Method for identifying contour features of convective storms and predicting positions at future times

Abstract

The present invention discloses a method for identifying the contour features of convective storms and predicting their future positions, which relates to the technical field of meteorological forecasting. The identification method includes: extracting radar basic reflectivity data; identifying one-dimensional storm segments; synthesizing two-dimensional storm components; and identifying storm contours. In addition to contour feature identification, the prediction method further includes calculating the characteristic parameters of storm segments, storm component characteristic parameters, and storm cell characteristic parameters, using the storm center position and characteristic information to match storm bodies at adjacent times, and finding the best adjacent matching storm cell; performing weight correction extrapolation based on the contour structure characteristics of the storm cell, calculating the average azimuth angle and distance of storm movement according to the centroid position of the storm reflectivity at the past time in combination with the weight coefficient, and predicting the future position of the storm. When identifying convective storms and predicting storm positions, the present invention not only has higher identification accuracy, but also can retain the overall contour features of storm cells, improving the prediction accuracy rate.

Description

Technical Field

[0001] The present invention relates to the technical field of weather forecasting, and in particular, to a method for identifying the contour features of convective storms and predicting the position at a future time. Background Art

[0002] The storm convection identification technology combines disciplines such as meteorological technology and computer technology. With the accumulation and development of meteorological big data, automatic algorithm identification, tracking, and early warning of storms based on radar data have become the premise and basic work for developing short-term and nowcasting weather forecasting technologies. Currently, the more widely used and relatively mature methods include the storm identification, tracking, analysis, and nowcasting algorithm based on the centroid of a single entity (TITAN), and the storm single entity identification, tracking, and forecasting algorithm (SCIT).

[0003] In the data preprocessing stage of the TITAN algorithm, radar data is projected onto a Cartesian coordinate system using the nearest neighbor method, and then the convective area is identified in the adjacent area exceeding the reflectivity threshold. When the identification target is an isolated storm single entity or a mesoscale storm system, TITAN has a good identification effect. The TITAN algorithm projects and interpolates radar data, which can well retain the overall contour features of the storm, but will ignore the internal structure and some other characteristic attributes of the storm. Since the interpolation algorithm is used, errors will inevitably occur, resulting in changes in the echo distribution, shape, and features, and may cause false mergers of storm single entities.

[0004] The SCIT algorithm separately performs radial storm segment search, storm component synthesis, storm single entity centroid calculation, storm single entity tracking, and storm position forecasting on the premise of retaining the characteristics of the original radar data. It can not only determine the position information of storm single entities and mesoscale convective systems, but also obtain the structural feature information of storm single entities. The SCIT algorithm retains the original characteristics of the radar base data, can well grasp the internal structure information of the storm, and reduce false mergers. However, due to the identification through multiple thresholds, the SCIT algorithm will discard some low-threshold areas and lose the overall characteristics of the storm. In addition, the SCIT algorithm introduces seven thresholds to identify the storm area azimuth by azimuth based on the radar base data. During the identification process, the algorithm will discard the low-threshold area of the outer boundary, so it cannot effectively identify the contour of convective storms. For the computational geometry method for storm belt identification, it can effectively identify the contour of a large-scale storm belt composed of multiple storm bodies. After identifying the storm segments, this method traverses and discriminates, combines all storm segments that meet the threshold conditions into a unified storm belt, and then identifies the contour of the storm belt. This method can only effectively identify the overall contour of a large-scale storm belt generated under strong convective processes, and cannot effectively identify storm single entities in cases such as weak storm intensity, small area, independent occurrence, and incipient convection.

[0005] The storm future position prediction methods in SCIT and TITAN algorithms only consider the position changes of the past storm centroids and use the least squares method for linear extrapolation. They do not consider the effects of the mass and contour features of storm cells and time changes on the storm position prediction results, resulting in an increase in the storm position prediction deviation and a decrease in the prediction accuracy. Summary of the Invention

[0006] The present invention aims to provide a method for identifying the contour features of convective storms and predicting their positions at future times, which can solve the above problems.

