CN113466868B - Method and system for processing differential phase of dual-polarization radar - Google Patents

Method and system for processing differential phase of dual-polarization radar Download PDF

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CN113466868B
CN113466868B CN202110645155.0A CN202110645155A CN113466868B CN 113466868 B CN113466868 B CN 113466868B CN 202110645155 A CN202110645155 A CN 202110645155A CN 113466868 B CN113466868 B CN 113466868B
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CN113466868A (en
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陈生
刘陈帅
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Sun Yat Sen University
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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
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4052—Means for monitoring or calibrating by simulation of echoes
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/411—Identification of targets based on measurements of radar reflectivity
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter
    • 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 invention discloses a method and a system for processing a differential phase of a dual-polarization radar, wherein the method comprises the following steps: acquiring first observation data of a dual-polarization weather radar, and preprocessing the first observation data to acquire second observation data; processing the second observation data by adopting a preset algorithm to obtain a boundary condition of the second observation data; filling missing values in the second observation data by adopting a physical constraint method to obtain third observation data; and performing variational fitting processing on the third observation data by a method of constructing a cost function to obtain a reconstructed dual-polarization radar differential phase. The invention can eliminate the observation error of the dual-polarization radar and solve the problem of negative differential propagation phase shift rate (a)KDP) The case (1).

Description

Method and system for processing differential phase of dual-polarization radar
Technical Field
The invention relates to the technical field of radar measurement, in particular to a method, a system, a terminal and a storage medium for processing a dual-polarization radar differential phase.
Background
Quantitative Precipitation Estimation (QPE) is one of the main tasks of radar meteorology, and dual-polarized weather radar has superior performance in QPE compared to conventional doppler radar. Dual polarization radar can provide more polarization variables, such as differential reflectivity factor (Z)DR) Differential phase (phi)DP) And horizontal reflectance (Z)H) And the like. Differential propagation phase shift ratio (K)DP) Is one half of the derivative of the differential phase with respect to distance, KDPPrecipitation estimation algorithm R (K)DP) Is less affected by the raindrop spectrum. Radar measurement error is one of the main errors of radar QPE, and measurement should be performed before QPE is performedPolarization variables (e.g. Z)H、ΦDPEtc.) to eliminate random errors in the measurements.
At present, the existing methods for eliminating random errors of polarization variables include: moving average, wavelet analysis, linear programming, variation method and the like, although most of the methods can eliminate measurement errors, the methods have differential propagation phase shift rate (K) after quality controlDP) Negative values may occur, which in turn may lead to negative rainfall rates, thereby affecting the effectiveness of the precipitation estimation.
Disclosure of Invention
The purpose of the invention is: the method, the system, the terminal and the storage medium for processing the differential phase of the dual-polarization radar can eliminate the observation error of the dual-polarization radar and simultaneously solve the problem of negative differential propagation phase shift rate (K)DP) The case (1).
In order to achieve the above object, the present invention provides a method for processing differential phase of dual-polarization radar, comprising:
acquiring first observation data of a dual-polarization weather radar, and preprocessing the first observation data to acquire second observation data;
processing the second observation data by adopting a preset algorithm to obtain a boundary condition of the second observation data;
filling missing values in the second observation data by adopting a physical constraint method to obtain third observation data;
and performing variational fitting processing on the third observation data by a method of constructing a cost function to obtain a reconstructed dual-polarization radar differential phase.
Further, the method further comprises:
and predicting precipitation according to the dual-polarized radar differential phase to obtain a precipitation prediction result.
Further, the preprocessing the first observation data to obtain second observation data includes:
z in the first observation dataH<3dBZ,ρhv<0.85,|ZDR|>2.3dB of data advanceLine culling, wherein ZHExpressed as a reflectivity factor, ρhvExpressed as the polarization correlation coefficient, ZDRExpressed as differential reflectivity.
Further, the processing the second observation data by using a preset algorithm to obtain a boundary range of the second observation data includes:
calculating a near-end boundary condition and a far-end boundary condition, wherein the calculating the near-end boundary condition comprises:
acquiring a radial data sample set of a near-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the minimum value in the distance set as a near-end boundary condition; and if not, taking the median in the distance set as a near-end boundary condition.
The calculating the far-end boundary condition comprises:
acquiring a radial data sample set of a far-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the maximum value in the distance set as a near-end boundary condition; and if not, taking the median in the distance set as a far-end boundary condition.
Further, the method using physical constraint fills up missing values in the second observation data to obtain third observation data, and the following calculation formula is used:
Figure GDA0003227682580000031
Figure GDA0003227682580000032
wherein the content of the first and second substances,
Figure GDA0003227682580000033
is provided with ZHAnd ZDRTwo variables are estimated to be obtained by the method,
Figure GDA0003227682580000034
obtained according to physical constraints
Figure GDA0003227682580000035
The resulting differential phase is inverted and the phase is inverted,
Figure GDA0003227682580000036
is the initial differential phase in the radial direction.
Further, the third observation data is subjected to variational fitting processing by a cost function constructing method to obtain a reconstructed dual-polarization radar differential phase, and the following calculation formula is specifically adopted:
J＝Jobs+Jlpf
Figure GDA0003227682580000037
Figure GDA0003227682580000038
wherein J is a cost function, JobsThe term is the mean square error of the observed term and the reconstructed theoretical term, JlpfIs the Laplacian of the parameter k, ClpfIs a parameter of the filter, N is the number of distance bins, H1And H2For forward operators, k2Non-negative operators, δ is the observed differential phase, δ' is the difference of the boundary conditions.
Further, according to the dual-polarization radar differential phase, precipitation is predicted, and a precipitation prediction result is obtained by adopting the following calculation formula:
Figure GDA0003227682580000041
wherein a and b are parameters of the observed data, R (K)DP) Is the predicted amount of precipitation.
The invention also provides a system for processing the differential phase of the dual-polarization radar, which comprises the following components: an acquisition module, a first processing module, a second processing module, and a reconstruction module, wherein,
the acquisition module is used for acquiring first observation data of the dual-polarization weather radar, and preprocessing the first observation data to acquire second observation data;
the first processing module is used for processing the second observation data by adopting a preset algorithm to obtain the boundary condition of the second observation data
The second processing module is configured to fill up missing values in the second observation data by using a physical constraint method to obtain third observation data;
and the reconstruction module is used for performing variational fitting processing on the third observation data by constructing a cost function method to obtain a reconstructed dual-polarization radar differential phase.
The present invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a method of processing dual-polarized radar differential phase as recited in any one of the above.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of processing differential phase of a dual-polarization radar as described in any one of the above.
Compared with the prior art, the method, the system, the terminal and the storage medium for processing the differential phase of the dual-polarization radar have the advantages that:
the invention uses a variational method to reconstruct the differential phase (phi)DP) The random error of measurement can be effectively removed, and the reconstructed differential phase (phi) is ensuredDP) Calculating the obtained differential propagation phase shift ratio (K)DP) Is non-negative.
Drawings
Fig. 1 is a schematic flow chart of a method for processing a differential phase of a dual-polarization radar according to the present invention;
FIG. 2 is a flow diagram of a quality control process for the radar differential phase provided by the present invention;
FIG. 3 is a schematic diagram of a radar differential phase calculation boundary condition provided by the present invention;
FIG. 4 is a schematic diagram of the radar differential phase physical constraints provided by the present invention;
FIG. 5 is a schematic diagram of the effect of the radar differential variational fit provided by the present invention;
FIG. 6 is a diagram of raw data of radar difference provided by the present invention
FIG. 7 is a schematic diagram of data after the variance fitting process of the radar difference provided by the present invention
Fig. 8 is a schematic structural diagram of a system for processing differential phase of dual-polarization radar according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1 to 7, a method for processing differential phases of a dual-polarization radar according to an embodiment of the present invention at least includes the following steps:
s1, acquiring first observation data of the dual-polarization weather radar, and preprocessing the first observation data to acquire second observation data;
specifically, radar observation data is subjected to quality control, and a reflectivity factor (Z) is subjected toH) Polarization correlation coefficient (p)hv) And differential reflectivity (Z)DR) Performing quality control on ZH<3dBZ,ρhv<0.85,|ZDR|>And deleting the 2.3dB sampling points to eliminate the non-meteorological echoes and ground clutter.
S2, processing the second observation data by adopting a preset algorithm to obtain a boundary condition of the second observation data;
specifically, the boundary conditions include: near end boundary condition phinearAnd far-end boundary condition phifar；
It should be noted that before the boundary condition is calculated, isolated point detection and deletion need to be performed on the observation data, and the specific steps are as follows:
1) selecting a phiDPTraversing all data to calculate continuous numbersSlice index of group IDX ═ (IDX)1,idx2,…,idxn) Wherein
Figure GDA0003227682580000061
idx1Is the index of the first consecutive array,
Figure GDA0003227682580000062
is the starting index of the first consecutive array,
Figure GDA0003227682580000063
indexing the end of the first continuous array;
2) traversing the continuous array indexes obtained in the step 1) and calculating the interval between two continuous arrays
Figure GDA0003227682580000064
If the spacing is less than 5, and
Figure GDA0003227682580000065
is less than 30 deg., the indices of the two arrays are merged into one index
Figure GDA0003227682580000066
Otherwise, if the distance library number in the jth index is less than 3, the continuous array is regarded as an isolated point and deleted;
3) go through all the consecutive array indexes if phi of a certain distance libraryDPIf the value of (c) is different from that of the neighboring range bin by more than 35 deg., the range bin is regarded as data contaminated by clutter and deleted.
At a pair of phiDPAfter quality control, the boundary phi of the radial data is calculatednearThe conditions are specifically calculated as follows:
1) and traversing the indexes of the radial data from beginning to end, and if the array elements represented by the indexes are more than 20, taking the first 20 data of the array as samples of the near boundary condition calculation.
2) Calculating the selected sample (X)1,X2,…,X20) And corresponding distance ((r)1,r2,…,r20) The slope K of the linear regression.
3) If K is>0, then the near-end boundary condition ΦnearIs a minimum distance r1Otherwise, the near-end boundary condition is the median of the taken samples.
Far end boundary condition phifarThe calculation method of (2) is similar to the near-end boundary condition:
1) and traversing the indexes of the radial data from tail to head, and if the array elements represented by the indexes are more than 20, taking the last 20 data of the array as samples for calculating the near boundary condition.
2) Calculating the selected sample (X)end-19,Xend-18,…,Xend) To corresponding distance (r)end-19,rend-18,…,rend) The slope K of the linear regression.
3) If K is>0, then the far-end boundary condition ΦfarIs the farthest distance rendOtherwise, the far-end boundary condition is the median of the taken samples.
S3, filling missing values in the second observation data by adopting a physical constraint method to obtain third observation data;
specifically, after the calculation of the boundary conditions of the far end and the near end of the differential phase is completed, the missing value of the observed data needs to be filledH，ZDRAnd KDPRelationship between the three, using ZHAnd ZDRTwo polarization variables calculate KDPThe specific calculation formula is as follows:
Figure GDA0003227682580000071
Figure GDA0003227682580000072
wherein Z isHIs in mm6m-3，ZDRThe unit of (a) is in dB,
Figure GDA0003227682580000073
is provided with ZHAnd ZDRTwo variables are estimated to be obtained by the method,
Figure GDA0003227682580000074
obtained according to physical constraints
Figure GDA0003227682580000075
The resulting differential phase is inverted and the phase is inverted,
Figure GDA0003227682580000076
in the invention, the near boundary condition is used as the initial differential phase, and other parameters in the formula are obtained by fitting the observation data of the S-band dual-polarization radar.
And S4, performing variational fitting processing on the third observation data by constructing a cost function method to obtain a reconstructed dual-polarization radar differential phase.
Specifically, after missing value filling is completed, variational fitting is performed by constructing a cost function, and a specific derivation formula is as follows:
before constructing the cost function, several intermediate variables need to be constructed, and for a piece of radar radial observation data with N distance libraries, a differential phase observation value (phi) is defined in the textDP)iThe theoretical differential phase is (phi)DP)iDifferential propagation phase shift ratio (K)DP)iWherein i is 1,2, … N.
ΦDP＝[(ΦDP)1,(ΦDP)2,…,(ΦDP)N]T
φDP＝[(φDP)1,(φDP)2,…,(φDP)N]T
KDP＝[(KDP)1,(KDP)2,…,(KDP)N]T
Then given
Figure GDA0003227682580000081
Definition of (1):
Figure GDA0003227682580000082
according to K given aboveDPIs introduced here a forward operator H1，
Figure GDA0003227682580000083
Can be expressed as:
Figure GDA0003227682580000084
Figure GDA0003227682580000085
where ar is the radar range resolution. Because of phiDPIs increasing, KDPIs non-negative, in order to ensure KDPNon-negative, introducing a non-negative operator k2It is expressed as follows:
Figure GDA0003227682580000086
(i＝1,2,…,N)
the above formula can be obtained in a simultaneous manner,
Figure GDA0003227682580000087
the expression of (c) can be written as:
Figure GDA0003227682580000088
by the same token, can give
Figure GDA0003227682580000089
Definition of (1):
Figure GDA00032276825800000810
by introducing antecedent operators
Figure GDA00032276825800000811
A similar form can be written:
Figure GDA00032276825800000812
Figure GDA0003227682580000091
the difference between the observed differential phase and the boundary condition is:
Figure GDA0003227682580000092
the cost function J is defined as:
J＝Jobs+Jlpf
Figure GDA0003227682580000093
Figure GDA0003227682580000094
Jobsthe term is the mean square error of the observation term and the reconstructed theoretical term, and is the main part in the cost function, so that the reconstructed differential phase can be better fitted with the observed differential phase. J. the design is a squarelpfIs the Laplacian of the parameter k, which is equivalent to a low-pass filter, ClpfAre parameters of the filter. Taking K when the cost function takes the minimum value as a final solution, and then calculating K through KDPThe iterative method chosen here is a quasi-Newton method, which requires a partial derivative of the cost functionAs the direction of iteration. The partial derivative of the cost function with respect to k is as follows:
Figure GDA0003227682580000095
Figure GDA0003227682580000096
Figure GDA0003227682580000097
solving by a cost function and partial derivatives related to k by using a Newton iteration method, and finally reconstructing phiDP。
In one embodiment of the present invention, the method further includes:
and predicting precipitation according to the dual-polarized radar differential phase to obtain a precipitation prediction result.
In an embodiment of the present invention, the preprocessing the first observation data to obtain second observation data includes:
z in the first observation dataH<3dBZ,ρhv<0.85,|ZDR|>2.3dB of data are removed, where ZHExpressed as a reflectivity factor, ρhvExpressed as the polarization correlation coefficient, ZDRExpressed as differential reflectivity.
In an embodiment of the present invention, the processing the second observation data by using a preset algorithm to obtain a boundary range of the second observation data includes:
calculating a near-end boundary condition and a far-end boundary condition, wherein the calculating the near-end boundary condition comprises:
acquiring a radial data sample set of a near-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the minimum value in the distance set as a near-end boundary condition; and if not, taking the median in the distance set as a near-end boundary condition.
The calculating the far-end boundary condition comprises:
acquiring a radial data sample set of a far-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the maximum value in the distance set as a near-end boundary condition; and if not, taking the median in the distance set as a far-end boundary condition.
In an embodiment of the present invention, the method that uses physical constraint fills up missing values in the second observation data to obtain third observation data, and uses the following calculation formula:
Figure GDA0003227682580000111
Figure GDA0003227682580000112
wherein the content of the first and second substances,
Figure GDA0003227682580000113
is provided with ZHAnd ZDRTwo variables are estimated to be obtained by the method,
Figure GDA0003227682580000114
obtained according to physical constraints
Figure GDA0003227682580000115
The resulting differential phase is inverted and the phase is inverted,
Figure GDA0003227682580000116
is the initial differential phase in the radial direction.
In a certain embodiment of the present invention, the third observation data is subjected to variational fitting processing by a method of constructing a cost function, so as to obtain a reconstructed dual-polarization radar differential phase, specifically using the following calculation formula:
J＝Jobs+Jlpf
Figure GDA0003227682580000117
Figure GDA0003227682580000118
wherein J is a cost function, JobsThe term is the mean square error of the observed term and the reconstructed theoretical term, JlpfIs the Laplacian of the parameter k, ClpfIs a parameter of the filter, N is the number of distance bins, H1And H2For forward operators, k2Non-negative operators, δ is the observed differential phase, δ' is the difference of the boundary conditions.
In an embodiment of the present invention, the predicting precipitation according to the dual-polarized radar differential phase to obtain a precipitation prediction result uses the following calculation formula:
Figure GDA0003227682580000119
wherein a and b are parameters of the observed data, R (K)DP) Is the predicted amount of precipitation.
Compared with the prior art, the method for processing the differential phase of the dual-polarization radar has the beneficial effects that:
the invention makesUses a variational method to reconstruct the differential phase (phi)DP) The random error of measurement can be effectively removed, and the reconstructed differential phase (phi) is ensuredDP) Calculating the obtained differential propagation phase shift ratio (K)DP) Is non-negative.
As shown in fig. 8, the present invention further provides a system 200 for processing differential phase of dual-polarization radar, comprising: an acquisition module 201, a first processing module 202, a second processing module 203, and a reconstruction module 204, wherein,
the acquiring module 201 is configured to acquire first observation data of the dual-polarization weather radar, and preprocess the first observation data to acquire second observation data;
the first processing module 202 is configured to process the second observation data by using a preset algorithm to obtain a boundary condition of the second observation data
The second processing module 203 is configured to fill up missing values in the second observation data by using a physical constraint method, so as to obtain third observation data;
the reconstruction module 204 is configured to perform variational fitting processing on the third observation data by constructing a cost function method, so as to obtain a reconstructed dual-polarization radar differential phase.
The present invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a method of processing dual-polarized radar differential phase as recited in any one of the above.
It should be noted 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), an application-specific programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., the general-purpose processor may be a microprocessor, or the processor may be any conventional processor, the processor is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.
The memory mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory may be a high speed random access memory, may also be a non-volatile memory, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (FlashCard), and the like, or may also be other volatile solid state memory devices.
It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the terminal device is only an example and does not constitute a limitation of the terminal device, and may include more or less components, or combine some components, or different components.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of processing differential phase of a dual-polarization radar as described in any one of the above.
It should be noted that the computer program may be divided into one or more modules/units (e.g., computer program), and the one or more modules/units are stored in the memory and executed by the processor to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.

Claims (6)

1. A method of processing differential phase of a dual-polarization radar, comprising:
acquiring first observation data of the dual-polarization weather radar, preprocessing the first observation data, and acquiring second observation data, wherein the method comprises the following steps: z in the first observation dataH＜3dBZ,ρhv＜0.85,|ZDREliminating data with the value greater than 2.3dB, wherein ZHExpressed as a reflectivity factor, ρhvExpressed as the polarization correlation coefficient, ZDRExpressed as differential reflectivity;
processing the second observation data by adopting a preset algorithm to obtain a boundary condition of the second observation data, wherein the boundary condition of the second observation data comprises a near-end boundary condition and a far-end boundary condition, and the calculating of the near-end boundary condition comprises the following steps:
acquiring a radial data sample set of a near-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the minimum value in the distance set as a near-end boundary condition; if not, taking the median in the distance set as a near-end boundary condition;
the calculating the far-end boundary condition comprises:
acquiring a radial data sample set of a far-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the maximum value in the distance set as a near-end boundary condition; if not, taking the median in the distance set as a far-end boundary condition;
filling missing values in the second observation data by adopting a physical constraint method to obtain third observation data, wherein the following calculation formula is specifically adopted:
Figure FDA0003450542890000021
Figure FDA0003450542890000022
wherein the content of the first and second substances,
Figure FDA0003450542890000023
is provided with ZHAnd ZDRTwo variables are estimated to be obtained by the method,
Figure FDA0003450542890000024
obtained according to physical constraints
Figure FDA0003450542890000025
The resulting differential phase is inverted and the phase is inverted,
Figure FDA0003450542890000026
is an initial differential phase in the radial direction;
performing variational fitting processing on the third observation data by a method for constructing a cost function to obtain a reconstructed dual-polarization radar differential phase, wherein the following calculation formula is specifically adopted:
J＝Jobs+Jlpf
Figure FDA0003450542890000027
Figure FDA0003450542890000028
wherein J is a cost function, JobsThe term is the mean square error of the observed term and the reconstructed theoretical term, JlpfIs the Laplacian of the parameter k, ClpfIs a parameter of the filter, N is the number of distance bins, H1And H2For forward operators, k2Non-negative operators, δ is the observed differential phase, δ' is the difference of the boundary conditions.
2. The method of processing dual polarized radar differential phase according to claim 1, further comprising:
and predicting precipitation according to the dual-polarized radar differential phase to obtain a precipitation prediction result.
3. The method for processing the differential phase of the dual-polarization radar according to claim 2, wherein the precipitation is predicted according to the differential phase of the dual-polarization radar, and a precipitation prediction result is obtained by using the following calculation formula:
Figure FDA0003450542890000031
wherein a and b are parameters of the observed data, R (K)DP) Is the predicted amount of precipitation.
4. A system for processing differential phase of a dual polarization radar, comprising: an acquisition module, a first processing module, a second processing module, and a reconstruction module, wherein,
the acquisition module is used for acquiring first observation data of the dual-polarization weather radar, preprocessing the first observation data and acquiring second observation data, and comprises: z in the first observation dataH<3dBZ,ρhv<0.85,|ZDREliminating data with the value greater than 2.3dB, wherein ZHExpressed as a reflectivity factor, ρhvExpressed as the polarization correlation coefficient, ZDRExpressed as differential reflectivity;
the first processing module is configured to process the second observation data by using a preset algorithm to obtain a boundary condition of the second observation data, where the boundary condition of the second observation data includes a near-end boundary condition and a far-end boundary condition, and the calculating of the near-end boundary condition includes:
acquiring a radial data sample set of a near-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the minimum value in the distance set as a near-end boundary condition; if not, taking the median in the distance set as a near-end boundary condition;
the calculating the far-end boundary condition comprises:
acquiring a radial data sample set of a far-end boundary condition, and calculating to obtain a distance set corresponding to the radial data sample set according to the radial data sample;
according to the radial data sample set and the distance set, the slope of linear regression of each sample data and the distance corresponding to each sample data;
judging whether the slope of the linear regression is larger than zero, if so, taking the maximum value in the distance set as a near-end boundary condition; if not, taking the median in the distance set as a far-end boundary condition;
the second processing module is configured to fill up missing values in the second observation data by using a physical constraint method to obtain third observation data, and specifically adopts the following calculation formula:
Figure FDA0003450542890000041
Figure FDA0003450542890000042
wherein the content of the first and second substances,
Figure FDA0003450542890000043
is provided with ZHAnd ZDRTwo variables are estimated to be obtained by the method,
Figure FDA0003450542890000044
obtained according to physical constraints
Figure FDA0003450542890000045
The resulting differential phase is inverted and the phase is inverted,
Figure FDA0003450542890000046
is an initial differential phase in the radial direction;
the reconstruction module is configured to perform variational fitting processing on the third observation data by constructing a cost function method to obtain a reconstructed dual-polarization radar differential phase, and specifically adopts the following calculation formula:
J＝Jobs+Jlpf
Figure FDA0003450542890000047
Figure FDA0003450542890000048
wherein J is a cost function, JobsThe term is the mean square error of the observed term and the reconstructed theoretical term, JlpfIs the Laplacian of the parameter k, ClpfIs a parameter of the filter, N is the number of distance bins, H1And H2For forward operators, k2Non-negative operators, δ is the observed differential phase, δ' is the difference of the boundary conditions.
5. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of processing dual-polarized radar differential phase according to any one of claims 1 to 3.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of processing dual-polarization radar differential phase according to any one of claims 1 to 3.
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