JP2011033528A - Method and system of automatically tracking weather phenomenon - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To reliably and automatically track only the weather phenomenon such as a thundercloud including a cumulonimbus by RHI observation. <P>SOLUTION: In an automatic tracking system 10, an analysis PC 22 uses the product of an echo top height and a vertical integrated rainwater amount based on echo intensity data of a weather phenomenon observed by a weather Doppler radar 23 to automatically determine the echo of interest as a tracking target, and automatically calculates an azimuth angle when performing the RHI observation for the weather phenomenon corresponding to the determined echo of interest. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an automatic tracking method and an automatic tracking system for tracking a weather phenomenon such as a thundercloud by RHI observation when the weather radar observes the weather phenomenon.

  Patent Documents 1 to 3 propose that a weather radar tracks a weather phenomenon such as a thundercloud by performing PPI (Plane Position Indicator) observation.

JP-A-8-122433 JP 9-318745 A Japanese Patent Laid-Open No. 11-271443

  By the way, in recent years, thunderclouds (meteorological phenomena) such as local cumulonimbus clouds frequently occur in summer due to the heat island phenomenon in large cities, and as a result, local flooding in urban areas due to sudden heavy rain caused by the thunderclouds. Natural disasters are increasing. Since the cumulonimbus is a local and vertical weather phenomenon, the PPI observation in Patent Documents 1 to 3 scans the radar beam in the azimuth direction with the elevation angle fixed and the azimuth angle variable. It is difficult to track the thundercloud. Therefore, it is conceivable to track the thundercloud by RHI (Range Height Indicator) observation in which the elevation angle is varied and the azimuth angle is fixed, and the radar beam is scanned in the elevation angle direction. Since RHI observation is performed at a fixed azimuth according to a schedule, it is difficult to track the cumulonimbus. On the other hand, based on the result of PPI observation, the user may change the azimuth angle in the observation schedule, and the weather radar may perform RHI observation at the changed azimuth angle. If the thundercloud moves in a direction with an azimuth angle different from the azimuth angle after the change, the thundercloud cannot be tracked even if RHI observation is performed at the azimuth angle after the change.

  The present invention has been made to solve the above-described problems, and provides an automatic tracking method and an automatic tracking system that can reliably and automatically track meteorological phenomena such as thunderclouds including cumulonimbus clouds by RHI observation. For the purpose.

The automatic tracking method of the meteorological phenomenon according to the present invention,
A first step of determining an echo top height and a vertical accumulated rainwater amount using echo intensity data indicating a meteorological phenomenon observed by a weather radar;
A second step of determining a predetermined echo in the echo intensity data as a target echo to be tracked based on the obtained echo peak altitude and the vertically integrated rainwater amount;
And a third step of calculating an azimuth angle when the weather radar performs RHI observation for a weather phenomenon corresponding to the attention echo.

An automatic tracking system for meteorological phenomena according to the present invention includes:
A weather radar for acquiring echo intensity data indicating a weather phenomenon by performing PPI observation according to an observation sequence;
Using the echo intensity data, the echo top height and the vertical accumulated rainwater amount are obtained, and based on the obtained echo top altitude and the vertical accumulated rainwater amount, a predetermined echo in the echo intensity data to be tracked to be tracked Command issuing means for calculating an azimuth angle when the weather radar performs RHI observation for the meteorological phenomenon corresponding to the determined echo of interest, and issuing an azimuth rewriting command indicating the calculated azimuth angle Have
When the command issuing means issues the direction rewriting command, the weather radar determines an azimuth angle of RHI observation for a meteorological phenomenon corresponding to the echo of interest from an azimuth angle set in advance in the observation sequence. The azimuth angle of the azimuth rewriting command is changed to perform RHI observation for the meteorological phenomenon.

