CN109444893B - Phase state recognition product jigsaw method and device based on dual-polarization radar network - Google Patents

Phase state recognition product jigsaw method and device based on dual-polarization radar network Download PDF

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CN109444893B
CN109444893B CN201811355636.2A CN201811355636A CN109444893B CN 109444893 B CN109444893 B CN 109444893B CN 201811355636 A CN201811355636 A CN 201811355636A CN 109444893 B CN109444893 B CN 109444893B
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polarization
phase
point
lattice
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CN109444893A (en
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吴翀
刘黎平
王红艳
胡志群
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Chinese Academy of Meteorological Sciences CAMS
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Chinese Academy of Meteorological Sciences CAMS
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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
    • 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

Abstract

The embodiment of the invention provides a phase recognition product jigsaw method and device based on a dual-polarization radar network, aiming at a first lattice point covered by M dual-polarization radars at the same time, a jigsaw scheme is determined after comprehensive consideration is carried out according to the number of stations of the dual-polarization radars in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network and an observation target of the dual-polarization radar, and then the phase recognition product jigsaw is carried out on the first lattice point according to the jigsaw scheme, so that a uniform phase jigsaw product with a large observation range is obtained, and an integral structure of large-scale disaster weather such as rainstorm, typhoon and the like can be given.

Description

Phase state recognition product jigsaw method and device based on dual-polarization radar network
Technical Field
The embodiment of the invention relates to the technical field of meteorological observation, in particular to a phase recognition product jigsaw puzzle method and device based on a dual-polarization radar network.
Background
At present, the conventional meteorological radar can only obtain a reflectivity factor, so that precipitation particles in different phases are difficult to distinguish. Compared with the conventional meteorological radar, the dual-polarization radar has a vertical polarization channel in addition to a horizontal polarization channel. Therefore, the dual-polarization radar can acquire more-dimensional information to distinguish precipitation particles in different phases.
The dual-polarization radar identifies precipitation particles based on the theory of scattering of electromagnetic waves by the precipitation particles. For a single radar, when different phases of the aqueous composition in the cloud are different due to their shape, size, density and orientation in space, the scattering and depolarization effects on the polarized wave will also be different. According to long-term observation facts, the dual-polarization parameter characteristics of common rainfall phases can be counted, then the observation parameters are compared with the dual-polarization parameter characteristics one by using a phase recognition algorithm, and the closest rainfall phase is screened out to serve as a phase recognition product. However, the coverage area of a single radar is limited, and the whole structure of a large-scale disaster weather such as rainstorm and typhoon cannot be provided. In order to solve the problem, a dual-polarization radar network is formed by deploying a plurality of dual-polarization radars in the same area, and phase recognition products of the dual-polarization radars in the dual-polarization radar network are subjected to jigsaw puzzle, so that a unified phase puzzle product with a large range is formed.
Therefore, how to jigsaw the phase recognition products of the dual-polarization radars of all parts in the dual-polarization radar network is a problem to be solved urgently at present.
Disclosure of Invention
The invention provides a phase state identification product jigsaw method and device based on a dual-polarization radar network, which are used for realizing jigsaw puzzle of phase state identification products of dual-polarization radars of all parts in the dual-polarization radar network.
In a first aspect, an embodiment of the present invention provides a phase recognition product puzzle method based on a dual-polarization radar network, including:
determining M dual-polarization radars of which the observation range comprises a first grid point, wherein the first grid point is a grid point in a Cartesian coordinate system, the M dual-polarization radars are included in a dual-polarization radar network, and M is more than or equal to 2;
determining a jigsaw scheme of the first grid point according to at least one of the number of stations of a dual-polarization radar in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network or an observation target of the dual-polarization radar;
and identifying the product jigsaw by the phase state of the first lattice point according to the jigsaw scheme.
In one possible design, the implementation of the puzzle scheme is a recognition-first puzzle scheme, and the phase recognition of the product puzzle for the first lattice according to the puzzle scheme includes:
for a first dual-polarization radar in the M dual-polarization radars, traversing the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase to obtain a phase recognition product of each point in a spherical coordinate system of the first dual-polarization radar;
determining the phase state identification product of the first grid point according to the beam width of the first dual-polarization radar and the phase state identification product of each point under the spherical coordinate system of the first dual-polarization radar, and further obtaining M phase state identification products aiming at the first grid point;
and performing picture splicing on the M phase state identification products to obtain the phase state identification product of the first lattice point of the dual-polarization radar network.
In one possible design, the determining the phase recognition product for the first grid point according to the beam width of the first dual-polarization radar and the phase recognition product for each point in the spherical coordinate system of the first dual-polarization radar includes:
dividing grid points in a Cartesian coordinate system into a first part and a second part according to the beam width of the first dual-polarization radar, wherein the grid points included in the first part are located within the range of the beam width, and the grid points included in the second part are located outside the range of the beam width;
projecting the phase state identification product of each point in the spherical coordinate system to a Cartesian coordinate system according to the beam width of the first dual-polarization radar to obtain a lattice phase state identification product contained in the first part;
interpolating each lattice point contained in the second part according to a precipitation type field in a Cartesian coordinate system and a phase recognition product of each lattice point contained in the first part to obtain a phase recognition product of each lattice point contained in the second part;
and determining phase state identification products of the M dual-polarization radars on the first lattice point respectively to obtain M phase state identification products aiming at the first lattice point, and further obtaining M phase state identification products aiming at the first lattice point.
In a possible design, the interpolating, according to the precipitation type field and the phase recognition products of the lattice points included in the first part, the phase recognition products of the lattice points included in the second part to obtain the phase recognition products of the lattice points included in the second part includes:
determining a rainfall type field of the grid points to be interpolated, wherein the grid points to be interpolated are contained in the second part;
when the rainfall type field of the lattice points to be interpolated is convection cloud rainfall, lattice points vertical to the lattice points to be interpolated are determined from the first part, and interpolation is carried out on the lattice points to be interpolated according to the lattice points vertical to the lattice points to be interpolated;
and when the rainfall type field of the lattice points to be interpolated is laminar cloud rainfall, determining lattice points parallel to the lattice points to be interpolated from the first part, and interpolating the lattice points to be interpolated according to the lattice points parallel to the lattice points to be interpolated.
In one possible design, the solution for picture arrangement is specifically a solution for picture arrangement first and then recognition, and the phase recognition of the product picture arrangement for the first lattice point according to the solution for picture arrangement includes:
for real-time observation data of a first dual-polarization radar in the M dual-polarization radars, determining a reference point in a spherical coordinate system corresponding to the first dual-polarization radar according to the position of the first grid point, determining an interpolation result of the dual-polarization parameter at the first grid point according to a coordinate parameter of the reference point under the spherical coordinate system, so as to obtain M interpolation results, performing jigsaw puzzle on the M interpolation results, so as to obtain a jigsaw result of the real-time observation data at the first grid point, wherein the first dual-polarization radar is any one of the M dual-polarization radars;
and traversing the membership functions of the dual-polarization parameters of each rainfall phase by combining the jigsaw result of the first grid point obtained according to the real-time observation data of the M dual-polarization radars with the rainfall type field and the environmental temperature field of the real-time observation data in a Cartesian coordinate system to obtain the phase recognition product of the first grid point.
