CN111398949B - Networking X-band radar-based self-adaptive collaborative scanning method - Google Patents
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- CN111398949B CN111398949B CN202010279053.7A CN202010279053A CN111398949B CN 111398949 B CN111398949 B CN 111398949B CN 202010279053 A CN202010279053 A CN 202010279053A CN 111398949 B CN111398949 B CN 111398949B
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- 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
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
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- G—PHYSICS
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- 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
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Abstract
The invention discloses a networking X-band radar-based adaptive collaborative scanning method. According to the method, different scale weather characteristics identified according to networking weather radar data are used as trigger conditions, different scanning strategies are started in a self-adaptive mode, the effective detection capability of each radar in the networking radar is exerted to the maximum extent, and the three-dimensional structural characteristics of the storm monomer with higher precision can be detected on the basis of not influencing the jigsaw effect of the networking radar.
Description
Technical Field
The invention relates to a meteorological detection method, in particular to a strategy formulation method for cooperatively scanning multiple weather types based on a networking X-band radar.
Background
In the field of meteorological detection, a single S (C) waveband weather radar is mainly used for detecting a weather system at present, the defect of insufficient detection capability on low-level atmosphere exists, the observation capability on a vertical structure of atmospheric flow field change influencing the occurrence and development of high-wind and medium-small scale strong convection weather events is weak, and the vertical observation capability on the atmospheric flow field evolution process is obviously insufficient. Aiming at gamma mesoscale convection single bodies and smaller-scale tornado weather, the time and space resolution of the volume scanning mode of the existing radar is poor, the monitoring and identifying capabilities of the observation mode on small-scale strong convection weather processes (such as tornado, hail and the like) with small scale, rapid change and complex structure are limited, and the effective detection and identification on locally generated small-scale strong convection weather cannot be met.
And the scanning mode of the networking X-waveband weather radar which is put into scientific research test is still unified into a volume scanning mode of a full airspace, the scanning strategy is mainly used for improving the capacity of quantitatively estimating precipitation in a detection test area by using the networking X-waveband weather radar, and no strategy exists for the targeted scanning of medium and small-scale convection monomers. The detection capability of the networking X-waveband weather radar cannot be exerted.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the prior art, the invention provides a networking X-band radar-based adaptive collaborative scanning method, which is characterized in that different trigger mechanisms are formulated according to different weather systems, an adaptive scanning strategy is started, and the advantages of a networking radar joint detection weather system are fully exerted, so that radar detection data with high space-time resolution and scientific research significance are obtained.
The technical scheme is as follows: the invention discloses a networking X-waveband radar-based adaptive collaborative scanning method, which is characterized in that a networking weather radar adaptively starts different scanning strategies according to different scale weather characteristics recognized by radar data in an observation area as trigger conditions, wherein the specific scanning strategies comprise:
a. under the weather of no precipitation and obvious clouds in an observation area, scanning by X-band radars participating in networking according to a clear sky mode scanning strategy, and adopting a low-layer multi-elevation three-dimensional scanning mode;
b. under the condition of a layered cloud precipitation system with a wide range in an observation area, scanning X-band radars participating in networking according to a precipitation mode scanning strategy, wherein all the X-band radars adopt a full-airspace three-dimensional scanning mode;
c. under the condition that a plurality of storm monomers exist in a convective precipitation system in an observation area, an X-band radar participating in networking carries out cooperative scanning according to a storm mode scanning strategy;
the radar closest to the storm monomer with the highest weight coefficient vertically scans the storm monomer with the largest speed measurement range; other radars capable of detecting the storm monomer or other storm monomers adopt a full airspace three-dimensional scanning mode; other radars which cannot detect any storm monomer adopt a low-level multi-elevation scanning mode;
d. under the condition that a super monomer storm exists in an observation area, the X-band radar participating in networking carries out cooperative scanning according to a tornado mode scanning strategy;
the radar closest to the super monomer storm with the highest weight coefficient vertically scans the super monomer storm with the largest speed measurement range; other radars capable of detecting the super monomer storm adopt a multi-elevation scanning working mode to carry out low-altitude encryption scanning; other radars capable of detecting other storm monomers adopt a full airspace stereo scanning mode; other radars which cannot detect any storm monomer adopt a low-level multi-elevation scanning mode.
