WO2017051647A1 - Precipitation particle determination device, weather radar device, precipitation particle determination method, and precipitation particle determination program - Google Patents

Precipitation particle determination device, weather radar device, precipitation particle determination method, and precipitation particle determination program Download PDF

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Publication number
WO2017051647A1
WO2017051647A1 PCT/JP2016/074328 JP2016074328W WO2017051647A1 WO 2017051647 A1 WO2017051647 A1 WO 2017051647A1 JP 2016074328 W JP2016074328 W JP 2016074328W WO 2017051647 A1 WO2017051647 A1 WO 2017051647A1
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Prior art keywords
precipitation
precipitation particle
target area
point
feature value
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PCT/JP2016/074328
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French (fr)
Japanese (ja)
Inventor
大石　哲
真理子 早野
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国立大学法人神戸大学
古野電気株式会社
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Priority to JP2017541483A priority Critical patent/JPWO2017051647A1/en
Publication of WO2017051647A1 publication Critical patent/WO2017051647A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/14—Rainfall or precipitation gauges
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present invention includes a precipitation particle determination device, a precipitation particle determination method, a precipitation particle determination program, and a precipitation particle determination device that determine whether the precipitation particles to be determined are rain, snow, hail, or the like. It relates to weather radar equipment.
  • Non-Patent Document 1 a technique for determining which precipitation particle type (rain, snow, hail, hail, etc.) the precipitation particles to be determined are.
  • a plurality of types of polarization parameters horizontal polarization reflection intensity Z hh , reflection factor difference Z dr , inter-polarization phase difference change rate K dp, etc.
  • a membership function is prepared in advance for each combination of the polarization parameter type and the precipitation particle type.
  • the membership function is a function indicating the relationship between the value of the polarization parameter of the precipitation particles to be discriminated and the degree to which the precipitation particles to be discriminated belong to the precipitation particle type of the membership function.
  • the evaluation value Rs for each precipitation particle type is obtained from the calculated polarization parameter and the membership function, and the precipitation particle type having the largest evaluation value Rs is used as the discrimination result.
  • the present invention is for solving the above-mentioned problems, and the object thereof is to further improve the accuracy of the result of precipitation particle discrimination.
  • a precipitation particle discriminating apparatus is based on a plurality of point-by-point polarization parameters obtained from a plurality of points included in a target area.
  • a discrimination process comprising: a feature value calculation unit that calculates a feature value indicating a feature of a particle; and a precipitation particle discrimination unit that discriminates a precipitation particle type in the target area based on the feature value calculated by the feature value calculation unit. And a section.
  • the precipitation particle determination unit determines the precipitation particle type based on a comparison result between the feature value and a threshold value.
  • the discrimination processing unit discriminates the precipitation particle type using fuzzy inference.
  • the feature value calculation unit calculates a plurality of types of the feature values
  • the discrimination processing unit generates a member generated for each combination of the feature value type and the precipitation particle type.
  • a membership function storage unit that stores a ship function
  • the precipitation particle determination unit includes the feature value calculated by the feature value calculation unit and a plurality of the feature functions stored in the membership function storage unit. The type of precipitation particles is determined based on a membership function.
  • the discrimination processing unit determines whether the precipitation particles to be discriminated from each of the membership functions generated for each combination of the certain precipitation particle type and the feature value type.
  • An index indicating the possibility that the precipitation particles to be identified are a certain type of precipitation particles by combining a plurality of the attribution levels and calculating the degree of attribution belonging to the precipitation particle type
  • an evaluation value calculation unit for calculating an evaluation value as a value, wherein the precipitation particle determination unit determines the precipitation particle type based on the evaluation value calculated for each precipitation particle type.
  • the feature value calculation unit calculates the feature value corresponding to each of the target areas whose positions are different from each other, and the precipitation particle determination unit calculates each of the targets based on each of the feature values. The type of precipitation particles in the area is determined.
  • the feature value is a value based on at least one of an average value and a dispersion value of the polarization parameter for each point.
  • a weather radar apparatus includes a transmission / reception unit capable of transmitting / receiving horizontal polarization and vertical polarization, and the horizontal wave received by the transmission / reception unit. Based on the reflected wave of polarization and the reflected wave of vertical polarization, a parameter calculation unit that calculates a plurality of point-by-point polarization parameters from a plurality of points included in the target area, and calculated by the parameter calculation unit.
  • a parameter calculation unit that calculates a plurality of point-by-point polarization parameters from a plurality of points included in the target area, and calculated by the parameter calculation unit
  • One of the above-described precipitation particle discriminating devices that discriminates the type of precipitation particles in the target area based on the plurality of polarization parameters for each point.
  • a precipitation particle discrimination method is based on a plurality of point-by-point polarization parameters obtained from a plurality of points included in a target area.
  • a precipitation particle determination program is based on a plurality of point-by-point polarization parameters obtained from a plurality of points included in a target area. Causing the computer to execute a step of calculating a feature value indicating a feature of the particle, and a step of determining the type of precipitation particles in the target area based on the feature value calculated in the step of calculating the feature value.
  • the accuracy of the precipitation particle discrimination result can be further increased.
  • FIG. 1 is a block diagram of a weather radar device according to an embodiment of the present invention. It is a top view which shows typically the observation area
  • determination part It is a flowchart for demonstrating the discrimination
  • the present invention can be widely applied to a precipitation particle discriminating device that discriminates the type of precipitation particles, a weather radar device equipped with the precipitation particle discrimination device, a precipitation particle discrimination method, and a precipitation particle discrimination program.
  • FIG. 1 is a block diagram of a weather radar apparatus 1 according to an embodiment of the present invention.
  • the weather radar device 1 according to the present embodiment is a so-called X-band radar, and is configured to transmit and receive radio waves in the X-band (8 to 12 GHz).
  • the spatial resolution and temporal resolution of the observed precipitation particles can be improved as compared with the C-band radar and S-band radar that have a large number of achievements.
  • the weather radar apparatus 1 is a so-called dual polarization weather radar, and is configured to transmit and receive both horizontal polarization and vertical polarization. Thereby, the weather radar apparatus 1 can calculate a plurality of types of polarization parameters (horizontal polarization reflection intensity Zhh , reflection factor difference Zdr , polarization phase difference change rate Kdp, etc.), and based on these parameters. Thus, the calculation of precipitation intensity and the determination of the precipitation particle type as described above can be performed.
  • polarization parameters horizontal polarization reflection intensity Zhh , reflection factor difference Zdr , polarization phase difference change rate Kdp, etc.
  • the weather radar device 1 includes an antenna 2 (wave transmission / reception unit), a transmission / reception unit 3, a signal processing unit 4, a display unit 5, and an external output unit 6.
  • the antenna 2 is a radar antenna capable of transmitting and receiving highly directional radio waves, and can transmit both horizontally polarized waves and vertically polarized waves and can receive these reflected waves.
  • the antenna 2 is configured to be mechanically rotatable, whereby the observation region Z can be scanned with a transmission wave and the reflected wave can be received as a reception wave.
  • the antenna 2 repeatedly transmits and receives transmission waves and reception waves while changing the direction (for example, azimuth and elevation angle) in which transmission waves and reception waves are transmitted and received.
  • the transmission / reception unit 3 includes a circulator 10, a transmission control unit 11, a transmission signal generation unit 12, an amplifier 13, a reception unit 14, an AD conversion unit 15, a pulse compression unit 16, and an unnecessary signal removal unit 17.
  • the circulator 10 is configured to output the transmission signal output from the amplifier 13 to the antenna 2.
  • the circulator 10 is configured to output a reception signal obtained from the reception wave received by the antenna 2 to the reception unit 14.
  • the transmission control unit 11 controls the timing at which the transmission signal generated by the transmission signal generation unit 12 is transmitted.
  • the transmission signal generator 12 generates a transmission signal that is the basis of the transmission wave transmitted from the antenna 2.
  • the transmission signal generated by the transmission signal generator 12 is amplified by the amplifier 13 and then output to the antenna 2 via the circulator 10.
  • the reception signal obtained from the reception wave received by the antenna 2 is output to the reception unit 14 via the circulator 10 and then converted into a digital signal by the AD conversion unit 15.
  • the received signal converted into the digital signal is pulse-compressed by the pulse compression unit 16, and unnecessary signals such as noise are removed by the unnecessary signal removal unit 17, and then output to the signal processing unit 4.
  • the signal processing unit 4 is configured to process the reception signal output from the transmission / reception unit 3 to calculate the precipitation intensity and determine the type of precipitation particles at each point P n in the observation region Z. As shown in FIG. 1, the signal processing unit 4 includes a parameter calculation unit 20, a Doppler velocity calculation unit 21, a precipitation intensity calculation unit 22, a precipitation particle discrimination device 19, and an image generation unit 24.
  • the signal processing unit 4 includes, for example, a processor (CPU, FPGA, etc.) not shown and a device such as a memory.
  • the CPU reads the program from the memory and executes it, so that the signal processing unit 4 becomes the parameter calculation unit 20, the Doppler velocity calculation unit 21, the precipitation intensity calculation unit 22, the precipitation particle determination device 19, and the image generation unit 24. Can function.
  • the above program includes a precipitation particle discrimination program.
  • the precipitation particle discrimination program is a program for causing the precipitation particle discrimination device 19 to execute the precipitation particle discrimination method according to the embodiment of the present invention.
  • the program is distributed in a state stored in a recording medium.
  • FIG. 2 is a plan view schematically showing an observation region Z that is a region in which the type of precipitation particles can be identified by the weather radar device 1 according to the present embodiment.
  • the observation area Z is divided into a mesh shape so that one divided section becomes a small area Z n of 100 m square.
  • a plurality of types of polarization parameters are calculated based on a received signal obtained from a reflected wave from n . These include plural types of polarization parameters, for example, horizontally polarized reflection intensity Z hh, the differential reflectivity factor Z dr, polarizations phase difference change ratio K dp, include a correlation coefficient [rho hv like.
  • the parameter calculation unit 20 for each point P n, and calculates horizontal polarization reflection intensity Z hh, the differential reflectivity factor Z dr, polarizations phase difference change ratio K dp, the polarization parameters, such as correlation coefficients [rho hv .
  • the description is abbreviate
  • the Doppler velocity calculation unit 21 detects the difference between the frequency of the transmitted wave and the frequency of the reflected wave (received wave), and calculates the Doppler velocity of the precipitation particles based on the difference. Thereby, the Doppler velocity calculation unit 21 calculates the movement velocity of the precipitation particles in the direction connecting the antenna 2 and the precipitation particles.
  • the precipitation intensity calculation unit 22 calculates the precipitation intensity at each point P n included in the observation region Z based on each polarization parameter calculated by the parameter calculation unit 20. Since the technique for calculating the precipitation intensity at each point Pn based on the polarization parameter is known, the description thereof is omitted.
  • FIG. 3 is a block diagram showing in detail the configuration of the feature value calculation unit 30 and the discrimination processing unit 23 of the precipitation particle discrimination device 19.
  • FIG. 4 is a schematic diagram showing the relationship between the observation area Z and the target area Za for which the feature value is calculated by the feature value calculation unit 30.
  • the target area Za is divided into nine small regions Za m.
  • the number of small areas included in the target area Za is not limited to this, and may be other numbers.
  • Feature value calculating section 30 based on the polarization parameters obtained from each point Pa m in the plurality of small regions Za m included in the target area Za (point each polarization parameters), precipitation particles in the target area Za.
  • the feature value indicating the feature (the feature value will be described in detail below) is calculated.
  • the target area Za moves little by little in the observation region Z as indicated by the straight arrows in FIG. Specifically, the target area Za moves little by little in the observation region Z such that the central point Pa 5 scans all the points P 1 to P N included in the observation region Z.
  • the feature value calculation unit 30 calculates a feature value for each of the target areas Za having different positions.
