CN104237890B - The heavy rain identification that one is caused by " train effect " and forecasting procedure - Google Patents
The heavy rain identification that one is caused by " train effect " and forecasting procedure Download PDFInfo
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Abstract
The present invention discloses heavy rain identification and the forecasting procedure that one is caused by " train effect ", comprises and real-time radar data is carried out pre-treatment; Carry out initial fitting by through the monomer of sieve at the beginning of area, eliminate interference, find " train effect " suspicious region; Just the automatic identification of " train effect " can be carried out after determining " train effect " suspicious region; The barycenter miles of relative movement of the banded echo in suspicious region and axis angle of rotation is utilized to carry out the tracking of banded echo, it is achieved the tracking of " train effect "; According to monomer motional inertia to the shifting of the suspicious region of " train effect " to, move speed, change in shape extrapolates. The invention has the beneficial effects as follows: when occur " train effect " phenomenon meteorological time, " train effect " can not only be identified give the alarm to forecaster, can also extrapolate according to the weather conditions of " the train effect " in multiple moment, forecaster carrying out follow-up forecast and provides important references, accuracy and rapidity are higher.
Description
Technical Field
The invention relates to the field of weather, in particular to a method for identifying and forecasting rainstorm by train effect.
Background
In cities, the strong convection weather of small and medium sizes often causes violent weather phenomena, such as local heavy storm, strong wind, hail and other disastrous weather, and seriously threatens the life and property safety of people. The weather radar is a main means for detecting a precipitation system and is a main tool for monitoring and early warning strong convection weather. The Doppler weather radar has all-weather detection capability and very abundant dynamic information reflecting the atmospheric layer cloud and rain digestion evolution process, greatly enhances the detection and early warning capability of medium and small scale weather systems, and lays a solid foundation for monitoring and forecasting short-time disastrous weather.
The 'train effect' means that a certain area frequently and continuously generates convection monomers with small space scale within a period of time, each generated convection monomer moves along a certain direction, then new convection monomers are generated at the same place and continuously move along the same direction, so that an arrangement similar to a 'train' is formed by arranging a series of convection monomers, and the 'train' can continuously and continuously affect a certain area at the downstream of the 'train', so that strong rainfall is caused, and the 'train effect' is a main echo characteristic of disasters such as flood and the like caused by extreme rainfall.
In recent years, along with the gradual establishment of Doppler weather radar networks in China, the monitoring and early warning functions of Doppler radars on disastrous weather are increasingly highlighted, so that the accuracy of the disastrous weather forecast in China is improved on the original basis, but if the functions of the Doppler radar in the weather monitoring and early warning are to be fully exerted, corresponding storm identification, tracking and forecasting algorithms based on Doppler weather radar data are matched with the storm identification, tracking and forecasting algorithms. At present, the method can be applied to a recognition tracking algorithm of train effect: thunderstorm identification tracking analysis forecast (TITAN), storm identification tracking algorithm (SCIT). The TITAN algorithm is used for identifying the strong convection storm by using a single reflectivity factor threshold, is suitable for the whole storm zone, but has less intensity threshold, and cannot well extract parameters in the storm and distinguish the fine structure of a storm cluster. The SCIT algorithm adopts seven broad values to identify the mass center of the storm, so that the storm monomers in the storm cluster can be well identified, but only the identification result with a high threshold value is retained, the identification result with a low threshold value is abandoned, and a large amount of storm body structure information is lost.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a rainstorm forecasting method which can not only identify the train effect to give an alarm to a forecaster when the train effect phenomenon occurs in real-time weather monitoring and forecasting, but also can extrapolate according to the weather conditions of the train effect at a plurality of moments.
The technical scheme adopted by the invention is as follows: a method for identifying and forecasting rainstorm caused by train effect comprises the following steps: step S1, preprocessing the real-time radar data: eliminating super refraction, fine monomer and false merging; step S2, carrying out primary fitting on the monomer subjected to the area primary screening: eliminating interference, clustering the result, performing secondary fitting on the clustering result, and searching a suspected area of the train effect; step S3, after determining the suspected area of "train effect": automatically recognizing the train effect, if so, judging the train effect as the train effect, performing S4, otherwise, skipping to the radar data processing at the next moment, namely, starting from S1; step S4, tracking the strip echo using the centroid movement distance and the axis rotation angle of the suspected area strip echo: after the position of the whole strip echo is determined, monomer tracking in a suspected area is carried out by using the Hu moment and the form change, and the tracking of a train effect is realized; and step S5, extrapolating the movement direction, the movement speed and the shape change of the suspected area of the train effect according to the movement inertia of the single body.
