TW201730582A - Weather phenomenon occurrence possibility determination system and method - Google Patents

Abstract

In an embodiment, this system is characterized by being provided with: an acquisition means for using a radar to irradiate radar waves into the sky, receive reflected or scattered waves resulting from the reflection or scattering of the radar waves by particles composing a cloud, and repeatedly acquire a series of three-dimensional data necessary to predict a weather phenomenon from the reflected or scattered waves within a period of time shorter than the time necessary for the weather phenomenon to occur; an analysis means for carrying out analysis for detecting a particular temporal variation in a parameter relating to the weather phenomenon from the three-dimensional data that is repeatedly acquired; and a determination means for, on the basis of the analysis result, determining the possibility of the occurrence of the weather phenomenon if the particular temporal variation has been detected.

Description

Meteorological phenomenon occurrence possibility determination system and method

Embodiments of the present invention relate to systems and methods for determining the likelihood of occurrence of a meteorological phenomenon.

From the past, the data obtained by parabolic meteorological radars were used to predict meteorological phenomena. For example, if such a parabolic meteorological radar is used, data showing the characteristics of the two-dimensional nature of the precipitation cloud is obtained. Then, based on the data obtained, the weather phenomenon such as the evaluation of the possibility of occurrence of meteorological phenomena such as precipitation clouds is predicted.

[Previous Technical Literature] [Patent Literature]

[Patent Document 1] Japanese Patent Laid-Open Publication No. 2011-520127

[Patent Document 2] WO2015/005020

[Patent Document 3] Japanese Patent Laid-Open Publication No. 2010-249550

However, the appearance of meteorological phenomena like precipitation clouds is characterized by three-dimensionality.

Therefore, there is a problem that it is difficult to predict various weather phenomena with high accuracy based on the characteristics of two-dimensionality.

In addition, in order to obtain radar data required for the analysis of the three-dimensional nature of the precipitation cloud or the like using a parabolic meteorological radar, for example, it takes about 5 to 10 minutes.

Therefore, it is also difficult to generate a significant meteorological phenomenon that occurs in a very short time of 1 minute, such as a tornado, based on the three-dimensional nature of the precipitation cloud obtained by using a parabolic meteorological radar. The possibility of making predictions.

In addition, even when a three-dimensional data is acquired by using a radar other than a parabolic meteorological radar, the same is true for the acquisition of a set of three-dimensional data necessary for the analysis of meteorological phenomena. The time required (that is, the time resolution) is not a high-resolution problem that can be predicted for the occurrence of significant weather phenomena occurring in a very short time of 1 minute such as a tornado.

Therefore, in the case of using such prior art, there is a prediction misalignment that causes a possibility of occurrence of a significant meteorological phenomenon such as a tornado, which occurs in a very short time, and causes a prediction to occur. The fault is wrong.

The present invention has been made in view of the above-described circumstances, and an object thereof is to provide a set of three-dimensional data necessary for obtaining a weather phenomenon analysis in a short period of time, even if it is for an image. A system and method in which a tornado generally makes a significant meteorological phenomenon in a very short time and can be predicted with high accuracy.

The system according to the embodiment is a meteorological phenomenon occurrence possibility determination system that uses a radar to determine the possibility of occurrence of a meteorological phenomenon, and is characterized in that the acquisition means, the analysis means, and the determination means are provided. The means of obtaining is to use a radar to transmit a radar wave toward the sky and receive a reflected wave or a scattered wave formed by the radar wave reflected or scattered by the particles constituting the cloud, and according to the reflected wave or the scattered wave, in a weather phenomenon In a shorter period of time, the series of 3D data required for the prediction of meteorological phenomena is obtained in a shorter period of time. The analysis means performs analysis for detecting the temporal change of the specificity of the parameter related to the meteorological phenomenon based on the three-dimensional data acquired repeatedly. The determination means determines whether or not there is a possibility of occurrence of a meteorological phenomenon when a temporal change in specificity is detected based on the result of the analysis.

1‧‧‧Weather occurrence probability determination system

8‧‧‧High-speed scanning weather radar

8a‧‧‧Parabolic antenna

9‧‧‧Radar Signal Processing Department

9a‧‧‧Acquisition Department

10‧‧‧Radar Analysis Department

11‧‧‧Communication interface

12‧‧‧RAW Data Processing Department

13‧‧‧RAW Data Storage Department

14‧‧‧Radar Data Analysis and Calculation Department

15‧‧‧Analytical data storage department

20‧‧‧ Tornado Forecasting Department

21‧‧‧ Tornado occurrence possibility analysis department

22‧‧‧Analytical data storage department

23‧‧‧Show notification department

30‧‧‧Analysis of Data Acquisition Department

32‧‧‧Parameter Analysis Department

34‧‧‧ Tornado Sign Detection Department

40‧‧‧Control Department

41‧‧‧Detection Information Acquisition Department

42‧‧‧Switching Department

44‧‧‧Azimuth calculation department

46‧‧‧Switching judgment department

48‧‧‧Control Information Generation Department

49‧‧‧Switching information memory

50‧‧‧ precipitation cloud

50a‧‧‧ precipitation cloud

50b‧‧‧ precipitation cloud

50c‧‧‧ precipitation cloud

50d‧‧‧ precipitation cloud

50e‧‧‧ precipitation cloud

50f‧‧‧ precipitation cloud

50-1‧‧‧ precipitation cloud

50-2‧‧‧ precipitation cloud

51‧‧‧Precipitation core

51a‧‧‧Precipitation core

51b‧‧‧Precipitation core

51c‧‧‧Precipitation core

51d‧‧‧Precipitation core

51e‧‧‧Precipitation core

51f‧‧‧Precipitation core

52‧‧‧ line

53‧‧‧ line

60‧‧‧ line

60a‧‧‧ line

60b‧‧‧ line

60c‧‧‧ line

60d‧‧‧ line

60e‧‧‧ line

70‧‧‧reflexion point

70a‧‧‧reflexion point

70c‧‧‧recurve

70d‧‧‧recurve point

70e‧‧‧reflexion point

90‧‧‧reflexion point

A‧‧‧ tornado prediction parameters

B‧‧‧ Tornado prediction parameters

C‧‧‧ Tornado prediction parameters

C1‧‧‧Scope of observation

C2‧‧‧Scope of observation

D‧‧‧ tornado prediction parameters

D1‧‧‧ tornado prediction parameters

D2‧‧‧ tornado prediction parameters

D3‧‧‧ Tornado prediction parameters

D4‧‧‧ tornado prediction parameters

D0-1‧‧‧Detecting position

D0-2‧‧‧Detecting position

E‧‧‧ tornado prediction parameters

P02‧‧‧Get the point

P03‧‧‧Get the point

P04‧‧‧Get the point

P05‧‧‧Get the point

P11‧‧‧Get the point

P12‧‧‧Get the point

P-1‧‧‧Get the point

P-2‧‧‧Get the point

P-3‧‧‧Get the point

P-4‧‧‧Get the point

P-5‧‧‧Get the point

P-6‧‧‧Get the point

P-7‧‧‧Get the point

Pa‧‧‧Get the point

Pb‧‧‧Get the point

Pc‧‧‧Get the point

Pd‧‧‧Get the point

Pe‧‧‧Get the point

R‧‧‧ Radius

T0‧‧‧ time

T01‧‧‧ time

T02‧‧‧ time

T03‧‧‧ time

T04‧‧‧ time

T05‧‧‧ time

T11‧‧‧ time

T12‧‧‧ time

T0a‧‧‧ time

T0b‧‧‧ time

T0c‧‧‧ time

T0d‧‧‧ time

T0e‧‧‧ time

T1m‧‧‧ time

T1n‧‧‧ time

A0‧‧‧ signal processing results

B0‧‧‧ signal processing results

c0‧‧‧RAW data

D0‧‧‧ radar data

E0‧‧‧ Analytical data

F‧‧‧ Results

g‧‧‧Information

i‧‧‧Information

k‧‧‧Location related information

n‧‧‧Results

R0‧‧‧Received signal data

r‧‧‧Detection distance

R1‧‧‧ distance

S1‧‧‧Detection information

S2‧‧‧Control information

W‧‧‧ radar information

Θ1‧‧‧ angle

Θ2‧‧‧ angle

Fig. 1 is a view showing a configuration example of a meteorological phenomenon occurrence possibility determination system to which the weather phenomenon occurrence possibility determination method according to the first embodiment of the present invention is applied.

