CN108020840A - A kind of Hail Cloud By Using Weather EARLY RECOGNITION method based on Doppler radar data - Google Patents
A kind of Hail Cloud By Using Weather EARLY RECOGNITION method based on Doppler radar data Download PDFInfo
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Abstract
The invention discloses a kind of Hail Cloud By Using Weather EARLY RECOGNITION method based on Doppler radar data for the identification of strong weather, and first, the reflectivity data based on Doppler radar, three-dimensional reflection rate Grid data is obtained using bilinearity difference arithmetic；10 features of extraction description Hail Cloud By Using Weather, then, intrinsic dimensionality is reduced according to principal component analytical method, obtains scale of construction comprehensive characteristics and altitudinal gradient comprehensive characteristics, passage time sequence analysis, above-mentioned two comprehensive characteristics value occur showing obvious jumping phenomenon in early days in Hail Cloud By Using Weather；Finally, the jumping phenomenon based on hail sample bidimensional comprehensive characteristics, using precipitation as negative sample, builds the EARLY RECOGNITION that a four-dimensional strong convection monomer description vectors carry out Hail Cloud By Using Weather.The present invention realizes the automatic identification of Hail Cloud By Using Weather early stage, can provide the opportune time for hail suppression and hail mitigation, reduce economic loss and casualties.Meanwhile provide new thinking for the strong convective weather identification based on weather radar data for communication.
Description
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 cloud is a product triggered by a ground convergent flow field under the condition of sufficient water vapor supply after an unstable atmospheric layer appears, and is classified as a strong convection storm. In strong weather identification, the identification of hail clouds occupies an important position.
Most of hail cloud recognition research is based on Doppler weather radars, and different hail cloud characteristics are constructed to recognize hail clouds based on reflectivity factor base data of various elevation radar according to structural characteristics and morphological characteristics of the hail clouds. 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 method provides a vertical accumulation liquid water content VIL, and the method assumes that all radar return reflectivity factors are caused by liquid water drop reflection, so the method converts the reflectivity factors into equivalent liquid water values and judges the hail reduction possibility by using the 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 identification method based on Doppler weather radar data for strong weather identification, which is used for obtaining combined reflectivity maps of Doppler weather radars at different moments according to Doppler weather radar base data at continuous moments, and comprises the following processes according to a time sequence:
the method comprises the following steps: extracting the characteristics of the hail cloud;
extracting 10 characteristics of each individual sweep after the precursor sweep of the convection monomer starts from a 40dBZ connected region where more than 10 pixel points appear in the three-dimensional reflectivity lattice point data of the convection monomer;
10 features include more than 0 ℃ layer thickness of convective monomerConvection monomer nuclear thickness CT, convection monomer nuclear core height CCH and convection monomer-10 DEG CCombined reflectivity kernel area above layer heightConvection monomer core volume above-10 ℃ layer heightVolume of zone of stronger reflectivity for convective monomerConvective monomer low reflectivity region volumeWev for the volume of the weak echo below the catenary with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRG CCH ；
Step two: processing hail cloud feature data to obtain two comprehensive features;
2-1) standardizing the thickness of the convection monomer layer above 0 deg.C based on 10 characteristic values obtained in step oneCore area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG CConvection monomer core volume above-10 ℃ layer heightVolume of zone of stronger reflectivity for convective monomerConvective monomer low reflectivity region volumeWith reflectivity thresholds of 40dBZ and 45dBZ respectivelyWeak echo volume WEV under pituitary 40 And WEV 45 And high reflectance gradient value MRG CCH Is recorded as: f = (F) 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 ,F 7 ,F 8 ,F 9 ,F 10 ) Using the formulaNormalized to obtain a signatureWherein:
2-2) to the characteristics obtained aboveThe principal component analysis treatment was performed to obtain 10 principal components, which were denoted as pw = (pw) 1 ,pw 2 ,pw 3 ,pw 4 ,pw 5 ,pw 6 ,pw 7 ,pw 8 ,pw 9 ,pw 10 )；
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: and respectively recording the characteristic as volume comprehensive characteristic VMCF and height-gradient comprehensive characteristic HGCF;
step three: carrying out early identification on hailstone cloud;
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△ 1 Difference of volume integration features VMCF for adjacent volume sweep, Δ 2 Is the difference of the height-gradient integrated characteristic HGCF of adjacent body sweeps,for the difference between the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF of the same volume,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 ＝pw i (n)-pw i (n-1),i＝1,2
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)
(4)
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 reflectivity graph of the Doppler weather radar, respectively obtaining an outsourcing rectangle of each convection monomer, matching the outsourcing rectangle with the position of the outsourcing 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;
performing bilinear interpolation operation on 9 elevation reflectivity data in each outsourcing rectangular range to obtain three-dimensional reflectivity lattice point data of a convection monomer, and setting the resolution of the three-dimensional reflectivity lattice point data to be 1km multiplied by 1km; 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 high M×N Medium, height value k =1,2, \8230, 17;
2-2) extracting convection monomer with thickness of more than 0 DEG CAnd convective monomer core thickness CT: creating two-dimensional data 2DA1 and 2DA2 with the size of M multiplied by N; p ij I =1, \8230, M, j =1, \8230, N is a position of a point in the two-dimensional data 2DA1 and 2DA2, and the two-dimensional data 2Dk is set M×N The data in (1) is R ijk Let the height value k go down in order from 17, R will be satisfied twice consecutively ijk The larger k value of ≥ 40dBZ is noted as h ij P stored in two-dimensional data 2DA1 ij The above step (1); then let k go up from 1 in sequence, and satisfy R twice in succession ijk The smaller k value of ≧ 40dBZ is noted as h ij P stored in two-dimensional data 2DA2 ij The above step (1); if the condition is not satisfied all the time, then 0 is assigned to P ij (ii) a To two isAny position P in dimension data 2DA1 and 2DA2 ij Calculate P ij The difference between the value of (A) and the value of its 8 neighbours l L =1, \ 8230;, 8, if Δ l If the number of not more than 1 exceeds 4, P is ij Otherwise, the point is considered as a noise point, and 0 is assigned to P ij (ii) a The maximum height in the 2DA1 minus the height of the 0 ℃ layer is the thickness CH of the convection monomer above the 0 ℃ layer 0 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, finding a maximum reflectivity value R in an outer rectangular wrapping range of a convection single body M Sequentially calculating two-dimensional data 2Dk M×N The median value is R M Number of (2), denoted as n k K =1, 2.., 17, and the average height of the calculated maximum reflectance value is the convective monomer core height CCH;
2-4) extracting convection monomer-the combined reflectivity nuclear zone area with the layer height of more than 10 DEG CCreating two-dimensional data 2DA3 with the size of M multiplied by N; calculate the layer height H at-10 deg.C -10℃ ＝(H 0 +H -20 ) /2 wherein H 0 At a layer height of 0 ℃ H -20 Is a layer height of-20 deg.C, and its adjacent height value is defined as k z1 ，k Z1 ∈[1,17](ii) a For P ij I =1, \ 8230;, M, j =1, \ 8230;, N, let k be from k z1 Starting with the sequence upward, and adding R ijk Is assigned to P of the two-dimensional data 2DA3 ij Positionally, as the value of k increases, if R ijk If the current value is larger, P is updated ij Value, otherwise P ij The value at (a) is constant and is searched until k =17; calculation of R in 2DA3 ijk The 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
2-5) extracting convection monomer-monomer core volume above layer height of-10 DEG CVolume of zone of stronger reflectivity for convective monomerAnd convection cell low reflectivity region volumeFor 2Dk M×N ，k＝k z1 \8230;, 17, 2Dk was calculated sequentially M×N In R ijk Number n of not less than 40dBZ 40k 、R ijk Number n of more than or equal to 45dBZ 45k And R ijk ∈[25,39]Number n of dBZ 25k Then n is 40k The value is the product of monomer nuclei with the height of a convection monomer layer higher than-10 DEG Cn 45k I.e. the volume of the zone with stronger reflectivity of convection monomern 25k The value is the volume of the convection monomer low-reflectivity area