  According to these inventions, a predetermined echo is automatically determined as an attention echo using an echo peak height and a vertical accumulated rainwater amount obtained from echo intensity data indicating a weather phenomenon observed by a weather radar, Since the azimuth angle at the time of RHI observation is automatically calculated for the meteorological phenomenon corresponding to the determined echo of interest, the meteorological phenomenon such as thunderclouds including the cumulonimbus corresponding to the echo of interest is reliably and automatically tracked. can do.

It is a block diagram of the automatic tracking system concerning this embodiment. 7 is a flowchart showing a process for issuing an RHI azimuth rewriting command in the analysis PC of FIG. 1. 3A to 3D are schematic explanatory diagrams of echo intensity data. 4A to 4D are schematic explanatory diagrams of echo intensity data. FIG. 5A is a schematic explanatory diagram of a product of echo peak altitude, and FIG. 5B is a schematic explanatory diagram of a product of vertical accumulated rainwater. It is a model explanatory drawing of the raster scan with respect to the mesh of a product. 7A to 7D are schematic explanatory diagrams of labeling. 8A and 8B are schematic explanatory diagrams for determining the correlation between the cell obtained by the previous process and the cell obtained by the current process. 9A and 9B are schematic explanatory diagrams for obtaining the center of gravity of the cell. FIG. 10A is an explanatory diagram of RHI observation when the azimuth is fixed, and FIG. 10B is an explanatory diagram of RHI observation in the present embodiment. It is explanatory drawing of RHI observation by a some weather Doppler radar.

  A preferred embodiment of a weather phenomenon automatic tracking method and automatic tracking system according to the present invention will be described with reference to FIGS.

  As shown in FIG. 1, the automatic tracking system 10 according to the present embodiment includes an antenna 12, an antenna driving unit 14, a transceiver 16, a signal processor 18, a host computer (hereinafter also referred to as a host PC) 20, and a database 24. A meteorological Doppler radar 23 composed of a computer and an online analysis computer (hereinafter also referred to as an analysis PC) 22 as command issuing means.

  The antenna 12 radiates the transmission signal output from the transmitter / receiver 16 via the antenna driving unit 14 as a radar beam to the outside, and on the other hand, the radar reflected by a thundercloud such as a cumulonimbus (hereinafter also referred to as a meteorological phenomenon). When a beam (reflected wave) is received, the reflected wave is output as a received signal to the transceiver 16 via the antenna driving unit 14. In this case, the weather Doppler radar 23 performs PPI observation (observation in which the antenna 12 is transmitted and received by rotating the antenna 12 in the azimuth direction while the elevation angle is fixed) by rotation of the antenna 12 by the antenna driving unit 14, and CAPPI. Observation (observation in which PPI observation is performed for a plurality of elevation angles) and RHI observation (observation in which a radar beam is transmitted and received by rotating the antenna 12 in the elevation angle direction with a fixed azimuth angle). Is possible.

  The signal processor 18 generates echo intensity data indicating the intensity of the reflected wave based on the received signals obtained by PPI observation, CAPPI observation, and RHI observation, and outputs the echo intensity data to the host PC 20. The reflected wave is generated by the reflection of the radar beam with respect to the water droplet in the thundercloud, and therefore the echo intensity data indicates data corresponding to the amount of the water droplet (moisture).

  The host PC 20 uses the echo intensity data from the signal processor 18 to display echo intensity data (image data) at the same altitude on the display as shown in FIG. 3A, while being stored in the database 24. Based on the observation sequence, the antenna drive unit 14, the transceiver 16 and the signal processor 18 are controlled.

  The observation sequence includes a scanning schedule of the antenna 12 in which an elevation angle in PPI observation or CAPPI observation and an azimuth angle in RHI observation are set in advance (for example, a schedule for performing RHI observation at a predetermined azimuth angle after performing CAPPI observation). This is a scanning schedule in which one cycle is repeated, and the user cannot operate the host PC 20 to change the setting of the scanning schedule. Accordingly, the host PC 20 controls the antenna drive unit 14, the transceiver 16, and the signal processor 18 according to the PPI observation, CAPPI observation, and RHI observation scan schedules set in the observation sequence, thereby controlling the echo intensity data (image data thereof). ) To get.