FIG. 2 is a view showing a configuration example of a tornado occurrence prediction unit in the meteorological phenomenon occurrence possibility determination system of the embodiment.

FIG. 3 is a view showing a configuration example of a tornado occurrence possibility analysis unit in the weather phenomenon occurrence possibility determination system of the embodiment.

Fig. 4 is a view showing an example of a tornado symptom detection processing program in the meteorological phenomenon occurrence possibility determination system of the embodiment. Flow chart.

Fig. 5 is a view for explaining temporal changes of the tornado prediction parameters used in the tornado detection processing in the meteorological phenomenon occurrence possibility determination system of the embodiment.

Fig. 6 is a diagram for explaining the risk of occurrence of a tornado in the tornado detection processing in the meteorological phenomenon occurrence possibility determination system of the embodiment.

Fig. 7 is a view showing a configuration example of a meteorological phenomenon occurrence possibility determination system to which the meteorological phenomenon occurrence possibility determination method according to the second embodiment of the present invention is applied.

FIG. 8 is a view showing a configuration example of a control unit in the meteorological phenomenon occurrence possibility determination system of the embodiment.

Fig. 9 is a view for explaining the orientation of the detection of the occurrence of a significant weather phenomenon in the meteorological phenomenon occurrence possibility determination system of the embodiment.

Fig. 10 is a flowchart showing an example of a tornado symptom detection processing program in the meteorological phenomenon occurrence possibility determination system of the embodiment.

Fig. 11 is a flowchart showing an example of a mode switching processing program in the weather phenomenon occurrence possibility determination system of the embodiment.

(First embodiment)

Hereinafter, a meteorological phenomenon occurrence possibility determination system to which the weather phenomenon occurrence possibility determination method according to the first embodiment of the present invention is applied will be described with reference to the drawings.

This meteorological phenomenon occurrence possibility determination system is a system for detecting a sign of a significant meteorological phenomenon such as a tornado, and is a system for determining the possibility of occurrence of a significant meteorological phenomenon. In other words, it is a system used to detect the signs of this significant meteorological phenomenon before the occurrence of significant meteorological phenomena, and to predict and determine the likelihood of significant meteorological phenomena based on the detected signs.

Moreover, the meteorological phenomenon occurrence possibility determination system, for example, is to analyze the radar data obtained by the high-speed scanning meteorological radar, and is based on the rise and fall of the precipitation cloud, the precipitation core in the precipitation cloud, or the precipitation cloud. In the case where the continuity of the vertical vortex is high and the time of high density is changed, and the possibility that the occurrence of a significant meteorological phenomenon is determined to be large, the user is notified of the matter.

A significant meteorological phenomenon occurs in a very short period of time ranging from 5 minutes to 10 minutes. For example, heavy rain, strong gusts, thunderstorms, or hail, etc., caused by tornadoes, heavy rains occurring in localized areas, etc. Extreme weather phenomena.

The high-speed scanning weather radar is a radar capable of scanning at a high speed for sky or the like, for example, a phase array weather radar. For example, it can be a shorter time (for example, a very short time of 5 minutes to 10 minutes) from the occurrence of a more significant meteorological phenomenon (for example, a very short time of 5 minutes to 10 minutes) (for example, a shorter time than 1 minute, below, High resolution Time") to scan the radar. In addition, instead of the phase array weather radar, a parabolic antenna capable of scanning at a high resolution time can also be used. Moreover, the phase array weather radar can be a double-biased phase array weather radar or a single-bias phase array weather radar.

In addition, a high-speed scanning weather radar is used to transmit a radar wave that is transmitted over a range that is connected to a significant meteorological phenomenon, and that can be reflected or reflected by a cloud or the like. .

In addition, although the following is a description of the tornado as a significant meteorological phenomenon, the other significant weather phenomena can be similarly explained.

Specifically, the weather phenomenon occurrence possibility determination system 1 to which the meteorological phenomenon occurrence possibility determination method of the present embodiment is applied will be described with reference to the configuration example of FIG.

The meteorological phenomenon occurrence possibility determination system 1 includes a radar signal processing unit 9 including an acquisition unit 9a, a radar analysis unit 10, and a tornado prediction unit 20.

The radar signal processing unit 9 performs signal processing such as general amplification processing or modulation processing on the received signal data r0 of the reflected wave or the scattered wave received by the high-speed scanning weather radar 8. Thereafter, the signal processing result a0 is output to the radar analysis unit 10.

The acquisition unit 9a repeatedly acquires a series of three-dimensional data necessary for determining the possibility of occurrence of a tornado in a shorter time period than that required for the occurrence of a tornado.

The radar analysis unit 10 includes a communication interface (I/F) 11, a RAW data processing unit 12, a RAW data storage unit 13, a radar data analysis calculation unit 14, and an analysis data storage unit 15.

The radar analysis unit 10 performs analysis of the received signal data r0 obtained by the high-speed scanning of the weather radar 8. Specifically, based on the signal processing result a0 obtained from the radar signal processing unit 9, three-dimensional data is generated, and based on the generated three-dimensional data, in order to predict the occurrence of the tornado, the calculation is performed for The analysis of the parameters required to detect the occurrence of a tornado and the parameters required for the occurrence of a tornado (hereinafter referred to as "tornado prediction parameters").

In addition, by generating three-dimensional data, it is possible to know the three-dimensional nature of the shape of the precipitation cloud.

The tornado prediction parameter, for example, is a parameter relating to the vorticity of the vertical vortex at the lowest layer of the precipitation cloud. For example, it is a parameter that represents the height or intensity of the ascending airflow in the vertical vortex. In addition, the tornado prediction parameters are described in detail with reference to FIG. 6.

The communication I/F 11 is an interface for communicating with the radar processing unit 9. For example, the signal processing result a0 obtained repeatedly from the radar processing unit 9 is output to the RAW data processing unit 12 as the signal processing result b0. At the office.

The RAW data processing unit 12 performs the RAW data c0 including the three-dimensional data based on the signal processing result b0 obtained from the communication I/F 11, and performs the radar signal included in the signal processing result b0. The processed signal data r0 processed by the processing unit 9 is converted into Processing of RAW data c0.

3-dimensional data is produced using well-known techniques in the art. Thereafter, the RAW data c0 including the three-dimensional data is output to the RAW data storage unit 13. Further, the RAW data c0, for example, when a double-biased phase array weather radar is used as a high-speed scanning weather radar, includes data such as polar coordinates of each polarization parameter. Polar coordinates, for example, contain information on the direction of elevation.

The RAW data storage unit 13 temporarily stores the RAW data c0 acquired from the RAW data processing unit 12, for example. Then, in order to calculate the volumetric scan data required for the tornado prediction parameter (that is, for example, the entire three-dimensional data corresponding to the entire height from the upper portion of the precipitation cloud to the lower portion), or The three-dimensional data of one of the three-dimensional data necessary for calculating the tornado prediction parameter is output to the radar data analysis calculation unit 14 as the radar data d0.

The three-dimensional data of a part of the whole of the three-dimensional data is, for example, a three-dimensional data of a part of the whole three-dimensional data obtained by transmitting the radar wave for all directions.

The radar data analysis calculation unit 14 performs an analysis and calculation process for calculating a tornado prediction parameter based on the radar data d0 acquired from the RAW data storage unit 13.

Thereafter, the analysis data e0 including the calculated tornado prediction parameter information is output to the analysis data storage unit 15.

Further, the radar data analysis calculation unit 14 may be configured to When the radar data d0 including all of the RAW data c0 necessary for analyzing the radar data d0 is acquired, the radar data d0 is started.

The radar data analysis calculation unit 14 first detects the precipitation core in the precipitation cloud. Then, when the precipitation core is detected, the parameters related to the precipitation core are calculated, for example, the parameters relating to the position and height of the center of gravity of the precipitation core (tornado prediction parameter A), or related to the precipitation core are calculated. Maximum reflection intensity parameter (tornado prediction parameter B).

In addition, the drop speed of the precipitation core related to the tornado prediction parameters A and B is generally changed within, for example, within 5 minutes. Therefore, it is conceivable that the tornado prediction parameter A will also change within 5 minutes.