  • the feature value calculation unit 30 includes a ⁇ hv variance value calculation unit 31, a ⁇ hv average value calculation unit 32, a Z dr variance value calculation unit 33, a Z dr average value calculation unit 34, A K dp dispersion value calculation unit 35, a K dp average value calculation unit 36, and a Zhh average value calculation unit 37 are included.
  • the ⁇ hv variance value calculation unit 31 uses the ⁇ hv variance value Var ( ⁇ hv ), which is a variance value of the correlation coefficients ⁇ hv obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
  • the ⁇ hv average value calculation unit 32 calculates ⁇ hv average value Avg ( ⁇ hv ), which is an average value of the correlation coefficients ⁇ hv obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
  • the Z dr variance value calculation unit 33 uses a Z dr variance value Var (Z dr ), which is a variance value of the reflection factor differences Z dr obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
  • the Z dr average value calculation unit 34 calculates a Z dr average value Avg (Z dr ), which is an average value of the reflection factor differences Z dr obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
  • the K dp dispersion value calculation unit 35 calculates a K dp dispersion value Var (K dp ), which is a dispersion value of the inter- polarization phase difference change rate K dp obtained from the points Pa 1 to Pa M in the target area Za. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of the target area Za.
  • the K dp average value calculation unit 36 calculates a K dp average value Avg (K dp ) that is an average value of the inter- polarization phase difference change rates K dp obtained from the points Pa 1 to Pa M in the target area Za. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of the target area Za.
  • the Zhh average value calculator 37 calculates the Zhh average value Avg ( Zhh ), which is the average value of the horizontally polarized wave reflection intensities Zhh obtained from the points Pa 1 to Pa M in the target area Za, It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of the target area Za.
  • the discrimination processing unit 23 has a precipitation particle discrimination unit 25.
  • FIG. 5 is a flowchart for explaining the precipitation particle determination operation performed by the precipitation particle determination unit 25.
  • the precipitation particle discriminating unit 25 compares each feature value calculated by the feature value calculation unit 30 with a predetermined threshold value, and discriminates the type of precipitation particle at each point P n based on the comparison result.
  • a method for determining the type of precipitation particles will be described with reference to the flowchart shown in FIG.
  • step S1 the ⁇ hv variance value Var ( ⁇ hv ) and the threshold value Thr_Var ( ⁇ hv ) of the variance value are compared, and the ⁇ hv average value Avg ( ⁇ hv ) and the threshold value Thr_Avg ( ⁇ hv ). If the ⁇ hv variance value Var ( ⁇ hv ) is larger than the threshold Thr_Var ( ⁇ hv ) and the ⁇ hv average value Avg ( ⁇ hv ) is smaller than the threshold Thr_Avg ( ⁇ hv ) (Yes in step S1), step Proceed to S2. On the other hand, when the condition of step S1 is not satisfied (No in step S1), the type of precipitation particles at the point Pn is determined to be rain (step S5).
  • step S2 the Z dr variance value Var (Z dr ) is compared with the threshold value Thr_Var (Z dr ) of the variance value, and the Z dr average value Avg (Z dr ) and the threshold value Thr_Avg ( Z dr ) is compared.
  • step Proceed to S3 step Proceed to S3.
  • step S6 the type of precipitation particles at that point Pn is determined to be snow (step S6).
  • step S3 the K dp variance value Var (K dp ) and the threshold value Thr_Var (K dp ) of the variance value are compared, and the K dp average value Avg (K dp ) and the threshold value Thr_Avg ( Kdp ) is compared.
  • step Proceed to S4 step Proceed to S4.
  • step S6 the type of precipitation particles at the point Pn is determined to be snow (step S6).
  • step S4 the threshold Thr_Avg (Z hh) of Z hh average Avg (Z hh) with the average value are compared.
  • Z hh average Avg (Z hh) is greater than the threshold value Thr_Avg (Z hh) (Yes in step S4), and precipitation particles type at that point it is determined to hail (step S8).
  • the condition of step S4 is not satisfied (No in step S4), it is determined that the precipitation particle type at that point Pn is hail (step S7).
  • the image generation unit 24 generates a distribution image of the movement speed of the precipitation particles in the observation region Z based on the movement speed of the precipitation particles at each point P n calculated by the Doppler velocity calculation unit 21. In addition, the image generation unit 24 generates a distribution image of the precipitation intensity in the observation region Z based on the precipitation intensity at each point Pn calculated by the precipitation intensity calculation unit 22. In addition, the image generation unit 24 generates a distribution image of precipitation particles in the observation region Z based on the precipitation particle type at each point Pn determined by the determination processing unit 23.
  • the display unit 5 displays the distribution image of the moving speed of the precipitation particles, the distribution image of the precipitation intensity, and the distribution image of the precipitation particle type generated by the image generation unit 24. Specifically, for example, these distribution images may be displayed on the display unit 5 at the same time, or any one of these distribution images may be displayed in accordance with user switching.
  • FIG. 6 is a diagram illustrating an example of a display screen displayed on the display unit 5, and is a distribution image of precipitation particle types in the observation region Z.
  • a point determined to be rain is represented by being painted
  • a point determined to be snow is represented by dot hatching. .
  • the external output unit 6 includes various polarization parameters calculated by the parameter calculation unit 20, the moving speed of precipitation particles calculated by the Doppler velocity calculation unit 21, the precipitation intensity at each point calculated by the precipitation intensity calculation unit 22, And an interface for outputting the precipitation particle type at each point determined by the precipitation particle determination device 19 to an external device.
  • An example of the external output unit 6 is an interface connector.
  • a feature value is calculated, and a precipitation particle type is determined based on the feature value.
  • the information is statistically integration obtained from each point Pa m range (target area Za) having a certain extent, for use in determination of the precipitation particles classification as a feature value, the conventional case (point The accuracy of the precipitation particle discrimination result can be improved more than the case where the precipitation particle type for each point is discriminated based on the polarization parameter calculated every time.
  • the type of precipitation particles is determined based on the feature value.
  • the range information is statistically integration obtained from each point Pa m of (target area Za) having a certain expanse, employed in determination of the precipitation particles type as the feature value.
  • the precipitation particle discriminating apparatus 19 it is possible to improve the accuracy of the precipitation particle discrimination result.
  • the precipitation particle discriminating apparatus 19 since the precipitation particle type is discriminated based on the comparison result between each feature value and the threshold value, the precipitation particle type can be discriminated relatively easily.
  • the dispersion value and the average value of the polarization parameter are used as the characteristic values. This makes it possible to appropriately determine the type of precipitation particles.
  • the meteorological radar apparatus 1 it is possible to provide a meteorological radar apparatus equipped with a precipitation particle determining apparatus that can accurately determine precipitation particles.
  • FIG. 7 is a block diagram showing a configuration of a precipitation particle discrimination device 19a of a weather radar device according to a modification.
  • the meteorological radar apparatus according to this modification is different in the configuration of the precipitation particle discriminating apparatus from the meteorological radar apparatus 1 according to the embodiment described above.
  • grain discrimination apparatus 19a are demonstrated, and description is abbreviate
  • the precipitation particle discriminating apparatus 19a discriminates the precipitation particle type using so-called fuzzy reasoning.
  • the precipitation particle discriminating apparatus 19a has a feature value calculation unit 30a and a discrimination processing unit 23a.
  • the discrimination processing unit 23a includes a membership function storage unit 26, an attribution degree calculation unit 27, an evaluation value calculation unit 28, and a precipitation particle discrimination unit 25a.
  • the feature value calculation unit 30a has a configuration in which the K dp variance value calculation unit 35, the K dp average value calculation unit 36, and the Zhh average value calculation unit 37 are omitted as compared to the feature value calculation unit 30 of the above embodiment. It has become. That is, the feature value calculation unit 30a of the present modification includes a ⁇ hv variance value calculation unit 31, a ⁇ hv average value calculation unit 32, a Z dr variance value calculation unit 33, and a Z dr average value calculation unit 34. is doing.
  • the configurations of the ⁇ hv variance value calculation unit 31, the ⁇ hv average value calculation unit 32, the Z dr variance value calculation unit 33, and the Z dr average value calculation unit 34 are the same as those in the above-described embodiment. Omitted.
  • FIG. 8 is a graph showing a part of the membership function MBF a_b stored in the membership function storage unit 26, and (A) to (D) show that precipitation particles to be discriminated belong to rain.
  • Graphs showing the degrees, (E) to (H) are graphs showing the degree to which the precipitation particles to be identified belong to the kites.
  • the horizontal axis is the Z dr dispersion value Var (Z dr ), and in (B) and (F), the horizontal axis is the Z dr average value Avg (Z dr ), and (C ) And (G), the horizontal axis is ⁇ hv dispersion value Var ( ⁇ hv ), and in (D) and (H), the horizontal axis is ⁇ hv average value Avg ( ⁇ hv ).
  • the membership function storage unit 26 stores membership functions related to rain and hail. However, the present invention is not limited to this, and members related to other precipitation particles (for example, snow, hail, etc.). A ship function may be stored.
  • the horizontal axis of the membership function indicates the Z dr variance value Var (Z dr ), the Z dr average value Avg (Z dr ), the ⁇ hv variance value Var ( ⁇ hv ), and the ⁇ hv average value Avg ( ⁇
  • the horizontal axis other values (e.g., horizontal polarization reflection intensity Z hh, the differential reflectivity factor Z dr, polarization phase difference change ratio K dp, K dp dispersion Value Var (K dp ), K dp average value Avg (K dp ), Zhh average value Avg (Z hh ), etc.).
  • the membership function storage unit 26 stores the above-described membership function MBF a_b .
  • a is an integer of 1 or more, and each integer here corresponds to each polarization parameter or each feature value.
  • 1 is a Z dr dispersion value
  • 2 is a Z dr average value
  • 3 is a ⁇ hv dispersion value
  • 4 is a ⁇ hv average value.
  • Each integer corresponds to each polarization parameter or each feature value.
  • b in the membership function MBF a_b is an integer of 1 or more, and each integer here corresponds to a precipitation particle type.
  • each integer corresponds to each precipitation particle type, such as 1 for rain, 2 for hail.
  • FIG. 9 is a diagram schematically showing a process in which precipitation particles are discriminated by the precipitation particle discrimination device 19a of the weather radar apparatus according to this modification.
  • the evaluation value calculation unit 28 based on the degree of attribution calculated by the degree of attribution calculation unit 27, evaluates the precipitation particle type Rs b (b is an integer) at each point, and each integer here is the type of precipitation particle Corresponding). The larger the evaluation value, the higher the possibility that the precipitation particles at that point are the type of precipitation particles for which the evaluation value was calculated. With reference to FIG. 9, the evaluation value calculation unit 28 calculates the evaluation value Rs b corresponding to each precipitation particle type by adding the degree of attribution for each precipitation particle type. Specifically, for example, referring to FIG.
  • the evaluation value calculation unit 28 adds the belonging degrees Ps 1_1 , Ps 2_1 , Ps 3_1 , and Ps 4_1 to which the precipitation particles to be identified belong to rain, The rain evaluation value Rs 1 is calculated. Similarly, the evaluation value calculation unit 28 adds the degree of attribution Ps 1_2 , Ps 2_2 , Ps 3_2 , and Ps 4_2 to which the precipitation particles to be discriminated belong to cocoon to calculate the evaluation value Rs 2 of cocoon .
  • the precipitation particle discriminating unit 25a discriminates that the precipitation particle type corresponding to the evaluation value having the largest value among the evaluation values calculated by the evaluation value calculation unit 28 is precipitation particles at the point.
  • the precipitation particle discriminating apparatus 19a discriminates the precipitation particle type using fuzzy reasoning. As a result, since the final precipitation particle type can be determined by comprehensively determining various inference results, the precipitation particle type can be determined with higher accuracy.
  • the precipitation particle type is discriminated based on each feature value calculated by the feature value calculation unit 30a and the membership function MBF a_b.
  • the type of precipitation particles can be determined.
  • the precipitation particles are discriminated based on the evaluation value calculated for each precipitation particle type, so that the precipitation particle type can be more appropriately discriminated.
