CN105139448A - Strong storm structure feature three-dimensional display method - Google Patents
Strong storm structure feature three-dimensional display method Download PDFInfo
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- CN105139448A CN105139448A CN201510490196.1A CN201510490196A CN105139448A CN 105139448 A CN105139448 A CN 105139448A CN 201510490196 A CN201510490196 A CN 201510490196A CN 105139448 A CN105139448 A CN 105139448A
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Abstract
The invention discloses a strong storm structure feature three-dimensional display method, comprising the following steps of: 1, pre-processing radar data; 2, identifying a strong storm region; 3, calculating radar horizontal reflectivity gradient distribution in the identified strong storm region, and according to the gradient changing characteristic, adaptively extracting boundary threshold values of characteristic regions including short-time heavy rainfall, thunderstorm strong wind and a hail type in the strong storm; and 4, three-dimensionally displaying the strong storm structure feature, wherein by calculating the extracted boundary threshold values of various characteristic regions, the strong storm region can be adaptively identified, the transparency of each region is automatically adjusted according to the boundary threshold values, and a drawn three-dimensional image can dynamically reflect the radar reflectivity data and the gradient changing rule thereof.
Description
Technical field
The present invention relates to a kind of display packing of strong storm architectural feature, especially relate to a kind of 3 D displaying method of strong storm architectural feature.
Background technology
Strong convective weather (comprising the phenomenons such as thunder and lightning, hail, thunderstorm, strong wind), also claims strong storm, is the diastrous weather of the movable strong development of atmosphere convection and generation, can causes huge social danger when it occurs.Because strong storm space scale is less, the duration is shorter, routine observation means are used to be difficult to effectively monitor it.Doppler radar, because have very high spatial and temporal resolution, is one of main remote sensing monitoring means of current strong storm.The three-D space structure of storm can be monitored by Analysis of Radar data.Therefore, how research carries out automatic analysis to the data of radar Doppler collection, accurately and rapidly identify the border of all kinds strong storm inner structure, the space structure of these strong storms is shown clearly by visual technology, thus auxiliary forecaster identifies the feature of strong storm body better, the development trend in its future is predicted accurately to there is earth shaking practical application meaning.
The data arrived with doppler radar are analytic target, existing much about stratus and convective cloud classification at present, and the research of Storm identification.Reflectivity threshold value is decided to be 30dBZ by the storm recognizer of very early time, and the region exceeding this threshold value in convective region is divided into storm monomer.This simple classification causes broad area such as violent wind line, mesoscale convective complex and the tropical storm etc. being greater than 30dBZ to be all identified as a monomer, unsatisfactory to the recognition effect of closely-spaced storm group.
Storm is structure fast-developing in three-dimensional space-time, various recognition methods mostly can only be used for the storm region identifying two dimension above, distinguish stratus and convective cloud, cannot identify the Stereo structure Characteristics of each generic attribute of storm, forecaster cannot observe Complicated Spatial Structure and the evolving state of strong storm body intuitively from recognition result.Therefore, according to the characteristic of spatial distribution of Doppler Radar Data, by direct for strong storm recognition result three-dimensional visualization rapidly, more intuitively and all sidedly represent the space structure of observed strong storm inside, thus auxiliary forecaster predicts effectively, is also a urgent problem.
Recognize that the three-dimensional visible of weather data is in the important application analyzed and in forecast, domestic and international each company and research institute (institute) develop the visualization system of multiple weather data.Find through more existing multiple display system, GR2analyst software is comparatively clear for the display effect of strong storm data.This software for input, can represent the state of development of storm structure by high-quality translucent 3-D effect with China New Generation Weather Radar secondary, three DBMSs.But arranging of transparency needs to rely on user by virtue of experience to carry out manual adjustment, not only operate slightly loaded down with trivial details, and display effect is subject to the impact of parameter adjustment.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of 3 D displaying method of strong storm architectural feature.The Changing Pattern of the method dynamic Analysis of Radar reflectivity data and its gradient, automatically extract and strong storm region, strong storm kernel area, heavy rain kernel area and hail region 4 quasi-representative structure in strong storm body in viewing area, automatic adjustment transparency, and reinforcement 3-D display is carried out to this architectural feature.
