JP2019138736A - Thunder risk determination device - Google Patents

Abstract

To provide a thunder risk determination device capable of accurately predicting a risk of a thunder.SOLUTION: A thunder risk determination device 10 comprises: a dual polarization information acquisition unit 11 which detects a cumulonimbus cloud; a three-dimensional data creation unit 12 which creates three-dimensional data from the dual polarization data acquired by the dual polarization information acquisition unit 11 ; a state threshold value input unit 16 which defines a predetermined threshold value related to a state change; a state change calculation unit 14 which calculates a state change in a cumulonimbus cloud higher than or equal to the threshold value input from the state threshold value input unit 16 from the present-state data and past data created by the three-dimensional data creation unit 12; a risk calculation unit 17 which calculates thunder risk data representing the risk of thunder occurrence from the state change calculated by the state change calculation unit 14; and a thunder information creation unit 19 which creates location information at high risk places from the thunder risk data calculated by the risk calculation unit 17.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a lightning risk determination device that determines the risk of lightning.

  Conventionally, a technique for determining the occurrence of lightning using a weather radar and outside air temperature has been disclosed (see Patent Document 1). The technique described in Patent Literature 1 determines the freezing altitude based on the outside air temperature, generates a lightning icon when the reflectance related to the altitude above the freezing altitude is larger than the lightning threshold, A wrinkle icon is generated when the high reflectance in the sum of is greater than the wrinkle threshold.

JP 2011-128150 A

Shingo Shimizu and Tsuyoshi Maesaka, “A method for analyzing multiple Doppler radar data using the variational method for estimating the three-dimensional wind velocity field”, Research Report No. 70 of the National Research Institute for Earth Science and Disaster Prevention, January 2007 (http: //dil-opac.bosai.go.jp/publication/nied_report/PDF/70/70shimizu.pdf) TAKEHARU KOUKETSU, 8 others, “A Hydrometeor Classification Method for X-Band Polarimetric Rader: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments”, American Meteorological Society, JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, VOLUME 32, pp2052-2074, Nov 2015 (https://journals.ametsoc.org/doi/abs/10.1175/JTECH-D-14-00124.1) HYPERLINK "https://journals.ametsoc.org/author/Hauser%2C+Dani%C3%A8le" Daniele Hauser HYPERLINK "https://journals.ametsoc.org/author/Amayenc%2C+Paul" Paul Amayenc, Retrieval of Cloud Water and Water Vapor Contents from Doppler Radar Data in a Tropical Squall Line, American Meteorological Society, JOURNAL OF ATMOSPHERIC SCIENCES, VOL 43, No. 8, pp823-838, 15 APRIL, 1986 (https: // journals. ametsoc.org/doi/abs/10.1175/1520-0469%281986%29043%3C0823%3AROCWAW%3E2.0.CO%3B2) Lawrence D. Carey and Steven A. Rutledge, “The Relationship between Precipitation and Lightning in Tropical Island Convection: A C-Band Polarimetric Radar Study”, American Meteorological Society, MONTHLY WEATHER REVIEW, VOL 128, pp2687-2710, AUGUST 2000 (https : //journals.ametsoc.org/doi/full/10.1175/1520-0493%282000%29128%3C2687%3ATRBPAL%3E2.0.CO%3B2) Gregory N. Seroka, Richard E. Orville, Courtney Schumacher, "Radar Nowcasting of Total Lightning over the Kennedy Space Center", American Meteorological Society, WEATHER AND FORECASTING, VOL 27, pp189-204, FEBRUARY 2012 (https: // journals. ametsoc.org/doi/pdf/10.1175/WAF-D-11-00035.1)

  However, since the technique described in Patent Document 1 is not a prediction based on the relationship between the hail and the lightning, the prediction accuracy is not good.

  An object of the present invention is to provide a lightning risk determination apparatus capable of accurately predicting the lightning risk as compared with the prior art.