The step of preprocessing the real-time radar data in S1 is: s101: by raising the elevation angle of the radar echo data, super-refraction echoes with strong interference on an automatic identification stage are filtered; s102: deleting the fine monomers which do not meet the threshold condition by setting an area threshold; s103: judging whether the reflectivity of the target connected region is greater than 40dBZ, if so, corroding one pixel point, and corroding a strong echo region less; if the detected signal is less than 40dBZ, corroding two pixel points, corroding a weak echo region a little more, then improving a first-level threshold value, judging whether a monomer with a higher first-level threshold value can be detected, if the monomer can be detected, expanding, repeating the process, and if the monomer cannot be detected, stopping.
The step S2 specifically includes the following steps: s201: firstly, a centroid method is used for obtaining representative points of each monomer, then straight line fitting is carried out on the points to obtain a fitting straight line of the train effect, then the monomer which deviates from the fitting straight line to a large extent is deleted, and interference of peripheral monomers irrelevant to the train effect is eliminated; s202: obtaining monomers meeting requirements after preliminary fitting, extracting the mass centers of the monomers, automatically clustering by using a neighbor propagation method, then respectively carrying out straight line fitting on each type of point set again, and deleting the monomers with larger deviation from the fitting straight line degree to obtain a suspected area of the train effect.
The step S3 specifically includes the following steps: s301: extracting information of the whole strip-shaped echo in the suspected area, judging whether the length-width ratio of the minimum external rectangle of the whole strip-shaped echo, the average reflectivity and the average speed of the whole strip-shaped echo meet the standard of the train effect, if the length-width ratio of the minimum external rectangle of the whole strip-shaped echo, the average reflectivity and the average speed meet the standard of the train effect, continuing the following judging process, if the length-width ratio does not meet the standard of the train effect, judging that the length-width ratio is not the train effect, then discarding the length-width ratio; s302: extracting various characteristics of all monomers in the suspected area, putting the characteristics into a rule base, judging whether the monomer echoes meet a 'train effect' monomer echo condition or not by using a standard voting method, counting the proportion of the monomers meeting the 'train effect' monomer echo condition in all the monomers after judging and identifying all the monomers in the suspected area, and judging the 'train effect' if the conditions meet a threshold value.
The step S4 specifically includes the following steps: s401: when the train effect is tracked, the attributes of the train effect integral strip echo and the single echo are fused; s402: calculating the centroid position and the long axis direction of the whole strip echo by taking the whole strip echo with the train effect as a research object, finding out all possible motion path combinations of the whole strip echo between two adjacent moments, calculating the moving distance of the centroid of the strip echo and the rotating angle of the long axis under each path combination, and selecting the shortest path combination as the moving path of the echo band in the path combinations meeting the conditions; s403: after the position of the whole band echo is determined, calculating the area difference between each monomer at the current moment and each monomer at the previous moment aiming at the same whole band echo, if the area difference is smaller than a threshold value, storing the monomers at the previous moment as possible matching with the current monomer, then calculating the similarity degree of the current monomer and the outlines of all the possible matched monomers through Hu moment, and considering the combination with the maximum similarity degree as the same monomer, and marking the same ID (identity) with the same monomer; s404: based on the recognition and judgment results of the train effect of the primary pair, the area range is defined, the secondary operation time is shortened, and the accuracy is improved; s405: the information between the integral belt with the maximum relevance and each monomer in the belt is obtained by comparing various characteristics of the front time sequence monomer and the rear time sequence monomer, comparing integral belt characteristics, comparing the long axis direction of the integral belt unique to the train effect with the moving direction, and comparing the speed with the moving direction, so that a one-to-one correspondence relationship is formed.
