CN108020840B - Hail cloud early-stage identification method based on Doppler weather radar data - Google Patents

Hail cloud early-stage identification method based on Doppler weather radar data Download PDF

Info

Publication number
CN108020840B
CN108020840B CN201711161224.0A CN201711161224A CN108020840B CN 108020840 B CN108020840 B CN 108020840B CN 201711161224 A CN201711161224 A CN 201711161224A CN 108020840 B CN108020840 B CN 108020840B
Authority
CN
China
Prior art keywords
monomer
height
reflectivity
value
volume
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201711161224.0A
Other languages
Chinese (zh)
Other versions
CN108020840A (en
Inventor
王萍
史金玉
侯谨毅
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University
Original Assignee
Tianjin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University filed Critical Tianjin University
Priority to CN201711161224.0A priority Critical patent/CN108020840B/en
Publication of CN108020840A publication Critical patent/CN108020840A/en
Application granted granted Critical
Publication of CN108020840B publication Critical patent/CN108020840B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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 hail cloud early-stage identification method for strong weather identification based on Doppler weather radar data, which comprises the steps of firstly, acquiring three-dimensional reflectivity grid point data by utilizing a bilinear difference algorithm based on reflectivity data of a Doppler weather radar; extracting 10 features describing the hail cloud, then reducing feature dimensions according to a principal component analysis method to obtain a bulk comprehensive feature and a height-gradient comprehensive feature, and through time sequence analysis, the two comprehensive feature values show an obvious jump increase phenomenon in the early stage of the hail cloud; and finally, constructing a four-dimensional strong convection monomer description vector to perform early recognition of the hail cloud by taking strong precipitation as a negative sample based on the jump increase phenomenon of the two-dimensional comprehensive characteristics of the hail sample. The invention realizes the early automatic recognition of hail clouds, can provide favorable opportunity for hail suppression and hail elimination, and reduces economic loss and casualties. Meanwhile, a new idea is provided for strong convection weather identification based on meteorological radar data.