The radar data analysis calculation unit 14 and the second method detect the vertical vortex in the precipitation cloud. Then, when a vertical vortex is detected, the parameter related to the vertical vortex is calculated, for example, a parameter representing the vorticity of the vertical vortex at the lowermost portion of the precipitation cloud (tornado prediction parameter C), or It is a parameter representing the depth of the vertical vortex of the vorticity of, for example, 1.0 × 10 ^-2 (S ^-1 ) or more (tornado prediction parameter D). Further, the vorticity is, for example, an index generally used to represent the degree of growth of the vertical vortex.

Further, the tornado prediction parameter C is, for example, a parameter representing the lowest height of each of the precipitation cores 51 shown in the graph (a) of Fig. 5 to be described later. Further, the tornado prediction parameter D is, for example, a parameter representing the depth of the cloud of each precipitation cloud 50 or the thickness of the cloud shown in the graph (a) of Fig. 5 to be described later.

The radar data analysis calculation unit 14 thirdly calculates the height of the radar echo top representing the degree of development of the precipitation cloud. Thereafter, a parameter representing the height of the echo top (tornado prediction parameter E) is calculated based on the calculated height of the echo top. Further, the tornado prediction parameter E is, for example, a parameter representing the highest height of each of the precipitation cores 51 shown in the graph (a) of Fig. 5 to be described later.

In addition, each of these tornado prediction parameters A, B, C, D, E, for example, can be calculated using a specific equation relating to each tornado prediction parameter.

In this manner, the radar data analysis calculation unit 14 calculates the tornado prediction parameters A, B, C, D, and E. Thereafter, the tornado prediction parameters A, B, C, D, and E are stored in the analysis data storage unit 15 as the analysis data e0.

Further, the radar data analysis calculation unit 14 may store the analysis data e0 on another network connected to the meteorological phenomenon occurrence possibility determination system 1 instead of the analysis data storage unit 15.

The tornado prediction unit 20 predicts the occurrence of a tornado based on the analysis data e0 stored in the analysis data storage unit 15 or another network.

The tornado prediction unit 20 includes a tornado occurrence possibility analysis unit 21, an analysis data storage unit 22, and a display notification unit 23 as shown in FIG.

The tornado occurrence possibility analysis unit 21 associates the tornado based on the analysis data e0 acquired from the analysis data storage unit 15 Analysis and judgment of the possibility of birth.

The tornado occurrence possibility analysis unit 21 includes an analysis data acquisition unit 30, a parameter analysis unit 32, and a tornado symptom detection unit 34 as shown in FIG.

The analysis data acquisition unit 30 outputs the tornado prediction parameters A, B, C, D, and E included in the analysis data e0 to the parameter analysis unit 32.

The parameter analysis unit 32 analyzes the tornado prediction parameters A, B, C, D, and E. Specifically, analysis is performed to detect temporal changes in the specificity of the tornado prediction parameters A, B, C, D, and E from the respective three-dimensional data acquired by the acquisition unit 9. For example, the inflection point of the specificity of the tornado prediction parameters A, B, C, D, E with respect to time is detected.

Before the occurrence of a tornado, the prediction parameters A, B, C, D, and E of each tornado will change temporally. Specifically, the tornado prediction parameter A will change over time if the precipitation core in the precipitation cloud occurs. The tornado prediction parameter B, if the maximum reflection intensity of the precipitation core in the precipitation cloud is, for example, 40 dBZ or more, it will change over time. The tornado prediction parameter C will change over time if the vorticity of the vertical vortex increases at the lowest layer of the precipitation cloud.

The tornado prediction parameter D, if the vertical vortex in the precipitation cloud becomes a tendency to increase, will change over time. The tornado prediction parameter E will change over time if the height of the echo top of 10 dBZ is 10 km or more.

The parameter analysis unit 32 analyzes the change in the tornado prediction parameters A, B, C, D, and E with respect to time, and outputs the result f to the tornado symptom detecting unit 34.

The tornado symptom detecting unit 34 determines whether or not the occurrence of the tornado is likely to occur when a temporal change in specificity is detected based on the result of the analysis by the parameter analyzing unit 32. Specifically, when the inflection point of the specificity of the tornado prediction parameters A, B, C, D, and E with respect to time is detected based on the result f output from the parameter analysis unit 32, the inflection point It is judged as a sign of a tornado.

Further, the tornado symptom detecting unit 34 determines the level of the possibility of occurrence of the tornado in response to the number of times the specific time change is detected by the parameter analyzing unit 32.

Thereafter, based on the determined level, the possibility of occurrence of a tornado is determined. This represents the level of the likelihood of a tornado occurring as a level of risk representing the occurrence of a tornado. In addition, this level is described in detail with reference to FIG.

Thereafter, the tornado symptom detecting unit 34 outputs the information g relating to the determination results of these to the analysis data storage unit 22.

The analysis data storage unit 22 temporarily stores the information g related to the determination result obtained by the tornado symptom detection unit 34, for example, for temporary storage.

The notification unit 23 displays information on the possibility of occurrence of a tornado when it is determined by the tornado symptom detecting unit 34 that there is a possibility of occurrence of a tornado. Specifically, the system will be stored in The information g in the analysis data storage unit 22 is taken out as needed, and processed into a material i for display from a screen or the like provided at the portable terminal held by the user, and thereafter, the data i is read from It is displayed on a screen or the like provided at the portable terminal held by the user.

By this, the display notification unit 23 displays information indicating whether or not there is a possibility of occurrence of a tornado from a screen or the like provided at the portable terminal held by the user.

The meteorological phenomenon occurrence possibility determination system 1 of the present embodiment is a screen from the acquisition of the received signal data r0 by the high-speed scanning weather radar 8 to the screen placed at the portable terminal held by the user. The series of processes until the occurrence of the occurrence of the tornado is performed in a shorter period of time, for example, within one minute of the time required for the occurrence of the tornado. As described above, the meteorological phenomenon occurrence possibility determination system 1 of the present embodiment has a higher temporal resolution than the prior art.

In addition, in the request item, a radar wave is used to transmit a radar wave toward the sky, and a reflected wave or a scattered wave formed by the radar wave reflected or scattered by the particles constituting the cloud is received, and the reflected wave or the scattered wave is used according to the reflected wave or the scattered wave. In a shorter period of time, the means for obtaining a series of three-dimensional data required for the prediction of meteorological phenomena is obtained in a shorter period of time, for example, corresponding to the communication I/F 11 or the analysis data acquisition unit 30. The analysis means for analyzing the temporal change of the specificity of the parameter related to the meteorological phenomenon based on the three-dimensional data acquired in the reverse, for example, corresponds to the parameter analysis unit 32. When the specificity is detected based on the result of the analysis In the case where the time of the sex changes, the means for determining the possibility of occurrence of the meteorological phenomenon is, for example, corresponding to the tornado symptom detecting unit 34.

Further, in the case of the request, when it is determined by the determination means that there is a possibility that the weather phenomenon is likely to occur, the information prompting means for information relating to the possibility of occurrence of the meteorological phenomenon, for example, corresponding to the display Notification unit 23.

Next, an example of the weather phenomenon occurrence possibility determination processing program will be described with reference to the flowchart of FIG.

First, by the acquisition unit 9a, the general three-dimensional data is repeatedly acquired in a shorter time period than the time required for the occurrence of the tornado (step S40). Thereafter, the analysis data storage unit 15 acquires the tornado prediction parameters A, B, C, D, and E by the analysis data acquisition unit 30 (step S42).

Next, the parameter analysis unit 32 performs analysis for detecting the temporal change in the specificity of the tornado prediction parameters A, B, C, D, and E acquired in step S42 (step S44).

Thereafter, when the time change of the specificity of the tornado prediction parameters A, B, C, D, and E is detected as a result of the analysis in step S44, the time variation of the detected specificity is changed. As a measure of the occurrence of the tornado, the tornado symptom detecting unit 34 determines whether or not a sign of occurrence of the tornado is detected (step S46). When it is determined that the sign of the occurrence of the tornado is not detected (S46: NO), the process returns to step S40, and the next dragon is again obtained temporally. The wind forecast parameters A, B, C, D, E.

On the other hand, when it is determined that the sign of the occurrence of the tornado is detected (step S46: YES), the tornado symptom detecting unit 34 determines the possibility of occurrence of the tornado in response to the detected sign. The presence or absence of sex (step S48). For example, the number of times of change in the specificity of the tornado prediction parameters A, B, C, D, and E, that is, the number of times the sign is detected, determines the risk of occurrence of a tornado. The degree of degree is determined, and the possibility of occurrence of a tornado is determined (step S48).