  • the method using a threshold and the method using fuzzy inference have been described as specific methods for determining the type of precipitation particles. Any method may be used as long as it is a method for discriminating the type of precipitation particles based on feature values obtained from a plurality of point-by-point polarization parameters obtained from a plurality of points included in.

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Abstract

In order to increase the accuracy of precipitation particle determination results, a precipitation particle determination device 19 comprises: a characteristics value calculation unit 30 that calculates characteristics values indicating the characteristics of precipitation particles within a target area, from a plurality of location-specific polarization parameters obtained from a plurality of locations included inside the target area; and a determination processing unit 23 having a precipitation particle determination unit that determines the type of precipitation particles within the target area, on the basis of the characteristics values calculated by the characteristics value calculation unit 30.

Description

降水粒子判別装置、気象レーダー装置、降水粒子判別方法、及び降水粒子判別プログラムPrecipitation particle discrimination device, weather radar device, precipitation particle discrimination method, and precipitation particle discrimination program
　本発明は、判別対象となる降水粒子が雨、雪、あられ等のいずれの種別であるかを判別する降水粒子判別装置、降水粒子判別方法、降水粒子判別プログラム、及び降水粒子判別装置を備えた気象レーダー装置に関する。 The present invention includes a precipitation particle determination device, a precipitation particle determination method, a precipitation particle determination program, and a precipitation particle determination device that determine whether the precipitation particles to be determined are rain, snow, hail, or the like. It relates to weather radar equipment.
　判別対象となる降水粒子がいずれの降水粒子種別（雨、雪、あられ、雹等）であるかを判別するための手法として、例えば非特許文献１に開示される手法が知られている。この非特許文献１で開示される手法では、地点毎に、複数種類の偏波パラメータ（水平偏波反射強度Ｚｈｈ、反射因子差Ｚｄｒ、偏波間位相差変化率Ｋｄｐ等）が算出される。そして、この手法では、偏波パラメータの種類と降水粒子種別との組み合わせ毎に、メンバシップ関数が予め準備されている。メンバシップ関数とは、判別対象となる降水粒子の偏波パラメータの値と、判別対象となる降水粒子がそのメンバシップ関数の降水粒子種別に帰属する度合と、の関係を示す関数である。そして、従来の手法では、算出された偏波パラメータ及び上記メンバシップ関数から降水粒子種別毎の評価値Ｒｓが求められ、最も大きい評価値Ｒｓを有する降水粒子種別が判別結果とされていた。 For example, a technique disclosed in Non-Patent Document 1 is known as a technique for determining which precipitation particle type (rain, snow, hail, hail, etc.) the precipitation particles to be determined are. In the method disclosed in Non-Patent Document 1, a plurality of types of polarization parameters (horizontal polarization reflection intensity Z hh , reflection factor difference Z dr , inter-polarization phase difference change rate K dp, etc.) are calculated for each point. The In this method, a membership function is prepared in advance for each combination of the polarization parameter type and the precipitation particle type. The membership function is a function indicating the relationship between the value of the polarization parameter of the precipitation particles to be discriminated and the degree to which the precipitation particles to be discriminated belong to the precipitation particle type of the membership function. In the conventional method, the evaluation value Rs for each precipitation particle type is obtained from the calculated polarization parameter and the membership function, and the precipitation particle type having the largest evaluation value Rs is used as the discrimination result.
　ところで、上述した手法の場合、降水粒子の判別結果の精度が不十分となる場合がある。 By the way, in the case of the above-described method, the accuracy of the precipitation particle discrimination result may be insufficient.
　本発明は、上記課題を解決するためのものであり、その目的は、降水粒子の判別結果の精度をより高めることである。 The present invention is for solving the above-mentioned problems, and the object thereof is to further improve the accuracy of the result of precipitation particle discrimination.
　（１）上記課題を解決するため、本発明のある局面に係る降水粒子判別装置は、対象エリア内に含まれる複数の地点から得られる複数の地点毎偏波パラメータから、前記対象エリア内の降水粒子の特徴を示す特徴値を算出する特徴値算出部と、前記特徴値算出部で算出された前記特徴値に基づき前記対象エリア内の降水粒子種別を判別する降水粒子判別部、を有する判別処理部と、を備えている。 (1) In order to solve the above-described problem, a precipitation particle discriminating apparatus according to an aspect of the present invention is based on a plurality of point-by-point polarization parameters obtained from a plurality of points included in a target area. A discrimination process comprising: a feature value calculation unit that calculates a feature value indicating a feature of a particle; and a precipitation particle discrimination unit that discriminates a precipitation particle type in the target area based on the feature value calculated by the feature value calculation unit. And a section.
　（２）好ましくは、前記降水粒子判別部は、前記特徴値と閾値との比較結果に基づき、前記降水粒子種別を判別する。 (2) Preferably, the precipitation particle determination unit determines the precipitation particle type based on a comparison result between the feature value and a threshold value.
　（３）好ましくは、前記判別処理部は、ファジィ推論を用いて前記降水粒子種別を判別する。 (3) Preferably, the discrimination processing unit discriminates the precipitation particle type using fuzzy inference.
　（４）更に好ましくは、前記特徴値算出部は、複数種類の前記特徴値を算出し、前記判別処理部は、前記特徴値の種類と前記降水粒子の種別との組み合わせ毎に生成されたメンバシップ関数を記憶するメンバシップ関数記憶部を更に有し、前記降水粒子判別部は、前記特徴値算出部で算出された前記特徴値と、前記メンバシップ関数記憶部に記憶されている複数の前記メンバシップ関数とに基づき、前記降水粒子種別を判別する。 (4) More preferably, the feature value calculation unit calculates a plurality of types of the feature values, and the discrimination processing unit generates a member generated for each combination of the feature value type and the precipitation particle type. A membership function storage unit that stores a ship function, wherein the precipitation particle determination unit includes the feature value calculated by the feature value calculation unit and a plurality of the feature functions stored in the membership function storage unit. The type of precipitation particles is determined based on a membership function.
　（５）更に好ましくは、前記判別処理部は、或る前記降水粒子種別と前記特徴値の種類との組み合わせ毎に生成された前記メンバシップ関数のそれぞれから、判別対象となる前記降水粒子が或る前記降水粒子種別に帰属する帰属度合を算出する帰属度合算出部と、複数の前記帰属度合を合成して、判別対象となる前記降水粒子が或る前記降水粒子種別である可能性を示す指標値としての評価値、を算出する評価値算出部とを更に有し、前記降水粒子判別部は、前記降水粒子種別毎に算出された前記評価値に基づいて前記降水粒子種別を判別する。 (5) More preferably, the discrimination processing unit determines whether the precipitation particles to be discriminated from each of the membership functions generated for each combination of the certain precipitation particle type and the feature value type. An index indicating the possibility that the precipitation particles to be identified are a certain type of precipitation particles by combining a plurality of the attribution levels and calculating the degree of attribution belonging to the precipitation particle type And an evaluation value calculation unit for calculating an evaluation value as a value, wherein the precipitation particle determination unit determines the precipitation particle type based on the evaluation value calculated for each precipitation particle type.
　（６）好ましくは、前記特徴値算出部は、互いに位置が異なる各前記対象エリアのそれぞれに対応する前記特徴値を算出し、前記降水粒子判別部は、各前記特徴値に基づき、各前記対象エリアの前記降水粒子種別を判別する。 (6) Preferably, the feature value calculation unit calculates the feature value corresponding to each of the target areas whose positions are different from each other, and the precipitation particle determination unit calculates each of the targets based on each of the feature values. The type of precipitation particles in the area is determined.
　（７）好ましくは、前記特徴値は、前記地点毎偏波パラメータの平均値及び分散値のうち少なくとも１つに基づく値である。 (7) Preferably, the feature value is a value based on at least one of an average value and a dispersion value of the polarization parameter for each point.
　（８）上記課題を解決するため、本発明のある局面に係る気象レーダー装置は、水平偏波及び垂直偏波を送受波可能な送受波部と、前記送受波部によって受波された前記水平偏波の反射波及び前記垂直偏波の反射波に基づいて、対象エリア内に含まれる複数の地点から複数の地点毎偏波パラメータを算出するパラメータ算出部と、前記パラメータ算出部によって算出された前記複数の地点毎偏波パラメータに基づいて、前記対象エリア内の降水粒子種別を判別する、上述したいずれかの降水粒子判別装置と、を備えている。 (8) In order to solve the above-described problem, a weather radar apparatus according to an aspect of the present invention includes a transmission / reception unit capable of transmitting / receiving horizontal polarization and vertical polarization, and the horizontal wave received by the transmission / reception unit. Based on the reflected wave of polarization and the reflected wave of vertical polarization, a parameter calculation unit that calculates a plurality of point-by-point polarization parameters from a plurality of points included in the target area, and calculated by the parameter calculation unit One of the above-described precipitation particle discriminating devices that discriminates the type of precipitation particles in the target area based on the plurality of polarization parameters for each point.
　（９）上記課題を解決するため、本発明のある局面に係る降水粒子判別方法は、対象エリア内に含まれる複数の地点から得られる複数の地点毎偏波パラメータから、前記対象エリア内の降水粒子の特徴を示す特徴値を算出するステップと、前記特徴値を算出するステップで算出された前記特徴値に基づき前記対象エリア内の降水粒子種別を判別するステップと、を含む。 (9) In order to solve the above problem, a precipitation particle discrimination method according to an aspect of the present invention is based on a plurality of point-by-point polarization parameters obtained from a plurality of points included in a target area. A step of calculating a feature value indicating a feature of the particle, and a step of discriminating a precipitation particle type in the target area based on the feature value calculated in the step of calculating the feature value.
　（１０）上記課題を解決するため、本発明のある局面に係る降水粒子判別プログラムは、対象エリア内に含まれる複数の地点から得られる複数の地点毎偏波パラメータから、前記対象エリア内の降水粒子の特徴を示す特徴値を算出するステップと、前記特徴値を算出するステップで算出された前記特徴値に基づき前記対象エリア内の降水粒子種別を判別するステップと、をコンピュータに実行させる。 (10) In order to solve the above-described problem, a precipitation particle determination program according to an aspect of the present invention is based on a plurality of point-by-point polarization parameters obtained from a plurality of points included in a target area. Causing the computer to execute a step of calculating a feature value indicating a feature of the particle, and a step of determining the type of precipitation particles in the target area based on the feature value calculated in the step of calculating the feature value.
　本発明によれば、降水粒子の判別結果の精度をより高めることができる。 According to the present invention, the accuracy of the precipitation particle discrimination result can be further increased.
本発明の実施形態に係る気象レーダー装置のブロック図である。1 is a block diagram of a weather radar device according to an embodiment of the present invention. 本実施形態に係る気象レーダー装置で降水粒子種別を判別可能な領域である観測領域を模式的に示す平面図である。It is a top view which shows typically the observation area | region which is an area | region which can discriminate | determine a precipitation particle type with the weather radar apparatus which concerns on this embodiment. 図１に示す降水粒子判別装置の構成を示すブロック図である。It is a block diagram which shows the structure of the precipitation particle | grain discrimination apparatus shown in FIG. 特徴値算出部によって特徴値が算出される対象となる対象エリアと、観測領域との関係を示す模式図である。It is a schematic diagram which shows the relationship between the object area used as the object for which a feature value is calculated by the feature value calculation part, and an observation area. 降水粒子判別部で行われる降水粒子の判別動作を説明するためのフローチャートである。It is a flowchart for demonstrating the discrimination | determination operation | movement of the precipitation particle | grains performed in the precipitation particle | grain discrimination | determination part. 表示部に表示される表示画面の一例を示す図であって、観測領域における降水粒子種別の分布画像である。It is a figure which shows an example of the display screen displayed on a display part, Comprising: It is a distribution image of the precipitation particle classification in an observation area | region. 変形例に係る気象レーダー装置の降水粒子判別装置の構成を示すブロック図である。It is a block diagram which shows the structure of the precipitation particle | grain discrimination apparatus of the weather radar apparatus which concerns on a modification. メンバシップ関数記憶部に記憶されているメンバシップ関数の一部を示すグラフであって、（Ａ）～（Ｄ）は、判別対象となる降水粒子が雨に帰属する度合を示すグラフであって、特徴値の種類毎に生成されたグラフ、（Ｅ）～（Ｈ）は、判別対象となる降水粒子が雹に帰属する度合を示すグラフであって、特徴値の種類毎に生成されたグラフ、である。7 is a graph showing a part of membership functions stored in a membership function storage unit, and (A) to (D) are graphs showing the degree to which precipitation particles to be identified belong to rain. , Graphs generated for each type of feature value, and (E) to (H) are graphs showing the degree to which precipitation particles to be identified belong to the kite, and are graphs generated for each type of feature value . 図７に示す変形例に係る気象レーダー装置の降水粒子判別装置によって降水粒子が判別される過程を模式的に示す図である。It is a figure which shows typically the process in which precipitation particle | grains are discriminate | determined by the precipitation particle | grain discrimination apparatus of the weather radar apparatus which concerns on the modification shown in FIG.