Description

Hail cloud early-stage identification method based on Doppler weather radar data
Technical Field
The invention relates to the field of strong weather identification, in particular to a hail cloud early identification method based on Doppler weather radar data.
Background
Hailstone clouds are products triggered by ground convergent flow fields under sufficient water vapor supply conditions after unstable atmospheric junctions appear, and are classified as strong convection storms. In strong weather identification, the identification of hail clouds occupies an important place.
Most of hail cloud identification researches are based on Doppler weather radars, and different hail cloud characteristics are constructed to identify hail clouds according to structural characteristics and morphological characteristics of the hail clouds and based on reflectivity factor base data of various elevation radar. Mason[1]Hail and precipitation were distinguished using a 55dBZ reflectivity value as a threshold. Waldvogel[2]The height difference characteristic of the echo top height of 45dBZ and the temperature layer of 0 ℃ is provided, so that a good foundation is laid for the development of a hail identification algorithm, and the characteristic is still used by most hail cloud identification algorithms until now. Delobbe and Holleman[3]Some uncertainty factors in measuring the 45dBZ echo top height are discussed in detail, while confirming the role of the 45dBZ echo top height feature in hail identification. Paxton and Shepherd[4]The vertical accumulation of the liquid water content VIL is proposed, whichThe method assumes that all radar-returned reflectivity factors are caused by liquid water drop reflection, so the method converts the reflectivity factors into liquid water values equivalent to the reflectivity factors, and judges the hail reduction possibility by using the liquid water values. Amburn and Wolf[5]The VIL method is further extended by defining a vertical cumulative liquid water content density VIL density based on the vertical cumulative liquid water, which represents the ratio of the vertical cumulative liquid water to the monomer top height.
In the process of implementing the invention, the inventor finds that at least the following disadvantages and shortcomings exist in the prior art: at present, most of hail cloud identification methods concern certain aspect characteristics of hail clouds; the hail cloud identification is carried out by the method, namely the hail cloud identification is carried out in a certain volume scanning process instead of the real-time identification of the whole hail process; when the methods can identify the hail clouds, the overall hail characteristics are obvious, and the result identification rate is low and the empty report rate is high. At present, no algorithm can identify hail clouds in the early period of the appearance of the hail clouds in China.
[ reference documents ]
[1]Ludlam F H,Mason B J.The Physics of Clouds[J].Physics Today,1957,11(12):26–27.
[2]Waldvogel A,Federer B,Grimm P.Criteria for the Detection of Hail Cells[J].Journal of Applied Meteorology,1979,18(12):1521-1525.
[3]Delobbe L,Holleman I.Uncertainties in radar echo top heights used for hail detection[J].Meteorological Applications,2006,13(4):361-374.
[4]Paxton,C.H.,and J.M.Shepherd.Radar diagnostic parameters as indicators of severe weather in central Florida[J].1993.
[5]Amburn S A,Wolf P L.VIL Density as a Hail Indicator[J].Weather&Forecasting,1997,12(3):473-478.
Disclosure of Invention
The invention provides a Doppler weather radar data-based hail cloud early identification method for strong weather identification.
In order to solve the technical problem, the invention provides a hail cloud early-stage identification method for strong weather identification based on Doppler weather radar data, which obtains combined reflectivity maps of Doppler weather radars at different moments according to Doppler weather radar base data at continuous moments, and carries out the following processes according to a time sequence, wherein the method comprises the following steps:
the method comprises the following steps: extracting the characteristics of the hail cloud;
extracting 10 characteristics of each individual sweep of the convection monomer after the precursor sweep from a 40dBZ connected region in which more than 10 pixel points appear in the three-dimensional reflectivity lattice point data of the convection monomer;
10 features including more than 0 ℃ layer thickness of convective monomer
Figure BDA0001475055710000021
Core area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG C
Figure BDA0001475055710000022
Convection monomer core volume above-10 ℃ layer height
Figure BDA0001475055710000023
Volume of zone of stronger reflectivity for convective monomer
Figure BDA0001475055710000024
Convective monomer low reflectivity region volume
Figure BDA0001475055710000025
Weak echo volume below pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRGCCH；
Step two: processing hail cloud feature data to obtain two comprehensive features;
2-1) based on the 10 characteristic values obtained in the step one, carrying out standardization treatment to enable convection to be conductedThickness of monomer layer above 0 DEG C
Figure BDA0001475055710000026
Core area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG C
Figure BDA0001475055710000027
Convection monomer core volume above-10 ℃ layer height
Figure BDA0001475055710000028
Volume of zone of stronger reflectivity for convective monomer
Figure BDA0001475055710000029
Convective monomer low reflectivity region volume
Figure BDA00014750557100000210
Weak echo volume below pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRGCCHIs recorded as: f ═ F1,F2,F3,F4,F5,F6,F7,F8,F9,F10) Using the formula
Figure BDA00014750557100000211
Normalized to obtain the characteristic
Figure BDA00014750557100000212
Wherein:
Figure BDA00014750557100000213
Figure BDA00014750557100000214
2-2) to the above-obtained features
Figure BDA0001475055710000031
The principal component analysis was carried out to obtain 10 principal components, which were designated as pw (pw)1,pw2,pw3,pw4,pw5,pw6,pw7,pw8,pw9,pw10)；
2-3) according to the difference of the information contribution rates of the 10 principal components, reserving the former two-dimensional principal components to obtain a new two-dimensional characteristic:
Figure BDA0001475055710000032
Figure BDA0001475055710000033
and
Figure BDA0001475055710000034
Figure BDA0001475055710000035
Figure BDA0001475055710000036
respectively recording the characteristic as volume comprehensive characteristic VMCF and height-gradient comprehensive characteristic HGCF;
step three: carrying out early recognition of hail clouds;
according to the jump phenomenon of hail cloud volume comprehensive characteristic VMCF and height-gradient comprehensive characteristic HGCF shown in the early stage, a four-dimensional strong convection monomer description vector is constructed
Figure BDA0001475055710000037
△1Difference of volume integration features VMCF for adjacent volume sweep, Δ2Is the difference of the height-gradient integrated characteristic HGCF of adjacent body sweeps,
