WO2017122408A1 - Weather forecasting device, weather forecasting method, and weather forecasting program - Google Patents

Abstract

A weather forecasting device according to an embodiment has a precipitation risk derivation unit and an output unit. The precipitation risk derivation unit derives a precipitation risk on land on the basis of weather conditions in the sky obtained from a radar device. The output unit outputs information based on the precipitation risk derived by the precipitation risk derivation unit.

Description

Weather forecasting device, weather forecasting method, and weather forecasting program

Embodiments described herein relate generally to a weather prediction device, a weather prediction method, and a weather prediction program.
This application claims priority on January 12, 2016 based on Japanese Patent Application No. 2016-003545 for which it applied to Japan, and uses the content here.

In recent years, meteorological phenomena such as guerrilla heavy rains have been observed, in which a lot of rain falls instantaneously, causing damage such as inundation and flooding. Related to this, a technique for observing rain clouds such as cumulonimbus clouds in the sky and estimating the weather condition in the sky is known. In addition, a technique for estimating the risk of flooding based on the measurement data of a rain gauge on the ground is known. However, with the conventional technology, there is a case where the degree of influence on the ground cannot be accurately predicted based on the weather conditions in the sky.

Japanese Patent Laid-Open No. 10-288664 JP 2009-128180 A

The problem to be solved by the present invention is to provide a weather prediction device, a weather prediction method, and a weather prediction program capable of accurately predicting the degree of influence on the ground based on the weather conditions in the sky. .

The weather prediction apparatus of the embodiment has a precipitation risk deriving unit and an output unit. The precipitation risk deriving unit derives the risk of precipitation on the ground based on the weather condition in the sky obtained by the radar device. The output unit outputs information based on precipitation risk derived by the precipitation risk deriving unit.

The figure which shows an example of a structure of the weather prediction apparatus 100 in 1st Embodiment. The figure which shows an example of the observation data 32 stored in the memory | storage part. The figure which matched the calculation result by the mesh parameter calculation part 12 with the cross section in the plane containing the vertical direction in three-dimensional space of the sky. Diagram showing an example of the formation results in the expected rainfall regions S _k. The figure which shows an example of the screen based on precipitation point information. The figure which shows an example of the screen based on rain cloud scale information. The flowchart which shows an example of the process by the weather prediction apparatus 100 in 1st Embodiment. It illustrates an example of a case where a plurality of predicted precipitation region S _k overlap each other. The figure which shows an example in case the ground surface G is not flat. Figure schematically showing how to derive a precipitation core region CR _k in the ground. The flowchart which shows an example of the process by the weather prediction apparatus 100 in 2nd Embodiment. The figure which shows an example of a structure of the weather prediction apparatus 100B in 3rd Embodiment. The figure which shows an example of a structure of the weather prediction apparatus 100C in 4th Embodiment. The figure for demonstrating the discrimination | determination method of the kind of particle | grains based on temperature and humidity. It is a figure which shows an example of the fall speed information 36 for every particle in 4th Embodiment. The flowchart which shows an example of the process by 100 C of weather prediction apparatuses in 4th Embodiment. The flowchart which shows the other example of the process by the weather prediction apparatus 100C in the modification of 4th Embodiment. The figure which shows an example of a structure of the weather prediction apparatus 100D in 5th Embodiment. The figure which shows an example of the fall speed information 36 for every particle in 5th Embodiment. The figure which shows an example of a structure of the weather prediction apparatus 100E in 6th Embodiment.

Hereinafter, a weather prediction device, a weather prediction method, and a weather prediction program according to an embodiment will be described with reference to the drawings.

(First embodiment)
FIG. 1 is a diagram illustrating an example of a configuration of a weather prediction apparatus 100 according to the first embodiment. The weather prediction device 100 according to the first embodiment estimates the amount of rain and snow falling on the ground based on the received power of the radio waves received by the weather radar device 200 or the signal strength of the radio waves, and calculates the estimated amount. Judge as a risk to users on the ground.

The weather radar apparatus 200 is an apparatus including a phased array antenna, for example, and electronically varies the directivity angle by controlling the phase of a signal input to an arrayed antenna element constituting the phased array antenna. The weather radar apparatus 200 transmits and receives radio waves while changing the directivity angle of the antenna. For example, the weather radar apparatus 200 changes the directivity angle in the elevation direction (vertical direction) within a certain angle range (for example, 90 degrees) by electrical phase control. The weather radar apparatus 200 mechanically varies the directivity angle in the azimuth direction (horizontal direction) by a drive mechanism (not shown). Further, the weather radar apparatus 200 may change the directivity angle by electrical phase control in both the azimuth direction and the elevation direction.

In addition, the weather radar apparatus 200 may be an apparatus including a parabolic antenna, a patch antenna, a pole antenna, a shunt feed antenna, a slot antenna, and the like in addition to the above-described phased array antenna. When the antenna is a parabolic antenna, the weather radar apparatus 200 transmits and receives radio waves while mechanically changing the antenna directivity angle by a driving mechanism (not shown).

The weather radar apparatus 200 converts received radio waves into electrical signals, and performs signal processing such as demodulation, signal strength amplification, and frequency conversion. Then, the weather radar apparatus 200 transmits a signal subjected to signal processing (hereinafter referred to as a processed signal) to the weather prediction apparatus 100 as observation data. For example, the weather radar device 200 transmits a plurality of processed signals generated during a predetermined search cycle to the weather prediction device 100 as one observation data. Observation data, for example, a three-dimensional space, the distance direction, divided by a predetermined width for each of the horizontal direction and the vertical direction, the divided regions each (hereinafter, referred to as a mesh area M _i), the physical quantity based on radio waves The volume data is associated with the volume data. Incidentally, the observation target of the meteorological radar apparatus 200 sufficiently far from the weather radar system 200, the mesh area M _i shall be regarded as a cube.

The weather prediction device 100 includes a communication interface 10, a mesh parameter calculation unit 12, a precipitation core region derivation unit 14, a wind direction and wind speed estimation unit 16, an advection prediction unit 18, a precipitation risk derivation unit 20, and an image generation unit 22. The output unit 24 and the storage unit 30 may be included, but are not limited thereto. Some or all of the constituent elements of the weather prediction apparatus 100 described above may be realized by a processor such as a CPU (Central Processing Unit) executing a program stored in the storage unit 30. Further, some or all of the components of the weather prediction apparatus 100 may be realized by hardware such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array).

The storage unit 30 includes, for example, a nonvolatile storage medium such as a ROM (Read Only Memory), a flash memory, an HDD (Hard Disk Drive), an SD card, an MRAM (Magnetoresistive Random Access Memory), a RAM (Random Access Memory), It may be realized by a volatile storage medium such as a register. The storage unit 30 stores programs executed by the processor of the weather prediction apparatus 100, and stores observation data 32, analysis data 34 for each core, and the like, which will be described later.

The communication interface 10 communicates with the weather radar apparatus 200 and the like, and receives observation data 32 from the weather radar apparatus 200. Observation data 32 received by the communication interface 10 is stored in the storage unit 30.

FIG. 2 is a diagram illustrating an example of observation data 32 stored in the storage unit 30. In the observation data 32, a radar reflection factor Z _i and a Doppler velocity D _i are associated with each mesh region M _i obtained by dividing a three-dimensional space including clouds in the sky. The radar reflection factor Z _i is a parameter that varies according to the particle size of particles that reflect radio waves. The received power when the weather radar device 200 receives radio waves and the cloud particles that reflect the radio waves from the weather radar device 200. It is calculated based on the distance to. The particles that reflect radio waves are, for example, particles that form clouds, and will be described below as cloud particles. Cloud droplets include, for example, water droplets and ice crystals. The Doppler velocity D _i is a parameter representing the moving direction and moving velocity of the cloud particles in the mesh region M _i , and the transmission frequency when the weather radar device 200 transmits the radio wave and the reception when the radio wave is received. It is calculated based on the difference from the frequency. The Doppler speed D _i is an index used when calculating the wind direction and wind speed of each mesh region M _i . These indices may be calculated as a result of signal processing in the weather radar apparatus 200, or may be calculated in the weather prediction apparatus 100.

The size of the mesh region M _i may be changed according to the time resolution and spatial resolution of the weather radar apparatus 200. In addition, each mesh area M _i, the position coordinates of the orthogonal coordinate system is associated with the origin position of the meteorological radar apparatus 200. For example, if the weather radar system 200 is installed in a high hill or summit like elevations, the position coordinates of a mesh area M _i may take a negative value in the altitude direction. The observation data 32 is an example of information representing weather conditions in the sky. The coordinate system is not limited to an orthogonal coordinate system, and may be a polar coordinate system.

The mesh parameter calculation unit 12 calculates the precipitation intensity R _i for each mesh region M _i of the observation data 32 stored in the storage unit 30. For example, precipitation intensity R _i is calculated by substituting the radar reflectivity factor Z _i for each mesh area M _i in Equation (1). Units of precipitation intensity _{R i} is, for example, mm / h. The precipitation intensity R _i may be calculated by other methods.

B and β in the above formula (1) are constants determined from the observation values by the rain gauge. When the cloud droplet is a water droplet, B is set to about 200, β is set to about 1.6, and the cloud particle is ice. In the case of crystals, B is set to about 500 to 2000, and β is set to about 2.0. The constants B and β may be set to the same value in all mesh areas M _i or may be set to different values for each mesh area M _i .

The precipitation core region deriving unit 14 classifies the degree of precipitation according to the precipitation intensity R _i inside the cloud, so that the mesh regions M _i having the same precipitation intensity R _i calculated by the mesh parameter calculation unit 12 are combined. The precipitation core region CR _k is derived in a three-dimensional space including the clouds above.

FIG. 3 is a diagram in which a calculation result by the mesh parameter calculation unit 12 is associated with a cross section in a plane including a vertical direction in the above three-dimensional space. In the figure, the Z axis indicates the vertical direction, and the X axis and the Y axis indicate orthogonal components included in the horizontal direction. In the illustrated example, only a cross section of a certain XZ plane in the above three-dimensional space is shown. Each mesh region M _i is associated with a vector (arrow V _i ) indicating the wind direction and wind speed based on the Doppler speed D _i described later, and the precipitation intensity R _i calculated by the mesh parameter calculation unit 12. In the drawing, the precipitation intensity R _i is expressed by R _xz in order to indicate the precipitation intensity R corresponding to the X axis and the Z axis. Direction of the vector indicated by the arrow V _i indicates the wind direction, the magnitude of the vector indicates the wind speed. The information in which the precipitation intensity R _i and the vector arrow V _i indicating the wind direction and wind speed are associated with each mesh region M _i that virtually represents the three-dimensional space in the sky as the analysis data 34 for each core. It is stored in the storage unit 30.

Precipitation core region deriving unit 14 refers to the precipitation intensity R _i for each mesh area M _i, precipitation intensity R _i is comparable mesh area M _{i (hereinafter,} referred to as matching mesh regions) bound to the 1 Two precipitation core regions CR _k are derived. The precipitation core region deriving unit 14 sets, for example, a mesh region M _{i in} which the precipitation intensity R _i falls between two threshold values Th _k selected in stages as a matching mesh region. The precipitation core region deriving unit 14 lined with boundaries of the region gathered a plurality of matching mesh regions, derives precipitation core region CR _k that the boundary line between contours. Incidentally, if the mesh area M _i around the mesh area M _i and precipitation intensity R _i is not at the same level is present alone, it may be assimilated with the surrounding ignoring it.

In the present embodiment, as an example, it is assumed that two threshold values Th ₁ and Th ₂ are set for the precipitation intensity R _i . The precipitation core region deriving unit 14 derives three precipitation core regions CR ₁ , CR ₂ , and CR ₃ using the _two threshold values Th ₁ and Th ₂ . For example, the core region CR ₁ has a precipitation intensity R _{i that} is greater than or equal to the threshold Th ₂ , and the core region CR ₂ has a precipitation intensity R _{i that} is greater than or equal to the threshold Th _{1 and} less than the threshold Th ₂ , and the core region CR ₃ Is a precipitation intensity R _i less than the threshold Th ₁ . The core region CR ₁ is, for example, precipitation intensity R _i centered at 80 mm / h, the core region CR ₂ is, for example, precipitation intensity R _i centered at 50 mm / h, and the core region CR ₃ is For example, precipitation intensity R _i centered at 30 mm / h.

Wind estimating unit 16, for example, on the basis of radar reflectivity factor Z _i and Doppler velocity D _i for each mesh area M _i, estimating the wind direction and wind speed for each mesh area M _i. For example, the wind direction and wind speed estimation unit 16 uses a plurality of observation data to estimate the cloud droplet fall speed, the azimuth and elevation angles when the radio wave is received by the weather radar device 200, the radar reflection factor Z _i and the Doppler. Based on the speed D _i , the wind direction and the wind speed are estimated. Further, the wind direction and wind speed estimation unit 16 may estimate the wind direction and the wind speed using a three-dimensional wind analysis method such as a VVP (Volume Velocity Processing) method or a Gal-Chen method.

Then, Wind estimating unit 16, for example, precipitation core region CR of the wind direction and wind velocity were estimated for each mesh area M _i in the _k, and the average for each downcomer core region CR _k, the average wind direction and precipitation core wind speed the wind direction and wind speed region CR _k.

The advection prediction unit 18 performs advection prediction for each precipitation core region CR _k derived by the precipitation core region deriving unit 14. The advection prediction, the precipitation core region CR _k internal cloud observation target is to predict whether flowing by how wind before reaching to or, or ground to what extent the diffusion in the air before reaching the ground It is.

First, advection prediction unit 18, to determine whether to predict to future extent destination, precipitation core region CR _k is time to reach the ground (hereinafter, referred to as the predicted arrival time) is calculated. Advection prediction unit 18, based on the combined vector of the vector U _k indicating the wind direction and wind speed of precipitation core region CR _k, and the vector obtained by multiplying the mass and gravitational acceleration of precipitation core region CR _k, precipitation core region CR _k Calculates the estimated arrival time from the current position (altitude) to the ground. The mass of the precipitation core region CR _k is determined according to the precipitation intensity R _i . For example, the mass of the precipitation core region CR _k tends to increase as the precipitation intensity R _i increases.

The advection prediction unit 18 calculates a position (hereinafter referred to as a predicted arrival position) when the precipitation core region CR _k reaches the ground according to an advection model such as CUL (Cubic Lagrange) by simulation. Formula (2) is an example of a formula indicating an advection model.

In the above equation, z is a value for each mesh region M _i at time t at the time of observation by the weather radar device 200 in an orthogonal coordinate system whose origin is the position of the weather radar device 200 set on the horizontal plane (x, y). This is a parameter representing precipitation intensity R _i (ie, z is a function of (x, y, t)). U is a vector indicating the wind direction and wind speed of precipitation core region CR _k in the x-axis direction, V is a vector indicating the wind direction and wind speed of precipitation core region CR _k the y-axis direction, W is precipitation core region It is a constant (developmental debilitating term) representing the amount of change in shape accompanying the movement of CR _k . The amount of change in shape accompanying movement is an index indicating the degree to which the shape changes due to rotation, shear distortion, expansion, contraction, or the like. The amount of change in the precipitation core region CR _k in the vertical direction (z-axis direction) is considered as a constant term of W. The advection prediction unit 18 determines these U, V, and W parameters by solving simultaneous linear equations shown in Equation (3) as a least square estimation problem or a sequential estimation problem.

The parameters c ₁ to c ₉ are determined using observation data observed in the past, and may be stored in the storage unit 30 in advance. These parameters c ₁ to c ₉ are treated as being constant for a predetermined period (for example, about 1 hour). The advection prediction unit 18 substitutes the parameters c ₁ to c ₉ into the characteristic differential equation shown in Equation (4), and calculates the predicted arrival position of the mesh region M _i corresponding to the precipitation intensity R _i represented by W. To do.

On the characteristic curve defined by the mathematical formulas dx / dt and dy / dt, the parameter W representing the precipitation intensity R _i changes according to dz / dt. For example, when the position coordinates at the current time t0 for each mesh area M _{i of interest} are represented as (x _t0 , y _t0 ), the advection prediction unit 18 determines the current position coordinates ( The position coordinates (x _τ , y _τ ) at the future time τ advanced by the predicted arrival time are calculated from x _t0 , y _t0 ). Coordinates (x _τ, y _τ) in this future time tau, since it represents only the amount of movement of the mesh area M _i in the horizontal direction, advection prediction unit 18 further mass and gravity precipitation core region CR _k Based on the acceleration, the position coordinates at a future time τ are corrected in the vertical direction (z-axis direction). As a result, the advection prediction unit 18 predicts the position coordinates (x _τ , y _τ , z _τ ) of the mesh region M _i at a future time τ advanced by the predicted arrival time. The advection prediction unit 18 further predicts the position coordinates (x _τ , y _τ , z _τ ) of the mesh region M _i in consideration of the air resistance received before the precipitation core region CR _k reaches the ground. May be. Thus the position coordinates of a mesh area M _i in the predicted future time tau by _{(x τ, y τ, z} τ) , since in consideration of the expected arrival time, on the ground of the ground surface or the ground surface near, Located in. In the present embodiment, the position coordinates of a mesh area M _i at a future time _{τ (x τ, y τ,} z τ) is described as being on the surface of the earth.

Advection prediction unit 18, a simulation of advection prediction described above is performed for all of the mesh area M _i or typified by several mesh area M _i, that make up the downcomer core region CR _k, the mesh area M _i The position coordinates at each future time τ are calculated. Advection prediction unit 18, a virtually mesh area M _i to the calculated position coordinates arranged, regions virtually formed by a plurality of mesh areas M _i on the ground surface (hereinafter, referred to as the expected precipitation region S _k the position of the) is calculated as the predicted arrival position of precipitation core region CR _k.

Figure 4 is a diagram showing an example of the formation results in the expected rainfall regions S _k. In the illustrated example, vectors U _k indicating the same wind direction and wind speed are set in the three precipitation core regions CR ₁ , CR ₂ , and CR ₃ . Moreover, the code | symbol G shown in a figure shows the ground surface on the ground.

In general, the precipitation core region CR _k having a higher precipitation intensity R _i tends to have more rainfall (or snowfall). The precipitation core region CR _k having such a large precipitation intensity R _i often has a high density of cloud particles per unit volume or a large size of the cloud particles themselves. Therefore, the precipitation core region CR _k having a higher precipitation intensity R _{i has} a larger mass and tends to have a shorter time until it reaches the ground from the sky during rainfall (or snowfall).

For example, most precipitation intensity _R larger downcomer core region CR ₁ of _i is less affected by wind for large mass, precipitation core near just below the cloud of the three precipitation core region _{CR 1,} CR 2, CR ₃ A predicted precipitation region S ₁ corresponding to the region CR ₁ is likely to be formed. Further, three precipitation core region _{CR 1,} CR 2, CR most precipitation intensity _{R i} lower downcomer core region CR ₃ of among the _three are susceptible to wind for small mass. Therefore, expected rainfall area S ₃ corresponding to the precipitation core region CR _3, rather than the position of the expected rainfall regions S _k corresponding to the precipitation core region CR ₁ and CR _2, likely to be formed farther. Also, expected rainfall area S ₃ corresponding to the precipitation core region CR ₃ is due to the influence of the wind, the area is likely to spread before reaching the ground. Therefore, it expected rainfall area S _3, an area likely to be formed as a larger area than the when precipitation core region CR ₃ were simply projected onto the ground surface G.

The precipitation risk deriving unit 20 calculates the precipitation on the ground based on the precipitation intensity R _i for each precipitation core region CR _k derived by the precipitation core region deriving unit 14 and the predicted arrival time calculated by the advection prediction unit 18. A risk P _k is derived. The risk _Pk of precipitation is an index according to the amount of precipitation and the time until precipitation. For example, the risk P _k of precipitation is derived for each arrival position of precipitation core region CR _k, the precipitation intensity R _i Precipitation core region CR _k to reach the ground, divided by the expected arrival time of precipitation core region CR _k Defined as a value. Therefore, the precipitation risk deriving unit 20 derives the precipitation risk P _k at a higher value for the precipitation core region CR _{k having} the larger precipitation intensity R _i .

Precipitation risk deriving unit 20, the prediction result of advection predictor 18 for each of the predicted rainfall regions S _k of the ground surface G, to derive the risk P _k of precipitation. For example, the precipitation risk deriving unit 20 classifies the precipitation risk P _k by comparing the derived precipitation risk P _k with the reference value D. In the present embodiment, the precipitation risk deriving unit 20 classifies the precipitation risk P _k into three categories using the two reference values Dx and Dy. For example, precipitation risk deriving unit 20, the risk P _k of rainfall than Dx case where (R _i ≧ Dx), classifies the risk P _k of precipitation category indicating that high risk (high risk), the precipitation If the risk P _k is less than Dx and greater than or equal to Dy (Dx> R _i ≧ Dy), the precipitation risk P _k is classified into a category indicating that the risk is medium (medium risk), and the precipitation risk P _k Is less than Dy (Dy> R _i ), the risk _Pk of precipitation is classified into a category indicating low risk (low risk). The reference value D may be one, or may be three or more. In this case, the precipitation risk deriving unit 20 classifies the precipitation risk P _k into two, or four or more categories.

In the example of FIG. 4 described above, precipitation risk deriving unit 20, to the predicted rainfall areas S _1, risk P ₁ of precipitation based on precipitation intensity R ₁ and predicted arrival time of the prediction source precipitation core region CR ₁ To derive. In the example shown in the figure, the precipitation risk P ₁ for the predicted precipitation region S ₁ is classified as “high risk”. Similarly, the precipitation risk deriving unit 20 applies the predicted precipitation region S _k of the precipitation core region CR _{2 to} the predicted precipitation region S ₂ of the precipitation core region CR ₂ and the predicted precipitation region S ₃ of the precipitation core region CR ₃ . A precipitation risk P _k is derived based on the precipitation intensity R _i and the predicted arrival time. In the illustrated example, the risk P ₂ precipitation for predicted rainfall area S ₂ which corresponds to the precipitation core region CR ₂ is classified as a "medium risk" of precipitation for the expected rainfall area S ₃ corresponding to the precipitation core region CR ₃ Risk P ₃ is classified as "low risk".

Image generating unit 22, the classification results of risk P _k of precipitation of each predicted rainfall regions S _k derived by the precipitation risk deriving unit 20, information of a combination of a Map of the ground surface G (hereinafter, the precipitation point information An image is generated based on the For example, the image generation unit 22 generates an image obtained by converting a representative value (for example, an average value) of precipitation risk P _k for each category into a luminance value. Here, the luminance value is information regarding three components that represent colors in the color space. For example, the image generation unit 22 converts the representative value of the risk _Pk of precipitation into a luminance value according to a predetermined color format such as YUV or YCbCr.

The image generation unit 22 also includes information indicating the precipitation intensity R _i for each precipitation core region CR _k derived by the precipitation core region deriving unit 14 (hereinafter referred to as rain cloud scale information) and the precipitation core region from the ground surface G. An image may be generated based on information indicating a distance (altitude) to CR _k . For example, the image generation unit 22 generates an image obtained by converting the precipitation intensity R _i for each precipitation core region CR _k into a luminance value.

The output unit 24 uses information indicating the image generated by the image generation unit 22 as a portable device that also serves as a display device such as a smartphone or a tablet terminal operated by a user via a network such as a WAN (Wide Area Network), for example. Output to a terminal device or a stationary terminal device. In this case, a screen as shown in FIGS. 5 and 6 is displayed on the terminal device.

FIG. 5 is a diagram illustrating an example of a screen based on precipitation point information. Like the example of illustration, on the screen of a terminal device, the prediction precipitation area | region _Sk is displayed on the map information which shows the ground surface G, and this prediction precipitation area | region _Sk is color-coded and displayed for every category of risk. . For example, the expected precipitation area S ₁ belonging to the “high risk” category is red, the expected precipitation area S ₂ belonging to the “medium risk” category is yellow, the expected precipitation area S ₃ belonging to the “low risk” category is blue, And so on.

FIG. 6 is a diagram illustrating an example of a screen based on rain cloud scale information. As in the illustrated example, the screen of the terminal device, precipitation core region CR _k are displayed in different colors at a position in the sky.

Further, the output unit 24 may output information indicating the image generated by the image generation unit 22 to the web server. This web server provides, for example, a web page that can be accessed by a terminal device via a web browser. The web server incorporates an image received from the weather prediction device 100 on the web page, and the web page is stored in the terminal device. provide. As a result, a screen based on precipitation point information or rain cloud scale information as shown in FIGS. 5 and 6 is displayed on the terminal device. Note that the output unit 24 may output both image information based on precipitation point information and image information based on rain cloud scale information to a terminal device, a web server, or the like.

FIG. 7 is a flowchart showing an example of processing performed by the weather prediction apparatus 100 according to the first embodiment. The processing of this flowchart is repeatedly performed at a predetermined cycle, for example.

First, when the observation data is received by the communication interface 10 (step S100; Yes), the mesh parameter calculation unit 12 calculates the precipitation intensity R _i for each mesh region M _i of the observation data 32 (step S102). Next, the precipitation core region deriving unit 14 calculates the precipitation core region CR _{k obtained} by combining the mesh regions M _i having the same precipitation intensity R _i calculated by the mesh parameter calculation unit 12 and includes the three-dimensional cloud including the sky above. Derivation is performed in space (step S104).

Then, Wind estimation unit 16, based on the radar reflectivity factor _{Z i} and Doppler velocity _{D i} for each mesh area _{M i,} estimating the wind direction and wind speed for each mesh area _{M i} (step S106). Next, the advection prediction unit 18 performs advection prediction for each precipitation core region CR _k derived by the precipitation core region deriving unit 14, and calculates a predicted arrival time and a predicted arrival position for each precipitation core region CR _k. (Step S108).

Next, the precipitation risk deriving unit 20 is based on the precipitation intensity R _i for each precipitation core region CR _k derived by the precipitation core region deriving unit 14 and the predicted arrival time calculated by the advection prediction unit 18. The risk _Pk of precipitation in is derived (step S110). Then, the image generator 22, based on the precipitation point information in combination with the classification results of risk P _k of precipitation of each predicted rainfall regions S _k derived by the precipitation risk deriving unit 20, and the map information of the ground surface G Then, an image is generated (step S112). Next, the output unit 24 outputs information indicating the image generated by the image generation unit 22 to a terminal device, a web server, or the like (step S114). Thereby, the process of this flowchart is complete | finished.

According to the weather prediction apparatus 100 in the first embodiment described above, the risk _Pk of precipitation on the ground is derived based on the weather condition in the sky obtained by the weather radar apparatus 200, so It is possible to accurately predict the degree of influence.

Further, according to the weather prediction apparatus 100 in the first embodiment, information indicating the risk _Pk of precipitation on the ground is transmitted as image information to a terminal device or the like operated by the user. The degree of influence can be known in advance.

(Modification of the first embodiment)
Hereinafter, modifications of the first embodiment will be described. Precipitation risk deriving unit 20 in the first embodiment described above, when a plurality of predicted rainfall regions S _k formed on the ground surface G overlap each other with respect to overlapping area S _x, new risk of precipitation P _k May be derived.

Figure 8 is a diagram showing an example of a case where a plurality of predicted precipitation region S _k overlap each other. In the illustrated example, the expected precipitation region S ₁ corresponding to the precipitation core region CR _1, and overlap the expected precipitation area S ₃ corresponding to the precipitation core region CR _3. In this case, the precipitation risk deriving unit 20 derives a region S _x where the predicted precipitation region S ₁ and the predicted precipitation region S ₃ overlap (hereinafter, referred to as a superimposed region S _x ). Then, the precipitation risk deriving unit 20 newly derives the precipitation risk P _k of the overlapping region S _x .

For example, precipitation risk deriving unit 20, in the overlapping area _{S x,} calculates the average of the precipitation intensity _{R 3} predicted precipitation region _{S 3} and precipitation intensity _{R 1} predicted precipitation area _{S 1,} the precipitation core region CR ₁ calculating the average of the predicted arrival time of the expected arrival time and precipitation core region CR _3. The precipitation risk deriving unit 20 derives the precipitation risk P _k in the overlapping region S _x based on the average precipitation intensity and the predicted arrival time. As a result, in the illustrated example, the overlapping region _Sx is classified into the “medium risk” category.

Also, precipitation risk deriving unit 20 overlaps among a plurality of predicted rainfall regions S _k, more large expected rainfall regions S _k risk P _k of precipitation that is derived for the precipitation intensity R _i, the overlapping area S _x It may be the risk _Pk of precipitation. In this case, in the example of FIG. 8, the precipitation risk deriving unit 20 treats the overlapping region S _x as a partial region of the predicted precipitation region S ₃ . On the contrary, precipitation risk deriving unit 20 overlaps among a plurality of predicted precipitation area S _k, the risk P _k of derived precipitation for a greater rainfall intensities R _i little expected rainfall regions S _k, superimposed area S _It is good also as the risk _Pk of precipitation of _x .

Also, if the ground surface G as mountainous areas is not flat, precipitation risk deriving unit 20 in accordance with the predicted arrival position of each downcomer core region CR _1, with respect to expected arrival time calculated by the advection predictor 18 May be weighted.

FIG. 9 is a diagram illustrating an example when the ground surface G is not flat. In the illustrated example, a part of a certain ground surface G rises in the vertical direction by a height (altitude) H along the direction indicated by the vector U _k indicating the wind direction and the wind speed. In this case, for example, when the precipitation risk deriving unit 20 derives the precipitation risks P ₂ and P ₃ of the predicted precipitation region S ₂ and the predicted precipitation region S ₃ formed at a higher position, these predicted precipitation regions S ₂ are derived. _2, to weight the predicted arrival time in accordance with a predicted arrival position of S _2. As a result, as shown in FIG. 4 described above would otherwise, the expected precipitation area S _3, are classified into the category of "low risk", in the example of FIG. 9, it has to weight the predicted arrival time Is classified into the “medium risk” category. Even within the same expected precipitation region _Sk , the expected arrival time varies depending on the altitude of the ground surface G, and therefore the precipitation risk _Pk varies depending on the position in the region. Therefore, precipitation risk deriving unit 20 may divide the category even with the same expected rainfall area S _k. In the example of FIG. 9, the precipitation risk deriving unit 20 divides the predicted precipitation region corresponding to the precipitation core region CR ₂ into S ₂ and S ₂ #, and classifies these two predicted precipitation regions into different categories. . Thus, the image generating unit 22, the same predicted rainfall area S _k, it is possible to generate an image color-coded depending on the altitude of the ground surface G.

(Second Embodiment)
Hereinafter, the weather prediction apparatus 100A in the second embodiment will be described. The weather prediction apparatus 100A according to the second embodiment is different from the first embodiment in that the precipitation core region CR _k is derived on the ground. Therefore, it demonstrates centering on such a difference and abbreviate | omits description about a common part.

FIG. 10 is a diagram schematically showing how the precipitation core region CR _k is derived on the ground. Advection predictor 18 in the second embodiment is based on the precipitation intensity R _i for each mesh area M _i, it calculates the predicted arrival time for each mesh area M _i. Then, the advection prediction unit 18 performs advection prediction simulation based on the wind direction and wind speed for each mesh region M _i and the precipitation intensity R _i , and determines the position coordinates of all the mesh regions M _i at the future time τ. calculated on the surface G, which virtually arranged the mesh area M _i to the position coordinates.

Precipitation core region deriving unit 14, the mesh area M _i virtually arranged on the surface of the earth G, precipitation core region CR _k where precipitation intensity R _i is the sum of comparable mesh area M _i between, ground Derived at the ground surface G. In the illustrated example, the precipitation core region deriving unit 14 derives the precipitation core region CR ₁ and the precipitation core region CR _{2 on} the ground surface G. The precipitation risk deriving unit 20 derives a precipitation risk P _k for the precipitation core region CR _k derived on the ground surface G by the precipitation core region deriving unit 14.

FIG. 11 is a flowchart illustrating an example of processing by the weather prediction apparatus 100 according to the second embodiment. The processing of this flowchart is repeatedly performed at a predetermined cycle, for example.

First, when the observation data is received by the communication interface 10 (step S200; Yes), the mesh parameter calculation unit 12 calculates the precipitation intensity R _i for each mesh region M _i of the observation data 32 (step S202). Then, Wind estimation unit 16, based on the radar reflectivity factor _{Z i} and Doppler velocity _{D i} for each mesh area _{M i,} estimating the wind direction and wind speed for each mesh area _{M i} (step S204).

Next, advection prediction unit 18 performs the advection prediction for each mesh area M _i, calculates the predicted arrival time and the predicted arrival position of each mesh area M _i (step S206). Then, the precipitation core region deriving unit 14, based on the predicted arrival position of each mesh area M _i, precipitation core region CR _k where precipitation intensity R _i is the sum of comparable mesh area M _i between ground land of Derivation is performed on the surface G (step S208).

Next, the precipitation risk deriving unit 20 calculates the precipitation intensity R _i for each precipitation core region CR _k derived on the ground surface G by the precipitation core region deriving unit 14 and the predicted arrival time calculated by the advection prediction unit 18. Based on this, the risk _Pk of precipitation on the ground is derived (step S210). Next, the image generation unit 22 is based on the precipitation point information obtained by combining the classification result of the precipitation risk P _k for each precipitation core region CR _k derived by the precipitation risk deriving unit 20 and the map information of the ground surface G. Thus, an image is generated (step S212). Next, the output unit 24 outputs information indicating the image generated by the image generation unit 22 to a terminal device, a web server, or the like (step S214). Thereby, the process of this flowchart is complete | finished.

According to the weather prediction apparatus 100A in the second embodiment described above, the risk _Pk of precipitation on the ground is calculated based on the weather condition in the sky obtained by the weather radar apparatus 200, as in the first embodiment. By deriving, it is possible to accurately predict the degree of influence of precipitation on the ground.

(Third embodiment)
Hereinafter, the weather prediction apparatus 100B in 3rd Embodiment is demonstrated. In weather forecasting apparatus 100B in the third embodiment, the wind direction and wind speed measured by the wind direction and speed measurement device 300, differs from the first and second embodiments in that handled as wind direction and wind speed of precipitation core region CR _k . Therefore, it demonstrates centering on such a difference and the description about a common part is abbreviate | omitted.

FIG. 12 is a diagram illustrating an example of the configuration of the weather prediction device 100B according to the third embodiment. In the weather prediction device 100B according to the third embodiment, the wind direction and wind speed estimation unit 16 may be omitted. The wind direction and wind speed measuring device 300 includes, for example, a propeller and a vertical tail, and when the wind blows, the fuselage is rotated by the vertical tail and the propeller faces the windward. The wind direction and wind speed measuring device 300 measures the wind direction from the direction of the fuselage and the wind speed from the rotation speed of the propeller. The wind direction and wind speed measuring apparatus 300 transmits information indicating the measured wind direction and wind speed (wind direction and wind speed information) and information indicating the position where the wind is installed (position information) to the weather prediction apparatus 100B. For example, the wind direction and wind speed measuring device 300 is installed to be scattered in various places.

The communication interface 10 in the third embodiment communicates with a plurality of wind direction wind speed measurement devices 300 and receives wind direction wind speed information and position information from the plurality of wind direction wind speed measurement devices 300, respectively. For example, advection prediction unit 18 compares the installation position of the wind speed and direction measuring device 300, and a position in the horizontal direction of precipitation core region CR _k, the closest Anemometer Measurements in position in the horizontal direction of precipitation core region CR _k identify device 300, the measured wind direction and wind speed wind measuring apparatus 300 in this particular, each of the wind direction and wind velocity of the plurality of mesh areas M _i constituting the precipitation core region CR k or precipitation core region CR _k, And The advection prediction unit 18 uses the measured wind direction and wind speed Wind measuring device 300, performs advection prediction for each mesh area M _i.

In addition, the weather prediction apparatus 100B replaces with the information obtained from the wind direction wind speed measurement apparatus 300 mentioned above, from the weather observation apparatus (radiosonde) provided in flying objects, such as a balloon, atmospheric pressure, temperature, humidity, wind direction, wind speed, by acquiring the information of the altitude and the like, may be performed advection prediction for each mesh area M _i.

According to the weather prediction device 100B in the third embodiment described above, the risk of precipitation on the ground based on the weather conditions in the sky obtained by the weather radar device 200, as in the first and second embodiments. By deriving P _k , it is possible to accurately predict the degree of influence of precipitation on the ground.

(Fourth embodiment)
Hereinafter, the weather prediction apparatus 100C according to the fourth embodiment will be described. In the weather prediction device 100C according to the fourth embodiment, the first to third points are used in that the type of cloud particle is discriminated based on at least the temperature and the humidity measured by the temperature / humidity measuring device 400. It is different from the embodiment. Therefore, it demonstrates centering on such a difference and the description about a common part is abbreviate | omitted.

FIG. 13 is a diagram illustrating an example of the configuration of the weather prediction apparatus 100C according to the fourth embodiment. The weather prediction apparatus 100C according to the fourth embodiment has a communication interface 10, a mesh parameter calculation unit 12, a precipitation core region derivation unit 14, a wind direction and wind speed estimation unit 16, and an advection prediction unit, as in the above-described embodiment. 18, a precipitation risk deriving unit 20, an image generation unit 22, an output unit 24, and a storage unit 30, and further includes a particle discrimination unit 26. Further, in addition to the observation data 32 and the analysis data 34 for each core, the particle fall speed information 36 is further stored in the storage unit 30.

The temperature / humidity measuring device 400 is a device that is provided on a flying object such as a balloon and measures the temperature and humidity of the atmosphere around the flying object. Moreover, the temperature / humidity measuring apparatus 400 may be an apparatus that is installed on the ground and measures the temperature and humidity on the ground. In this case, the temperature / humidity measuring apparatus 400 may estimate the temperature and humidity of the altitude above the sky to be observed by the weather radar apparatus 200 based on the temperature and humidity measured on the ground. For example, the temperature / humidity measuring apparatus 400 corrects the temperature and humidity measured on the ground based on the difference between the installed altitude and the altitude of the sky that the weather radar apparatus 200 observes. Estimate temperature and humidity.

Also, the temperature / humidity measuring apparatus 400 may estimate the current temperature and humidity with reference to the temperature and humidity measured or estimated in the past. For example, when the observation season is April, the temperature / humidity measuring apparatus 400 derives the average temperature and average humidity for a predetermined period (for example, about 10 years) in the past April as the temperature and humidity of the present April. You can do it.

Then, the temperature / humidity measuring apparatus 400 transmits the measured or estimated temperature and humidity information (hereinafter referred to as temperature / humidity information) to the weather prediction apparatus 100C.

The communication interface 10 according to the fourth embodiment communicates with the weather radar device 200 and the temperature / humidity measurement device 400, receives the observation data 32 from the weather radar device 200, and receives the temperature / humidity information from the temperature / humidity measurement device 400. To do.

Particle determination unit 26, based on the communication interface 10 to the temperature and humidity information received from the temperature and humidity measuring unit 400 determines the type of particle cloud particles per mesh area M _i. For example, the particle discriminating unit 26 discriminates whether the cloud particles observed by the weather radar apparatus 200 are in a liquid phase or a solid phase according to the temperature and humidity in the sky indicated by the temperature / humidity information. That is, the particle discriminating unit 26 discriminates the phase of particles.

FIG. 14 is a diagram for explaining a method of determining the type of particles based on temperature and humidity. As illustrated, for example, when the temperature T is higher than the reference temperature Tx or when the humidity H is higher than the reference humidity Hx, the particle determination unit 26 determines that the particles are in the liquid phase. The reference temperature Tx is, for example, about 0 [° C.], and the reference humidity Hx is about 30 [%]. Moreover, the particle | grain discrimination | determination part 26 determines with particle | grains being a solid phase, when the temperature T is less than the reference temperature Tx and the humidity H is less than the reference humidity Hx. As a result, even if the precipitation intensity R _i is the same mesh area M _i , it is possible to determine whether the particles existing in the area are in a solid phase or a liquid phase. As a result, it is possible to distinguish whether the object falling on the ground is rain, snow, hail, or hail.

The advection prediction unit 18 in the fourth embodiment uses the particle fall velocity information 36 stored in the storage unit 30 based on the type of particles determined by the particle determination unit 26 in the process of performing advection prediction. Referring to, it derives the falling speed of each mesh area M _i.

FIG. 15 is a diagram illustrating an example of the particle fall speed information 36 according to the fourth embodiment. As shown in the figure, the drop speed information 36 for each particle associates the drop speed of the particle with the radar reflection factor Z _{i in} both the case where the particle is in the liquid phase and the case where the particle is in the solid phase. Information. In the figure, (a) represents the falling speed when the particles are in a liquid phase (raindrops), and (b) represents the falling speed when the particles are in a solid phase (for example, snow). The advection prediction unit 18 refers to the corresponding information based on the type of particle determined by the particle determination unit 26, and acquires the falling velocity corresponding to the radar reflection factor Z _i included in the observation data 32, thereby obtaining a mesh. It derives the falling speed of each region M _i. The drop speed indicated by the drop speed information 36 for each particle may be taken into consideration for air resistance according to the shape of the particle.

Then, the advection prediction unit 18 calculates a predicted arrival time using the derived fall speed for each mesh region M _i , and based on the calculated predicted arrival time, the precipitation core region CR _k is calculated on the ground. The predicted arrival position when arriving at is calculated. For example, the precipitation core region CR _k , in which most of the cloud particles are composed of raindrops, has a faster fall speed than the precipitation core region CR _k , which is composed of solid phase particles such as snow and hail, Estimated arrival time tends to be short. Thus, to determine the fall velocity for each mesh area M _i constituting the precipitation core region CR _k, it is possible to more accurately calculate the expected arrival time for each downcomer core region CR _k.

Moreover, even with the same radar reflectivity factor Z _i, when calculating the predicted arrival position, to alter the falling velocity in a liquid phase and a liquid phase, the position of a mesh area M _i at time of future advanced expected arrival time period τ The coordinates (x _τ , y _τ , z _τ ) can also be predicted with higher accuracy.

FIG. 16 is a flowchart illustrating an example of processing performed by the weather prediction apparatus 100C according to the fourth embodiment. The processing of this flowchart is repeatedly performed at a predetermined cycle, for example.

First, when the observation data is received by the communication interface 10 (step S300; Yes), the mesh parameter calculation unit 12 calculates the precipitation intensity R _i for each mesh region M _i of the observation data 32 (step S302). Next, the precipitation core region deriving unit 14 calculates the precipitation core region CR _{k obtained} by combining the mesh regions M _i having the same precipitation intensity R _i calculated by the mesh parameter calculation unit 12 and includes the three-dimensional cloud including the sky above. Derivation is performed in space (step S304).

Particle determination unit 26, based on the communication interface 10 to the temperature and humidity information received from the temperature and humidity measuring unit 400 determines the type of particle cloud particles per mesh area M _i (step S306).

Then, Wind estimation unit 16, based on the radar reflectivity factor _{Z i} and Doppler velocity _{D i} for each mesh area _{M i,} estimating the wind direction and wind speed for each mesh area _{M i} (step S308).

Next, the advection prediction unit 18 performs advection prediction based on the falling speed of each mesh region M _i of the precipitation core region CR _k derived by the precipitation core region deriving unit 14, and performs the advection prediction for each precipitation core region CR _k . A predicted arrival time and a predicted arrival position are calculated (step S310).

Next, the precipitation risk deriving unit 20 is based on the precipitation intensity R _i for each precipitation core region CR _k derived by the precipitation core region deriving unit 14 and the predicted arrival time calculated by the advection prediction unit 18. The risk _Pk of precipitation in is derived (step S312).

Then, the image generator 22, based on the precipitation point information in combination with the classification results of risk P _k of precipitation of each predicted rainfall regions S _k derived by the precipitation risk deriving unit 20, and the map information of the ground surface G Then, an image is generated (step S314).

Next, the output unit 24 outputs information indicating the image generated by the image generation unit 22 to a terminal device, a web server, or the like (step S316). Thereby, the process of this flowchart is complete | finished.

Also, weather forecasting apparatus 100C in the fourth embodiment, if the place to derive precipitation core region CR _k in the sky, to derive a precipitation core region CR _k at ground, i.e., the second embodiment described above When the same processing is performed, the following flowchart is followed.

FIG. 17 is a flowchart illustrating another example of the process performed by the weather prediction device 100C according to the modification of the fourth embodiment. The processing of this flowchart is repeatedly performed at a predetermined cycle, for example.

First, when the observation data is received by the communication interface 10 (step S400; Yes), the mesh parameter calculation unit 12 calculates the precipitation intensity R _i for each mesh region M _i of the observation data 32 (step S402).

Then, the particle determination unit 26, based on the communication interface 10 to the temperature and humidity information received from the temperature and humidity measuring unit 400 determines the type of particle cloud particles per mesh area M _i (step S404).

Then, Wind estimation unit 16, based on the radar reflectivity factor _{Z i} and Doppler velocity _{D i} for each mesh area _{M i,} estimating the wind direction and wind speed for each mesh area _{M i} (step S406).

Next, the advection prediction unit 18 performs advection prediction for each mesh region M _i in which the particle type is determined by the particle determination unit 26, and calculates a predicted arrival time and a predicted arrival position for each mesh region M _i ( Step S408). For example, advection prediction unit 18, by calculating the falling speed of each mesh area M _i for each type of particles of cloud particles, it is possible to accurately calculate the predicted arrival time and the predicted arrival position.

Then, the precipitation core region deriving unit 14, based on the predicted arrival position of each mesh area M _i, precipitation core region CR _k where precipitation intensity R _i is the sum of comparable mesh area M _i between ground land of Derivation is performed on the surface G (step S410).

Next, the precipitation risk deriving unit 20 calculates the precipitation intensity R _i for each precipitation core region CR _k derived on the ground surface G by the precipitation core region deriving unit 14 and the predicted arrival time calculated by the advection prediction unit 18. Based on this, the risk _Pk of precipitation on the ground is derived (step S412).

Next, the image generation unit 22 is based on the precipitation point information obtained by combining the classification result of the precipitation risk P _k for each precipitation core region CR _k derived by the precipitation risk deriving unit 20 and the map information of the ground surface G. Thus, an image is generated (step S414).

Next, the output unit 24 outputs information indicating the image generated by the image generation unit 22 to a terminal device, a web server, or the like (step S416). Thereby, the process of this flowchart is complete | finished.

According to the weather forecast apparatus 100C in the fourth embodiment described above, when deriving the downcomer core region CR _k in the sky, dropping each mesh area M _i constituting the precipitation core region CR _k based on the type of particle to determine the rate, it is possible to more accurately calculate the expected arrival time for each downcomer core region CR _k, the position coordinates (x _tau of a mesh area M _i in the prediction arrival time min advanced future time tau, y _tau , Z _τ ) can be predicted with higher accuracy. As a result, the degree of the influence of precipitation on the ground can be predicted with higher accuracy.

Further, according to the weather forecast apparatus 100C in the fourth embodiment described above, when deriving the downcomer core region CR _k in the ground, in the sky, the predicted arrival time and predicted for each mesh area M _i based on the type of particle In order to calculate the arrival position, the precipitation core region CR _k can be accurately derived on the ground. As a result, the degree of the influence of precipitation on the ground can be predicted with higher accuracy.

(Fifth embodiment)
Hereinafter, the weather prediction apparatus 100D in 5th Embodiment is demonstrated. The weather prediction device 100D in the fifth embodiment is different from the first to fourth embodiments in that the observation data observed by the dual polarization radar device 200A is acquired. Therefore, it demonstrates centering on such a difference and the description about a common part is abbreviate | omitted.

The dual-polarization radar device 200A transmits and receives two radio waves of horizontal polarization and vertical polarization. The dual-polarization radar apparatus 200A includes a radar reflection factor Z _h for horizontal polarization, a radar reflection factor Z _V for vertical polarization, a radar reflection factor difference Z _DR , an inter-polarization phase difference φ _DP , and a propagation phase difference change rate. Observation data including parameters such as K _DP and correlation coefficient ρ _hv between polarizations is acquired. Radar reflectivity factor difference Z _DR is, for example, a logarithm of a value obtained by dividing the radar reflectivity factor Z _h about horizontal polarization radar reflectivity factor Z _V about a vertical polarization, a parameter that depends on the ratio of the vertical and horizontal size of the particles is there. The dual-polarization radar apparatus 200A transmits observation data including various parameters related to the acquired dual-polarization (hereinafter referred to as dual-polarization observation data) to the weather prediction apparatus 100D.

FIG. 18 is a diagram illustrating an example of the configuration of the weather prediction device 100D according to the fifth embodiment. The communication interface 10 of the weather prediction apparatus 100D in the fifth embodiment receives the dual polarization observation data from the dual polarization radar apparatus 200A.

Particle determination unit 26 in the fifth embodiment, on the basis of the communication interface 10 to the dual polarization observation data received from the dual polarization radar apparatus 200A, the type of particle cloud particles per mesh area M _i Determine. For example, the particle discriminating unit 26 determines the degree of particle flatness, the particle size, and the like based on various parameters (particularly, the radar reflection factor difference _ZDR and the propagation phase difference change rate _KDP ) included in the dual polarization observation data. To determine the type of cloud particle. For example, when the particle is flat, the particle determination unit 26 determines the particle as raindrop, and when the particle size is larger than a reference diameter (for example, 5 [mm]) and is not flat, the particle Is identified as hail. Moreover, the particle | grain discrimination | determination part 26 discriminate | determines the said particle | grain as hail or snow, when a particle size is smaller than a reference | standard diameter (for example, 5 [mm]) and it is not flat. Moreover, the particle _| grain discrimination _| determination part 26 may discriminate _| determine also about the state (molten layer) which the solid phase and the liquid phase mixed based on the correlation coefficient (rho) _hv between polarization _| polarized- _lights . For example, the particle determination unit 26 may determine the sleet as a state where the solid phase and the liquid phase are mixed.

The advection prediction unit 18 in the fifth embodiment uses the particle fall velocity information 36 stored in the storage unit 30 based on the type of particle determined by the particle determination unit 26 in the process of performing advection prediction. Referring to, it derives the falling speed of each mesh area M _i.

FIG. 19 is a diagram illustrating an example of the particle fall speed information 36 according to the fifth embodiment. As shown in the figure, the particle fall velocity information 36 is information in which the particle fall velocity is associated with the radar reflection factor Z _i for each particle type. In the figure, (a) shows the falling speed when the particles are rain, (b) shows the falling speed when the particles are snow (or sleet), and (c) shows that the particles are hit. (D) represents the drop speed when the particles are hail. The advection prediction unit 18 refers to the corresponding information based on the type of particle determined by the particle determination unit 26, and acquires the falling velocity corresponding to the radar reflection factor Z _i included in the observation data 32, thereby obtaining a mesh. It derives the falling speed of each region M _i. Note that the drop speed indicated by the drop speed information 36 for each particle may be considered in advance for air resistance corresponding to the shape of the particle.

According to the weather prediction apparatus 100D in the fifth embodiment described above, when the precipitation core region CR _k is derived above the precipitation core region CR _k based on the type of particles as in the fourth embodiment described above. _{Since the} drop speed is obtained for each mesh region M _i constituting _k , the predicted arrival time for each precipitation core region CR _k can be calculated more accurately, and the mesh region at a future time τ advanced by the predicted arrival time M _i position coordinates _{(x τ, y τ, z} τ) can be further predicted accurately also. As a result, the degree of the influence of precipitation on the ground can be predicted with higher accuracy.

Further, according to the weather forecast apparatus 100D of the fifth embodiment described above, when deriving the downcomer core region CR _k at ground, as in the fourth embodiment described above, in the sky, on the basis of the type of particle Since the predicted arrival time and predicted arrival position for each mesh region M _i are calculated, the precipitation core region CR _k can be accurately derived on the ground. As a result, the degree of the influence of precipitation on the ground can be predicted with higher accuracy.

(Sixth embodiment)
Hereinafter, the weather prediction apparatus 100E in 6th Embodiment is demonstrated. The weather prediction apparatus 100E according to the sixth embodiment is based on double polarization observation data observed by the dual polarization radar apparatus 200A and temperature / humidity information measured by the temperature / humidity measurement apparatus 400. This is different from the first to fifth embodiments in that the type of the particles is discriminated. Therefore, it demonstrates centering on such a difference and the description about a common part is abbreviate | omitted.

FIG. 20 is a diagram illustrating an example of a configuration of a weather prediction device 100E according to the sixth embodiment. The communication interface 10 of the weather prediction apparatus 100E in the sixth embodiment receives the dual polarization observation data from the dual polarization radar apparatus 200A. The communication interface 10 of the weather prediction device 100E receives temperature / humidity information from the temperature / humidity measurement device 400.

Sixth particle determination unit 26 in the embodiment of, based on the dual polarization observation data and temperature and humidity information, determines the type of particle cloud particles per mesh area M _i. Thereby, the kind of particle | grain can be discriminate | determined still more accurately than embodiment mentioned above.

According to at least one embodiment described above, the risk of precipitation on the ground is derived by deriving the risk _Pk of precipitation on the ground based on the weather conditions in the sky obtained by the weather radar apparatus 200. Predict with high accuracy.

Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 10 ... Communication interface, 12 ... Mesh parameter calculation part, 14 ... Precipitation core area | region derivation part, 16 ... Wind direction wind speed estimation part, 18 ... Advection prediction part, 20 ... Precipitation risk derivation part, 22 ... Image generation part, 24 ... Output part , 26 ... Particle prediction unit, 30 ... Storage unit, 100 ... Weather prediction device, 200 ... Weather radar device, 200A ... Dual polarization radar device, 300 ... Wind direction and wind speed measurement device, 400 ... Temperature and humidity measurement device

Claims (12)

1. A precipitation risk deriving unit for deriving the risk of precipitation on the ground based on the weather conditions in the sky obtained by the radar device;
   An output unit for outputting information based on the risk of precipitation derived by the precipitation risk deriving unit;
   A weather prediction apparatus comprising:
2. Receive information from the radar apparatus that associates information on the intensity of radio waves with information indicating the wind direction and wind speed in the sky for each of a plurality of mesh regions virtually dividing the above three-dimensional space. Interface,
   Based on information received by the interface, a mesh parameter calculation unit that calculates precipitation intensity for each of the plurality of mesh regions;
   A precipitation core region deriving unit for deriving a precipitation core region obtained by combining the mesh regions having the same value indicating the precipitation intensity calculated by the mesh parameter calculation unit;
   For each precipitation core region derived by the precipitation core region deriving unit, based on the wind direction and wind speed received by the interface, the time until the precipitation core region reaches the ground, and the precipitation core region An advection prediction unit that predicts a position reaching the ground,
   The precipitation risk deriving unit is based on the time predicted by the advection prediction unit and the precipitation intensity for each mesh region included in the precipitation core region derived by the precipitation core region deriving unit. Deriving the risk of precipitation at the ground position predicted by the advection prediction unit,
   The weather prediction apparatus according to claim 1.
3. When the position predicted by the advection prediction unit overlaps, the precipitation risk deriving unit derives a risk of precipitation on the ground according to the precipitation intensity corresponding to each of the precipitation core regions where the position overlaps.
   The weather prediction apparatus according to claim 2.
4. The precipitation risk deriving unit weights the time predicted by the advection prediction unit according to the altitude of the position predicted by the advection prediction unit, and calculates a risk of precipitation in the ground precipitation core region. To derive,
   The weather prediction apparatus according to claim 2 or 3.
5. Receive information from the radar apparatus that associates information on the intensity of radio waves with information indicating the wind direction and wind speed in the sky for each of a plurality of mesh regions virtually dividing the above three-dimensional space. Interface,
   Based on information received by the interface, a mesh parameter calculation unit that calculates precipitation intensity for each of the plurality of mesh regions;
   Time until the mesh area reaches the ground based on the precipitation intensity calculated by the mesh parameter calculation unit and the wind direction and wind speed associated with each mesh area for each mesh area An advection prediction unit that predicts a position where the mesh region reaches the ground;
   A precipitation core region deriving unit for deriving a precipitation core region combining the mesh regions having the same value indicating the precipitation intensity on the ground based on the position predicted by the advection prediction unit; Prepared,
   The precipitation risk deriving unit is based on the time predicted by the advection prediction unit and the precipitation intensity for each mesh region included in the precipitation core region derived by the precipitation core region deriving unit. Deriving the risk of precipitation in the ground precipitation core area,
   The weather prediction apparatus according to claim 1.
6. When the position predicted by the advection prediction unit overlaps, the precipitation risk deriving unit derives a risk of precipitation on the ground according to the precipitation intensity corresponding to each mesh region where the position overlaps.
   The weather prediction apparatus according to claim 5.
7. An image based on the information indicating the risk of precipitation on the ground derived by the precipitation risk deriving unit, the map information on the ground, and the precipitation intensity for each precipitation core region derived by the precipitation core region deriving unit An image generation unit that generates one or both of the image based on the information;
   The output unit outputs information indicating the image generated by the image generation unit;
   The weather prediction apparatus according to any one of claims 2 to 6.
8. The interface unit further receives information on the temperature of the sky from an external device,
   Based on the information on the temperature of the sky received by the interface and the information on the intensity of the radio wave, further includes a determination unit that determines the type of particles of the cloud particles in the sky,
   The advection prediction unit, for each precipitation core region, based on the wind direction and wind speed and the type of particle determined by the determination unit, the time until the precipitation core region reaches the ground, the precipitation Predict where the core area will reach the ground,
   The weather prediction apparatus according to any one of claims 2 to 7.
9. The interface unit further receives information on the humidity of the sky from the external device,
   The determining unit determines the type of particles of the cloud particles in the sky based on the information on the temperature and humidity in the sky received by the interface and the information on the intensity of the radio wave.
   The weather prediction apparatus according to claim 8.
10. The interface unit further receives information including a dual polarization parameter from the radar device,
    Based on the dual polarization parameter included in the information received by the interface, further comprising a determination unit for determining the type of cloud particles above the cloud,
    The advection prediction unit, for each precipitation core region, based on the wind direction and wind speed and the type of particle determined by the determination unit, the time until the precipitation core region reaches the ground, the precipitation Predict where the core area will reach the ground,
    The weather prediction apparatus according to any one of claims 2 to 9.
11. Computer
    Based on the weather conditions in the sky obtained by the radar device, the risk of precipitation on the ground is derived,
    Outputting information based on the derived risk of precipitation;
    Weather forecast method.
12. On the computer,
    Based on the weather conditions of the sky obtained by the radar device, the risk of precipitation on the ground is derived,
    Outputting information based on the derived risk of precipitation;
    Weather forecast program.

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