According to a further preferable technical scheme, the scanning strategies a, c and d adopt a low-layer multi-elevation scanning mode, and the default low-layer scanning elevation angles are 0.5 degrees, 1.5 degrees, 2.4 degrees and 3.4 degrees respectively;
the scanning strategies b, c and d adopt full airspace stereo scanning modes, and the default full elevation angle scanning angles are 0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees respectively;
the scanning strategy d adopts a multi-elevation low-altitude encryption scanning mode, and the default scanning elevation angles are respectively 0.5 °, 0.7 °, 1.0 °, 1.5 °, 2.0 °, 2.4 °, 3.3 °, 4.3 ° and 6.0 °.
Preferably, in the scanning strategy b, whether the observation area is a wide range of lamellar cloud precipitation system is determined according to the precipitation echo intensity and the echo area.
Preferably, in the scanning strategy c, whether the observation area is a convective water-lowering system is determined according to the fact that whether a plurality of radar vertical liquid water content VIL central areas exist in the observation area or not;
meanwhile, numerical sequencing is carried out on a plurality of vertical liquid water content VIL central areas of the radar in the identification observation area, and the VIL central area with the highest numerical value is determined as a storm monomer with the highest weight coefficient.
Preferably, in the scanning strategy d, storm monomers with mesocyclone characteristics are identified as super-monomer storms; when a plurality of super monomer storms exist, the super monomer storms with the highest numerical value are determined as the super monomer storms with the highest weight coefficient according to the ranking of the minimum shear value of mesowhirl.
Has the advantages that: according to the method, different scale weather characteristics identified according to networking weather radar data are used as trigger conditions, different scanning strategies are started in a self-adaptive mode, the effective detection capability of each radar in the networking radar is exerted to the maximum extent, and on the basis that the jigsaw effect of the networking radar is not influenced, the three-dimensional structural characteristics of the storm monomer with higher precision can be detected;
under the condition of no obvious weather system, the networking radar is kept in a low-level scanning mode, and on the premise of achieving the joint defense purpose, the detection time cost and radar base data storage resources are saved; under the background of large-scale rainfall weather, the designed scanning strategy can meet the requirement of a networking radar quantitative estimation rainfall algorithm on data; under the condition that a storm monomer, particularly a super monomer storm, occurs, the designed scanning strategy can meet the scanning requirement on the area below a boundary layer (1 km lower layer) under the condition that the fine three-dimensional structure of the super monomer storm can be detected, a data base is provided for utilizing a networking radar to invert the information of the lower layer wind field, and the capability of an X-waveband weather radar for making up the insufficient detection of an S-waveband weather radar on the lower layer is fully exerted.
Drawings
FIG. 1 is a flow chart of adaptive scanning policy switching according to the present invention;
FIG. 2 is a schematic diagram illustrating identification of a VIL center region in an embodiment of the present invention;
figure 3 is a schematic view of cyclonic merge in an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the embodiments.
Example (b): a networking X-band radar-based adaptive collaborative scanning method is characterized in that a networking weather radar adaptively starts different scanning strategies by taking different scale weather characteristics identified by the networking weather radar as trigger conditions according to radar data in an observation area. And the cooperative command system of the networking radar commands the scanning mode of each radar in the networking radar according to the scanning strategy.
In this embodiment, four scanning strategies are designed according to different weather systems, which are respectively a, a clear sky mode, b, a precipitation mode, c, a storm mode, d, and a tornado mode, and the scanning grades of the scanning strategies are sequentially increased and respectively are 1, 2, 3, and 4 grades. When features meeting various trigger conditions are identified by the weather system, the scanning strategy mode with the highest scanning level is preferentially started. Namely, when various weather systems appear in an observation area, the scanning strategy of d > c > b > a is adopted.
