CN102129566A - Method for identifying rainstorm cloud cluster based on stationary meteorological satellite - Google Patents
Method for identifying rainstorm cloud cluster based on stationary meteorological satellite Download PDFInfo
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- CN102129566A CN102129566A CN201110056843XA CN201110056843A CN102129566A CN 102129566 A CN102129566 A CN 102129566A CN 201110056843X A CN201110056843X A CN 201110056843XA CN 201110056843 A CN201110056843 A CN 201110056843A CN 102129566 A CN102129566 A CN 102129566A
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Abstract
The invention relates to a method for identifying a rainstorm cloud cluster based on a stationary meteorological satellite, and belongs to the technical field of atmospheric monitoring. In order to improve the accuracy of identifying the rainstorm cloud cluster, the method comprises the following steps: segmenting the cloud picture to obtain the category of each cloud cluster at the current moment; synthesizing a plurality of cloud pictures in a set time period to obtain a short-time basic brightness temperature picture; calculating a gray value difference image of the cloud image at the current moment and the short-time basic brightness temperature image; dividing the gray value difference image, and identifying a rainstorm weather alternative cloud cluster; and identifying the final rainstorm cloud cluster by using historical sample data by combining the various obtained cloud clusters with the alternative cloud clusters. Compared with the existing rainstorm identification method only considering the static intensity characteristics and the textural characteristics of cloud clusters, the technical scheme of the invention considers the evolution processes of generation, development, splitting, combination and the like of the cloud clusters and provides the concept and the calculation method of the short-time basic light-temperature diagram, so that the position of the cloud clusters which change violently in a short time can be identified, the early discovery of the target in the formation of the rainstorm cloud clusters is facilitated, and the method has high accuracy.
Description
Technical field
The present invention relates to the atmospheric surveillance technical field, be specifically related to a kind of method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster.
Background technology
Heavy showers is the key factor that triggers flood, landslide, rubble flow.Put accurate identification with scope for the convective cloud cumularsharolith that causes heavy showers, and the cloud cluster mobile route is followed the trail of, can provide correct trigger condition and relevant parameters for the startup of downstream disaster subchains such as flood, landslide, rubble flow.
Geostationary meteorological satellite (GMS) can be carried out Continuous Observation to the zone on the face of land about 1/3rd in 24 hours incessantly, produce one group of remote sensing data per half an hour, observation scope is wide, observation frequency height, can change weather phenomenon faster the time of capturing, be particularly suitable for early warning the mesoscale strong convective weather.These advantages are that polar orbiting meteorological satellite and ground observation means are not available.Therefore, use the stationary satellite remote sensing data that the strong convection cloud cluster is discerned and followed the trail of, very important practical significance is arranged.
Summary of the invention
(1) technical matters that will solve
The technical problem to be solved in the present invention is how to improve the accuracy rate of heavy rain cloud cluster recognition methods.
(2) technical scheme
For solving the problems of the technologies described above, the invention provides a kind of method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster, described method comprises the steps:
Step S1: the cloud atlas that geostationary meteorological satellite (GMS) is obtained is cut apart, obtained the classification of each cloud cluster under the current time in the observation area;
Step S2: the cloud atlas that obtains according to geostationary meteorological satellite (GMS), several cloud atlas in the setting-up time section before the current time are synthesized, obtain the bright substantially temperature figure of each cloud cluster in the setting-up time section, the bright substantially temperature figure that this synthetic back is obtained is defined as bright substantially in short-term temperature figure;
Step S3: calculate the cloud atlas of current time and the gray-scale value difference image of described bright substantially temperature figure in short-term;
Step S4: described gray-scale value difference image is cut apart, identified the alternative cloud cluster that rainstorm weather may occur;
Step S5: for all kinds of cloud clusters of cutting apart acquisition among the described step S1, the alternative cloud cluster that identifies among the integrating step S4, the historical sample data of the observation area that the use geostationary meteorological satellite (GMS) is obtained are discerned and are drawn final heavy rain cloud cluster.
Among the described step S1, specifically comprise the steps:
Step S101: read current time t that geostationary meteorological satellite (GMS) obtains and the last one hour cloud atlas of t-1 constantly, use the gray-scale value threshold method that described cloud atlas is cut apart, lattice point in the cloud atlas is divided into more than or equal to threshold value with less than two classes of threshold value by the gray-scale value size, wherein is designated as a set S (t) respectively and point is gathered S (t-1) more than or equal to the part of threshold value; Wherein, some set S (t) is the cloud cluster picture point set of current time t, and some set S (t-1) is last one hour of the cloud cluster picture point set of t-1 constantly;
Step S102: the connected region among described some set S (t) of mark and the some set S (t-1), write down wherein relevant with cloud cluster precipitation intensity parameter, described parameter specifically comprises: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster;
Step S103: for each cloud cluster among the set S (t), by judge its whether in a set S (t-1), exist corresponding source cloud cluster, in a set S (t-1) the source cloud cluster quantity, when comparing with the source cloud cluster its average gray value be increase or reduce with and cloud cluster area change situation, will put the cloud cluster of gathering among the S (t) and be divided into ten classifications.