The present invention solves the problems of the technologies described above adopted technical scheme: a kind of 3 D displaying method of strong storm architectural feature, comprises the following steps: 1. radar data pre-service; 2. strong storm region recognition; 3. calculate the radar horizon reflectivity gradient distribution in the strong storm region identified, the variation characteristic according to gradient extracts in strong storm the marginal threshold of the characteristic area comprising short-time strong rainfall, thunderstorm gale and hail type adaptively; 4. the 3-D display of strong storm architectural feature, step concrete mode is 3.:
3.-1: the distribution curve calculating the average reflectance Grad in strong storm region;
For single strong storm, all lattice points in traversal strong storm region, with the Grad corresponding to the reflectivity intensity of each lattice point of the form calculus of discrete differential, then the mean value of the Grad that the reflectivity intensity of all lattice points is corresponding in strong storm region is calculated, obtain distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity, wherein, r ∈ [25,60] dBZ, represents the changing value region of reflectivity intensity;
3.-2: the marginal threshold T calculating strong storm region
cov, namely determine the inner boundary of strong storm body;
First, according to distribution curve G (r) that average gradient value in strong storm region changes relative to reflectivity intensity, calculate the mean value of distribution curve G (r) that average gradient value changes relative to reflectivity intensity in r ∈ [25,60] dBZ regional extent, be defined as G
d,
wherein, abs represents and gets absolute value, the maximal value G (r on distribution curve G (r) then finding average gradient value in strong storm region to change relative to reflectivity intensity according to above-mentioned formula
p) and minimum value G (r
v), and define G (r
p)=max (G (r)), r ∈ [25,45], G (r
v)=min (G (r)), r ∈ [35,55], wherein, r
pand r
vmaximal value G (r respectively
p) and minimum value G (r
v) corresponding to reflectivity intensity, then according to formula G
a=(G (r
p)-G (r
v))/(r
v-r
p) calculate r ∈ [r in strong storm region
v, r
p] the average lapse rate G of distribution curve G (r) that changes relative to reflectivity intensity of the average gradient value in dBZ interval
a; Then, continue to analyze distribution curve G (r) that average gradient value in strong storm region changes relative to reflectivity intensity at r ∈ [30,38] curvilinear characteristic in dBZ interval, and definition Boolean relation formula below: B (r, s)=(G (r)-G (r-s)) >G
daMP.AMp.Amp (G (r)-G (r+s)) >G
d, S (r, s)=(G (r)-G (r+s)) >s × G
awherein & presentation logic with, × represent scalar multiplication, s is 1 or 2 or 3, then node-by-node algorithm B (r, s) with S (r, s), if B is (r, 1) & B (r, 2) & B (r, 3) is true, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has local peaking's feature; If S is (r, 1) & S (r, 2) & S (r, 3) be true, then on the right side of the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding, there is remarkable decreasing characteristic;
When average gradient value in strong storm region has local peaking's feature or the remarkable decreasing characteristic in right side relative to distribution curve G (r) that reflectivity intensity changes, the reflectivity intensity r defining this place is the marginal threshold T in strong storm region
cov;
3.-3: the marginal threshold T calculating strong storm kernel area
incov, namely determine the inner boundary of strong storm kernel area;
Traversal reflectivity intensity r ∈ [40, 48] distribution curve G (r) that changes relative to reflectivity intensity of the average gradient value in dBZ region, calculate B (r, 1) & B (r, 2) & B (r, 3) and S (r, 1) & S (r, 2) & S (r, 3), if B is (r, 1) & B (r, 2) & B (r, 3) be true, the position that reflectivity intensity r in distribution curve G (r) that then in this strong storm kernel area, average gradient value changes relative to reflectivity intensity is corresponding has local peaking's feature, if S is (r, 1) & S (r, 2) & S (r, 3) be true, then on the right side of the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm kernel area, average gradient value changes relative to reflectivity intensity is corresponding, there is remarkable decreasing characteristic,
When average gradient value in strong storm kernel area has local peaking's feature or the remarkable decreasing characteristic in right side relative to distribution curve G (r) that reflectivity intensity changes, the reflectivity intensity r defining this place is the marginal threshold T of strong storm kernel area
incovif, the strong storm kernel area boundary threshold T calculated
incovmeet abs (G (T
incov)-G (T
cov)) <G
dtime, then by the marginal threshold T of strong storm kernel area
incovwith the marginal threshold T in strong storm region
covrepresent;
3.-4: calculate strong storm heavy rain kernel area marginal threshold T
heavyrain, whether the existence of judgement short-time strong rainfall feature;
Definition B
hr1(r, s)=(G (r+s)-G (r)) >s × G
d, B
hr2(r, s)=(G (r+s)-G (r)) <s × G