  By executing a scanning schedule for one cycle of PPI observation or CAPPI observation and RHI observation (for example, a scanning schedule for 10 to 20 minutes of PPI observation or CAPPI observation and RHI observation as one cycle), echo intensity data is obtained. Thus, as shown in the order of FIGS. 3A to 4D, the display content on the screen 30 of the display of the host PC 20 (or the analysis PC 22) is sequentially updated every time one cycle of observation is completed. That is, until the display content of the screen 30 is switched (for example, within the time from when the screen 30 displays FIG. 3A to when it displays FIG. 3B), the weather Doppler radar 23 performs PPI observation or CAPPI observation and RHI. Observations are being made. 3A to 4D, the center 34 of the circle 32 indicating the observable range of the weather Doppler radar 23 indicates the position of the antenna 12, and the circle 32 includes a thundercloud or the like that moves with time. Echoes 36 to 60 indicating weather phenomena are displayed.

  The PC 22 for analysis is used when it is difficult to track the weather phenomenon even if the antenna 12 is rotated based on the observation sequence with respect to a weather phenomenon that grows locally and vertically like a thundercloud such as a cumulonimbus. Using the echo intensity data from the host PC 20, issue an RHI azimuth rewrite command to change the azimuth angle of RHI observation from the azimuth angle preset in the observation sequence to the azimuth angle of the meteorological phenomenon. And stored in the database 24. Only when the RHI azimuth rewriting command is stored in the database 24, the host PC 20 sets the RHI observation azimuth set in the observation sequence in the RHI azimuth rewriting command. The azimuth angle is changed, and the antenna driving unit 14, the transceiver 16 and the signal processor 18 are controlled based on the changed azimuth angle (observation sequence).

  Next, the RHI azimuth rewriting command issuance process in the analysis PC 22 will be described with reference to FIGS.

  FIG. 2 is a flowchart showing the RHI azimuth rewriting command issuance process, and the process of this flowchart is executed once per cycle.

  In step S1, echo intensity data by PPI observation as primary data is input from the host PC 20 (see FIG. 1) to the analysis PC 22. In this case, if the weather Doppler radar 23 is performing CAPPI observation, the echo intensity data is input from the host PC 20 to the analysis PC 22 every time PPI observation is performed for each elevation angle. Each echo intensity data is accumulated until the observation echo intensity data is acquired (step S2).

  Next, in step S3 (first step), the analysis PC 22 performs echo intensity at the same altitude (see FIG. 3A), echo top altitude (see FIG. 5A), and vertical integration based on the accumulated echo intensity data. A product (image data) indicating the amount of rainwater (see FIG. 5B) is generated. 3A, 5A, and 5B are schematic explanatory diagrams showing image data of echo intensity, echo top height, and vertical accumulated rainwater amount displayed on the display screen 30 of the analysis PC 22 (or the host PC 20).

  Corresponding to the echoes 36-40 in FIG. 3A, FIG. 5A shows echo top heights indicating the top altitude of thunderclouds as echoes 62-78, while FIG. 5B shows the rainwater in the thunderclouds vertically. The vertically accumulated rainwater amount accumulated in the direction is displayed as echoes 82-98. In FIG. 3A and FIGS. 5A and 5B, there are echoes that do not correspond to each other. As described above, FIG. 3A is the echoes 36 to 40 at the same altitude. The thunderclouds occurring at the altitude are not displayed on the screen 30, while the echoes 62 to 78 and 82 to 98 in FIGS. 5A and 5B are generated within the observable range (circle 32). This is because the echoes corresponding to all thunderclouds are shown.