2-6) extracting Wev volumes of Wev under pendants 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 H 2 ＝H 0 -1，H 2 Is the height of 1km below the 0 ℃ layer, and the height value k adjacent to the height value is determined z2 ，k Z2 ∈[1,17](ii) a For P ij I =1, \ 8230;, M, j =1, \ 8230;, N, let k be from k z2 Starting sequentially upwards, R will be satisfied twice in succession ijk Smaller k-k of not less than 40dBZ z2 Is denoted as W1 ij Storing the two-dimensional data 2DA4, otherwise, assigning 0 to P ij (ii) a K for storing each point in two-dimensional data 2DA4 z2 The height values are converted into gray levels and stored in two-dimensional data 2DA6; will satisfy R twice in succession ijk Smaller k-k of not less than 45dBZ z2 Is marked as W2 ij Storing the two-dimensional data 2DA5, otherwise, giving 0 to P ij (ii) a K for storing each point in two-dimensional data 2DA5 z2 Converting the height value into gray scale and storing the gray scale into two-dimensional data 2DA7; respectively reserving the areas with the largest connected areas in the two-dimensional data 2DA6 and the two-dimensional data 2DA7, and enabling the W1 corresponding to the connected areas ij Value sum W2 ij Values are respectively recorded as W3 ij And W4 ij Respectively storing the two-dimensional data 2DA8 and the two-dimensional data 2DA9; all W3 in the two-dimensional data 2DA8 ij The sum is the Weak echo volume WEV under the suspension body with the reflectivity threshold value of 40dBZ 40 All W4 in the two-dimensional data 2DA9 ij The sum is the volume WEV of the weak echo body below the suspension body with the reflectivity threshold value of 45dBZ 45 ；
2-7) extracting high-value MRG of reflectivity gradient CCH : 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 2DK M×N In, let max { R ijk }＝R M A number n M 、R M -5dBZ in a number n M-5 、R M Number n of-10 dBZ M-10 Calculating the sum of n M 、n M-5 And n M-10 Barycentric coordinates P (x) of all corresponding points 0 ,y 0 ) (ii) a With P (x) 0 ,y 0 ) Centering on the two-dimensional data 2DK M×N All the points in (a) are divided into 8 regions in total, one region every 45 DEG with the OX direction as the starting position; for the data points falling into each region, the direction gradient GK is obtained ij And storing the data into a two-dimensional array 2DA10; respectively counting R in the 8 region ranges ijk The number of points ≧ 25dBZ, noted n i I =1,2, \8230;, 8; calculating n 1 To n 8 Minimum value n of m Respectively 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 range of the high gradient area according to the gradient matrixDegree is the high value MRG of the reflectivity gradient CCH 。
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 for two hail processes, illustrating the jump-up phenomena of the massively integrated characteristic VMCF and the altitude-gradient integrated characteristic HGCF;
FIG. 4 is a comparison distribution 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 detailed flowchart of a method for early hail cloud identification according to 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,the design idea is as follows: firstly, 10 characteristics reflecting different characteristics of hail cloud in early stage are extracted, wherein the 10 characteristics include the thickness of more than 0 ℃ layer of convection monomerCore area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG CConvection monomer core product with layer height above-10 DEG CVolume of zone of stronger reflectivity for convective monomerConvective monomer low reflectivity zone volumeWeak echo volume below pendants WEV with reflectivity thresholds of 40dBZ and 45dBZ respectively 40 And WEV 45 And high reflectance gradient value MRG CCH (ii) a Performing characteristic transformation by using a principal component analysis method to obtain an integrated mass characteristic VMCF and an integrated height-gradient HGCF for describing the hail cloud, wherein the integrated mass characteristic VMCF or the integrated height-gradient HGCF presents an obvious jump phenomenon in the early stage of the appearance of the hail cloud; 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, provides 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 content is as follows: obtaining combined reflectivity graphs of the Doppler weather radars 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 after the precursor sweep of the convection monomer starts from a 40dBZ connected region where 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 monomerArea of core region with combined reflectivity including convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG CConvection monomer core volume above-10 ℃ layer heightVolume of zone of stronger reflectivity for convective monomerConvective monomer low reflectivity region volumeWeak echo volume below pendants WEV with reflectivity thresholds of 40dBZ and 45dBZ respectively 40 And WEV 45 And high reflectance gradient value MRG CCH ；