d, then travel through curve G (r) in reflectivity intensity r ∈ [48,55] dBZ region, work as B
hr1(r, 1) & B
hr1(r, 2) & B
hr1(r, 3) are true time, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has right side increment feature continuously; Work as B
hr2(r, 1) & B
hr2(r, 2) & B
hr2(r, 3) are true time, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has right side decreasing characteristic continuously; When average gradient value in strong storm region relative to distribution curve G (r) that reflectivity intensity changes have right side continuously increment feature or the continuous decreasing characteristic in right side time, definition short-time strong rainfall feature exists, and the reflectivity intensity r at this place is defined as strong storm heavy rain kernel area marginal threshold T
heavyrain;
3.-5: calculate strong storm hail region marginal threshold T
hail, whether the existence of judgement hail architectural feature;
Definition B
h(r, s)=(G (r)-G (r+s)) >G
d, curve G (r) in traversal reflectivity intensity r ∈ [52,60] dBZ region, works as B
h(r, 1) & B
h(r, 2) & B
h(r, 3) & (G (r) >G (r-1)) is true time, judge the existence of hail architectural feature, and the reflectivity intensity r at this place is defined as the marginal threshold T in strong storm hail region
hail;
Step concrete mode is 4.:
4.-1: point sampling, at image space, a ray is launched to each screen pixels with light projecting algorithm, get equal intervals and point sampling is carried out to 3D grid volume data, first the reflectivity intensity r that each sampled point on inspection ray is corresponding, when reflectivity intensity r belongs within the marginal threshold of characteristic area, defining this sampled point is effective sampling points;
4.-2: adopt following steps that the transparency of all effective sampling points is set:
1) if only there is the marginal threshold T in strong storm region
cov, then reflectivity intensity is greater than T
covsampled point transparency be set to 1.0, i.e. all-transparent;
2) if only there is the marginal threshold T in strong storm region
covwith the marginal threshold T of strong storm kernel area
incov, then reflectivity intensity is greater than T
incovsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] sampled point transparency is set to 0.3;
3) if there is the marginal threshold T in strong storm region
cov, strong storm kernel area marginal threshold T
incovwith strong storm heavy rain kernel area marginal threshold T
heavyrain, then reflectivity intensity is greater than T
heavyrainsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] sampled point transparency is set to 0.1, reflectivity intensity is met r ∈ [T
incov-1, T
incov+ 1] sampled point transparency is set to 0.5;
4) if there is the marginal threshold T in strong storm region
cov, strong storm kernel area marginal threshold T
incovwith the marginal threshold T in strong storm hail region
hail, then reflectivity intensity is greater than T
hailsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] effective sampling points transparency is set to 0.1, reflectivity intensity is met r ∈ [T
incov-1, T
incov+ 1] sampled point transparency is set to 0.5;
4.-3: according to the architectural feature of the strong storm that 3. step obtains, according to the principle arranging sampled point transparency in step 4.-2, adopt direct volume rendering technology, all effective sampling points carry out 3 D rendering, obtain the shape facility of this characteristic area.
Definition all kinds of structure boundary place of strong storm body gradient magnitude be | D (x, y, z) |, by following formula adjust all kinds of architectural feature boundary transparency a'=1-(A '
i+ k|D (x, y, z) |) × (1-attenuate), in formula, attenuate represents light attenuation factor.
The pretreated concrete grammar of step radar data is 1.: first process with reference to tangent plane algorithm with adaptive the data of Doppler radar collection, then by under being inserted into unified cartesian coordinate system in each layer 2-D data after process, the interpolation method of vertical-horizontal interpolation and adjacent data value is nearby adopted to generate three-dimensional uniform grid data.
Step 2. strong storm region two dimension identify concrete grammar be: the boundary intensity of strong storm is defined as 30dBZ, by radar reflectivity more than 30dBZ and area more than 100km
2echo group region be defined as strong storm, then strong storm region is gone out at this echo group region recognition: first radar reflectivity data in the analyst coverage on 3km height is analyzed, the lattice point of all 30dBZ of being greater than is labeled as point in strong storm, from the lattice point that interior some search is arbitrarily adjacent, if consecutive point are also points in strong storm, then be classified as same strong storm region and regarded as point in strong storm new in this strong storm region, point in the strong storm in same connected region is merged by continuous recursive search, complete the two dimension identification in this strong storm region, repeat above process, until point is attributed to a certain strong storm region in all strong storms.
