raw data; the raw data includes radial wind speed data and signal-to-noise ratio data.

[0024] The quality of the raw data was controlled based on the K-neighbor frequency algorithm.

[0025] The radial wind speed data were quality controlled based on the K-neighbor frequency algorithm, and the abnormal radial wind speed data and incomplete and missing radial wind speed data were eliminated to obtain the radial wind speed data that had passed the quality control.

[0026] The K-neighbor frequency algorithm is a general algorithm that removes outliers and fills in missing values ​​for the radial wind speed sector set obtained in each scanning cycle. The details are as follows: (1) Statistics based on radial wind speed The frequency of the radial wind speed data set of M×N size in different intervals in the M×N sliding window with centered is calculated. The intervals are divided into equal wind speed intervals. The minimum radial wind speed and the maximum radial wind speed are evenly divided into 10 wind speed intervals. The frequency P of each wind speed interval is counted, and the maximum frequency Pmax is counted. (2) Eliminate isolated points, isolated small areas, and singular points in continuous areas. It can be expressed as: ; (3) Fill in the missing values, which can be expressed as: ; in represents the median value of the radial wind speed in the sliding window. In the example of the present invention, K1=0.1×M×N, K2=0.5×M×N, such as Figure 3 shown.

[0027] Based on the least squares method, the radial shear value is calculated for m range gates in the radial direction of the radar. ; Calculate the tangential shear value for n range gates in the radar tangential direction .

[0028] , , in, Indicates starting from the radial calculation point The radial velocity of the range gate, Indicates The distance from the range gate to the center of the radar, is the interval between the range gates, Indicates the length of the unit distance gate; represents the radial velocity of the jth range gate starting from the tangential calculation point, represents the azimuth of the jth range gate starting from the tangential calculation point, is the radar azimuth step length, is the angular resolution of the radar, Indicates the radar beam transmission distance.

[0029] according to , calculate the wind shear value of the horizontal wind field .

[0030] The inversion of visibility field comprises the following steps: Determine whether the wavelength of the laser used deviates from the wavelength of 550nm.

[0031] If the laser wavelength does not deviate from 550nm, based on the visibility inversion algorithm, , combined with the intensity information of the echo signal, the visibility is calculated ;in, For visibility, is the extinction coefficient, To identify the target contrast threshold, .

[0032] If the laser wavelength deviates from 550nm, the extinction coefficient Make corrections.

[0033] After correction, according to , calculate the visibility , in, , D is the distance from the starting point of the local detection light source; Set t as the time statistical period, calculate the average visibility at different altitudes, and obtain the visibility profile of visibility changing with altitude within time t.

[0034] S2. Real-time detection of atmospheric parameters in the area above the airport to form airport auxiliary observation data.

[0035] Through routine civil aviation meteorological observations, the near-ground wind speed, near-ground visibility value, precipitation, temperature, humidity and pressure data of the current airport are obtained to form a corresponding data set.

[0036] Based on the percentile method, the partition noise is filtered to obtain effective airport auxiliary observation data. The data sets are sorted from small to large, and the top 10% of the minimum points and the bottom 10% of the maximum points are eliminated.

[0037] S3. Based on the wind shear field, visibility field and airport auxiliary observation data, artificial intelligence machine learning is used to process data and form a forecast plan.

[0038] The wind shear field data, visibility field data and airport auxiliary observation data are preprocessed, and sample screening is performed with the preprocessed data.

[0039] The filtered data is divided into a training sample set and a test sample set.

[0040] The data sets are normalized separately, with the value range of 0~1; the normalized data sets are divided into training sample sets and test sample sets, where 70% of the total number of samples are used as training sample sets and 30% of the total number of samples are used as test sample sets.

[0041] The prediction model is constructed based on the generalized regression neural network method.

[0042] By inputting the wind shear field data, visibility field data and airport auxiliary observation data at historical moments, the visibility value and wind shear value at future moments can be obtained.

[0043] An early warning report is generated based on the predicted visibility and wind shear values ​​at future times.

[0044] The wind shear warning report includes: When the wind shear value is less than 0.03, it is considered mild; When 0.03≤wind shear value<0.2, it is considered strong; When 0.2 ≤ wind shear value, it is considered severe; The visibility warning report includes: When visibility is ≤1km, visibility level is 1; When 1km<visibility≤10km, visibility level is 2; When visibility is less than 10km, the visibility level is 3.

[0045] Embodiment 2: A weather warning device based on radar data and meteorological data, the device comprising one or more processors and a memory, the memory being used to store one or more programs; when the one or more programs are executed by the processor, the processor is enabled to implement a weather warning method based on radar data and meteorological data.

[0046] Embodiment 3: A computer storage device stores at least one program instruction, and the at least one program instruction is used to be loaded and executed by a processor to implement a weather warning method based on radar data and meteorological data.