  Next, in step S4, the analysis PC 22 performs raster scanning simultaneously on the meshes constituting the echo peak height image data in FIG. 5A and the vertical accumulated rainwater image data (product) in FIG. The threshold value is determined by the evaluation function method from the mesh information.

Specifically, as shown in the schematic explanatory diagram of FIG. 6, the mesh 102 (the mesh 102 a constituting the echo and the mesh not constituting the echo) constituting the product 100 indicating the image data of the echo top height or the vertical accumulated rainwater amount. 102b) is raster-scanned, and the mesh value at the scanning location 104 in the image data of the echo peak height and the mesh value at the scanning location 104 in the image data of the vertical accumulated rainwater amount are as follows (1). ) The weighting shown in the equation is performed, and each weighted value is added. Note that the values of the first weight and the second weight are appropriately set according to the type of weather phenomenon such as thunderclouds.
(Vertical accumulated rainwater volume with one mesh 102) x (first weight)
+ (Echo peak height of corresponding mesh 102) × (second weight)> (threshold)
(1)

  If the relationship of the expression (1) is satisfied between the addition result and the predetermined threshold value, the value of the mesh 102 at the scanning location 104 is set to “1”. On the other hand, if the relationship of the expression (1) is not satisfied, the mesh 102 By performing binarization processing with a value of “0”, image data of the echo peak altitude, image data of the vertical accumulated rainwater amount, and new image data (binarized array) based on the equation (1) are generated. .

  Next, in step S5, the analysis PC 22 performs the following labeling by performing raster scanning on the new image data mesh of the binarized array generated in step S4.

  Specifically, as shown in the schematic explanatory diagrams of FIGS. 7A to 7D, the analysis PC 22 uses the meshes 106 (the meshes 106 a and “0” indicating “1”) that form the image data 105 of the binarized array. The mesh 106b) is subjected to raster scanning, and when the scanned portion 108 reaches the mesh 106a (see FIG. 7A), a labeling number is assigned to the mesh 106a. Next, the analysis PC 22 checks whether the upper, lower, left, and right meshes 106a (mesh 106c) to which the labeling numbers are assigned are meshes 106a indicating “1” (see FIG. 7B). If there is, a labeling number is assigned to the mesh 106a (see FIG. 7C). By repeating such labeling processing until the presence of the mesh 106a can no longer be confirmed, a cell 109 composed of a plurality of meshes 106c to which a labeling number is assigned is configured in the image data 105 (FIG. 7D). reference). When the area of the cell 109 does not reach the predetermined minimum cellization area (when the number of meshes 106c constituting the cell 109 does not reach the predetermined number), the analysis PC 22 discards the cell 109.

  Next, in step S6, the analysis PC 22 takes a correlation between the cell obtained by the process of the current step S5 and the cell obtained by the process of the previous step S5, and if the correlation is high, The cell and the current cell are the same cell, and it is determined that the cell has moved from the previous cell position to the current cell position, and the labeling information stored in the analysis PC 22 is used as the content of the previous cell. To update the contents of this cell.

  Specifically, as shown in the schematic explanatory diagrams of FIGS. 8A and 8B, the analysis PC 22 determines that the previous cell 116 in the image data 110 of FIG. 8A (the cell obtained by the process of the previous step S5). Is moved up, down, left, and right, and the correlation with the current cell 118 (the cell obtained by the processing of the current step S5) in the image data 112 in FIG. The cells 116 and 118 (the areas of the overlapping portions are maximized each other) are regarded as the same cell, and the labeling information (labeling number, vertical accumulated rainwater amount, echo peak intensity, observation time, etc.) of the stored cells 116 is stored. Information) is updated to the labeling information of the cell 118. That is, the analysis PC 22 determines that the cell 116 has moved to the position of the cell 118 between the previous process (previous PPI observation) and the current process (current PPI observation). The update processing is performed by regarding the cell 118 positioned at the movement destination as a cell in the current processing. Since there are a plurality of cells 120 in the image data 110 and 112, the analysis PC 22 performs the same correlation determination for each cell 120.