The lightning danger determination device according to the present invention,
A dual polarization information acquisition unit for detecting cumulonimbus clouds;
A three-dimensional data creation unit that creates three-dimensional data from the dual polarization data acquired by the dual polarization information acquisition unit;
A state threshold value input part for defining a predetermined threshold value regarding the state change;
From the current state data and past data created by the three-dimensional data creation unit, a state change calculation unit that identifies a cumulonimbus whose state change is equal to or greater than the threshold, and calculates a state change in the cumulonimbus cloud; and
A risk calculation unit for calculating lightning risk data representing the risk of lightning occurrence from the state change calculated by the state change calculation unit;
A lightning information creation unit that creates location information of a high risk location from the lightning risk data calculated by the risk calculation unit;
It is characterized by providing.

The lightning danger determination device according to the present invention,
Predicting the movement of cumulonimbus clouds from the current status data and the past data, comprising a movement prediction unit for creating future prediction data representing the status of future cumulonimbus clouds,
The state change calculation unit specifies a cumulonimbus whose state change is equal to or greater than the threshold from the current state data, the past data, and the future prediction data, and calculates a future state change in the cumulonimbus cloud,
The risk calculation unit calculates future lightning risk data from state changes in the future cumulonimbus,
The lightning information creation unit creates position information, a moving direction, and a moving speed of a place with a high future risk from the future lightning risk data.

The lightning danger determination device according to the present invention,
An observation data input unit that inputs statistical information created by performing learning processing using at least one of the three-dimensional data, the lightning risk data, and the observation data to the risk calculation unit; To do.

In the lightning danger determination device according to the present invention,
The state change calculation unit calculates the volume change of the upward flow in the cumulonimbus,
The state threshold value input unit defines a threshold value of an upward flow volume to be compared with the value calculated by the state change calculation unit.

In the lightning danger determination device according to the present invention,
The state change calculation unit calculates the volume change of the soot in the cumulonimbus,
The state threshold value input unit may define a threshold value of the eyelid volume to be compared with the value calculated by the state change calculation unit.

  According to such a lightning risk determination device, it is possible to accurately predict the lightning risk.

The mechanism of lightning in the cumulonimbus is shown. 1 shows a system block of a lightning risk determination device of the present embodiment. The image of the cumulonimbus detection of this embodiment is shown. It shows the volume of the kite at each altitude with respect to the time elapsed since the formation of cumulonimbus clouds. The system block of the cumulonimbus cloud prediction system of this embodiment is shown. The flowchart of the cumulonimbus cloud prediction system of this embodiment is shown.

  An embodiment according to the present invention will be described with reference to the drawings.

  FIG. 1 shows the mechanism of lightning in the cumulonimbus.

  Thunder is generated from cumulonimbus clouds. A cumulonimbus cloud is formed when the lower air is lifted by a strong updraft and water vapor in the air becomes water droplets in the sky. At temperatures below freezing, not only raindrops but also ice grains such as hail and ice crystals are formed. Ice grains grow by colliding with surrounding water droplets called supercooled water droplets in the upward flow. Eventually, ice particles begin to fall when they become too large to support the updraft.

  During the rising and falling, the ice particles collide with each other, and charge transfer occurs between the large and small particles. The sign of the charge of each ice particle is determined by the mass of water contained in the atmosphere per unit volume called the amount of cloud water and the ambient temperature. When there is an appropriate amount of cloud water, large ice particles are negatively charged and small ice particles are positively charged when the temperature is lower than −10 ° C. When such collisions of ice particles continue, a lot of electric charge is stored in the cumulonimbus.

  Air is an insulator that does not conduct electricity. However, when the potential difference exceeds 3 million V per meter, a phenomenon called dielectric breakdown occurs, and a discharge starts when electricity passes through the air, generating lightning. Lightning strikes include lightning strikes and cloud discharges. Lightning strikes are a phenomenon in which electricity flows between cumulonimbus clouds and the ground, and cloud discharges are phenomena in which electricity flows in cumulonimbus clouds or between different cumulonimbus clouds.

  The thunder danger determination device 10 according to the present embodiment obtains volume changes of soot and airflow in the cumulonimbus and determines that it is dangerous when a rapid increase is recognized.

  FIG. 2 shows a system block of the lightning danger determination device 10 of the present embodiment. FIG. 3 shows an image of cumulonimbus detection according to this embodiment.