[0007] To solve the above problems, the technical solutions adopted by the present invention are as follows:

[0008] In the first aspect, the present invention provides a method for identifying the contour features of convective storms, including the following steps:

[0009] A1. Radar-based data decoding, preprocessing the radar-based data, and extracting the radar basic reflectivity data from the preprocessed radar-based data;

[0010] A2. Storm segment identification, identifying several one-dimensional storm segments from the radar basic reflectivity data;

[0011] A3. Storm component synthesis, based on the azimuth spacing between each one-dimensional storm segment and the overlapping distance between two adjacent one-dimensional storm segments, synthesizing the continuous one-dimensional storm segments into two-dimensional storm components. If the number of one-dimensional storm segments in the synthesized two-dimensional storm component is less than the storm segment number threshold or the area is less than the two-dimensional storm component area threshold, it is excluded; otherwise, it is retained;

[0012] A4. Storm contour identification, starting from the 0° azimuth angle, performing contour identification on each two-dimensional storm component one by one; for each two-dimensional storm component, its storm contour identification method is to start searching and identifying from its first storm segment, using the farthest storm segment endpoint at the first azimuth angle as the starting point of the storm contour endpoint for counterclockwise search, using the farthest endpoints of the convective storm segments at each adjacent azimuth angle as the contour endpoints until there are no adjacent storm segments in the search direction, which is judged as an inflection point. The farthest and nearest endpoints of each storm segment at the inflection point are used as the contour endpoints, and the search direction is changed to perform clockwise search, using the nearest endpoints of the storm segments at each adjacent azimuth angle as the contour endpoints until there are no adjacent storm segments, and the farthest and nearest endpoints of all storm segments at the inflection point are also used as the contour endpoints. The search returns to the starting point and the search ends.

[0013] As a further description of the above technical solution:

[0014] The method for preprocessing the radar-based data includes ground clutter suppression, missing data filling, and reflectivity factor attenuation correction.

[0015] As a further description of the above technical solution:

[0016] Step A2 specifically includes: starting from any initial azimuth angle, searching for the radar basic reflectivity data along the radial direction until the initial azimuth angle is reached, obtaining several storm segments, removing the storm segments with a length less than the length threshold L, and the remaining storm segments are the identified one-dimensional storm segments, and recording the starting distance, the ending distance, and the length of each storm segment, which are used to calculate the overlapping distance between adjacent storm segments and the two-dimensional storm component area;

[0017] The starting distance R of the storm segment beg = R1 - SVL / 2;

[0018] The ending distance R of the storm segment end = R n + SVL / 2;

[0019] The length L of the storm segment s = R end - R beg ;

[0020] Among them, R1 and R n respectively represent the radial distances of the first and the last points in the storm segment, and SVL represents the bin length.

[0021] As a further description of the above technical solution:

[0022] The search process for each storm segment includes: defining a reflectivity threshold T Z , a secondary reflectivity threshold D Z and an allowable number of points N P ; classifying the points in the radar basic reflectivity data that continuously satisfy Z > T Z into the storm segment, where Z is the reflectivity in the radar basic reflectivity data. When encountering a data point with Z ≤ T Z , judge whether the number of points with D Z < Z ≤ T Z reaches N P . If it reaches, then classify N P points with D Z < Z ≤ T Z into the storm segment, and the search for this storm segment is completed.

[0023] As a further description of the above technical solution:

[0024] In step A3, starting from 0°, search for continuous one-dimensional storm segments along the tangential direction until 360° is reached. During this search period, synthesize the continuous one-dimensional storm segments that meet the synthesis conditions into two-dimensional storm components;

[0025] Among them, the synthesis conditions are as follows: for the synthesized two-dimensional storm component, it includes a plurality of consecutive one-dimensional storm segments, and the azimuth spacing between any two adjacent one-dimensional storm segments is less than the azimuth angle spacing threshold A z , and the overlapping distance L cd is greater than the overlapping distance threshold L ch , and the area Area of the two-dimensional storm component is greater than the area threshold Area m ;

[0026] Overlapping distance

[0027] Area of two-dimensional storm component

[0028] Among them, the subscripts 1 and 2 respectively represent two adjacent storm segments, and i represents the i-th storm segment in the two-dimensional storm component;

[0029] When synthesizing the two-dimensional storm component, reverse storm synthesis starting from 360° is performed until it returns to 0° to end.