On the other hand, when it is determined that there is no possibility of occurrence of a tornado (step S48: NO), the process returns to step S40, and the next tornado prediction parameters A, B, C, D, E are again obtained temporally. .

On the other hand, when it is determined that there is a possibility that the tornado is likely to occur (step S48: YES), the information relating to the possibility of occurrence of the tornado is displayed by the display notification unit 23, for example, The level determined in step S48 is additionally related and presented to the user (step S50). Specifically, the tornado occurrence prediction information is notified to the user as the information g, and is displayed as a material i from a screen or the like provided at the portable terminal held by the user (step S50).

Next, with reference to FIG. 5, the time variation of the tornado prediction parameters will be described.

Diagram (a) is a schematic representation of the time variation of one of the rise and fall of precipitation cloud 50. Again, chart (b) is for prior art The time variation of the parameters used to predict significant meteorological phenomena is shown. On the other hand, the graph (c) shows the temporal change of the tornado prediction parameter in the meteorological phenomenon occurrence possibility determination processing of the present embodiment.

In addition, the parameters of the prior art shown in the diagram (b) may not be the tornado prediction parameters. For example, it is a parameter that changes with time variation of the precipitation cloud 50, and is a parameter that can be compared with the tornado prediction parameter. Further, the tornado prediction parameter shown in the graph (c) is a parameter necessary for determining the possibility of occurrence of a tornado as in the above-described tornado prediction parameters A, B, C, D, and E.

The horizontal axes of the graphs (a), (b), and (c) of Fig. 5 represent time (minutes), respectively. Further, the vertical axis of the graph (a) represents the height, and the vertical axes of the graph (b) and the graph (c) represent the values of the respective parameters.

First, a description of time series will be given for the graph (a) of FIG. 5.

First, at the time point T05 (in the figure, it is 0 minutes), the precipitation cloud 50a occurs. In FIG. 5, the time point T05 is taken as a time point which becomes a reference in the meteorological phenomenon occurrence possibility determination process.

Next, at time point T04 (in the figure, it is 10 minutes), the precipitation cloud 50 grows into a precipitation cloud 50b which is larger than the precipitation cloud 50a, that is, a precipitation cloud 50 which grows to a high height. In the figure, the height of the precipitation cloud 50b is about 8 km. Further, the precipitation cloud 50b includes a precipitation core 51b corresponding to the precipitation area.

Thereafter, at time point T03 (in the figure, 20 minutes), the precipitation cloud 50 grows into a precipitation cloud 50c which is higher than the precipitation cloud 50b and has a higher height and includes the precipitation region 51c. Also, about the equivalent of the precipitation core The echo top of the highest height of 51 is equivalent to the highest height of the precipitation core 51 of the echo top of the precipitation core 51c (in the figure, the height is about 12km), and it is also the echo top equivalent to the precipitation core 51b. The highest height of the precipitation core 51 (in the figure, the height is about 7km) is higher.

Thereafter, at time point T02 (in the figure, it is 30 minutes), the precipitation cloud 50 is grown to have a precipitation core 51d having a size larger than that of the precipitation core 51c.

Thereafter, at the time point T0 (in the figure, the time point between 30 minutes and 40 minutes), a tornado (not shown) occurs.

Thereafter, at time point T11 (40 minutes in the figure), the precipitation cloud 50 is degraded along with the precipitation core 51. For example, the precipitation cloud 50 is a precipitation cloud 50e that is reduced to a maximum height of 50d from the precipitation cloud (about 11km in the figure) and a lower maximum height (about 9km in the figure). Further, the precipitation core 51 is a precipitation core 51e which is reduced to the highest height (in the figure, about 10 km) and the lower highest height (in the figure, about 8 km).

Thereafter, at time point T12 (in the figure, 50 minutes), the precipitation cloud 50 is reduced to a precipitation cloud 50f having a precipitation core 51f having a height higher than that of the precipitation core 51e.

In addition, the tornado occurring at the time point T0, for example, is eliminated together with the decline of the precipitation cloud 50.

Next, the time variation of the parameters shown in the graph (b) of FIG. 5 and the tornado prediction parameters shown in the graph (c) of FIG. 5 is made. Compare, one side for explanation.

First, at the acquisition point P05 corresponding to the time point T05, the parameter value (P05) (not shown) is acquired. Hereinafter, the point P is obtained, for example, the time point at which the parameter is acquired. Further, the acquisition point P is also a time point at which the analysis for determining the possibility of occurrence of the tornado is performed. Further, the point P05 is obtained, which corresponds to the precipitation cloud 50a.

Further, the value of the parameter (P05), for example, represents that the precipitation cloud 50a has occurred.

Next, at the acquisition point P04 corresponding to the time point T04, the value of the parameter (P04) is obtained. Further, the point P04 is obtained, which corresponds to the precipitation cloud 50b. Further, the value of the parameter (P04), for example, represents that the precipitation cloud 50a grows into the precipitation cloud 50b. Further, in Fig. 5, the value of the parameter becomes larger as the precipitation cloud 50 grows from the precipitation cloud 50a to the precipitation cloud 50b.

Further, at the time line 52 representing the time variation of the parameters of the prior art shown in the graph (b) of Fig. 5, the value of the parameter (P04) exhibits a value which is slightly the same as the value of the parameter (P05). On the other hand, at the line 53 representing the time variation of the tornado prediction parameter of the present embodiment shown in the graph (c) of Fig. 5, the value of the parameter (P04) is the same as that of the prior art and exhibits parameters and parameters. The value (P05) is the same value. However, between 10 minutes from the time point T05 to the time point T04, the value of the parameter is different from the prior art, and is not necessarily constant.

This is because, in the present embodiment, for example, a general high-speed scanning weather radar as described above is used. Also, because this embodiment The tornado prediction parameters are, for example, used to predict parameters used in the occurrence of tornadoes, particularly in meteorological phenomena.

Further, the acquisition point P04 corresponds to the precipitation cloud 50b, and comparing the tornado prediction parameter at the acquisition point P05 with the tornado prediction parameter at the acquisition point P04 is equivalent to comparing the precipitation cloud 50a with the precipitation cloud 50b.

Next, at the acquisition point P03 corresponding to the time point T03, the value of the parameter (P03) is acquired. Further, the acquisition point P03 corresponds to the precipitation cloud 50c. Further, the value of the parameter (P03), for example, represents that the precipitation cloud 50b grows into the precipitation cloud 50c. Further, in Fig. 5, it is shown that the value of the parameter becomes smaller as the precipitation cloud 50 grows from the precipitation cloud 50b to the precipitation cloud 50c.

In this way, there will be cases where the value of the parameter becomes smaller as the precipitation cloud 50 grows. This matter, for example, represents the parameters used for the tornado prediction parameters, as will be described later with reference to Figure 6 and generally there are complex numbers, and each tornado prediction parameter represents a different time variation.

Next, at the acquisition point P02 corresponding to the time point T02, the value of the parameter (P02) is obtained. In addition, the point P02 is obtained, which corresponds to the precipitation cloud 50d. Further, the value of the parameter (P02), for example, represents that the precipitation cloud 50c grows into a precipitation cloud 50d. Further, in Fig. 5, it is shown that the value of the parameter hardly changes as the precipitation cloud 50 grows from the precipitation cloud 50c to the precipitation cloud 50d.

However, between the time point T02 and the time point T11, a tornado wind occurs. During this period, in the prior art, because the system did not use a high-speed sweep The weather radar is described so that, for example, new parameters can only be obtained every 5 to 10 minutes. On the other hand, in the present embodiment, by using a high-speed scanning weather radar, it is possible to acquire points P-2, P-3, P-4, and P-5 every about 30 seconds to 1 minute. At the time point corresponding to P-6, new tornado prediction parameters are obtained. Further, the acquisition point P02 corresponds to the acquisition point P-1, and the acquisition point P11 corresponds to the acquisition point P-7.

Further, although not shown in detail in FIG. 5, in the graph (c) of FIG. 5, in all the sections from the time point T05 to the time point T12, from the time point T02 to Similarly, at time point T11, for example, there are five acquisition points P between about 10 minutes. Further, for example, it may be configured to acquire a new tornado prediction parameter only during a specific period after the occurrence of the occurrence of the tornado, for example, only between the time point T02 and the time point T11.