Figure BDA0001475055710000038
for the difference between the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF of the same volume sweep,
Figure BDA0001475055710000039
the sum of the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF for the same volume sweep, n represents the current volume sweep, n-1 represents the previous volume sweep,
△i＝pwi(n)-pwi(n-1),i＝1,2
Figure BDA00014750557100000310
there is one of the following cases, the convective monomer is the hail monomer, then it is considered to be the hail cloud early stage at this moment,
(1)△1≥1.24；
(2)△2≥1.50；
(3)
Figure BDA00014750557100000311
(4)
Figure BDA00014750557100000312
recording the body scanning number and time at the moment; otherwise, the convection current monomer is not the hail monomer, the step one is returned, and the process processing of the next moment is carried out until the judgment of the radar base data at all the moments is completed.
Further, the specific contents of the 10 features in the step one are as follows:
2-1) segmenting convection monomers on a combined reflectance graph of the Doppler weather radar, respectively obtaining an outer-wrapping rectangle of each convection monomer, matching the outer-wrapping rectangle with the position of the outer-wrapping rectangle at the previous moment, if matching is successful, the convection monomer is the convection monomer at the previous moment, and if not, the convection monomer is a new convection monomer;
carrying out bilinear interpolation operation on the 9 elevation reflectivity data in each outsourcing rectangular range to obtain three-dimensional reflectivity grid point data of a convection monomer, and setting the resolution of the three-dimensional reflectivity grid point data to be 1km multiplied by 1 km; sequentially arranging the three-dimensional reflectivity lattice point data at the same horizontal height position from low to highStoring two-dimensional data 2Dk of size MxNM×NThe height value k is 1,2, …, 17;
2-2) extracting convection monomer with thickness of more than 0 DEG C
Figure BDA0001475055710000049
And convective monomer core thickness CT: creating two-dimensional data 2DA1 and 2DA2 of size M × N; pijI is 1, …, M, j is 1, …, N is a point position in the two-dimensional data 2DA1 and 2DA2, and the two-dimensional data 2Dk is assumedM×NThe data in (1) is RijkLet the height value k go down in the order from 17, R will be satisfied twice in successionijkThe larger k value of ≥ 40dBZ is noted as hijP stored to two-dimensional data 2DA1ijThe above step (1); then let k go up from 1 in sequence, and satisfy R twice in successionijkThe smaller k value of ≧ 40dBZ is noted as hijP stored to two-dimensional data 2DA2ijThe above step (1); if the condition is not satisfied all the time, then 0 is assigned to Pij(ii) a For any position P in two-dimensional data 2DA1 and 2DA2ijCalculate PijThe difference between the value of (A) and the value of its 8 neighbours l1, …,8, if ΔlIf the number of not more than 1 exceeds 4, P isijOtherwise, the point is considered as noise, and P is assigned 0ij(ii) a The height of the layer with the temperature of 0 ℃ subtracted from the maximum height in the 2DA1 is the thickness CH of the convection monomer above the layer with the temperature of 0 DEG C0The maximum value of the height value difference of the same position in the 2DA1 and the 2DA2 is the convection monomer core thickness CT;
2-3) extracting convective monomer core height CCH: on a combined reflectivity graph of a Doppler weather radar, a maximum reflectivity value R is found in an outer-wrapping rectangular range of a convection single bodyMSequentially calculating two-dimensional data 2DkM×NThe median value is RMThe number of (2) is denoted by nkAnd k is 1,2, 17, and the average height of the calculated maximum reflectance value is the convective single core height CCH;
2-4) extracting convection monomer-combined reflectivity nuclear region area with layer height above-10 DEG C
Figure BDA0001475055710000041
Creating two-dimensional data 2DA3 of size M × N; computingLayer height H at-10 DEG C-10℃＝(H0+H-20) /2 wherein H0At a layer height of 0 ℃ H-20Is a layer height of-20 deg.C, and its adjacent height value is defined as kz1，kZ1∈[1,17](ii) a For PijI 1, …, M, j 1, …, N, let k be from kz1Starting with the sequence upward, and adding RijkThe value of (A) is assigned to P of the two-dimensional data 2DA3ijPositionally, as the value of k increases, if RijkIf the current value is larger, P is updatedijValue, otherwise PijThe value of (c) is not changed, and the search is carried out until k is 17; calculating R in 2DA3ijkThe number n is more than or equal to 40dBZ, and the value of n is the combined reflectivity nuclear area of the convection monomer with the layer height of more than 10 ℃ below zero
Figure BDA0001475055710000042
2-5) extracting convective monomer-monomer core volume above-10 ℃ layer height
Figure BDA0001475055710000043
Volume of zone of stronger reflectivity for convective monomer
Figure BDA0001475055710000044
And convection cell low reflectivity region volume
Figure BDA0001475055710000045
For 2DkM×N，k＝kz1… …,17, calculating 2Dk sequentiallyM×NIn RijkThe number n of not less than 40dBZ40k、RijkThe number n of not less than 45dBZ45kAnd Rijk∈[25,39]Number n of dBZ25kThen n is40kThe value is the volume of monomer core above the layer height of-10 ℃ of the convective monomer
Figure BDA0001475055710000046
n45kI.e. the volume of the zone with stronger reflectivity of convection monomer
Figure BDA0001475055710000047
n25kThe value is the convection monomer low reflectivityVolume of zone
Figure BDA0001475055710000048
2-6) extraction of Weak echo volume under pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 : creating two-dimensional data 2DA4, 2DA5, 2DA6, 2DA7, 2DA8, and 2DA9 of size M × N; definition H2＝H0-1，H2Is the height of 1km below the 0 ℃ layer, and the height value adjacent to the height is determined as kz2，kZ2∈[1,17](ii) a For PijI 1, …, M, j 1, …, N, let k be from kz2Starting sequentially upwards, R will be satisfied twice in successionijkSmaller k-k of not less than 40dBZz2Is marked as W1ijStoring the two-dimensional data 2DA4, otherwise, assigning 0 to Pij(ii) a K for storing each point in two-dimensional data 2DA4z2Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA 6; will satisfy R twice in successionijkSmaller k-k of not less than 45dBZz2Is marked as W2ijStoring the two-dimensional data 2DA5, otherwise, assigning 0 to Pij(ii) a K for storing each point in two-dimensional data 2DA5z2Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA 7; respectively reserving areas with the largest communication areas in the two-dimensional data 2DA6 and the two-dimensional data 2DA7, and enabling W1 corresponding to the communication areasijValue sum W2ijThe values are respectively denoted as W3ijAnd W4ijRespectively storing the two-dimensional data 2DA8 and the two-dimensional data 2DA 9; all W3 in the two-dimensional data 2DA8ijThe sum is the volume WEV of the weak echo below the pendants with a reflectivity threshold of 40dBZ 40 All W4 in the two-dimensional data 2DA9ijThe sum is the volume WEV of the weak echo below the pendants with a reflectivity threshold of 45dBZ 45 ；