Compared with prior art, the invention has the advantages that by calculating the boundary threshold extracting various types of characteristic area, the region of strong storm can be gone out by self-adapting estimation, and and then automatically regulate the transparency of regional according to above-mentioned boundary threshold, reflect to the 3-D view dynamic drawn out the Changing Pattern of radar reflectivity data and its gradient; And the transparency of all kinds of architectural feature boundary is adjusted, three-dimensional can be carried out to the architectural feature of strong storm and strengthen display.
Accompanying drawing explanation
Fig. 1 is the shape facility figure of the 3 D stereo characteristic area using method of the present invention to obtain:
Fig. 2 is the shape facility figure of the 3 D stereo characteristic area using the method for this prior art to obtain.
Embodiment
Below in conjunction with accompanying drawing embodiment, the present invention is described in further detail.
A 3 D displaying method for strong storm architectural feature, comprises the following steps:
1. radar data pre-service: first use introduce in list of references [1] adaptive to process with reference to tangent plane algorithm to the data of Doppler radar collection, then by under being inserted into unified cartesian coordinate system in each layer 2-D data after process, the interpolation method of the vertical-horizontal interpolation then adopting list of references [2] to introduce and adjacent data value nearby generates three-dimensional uniform grid data;
2. strong storm region recognition: according to the definition to strong storm in list of references [3], the boundary intensity of strong storm is defined as 30dBZ, by radar reflectivity more than 30dBZ and area more than 100km
2echo group region be defined as strong storm, then strong storm region is gone out at this echo group region recognition: first radar reflectivity data in the analyst coverage on 3km height is analyzed, the lattice point of all 30dBZ of being greater than is labeled as point in strong storm, from the lattice point that interior some search is arbitrarily adjacent, if consecutive point are also points in strong storm, then be classified as same strong storm region and regarded as point in strong storm new in this strong storm region, point in the strong storm in same connected region is merged by continuous recursive search, complete the identification in this strong storm region, repeat above process, until point is attributed to a certain strong storm region in all strong storms,
3. calculate the radar horizon reflectivity gradient distribution in the strong storm region identified, the variation characteristic according to gradient extracts in strong storm the boundary threshold of the characteristic area comprising short-time strong rainfall, thunderstorm gale and hail type adaptively, and concrete mode is:
3.-1: the distribution curve calculating the average reflectance Grad in strong storm region;
For single strong storm, all lattice points in traversal strong storm region, with the Grad corresponding to the reflectivity intensity of each lattice point of the form calculus of discrete differential, then the mean value of the Grad that the reflectivity intensity of all lattice points is corresponding in strong storm region is calculated, obtain distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity, wherein, r ∈ [25,60] dBZ, represents the changing value region of reflectivity intensity;
3.-2: the marginal threshold T calculating strong storm region
cov, namely determine the inner boundary of strong storm body;
First, according to distribution curve G (r) that average gradient value in strong storm region changes relative to reflectivity intensity, calculate the mean value of distribution curve G (r) that average gradient value changes relative to reflectivity intensity in r ∈ [25,60] dBZ regional extent, be defined as G
d,
wherein, abs represents and gets absolute value, the maximal value G (r on distribution curve G (r) then finding average gradient value in strong storm region to change relative to reflectivity intensity according to above-mentioned formula
p) and minimum value G (r
v), and define G (r
p)=max (G (r)), r ∈ [25,45], G (r
v)=min (G (r)), r ∈ [35,55], wherein, r
pand r
vmaximal value G (r respectively
p) and minimum value G (r
v) corresponding to reflectivity intensity, then according to formula G
a=(G (r
p)-G (r
v))/(r
v-r
p) calculate r ∈ [r in strong storm region
v, r
p] the average lapse rate G of distribution curve G (r) that changes relative to reflectivity intensity of the average gradient value in dBZ interval
a; Then, continue to analyze distribution curve G (r) that average gradient value in strong storm region changes relative to reflectivity intensity at r ∈ [30,38] curvilinear characteristic in dBZ interval, and definition Boolean relation formula below: B (r, s)=(G (r)-G (r-s)) >G
daMP.AMp.Amp (G (r)-G (r+s)) >G
d, S (r, s)=(G (r)-G (r+s)) >s × G
awherein & presentation logic with, × represent scalar multiplication, s is 1 or 2 or 3, then node-by-node algorithm B (r, s) with S (r, s), if B is (r, 1) & B (r, 2) & B (r, 3) is true, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has local peaking's feature; If S is (r, 1) & S (r, 2) & S (r, 3) be true, then on the right side of the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding, there is remarkable decreasing characteristic;
When average gradient value in strong storm region has local peaking's feature or the remarkable decreasing characteristic in right side relative to distribution curve G (r) that reflectivity intensity changes, the reflectivity intensity r defining this place is the marginal threshold T in strong storm region
cov;
3.-3: the boundary threshold T calculating strong storm kernel area
incov, namely determine the inner boundary of strong storm kernel area;
Traversal reflectivity intensity r ∈ [40, 48] distribution curve G (r) that changes relative to reflectivity intensity of the average gradient value in dBZ region, calculate B (r, 1) & B (r, 2) & B (r, 3) and S (r, 1) & S (r, 2) & S (r, 3), if B is (r, 1) & B (r, 2) & B (r, 3) be true, the position that reflectivity intensity r in distribution curve G (r) that then in this strong storm kernel area, average gradient value changes relative to reflectivity intensity is corresponding has local peaking's feature, if S is (r, 1) & S (r, 2) & S (r, 3) be true, then on the right side of the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm kernel area, average gradient value changes relative to reflectivity intensity is corresponding, there is remarkable decreasing characteristic,
When average gradient value in strong storm kernel area has local peaking's feature or the remarkable decreasing characteristic in right side relative to distribution curve G (r) that reflectivity intensity changes, the reflectivity intensity r defining this place is the boundary threshold T of strong storm kernel area
incovif, the strong storm kernel area boundary threshold T calculated
incovmeet abs (G (T
incov)-G (T
cov)) <G
dtime, then by the boundary threshold T of strong storm kernel area
incovwith the boundary threshold T in strong storm region
covrepresent;
3.-4: calculate strong storm heavy rain kernel area boundary threshold T
heavyrain, whether the existence of judgement short-time strong rainfall feature;
Definition B
hr1(r, s)=(G (r+s)-G (r)) >s × Gd, B
hr2(r, s)=(G (r+s)-G (r)) <s × G
d, then travel through curve G (r) in reflectivity intensity r ∈ [48,55] dBZ region, work as B
hr1(r, 1) & B
hr1(r, 2) & B
hr1(r, 3) are true time, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has right side increment feature continuously; Work as B
hr2(r, 1) & B
hr2(r, 2) & B
hr2(r, 3) are true time, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has right side decreasing characteristic continuously; When average gradient value in strong storm region relative to distribution curve G (r) that reflectivity intensity changes have right side continuously increment feature or the continuous decreasing characteristic in right side time, definition short-time strong rainfall feature exists, and the reflectivity intensity r at this place is defined as strong storm heavy rain kernel area boundary threshold T
heavyrain;
3.-5: calculate strong storm hail region boundary threshold T
hail, whether the existence of judgement hail architectural feature;
Definition B
h(r, s)=(G (r)-G (r+s)) >G
d, curve G (r) in traversal reflectivity intensity r ∈ [52,60] dBZ region, works as B
h(r, 1) & B
h(r, 2) & B
h(r, 3) & (G (r) >G (r-1)) is true time, judge the existence of hail architectural feature, and the reflectivity intensity r at this place is defined as the boundary threshold T in strong storm hail region
hail;
4. the 3-D display of strong storm architectural feature, concrete mode is:
4.-1: point sampling, at image space, a ray is launched to each screen pixels with light projecting algorithm, get equal intervals and point sampling is carried out to 3D grid volume data, first the reflectivity intensity r that each sampled point on inspection ray is corresponding, when reflectivity intensity r belongs within the marginal threshold of characteristic area, defining this sampled point is effective sampling points;
4.-2: adopt following steps that the transparency of all effective sampling points is set:
1) if only there is the boundary threshold T in strong storm region
cov, then reflectivity intensity is greater than T
covsampled point transparency be set to 1.0, i.e. all-transparent;
2) if only there is the boundary threshold T in strong storm region