[0030] As a further description of the above technical solution:

[0031] In step A4, the formula model used for storm contour recognition is:

[0032]

[0033] Among them, (θ, r) represents the polar coordinates of the storm contour endpoint, θ is the azimuth angle, r is the distance, θ i represents the azimuth angle of the corresponding storm segment, r imin and r imax respectively represent the nearest endpoint distance and the farthest endpoint distance; i = 0 represents the starting storm segment, i = 1, 2,..., n - 1 represents counterclockwise search, i = n - 1,..., 2, 1 represents clockwise search, and i = n represents the inflection point.

[0034] In a second aspect, the present invention provides a method for predicting the future position of a storm, including the following steps:

[0035] B1. Use the convective storm contour feature recognition method described in the first aspect to recognize the storm contour features;

[0036] For the one-dimensional storm segments recognized during the storm segment recognition process, obtain their storm segment characteristic parameters, including the starting distance of the storm segment, the ending distance of the storm segment, the length of the storm segment, the mass-weighted length, the mass-weighted area, and the average adjacent azimuth difference;

[0037] For the two-dimensional storm components synthesized during the storm component synthesis process, obtain their storm component characteristic parameters, including the two-dimensional storm component area, storm component mass, Cartesian coordinate position of the storm component centroid weight, and polar coordinate position of the storm component centroid weight;

[0038] B2. Calculate the storm cell characteristic parameters based on the storm segment characteristic parameters and storm component characteristic parameters, including the storm cell height, storm cell mass, and storm cell weighted center position;

[0039] B3. Use the storm contour, storm cell weighted center position, and storm cell mass to match the storm bodies at adjacent times, and find the storm cells that meet the best adjacent match;

[0040] B4. Perform weighted correction extrapolation based on the storm cell contour structure characteristics, calculate the correlation and change range of the mass and contour characteristics of historical storm cells, combine the reflectivity centroid position at the past time of the storm, formulate a weight coefficient to calculate the weighted average azimuth and distance of storm movement, and use it as the prediction result of the azimuth and distance of storm movement to forecast the future position of the storm.

[0041] As a further description of the above technical solution:

[0042] Mass-weighted length

[0043] Mass-weighted area

[0044] Average adjacent azimuth difference

[0045] Among them, Z k and R k respectively represent the reflectivity factor and radial distance of the k-th point in the storm segment, j represents the adjacent storm segment, θ is the azimuth difference between adjacent storm segments, and N az is the total number of adjacent storm segments.

[0046] Storm component mass

[0047] The Cartesian coordinate position of the storm component centroid weight, including the abscissa XC and the ordinate YC,

[0048]

[0049]

[0050] The polar coordinate position of the storm component centroid weight, including the azimuth AC, the distance RC, and the height HC,

[0051]

[0052]

[0053] HC = sin(β)(RC) + RC 2 / (2×IR×RE);

[0054] Wherein, β is the elevation angle, IR represents the refractive index, and RE is the radius of the earth.

[0055] As a further description of the above technical solution:

[0056] In step B2, the calculation formula of the storm cell characteristic parameters is as follows:

[0057] Storm cell height

[0058] Storm cell mass

[0059] The position of the weight center of the storm cell, including the abscissa XSC and the ordinate YSC,

[0060]

[0061]

[0062] Wherein, l represents the l-th layer storm component in the storm body, and m represents the elevation angle of the m-th layer of the basic reflectivity data at the bottom layer of the storm body.

[0063] As a further description of the above technical solution:

[0064] In step B4, the calculation formulas for the weighted average azimuth angle and distance of storm movement are:

[0065]

[0066]

[0067] Wherein, α represents the weighted average azimuth angle of storm movement, d represents the weighted average distance of storm movement, t represents the matching storm at adjacent times, tjn is the number of historical trajectories, XSC and YSC represent the centroid positions of the storm cell at each historical moment, a t and b t are the weight coefficients of the storm azimuth angle and distance changing with time.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] 1) While obtaining the convective contour, it can retain information such as the internal structure of the storm, which is not only beneficial for more detailed identification of the static characteristics of convective storms, but also can provide richer and more detailed information for the identification of dynamic characteristics such as the movement path of convective storms.

[0070] 2) The present invention directly uses the original basic data for recognition. First, storm cell recognition is performed based on radar polar coordinate data. After the recognition is completed, endpoint determination is performed on the storm segments composed of each recognized storm cell, and each determined endpoint is used as the contour point of the convective storm. Using this method to start the recognition from the radar basic data preserves the true storm segment composition of the storm body, as well as its original azimuth and distance structure, and can also obtain clearer and more specific geometric features of the storm contour, reducing errors and false merger phenomena and increasing the recognition reliability.

[0071] 3) When predicting the storm position, the present invention not only considers the change of the past storm centroid position, but also comprehensively considers the influence of storm cell mass, storm contour features, and time change on the storm position, making the calculation method more in line with the actual situation, reducing the prediction error of the storm position, and improving the prediction accuracy.

[0072] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically gives embodiments of the present invention and, in conjunction with the accompanying drawings, makes a detailed description as follows. Description of the Drawings

[0073] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as a limitation of the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0074] Figure 1 is the general flowchart of the convective storm contour feature recognition method;

[0075] Figure 2 is the refined flowchart of the contour recognition algorithm;

[0076] Figure 3 is the refined flowchart of the convective tracking algorithm;

[0077] Figure 4 is the schematic diagram of the tracking path;

[0078] Figure 5 is the storm cell recognition diagram. From left to right, they are the TITAN recognition method (the red elliptical area is the recognition result of the convective storm), the SCIT recognition method (the black marked point is the centroid position of the convective storm echo), and the method of the present invention (the purple line is the recognition result of the convective storm contour). Detailed Embodiments

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention.

[0080] Please refer to Figure 3 , the embodiments of the present invention provide a method for predicting the future position of a storm, which is specifically as follows:

[0081] 1. Recognition of convective storm contour features. Refer to Figure 1 as shown, specifically:

[0082] 1) Radar base data decoding: Preprocess the radar base data, and extract the radar basic reflectivity data from the preprocessed radar base data.

[0083] Among them, the methods for preprocessing the radar base data include ground clutter suppression, missing data filling, and reflectivity factor attenuation correction.

[0084] 2) Storm segment recognition: Identify several one-dimensional storm segments from the radar basic reflectivity data, specifically as follows:

[0085] Start from an arbitrary initial azimuth angle, search the radar basic reflectivity data along the radial direction until the initial azimuth angle is reached, and obtain several storm segments. Eliminate the storm segments with a length less than the length threshold L. The remaining storm segments are the identified one-dimensional storm segments, and record the starting distance, ending distance, and length of each storm segment, which are used to calculate the overlapping distance between adjacent storm segments and the two-dimensional storm component area.

[0086] The search process for each storm segment includes: Define the reflectivity threshold T Z , the secondary reflectivity threshold D Z and the allowable number of points N P ; Classify the points in the radar basic reflectivity data that continuously satisfy Z>T Z into the storm segment, where Z is the reflectivity in the radar basic reflectivity data. When encountering a data point with Z≤T Z , judge whether the number of points with D Z <Z≤T Z reaches N P . If it reaches, then classify N P points with D Z <Z≤T Z into this storm segment, and the search for this storm segment is completed.

[0087] For the one-dimensional storm segments identified during the storm segment identification process, calculate their storm segment characteristic parameters, including the starting distance of the storm segment, the ending distance of the storm segment, the length of the storm segment, the mass-weighted length, the mass-weighted area, and the average adjacent azimuth difference.

[0088] The calculation formulas for the storm segment characteristic parameters are as follows:

[0089] The starting distance R of the storm segment beg = R1 - SVL / 2;

[0090] The ending distance R of the storm segment end = R n + SVL / 2;

[0091] The length L of the storm segment = R end - R beg ;

[0092] Mass-weighted length

[0093] Mass-weighted area

[0094] Average adjacent azimuth difference

[0095] Among them, Z k and R k respectively represent the reflectivity factor and the radial distance of the k-th point in the storm segment, R1 and R n respectively represent the radial distances of the first and the last points in the storm segment, SVL represents the bin length, j represents the adjacent storm segments, θ is the azimuth difference between adjacent storm segments, and N az is the total number of adjacent storm segments. Store the calculation results of the above parameters and the original radar data including [elevation angle, azimuth angle, reflectivity factor threshold, starting distance, ending distance, segment length, mass-weighted length, mass-weighted area, average adjacent azimuth difference] for future use.

[0096] In this embodiment, the reflectivity factor threshold T Z = 35 dBZ, the length threshold L = 4.5 km, the reflectivity sub-threshold D Z = 30 dBZ, and the allowable number of points NP = 2.

[0097] 3) Storm component synthesis: Based on the azimuth spacing between each one-dimensional storm segment and the overlapping distance between two adjacent one-dimensional storm segments, synthesize the continuous one-dimensional storm segments into two-dimensional storm components. If the number of one-dimensional storm segments in the synthesized two-dimensional storm component is less than the storm segment number threshold or the area is less than the two-dimensional storm component area threshold, then eliminate it; otherwise, retain it.

[0098] Among them, starting from 0° and searching tangentially for continuous one-dimensional storm segments until reaching 360°, during this search period, the continuous one-dimensional storm segments that meet the synthesis conditions are synthesized into two-dimensional storm components;

[0099] The synthesis conditions are as follows: for the synthesized two-dimensional storm component, it includes multiple continuous one-dimensional storm segments, and the azimuth spacing between any two adjacent one-dimensional storm segments is less than the azimuth angle spacing threshold A Z , and the overlapping distance L cd is greater than the overlapping distance threshold L ch , and the area Area of the two-dimensional storm component is greater than the area threshold Area m ;

[0100] Overlapping distance

[0101] Area of two-dimensional storm component

[0102] Among them, the subscripts 1 and 2 respectively represent two adjacent storm segments, and i represents the i-th storm segment in the two-dimensional storm component;

[0103] When synthesizing the two-dimensional storm component, perform reverse storm synthesis starting from 360° until returning to 0°.

[0104] For each two-dimensional storm component synthesized during the storm component synthesis process, calculate its storm component characteristic parameters, including the geometric area of the storm component, the mass of the storm component, the Cartesian coordinate position of the centroid weight of the storm component, and the polar coordinate position of the centroid weight of the storm component. The calculation formulas are as follows:

[0105] Mass of storm component

[0106] The Cartesian coordinate position of the centroid weight of the storm component, including the abscissa XC and the ordinate YC,

[0107]

[0108]

[0109] The polar coordinate position of the centroid weight of the storm component, including the azimuth angle AC, the distance RC, and the height HC,

[0110]

[0111]

[0112] HC = sin(β)(RC) + RC 2 / (2×IR×RE);

[0113] Among them, β is the elevation angle, IR represents the refractive index (IR = 1.21), and RE is the radius of the earth (RE = 6371 km).

[0114] In this embodiment, the minimum azimuth spacing is θ max = 1°, the minimum overlapping distance L of adjacent storm segments min = 1.5 km, the threshold N of the number of storm segments included in the storm component min = 12.

[0115] 4) Storm contour recognition, starting from the 0° azimuth angle, perform individual contour recognition on each two-dimensional storm component.

[0116] Combined Figure 2 As shown, for each two-dimensional storm component, its storm contour recognition method is as follows: start searching and recognizing from its first storm segment, use the farthest storm segment endpoint at the first azimuth angle as the starting point of the storm contour endpoint to search counterclockwise, use the farthest endpoint of the convective storm segment at each adjacent azimuth angle as the contour endpoint until there are no adjacent storm segments in the search direction, judge it as an inflection point, use the farthest and nearest endpoints of each storm segment at the inflection point as the contour endpoints, and change the search direction to search clockwise, use the nearest endpoint of the storm segment at each adjacent azimuth angle as the contour endpoint until there are no adjacent storm segments, and also use the farthest and nearest endpoints of all storm segments at the inflection point as the contour endpoints, and the search returns to the starting point to end the search.

[0117] The formula model used for storm contour recognition is:

[0118]

[0119] Among them, (θ, r) represents the polar coordinates of the storm contour endpoint, θ is the azimuth angle, r is the distance, θ i represents the azimuth angle of the corresponding storm segment, r imin and r imax respectively represent the nearest endpoint distance and the farthest endpoint distance; i = 0 represents the starting storm segment, i = 1, 2,..., n - 1 represents counterclockwise search, i = n - 1,..., 2, 1 represents clockwise search, and i = n represents the inflection point.

[0120] 2. Calculate the storm cell characteristic parameters according to the storm segment characteristic parameters and the storm component characteristic parameters, including the storm cell height, the storm cell mass, and the position of the storm cell weight center.

[0121] The calculation formulas for the storm cell characteristic parameters are as follows:

[0122] Storm cell height

[0123] Storm cell mass

[0124] The position of the weighted center of the storm cell, including the abscissa XSC and the ordinate YSC,

[0125]

[0126]

[0127] where l represents the l-th layer of storm components in the storm body, and m represents the elevation angle of the m-th layer of the basic reflectivity data at the bottom layer of the storm body.

[0128] 3. Match the storm bodies at adjacent times using the storm contour, the position of the weighted center of the storm cell, and the storm cell mass, and find the storm cell that satisfies the best adjacent match.

[0129] The best adjacent match satisfies the following rules:

[0130] Spatial proximity: The position of the storm center at the new time should be close to that at the old time;

[0131] Motion continuity: The movement of the storm should be relatively smooth, that is, the speed and direction should remain relatively consistent;

[0132] Feature similarity: The features of the storm, such as size and intensity, should have a certain degree of similarity at adjacent times.

[0133] Perform screening and matching in sequence according to the above rules, and finally obtain the best-matched storm body. The calculation formula is as follows:

[0134]

[0135]

[0136] C = |MA t+1 -MA t |

[0137]

[0138] where M is the storm matching coefficient, A, B, and C are the indicators of each rule, corresponding to the distance between the old and new storm centers, the distance between the predicted storm center and the new storm, and the change in storm cell mass respectively. MA represents the storm cell mass, t represents the storm at adjacent times, time represents the time interval between adjacent storms, XSV and YSV represent the storm speed; by performing normalization calculations on the indicators of all storm bodies and combining the weight coefficients a, b, and c, which are 0.5, 0.3, and 0.2 in this embodiment respectively, the storm matching coefficient is finally obtained. Storm matching is performed according to the size of the storm matching coefficient, making the storm matching process objective and having a good matching effect, especially providing a more objective basis for the matching of the storm merger and splitting processes.

[0139] 4. Based on the contour structure characteristics of storm cells, perform weight correction extrapolation, calculate the correlation and variation range of the mass and contour characteristics of historical storm cells, combine with the centroid position of reflectivity at the past moment of the storm, formulate weight coefficients to calculate the weighted average azimuth and distance of storm movement, and use them as the predicted results of the azimuth and distance of storm movement to forecast the future position of the storm.

[0140] The calculation formulas for the average azimuth and distance of storm movement are as follows:

[0141]

[0142]

[0143] Among them, α represents the weighted average azimuth of storm movement, d represents the average distance of storm movement, tjn is the number of historical trajectories, XSC and YSC represent the centroid positions of the storm cell at each historical moment, a t and b t are the weight coefficients for the change of storm azimuth and distance with time.

[0144] The weight coefficient a t and b t The specific calculation rules are as follows: (1) Use the variation range of the mass of historical storm cells as the basis for judging the reliability of extrapolation results. When the variation range is small, it is judged that the extrapolation results are reliable and the weight coefficients are high. (2) For the extrapolation results of historical storms, the results closer to the predicted time step have a greater weight. The extrapolation results are usually faster than the actual ones, so follow the principle of "better slow than fast". (3) Conduct a similarity analysis on the historical contour recognition images of the storm body, and use the similarity as the weight basis. The higher the similarity, the higher the weight ratio.

[0145] The schematic diagram of the final path tracking is as Figure 4 shown. The solid line is the position of the original storm contour, and the dashed line is the situation of tracking and predicting the storm contour position.

[0146] Figure 5It is a storm cell recognition diagram. From left to right, they are the existing TITAN recognition method, the SCIT recognition method, and the storm contour feature recognition method of the present invention. As can be seen from the figure, the TITAN recognition method can recognize storm cells, but the recognition result is relatively rough, and only an ellipse can be used to replace the storm cell contour; the SCIT recognition method cannot recognize the storm contour and can only recognize the centroid position of the convective storm; this method starts the recognition from the radar base data and obtains the storm segment data that makes up the storm cell. The storm segment data consists of parameters such as azimuth, start endpoint distance, end endpoint distance, etc., which is consistent with the original data format. Then, endpoint judgment is performed based on the storm segment to obtain independent and accurate storm cell contour features, and adjacent storm cells can also be accurately distinguished. It can be seen from the figure that this method accurately and completely recognizes the contour features of the storm cell, and the two adjacent storm cells in the lower left corner are also successfully separated and recognized. Due to interpolation operation, TITAN recognizes the two storm cells as a whole, resulting in serious recognition errors, while this method avoids this situation.

[0147] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying convective storm contour features, characterized in that: The following steps are involved: A1. Decoding radar base data, preprocessing the radar base data, and extracting radar basic reflectivity data from the preprocessed radar base data; A2. Storm segment identification: identifying several one-dimensional storm segments from the radar basic reflectivity data, specifically including: starting from any initial azimuth, searching the radar basic reflectivity data radially until the initial azimuth is searched to obtain several storm segments, and removing the storm segments whose length is less than the length threshold L. The remaining storm segments are the identified one-dimensional storm segments, and the starting distance, ending distance and length of each storm segment are recorded to calculate the overlapping distance of adjacent storm segments and the area of ​​the two-dimensional storm component; The starting distance R of the storm segment beg =R1-SVL / 2; Storm segment end distance R end =R n +SVL / 2; Storm segment length L s =R end -R beg ; Among them, R1 and R n They represent the radial distances of the first and last points in the storm segment, respectively, and SVL represents the reservoir length; The search process for each storm segment includes: defining the reflectivity threshold T Z , reflectivity subthreshold D Z and the number of allowed points N P ; The radar basic reflectivity data continuously satisfies Z>T Z The point is classified as the storm segment, Z is the reflectivity in the radar basic reflectivity data, when Z≤T Z When the data point is Z <Z≤T Z Whether the number of points reaches N P , if reached, then N P D Z <Z≤T Z The points are also included in this storm segment, and the search for this storm segment is now completed; A3, storm component synthesis, based on the azimuth spacing between each one-dimensional storm segment and the overlapping distance between two adjacent one-dimensional storm segments, the continuous one-dimensional storm segments are synthesized into two-dimensional storm components. If the number of one-dimensional storm segments in the synthesized two-dimensional storm component is less than the threshold of the number of storm segments or the area is less than the threshold of the area of ​​the two-dimensional storm component, it will be eliminated, otherwise it will be retained; A4. Storm contour identification. The contours of each two-dimensional storm component are identified one by one starting from the 0° azimuth. For each two-dimensional storm component, the storm contour identification method is to start searching and identifying from its first storm segment, and use the farthest storm segment endpoint on the first azimuth as the starting point of the storm contour endpoint for counterclockwise search, and use the farthest endpoint of the convective storm segment on each adjacent azimuth as the contour endpoint, until no adjacent storm segments appear in the search direction, it is judged as an inflection point, and the farthest and nearest endpoints of each storm segment on the inflection point are used as contour endpoints, and the search direction is changed to search clockwise, and the nearest endpoint of the storm segment on each adjacent azimuth is used as the contour endpoint, until no adjacent storm segments appear, and similarly use the farthest and nearest endpoints of all storm segments on the inflection point as contour endpoints, and the search ends when it returns to the starting point.

2. The method for identifying convective storm contour features according to claim 1, characterized in that: The methods for preprocessing radar-based data include ground clutter suppression, missing data filling and reflectivity factor attenuation correction.

3. The method for identifying convective storm contour features according to claim 1, characterized in that: In step A3, continuous one-dimensional storm segments are searched tangentially from 0° until 360° is reached. During the search, continuous one-dimensional storm segments that meet the synthesis conditions are synthesized into two-dimensional storm components. The synthesis condition is as follows: for the synthesized two-dimensional storm component, it includes multiple continuous one-dimensional storm segments, where the azimuth spacing between any two adjacent one-dimensional storm segments is less than the azimuth spacing threshold A. z , and the overlap distance L cd Greater than the overlap distance threshold L ch , the area of ​​the two-dimensional storm component Area is greater than the area threshold Area m ; Overlap distance Two-dimensional storm component area Among them, the subscripts 1 and 2 represent two adjacent storm segments, respectively, and i represents the i-th storm segment in the two-dimensional storm component; When synthesizing the two-dimensional storm component, reverse storm synthesis is performed starting from 360° and ending when returning to 0°.

4. The method for identifying convective storm contour features according to claim 3, characterized in that: In step A4, the formula model used for storm contour identification is: Among them, (θ, r) represents the polar coordinates of the endpoints of the storm contour, θ is the azimuth, r is the distance, θ i represents the azimuth of the corresponding storm segment, r imin and r imax Represent the nearest endpoint distance and the farthest endpoint distance respectively; i=0 represents the starting storm segment, i=1,2,...,n-1 represents counterclockwise search, i=n-1,...,2,1 represents clockwise search, and i=n represents the inflection point.

5. A method for predicting the future position of a storm, characterized in that: The following steps are involved: B1. Using the convective storm contour feature identification method described in claim 4 to identify the storm contour features; For the one-dimensional storm segment identified in the storm segment identification process, obtain its storm segment characteristic parameters, including the storm segment start distance, storm segment end distance, storm segment length, mass weight length, mass weight area and average adjacent azimuth difference; For the two-dimensional storm component synthesized in the storm component synthesis process, obtain its storm component characteristic parameters, including the two-dimensional storm component area, storm component mass, storm component centroid weighted Cartesian coordinate position and storm component centroid weighted polar coordinate position; B2. Calculate storm cell characteristic parameters based on storm segment characteristic parameters and storm component characteristic parameters, including storm cell height, storm cell mass and storm cell weight center position; B3. Use storm contours, storm cell weight center positions, and storm cell masses to match storm bodies at adjacent moments, and find storm cells that satisfy the best adjacent matching; B4. Based on the structural characteristics of the storm cell contour, weight correction and extrapolation are performed to calculate the mass of historical storm cells, the correlation and variation range of contour characteristics. Combined with the position of the reflectivity centroid of the storm in the past, weight coefficients are formulated to calculate the weighted average azimuth and distance of the storm movement. The azimuth and distance prediction results of the storm movement are used to forecast the future position of the storm.

6. The method for predicting the future position of a storm according to claim 5, characterized in that: In step B1, Quality Weight Length Mass Weight Area Average adjacent bearing difference Among them, Z k and R k represent the reflectivity factor and radial distance of the kth point in the storm segment respectively; j represents the jth adjacent storm segment, θ is the difference in azimuth between adjacent storm segments, N az is the total number of adjacent storm segments; Storm component mass The Cartesian coordinate position of the storm component centroid weight, including the horizontal coordinate XC and the vertical coordinate YC, The weighted polar coordinate position of the storm component centroid, including azimuth AC, distance RC and height HC, HC=sin(β)(RC)+RC 2 / (2×IR×RE); Where β is the elevation angle, IR is the refractive index, and RE is the radius of the Earth.

7. The method for predicting the future position of a storm according to claim 6, characterized in that: In step B2, the calculation formula of the storm cell characteristic parameters is as follows: Storm cell height Storm cell mass The center position of the storm cell weight, including the horizontal coordinate XSC and the vertical coordinate YSC, Among them, l represents the lth layer of storm component in the storm body, and m represents the elevation angle of the bottom layer of the storm body at the mth layer of basic reflectivity data.

8. The method for predicting the future position of a storm according to claim 6, characterized in that: In step B4, the weighted average azimuth and distance of the storm movement are calculated as follows: Among them, α represents the weighted average azimuth of the storm movement, d represents the weighted average distance of the storm movement, t represents the matching storms at adjacent moments, tjn is the number of historical trajectories, and a t With b t is the weight coefficient of the storm azimuth and distance changing with time.

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