CN118732089B - A method and device for coordinated dispatching of weather radar based on hail potential forecast - Google Patents
A method and device for coordinated dispatching of weather radar based on hail potential forecast Download PDFInfo
- Publication number
- CN118732089B CN118732089B CN202411230787.0A CN202411230787A CN118732089B CN 118732089 B CN118732089 B CN 118732089B CN 202411230787 A CN202411230787 A CN 202411230787A CN 118732089 B CN118732089 B CN 118732089B
- Authority
- CN
- China
- Prior art keywords
- hail
- radar
- scheduling
- potential
- mode
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000006855 networking Effects 0.000 claims abstract description 23
- 230000008569 process Effects 0.000 claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000002310 reflectometry Methods 0.000 claims description 4
- 230000008859 change Effects 0.000 abstract description 6
- 230000007613 environmental effect Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005293 physical law Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention relates to a cooperative scheduling method and a cooperative scheduling device for weather radar based on hail potential prediction, belonging to the field of radio, and comprising the following steps: the hail potential data information is read, a hail potential early warning area is obtained, and meanwhile radar networking jigsaw data is read; calculating a hail potential coordination area according to the hail potential early warning area and radar networking jigsaw data, and judging whether weather echo in the area meets the condition of the hail in the early development stage; if yes, calculating an optimal scheduling radar, generating a cooperative scheduling strategy, and scheduling the radar to execute cooperative observation according to the cooperative scheduling strategy until the hail process is ended. The method utilizes hail potential early warning data, and the identified hail weather occurrence area and hail size are combined with the radar real-time observation data area and intensity, so that the radar is scheduled to switch the hail mode in advance in the early stage of hail development to rapidly capture the dynamic change of the hail weather.
Description
Technical Field
The invention relates to the field of radio, in particular to a weather radar collaborative scheduling method and device based on hail potential prediction.
Background
The numerical potential prediction is a method for predicting potential variation trend of future atmospheric environmental elements based on numerical model and mass data calculation; the method utilizes the strong computing power of a computer and combines a physical law and a mathematical algorithm to simulate and predict a complex environmental system so as to provide scientific decision support; based on the physical law and mathematical equation, a numerical model describing the change of the environmental system is constructed, a large number of mathematical calculations are carried out by a computer by inputting initial conditions and boundary conditions, the evolution process of the system is simulated, and the future state is predicted. The numerical potential forecast can be applied to forecast future weather changes, including factors such as temperature, humidity, wind direction and speed, precipitation and the like, and potential influences of extreme weather events such as hail, heavy rain, strong wind and the like.
Weather radar is one of important tools for observing hail weather, and can monitor the development, movement and evolution of cloud clusters in real time through radar echo images, so that the intensity and the range of the hail weather are judged; in the early stage of hail weather, the radar needs to pay attention to the change and structure of cloud clusters faster and finer, and needs to automatically switch to hail mode scanning; compared with the rainfall mode, the radar has denser scanning elevation angle, can finely observe the internal structure of the hail weather process, adopts the DPRF mode to fade the speed to blur, meets the requirement of larger maximum non-blurring speed of the hail weather process, reduces the accumulation number, improves the rotating speed of the antenna, focuses on strong echo, and can rapidly capture the dynamic change of the hail weather. However, since there is no better way to predict whether the weather process will develop into hail weather process in the early stage of occurrence through real-time observation data of radar, how to solve the problem is currently considered.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a weather radar collaborative scheduling method and device based on hail potential prediction, and solves the problem that no better mode exists at present to predict whether the weather process will develop into the hail weather process in the early stage of occurrence.
The aim of the invention is achieved by the following technical scheme: a weather radar collaborative scheduling method based on hail potential forecast, the method comprising:
Step one, reading hail potential data information, obtaining a hail potential early warning area, and simultaneously reading radar networking jigsaw data;
calculating a hail potential coordination area according to the hail potential early warning area and radar networking jigsaw data, and judging whether weather echo in the area meets the condition of the hail in the early development stage;
and thirdly, if the hail is met, calculating an optimal scheduling radar, generating a cooperative scheduling strategy, and scheduling the radar to execute cooperative observation according to the cooperative scheduling strategy until the hail process is ended.