2-7) extracting high-value MRG of reflectivity gradientCCH: creating a two-dimensional array 2DA10 with the size of M multiplied by N, setting the K value nearest to the convective single core height CCH as K, and performing two-dimensional data 2DKM×NIn, let max { Rijk}＝RMA number nM、RM-5dBZ in a number nM-5、RMA quantity of-10 dBZ ofnM-10Calculating the sum of nM、nM-5And nM-10Barycentric coordinates P (x) of all corresponding points0,y0) (ii) a With P (x)0,y0) Centering on the two-dimensional data 2DKM×NAll the points in (a) are divided into 8 regions in total, one region every 45 degrees with the OX direction as the starting position; for the data points falling into each region, the direction gradient GK is obtainedijAnd storing the data into a two-dimensional array 2DA 10; respectively counting R in the 8 region rangesijkThe number of points ≧ 25dBZ, noted niI is 1,2, …, 8; calculating n1To n8Minimum value n ofmRespectively expanding two areas before and after the m-th area is taken as a reference, then calculating the number of points with the reflectivity of more than 25dBZ of all 3 connected areas in the 5 adjacent areas, and calling the area corresponding to the area with the minimum number in all 3 connected areas as a high gradient area; calculating the average gradient in the high gradient area range according to the gradient matrix, namely the reflectivity gradient high value MRGCCH。
Compared with the prior art, the invention has the beneficial effects that: the invention decides a complete hail process and not only aims at one of the hail process and the hail process; meanwhile, the method can identify the hail cloud in the early stage, and can provide favorable opportunity for hail suppression and hail elimination; finally, the effectiveness of the invention is verified through experiments.
Drawings
FIGS. 1(a) and 1(b) illustrate the processing of the weak echo volume under the overhung structure before its feature calculation, where FIG. 1(a) is the result of converting the height value to grayscale and FIG. 1(b) is the determination of the location of the weak echo region after connected domain processing;
FIG. 2 is a schematic of gradient feature computation showing the location of a center point and the division of 8 regions;
FIGS. 3(a) and 3(b) are examples of time series plots of two-dimensional integration characteristics of two hail processes, illustrating the jump-up phenomena of the massively integrated characteristic VMCF and the altitudinal-gradient integrated characteristic HGCF;
FIG. 4 is a comparison distribution plot of two-dimensional integration characteristics of 49 hail events and 35 heavy precipitation events;
FIG. 5 is a graphical representation of the statistics of early identification times leading hail time for all hail processes containing specific hail time;
fig. 6 is a specific flowchart of an early hail cloud identification method provided by the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings and specific examples, which are only illustrative of the present invention and are not intended to limit the present invention.
The invention provides a hail cloud early-stage identification method based on Doppler weather radar data for strong weather identification, which is designed according to the following steps: firstly, 10 characteristics reflecting different characteristics of hail clouds in early stage are extracted, wherein the characteristics comprise the thickness of more than 0 ℃ layer of convection monomer
Figure BDA0001475055710000051
Core area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG C
Figure BDA0001475055710000061
Convection monomer core volume above-10 ℃ layer height
Figure BDA0001475055710000062
Volume of zone of stronger reflectivity for convective monomer
Figure BDA0001475055710000063
Convective monomer low reflectivity region volume
Figure BDA0001475055710000064
Weak echo volume below pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRGCCH(ii) a Performing characteristic transformation by using a principal component analysis method to obtain an integrated characteristic VMCF of the hailstone cloud, an integrated height-gradient HGCF, and an integrated characteristic VMCF or height of the hailstone cloudGradient synthesis HGCF exhibits a pronounced jump phenomenon in the early appearance of hail clouds; and finally, constructing a four-dimensional strong convection monomer description vector to perform early hail cloud identification based on the jump phenomenon of two-dimensional comprehensive characteristics appearing in the early hail cloud. The invention realizes early recognition of hail clouds, can provide favorable opportunity for hail suppression and hail elimination, and reduces economic loss and casualties.
As shown in fig. 6, the method mainly includes: obtaining three-dimensional reflectivity lattice point data of Doppler weather radar base data, calculating 10 characteristics, obtaining two-dimensional comprehensive characteristics and calculating increment thereof, and comparing the increment with a threshold value;
the specific contents are as follows: obtaining a combined reflectivity diagram of the Doppler weather radar at different moments according to the Doppler weather radar base data at continuous moments, and performing the following processes according to a time sequence, wherein the processes comprise:
the method comprises the following steps: extracting the characteristics of the hail cloud;
extracting 10 characteristic values of each individual sweep of the convection monomer after the precursor sweep from a 40dBZ connected region in which more than 10 pixel points appear in the three-dimensional reflectivity lattice point data of the convection monomer;
10 features including more than 0 ℃ layer thickness of convective monomer
Figure BDA0001475055710000065
Core area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG C
Figure BDA0001475055710000066
Convection monomer core volume above-10 ℃ layer height
Figure BDA0001475055710000067
Volume of zone of stronger reflectivity for convective monomer
Figure BDA0001475055710000068
Convective monomer low reflectivity region volume
Figure BDA0001475055710000069
Weak echo volume below pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRGCCH；
The specific extraction process of 10 features is as follows:
1-1) segmenting convection monomers on a combined reflectance graph of the Doppler weather radar, respectively obtaining an outer-wrapping rectangle of each convection monomer, matching the outer-wrapping rectangle with the position of the outer-wrapping rectangle at the previous moment, if matching is successful, the convection monomer is the convection monomer at the previous moment, and if not, the convection monomer is a new convection monomer;