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;
for each outsourcing rectangular rangeCarrying out bilinear interpolation operation on the 9 pieces of elevation reflectivity data in the convection cell to obtain three-dimensional reflectivity grid point data of a convection cell, and setting the resolution of the three-dimensional reflectivity grid point data to be 1km multiplied by 1km; 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 high M×N Medium, height values k =1,2, \ 8230;, 17;
1-2) extracting convective monomer with thickness of more than 0 deg.C layerAnd convective monomer core thickness CT: creating two-dimensional data 2DA1 and 2DA2 with the size of M multiplied by N; p ij I =1, \8230, M, j =1, \8230, N is a position of a point in the two-dimensional data 2DA1 and 2DA2, and the two-dimensional data 2Dk is set M×N The data in (1) is R ijk Let the height value k go down in the order from 17, R will be satisfied twice in succession ijk The larger k value of not less than 40dBZ is recorded as h ij P stored in two-dimensional data 2DA1 ij The above step (1); then let k go up from 1 in sequence, and satisfy R twice in succession ijk The smaller k value of ≧ 40dBZ is noted as h ij P stored in two-dimensional data 2DA2 ij The above step (1); if the condition is not satisfied all the time, 0 is given to P ij (ii) a For any position P in two-dimensional data 2DA1 and 2DA2 ij Calculating P ij The difference Δ between the value of (X) and the value of its 8 neighborhood points l L =1, \ 8230;, 8, if Δ l If the number of less than or equal to 1 exceeds 4, P is ij Otherwise, the point is considered as noise, and P is assigned 0 ij (ii) a The height of the layer obtained by subtracting 0 ℃ from the maximum height in the 2DA1 is the thickness of the convection monomer above the 0 ℃ layerThe 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 formula is as follows, wherein H 0 Is the height of the layer at 0 ℃,
1-3) extracting convective monomer core height CCH: on a combined reflectivity graph of a Doppler weather radar, finding a maximum reflectivity value R in an outer rectangular wrapping range of a convection single body M Sequentially calculating two-dimensional data 2Dk M×N The median value is R M The number of (2) is denoted by n k K =1, 2.., 17, and the average height of the calculated maximum reflectance value is the convective monomer core height CCH; the specific calculation formula is
1-4) extracting convection monomer-combined reflectivity nuclear region area with layer height above-10 DEG CCreating two-dimensional data 2DA3 with the size of M multiplied by N; calculate the layer height H at-10 deg.C -10℃ ＝(H 0 +H -20 ) /2 wherein H 0 At a layer height of 0 ℃ H -20 Is the layer height at-20 deg.C, and the height value adjacent to it is determined as k z1 ，k Z1 ∈[1,17](ii) a For P ij I =1, \8230, M, j =1, \8230, N, let k be from k z1 Starting with the sequence up, and adding R ijk Is assigned to P of the two-dimensional data 2DA3 ij Positionally, as the value of k increases, if R ijk If the current value is larger, P is updated ij Value, otherwise P ij The value at (a) is constant and is searched until k =17; calculation of R in 2DA3 ijk The number n of more than or equal to 40dBZ, wherein the value of n is the combined reflectivity nuclear area of the convection monomer with the layer height of more than-10 DEG CIs expressed as
1-5) extracting convective monomer-monomer core product with layer height above-10 DEG CVolume of zone with stronger reflectivity for convection monomerAnd convection cell low reflectivity region volumeFor 2Dk M×N ，k＝k z1 \8230;, 17, 2Dk was calculated sequentially M×N In R ijk Number n of not less than 40dBZ 40k 、R ijk Number n of more than or equal to 45dBZ 45k And R ijk ∈[25,39]Number n of dBZ 25k Then n is 40k The value is the product of monomer nuclei with the height of a convection monomer layer higher than-10 DEG Cn 45k I.e. the volume of the zone with stronger reflectivity of convection monomern 25k The value is the volume of the convection monomer low reflectivity regionThe specific calculation formula is
1-6) extracting Weak echo volume WEV under pendants 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 H 2 ＝H 0 -1，H 2 Is the height of 1km below the 0 ℃ layer, and the height value adjacent to the height is determined as k z2 ，k Z2 ∈[1,17](ii) a For P ij I =1, \ 8230;, M, j =1, \ 8230;, N, let k be from k z2 Starting sequentially upwards, R will be satisfied twice in succession ijk Smaller k-k of not less than 40dBZ z2 Is denoted as W1 ij Storing the two-dimensional data 2DA4, otherwise, assigning 0 to P ij (ii) a K for storing each point in the two-dimensional data 2DA4 z2 Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA6; as shown in FIG. 1 (a), white represents W1 ij =0, the darker the color, W1 ij The larger the value, W1 ij &Points of 4.2km are indicated in black; will satisfy R twice in succession ijk Smaller k-k of not less than 45dBZ z2 Is marked as W2 ij Storing the two-dimensional data 2DA5, otherwise, giving 0 to P ij (ii) a K for storing each point in two-dimensional data 2DA5 z2 Converting the height value into gray scale and storing the gray scale into two-dimensional data 2DA7; the areas of the two-dimensional data 2DA6 and 2DA7 having the largest connected areas are retained, and as shown in FIG. 1 (b), W1 corresponding to the connected areas is assigned ij Value sum W2 ij The values are respectively denoted as W3 ij And W4 ij Respectively storing the two-dimensional data 2DA8 and the two-dimensional data 2DA9; all W3 in the two-dimensional data 2DA8 ij The sum is the Weak echo volume WEV under the suspension body with the reflectivity threshold value of 40dBZ 40 All W4 in the two-dimensional data 2DA9 ij The sum is the Weak echo volume WEV under the suspension body with the reflectivity threshold value of 45dBZ 45 (ii) a The specific calculation formula is
WEV 40 ＝∑{W3 ij | ij∈2DA8 } (8)
WEV 45 ＝∑{W4 ij | j∈2DA9 } (9)
1-7) extracting high-value MRG of reflectivity gradient CCH : creating a bigA two-dimensional array 2DA10 with a size of M multiplied by N, the K value nearest to the convective single core height CCH is set as K, and two-dimensional data 2DK M×N In, let max { R ijk }＝R M A number n M 、R M -5dBZ in a number n M-5 、R M Number n of-10 dBZ M-10 Calculating the sum of n M 、n M-5 And n M-10 Barycentric coordinates P (x) of all corresponding points 0 ,y 0 ) The formula is as follows, wherein a =1, b =1.5,
with P (x) 0 ,y 0 ) Centering on the two-dimensional data 2DK M×N All 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 obtained ij And storing the data into a two-dimensional array 2DA10; respectively counting R in the 8 region ranges ijk The number of points ≧ 25dBZ, noted n i I =1,2, \ 8230;, 8, as shown in fig. 2; calculating n 1 To n 8 Minimum value n of (1) m Respectively 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; in FIG. 2, m =2, then n ∑1 ＝n 8 +n 1 +n 2 ，n ∑2 ＝n 1 +n 2 +n 3 ，n ∑3 ＝n 2 +n 3 +n 4 Wherein n is ∑2 At a minimum, therefore region No. 1,2,3 is collectively 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 MRG CCH 。
Step two: processing the hail cloud characteristic data to obtain two comprehensive characteristics;
2-1) normalizing the 0 ℃ layer of the convective monomer based on the 10 characteristic values obtained in step oneAbove thicknessCore area of combined reflectivity of convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG CConvection monomer core volume above-10 ℃ layer heightVolume of zone of stronger reflectivity for convective monomerConvective monomer low reflectivity region volumeWev for the volume of the weak echo below the catenary with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRG CCH Is recorded as: f = (F) 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 ,F 7 ,F 8 ,F 9 ,F 10 ) By the formulaNormalized to obtain a signatureWherein
2-2) to the characteristics obtained aboveThe principal component analysis was carried out to obtain 10 principal components, which were designated as pw = (pw) 1 ,pw 2 ,pw 3 ,pw 4 ,pw 5 ,pw 6 ,pw 7 ,pw 8 ,pw 9 ,pw 10 ) (ii) a Wherein the information contribution ratio of each principal component is shown in Table 1, wherein the first two-dimensional principal component pw 1 And pw 2 The weighting coefficients of (d) are shown in table 2:
TABLE 1 information contribution ratio of principal component vectors
TABLE 2 principal Components pw 1 And pw 2 Weight coefficient of (2)
2-3) according to the difference of the information contribution rates of the 10 principal components, reserving the previous two-dimensional principal components to obtain a new two-dimensional characteristic: and respectively recording the characteristic as volume comprehensive characteristic VMCF and height-gradient comprehensive characteristic HGCF;
step three: carrying out early identification on hailstone cloud;
based on hail cloud volumeThe jump phenomenon exhibited by the characteristic VMCF and the altitude-gradient integrated characteristic HGCF at an early stage is shown in FIGS. 3 (a) and 3 (b), in which the abscissa represents the number of sweeps and the ordinate represents the two-dimensional integrated characteristic value, in which the dotted line represents the VMCF and the solid line represents the HGCF, and a four-dimensional strong convection monomer description vector is constructed△ 1 Difference of volume integration features VMCF for adjacent volume sweep, Δ 2 Is the difference of the height-gradient integrated characteristic HGCF of adjacent body sweeps,for the difference between the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF of the same volume sweep,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 ＝pw i (n)-pw i (n-1),i＝1,2
there is one of the following cases, the convection cell is a hail cell, and then it is considered as the hail cloud early stage at the moment,
(1)△ 1 ≥1.24；
(2)△ 2 ≥1.50；
(3)
(4)
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
TABLE 4 time results for early recognition of hail clouds
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 lead 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 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 include more than 0 ℃ layer thickness of convective monomerArea of core region with combined reflectivity including convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG CConvection monomer core product with layer height above-10 DEG CVolume of zone of stronger reflectivity for convective monomerConvective monomer low reflectivity region volumeWev for the volume of the weak echo below the catenary with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRG CCH ；
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 CArea of core region with combined reflectivity including convection monomer core thickness CT, convection monomer core height CCH and convection monomer layer height above-10 DEG CConvection monomer core volume above-10 ℃ layer heightVolume of zone of stronger reflectivity for convective monomerConvective monomer low reflectivity region volumeWev for the volume of the weak echo below the catenary with reflectivity thresholds of 40dBZ and 45dBZ, respectively 40 And WEV 45 And high reflectance gradient value MRG CCH Is recorded as: f = (F) 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 ,F 7 ,F 8 ,F 9 ,F 10 ) Using the formulaNormalized to obtain the characteristicWherein:
2-2) to the above-obtained featuresThe principal component analysis was carried out to obtain 10 principal components, which were designated as pw = (pw) 1 ,pw 2 ,pw 3 ,pw 4 ,pw 5 ,pw 6 ,pw 7 ,pw 8 ,pw 9 ,pw 10 )；
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:
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△ 1 Difference of volume integration features VMCF for adjacent volume sweeps, Δ 2 Is the difference of the height-gradient integrated characteristic HGCF of adjacent body sweeps,for the difference between the volume integration characteristic VMCF and the height-gradient integration characteristic HGCF of the same volume,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 ＝pw i (n)-pw i (n-1),i＝1,2
there is one of the following cases, the convection cell is a hail cell, and then it is considered as the hail cloud early stage at the moment,
(1)△ 1 ≥1.24；
(2)△ 2 ≥1.50；
(3)
(4)
recording the body scanning number and time at the moment; otherwise, the convection current monomer is not considered as 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 step one 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 1km; sequentially storing the three-dimensional reflectivity lattice point data at the same horizontal height position into two-dimensional data 2Dk with size of MxN from low to high M×N Medium, height value k =1,2, \8230, 17;
2-2) extracting convective monomer with thickness of more than 0 deg.C layerAnd convective monomer core thickness CT: creating two-dimensional data 2DA1 and 2DA2 with the size of M multiplied by N; p ij I =1, \ 8230, M, j =1, \ 8230, N is a point position in the two-dimensional data 2DA1 and 2DA2, and the two-dimensional data 2Dk is set M×N In (b) is R ijk Let the height value k go down in order from 17, R will be satisfied twice consecutively ijk The larger k value of ≥ 40dBZ is noted as h ij P stored in two-dimensional data 2DA1 ij The above step (1); let k go up from 1 sequentially and satisfy R twice ijk The smaller k value of ≧ 40dBZ is noted as h i j stores P to two-dimensional data 2DA2 i j above; if the condition is not satisfied all the time, 0 is given to P ij (ii) a For any position P in two-dimensional data 2DA1 and 2DA2 ij Calculate P ij The difference between the value of (A) and the value of its 8 neighbours l L =1, \ 8230;, 8, if Δ l If the number of not more than 1 exceeds 4, P is ij Otherwise, the point is considered as noise, and P is assigned 0 ij (ii) a The maximum height in the 2DA1 minus the height of the layer at 0 ℃ is the thickness of the convection monomer above the layer at 0 DEG CHeight value of the same position in 2DA1 and 2DA2The maximum value of the difference 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, finding a maximum reflectivity value R in an outer rectangular wrapping range of a convection single body M Sequentially calculating two-dimensional data 2Dk M×N The median value is R M The number of (2) is denoted by n k K =1, 2.., 17, and the average height of the calculated maximum reflectance value is the convective monomer core height CCH;
2-4) extracting convection monomer-the combined reflectivity nuclear zone area with the layer height of more than 10 DEG CCreating two-dimensional data 2DA3 with the size of M multiplied by N; calculate the layer height H at-10 deg.C -10℃ ＝(H 0 +H -20 ) /2 wherein H 0 At a layer height of 0 ℃ H -20 Is a layer height of-20 deg.C, and its adjacent height value is defined as k z1 ，k Z1 ∈[1,17](ii) a For P ij I =1, \ 8230;, M, j =1, \ 8230;, N, let k be from k z1 Starting with the sequence upward, and adding R ijk The value of (2) is assigned to P of the two-dimensional data 2DA3 ij Positionally, as the value of k increases, if R ijk If the current value is larger, P is updated ij Value, otherwise P ij The value of (a) is unchanged, and the search is carried out until k =17; calculation of R in 2DA3 ijk The 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
2-5) extracting convection monomer-monomer core volume above layer height of-10 DEG CVolume of zone with stronger reflectivity for convection monomerAnd volume of convection monomer low reflectivity regionFor 2Dk M×N ，k＝k z1 8230, 823017, calculating 2Dk sequentially M×N In R ijk The number n of not less than 40dBZ 40k 、R ijk Number n of more than or equal to 45dBZ 45k And R ijk ∈[25,39]Number n of dBZ 25k Then n is 40k The value is the volume of monomer core above the layer height of-10 ℃ of the convective monomern 45k I.e. the volume of the zone with stronger reflectivity of convection monomern 25k The value is the volume of the convection monomer low-reflectivity area
2-6) extracting Wev volumes of Wev under pendants 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 H 2 ＝H 0 -1，H 2 Is the height of 1km below the 0 ℃ layer, and the height value k adjacent to the height value is determined z2 ，k Z2 ∈[1,17](ii) a For P ij I =1, \ 8230;, M, j =1, \ 8230;, N, let k be from k z2 Starting sequentially upwards, R will be satisfied twice in succession ijk Smaller k-k of not less than 40dBZ z2 Is denoted as W1 ij Storing the two-dimensional data 2DA4, otherwise, assigning 0 to P ij (ii) a K for storing each point in the two-dimensional data 2DA4 z2 Converting the height values into gray scales and storing the gray scales into two-dimensional data 2DA6; will satisfy R twice in succession ijk Smaller k-k of not less than 45dBZ z2 Is denoted as W2 ij Storing the two-dimensional data 2DA5, otherwise, assigning 0 to P ij (ii) a K for storing each point in two-dimensional data 2DA5 z2 Converting the height value into gray scale and storing the gray scale into two-dimensional data 2DA7; respectively keeping the two-dimensional data 2DA6 and the two-dimensional data 2DA7 connectedThe region with the largest through area is W1 corresponding to the connected region ij Value sum W2 ij Values are respectively recorded as W3 ij And W4 ij Respectively storing the two-dimensional data 2DA8 and the two-dimensional data 2DA9; all W3 in the two-dimensional data 2DA8 ij The sum is the Weak echo volume WEV under the suspension body with the reflectivity threshold value of 40dBZ 40 All W4 in the two-dimensional data 2DA9 ij The sum is the volume WEV of the weak echo body below the suspension body with the reflectivity threshold value of 45dBZ 45 ；
2-7) extracting the high-value MRG of the reflectivity gradient CCH : 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 2DK M×N In, let max { R ijk }＝R M A number n M 、R M -5dBZ in a number n M-5 、R M Number n of-10 dBZ M-10 Calculating the sum of n M 、n M-5 And n M-10 Barycentric coordinates P (x) of all corresponding points 0 ,y 0 ) (ii) a With P (x) 0 ,y 0 ) Centering on the two-dimensional data 2DK M×N All 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 obtained ij And storing the data into a two-dimensional array 2DA10; respectively counting R in the 8 region ranges ijk The number of points ≧ 25dBZ, noted n i I =1,2, \8230;, 8; calculating n 1 To n 8 Minimum value n of (1) m Respectively expanding two areas before and after by taking the m-th area as a reference, then calculating the number of points with the reflectivity of all 3 connected areas more than 25dBZ 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 range of the high gradient zone according to the gradient matrix, namely the reflectivity gradient high value MRG CCH 。
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| CN112782699B (en) * | 2020-12-24 | 2024-05-03 | 中国气象局公共气象服务中心(国家预警信息发布中心) | Precipitation prediction method and device based on three-dimensional radar echo, electronic equipment and storage medium |
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| 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 |
| CN114280571A (en) * | 2022-03-04 | 2022-04-05 | 北京海兰信数据科技股份有限公司 | Processing method, device and equipment of rain clutter signals |
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