covwith the boundary threshold T of strong storm kernel area
incov, then reflectivity intensity is greater than T
incovsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] sampled point transparency is set to 0.3;
3) if there is the boundary threshold T in strong storm region
cov, strong storm kernel area boundary threshold T
incovwith strong storm heavy rain kernel area boundary threshold T
heavyrain, then reflectivity intensity is greater than T
heavyrainsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] sampled point transparency is set to 0.1, reflectivity intensity is met r ∈ [T
incov-1, T
incov+ 1] sampled point transparency is set to 0.5;
4) if there is the boundary threshold T in strong storm region
cov, strong storm kernel area boundary threshold T
incovwith the boundary threshold T in strong storm hail region
hail, then reflectivity intensity is greater than T
hailsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] sampled point transparency is set to 0.1, reflectivity intensity is met r ∈ [T
incov-1, T
incov+ 1] sampled point transparency is set to 0.5;
On this basis, definition all kinds of structure boundary place of strong storm body gradient magnitude be | D (x, y, z) |, by following formula adjust all kinds of architectural feature zone boundary place transparency a'=1-(A '
i+ k|D (x, y, z) |) × (1-attenuate), in formula, attenuate represents light attenuation factor;
4.-3: according to the architectural feature of the strong storm that 3. step obtains, according to the principle arranging sampled point transparency in step 4.-2, adopt direct volume rendering technology, all effective sampling points carry out 3 D rendering, obtain the shape facility of this characteristic area.
List of references
[1].ZhangJ,WangS,ClarkeB.P.WSR-88DReflectivityQualityControlusingHorizontalandVerticalReflectivityStructure[C].Preprints,Proceedingsofthe11thConferenceonAviation,RangeandAerospaceMeteorology.IS,2004,1:5-6.
[2].ZhangJ,HowardK,GourleyJ.J.ConstructingThree-DimensionalMultiple-RadarReflectivityMosaics:ExamplesofConvectiveStormsandStratiformRainEchoes[J].JournalofAtmosphericandOceanicTechnologys(0739-0572),2005,22(1):30-42.
[3].MichaelD,GerryW.TITAN:ThunderstormIdentification,Tracking,Analysis,andNowcasting—ARadar-basedMethodology.J.Atmos.OceanicTechnol.,1993,10,785–797.
Claims (4)
1. a 3 D displaying method for strong storm architectural feature, comprises the following steps: 1. radar data pre-service; 2. strong storm region recognition; 3. calculate the radar horizon reflectivity gradient distribution in the strong storm region identified, the variation characteristic according to gradient extracts in strong storm the marginal threshold of the characteristic area comprising short-time strong rainfall, thunderstorm gale and hail type adaptively; 4. the 3-D display of strong storm architectural feature, is characterized in that:
Step concrete mode is 3.:
3.-1: the distribution curve calculating the average reflectance Grad in strong storm region;
For single strong storm, all lattice points in traversal strong storm region, with the Grad corresponding to the reflectivity intensity of each lattice point of the form calculus of discrete differential, then the mean value of the Grad that the reflectivity intensity of all lattice points is corresponding in strong storm region is calculated, obtain distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity, wherein, r ∈ [25,60] dBZ, represents the changing value region of reflectivity intensity;
3.-2: the marginal threshold T calculating strong storm region
cov, namely determine the inner boundary of strong storm body;
First, according to distribution curve G (r) that average gradient value in strong storm region changes relative to reflectivity intensity, calculate the mean value of distribution curve G (r) that average gradient value changes relative to reflectivity intensity in r ∈ [25,60] dBZ regional extent, be defined as G
d,
wherein, abs represents and gets absolute value, the maximal value G (r on distribution curve G (r) then finding average gradient value in strong storm region to change relative to reflectivity intensity according to above-mentioned formula
p) and minimum value G (r
v), and define G (r
p)=max (G (r)), r ∈ [25,45], G (r
v)=min (G (r)), r ∈ [35,55], wherein, r
pand r
vmaximal value G (r respectively
p) and minimum value G (r
v) corresponding to reflectivity intensity, then according to formula G
a=(G (r
p)-G (r
v))/(r
v-r
p) calculate r ∈ [r in strong storm region
v, r
p] average gradient value in dBZ interval change relative to reflectivity intensity distribution curve G (
r) average lapse rate G
a; Then, continue to analyze average gradient value changes relative to reflectivity intensity in strong storm region distribution curve G (
r) curvilinear characteristic in r ∈ [30,38] dBZ interval, and definition Boolean relation formula below:
B (r, s)=(G (r)-G (r-s)) >G
daMP.AMp.Amp (G (r)-G (r+s)) >G
d, S (r, s)=(G (r)-G (r+s)) >s × G
awherein & presentation logic with, × represent scalar multiplication, s is 1 or 2 or 3, then node-by-node algorithm B (r, s) with S (r, s), if B is (r, 1) & B (r, 2) & B (r, 3) is true, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has local peaking's feature; If S is (r, 1) & S (r, 2) & S (r, 3) be true, then on the right side of the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding, there is remarkable decreasing characteristic;
When average gradient value in strong storm region has local peaking's feature or the remarkable decreasing characteristic in right side relative to distribution curve G (r) that reflectivity intensity changes, the reflectivity intensity r defining this place is the marginal threshold T in strong storm region
cov;
3.-3: the marginal threshold T calculating strong storm kernel area
incov, namely determine the inner boundary of strong storm kernel area;
Traversal reflectivity intensity r ∈ [40, 48] distribution curve G (r) that changes relative to reflectivity intensity of the average gradient value in dBZ region, calculate B (r, 1) & B (r, 2) & B (r, 3) and S (r, 1) & S (r, 2) & S (r, 3), if B is (r, 1) & B (r, 2) & B (r, 3) be true, the position that reflectivity intensity r in distribution curve G (r) that then in this strong storm kernel area, average gradient value changes relative to reflectivity intensity is corresponding has local peaking's feature, if S is (r, 1) & S (r, 2) & S (r, 3) be true, then on the right side of the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm kernel area, average gradient value changes relative to reflectivity intensity is corresponding, there is remarkable decreasing characteristic,
When average gradient value in strong storm kernel area has local peaking's feature or the remarkable decreasing characteristic in right side relative to distribution curve G (r) that reflectivity intensity changes, the reflectivity intensity r defining this place is the marginal threshold T of strong storm kernel area
incovif, the strong storm kernel area boundary threshold T calculated
incovmeet abs (G (T
incov)-G (T
cov)) <G
dtime, then by the marginal threshold T of strong storm kernel area
incovwith the marginal threshold T in strong storm region
covrepresent;
3.-4: calculate strong storm heavy rain kernel area marginal threshold T
heavyrain, whether the existence of judgement short-time strong rainfall feature;
Definition B
hr1(r, s)=(G (r+s)-G (r)) >s × G
d, B
hr2(r, s)=(G (r+s)-G (r)) <s × G
d, then travel through curve G (r) in reflectivity intensity r ∈ [48,55] dBZ region, work as B
hr1(r, 1) & B
hr1(r, 2) & B
hr1(r, 3) are true time, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has right side increment feature continuously; Work as B
hr2(r, 1) & B
hr2(r, 2) & B
hr2(r, 3) are true time, then the position that the reflectivity intensity r in distribution curve G (r) that in this strong storm region, average gradient value changes relative to reflectivity intensity is corresponding has right side decreasing characteristic continuously; When average gradient value in strong storm region relative to distribution curve G (r) that reflectivity intensity changes have right side continuously increment feature or the continuous decreasing characteristic in right side time, definition short-time strong rainfall feature exists, and the reflectivity intensity r at this place is defined as strong storm heavy rain kernel area marginal threshold T
heavyrain;
3.-5: calculate strong storm hail region marginal threshold T
hail, whether the existence of judgement hail architectural feature;
Definition B
h(r, s)=(G (r)-G (r+s)) >G
d, curve G (r) in traversal reflectivity intensity r ∈ [52,60] dBZ region, works as B
h(r, 1) & B
h(r, 2) & B
h(r, 3) & (G (r) >G (r-1)) is true time, judge the existence of hail architectural feature, and the reflectivity intensity r at this place is defined as the marginal threshold T in strong storm hail region
hail;
Step concrete mode is 4.:
4.-1: point sampling, at image space, a ray is launched to each screen pixels with light projecting algorithm, get equal intervals and point sampling is carried out to 3D grid volume data, first the reflectivity intensity r that each sampled point on inspection ray is corresponding, when reflectivity intensity r belongs within the marginal threshold of characteristic area, defining this sampled point is effective sampling points;
4.-2: adopt following steps that the transparency of all effective sampling points is set:
1) if only there is the marginal threshold T in strong storm region
cov, then reflectivity intensity is greater than T
covsampled point transparency be set to 1.0, i.e. all-transparent;
2) if only there is the marginal threshold T in strong storm region
covwith the marginal threshold T of strong storm kernel area
incov, then reflectivity intensity is greater than T
incovsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] sampled point transparency is set to 0.3;
3) if there is the marginal threshold T in strong storm region
cov, strong storm kernel area marginal threshold T
incovwith strong storm heavy rain kernel area marginal threshold T
heavyrain, then reflectivity intensity is greater than T
heavyrainsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] sampled point transparency is set to 0.1, reflectivity intensity is met r ∈ [T
incov-1, T
incov+ 1] sampled point transparency is set to 0.5;
4) if there is the marginal threshold T in strong storm region
cov, strong storm kernel area marginal threshold T
incovwith the marginal threshold T in strong storm hail region
hail, then reflectivity intensity is greater than T
hailsampled point transparency be set to 1.0, reflectivity intensity is met r ∈ [T
cov-1, T
cov+ 1] effective sampling points transparency is set to 0.1, reflectivity intensity is met r ∈ [T
incov-1, T
incov+ 1] sampled point transparency is set to 0.5;
4.-3: according to the architectural feature of the strong storm that 3. step obtains, according to the principle arranging sampled point transparency in step 4.-2, adopt direct volume rendering technology, all effective sampling points carry out 3 D rendering, obtain the shape facility of this characteristic area.
2. the 3 D displaying method of a kind of strong storm architectural feature as claimed in claim 1, it is characterized in that the gradient magnitude defining all kinds of structure boundary place of strong storm body is | D (x, y, z) |, by following formula adjust all kinds of architectural feature boundary transparency a'=1-(A '
i+ k|D (x, y, z) |) × (1-attenuate), in formula, attenuate represents light attenuation factor.
3. the 3 D displaying method of a kind of strong storm architectural feature as claimed in claim 1, it is characterized in that the pretreated concrete grammar of step radar data is 1.: first the data of Doppler radar collection are processed with reference to tangent plane algorithm with adaptive, then by under being inserted into unified cartesian coordinate system in each layer 2-D data after process, the interpolation method of vertical-horizontal interpolation and adjacent data value is nearby adopted to generate three-dimensional uniform grid data.
4. the 3 D displaying method of a kind of strong storm architectural feature as claimed in claim 1, it is characterized in that step 2. strong storm region two dimension identify concrete grammar be: the boundary intensity of strong storm is defined as 30dBZ, by radar reflectivity more than 30dBZ and area more than 100km
2echo group region be defined as strong storm, then strong storm region is gone out at this echo group region recognition: first radar reflectivity data in the analyst coverage on 3km height is analyzed, the lattice point of all 30dBZ of being greater than is labeled as point in strong storm, from the lattice point that interior some search is arbitrarily adjacent, if consecutive point are also points in strong storm, then be classified as same strong storm region and regarded as point in strong storm new in this strong storm region, point in the strong storm in same connected region is merged by continuous recursive search, complete the two dimension identification in this strong storm region, repeat above process, until point is attributed to a certain strong storm region in all strong storms.
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| CN105678846A (en) * | 2016-02-22 | 2016-06-15 | 武汉华信联创技术工程有限公司 | Three-dimensional visualization method and system for real-time meteorological networking radar data |
| CN107301272A (en) * | 2017-05-24 | 2017-10-27 | 天津大学 | Strong convection monomer structure and architectural feature visualization space-time method for decomposing |
| CN113254417A (en) * | 2021-06-29 | 2021-08-13 | 南京满星数据科技有限公司 | Meteorological grid data service method and system based on big data technology |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105678846A (en) * | 2016-02-22 | 2016-06-15 | 武汉华信联创技术工程有限公司 | Three-dimensional visualization method and system for real-time meteorological networking radar data |
| CN105678846B (en) * | 2016-02-22 | 2018-09-28 | 武汉华信联创技术工程有限公司 | A kind of three-dimensional visualization method and system of real-time weather radar network data |
| CN107301272A (en) * | 2017-05-24 | 2017-10-27 | 天津大学 | Strong convection monomer structure and architectural feature visualization space-time method for decomposing |
| CN113254417A (en) * | 2021-06-29 | 2021-08-13 | 南京满星数据科技有限公司 | Meteorological grid data service method and system based on big data technology |
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