The specific scanning strategies are respectively as follows:
a. under normal conditions, under the condition that no precipitation exists in the observation area of the networking radar and the weather of the cloud system is obvious, all the radars are in a scanning mode of a clear sky mode, namely, the X-band radars participating in networking execute scanning according to a clear sky mode scanning strategy. The default low-level scan elevation angles are 0.5 °, 1.5 °, 2.4 °, and 3.4 °, respectively.
b. And calculating combined reflectivity jigsaw data by using the networking radar reflectivity data, and judging whether a layered cloud precipitation system appears in the observation area.
(1) Judging a wide-range layered cloud weather system: identifying the area with the combined reflectivity data of the networking X-band radar jigsaw puzzle not less than 20dBZ and not more than 35dBZ, and when the continuous area meeting the threshold interval is not less than 400km2And when the weather system in the observation area is judged to be converted into lamellar cloud precipitation, the scanning strategy is automatically adjusted to be a precipitation mode.
(2) The X-band radar participating in networking scans according to a precipitation mode scanning strategy, all the X-band radars adopt a full airspace stereo scanning mode, and the default full elevation angle scanning angles are 0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees respectively.
(3) And when the area of the laminar cloud precipitation echo in the observation area does not meet the upper threshold value condition for 2 times of continuous full elevation angles, automatically switching the scanning strategy mode to a clear sky mode.
c. And calculating vertical accumulated liquid water content (VIL) jigsaw data by using the networking radar reflectivity data, and judging whether a convective precipitation system exists in the observation area.
(1) Judging storm monomers based on the identification of the vertical accumulated liquid water content central area: firstly, calculating vertical accumulated liquid water content (VIL) jigsaw data by using reflectivity data of a networking radar, and realizing identification of a VIL central area by using an expansion method idea on the basis. As shown in fig. 2, a threshold VILmin is given, grid points larger than the threshold are searched in VIL data, when a certain grid point meets the threshold condition, 3 × 3, 5 × 5 and 7 × 7 … are expanded continuously with the grid point as the center, data points meeting the threshold in a grid point area are recorded, all VIL values are sorted, when the proportion of grid point data with VIL values < VILmin in a certain expanded grid N × N exceeds 90%, expansion is stopped, the average position of grids where the first 10 percentile VIL value sets sorted according to the size of the values are located is used as the center position of the VIL area, and the average value of the first 10 percentile VIL values is used as the core value of the VIL center area. The grid data that already has the center position is no longer entered into the calculation. And so on to obtain a plurality of regions having a central region of VIL. And sequencing according to the core value of the central area, and finishing the calculation of the triggering condition of using the VIL central area as a thunderstorm monomer.
(2) When a plurality of storm single convection precipitation systems are identified in an observation area, the X-band radar participating in networking is switched to a storm mode scanning strategy for collaborative scanning: the radar closest to the target storm monomer with the largest VIL core value performs vertical scanning (RHI) with the largest speed measurement range on the storm monomer; other radars capable of detecting the storm monomer or other storm monomers adopt a full airspace stereo scanning mode, and an elevation scanning mode under the default elevation and precipitation mode; other radars which cannot detect any storm monomer adopt a low-level multi-elevation scanning mode and an elevation scanning mode in a default elevation and clear sky mode.
(3) When the storm monomer structure in the observation area disappears for 2 times of full elevation scanning, the scanning strategy mode is automatically switched to the precipitation mode.
d. And calculating and identifying the mesowhirl characteristics by using the radial velocity data of the single radar, and judging whether the super monomer storm occurs in the observation area.
(1) Mesocyclone identification and merging based on multiple radars: and when the mesocyclone features are calculated, the mesocyclone features of each weather radar are independently calculated, and the mesocyclone features are subjected to jigsaw puzzle after calculation. Since the characteristics of the medium cyclones obtained by detecting the same vortex structure by different weather radars vary according to the detection distance, height, direction and the like, a plurality of medium cyclones representing the same vortex structure appear during jigsaw puzzle splicing, and therefore the plurality of medium cyclones are reasonably combined. The specific way is shown in fig. 3: calculating the area of the middle cyclone according to the diameter of the middle cyclone, and when the overlapped area of the two circles exceeds 80%, taking the center of the connecting line of the two circle centers as the center of the new middle cyclone, taking the larger of the two circle diameters as the diameter of the new middle cyclone, and taking the larger of the lowest elevation tangent values of the two middle cyclones as the value of the new middle cyclone, so as to construct the new middle cyclone to replace the original two middle cyclones.