　以下、本発明を実施するための形態について、図面を参照しつつ説明する。本発明は、降水粒子種別を判別する降水粒子判別装置、及びこの降水粒子判別装置を備えた気象レーダー装置、降水粒子判別方法、及び降水粒子判別プログラムに広く適用することができる。 Hereinafter, modes for carrying out the present invention will be described with reference to the drawings. The present invention can be widely applied to a precipitation particle discriminating device that discriminates the type of precipitation particles, a weather radar device equipped with the precipitation particle discrimination device, a precipitation particle discrimination method, and a precipitation particle discrimination program.
　図１は、本発明の実施形態に係る気象レーダー装置１のブロック図である。本実施形態に係る気象レーダー装置１は、いわゆるＸバンド帯レーダーであって、Ｘバンド帯（８～１２ＧＨｚ）の電波を送受波可能に構成されている。このＸバンド帯レーダーによれば、多数の実績があるＣバンド帯レーダー及びＳバンド帯レーダーと比べて、観測される降水粒子の空間的分解能及び時間的分解能を向上することができる。この気象レーダー装置１では、降水を観測可能な領域（観測領域Ｚ、図２参照）内の各地点Ｐｎ（ｎ＝１，２，…，Ｎ）における降水強度が算出されるだけでなく、観測領域Ｚ内の各地点Ｐｎにおける降水粒子種別（雨、雪、あられ、雹等）を精度よく判別することができる。 FIG. 1 is a block diagram of a weather radar apparatus 1 according to an embodiment of the present invention. The weather radar device 1 according to the present embodiment is a so-called X-band radar, and is configured to transmit and receive radio waves in the X-band (8 to 12 GHz). According to this X-band radar, the spatial resolution and temporal resolution of the observed precipitation particles can be improved as compared with the C-band radar and S-band radar that have a large number of achievements. The weather radar device 1 not only calculates precipitation intensity at each point P n (n = 1, 2,..., N) in an area where precipitation can be observed (observation area Z, see FIG. 2), The type of precipitation particles (rain, snow, hail, hail, etc.) at each point P n in the observation region Z can be accurately determined.
　また、本実施形態に係る気象レーダー装置１は、いわゆる二重偏波気象レーダーであって、水平偏波及び垂直偏波の双方を送受波可能に構成されている。これにより、気象レーダー装置１では、複数種類の偏波パラメータ（水平偏波反射強度Ｚｈｈ、反射因子差Ｚｄｒ、偏波間位相差変化率Ｋｄｐ等）を算出することができ、これらに基づいて、上述したような降水強度の算出及び降水粒子種別の判別を行うことができる。 The weather radar apparatus 1 according to the present embodiment is a so-called dual polarization weather radar, and is configured to transmit and receive both horizontal polarization and vertical polarization. Thereby, the weather radar apparatus 1 can calculate a plurality of types of polarization parameters (horizontal polarization reflection intensity Zhh , reflection factor difference Zdr , polarization phase difference change rate Kdp, etc.), and based on these parameters. Thus, the calculation of precipitation intensity and the determination of the precipitation particle type as described above can be performed.
　気象レーダー装置１は、図１に示すように、アンテナ２（送受波部）と、送受信部３と、信号処理部４と、表示部５と、外部出力部６とを備えている。 As shown in FIG. 1, the weather radar device 1 includes an antenna 2 (wave transmission / reception unit), a transmission / reception unit 3, a signal processing unit 4, a display unit 5, and an external output unit 6.
　アンテナ２は、指向性の強い電波を送受波可能なレーダアンテナであって、水平偏波及び垂直偏波の双方を送波可能且つこれらの反射波を受波可能である。アンテナ２は、機械的に回転可能に構成されていて、これにより、観測領域Ｚを送信波で走査し且つその反射波を受信波として受波することができる。アンテナ２は、送信波及び受信波を送受信する方向（例えば、方位及び仰角）を変えながら、送信波及び受信波の送受波を繰り返し行う。 The antenna 2 is a radar antenna capable of transmitting and receiving highly directional radio waves, and can transmit both horizontally polarized waves and vertically polarized waves and can receive these reflected waves. The antenna 2 is configured to be mechanically rotatable, whereby the observation region Z can be scanned with a transmission wave and the reflected wave can be received as a reception wave. The antenna 2 repeatedly transmits and receives transmission waves and reception waves while changing the direction (for example, azimuth and elevation angle) in which transmission waves and reception waves are transmitted and received.
　送受信部３は、サーキュレータ１０、送信制御部１１、送信信号発生部１２、アンプ１３、受信部１４、ＡＤ変換部１５、パルス圧縮部１６、及び不要信号除去部１７、を有している。 The transmission / reception unit 3 includes a circulator 10, a transmission control unit 11, a transmission signal generation unit 12, an amplifier 13, a reception unit 14, an AD conversion unit 15, a pulse compression unit 16, and an unnecessary signal removal unit 17.
　サーキュレータ１０は、アンプ１３から出力された送信信号をアンテナ２へ出力するように構成されている。また、サーキュレータ１０は、アンテナ２で受波された受信波から得らえる受信信号を受信部１４へ出力するように構成されている。 The circulator 10 is configured to output the transmission signal output from the amplifier 13 to the antenna 2. The circulator 10 is configured to output a reception signal obtained from the reception wave received by the antenna 2 to the reception unit 14.
　送信制御部１１は、送信信号発生部１２によって発生させられる送信信号が送信されるタイミング等を制御する。送信信号発生部１２は、アンテナ２から送波される送信波の基となる送信信号を発生させる。送信信号発生部１２によって発生した送信信号は、アンプ１３によって増幅された後、サーキュレータ１０を介してアンテナ２に出力される。 The transmission control unit 11 controls the timing at which the transmission signal generated by the transmission signal generation unit 12 is transmitted. The transmission signal generator 12 generates a transmission signal that is the basis of the transmission wave transmitted from the antenna 2. The transmission signal generated by the transmission signal generator 12 is amplified by the amplifier 13 and then output to the antenna 2 via the circulator 10.
　アンテナ２で受波された受信波から得られる受信信号は、サーキュレータ１０を介して受信部１４に出力された後、ＡＤ変換部１５によってデジタル信号に変換される。デジタル信号に変換された受信信号は、パルス圧縮部１６によってパルス圧縮され、不要信号除去部１７によってノイズ等の不要信号が除去された後、信号処理部４に出力される。 The reception signal obtained from the reception wave received by the antenna 2 is output to the reception unit 14 via the circulator 10 and then converted into a digital signal by the AD conversion unit 15. The received signal converted into the digital signal is pulse-compressed by the pulse compression unit 16, and unnecessary signals such as noise are removed by the unnecessary signal removal unit 17, and then output to the signal processing unit 4.
　信号処理部４は、送受信部３から出力される受信信号を処理して、観測領域Ｚ内の各地点Ｐｎにおける降水強度の算出及び降水粒子種別の判別を行うように構成されている。信号処理部４は、図１に示すように、パラメータ算出部２０、ドップラ速度算出部２１、降水強度算出部２２、降水粒子判別装置１９、及び画像生成部２４を有している。 The signal processing unit 4 is configured to process the reception signal output from the transmission / reception unit 3 to calculate the precipitation intensity and determine the type of precipitation particles at each point P n in the observation region Z. As shown in FIG. 1, the signal processing unit 4 includes a parameter calculation unit 20, a Doppler velocity calculation unit 21, a precipitation intensity calculation unit 22, a precipitation particle discrimination device 19, and an image generation unit 24.
　この信号処理部４は、例えば図示しないプロセッサ（ＣＰＵ、ＦＰＧＡ等）及びメモリ等のデバイスで構成されている。例えば、ＣＰＵがメモリからプログラムを読み出して実行することにより、信号処理部４を、パラメータ算出部２０、ドップラ速度算出部２１、降水強度算出部２２、降水粒子判別装置１９、及び画像生成部２４として機能させることができる。 The signal processing unit 4 includes, for example, a processor (CPU, FPGA, etc.) not shown and a device such as a memory. For example, the CPU reads the program from the memory and executes it, so that the signal processing unit 4 becomes the parameter calculation unit 20, the Doppler velocity calculation unit 21, the precipitation intensity calculation unit 22, the precipitation particle determination device 19, and the image generation unit 24. Can function.
　上記プログラムには、降水粒子判別プログラムが含まれている。降水粒子判別プログラムは、本発明の一実施形態に係る降水粒子判別方法を、降水粒子判別装置１９に実行させるためのプログラムである。上記プログラムは、例えば、記録媒体に格納された状態で流通する。 The above program includes a precipitation particle discrimination program. The precipitation particle discrimination program is a program for causing the precipitation particle discrimination device 19 to execute the precipitation particle discrimination method according to the embodiment of the present invention. For example, the program is distributed in a state stored in a recording medium.
　図２は、本実施形態に係る気象レーダー装置１で降水粒子種別を判別可能な領域である観測領域Ｚを模式的に示す平面図である。観測領域Ｚは、例えば一例として、分割された１区画が１００ｍ角の小領域Ｚｎとなるように、メッシュ状に分割される。 FIG. 2 is a plan view schematically showing an observation region Z that is a region in which the type of precipitation particles can be identified by the weather radar device 1 according to the present embodiment. For example, the observation area Z is divided into a mesh shape so that one divided section becomes a small area Z n of 100 m square.
　パラメータ算出部２０は、このように分割された小領域Ｚｎ（ｎ＝１，２，…，Ｎ）毎に（すなわち、各小領域Ｚｎに含まれる地点Ｐｎ毎に）、各地点Ｐｎからの反射波から得られる受信信号に基づいて、複数種類の偏波パラメータを算出するように構成されている。これら複数種類の偏波パラメータとしては、例えば、水平偏波反射強度Ｚｈｈ、反射因子差Ｚｄｒ、偏波間位相差変化率Ｋｄｐ、相関係数ρｈｖ等が挙げられる。すなわち、パラメータ算出部２０は、地点Ｐｎ毎に、水平偏波反射強度Ｚｈｈ、反射因子差Ｚｄｒ、偏波間位相差変化率Ｋｄｐ、相関係数ρｈｖ等の偏波パラメータを算出する。なお、これらの偏波パラメータの算出方法は公知であるため、その説明を省略する。 The parameter calculation unit 20 sets each point P for each small region Z n (n = 1, 2,..., N) divided in this way (that is, for each point P n included in each small region Z n ). A plurality of types of polarization parameters are calculated based on a received signal obtained from a reflected wave from n . These include plural types of polarization parameters, for example, horizontally polarized reflection intensity Z hh, the differential reflectivity factor Z dr, polarizations phase difference change ratio K dp, include a correlation coefficient [rho hv like. That is, the parameter calculation unit 20, for each point P n, and calculates horizontal polarization reflection intensity Z hh, the differential reflectivity factor Z dr, polarizations phase difference change ratio K dp, the polarization parameters, such as correlation coefficients [rho hv . In addition, since the calculation method of these polarization parameters is well-known, the description is abbreviate | omitted.