The first step specifically comprises the following steps:
Combining hail cloud development characteristics and a refined monitoring service of advanced scheduling, encryption and observation, and reading hail potential forecast data of one hour in the future, wherein the hail potential forecast data is meshing data of one hour in the future;
traversing all points in the grid, checking whether the hail size of each point meets the condition that the hail size is larger than a preset value, connecting all points meeting the condition to form a plurality of continuous areas, extracting boundaries, centers and areas, selecting the area with the area larger than a threshold value as a hail potential early warning area, and expanding the hail potential early warning area so as to reduce the influence of early warning errors on the cooperative scanning of a radar scheduled in advance;
taking the minimum longitude, the maximum longitude, the minimum latitude and the maximum latitude of the live data combined reflectivity networking data range.
The calculating the optimal scheduling radar comprises the following steps:
calculating the duty ratio of each radar covering each cooperative area from 30km to 70 km;
and judging the radar of hail observation with the area ratio larger than N according to the ratio of the coverage cooperative areas of all the schedulable radars in the hail modes, and collecting the radar of hail observation in each area, wherein the collected radar is the scheduling radar for the observation of the optimal hail mode.
Judging the scanning mode of each radar according to the identified dispatching radar observed in the optimal hail mode and combining the last dispatching strategy;
if a radar is scheduled to execute the hail mode last time, the radar is still identified to be required to be scheduled to execute the hail mode, a scheduling command is not sent to the radar, and the hail mode is continuously maintained;
if a radar performs hail mode in the last scheduling strategy, the radar is not required to be scheduled to perform hail mode in the current identification, and the radar is scheduled to perform non-hail mode in the current scheduling;
If a radar performs a non-hail mode in the last scheduling strategy, the radar is required to be scheduled to perform the hail mode in the current time, and the radar is scheduled to perform the non-hail mode in the current time;
if a radar performs a non-hail mode in the last scheduling strategy, the radar does not need to be scheduled to perform the hail mode in the current identification, a scheduling command is not sent to the radar, and the non-hail mode is continuously maintained;
All the radars are identified in turn, forming a scheduling policy.
The device comprises a data acquisition module, a hail potential cooperation area calculation module and an optimal scheduling radar scheduling strategy generation module;
the data acquisition module is used for: the method comprises the steps of reading hail potential data information, obtaining a hail potential early warning area, and simultaneously reading radar networking jigsaw data;
The hail potential cooperation area calculating module is used for: the method comprises the steps of calculating a hail potential coordination area according to hail potential early warning areas and radar networking jigsaw data, and judging whether weather echo in the area meets the condition of the early development stage of hail or not;
The optimal scheduling radar scheduling strategy generation module: and when the conditions of the early stage of hail development are met, calculating an optimal scheduling radar, generating a cooperative scheduling strategy, and executing cooperative observation by the scheduling radar according to the cooperative scheduling strategy until the hail process is ended.
The data acquisition module comprises a hail potential data information acquisition unit, a hail potential early warning area generation unit and a radar networking jigsaw data acquisition unit;
The hail potential data information acquisition unit: the hail cloud monitoring system comprises a refined monitoring service for combining hail cloud development characteristics and scheduling, encrypting and observing in advance, and reading hail potential prediction data of one hour in the future, wherein the hail potential prediction data is meshing data of one hour in the future;
The hail potential early warning area generating unit: the method comprises the steps of traversing all points in a grid, checking whether the hail size of each point is larger than a preset value, connecting all points meeting the conditions to form a plurality of continuous areas, extracting boundaries, centers and areas, selecting the area larger than a threshold value as a hail potential early warning area, and expanding the hail potential early warning area to reduce the influence of early warning errors on cooperative scanning of a radar scheduled in advance;
The radar networking jigsaw data acquisition unit comprises: the minimum longitude, maximum longitude, minimum latitude and maximum latitude for taking the combined reflectance networking data range of live data.