The step S5 specifically includes the following steps: s501: continuously inputting 'train effect' data at three moments, acquiring relevant characteristics, constructing a time sequence of the continuous moments, establishing a monomer family relation, checking and comparing the similarity of adjacent time monomers, and identifying a motion track of the monomer which develops and disappears through calculation so as to predict the motion trend of the monomer; s502: firstly, extrapolating the integral band echo, respectively determining the moving speed, the moving direction and the long axis direction of the integral band at the next moment according to the average speed, the moving direction and the long axis direction of the integral band among three recorded continuous moments according to the inertia of the movement of the single body, and integrating the three information to finish the tracking of the integral band echo with the train effect; s503: on the basis of extrapolating the position of the whole strip echo, the position of each single echo is extrapolated by the same method.
The three times were extrapolated for 6 minutes, 12 minutes, and 18 minutes, respectively.
The invention has the beneficial effects that: when the weather of the train effect phenomenon occurs, the train effect can be identified to give an alarm to a forecaster, extrapolation can be performed according to the weather condition of the train effect at multiple moments, important reference is provided for the forecaster to perform subsequent forecasting, and accuracy and rapidity are high.
Drawings
FIG. 1 is a graph a showing the original reflectivity, and a graph b showing the reflectivity after the super-refraction is filtered;
FIG. 2 is a preliminary fit plot of the residual monomer after area prescreening;
in FIG. 3, the graphs a and b are the original contour and its corresponding representative points, respectively;
FIG. 4 is the auto-clustering map of FIG. 3;
in fig. 5, the images a and b are respectively the images after quadratic fitting of each local region;
FIG. 6 is a typical "train effect" diagram where there are two processes;
in FIG. 7, the graphs a, b and c are respectively the comparison graph of the real graph of the "train effect" at 6 minutes, 12 minutes and 18 minutes and the extrapolated graph,
the images are compared with real images and external images, wherein a1, b1, c1 and a3, b3 and c3 are real reflectivity maps of 6, 12 and 18 minutes 'train effect' and partial enlarged images thereof, and a2, b2, c2 and a4, b4 and c4 are extrapolation maps of 6, 12 and 18 minutes 'train effect' and partial enlarged images thereof.
Detailed Description
The invention is described in further detail below with reference to the following figures and detailed description:
a method for identifying and forecasting rainstorm caused by train effect comprises the following steps:
step S1, preprocessing the real-time radar data: eliminating super refraction, fine monomer and false merging; s101: by raising the elevation angle of the radar echo data, super-refraction echoes with strong interference on an automatic identification stage are filtered; s102: deleting the fine monomers which do not meet the threshold condition by setting an area threshold; s103: judging whether the reflectivity of the target connected region is greater than 40dBZ, if so, corroding one pixel point, and corroding a strong echo region less; if the detected signal is less than 40dBZ, corroding two pixel points, corroding a weak echo region a little more, then improving a first-level threshold value, judging whether a monomer with a higher first-level threshold value can be detected, if the monomer can be detected, expanding, repeating the process, and if the monomer cannot be detected, stopping.
Step S2, carrying out primary fitting on the monomer subjected to the area primary screening: eliminating interference, clustering the result, performing secondary fitting on the clustering result, and searching a suspected area of the train effect; s201: firstly, a centroid method is used for obtaining representative points of each monomer, then straight line fitting is carried out on the points to obtain a fitting straight line of the train effect, then the monomer which deviates from the fitting straight line to a large extent is deleted, and interference of peripheral monomers irrelevant to the train effect is eliminated; s202: obtaining monomers meeting requirements after preliminary fitting, extracting the mass centers of the monomers, automatically clustering by using a neighbor propagation method, then respectively carrying out straight line fitting on each type of point set again, and deleting the monomers with larger deviation from the fitting straight line degree to obtain a suspected area of the train effect.