Thereafter, at the time point T11 to the time point T12, the precipitation cloud 50 is degraded, and at the same time, the value of the parameter shown in Fig. 5 is also reduced. Further, it is shown that during this period, the temporal change of the parameters of the prior art is linear, and the temporal change of the parameters of the present embodiment is gently changed in a curved shape.

Next, with reference to FIG. 6, the determination of the risk degree of occurrence of a tornado caused by the temporal change of the tornado prediction parameter will be described.

The graphs (a), (b), (c), (d), and (e) of Fig. 6 represent temporal changes of the above-described tornado prediction parameters A, B, C, D, and E, respectively. Figure (f) of Figure 6 is the risk determined by the number of times the time variation of the specificity of each tornado prediction parameter is detected. For display.

The horizontal axes of the graphs (a), (b), (c), (d), (e), and (f) of Fig. 6 represent time (minutes). In addition, the lines shown in the graphs (a), (b), (c), (d), (e), and (f) of Fig. 6 represent the tornado prediction parameters obtained in about one hour. Change of time.

Moreover, the vertical axes of the graphs (a), (b), (c), (d), and (e) of Fig. 6 represent the value of the tornado prediction parameter A (km) and the value of the tornado prediction parameter B (dBZ, respectively). ), the value of the tornado prediction parameter C (s-1), the value of the tornado prediction parameter D (km), and the value of the tornado prediction parameter E (km). Moreover, the vertical axis of the graph (f) of Fig. 6 represents the level of risk.

The tornado prediction parameter A is generally as shown in the line 60a, and has an inflection point 70a in a period from the time point T0b to the time point T0d (between about 10 minutes). Further, it is shown that the value of the tornado prediction parameter A increases during the period up to the time point T0a, but starts to decrease at the time point Ta. In this case, at the time point T0a corresponding to the acquisition point Pa or the time point (not shown) corresponding to the inflection point 70a, a specific time change is detected and is regarded as detection. It is a sign of the occurrence of a tornado.

Further, the inflection point 70 is, for example, a point corresponding to a change in the sign of the slope of each line 60.

The tornado prediction parameter B is as shown in line 60b. The value of the tornado prediction parameter B is greater than the specific threshold at time T0b. Value (in the figure, it is 40dBZ). In this case, the specific threshold value (40 dBZ) is detected as a specific time change. Thereafter, at the time point T0b corresponding to the acquisition point Pb, it is considered that the occurrence of the tornado is detected.

The tornado prediction parameter C is generally as shown in the line 60c, and has an inflection point 70c in a period from the time point T0c to the time point T0 (between about 5 minutes). Further, it is shown that the value of the tornado prediction parameter C exhibits a slightly constant value during the period up to the time point T0c, and increases sharply at the time point T0c. In this case, the tornado prediction parameter C is rapidly increased, and is detected as a specific time change. Further, at the time point T0c corresponding to the acquisition point Pc or the time point (not shown) corresponding to the inflection point 70c, it is considered that the occurrence of the tornado is detected.

The tornado prediction parameter D is generally as shown in the line 60d, and has an inflection point 70d in a period from the time point T0d to the time point T0e. Further, it is shown that the value of the tornado prediction parameter D is slightly longer in the period up to the time point T0d, and starts to increase at the time point T0d. In this case, at the time point T0d corresponding to the acquisition point Pd or the time point (not shown) corresponding to the inflection point 70d, a specific time change is detected, which is regarded as detection. It is a sign of the occurrence of a tornado.

The tornado prediction parameter E is generally as shown in the line 60e, and has an inflection point 70e in a period from the time point T0d to the time point T0e. Again, the value of the tornado prediction parameter E is at time T0e, The system exceeds a certain threshold (in the figure, it is 10km). In this case, the specific threshold value (10 km) is detected as a specific time change. Further, at the time point T0e corresponding to the acquisition point Pe or the time point (not shown) corresponding to the inflection point 70e, it is considered that the occurrence of the tornado is detected.

In this way, the temporal change in specificity, for example, includes changes in the value of the tornado prediction parameter from an increase to a decrease or from a decrease to an increase, and the value of the tornado prediction parameter exceeds a certain threshold. The change, the change in the value of the tornado prediction parameter, the change in the rate of change, and the change in the equivalent of the inflection point 70.

Next, referring to the graph (f) of Fig. 6, the risk of occurrence of a tornado will be explained.

The degree of risk is determined as described above in response to the number of times the time variation of the specificity of each tornado prediction parameter is detected. Further, the information related to the risk is notified to the user in response to the number of times the specific time change is detected.

In FIG. 6, the change of the tornado prediction parameter B corresponding to the acquisition point Pb, the change of the tornado prediction parameter D corresponding to the acquisition point Pd, and the change of the tornado prediction parameter E corresponding to the acquisition point Pe, The change in the tornado prediction parameter A corresponding to the acquisition point Pa and the change in the tornado prediction parameter C corresponding to the acquisition point Pc are detected, and the time variation of each specificity is detected. Further, it is not limited to this order, and it is also possible to detect temporal changes in specificity in other orders.

The level (risk degree) 0 is, for example, a level representing a normal situation set until the change of the tornado prediction parameter B is detected. Also, the system is also the lowest level of possibility for the occurrence of a tornado. Further, it is a level that represents the state of safety that is the lowest in the risk of disasters caused by the occurrence of a tornado.

Level 1 is the level at which the change in the tornado prediction parameter B is detected. Also, the rating is a level that represents a higher risk than level 0.

Level 2 is a level indicating that the change in the tornado prediction parameter D is detected, and is also a level indicating that the risk is higher than level 1.

Level 3 is a level that indicates that the change in the tornado prediction parameter E is detected, and is also a level that represents a higher risk than level 2.

Level 4 is the level at which the change in the tornado prediction parameter A is detected, and is also the level at which the risk is higher than level 3.

Level 5 is the level at which the change in the tornado prediction parameter C is detected, and is also the level at which the risk is higher than level 4.

In addition, the degree of danger shown in the graph (f) of FIG. 6 is only one example. For example, the change of the tornado prediction parameter D may be detected earlier in time than the change of the tornado prediction parameter B. come out.

In this case, when the change of the tornado prediction parameter B is detected In the case of level 1, the change in the tornado prediction parameter D is detected. On the other hand, level 2 represents the detection of a change in the tornado prediction parameter B.

Further, in FIG. 6, although five tornado prediction parameters A, B, C, D, and E are described, for example, it is also possible to use only tornado prediction parameters A, B, C, D, and E. In the event of a tornado forecasting parameter C that is changing before the tornado is about to occur, the risk is judged. In this case, for example, the system may not have a multi-stage level as shown in the diagram (f) of Fig. 6. For example, the risk can also be determined by a value representing two risks or safety.

In addition, in the case of detection of significant meteorological phenomena other than tornadoes, such as the detection of signs of heavy rain, it is also possible to use the general parameters as shown in diagram (a) and diagram (e) of Figure 6. It is used as a parameter to detect signs of significant meteorological phenomena other than tornadoes, and to detect signs of significant meteorological phenomena other than tornadoes.

Further, in the present embodiment, a plurality of high-speed scanning weather radars can be used. In this case, the tornado prediction parameters are calculated based on the three-dimensional data obtained by using the received signal data r0 obtained from each high-speed scanning weather radar. Thereafter, for example, it is also possible to detect the occurrence of the tornado with higher accuracy by performing evaluations such as comparisons with the calculated tornado prediction parameters.

Moreover, in addition to the above-mentioned general tornado prediction parameters, as a tornado prediction parameter, for example, the highest representative and precipitation cloud 50 can also be used. The parameter of the elevation angle of the apex of the height corresponding precipitation cloud 50 is either a parameter representing the elevation angle of the apex of the precipitation core 51 corresponding to the highest height of the precipitation core 51.

As described above, according to the first embodiment, it is possible to obtain a set of three-dimensional data necessary for analysis of a significant weather phenomenon in a short period of time, even for a very short time like a tornado. Significant meteorological phenomena have also become predictive of high accuracy. Moreover, it is possible to analyze the three-dimensionality of the precipitation cloud 50, and analyze it at each time of high resolution, and notify the user of the possibility of occurrence of a tornado.

For example, a significant meteorological phenomenon in which there is still a large number of scientifically unconstrained aspects such as a tornado is able to analyze the three-dimensional nature of the precipitation cloud 50 at various times of high resolution. Further, by using a three-dimensional data obtained by using a high-speed scanning weather radar in each of several tens of seconds, the time variation of the specificity of the tornado prediction parameter is detected, and the time specificity of the detected specificity is changed. The sign of the occurrence of a tornado is an increase in the accuracy of the prediction of the possibility of a tornado.

