CN111929687A - Automatic recognition algorithm for tornado vortex characteristics - Google Patents
Automatic recognition algorithm for tornado vortex characteristics Download PDFInfo
- Publication number
- CN111929687A CN111929687A CN202010861316.5A CN202010861316A CN111929687A CN 111929687 A CN111929687 A CN 111929687A CN 202010861316 A CN202010861316 A CN 202010861316A CN 111929687 A CN111929687 A CN 111929687A
- Authority
- CN
- China
- Prior art keywords
- shear
- dimensional
- azimuthal
- azimuth
- feature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 53
- 239000000178 monomer Substances 0.000 claims abstract description 22
- 238000000034 method Methods 0.000 claims abstract description 14
- 238000002592 echocardiography Methods 0.000 claims abstract description 6
- 230000002401 inhibitory effect Effects 0.000 claims abstract description 3
- 230000000717 retained effect Effects 0.000 claims description 8
- 230000001419 dependent effect Effects 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 description 12
- 230000002596 correlated effect Effects 0.000 description 9
- 238000002310 reflectometry Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 6
- 230000010349 pulsation Effects 0.000 description 4
- 239000005435 mesosphere Substances 0.000 description 3
- 230000002829 reductive effect Effects 0.000 description 3
- 230000000903 blocking effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 230000036961 partial effect Effects 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- CLOMYZFHNHFSIQ-UHFFFAOYSA-N clonixin Chemical compound CC1=C(Cl)C=CC=C1NC1=NC=CC=C1C(O)=O CLOMYZFHNHFSIQ-UHFFFAOYSA-N 0.000 description 1
- 230000001351 cycling effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000010008 shearing Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
- G01S13/958—Theoretical aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses an automatic recognition algorithm for the characteristics of a tornado vortex, which comprises the following steps: step 1: identifying and inhibiting non-meteorological echoes in radar base data, and performing de-fuzzy processing on radial speed; step 2: identifying storm monomers by using a storm identification tracking algorithm, and recording the centroid position of the storm monomers; and step 3: estimating azimuth vorticity shear by using a two-dimensional local linear least square difference method; and 4, step 4: identifying a one-dimensional azimuthal shear segment using multiple thresholds; and 5: identifying two-dimensional azimuth shear characteristics based on a spatial proximity principle; step 6: identifying three-dimensional azimuth shear characteristics through vertical correlation; and 7: associating three-dimensional azimuth shear characteristics with the storm monomers; and 8: identifying the TVS based on the three-dimensional azimuthal shear signature of the storm. The method can reduce the false alarm rate of TVS identification and improve the hit rate of TVS identification.
Description
Technical Field
The invention relates to the technical field of weather radar systems, in particular to an automatic recognition algorithm for tornado vortex characteristics.
Background
An essential feature of the supermonomer is the permanent deep mesowhirl, which is a small scale vortex (3-10 km in diameter) closely linked to the updraft and the rear-side downdraft of a strong convection storm, appearing as an azimuthal pair on the radar radial velocity plotCalled the distributed velocity vortex couple. Blue-gold vortices are often used to simulate mesocyclone, i.e. the rotational speed within the radius of the mesocyclone nucleus region increases linearly with distance, and the rotational speed decreases inversely with distance outside the nucleus region radius, so that the radar radial speed in a clockwise direction has a tendency to increase gradually, i.e. from a maximum negative speed to zero and then from zero to a maximum positive speed, on the same distance circle of the mesocyclone nucleus region. More than 90% of the mesowhirlpools are accompanied by severe weather such as hail, strong wind, and tornado, but only about 25% of the mesowhirlpools develop into tornado. In addition to mesocyclone, a closely related cyclone can be identified on the radar radial velocity map as being smaller in size (about 1km) and faster in rotation (0.05 s for velocity azimuth shear) than mesocyclone-1) Is called a tornado vortex feature (TVS: tornado Vortex Signature).
The TVS identification algorithm used in the Chinese new-generation weather radar system is from a U.S. TVS identification algorithm, and the domestic literature of the automatic tornado vortex feature identification algorithm which is developed autonomously is almost absent.
The TVS recognition Algorithm (hereinafter 88D TVS) (Crum et al, 1993) by WSR-88D (Weather Surveillance Radar-1988Doppler) and Tornado Detection Algorithm (hereinafter NSSL) (NSSL) by The American Strong storm Laboratory (NSSL) (NSSL TDA) (Mitchell et al, 1998) are used in The U.S. Business. Both algorithms are designed on the basis of searching for the velocity difference of adjacent directions at equal distances from the radar, and both perform filtering processing on the radial velocity corresponding to the low reflectivity factor.
The 88D TVS algorithm is completed on the basis of the mesowhirl identification algorithm, and the algorithm comprises the following steps:
a. one-dimensional azimuth shear segment: firstly, filtering radial velocity data with the reflectivity factor smaller than a set threshold ZT (15dBZ), then searching the deblurred radial velocity data, searching a distance library with adjacent azimuth angles with the velocity value continuously increasing clockwise at the same distance from the radar, and forming a one-dimensional distance library sequence when the velocity value is not increased any more, which is called a one-dimensional azimuth shear section. Is reservedThe one-dimensional azimuthal shear segment needs to pass through a low doppler angular momentum threshold TLM (180 km)2·h-1) Low shear threshold TLS (7.2 h)-1) And a high Doppler angular momentum threshold THM (540 km)2·h-1) Or a high shear threshold TLS (14.4 h)-1) And (6) checking.