  The thunder hazard determination device 10 includes a dual polarization information acquisition unit 11 that detects cumulonimbus clouds, a three-dimensional data generation unit 12 that generates three-dimensional data, a movement prediction unit 13 that predicts the movement of cumulonimbus clouds, State change calculation unit 14 for calculating the state change of the state, state threshold value input unit 16 for defining and inputting a threshold value to be compared with the value calculated by the state change calculation unit 14, and the state change calculated by the state change calculation unit 14 A risk level calculation unit 17 that calculates the risk level from the observation data, an observation data input unit 18 that inputs statistical information created by learning the three-dimensional data, and the risk level from the data calculated by the risk level calculation unit 17 A lightning information creation unit 19 that creates position information, a moving direction, and a moving speed of a high place.

  The dual polarization information acquisition unit 11 of the present embodiment acquires dual polarization data using a multi-parameter radar. The dual polarization data may be data representing the elevation angle of the polar coordinate system. Multi-parameter radar can measure more observation parameters than ordinary weather radar by sending and receiving two types of radio waves (horizontal polarization and vertical polarization) simultaneously, including information on the shape of raindrops and icedrops. It is possible to accurately grasp rainfall, observe wind in rain clouds, and discriminate the types of particles such as rain, snow and hail.

  The three-dimensional data creation unit 12 converts elevation angle data of a plurality of polar coordinate systems acquired by the dual polarization information acquisition unit 11 into lattice-shaped three-dimensional data in an orthogonal coordinate system. For example, the generated three-dimensional lattice data includes CAPPI (Constant Altitude Plan Position Indicator) that cuts out cumulonimbus clouds at the same altitude plane like a circular slice for reflection intensity, reflection factor difference, polarization phase difference change rate, polarization correlation coefficient, etc. )

  The three-dimensional data creation unit 12 creates current status data indicating the current status of cumulonimbus clouds, and stores the past status of cumulonimbus clouds as past data.

  When two or more multi-parameter radars can be used, the airflow may be created with three-dimensional data by dual analysis. The input value of the dual analysis may be a polar coordinate PPI (Plan Position Indicator) format Doppler speed.

  The Doppler velocity is a velocity component in the line-of-sight direction in each radar. Only one radar can observe components in the direction in which the cumulonimbus approaches or separates from the radar, but by solving multiple radars and fluid dynamics, the wind of the three-dimensional component (east-west, north-south, vertical) 3D distribution can be created.

  The movement prediction unit 13 predicts the movement of cumulonimbus clouds using the current status data and past data created by the three-dimensional data creation unit 12. Specifically, the movement vector may be calculated by performing pattern matching by the cross correlation method or the like from the past data and the current data, and output as future prediction data. By using the movement prediction unit 13, it is possible to accurately predict future lightning. Note that the movement prediction unit 13 may not be used when only past data and current data are used without performing prediction.

  FIG. 4 shows the volume of the kite at each altitude with respect to the time elapsed since the formation of cumulonimbus clouds.

  The state change calculation unit 14 calculates a change in the state of the cumulonimbus cloud from the current state data and past data created by the three-dimensional data creation unit 12 and the future prediction data created by the movement prediction unit 13. Variables to evaluate the state change include the volume of the updraft, the volume of the area identified as cocoon, the mass per unit volume of the cocoon, the mass of precipitation particles accumulated vertically, the echo top height, or the isothermal surface echo intensity. At least one of them.

  For example, the calculated state change is not less than a predetermined threshold value input from the state threshold value input unit 16. The state change calculation unit 14 identifies cumulonimbus clouds that appear to be the same in past, present, and future three-dimensional data, and calculates the state change. To identify cumulonimbus clouds that appear to be identical, existing cumulonimbus automatic tracking technology may be used.

  Here, the variables for evaluating the state change will be described. FIG. 4 shows the volume of the bag as an example of these state changes.

  To determine the volume of the updraft, the three components of the wind are estimated by combining Doppler velocities observed by multiple radars. The three components are east-west wind, north-south wind, and vertical wind. In the coordinate system in which the vertical upward direction is positive, the positive vertical wind is called ascending current. The total volume of the grid grid in which the upward flow exceeding the threshold is detected is defined as “upward flow volume”. For a method for estimating the three components of wind, Non-Patent Document 1 may be referred to.