Further, it is possible to analyze and determine the possibility of occurrence of a tornado at intervals of about several tens of seconds. Therefore, for example, the department has become able to predict the occurrence of a tornado, and there is no misjudgment for the occurrence of a tornado (that is, for the occurrence of a tornado prediction information for the user, but the tornado did not occur. In the case of the case where the prediction is unsuccessful, the situation is continuously and frequently provided. Therefore However, the system has also become a predictive information for tornadoes that can notify users of high accuracy.

Moreover, it is also possible to provide the user with prediction information about the tornado occurrence in real time. For example, an application software such as a general user such as a general citizen is used as a service to provide a tornado prediction information to a general user. By this, it is possible to immediately arouse the attention of the general user to the tornado, and it is also helpful to remind the evacuation and the like.

(Second embodiment)

In the following, a weather phenomenon occurrence possibility determination system 1 to which the meteorological phenomenon occurrence possibility determination method according to the second embodiment of the present invention is applied will be described with reference to the drawings. The same components and contents as those in the first embodiment are denoted by the same reference numerals, and their description will be omitted.

The meteorological phenomenon occurrence possibility determination system 1 of the present embodiment is an omnidirectional scanning mode for obtaining a series of three-dimensional data for omnidirectional by responding to whether or not a symptom of occurrence of a significant meteorological phenomenon is detected. (1st observation mode) and a sector scan mode (second observation mode) for obtaining a series of 3-dimensional data for a specific orientation related to the occurrence of a significant meteorological phenomenon, and predicting with high accuracy The occurrence of meteorological phenomena.

With reference to the block diagram of Fig. 7, a configuration example of the meteorological phenomenon occurrence possibility determination system 1 to which the meteorological phenomenon occurrence possibility determination method of the present embodiment is applied will be described. Hereinafter, the same as in the first embodiment, as a display One of the meteorological phenomena is described for the tornado.

The meteorological phenomenon occurrence possibility determination system 1 includes the control unit 40 in addition to the components of the meteorological phenomenon occurrence possibility determination system 1 of the first embodiment shown in FIG.

In the present embodiment, the high-speed scanning weather radar 8 is a weather radar equipped with a parabolic surface having a small parabolic antenna 8a. The high-speed scanning weather radar 8 can rotate at least at a high speed in the omni-directional scanning mode by, for example, rotating the parabolic antenna 8a at a faster rotational speed by the rotational speed of the phase-aligned weather radar. High-precision 3D data of a series of 3D data (hereinafter referred to as "a series of full 3D data") necessary for the occurrence of a tornado (hereinafter referred to as "a series of 3D data" "), for example, to scan the minimum 3-dimensional data needed to detect signs of a tornado. Further, the high-speed scanning weather radar 8 is capable of scanning a series of full 3-dimensional data at a high speed in the sector scanning mode.

In addition, in the following, when it is not necessary to distinguish between a series of full 3-dimensional data and a series of partial 3-dimensional data, a series of full 3-dimensional data and a series of partial 3-dimensional data are recorded as a series of 3 dimensions. data.

The high-speed scanning weather radar 8 performs control corresponding to each observation mode on the parabolic antenna 8a based on the control information s2 associated with the switching of the observation mode sent from the control unit 40, which will be described later.

The radar signal processing unit 9 switches the observation mode based on the control information s2, and is, for example, related to obtaining a series of 3-dimensional data. Synchronization of timing is performed, and in each observation mode, a series of 3-dimensional data is repeatedly obtained from the high-speed scanning weather radar 8.

The tornado prediction unit 20 is based on a series of three-dimensional data that is repeatedly acquired in each observation mode by the radar signal processing unit 9, and is determined by the tornado occurrence possibility analysis unit 21 in order to predict the occurrence of the tornado. As a result of the analysis, when the time change of the specificity is detected in the omnidirectional scanning mode or the sector scanning mode and the sign of the occurrence of the tornado is detected, the possibility of occurrence of the tornado is determined.

Further, the tornado prediction unit 20 detects that the occurrence of the tornado is detected when the time change of the specificity is detected in the omnidirectional scanning mode or the sector scanning mode and the sign of the occurrence of the tornado is detected. The detection information s1 of the symptom is sent to the control unit 40. Moreover, when the time change of the specificity is not detected in the sector scan mode and the sign of the occurrence of the tornado is not detected, the detection of the sign that the occurrence of the tornado is not detected will be detected. The information is sent to the control unit 40.

The control unit 40 performs control processing for switching the observation mode (hereinafter referred to as "mode switching processing") based on the detection information s1 from the tornado prediction unit 20. Thereafter, as a result of the mode switching process, the control information s2 is sent to the high-speed scanning weather radar 8 and the radar signal processing unit 9.

Next, a more detailed configuration example of the control unit 40 will be described with reference to the block diagram of FIG.

The control unit 40 includes a detection information acquisition unit 41 and a switching unit. 42. And switching the information storage unit 49. Further, the switching unit 42 includes an azimuth calculating unit 44, a switching determining unit 46, and a control information generating unit 48.

The detection information acquisition unit 41 acquires the detection information s1 sent from the tornado prediction unit 20, for example, in each of the predetermined periods defined in advance. Then, the azimuth related information k(D) related to the detected azimuth D included in the acquired detection information s1 is sent to the azimuth calculating unit 44, which is related to the tornado. The specific orientation that occurred.

In the azimuth related information k(D), it is used as information related to the detected position D, and for example, includes a position connected to the sign of the occurrence of the detected tornado (hereinafter referred to as "detected position"). The position information, or the distance from the position where the high-speed scanning weather radar 8 is set (hereinafter referred to as "set position") to the detected position (hereinafter, referred to as "detection distance r") News. The detected position, for example, is the center position of the precipitation cloud 50 or the precipitation core 51.

The azimuth calculation unit 44 calculates the detected orientation D at the installation position based on the position information included in the orientation-related information k(D) sent from the detection information acquisition unit 41. Further, using the calculated detected orientation D, for example, the observation range C in which the orientation centered on the detected orientation D is calculated is calculated. Further, the detection distance r is calculated based on the distance information included in the orientation-related information k(D). Thereafter, the calculated detection direction D, observation range C, and detection distance r are sent to the switching determination unit 46.

Here, referring to FIG. 9, the detection orientation D and the like are described in more detail. Bright.

The meteorological phenomenon occurrence possibility determination system 1 observes the precipitation cloud 50 which is the target of the detection of the occurrence of the tornado in the radar range 90 having the radius R centered on the high-speed scanning weather radar 8.

The azimuth calculating unit 44 is based on the case where the precipitation cloud 50-1 exists at the detected azimuth D0-1, and the sign of the occurrence of the tornado is detected for the precipitation cloud 50-1, based on the relationship with the precipitation cloud 50-1 The orientation is related to the information k (D0-1) to calculate the detected orientation D0-1. Thereafter, the observation range C1 from the orientation D1 to the orientation D2 centering on the calculated detection direction D0-1 is calculated. Here, the angle θ1 between the orientation D0-1 and the orientation D1 and the angle θ1 between the detection orientation D0-1 and the orientation D2 are detected, for example, 30 degrees.

Further, when the precipitation cloud 50-1 is at a position at a distance r1 in the radial direction from the high-speed scanning weather radar 8, the azimuth calculating unit 44 calculates the distance r1 as the detection distance r, for example.

Further, the azimuth calculating unit 44 is based on the case where the precipitation cloud 50-1 moves to the precipitation cloud 50-2 and detects the occurrence of the tornado against the precipitation cloud 50-2, based on the correlation with the precipitation cloud 50-2. The orientation information k (D0-2) is used to calculate the detected orientation D0-2. Thereafter, the observation range C2 from the orientation D3 to the orientation D4 centered on the calculated detection direction D0-2 is calculated. Here, the angle θ2 between the azimuth D0-2 and the azimuth D3 is detected, and the angle θ2 between the detected azimuth D0-2 and the azimuth D4 is, for example, 30 degrees.

In this way, the meteorological phenomenon occurrence possibility determination system 1 is capable of As the precipitation cloud 50 moves, the orientation of the observation range C is changed to track the precipitation cloud 50 that detects the occurrence of the tornado and observe the precipitation cloud 50.