[0047] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A weather warning method based on radar data and meteorological data, characterized in that: The following steps are involved: S1. High-precision and high-temporal-resolution coherent lidar, which can obtain wind field data with high temporal-spatial resolution and invert wind shear field and visibility field; S2. Real-time detection of atmospheric parameters in the area above the airport to form airport auxiliary observation data; S3. Based on the wind shear field, visibility field and airport auxiliary observation data, artificial intelligence machine learning is used to process data and form a forecast plan.

2. The weather warning method based on radar data and meteorological data according to claim 1, characterized in that: The inversion of the wind shear field comprises the following steps: Collecting echo signals by high-precision and high-temporal-spatial resolution coherent laser radar; the echo signals include frequency information and intensity information; according to , where V is the radial wind speed, is the laser wavelength, Indicates the frequency change; the radial wind speed V is calculated; The coherent wind laser radar is scanned to obtain the raw data of the laser radar; the raw data includes radial wind speed data and signal-to-noise ratio data; The quality of the raw data was controlled based on the K-neighbor frequency algorithm; Based on the least squares method, the radial shear value is calculated for m range gates in the radial direction of the radar. ; Calculate the tangential shear value for n range gates in the radar tangential direction ; according to , calculate the wind shear value of the horizontal wind field .

3. The weather warning method based on radar data and meteorological data according to claim 2, characterized in that: The coherent wind laser radar is scanned in a PPI scanning mode, a glide slope scanning mode or a DBS scanning mode.

4. The weather warning method based on radar data and meteorological data according to claim 2, characterized in that: according to , calculate the radial shear value of m range gates in the radial direction of the radar ; according to , calculate the tangential shear value of n range gates in the tangential direction of the radar ; in, Indicates starting from the radial calculation point The radial velocity of the range gate, Indicates The distance from the range gate to the center of the radar, is the interval between the range gates, Indicates the length of the unit distance gate; represents the radial velocity of the jth range gate starting from the tangential calculation point, represents the azimuth of the jth range gate starting from the tangential calculation point, is the radar azimuth step length, is the angular resolution of the radar, Indicates the radar beam transmission distance.

5. The weather warning method based on radar data and meteorological data according to claim 1, characterized in that: The inversion of visibility field comprises the following steps: Determine whether the laser wavelength used deviates from the wavelength of 550nm; If the laser wavelength does not deviate from 550nm, based on the visibility inversion algorithm, , combined with the intensity information of the echo signal, the visibility is calculated ;in, For visibility, is the extinction coefficient, To identify the target contrast threshold, ; If the laser wavelength deviates from 550nm, the extinction coefficient make corrections; After correction, according to , calculate the visibility , in, , D represents the distance from the starting point of the local detection light source; Set t as the time statistical period, calculate the average visibility at different altitudes, and obtain the visibility profile of visibility changing with altitude within time t.

6. The weather warning method based on radar data and meteorological data according to claim 1, characterized in that: The S2 comprises the following steps: Through routine civil aviation meteorological observations, the near-surface wind speed, near-surface visibility value, precipitation, temperature, humidity and pressure data of the current airport are obtained to form a corresponding data set; Based on the percentile method, the noise is filtered out in different areas to obtain effective airport auxiliary observation data.

7. The weather warning method based on radar data and meteorological data according to claim 1, characterized in that: The S3 comprises the following steps: Preprocess the wind shear field data, visibility field data and airport auxiliary observation data, and perform sample screening with the preprocessed data; Divide the filtered data into a training sample set and a test sample set; Construct a prediction model based on the generalized regression neural network method; By inputting the wind shear field data, visibility field data and airport auxiliary observation data at historical moments, the visibility value and wind shear value at future moments can be calculated; An early warning report is generated based on the predicted visibility and wind shear values ​​at future times.

8. The weather warning method based on radar data and meteorological data according to claim 7, characterized in that: The warning reports include wind shear warning reports and visibility warning reports; The wind shear warning report includes: When the wind shear value is less than 0.03, it is considered slight; When 0.03≤wind shear value<0.2, it is considered strong; When 0.2 ≤ wind shear value, it is considered severe; The visibility warning report includes: When visibility is ≤1km, visibility level is 1; When 1km<visibility≤10km, visibility level is 2; When visibility is less than 10km, the visibility level is 3.

9. A weather warning device based on radar data and meteorological data, characterized in that: The device includes one or more processors and a memory, and the memory is used to store one or more programs; when the one or more programs are executed by the processor, the processor is enabled to implement a weather warning method based on radar data and meteorological data as described in any one of claims 1-8.

10. A computer storage device, characterized in that: The computer storage device stores at least one program instruction, and the at least one program instruction is used to be loaded and executed by the processor to implement the weather warning method based on radar data and meteorological data as described in any one of claims 1-8.

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