(2) When mesowhirlwind is identified in an observation area, namely under the condition that a super monomer storm exists, the X-band radar participating in networking carries out collaborative scanning according to a tornado mode scanning strategy, and when a plurality of mesowhirlwind exist at the same time, the super monomer storm with the highest numerical value is determined as a target super monomer storm with the highest weight coefficient according to the sequencing of the lowest elevation angle shear value of the mesowhirlwind. The radar closest to the target super monomer storm performs vertical scanning (RHI) with the largest speed measurement range on the monomer; and other radars capable of detecting the target super monomer storm adopt a multi-elevation scanning working mode to carry out low-altitude encryption scanning. Default scanning elevation angle: 0.5 °, 0.7 °, 1.0 °, 1.5 °, 2.0 °, 2.4 °, 3.3 °, 4.3 °, 6.0 °; the radar capable of detecting other storm monomers adopts a full airspace stereo scanning mode, and defaults an elevation angle scanning mode under the same storm mode; the radar which can not detect any storm monomer adopts a low-level multi-elevation scanning mode and an elevation scanning mode under the default elevation and precipitation mode.
(3) When the mesocyclonic structure disappears in the observation area for 2 times of full elevation scanning, the scanning strategy mode is automatically switched to the storm mode.
As noted above, while the present invention has been shown and described with reference to certain preferred embodiments, it is not to be construed as limited thereto. Various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (2)
1. The self-adaptive collaborative scanning method based on the networking X-band radar is characterized in that the networking weather radar takes different scale weather characteristics recognized as trigger conditions according to radar data in an observation area, different scanning strategies are started in a self-adaptive mode, and the specific scanning strategies comprise:
a. under the weather of no precipitation and obvious clouds in an observation area, scanning by X-band radars participating in networking according to a clear sky mode scanning strategy, and adopting a low-layer multi-elevation three-dimensional scanning mode;
b. determining whether the observation area is a wide range of layered cloud precipitation systems or not according to precipitation echo intensity and echo area, and under the condition that the observation area is a wide range of layered cloud precipitation systems, scanning X-band radars participating in networking according to a precipitation mode scanning strategy, wherein all three-dimensional scanning modes in a full airspace are adopted;
c. determining whether the observation area is a convective precipitation system or not according to the fact that whether a plurality of radar vertical liquid water content VIL central areas exist in the observation area or not is recognized, and under the condition that the observation area is the convective precipitation system with a plurality of storm monomers, performing collaborative scanning on the X-band radar participating in networking according to a storm mode scanning strategy;
meanwhile, numerical sequencing is carried out on a plurality of radar vertical liquid water content VIL central areas in the identification observation area, the VIL central area with the highest numerical value is determined as a storm monomer with the highest weight coefficient, and the radar closest to the storm monomer with the highest weight coefficient carries out vertical scanning with the largest speed measurement range on the storm monomer; other radars capable of detecting the storm monomer or other storm monomers adopt a full airspace three-dimensional scanning mode; other radars which cannot detect any storm monomer adopt a low-level multi-elevation scanning mode;
d. determining a storm monomer with mesocyclone characteristics as a super monomer storm, and carrying out cooperative scanning on an X-band radar participating in networking according to a tornado mode scanning strategy under the condition that the super monomer storm exists in an observation area;
when a plurality of super monomer storms exist, sequencing according to the minimum shear value of mesocyclone, determining the super monomer storm with the highest numerical value as the super monomer storm with the highest weight coefficient, and vertically scanning the super monomer storm with the largest speed measurement range by the radar closest to the super monomer storm with the highest weight coefficient; other radars capable of detecting the super monomer storm adopt a multi-elevation scanning working mode to carry out low-altitude encryption scanning; other radars capable of detecting other storm monomers adopt a full airspace stereo scanning mode; other radars which cannot detect any storm monomer adopt a low-level multi-elevation scanning mode.
