JP2019138737A - 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 weather information acquisition unit 11 which detects a cumulonimbus cloud; a three-dimensional data creation unit 12; a movement prediction unit 13 which creates nowcast prediction data and future prediction data; a state threshold value input unit 15 which defines a threshold value; a state change calculation unit 14 which identifies a cumulonimbus cloud whose state change is higher than or equal to the threshold value from present-state data, past data, and future prediction data and calculates a state change in the cumulonimbus cloud; a risk calculation unit 16 which calculates thunder risk data representing a risk of thunder occurrence from the state change calculated by the state change calculation unit 14; a blending unit 18 which blends the results of nowcast prediction data and numeric value prediction data about the future prediction data; 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 16 and the blending unit 18.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

Numerical Forecasting Division, “Basic Knowledge of Numerical Forecasting and Latest Numerical Forecasting System”, Meteorological Agency Forecasting Department, 2012 Numerical Forecast Training Text, November 2012 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 weather information acquisition unit for detecting cumulonimbus clouds;
A three-dimensional data creation unit that creates current data representing the current cumulonimbus status and past data representing past cumulonimbus status using objective analysis technology from the weather data acquired by the weather information acquisition unit When,
Predicted movement of cumulonimbus clouds from the current data and the past data, nowcast prediction data representing the state of future cumulonimbus clouds, and numerical values created by performing weather prediction using a cloud resolving numerical model with the current data as initial values A movement prediction unit for creating future prediction data including prediction data;
A state threshold value input part for defining a predetermined threshold value regarding the state change;
A state change calculation unit that identifies a cumulonimbus whose state change is equal to or greater than the threshold from the current data, the past data, and the future prediction data, and calculates a state change in the cumulonimbus cloud;
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;
For the future prediction data, a blending unit that blends the results of the nowcast prediction data and the numerical prediction data;
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 and the blending unit;
It is characterized by providing.

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.

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.

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. The three-dimensional data creation part of this embodiment is shown. The flow of the analysis of the three-dimensional data creation part of this embodiment is shown. The data in each time of the three-dimensional data creation part of this embodiment are shown. The control flowchart of the three-dimensional data creation part 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 diagram of the precipitation prediction apparatus of this embodiment is shown. The ratio of the prediction accuracy rate by numerical prediction to the prediction time and the prediction accuracy rate by nowcast prediction is shown. Comparison of observation data and prediction data for each grid is shown. The relationship of the accuracy rate to the prediction time is shown. The flowchart of the precipitation prediction method of this embodiment is shown. The precipitation prediction data obtained by the precipitation prediction apparatus and precipitation prediction method of this embodiment are shown. 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 danger determination device 10 includes a meteorological information acquisition unit 11 that detects cumulonimbus clouds, a three-dimensional data creation unit 12 that creates three-dimensional data, a movement prediction unit 13 that predicts the movement of the cumulonimbus clouds, and a state change in the cumulonimbus clouds. A state change calculation unit 14 for calculating the threshold value, a state threshold value input unit 15 for defining and inputting a threshold value to be compared with the value calculated by the state change calculation unit 14, and a risk level based on the state change calculated by the state change calculation unit 14 A risk level calculation unit 16 that calculates statistic data, an observation data input unit 17 that inputs statistical information created by learning the three-dimensional data, and a movement prediction unit 13 that are obtained from nowcasting and numerical prediction, respectively. The blending unit 18 blends the results calculated by the risk calculation unit 16 from the future prediction data, and the current data and blending unit 18 calculated by the risk calculation unit 16 blend. Comprising future high places positional information of the degree of risk from the prediction data, and lightning information creating unit 19 for creating a moving direction and a moving speed, a.

  The meteorological information acquisition unit 11 according to the present embodiment acquires observation data obtained from an on-the-job single polarized wave radar such as the Japan Meteorological Agency. The observation data may be data representing the elevation angle of the polar coordinate system.

  The three-dimensional data creation unit 12 uses the observation data acquired by one or more weather information acquisition units 11 as an input for meteorological field objective analysis, and converts elevation angle data of a plurality of polar coordinate systems into orthogonal coordinates using the meteorological field objective analysis technology. Convert to grid-like 3D data in the system.