The optimal scheduling radar scheduling strategy generation module comprises an optimal scheduling radar calculation unit and a cooperative scheduling strategy generation unit;
The optimal scheduling radar calculating unit: the method comprises the steps of calculating the duty ratio of each radar covering each cooperative area from 30km to 70km, judging the radar of hail observation with the duty ratio of each area being larger than N according to the duty ratio of the hail mode covering cooperative areas of all schedulable radars, and taking a set of the hail observation radars of each area, wherein the radar of the set is the scheduling radar of the optimal hail mode observation;
the cooperative scheduling policy generation unit: and the method is used for judging the scanning mode of each radar according to the identified optimal hail mode observation dispatching radar and combining with the last dispatching strategy, if the last dispatching strategy of a certain radar is the hail mode execution, the radar is still identified to be required to dispatch the radar to execute the hail mode, a dispatching command is not sent to the radar to keep the hail mode, if the last dispatching strategy of the certain radar is the hail mode execution, the radar is not required to dispatch the radar to execute the hail mode, the radar is required to execute the non-hail mode, if the last dispatching strategy of the certain radar is the non-hail mode execution, the radar is required to dispatch the hail mode execution, the radar is required to execute the non-hail mode, if the last dispatching strategy of the certain radar is the non-hail mode execution, the radar is not required to dispatch the hail mode execution, the dispatching command is not sent to the radar to keep the non-hail mode, and all the radars are sequentially identified to form the dispatching strategy.
The invention has the following advantages: the utility model provides a weather radar collaborative scheduling method and device based on hail potential forecast, utilizes hail potential early warning data, and the regional and the intensity of hail size combination radar real-time observation data that the hail weather that recognizes takes place, and the dynamic change of hail weather is caught fast in early stage early scheduling radar switching hail mode in hail development.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic illustration of hail potential warning areas;
FIG. 3 is a flow chart diagram for generating a coordinated scheduling policy.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Accordingly, the following detailed description of the embodiments of the application, as presented in conjunction with the accompanying drawings, is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application. The application is further described below with reference to the accompanying drawings.
The invention particularly relates to a cooperative scheduling method of a weather radar based on hail potential prediction, which is suitable for early scheduling, encryption, observation and refinement hail early warning and monitoring service of disastrous strong weather, and mainly utilizes numerical potential prediction to predict the occurrence area and hail size of the hail weather to a certain extent, and combines the area and the intensity of radar real-time observation data to determine that the scheduling radar switches the hail mode to quickly capture the dynamic change of the strong convection weather.
As shown in fig. 1, the following are specifically included:
And (5) reading hail potential data information: the hail potential forecast data is future hour-by-hour gridding data, and the hail potential forecast data of one hour in the future is taken by combining with hail cloud development characteristics and a refined monitoring service of scheduling, encrypting and observing in advance.
Identifying a hail potential early warning area: as shown in fig. 2, the horizontal axis is longitude, the vertical axis is latitude, all points in the grid are traversed based on hail potential prediction data, whether the conditions of the grid with hail size larger than 7mm are met or not is checked for each point, continuity of meeting the conditions is judged, a plurality of continuous areas are formed, and boundaries, centers and areas are extracted. And selecting an area with an area larger than 3 square kilometers as a hail potential early warning area.
Enlarging the hail potential early warning area: because the hail potential forecast has a certain range of errors, the hail potential area identified in the data is enlarged by 5km relative to the central point, so that the influence of the early warning errors on the cooperative scanning of the radar scheduled in advance subsequently is reduced.
Reading radar networking jigsaw data: the live data combined reflectivity networking data range is taken to be the minimum longitude (minLonPZ), the maximum longitude (maxLonPZ), the minimum latitude (minLatPZ) and the maximum latitude (maxLatPZ).
Calculating hail potential synergy zones: the area of the hail potential early warning area is enlarged to have a longitude and latitude boundary range, in the range, the data with the reflectivity intensity larger than 10dB (the weather echo is small rain) in the radar networking jigsaw data indicate that the weather echo is likely to develop into hail cloud, and the minimum longitude and latitude in the radar networking jigsaw data and the maximum longitude and latitude are used for describing the storage mode of the radar networking data.