Ten classifications among the described step S103 belong to four big classes, and described four big classes specifically comprise: newly-increased class cloud cluster, growth change class cloud cluster, division class cloud cluster and merging class cloud cluster;
If a certain cloud cluster among the some set S (t) is all non-intersect with the arbitrary cloud cluster among the some set S (t-1), assert that then it is newly-increased class cloud cluster;
If a certain cloud cluster that point is gathered among the S (t) only intersects with an a certain cloud cluster of gathering among the S (t-1), assert that then it is to be changed and next growth change class cloud cluster by the cloud cluster among the set S (t-1); Further, according to the situation of change of cloud cluster area, described growth change class cloud cluster also specifically is divided into translation and changes class cloud cluster, expansion variation class cloud cluster and contraction change class cloud cluster; If this cloud cluster of note is respectively A at t-1 and t area constantly
T-1And A
t, then work as m
1* A
T-1≤ A
t≤ n
1* A
T-1The time, assert that this growth change class cloud cluster is that translation changes the class cloud cluster; Work as A
t＞n
1* A
T-1The time, assert that this growth change class cloud cluster changes the class cloud cluster for expanding; Work as A
t＜m
1* A
T-1The time, assert that this growth change class cloud cluster is a contraction change class cloud cluster; Wherein, parameter value m
1, n
1Be according to the predefined numerical value of actual conditions, and n
1＞m
1〉=1;
If a plurality of cloud clusters among the some set S (t) all with put a certain cloud cluster C that gathers among the S (t-1)
jIntersect, then these cloud clusters can be regarded as by the cloud cluster C among the set S (t-1)
jThe division class cloud cluster that develops and come; Further, according to a plurality of cloud clusters and cloud cluster C among the set S (t)
jArea relationship, described division class cloud cluster also specifically is divided into and increases division class cloud cluster, common division class cloud cluster and independent division class cloud cluster; If among the note point set S (t) with C
jThe area of some cloud clusters is A in these cloud clusters that intersect
t,
CjArea be A
Cj, then work as A
t＞n
2* A
CjThe time, assert that this division class cloud cluster is for increasing division class cloud cluster; Work as m
2* A
Cj≤ A
t≤ n
2* A
CjThe time, assert that this division class cloud cluster is common division class cloud cluster; Work as A
t＜m
2* A
CjThe time, assert that this division class cloud cluster is independent division class cloud cluster; Wherein, parameter value m
2, n
2Be according to the predefined numerical value of actual conditions, and n
2＞m
2＞0;
If a plurality of cloud clusters among a certain cloud cluster in the current cloud cluster picture point S set (t) and the some set S (t-1) intersect, then this cloud cluster can be regarded as the merging class cloud cluster that comes by a plurality of cloud clusters merging among the set S (t-1); Further, whether greater than the area summation of a plurality of cloud clusters among the S (t-1), described merging class cloud cluster also specifically is divided into increasing and merges class cloud cluster, common merging class cloud cluster and may falsely merge the class cloud cluster according to the area of current cloud cluster; If the current cloud cluster area of note is A
t, the area of n cloud cluster among the some set S (t-1) that intersects with it is respectively A
i, i=1,2 ... n; Then work as A
t＞sum (A
i) time, assert that this merging class cloud cluster merges the class cloud cluster for increasing; As max (A
i)≤A
t≤ sum (A
i) time, assert that this merging class cloud cluster is common merging class cloud cluster; Work as A
t＜max (A
i) time, assert that this merging class cloud cluster is for may falsely merging the class cloud cluster.
Among the described step S2, specifically comprise:
Step S201: several cloud atlas in the setting-up time section before the described current time that aligns;
Step S202:, choose gray-scale value minimum in a plurality of gray-scale values that are in this identical lattice point place in several cloud atlas for the gray-scale value at a certain lattice point place among the described bright substantially in short-term temperature figure;
Step S203: the gray-scale value for lattice point places all among the described bright substantially in short-term temperature figure, all adopt the method among the described step S202 to carry out the gray-scale value value, thus the described bright substantially in short-term temperature figure of synthetic acquisition.