  In step S7, the analysis PC 22 determines whether or not the weather Doppler radar 23 is tracking an echo (for example, the echoes 36 to 40 in FIG. 3A) in the echo intensity data, and if the echo is not being tracked. In the echo intensity data, it is determined whether or not there is a notable echo (step S8). If there is a notable echo, the echo is selected as a notable echo (notable echo) (step S9). , The second step). In steps S8 and S9, as a determination process for determining whether or not there is an echo of interest and a determination process for selecting the echo of interest, for example, cells 109 and 116 generated by labeling and correlation determination in steps S5 and S6 are used. It is determined whether or not an echo corresponding to 118 is present in the image data of the echo intensity data in FIG. 3A. If such an echo (echo 36 in FIG. 3A) is present, the echo 36 is selected as an attention echo. To do.

  Next, in step S10, the analysis PC 22 moves the attention echo (echo 36) in the direction and distance in which the attention echo (echo 36) moves between the observation time in FIG. 3A (for example, the time when PPI observation is completed) and the observation time in FIG. Predict (destination).

Specifically, as shown in FIG. 9A, first, for the cell 130 corresponding to the echo 36 (the cell used in the processing of steps S5 and S6), based on the following equations (2) and (3): The center of gravity of the cell 130 in the process of the previous step S6 and the center of gravity of the cell 130 in the process of the current step S6 are respectively calculated, and the movement vector indicating the moving direction and the moving distance of the cell 130 from each calculated center of gravity. Ask for.
(X coordinate of the center of gravity) = Σ (Xn × number of meshes 132a)
/ (Number of coordinates in the X direction) (2)
(Y coordinate of the center of gravity) = Σ (Yn × number of meshes 132a)
/ (Number of coordinates in Y direction) (3)

  FIG. 9A shows the center of gravity of the cell 130 when the mesh 132 (mesh 132a constituting the cell 130 and mesh 132b not constituting the cell 130) have substantially the same value (when they are all “1”). Is based on the number of meshes 132a at the X coordinate (X1 to X5) and the Y coordinate (Y1 to Y5), and the X coordinate of the center of gravity by the above formulas (2) and (3) and Y coordinate is calculated. In Equations (2) and (3), Σ is a mathematical symbol that indicates the sum of n = 1 to 5 (Xn = X1 to X5, Yn = Y1 to Y5).

  Next, after considering the position of the echo 36 corresponding to the center of gravity of the cell 130 this time as the position of the center of gravity of the echo 36, the movement is performed so that the start point of the movement vector coincides with the position of the center of gravity of the echo 36. The destination of the echo 36 is predicted by superimposing the vector on the echo 36. That is, the end point of the movement vector starting from the position of the center of gravity of the echo 36 is predicted as the movement destination of the echo 36.

On the other hand, FIG. 9B shows a case where the values of the meshes 134a and 134b are set to different values for the mesh 134 (the meshes 134a and 134b that constitute the cell 130 and the mesh 134c that does not constitute the cell 130) (in steps S4 to S6). It is a schematic explanatory drawing regarding calculation of the center of gravity of the cell 130 (when processing digitized into a plurality of values is performed instead of binarization processing). In the case of FIG. 9B, as shown in FIGS. 3A to 4D, the X coordinate (X1 to X7) and the Y coordinate (Y1 to Y7) are considered in consideration that the echo intensity data indicates different echo intensities for each coordinate. The X and Y coordinates of the center of gravity are calculated by the following equations (4) and (5) using the weights Wxn and Wyn.
(X coordinate of the center of gravity) = Σ (Xn × Wxn) / (number of coordinates in the X direction) (4)

(Y coordinate of the center of gravity) = Σ (Yn × Wyn) / (number of coordinates in the Y direction) (5)

  In equations (4) and (5), Σ is the sum of n = 1 to 7 (Xn = X1 to X7, Yn = Y1 to Y7, Wxn = Wx1 to Wx7, Wyn = Wy1 to Wy7). Mathematical symbol shown. Further, the method of predicting the movement destination of the echo 36 is the same as that in the case of FIG. 9A, and thus detailed description thereof is omitted.