　ドップラ速度算出部２１は、送信波の周波数と、反射波（受信波）の周波数との差を検出し、その差に基づいて降水粒子のドップラ速度を算出する。これにより、ドップラ速度算出部２１は、アンテナ２と降水粒子とを結ぶ方向における、降水粒子の移動速度を算出する。 The Doppler velocity calculation unit 21 detects the difference between the frequency of the transmitted wave and the frequency of the reflected wave (received wave), and calculates the Doppler velocity of the precipitation particles based on the difference. Thereby, the Doppler velocity calculation unit 21 calculates the movement velocity of the precipitation particles in the direction connecting the antenna 2 and the precipitation particles.
　降水強度算出部２２は、パラメータ算出部２０によって算出された各偏波パラメータに基づいて、観測領域Ｚに含まれる各地点Ｐｎの降水強度を算出する。偏波パラメータに基づいて各地点Ｐｎの降水強度を算出する手法は公知であるため、その説明を省略する。 The precipitation intensity calculation unit 22 calculates the precipitation intensity at each point P n included in the observation region Z based on each polarization parameter calculated by the parameter calculation unit 20. Since the technique for calculating the precipitation intensity at each point Pn based on the polarization parameter is known, the description thereof is omitted.
　図３は、降水粒子判別装置１９が有する特徴値算出部３０及び判別処理部２３の構成を詳細に示すブロック図である。また、図４は、特徴値算出部３０によって特徴値が算出される対象となる対象エリアＺａと、観測領域Ｚとの関係を示す模式図である。図４を参照して、対象エリアＺａは、分割された１区画が１００ｍ角の小領域Ｚａｍ（ｍ＝１，２，…，Ｍ）となるようにメッシュ状に分割される。図４に示す例では、対象エリアＺａは、９つの小領域Ｚａｍに分割されている。なお、対象エリアＺａに含まれる小領域の数はこれに限らず、その他の数であってもよい。 FIG. 3 is a block diagram showing in detail the configuration of the feature value calculation unit 30 and the discrimination processing unit 23 of the precipitation particle discrimination device 19. FIG. 4 is a schematic diagram showing the relationship between the observation area Z and the target area Za for which the feature value is calculated by the feature value calculation unit 30. Referring to FIG. 4, target area Za is divided into a mesh shape so that one divided section is a small area Za m (m = 1, 2,..., M) of 100 m square. In the example shown in FIG. 4, the target area Za is divided into nine small regions Za m. The number of small areas included in the target area Za is not limited to this, and may be other numbers.
　特徴値算出部３０は、対象エリアＺａに含まれる複数の小領域Ｚａｍ内の各地点Ｐａｍから得られた偏波パラメータ（地点毎偏波パラメータ）に基づいて、対象エリアＺａ内の降水粒子の特徴を示す特徴値（特徴値については、以下で詳しく説明する）を算出する。対象エリアＺａは、図４の直線矢印で示すように、観測領域Ｚ内において少しずつ移動する。具体的には、対象エリアＺａは、その中心地点Ｐａ５が、観測領域Ｚに含まれる各地点Ｐ１～ＰＮの全てを走査するように、観測領域Ｚ内において少しずつ移動する。そして、特徴値算出部３０は、互いに位置が異なる対象エリアＺａのそれぞれについて、特徴値を算出する。 Feature value calculating section 30, based on the polarization parameters obtained from each point Pa m in the plurality of small regions Za m included in the target area Za (point each polarization parameters), precipitation particles in the target area Za The feature value indicating the feature (the feature value will be described in detail below) is calculated. The target area Za moves little by little in the observation region Z as indicated by the straight arrows in FIG. Specifically, the target area Za moves little by little in the observation region Z such that the central point Pa 5 scans all the points P 1 to P N included in the observation region Z. Then, the feature value calculation unit 30 calculates a feature value for each of the target areas Za having different positions.
　特徴値算出部３０は、図３を参照して、ρｈｖ分散値算出部３１と、ρｈｖ平均値算出部３２と、Ｚｄｒ分散値算出部３３と、Ｚｄｒ平均値算出部３４と、Ｋｄｐ分散値算出部３５と、Ｋｄｐ平均値算出部３６と、Ｚｈｈ平均値算出部３７とを有している。 With reference to FIG. 3, the feature value calculation unit 30 includes a ρ hv variance value calculation unit 31, a ρ hv average value calculation unit 32, a Z dr variance value calculation unit 33, a Z dr average value calculation unit 34, A K dp dispersion value calculation unit 35, a K dp average value calculation unit 36, and a Zhh average value calculation unit 37 are included.
　ρｈｖ分散値算出部３１は、対象エリアＺａ内の各地点Ｐａ１～ＰａＭから得られた相関係数ρｈｖ、の分散値であるρｈｖ分散値Ｖａｒ（ρｈｖ）を、当該対象エリアＺａの中心地点Ｐａ５における降水粒子の特徴値の１つとして算出する。 The ρ hv variance value calculation unit 31 uses the ρ hv variance value Var (ρ hv ), which is a variance value of the correlation coefficients ρ hv obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
　ρｈｖ平均値算出部３２は、対象エリアＺａ内の各地点Ｐａ１～ＰａＭから得られた相関係数ρｈｖ、の平均値であるρｈｖ平均値Ａｖｇ（ρｈｖ）を、当該対象エリアＺａの中心地点Ｐａ５における降水粒子の特徴値の１つとして算出する。 The ρ hv average value calculation unit 32 calculates ρ hv average value Avg (ρ hv ), which is an average value of the correlation coefficients ρ hv obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
　Ｚｄｒ分散値算出部３３は、対象エリアＺａ内の各地点Ｐａ１～ＰａＭから得られた反射因子差Ｚｄｒ、の分散値であるＺｄｒ分散値Ｖａｒ（Ｚｄｒ）を、当該対象エリアＺａの中心地点Ｐａ５における降水粒子の特徴値の１つとして算出する。 The Z dr variance value calculation unit 33 uses a Z dr variance value Var (Z dr ), which is a variance value of the reflection factor differences Z dr obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
　Ｚｄｒ平均値算出部３４は、対象エリアＺａ内の各地点Ｐａ１～ＰａＭから得られた反射因子差Ｚｄｒ、の平均値であるＺｄｒ平均値Ａｖｇ（Ｚｄｒ）を、当該対象エリアＺａの中心地点Ｐａ５における降水粒子の特徴値の１つとして算出する。 The Z dr average value calculation unit 34 calculates a Z dr average value Avg (Z dr ), which is an average value of the reflection factor differences Z dr obtained from the points Pa 1 to Pa M in the target area Za, as the target area. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of Za.
　Ｋｄｐ分散値算出部３５は、対象エリアＺａ内の各地点Ｐａ１～ＰａＭから得られた偏波間位相差変化率Ｋｄｐ、の分散値であるＫｄｐ分散値Ｖａｒ（Ｋｄｐ）を、当該対象エリアＺａの中心地点Ｐａ５における降水粒子の特徴値の１つとして算出する。 The K dp dispersion value calculation unit 35 calculates a K dp dispersion value Var (K dp ), which is a dispersion value of the inter- polarization phase difference change rate K dp obtained from the points Pa 1 to Pa M in the target area Za. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of the target area Za.
　Ｋｄｐ平均値算出部３６は、対象エリアＺａ内の各地点Ｐａ１～ＰａＭから得られた偏波間位相差変化率Ｋｄｐ、の平均値であるＫｄｐ平均値Ａｖｇ（Ｋｄｐ）を、当該対象エリアＺａの中心地点Ｐａ５における降水粒子の特徴値の１つとして算出する。 The K dp average value calculation unit 36 calculates a K dp average value Avg (K dp ) that is an average value of the inter- polarization phase difference change rates K dp obtained from the points Pa 1 to Pa M in the target area Za. It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of the target area Za.
　Ｚｈｈ平均値算出部３７は、対象エリアＺａ内の各地点Ｐａ１～ＰａＭから得られた水平偏波反射強度Ｚｈｈ、の平均値であるＺｈｈ平均値Ａｖｇ（Ｚｈｈ）を、当該対象エリアＺａの中心地点Ｐａ５における降水粒子の特徴値の１つとして算出する。 The Zhh average value calculator 37 calculates the Zhh average value Avg ( Zhh ), which is the average value of the horizontally polarized wave reflection intensities Zhh obtained from the points Pa 1 to Pa M in the target area Za, It is calculated as one of the feature values of the precipitation particles at the center point Pa 5 of the target area Za.
　判別処理部２３は、降水粒子判別部２５を有している。降水粒子判別部２５は、特徴値算出部３０で算出された各特徴値に基づいて、観測領域Ｚ内の各地点Ｐｎ（ｎ＝１，２，…，Ｎ、図２参照）の降水粒子の種別を判別する。 The discrimination processing unit 23 has a precipitation particle discrimination unit 25. The precipitation particle discriminating unit 25, based on the feature values calculated by the feature value calculation unit 30, the precipitation particles at each point P n (n = 1, 2,..., N, see FIG. 2) in the observation region Z. The type of is determined.
　図５は、降水粒子判別部２５で行われる降水粒子の判別動作を説明するためのフローチャートである。降水粒子判別部２５は、特徴値算出部３０で算出された各特徴値を所定の閾値と比較し、その比較結果に基づいて、各地点Ｐｎの降水粒子種別を判別する。以下では、図５で示すフローチャートを用いて、降水粒子種別の判別手法について説明する。 FIG. 5 is a flowchart for explaining the precipitation particle determination operation performed by the precipitation particle determination unit 25. The precipitation particle discriminating unit 25 compares each feature value calculated by the feature value calculation unit 30 with a predetermined threshold value, and discriminates the type of precipitation particle at each point P n based on the comparison result. Hereinafter, a method for determining the type of precipitation particles will be described with reference to the flowchart shown in FIG.
　まず、ステップＳ１では、ρｈｖ分散値Ｖａｒ（ρｈｖ）と該分散値の閾値Ｔｈｒ_Ｖａｒ（ρｈｖ）とが比較され、且つρｈｖ平均値Ａｖｇ（ρｈｖ）と該平均値の閾値Ｔｈｒ_Ａｖｇ（ρｈｖ）とが比較される。そして、ρｈｖ分散値Ｖａｒ（ρｈｖ）が閾値Ｔｈｒ_Ｖａｒ（ρｈｖ）よりも大きく且つρｈｖ平均値Ａｖｇ（ρｈｖ）が閾値Ｔｈｒ_Ａｖｇ（ρｈｖ）よりも小さい場合（ステップＳ１のＹｅｓ）、ステップＳ２へ進む。一方、ステップＳ１の条件が満たされない場合（ステップＳ１のＮｏ）、その地点Ｐｎでの降水粒子種別は雨と判別される（ステップＳ５）。 First, in step S1, the ρ hv variance value Var (ρ hv ) and the threshold value Thr_Var (ρ hv ) of the variance value are compared, and the ρ hv average value Avg (ρ hv ) and the threshold value Thr_Avg (ρ hv ). If the ρ hv variance value Var (ρ hv ) is larger than the threshold Thr_Var (ρ hv ) and the ρ hv average value Avg (ρ hv ) is smaller than the threshold Thr_Avg (ρ hv ) (Yes in step S1), step Proceed to S2. On the other hand, when the condition of step S1 is not satisfied (No in step S1), the type of precipitation particles at the point Pn is determined to be rain (step S5).
　次に、ステップＳ２では、Ｚｄｒ分散値Ｖａｒ（Ｚｄｒ）と該分散値の閾値Ｔｈｒ_Ｖａｒ（Ｚｄｒ）とが比較され、且つＺｄｒ平均値Ａｖｇ（Ｚｄｒ）と該平均値の閾値Ｔｈｒ_Ａｖｇ（Ｚｄｒ）とが比較される。そして、Ｚｄｒ分散値Ｖａｒ（Ｚｄｒ）が閾値Ｔｈｒ_Ｖａｒ（Ｚｄｒ）よりも大きく且つＺｄｒ平均値Ａｖｇ（Ｚｄｒ）が閾値Ｔｈｒ_Ａｖｇ（Ｚｄｒ）よりも小さい場合（ステップＳ２のＹｅｓ）、ステップＳ３へ進む。一方、ステップＳ２の条件が満たされない場合（ステップＳ２のＮｏ）、その地点Ｐｎでの降水粒子種別は雪と判別される（ステップＳ６）。 Next, in step S2, the Z dr variance value Var (Z dr ) is compared with the threshold value Thr_Var (Z dr ) of the variance value, and the Z dr average value Avg (Z dr ) and the threshold value Thr_Avg ( Z dr ) is compared. When the Z dr variance value Var (Z dr ) is larger than the threshold Thr_Var (Z dr ) and the Z dr average value Avg (Z dr ) is smaller than the threshold Thr_Avg (Z dr ) (Yes in step S2), step Proceed to S3. On the other hand, when the condition of step S2 is not satisfied (No in step S2), the type of precipitation particles at that point Pn is determined to be snow (step S6).
　次に、ステップＳ３では、Ｋｄｐ分散値Ｖａｒ（Ｋｄｐ）と該分散値の閾値Ｔｈｒ_Ｖａｒ（Ｋｄｐ）とが比較され、且つＫｄｐ平均値Ａｖｇ（Ｋｄｐ）と該平均値の閾値Ｔｈｒ_Ａｖｇ（Ｋｄｐ）とが比較される。そして、Ｋｄｐ分散値Ｖａｒ（Ｋｄｐ）が閾値Ｔｈｒ_Ｖａｒ（Ｋｄｐ）よりも大きく且つＫｄｐ平均値Ａｖｇ（Ｋｄｐ）が閾値Ｔｈｒ_Ａｖｇ（Ｋｄｐ）よりも大きい場合（ステップＳ３のＹｅｓ）、ステップＳ４へ進む。一方、ステップＳ３の条件が満たされない場合（ステップＳ３のＮｏ）、その地点Ｐｎでの降水粒子種別は雪と判別される（ステップＳ６）。 Next, in step S3, the K dp variance value Var (K dp ) and the threshold value Thr_Var (K dp ) of the variance value are compared, and the K dp average value Avg (K dp ) and the threshold value Thr_Avg ( Kdp ) is compared. When the K dp variance value Var (K dp ) is larger than the threshold value Thr_Var (K dp ) and the K dp average value Avg (K dp ) is larger than the threshold value Thr_Avg (K dp ) (Yes in step S3), step Proceed to S4. On the other hand, when the condition of step S3 is not satisfied (No in step S3), the type of precipitation particles at the point Pn is determined to be snow (step S6).
　次に、ステップＳ４では、Ｚｈｈ平均値Ａｖｇ（Ｚｈｈ）と該平均値の閾値Ｔｈｒ_Ａｖｇ（Ｚｈｈ）とが比較される。そして、Ｚｈｈ平均値Ａｖｇ（Ｚｈｈ）が閾値Ｔｈｒ_Ａｖｇ（Ｚｈｈ）よりも大きい場合（ステップＳ４のＹｅｓ）、その地点での降水粒子種別は雹と判別される（ステップＳ８）。一方、ステップＳ４の条件が満たされない場合（ステップＳ４のＮｏ）、その地点Ｐｎでの降水粒子種別はあられと判別される（ステップＳ７）。 Next, in step S4, the threshold Thr_Avg (Z hh) of Z hh average Avg (Z hh) with the average value are compared. When Z hh average Avg (Z hh) is greater than the threshold value Thr_Avg (Z hh) (Yes in step S4), and precipitation particles type at that point it is determined to hail (step S8). On the other hand, when the condition of step S4 is not satisfied (No in step S4), it is determined that the precipitation particle type at that point Pn is hail (step S7).
　画像生成部２４は、ドップラ速度算出部２１によって算出された各地点Ｐｎの降水粒子の移動速度に基づき、観測領域Ｚにおける降水粒子の移動速度の分布画像を生成する。また、画像生成部２４は、降水強度算出部２２によって算出された各地点Ｐｎの降水強度に基づき、観測領域Ｚの降水強度の分布画像を生成する。また、画像生成部２４は、判別処理部２３によって判別された各地点Ｐｎの降水粒子種別に基づき、観測領域Ｚの降水粒子の分布画像を生成する。 The image generation unit 24 generates a distribution image of the movement speed of the precipitation particles in the observation region Z based on the movement speed of the precipitation particles at each point P n calculated by the Doppler velocity calculation unit 21. In addition, the image generation unit 24 generates a distribution image of the precipitation intensity in the observation region Z based on the precipitation intensity at each point Pn calculated by the precipitation intensity calculation unit 22. In addition, the image generation unit 24 generates a distribution image of precipitation particles in the observation region Z based on the precipitation particle type at each point Pn determined by the determination processing unit 23.
　表示部５には、画像生成部２４によって生成された降水粒子の移動速度の分布画像、降水強度の分布画像、及び降水粒子種別の分布画像が表示される。具体的には、例えば、表示部５に、これらの分布画像が同時に表示されてもよいし、或いは、ユーザの切り替えに応じてこれらの分布画像のいずれか１つが表示されてもよい。 The display unit 5 displays the distribution image of the moving speed of the precipitation particles, the distribution image of the precipitation intensity, and the distribution image of the precipitation particle type generated by the image generation unit 24. Specifically, for example, these distribution images may be displayed on the display unit 5 at the same time, or any one of these distribution images may be displayed in accordance with user switching.
　図６は、表示部５に表示される表示画面の一例を示す図であって、観測領域Ｚにおける降水粒子種別の分布画像である。表示部５には、例えば、観測領域Ｚが表示され、その観測領域Ｚに含まれる各地点Ｐｎ（ｎ＝１，２，…，Ｎ）での降水粒子種別が、互いに異なる色調又は模様によって表される。図６に示す例では、雨と判別された地点が塗りつぶされることにより表され、あられと判別された地点が斜線のハッチングにより表され、雪と判別された地点がドットのハッチングにより表されている。 FIG. 6 is a diagram illustrating an example of a display screen displayed on the display unit 5, and is a distribution image of precipitation particle types in the observation region Z. The display unit 5 displays, for example, an observation region Z, and the types of precipitation particles at each point P n (n = 1, 2,..., N) included in the observation region Z are different from each other in tone or pattern. expressed. In the example shown in FIG. 6, a point determined to be rain is represented by being painted, a point determined to be that is represented by hatched hatching, and a point determined to be snow is represented by dot hatching. .
　外部出力部６は、パラメータ算出部２０で算出された各種偏波パラメータ、ドップラ速度算出部２１で算出された降水粒子の移動速度、降水強度算出部２２で算出された各地点での降水強度、及び降水粒子判別装置１９で判別された各地点での降水粒子種別を、外部機器へ出力するためのインターフェースである。外部出力部６としては、例えばインターフェースコネクタを挙げることができる。このように外部出力部６を設けることで、本実施形態に係る気象レーダー装置１で算出された降水粒子種別等のデータを、外部へ出力することが可能となる。 The external output unit 6 includes various polarization parameters calculated by the parameter calculation unit 20, the moving speed of precipitation particles calculated by the Doppler velocity calculation unit 21, the precipitation intensity at each point calculated by the precipitation intensity calculation unit 22, And an interface for outputting the precipitation particle type at each point determined by the precipitation particle determination device 19 to an external device. An example of the external output unit 6 is an interface connector. By providing the external output unit 6 in this way, it is possible to output data such as precipitation particle types calculated by the weather radar apparatus 1 according to the present embodiment to the outside.
　ところで、従来技術では、複数種類の偏波パラメータ（水平偏波反射強度Ｚｈｈ、反射因子差Ｚｄｒ、偏波間位相差変化率Ｋｄｐ等）が地点毎に算出され、これらの偏波パラメータに基づいて、各地点での降水粒子種別が判別されていた。しかし、この手法では、降水粒子の判別結果の精度が不十分となる場合がある。 By the way, in the prior art, a plurality of types of polarization parameters (horizontal polarization reflection intensity Zhh , reflection factor difference Zdr , inter-polarization phase difference change rate Kdp, etc.) are calculated for each point. Based on this, the type of precipitation particles at each point was identified. However, with this method, the accuracy of the precipitation particle discrimination result may be insufficient.
　この点につき、本実施形態に係る気象レーダー装置１の降水粒子判別装置１９では、複数地点Ｐａｍ（ｍ＝１，２，…Ｍ）から得られる複数の偏波パラメータ（地点毎偏波パラメータ）から、特徴値が算出され、その特徴値に基づいて降水粒子種別が判別される。こうすると、ある程度の広がりをもつ範囲（対象エリアＺａ）の各地点Ｐａｍから得られる情報が統計的に統合され、１つの特徴値として降水粒子種別の判別に用いられるため、従来の場合（地点毎に算出された偏波パラメータに基づき、地点毎の降水粒子種別が判別される場合）よりも、降水粒子の判別結果の精度を高めることができる。 In this regard, in the precipitation particle discriminating apparatus 19 of the weather radar apparatus 1 according to this embodiment, a plurality of polarization parameters (polarization parameters for each point) obtained from a plurality of points Pa m (m = 1, 2,... M). Thus, a feature value is calculated, and a precipitation particle type is determined based on the feature value. In this way, the information is statistically integration obtained from each point Pa m range (target area Za) having a certain extent, for use in determination of the precipitation particles classification as a feature value, the conventional case (point The accuracy of the precipitation particle discrimination result can be improved more than the case where the precipitation particle type for each point is discriminated based on the polarization parameter calculated every time.
　［効果］
　以上のように、本実施形態に係る気象レーダー装置１の降水粒子判別装置１９は、複数地点Ｐａｍ（ｍ＝１，２，…Ｍ）から得られる複数の偏波パラメータから特徴値を算出し、その特徴値に基づいて降水粒子種別を判別している。こうすると、ある程度の広がりをもつ範囲（対象エリアＺａ）の各地点Ｐａｍから得られる情報が統計的に統合され、特徴値として降水粒子種別の判別に用いられる。 [effect]
As described above, the precipitation particle discriminating apparatus 19 of the weather radar apparatus 1 according to the present embodiment calculates a feature value from a plurality of polarization parameters obtained from a plurality of points Pa m (m = 1, 2,... M). The type of precipitation particles is determined based on the feature value. In this way, the range information is statistically integration obtained from each point Pa m of (target area Za) having a certain expanse, employed in determination of the precipitation particles type as the feature value.
　従って、降水粒子判別装置１９によれば、降水粒子の判別結果の精度を高めることができる。 Therefore, according to the precipitation particle discriminating apparatus 19, it is possible to improve the accuracy of the precipitation particle discrimination result.
　また、降水粒子判別装置１９では、各特徴値と閾値との比較結果に基づいて降水粒子種別が判別されるため、比較的容易に降水粒子種別を判別することができる。 Further, in the precipitation particle discriminating apparatus 19, since the precipitation particle type is discriminated based on the comparison result between each feature value and the threshold value, the precipitation particle type can be discriminated relatively easily.
　また、降水粒子判別装置１９では、互いに位置が異なる各対象エリアＺａの特徴値に基づいて各地点の降水粒子種別が判別されるため、複数の地点Ｐｎ（ｎ＝１，２，…，Ｎ）の降水粒子種別を判別することができる。 Moreover, since the precipitation particle discriminating apparatus 19 discriminates the precipitation particle type at each point based on the feature values of the target areas Za having different positions, a plurality of points P n (n = 1, 2,..., N ) Precipitation particle types.
　また、降水粒子判別装置１９では、特徴値として、偏波パラメータの分散値及び平均値が用いられている。これにより、降水粒子種別の判別を適切に行うことができる。 Further, in the precipitation particle discriminating apparatus 19, the dispersion value and the average value of the polarization parameter are used as the characteristic values. This makes it possible to appropriately determine the type of precipitation particles.
　また、気象レーダー装置１によれば、降水粒子の判別を正確に行うことができる降水粒子判別装置を備えた気象レーダー装置を提供することができる。 Further, according to the meteorological radar apparatus 1, it is possible to provide a meteorological radar apparatus equipped with a precipitation particle determining apparatus that can accurately determine precipitation particles.
　［変形例］
　以上、本発明の実施形態について説明したが、本発明はこれらに限定されるものではなく、本発明の趣旨を逸脱しない限りにおいて種々の変更が可能である。 [Modification]
As mentioned above, although embodiment of this invention was described, this invention is not limited to these, A various change is possible unless it deviates from the meaning of this invention.
　（１）図７は、変形例に係る気象レーダー装置の降水粒子判別装置１９ａの構成を示すブロック図である。本変形例に係る気象レーダー装置は、上述した実施形態に係る気象レーダー装置１と比べて、降水粒子判別装置の構成が異なる。以下では、降水粒子判別装置１９ａの構成及び動作について説明し、その他については説明を省略する。 (1) FIG. 7 is a block diagram showing a configuration of a precipitation particle discrimination device 19a of a weather radar device according to a modification. The meteorological radar apparatus according to this modification is different in the configuration of the precipitation particle discriminating apparatus from the meteorological radar apparatus 1 according to the embodiment described above. Below, the structure and operation | movement of the precipitation particle | grain discrimination apparatus 19a are demonstrated, and description is abbreviate | omitted about others.
　本変形例に係る降水粒子判別装置１９ａは、いわゆるファジィ推論を用いて降水粒子種別を判別する。降水粒子判別装置１９ａは、特徴値算出部３０ａと、判別処理部２３ａとを有している。判別処理部２３ａは、メンバシップ関数記憶部２６と、帰属度合算出部２７と、評価値算出部２８と、降水粒子判別部２５ａと、を有している。 The precipitation particle discriminating apparatus 19a according to this modification discriminates the precipitation particle type using so-called fuzzy reasoning. The precipitation particle discriminating apparatus 19a has a feature value calculation unit 30a and a discrimination processing unit 23a. The discrimination processing unit 23a includes a membership function storage unit 26, an attribution degree calculation unit 27, an evaluation value calculation unit 28, and a precipitation particle discrimination unit 25a.
　特徴値算出部３０ａは、上記実施形態の特徴値算出部３０と比べて、Ｋｄｐ分散値算出部３５、Ｋｄｐ平均値算出部３６、及びＺｈｈ平均値算出部３７が省略された構成となっている。すなわち、本変形例の特徴値算出部３０ａは、ρｈｖ分散値算出部３１と、ρｈｖ平均値算出部３２と、Ｚｄｒ分散値算出部３３と、Ｚｄｒ平均値算出部３４とを有している。ρｈｖ分散値算出部３１、ρｈｖ平均値算出部３２、Ｚｄｒ分散値算出部３３、及びＺｄｒ平均値算出部３４の構成は、上記実施形態の場合と同様であるため、その説明を省略する。 The feature value calculation unit 30a has a configuration in which the K dp variance value calculation unit 35, the K dp average value calculation unit 36, and the Zhh average value calculation unit 37 are omitted as compared to the feature value calculation unit 30 of the above embodiment. It has become. That is, the feature value calculation unit 30a of the present modification includes a ρ hv variance value calculation unit 31, a ρ hv average value calculation unit 32, a Z dr variance value calculation unit 33, and a Z dr average value calculation unit 34. is doing. The configurations of the ρ hv variance value calculation unit 31, the ρ hv average value calculation unit 32, the Z dr variance value calculation unit 33, and the Z dr average value calculation unit 34 are the same as those in the above-described embodiment. Omitted.
　図８は、メンバシップ関数記憶部２６に記憶されているメンバシップ関数ＭＢＦａ＿ｂの一部を示すグラフであって、（Ａ）～（Ｄ）は、判別対象となる降水粒子が雨に帰属する度合を示すグラフ、（Ｅ）～（Ｈ）は、判別対象となる降水粒子が雹に帰属する度合を示すグラフである。（Ａ）及び（Ｅ）では、横軸がＺｄｒ分散値Ｖａｒ（Ｚｄｒ）であり、（Ｂ）及び（Ｆ）では、横軸がＺｄｒ平均値Ａｖｇ（Ｚｄｒ）であり、（Ｃ）及び（Ｇ）では、横軸がρｈｖ分散値Ｖａｒ（ρｈｖ）であり、（Ｄ）及び（Ｈ）では、横軸がρｈｖ平均値Ａｖｇ（ρｈｖ）である。なお、ここでは、メンバシップ関数記憶部２６に、雨及び雹に関するメンバシップ関数が記憶されている例を挙げたが、これに限らず、その他の降水粒子（例えば、雪、あられ等）に関するメンバシップ関数が記憶されていてもよい。また、ここでは、メンバシップ関数の横軸がＺｄｒ分散値Ｖａｒ（Ｚｄｒ）、Ｚｄｒ平均値Ａｖｇ（Ｚｄｒ）、ρｈｖ分散値Ｖａｒ（ρｈｖ）、及びρｈｖ平均値Ａｖｇ（ρｈｖ）である例を挙げたが、これに限らず、横軸がその他の値（例えば、水平偏波反射強度Ｚｈｈ、反射因子差Ｚｄｒ、偏波間位相差変化率Ｋｄｐ、Ｋｄｐ分散値Ｖａｒ（Ｋｄｐ）、Ｋｄｐ平均値Ａｖｇ（Ｋｄｐ）、Ｚｈｈ平均値Ａｖｇ（Ｚｈｈ）等）であってもよい。 FIG. 8 is a graph showing a part of the membership function MBF a_b stored in the membership function storage unit 26, and (A) to (D) show that precipitation particles to be discriminated belong to rain. Graphs showing the degrees, (E) to (H) are graphs showing the degree to which the precipitation particles to be identified belong to the kites. In (A) and (E), the horizontal axis is the Z dr dispersion value Var (Z dr ), and in (B) and (F), the horizontal axis is the Z dr average value Avg (Z dr ), and (C ) And (G), the horizontal axis is ρ hv dispersion value Var (ρ hv ), and in (D) and (H), the horizontal axis is ρ hv average value Avg (ρ hv ). In this example, the membership function storage unit 26 stores membership functions related to rain and hail. However, the present invention is not limited to this, and members related to other precipitation particles (for example, snow, hail, etc.). A ship function may be stored. Also, here, the horizontal axis of the membership function indicates the Z dr variance value Var (Z dr ), the Z dr average value Avg (Z dr ), the ρ hv variance value Var (ρ hv ), and the ρ hv average value Avg (ρ Although an example is hv), not limited to this, the horizontal axis other values (e.g., horizontal polarization reflection intensity Z hh, the differential reflectivity factor Z dr, polarization phase difference change ratio K dp, K dp dispersion Value Var (K dp ), K dp average value Avg (K dp ), Zhh average value Avg (Z hh ), etc.).
　メンバシップ関数記憶部２６は、上述したメンバシップ関数ＭＢＦａ＿ｂを記憶している。メンバシップ関数ＭＢＦａ＿ｂにおけるａは、１以上の整数であって、ここでの各整数は、各偏波パラメータ又は各特徴値に対応する。例えば、１はＺｄｒ分散値、２はＺｄｒ平均値、３はρｈｖ分散値、４はρｈｖ平均値、というように、各整数が各偏波パラメータ又は各特徴値に対応する。また、メンバシップ関数ＭＢＦａ＿ｂにおけるｂは、１以上の整数であって、ここでの各整数は、降水粒子種別に対応する。例えば、１は雨、２は雹、というように、各整数が各降水粒子種別に対応する。なお、これらのメンバシップ関数は、実験等によって予め決定された関数である。 The membership function storage unit 26 stores the above-described membership function MBF a_b . In the membership function MBF a_b , a is an integer of 1 or more, and each integer here corresponds to each polarization parameter or each feature value. For example, 1 is a Z dr dispersion value, 2 is a Z dr average value, 3 is a ρ hv dispersion value, and 4 is a ρ hv average value. Each integer corresponds to each polarization parameter or each feature value. Further, b in the membership function MBF a_b is an integer of 1 or more, and each integer here corresponds to a precipitation particle type. For example, each integer corresponds to each precipitation particle type, such as 1 for rain, 2 for hail. These membership functions are functions determined in advance by experiments or the like.
　図９は、本変形例に係る気象レーダー装置の降水粒子判別装置１９ａによって降水粒子が判別される過程を模式的に示す図である。 FIG. 9 is a diagram schematically showing a process in which precipitation particles are discriminated by the precipitation particle discrimination device 19a of the weather radar apparatus according to this modification.
　帰属度合算出部２７は、地点Ｐｎ（ｎ＝１，２，…，Ｎ、図２参照）毎に得られた各種偏波パラメータ（水平偏波反射強度Ｚｈｈ等）及び各特徴値（Ｚｄｒ平均値Ａｖｇ（Ｚｄｒ）等）に基づき、判別対象となる降水粒子がいずれの種別に帰属する可能性が高いかを示す度合（帰属度合Ｐｓａ＿ｂ）を、偏波パラメータ毎及び特徴値毎に算出する。例えば一例として、図８（Ａ）及び（Ｅ）を参照して、帰属度合算出部２７は、ある地点でのＺｄｒ分散値Ｖａｒ（Ｚｄｒ）がＸ１であった場合、図８（Ａ）に示すメンバシップ関数ＭＢＦ1_1に基づいて、前記ある地点での降水粒子が雨に帰属する帰属度合Ｐｓ1_1を０．２と算出し、図８（Ｅ）に示すメンバシップ関数ＭＢＦ1_２に基づいて、前記ある地点での降水粒子が雹に帰属する帰属度合Ｐｓ1_2を０．８と算出する。 The degree-of-assignment calculation unit 27 performs various polarization parameters (such as horizontal polarization reflection intensity Zhh ) obtained for each point P n (n = 1, 2,..., N, see FIG. 2) and characteristic values (Z based on the dr average value Avg (Z dr )), the degree (attribution degree Ps a — b ) indicating to which type the precipitation particles to be identified are likely to belong is classified for each polarization parameter and feature value To calculate. For example, as an example, referring to FIGS. 8A and 8E, the degree-of-affiliation calculation unit 27 determines that the Z dr variance value Var (Zdr) at a certain point is X 1 , as shown in FIG. based on the membership functions MBF 1_1 shown in, the attribution degree Ps 1_1 of precipitation particles at the point where the there is attributable to rain and the calculated 0.2, based on the membership function MBF 1_2 shown in FIG. 8 (E) Then, the degree of attribution Ps 1_2 at which the precipitation particles at the certain point belong to the kite is calculated as 0.8.
　評価値算出部２８は、帰属度合算出部２７で算出された帰属度合に基づき、各地点での降水粒子種別の評価値Ｒｓｂ（ｂは整数であって、ここでの各整数は降水粒子種別に対応）を算出する。評価値が大きいほど、その地点での降水粒子が、前記評価値が算出された降水粒子種別である可能性が高い。評価値算出部２８は、図９を参照して、帰属度合を降水粒子種別毎に加算することにより、各降水粒子種別に対応する評価値Ｒｓｂを算出する。具体的には、例えば図９を参照して、評価値算出部２８は、判別対象となる降水粒子が雨に帰属する帰属度合Ｐｓ1_1，Ｐｓ２_1，Ｐｓ３_1，Ｐｓ４_1を互いに加算して、雨の
評価値Ｒｓ１を算出する。同様に、評価値算出部２８は、判別対象となる降水粒子が雹に帰属する帰属度合Ｐｓ1_２，Ｐｓ２_２，Ｐｓ３_２，Ｐｓ４_２を互いに加算して、雹の
評価値Ｒｓ２を算出する。 The evaluation value calculation unit 28, based on the degree of attribution calculated by the degree of attribution calculation unit 27, evaluates the precipitation particle type Rs b (b is an integer) at each point, and each integer here is the type of precipitation particle Corresponding). The larger the evaluation value, the higher the possibility that the precipitation particles at that point are the type of precipitation particles for which the evaluation value was calculated. With reference to FIG. 9, the evaluation value calculation unit 28 calculates the evaluation value Rs b corresponding to each precipitation particle type by adding the degree of attribution for each precipitation particle type. Specifically, for example, referring to FIG. 9, the evaluation value calculation unit 28 adds the belonging degrees Ps 1_1 , Ps 2_1 , Ps 3_1 , and Ps 4_1 to which the precipitation particles to be identified belong to rain, The rain evaluation value Rs 1 is calculated. Similarly, the evaluation value calculation unit 28 adds the degree of attribution Ps 1_2 , Ps 2_2 , Ps 3_2 , and Ps 4_2 to which the precipitation particles to be discriminated belong to cocoon to calculate the evaluation value Rs 2 of cocoon .
　降水粒子判別部２５ａは、評価値算出部２８によって算出された評価値のうち最も値が大きい評価値に対応する降水粒子種別が、その地点での降水粒子であると判別する。 The precipitation particle discriminating unit 25a discriminates that the precipitation particle type corresponding to the evaluation value having the largest value among the evaluation values calculated by the evaluation value calculation unit 28 is precipitation particles at the point.
　［効果］
　以上のように、本変形例に係る降水粒子判別装置１９ａでは、ファジィ推論を用いて降水粒子種別を判別している。これにより、様々な推論結果を総合的に判断して最終的な降水粒子種別を判別できるので、降水粒子種別の判別をより精度高く行うことができる。 [effect]
As described above, the precipitation particle discriminating apparatus 19a according to the present modification discriminates the precipitation particle type using fuzzy reasoning. As a result, since the final precipitation particle type can be determined by comprehensively determining various inference results, the precipitation particle type can be determined with higher accuracy.
　また、本変形例に係る降水粒子判別装置１９ａによれば、特徴値算出部３０ａで算出された各特徴値とメンバシップ関数ＭＢＦａ＿ｂとに基づいて降水粒子種別が判別されているため、適切に降水粒子種別を判別することができる。 Moreover, according to the precipitation particle discriminating apparatus 19a according to the present modification, the precipitation particle type is discriminated based on each feature value calculated by the feature value calculation unit 30a and the membership function MBF a_b. The type of precipitation particles can be determined.
　また、本変形例に係る降水粒子判別装置１９ａによれば、降水粒子種別毎に算出された評価値に基づいて降水粒子が判別されるため、より適切に降水粒子種別を判別することができる。 Moreover, according to the precipitation particle discriminating apparatus 19a according to the present modification, the precipitation particles are discriminated based on the evaluation value calculated for each precipitation particle type, so that the precipitation particle type can be more appropriately discriminated.
　（２）上述した実施形態及び変形例では、降水粒子種別を判別する具体的な手法として、閾値を用いる手法、及びファジィ推論を用いる手法を挙げて説明したが、これらに限らず、対象エリア内に含まれる複数の地点から得られる複数の地点毎偏波パラメータから得られる特徴値に基づいて降水粒子種別を判別する手法であれば、どのような手法が用いられていてもよい。 (2) In the embodiment and the modification described above, the method using a threshold and the method using fuzzy inference have been described as specific methods for determining the type of precipitation particles. Any method may be used as long as it is a method for discriminating the type of precipitation particles based on feature values obtained from a plurality of point-by-point polarization parameters obtained from a plurality of points included in.
　（３）上述した実施形態では、本発明をＸバンド帯レーダーに適用する例を挙げて説明したが、これに限らず、本発明は、その他の周波数帯のレーダーに適用することもできる。 (3) In the above-described embodiment, an example in which the present invention is applied to an X-band radar has been described. However, the present invention is not limited thereto, and the present invention can also be applied to radars in other frequency bands.
　１　　　　　　　　　　　　　　気象レーダー装置
　１９，１９ａ　　　　　　　　　降水粒子判別装置
　２３　　　　　　　　　　　　　判別処理部
　３０　　　　　　　　　　　　　特徴値算出部
　Ｚａ　　　　　　　　　　　　　対象エリア DESCRIPTION OF SYMBOLS 1 Weather radar device 19, 19a Precipitation particle discrimination device 23 Discrimination processing part 30 Feature value calculation part Za Target area

Claims (10)

  1. 　対象エリア内に含まれる複数の地点から得られる複数の地点毎偏波パラメータから、前記対象エリア内の降水粒子の特徴を示す特徴値を算出する特徴値算出部と、
    　前記特徴値算出部で算出された前記特徴値に基づき前記対象エリア内の降水粒子種別を判別する降水粒子判別部、を有する判別処理部と、
    　を備えていることを特徴とする、降水粒子判別装置。     A feature value calculation unit for calculating a feature value indicating characteristics of precipitation particles in the target area from a plurality of point-by-point polarization parameters obtained from a plurality of points included in the target area;
    A discrimination processing unit having a precipitation particle discriminating unit for discriminating the type of precipitation particles in the target area based on the feature value calculated by the feature value calculation unit;
    A precipitation particle discriminating apparatus characterized by comprising:
  2. 　請求項１に記載の降水粒子判別装置において、
    　前記降水粒子判別部は、前記特徴値と閾値との比較結果に基づき、前記降水粒子種別を判別することを特徴とする、降水粒子判別装置。     In the precipitation particle discriminating device according to claim 1,
    The precipitation particle discrimination device, wherein the precipitation particle discrimination unit discriminates the precipitation particle type based on a comparison result between the feature value and a threshold value.
  3. 　請求項１に記載の降水粒子判別装置において、
    　前記判別処理部は、ファジィ推論を用いて前記降水粒子種別を判別することを特徴とする、降水粒子判別装置。     In the precipitation particle discriminating device according to claim 1,
    The said discrimination | determination processing part discriminate | determines the said precipitation particle classification using a fuzzy reasoning, The precipitation particle discrimination apparatus characterized by the above-mentioned.
  4. 　請求項３に記載の降水粒子判別装置において、
    　前記特徴値算出部は、複数種類の前記特徴値を算出し、
    　前記判別処理部は、前記特徴値の種類と前記降水粒子の種別との組み合わせ毎に生成されたメンバシップ関数を記憶するメンバシップ関数記憶部を更に有し、
    　前記降水粒子判別部は、前記特徴値算出部で算出された前記特徴値と、前記メンバシップ関数記憶部に記憶されている複数の前記メンバシップ関数とに基づき、前記降水粒子種別を判別することを特徴とする、降水粒子判別装置。     In the precipitation particle discriminating device according to claim 3,
    The feature value calculation unit calculates a plurality of types of the feature values,
    The discrimination processing unit further includes a membership function storage unit that stores a membership function generated for each combination of the feature value type and the precipitation particle type,
    The precipitation particle determination unit determines the precipitation particle type based on the feature value calculated by the feature value calculation unit and the plurality of membership functions stored in the membership function storage unit. A precipitation particle discrimination device characterized by
  5. 　請求項４に記載の降水粒子判別装置において、
    　前記判別処理部は、
    　或る前記降水粒子種別と前記特徴値の種類との組み合わせ毎に生成された前記メンバシップ関数のそれぞれから、判別対象となる前記降水粒子が或る前記降水粒子種別に帰属する帰属度合を算出する帰属度合算出部と、
    　複数の前記帰属度合を合成して、判別対象となる前記降水粒子が或る前記降水粒子種別である可能性を示す指標値としての評価値、を算出する評価値算出部と
    　を更に有し、
    　前記降水粒子判別部は、前記降水粒子種別毎に算出された前記評価値に基づいて前記降水粒子種別を判別することを特徴とする、降水粒子判別装置。     In the precipitation particle discriminating device according to claim 4,
    The discrimination processing unit
    From each of the membership functions generated for each combination of the certain precipitation particle type and the feature value type, the degree of belonging to which the precipitation particle to be identified belongs to the certain precipitation particle type is calculated. Attribution degree calculation part,
    An evaluation value calculation unit that synthesizes a plurality of the degrees of attribution and calculates an evaluation value as an index value indicating the possibility that the precipitation particles to be identified are a certain type of precipitation particles; and
    The precipitation particle determination device, wherein the precipitation particle determination unit determines the precipitation particle type based on the evaluation value calculated for each precipitation particle type.
  6. 　請求項１から請求項５のいずれか１項に記載の降水粒子判別装置において、
    　前記特徴値算出部は、互いに位置が異なる各前記対象エリアのそれぞれに対応する前記特徴値を算出し、
    　前記降水粒子判別部は、各前記特徴値に基づき、各前記対象エリアの前記降水粒子種別を判別することを特徴とする、降水粒子判別装置。     In the precipitation particle discriminating device according to any one of claims 1 to 5,
    The feature value calculation unit calculates the feature value corresponding to each of the target areas having different positions;
    The said precipitation particle discrimination | determination part discriminate | determines the said precipitation particle classification of each said target area based on each said characteristic value, The precipitation particle discrimination device characterized by the above-mentioned.
  7. 　請求項１から請求項６のいずれか１項に記載の降水粒子判別装置において、
    　前記特徴値は、前記地点毎偏波パラメータの平均値及び分散値のうち少なくとも１つに基づく値であることを特徴とする、降水粒子判別装置。     In the precipitation particle discriminating device according to any one of claims 1 to 6,
    The feature particle value is a value based on at least one of an average value and a dispersion value of the polarization parameter for each point.
  8. 　水平偏波及び垂直偏波を送受波可能な送受波部と、
    　前記送受波部によって受波された前記水平偏波の反射波及び前記垂直偏波の反射波に基づいて、対象エリア内に含まれる複数の地点から複数の地点毎偏波パラメータを算出するパラメータ算出部と、
    　前記パラメータ算出部によって算出された前記複数の地点毎偏波パラメータに基づいて前記対象エリア内の降水粒子種別を判別する、請求項１から請求項７のいずれか１項に記載の降水粒子判別装置と、
    　を備えていることを特徴とする、気象レーダー装置。     A transmission / reception unit capable of transmitting / receiving horizontal polarization and vertical polarization;
    Parameter calculation for calculating a plurality of point-by-point polarization parameters from a plurality of points included in the target area based on the horizontally polarized wave reflected wave and the vertically polarized wave reflected wave received by the wave transmitting / receiving unit And
    The precipitation particle discriminating device according to any one of claims 1 to 7, wherein the precipitation particle type in the target area is discriminated based on the plurality of point-by-point polarization parameters calculated by the parameter calculation unit. When,
    A meteorological radar device comprising:
  9. 　対象エリア内に含まれる複数の地点から得られる複数の地点毎偏波パラメータから、前記対象エリア内の降水粒子の特徴を示す特徴値を算出するステップと、
    　前記特徴値を算出するステップで算出された前記特徴値に基づき前記対象エリア内の降水粒子種別を判別するステップと、
    　を含むことを特徴とする、降水粒子判別方法。     Calculating feature values indicating characteristics of precipitation particles in the target area from a plurality of point-by-point polarization parameters obtained from a plurality of points included in the target area;
    Determining a precipitation particle type in the target area based on the feature value calculated in the step of calculating the feature value;
    A method for discriminating precipitation particles, comprising:
  10. 　対象エリア内に含まれる複数の地点から得られる複数の地点毎偏波パラメータから、前記対象エリア内の降水粒子の特徴を示す特徴値を算出するステップと、
    　前記特徴値を算出するステップで算出された前記特徴値に基づき前記対象エリア内の降水粒子種別を判別するステップと、
    　をコンピュータに実行させることを特徴とする、降水粒子判別プログラム。     Calculating feature values indicating characteristics of precipitation particles in the target area from a plurality of point-by-point polarization parameters obtained from a plurality of points included in the target area;
    Determining a precipitation particle type in the target area based on the feature value calculated in the step of calculating the feature value;
    A program for discriminating precipitation particles, which causes a computer to execute.
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