Among the described step S3, specifically comprise:
Step S301: the cloud atlas of the described current time that aligns and described bright substantially in short-term temperature figure;
Step S302: for the gray-scale value at a certain lattice point place in the described gray-scale value difference image, the gray-scale value that is in this identical lattice point place in the cloud atlas with described current time deducts the gray-scale value that is in this identical lattice point place among the described bright substantially in short-term temperature figure, and the difference that obtains is chosen for the gray-scale value at this lattice point place in the described gray-scale value difference image; If this difference is less than zero, the gray-scale value at this lattice point place gets 0 in the then described gray-scale value difference image;
Step S303: for the gray-scale value at lattice point places all in the described gray-scale value difference image, all adopt the method among the described step S302 to carry out the gray-scale value value, thereby obtain described gray-scale value difference image.
Among the described step S4, utilize threshold method to cut apart described gray-scale value difference image.
Among the described step S5, the newly-increased class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501a: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502a, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502a: whether the maximum gradation value in the lattice point that it comprised is greater than the threshold value T that presets
1, if then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster; Wherein, threshold value T
1One hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
Among the described step S5, the growth change class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501b: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502b or S503b, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502b: change class and the variation class cloud cluster that expands for translation, if the maximum gradation value in the lattice point that it comprised is greater than threshold value T
2, then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster;
Step S503b: for contraction change class cloud cluster, if its area is greater than threshold value V
1, then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster; Wherein, threshold value T
2And V
1All the one hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
Among the described step S5, the division class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501c: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502c or S503c, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502c:, discern it and be the heavy rain cloud cluster for increasing division class and common division class cloud cluster;
Step S503c:, discern it and be non-heavy rain cloud cluster for independent division class cloud cluster.
Among the described step S5, the merging class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501d: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502d or S503d, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502d: merge class and common merging class cloud cluster for increasing, discern it and be the heavy rain cloud cluster.
Step S503d: for may falsely merging the class cloud cluster, if its area is less than thresholding V
2, then discern it and be non-heavy rain cloud cluster, otherwise, discern it and be the heavy rain cloud cluster; Wherein, threshold value V
2One hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
(3) beneficial effect
The present invention compared with prior art, its beneficial effect is:
(1) the heavy rain recognition methods of cloud cluster static strength feature, textural characteristics is considered in existing of technical solution of the present invention contrast, has considered the evolution process such as generation, development, division, merging of cloud cluster, has improved the accuracy of heavy rain cloud cluster identification.
(2) technical solution of the present invention proposes notion and the computing method of bright substantially temperature figure in short-term, can be used for discerning the cloud cluster position of interior acute variation in short-term, helps the early detection target in the formation of heavy rain cloud cluster, therefore contrasts prior art, has higher accuracy.
Description of drawings
Fig. 1 is the process flow diagram of the method for the related identification heavy rain cloud cluster of the specific embodiment of the invention;
Fig. 2 is the related bright substantially in short-term temperature figure of the specific embodiment of the invention;
Fig. 3 is the related gray-scale value difference image of the specific embodiment of the invention;
Fig. 4 is the related heavy rain cloud cluster recognition result figure of the specific embodiment of the invention;
Fig. 5 is the comparison diagram of related recognition result of the specific embodiment of the invention and following actual rainfall situation.
Embodiment
For making purpose of the present invention, content and advantage clearer,, the specific embodiment of the present invention is described in further detail below in conjunction with drawings and Examples.
By a large amount of historical sample data are analyzed, ultimate principle in conjunction with atmosphere radiation, study radiation feature, textural characteristics and the motion change feature of heavy rain cloud cluster on each passage of FY2 satellite, on this basis, proposed the detection method of heavy rain cloud cluster.
By analysis, find that the feature of heavy rain cloud cluster on infrared division window passage 1 (hereinafter to be referred as the IR1 passage) image is the most obvious, conclude get up to mainly contain following some:
(1) strong convection cloud cluster average gray value is higher, and promptly temperature is lower.Gray-scale value accounts for the over half of all precipitation processes at the precipitation cloud cluster more than 200.But because the influence of factors such as latitude, sea level elevation, season, therefore the average gray value of the non-precipitation cloud cluster of part can't only just judge whether precipitation with gray-scale value also more than 200;
(2) possibility of precipitation appears greater than the possibility that appears at other positions in the windward side of cloud cluster;
(3) in short-term in the cloud cluster of acute variation the possibility of precipitation to occur bigger;
(4) several newly-generated little cloud clusters, merge into a big cloud cluster after, the possibility that precipitation occurs is bigger;
(5) from the original bigger independent less cloud cluster of area of cloud cluster, generally precipitation can not appear.