In addition, the angle θ can be set to be different depending on, for example, the performance of the high-speed scanning weather radar 8, the meteorological phenomenon such as the precipitation cloud 50, or the type of significant weather phenomenon such as a tornado, or the detection distance r. The value. For example, when the high-speed scanning weather radar 8 has a high temporal resolution, the angle θ is reduced. Further, when a weather phenomenon occurs in a wide range, or when a weather phenomenon in which the moving speed is fast is targeted, the angle θ is increased. Further, when the detection distance r is short, the angle θ is reduced.

In this way, by changing the angle θ, for example, it is possible to perform sector scanning for a more suitable observation range C in order to predict the occurrence of a tornado with high accuracy.

In addition, the sector scanning is performed by, for example, moving the parabolic antenna 8a in the elevation direction while scanning the observation range C in the azimuth direction, and narrowing it in a shorter time for scanning in a shorter time. Scanning is performed, and a series of full 3-dimensional data scans can be obtained in a high resolution time.

Further, in FIG. 9, as the observation range C, although the range of the specific orientation at the specific elevation angle is shown, the observation range C is the range of the specific orientation in the range of the specific elevation angle. That is, it is a range of 2 dimensions.

Referring back to FIG. 8, the azimuth calculation unit 44 can also replace the orientation related information. k(D), and the radar data d0 stored in the RAW data storage unit 13 is used. In this case, for example, the radar information of the acquired detection information s1 is obtained from the RAW data storage unit 13 by using the detection information acquisition unit 41 to obtain the detection information s1 as a trigger. D0, and based on the polar coordinate data related to the three-dimensional data of the tornado included in the obtained radar data d0, the detection direction D and the like are calculated.

Further, the azimuth calculating unit 44 calculates the detected azimuth D by the tornado prediction unit 20 or the like, and does not calculate the detection when the azimuth related information k(D) includes the detected azimuth D. The detection direction A included in the orientation-related information k(D) is sent to the switching determination unit 46.

In the mode switching process, the switching determination unit 46 determines whether or not it is possible to acquire a series of full three-dimensional data in the high-resolution time in the omni-scan mode based on the performance of the high-speed scanning weather radar 8 (hereinafter). This is referred to as "switching determination process", and the determination result n of the switching determination process is sent to the control information generating unit 48. For example, in the switching determination process, it is determined whether or not a series of all three-dimensional data within a range including the specific elevation angle of the observation range C can be acquired in the high-resolution time in the omni-directional scanning mode.

The switching determination unit 46 is configured to switch from the omnidirectional scanning mode to the case where it is not possible to obtain a series of full three-dimensional data in the high-resolution time in the omnidirectional scanning mode in the switching determination process. The result of the determination of the sector scan mode is sent to the control information generating unit 48. another On the one hand, when the decision is made to obtain a series of full 3-dimensional data in the high-resolution time in the omni-scan mode, it will be maintained in the omni-directional scan mode instead of switching to the sector scan mode. The result of the determination n is sent to the control information generating unit 48.

The switching information storage unit 49 pre-memorizes the radar information w related to the performance of the high-speed scanning weather radar 8, and the time information related to the high-resolution time which is different from each other in each significant weather phenomenon. And angle information related to the angle θ of the observation range C which is different from each other in each of the significant weather phenomena, and mode information related to the current observation mode.

The switching determination unit 46 performs switching determination processing based on, for example, the observation range C sent from the orientation calculation unit 44 and the radar information w stored in the switching information storage unit 49.

For example, the switching determination unit 46 determines that it is not possible in the omnidirectional scanning mode when the performance of the high-speed scanning weather radar 8 being used is lower than that of a specific weather radar (for example, a phase array weather radar). A series of full 3-dimensional data is acquired in the high-resolution time, and the determination result n for switching from the omni-scan mode to the sector scanning mode is sent to the control information generating unit 48.

On the other hand, when the performance of the high-speed scanning weather radar 8 being used is equal to the performance of a specific weather radar (for example, phase array weather radar), the switching determination unit 46 determines that it can be in the omnidirectional scanning mode. And a series of full 3D data is obtained in a high resolution time, and will be maintained in a full sweep without switching to the sector scan mode. The determination result n of the drawing mode is sent to the control information generating unit 48.

Further, the switching determination unit 46 determines that the phase omnidirectional weather radar is used as the high-speed scanning weather radar 8 based on the time information or the like stored in the switching information storage unit 49, for example, even in the omnidirectional scanning mode. In the case where a series of full 3-dimensional data is acquired in the high-resolution time, the determination result n for switching from the omni-scan mode to the sector scanning mode is sent to the control information generating unit 48.

In addition, when the current observation mode is determined to be the sector scan mode based on the mode information or the like stored in the switching information storage unit 49, the switching determination unit 46 sends the information from the tornado prediction unit 20, for example. When the representative does not detect the detection information of the occurrence of the tornado by the sector scanning mode, the determination result n for switching from the sector scanning mode to the omnidirectional scanning mode is sent to the control information. The generating portion 48 is located.

The control information generating unit 48 generates control information s2 for switching the observation mode or maintaining the observation mode based on the determination result n, and sends the generated control information s2 to the high-speed scanning weather radar 8 and the radar signal. At the processing unit 9.

Further, in the meteorological phenomenon occurrence possibility determination system 1 of the present embodiment, when the symptom of the occurrence of a significant weather phenomenon is detected in the omnidirectional scanning mode, the mode switching process may not be performed. The switching determination process is performed to switch from the omnidirectional scanning mode to the sector scanning mode. In this case, the control information generating unit 48, for example, triggers the detection information s1 by the detection information acquisition unit 41 as a trigger. To generate control information s2 for switching the observation mode.

Next, an example of the tornado symptom detection processing program of the present embodiment will be described with reference to the flowchart of FIG.

First, in the omnidirectional scan mode, the processes of steps S40, S42, S44, and S46 are performed in the same manner as in the first embodiment.

Next, the mode switching process is performed by the control unit 40 (step S47). In the omnidirectional scanning mode, the processes of steps S48 and S50 are performed in the same manner as in the first embodiment.

Further, in the case where the mode is switched from the omnidirectional scanning mode to the sector scanning mode in step S47, the processing of step S40 is performed in the sector scanning mode.

In addition, the processing of step S47 may be performed after the processing of step S48 or step S50.

Next, an example of the mode switching processing procedure of step S47 will be described with reference to the flowchart of FIG. In addition, the flowchart of FIG. 11 shows an example of a mode switching processing program for switching from the omnidirectional scanning mode to the sector scanning mode.

First, the azimuth calculating unit 44 calculates the detected azimuth D (step S110).

Next, by the switching determination unit 46, it is determined whether or not it is possible to acquire a series of all three-dimensional data in the high-resolution time in the omni-directional scanning mode, that is, whether it is possible to determine whether it is possible to perform the high-resolution time or the like within a specific time. The 3-dimensional data of the detected orientation D is obtained (step S112).

However, when it is determined that it is not possible to perform high resolution in the omnidirectional scan mode When a series of all three-dimensional data is acquired in a time (step S112: NO), the switching mode is switched from the omnidirectional scanning mode to the sector scanning mode by the switching determination unit 46 (step S114).

On the other hand, when it is determined that the series of full three-dimensional data can be acquired in the high-resolution time in the omnidirectional scanning mode (step S112: YES), the switching determination unit 46 is maintained in all directions. In the state of the scan mode, the observation mode is not switched (step S116), and the mode switching process of Fig. 11 is ended.

In addition, the processing of step S112 may be skipped, and after step S110, the processing of step S114 is performed. In this case, for example, when the detection direction D is calculated in step S110, the observation mode is switched in step S114, and the sector is performed for the observation range C including the calculated detection direction D. scanning.

Further, for example, when the performance of the high-speed scanning weather radar 8 used, the high-resolution time of the significant weather phenomenon as the target, or the observation range C is known in advance, it may be processed in step S110. The processing of step S112 is performed in advance.

In addition, even when a phase array weather radar is used as the high-speed scanning weather radar 8, the present embodiment can be applied.