2. The networking-based X-band radar adaptive collaborative scanning method according to claim 1,
the scanning strategies a, c and d adopt a low-layer multi-elevation scanning mode, and the default low-layer scanning elevation angles are 0.5 degrees, 1.5 degrees, 2.4 degrees and 3.4 degrees respectively;
the scanning strategies b, c and d adopt full airspace stereo scanning modes, and the default full elevation angle scanning angles are 0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees respectively;
the scanning strategy d adopts a multi-elevation low-altitude encryption scanning mode, and the default scanning elevation angles are respectively 0.5 °, 0.7 °, 1.0 °, 1.5 °, 2.0 °, 2.4 °, 3.3 °, 4.3 ° and 6.0 °.
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| CN112379345B (en) * | 2020-10-23 | 2024-04-19 | 吴海英 | Radar short-term extrapolation prediction method integrating numerical modes |
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| CN113687360B (en) * | 2021-10-26 | 2022-02-18 | 南京恩瑞特实业有限公司 | Phased array weather radar scanning strategy suitable for civil aviation air traffic control meteorological guarantee |
| CN115047463B (en) * | 2022-08-16 | 2022-11-01 | 成都远望科技有限责任公司 | Collaborative scanning scheduling method considering radar echo attenuation in rainfall area |
| CN116911526A (en) * | 2023-06-19 | 2023-10-20 | 成都信息工程大学 | Cooperative control method for weather radar networking |
| CN116975716B (en) * | 2023-07-31 | 2024-06-07 | 云南省大气探测技术保障中心 | Important weather identification method and system based on weather radar |
| CN118276094B (en) * | 2024-04-15 | 2024-08-30 | 上海市气象信息与技术支持中心 | Multi-band radar self-adaptive collaborative observation method and system for convection process |
| CN118732087A (en) * | 2024-09-02 | 2024-10-01 | 成都远望科技有限责任公司 | Weather radar collaborative scheduling method and device based on thunderstorm high wind potential forecast |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102007058345A1 (en) * | 2007-12-03 | 2009-06-04 | Selex Sistemi Integrati Gmbh | Method for determining composite data of weather radars in an overlapping region of the observation regions of at least two weather radars |
| CN104569981A (en) * | 2015-01-28 | 2015-04-29 | 中国科学院大气物理研究所 | Synergy self-adaption observing method |
| CN110297246A (en) * | 2019-07-16 | 2019-10-01 | 上海市气象信息与技术支持中心 | A kind of cooperative self-adapted control method of networking X-band weather radar and system |
| CN110531360A (en) * | 2019-08-28 | 2019-12-03 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | A kind of X-band weather radar networking data processing method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107450076B (en) * | 2017-07-19 | 2019-11-22 | 清华大学 | The collaboration networking measurement method and device of cloud detection radar |
| CN107300703B (en) * | 2017-07-27 | 2020-05-19 | 华云信息技术工程有限公司 | Radar data networking processing system |
| CN108051816B (en) * | 2017-12-20 | 2021-10-12 | 雷象科技（北京）有限公司 | Array weather radar collaborative scanning system and method |
| CN108693534A (en) * | 2018-03-27 | 2018-10-23 | 南京恩瑞特实业有限公司 | NRIET X band radars cooperate with networking analysis method |
-
2020
- 2020-04-10 CN CN202010279053.7A patent/CN111398949B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102007058345A1 (en) * | 2007-12-03 | 2009-06-04 | Selex Sistemi Integrati Gmbh | Method for determining composite data of weather radars in an overlapping region of the observation regions of at least two weather radars |
| CN104569981A (en) * | 2015-01-28 | 2015-04-29 | 中国科学院大气物理研究所 | Synergy self-adaption observing method |
| CN110297246A (en) * | 2019-07-16 | 2019-10-01 | 上海市气象信息与技术支持中心 | A kind of cooperative self-adapted control method of networking X-band weather radar and system |
| CN110531360A (en) * | 2019-08-28 | 2019-12-03 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | A kind of X-band weather radar networking data processing method |
Non-Patent Citations (1)
| Title |
|---|
| 新一代天气雷达灾害性天气监测能力分析及未来发展;李柏等;《气象》;20130321;第39卷(第03期);正文第1-4小节 * |
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