In one possible design, before the phase recognizing product puzzle according to the puzzle scheme for the first lattice point, the method further includes:
dividing historical observation data of the first dual-polarization radar into N types of subdata according to the precipitation phase state, wherein the N types of subdata correspond to N different types of precipitation phase states;
and determining a membership function of each dual-polarization parameter in a first type precipitation phase state according to first type subdata, wherein the first type subdata is contained in the N types of subdata, and the first type subdata corresponds to the first type precipitation phase state.
In one possible design, before the phase recognizing product puzzle according to the puzzle scheme for the first lattice point, the method further includes:
for a first dual-polarization radar in the M dual-polarization radars, determining a precipitation type field of each point in a spherical coordinate system according to real-time observation data of the first dual-polarization radar; converting the precipitation type field of each point in the spherical coordinate system into a Cartesian coordinate system to obtain the precipitation type field of each grid point of the first dual-polarization radar in the Cartesian coordinate system;
for the first lattice point, determining a precipitation type field of the lattice point according to M precipitation type fields, wherein the M precipitation type fields are obtained according to M dual-polarization radars covering the first lattice point at the same time;
when the laminar cloud precipitation in the M precipitation type fields is more than the convection cloud precipitation, determining that the precipitation type field of the first lattice point is laminar cloud precipitation;
when the convective cloud precipitation in the M precipitation type fields is equal to or more than laminar cloud precipitation, determining that the precipitation type field of the first lattice point is convective cloud precipitation.
In a possible design, after traversing the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase to obtain the phase identification product of each point, the method further includes: and correcting the phase recognition product of the corresponding point according to the precipitation type field and the environment temperature field of each point in the spherical coordinate system.
In a feasible design, after traversing the membership functions of the dual-polarization parameters of each precipitation phase according to the puzzle result of the first lattice obtained from the real-time observation data of the M dual-polarization radars to obtain the phase identification product of the first lattice, the method further includes:
and correcting the phase recognition product of the first lattice point according to the precipitation type field and the ambient temperature field of the first lattice point.
In a second aspect, an embodiment of the present invention provides a jigsaw device, including:
the first determining module is used for determining M dual-polarization radars of which the observation range comprises a first grid point, wherein the first grid point is a grid point in a Cartesian coordinate system, the M dual-polarization radars are included in a dual-polarization radar network, and M is more than or equal to 2;
the second determining module is used for determining a jigsaw scheme of the first grid point according to at least one of the station number of the dual-polarization radar in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network or an observation target of the dual-polarization radar;
and the picture splicing module is used for carrying out phase recognition on the product picture splicing on the first lattice point according to the picture splicing scheme.
In a feasible design, when the second determining module determines that the jigsaw puzzle scheme is the jigsaw puzzle scheme after recognition, the jigsaw module is specifically configured to traverse, for a first dual-polarization radar of the M dual-polarization radars, the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase, so as to obtain a phase recognition product of each point in a spherical coordinate system of the first dual-polarization radar; determining the phase state identification product of the first grid point according to the beam width of the first dual-polarization radar and the phase state identification product of each point under the spherical coordinate system of the first dual-polarization radar, and further obtaining M phase state identification products aiming at the first grid point; and performing picture splicing on the M phase state identification products to obtain the phase state identification product of the first lattice point of the dual-polarization radar network.
In one possible design, the puzzle module, when determining the phase recognition product of the first grid point according to the beam width of the first dual-polarization radar and the phase recognition product of each point in the spherical coordinate system of the first dual-polarization radar, is specifically configured to divide the grid points in the cartesian coordinate system into a first part and a second part according to the beam width of the first dual-polarization radar, where the first part includes grid points located within the beam width range, and the second part includes grid points located outside the beam width range; projecting the phase state identification product of each point in the spherical coordinate system to a Cartesian coordinate system according to the beam width of the first dual-polarization radar to obtain a lattice phase state identification product contained in the first part; interpolating each lattice point contained in the second part according to a precipitation type field in a Cartesian coordinate system and a phase recognition product of each lattice point contained in the first part to obtain a phase recognition product of each lattice point contained in the second part; and determining phase state identification products of the M dual-polarization radars on the first lattice point respectively to obtain M phase state identification products aiming at the first lattice point, and further obtaining M phase state identification products aiming at the first lattice point.
In a feasible design, the puzzle module is specifically configured to determine a precipitation type field of a lattice point to be interpolated, a precipitation type field of the lattice point to be interpolated, and a phase recognition product of each lattice point included in the first part according to the precipitation type field and the phase recognition product of each lattice point included in the second part, to obtain the phase recognition product of each lattice point included in the second part; when the rainfall type field of the lattice points to be interpolated is convection cloud rainfall, lattice points vertical to the lattice points to be interpolated are determined from the first part, and interpolation is carried out on the lattice points to be interpolated according to the lattice points vertical to the lattice points to be interpolated; and when the rainfall type field of the lattice points to be interpolated is laminar cloud rainfall, determining lattice points parallel to the lattice points to be interpolated from the first part, and interpolating the lattice points to be interpolated according to the lattice points parallel to the lattice points to be interpolated.
In a feasible design, when the second determining module determines that the jigsaw puzzle scheme is a jigsaw-first and recognition scheme, the jigsaw module is specifically configured to determine, for real-time observation data of a first dual-polarization radar in the M dual-polarization radars, a reference point in a spherical coordinate system corresponding to the first dual-polarization radar according to a position of the first lattice point, determine, according to a coordinate parameter of the reference point in the spherical coordinate system, an interpolation result of the dual-polarization parameter at the first lattice point, thereby obtaining M interpolation results, and perform jigsaw on the M interpolation results, thereby obtaining a jigsaw result of the real-time observation data at the first lattice point, where the first dual-polarization radar is any one of the M dual-polarization radars; and traversing the membership functions of the dual-polarization parameters of each rainfall phase by combining the jigsaw result of the first grid point obtained according to the real-time observation data of the M dual-polarization radars with the rainfall type field and the environmental temperature field of the real-time observation data in a Cartesian coordinate system to obtain the phase recognition product of the first grid point.
In a possible design, the above apparatus further includes:
a third determining module, configured to divide historical observation data of the first dual-polarization radar into N types of sub-data according to a precipitation phase before the phase recognition product puzzle is performed on the first lattice point by the puzzle module according to the puzzle scheme, where the N types of sub-data correspond to N different types of precipitation phase; and determining a membership function of each dual-polarization parameter in a first type precipitation phase state according to first type subdata, wherein the first type subdata is contained in the N types of subdata, and the first type subdata corresponds to the first type precipitation phase state.