  The three-dimensional data creation unit 12 of the present embodiment may create data using a technique for restoring a three-dimensional cumulonimbus cloud by objective analysis. 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. Here, a technique for restoring a three-dimensional cumulonimbus by objective analysis will be described.

  FIG. 4 shows the three-dimensional data creation unit 12 of this embodiment. FIG. 5 shows the flow of objective analysis of the three-dimensional data creation unit 12 of this embodiment. The horizontal axis in FIG. 5 represents the flow of time. FIG. 6 shows data at each time of the three-dimensional data creation unit 12 of the present embodiment.

  The three-dimensional data creation unit 12 includes an observation data acquisition unit 121, an initial value creation unit 122, a forecast value creation unit 123, a restart file creation unit 124, an estimated value creation unit 125, a temporary analysis unit 126, A main analysis unit 127.

  The observation data acquisition unit 121 acquires observation data such as ground observations, meteorological satellites, and radars from meteorological agencies or space agencies around the world. Since observation is performed at various places and times, observation data is acquired from each acquisition destination at predetermined time intervals. Moreover, it is preferable that the observation data acquisition unit 121 also acquires background field data such as grid data of a global model or meso model of the Japan Meteorological Agency. Some observation data are excluded because they are not accurate and cannot be used due to human error or equipment failure. The acquired observation data is output to the initial value creation unit 122.

  The initial value creation unit 122 creates an initial value at the initial time from the background field data and the observation data acquired by the observation data acquisition unit 121. As shown in FIG. 6, the observation data cannot be used as it is because the distribution is non-uniform. Therefore, the initial value creation unit 122 creates an initial value in a spatially and temporally uniform format.

  The forecast value creation unit 123 creates forecast values for the weather field continuously from the initial value created by the initial value creation unit 122 every predetermined time. The forecast value is created in a spatially and temporally uniform format as with the initial value. Here, the meteorological field refers to atmospheric pressure, temperature, humidity, wind direction, wind speed, precipitation, snow depth, snow depth, sunshine duration, solar radiation, clouds, visibility, atmospheric phenomena, and the like.

  The restart file creation unit 124 creates a restart file used for this analysis. The restart file is a forecast variable (wind, temperature, barometric pressure, water substance, etc.) at a predetermined restart time t before the analysis time, a time change term of the forecast variable, a boundary value, and one time before the restart time. Is generated by a forecast variable or the like at the time step time t−Δt (see Non-Patent Document 1).

  The estimated value creating unit 125 creates, as an estimated value, a predicted value at the analysis time when the observation data acquired by the observed data acquiring unit 121 is input every predetermined time after the initial time. The estimated value is created in a spatially and temporally uniform format, similar to the forecast value.

  The temporary analysis unit 126 calculates an analysis increment from the new observation data acquired by the observation data acquisition unit 121 and the estimated value created by the estimated value creation unit 125. The analysis increment is a value for correcting an error between the estimated value and the new observed value. The analysis increment is created in a spatially and temporally uniform format, similar to the estimated value. In the present embodiment, the analysis is preferably performed using a three-dimensional variational method.

Here, the three-dimensional variational method will be briefly described. In the three-dimensional variational method, the following equations (1) and (2) are used.
[Equation 1]
J = 1/2 (xx ^b ) ^T B ^-1 (xx ^b ) + 1/2 [H (x) -y] ^T R ^-1 [H (x) -y] (1)
∇xJ = B ^-1 (xx ^b ) + H ^T R ^-1 [H (x) -y] (2)
However,
B ^-1 is the inverse of the background error covariance matrix,
R ^-1 is the inverse of the observation error covariance matrix,
y is the observed value,
x ^b is an estimate,
H (x) is a function (observation operator) that calculates the amount corresponding to the observation from the analysis value,
H ^T is a transposed matrix of the matrix H when the observation operator corresponds to a linear transformation of the analysis value.

  Equation (1) is called a cost function and quantifies the error of the observed value or the estimated value of the numerical model in a quadratic form. Expression (2) is called a cost function gradient vector, and is a vector quantity having the same dimensions as the estimated value, which is obtained by differentiating the cost function of the scalar quantity with the estimated value of the vector quantity to be obtained.

  The gradient vector gives quantitative information on how to correct the estimate to reduce the error. The inverse matrix of the background error covariance matrix and the inverse matrix of the observation error covariance matrix are known and stored in advance. That is, the three-dimensional variational method is a problem in which an unknown value x that minimizes the cost function is searched as an analysis value using a gradient vector.

  The analysis unit 127 goes back to the restart time, smoothes and uses the analysis increment calculated by the temporary analysis unit 126 at the analysis time, and continuously corrects the restart forecast value from the data at the restart time. Output analysis value. As a correction method, an IAU (Incremental Analysis Update) filter or the like is preferably used. After the main analysis, the forecast value creation unit 13 continuously creates a forecast value by using a new main analysis value as an initial value. That is, the calculation can be resumed as soon as the next observed value is acquired.

Here, the IAU filter will be described. The following equation (3) is used for the IAU filter.
[Equation 2]
∂A / ∂t = Adv.A + Diff.A + Src.A + β (t-t ⁰ ) * Inc.A (3)

The time evolution of numerical prediction is generally determined by the advection term (Adv), the diffusion term (Diff), and the generation / annihilation term (Src) as shown in Equation (3). In the IAU filter, β (t−t ⁰ ) * Inc. A is added to change the time development method and improve the prediction.

β (t−t ⁰ ) is given as a function of the time difference between a certain time t and the time t ^{0 when} assimilation is performed. It becomes maximum when t matches t ⁰ , and approaches 0 as the time difference increases. Inc. A is xx ^b obtained by the equations (1) and (2) at a certain time t ⁰ .

  FIG. 7 shows a control flowchart of the three-dimensional data creation unit 12 of the present embodiment.

  First, in step 1, the observation data acquisition unit 121 assimilates the background field data and the observation data acquired from the Japan Meteorological Agency, etc., by the initial value generation unit 122 to create an initial value (ST1). The background field data may be acquired when the initial value is created.

  Subsequently, in step 2, the forecast value creation unit 123 continuously creates forecast values for the weather field from the initial values (ST2).

  Subsequently, in step 3, the restart file creation unit 124 creates a restart file (ST3). The restart file is created at a predetermined restart time before the analysis time.

  In step 4, it is determined whether there is new observation data (ST4). Since new observation data is input at the analysis time, normally, new observation data is not input until the analysis time is reached. If there is new observation data, go to step 5. If there is no new observation data, the process returns to step 4.

  In step 5, the estimated value creation unit 125 creates a predicted value of the analysis time as an estimated value (ST5).

  Subsequently, in step 6, the temporary analysis unit 126 calculates an analysis increment from the estimated value and new observation data (ST6).

  Next, in step 7, the analysis unit 127 holds the analysis increment and goes back to the restart time (ST7).

  Next, in step 8, the main analysis unit 127 performs the main analysis from the restart file and the analysis increment, and outputs the main analysis value (ST8). In this analysis, it is preferable that the restart prediction value is continuously corrected from the data at the restart time by using the analysis increment after being smoothed.

  Next, in step 9, it is determined whether or not the analysis period has ended (ST9). When the analysis period ends, the control ends.

When the analysis period ends, in step 10, the analysis value is set as a new initial value, the analysis increment is deleted, and the process returns to step 2 (ST10). That is, the objective analysis device of the present embodiment repeats the objective analysis until the analysis period ends.

  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.

  As an example, a movement vector may be calculated by performing pattern matching using the cross-correlation number method or the like from past data and current data, and output as future prediction data. By using the movement prediction unit 13, it is possible to accurately predict future lightning. The prediction data obtained in this way is called nowcast prediction data.

  Moreover, prediction data may be created by using current state data as an initial value and performing weather prediction using a cloud resolution numerical model. The prediction data obtained in this way is called numerical prediction data. As the cloud resolving numerical model, for example, CReSS (Cloud Resolving Storm Simulator) may be used.

  FIG. 8 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. As an indicator of the change in state, the volume of the upward flow for a predetermined time, the volume for a predetermined time of the region determined to be the soot, the mass per unit volume of the soot, the mass of the soot vertically integrated or the echo It may be at least one of the top altitudes.

  For example, the calculated state change is equal to or greater than a predetermined threshold value input from the state threshold value input unit 15. The state change calculation unit 14 identifies cumulonimbus clouds that are considered to be identical from the past 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. 8 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 the wind, Non-Patent Document 2 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 3 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 4 and 5 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 6).

  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 risk calculation unit 16 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, using the isothermal surface echo intensity change rate and the statistical information input from the observation data input unit 17, 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 17 is a risk calculation unit for statistical information created by learning processing using at least one of observation data such as three-dimensional data, lightning risk data, and LMA (Lightning Mapping Array) sensor. 16 By performing the learning process, it is possible to predict the risk of lightning with higher accuracy. Note that the observation data input unit 17 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

  Among these, regarding the future prediction data, the blending unit 18 blends the results of the nowcast prediction data and the numerical prediction data created by the movement prediction unit. Here, blending prediction will be described.

  FIG. 9 shows a system block diagram of the blending unit 18 of the present embodiment.

  The blending unit 18 includes an observation data acquisition unit 181, a numerical prediction unit 182, a nowcast prediction unit 183, a correct answer rate calculation unit 184, a positional shift lattice number input unit 185, and a synthesis unit 186.

  The observation data acquisition unit 181 acquires observation data such as ground observations, meteorological satellites, and radars from meteorological agencies or space agencies around the world. Since observation is performed at various places and times, observation data is acquired from each acquisition destination at predetermined time intervals. Some observation data are excluded because they are not accurate and cannot be used due to human error or equipment failure. The acquired observation data is output to the numerical prediction unit 182 and the nowcast prediction unit 183.

  The numerical prediction unit 182 is a part that divides the observation region into a lattice and performs numerical prediction for each lattice using the observation data acquired from the observation data acquisition unit 181. Numerical forecasting predicts future precipitation by calculating the time change of precipitation with a computer using the laws of physics.

  The nowcast prediction unit 183 divides the observation region into a grid and uses the observation data acquired from the observation data acquisition unit 181 to use the past precipitation region movement and the current precipitation distribution to This is the part that predicts the precipitation distribution for each grid.

The accuracy rate calculation unit 184 allows the positional deviation lattice number (allows positional deviation) while allowing the positional deviation between the numerical prediction data obtained from the numerical prediction unit 182 and the nowcast prediction data obtained from the nowcast prediction unit 183. The accuracy rate for each spatial scale) is calculated using the following equation (4).
However,
O (n) (i, j) is the Fraction of observations in the lattice (i, j),
M (n) (i, j) is the Fraction of the predicted value in the lattice (i, j),
Fraction is the percentage of grids where precipitation is greater than or equal to a predetermined value for the n × n region,
n is the number of grids on one side of the square for calculating Fraction,
N is the total number of grids in the evaluation area,
Σ _i Σ _j is the sum in the evaluation region.

  The misalignment grid number input unit 185 appropriately inputs a necessary misalignment grid number. Increasing the number of misalignment grids n increases the prediction accuracy of the probability of precipitation. However, the forecast that it will rain anywhere in a wide range is correct, but it is not practical. Conversely, when the number of misalignment grids n is reduced, the prediction accuracy of the precipitation occurrence probability is lowered. In other words, it is difficult to make a pinpoint forecast that it will rain several tens of minutes after this point. Therefore, it is preferable to appropriately determine the number n of misalignment lattices according to the user's request.

The synthesis unit 186 estimates the optimal synthesis coefficient in real time in consideration of the accuracy rate obtained by the accuracy rate calculation unit 184, and uses the synthesis coefficient to calculate numerical prediction data and nowcast prediction data using the following formula ( 5).
R (n, t) = C (n, t) × R1 (n, t) + (1−C (n, t)) × R2 (n, t) (5)
However,
n is the number of grids on one side of the square for calculating Fraction,
t is time,
R1 (n, t) is Nowcast prediction data after t hours with the number of lattices n on one side,
R2 (n, t) is numerical prediction data after t hours with the number of lattices n on one side,
C (n, t) is a composite coefficient (a function of n and t) after t hours of the nowcast prediction data R1 (n, t) with respect to the numerical prediction data R2 (n, t) with a lattice number n of one side. 0 ≦ C (n, t) ≦ 1),
It is.

  The composite coefficient is determined in consideration of the accuracy rate of the numerical prediction data and the nowcast prediction data. The accuracy rate of the prediction data is calculated in real time by comparing with the true value of the past one hour.

  FIG. 10 shows the ratio of the prediction accuracy rate based on numerical prediction to the prediction time and the prediction accuracy rate based on nowcast prediction.

  As shown in FIG. 10, in the numerical prediction, when the prediction time is short, the accuracy rate is low, and when the prediction time is long, the accuracy rate is high. Nowcast prediction has a high accuracy rate when the prediction time is short, and a low accuracy rate when the prediction time is long. Therefore, it is preferable to obtain a synthesis coefficient that places importance on nowcast prediction when the prediction time is short, and places importance on numerical prediction when the prediction time is long.

  Moreover, when predicting local heavy rain, prediction with high resolution with a grid of 1 km or less is required. When prediction is performed with high lattice resolution, the accuracy of prediction data for each lattice is generally low. That is, the prediction with a high lattice resolution has a large error.

  FIG. 11 shows a comparison between observation data and prediction data for each grid. FIG. 11A shows observation data, and FIG. 11B shows prediction data. The grid in the shaded area indicates the position of precipitation that is greater than or equal to a predetermined amount.

  In the example illustrated in FIG. 11, when a grid scale of n = 1 that does not allow positional deviation is selected, the prediction data is determined to be different from the observation data. However, if a grid scale of n = 5 that allows 5 misregistrations is selected, both the predicted data and the observed data have six precipitation positions that are equal to or greater than the predetermined amount, so the predicted data has the same result as the observed data. Determined.

  In the present embodiment, the accuracy rate calculation unit 184 allows a certain amount of positional deviation using Expression (4), and can maintain the prediction accuracy even at high lattice resolution. Then, the synthesizing unit 186 synthesizes the numerical prediction data and the nowcast prediction data using the result of the accuracy rate calculation unit 184.

  Equation (4) shows the accuracy rate of Fraction when the number of misalignment grids n at the coordinates (i, j) to be evaluated is allowed. Fraction includes O and M. FractionO is a precipitation occurrence probability when positional deviation is allowed with respect to actually observed rain. FractionM is the precipitation occurrence probability when the positional deviation is allowed with respect to the predicted rain.

  Expression (4) is obtained for each number of misalignment grids with respect to numerical prediction data and nowcast prediction data obtained in the past, and the accuracy rate of prediction can be calculated. Equation (4) is calculated for each prediction lead time. Then, the synthesis coefficient is determined from the ratio of the correct answer rate for each lead time of numerical prediction and nowcast prediction. Therefore, the synthesis coefficient is determined by the number of misalignment grids and the lead time.

  That is, the correct answer rate calculation unit 184 calculates the precipitation distribution and the precipitation occurrence probability distribution in which the numerical prediction data and the nowcast prediction data are allowed by the number of misalignment grids n for each lead time. The synthesizing unit 186 synthesizes the latest numerical prediction data and nowcast prediction data based on the synthesis coefficient with respect to the prediction for each lead time and misalignment grid number n, and the precipitation distribution and the precipitation occurrence probability distribution. Ask for.

  Thus, the blending unit 18 of the present embodiment can obtain the precipitation occurrence probability distribution in addition to the precipitation prediction distribution.

  FIG. 12 shows the relationship of the correct answer rate to the predicted time. FIG. 12A shows a case where 50 mm of precipitation is predicted per hour, and FIG. 12B shows a case where 20 mm of precipitation is predicted per hour. L in the graph shown in FIG. 12 indicates a positional deviation allowable scale (km).

  For example, when it is determined that a prediction accuracy rate of 50% is necessary, it is preferable to use data that is above the allowable line shown in FIG.

  Next, the control flow of the blending unit 18 of this embodiment will be described.

  FIG. 13 shows a flowchart of the precipitation prediction method of this embodiment.

  First, in step 11, past actual precipitation data is read (ST11). Subsequently, in step 12, past numerical prediction data and nowcast prediction data are read (ST12).

  Next, in step 13, the number n of misalignment lattices is determined (ST13). The number n of misalignment lattices can be determined as appropriate by the user.

  Next, in step 14, the correct answer rate shown in equation (4) is calculated (ST14). Subsequently, in step 15, a composite coefficient of the numerical prediction data and the nowcast prediction data is calculated (ST15). At the time of calculating the correct answer rate and the composite coefficient, the precipitation data, the prediction data, the number of misalignment grids n and the like obtained in steps 11 to 13 are used.

  Next, in step 16, the latest numerical prediction data and nowcast prediction data are read (ST16). Subsequently, in step 17, precipitation prediction and precipitation probability prediction are obtained for each number of position shift grids in the latest numerical prediction data and nowcast prediction data read in step 16 (ST17).

  Next, in step 18, the prediction based on the numerical prediction data and the prediction based on the nowcast prediction data are combined (ST18). At the time of composition, the composition coefficient calculated in step 15 is used to obtain from the equation (5). Further, it is preferable to consider the allowable line shown in FIG.

  Finally, in step 19, a combined prediction file that predicts the precipitation amount and the precipitation probability for each number of misalignment grids is output (ST19).

  FIG. 14 shows precipitation prediction data obtained by the blending unit 18 of the present embodiment. FIG. 14A shows rainfall prediction data, and FIG. 14B shows precipitation occurrence probability data.

  As shown in FIGS. 14A and 14B, the blending unit 18 of the present embodiment can obtain precipitation occurrence probability data in addition to precipitation prediction data. Further, in the precipitation occurrence probability data, it is possible to appropriately obtain the probability that a predetermined amount or more of rain will fall in a predetermined region by selecting the number n of misalignment grids. For example, in the example shown in FIG. 14 (b), the probability of rain of 50 mm or more in the 5 km square is obtained.

  The lightning information creation unit 19 predicts a three-dimensional distribution using the time-series lightning risk data calculated by the risk calculation unit 16 and the blending unit 18 and the movement vector calculated by the movement prediction unit 13. 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. 15 shows a system block of the cumulonimbus cloud prediction system of this embodiment.

  The cumulonimbus cloud prediction system 21 receives the thunder danger determination device 10, the receiver information input unit 24 that inputs the receiver information, and the cumulonimbus information calculated by the thunder hazard determination device 10 and the receiver information input unit 24. A cumulonimbus / recipient relationship computing unit 25 that computes each relationship from the receiver's information input by the user, and an output unit 26 that outputs the results computed by the cumulonimbus / receiver relationship computing unit 25.

  The receiver information input unit 24 is used by the receiver to input his / her information in advance. For example, the recipient information input unit 24 may use a mobile terminal or the like. Recipient's position information 24a that informs the receiver of whether or not the recipient wants to know the danger, the receiver's risk setting information 24b that informs the danger level and distance set by the receiver, and the positional deviation set by the receiver The receiver's positional deviation allowable information 24c that informs the allowable range is input.

  The location information 24a of the recipient may be a location where the recipient currently exists, a location where the recipient will move from now on, or a location that the recipient wants to know. 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 24b 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 24c 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 25 converts the cumulonimbus coordinate system to 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 26 outputs the result calculated by the cumulonimbus / recipient relationship calculation unit 5. The output unit 26 includes dangerous position information 26a for notifying whether or not the place set by the receiver using the receiver information input unit 24 is a dangerous position, and whether the time set by the receiver using the receiver information input unit 24 is a dangerous time. Risk time information 26b that informs whether or not, the risk type information 26c that informs whether or not the type of rain, wind, thunder or hail set by the receiver in the receiver information input unit 24 is dangerous, and the receiver is the receiver At least one of the danger level information 26d that informs which of the danger levels set by the information input unit 24 is output.

  The recipient information input unit 24 and the output unit 26 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. 16 shows a flowchart of the cumulonimbus prediction system of the present embodiment.

  First, at step 21, the lightning risk determination device 10 calculates and outputs dangerous lightning information (ST21).

  Next, in step 22, the recipient information input unit 24 acquires recipient information (ST22). The information on the recipient that is acquired may be the location information 24a of the recipient, the danger setting information 24b of the recipient, the positional deviation tolerance information 24c of the recipient, and the like.

  Next, in step 23, the cumulonimbus / recipient relationship calculating unit 25 calculates the relationship between the cumulonimbus and the receiver (ST23). 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 24, the output unit 25 outputs the relationship between the cumulonimbus and the receiver (ST24). The output unit 26 may output at least one of the dangerous position information 26a, the dangerous time information 26b, the dangerous type information 26c, the dangerous level information 26d, and the like.

  As described above, according to the cumulonimbus cloud prediction system 1, it is possible to accurately predict the risk of lightning by the lightning risk determination device 10, and to accurately notify the receiver of information on cumulonimbus clouds.

  As described above, the lightning risk determination device 10 according to the present embodiment represents the current state of cumulonimbus clouds using the meteorological information acquisition unit 11 that detects cumulonimbus clouds and the objective analysis technology from the weather data acquired by the weather information acquisition unit 11. A three-dimensional data creation unit 12 that creates three-dimensional data including current data and past data representing past cumulonimbus conditions, and nowcast prediction that predicts cumulonimbus movement from the current data and past data and represents future cumulonimbus conditions Data and the movement prediction unit 13 for generating future prediction data including numerical prediction data generated by performing weather prediction using a cloud resolving numerical model with current data as an initial value, and a predetermined threshold for state change are defined A state threshold value input unit, a state change calculation unit 14 that calculates a state change in the cumulonimbus cloud from current data, past data, and future prediction data, and a state change A risk calculation unit 16 that calculates lightning risk data representing the risk of lightning from the state change calculated by the calculation unit 14, and a blending unit that blends the results of nowcast prediction data and numerical prediction data with respect to future prediction data 18, and a lightning information creation unit 19 that creates location information of a place with a high risk from the lightning risk data calculated by the risk calculation unit 16 and the blending unit 18. Therefore, it is possible to accurately predict the risk of lightning.

  In addition, the lightning risk determination device 10 of the present embodiment stores statistical information created by learning processing using at least one of three-dimensional data, lightning risk data, and observation data in the risk calculation unit 16. An observation data input unit 17 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 cloud receiver relation calculation part 6 ... Output part 10 ... Lightning risk determination apparatus 11 ... Weather information acquisition part 12 ... Three-dimensional data creation unit 13 ... movement prediction unit 14 ... state change calculation unit 15 ... state threshold value input unit 16 ... risk level calculation unit 17 ... observation data input unit 18 ... blending unit 19 ... lightning information creation unit

Claims (4)

A weather information acquisition unit for detecting cumulonimbus clouds;
A three-dimensional data creation unit that creates current data representing the current cumulonimbus status and past data representing past cumulonimbus status using objective analysis technology from the weather data acquired by the weather information acquisition unit When,
A state threshold value input part for defining a predetermined threshold value regarding the state change;
Predicted movement of cumulonimbus clouds from the current data and the past data, nowcast prediction data representing the state of future cumulonimbus clouds, and numerical values created by performing weather prediction using a cloud resolving numerical model with the current data as initial values A movement prediction unit for creating future prediction data including prediction data;
A state change calculation unit that identifies a cumulonimbus whose state change is equal to or greater than the threshold from the current data, the past data, and the future prediction data, and calculates a state change in the cumulonimbus cloud;
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 blending unit for blending the results of the nowcast prediction data and the numerical prediction data with respect to the future prediction data;
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 and the blending unit;
A lightning risk determination device comprising: 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 device according to claim 1. The state change calculation unit calculates the volume change of the upward flow in the cumulonimbus,
The thunder hazard level determination device according to claim 1, wherein the state threshold value input unit defines a volume threshold value of an upflow volume to be compared with a value calculated by a state change calculation unit. The state change calculation unit calculates the volume change of the soot in the cumulonimbus,
The lightning danger determination device according to claim 1, 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.