b. Two-dimensional characteristics: the azimuthal distances and radial distances between one-dimensional azimuthal shear segment and all one-dimensional azimuthal shear segments classified into two-dimensional features are calculated and classified as the same two-dimensional feature when preset azimuthal and radial distance thresholds L A (2.2 °) and LR (1km) are met. A two-dimensional feature comprises a one-dimensional azimuthal shear segment that satisfies a predetermined threshold TPV (10) having a ratio of radial dimension to azimuthal dimension that is within a distance-dependent threshold (symmetric two-dimensional feature).
c. Vertical correlation and classification: two-dimensional features at different elevation angles are vertically correlated, and only two-dimensional features with center heights lower than a preset threshold TFM (8km) can be vertically correlated. Two-dimensional features are considered to be circular, the larger of the azimuthal and radial dimensions is considered to be the diameter, and if a small two-dimensional feature falls vertically within a larger two-dimensional feature area, the 2 two-dimensional features are considered to be vertically related. And forming three-dimensional features after completing all vertical correlations from a low elevation angle to a high elevation angle, wherein one three-dimensional feature comprises at least 2 symmetrical two-dimensional features.
The three-dimensional feature that is retained after passing through the above 3 steps is mesocyclone.
d, TVS: the TVS recognition is completed on the basis of mesocyclone recognition. The TVS algorithm works by searching for the maximum and minimum velocities of each two-dimensional feature in the cyclone, and then computing the differential shear if this is greater than 20 m-s-1·km-1Then the vortices associated with the shear are identified as potential TVS. If there are potential TVSs in a cyclone with 2 or more elevation angles, then the cyclone is identified as a TVS.
2) NSSL TDA algorithm
NSSL TDA is an improvement over the 88D TVS algorithm. The 88D TVS algorithm is based on identifying the cyclone-in-the-middle and determines whether there is a TVS in the cyclone-in-the-middle, whereas NSSL TDA is independent of the cyclone-in-the-middle algorithm without first identifying the cyclone-in-the-middle. Compared with the 88D TVS algorithm, the NSSL TDA has the main characteristics of vortex identification: a) searching radial velocity shear between two distance libraries at the same distance from the radar and adjacent azimuth angles; b) the algorithm is not required to first identify a medium cyclone. The algorithm process is as follows:
a. one-dimensional azimuth shear segment: radial velocity data with a reflectivity factor less than a set threshold ZT (0dBZ) are first filtered out and are also disregarded for radial velocity data with distance folding and data missing. Then for each elevation scan the radial velocity difference between two range bins for adjacent azimuths at the same distance from the radar is found, the finding process needs to be within 150km radius and below 10km altitude. If the difference in velocity is greater than a predetermined adjustable threshold (e.g., 11m/s), the velocity pair is stored as a shear segment and a number of attribute parameters are calculated. The process repeats until all velocity data within one radar elevation scan is processed and all shear segments exceeding the minimum velocity difference threshold are found.
b. Two-dimensional characteristics: two-dimensional features are constructed using 6 thresholds (35, 30, 25, 20, 15, 11m/s) cycling from high to low thresholds. For each threshold level, the two-dimensional features are composed of at least 3 shear segments, and the centroids of each shear segment are less than 1 ° apart from the orientation of the centroids of its neighboring shear segments, and the radial distance is less than 500 m. The aspect ratio (radial dimension/azimuthal dimension) of all two-dimensional features is calculated, and only two-dimensional features with ratios less than 4 are retained. The above process is repeated using a low threshold, which is discarded if the two-dimensional features of different thresholds overlap.
c. Three-dimensional characteristics: carrying out vertical correlation continuity check in the whole body scanning range, wherein one three-dimensional feature at least consists of 2 two-dimensional features, and the three-dimensional features are spaced by an elevation angle at most; the horizontal distance between two-dimensional features that make up a three-dimensional feature is less than 2.5 km. All three-dimensional features consisting of 3 and more two-dimensional features are called three-dimensional vortices. Each three-dimensional vortex is divided into 2 types: TVS and overhead TVS (ETVS). If a three-dimensional vortex satisfies: (1) the minimum strength and thickness criterion, (2) the base extends to 0.5 degrees elevation or a specified height (e.g., 600m), the three-dimensional vortex is called a TVS. If only condition (1) is satisfied then it is called ETVS.
The defects and shortcomings of the prior art are as follows:
1) the false alarm rate FAR of the 88D TVS algorithm for tornado is close to zero, but the hit rate POD is also low. The reason is that when the maximum and minimum speed difference in the middle cyclone is larger, the azimuth spacing may also be larger, so that the corresponding azimuth shear is smaller, and the shear standard of the TVS cannot be achieved. In fact, the maximum minimum speed difference of the smaller-scale vortexes contained in the mesosphere is slightly smaller than that of the mesosphere, but the corresponding azimuth spacing thereof is far smaller than that of the mesosphere, so that the calculated azimuth cut can meet the standard of the TVS, and the 88D TVS algorithm cannot identify the TVS of the type, so that the hit rate POD is low.
2) A single differential azimuth shear threshold is used in the 88D TVS algorithm, if the threshold is obtained too high, the area of the identified two-dimensional features occupying the real cyclone nucleus area is too small, and if the threshold is obtained too low, some weak shear areas outside the cyclone nucleus area are added, which affect the estimation of the azimuth shear and further affect the identification result of the TVS.
3) The hit rate POD of the NSSL TDA algorithm is higher, but the false alarm rate FAR is also higher. The reason is that the algorithm only uses the adjacent azimuth velocity difference and the differential azimuth shear to construct the one-dimensional feature and the two-dimensional feature, and for the radial velocity field, small-scale natural pulsation is common, and the identification of the TVS is easily affected by the small-scale natural pulsation of the velocity, so that the false alarm rate is high. In addition, the differential azimuth shear criterion is influenced by radial velocity quality problems caused by velocity de-blurring error or failure, noise, ground clutter, partial beam blocking and the like, so that the false alarm rate is high.
4) The 88D TVS algorithm and the NSSL TDA algorithm both use a low reflectivity factor threshold to filter radial velocity data, the mesowhirl is a small-scale vortex closely connected with the updraft and the rear-side downdraft of a strong convection storm, the precipitation particles in the strong updraft area are few, and the corresponding reflectivity factor may be lower than the threshold given in the algorithm, so that some large radial velocity values are lost.
5) The NSSL TDA algorithm requires that the TVS contain at least a two-dimensional azimuth shear signature of 3 elevation angles, which may make some low-quality spinning-reel supermonomers unrecognizable.
6) In the NSSL TDA algorithm, only the radial velocities of adjacent azimuths are used to calculate the differential shear, and at a short distance, the maximum minimum velocity may not be the adjacent azimuths, but the differential shear is larger than the difference between the maximum adjacent azimuths, which may result in missed recognition of TVS at a short distance.
Disclosure of Invention
The invention aims to provide an automatic recognition algorithm for the characteristics of a tornado vortex, which can solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
an automatic recognition algorithm for the characteristics of a tornado vortex comprises the following steps:
step 1: firstly, recognizing and inhibiting non-meteorological echoes such as ground clutter, super-refraction echoes, noise and the like in radar base data by adopting the prior art, and performing de-blurring processing on the radial speed;
step 2: identifying storm monomers by using a storm identification tracking algorithm, and recording the centroid position of the storm monomers;
and step 3: estimating azimuth vorticity shear using a two-dimensional local Linear Least Square Deviation (LLSD) method;
and 4, step 4: one-dimensional azimuthal shear segment identification
The one-dimensional azimuth shear segment is identified by searching for azimuth vorticity shear greater than a preset threshold value ( default grade 6, 70, 60, 50, 40, 30, 20, respectively, unit 10) in the radial direction-4s-1) A continuous library of (a). When the azimuth vorticity cutting library which is larger than the threshold value is searched for the first time from the radar outwards along the radial direction, the subsequent azimuth vorticity cutting libraries which are larger than the threshold value are gathered together until the azimuth vorticity cutting library which is smaller than the threshold value is searched; if the azimuth vorticity shear value of the subsequent library is equal toThe difference is less than or equal to the threshold value of the azimuthal vorticity variation (default value is 4, unit is 10)-4s-1) And the number of consecutive distance bins that satisfy the condition is less than the interrupt count threshold (default value is 2), then the shear segment search continues, otherwise the shear segment is cut before the first bin that is less than the threshold. If the length of the shear segment is greater than the segment length threshold (default value is 0.95km), then the segment is saved, otherwise, the segment is culled. The range bins, bearing, maximum and minimum velocity, etc. for the beginning and end of each shear segment are recorded.
And 5: identification of two-dimensional orientation shear features based on spatial proximity principle
And when the last radial one-dimensional azimuth shear section of an elevation angle is searched, merging the one-dimensional azimuth shear sections into a two-dimensional azimuth shear characteristic based on a spatial proximity principle. The merging of adjacent segments needs to satisfy 2 criteria: one is that the azimuth difference of 2 segments is less than the azimuth separation threshold (default of 1.5 °), and the other is that the overlap distance of 2 segments in the radial direction is greater than the segment overlap threshold (default of 0.45 km). A two-dimensional azimuthal shear feature must contain a number of one-dimensional azimuthal shear segments greater than a segment number threshold (default of 2) and an area greater than a two-dimensional azimuthal shear feature area threshold (default of 3 km)2). For the searched two-dimensional azimuth shear feature, some feature quantities are calculated and stored, including a central position (skew distance, azimuth angle and elevation angle), azimuth and skew distance of start and end, maximum azimuth vorticity shear, maximum minimum speed and azimuth and skew distance where the maximum speed and the maximum minimum speed are located and corresponding differential azimuth shear, maximum adjacent azimuth speed difference and corresponding differential azimuth shear, and the like.
After the identification of the two-dimensional azimuth shear features of all level thresholds is completed, in order to extract the strongest azimuth vorticity shear region information, the low-threshold two-dimensional azimuth shear feature wrapped by the high-threshold two-dimensional azimuth shear feature needs to be abandoned. If the center of the high threshold two-dimensional azimuthal shear feature falls within the low threshold two-dimensional azimuthal shear feature area, then the low threshold two-dimensional azimuthal shear feature is discarded, but parametric information for the two-dimensional azimuthal shear feature that records all of the level thresholds that wrap it is added to the retained two-dimensional azimuthal shear feature information for the highest level threshold.
Step 6: identifying three-dimensional azimuthal shear features through vertical correlation
And sequencing the reserved two-dimensional azimuth shear characteristics according to the maximum azimuth vorticity shear size, and then performing vertical correlation. Each identified three-dimensional azimuth shear feature contains at least 2 two-dimensional azimuth shear features in successive elevation angles. Vertical correlation is an iterative process starting from the lowest elevation angle. Firstly, two-dimensional azimuth shear features with the center distance of adjacent elevation angles smaller than a threshold value (default value of 2.5km) are vertically associated, and if a plurality of two-dimensional azimuth shear features can be associated, only the two-dimensional azimuth shear feature with the largest azimuth vorticity shear is selected for association. If two-dimensional azimuth shear features which are not vertically correlated exist after the first vertical correlation is finished, the search radius is increased to 5km, and the steps are repeated for all the two-dimensional azimuth shear features which are not vertically correlated. If the two-dimensional azimuth shear feature which is not vertically correlated still exists after the second vertical correlation, the search radius is increased to 7.5km, and the steps are repeated again to perform the 3 rd vertical correlation. This process forms a three-dimensional azimuthal shear feature after all adjacent elevation angles have been performed. And calculating and storing a plurality of parameters including the position of the mass center (azimuth, slant range and height), the bottom height, the top height, the maximum azimuth vorticity shear value and the height thereof, the vertical integral azimuth vorticity, all two-dimensional azimuth shear characteristic parameter information and the like for the reserved three-dimensional azimuth shear characteristics.
And 7: correlating three-dimensional azimuthal shear features and storm monomers
The storm monomer positions identified based on the reflectivity factor data are used for conducting null processing on the three-dimensional azimuth shear characteristics which are irrelevant to the storm, namely, when the storm exists within the radius range of 30km of the center of one three-dimensional azimuth shear characteristic, the three-dimensional azimuth shear characteristic can be finally reserved. The distance nearest principle is used to associate the three-dimensional azimuth shear feature that is finally retained with the identified storm monomer.
And 8: TVS identification
The TVS is identified based on the identified three-dimensional azimuth shear characteristics and the statistical analysis results of the three-dimensional azimuth shear characteristics of the super monomer tornado cases, and the identification algorithm is limited to be within 150km and below 6km with the radar as the center. Using 1.25km as a default TVS size to estimate the number n of bearing number intervals of the maximum and minimum speed, i.e. n equals 1.25/(r Δ θ), where r is the slant distance and Δ θ is the radar beam width, and when n <1, n equals 1, and when n >13, n equals 13. Identification of a TVS requires that the following 3 criteria be met:
(1) the rotation speed of the two-dimensional azimuth shear feature must be greater than the rotation speed (half of the maximum minimum speed difference) standard based on the statistics of the three-dimensional azimuth shear feature of the super monomer tornado (RV-42 r)-0.215R is the pitch, km in unit, RV is the rotational speed, m.s in unit-1)
(2) The azimuth number interval of the maximum and minimum speed of the rotation speed is calculated to be less than or equal to n.
(3) The maximum azimuthal vorticity shear of the two-dimensional azimuthal shear feature must be greater than the azimuthal vorticity shear criteria (u) of the statistics based on the three-dimensional azimuthal shear feature of the Supermonomer Tornadoa＝1104.3r-0.577R is the slant distance in km, uaFor azimuthal vorticity shear, unit 10-4s-1)。
The rotation speed of the three-dimensional azimuth shear feature and the azimuth number interval number corresponding to the maximum and minimum speed have a plurality of sets of values, that is, the rotation speed calculated by the maximum and minimum speed in the two-dimensional azimuth shear features of different levels of shear thresholds, the rotation speed calculated by the maximum adjacent azimuth speed difference in the two-dimensional azimuth shear feature of the lowest level of shear thresholds, and the azimuth number interval number corresponding to the maximum and minimum speed for calculating the rotation speed are only required to have one set which simultaneously meets the judgment criteria (1) and (2).
When at least 2 two-dimensional azimuth shear features meeting the conditions (1) to (3) below the height of 6km, determining that a potential TVS exists, and when the height of the two-dimensional azimuth shear feature at the bottommost layer of the potential TVS is less than 1km or the elevation angle at which the two-dimensional azimuth shear feature is located is the lowest scanning elevation angle, determining that the potential TVS is a TVS, otherwise, determining that the potential TVS is an overhead TVS; when the height below 6km is only 1 two-dimensional azimuth shear feature meeting the conditions (1) - (3), at least one two-dimensional azimuth shear feature at the adjacent elevation angle is required to meet the mesocyclone standard, the potential TVS is determined to exist, when the height of the two-dimensional azimuth shear feature meeting the conditions (1) - (3) is less than 1km or the elevation angle at which the two-dimensional azimuth shear feature is located is the lowest scanning elevation angle, the potential TVS is determined to be the TVS, otherwise, the potential TVS is determined to be the overhead TVS.
Compared with the prior art, the invention has the beneficial effects that:
1) the quality control of the radar data before identification can reduce the influence of problems such as non-meteorological echo and speed ambiguity on TVS identification. The radial velocity is filtered without using a low reflectivity factor threshold, so that the removal of weak echoes or large radial velocity values of a bounded weak echo region (a strong ascending airflow region) is avoided.
2) Compared with finite difference azimuth shear, the azimuth vorticity shear calculated by using the two-dimensional local LLSD method is more stable, the influence of speed quality (such as noise, partial beam blocking, ground clutter and the like) and small-scale natural pulsation of the speed on one-dimensional azimuth shear section identification can be reduced, the false alarm rate of TVS identification is reduced, and the hit rate of TVS identification is improved.
3) The core vortices that may be embedded within the rectangular shear region (e.g., the squall line along the radial) may be separated using a multi-threshold one-dimensional azimuthal shear segment search method. When the one-dimensional azimuth shear section is identified, a limited azimuth vorticity shear library smaller than a set threshold value is allowed to be contained, so that the influence of small-scale natural pulsation of the speed on the search of the one-dimensional azimuth shear section can be reduced, and the hit rate of TVS identification is improved.
4) In the two-dimensional azimuth shear feature identification, in order to accurately find the strongest azimuth vorticity shear region, the low-threshold two-dimensional azimuth shear feature wrapped by the high-threshold two-dimensional azimuth shear feature is abandoned, but the parameter information of all the abandoned low-threshold two-dimensional azimuth shear features is retained. In TVS identification, a plurality of groups of rotating speeds and corresponding maximum and minimum speed azimuth sequence number intervals are obtained by using the parameter information, and as long as one group meets the standard, the hit rate of TVS identification can be improved even if one group meets one of the criteria of TVS. Because the position serial number interval of the maximum and minimum speed of the TVS can change along with the distance from the TVS to the radar, the rotational speeds of different TVSs can have some differences, and the TVSs with different strengths, sizes and distances from the radar can be dynamically checked by using a plurality of groups of rotational speeds and the maximum and minimum speed position serial number interval, so that the condition that whether the position shear meets the TVS standard or not and the TVS is missed to be identified due to the fact that the maximum and minimum speed difference of the cyclone in a single threshold is used for judging whether the position shear meets the TVS standard or not is improved. In addition, since the mesocyclone and the TVS are not all azimuthally symmetrically distributed (for example, the lower layer is a convergent cyclone), if only the velocity difference of adjacent azimuths at the same distance is calculated, the maximum rotation speed and azimuth shear cannot be found, and the algorithm adopts multiple sets of rotation speeds to compensate for the defect.
5) The advantage of using three sets of search radii for vertical correlation in three-dimensional azimuthal shear feature identification is that three-dimensional azimuthal shear features that are relatively steeply pitched are completely identified as much as possible, because a macromer storm is often generated in a strong vertical wind shear environment, and the storm is relatively steeply pitched. The identified storm monomer is used for conducting null elimination processing on the three-dimensional azimuth shear characteristic irrelevant to the storm, and the three-dimensional azimuth shear characteristic and the TVS which are mistakenly identified due to the quality problem of radial velocity data can be effectively removed.
6) In TVS identification, using 1.25km as the default characteristic dimension of the tornado scroll to estimate the azimuth sequence number interval threshold for maximum minimum speed may solve the problem of variation of azimuth sequence number interval for maximum minimum speed with distance in TVS, since not all TVS' maximum minimum speeds are located at adjacent azimuths. The rotation speed standard and the azimuth vorticity shear standard of the TVS used in the algorithm are obtained by statistics according to batch tornado examples in China, and the 2 standards are power functions which change along with distances, so that the hit rate of TVS identification in China can be improved. The hit rate of low-mass TVS identification can be improved by using 2 continuous elevation angles to meet the TVS judgment standard or one elevation angle to meet the TVS standard and the other adjacent elevation angle to meet the mesocyclone standard.
Drawings
FIG. 1 is a flow chart of an automatic identification algorithm for the characteristics of a tornado scroll in accordance with the present invention;
FIG. 2 illustrates the radial identification 70, 60, 50, 40, 30 and 20 × 10 of the present invention-4s-1A shear segment map of a threshold;
FIG. 3 is a schematic view of 3 shear segments of the present invention;
FIG. 4 is a two-dimensional azimuthal shear profile of the present invention;
FIG. 5 is a graph of 2 two-dimensional azimuthal shear signatures of the present invention;
FIG. 6 is a two-dimensional azimuthal shear signature graph with which the present invention is vertically correlated;
FIG. 7 is a plot of cyclonic rotational speed (a) and azimuthal vorticity shear (b) versus distance from the radar in a tornado of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows a flow chart of an automatic recognition algorithm for the characteristics of the tornado vortex, which is proposed by the present invention, wherein the reflectivity factor clutter suppression, radial velocity de-ambiguity and storm monomer have been studied and the other modules are specifically described below.
1) Azimuthal vorticity shear estimation
Taking a two-dimensional local window with the size of (2N +1) × (2M +1) under polar coordinates, and setting the radial speed of each azimuth-slope distance library as u in the windowi,j(i ═ N, … 0,1 … N; j ═ M, … 0,1 … M). Using the window center as a reference (i is 0, j is 0), and taking the radial center far away, i is positive, clockwise along the tangential direction, j is positive, the azimuthal vorticity shear u at the window center is obtainedaComprises the following steps:
wherein the content of the first and second substances,
r is the slant distance (km) from the center of the window to the radar, Deltar is the length of the radial velocity library (km),
is the beam width (in radians). w is ai,jFor weight, there should be w for all i and jij>0，wi,j＝w-i,j，wi,j＝wi,-jThe algorithm takes wi，j1. If there is cyclonic rotation in the velocity field, ua＞0，uaThe larger the square vorticity shear.
Since polar radial velocity data is used in the calculation of azimuthal vorticity shear using the LLSD method, its azimuthal resolution varies with the distance r from the radar, so the size of the local window taken also varies with r. Assuming that the size of the cyclone core is a constant D (default 1.25km), then the values of N and M are:
where (int) denotes rounding. When the azimuth resolution of the radial speed data is 1 DEG, if M is less than 1, making M equal to 1; if M >13, let M be 13.
2) One-dimensional azimuthal shear segment identification
The one-dimensional azimuth shear segment is identified by searching for azimuth vorticity shear greater than a preset threshold value ( default grade 6, 70, 60, 50, 40, 30, 20, respectively, unit 10) in the radial direction-4s-) A continuous library of (a). As shown in FIG. 2, when the bank of azimuth vorticity cuts greater than the threshold is first searched radially outward from the radar, then its subsequent successive azimuth vorticity cuts greater than the threshold are followedThe shear banks are grouped together until the azimuth vorticity shear bank smaller than the threshold is searched; if the difference between the azimuthal vorticity cut value of the subsequent library and the threshold is less than or equal to the azimuthal vorticity cut variation threshold (default value of 4, unit of 10)-4s-1) And the number of consecutive distance bins that satisfy the condition is less than the interrupt count threshold (default value is 2), then the shear segment search continues, otherwise the shear segment is cut before the first bin that is less than the threshold. If the length of the shear segment is greater than the segment length threshold (default value is 0.95km), then the segment is saved, otherwise, the segment is culled. FIG. 3 shows 3 shear segments (shaded areas represent the portions being preserved, and brackets indicate azimuthal vorticity shear values less than the corresponding threshold, in units of 10-4s-1。(a)40×10-4s-1Shear segment, (b)30 × 10-4s-1Shear segment, (c)20 × 10-4s-1And (4) shearing the section. ) It can be seen that for those azimuthal vorticity shear values greater than the threshold, only those that satisfy both the azimuthal vorticity shear difference threshold and the discontinuity count threshold can be included in the saved shear segment.
3) Two-dimensional azimuthal shear feature identification
And when the last radial one-dimensional azimuth shear section of an elevation angle is searched, merging the one-dimensional azimuth shear sections into a two-dimensional azimuth shear characteristic based on a spatial proximity principle. The merging of adjacent segments needs to satisfy 2 criteria: one is that the azimuth difference of 2 segments is less than the azimuth separation threshold (default of 1.5 °), and the other is that the overlap distance of 2 segments in the radial direction is greater than the segment overlap threshold (default of 0.45 km). A two-dimensional azimuthal shear feature must contain a number of one-dimensional azimuthal shear segments greater than a segment number threshold (default of 2) and an area greater than a two-dimensional azimuthal shear feature area threshold (default of 3km 2). FIG. 4 shows a threshold of 20 × 10-4s-1The two-dimensional azimuth shear feature of (1), wherein 6 one-dimensional azimuth shear segments are included. For the two-dimensional azimuth shear feature found, some feature quantities are calculated and saved.
After the two-dimensional azimuth shear feature identification of all level thresholds is completed, packages need to be abandoned in order to extract the strongest azimuth vorticity shear region informationA low threshold two-dimensional azimuthal shear feature that is a high threshold two-dimensional azimuthal shear feature. If the center of the high threshold two-dimensional azimuthal shear feature falls within the low threshold two-dimensional azimuthal shear feature area, then the low threshold two-dimensional azimuthal shear feature is discarded, but parametric information for the two-dimensional azimuthal shear feature that records all of the level thresholds that wrap it is added to the retained two-dimensional azimuthal shear feature information for the highest level threshold. FIG. 5 shows 2 two-dimensional azimuthal shear features with a threshold of 40 × 10-4s-1Is embedded in a threshold of 30 x 10-4s-1Within the two-dimensional azimuthal shear feature.
4) Three-dimensional azimuthal shear feature identification
And sequencing the reserved two-dimensional azimuth shear characteristics according to the maximum azimuth vorticity shear size, and then performing vertical correlation. Each identified three-dimensional azimuth shear feature contains at least 2 two-dimensional azimuth shear features in successive elevation angles. Vertical correlation is an iterative process starting from the lowest elevation angle. Firstly, two-dimensional azimuth shear features with the center distance of adjacent elevation angles smaller than a threshold value (default value of 2.5km) are vertically associated, and if a plurality of two-dimensional azimuth shear features can be associated, only the two-dimensional azimuth shear feature with the largest azimuth vorticity shear is selected for association. If two-dimensional azimuth shear features which are not vertically correlated exist after the first vertical correlation is finished, the search radius is increased to 5km, and the steps are repeated for all the two-dimensional azimuth shear features which are not vertically correlated. If the two-dimensional azimuth shear feature which is not vertically correlated still exists after the second vertical correlation, the search radius is increased to 7.5km, and the above steps are repeated again to perform the 3 rd vertical correlation (see the schematic diagram in fig. 6). This process forms a three-dimensional azimuthal shear feature after all adjacent elevation angles have been performed. For the three-dimensional azimuth shear feature that is kept, a plurality of parameters are calculated and saved.
5) Correlating three-dimensional azimuthal shear features and storm monomers
The storm monomer positions identified based on the reflectivity factor data are used for conducting null processing on the three-dimensional azimuth shear characteristics which are irrelevant to the storm, namely, when the storm exists within the radius range of 30km of the center of one three-dimensional azimuth shear characteristic, the three-dimensional azimuth shear characteristic can be finally reserved. The distance nearest principle is used to associate the three-dimensional azimuth shear feature that is finally retained with the identified storm monomer.
6) Tornado vortex feature identification and typing
The TVS is identified based on the identified three-dimensional azimuth shear characteristics and the statistical analysis result of the three-dimensional azimuth shear characteristics of the super monomer tornado, and the identification algorithm is limited to be within 150km range centered on the radar and is lower than 6km in height. The azimuth number interval n of the maximum minimum speed is estimated using 1.25km as the default characteristic dimension of the tornado vortex, i.e. n is 1.25/(r Δ θ), where r is the skew distance (calculated from 5km), Δ θ is the radar beam width, when n <1, n is 1, and when n >13, n is 13. Identification of TVS requires the following 3 criteria to be met:
(1) the rotation speed of the two-dimensional azimuth shear feature must be greater than the rotation speed (half of the maximum minimum speed difference) standard based on the statistics of the three-dimensional azimuth shear feature of the super monomer tornado (see fig. 7a) (RV 42r ═ r)-0.215R is the pitch, km in unit, RV is the rotational speed, m.s in unit-1)
(2) The number of radial serial number intervals for calculating the maximum and minimum speed of the rotating speed is less than or equal to n.
(3) The maximum azimuthal vorticity shear of the two-dimensional azimuthal shear feature must be greater than the azimuthal vorticity shear criteria based on the statistics of the three-dimensional azimuthal shear feature of the Supermonomer Tornado (see FIG. 7b) (u)a＝1104.3r-0.577R is the slant distance in km, uaFor azimuthal vorticity shear, unit 10-4s-1)。
The rotation speed of the three-dimensional azimuth shear feature and the azimuth number interval number corresponding to the maximum and minimum speed have a plurality of sets of values, that is, the rotation speed calculated by the maximum and minimum speed in the two-dimensional azimuth shear features of different levels of shear thresholds, the rotation speed calculated by the maximum adjacent azimuth speed difference in the two-dimensional azimuth shear feature of the lowest level of shear thresholds, and the azimuth number interval number corresponding to the maximum and minimum speed for calculating the rotation speed are only required to have one set which simultaneously meets the judgment criteria (1) and (2).
When at least 2 two-dimensional azimuth shear features meeting the conditions (1) to (3) below the height of 6km, determining that a potential TVS exists, and when the height of the two-dimensional azimuth shear feature at the bottommost layer of the potential TVS is less than 1km or the elevation angle at which the two-dimensional azimuth shear feature is located is the lowest scanning elevation angle, determining that the potential TVS is a TVS, otherwise, determining that the potential TVS is an overhead TVS; when the height below 6km is only 1 two-dimensional azimuth shear feature meeting the conditions (1) - (3), at least one two-dimensional azimuth shear feature at the adjacent elevation angle is required to meet the mesocyclone standard, the potential TVS is determined to exist, when the height of the two-dimensional azimuth shear feature meeting the conditions (1) - (3) is less than 1km or the elevation angle at which the two-dimensional azimuth shear feature is located is the lowest scanning elevation angle, the potential TVS is determined to be the TVS, otherwise, the potential TVS is determined to be the overhead TVS.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (10)
1. An automatic recognition algorithm for the characteristics of a tornado vortex is characterized by comprising the following steps:
step 1: firstly, recognizing and inhibiting non-meteorological echoes such as ground clutter, super-refraction echoes, noise and the like in radar base data by adopting the prior art, and performing de-blurring processing on the radial speed;
step 2: identifying storm monomers by using a storm identification tracking algorithm, and recording the centroid position of the storm monomers;
and step 3: estimating azimuth vorticity shear by using a two-dimensional local linear least square difference method;
and 4, step 4: identifying a one-dimensional azimuthal shear segment using multiple thresholds;
and 5: identifying two-dimensional azimuth shear characteristics based on a spatial proximity principle;
step 6: identifying three-dimensional azimuth shear characteristics through vertical correlation;
and 7: associating three-dimensional azimuth shear characteristics with the storm monomers;
and 8: based on the three-dimensional azimuth shear characteristics of the storm, the distance-dependent rotating speed obtained by statistics of the super monomer tornado examples, the azimuth sequence number interval of the corresponding maximum and minimum speed, the azimuth vorticity shear and other 3 standards are used for identifying the TVS of the tornado vortex characteristics.
2. The automated tornado-vortex feature identification algorithm of claim 1, wherein azimuthal vorticity shear is estimated using a two-dimensional local linear least squares difference method.
3. The automated tornado feature identification algorithm of claim 1, wherein the identification of the one-dimensional azimuthal shear segment is a search in the radial direction for a contiguous pool of azimuthal vorticity shears greater than a preset threshold, with up to 2 pools of azimuthal vorticity shears within 4 less than the set threshold allowed to be interspersed.
4. The automatic identification algorithm for characteristics of a tornado scroll according to claim 2, wherein the default of the preset threshold is classified into 6 grades with the unit of 10, 70, 60, 50, 40, 30 and 20 respectively-4s-1。
5. The automated tornado feature identification algorithm of claim 1, wherein the one-dimensional azimuthal shear segments are merged into the two-dimensional azimuthal shear feature based on a spatial proximity principle after the last radial one-dimensional azimuthal shear segment of an elevation is searched.
6. The automated tornado-scroll feature identification algorithm of claim 1, wherein the retained two-dimensional azimuthal shear features are ordered by maximum azimuthal vorticity shear magnitude, followed by a vertical correlation, each identified three-dimensional azimuthal shear feature comprising at least 2 two-dimensional azimuthal shear features at successive elevations.
7. The automated tornado feature identification algorithm of claim 1, wherein a three-dimensional azimuthal shear feature and a storm cell are associated.
8. The automated tornado vortex feature identification algorithm of claim 1, wherein TVS identification based on three-dimensional azimuthal shear feature uses 3 criteria of rotational speed, azimuthal order number interval of maximum and minimum speed for rotational speed, and azimuthal vorticity shear.
9. The automated tornado feature identification algorithm of claim 1, wherein the three dimensional azimuthal shear feature rotation speed and its corresponding azimuthal index of maximum minimum speed are separated by a plurality of sets of calculated values, provided that one set meets the criteria.