  Next, in step S11, the analysis PC 22 determines whether or not the echo 36 is outside the circle 32 (outside the observable range), and if it is within the circle 32, the center 34 and the end point of the movement vector ( An RHI azimuth rewriting command is issued and stored in the database 24 (step S12, third step). The RHI azimuth rewriting command uses the direction connecting with the (destination) (the direction of the straight line 44 shown in FIG. .

  Thereby, in the RHI observation of the next cycle, the weather Doppler radar 23 performs RHI observation at the azimuth angle of the RHI azimuth rewriting command, and as a result, the screen 46 shown in FIG. And a straight line 44 indicating that the echo 36 (corresponding meteorological phenomenon) is being tracked by the RHI observation is displayed.

  Therefore, whenever the echo intensity data obtained in each cycle is input from the host PC 20 to the analysis PC 22, the processing of steps S <b> 1 to S <b> 12 is repeatedly performed in the analysis PC 22, and the results are shown in FIGS. 3B to 4C. Thus, it is possible to reliably and automatically track the echo of interest (echo 36) that moves over time. As described above, the straight line 44 is a straight line connecting the center 34 and the destination predicted by the analysis PC 22, while the circle 46 indicates the center of gravity of the echo 36 at the destination. Yes. Therefore, even if the echo 36 (according to the meteorological phenomenon) grows or decays with time, the echo 36 can be reliably captured. 3B to 4C, echoes 38 to 60 are displayed in addition to the echo 36. These echoes 38 to 60 are a thundercloud that declines, and the observation range of the weather Doppler radar 23 at the next observation. Even a thundercloud that is outside, or a thundercloud that grows, does not grow as much as the thundercloud according to the echo 36.

  In the flowchart of FIG. 2, if it is determined in step S8 that there is no echo of interest, the analysis PC 22 does not perform the processing after step S9, that is, the RHI azimuth rewriting command issuance processing.

  Further, when it is determined in step S7 that the target echo (echo 36) is being tracked, the analysis PC 22 determines whether or not the echo 36 being tracked has disappeared. The movement destination of the echo 36 predicted in the process of step S10 is compared with the position of the echo 36 included in the current echo intensity data to calculate a movement prediction error (step S14), and the calculated movement prediction error is calculated. The destination of the echo 36 is accurately calculated by reflecting it in the processing of the next step S10.

  Further, when it is determined in step S13 that the echo 36 has disappeared, the analysis PC 22 does not perform the processing subsequent to step S14, and therefore does not issue the RHI azimuth rewriting command.

  Furthermore, as shown in FIG. 2 and FIG. 4D, the circle 46 is located outside the circle 32 because the center of gravity of the echo 36 is located outside the circle 32 (outside the observable range) after the process of step S <b> 10. If this is the case, the analysis PC 22 stops tracking the echo 36 (step S15).

  As described above, according to the automatic tracking system 10 and the automatic tracking method according to the present embodiment, the product 100 of the echo top height and the vertical accumulated rainwater amount based on the echo intensity data of the weather phenomenon observed by the weather Doppler radar 23 is obtained. The echo 36 to be noticed is automatically determined as a tracking target, and the azimuth angle when the RHI observation is performed for the meteorological phenomenon corresponding to the determined echo 36 is automatically calculated. It is possible to reliably and automatically track meteorological phenomena such as thunderclouds including cumulonimbus clouds corresponding to 36.

  That is, as shown in FIG. 10A, when the azimuth angle at the time of RHI observation is fixed by the observation sequence, the azimuth angle is directed in the direction of an echo 140 indicating a meteorological phenomenon such as a thundercloud that suddenly occurs. And the echo 140 cannot be tracked.

  In contrast, in the present embodiment, as shown in FIG. 10B, when the RHI azimuth rewriting command is issued by the analysis PC 22, the weather Doppler radar 23 sets the azimuth angle in the RHI azimuth rewriting command. Since RHI observation is performed, it is possible to perform tracking so that the echo 140 to be noticed can be aimed at reliably. As a result, it is possible to observe a vertical-structured meteorological phenomenon such as a cumulonimbus that suddenly occurs in a short time, so that significant research data on natural disasters caused by the cumulonimbus can be easily collected. . Further, by providing the weather Doppler radar 23 with the above tracking function, it is possible to perform RHI observation from the occurrence of the weather phenomenon to the decline.

  The automatic tracking system 10 and the automatic tracking method according to the present embodiment are not limited to the above description, and as shown in FIG. 11, for example, the observable ranges 152a to 150m of three weather Doppler radars 150a to 150c are provided. When there is a meteorological phenomenon 156 in the overlapping portion 154 of 152c, the analysis PC 22 (see FIG. 1) issues RHI scanning azimuth rewriting commands having different azimuth angles to each of the weather Doppler radars 150a to 150c. The meteorological Doppler radars 150a to 150c may perform RHI observation based on the straight lines 44a to 44c indicating the azimuth angles of the RHI scanning azimuth angle rewriting command.

  In this case, even if each of the weather Doppler radars 150a to 150c performs RHI observation at the same time, or separates the weather Doppler radar that performs PPI observation and the weather Doppler radar that performs RHI observation, the meteorological phenomenon 156 is observed. Since the observation by the plurality of weather Doppler radars 150a to 150c is possible, the weather phenomenon 156 can be tracked efficiently and reliably, and the analysis accuracy of the weather phenomenon 156 can be improved. Further, by performing observation on the meteorological phenomenon 156 separately for a weather Doppler radar that performs PPI observation and a weather Doppler radar that performs RHI observation, it is possible to obtain both RHI observation data and PPI observation data. Become.

  Of course, the present invention is not limited to the above-described embodiment, and various configurations can be adopted.

10 ... Automatic tracking system 20 ... Host PC
22 ... PC for analysis 23 ... Weather Doppler radar 24 ... Database 36, 38, 40, 60, 62, 78, 82, 98, 140 ... Echo 44 ... Straight line 46 ... Circle 109, 116, 118, 120, 130 ... Cell

Claims (2)

A first step of determining an echo top height and a vertical accumulated rainwater amount using echo intensity data indicating a meteorological phenomenon observed by a weather radar;
A second step of determining a predetermined echo in the echo intensity data as a target echo to be tracked based on the obtained echo peak altitude and the vertically integrated rainwater amount;
A third step of calculating an azimuth angle when the weather radar performs RHI observation on a meteorological phenomenon corresponding to the echo of interest;
A method for automatically tracking meteorological phenomena. A weather radar for acquiring echo intensity data indicating a weather phenomenon by performing PPI observation according to an observation sequence;
Using the echo intensity data, the echo top height and the vertical accumulated rainwater amount are obtained, and based on the obtained echo top altitude and the vertical accumulated rainwater amount, a predetermined echo in the echo intensity data to be tracked to be tracked Command issuing means for calculating an azimuth angle when the weather radar performs RHI observation for a meteorological phenomenon corresponding to the determined echo of interest, and issuing an azimuth rewriting command indicating the calculated azimuth angle;
Have
When the command issuing means issues the direction rewriting command, the weather radar determines an azimuth angle of RHI observation for a meteorological phenomenon corresponding to the echo of interest from an azimuth angle set in advance in the observation sequence. An automatic weather phenomenon tracking system, wherein the azimuth angle of the direction rewriting command is changed to perform RHI observation for the weather phenomenon.

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