Step S3, after determining the suspected area of "train effect": automatically identifying the train effect, if so, judging the train effect to be the train effect, performing S4, and if not, skipping to the radar data processing at the next moment, namely, starting from S1; s301: extracting information of the whole strip-shaped echo in the suspected area, judging whether the length-width ratio of the minimum external rectangle of the whole strip-shaped echo, the average reflectivity and the average speed of the whole strip-shaped echo meet the standard of the train effect, if the length-width ratio of the minimum external rectangle of the whole strip-shaped echo, the average reflectivity and the average speed meet the standard of the train effect, continuing the following judging process, if the length-width ratio does not meet the standard of the train effect, judging that the length-width ratio is not the train effect, then discarding the length-width ratio; s302: extracting various characteristics of all monomers in the suspected area, putting the characteristics into a rule base, judging whether the monomer echoes meet a 'train effect' monomer echo condition or not by using a standard voting method, counting the proportion of the monomers meeting the 'train effect' monomer echo condition in all the monomers after judging and identifying all the monomers in the suspected area, and judging the 'train effect' if the conditions meet a threshold value.
Step S4, tracking the strip echo using the centroid movement distance and the axis rotation angle of the suspected area strip echo: after the position of the whole strip echo is determined, monomer tracking in a suspected area is carried out by using the Hu moment and the form change, and the tracking of a train effect is realized; s401: when the train effect is tracked, the attributes of the train effect integral strip echo and the single echo are fused; s402: calculating the centroid position and the long axis direction of the whole strip echo by taking the whole strip echo with the train effect as a research object, finding out all possible motion path combinations of the whole strip echo between two adjacent moments, calculating the moving distance of the centroid of the strip echo and the rotating angle of the long axis under each path combination, and selecting the shortest path combination as the moving path of the echo band in the path combinations meeting the conditions; s403: after the position of the whole band echo is determined, calculating the area difference between each monomer at the current moment and each monomer at the previous moment aiming at the same whole band echo, if the area difference is smaller than a threshold value, storing the monomers at the previous moment as possible matching with the current monomer, then calculating the similarity degree of the current monomer and the outlines of all the possible matched monomers through Hu moment, and considering the combination with the maximum similarity degree as the same monomer, and marking the same ID (identity) with the same monomer; s404: based on the recognition and judgment results of the train effect of the primary pair, the area range is defined, the secondary operation time is shortened, and the accuracy is improved; s405: the information between the integral belt with the maximum relevance and each monomer in the belt is obtained by comparing various characteristics of the front time sequence monomer and the rear time sequence monomer, comparing integral belt characteristics, comparing the long axis direction of the integral belt unique to the train effect with the moving direction, and comparing the speed with the moving direction, so that a one-to-one correspondence relationship is formed.
And step S5, extrapolating the movement direction, the movement speed and the shape change of the suspected area of the train effect according to the movement inertia of the single body. S501: continuously inputting 'train effect' data at three moments, acquiring relevant characteristics, constructing a time sequence of the continuous moments, establishing a monomer family relation, checking and comparing the similarity of adjacent time monomers, and identifying a motion track of the monomer which develops and disappears through calculation so as to predict the motion trend of the monomer; s502: firstly, extrapolating the integral band echo, respectively determining the moving speed, the moving direction and the long axis direction of the integral band at the next moment according to the average speed, the moving direction and the long axis direction of the integral band among three recorded continuous moments according to the inertia of the movement of the single body, and integrating the three information to finish the tracking of the integral band echo with the train effect; s503: on the basis of extrapolating the position of the whole strip echo, extrapolating the position of each single echo by the same method; the three times were extrapolated for 6 minutes, 12 minutes, and 18 minutes, respectively.
Example (b):
as shown in the process of fig. 1, panels a-b, the real-time radar data is preprocessed to eliminate super-refraction, subtle singles, and spurious mergers. By raising the elevation angle of the radar echo data, super-refraction echoes with strong interference on an automatic identification stage are filtered; deleting the fine monomers which do not meet the threshold condition by setting an area threshold; and judging whether the reflectivity of the target connected region is greater than 40dBZ, corroding one pixel point if the reflectivity of the target connected region is greater than 40dBZ, and corroding two pixel points if the reflectivity of the target connected region is less than 40dBZ so as to corrode a strong echo region less and a weak echo region more. And then, identifying by using a higher threshold, judging whether the monomer with the higher threshold can be detected or not, if so, expanding, repeating the process, and if not, stopping.
As shown in fig. 2 to 5, the monomers after area primary screening are preliminarily fitted to eliminate interference, the results are clustered by using a neighbor propagation method, and then the clustering results are secondarily fitted to find a suspected train effect areai-a-bxi)2Where Q is the sum of the residual squares of the fitted straight lines formed, a represents the intercept of the fitted straight line, b is the fit and slope of the line In order to minimize the target residual sum of squares, the partial derivative can be calculated by:and obtaining a fitting straight line, and obtaining the fitting straight line and two end points. The thick line in fig. 2 is a straight line obtained by fitting each centroid of the single body; and calculating the slope formed by each centroid and the two end points, comparing the slope with a fitting straight line, and deleting the profile corresponding to the centroid of which the difference is greater than the set threshold value. And respectively fitting the blocked processes formed by the clustered monomers to obtain different fitted straight lines, wherein one clustering process is shown in the graphs from 3 to 4, the centroid of each monomer is obtained through primary fitting in the graph from 3, the clustering result is shown in the graph from 4, and the classified secondary fitting result is shown in the graph from 5. Through the processing of the process, the interference factors in the train effect identification process can be greatly eliminated, and the suspected area of the train effect is obtained.
In the process, a neighbor propagation method is adopted for the selection of the clustering algorithm. The theoretical basis is a similarity matrix of data points. For a large-scale data set, the neighbor propagation method can perform clustering quickly and efficiently, and the result is ideal. The core of the method is to find an optimal representative point of a target area. This class represents points such that the sum of the similarities to all data in this region is maximized, they can be grouped into a class. Unlike other clustering algorithms, the neighbor propagation method assumes all data points as class representative points when clustering is started, so that the situations of receiving initial value setting and artificial participation in clustering results cannot occur in the subsequent clustering process, and the neighbor propagation method is an important link for realizing automatic identification and tracking of the train effect. The formula is as follows:
where s (i, k) represents data point xkTo what extent data x is suitable foriThe class of (1) represents a point; representative matrix R ═ R (i, k)]n×nAnd the fitting matrix a ═ a (i, k)]n×nAnd r (i, k) represents the data point xkAs data point xiA (i, k) represents the data point xiSelecting a data point xkAs the suitability degree of its representative point, the sum of these two degrees needs to be calculated:
by solving the optimal representative point of the class, the automatic clustering can be carried out without manually indicating the number of the required clusters at the initial clustering stage. And extracting the central point data obtained by the outline through the reflectivity image, enabling the information of the adjacent points to form mutual influence, and automatically clustering in real time so as to meet the current situation of a plurality of 'train effect' processes. The references are: xiaoyu, yu, semi-supervised clustering based on neighbor propagation algorithm [ J ]. software science, 2008, 19 (11): 2803-2813. the clustering process of fig. 3 to 4 is implemented by using the algorithm described above.
As shown in fig. 6, after the suspected area is determined, the train effect can be automatically identified. After each suspected train effect area is obtained, extracting the information of the whole strip-shaped echo in the suspected area, and judging whether the aspect ratio of the minimum external rectangle of the whole strip-shaped echo is between 1.5:1 and 3: 1; whether the average reflectivity of the whole strip echo is between 30dbz and 45 dbz; whether the moving speed is less than 1.5km/min or not, if so, extracting all the characteristics of all the monomers in the suspected area, putting the monomers into a rule base for judgment by using a standard voting method, seeing whether the echoes of the monomers meet the train effect monomer echo condition or not, counting the proportion of the monomers meeting the rule base after judging and identifying all the monomers in the suspected area, and if the proportion of the monomers meeting the condition to the total number of the monomers exceeds 2/3, judging the train effect; if these 3 criteria are not satisfied, it is determined that the train effect is not present. The result of the automatic identification is shown in region I, II of fig. 6, which is a typical "train effect" of two processes.