In a possible design, the above apparatus further includes:
a fourth determining module, configured to determine, for a first dual-polarization radar of the M dual-polarization radars, a precipitation type field of each point in a spherical coordinate system according to real-time observation data of the first dual-polarization radar before the phase recognition product puzzle is performed on the first point by the puzzle module according to the puzzle scheme; converting the precipitation type field of each point in the spherical coordinate system into a Cartesian coordinate system to obtain the precipitation type field of each grid point of the first dual-polarization radar in the Cartesian coordinate system; for the first lattice point, determining a precipitation type field of the lattice point according to M precipitation type fields, wherein the M precipitation type fields are obtained according to M dual-polarization radars covering the first lattice point at the same time; when the laminar cloud precipitation in the M precipitation type fields is more than the convection cloud precipitation, determining that the precipitation type field of the first lattice point is laminar cloud precipitation; when the convective cloud precipitation in the M precipitation type fields is equal to or more than laminar cloud precipitation, determining that the precipitation type field of the first lattice point is convective cloud precipitation.
In a possible design, the above apparatus further includes:
and the correcting module is used for correcting the phase recognition product of the corresponding point according to the precipitation type field and the environment temperature field of each point under the spherical coordinate system after the jigsaw module traverses the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase to obtain the phase recognition product of each point.
In a feasible design, the correcting module is further configured to, after the puzzle module obtains the puzzle result of the first lattice point according to the real-time observation data of the M dual-polarization radars and traverses the membership function of each dual-polarization parameter of each rainfall phase to obtain the phase identification product of the first lattice point, correct the phase identification product of the first lattice point according to the rainfall type field and the ambient temperature field of the first lattice point.
According to the phase recognition product jigsaw method and device based on the dual-polarization radar network, provided by the embodiment of the invention, aiming at a first lattice point covered by M dual-polarization radars, a jigsaw scheme is determined after comprehensive consideration is carried out according to the station number of the dual-polarization radars in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network and the observation targets of the dual-polarization radars, and then the phase recognition product jigsaw is carried out on the first lattice point according to the jigsaw scheme, so that a uniform phase jigsaw product with a large observation range is obtained, and an integral structure of large-range disastrous weather such as rainstorm, typhoon and the like can be given.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a phase recognition result stitching method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a phase recognition result puzzle according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating membership functions applied to a phase recognition result puzzle method according to an embodiment of the present invention;
FIG. 4 is a schematic view of a jigsaw device according to an embodiment of the present invention;
FIG. 5 is a schematic view of another embodiment of a jigsaw device of the present invention;
fig. 6 is a schematic structural view of a jigsaw device according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a phase recognition result stitching method according to an embodiment of the present invention. The execution subject of the embodiment is a puzzle mechanism, which can be implemented by software, hardware or a combination of software and hardware, and can be part or all of an electronic device.
As shown in fig. 1, the present embodiment includes:
101. and determining M dual-polarization radars of which the observation range comprises a first grid point, wherein the first grid point is a grid point in a Cartesian coordinate system, the M dual-polarization radars are included in a dual-polarization radar network, and M is more than or equal to 2.
In the embodiment of the invention, the meteorological radar adopted in the meteorological observation process is a dual-polarization radar. Compared with the conventional meteorological radar, the dual-polarization radar has a vertical polarization channel in addition to a horizontal polarization channel. Therefore, the dual-polarization radar can acquire not only the echo intensity (Z) and the radial velocity (V), but also the following dual-polarization parameters: differential reflectivity factor ZDRDifferential phase phiDPDifferential propagation phase shift ratio KDPDepolarization polarization ratio LDRDenoted by zero lag correlation coefficient phvAnd (4) showing.
Because a plurality of stations are deployed in the same area, each station is provided with a dual-polarization radar. For example, in the region corresponding to the china territory, more than 200 dual-polarization radars are deployed, and the more than 200 dual-polarization radars can observe the weather conditions in all ranges of the china territory. Wherein the observation area of each radar may be divided into a plurality of grid points of the same size. In order to make the observation areas of the plurality of radars cover the whole area to be observed, the coverage area of each dual-polarization radar partially overlaps with the coverage area of the dual-polarization radar around the dual-polarization radar. That is, the same grid point may belong to the observation regions of different dual-polarization radars. Therefore, if the same grid point is covered by multiple dual-polarization radars at the same time, the phase recognition products of different dual-polarization radars need to be subjected to jigsaw puzzle splicing.
In this step, for a first grid point in a cartesian coordinate system, the first grid point is simultaneously covered by M sections of dual-polarization radars.
102. And determining a jigsaw scheme of the first grid point according to the station number of the dual-polarization radar in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network and an observation target of the dual-polarization radar.
In this step, the number of radar sites of the dual-polarization radar network, the performance of the radar, and the observation target of the dual-polarization radar network need to be considered comprehensively, and the jigsaw scheme with the best efficiency is determined.
103. And identifying the product jigsaw by the phase state of the first lattice point according to the jigsaw scheme.
In this step, the phase recognition product puzzle is performed on the first lattice point according to the puzzle scheme determined in the above step 102.
The phase recognition product jigsaw method provided by the embodiment of the invention determines a jigsaw scheme after comprehensive consideration according to the number of the dual-polarization radars in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network and the observation targets of the dual-polarization radars aiming at the first lattice points covered by the M dual-polarization radars at the same time, and then carries out phase recognition product jigsaw on the first lattice points according to the jigsaw scheme, thereby obtaining a uniform phase jigsaw product with a large observation range, and further being capable of providing an integral structure of large-scale weather disasters such as rainstorm, typhoon and the like.
Fig. 2 is a process diagram of phase recognition result mosaics according to an embodiment of the present invention. In the process of picture arrangement, firstly, a localized phase state identification parameter such as a membership function is established through the statistics of a large amount of historical observation data; secondly, determining an optimal jigsaw scheme according to the distribution of the dual-polarization radar in the dual-polarization radar network, the hardware performance of the dual-polarization radar, an observation target and the like; thirdly, determining a lattice and picture splicing method of the precipitation type field; thirdly, determining a lattice point method and a jigsaw method of the environment temperature field; finally, phase recognition and picture splicing are carried out on the observation data of the polarization radar network, the precipitation phase of each dual-polarization radar can be recognized station by station, and then picture splicing is carried out on the phase product in the spherical coordinate system; or firstly carrying out picture splicing on the observation parameters of the dual-polarization radar network, and then carrying out phase state identification on the parameters which are positioned under the first lattice point after picture splicing; in the phase recognition and picture arrangement process, the obtained product is verified by using a precipitation type field and/or an environment temperature field. This process will be described in detail below.
First, localized phase identification parameters are established.
In a feasible implementation manner, historical observation data of a first dual-polarization radar is divided into N types of sub-data according to a precipitation phase state, the N types of sub-data correspond to N different types of precipitation phase states, the first dual-polarization radar is included in M portions of dual-polarization radars, the M portions of dual-polarization radars are dual-polarization radars whose coverage areas include first grid points, and the first grid points are grid points in a cartesian coordinate system; and determining a membership function of each dual-polarization parameter in a first type precipitation phase state according to first type subdata, wherein the first type subdata is contained in the N types of subdata, and the first type subdata corresponds to the first type precipitation phase state.
In meteorological observation, for example, the dual-polarization radar identifies precipitation particles based on the theory of scattering of electromagnetic waves by precipitation particles. Specifically, when different phases of the aqueous composition in the cloud are oriented in different directions due to their shape, size, density and orientation in space, the scattering and depolarization effects on the polarized wave will be different. Therefore, the dual-polarization parameter characteristics of the common precipitation phase can be counted according to long-term observation facts, the observation parameters are compared with the dual-polarization parameter characteristics one by using a phase recognition algorithm, and the most approximate precipitation phase is screened out to serve as a phase recognition result. Therefore, the localized phase identification parameters can be established through statistics of a large number of historical observations.
Specifically, the phase recognition algorithm essentially compares each observation parameter of the dual-polarization radar with the characteristic of a typical phase through simple data operation, and selects the closest phase as a phase recognition result. The membership function is a basic calculation unit of a phase equipment algorithm and represents the matching degree of a certain polarization parameter and a precipitation phase. Fig. 3 is a schematic diagram of a membership function applicable to the phase recognition result mosaic method according to an embodiment of the present invention.
Referring to FIG. 3, the X-axis is an arbitrary dual polarization parameter, and P (X) is the value of a membership function, which is described by four vertices X1, X2, X3 and X4. When x is in the range of x 2-x 3, the value of P (x) is 1, which indicates that the matching degree of the observed parameter and the phase state is good; when x < x1 or x > x4, the value of P (x) is 0, indicating that the observed parameter does not belong to the phase class. When x is in the range of x 1-x 2 or x 3-x 4, the values of P (x) are 0-1 and 1-0, respectively. Because the observation parameters of the dual-polarization radars in different regions and different models are not completely the same, a localized membership function needs to be established for each dual-polarization radar, and a phase identification parameter needs to be established according to the membership function. In the establishing process, according to the observation result of sounding, the layered cloud precipitation is divided into a snow and ice crystal area, a melting area and a rainwater area according to the height; determining a hail area according to an observation result acquired by a ground station; then, counting the dual-polarization parameter characteristics of the real-time phase state; and finally, sorting the statistical results from small to large, and selecting points with the positions of 5%, 20%, 80% and 95% as x1, x2, x3 and x4 of the trapezoidal membership function for phase recognition, namely the first parameter, the second parameter, the third parameter and the fourth parameter.
Second, an optimal puzzle scheme is determined.
For example, the mosaicing algorithm first needs to convert the observation data of the dual-polarization radar in the spherical coordinate system to the cartesian coordinate system. Because grid parameters (grid point resolution and vertical layer number) of a cartesian coordinate system are not fixed, parameter setting in the conversion process directly affects the efficiency of phase recognition. For example, after the dual polarization radar is rasterized according to the original range resolution, the cartesian coordinate system and the spherical coordinate system exhibit a many-to-one relationship, that is, one point of the spherical coordinate system corresponds to at least one grid point in the cartesian coordinate system, and the data flow is increased by about 10 times, which increases the amount of computation required by the phase recognition algorithm.
Referring to the portion shown by the dotted line in fig. 2, in order to ensure that the phase recognition algorithm has sufficient timeliness, it is necessary to scientifically set grid parameters, i.e. determine the puzzle scheme, according to the site distribution, radar performance and observation targets of the dual-polarization radar network. The monitoring target mainly refers to the lattice point spacing, the number of layers in the vertical direction and the like. In the process of determining the jigsaw scheme, the data volume before and after the spherical coordinates are converted into the cartesian coordinates needs to be evaluated, the identification algorithm is arranged in the step of the minimum data volume to reduce the calculation amount, and the horizontal and vertical resolutions of the grid points are properly reduced if necessary. For a service radar network with a large number of stations and a large detection range, a monitoring target is to use 2-4 times of distance resolution as a grid point distance and about 10 layers of ascending plane Position indicators (CAPPI) in the vertical direction, at this time, a phase recognition algorithm is arranged behind a jigsaw algorithm, namely, a scheme of firstly jigsaw puzzle and then recognition is adopted for jigsaw of a phase recognition result, and the operational efficiency of phase recognition can be greatly improved. For a fine radar network with a small number of stations and a small monitoring range in the dual-polarization radar network, the monitoring target is to use 1-2 times of range resolution as grid resolution and about 20 layers of CAPPI in the vertical direction, and at the moment, the phase recognition result is set before the jigsaw algorithm, namely, the scheme of firstly recognizing and then jigsaw is adopted to perform jigsaw of the phase recognition result, so that the operation efficiency can be improved.
And thirdly, determining a gridding and picture splicing method of the precipitation type field.
In a possible implementation manner, for a first dual-polarization radar in the M dual-polarization radars, a precipitation type field of each point in a spherical coordinate system is determined according to real-time observation data of the first dual-polarization radar; converting the precipitation type field of each point in the spherical coordinate system into a Cartesian coordinate system to obtain the precipitation type field of each grid point of the first dual-polarization radar in the Cartesian coordinate system; for the first lattice point, determining a precipitation type field of the lattice point according to M precipitation type fields, wherein the M precipitation type fields are obtained according to M dual-polarization radars covering the first lattice point at the same time; when the laminar cloud precipitation in the M precipitation type fields is more than the convection cloud precipitation, determining that the precipitation type field of the first lattice point is laminar cloud precipitation; when the convective cloud precipitation in the M precipitation type fields is equal to or more than laminar cloud precipitation, determining that the precipitation type field of the first lattice point is convective cloud precipitation.
Specifically, referring to the part shown by the two-dot chain line in fig. 2, in the networking and puzzle process of the dual-polarization parameter, the characteristics of convection cloud and layered cloud are identified according to the real-time radar observation data, and the precipitation type field in the dual-polarization radar observation area is determined for correcting the phase recognition result. The precipitation type field is stored in a two-dimensional spherical coordinate plane formed by the direction and the distance, and represents the precipitation type projected to the area under all points of the spherical coordinate system.
In the process of lattice localization and picture splicing of the precipitation type field, firstly, points in a spherical coordinate system are converted into a two-dimensional Cartesian coordinate system formed by longitude and latitude, and a result corresponding to the three-dimensional Cartesian coordinate system is formed. And then, assigning the precipitation type of each grid point in the two-dimensional Cartesian coordinate system to all three-dimensional grid points with observed values right above the first grid point to obtain a grid-point precipitation type field. Since the first lattice point is covered by M sections of dual-polarization radars at the same time, there are M precipitation type fields for the first lattice point. When the M precipitation type fields have differences, the phase recognition result of most dual-polarization radars is used as the precipitation type field of the lattice point. If the number of dual polarization radar sites identified as lamellar cloud precipitation and convective cloud precipitation is the same, the grid point may be located at the echo edge of the convective cloud and the mosaic algorithm will tend to be a convective cloud precipitation type field.
And thirdly, determining a gridding and picture splicing method of the environment temperature field.
In a possible implementation manner, for a first dual-polarization radar in the M dual-polarization radars, an ambient temperature field of each point in a spherical coordinate system is determined according to real-time observation data of the first dual-polarization radar; converting the environment temperature field of each point in the spherical coordinate system into a Cartesian coordinate system to obtain the environment temperature field of each point of the first dual-polarization radar in the Cartesian coordinate system; for the first grid point, determining the average melting layer height according to melting layer heights respectively corresponding to M environmental temperature fields, wherein the M environmental temperature fields are obtained according to M dual-polarization radars covering the first grid point at the same time; and determining the ambient temperature field of the first lattice point according to the average melting layer height.
Specifically, the grid-point transformation of the ambient temperature field is to project the top and bottom heights of the fusion layer stored in the one-dimensional direction to a two-dimensional cartesian coordinate system formed by longitude and latitude within the coverage range of the dual-polarization radar. When the environment temperature field is subjected to jigsaw puzzle, firstly, the heights of the melting layers of different dual-polarization radars in the common coverage area are averaged to obtain the average height of the melting layer; and then, according to the average height of the melting layer, marking the position of the first grid point in a Cartesian coordinate system, wherein the corresponding heights of the grid point are greater than 0 ℃, about 0 ℃ and less than 0 ℃, and the grid point is used for correcting the phase recognition product. And then combining the result with a numerical prediction mode to obtain the specific temperature of each lattice point.
Thirdly, phase recognition product jigsaw stage:
(1) the phase recognition product picture splicing process of recognizing picture splicing firstly and then picture splicing:
according to the jigsaw scheme, for a first dual-polarization radar in M dual-polarization radars covering a first grid point at the same time, traversing real-time observation data of the first dual-polarization radar through a membership function of each dual-polarization parameter of each precipitation phase, and combining a precipitation type field and an environment temperature field under a spherical coordinate system to obtain a phase recognition product of each point under the spherical coordinate system of the first dual-polarization radar; dividing grid points in a Cartesian coordinate system into a first part and a second part according to the beam width of the first dual-polarization radar, wherein the grid points included in the first part are located within the range of the beam width, and the grid points included in the second part are located outside the range of the beam width; projecting the phase state identification product of each point in the spherical coordinate system to a Cartesian coordinate system according to the beam width of the first dual-polarization radar to obtain a lattice phase state identification product contained in the first part; interpolating each lattice point contained in the second part according to a precipitation type field in a Cartesian coordinate system and a phase recognition product of each lattice point contained in the first part to obtain a phase recognition product of each lattice point contained in the second part; determining phase state identification products of the M dual-polarization radars on the first grid point respectively to obtain M phase state identification products aiming at the first grid point; and performing picture splicing on the M phase state identification products to obtain the phase state identification product of the first lattice point of the dual-polarization radar network.
In the scheme, for any first dual-polarization radar in M dual-polarization radars, firstly, according to real-time observation data of the dual-polarization radar, a phase recognition product of each point in a spherical coordinate system is recognized, then, the phase recognition product in the spherical coordinate system is interpolated into a Cartesian coordinate system to obtain the phase recognition product of the first grid point, and for the first grid point, M phase recognition products exist. Then, the M phase recognition products are subjected to jigsaw puzzle.
Specifically, please refer to the portion outlined by the black dots in fig. 2. In the scheme, firstly, observation parameters under a spherical coordinate system are identified by using localized phase identification parameters to obtain a phase identification result of a single radar; and then, performing lattice transformation on the phase recognition result in the spherical coordinate system. Since the phase recognition result cannot be interpolated in space, in the process of converting from a spherical coordinate system to a cartesian coordinate system, the projection is performed according to the beam width of the dual-polarization radar, and a three-dimensional lattice point (hereinafter referred to as a lattice point included in the second part) located inside the start-stop elevation and outside the beam coverage is given to the mark to be added. And finally, carrying out jigsaw puzzle on the data subjected to lattice point formation by the dual-polarization radar, wherein the weight of the jigsaw puzzle in the common coverage area uses the data quality coefficient, and therefore the point with higher data quality is selected as a final jigsaw puzzle value. The data quality coefficient can be determined according to the influence of non-precipitation echoes, the influence of electromagnetic wave transmission and scattering special effects or the influence of radar hardware in the observation data of the dual-polarization radar network.
In the above embodiment, since the phase recognition result is projected strictly according to the beam width during the lattice spotting, the spatial discontinuity of a large area is stored after the puzzle is pieced. In order to improve the puzzle of the phase recognition result of the unknown lattice points outside the beam range of the dual-polarization radar, in the embodiment of the invention, the lattice points marked to be filled are subjected to post-processing according to the filling scheme of the physical characteristics of precipitation. In the process, as the space continuity of the convection cloud in the vertical direction is good, for the lattice points to be filled, the precipitation type of which is the convection cloud, the phase recognition product result closest to the lattice point in the vertical direction is selected for filling. And for the points to be filled with the precipitation type of the layered cloud, considering that the phase distribution of the layered cloud precipitation in the horizontal direction is continuous, selecting the phase state of the layered cloud precipitation within a certain range in the horizontal direction of the grid points to be filled for statistics, and taking the precipitation phase state which has the highest occurrence frequency and is located in the environmental temperature field as a phase state identification result.
(2) And identifying the phase state of the product by picture arrangement first and then identifying the product.
In a feasible implementation manner, when performing phase recognition product puzzle according to the puzzle scheme, determining M dual-polarization radars whose observation ranges include a first grid point, where the first grid point is a grid point in a cartesian coordinate system, and M is greater than or equal to 2; for real-time observation data of a first dual-polarization radar in the M dual-polarization radars, determining a reference point in a spherical coordinate system corresponding to the first dual-polarization radar according to the position of the first grid point, determining an interpolation result of the dual-polarization parameter at the first grid point according to a coordinate parameter of the reference point under the spherical coordinate system, so as to obtain M interpolation results, performing jigsaw puzzle on the M interpolation results, so as to obtain a jigsaw result of the real-time observation data at the first grid point, wherein the first dual-polarization radar is any one of the M dual-polarization radars; and traversing the membership functions of the dual-polarization parameters of each rainfall phase by combining the jigsaw result of the first grid point obtained according to the real-time observation data of the M dual-polarization radars with the rainfall type field and the environmental temperature field in the Cartesian coordinate system to obtain the phase recognition product of the first grid point.
In the scheme, for any first dual-polarization radar in M dual-polarization radars, real-time observation data in a spherical coordinate system is interpolated into a Cartesian coordinate system, and then a picture is spliced on a first grid point; and finally, carrying out phase product identification on the first lattice point after picture splicing.
For example, referring to a part outlined by a dense black dot in fig. 2, a scheme is first jigsaw puzzle and then recognition, dual polarization parameters located under a cartesian coordinate system after the jigsaw puzzle are directly used, fuzzy logic operation is directly performed by combining localized phase state recognition parameters, and a recognition result is corrected by an environment temperature field and a precipitation type field which can be in one-to-one correspondence after the jigsaw puzzle. And the identified result is directly used as the phase state identification result of the dual-polarization radar network.
And finally, a correction phase.
In the process of correcting the phase state identification product, no matter the scheme of picture arrangement first and then identification or the scheme of picture arrangement first and then identification, the identification product needs to be corrected by using a precipitation type field and an environment temperature field after fuzzy logic operation.
For the scheme of first identification and then jigsaw puzzle, the real-time observation data of the first dual-polarization radar traverses the membership functions of the dual-polarization parameters of each precipitation phase to obtain the phase identification product of each point, and then the phase identification product of the corresponding point is corrected according to the precipitation type field and the environment temperature field of each point in a spherical coordinate system. In the correction process, the observation parameters under the spherical coordinate system are identified, then the precipitation type field and the environment temperature field under the spherical coordinate system are used for correction, and then the jigsaw is carried out on the identified products. For example, when the jigsaw puzzle scheme is a jigsaw first and then recognition scheme, for a first dual-polarization radar in the M dual-polarization radars, the real-time observation data of the first dual-polarization radar is traversed through the membership functions of the dual-polarization parameters of each rainfall phase to obtain a phase recognition product of each point in a spherical coordinate system of the first dual-polarization radar, and then the phase recognition product of the corresponding point in the spherical coordinate system is corrected by combining the rainfall type field and the ambient temperature field of the real-time observation data in the spherical coordinate system.
For the scheme of first jigsaw and then recognition, according to the jigsaw result of the first lattice point obtained by the real-time observation data of the M dual-polarization radars, the membership function of each dual-polarization parameter of each precipitation phase is traversed, after the phase recognition product of the first lattice point is obtained, the phase recognition product of the first lattice point is corrected according to the precipitation type field and the environment temperature field of the first lattice point. In the process, after fuzzy logic operation is carried out on each point under the spherical coordinate system, the point is combined with a precipitation type field and an environment temperature field of the jigsaw puzzle to be corrected, and the phase recognition product jigsaw puzzle is obtained.
Next, how to correct the phase recognition product will be described in detail.
According to the height of the melting layer, the specific limiting conditions are as follows: 1) snow can only appear at the bottom of the melting layer and at a height above the bottom; 2) pure raindrops can only appear at the top of the melt layer and at a height below it. And for the phase recognition product under the spherical coordinate system in the method of recognizing the picture mosaic firstly and then correcting by using the environment temperature field under the spherical coordinate system. And for the phase state identification product under the Cartesian coordinate system in the method of first jigsaw puzzle and then identification, the environment temperature field after gridding and jigsaw puzzle is used for correction.
Depending on the type of precipitation, specific constraints are: 1) the convection cloud precipitation cannot generate two precipitation phases of dry snow and wet snow in the whole layer; 2) the lamellar cloud precipitation cannot generate three precipitation phases of large drops, aragonite and hail in the whole layer. And correcting the phase recognition product under the spherical coordinate system in the scheme of picture splicing after recognition by using the precipitation type field under the spherical coordinate system. And correcting the phase state identification product under the Cartesian coordinate system in the scheme of firstly splicing the images and then identifying the images by using the latticed precipitation type field after splicing the images. In addition, the precipitation phase state belonging to the convection cloud does not appear in the laminar cloud precipitation, and conversely, the precipitation phase state belonging to the laminar cloud does not appear in the convection cloud precipitation. The precipitation phase state belonging to the melting layer does not appear at a height other than the melting layer, the precipitation phase state belonging to the melting layer or lower does not appear at a height above the melting layer, and the precipitation phase state belonging to the melting layer or higher does not appear at a height below the melting layer.
Fig. 4 is a schematic structural diagram of a jigsaw device provided in an embodiment of the present invention, which can be implemented by software and/or hardware. As shown in fig. 4, the jigsaw puzzle 100 includes:
the first determining module 11 is configured to determine M dual-polarization radars of which an observation range includes a first grid point, where the first grid point is a grid point in a cartesian coordinate system, the M dual-polarization radars are included in a dual-polarization radar network, and M is greater than or equal to 2;
a second determining module 12, configured to determine a jigsaw scheme of the first grid point according to at least one of the number of stations of a dual-polarization radar in the dual-polarization radar network, performance of each dual-polarization radar in the dual-polarization radar network, or an observation target of the dual-polarization radar;
and the jigsaw module 13 is configured to perform phase recognition on the first lattice point to produce a jigsaw according to the jigsaw scheme.
In a feasible design, when the second determining module 12 determines that the jigsaw puzzle is the jigsaw puzzle after recognition, the jigsaw module 13 is specifically configured to traverse, for a first dual-polarization radar of the M dual-polarization radars, the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase, so as to obtain a phase recognition product of each point in a spherical coordinate system of the first dual-polarization radar; determining the phase state identification product of the first grid point according to the beam width of the first dual-polarization radar and the phase state identification product of each point under the spherical coordinate system of the first dual-polarization radar, and further obtaining M phase state identification products aiming at the first grid point; and performing picture splicing on the M phase state identification products to obtain the phase state identification product of the first lattice point of the dual-polarization radar network.
In one possible design, the puzzle module 13, when determining the phase recognition product of the first grid point according to the beam width of the first dual-polarization radar and the phase recognition product of each point in the spherical coordinate system of the first dual-polarization radar, is specifically configured to divide the grid points in the cartesian coordinate system into a first part and a second part according to the beam width of the first dual-polarization radar, where the grid points included in the first part are located within the beam width range, and the grid points included in the second part are located outside the beam width range; projecting the phase state identification product of each point in the spherical coordinate system to a Cartesian coordinate system according to the beam width of the first dual-polarization radar to obtain a lattice phase state identification product contained in the first part; interpolating each lattice point contained in the second part according to a precipitation type field in a Cartesian coordinate system and a phase recognition product of each lattice point contained in the first part to obtain a phase recognition product of each lattice point contained in the second part; and determining phase state identification products of the M dual-polarization radars on the first lattice point respectively to obtain M phase state identification products aiming at the first lattice point, and further obtaining M phase state identification products aiming at the first lattice point.
In a feasible design, the puzzle module 13 is specifically configured to determine a precipitation type field of a lattice point to be interpolated, a precipitation type field of the lattice point to be interpolated, and a lattice point to be interpolated included in the second part, when interpolating each lattice point included in the second part according to a precipitation type field and a phase recognition product of each lattice point included in the first part to obtain a phase recognition product of each lattice point included in the second part; when the rainfall type field of the lattice points to be interpolated is convection cloud rainfall, lattice points vertical to the lattice points to be interpolated are determined from the first part, and interpolation is carried out on the lattice points to be interpolated according to the lattice points vertical to the lattice points to be interpolated; and when the rainfall type field of the lattice points to be interpolated is laminar cloud rainfall, determining lattice points parallel to the lattice points to be interpolated from the first part, and interpolating the lattice points to be interpolated according to the lattice points parallel to the lattice points to be interpolated.
In a feasible design, when the second determining module 12 determines that the jigsaw puzzle is a jigsaw first and then recognition scheme, the jigsaw module 13 is specifically configured to determine, for real-time observation data of a first dual-polarization radar in the M dual-polarization radars, a reference point in a spherical coordinate system corresponding to the first dual-polarization radar according to a position of the first lattice point, determine, according to a coordinate parameter of the reference point in the spherical coordinate system, an interpolation result of the dual-polarization parameter at the first lattice point, thereby obtaining M interpolation results, and perform jigsaw on the M interpolation results, thereby obtaining a jigsaw puzzle result of the real-time observation data at the first lattice point, where the first dual-polarization radar is any one of the M dual-polarization radars; and traversing the membership functions of the dual-polarization parameters of each rainfall phase by combining the jigsaw result of the first grid point obtained according to the real-time observation data of the M dual-polarization radars with the rainfall type field and the environmental temperature field of the real-time observation data in a Cartesian coordinate system to obtain the phase recognition product of the first grid point.
Fig. 5 is a schematic structural diagram of another jigsaw device provided by an embodiment of the present invention, as shown in fig. 5, the jigsaw device 100 further includes, on the basis of fig. 4:
a third determining module 14, configured to divide historical observation data of the first dual-polarization radar into N types of sub-data according to a precipitation phase before the phase recognition product puzzle is performed on the first lattice point by the puzzle module 13 according to the puzzle scheme, where the N types of sub-data correspond to N different types of precipitation phase; and determining a membership function of each dual-polarization parameter in a first type precipitation phase state according to first type subdata, wherein the first type subdata is contained in the N types of subdata, and the first type subdata corresponds to the first type precipitation phase state.
Referring to fig. 5 again, the apparatus further includes:
a fourth determining module 15, configured to determine, for a first dual-polarization radar in the M dual-polarization radars, a rainfall type field of each point in a spherical coordinate system according to real-time observation data of the first dual-polarization radar before the puzzle module 13 performs phase recognition product puzzle on the first point according to the puzzle scheme; converting the precipitation type field of each point in the spherical coordinate system into a Cartesian coordinate system to obtain the precipitation type field of each grid point of the first dual-polarization radar in the Cartesian coordinate system; for the first lattice point, determining a precipitation type field of the lattice point according to M precipitation type fields, wherein the M precipitation type fields are obtained according to M dual-polarization radars covering the first lattice point at the same time; when the laminar cloud precipitation in the M precipitation type fields is more than the convection cloud precipitation, determining that the precipitation type field of the first lattice point is laminar cloud precipitation; when the convective cloud precipitation in the M precipitation type fields is equal to or more than laminar cloud precipitation, determining that the precipitation type field of the first lattice point is convective cloud precipitation.
Referring to fig. 5 again, the apparatus further includes: and the correcting module 16 is configured to, after the puzzle module 13 traverses the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase to obtain the phase identification product of each point, correct the phase identification product of the corresponding point according to the precipitation type field and the ambient temperature field of each point in the spherical coordinate system.
In a feasible design, the correcting module 16 is configured to, after the puzzle module 13 obtains the puzzle result of the first lattice point according to the real-time observation data of the M dual-polarization radars and traverses the membership function of each dual-polarization parameter of each rainfall phase to obtain the phase identification product of the first lattice point, correct the phase identification product of the first lattice point according to the rainfall type field and the ambient temperature field of the first lattice point.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 6, the electronic apparatus 200 includes:
at least one processor 21 and memory 22;
the memory 22 stores computer-executable instructions;
the at least one processor 21 executes the computer-executable instructions stored by the memory 22 to cause the at least one processor 21 to perform the dual-polarization parametric radar web-based parametric tiling method described above.
For a specific implementation process of the processor 21, reference may be made to the above method embodiments, which implement similar principles and technical effects, and this embodiment is not described herein again.
Optionally, the user equipment 20 further comprises a communication section 23. The processor 21, the memory 22, and the communication unit 23 may be connected by a bus 24.
The embodiment of the present invention further provides a storage medium, where the storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the parametric stitching method based on the dual-polarization-parameter radar network.
The embodiment of the present invention further provides a computer program product, which when running on a computer, causes the computer to execute the parametric jigsaw method based on the dual-polarization parametric radar network.
In the above embodiments, it should be understood that the described apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The unit formed by the modules can be realized in a hardware form, and can also be realized in a form of hardware and a software functional unit.
The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable an electronic device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the method according to various embodiments of the present invention.
It should be understood that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present invention are not limited to only one bus or one type of bus.
The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in a terminal or server.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A phase state identification product jigsaw method based on a dual-polarization radar network is characterized by comprising the following steps:
determining M dual-polarization radars of which the observation range comprises a first grid point, wherein the first grid point is a grid point in a Cartesian coordinate system, the M dual-polarization radars are included in a dual-polarization radar network, and M is more than or equal to 2;
determining a jigsaw scheme of the first grid point according to at least one of the number of stations of a dual-polarization radar in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network or an observation target of the dual-polarization radar;
according to the picture splicing scheme, carrying out phase recognition on the first lattice point to obtain a product picture splicing;
wherein, according to the jigsaw scheme, the phase recognition product jigsaw for the first lattice point comprises:
when the jigsaw scheme is a scheme of jigsaw after recognition, for a first dual-polarization radar in the M dual-polarization radars, traversing the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase to obtain a phase recognition product of each point under a spherical coordinate system of the first dual-polarization radar, determining the phase recognition product of the first lattice according to the beam width of the first dual-polarization radar and the phase recognition product of each point under the spherical coordinate system of the first dual-polarization radar, further obtaining M phase recognition products for the first lattice, and performing jigsaw on the M phase recognition products to obtain the phase recognition product of the first lattice of the dual-polarization radar network;
alternatively, the first and second electrodes may be,
when the jigsaw scheme is a scheme of jigsaw firstly and then recognizing, determining a reference point in a spherical coordinate system corresponding to a first dual-polarization radar in the M dual-polarization radars according to the position of a first lattice point for real-time observation data of the first dual-polarization radar, determining an interpolation result of the dual-polarization parameter at the first lattice point according to a coordinate parameter of the reference point under the spherical coordinate system, thereby obtaining M interpolation results, jigsaw the M interpolation results, so as to obtain a jigsaw result of the real-time observation data at the first lattice point, wherein the first dual-polarization radar is any one dual-polarization radar in the M dual-polarization radars, and a membership function of each dual-polarization of each phase precipitation state is traversed according to the jigsaw result of the first lattice point obtained according to the real-time observation data of the M dual-polarization radars, and obtaining the phase state identification product of the first lattice point.
2. The method of claim 1, wherein determining the phase identification product for the first grid point from the beamwidth of the first dual-polarization radar and the phase identification product for each point in the spherical coordinate system of the first dual-polarization radar comprises:
dividing grid points in a Cartesian coordinate system into a first part and a second part according to the beam width of the first dual-polarization radar, wherein the grid points included in the first part are located within the range of the beam width, and the grid points included in the second part are located outside the range of the beam width;
projecting the phase state identification product of each point in the spherical coordinate system to a Cartesian coordinate system according to the beam width of the first dual-polarization radar to obtain a lattice phase state identification product contained in the first part;
interpolating each lattice point contained in the second part according to a precipitation type field in a Cartesian coordinate system and a phase recognition product of each lattice point contained in the first part to obtain a phase recognition product of each lattice point contained in the second part;
and determining phase state identification products of the M dual-polarization radars on the first lattice point respectively to obtain M phase state identification products aiming at the first lattice point, and further obtaining M phase state identification products aiming at the first lattice point.
3. The method of claim 2, wherein the interpolating the lattice points included in the second portion according to the precipitation type field and the phase recognition products of the lattice points included in the first portion to obtain the phase recognition products of the lattice points included in the second portion comprises:
determining a rainfall type field of the grid points to be interpolated, wherein the grid points to be interpolated are contained in the second part;
when the rainfall type field of the lattice points to be interpolated is convection cloud rainfall, lattice points vertical to the lattice points to be interpolated are determined from the first part, and interpolation is carried out on the lattice points to be interpolated according to the lattice points vertical to the lattice points to be interpolated;
and when the rainfall type field of the lattice points to be interpolated is laminar cloud rainfall, determining lattice points parallel to the lattice points to be interpolated from the first part, and interpolating the lattice points to be interpolated according to the lattice points parallel to the lattice points to be interpolated.
4. The method according to any one of claims 1 to 3, wherein before performing phase recognition on the first lattice point according to the puzzle scheme to produce a product puzzle, the method further comprises:
dividing historical observation data of the first dual-polarization radar into N types of subdata according to the precipitation phase state, wherein the N types of subdata correspond to N different types of precipitation phase states;
and determining a membership function of each dual-polarization parameter in a first type precipitation phase state according to first type subdata, wherein the first type subdata is contained in the N types of subdata, and the first type subdata corresponds to the first type precipitation phase state.
5. The method according to any one of claims 1 to 3, wherein before performing phase recognition on the first lattice point according to the puzzle scheme to produce a product puzzle, the method further comprises:
for a first dual-polarization radar in the M dual-polarization radars, determining a precipitation type field of each point in a spherical coordinate system according to real-time observation data of the first dual-polarization radar; converting the precipitation type field of each point in the spherical coordinate system into a Cartesian coordinate system to obtain the precipitation type field of each grid point of the first dual-polarization radar in the Cartesian coordinate system;
for the first lattice point, determining a precipitation type field of the lattice point according to M precipitation type fields, wherein the M precipitation type fields are obtained according to M dual-polarization radars covering the first lattice point at the same time;
when the laminar cloud precipitation in the M precipitation type fields is more than the convection cloud precipitation, determining that the precipitation type field of the first lattice point is laminar cloud precipitation;
when the convective cloud precipitation in the M precipitation type fields is equal to or more than laminar cloud precipitation, determining that the precipitation type field of the first lattice point is convective cloud precipitation.
6. The method according to any one of claims 1 to 3, wherein after traversing the real-time observation data of the first dual-polarization radar through the membership functions of the dual-polarization parameters of each precipitation phase to obtain the phase identification product of each point, the method further comprises:
and correcting the phase recognition product of the corresponding point according to the precipitation type field and the environment temperature field of each point in the spherical coordinate system.
7. The method of claim 1, wherein after the traversing the membership functions of the dual-polarization parameters of each precipitation phase according to the puzzle result of the first lattice obtained from the real-time observation data of the M dual-polarization radars to obtain the phase identification product of the first lattice, the method further comprises:
and correcting the phase recognition product of the first lattice point according to the precipitation type field and the ambient temperature field of the first lattice point.
8. A jigsaw puzzle device is characterized in that,
the first determining module is used for determining M dual-polarization radars of which the observation range comprises a first grid point, wherein the first grid point is a grid point in a Cartesian coordinate system, the M dual-polarization radars are included in a dual-polarization radar network, and M is more than or equal to 2;
the second determining module is used for determining a jigsaw scheme of the first grid point according to at least one of the station number of the dual-polarization radar in the dual-polarization radar network, the performance of each dual-polarization radar in the dual-polarization radar network or an observation target of the dual-polarization radar;
the picture splicing module is used for carrying out phase recognition on the first lattice point to obtain a product picture splicing according to the picture splicing scheme;
when the second determining module determines that the jigsaw scheme is a jigsaw scheme after recognition, the jigsaw module is specifically configured to traverse, for a first dual-polarization radar in the M dual-polarization radars, a membership function of each dual-polarization parameter of each precipitation phase by using real-time observation data of the first dual-polarization radar to obtain a phase recognition product of each point in a spherical coordinate system of the first dual-polarization radar, determine, according to a beam width of the first dual-polarization radar and the phase recognition product of each point in the spherical coordinate system of the first dual-polarization radar, the phase recognition product of the first grid point, further obtain M phase recognition products for the first grid point, perform jigsaw on the M phase recognition products, and obtain a phase recognition product of the first grid point of the dual-polarization radar network;
alternatively, the first and second electrodes may be,
the jigsaw module, when the second determining module determines that the jigsaw scheme is a jigsaw-first recognition scheme, is specifically configured to determine, for real-time observation data of a first dual-polarization radar of the M dual-polarization radars, a reference point in a spherical coordinate system corresponding to the first dual-polarization radar according to a position of the first lattice point, determine an interpolation result of the dual-polarization parameter at the first lattice point according to a coordinate parameter of the reference point in the spherical coordinate system, thereby obtaining M interpolation results, perform jigsaw on the M interpolation results, thereby obtaining a jigsaw result of the real-time observation data at the first lattice point, where the first dual-polarization radar is any one of the M dual-polarization radars, and obtain a jigsaw result of the first lattice point according to real-time observation data of the M dual-polarization radars, and traversing the membership functions of the dual-polarization parameters of each precipitation phase by combining the precipitation type field and the environment temperature field of the real-time observation data in a Cartesian coordinate system to obtain the phase identification product of the first grid point.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6377202B1 (en) * 2000-03-24 2002-04-23 The United States Of America As Represented By The Secretary Of Commerce Icing hazard avoidance system and method using dual-polarization airborne radar
CN105891833A (en) * 2016-04-15 2016-08-24 兰州大学 Method of identifying warm cloud precipitation rate based on Doppler radar information
CN107015229A (en) * 2017-05-22 2017-08-04 新疆维吾尔自治区人工影响天气办公室 Artificial Hail Suppression operation command method based on dual-polarization weather radar

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6697009B2 (en) * 2001-06-15 2004-02-24 Lockheed Martin Corporation Adaptive digital beamforming architecture for target detection and angle estimation in multiple mainlobe and sidelobe jamming
CN108693534A (en) * 2018-03-27 2018-10-23 南京恩瑞特实业有限公司 NRIET X band radars cooperate with networking analysis method
CN108680920A (en) * 2018-04-28 2018-10-19 深圳市雅码科技有限公司 A kind of hazard weather identification early warning system and method based on dual polarization radar

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6377202B1 (en) * 2000-03-24 2002-04-23 The United States Of America As Represented By The Secretary Of Commerce Icing hazard avoidance system and method using dual-polarization airborne radar
CN105891833A (en) * 2016-04-15 2016-08-24 兰州大学 Method of identifying warm cloud precipitation rate based on Doppler radar information
CN107015229A (en) * 2017-05-22 2017-08-04 新疆维吾尔自治区人工影响天气办公室 Artificial Hail Suppression operation command method based on dual-polarization weather radar

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
《Dual-Polarization X-Band Phased Array Weather》;Peter R. Drake;《2014 International Radar Conference》;20141231;第1页-第6页 *
《多波段双偏振天气雷达识别降水类型的模拟研究》;杨通晓;《高原气象》;20170228(第01期);第241页-第255页 *

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