Calculating an optimal scheduling radar: because hail clouds generally reach the highest energy of 8km to 12km, and the cooperative areas are the optimal observation distances from 30km to 70km to the radar in combination with hail mode characteristics and X-band radar attenuation and detection ranges, the duty ratio of each radar covering each cooperative area from 30km to 70km is calculated. And according to the duty ratio of the coverage cooperative areas of all the schedulable radars in the hail mode, if the duty ratio is more than 70%, the radar is suitable for carrying out hail simulation observation on the areas, the radars meeting the hail observation in each area are judged, and the radars are the scheduling radars with the optimal hail mode observation in each area.
Generating a cooperative scheduling policy: as shown in fig. 3, according to the identified scheduling radar observed in the optimal hail mode, in combination with the last scheduling policy, a scanning mode of each radar is determined, for example: 1. if a radar is scheduled to execute the hail mode last time, the radar is still identified to be required to be scheduled to execute the hail mode, a scheduling command is not sent to the radar, and the hail mode is continuously maintained; 2. if a radar performs hail mode in the last scheduling strategy, the radar is not required to be scheduled to perform hail mode in the current identification, and then the radar is scheduled to perform rainfall mode; 3. if a radar performs a rainfall mode in the last scheduling strategy, the radar is identified to be scheduled to perform a hail mode, and the radar is scheduled to perform the rainfall mode; 4. if the last scheduling strategy of a certain radar is to execute a rainfall mode, the radar does not need to be scheduled to execute a hail mode in the identification, and a scheduling command is not sent to the radar, so that the rainfall mode is continuously maintained; all the radars are identified in turn, forming a scheduling policy.
And according to the cooperative scheduling strategy, the scheduling radar executes cooperative observation and is executed continuously and periodically until the hail process is finished.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and adaptations, and of being modified within the scope of the inventive concept described herein, by the foregoing teachings or by the skilled person or knowledge of the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (4)
1. A weather radar collaborative scheduling method based on hail potential forecast is characterized by comprising the following steps: the method comprises the following steps:
Step one, reading hail potential data information, obtaining a hail potential early warning area, and simultaneously reading radar networking jigsaw data;
calculating a hail potential coordination area according to the hail potential early warning area and radar networking jigsaw data, and judging whether weather echo in the area meets the condition of the hail in the early development stage;
thirdly, if the hail is met, calculating an optimal scheduling radar, generating a cooperative scheduling strategy, and scheduling the radar to execute cooperative observation according to the cooperative scheduling strategy until the hail process is finished;
the calculating the optimal scheduling radar comprises the following steps:
calculating the duty ratio of each radar covering each cooperative area from 30km to 70 km;
Judging the radar of hail observation with the occupation ratio of each area being larger than N according to the occupation ratio of all the schedulable radar hail modes covering the cooperative area, and collecting the radar of hail observation in each area, wherein the collected radar is the scheduling radar of the optimal hail mode observation;
The generating the cooperative scheduling policy includes:
Judging the scanning mode of each radar according to the identified dispatching radar observed in the optimal hail mode and combining the last dispatching strategy;
if a radar is scheduled to execute the hail mode last time, the radar is still identified to be required to be scheduled to execute the hail mode, a scheduling command is not sent to the radar, and the hail mode is continuously maintained;
if a radar performs hail mode in the last scheduling strategy, the radar is not required to be scheduled to perform hail mode in the current identification, and the radar is scheduled to perform non-hail mode in the current scheduling;
If a radar performs a non-hail mode in the last scheduling strategy, the radar is required to be scheduled to perform the hail mode in the current time, and the radar is scheduled to perform the non-hail mode in the current time;
if a radar performs a non-hail mode in the last scheduling strategy, the radar does not need to be scheduled to perform the hail mode in the current identification, a scheduling command is not sent to the radar, and the non-hail mode is continuously maintained;
All the radars are identified in turn, forming a scheduling policy.