According to above analysis, we adopt the IR1 passage as Data Source.Cloud atlas during with the plurality of continuous before the current time time is looked as a whole, and is that unit is studied with the cloud cluster, investigates the generation and the evolution process of cloud cluster simultaneously, and comprehensive various indexs are discerned.The important indicator relevant with the precipitation intensity of cloud cluster has, the maximum of cloud cluster, minimum gradation value, the area of cloud cluster, the change in location of cloud cluster, and the differentiation situation of cloud cluster (newly-generated, division merges etc.).
So far, for improving the accuracy rate of heavy rain cloud cluster recognition methods, the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster provided by the present invention as shown in Figure 1, specifically comprises:
One, strong convection cloud cluster identification
Step S1: the cloud atlas that geostationary meteorological satellite (GMS) is obtained is cut apart, obtained the classification of each cloud cluster under the current time in the observation area;
Among the described step S1, specifically comprise the steps:
Step S101: read current time t that geostationary meteorological satellite (GMS) obtains and the last one hour cloud atlas of t-1 constantly, use the gray-scale value threshold method that described cloud atlas is cut apart, lattice point in the cloud atlas is divided into more than or equal to threshold value with less than two classes of threshold value by the gray-scale value size, wherein is designated as a set S (t) respectively and point is gathered S (t-1) more than or equal to the part of threshold value; Wherein, some set S (t) is the cloud cluster picture point set of current time t, and some set S (t-1) is last one hour of the cloud cluster picture point set of t-1 constantly;
Step S102: the connected region among described some set S (t) of mark and the some set S (t-1), write down wherein relevant with cloud cluster precipitation intensity parameter, described parameter specifically comprises: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster; Do opening operation then one time, remove isolated point and smooth region border;
Step S103: for each cloud cluster among the set S (t), by judge its whether in a set S (t-1), exist corresponding source cloud cluster, in a set S (t-1) the source cloud cluster quantity, when comparing with the source cloud cluster its average gray value be increase or reduce with and cloud cluster area change situation, will put the cloud cluster of gathering among the S (t) and be divided into ten classifications.
Ten classifications among the described step S103 belong to four big classes, and described four big classes specifically comprise: newly-increased class cloud cluster, growth change class cloud cluster, division class cloud cluster and merging class cloud cluster;
If a certain cloud cluster among the some set S (t) is all non-intersect with the arbitrary cloud cluster among the some set S (t-1), assert that then it is newly-increased class cloud cluster;
If a certain cloud cluster that point is gathered among the S (t) only intersects with an a certain cloud cluster of gathering among the S (t-1), assert that then it is to be changed and next growth change class cloud cluster by the cloud cluster among the set S (t-1); Further, according to the situation of change of cloud cluster area, described growth change class cloud cluster also specifically is divided into translation and changes class cloud cluster, expansion variation class cloud cluster and contraction change class cloud cluster; If this cloud cluster of note is respectively A at t-1 and t area constantly
T-1And A
t, then work as m
1* A
T-1≤ A
t≤ n
1* A
T-1The time, assert that this growth change class cloud cluster is that translation changes the class cloud cluster; Work as A
t＞n
1* A
T-1The time, assert that this growth change class cloud cluster changes the class cloud cluster for expanding; Work as A
t＜m
1* A
T-1The time, assert that this growth change class cloud cluster is a contraction change class cloud cluster; Wherein, parameter value m
1, n
1Be according to the predefined numerical value of actual conditions, and n
1＞m
1〉=1; Such as, can set m
1=1, n
1=2;
If a plurality of cloud clusters among the some set S (t) all with put a certain cloud cluster C that gathers among the S (t-1)
jIntersect, then these cloud clusters can be regarded as by the cloud cluster C among the set S (t-1)
jThe division class cloud cluster that develops and come; Further, according to a plurality of cloud clusters among the set S (t) and the area relationship of cloud cluster Cj, described division class cloud cluster also specifically is divided into increasing and divides class cloud cluster, common division class cloud cluster and independently divide the class cloud cluster; If among the note point set S (t) with C
jThe area of some cloud clusters is A in these cloud clusters that intersect
t, C
jArea be A
Cj, then work as A
t＞n
2* A
CjThe time, assert that this division class cloud cluster is for increasing division class cloud cluster; Work as m
2* A
Cj≤ A
t≤ n
2* A
CjThe time, assert that this division class cloud cluster is common division class cloud cluster; Work as A
t＜m
2* A
CjThe time, assert that this division class cloud cluster is independent division class cloud cluster; Wherein, parameter value m
2, n
2Be according to the predefined numerical value of actual conditions, and n
2＞m
2＞0; Such as, can set m
2=0.5, n
2=1;
If a plurality of cloud clusters among a certain cloud cluster in the current cloud cluster picture point S set (t) and the some set S (t-1) intersect, then this cloud cluster can be regarded as the merging class cloud cluster that comes by a plurality of cloud clusters merging among the set S (t-1); Further, whether greater than the area summation of a plurality of cloud clusters among the S (t-1), described merging class cloud cluster also specifically is divided into increasing and merges class cloud cluster, common merging class cloud cluster and may falsely merge the class cloud cluster according to the area of current cloud cluster; If the current cloud cluster area of note is A
t, the area of n cloud cluster among the some set S (t-1) that intersects with it is respectively A
i, i=1,2 ... n; Then work as A
t＞sum (A
i) time, assert that this merging class cloud cluster merges the class cloud cluster for increasing; As max (A
i)≤A
t≤ sum (A
i) time, assert that this merging class cloud cluster is common merging class cloud cluster; Work as A
t＜max (A
i) time, assert that this merging class cloud cluster is for may falsely merging the class cloud cluster.
Two, image pre-service
Step S2: the cloud atlas that obtains according to geostationary meteorological satellite (GMS), N width of cloth cloud atlas in the setting-up time section before the current time is synthesized, calculate the gray-scale value minimum value at each self-corresponding each lattice point place of N width of cloth cloud atlas, obtain the bright substantially temperature figure of each cloud cluster in the setting-up time section, the bright substantially temperature figure that this synthetic back is obtained is defined as bright substantially in short-term temperature figure;
Among the described step S2, specifically comprise:
Step S201: several cloud atlas in the setting-up time section before the described current time that aligns;
Step S202:, choose gray-scale value minimum in a plurality of gray-scale values that are in this identical lattice point place in several cloud atlas for the gray-scale value at a certain lattice point place among the described bright substantially in short-term temperature figure;
Step S203: the gray-scale value for lattice point places all among the described bright substantially in short-term temperature figure, all adopt the method among the described step S202 to carry out the gray-scale value value, thus the described bright substantially in short-term temperature figure of synthetic acquisition;
With per half an hour of satellite image calculating,, that is, use three pictures in 2 hours in the past to calculate the most highlighted temperature, the basic bright temperature of each cloud cluster in the past 2 hours of expression if N gets 3.As shown in Figure 2, Fig. 2 for 0 of August 1 first three constantly (be 23:30 on July 31,23:00,22:30) cloud atlas the most highlighted in short-term synthetic temperature is schemed.
Step S3: calculate the cloud atlas of current time and the gray-scale value difference image of described bright substantially temperature figure in short-term;
Among the described step S3, specifically comprise:
Step S301: the cloud atlas of the described current time that aligns and described bright substantially in short-term temperature figure;
Step S302: for the gray-scale value at a certain lattice point place in the described gray-scale value difference image, the gray-scale value that is in this identical lattice point place in the cloud atlas with described current time deducts the gray-scale value that is in this identical lattice point place among the described bright substantially in short-term temperature figure, and the difference that obtains is chosen for the gray-scale value at this lattice point place in the described gray-scale value difference image; If this difference is less than zero, the gray-scale value at this lattice point place gets 0 in the then described gray-scale value difference image;
Step S303: for the gray-scale value at lattice point places all in the described gray-scale value difference image, all adopt the method among the described step S302 to carry out the gray-scale value value, thereby obtain described gray-scale value difference image.
The involved picture of the most highlighted in short-term temperature figure is less, and time span is little, therefore is not " the bright temperature of the clear air " figure that is similar to, but the bright substantially temperature of each cloud cluster in short-term.And after doing difference with current cloud atlas, in the recent period obviously the cloud cluster of enhancing identify out.As shown in Figure 3, Fig. 3 on August 10 point cloud chart do with Fig. 2 and differ from the image obtain.Among Fig. 3, gray-scale value is big more is illustrated in in 2 hours should zone cloud amount increase many more.The possibility that produces precipitation in these zones is bigger.Use image segmentation algorithm,, the cloud cluster among Fig. 3 is cut apart, identify the alternative area of heavy rain cloud cluster as threshold method.
Step S4: utilize threshold method that described gray-scale value difference image is cut apart, identify the alternative cloud cluster that rainstorm weather may occur;
Wherein, the order of step S1 and step S2-step S4 can be put upside down, and promptly the identifying of strong convection cloud layer and image preprocessing process can be put upside down.
Three, identification heavy rain cloud cluster
Step S5: for all kinds of cloud clusters of cutting apart acquisition among the described step S1, the alternative cloud cluster that identifies among the integrating step S4, the historical sample data of the observation area that the use geostationary meteorological satellite (GMS) is obtained are discerned and are drawn final heavy rain cloud cluster.