carrying out bilinear interpolation operation on the 9 elevation reflectivity data in each outsourcing rectangular range to obtain three-dimensional reflectivity grid point data of a convection monomer, and setting the resolution of the three-dimensional reflectivity grid point data to be 1km multiplied by 1 km; sequentially storing the three-dimensional reflectivity lattice point data at the same horizontal height position into two-dimensional data 2Dk with the size of M multiplied by N from low to highM×NThe height value k is 1,2, …, 17;
1-2) extracting convection monomer with thickness of more than 0 DEG C
Figure BDA00014750557100000610
And convective monomer core thickness CT: creating two-dimensional data 2DA1 and 2DA2 of size M × N; pijI is 1, …, M, j is 1, …, N is a point position in the two-dimensional data 2DA1 and 2DA2, and the two-dimensional data 2Dk is assumedM×NThe data in (1) is RijkLet the height value k go down in the order from 17, R will be satisfied twice in successionijkThe larger k value of ≥ 40dBZ is noted as hijP stored to two-dimensional data 2DA1ijThe above step (1); then let k go up from 1 in sequence, and satisfy R twice in successionijkThe smaller k value of ≧ 40dBZ is noted as hijP stored to two-dimensional data 2DA2ijThe above step (1); if the condition is not satisfied all the time, then 0 is assigned to Pij(ii) a For any position P in two-dimensional data 2DA1 and 2DA2ijCalculate PijThe difference between the value of point and the value of 8 neighborhood pointsl1, …,8, if ΔlThe number of the particles is more than 14, then PijOtherwise, the point is considered as noise, and P is assigned 0ij(ii) a The height of the layer with the temperature of 0 ℃ subtracted from the maximum height in the 2DA1 is the thickness of the convection monomer above the layer with the temperature of 0 DEG C
Figure BDA00014750557100000716
The maximum value of the height value difference of the same position in the 2DA1 and the 2DA2 is the convective monomer core thickness CT; the specific calculation formula is as follows, wherein H0Is the layer height of 0 ℃ and is,
Figure BDA0001475055710000071
Figure BDA0001475055710000072
1-3) extracting convective monomer core height CCH: on a combined reflectivity graph of a Doppler weather radar, a maximum reflectivity value R is found in an outer-wrapping rectangular range of a convection single bodyMSequentially calculating two-dimensional data 2DkM×NThe median value is RMThe number of (2) is denoted by nkAnd k is 1,2, 17, and the average height of the calculated maximum reflectance value is the convective single core height CCH; the specific calculation formula is
Figure BDA0001475055710000073
1-4) extracting convection monomer-combined reflectivity nuclear region area with layer height above-10 DEG C
Figure BDA0001475055710000074
Creating two-dimensional data 2DA3 of size M × N; calculate the layer height H at-10 deg.C-10℃＝(H0+H-20) /2 wherein H0At a layer height of 0 ℃ H-20Is a layer height of-20 deg.C, and its adjacent height value is defined as kz1，kZ1∈[1,17](ii) a For PijI 1, …, M, j 1, …, N, let k be from kz1Starting with the sequence upward, and adding RijkThe value of (A) is assigned to P of the two-dimensional data 2DA3ijPositionally, as the value of k increases, if RijkIf the current value is larger, P is updatedijValue, otherwise PijThe value of (c) is not changed, and the search is carried out until k is 17; calculating R in 2DA3ijkThe number n is more than or equal to 40dBZ, and the value of n is the combined reflectivity nuclear area of the convection monomer with the layer height of more than 10 ℃ below zero
Figure BDA00014750557100000715
Is expressed as
Figure BDA0001475055710000075
1-5) extracting convective monomer-monomer core volume above-10 ℃ layer height
Figure BDA0001475055710000076
Volume of zone of stronger reflectivity for convective monomer
Figure BDA0001475055710000077
And convection cell low reflectivity region volume
Figure BDA0001475055710000078
For 2DkM×N，k＝kz1… …,17, calculating 2Dk sequentiallyM×NIn RijkThe number n of not less than 40dBZ40k、RijkThe number n of not less than 45dBZ45kAnd Rijk∈[25,39]Number n of dBZ25kThen n is40kThe value is the volume of monomer core above the layer height of-10 ℃ of the convective monomer
Figure BDA0001475055710000079
n45kI.e. the volume of the zone with stronger reflectivity of convection monomer
Figure BDA00014750557100000710
n25kThe value is the volume of the convection monomer low-reflectivity area
Figure BDA00014750557100000711
The specific calculation formula is
Figure BDA00014750557100000712
Figure BDA00014750557100000713
Figure BDA00014750557100000714
1-6) extracting Weak echo volume under pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 : creating two-dimensional data 2DA4, 2DA5, 2DA6, 2DA7, 2DA8, and 2DA9 of size M × N; definition H2＝H0-1，H2Is the height of 1km below the 0 ℃ layer, and the height value adjacent to the height is determined as kz2，kZ2∈[1,17](ii) a For PijI 1, …, M, j 1, …, N, let k be from kz2Starting sequentially upwards, R will be satisfied twice in successionijkSmaller k-k of not less than 40dBZz2Is marked as W1ijStoring the two-dimensional data 2DA4, otherwise, assigning 0 to Pij(ii) a K for storing each point in two-dimensional data 2DA4z2Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA 6; as shown in FIG. 1(a), white represents W1ijThe darker the color, W1, 0ijThe larger the value, W1ij>The 4.2km dots are shown in black; will satisfy R twice in successionijkSmaller k-k of not less than 45dBZz2Is marked as W2ijStoring the two-dimensional data 2DA5, otherwise, assigning 0 to Pij(ii) a K for storing each point in two-dimensional data 2DA5z2Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA 7; the areas with the largest connected areas in the two-dimensional data 2DA6 and the two-dimensional data 2DA7 are reserved, and as shown in FIG. 1(b), the W1 corresponding to the connected areas is reservedijValue sum W2ijThe values are respectively denoted as W3ijAnd W4ijRespectively storing the two-dimensional data 2DA8 and the two-dimensional data 2DA 9; all of the two-dimensional data 2DA8W3ijThe sum is the volume WEV of the weak echo below the pendants with a reflectivity threshold of 40dBZ 40 All W4 in the two-dimensional data 2DA9ijThe sum is the volume WEV of the weak echo below the pendants with a reflectivity threshold of 45dBZ 45 (ii) a The specific calculation formula is
WEV 40 ＝∑{W3ij|ij∈2DA8} (8)
WEV45＝∑{W4ij|j∈2DA9} (9)
1-7) extracting high-value MRG of reflectivity gradientCCH: creating a two-dimensional array 2DA10 with the size of M multiplied by N, setting the K value nearest to the convective single core height CCH as K, and performing two-dimensional data 2DKM×NIn, let max { Rijk}＝RMA number nM、RM-5dBZ in a number nM-5、RMNumber n of-10 dBZM-10Calculating the sum of nM、nM-5And nM-10Barycentric coordinates P (x) of all corresponding points0,y0) The formula is as follows, wherein a is 1, b is 1.5,
Figure BDA0001475055710000081
with P (x)0,y0) Centering on the two-dimensional data 2DKM×NAll the points in (a) are divided into 8 regions in total, one region every 45 degrees with the OX direction as the starting position; for the data points falling into each region, the direction gradient GK is obtainedijAnd storing the data into a two-dimensional array 2DA 10; respectively counting R in the 8 region rangesijkThe number of points ≧ 25dBZ, noted niI ═ 1,2, …,8, as shown in fig. 2; calculating n1To n8Minimum value n ofmRespectively expanding two areas before and after the m-th area is taken as a reference, then calculating the number of points with the reflectivity of more than 25dBZ of all 3 connected areas in the 5 adjacent areas, and calling the area corresponding to the area with the minimum number in all 3 connected areas as a high gradient area; as shown in fig. 2, when m is 2, n is∑1＝n8+n1+n2，n∑2＝n1+n2+n3，n∑3＝n2+n3+n4Wherein n is∑2At the minimum, therefore, regions No. 1,2,3 are collectively referred to as high gradient regions; calculating the average gradient in the high gradient area range according to the gradient matrix, namely the reflectivity gradient high value MRGCCH。
Step two: processing hail cloud feature data to obtain two comprehensive features;
2-1) based on the 10 characteristic values obtained in the step one, carrying out standardization treatment to ensure that the thickness of the convection monomer is more than 0 DEG C
Figure BDA0001475055710000082
Core area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG C
Figure BDA0001475055710000091
Convection monomer core volume above-10 ℃ layer height
Figure BDA0001475055710000092
Volume of zone of stronger reflectivity for convective monomer
Figure BDA0001475055710000093
Convective monomer low reflectivity region volume
Figure BDA0001475055710000094
Weak echo volume below pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRGCCHIs recorded as: f ═ F1,F2,F3,F4,F5,F6,F7,F8,F9,F10) Using the formula
Figure BDA0001475055710000095
Normalized to obtain the characteristic
Figure BDA0001475055710000096
Wherein
Figure BDA0001475055710000097
Figure BDA0001475055710000098
2-2) to the above-obtained features
Figure BDA0001475055710000099
The principal component analysis was carried out to obtain 10 principal components, which were designated as pw (pw)1,pw2,pw3,pw4,pw5,pw6,pw7,pw8,pw9,pw10) (ii) a Wherein the information contribution ratio of each principal component is shown in Table 1, wherein the first two-dimensional principal component pw1And pw2The weight coefficients of (d) are shown in table 2:
TABLE 1 information contribution ratio of principal component vector
Figure BDA00014750557100000910
TABLE 2 principal Components pw1And pw2Weight coefficient of
Figure BDA00014750557100000911
2-3) according to the difference of the information contribution rates of the 10 principal components, reserving the former two-dimensional principal components to obtain a new two-dimensional characteristic:
Figure BDA0001475055710000101
Figure BDA0001475055710000102
and
Figure BDA0001475055710000103
Figure BDA0001475055710000104
Figure BDA0001475055710000105
respectively recording the characteristic as volume comprehensive characteristic VMCF and height-gradient comprehensive characteristic HGCF;
step three: carrying out early recognition of hail clouds;
according to the jump phenomenon of the hail cloud volume comprehensive characteristic VMCF and the height-gradient comprehensive characteristic HGCF shown in the early stage, as shown in fig. 3(a) and 3(b), wherein the abscissa represents the number of body sweeps, the ordinate represents the two-dimensional comprehensive characteristic value, wherein the dotted line represents VMCF, the solid line represents HGCF, a four-dimensional strong convection monomer description vector is constructed
Figure BDA0001475055710000106
△1Difference of volume integration features VMCF for adjacent volume sweep, Δ2Is the difference of the height-gradient integrated characteristic HGCF of adjacent body sweeps,
Figure BDA0001475055710000107
for the difference between the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF of the same volume sweep,
Figure BDA0001475055710000108
the sum of the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF for the same volume sweep, n represents the current volume sweep, n-1 represents the previous volume sweep,
△i＝pwi(n)-pwi(n-1),i＝1,2
Figure BDA0001475055710000109
there is one of the following cases, the convective monomer is the hail monomer, then it is considered to be the hail cloud early stage at this moment,
(1)△1≥1.24；
(2)△2≥1.50；
(3)
Figure BDA00014750557100001010
(4)
Figure BDA00014750557100001011
recording the body scanning number and time at the moment; otherwise, the convective monomer is considered not to be a hail monomer; and returning to the step one, and carrying out process processing at the next moment until the judgment of the radar base data at all the moments is finished.
The feasibility of the early hail cloud identification method based on doppler weather radar data provided by the invention is verified by specific tests, which are described in detail in the following description:
data set 1: the total number of the 49 hail processes and the 35 strong precipitation processes is 84 processes, and the series of data participates in algorithm construction and debugging of algorithm parameters.
Data set 2: the 31 hail processes and the 33 strong precipitation processes are 64 processes, and the series of data does not participate in the debugging of algorithm parameters and is only used for the testing of the algorithm.
Fig. 4 shows the statistics of the distribution of the two-dimensional comprehensive characteristics of all hail samples and heavy precipitation samples in the data set 1, and it is easy to see that there is a great difference between hail and heavy precipitation. Table 3 lists the recognition results of the data set 2 of the present invention. The hits indicate that the sample is a hail cloud and is correctly identified, and the empty report indicates that the rain cloud of strong precipitation is identified as a hail cloud, where the data set 2 detects a total of 29 hail processes. Table 4 lists statistics of the time taken by the hail cloud early identification algorithm from the detection of a strong convective monomer to its identification as a hail cloud, most hail samples can be identified within 18 minutes of the occurrence of a hail monomer.
TABLE 3 hail procedure test results
Figure BDA0001475055710000111
TABLE 4 time results for early recognition of hail clouds
Figure BDA0001475055710000112
The early recognition of all hail samples with specific hail time information is carried out in advance of the statistics of the hail time, and the specific statistical result is shown in fig. 5. Through statistics and calculation, 52 hail samples with specific hail suppression time exist, and the average time value of early identification before the hail suppression time is 8.23 volume sweeps, namely 49 minutes. Compared with a general hail cloud identification algorithm, the method has the advantage that the advance is increased greatly.
While the present invention has been described with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are illustrative only and not restrictive, and various modifications which do not depart from the spirit of the present invention and which are intended to be covered by the claims of the present invention may be made by those skilled in the art.

Claims (2)

1. The early hail cloud identification method based on Doppler weather radar data is characterized in that combined reflectivity maps of Doppler weather radars at different moments are obtained according to Doppler weather radar base data at continuous moments, and the following processes are performed according to a time sequence, and the method comprises the following steps of:
the method comprises the following steps: extracting the characteristics of the hail cloud;
extracting 10 characteristics of each individual sweep of the convection monomer after the precursor sweep from a 40dBZ connected region in which more than 10 pixel points appear in the three-dimensional reflectivity lattice point data of the convection monomer;
10 features including more than 0 ℃ layer thickness of convective monomer
Figure FDA0001475055700000011
Core area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG C
Figure FDA0001475055700000012
Convection monomer core volume above-10 ℃ layer height
Figure FDA0001475055700000013
Volume of zone of stronger reflectivity for convective monomer
Figure FDA0001475055700000014
Convective monomer low reflectivity region volume
Figure FDA0001475055700000015
Weak echo volume below pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRGCCH；
Step two: processing hail cloud feature data to obtain two comprehensive features;
2-1) based on the 10 characteristic values obtained in the step one, carrying out standardization treatment to ensure that the thickness of the convection monomer is more than 0 DEG C
Figure FDA0001475055700000016
Core area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG C
Figure FDA0001475055700000017
Convection monomer core volume above-10 ℃ layer height
Figure FDA0001475055700000018
Volume of zone of stronger reflectivity for convective monomer
Figure FDA0001475055700000019
Convective monomer low reflectivity region volume
Figure FDA00014750557000000110
Threshold value of reflectivity respectivelyWeak echo volume below pendant WEV for 40dBZ and 45dBZ 40 And WEV 45 And high reflectance gradient value MRGCCHIs recorded as: f ═ F1,F2,F3,F4,F5,F6,F7,F8,F9,F10) Using the formula
Figure FDA00014750557000000111
Normalized to obtain the characteristic
Figure FDA00014750557000000112
Wherein:
Figure FDA00014750557000000113
Figure FDA00014750557000000114
2-2) to the above-obtained features
Figure FDA00014750557000000115
The principal component analysis was carried out to obtain 10 principal components, which were designated as pw (pw)1,pw2,pw3,pw4,pw5,pw6,pw7,pw8,pw9,pw10)；
2-3) according to the difference of the information contribution rates of the 10 principal components, reserving the former two-dimensional principal components to obtain a new two-dimensional characteristic:
Figure FDA00014750557000000116
Figure FDA0001475055700000021
and respectively mark asVolume integrated feature VMCF and height-gradient integrated feature HGCF;
step three: carrying out early recognition of hail clouds;
according to the jump phenomenon of hail cloud volume comprehensive characteristic VMCF and height-gradient comprehensive characteristic HGCF shown in the early stage, a four-dimensional strong convection monomer description vector is constructed
Figure FDA0001475055700000022
△1Difference of volume integration features VMCF for adjacent volume sweep, Δ2Is the difference of the height-gradient integrated characteristic HGCF of adjacent body sweeps,
Figure FDA0001475055700000023
for the difference between the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF of the same volume sweep,
Figure FDA0001475055700000024
the sum of the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF for the same volume sweep, n represents the current volume sweep, n-1 represents the previous volume sweep,
△i＝pwi(n)-pwi(n-1),i＝1,2
Figure FDA0001475055700000025
there is one of the following cases, the convective monomer is the hail monomer, then it is considered to be the hail cloud early stage at this moment,
(1)△1≥1.24；
(2)△2≥1.50；
(3)
Figure FDA0001475055700000026
(4)
Figure FDA0001475055700000027
recording the body scanning number and time at the moment; otherwise, the convection current monomer is not the hail monomer, the step one is returned, and the process processing of the next moment is carried out until the judgment of the radar base data at all the moments is completed.
2. The doppler weather radar data-based hail cloud early identification method according to claim 1, wherein the specific content of the first step is as follows:
2-1) segmenting convection monomers on a combined reflectance graph of the Doppler weather radar, respectively obtaining an outer-wrapping rectangle of each convection monomer, matching the outer-wrapping rectangle with the position of the outer-wrapping rectangle at the previous moment, if matching is successful, the convection monomer is the convection monomer at the previous moment, and if not, the convection monomer is a new convection monomer;
carrying out bilinear interpolation operation on the 9 elevation reflectivity data in each outsourcing rectangular range to obtain three-dimensional reflectivity grid point data of a convection monomer, and setting the resolution of the three-dimensional reflectivity grid point data to be 1km multiplied by 1 km; sequentially storing the three-dimensional reflectivity lattice point data at the same horizontal height position into two-dimensional data 2Dk with the size of M multiplied by N from low to highM×NThe height value k is 1,2, …, 17;
2-2) extracting convection monomer with thickness of more than 0 DEG C
Figure FDA0001475055700000028
And convective monomer core thickness CT: creating two-dimensional data 2DA1 and 2DA2 of size M × N; pijI is 1, …, M, j is 1, …, N is a point position in the two-dimensional data 2DA1 and 2DA2, and the two-dimensional data 2Dk is assumedM×NThe data in (1) is RijkLet the height value k go down in the order from 17, R will be satisfied twice in successionijkThe larger k value of ≥ 40dBZ is noted as hijP stored to two-dimensional data 2DA1ijThe above step (1); then let k go up from 1 in sequence, and satisfy R twice in successionijkThe smaller k value of ≧ 40dBZ is noted as hij stores P to two-dimensional data 2DA2ij above; if the condition is not satisfied all the time, then 0 is assigned to Pij(ii) a For any position P in two-dimensional data 2DA1 and 2DA2ijCalculate PijThe difference between the value of (A) and the value of its 8 neighboursl1, …,8, if ΔlIf the number of not more than 1 exceeds 4, P isijOtherwise, the point is considered as noise, and P is assigned 0ij(ii) a The height of the layer with the temperature of 0 ℃ subtracted from the maximum height in the 2DA1 is the thickness of the convection monomer above the layer with the temperature of 0 DEG C
Figure FDA0001475055700000039
The maximum value of the height value difference of the same position in the 2DA1 and the 2DA2 is the convective monomer core thickness CT;
2-3) extracting convective monomer core height CCH: on a combined reflectivity graph of a Doppler weather radar, a maximum reflectivity value R is found in an outer-wrapping rectangular range of a convection single bodyMSequentially calculating two-dimensional data 2DkM×NThe median value is RMThe number of (2) is denoted by nkAnd k is 1,2, 17, and the average height of the calculated maximum reflectance value is the convective single core height CCH;
2-4) extracting convection monomer-combined reflectivity nuclear region area with layer height above-10 DEG C
Figure FDA0001475055700000031
Creating two-dimensional data 2DA3 of size M × N; calculate the layer height H at-10 deg.C-10℃＝(H0+H-20) /2 wherein H0At a layer height of 0 ℃ H-20Is a layer height of-20 deg.C, and its adjacent height value is defined as kz1，kZ1∈[1,17](ii) a For PijI 1, …, M, j 1, …, N, let k be from kz1Starting with the sequence upward, and adding RijkThe value of (A) is assigned to P of the two-dimensional data 2DA3ijPositionally, as the value of k increases, if RijkIf the current value is larger, P is updatedijValue, otherwise PijThe value of (c) is not changed, and the search is carried out until k is 17; calculating R in 2DA3ijkThe number n is more than or equal to 40dBZ, and the value of n is the combined reflectivity nuclear area of the convection monomer with the layer height of more than 10 ℃ below zero
Figure FDA0001475055700000032
2-5) extracting convective monomer-monomer core volume above-10 ℃ layer height
Figure FDA0001475055700000033
Volume of zone of stronger reflectivity for convective monomer
Figure FDA0001475055700000034
And convection cell low reflectivity region volume
Figure FDA0001475055700000035
For 2DkM×N，k＝kz1… …,17, calculating 2Dk sequentiallyM×NIn RijkThe number n of not less than 40dBZ40k、RijkThe number n of not less than 45dBZ45kAnd Rijk∈[25,39]Number n of dBZ25kThen n is40kThe value is the volume of monomer core above the layer height of-10 ℃ of the convective monomer
Figure FDA0001475055700000036
n45kI.e. the volume of the zone with stronger reflectivity of convection monomer
Figure FDA0001475055700000037
n25kThe value is the volume of the convection monomer low-reflectivity area
Figure FDA0001475055700000038
2-6) extraction of Weak echo volume under pendant WEV with reflectivity thresholds of 40dBZ and 45dBZ, respectively40And WEV 45 : creating two-dimensional data 2DA4, 2DA5, 2DA6, 2DA7, 2DA8, and 2DA9 of size M × N; definition H2＝H0-1，H2Is the height of 1km below the 0 ℃ layer, and the height value adjacent to the height is determined as kz2，kZ2∈[1,17](ii) a For PijI 1, …, M, j 1, …, N, let k be from kz2Starting sequentially upwards, R will be satisfied twice in successionijkSmaller k-k of not less than 40dBZz2Is marked as W1ijStoring the two-dimensional data 2DA4, otherwise, assigning 0 to Pij(ii) a K for storing each point in two-dimensional data 2DA4z2Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA 6; will satisfy R twice in successionijkSmaller k-k of not less than 45dBZz2Is marked as W2ijStoring the two-dimensional data 2DA5, otherwise, assigning 0 to Pij(ii) a K for storing each point in two-dimensional data 2DA5z2Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA 7; respectively reserving areas with the largest communication areas in the two-dimensional data 2DA6 and the two-dimensional data 2DA7, and enabling W1 corresponding to the communication areasijValue sum W2ijThe values are respectively denoted as W3ijAnd W4ijRespectively storing the two-dimensional data 2DA8 and the two-dimensional data 2DA 9; all W3 in the two-dimensional data 2DA8ijThe sum is the volume WEV of the weak echo below the pendants with a reflectivity threshold of 40dBZ 40 All W4 in the two-dimensional data 2DA9ijThe sum is the volume WEV of the weak echo below the pendants with a reflectivity threshold of 45dBZ 45 ；
2-7) extracting high-value MRG of reflectivity gradientCCH: creating a two-dimensional array 2DA10 with the size of M multiplied by N, setting the K value nearest to the convective single core height CCH as K, and performing two-dimensional data 2DKM×NIn, let max { Rijk}＝RMA number nM、RM-5dBZ in a number nM-5、RMNumber n of-10 dBZM-10Calculating the sum of nM、nM-5And nM-10Barycentric coordinates P (x) of all corresponding points0,y0) (ii) a With P (x)0,y0) Centering on the two-dimensional data 2DKM×NAll the points in (a) are divided into 8 regions in total, one region every 45 degrees with the OX direction as the starting position; for the data points falling into each region, the direction gradient GK is obtainedijAnd storing the data into a two-dimensional array 2DA 10; respectively counting R in the 8 region rangesijkThe number of points ≧ 25dBZ, noted niI is 1,2, …, 8; calculating n1To n8Minimum value n ofmRespectively expanding two areas before and after the m-th area is taken as a reference, and then calculating that the reflectivity of all 3 connected areas in the 5 adjacent areas is more than 25dBZThe number of the above points, and the region corresponding to the minimum number of all the 3-linked regions is referred to as a high gradient region; calculating the average gradient in the high gradient area range according to the gradient matrix, namely the reflectivity gradient high value MRGCCH。
CN201711161224.0A 2017-11-20 2017-11-20 Hail cloud early-stage identification method based on Doppler weather radar data Active CN108020840B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711161224.0A CN108020840B (en) 2017-11-20 2017-11-20 Hail cloud early-stage identification method based on Doppler weather radar data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711161224.0A CN108020840B (en) 2017-11-20 2017-11-20 Hail cloud early-stage identification method based on Doppler weather radar data

Publications (2)

Publication Number Publication Date
CN108020840A CN108020840A (en) 2018-05-11
CN108020840B true CN108020840B (en) 2021-08-13

Family

ID=62079930

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711161224.0A Active CN108020840B (en) 2017-11-20 2017-11-20 Hail cloud early-stage identification method based on Doppler weather radar data

Country Status (1)

Country Link
CN (1) CN108020840B (en)

Families Citing this family (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108802733A (en) * 2018-06-15 2018-11-13 安徽诺安信息科技有限公司 A kind of algorithm that hail is detected based on weather radar data for communication
CN109188439B (en) * 2018-08-02 2020-05-19 大连市人工影响天气办公室 Artificial hail suppression early hail cloud identification technical method and system
CN109116358B (en) * 2018-08-09 2022-11-22 成都信息工程大学 Hail identification and area forecast method based on new-generation weather radar
CN109061649A (en) * 2018-08-10 2018-12-21 中国气象局气象探测中心 A kind of hail monitoring method and system
CN109164450B (en) * 2018-09-12 2022-11-22 天津大学 Downburst prediction method based on Doppler radar data
CN109828275B (en) * 2019-01-28 2021-03-02 北京城市气象研究院 Identification method and system for zero-degree layer bright band
CN110574614B (en) * 2019-08-07 2021-05-18 大连市人工影响天气办公室 Technical method and system for scientific and effective application of artificial hail suppression safety radial boundary diagram
CN110488296B (en) * 2019-08-21 2022-11-25 成都信息工程大学 Online monitoring data early warning method for convective monomer hail-suppression polarization radar ZDR column
CN110488298B (en) * 2019-08-30 2022-11-11 成都信息工程大学 Hail early warning method based on various scale features
CN111289983B (en) * 2020-04-16 2022-12-09 内蒙古工业大学 Inversion method for vertically accumulated liquid water content of radar
CN111505645A (en) * 2020-04-26 2020-08-07 上海眼控科技股份有限公司 Radar cloud chart data display method and device, computer equipment and storage medium
CN112651463A (en) * 2021-01-05 2021-04-13 天津大学 Construction method of double-forecast model of hail weather in plateau area
CN112819064B (en) * 2021-01-28 2022-04-22 南京航空航天大学 Terminal area time sequence meteorological scene identification method based on spectral clustering
CN112558076B (en) * 2021-02-07 2021-08-10 上海市气象信息与技术支持中心 Volume scanning mode calculation method based on networking weather radar coverage area and application
CN114280571B (en) * 2022-03-04 2022-07-19 北京海兰信数据科技股份有限公司 Method, device and equipment for processing rain clutter signals

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09329672A (en) * 1996-06-07 1997-12-22 Kansai Electric Power Co Inc:The Method for predicting thunder
US7069258B1 (en) * 2002-09-26 2006-06-27 Bothwell Phillip D Weather prediction method for forecasting selected events
CN102129566A (en) * 2011-03-09 2011-07-20 国家卫星气象中心 Method for identifying rainstorm cloud cluster based on stationary meteorological satellite
CN102721987A (en) * 2012-06-12 2012-10-10 中国海洋大学 Method for prewarning Doppler radar remote sensing strong storm

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09329672A (en) * 1996-06-07 1997-12-22 Kansai Electric Power Co Inc:The Method for predicting thunder
US7069258B1 (en) * 2002-09-26 2006-06-27 Bothwell Phillip D Weather prediction method for forecasting selected events
CN102129566A (en) * 2011-03-09 2011-07-20 国家卫星气象中心 Method for identifying rainstorm cloud cluster based on stationary meteorological satellite
CN102721987A (en) * 2012-06-12 2012-10-10 中国海洋大学 Method for prewarning Doppler radar remote sensing strong storm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于显著性特征的大冰雹识别模型;王萍 等;《物理学报》;069202-1-069202-10;20131231;第62卷(第6期);069202-01-069202-10 *

Also Published As

Publication number Publication date
CN108020840A (en) 2018-05-11

Similar Documents

Publication Publication Date Title
CN108020840B (en) Hail cloud early-stage identification method based on Doppler weather radar data
Zhou et al. Forecasting different types of convective weather: A deep learning approach
CN107016677A (en) A kind of cloud atlas dividing method based on FCN and CNN
CN108693534A (en) NRIET X band radars cooperate with networking analysis method
Panziera et al. The relation between airflow and orographic precipitation on the southern side of the Alps as revealed by weather radar
CN109975809B (en) Method for assimilating radar and lightning data
Goudenhoofdt et al. Generation and verification of rainfall estimates from 10-yr volumetric weather radar measurements
Lakshmanan et al. Data mining storm attributes from spatial grids
CN111983732B (en) Rainfall intensity estimation method based on deep learning
CN107229084B (en) A kind of automatic identification tracks and predicts contracurrent system mesh calibration method
CN110415346B (en) Method for simulating water and soil loss by using object-oriented three-dimensional cellular automaton
CN108254750B (en) Down-blast intelligent identification early warning method based on radar data
CN109459751A (en) A kind of biological information monitor method of migrating based on weather radar data
CN103529492A (en) Storm body position and form prediction method based on Doppler radar reflectivity image
CN101114337A (en) Ground buildings recognition positioning method
CN107301272A (en) Strong convection monomer structure and architectural feature visualization space-time method for decomposing
CN102662173A (en) Thunderstorm forecasting method based on level set
CN109658477A (en) A kind of DEM generating algorithm based on LIDAR data
Houston et al. Thunderstorm Observation by Radar (ThOR): An algorithm to develop a climatology of thunderstorms
CN115857059B (en) Ice accumulation forecast management method, system, readable storage medium and device
Zou et al. A method of radar echo extrapolation based on TREC and Barnes filter
CN116384733A (en) Power transmission line risk early warning method based on weather radar in strong convection weather
Wang et al. A quantitative comparison of precipitation forecasts between the storm-scale numerical weather prediction model and auto-nowcast system in Jiangsu, China
CN116189147A (en) YOLO-based three-dimensional point cloud low-power-consumption rapid target detection method
CN114898206A (en) Short-time heavy rainfall forecasting method, computer equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant