CN115453665B - Automatic optimization method for radar quantitative estimation of precipitation - Google Patents

Automatic optimization method for radar quantitative estimation of precipitation Download PDF

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Publication number
CN115453665B
CN115453665B CN202211413378.5A CN202211413378A CN115453665B CN 115453665 B CN115453665 B CN 115453665B CN 202211413378 A CN202211413378 A CN 202211413378A CN 115453665 B CN115453665 B CN 115453665B
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rainfall
station
stations
radar
precipitation
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CN115453665A (en
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曾燕
邱新法
朱晓晨
王勇
谢志清
韦翔鸿
刘岩
王珂清
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Nanjing Institute Of Meteorological Science And Technology Innovation
Nanjing University of Information Science and Technology
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Nanjing Institute Of Meteorological Science And Technology Innovation
Nanjing University of Information Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/14—Rainfall or precipitation gauges
    • 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
    • G01S7/41—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
    • 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 an automatic optimization method for quantitatively estimating precipitation by radar, which is characterized in that an automatic optimization algorithm for quantitatively estimating precipitation by combining radar reflectivity data with ground station rain gauge data is developed, and dynamic determination is realizedZ‑IOptimization of relational expressionsA、bEmpirical coefficient, the algorithm has strong operability, and overcomes the defects of the traditional radar rainfall estimationA、bThe empirical coefficient is fixed, and the precipitation estimation precision is difficult to improve.

Description

Automatic optimization method for radar quantitative estimation of rainfall
Technical Field
The invention belongs to the technical field of atmospheric and environmental science, and particularly relates to an automatic optimization method for quantitatively estimating precipitation by a radar, which can be applied to precipitation estimation of meteorological and hydrological departments.
Background
Precipitation is one of the important weather elements. The method has very important significance for accurately and quantitatively estimating the precipitation. By using weatherRadar based precipitation estimation is a common problem in meteorological and hydrological services. At present, radar measurement precipitation is mainly based on radar reflectivity factor (echo intensity)ZIntensity of precipitation from groundIThe relationship between them, namely:
Figure 54089DEST_PATH_IMAGE001
in the formulaA、bAre empirical coefficients. The accuracy of radar quantitative estimation of precipitation depends to a large extent onZ-IIn the relationA、bAnd (4) determining coefficients.
Combining the rain gauge data and radar observation data of the ground station point and surface, and determining the combination through a certain mathematical statistical methodZ-IAnd (4) relation, thereby realizing radar precipitation estimation. The empirical coefficients determined by conventional methods are generally fixed, and in factA、bThe empirical coefficients are not fixed and vary with the area, season, type of precipitation, distance from the radar centre, etc.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the existing problems and defects, the invention aims to provide an automatic optimization algorithm for quantitatively estimating precipitation by combining radar reflectivity data with ground rainfall station data, so that dynamic determination is realizedZ-IOptimization of relational expressionsA、bEmpirical coefficient, the algorithm has strong operability, and overcomes the defect of the traditional radar rainfall estimationA、bThe empirical coefficient is fixed, and the precipitation estimation precision is difficult to improve.
The technical scheme is as follows: in order to realize the purpose, the invention adopts the following technical scheme: an automatic optimization method for radar quantitative estimation of precipitation comprises the following steps:
step S1, single factor index value
Figure 100002_DEST_PATH_IMAGE002
Is calculated by
By distance of the rainfall station from the central position of the radar or by radar echo intensity corresponding to the rainfall station
Figure 687064DEST_PATH_IMAGE003
As a target rainfall station
Figure 100002_DEST_PATH_IMAGE004
A single factor index of (1), wherein
Figure 958777DEST_PATH_IMAGE005
The calculation is carried out according to the formula (1),
Figure 100002_DEST_PATH_IMAGE006
（1）
in the formula (I), the compound is shown in the specification,
Figure 251962DEST_PATH_IMAGE007
is the coordinates of the center position of the radar,
Figure 100002_DEST_PATH_IMAGE008
for the rainfall station
Figure 225603DEST_PATH_IMAGE004
Position coordinates of, lower corner marks
Figure 209739DEST_PATH_IMAGE009
A serial number representing the rain station,
Figure 100002_DEST_PATH_IMAGE010
n is the total number of rainfall stations monitoring rainfall in the radar scanning area;
s2, determining the type of precipitation process
Is provided with
Figure 352270DEST_PATH_IMAGE011
And
Figure 100002_DEST_PATH_IMAGE012
respectively the minimum modeling rainfall station number and the maximum modeling rainfall station number, the total number of rainfall stations with rainfall in a radar scanning area is N,
if it is
Figure 989925DEST_PATH_IMAGE013
The precipitation process is a small-area precipitation process, a double-station model is adopted for parameter calculation, and the step S3 is carried out;
if it is
Figure 100002_DEST_PATH_IMAGE014
The precipitation process is a large-area precipitation process, a multi-station model is adopted for parameter calculation, and the step S4 is carried out;
step S3, a small-area precipitation processZ-IRelational model parameter solution
(1) Firstly, sequencing all rainfall stations in an ascending order according to the corresponding radar echo intensities;
(2) then, selecting target rainfall stations from the rainfall station arrangement in sequence
Figure 214976DEST_PATH_IMAGE004
Taking other rainfall stations as matched rainfall stations
Figure 2804DEST_PATH_IMAGE015
And calculating by the formula (2) to obtain a target rainfall station
Figure 29534DEST_PATH_IMAGE004
And matched rainfall station
Figure 182298DEST_PATH_IMAGE015
Double-station model empirical parameters
Figure 100002_DEST_PATH_IMAGE016
And
Figure 631996DEST_PATH_IMAGE017
，
Figure 100002_DEST_PATH_IMAGE018
，
Figure 144886DEST_PATH_IMAGE019
（2）
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE020
and
Figure 511276DEST_PATH_IMAGE021
respectively the radar echo intensity and the precipitation intensity corresponding to the target rainfall station,
Figure 100002_DEST_PATH_IMAGE022
and
Figure 340603DEST_PATH_IMAGE023
respectively matching radar echo intensity and rainfall intensity corresponding to the rainfall station;
(3) according to the target rainfall station obtained by calculation in the step (2) and the empirical parameters obtained by calculation of the matched rainfall station
Figure 100002_DEST_PATH_IMAGE024
And
Figure 244973DEST_PATH_IMAGE017
by passingZ-IRelational calculation target rainfall station
Figure 374604DEST_PATH_IMAGE004
Matching the estimated rainfall of all rainfall stations except the rainfall station, and comparing the estimated rainfall with the actually measured rainfall value corresponding to the rainfall station to obtain an average value of the estimated absolute errors of the rainfall;
(4) traversing all matched rainfall stations, and estimating the model parameter corresponding to the minimum absolute error average value by using the rainfallAAndbas target rainfall station
Figure 142971DEST_PATH_IMAGE004
Optimizing the model parameters;
(5) continuously selecting the next target rainfall station
Figure 168695DEST_PATH_IMAGE025
And repeating the steps (2) - (4) to obtain the optimal rainfall station model of all targetsA parameter;
step S4, large area precipitation processZ-IRelational model parameter solution
(1) Selecting a target rainfall station from rainfall stations monitored in a radar scanning area
Figure 622679DEST_PATH_IMAGE004
The corresponding single factor index is
Figure 100002_DEST_PATH_IMAGE026
；
(2) Station for obtaining target rainfall through calculation of formula (3)
Figure 962525DEST_PATH_IMAGE004
With other rainfall stations
Figure 848048DEST_PATH_IMAGE015
Absolute difference of single factor index
Figure 779095DEST_PATH_IMAGE027
，
Figure 100002_DEST_PATH_IMAGE028
（3）
(3) Then, according to the single factor index absolute difference
Figure 189216DEST_PATH_IMAGE027
Ascending, namely sequencing other rainfall stations to obtain a rainfall station queue;
(4) selecting the first n rainfall stations from the rainfall station queue as a sample set, and establishing the rainfall stations by using the sample setZ-IRelation, determination of target rainfall station by fitting
Figure 660649DEST_PATH_IMAGE004
Model parameters ofAAndband fitting the absolute error mean value to the precipitation, wherein
Figure 652876DEST_PATH_IMAGE029
；
(5) Increasing the number of sample rainfall stations to
Figure 100002_DEST_PATH_IMAGE030
And repeating the step (4) until the number of the rainfall stations of the sample set is equal to
Figure 239977DEST_PATH_IMAGE031
At the moment, selecting the model parameter corresponding to the minimum average value of the absolute error of precipitation fittingAAndbas a target rainfall station
Figure 402974DEST_PATH_IMAGE004
Optimizing the model parameters;
(6) continuously selecting the next target rainfall station
Figure 100002_DEST_PATH_IMAGE032
Repeating the steps (2) - (5) so as to traverse all the rainfall stations to obtain the optimal model parameters of all the rainfall stations;
step S5, spatial distribution interpolation of model parameters
In the radar scanning image, for the grids without rainfall stations, the model parameters are obtained according to the following method:
(1) calculating a grid point by the formula (4)
Figure 84622DEST_PATH_IMAGE033
Single factor index absolute difference with related rainfall station
Figure 100002_DEST_PATH_IMAGE034
，
Figure 882421DEST_PATH_IMAGE035
（4）
In the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE036
as a grid point
Figure 873379DEST_PATH_IMAGE037
N is the number of rainfall stations;
(2) all rainfall stations are subjected to absolute difference according to single factor indexes
Figure 336722DEST_PATH_IMAGE034
Sort in ascending order and select the first K rainfall stations in the sort queue (note:
Figure 838372DEST_PATH_IMAGE034
the smaller the grid point
Figure 805191DEST_PATH_IMAGE037
The more similar) as an interpolated reference station;
(3) and calculating the weight of each interpolation reference station by the formula (5)
Figure 100002_DEST_PATH_IMAGE038
，
Figure 967051DEST_PATH_IMAGE039
（5）
(4) Calculating grid points by equation (6)
Figure 917690DEST_PATH_IMAGE037
The corresponding parameters of the model are set to be,
Figure 100002_DEST_PATH_IMAGE040
（6）
in the formula (I), the compound is shown in the specification,
Figure 954523DEST_PATH_IMAGE041
and
Figure 100002_DEST_PATH_IMAGE042
respectively representlModel parameters of each reference station.
Step S6, combining the model parameters according to the grid pointsZ-IRelational expression, generateThe surface rainfall spatial distribution map.
Further, the single-factor index is radar echo intensity or distance from a center position of the radar.
Preferably, the minimum number of rainfall stations modeled is
Figure 447952DEST_PATH_IMAGE043
And maximum number of rainfall stations modeled
Figure 100002_DEST_PATH_IMAGE044
Are 20 and 40 respectively.
Preferably, K in step S5 takes a value of 5.
Further, in step (3) of step S5, if
Figure 46292DEST_PATH_IMAGE045
Is calculated to obtain
Figure 218648DEST_PATH_IMAGE045
The number of rainfall stations ismThen the corresponding reference station is weighted 1 ″mAnd the weights of the other rainfall stations are marked as 0.
Has the advantages that: compared with the prior art, the invention aims at the radar reflectivity factorZIntensity of precipitationIThe automatic optimization algorithm for quantitatively estimating rainfall by combining radar reflectivity data with the rain gauge data of the ground station is developed, and dynamic determination can be carried outZ-IOptimization of relational expressionsA、bThe empirical coefficient has better precipitation estimation precision.
Drawings
Fig. 1 is a schematic flow chart of the automatic optimization method for quantitatively estimating precipitation by using the radar of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
With reference to the flow of fig. 1, the technical idea and flow of the method of the present invention are described in detail as follows:
1. model classification
According to radar principles of operation, resolvingZ-IIn the relational modelA、bAnd classifying the models according to different sample data modeling modes when the coefficients are experienced.
Classifying according to the number of rainfall stations:
1) A multi-station model: in a circular area with the center of a radar as the center of a circle, a plurality of rainfall stations (stations with rainfall meter observation, called rainfall stations for short, the same below) in a radar scanning area are used as a sample set to establish rainfall and radar echo intensityZ-IRelationship, determinationA、bEmpirical coefficients.
2) The two-station model: resolving according to the corresponding rainfall and radar echo intensity of the two rainfall stationsZ-IOf a relationshipA、bEmpirical coefficients.
Classifying according to factors:
1) Distance model: grouping the rainfall stations according to the distance from the radar center to establishZ-IRelationship, i.e.A、bBy distance from the center of the radarrAnd (4) leading.
2) Echo model: grouping the rainfall stations according to the radar echo intensity to establishZ-IIn relation to, namelyA、bBy radar echo intensityZAnd (4) leading.
2. Model building step
1) Calculating single factor index value
Figure 100002_DEST_PATH_IMAGE046
Set a target rainfall station as
Figure 999784DEST_PATH_IMAGE047
The single-factor index of the distance model is the distance from the rainfall station to the center position of the radar
Figure 100002_DEST_PATH_IMAGE048
Namely:
Figure 128146DEST_PATH_IMAGE049
（1）
in the formula
Figure 100002_DEST_PATH_IMAGE050
As the center position of the radar, there is a center position,
Figure 130344DEST_PATH_IMAGE051
for the rainfall station
Figure 993258DEST_PATH_IMAGE047
The position of the mobile phone is determined,
Figure 100002_DEST_PATH_IMAGE052
，Nthe total number of rainfall stations monitoring precipitation in the radar scanning area.
Set a target rainfall station as
Figure 873358DEST_PATH_IMAGE047
The single-factor index of the echo model is the radar echo intensity corresponding to the rainfall station
Figure 934855DEST_PATH_IMAGE053
Namely:
Figure 100002_DEST_PATH_IMAGE054
；
2) Setting minimum value of rainfall station number of multi-station model modeling
Figure 124265DEST_PATH_IMAGE055
And maximum value
Figure DEST_PATH_IMAGE056
Setting minimum value of rainfall station number of multi-station model modeling
Figure 192584DEST_PATH_IMAGE055
And maximum value
Figure 174578DEST_PATH_IMAGE056
Minimum number of rainfall stations modeled
Figure 776067DEST_PATH_IMAGE055
Determining the minimum number of samples for modeling and the maximum number of rainfall stations for modeling
Figure 965740DEST_PATH_IMAGE056
And determining the maximum number of samples for modeling. Advising
Figure 537667DEST_PATH_IMAGE055
Taking out the raw materials of 20 percent,
Figure 87466DEST_PATH_IMAGE056
and taking 40.
3) Determining precipitation process type
Total number of rainfall stations with precipitation in radar scanning areaNIf, if
Figure 795659DEST_PATH_IMAGE057
Step 4) for a small-area precipitation process, performing model parameter calculation on the small-area precipitation process; if it is
Figure DEST_PATH_IMAGE058
And 5) turning to the step 5) for the large-area precipitation process, and calculating the model parameters of the large-area precipitation process.
4) Small area precipitation processZ-IRelational model parameter solution (two-station model):
(1) sequencing all rainfall stations in ascending order according to the corresponding radar echo intensities;
(2) station for selecting target rainfall
Figure 375807DEST_PATH_IMAGE047
Initially, the 1 st rainfall station in the rainfall station queue is used as the target rainfall station, that is, an initial value is set
Figure 573046DEST_PATH_IMAGE059
. The target rainfall station is
Figure 365683DEST_PATH_IMAGE047
Corresponding to a radar echo intensity of
Figure DEST_PATH_IMAGE060
The precipitation strength is
Figure 380913DEST_PATH_IMAGE061
；
(3) Selecting and matching rainfall station
Figure DEST_PATH_IMAGE062
Initially, the 1 st rainfall station in the rainfall station queue is taken as a matched rainfall station, that is, an initial value is set
Figure 584492DEST_PATH_IMAGE063
. Matched with rainfall station
Figure 511489DEST_PATH_IMAGE062
Corresponding to a radar echo intensity of
Figure DEST_PATH_IMAGE064
The precipitation strength is
Figure 91506DEST_PATH_IMAGE065
；
(4) Calculating parameters of the double-station model if the target rainfall station and the matched rainfall station have
Figure DEST_PATH_IMAGE066
And is provided with
Figure 757980DEST_PATH_IMAGE067
Then the two-station model parameters can be calculated, whose solution is:
Figure DEST_PATH_IMAGE068
（2）
otherwise, turning to the step (6), and selecting the next matched rainfall station;
(5) according to the empirical coefficient calculated by the target rainfall station and the matched rainfall station
Figure 883193DEST_PATH_IMAGE069
、
Figure DEST_PATH_IMAGE070
Value, applicationZ-IAnd calculating the estimated rainfall of all rainfall stations except the target rainfall station and the matched rainfall station by using the relational expression, and comparing the estimated rainfall with the actually measured rainfall value corresponding to the rainfall station to obtain the average value of the estimated absolute errors of the rainfall.
(6) Selecting the next matched rainfall station, i.e.
Figure 635117DEST_PATH_IMAGE071
If, if
Figure DEST_PATH_IMAGE072
Turning to (4), calculating parameters of the two-station model; otherwise, the next step is carried out, and a next target rainfall station is selected;
(7) selecting the next target rainfall station, namely if the next target rainfall station is selected, turning to (3), and calculating the model parameters of the new target station; otherwise, completing calculation of the double-station model parameters of all the rainfall stations, and turning to the next step to preferably select the model parameters;
(8) determining the optimal model parameters of the target rainfall station, taking the average value of the absolute errors of the rainfall estimation as the judgment standard, and taking the model parameter corresponding to the minimum average value of the absolute errors of the rainfall estimationA、bIs the optimal of the target rainfall stationA、bA value;
(9) and (6) calculating the spatial distribution of the model parameters, namely, turning to the step 6) to perform spatial interpolation on the model parameters.
5) Large area precipitation processZ-IRelational model parameter resolution (Multi-station model)
(1) Station for selecting target rainfall
Figure 487666DEST_PATH_IMAGE047
The corresponding single factor index is
Figure 38340DEST_PATH_IMAGE073
；
(2) Station for calculating target rainfall and other rainfall stations
Figure DEST_PATH_IMAGE074
Absolute difference of single factor index
Figure 380460DEST_PATH_IMAGE075
（3）
(3) All rainfall stations are subjected to absolute difference according to single factor indexes
Figure DEST_PATH_IMAGE076
Sorting in ascending order;
(4) setting initial modeling rainfall station sample number
Figure 619680DEST_PATH_IMAGE077
；
(5) Absolute difference of single factor index
Figure 292232DEST_PATH_IMAGE076
Front in ascending queuenThe rainfall station is used as a sample set to establishZ-IRelationship, determination of model parametersAAndband fitting the mean absolute error value of the precipitation.
(6) Increasing the number of rainfall stations in the model, i.e. taking
Figure DEST_PATH_IMAGE078
If at all
Figure 684030DEST_PATH_IMAGE079
Turning to (5) until the maximum number of stations for rainfall is reached
Figure DEST_PATH_IMAGE080
；
(7) If it is
Figure 446319DEST_PATH_IMAGE081
Selecting the next target rainMeasuring stations, namely turning to (1) until all rainfall stations are traversed;
(8) evaluating the performance of the model, selecting the optimal model, and establishing each target rainfall station through different data sets (namely different sample numbers and different single-factor indexes)Z-IIn the relation model, the model with the minimum absolute error mean value is fitted by the precipitation amount and is used as the optimal model, and the model is determined by the optimal modelA、bCoefficient of the target rainfall stationA、bThe value is obtained.
6) Spatial distributed interpolation of model parameters
And determining the model parameters of each grid point in the radar scanning area by using the existing rainfall station model parameters by adopting an interpolation method.
(1) Setting the number of rainfall stations participating in interpolationkTo suggestkTaking 5;
(2) calculating grid points
Figure DEST_PATH_IMAGE082
The corresponding single factor index is;
(3) calculating grid points
Figure 671370DEST_PATH_IMAGE082
Single factor index absolute difference with related rainfall station
Figure 396881DEST_PATH_IMAGE083
（4）
In the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE084
as a grid point
Figure 158032DEST_PATH_IMAGE082
N is the number of rainfall stations;
(4) all rainfall stations are subjected to absolute difference according to single factor indexes
Figure 310796DEST_PATH_IMAGE085
Sorting in ascending order;
(5) in the single factor meansStandard absolute difference
Figure 88390DEST_PATH_IMAGE085
Before selection in ascending queuekEach rainfall station (note:
Figure 414330DEST_PATH_IMAGE085
the smaller and the grid point
Figure 780720DEST_PATH_IMAGE082
The more similar) as an interpolated reference station;
(6) calculating weights of reference stations
Figure DEST_PATH_IMAGE086
If present, is
Figure 353653DEST_PATH_IMAGE087
Calculating
Figure 818876DEST_PATH_IMAGE087
The number m of rainfall stations and the corresponding weight of the reference station is
Figure DEST_PATH_IMAGE088
The weight of other rainfall stations is 0;
(7) calculating grid points
Figure 620610DEST_PATH_IMAGE082
The corresponding model parameters are:
Figure 153091DEST_PATH_IMAGE089
Figure DEST_PATH_IMAGE090
Figure DEST_PATH_IMAGE092
、
Figure 70494DEST_PATH_IMAGE093
is as followslModel parameters of each reference station.
7) According to grid pointA、bValue, combinationZ-IAnd generating a surface rainfall spatial distribution map according to the relational expression.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (4)

1. An automatic optimization method for radar quantitative estimation of precipitation is characterized by comprising the following steps:
step S1, single factor index value
Figure DEST_PATH_IMAGE001
Is calculated by
By the distance of the rainfall station from the central position of the radar
Figure DEST_PATH_IMAGE002
Or radar echo intensity corresponding to rainfall station
Figure DEST_PATH_IMAGE003
As a target rainfall station
Figure DEST_PATH_IMAGE004
A single factor index of (1), wherein
Figure 740043DEST_PATH_IMAGE002
The calculation is carried out according to the formula (1),
Figure DEST_PATH_IMAGE005
（1）
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE006
is the coordinates of the center position of the radar,
Figure DEST_PATH_IMAGE007
for the rainfall station
Figure 926305DEST_PATH_IMAGE004
Position coordinates of, lower corner marks
Figure DEST_PATH_IMAGE008
A serial number of the rainfall station is indicated,
Figure DEST_PATH_IMAGE009
，
Figure DEST_PATH_IMAGE010
the total number of rainfall stations monitoring rainfall in a radar scanning area;
s2, determining the type of precipitation process
Is provided with
Figure DEST_PATH_IMAGE011
And
Figure DEST_PATH_IMAGE012
respectively the minimum modeling rainfall station number and the maximum modeling rainfall station number, and the total number of rainfall stations with rainfall in a radar scanning area is
Figure 180569DEST_PATH_IMAGE010
；
If it is
Figure DEST_PATH_IMAGE013
The precipitation process is a small-area precipitation process, a double-station model is adopted for parameter calculation, and the step S3 is carried out;
if it is
Figure DEST_PATH_IMAGE014
The precipitation process is a large-area precipitation process, a multi-station model is adopted for parameter calculation, and the step S4 is carried out;
step S3, a small-area precipitation processZ-IRelational model parameter solution
(1) Firstly, sequencing all rainfall stations in an ascending order according to the corresponding radar echo intensities;
(2) then, selecting target rainfall stations from the rainfall station arrangement in sequence
Figure 181892DEST_PATH_IMAGE004
Taking other rainfall stations as matched rainfall stations
Figure DEST_PATH_IMAGE015
And calculating to obtain a target rainfall station by the formula (2)
Figure 207617DEST_PATH_IMAGE004
And matched rainfall station
Figure 209071DEST_PATH_IMAGE015
Double-station model empirical parameters
Figure DEST_PATH_IMAGE016
And
Figure DEST_PATH_IMAGE017
，
Figure DEST_PATH_IMAGE018
，
Figure DEST_PATH_IMAGE019
（2）
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE020
and
Figure DEST_PATH_IMAGE021
respectively the radar echo intensity and the precipitation intensity corresponding to the target rainfall station,
Figure DEST_PATH_IMAGE022
and
Figure DEST_PATH_IMAGE023
respectively matching radar echo intensity and rainfall intensity corresponding to the rainfall station;
(3) according to the target rainfall station obtained by calculation in the step (2) and the empirical parameters obtained by calculation of the matched rainfall station
Figure DEST_PATH_IMAGE024
And
Figure DEST_PATH_IMAGE025
by passingZ-IRelational calculation target rainfall station
Figure DEST_PATH_IMAGE026
Matching the estimated rainfall of all rainfall stations except the rainfall station, and comparing the estimated rainfall with the actually measured rainfall value corresponding to the rainfall station to obtain an average value of the estimated absolute errors of the rainfall;
(4) traversing all matched rainfall stations, and estimating the model parameter corresponding to the minimum absolute error average value by using the rainfallAAndbas a target rainfall station
Figure DEST_PATH_IMAGE027
Optimizing the model parameters;
(5) continuously selecting the next target rainfall station
Figure DEST_PATH_IMAGE028
Repeating the steps (2) - (4) to obtain optimal model parameters of all target rainfall stations;
step S4, large area precipitation processZ-IRelational model parameter solution
(1) Selecting a target rainfall station from the rainfall stations monitored in the radar scanning area
Figure DEST_PATH_IMAGE029
The corresponding single factor index is
Figure DEST_PATH_IMAGE030
；
(2) Station for obtaining target rainfall through calculation of formula (3)
Figure 34070DEST_PATH_IMAGE029
With other rainfall stations
Figure DEST_PATH_IMAGE031
Absolute difference of single factor index
Figure DEST_PATH_IMAGE032
，
Figure DEST_PATH_IMAGE033
（3）
(3) Then, according to the single factor index absolute difference
Figure 355811DEST_PATH_IMAGE032
Ascending, namely sequencing other rainfall stations to obtain a rainfall station queue;
(4) selecting the first n rainfall stations from the rainfall station queue as a sample set, and establishing the rainfall stations by using the sample setZ-IRelation, determination of target rainfall station by fitting
Figure DEST_PATH_IMAGE034
Model parameters ofAAndband fitting the mean absolute error to the precipitation, wherein
Figure DEST_PATH_IMAGE035
；
(5) Increase theNumber of sample adding book rainfall collecting stations is up to
Figure DEST_PATH_IMAGE036
And repeating the step (4) until the number of the rainfall stations of the sample set is equal to
Figure DEST_PATH_IMAGE037
At the moment, selecting the model parameter corresponding to the minimum average value of the absolute error of precipitation fittingAAndbas a target rainfall station
Figure 739388DEST_PATH_IMAGE034
Optimizing the model parameters;
(6) continuously selecting the next target rainfall station
Figure DEST_PATH_IMAGE038
Repeating the steps (2) - (5) so as to traverse all the rainfall stations to obtain the optimal model parameters of all the rainfall stations;
step S5, interpolation of spatial distribution of model parameters
In the radar scanning image, for the grids without rainfall stations, the model parameters are obtained according to the following method:
(1) calculating and obtaining grid points by the formula (4)
Figure DEST_PATH_IMAGE039
Absolute difference of single-factor index from each rainfall station
Figure DEST_PATH_IMAGE040
，
Figure DEST_PATH_IMAGE041
（4）
In the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE042
as a grid point
Figure DEST_PATH_IMAGE043
N is the number of rainfall stations;
(2) all rainfall stations are subjected to absolute difference according to single factor indexes
Figure DEST_PATH_IMAGE044
Sorting in ascending order and selecting in the sorting queue
Figure DEST_PATH_IMAGE046
The rainfall station is used as an interpolation reference station;
(3) and calculating the weight of each interpolation reference station by the formula (5)
Figure DEST_PATH_IMAGE047
，
Figure DEST_PATH_IMAGE048
（5）
(4) Calculating grid points by equation (6)
Figure 8564DEST_PATH_IMAGE043
The corresponding parameters of the model are set to be,
Figure DEST_PATH_IMAGE049
（6）
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE050
and
Figure DEST_PATH_IMAGE051
respectively representlModel parameters of the individual reference stations;
step S6, combining the grid points according to the model parameters of the grid pointsZ-IAnd generating a surface rainfall spatial distribution map by using the relational expression.
2. A mine as claimed in claim 1The automatic optimization method for quantitatively estimating precipitation is characterized by comprising the following steps: the minimum number of rainfall stations modeled
Figure DEST_PATH_IMAGE052
And maximum number of rainfall stations modeled
Figure DEST_PATH_IMAGE053
Are 20 and 40 respectively.
3. The method of claim 1, wherein the radar is configured to perform an automatic optimization of the quantitative estimation of precipitation: in step S5
Figure 607560DEST_PATH_IMAGE046
Is 5.
4. The method of claim 1, wherein the radar is configured to perform an automatic optimization of the quantitative estimation of precipitation: in step (3) of step S5, if
Figure DEST_PATH_IMAGE054
Is calculated to obtain
Figure 599787DEST_PATH_IMAGE054
The number of rainfall stations ismThen the corresponding reference station weight is 1%mAnd the weights of the other rainfall stations are marked as 0.
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