  The volume of the region determined to be cocoon may be obtained, for example, by determining the total volume of the grid grid classified as cocoon using a precipitation particle discrimination method using dual polarization radar. Non-patent document 2 may be referred to for a method for discriminating precipitation particles using a dual polarization radar.

  The mass per unit volume of the kite is calculated from the measured values of reflection intensity, polarization parameter, etc. in the lattice grid classified as kite using the method of discriminating precipitation particles using dual polarization radar. It is the mass per unit volume in each lattice grid calculated using the method of estimating the mass of. Non-patent literatures 3 and 4 may be referred to for a method for estimating the mass per unit volume of the bag.

  The vertically accumulated mass of precipitation particles is calculated using a method that estimates the mass of precipitation particles (rain, hail, snow) per unit volume from the measured values of reflection intensity and polarization parameters. The mass is calculated and the mass of each grid grid is integrated in the vertical direction (see Non-Patent Document 5).

  The echo peak altitude is the highest altitude reached on the isosurface of the radar reflection intensity in the cumulonimbus cloud.

  The isothermal surface echo intensity refers to a reflection intensity on an isosurface of a certain temperature by creating an isosurface of the air temperature from a three-dimensional distribution of the air temperature. Specifically, the temperature at which the eyelids of about −10 degrees are negatively charged is selected, and the reflection intensity at the altitude of −10 degrees may be extracted.

  The degree-of-risk calculation unit 17 calculates the volume change rate of the upflow calculated by the state change calculation unit 14, the volume change rate of the kite, the mass change rate of the kite, the rate of change of the mass of precipitation particles vertically integrated, and the rate of change of the echo peak height Alternatively, by using the isothermal surface echo intensity change rate and the statistical information input from the observation data input unit 18, a cumulonimbus with a change rate equal to or higher than a predetermined value is determined to have a high lightning risk. The determination of the risk level is modeled by statistical information.

  The observation data input unit 18 inputs statistical information created by learning processing using observation data such as three-dimensional data, lightning risk data, and LMA (Lightning Mapping Array) sensor to the risk calculation unit 17. By performing the learning process, it is possible to predict the risk of lightning with higher accuracy. Note that the observation data input unit 18 is not necessarily used.

  The modeled lightning risk data is output as a horizontal distribution map. The horizontal distribution map is output as a current horizontal distribution map and a future horizontal distribution map using the current data and past data created by the three-dimensional data creation unit 12 and the future prediction data created by the movement prediction unit 13, respectively. The

  The lightning information creation unit 19 uses the time-series lightning risk data calculated by the risk calculation unit 17 and the movement vector calculated by the movement prediction unit 13 to predict a three-dimensional distribution. Thereafter, the position of the current cumulonimbus cloud with a high lightning risk, and the future moving direction and speed of the cumulonimbus cloud are calculated.

  As described above, according to the lightning risk determination device 1 of the present embodiment, it is possible to accurately predict the lightning risk.

  FIG. 5 shows system blocks of the cumulonimbus cloud prediction system of this embodiment.

  The cumulonimbus cloud prediction system 1 includes a thunder danger determination device 10, a receiver information input unit 4 for inputting receiver information, and information on the cumulonimbus cloud calculated by the cumulonimbus information calculation unit 3 and the receiver information input unit 4. The cumulonimbus cloud / recipient relationship computing unit 5 computes each relationship from the information of the recipients input, and the output unit 6 outputs the result computed by the cumulonimbus / receiver relationship computing unit 5.

  The receiver information input unit 4 is for the receiver to input his / her information in advance. For example, the recipient information input unit 4 may use a mobile terminal or the like. Recipient's position information 4a that informs the receiver of the location where the receiver wants to know whether or not it is dangerous, the receiver's risk setting information 4b that informs the level and distance of the risk set by the receiver, and the positional deviation set by the receiver The receiver's positional deviation allowable information 4c that informs the allowable range is input.

  The location information 4a of the recipient may be a location where the recipient currently exists, a location where the recipient will move, a location that the recipient wants to know, or the like. The location may be specified from latitude and longitude information such as GPS. The recipient selects at least one of these locations.