Among the described step S5, the newly-increased class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501a: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502a, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502a: whether the maximum gradation value in the lattice point that it comprised is greater than the threshold value T that presets
1, if then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster; Wherein, threshold value T
1One hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
Among the described step S5, the growth change class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501b: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502b or S503b, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502b: change class and the variation class cloud cluster that expands for translation, if the maximum gradation value in the lattice point that it comprised is greater than threshold value T
2, then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster;
Step S503b: for contraction change class cloud cluster, if its area is greater than threshold value V
1, then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster; Wherein, threshold value T
2And V
1All the one hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
Among the described step S5, the division class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501c: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502c or S503c, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502c:, discern it and be the heavy rain cloud cluster for increasing division class and common division class cloud cluster;
Step S503c:, discern it and be non-heavy rain cloud cluster for independent division class cloud cluster.
Among the described step S5, the merging class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501d: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502d or S503d, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502d: merge class and common merging class cloud cluster for increasing, discern it and be the heavy rain cloud cluster.
Step S503d: for may falsely merging the class cloud cluster, if its area is less than thresholding V
2, then discern it and be non-heavy rain cloud cluster, otherwise, discern it and be the heavy rain cloud cluster; Wherein, threshold value V
2One hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
If wherein, 30 days historical sample data are preferably pass by in the selection of above-mentioned historical sample data, but when current month being May because heavy rain occurs mainly concentrating in month 6,7, August, then the historical sample data select last year 6,7, the data in August.
Finally, as shown in Figure 4, be the recognition result of 1 day 0 point cloud chart in August, the position of heavy rain cloud cluster is identified out by the boundary line.
Four, the method for inspection
Below, the true and reliable method that detects whether to the above-mentioned heavy rain cloud cluster result who identifies is described.
Because weather system can not reappear, and do not have a kind of technological means can continuous, complete all weather phenomena of detection at present, therefore which kind of method of inspection no matter all has certain limitation.
Here we adopt one hour precipitation record of robotization precipitation station as the reference standard, and algorithm is estimated.China has more than 20,000 robotization precipitation station at present, and denser in the distributions in overwhelming majority area, east, spatial resolution is near the resolution of FY2 satellite infrared passage.In addition, because west area precipitation station negligible amounts, it is sparse to distribute, and can't test.When therefore checking, only consider the accuracy of algorithm in the eastern region.
Test stone is, the heavy rain cloud cluster for above-mentioned algorithm identified goes out surpasses 8mm if a certain precipitation station in cloud cluster location records one hour quantity of precipitation at current time and in following 2 hours, thinks that then identification is correct, otherwise thinks identification error.
Concrete checking procedure is as follows:
(1) establishes current time t, each heavy rain cloud cluster that above-mentioned algorithm identified is gone out, all are from one hour quantity of precipitation information changing precipitation station to read cloud cluster location current time and two hours futures, promptly, if current is 8 points, then read each website at 7 o'clock to 8 o'clock, 8 o'clock to 9 o'clock, 9 o'clock to 10 o'clock 3 one hour quantity of precipitation information;
(2) if one hour quantity of precipitation of certain website surpasses 8mm, think that then this cloud cluster is the heavy rain cloud cluster really, identification is correct;
(3) if three one hour quantity of precipitation of all websites all are no more than 8mm, think that then this cloud cluster is not the heavy rain cloud cluster, identification error;
(4) write down that all identification is correct, the number of times of mistake, calculate recognition correct rate.
As shown in Figure 5, be August 10 recognition result and following 2 hours one hour quantity of precipitation comparison diagram.Wherein one hour quantity of precipitation divides out with the square frame boundary line less than 5mm, greater than areal distribution the fine some place in square frame border top-right dot matrix zone in of 5mm, greater than the bulky grain point place of areal distribution in the dot matrix zone of lower left, square frame border of 8mm less than 8mm.
Five, product test
To 0 of on July 1st, 2010 to 23 of July 31 totally 738 Zhang Yuns figure (6 Zhang Yuns scheme disappearance) detect, the area, Sichuan detects 1565 heavy rain cloud clusters altogether, 1335 correct, accuracy 85.30%, nationwide (except the west area) detects 6217 heavy rain cloud clusters altogether, 4746 correct, accuracy 76.34%.
Algorithm to 0 of on August 1st, 2010 to 23 of Augusts 10 totally 238 Zhang Yuns figure (2 Zhang Yuns scheme disappearance) detect, the area, Sichuan detects 329 heavy rain cloud clusters altogether, 272 correct, accuracy 82.67%, nationwide (except the west area) detects 2021 heavy rain cloud clusters altogether, 1476 correct, accuracy 73.03%.
The above only is a preferred implementation of the present invention; should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the technology of the present invention principle; can also make some improvement and distortion, these improvement and distortion also should be considered as protection scope of the present invention.
Claims (10)
1. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster is characterized in that described method comprises the steps:
Step S1: the cloud atlas that geostationary meteorological satellite (GMS) is obtained is cut apart, obtained the classification of each cloud cluster under the current time in the observation area;
Step S2: the cloud atlas that obtains according to geostationary meteorological satellite (GMS), several cloud atlas in the setting-up time section before the current time are synthesized, obtain the bright substantially temperature figure of each cloud cluster in the setting-up time section, the bright substantially temperature figure that this synthetic back is obtained is defined as bright substantially in short-term temperature figure;
Step S3: calculate the cloud atlas of current time and the gray-scale value difference image of described bright substantially temperature figure in short-term;
Step S4: described gray-scale value difference image is cut apart, identified the alternative cloud cluster that rainstorm weather may occur;
Step S5: for all kinds of cloud clusters of cutting apart acquisition among the described step S1, the alternative cloud cluster that identifies among the integrating step S4, the historical sample data of the observation area that the use geostationary meteorological satellite (GMS) is obtained are discerned and are drawn final heavy rain cloud cluster.
2. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 1 is characterized in that, among the described step S1, specifically comprises the steps:
Step S101: read current time t that geostationary meteorological satellite (GMS) obtains and the last one hour cloud atlas of t-1 constantly, use the gray-scale value threshold method that described cloud atlas is cut apart, lattice point in the cloud atlas is divided into more than or equal to threshold value with less than two classes of threshold value by the gray-scale value size, wherein is designated as a set S (t) respectively and point is gathered S (t-1) more than or equal to the part of threshold value; Wherein, some set S (t) is the cloud cluster picture point set of current time t, and some set S (t-1) is last one hour of the cloud cluster picture point set of t-1 constantly;
Step S102: the connected region among described some set S (t) of mark and the some set S (t-1), write down wherein relevant with cloud cluster precipitation intensity parameter, described parameter specifically comprises: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster;
Step S103: for each cloud cluster among the set S (t), by judge its whether in a set S (t-1), exist corresponding source cloud cluster, in a set S (t-1) the source cloud cluster quantity, when comparing with the source cloud cluster its average gray value be increase or reduce with and cloud cluster area change situation, will put the cloud cluster of gathering among the S (t) and be divided into ten classifications.
3. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 2, it is characterized in that, ten classifications among the described step S103 belong to four big classes, and described four big classes specifically comprise: newly-increased class cloud cluster, growth change class cloud cluster, division class cloud cluster and merging class cloud cluster;
If a certain cloud cluster among the some set S (t) is all non-intersect with the arbitrary cloud cluster among the some set S (t-1), assert that then it is newly-increased class cloud cluster;
If a certain cloud cluster that point is gathered among the S (t) only intersects with an a certain cloud cluster of gathering among the S (t-1), assert that then it is to be changed and next growth change class cloud cluster by the cloud cluster among the set S (t-1); Further, according to the situation of change of cloud cluster area, described growth change class cloud cluster also specifically is divided into translation and changes class cloud cluster, expansion variation class cloud cluster and contraction change class cloud cluster; If this cloud cluster of note is respectively A at t-1 and t area constantly
T-1And A
t, then work as m
1* A
T-1≤ A
t≤ n
1* A
T-1The time, assert that this growth change class cloud cluster is that translation changes the class cloud cluster; Work as A
t＞n
1* A
T-1The time, assert that this growth change class cloud cluster changes the class cloud cluster for expanding; Work as A
t＜m
1* A
T-1The time, assert that this growth change class cloud cluster is a contraction change class cloud cluster; Wherein, parameter value m
1, n
1Be according to the predefined numerical value of actual conditions, and n
1＞m
1〉=1;
If a plurality of cloud clusters among the some set S (t) all with put a certain cloud cluster C that gathers among the S (t-1)
jIntersect, then these cloud clusters can be regarded as by the cloud cluster C among the set S (t-1)
jThe division class cloud cluster that develops and come; Further, according to a plurality of cloud clusters and cloud cluster C among the set S (t)
jArea relationship, described division class cloud cluster also specifically is divided into and increases division class cloud cluster, common division class cloud cluster and independent division class cloud cluster; If among the note point set S (t) with C
jThe area of some cloud clusters is A in these cloud clusters that intersect
t, C
jArea be A
Cj, then work as A
t＞n
2* A
CjThe time, assert that this division class cloud cluster is for increasing division class cloud cluster; Work as m
2* A
Cj≤ A
t≤ n
2* A
CjThe time, assert that this division class cloud cluster is common division class cloud cluster; Work as A
t＜m
2* A
CjThe time, assert that this division class cloud cluster is independent division class cloud cluster; Wherein, parameter value m
2, n
2Be according to the predefined numerical value of actual conditions, and n
2＞m
2＞0;
If a plurality of cloud clusters among a certain cloud cluster in the current cloud cluster picture point S set (t) and the some set S (t-1) intersect, then this cloud cluster can be regarded as the merging class cloud cluster that comes by a plurality of cloud clusters merging among the set S (t-1); Further, whether greater than the area summation of a plurality of cloud clusters among the S (t-1), described merging class cloud cluster also specifically is divided into increasing and merges class cloud cluster, common merging class cloud cluster and may falsely merge the class cloud cluster according to the area of current cloud cluster; If the current cloud cluster area of note is A
t, the area of n cloud cluster among the some set S (t-1) that intersects with it is respectively A
i, i=1,2 ... n; Then work as A
t＞sum (A
i) time, assert that this merging class cloud cluster merges the class cloud cluster for increasing; As max (A
i)≤A
t≤ sum (A
i) time, assert that this merging class cloud cluster is common merging class cloud cluster; Work as A
t＜max (A
i) time, assert that this merging class cloud cluster is for may falsely merging the class cloud cluster.
4. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 1 is characterized in that, among the described step S2, specifically comprises:
Step S201: several cloud atlas in the setting-up time section before the described current time that aligns;
Step S202:, choose gray-scale value minimum in a plurality of gray-scale values that are in this identical lattice point place in several cloud atlas for the gray-scale value at a certain lattice point place among the described bright substantially in short-term temperature figure;
Step S203: the gray-scale value for lattice point places all among the described bright substantially in short-term temperature figure, all adopt the method among the described step S202 to carry out the gray-scale value value, thus the described bright substantially in short-term temperature figure of synthetic acquisition.
5. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 1 is characterized in that, among the described step S3, specifically comprises:
Step S301: the cloud atlas of the described current time that aligns and described bright substantially in short-term temperature figure;
Step S302: for the gray-scale value at a certain lattice point place in the described gray-scale value difference image, the gray-scale value that is in this identical lattice point place in the cloud atlas with described current time deducts the gray-scale value that is in this identical lattice point place among the described bright substantially in short-term temperature figure, and the difference that obtains is chosen for the gray-scale value at this lattice point place in the described gray-scale value difference image; If this difference is less than zero, the gray-scale value at this lattice point place gets 0 in the then described gray-scale value difference image;
Step S303: for the gray-scale value at lattice point places all in the described gray-scale value difference image, all adopt the method among the described step S302 to carry out the gray-scale value value, thereby obtain described gray-scale value difference image.
6. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 1 is characterized in that, among the described step S4, utilizes threshold method to cut apart described gray-scale value difference image.
7. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 3 is characterized in that among the described step S5, the newly-increased class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501a: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502a, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502a: whether the maximum gradation value in the lattice point that it comprised is greater than the threshold value T that presets
1, if then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster; Wherein, threshold value T
1One hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
8. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 3 is characterized in that among the described step S5, the growth change class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501b: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502b or S503b, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502b: change class and the variation class cloud cluster that expands for translation, if the maximum gradation value in the lattice point that it comprised is greater than threshold value T
2, then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster;
Step S503b: for contraction change class cloud cluster, if its area is greater than threshold value V
1, then discern it and be the heavy rain cloud cluster; Otherwise, discern it and be non-heavy rain cloud cluster; Wherein, threshold value T
2And V
1All the one hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
9. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 3 is characterized in that among the described step S5, the division class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501c: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502c or S503c, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502c:, discern it and be the heavy rain cloud cluster for increasing division class and common division class cloud cluster;
Step S503c:, discern it and be non-heavy rain cloud cluster for independent division class cloud cluster.
10. the method based on geostationary meteorological satellite (GMS) identification heavy rain cloud cluster as claimed in claim 3 is characterized in that among the described step S5, the merging class cloud cluster for cutting apart acquisition among the step S1 specifically comprises the steps:
Step S501d: judge whether it is the alternative cloud cluster that identifies among the step S4, if, then proceed S502d or S503d, otherwise, discern it and be non-heavy rain cloud cluster;
Step S502d: merge class and common merging class cloud cluster for increasing, discern it and be the heavy rain cloud cluster.
Step S503d: for may falsely merging the class cloud cluster, if its area is less than thresholding V
2, then discern it and be non-heavy rain cloud cluster, otherwise, discern it and be the heavy rain cloud cluster; Wherein, threshold value V
2One hour minimum quantity of precipitation about heavy rain with the meteorology definition is foundation, set month in one's duty historical sample data by the past and determine that according to minimum probability of miscarriage of justice criterion described historical sample data comprise: the change in location situation of the maximum gradation value of cloud cluster, the minimum gradation value of cloud cluster, cloud cluster area and cloud cluster.
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