WO2017051647A1 - Dispositif de détermination de particules de précipitation, dispositif radar météorologique, procédé de détermination de particules de précipitation, et programme de détermination de particules de précipitation - Google Patents

Dispositif de détermination de particules de précipitation, dispositif radar météorologique, procédé de détermination de particules de précipitation, et programme de détermination de particules de précipitation Download PDF

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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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English (en)
Japanese (ja)
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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/ja
Publication of WO2017051647A1 publication Critical patent/WO2017051647A1/fr

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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

Afin d'augmenter la précision de résultats de détermination de particules de précipitation, un dispositif de détermination de particules de précipitation (19) comprend : une unité de calcul de valeurs de caractéristiques (30) qui calcule des valeurs de caractéristiques indiquant les caractéristiques des particules de précipitation à l'intérieur d'une zone cible, à partir d'une pluralité de paramètres de polarisation spécifiques à un emplacement obtenus à partir d'une pluralité d'emplacements inclus à l'intérieur de la zone cible ; et une unité de traitement de détermination (23) ayant une unité de détermination de particules de précipitation qui détermine le type de particules de précipitation à l'intérieur de la zone cible, sur la base des valeurs de caractéristiques calculées par l'unité de calcul de valeurs de caractéristiques (30).
PCT/JP2016/074328 2015-09-24 2016-08-22 Dispositif de détermination de particules de précipitation, dispositif radar météorologique, procédé de détermination de particules de précipitation, et programme de détermination de particules de précipitation WO2017051647A1 (fr)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019111641A1 (fr) * 2017-12-06 2019-06-13 古野電気株式会社 Dispositif, procédé et programme d'identification de particules de précipitation
WO2019176462A1 (fr) * 2018-03-13 2019-09-19 古野電気株式会社 Dispositif de discrimination de particules de précipitation, système de discrimination de particules de précipitation, procédé de discrimination de particules de précipitation et programme de discrimination de particules de précipitation
CN111045012A (zh) * 2018-10-15 2020-04-21 通用汽车环球科技运作有限责任公司 使用雷达检测降水的系统和方法
US20210088653A1 (en) * 2019-09-25 2021-03-25 Korea Meteorological Administration Apparatus and method for estimating rainfall of hail and rain using dual-polarization weather radar
JP2021092407A (ja) * 2019-12-09 2021-06-17 コリア インスティテュート オフ コンストラクション テクノロジー 超短距離二重偏波レーダの多重高度観測資料を用いた降雨強度推定方法，及び推定装置
JP7479768B2 (ja) 2020-09-18 2024-05-09 日本無線株式会社 物標検出装置

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WO2019111641A1 (fr) * 2017-12-06 2019-06-13 古野電気株式会社 Dispositif, procédé et programme d'identification de particules de précipitation
JPWO2019111641A1 (ja) * 2017-12-06 2020-12-17 古野電気株式会社 降水粒子判別装置、降水粒子判別方法、及び降水粒子判別プログラム
EP3745162A4 (fr) * 2017-12-06 2021-11-17 Furuno Electric Co., Ltd. Dispositif, procédé et programme d'identification de particules de précipitation
WO2019176462A1 (fr) * 2018-03-13 2019-09-19 古野電気株式会社 Dispositif de discrimination de particules de précipitation, système de discrimination de particules de précipitation, procédé de discrimination de particules de précipitation et programme de discrimination de particules de précipitation
CN112005130A (zh) * 2018-03-13 2020-11-27 古野电气株式会社 降水粒子判别装置、系统、方法及程序
CN112005130B (zh) * 2018-03-13 2024-03-26 古野电气株式会社 降水粒子判别装置、系统、方法及存储介质
US11520040B2 (en) 2018-03-13 2022-12-06 Furuno Electric Co., Ltd. Precipitation particle discrimination device, precipitation particle discrimination system, precipitation particle discrimination method and precipitation particle discrimination program
CN111045012A (zh) * 2018-10-15 2020-04-21 通用汽车环球科技运作有限责任公司 使用雷达检测降水的系统和方法
CN111045012B (zh) * 2018-10-15 2023-07-28 通用汽车环球科技运作有限责任公司 使用雷达检测降水的系统和方法
US11550051B2 (en) * 2019-09-25 2023-01-10 Korea Meteorological Administration Apparatus and method for estimating rainfall of hail and rain using dual-polarization weather radar
US20210088653A1 (en) * 2019-09-25 2021-03-25 Korea Meteorological Administration Apparatus and method for estimating rainfall of hail and rain using dual-polarization weather radar
JP2021092407A (ja) * 2019-12-09 2021-06-17 コリア インスティテュート オフ コンストラクション テクノロジー 超短距離二重偏波レーダの多重高度観測資料を用いた降雨強度推定方法，及び推定装置
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