  The receiver risk setting information 4b is information on the degree of risk set by the receiver. For example, the receiver specifies at least one phenomenon of concern from rain, wind, thunder, and hail, and selects the risk level of the phenomenon for each level. The degree of danger is the hourly or cumulative rainfall in the case of rain, wind speed in the case of wind, etc. It is sufficient to set at least two levels such as caution and vigilance with reference to. The recipient selects at least one of these levels.

  The receiver positional deviation allowable information 4c is a range in which the positional deviation set by the receiver can be allowed. For example, the receiver only needs to adjust the positional deviation up to 20 km. The positional deviation distance only needs to be set continuously or stepwise. Since the cumulonimbus is about 10km in size, it is preferable to set the maximum value to twice that size.

  The cumulonimbus / recipient relationship calculation unit 5 converts the cumulonimbus coordinate system into the receiver's coordinate system, and calculates the risk level at the position set by the receiver in time series for each phenomenon. Cumulonimbus always changes size and moves. In addition, the receiver may want to know the degree of danger, the position, etc., from time to time. Therefore, the relationship between the cumulonimbus and the receiver is calculated with the coordinate system.

  The output unit 6 outputs the result calculated by the cumulonimbus / recipient relationship calculation unit 5. The output unit 6 includes dangerous position information 6a for notifying whether or not the place set by the receiver in the receiver information input unit 4 is a dangerous position, and whether the time set by the receiver in the receiver information input unit 4 is a dangerous time Risk time information 6b that informs whether or not, the risk type information 6c that informs whether or not the type of rain, wind, thunder, or hail set by the receiver in the receiver information input unit 4 is dangerous, and the receiver is the receiver At least one of the danger level information 6d and the like for informing which level of the danger level set by the information input unit 4 is output.

  The recipient information input unit 4 and the output unit 6 may be a personal computer or a portable terminal. The receiver can input the information of the receiver and the information he / she wants to know from a personal computer or a portable terminal, and can view information about the cumulonimbus cloud after the calculation on the portable terminal or the like.

  FIG. 6 shows a flowchart of the cumulonimbus cloud prediction system of this embodiment.

  First, in step 1, the lightning risk determination device 10 calculates and outputs dangerous lightning information (ST1).

  Next, in step 2, the recipient information input unit 4 acquires recipient information (ST2). The acquired receiver information may be the receiver's position information 4a, the receiver's danger setting information 4b, the receiver's positional deviation allowable information 4c, and the like.

  Next, in step 3, the cumulonimbus / recipient relationship calculation unit 5 calculates the relationship between the cumulonimbus and the receiver (ST3). As for the relationship between the cumulonimbus and the receiver, the coordinate system of the cumulonimbus cloud may be converted into the coordinate system of the receiver, and the risk level at the position set by the receiver may be calculated in time series for each phenomenon.

  Next, in step 4, the output unit 5 outputs the relationship between the cumulonimbus and the receiver (ST4). The output unit 6 may output at least one of the dangerous position information 6a, the dangerous time information 6b, the dangerous type information 6c, the dangerous level information 6d, and the like.

  As described above, according to the cumulonimbus prediction system 1, the thunder danger determination device 1 can accurately predict the danger of thunder, and can accurately notify the receiver of information on the cumulonimbus.

  As described above, the lightning risk determination device 10 according to the present embodiment has a dual polarization information acquisition unit 11 that detects cumulonimbus and three-dimensional data from the dual polarization data acquired by the dual polarization information acquisition unit 11. A state for calculating a state change in the cumulonimbus cloud from the current data and past data created by the three-dimensional data creation unit 12 to be created, a state threshold value input unit for defining a predetermined threshold for state change, and the three-dimensional data creation unit 12 A change calculating unit 14; a risk calculating unit 17 that calculates lightning risk data representing the risk of lightning from the state change calculated by the state change calculating unit 14; and a lightning risk calculated by the risk calculating unit 17. A lightning information creation unit 19 that creates position information of a high risk location from the data. Therefore, it is possible to accurately predict the risk of lightning.

  Further, the lightning danger determination device 10 of the present embodiment includes a movement prediction unit 13 that predicts the movement of cumulonimbus clouds from current data and past data, and creates future prediction data representing the state of future cumulonimbus clouds. The unit 14 identifies a cumulonimbus cloud whose state change is equal to or greater than the threshold value from the current state data, past data, and future prediction data, calculates a future state change in the cumulonimbus cloud, and the risk level calculation unit 17 The future lightning risk data is calculated from the state change in the cumulonimbus cloud, and the lightning information creation unit 19 creates the position information, the moving direction and the moving speed of the future high-risk place from the future lightning risk data. Therefore, it is possible to accurately predict future lightning.

  In addition, the lightning risk determination device 10 according to the present embodiment stores statistical information created by performing learning processing using at least one of three-dimensional data, lightning risk data, and observation data in the risk calculation unit 17. An observation data input unit 18 is provided. Therefore, it is possible to predict the risk of lightning with higher accuracy.

  Further, in the lightning risk determination device 10 of the present embodiment, the state change calculation unit 14 calculates the volume change of the upward flow in the cumulonimbus, and the state threshold input unit 16 has the value calculated by the state change calculation unit. Define the upflow volume threshold to compare. Therefore, it is possible to predict the risk of lightning with higher accuracy.

  Further, in the lightning risk determination device 10 of the present embodiment, the state change calculation unit 14 calculates the volume change of the soot in the cumulonimbus, and the state threshold input unit 16 compares the value calculated by the state change calculation unit. Define a threshold for the volume of the kite to be. Therefore, it is possible to predict the risk of lightning with higher accuracy.

  In addition, this invention is not limited by this embodiment. That is, in describing the embodiment, many specific details are included for illustration, but those skilled in the art may add various variations and changes to these details.

DESCRIPTION OF SYMBOLS 1 ... Cumulonimbus cloud prediction system 2 ... Cumulonimbus cloud detection part 3 ... Cumulonimbus cloud information calculation part 4 ... Receiver information input part 5 ... Cumulonimbus receiver relation calculation part 6 ... Output part 10 ... Lightning risk determination apparatus 11 ... Double polarization information acquisition Unit 12 ... Three-dimensional data creation unit 13 ... Movement prediction unit 14 ... State change calculation unit 16 ... State threshold value input unit 17 ... Risk level calculation unit 18 ... Observation data input unit 19 ... Lightning information creation unit

Claims (5)

A dual polarization information acquisition unit for detecting cumulonimbus clouds;
A three-dimensional data creation unit that creates three-dimensional data from the dual polarization data acquired by the dual polarization information acquisition unit;
A state threshold value input part for defining a predetermined threshold value regarding the state change;
From the current state data and past data created by the three-dimensional data creation unit, a state change calculation unit that identifies a cumulonimbus whose state change is equal to or greater than the threshold, and calculates a state change in the cumulonimbus cloud; and
A risk calculation unit for calculating lightning risk data representing the risk of lightning occurrence from the state change calculated by the state change calculation unit;
A lightning information creation unit that creates location information of a high risk location from the lightning risk data calculated by the risk calculation unit;
A lightning risk determination device comprising: Predicting the movement of cumulonimbus clouds from the current status data and the past data, comprising a movement prediction unit for creating future prediction data representing the status of future cumulonimbus clouds,
The state change calculation unit specifies a cumulonimbus whose state change is equal to or greater than the threshold from the current state data, the past data, and the future prediction data, and calculates a future state change in the cumulonimbus cloud,
The risk calculation unit calculates future lightning risk data from state changes in the future cumulonimbus,
2. The lightning risk determination device according to claim 1, wherein the lightning information generation unit generates position information, a moving direction, and a moving speed of a place with a high future risk from the future lightning risk data. . An observation data input unit that inputs statistical information created by performing learning processing using at least one of the three-dimensional data, the lightning risk data, and the observation data to the risk calculation unit; The lightning danger determination apparatus according to claim 1 or 2. The state change calculation unit calculates the volume change of the upward flow in the cumulonimbus,
The lightning risk determination according to any one of claims 1 to 3, wherein the state threshold value input unit defines a threshold value of an upflow volume to be compared with a value calculated by a state change calculation unit. apparatus. The state change calculation unit calculates the volume change of the soot in the cumulonimbus,
The lightning risk determination device according to any one of claims 1 to 3, wherein the state threshold value input unit defines a threshold value of the volume of the kite to be compared with the value calculated by the state change calculation unit. .