CN106097400A - The Design of Mathematical Model method of hail prediction - Google Patents
The Design of Mathematical Model method of hail prediction Download PDFInfo
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- CN106097400A CN106097400A CN201610405807.2A CN201610405807A CN106097400A CN 106097400 A CN106097400 A CN 106097400A CN 201610405807 A CN201610405807 A CN 201610405807A CN 106097400 A CN106097400 A CN 106097400A
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- hail
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30192—Weather; Meteorology
Abstract
The Design of Mathematical Model method of hail prediction belongs to field of statistics, especially belongs to art of image analysis.The existing prediction to hail cloud depends on experience often and differentiates, such method exists a lot of not enough.In order to overcome the deficiencies in the prior art, the present invention is open: a kind of method predicting hail, it is characterized in that, described method comprises the following steps: the first step, utilize cloud atlas to obtain dBZ, second step, RGB image is converted into gray level image, 3rd step, the reflectance of gray level image is divided into 15 magnitudes, the 4th step, calculates the proportion of each magnitude, 5th step, calculates average x of each magnitude1, variance x2, degree of bias x3, kurtosis x4, the 6th step, utilize formula: y=515.19091 x1+89.93360·x2‑488.50192·x3+548.22732·x4y0If=2.38415 thus have: y > y0, then X ∈ G1, otherwise X ∈ G2, G1Conjunction, G is converged for hail shooting2For converging conjunction without hail.The present invention can pre-hail detection cloud.
Description
Technical field
The invention belongs to field of statistics, especially belong to art of image analysis.
Background technology
Hail, as a kind of strong convective weather, is characterized in that space scale is little, life cycle is short, sudden strong, development evolvement
Rapidly, its forecast difficulty is well-known.Through " 15 ", the development of Eleventh Five-Year Plan and construction, Xinjiang meteorology hail suppression technology
Level has had the biggest lifting, but compared with the demand that national economy and social development is growing, however it remains necessarily not enough, main
Accuracy rate and the level of becoming more meticulous of hail weather to be showed themselves in that prediction have much room for improvement, and particularly close on local hail weather
(0~3 hour) and in short-term (3~12 hours) prediction ability urgently strengthen." 13 " period is Xinjiang social stability and economy
The critical period of development, in the urgent need to providing the monitoring and forecast more become more meticulous to Xinjiang hail weather.And to hail cloud recognition
Service basic is mainly derived from weather radar, the radar image of real-time monitored Convective Cloud, and image covers cloud cluster life development
The information of change, by processing the radar image of this cloud cluster in real time, whether hail shooting is for anti-for look-ahead to this cloud cluster
Hail mitigation is significant.
The existing prediction to hail cloud depends on experience often and differentiates, such method exists a lot of not enough.
Summary of the invention
In order to overcome the deficiencies in the prior art, the present invention is open:
1, the Design of Mathematical Model method of hail prediction, it is characterized in that, described method comprises the following steps:
The first step, gathers radar RGB cloud atlas, utilizes cloud atlas to obtain dBZ,
Second step, is converted into gray level image the RGB image of first step gained,
3rd step, is divided into 15 magnitudes the reflectance of the gray level image of second step gained,
4th step, calculates each magnitude proportion in the picture,
5th step, calculates average x of each magnitude1, variance x2, degree of bias x3, kurtosis x4,
6th step, average x of the 5th step gained1, variance x2, degree of bias x3, kurtosis x4Substitution formula:
Y=515.19091 x1+89.93360·x2-488.50192·x3+548.22732·x4
If therefore have: y > y0Then X ∈ G1, otherwise X ∈ G2。G1Conjunction, G is converged for hail shooting2For converging conjunction without hail.
2, it is characterized in that: described y0=2.38415.
3, it is characterized in that: also include:
Utilize formula:
Obtain y0。
Hail shooting converges conjunction G1In meansigma methods,Hail shooting converges conjunction G2In meansigma methods, n1Conjunction G is converged for hail shooting1In
Element number, n2For converging conjunction G without hail2In element number.
Reflectance separates for unit according to 5, dBZ value s1Corresponding gray value is calculated as s1-2.5 arrive s1The gray scale of+2.5 is put down
Average.
4, it is characterized in that: described reflectance, magnitude, rgb value, relation that gray value is corresponding be:
Reflectance is :-5, and corresponding magnitude is: 1, and corresponding rgb value is :-201,201,201, and corresponding gray value is:
201;
Reflectance is: 0, and corresponding magnitude is: 2, and corresponding rgb value is :-118,118,118, and corresponding gray value is:
118;
Reflectance is: 5, and corresponding magnitude is: 3, and corresponding rgb value is :-255,170,170, and corresponding gray value is:
196;
Reflectance is: 10, and corresponding magnitude is: 4, and corresponding rgb value is :-238,140,140, and corresponding gray value is:
170;
Reflectance is: 15, and corresponding magnitude is: 5, and corresponding rgb value is :-201,112,112, and corresponding gray value is:
139;
Reflectance is: 20, and corresponding magnitude is: 6, and corresponding rgb value is: 0,251,144, and corresponding gray value is:
164;
Reflectance is: 25, and corresponding magnitude is: 7, and corresponding rgb value is: 0,187,0, and corresponding gray value is: 110;
Reflectance is: 30, and corresponding magnitude is: 8, and corresponding rgb value is :-255,255,112, and corresponding gray value is:
239;
Reflectance is: 35, and corresponding magnitude is: 9, and corresponding rgb value is: 208,208,96, and corresponding gray value is:
195;
Reflectance is: 40, and corresponding magnitude is: 10, and corresponding rgb value is: 255,96,96, and corresponding gray value is:
144;
Reflectance is: 45, and corresponding magnitude is: 11, and corresponding rgb value is: 218,0,0, and corresponding gray value is: 65;
Reflectance is: 50, and corresponding magnitude is 12, and corresponding rgb value is: 174,0,0, and corresponding gray value is: 52;
Reflectance is: 55, and corresponding magnitude is: 13, and corresponding rgb value is: 0,0,255, and corresponding gray value is: 29;
Reflectance is: 60, and corresponding magnitude is: 14, and corresponding rgb value is :-160,255,255, corresponding gray value
For: 227;
Reflectance is: 65, and corresponding magnitude is: 15, and corresponding rgb value is: 231,0,255, and corresponding gray value is:
98。
5, it is characterized in that: by the conversion relation formula of RGB Yu gray value
Gray=R × 0.2999+G × 0.587+B × 0.114
Obtain corresponding gray value.The method can improve the accuracy rate of hail cloud forecast, reduces rate of false alarm.Effective raising
Identification hail cloud atlas, as accuracy rate, provides reference for Forecast of Hail.
Detailed description of the invention:
1 intensity statistics
Radar reflectivity image is based on RGB color space, based on the legend of base reflectivity figure, utilizes
The color production theory of RGB color space, reads echo reflection figure.Will reflectance be-5dBZ 65dBZ, be divided into 15 kinds accordingly
Magnitude.Wherein it is as shown in the table, by the conversion relation formula (1) of RGB Yu gray value for different rgb values corresponding to magnitude, it is known that corresponding
Gray value.
Gray=R × 0.2999+G × 0.587+B × 0.114 (1)
Table 1 is used for reflectance and rgb value, gray value corresponding relation are described.
Table 1:
What the different colours of radar reflectivity image represented is different reflex strengths, i.e. can be by statistics different colours
Proportion shared in the picture, reflects the feature of echo strength in image, and the different proportion shared by intensity can be by probability
Represent, calculate as shown in formula (2).If a width size is the image of M × N, then containing M × N number of pixel, reflectivity intensity is divided into 15
Individual magnitude, this varying strength proportion in the picture is:
pi=si/ M × N i=1,2 ..., 15 (2)
Wherein, siFor the color of the i-th magnitude number of pixels on image.
2, the modelling of quadravalence hail cloud recognition
According to meteorological theory, the extreme weather of strong convection, it is only possible to occur in reflex strength more than 40dBZ's
Region.Therefore only choosing magnitude is 10 15, four first-order statistics being calculated hail cloud and non-hail cloud in accordance with the following methods are surveyed
Angle value.
Average:
Variance:
The degree of bias:
Kurtosis:
Determine differentiation critical point, take y0ForWithWeighted mean, it may be assumed that
n1Conjunction G is converged for hail shooting1In element number, n2For converging conjunction G without hail2In element number.
Discriminant function is:
Y=515.19091 x1+89.93360·x2-488.50192·x3+548.22732·x4
(3)
Differentiate that critical point is: y0=2.38415, andIf therefore have: y > y0Then X ∈ G1, otherwise X ∈ G2。G1For
Hail shooting converges conjunction, G2For converging conjunction without hail.
3, model testing
Utilizing method of the present invention, inventor has carried out following checking, to prove that prediction hail is had relatively by the present invention
High accuracy rate.
Data decimation is Shihezi, area, Shawan and the data being different from training sample of Aksu Prefecture between 2009, make
For sample to be tested table 2.Owing to the classification of sample to be tested is it is known that then utilize this discriminant function to differentiate, and will differentiate result with
Legitimate reading compares, and i.e. may know that the accuracy rate that discriminant function differentiates for albedo image.Result is as shown in table 3.
The moment of inertia dependency-the sample to be tested of the albedo image intensity of table 2 each department
Table 3 differentiates result
From table 2 and table 3, the differentiation accuracy rate of this discrimination model is fine, and this model is ratio for the identification of hail cloud
Accurate, accuracy rate is 100%.
Claims (5)
1. the Design of Mathematical Model method of hail prediction, is characterized in that, described method comprises the following steps:
The first step, gathers radar RGB cloud atlas, utilizes cloud atlas to obtain dBZ,
Second step, is converted into gray level image the RGB image of first step gained,
3rd step, is divided into 15 magnitudes the reflectance of the gray level image of second step gained,
4th step, calculates each magnitude proportion in the picture,
5th step, calculates average x of each magnitude1, variance x2, degree of bias x3, kurtosis x4,
6th step, average x of the 5th step gained1, variance x2, degree of bias x3, kurtosis x4Substitution formula:
Y=515.19091 x1+89.93360·x2-488.50192·x3+548.22732·x4
If therefore have: y > y0, then X ∈ G1, otherwise X ∈ G2, G1Conjunction, G is converged for hail shooting2For converging conjunction without hail.
2. the Design of Mathematical Model method of hail prediction as claimed in claim 1, is characterized in that: described y0=2.38415.
3. the Design of Mathematical Model method of hail prediction as claimed in claim 1, is characterized in that: also include:
Utilize formula:
Hail shooting converges conjunction G1In meansigma methods,Hail shooting converges conjunction G2In meansigma methods, n1Conjunction G is converged for hail shooting1In unit
Element number, n2For converging conjunction G without hail2In element number, obtain y0。
4. the Design of Mathematical Model method of hail prediction as claimed in claim 1, is characterized in that: described reflectance, magnitude,
The relation that rgb value, gray value are corresponding is:
Reflectance is :-5, and corresponding magnitude is: 1, and corresponding rgb value is :-201,201,201, and corresponding gray value is: 201;
Reflectance is: 0, and corresponding magnitude is: 2, and corresponding rgb value is :-118,118,118, and corresponding gray value is: 118;
Reflectance is: 5, and corresponding magnitude is: 3, and corresponding rgb value is :-255,170,170, and corresponding gray value is: 196;
Reflectance is: 10, and corresponding magnitude is: 4, and corresponding rgb value is :-238,140,140, and corresponding gray value is: 170;
Reflectance is: 15, and corresponding magnitude is: 5, and corresponding rgb value is :-201,112,112, and corresponding gray value is: 139;
Reflectance is: 20, and corresponding magnitude is: 6, and corresponding rgb value is: 0,251,144, and corresponding gray value is: 164;
Reflectance is: 25, and corresponding magnitude is: 7, and corresponding rgb value is: 0,187,0, and corresponding gray value is: 110;
Reflectance is: 30, and corresponding magnitude is: 8, and corresponding rgb value is :-255,255,112, and corresponding gray value is: 239;
Reflectance is: 35, and corresponding magnitude is: 9, and corresponding rgb value is: 208,208,96, and corresponding gray value is: 195;
Reflectance is: 40, and corresponding magnitude is: 10, and corresponding rgb value is: 255,96,96, and corresponding gray value is: 144;
Reflectance is: 45, and corresponding magnitude is: 11, and corresponding rgb value is: 218,0,0, and corresponding gray value is: 65;
Reflectance is: 50, and corresponding magnitude is 12, and corresponding rgb value is: 174,0,0, and corresponding gray value is: 52;
Reflectance is: 55, and corresponding magnitude is: 13, and corresponding rgb value is: 0,0,255, and corresponding gray value is: 29;
Reflectance is: 60, and corresponding magnitude is: 14, and corresponding rgb value is :-160,255,255, and corresponding gray value is:
227;
Reflectance is: 65, and corresponding magnitude is: 15, and corresponding rgb value is: 231,0,255, and corresponding gray value is: 98.
5. the Design of Mathematical Model method of hail prediction as claimed in claim 1, is characterized in that: by changing of RGB and gray value
Calculate relational expression
Gray=R × 0.2999+G × 0.587+B × 0.114, obtains corresponding gray value.
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CN113657610A (en) * | 2021-08-27 | 2021-11-16 | 无锡九方科技有限公司 | Hail climate characteristic prediction method based on random forest |
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CN102622515A (en) * | 2012-02-21 | 2012-08-01 | 北京联合大学 | Weather prediction method |
CN103258142A (en) * | 2013-06-04 | 2013-08-21 | 哈尔滨工业大学 | Wind power plant short-term wind speed forecasting method based on weather patterns |
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Application publication date: 20161109 |