Respectively extracting the whole strip echo information and the monomer characteristics in the suspected area, combining the tracking of the strip echo with the tracking of the monomer, and tracking the train effect: firstly, the centroid moving distance and the axis rotation angle of the suspected area strip echo are utilized to track the strip echo. After the position of the whole strip echo is determined, monomer tracking in the suspected area is carried out by using the Hu moment and the morphological change. And completing one-time complete data entry, judging whether a train effect phenomenon exists or not, if so, judging that several processes exist at the same time, and recording various characteristics of each monomer and whole belt of the target area. When the train effect is tracked, the attributes of the train effect integral strip echo and the single echo are fused, so that the accurate tracking of the train effect is realized; firstly, starting from the overall angle, determining the position of the overall strip echo, and the specific process is as follows:
A. calculating the centroid position and the long axis direction of the whole strip echo by taking the whole strip echo with the train effect as a research object;
B. finding out all possible motion path combinations of the whole strip-shaped echo between two adjacent moments, and calculating the moving distance of the mass center of the strip-shaped echo and the rotation angle of the long axis under each path combination;
C. because the time delta t (6 minutes) spent on acquiring one volume scan is short, the rotation angle of the whole strip-shaped echo has an upper bound, if the rotation angle of the whole strip-shaped echo is larger than the upper bound in a certain path combination, the path combination is deleted, and only the path combinations with the rotation angles of the whole strip-shaped echo smaller than the upper bound are left;
D. and selecting the shortest path combination as the moving path of the loop band from the remaining path combinations. Since the ratio of the size of the whole band echo to its distance traveled in Δ t (6 minutes) determines that it cannot move far in a short time, and that its previous position, or a position adjacent to it, cannot be occupied by another whole band echo, it is more likely that a combination of paths is left that is shorter, and that is true. Therefore, the path combination with the shortest moving distance of the centroid of the band echo is determined as the real moving path of the whole band echo.
After the position of the whole strip echo is determined, the single echoes can be tracked, and the specific process is as follows:
since the change of the single echo in Δ t (6 minutes) is limited, the two single echoes in the reflectance maps at two adjacent times may be the same single echo as the shapes and sizes of the two single echoes are closer to each other.
And calculating the area difference between each monomer at the current moment and each monomer at the previous moment aiming at the same whole strip echo. If the area difference is less than a threshold, the cells in the previous time instance are stored as a possible match to the current cell. The similarity of the contours of the current monomer to all its possible matching monomers is then calculated by the Hu moments, and the combination with the greatest similarity is considered to be the same monomer, with both labeled with the same ID.
The formula for the Hu moment is as follows:
normal and central moments of order (p + q) of f (x, y):
we process the digitized image after dispersion, and can replace it with the form of integration:
whereinIs the center of gravity.
Normalized central moment ηpq：
r＝(p+q)/2，p+q＝2，3，4，......
η when the reflectivity map we have processed undergoes a morphological changepqThe following steps are changed:
where ρ is a scaling factor.
Hu constructs 7 geometric moments that satisfy invariant features:
for comparison, we can obtain a degree of similarity with respect to the two monomer profiles:
wherein,
when the profiles of the two monomers are completely the same, I (a, B) ═ 0, the larger the difference between the two profiles, the larger I (a, B);
based on the recognition and judgment result of the train effect for the first time, the area range is defined, the secondary operation time is shortened, and the accuracy is improved. Namely, the suspected area is defined in a certain range, and in the subsequent data entry, the storm body in the suspected area can be directly judged firstly, and then other non-suspected areas are judged. Obtaining the characteristics of the single body and the whole belt shape of the target area again;
the information between the integral belt with the maximum relevance and each monomer in the belt is obtained by comparing various characteristics of the front time sequence monomer and the rear time sequence monomer, comparing integral belt characteristics, comparing the long axis direction of the integral belt unique to the train effect with the moving direction, comparing the speed and the moving direction and the like, so that a one-to-one corresponding relation is formed.
And extrapolating the movement direction, the movement speed and the shape change of the suspected area of the train effect according to the movement inertia of the single body.
Continuously inputting train effect data at three moments, acquiring relevant characteristics, constructing a time sequence of the continuous moments, establishing a monomer family spectrum relationship, checking and comparing the similarity of adjacent secondary monomers, and identifying the motion track of monomer development and extinction through calculation, thereby predicting the motion trend of the monomer, wherein the extrapolation of the train effect is also divided into two parts of extrapolation of integral strip echoes and extrapolation of each monomer echo inside;
firstly, extrapolation of the whole zonal echo is performed, and the body contents are as follows:
A. estimating the moving speed of the whole belt at the next moment according to the inertia of the movement of the monomer and the average speed of the whole belt between the recorded continuous three moments;
B. estimating the moving direction of the whole belt at the next moment according to the inertia of the monomer and the moving direction of the whole belt recorded among three continuous moments;
C. determining the long axis direction of the whole belt at the next moment according to the recorded long axis direction of the whole belt between three continuous moments;
the tracking of the whole strip echo of the train effect is finished by integrating the information.
On the basis of extrapolating the position of the whole strip echo, the position of each single echo can be extrapolated by the same method, but because the change speed of the single echo is relatively fast, the method may have a large error, and therefore the extrapolated position needs to be corrected, and the specific contents are as follows:
A. the principle of extrapolation correction for the monomer is: the speed difference between a single body and the adjacent single body cannot exceed 6m/s, and the difference with the moving direction of the whole strip-shaped echo cannot exceed 10 degrees. If the threshold value is exceeded, the average moving direction and moving speed of the surrounding monomer are used for replacing, so that the distribution situation of the echo of each monomer at the next moment is predicted.
B. In the prediction of the train effect, the single body forming the train effect is generally small, so that the position and the moving speed of the echo of the single body are mainly concerned, and the shape change of the single body does not need to be accurately estimated. Therefore, the patent only roughly extrapolates the shape of the single echo in the "train effect": and expanding and corroding the monomer according to the change rate of the echo area of the monomer.
As shown in fig. 7, the extrapolation of the "train effect" is also divided into two parts, the extrapolation of the whole band echo and the extrapolation of the internal individual echoes, combined, and extrapolated for 6 minutes, 12 minutes, and 18 minutes, respectively. FIGS. 7a, b, c are real graphs of "train effect" at 6, 12, 18 minutes compared to extrapolated graphs. The a1, b1, c1, a3, b3 and c3 are real reflectivity maps of 6, 12 and 18 minutes 'train effect' and partial enlarged views thereof, and the a2, b2, c2, a4, b4 and c4 are extrapolation maps of 6, 12 and 18 minutes 'train effect' and partial enlarged views thereof.
Claims (6)
1. A method for identifying and forecasting rainstorm caused by train effect is characterized by comprising the following steps:
step S1, preprocessing the real-time radar data: eliminating super-refraction, fine monomers and false combination, filtering out super-refraction echoes with strong interference on an automatic identification stage by raising the elevation angle of radar echo data, deleting the fine monomers which do not meet the condition of a threshold value by setting an area threshold value, judging whether the reflectivity of a target communication area is more than 40dBZ or not, corroding one pixel point if the reflectivity of the target communication area is more than 40dBZ, and corroding a strong echo area less; if the number of the pixels is less than 40dBZ, corroding two pixels, corroding a weak echo region a little more, then increasing a first-level threshold value, judging whether a monomer with a higher first-level threshold value can be detected, if the monomer can be detected, expanding, repeating the process, and if the monomer cannot be detected, stopping;
step S2, primarily fitting the monomers subjected to area primary screening to eliminate interference, clustering the result, secondarily fitting the clustering result, and searching a suspected area of train effect;
step S3, automatically identifying the train effect after determining the suspected area of the train effect, judging the suspected area of the train effect as the train effect if the suspected area of the train effect is the train effect, and carrying out step S4, if the suspected area of the train effect is not the train effect, skipping to the radar data processing at the next moment, namely starting from step S1;
step S4, tracking the strip echo by using the centroid moving distance and the axis rotation angle of the strip echo in the suspected area, and after the position of the whole strip echo is determined, tracking the monomer in the suspected area by using the Hu moment and the form change to realize the tracking of the train effect;
and step S5, extrapolating the movement direction, the movement speed and the shape change of the suspected area of the train effect according to the movement inertia of the single body.
2. The method for identifying and forecasting rainstorms caused by the "train effect" as claimed in claim 1, wherein said step S2 includes the following steps:
s201: firstly, a centroid method is used for obtaining representative points of each monomer, then straight line fitting is carried out on the points to obtain a fitting straight line of the train effect, then the monomer which deviates from the fitting straight line to a large extent is deleted, and interference of peripheral monomers irrelevant to the train effect is eliminated;
s202: obtaining monomers meeting requirements after preliminary fitting, extracting the mass centers of the monomers, automatically clustering by using a neighbor propagation method, then respectively carrying out straight line fitting on each type of point set again, and deleting the monomers with larger deviation from the fitting straight line degree to obtain a suspected area of the train effect.
3. The method for identifying and forecasting rainstorms caused by the "train effect" as claimed in claim 1, wherein said step S3 includes the following steps:
s301: extracting information of the whole strip-shaped echo in the suspected area, judging whether the length-width ratio of the minimum external rectangle of the whole strip-shaped echo, the average reflectivity and the average speed of the whole strip-shaped echo meet the standard of the train effect, if the length-width ratio of the minimum external rectangle of the whole strip-shaped echo, the average reflectivity and the average speed meet the standard of the train effect, continuing the following judging process, if the length-width ratio does not meet the standard of the train effect, judging that the length-width ratio is not the train effect, then discarding the length-width ratio;
s302: extracting various characteristics of all monomers in the suspected area, putting the characteristics into a rule base, judging whether the monomer echoes meet a 'train effect' monomer echo condition or not by using a standard voting method, counting the proportion of the monomers meeting the 'train effect' monomer echo condition in all the monomers after judging and identifying all the monomers in the suspected area, and judging the 'train effect' if the conditions meet a threshold value.
4. The method for identifying and forecasting rainstorms caused by the "train effect" as claimed in claim 1, wherein said step S4 includes the following steps:
s401: when the train effect is tracked, the attributes of the train effect integral strip echo and the single echo are fused;
s402: calculating the centroid position and the long axis direction of the whole strip echo by taking the whole strip echo with the train effect as a research object, finding out all possible motion path combinations of the whole strip echo between two adjacent moments, calculating the moving distance of the centroid of the strip echo and the rotating angle of the long axis under each path combination, and selecting the shortest path combination as the moving path of the echo band in the path combinations meeting the conditions;
s403: after the position of the whole band echo is determined, calculating the area difference between each monomer at the current moment and each monomer at the previous moment aiming at the same whole band echo, if the area difference is smaller than a threshold value, storing the monomers at the previous moment as possible matching with the current monomer, then calculating the similarity degree of the current monomer and the outlines of all the possible matched monomers through Hu moment, and considering the combination with the maximum similarity degree as the same monomer, and marking the same ID (identity) with the same monomer;
s404: based on the recognition and judgment results of the train effect of the primary pair, the area range is defined, the secondary operation time is shortened, and the accuracy is improved;
s405: the information between the integral belt with the maximum relevance and each monomer in the belt is obtained by comparing various characteristics of the front time sequence monomer and the rear time sequence monomer, comparing integral belt characteristics, comparing the long axis direction of the integral belt unique to the train effect with the moving direction, and comparing the speed with the moving direction, so that a one-to-one correspondence relationship is formed.
5. The method for identifying and forecasting rainstorms caused by the "train effect" as claimed in claim 1, wherein said step S5 includes the following steps:
s501: continuously inputting 'train effect' data at three moments, acquiring relevant characteristics, constructing a time sequence of the continuous moments, establishing a monomer family relation, checking and comparing the similarity of adjacent time monomers, and identifying a motion track of the monomer which develops and disappears through calculation so as to predict the motion trend of the monomer;
s502: firstly, extrapolating the integral band echo, respectively determining the moving speed, the moving direction and the long axis direction of the integral band at the next moment according to the average speed, the moving direction and the long axis direction of the integral band among three recorded continuous moments according to the inertia of the movement of the single body, and integrating the three information to finish the tracking of the integral band echo with the train effect;
s503: on the basis of extrapolating the position of the whole strip echo, the position of each single echo is extrapolated by the same method.
6. The method for identifying and forecasting rainstorms caused by the "train effect" according to claim 5, characterized in that the extrapolation is carried out for three times, respectively 6 minutes, 12 minutes and 18 minutes.
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