Further, in the case of the request, when the time change of the specificity is detected by the first mode based on the result of the analysis by the analysis means, the switching means is switched from the first mode to the second mode. For example, it corresponds to the switching unit 42. The time variation in which the specificity is detected by the first mode or the second mode based on the result of the analysis by the analysis means The first determining means for determining the possibility of occurrence of a meteorological phenomenon in the case of the case, for example, corresponds to the tornado symptom detecting unit 34. Further, when the time change of the specificity is detected by the first mode based on the result of the analysis by the analysis means, it is determined whether or not the time required for the occurrence of the meteorological phenomenon can be shorter. The second determination means for obtaining a series of three-dimensional data by the first mode corresponds to, for example, the switching determination unit 46.

As described above, according to the second embodiment, in addition to the operational effects that can be exhibited by the first embodiment, the following general effects can be obtained.

In the meteorological phenomenon occurrence possibility determination process, the observation mode of the meteorological phenomenon occurrence possibility determination system 1 is switched to an appropriate condition when the symptom of the significant weather phenomenon is detected in response to the performance of the high-speed scanning weather radar 8 or the like. The observation mode, for example, even when a parabolic meteorological radar is used, it is possible to obtain a series of full 3-dimensional data in a high resolution time, and it is possible to predict significant weather phenomena with high accuracy. occur.

Although the embodiments of the present invention have been described, the embodiments are not intended to limit the scope of the present invention. The present invention may be embodied in various other forms, and various omissions, substitutions and changes may be made without departing from the scope of the invention. The embodiments and variations thereof are included in the scope of the invention and the scope of the invention, and are also included in the scope of the invention described in the claims.

21‧‧‧ Tornado occurrence possibility analysis department

30‧‧‧Analysis of Data Acquisition Department

32‧‧‧Parameter Analysis Department

34‧‧‧ Tornado Sign Detection Department

E0‧‧‧ Analytical data

A, B, C, D, E‧‧‧ tornado prediction parameters

F‧‧‧ Results

g‧‧‧Information

Claims (17)

A system is a system for determining the possibility of occurrence of a meteorological phenomenon using a radar, and is characterized by: acquiring means for transmitting a radar wave toward the sky and receiving the particle of the radar wave Reflecting or scattering the reflected wave or scattered wave, and according to the reflected wave or the scattered wave, it is repeatedly obtained in the prediction of the meteorological phenomenon in a shorter time than the time required for the occurrence of the meteorological phenomenon. A series of three-dimensional data required; and an analytical means for performing a time change for detecting the specificity of a parameter related to the meteorological phenomenon based on the respective three-dimensional data obtained in the foregoing; and determining means When the temporal change of the specificity is detected based on the result of the above analysis, the possibility of occurrence of the aforementioned meteorological phenomenon is determined. The system of claim 1, wherein the radar comprises a phased array weather radar. The system according to claim 1 or 2, wherein the analysis means detects an inflection point of the specificity of the parameter with respect to time. The system according to claim 1 or 2, wherein the parameter includes a plurality of parameters, and the analyzing means detects a temporal change of the specificity for each of the plurality of parameters. The determination means determines the level of the possibility of occurrence of the meteorological phenomenon based on the number of times the temporal change of the specificity is detected by the analysis means, and based on the determined level. The possibility of determining the occurrence of the aforementioned meteorological phenomenon is determined. The system according to the first or second aspect of the invention, wherein the system further includes: a prompting means for determining that there is a possibility of occurrence of the weather phenomenon by the determining means; , prompts information about the likelihood of occurrence of the aforementioned meteorological phenomena. A method is a method for determining the possibility of occurrence of a meteorological phenomenon using a radar, characterized in that a radar wave is transmitted over a range covering a occurrence of a meteorological phenomenon, and the radar wave is received to constitute a cloud. The reflected or scattered wave formed by the particles reflected or scattered by the particles, and according to the reflected wave or the scattered wave, the meteorological phenomenon is repeatedly obtained in a shorter time than the time required for the occurrence of the meteorological phenomenon. A series of three-dimensional data required for the prediction, based on the three-dimensional data obtained in the above-mentioned manner, and the analysis of the temporal change of the specificity of the parameter related to the meteorological phenomenon, based on the result of the foregoing analysis When the time change of the specificity is detected, the possibility of occurrence of the weather phenomenon is determined. The method of claim 6, wherein the radar comprises a phased array weather radar. If the method described in item 6 or item 7 of the patent application is applied, Among them, the above analysis includes the detection of the inflection point of the specificity of the aforementioned parameter with respect to time. The method of claim 6 or 7, wherein the parameter includes a plurality of parameters, and the analyzing is performed by including a time for detecting the specificity for each of the parameters of the plurality of parameters. The change is performed by determining the level of the possibility of occurrence of the meteorological phenomenon in response to the number of times of the change in the specificity of the specificity, and determining the weather based on the determined level. The possibility of occurrence of a phenomenon. The method of claim 6 or 7, wherein the method further includes: when the determination is that the possibility of occurrence of the meteorological phenomenon occurs, the prompt is related to the occurrence of the meteorological phenomenon. Information about the possibilities. A system is a system for determining the possibility of occurrence of a meteorological phenomenon using a radar, and is characterized by: acquiring means for transmitting a radar wave toward the sky and receiving the particle of the radar wave Reflecting or scattering a reflected wave or a scattered wave, and repeatedly obtaining a series of three-dimensional data required for prediction of the weather phenomenon by the first mode or the second mode based on the reflected wave or the scattered wave. In the first mode, the series of three-dimensional data is obtained for all directions, and the second mode is related to the foregoing. Obtaining the series of three-dimensional data in a specific orientation of the occurrence of the meteorological phenomenon; and the analyzing means is based on each of the three-dimensional data acquired by the above-mentioned first mode or the second mode Detecting a temporal change in the specificity of the parameter related to the meteorological phenomenon; and switching means detecting the specificity by the first mode based on the result of the analysis by the analysis means When the time is changed, switching from the first mode to the second mode and the first determining means are based on the result of the analysis by the analyzing means, and the first mode or the first In the case where the time change of the specificity is detected in the 2 mode, the possibility of occurrence of the weather phenomenon is determined. The system of claim 11, wherein the radar includes a parabolic meteorological radar. The system according to claim 11 or 12, further comprising: the second determining means, wherein the first mode is based on a result of the analysis by the analyzing means When the time change of the specificity is detected, it is determined whether the series of three-dimensional data can be obtained by the first mode in a shorter time than the time required for the occurrence of the meteorological phenomenon. The switching means is determined by the second determining means When it is not possible to obtain the above-described series of three-dimensional data by the first mode, it is possible to switch from the first mode to the second mode, and it is determined by the second determination means that the first When the first three-dimensional data is acquired in the first mode, the first mode is not switched from the first mode to the second mode. The system according to claim 11, wherein the switching means detects the specificity by the second mode based on the result of the analysis by the analyzing means. When the time changes, the second mode is switched to the first mode. A method for determining the possibility of occurrence of a meteorological phenomenon using a radar is characterized in that a radar wave is emitted toward the sky and receives a reflection of the radar wave reflected or scattered by particles constituting the cloud. a series of three-dimensional data required for the prediction of the weather phenomenon by the first mode or the second mode, based on the reflected wave or the scattered wave, and the first mode, Obtaining the series of three-dimensional data for all directions, wherein the second mode acquires the series of three-dimensional data for a specific orientation related to the occurrence of the meteorological phenomenon, according to the first mode or the foregoing In the three-dimensional data obtained by the above-mentioned two modes, the three-dimensional data obtained by the above-mentioned repeated detection, the analysis of the temporal change of the specificity of the parameter related to the meteorological phenomenon is performed, and based on the result of the analysis, the first mode is used. When the time change of the specificity is detected, the first mode is cut. In the second mode, when the temporal change of the specificity is detected by the first mode or the second mode based on the result of the analysis, it is determined whether or not the possibility of occurrence of the meteorological phenomenon is present. . The method according to claim 15, wherein when the temporal change of the specificity is detected by the first mode based on the result of the analysis, it is determined whether the When the meteorological phenomenon takes place in a shorter period of time, the aforementioned three-dimensional data is acquired by the first mode, and the switching is performed by determining whether or not the series of three-dimensional data can be obtained. When it is determined that it is not possible to obtain the series of three-dimensional data by the first mode, the first mode is switched to the second mode, and it is determined whether or not the series of three-dimensional data can be obtained. When it is determined that the above-described series of three-dimensional data is acquired by the first mode, the first mode is not switched from the first mode to the second mode. The method of claim 15 or claim 16, wherein the switching is performed, and when the temporal change of the specificity is not detected by the second mode based on the result of the analysis The system switches from the second mode to the first mode.