10. The automated dragon scroll feature identification algorithm of claim 1, wherein the rotational speed and azimuthal vorticity shear criteria used are distance dependent power functions based on statistics of the three-dimensional azimuthal shear features of a super-monomer dragon scroll, and the azimuthal index spacing criteria for the maximum minimum speed for the rotational speed is calculated using a geometric pitch of 1.25km, with azimuthal index spacing equal to 13 when greater than 13 and 1 when less than 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010861316.5A CN111929687B (en) | 2020-08-25 | 2020-08-25 | Automatic recognition algorithm for characteristics of tornado vortex |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010861316.5A CN111929687B (en) | 2020-08-25 | 2020-08-25 | Automatic recognition algorithm for characteristics of tornado vortex |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111929687A true CN111929687A (en) | 2020-11-13 |
| CN111929687B CN111929687B (en) | 2023-11-21 |
Family
ID=73305089
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010861316.5A Active CN111929687B (en) | 2020-08-25 | 2020-08-25 | Automatic recognition algorithm for characteristics of tornado vortex |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111929687B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112698333A (en) * | 2021-03-24 | 2021-04-23 | 成都千嘉科技有限公司 | Ultrasonic flight time measuring method and system suitable for gas and liquid |
| CN115542326A (en) * | 2022-09-14 | 2022-12-30 | 江苏省气象台 | Isolated downburst identification method |
| CN112748434B (en) * | 2021-02-07 | 2023-04-11 | 江西师范大学 | Doppler weather radar speed de-ambiguity method based on polynomial fitting |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5523759A (en) * | 1992-04-14 | 1996-06-04 | Honeywell Inc. | In flight doppler weather radar wind shear detection system |
| US7486220B1 (en) * | 2006-09-28 | 2009-02-03 | Rockwell Collins, Inc. | Storm top detection and prediction |
| CN106199606A (en) * | 2016-07-20 | 2016-12-07 | 国网河南省电力公司电力科学研究院 | A kind of multi thresholds squall line recognition methods based on radar return 3 d mosaics |
| CN106772387A (en) * | 2016-12-21 | 2017-05-31 | 中国航空工业集团公司雷华电子技术研究所 | A kind of wind shear recognition methods |
| CN106919792A (en) * | 2017-02-24 | 2017-07-04 | 天津大学 | Vortex center automatic identifying method based on high accuracy numerical value Wind Data |
| CN108680920A (en) * | 2018-04-28 | 2018-10-19 | 深圳市雅码科技有限公司 | A kind of hazard weather identification early warning system and method based on dual polarization radar |
-
2020
- 2020-08-25 CN CN202010861316.5A patent/CN111929687B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5523759A (en) * | 1992-04-14 | 1996-06-04 | Honeywell Inc. | In flight doppler weather radar wind shear detection system |
| US7486220B1 (en) * | 2006-09-28 | 2009-02-03 | Rockwell Collins, Inc. | Storm top detection and prediction |
| CN106199606A (en) * | 2016-07-20 | 2016-12-07 | 国网河南省电力公司电力科学研究院 | A kind of multi thresholds squall line recognition methods based on radar return 3 d mosaics |
| CN106772387A (en) * | 2016-12-21 | 2017-05-31 | 中国航空工业集团公司雷华电子技术研究所 | A kind of wind shear recognition methods |
| CN106919792A (en) * | 2017-02-24 | 2017-07-04 | 天津大学 | Vortex center automatic identifying method based on high accuracy numerical value Wind Data |
| CN108680920A (en) * | 2018-04-28 | 2018-10-19 | 深圳市雅码科技有限公司 | A kind of hazard weather identification early warning system and method based on dual polarization radar |
Non-Patent Citations (3)
| Title |
|---|
| JAMES G. LADUE 等: "A COMPARISON OF HIGH RESOLUTION TORNADO SURVEYS TO DOPPLER RADAR OBSERVED VORTEX PARAMETERS: 2011-2012 CASE STUDIES", 《26TH CONF. ON SEVERE LOCAL STORMS》, pages 1 - 13 * |
| 张一平 等: "区域暴雨过程中两次龙卷风事件分析", 《气象学报》, vol. 70, no. 05, pages 961 - 973 * |
| 马中元: "利用CINRAD雷达产品进行风暴外推与预警", 《气象水文海洋仪器》, vol. 27, no. 02, pages 47 - 52 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112748434B (en) * | 2021-02-07 | 2023-04-11 | 江西师范大学 | Doppler weather radar speed de-ambiguity method based on polynomial fitting |
| CN112698333A (en) * | 2021-03-24 | 2021-04-23 | 成都千嘉科技有限公司 | Ultrasonic flight time measuring method and system suitable for gas and liquid |
| CN115542326A (en) * | 2022-09-14 | 2022-12-30 | 江苏省气象台 | Isolated downburst identification method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111929687B (en) | 2023-11-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111929687A (en) | Automatic recognition algorithm for tornado vortex characteristics | |
| CN111239766B (en) | Water surface multi-target rapid identification and tracking method based on laser radar | |
| CN110781827B (en) | Road edge detection system and method based on laser radar and fan-shaped space division | |
| CN110018489B (en) | Target tracking method and device based on laser radar, controller and storage medium | |
| Yi et al. | Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly | |
| Zrnić et al. | Automatic detection of mesocyclonic shear with Doppler radar | |
| CN104931949B (en) | Wind turbine clutter background is got off the plane object detection method under radar scanning pattern | |
| CN112180354B (en) | High-frequency radar target joint detection method utilizing time-frequency analysis and constant false alarm technology | |
| CN110261857B (en) | Spatial interpolation method for weather radar | |
| CN108181620B (en) | Three-coordinate radar trace point quality evaluation method | |
| CN109324335B (en) | Method and system for identifying wind shear based on laser radar | |
| WO2018000252A1 (en) | Oceanic background modelling and restraining method and system for high-resolution remote sensing oceanic image | |
| CN111931409A (en) | Vortex three-dimensional posture recognition and tracking method based on high-resolution ocean mode data | |
| CN108288268B (en) | Method for detecting ship landing in high-resolution remote sensing image based on projection analysis | |
| CN113570005A (en) | Long-distance ship type identification method based on airborne photon radar | |
| CN116051822A (en) | Concave obstacle recognition method and device, processor and electronic equipment | |
| CN111736156B (en) | Upwind area identification method and device based on weather radar | |
| CN113780085B (en) | Offshore single photon denoising classification method | |
| CN113256990B (en) | Method and system for collecting road vehicle information by radar based on clustering algorithm | |
| CN113030997B (en) | Method for detecting travelable area of open-pit mine area based on laser radar | |
| CN108151739B (en) | Gravity matching positioning error suppression method based on vector matching algorithm | |
| CN115128571B (en) | Multi-person and non-motor vehicle identification method based on millimeter wave radar | |
| US20220397639A1 (en) | Method for correction of reflectivity on identified bright band based on polarimetric observations, recording medium and device for performing the method | |
| CN113552563B (en) | Method for analyzing correspondence between vertical measurement information and high-frequency ground wave radar clutter information | |
| KR101203044B1 (en) | Clutter reduction method for moving object and target detection system using the method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |