Horizontal divergence profile retrieval method based on wind profile radar networking

Abstract

This invention provides a method for inverting horizontal divergence profiles based on a network of wind profiler radars. The method includes: employing simulation analysis to evaluate the sensitivity of the horizontal divergence inversion results of each wind profiler radar to its horizontal wind, as well as the rules for determining the distance to the wind profiler radars and the rules for determining the maximum interior angle and area of ​​the triangle; based on the determined results, selecting three target wind profiler radars, and obtaining the horizontal divergence at a certain vertical height layer within the triangle formed by the three target wind profiler radars using near-real-time wind vertical profile product data files from the mesoscale network of wind profiler radars. This invention provides a method for inverting horizontal divergence profiles based on a network of wind profiler radars, which is an optimal method for inverting horizontal divergence vertical profiles suitable for monitoring and warning of signals in the early stages of convection. It can reduce horizontal divergence inversion errors, improve the precision and accuracy of strong convection monitoring and short-term warnings, and thus provide support for optimizing the layout of the mesoscale network of wind profiler radars.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mesoscale weather and atmospheric sounding, and particularly relates to a horizontal divergence profile inversion method based on a wind profiler radar network. BACKGROUND

[0002] Severe convective weather is one of the most serious disastrous weather, and many studies at home and abroad show that thermal instability conditions and dynamic lifting signals play an important role in the triggering and subsequent evolution of severe convection. In the aspect of the inversion of atmospheric divergence and vertical velocity closely related to dynamic lifting before the occurrence of convection, the predecessors use the triangular method based on three non-collinear wind profiler observation sites to invert the atmospheric dynamic parameters such as horizontal divergence, relative vorticity and vertical velocity in the air above the triangle by using the longitude and latitude coordinates of each vertex of the triangle and the vertical wind profile data, which can avoid the error caused by the interpolation of the wind field to the grid.

[0003] The wind profiler radar that can provide continuous wind profile observation is a representative device for realizing the divergence profile of atmospheric dynamic parameters. The triangle composed of three wind profilers can realize continuous inversion of atmospheric dynamic parameter profile, and further reveal the evolution characteristics of atmospheric dynamic structure, helping to monitor and warn the development process of mesoscale systems. However, the distance between different wind profilers and the change of the shape of the triangle (such as obtuse angle, right angle and acute angle) have a great influence on the divergence inversion result, resulting in a great error in the above divergence profile inversion algorithm and technology with different triangle networking. Therefore, how to reduce the divergence profile inversion error is a problem that needs to be solved at present. SUMMARY

[0004] In view of the defects in the prior art, the application provides a horizontal divergence profile inversion method based on a wind profiler radar network, which can effectively solve the above problems.

[0005] The technical scheme adopted by the application is as follows:

[0006] The application provides a horizontal divergence profile inversion method based on a wind profiler radar network, comprising the following steps:

[0007] Step 1, for a mesoscale network composed of multiple wind profilers, the sensitivity of the horizontal divergence inversion result of each wind profiler to its horizontal wind is evaluated by using a simulation analysis method;

[0008] Step 2, the sensitivity of the horizontal divergence inversion result to the distance of the wind profiler is evaluated by using a simulation analysis method, and a determination rule of the distance of the wind profiler is obtained;

[0009] Step 3, the sensitivity of the horizontal divergence inversion result to the shape of the triangle is evaluated by using a simulation analysis method, and a determination rule of the maximum internal angle of the triangle and the area of the triangle is obtained;

[0010] Step 4, for the mesoscale network composed of multiple wind profiler radars, the sensitivity of the horizontal divergence inversion result of the wind profiler radar determined in step 1 to the horizontal wind of the wind profiler radar, select the wind profiler radar whose sensitivity to the horizontal divergence inversion result is lower than the set threshold, and select three target wind profiler radars according to the determination rules of the wind profiler radar distance, the maximum internal angle of the triangle and the triangle area determined in steps 2 and 3, and obtain the horizontal divergence D of a certain vertical height layer in the triangle formed by the three target wind profiler radars according to the wind profiler radar mesoscale network near real-time wind vertical profile product data file.

[0011] Preferably, step 1 is specifically:

[0012] Step 1.1, for the wind profiler radar to be evaluated, denoted as: the third wind profiler radar P3; an equilateral triangle composed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 is constructed;

[0013] Step 1.2, a plane rectangular coordinate system is established, the x-axis and the y-axis are the east-west and south-north directions respectively, and the east and north directions are defined as positive directions; in the plane rectangular coordinate system, the coordinates of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 are (x1, y1), (x2, y2) and (x3, y3) respectively;

[0014] Step 1.3, let the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 be 0, continuously change the horizontal wind direction of the third wind profiler radar P3 in the range of 0-360°, and obtain the horizontal divergence D in the triangle ΔP1P2P3 at a certain height layer and the relative error of the horizontal divergence D under each horizontal wind direction of the third wind profiler radar P3, so as to obtain the sensitivity of the horizontal divergence inversion relative error of the third wind profiler radar P3 to its horizontal wind direction;

[0015] Step 1.4, let the wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 be 0, continuously change the horizontal wind speed of the third wind profiler radar P3 in the set range, and obtain the horizontal divergence D in the triangle ΔP1P2P3 at a certain height layer and the relative error of the horizontal divergence D under each horizontal wind speed of the third wind profiler radar P3, so as to obtain the sensitivity of the horizontal divergence inversion relative error of the third wind profiler radar P3 to its horizontal wind speed;

[0016] Step 1.5, the sensitivity of the horizontal divergence inversion result of the third wind profiler radar P3 to the horizontal wind of the third wind profiler radar P3 is obtained by comprehensively considering the sensitivity of the horizontal divergence inversion relative error of the third wind profiler radar P3 to its horizontal wind direction and the sensitivity of the horizontal divergence inversion relative error of the third wind profiler radar P3 to its horizontal wind speed.

[0017] Preferably, step 2 is specifically:

[0018] Step 2.1, constructing an equilateral triangle composed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3;

[0019] Step 2.2, setting the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 to be 0, and the horizontal wind speed of the third wind profiler radar P3 to be a set value; the horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 is 0°;

[0020] Step 2.3, keeping the horizontal wind direction and the horizontal wind speed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 unchanged;

[0021] Making the side length L of the equilateral triangle change in a set distance by a step, at each distance, calculating the horizontal divergence D in the triangle ΔP1P2P3 of a certain height layer, obtaining the function curve of the side length L and the horizontal divergence D, and The function curve of

[0022] Step 2.4, analyzing the function curve of the side length L and the horizontal divergence D, and The function curve of , obtaining the side length L which has the least influence on the inversion result of the horizontal divergence D, which is the minimum threshold L of the wind profiler radar distance min , that is, when constructing the triangle, the wind profiler radar distance needs to be greater than the minimum threshold L min .

[0023] Preferably, step 3 is specifically:

[0024] Step 3.1, constructing a triangle composed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3, wherein the first wind profiler radar P1 and the second wind profiler radar P2 are located on the x-axis and symmetric about the origin, and the positions of the first wind profiler radar P1 and the second wind profiler radar P2 are fixed;

[0025] The initial position of the third wind profiler radar P3 is located on the y-axis and forms an equilateral triangle with the first wind profiler radar P1 and the second wind profiler radar P2, and at this time the area of the triangle is S0;

[0026] Step 3.2: Keep the area S0 of the triangle constant; the third wind profiler radar P3 moves horizontally to the right from its initial position, that is, keep the ordinate of the third wind profiler radar P3 constant, and its abscissa increases continuously from 0, so that the interior angle ∠P1P2P3 increases continuously to infinitely close to 180°; where, at each position, the interior angle ∠P1P2P3 is the largest interior angle of triangle P1P2P3, called the largest interior angle A of the triangle;

[0027] Step 3.3: Set the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 to 0, and the horizontal wind speed of the third wind profiler radar P3 to a set value; set the horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3 to 0°; keep the horizontal wind speed and horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3 fixed.

[0028] During the movement of the third wind profiler radar P3 according to step 3.2, at each position, when the maximum interior angle A of the current triangle is reached, the horizontal divergence D within the triangle ΔP1P2P3 at a certain height layer is calculated. Thus, under the current triangle area S0, the function curve of the maximum interior angle A of the triangle versus the horizontal divergence D is obtained. Function curve;

[0029] Step 3.4: Change the area S0 of the triangle and repeat steps 3.1-3.3;

[0030] Therefore, for each selected triangle area S0, we obtain the function curves of the maximum interior angle A and the horizontal divergence D of the triangle, and Function curve;

[0031] The function curves of the maximum interior angle A and the horizontal divergence D for each triangle, and By analyzing the function curve, we obtain the influence of the triangle area S0 and the maximum interior angle A on the horizontal divergence inversion results, and obtain the minimum threshold S0(min) of the triangle area S0 and the maximum value A(max) of the maximum interior angle A. Therefore, when constructing the triangle, we need to make the constructed triangle area S0 greater than the minimum threshold S0(min) and the maximum interior angle A of the triangle less than the maximum value A(max).

[0032] The horizontal divergence profile inversion method based on wind profiler radar networking provided by this invention has the following advantages:

[0033] The application provides a horizontal divergence profile inversion method based on a wind profile radar network, which is a horizontal divergence vertical profile optimal inversion method suitable for monitoring and early warning of preconvective signals, mainly based on any three wind profile radars, quantitative evaluation of the sensitivity of horizontal divergence profile inversion results to single-station horizontal wind observation errors, distances between different wind profile radars and maximum internal angles and other variables, thereby reducing the horizontal divergence inversion error, improving the fine and accurate degree of severe convective monitoring and short-term warning, and further providing support for optimizing the layout of the wind profile radar mesoscale station network. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 A flowchart of the horizontal divergence profile inversion method based on the wind profile radar network provided by the application is provided.

[0035] Figure 2 A triangular graph for evaluating the sensitivity of divergence inversion to horizontal wind observation errors is provided by the application.

[0036] Figure 3 A graph of the horizontal divergence calculated by the equilateral triangle changing with the horizontal wind of one of the three vertices is provided by the application.

[0037] Figure 4 A triangular graph for evaluating the sensitivity of horizontal divergence inversion to site distance is provided by the application.

[0038] Figure 5 A graph of the functional relationship between the side length of the equilateral triangle and the average divergence of the area is provided by the application.

[0039] Figure 6 A triangular graph for evaluating the sensitivity of divergence inversion to the shape of the triangle is provided by the application.

[0040] Figure 7 A graph of the functional relationship between the maximum internal angle of the triangle and the average divergence of the area is provided by the application.

[0041] Figure 8 A ground temperature and ground flow line graph of the Yangtze River Delta region obtained from the observation of the ground automatic station from 14:00 to 16:30 on July 28, 2018 is provided by the application.

[0042] Figure 9 A divergence vertical profile graph above the triangle from 14:00 to 16:30 on July 28, 2018 in Beijing time is provided by the application. DETAILED DESCRIPTION

[0043] In order to make the technical problems, technical solutions and beneficial effects solved by the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0044] The method belongs to the field of mesoscale meteorology and atmospheric detection, and particularly relates to a horizontal divergence inversion method based on a wind profile radar mesoscale observation network, which can be applied to the monitoring and early warning of thunderstorm gale, hail, tornado, short-time heavy precipitation and other severe convective weather. The horizontal divergence profile inversion method based on the wind profile radar networking provided by the present application mainly uses any three wind profile radars to simulate the influence of single-station horizontal wind observation error, the distance between different wind profile radars, the area of a triangle and the largest internal angle on the horizontal divergence profile inversion result, quantitatively evaluates the sensitivity to the inversion result, and proposes an optimal horizontal divergence profile inversion method, which can effectively reduce the horizontal divergence inversion error and improve the monitoring capability of the early stage signal of convection, and at the same time, provides a scientific basis for the reasonable arrangement of the future wind profile radar mesoscale station network.

[0045] Convergence and divergence are closely related to atmospheric vertical motion, and horizontal divergence is an important atmospheric dynamic parameter for describing convergence and divergence, therefore, horizontal divergence is often applied to the diagnostic analysis of atmospheric vertical motion. The present application inverses the horizontal divergence and other atmospheric dynamic parameters in the air above a triangle based on three wind profile radars which are not collinear, and uses the longitude and latitude coordinates of each vertex of the triangle and wind vertical profile data.

[0046] The L-band wind profile radar deployed by the meteorological department has been arranged for more than 160, which can provide near real-time wind vertical profile products with a time resolution of 6 minutes. As an important part of the new generation of ground-based atmospheric remote sensing observation system, the L-band wind profile radar has the advantages of continuity, unattended, all-weather monitoring and providing low-layer atmospheric three-dimensional wind field. Based on any three wind profile radars, the present application simulates and analyzes the uncertainty caused by three variables, i.e., single-station horizontal wind observation error, wind profile radar distance and triangle shape, to the horizontal divergence inversion result, and proposes an optimal horizontal divergence profile method suitable for monitoring the early stage signal of convection, and the overall technical route is shown in Figure 1 The detailed steps are as follows:

[0047] In the following description, in order to calculate the horizontal divergence D, the calculation method of the horizontal divergence D is first given:

[0048] Let P1, P2, P3 be three wind stations, P1, P2, P3 three wind stations as three vertices, the triangle formed by the approximate plane triangle, establish a plane rectangular coordinate system, the x axis and y axis are east and south respectively, and define east and north as positive direction; in the plane rectangular coordinate system, the coordinates of P1, P2, P3 wind stations are (x1, y1), (x2, y2), (x3, y3) respectively;

[0049] The longitude direction wind speed components observed by P1, P2, P3 at a certain time on a certain height layer are u1, u2, u3 respectively, and the latitude direction wind speed components are v1, v2, v3 respectively. Through the following formula, the horizontal divergence D in the triangle of the height layer is obtained:

[0050]

[0051] The application provides a horizontal divergence profile inversion method based on wind profile radar networking, referring to Figure 1 , comprising the following steps:

[0052] Step 1, for the mesoscale network composed of multiple wind profile radars, the sensitivity of the horizontal divergence inversion result of each wind profile radar to its horizontal wind is evaluated by using simulation analysis method;

[0053] This step is specifically:

[0054] Step 1.1, for the wind profile radar to be evaluated, represented as: the third wind profile radar P3; an equilateral triangle composed of the first wind profile radar P1, the second wind profile radar P2 and the third wind profile radar P3 is constructed;

[0055] Step 1.2, a plane rectangular coordinate system is established, the x axis and y axis are east and south respectively, and the east and north are defined as positive direction; in the plane rectangular coordinate system, the coordinates of the first wind profile radar P1, the second wind profile radar P2 and the third wind profile radar P3 are (x1, y1), (x2, y2), (x3, y3) respectively;

[0056] Step 1.3, let the horizontal wind speed of the first wind profile radar P1 and the second wind profile radar P2 be 0, continuously change the horizontal wind direction of the third wind profile radar P3 in the range of 0-360°, under each horizontal wind direction of the third wind profile radar P3, the horizontal divergence D in the triangle ΔP1P2P3 of a certain height layer and the relative error of the horizontal divergence D are obtained, so as to obtain the sensitivity of the horizontal divergence inversion relative error of the third wind profile radar P3 to its horizontal wind direction;

[0057] Step 1.4, let the wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 be 0, continuously change the horizontal wind speed of the third wind profiler radar P3 within a set range, at each horizontal wind speed of the third wind profiler radar P3, obtain the horizontal divergence D in the triangle ΔP1P2P3 at a certain height layer and the relative error of the horizontal divergence D, so as to obtain the sensitivity of the horizontal divergence retrieval relative error of the third wind profiler radar P3 to its horizontal wind speed;

[0058] Step 1.5, comprehensively consider the sensitivity of the horizontal divergence retrieval relative error of the third wind profiler radar P3 to its horizontal wind direction and the sensitivity of the horizontal divergence retrieval relative error of the third wind profiler radar P3 to its horizontal wind speed, to obtain the sensitivity of the horizontal divergence retrieval result of the third wind profiler radar P3 to its horizontal wind.

[0059] Step 2, using simulation analysis method, evaluate the sensitivity of the horizontal divergence retrieval result to the wind profiler radar distance, to obtain the determination rule of the wind profiler radar distance;

[0060] This step is specifically:

[0061] Step 2.1, construct an equilateral triangle composed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3;

[0062] Step 2.2, let the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 be 0, and the horizontal wind speed of the third wind profiler radar P3 be a set value; the horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 is 0°;

[0063] Step 2.3, keep the horizontal wind direction and the horizontal wind speed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 unchanged;

[0064] Change the side length L of the equilateral triangle by a step within a set distance, at each distance, calculate the horizontal divergence D in the triangle ΔP1P2P3 at a certain height layer, obtain the function curve of the side length L and the horizontal divergence D, and The function curve of

[0065] Step 2.4, analyze the function curve of the side length L and the horizontal divergence D, and The function curve of min , that is, when constructing the triangle, the wind profiler radar distance needs to be greater than the minimum threshold L min .

[0066] Step 3, using the simulation analysis method, the sensitivity of the horizontal divergence inversion result to the triangle shape is evaluated to obtain the determination rule of the maximum internal angle of the triangle and the area of the triangle;

[0067] This step is specifically:

[0068] Step 3.1, a triangle composed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 is constructed, wherein the first wind profiler radar P1 and the second wind profiler radar P2 are located on the x-axis and symmetric about the origin, and the positions of the first wind profiler radar P1 and the second wind profiler radar P2 are fixed and unchanged;

[0069] The initial position of the third wind profiler radar P3 is located on the y-axis and forms an equilateral triangle with the first wind profiler radar P1 and the second wind profiler radar P2, at this time the area of the triangle is S0;

[0070] Step 3.2, keeping the area S0 of the triangle fixed and unchanged, the third wind profiler radar P3 is moved horizontally to the right from the initial position, that is, the longitudinal coordinate of the third wind profiler radar P3 is fixed and unchanged, and the transverse coordinate is continuously increased from 0, so that the internal angle ∠P1P2P3 is continuously increased to infinitely close to 180°; wherein at each position, the internal angle ∠P1P2P3 is the maximum internal angle of the triangle P1P2P3, referred to as the maximum internal angle A of the triangle;

[0071] Step 3.3, the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 is 0, and the horizontal wind speed of the third wind profiler radar P3 is a set value; the horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 is 0°; keeping the horizontal wind speed and the horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 fixed and unchanged;

[0072] During the movement of the third wind profiler radar P3 according to the method of step 3.2, at each position, the horizontal divergence D in the triangle ΔP1P2P3 at a certain height layer is calculated at the current maximum internal angle A of the triangle, thereby obtaining the function curve of the maximum internal angle A of the triangle and the horizontal divergence D under the current triangle area S0, and the function curve;

[0073] Step 3.4, changing the triangle area S0, repeating steps 3.1-3.3;

[0074] Therefore, under each selected triangle area S0, the function curve of the maximum internal angle A of the triangle and the horizontal divergence D is obtained, and the function curve;

[0075] The function curves of the maximum internal angle A of each triangle and the horizontal divergence D, and The function curves of the maximum internal angle A of each triangle and the horizontal divergence D, and

[0076] In practical application, after the relevant rules determined by steps 1, 2 and 3, the mesoscale network near real-time wind vertical profile product data file of wind profile radar can be verified.

[0077] The mesoscale network near real-time wind vertical profile product data file of wind profile radar is read by the following method:

[0078] The mesoscale network near real-time wind vertical profile product data file of wind profile radar provided by the meteorological bureau is in txt format. The file name includes district station number, observation date and world time, product identification, radar model and other information, represented as Z_RADR_IIiii_WPRD_CAMS_NWQC_product identification_radar model_QI_yyyyMMddhhmmss.TXT.

[0079] For example, the following file name:

[0080] Z_RADA_54511_WPRD_CAMS_NWQC_OOBS_LC_QI_20180601000000.TXT, wherein Z is the domestic exchange file; RADA is the radar data; IIiii is the station number of the wind profile radar station; WPRD is the wind profile radar data; yyyyMMddhhmmss is the observation universal time (wherein yyyy is the year, mm is the month, dd is the day, hh is the hour, mm is the minute, and ss is the second, which are the same below); LC is the radar model identifier, representing the L-band boundary layer wind profile radar; ROBS is the wind profile radar product identifier. It represents the real-time product data file. The daily real-time wind vertical profile product data text file of each station in the wind profile radar network is stored in the daily date folder in the compressed package format. The folder name format is the observation universal time year month day, that is, yyyymmdd, for example, 20180601, 20180602, and the like. The number of compressed packages in each date folder is the number of stations returning data on the day. A subfolder is established in the date folder, and the subfolder name is the station number, in the format of IIiii, for example, 54511, 54399, and the like. The real-time wind vertical profile product data text file of each station on the day is decompressed into the corresponding folder. The time resolution of the real-time wind vertical profile product data text file is 6 minutes. If there is no missing data, there are 240 observation files in each station subfolder on the day.

[0081] Select a certain time and a certain station. The latitude, longitude, and altitude of the station and the wind field product entity data at that time are needed to be read, and the horizontal wind at the sampling height is vector decomposed to obtain the wind speed components in the latitude direction and the longitude direction. If the observation file of a station at a time is missing, the column is set as the missing value NAN. Before applying the wind profile radar data, the data quality should be investigated to avoid errors in horizontal wind inversion caused by instrument failure.

[0082] Step 4, for a mesoscale network composed of multiple wind profile radars, according to the sensitivity of the horizontal divergence inversion result of the wind profile radar determined in step 1 to the horizontal wind, select the wind profile radar with a sensitivity lower than a set threshold, and select three target wind profile radars according to the determination rules of the wind profile radar distance, the maximum internal angle of the triangle, and the triangle area determined in steps 2 and 3, and obtain the horizontal divergence D of a certain vertical height layer in the triangle formed by the three target wind profile radars according to the near real-time wind vertical profile product data file of the wind profile radar mesoscale network.

[0083] A specific embodiment is introduced below:

[0084] Step 1) Evaluate the sensitivity of the horizontal divergence inversion result to the horizontal wind of a single station

[0085] In this module, the shape of the triangle is fixed as an equilateral triangle. Take the triangle side length as 100 km for example. Assume the coordinates of P1, P2, P3 are (-50 km, 0), (50 km, 0), (0, 100 km) respectively. As shown in FIG. 1, a plot of the equilateral triangle formed by P1, P2, P3 is used to evaluate the sensitivity of horizontal divergence retrieval to horizontal wind observation error. Figure 2

[0086] Let the wind speed at P1 and P2 be both 0. Only change the horizontal wind direction at P3 continuously in the range of 0-360° and change the horizontal wind speed at P3 continuously in the range of 1-20 m s -1 As shown in FIG. 2, a plot of the horizontal divergence calculated for the equilateral triangle as a function of the horizontal wind (including wind speed and direction) at one of the three vertices. Figure 3 Figure 3 It is shown that when the horizontal wind direction is constant, changing the horizontal wind speed at one station only changes the absolute value of the horizontal divergence but not the sign. Under the condition of strong wind, i.e. the horizontal wind speed is greater than 15 m s -1 , the relative error of horizontal divergence retrieval caused by 1 m s -1 horizontal wind speed deviation is less than 5%. When the horizontal wind speed is constant, the horizontal divergence retrieval result shows a two-pole distribution as the horizontal wind direction changes from 0° to 360°. The relative error of horizontal divergence caused by 1° change in horizontal wind direction is less than 5‰.

[0087] Step 2) Evaluate the sensitivity of horizontal divergence retrieval to station distance

[0088] In this module, the shape of the triangle is fixed as an equilateral triangle. Take the triangle side length as 100 km for example. Assume the coordinates of P1, P2, P3 are (-50 km, 0), (50 km, 0), (0, 100 km) respectively. P3 coordinates are As shown in FIG. 3, a plot of the equilateral triangle is used to evaluate the sensitivity of horizontal divergence retrieval to station distance. Figure 4

[0089] Let the horizontal wind speed at P1 and P2 be both 0; the horizontal wind speed at P3 be 10 m s -1 , the horizontal wind direction be 0°, and the horizontal wind at the three vertices remain unchanged. Calculate the horizontal divergence in the triangle. As shown in FIG. 4, a plot of the equilateral triangle side length L as a function of horizontal divergence D (solid line) and Figure 5

[0090] Figure 5 ​​​​​This indicates that as the side length of the equilateral triangle increases from 10 km to 20 km, the absolute value of the area-average divergence D decreases sharply. When the side length is 10 km, an increase of 1 km in side length results in a decrease in divergence of approximately 10 × 10⁻⁶. -5 s -1 However, when the side length exceeds 50km, the change in divergence is less affected by the side length of the triangle.

[0091] Step 3) Evaluate the sensitivity of divergence inversion to triangle shape.

[0092] In this module, the area S0 of the triangle is fixed at 250 km². 2 Assume that P1 and P2 are both located on the x-axis and are symmetric about the origin. The coordinates of P1 and P2 are respectively... P3 ordinate remains unchanged The x-coordinate remains unchanged, and the x-coordinate continuously increases from 0, making ∠P1P2P3 the largest interior angle of triangle P1P2P3. The angle of ∠P1P2P3 is denoted as A, and angle A continuously increases from 60° to almost 180°. For example... Figure 6 The figure shown is a triangle diagram used to evaluate the sensitivity of divergence inversion to the shape of a triangle.

[0093] Set the horizontal wind speed at points P1 and P2 to 0, and the horizontal wind speed at point P3 to 10 m / s. -1 Given a horizontal wind direction of 0°, with the horizontal winds at all three vertices remaining constant, calculate the magnitude of the horizontal divergence within the triangle. Then, change the area S0 of the triangle to 250 km². 2 500km 2 1000km 2 2000km 2 4000km 2 Repeat the above steps. Figure 7 The figure shows the functions of the maximum interior angle (A) and the horizontal divergence (D, solid line) of a triangle, and (Dashed lines), different colors represent different triangle areas.

[0094] Figure 7 This indicates that, for the same area, the absolute value of the area-average divergence increases as the maximum interior angle A increases from 60°. When the maximum interior angle is less than 120°, the shape of the triangle has a relatively small impact on the divergence calculation results. The smaller the area of ​​the triangle, the greater the impact of changes in the triangle shape on the divergence calculation. Therefore, it is advisable to avoid selecting triangles with a maximum interior angle exceeding 140° and an area less than 500 km². 2 A triangle is used to obtain more accurate divergence calculation results.

[0095] Step 4) Dynamic Parameter Calculation Module

[0096] In the practical application of the optimal triangle method, for the mesoscale network composed of multiple wind profile radars, the horizontal wind should be obtained by referring to step 1 and trying to eliminate the data of the station with high horizontal wind inversion error. The triangle should be constructed by referring to steps 2 and 3 to avoid selecting a triangle with a maximum internal angle greater than 140° and an area less than 500 km2. If P1, P2, and P3 are three wind profile radar stations selected according to the above-mentioned standards, the positions are wherein λ1 is the longitude value of the target wind profile radar station P1, is the latitude value of the target wind profile radar station P1; λ2 is the longitude value of the target wind profile radar station P2, is the latitude value of the target wind profile radar station P2; λ3 is the longitude value of the target wind profile radar station P3, is the latitude value of the target wind profile radar station P3.

[0097] The east-west direction distance x3-x1 and the north-south direction distance y3-y1 between P3 and P1 are calculated by the following formula:

[0098]

[0099]

[0100] The east-west direction distance x2-x1 and the north-south direction distance y2-y1 between P2 and P1 are calculated by the following formula:

[0101]

[0102]

[0103] wherein R is the average radius of the earth, and π is the circular constant;

[0104] The latitude direction wind speed components observed by P1, P2, and P3 at a certain height layer at a certain time are u1, u2, and u3 respectively, and the latitude direction wind speed components are v1, v2, and v3 respectively. The horizontal divergence D in each vertical standard layer is calculated by substituting the following formula:

[0105]

[0106] An embodiment is introduced as follows:

[0107] In this embodiment, the Jiading station in the north of Shanghai, the Jinshan station in the south of Shanghai, and the Jiaxing station in the east of Zhejiang are selected to form a triangle, the maximum internal angle of the triangle is about 75°, and the side length is in the range of 70-150 km, which meets the optimal triangle standard. For example, Figure 8The ground temperature (filled) and ground streamlines (blue solid line) in the Yangtze River Delta region from 14:00 to 16:30 on July 28, 2018 are shown, which are obtained from the automatic ground station observation. Figure 8 It can be seen that the streamlines converge and continuously strengthen in the wind profile radar triangular network area after 14:00, which means that there is convergent ascending motion, and the near-surface temperature decreases after 15:30 when the precipitation occurs. The high-precision vertical wind profile provided by the three wind profile radars is used to calculate the distribution of horizontal divergence at each height layer in the triangle, as shown in Figure 9 , Figure 9 The vertical divergence profile (filled) in the triangle from 14:00 to 16:30 on July 28, 2018 is shown, and red (blue) represents positive (negative) divergence. The green and purple solid lines represent the 6-minute maximum rainfall and cloud top brightness temperature, respectively. It can be seen that the local convection is closely related to the development of convective clouds caused by the dynamic lifting effect of low-level convergence and high-level divergence above the triangle. According to the convergence and divergence field retrieved from the three-dimensional wind field provided by the wind profile radar, the variation characteristics of the atmospheric dynamic conditions before the occurrence of severe convective weather can be analyzed, and the ascending motion in the mesoscale network can be precisely captured, thereby providing a basis for judging the occurrence and development of mesoscale convective systems.

[0108] When the traditional method is used to retrieve atmospheric dynamic parameters such as horizontal divergence above the triangle, the twice-a-day observation of the sounding balloon cannot capture the variation characteristics of the troposphere, while the wind profile radar has the advantages of continuous observation, high degree of automation, etc. It can provide real-time information of horizontal wind direction, wind speed, vertical speed, refractive index structure constant and other element information, which can make up for the low resolution of sounding observation. The innovation of the present application lies in quantitatively evaluating the sensitivity of divergence profile retrieval to single station horizontal wind observation error, site distance and triangle shape, and thus an optimal method for revealing the horizontal divergence profile of one of the signals before the occurrence of convection is proposed. The results show that when the triangle is actually selected, the data quality of the wind profile radar needs to be evaluated, and the station with smaller horizontal wind error should be selected. In addition, the triangle with a maximum internal angle of more than 140° and an area of less than 500km 2 should be avoided to obtain more accurate horizontal divergence calculation results. The optimal method can capture and more accurately judge the occurrence and development process of mesoscale systems. In addition, it can provide an important scientific basis for the layout of the mesoscale station network of the wind profile radar in the future.

[0109] Therefore, the application provides a horizontal divergence profile inversion method based on a wind profile radar network, which is an optimal horizontal divergence vertical profile inversion method suitable for monitoring and early warning of a convective trigger early stage signal, mainly based on any three wind profile radars, and quantitatively evaluating the sensitivity of the horizontal divergence profile inversion result to variables such as a single-station horizontal wind observation error, a distance between different wind profile radars, and a maximum internal angle, so as to reduce the horizontal divergence inversion error, improve the fine and accurate degree of strong convective monitoring and short-term early warning, and further provide support for optimizing a wind profile radar mesoscale station network layout.

[0110] The above only describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for inverting horizontal divergence profiles based on wind profiler radar networking, characterized in that, Includes the following steps: Step 1: For a mesoscale network composed of multiple wind profiler radars, a simulation analysis method is used to evaluate the sensitivity of the horizontal divergence inversion results of each wind profiler radar to its horizontal wind. Step 2: Using simulation analysis, the sensitivity of the horizontal divergence inversion results to the wind profiler radar range is evaluated, and the determination rule for the wind profiler radar range is obtained. Step 3: Using simulation analysis, evaluate the sensitivity of the horizontal divergence inversion results to the shape of the triangle, and obtain the rules for determining the maximum interior angle and area of ​​the triangle. Step 4: For a mesoscale network composed of multiple wind profiler radars, based on the sensitivity of the horizontal divergence inversion results of the wind profiler radars to horizontal wind determined in Step 1, select wind profiler radars whose sensitivity to the horizontal divergence inversion results is lower than a set threshold. Then, according to the rules for determining the wind profiler radar range, the maximum interior angle of the triangle, and the area of ​​the triangle determined in Steps 2 and 3, select three target wind profiler radars. Finally, based on the near-real-time vertical wind profiler product data file of the mesoscale network of wind profiler radars, obtain the horizontal divergence D of a certain vertical height layer within the triangle formed by the three target wind profiler radars.

2. The horizontal divergence profile inversion method based on wind profiler radar networking according to claim 1, characterized in that, Step 1 is as follows: Step 1.1, for the wind profiler radar being evaluated, it is represented as: the third wind profiler radar P3; construct an equilateral triangle composed of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3; Step 1.2: Establish a Cartesian coordinate system, with the x-axis and y-axis representing the east-west and north-south directions, respectively, and define east and north as positive directions; in the Cartesian coordinate system, the coordinates of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3 are (x1, y1), (x2, y2), and (x3, y3), respectively; Step 1.3: Set the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 to 0. Continuously change the horizontal wind direction of the third wind profiler radar P3 within the range of 0-360°. Under each horizontal wind direction of the third wind profiler radar P3, obtain the horizontal divergence D and the relative error of the horizontal divergence D within the triangle ΔP1P2P3 at a certain height layer. This will give us the sensitivity of the horizontal divergence inversion relative error of the third wind profiler radar P3 to its horizontal wind direction. Step 1.4: Set the wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 to 0. Continuously change the horizontal wind speed of the third wind profiler radar P3 within a set range. Under each horizontal wind speed of the third wind profiler radar P3, obtain the horizontal divergence D and the relative error of the horizontal divergence D within the triangle ΔP1P2P3 at a certain height layer. This will give us the sensitivity of the horizontal divergence inversion relative error of the third wind profiler radar P3 to its horizontal wind speed. Step 1.5: By combining the sensitivity of the relative error of the horizontal divergence inversion of the third wind profiler radar P3 to its horizontal wind direction, and the sensitivity of the relative error of the horizontal divergence inversion of the third wind profiler radar P3 to its horizontal wind speed, the sensitivity of the horizontal divergence inversion result of the third wind profiler radar P3 to its horizontal wind is obtained.

3. The horizontal divergence profile inversion method based on wind profiler radar networking according to claim 1, characterized in that, Step 2 is as follows: Step 2.1: Construct an equilateral triangle consisting of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3. Step 2.2: Set the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 to 0, and the horizontal wind speed of the third wind profiler radar P3 to a set value; set the horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2 and the third wind profiler radar P3 to 0°. Step 2.3: Keep the horizontal wind direction and horizontal wind speed of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3 constant. The side length L of an equilateral triangle is varied in steps within a set distance. At each distance, the horizontal divergence D within the triangle ΔP1P2P3 at a certain height level is calculated, resulting in a function curve of side length L versus horizontal divergence D. The function curve; Step 2.4, analyze the function curve of side length L versus horizontal divergence D, and The function curve is used to obtain the side length L that has the least impact on the horizontal divergence D inversion result, which is the minimum threshold L for the final wind profile radar range. min In other words, when constructing the triangle, the wind profiler radar distance needs to be greater than the minimum threshold L. min .

4. The horizontal divergence profile inversion method based on wind profiler radar networking according to claim 1, characterized in that, Step 3 specifically involves: Step 3.1: Construct a triangle consisting of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3. The first wind profiler radar P1 and the second wind profiler radar P2 are located on the x-axis and are symmetrical about the origin. The positions of the first wind profiler radar P1 and the second wind profiler radar P2 are fixed. The third wind profiler radar P3 is initially located on the y-axis and forms an equilateral triangle with the first wind profiler radar P1 and the second wind profiler radar P2. At this time, the area of ​​the triangle is S0. Step 3.2: Keep the area S0 of the triangle constant; the third wind profiler radar P3 moves horizontally to the right from its initial position, that is, keep the ordinate of the third wind profiler radar P3 constant, and its abscissa increases continuously from 0, so that the interior angle ∠P1P2P3 increases continuously to infinitely close to 180°; where, at each position, the interior angle ∠P1P2P3 is the largest interior angle of triangle P1P2P3, called the largest interior angle A of the triangle; Step 3.3: Set the horizontal wind speed of the first wind profiler radar P1 and the second wind profiler radar P2 to 0, and the horizontal wind speed of the third wind profiler radar P3 to a set value; set the horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3 to 0°; keep the horizontal wind speed and horizontal wind direction of the first wind profiler radar P1, the second wind profiler radar P2, and the third wind profiler radar P3 fixed. During the movement of the third wind profiler radar P3 according to step 3.2, at each position, when the maximum interior angle A of the current triangle is reached, the horizontal divergence D within the triangle ΔP1P2P3 at a certain height layer is calculated. Thus, under the current triangle area S0, the function curve of the maximum interior angle A of the triangle versus the horizontal divergence D is obtained. Function curve; Step 3.4: Change the area S0 of the triangle and repeat steps 3.1-3.3; Therefore, for each selected triangle area S0, we obtain the function curves of the maximum interior angle A and the horizontal divergence D of the triangle, and Function curve; The function curves of the maximum interior angle A and the horizontal divergence D for each triangle, and By analyzing the function curve, we obtain the influence of the triangle area S0 and the maximum interior angle A on the horizontal divergence inversion results, and obtain the minimum threshold S0(min) of the triangle area S0 and the maximum value A(max) of the maximum interior angle A. Therefore, when constructing the triangle, we need to make the constructed triangle area S0 greater than the minimum threshold S0(min) and the maximum interior angle A of the triangle less than the maximum value A(max).

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