2. The cooperative scheduling method for weather radar based on hail potential prediction according to claim 1, wherein the cooperative scheduling method comprises the following steps: the first step specifically comprises the following steps:
Combining hail cloud development characteristics and a refined monitoring service of advanced scheduling, encryption and observation, and reading hail potential forecast data of one hour in the future, wherein the hail potential forecast data is meshing data of one hour in the future;
traversing all points in the grid, checking whether the hail size of each point meets the condition that the hail size is larger than a preset value, connecting all points meeting the condition to form a plurality of continuous areas, extracting boundaries, centers and areas, selecting the area with the area larger than a threshold value as a hail potential early warning area, and expanding the hail potential early warning area so as to reduce the influence of early warning errors on the cooperative scanning of a radar scheduled in advance;
taking the minimum longitude, the maximum longitude, the minimum latitude and the maximum latitude of the live data combined reflectivity networking data range.
3. Weather radar cooperative scheduling device based on hail potential forecast, its characterized in that: the device comprises a data acquisition module, a hail potential cooperation area calculation module and an optimal scheduling radar scheduling strategy generation module;
the data acquisition module is used for: the method comprises the steps of reading hail potential data information, obtaining a hail potential early warning area, and simultaneously reading radar networking jigsaw data;
The hail potential cooperation area calculating module is used for: the method comprises the steps of calculating a hail potential coordination area according to hail potential early warning areas and radar networking jigsaw data, and judging whether weather echo in the area meets the condition of the early development stage of hail or not;
The optimal scheduling radar scheduling strategy generation module: when the conditions of the early stage of hail development are met, calculating an optimal scheduling radar, generating a cooperative scheduling strategy, and performing cooperative observation according to the cooperative scheduling strategy, until the hail process is finished;
the optimal scheduling radar scheduling strategy generation module comprises an optimal scheduling radar calculation unit and a cooperative scheduling strategy generation unit;
The optimal scheduling radar calculating unit: the method comprises the steps of calculating the duty ratio of each radar covering each cooperative area from 30km to 70km, judging the radar of hail observation with the duty ratio of each area being larger than N according to the duty ratio of the hail mode covering cooperative areas of all schedulable radars, and taking a set of the hail observation radars of each area, wherein the radar of the set is the scheduling radar of the optimal hail mode observation;
the cooperative scheduling policy generation unit: and the method is used for judging the scanning mode of each radar according to the identified optimal hail mode observation dispatching radar and combining with the last dispatching strategy, if the last dispatching strategy of a certain radar is the hail mode execution, the radar is still identified to be required to dispatch the radar to execute the hail mode, a dispatching command is not sent to the radar to keep the hail mode, if the last dispatching strategy of the certain radar is the hail mode execution, the radar is not required to dispatch the radar to execute the hail mode, the radar is required to execute the non-hail mode, if the last dispatching strategy of the certain radar is the non-hail mode execution, the radar is required to dispatch the hail mode execution, the radar is required to execute the non-hail mode, if the last dispatching strategy of the certain radar is the non-hail mode execution, the radar is not required to dispatch the hail mode execution, the dispatching command is not sent to the radar to keep the non-hail mode, and all the radars are sequentially identified to form the dispatching strategy.
4. A hail potential forecast based weather radar co-scheduling device according to claim 3, wherein: the data acquisition module comprises a hail potential data information acquisition unit, a hail potential early warning area generation unit and a radar networking jigsaw data acquisition unit;
The hail potential data information acquisition unit: the hail cloud monitoring system comprises a refined monitoring service for combining hail cloud development characteristics and scheduling, encrypting and observing in advance, and reading hail potential prediction data of one hour in the future, wherein the hail potential prediction data is meshing data of one hour in the future;
The hail potential early warning area generating unit: the method comprises the steps of traversing all points in a grid, checking whether the hail size of each point is larger than a preset value, connecting all points meeting the conditions to form a plurality of continuous areas, extracting boundaries, centers and areas, selecting the area larger than a threshold value as a hail potential early warning area, and expanding the hail potential early warning area to reduce the influence of early warning errors on cooperative scanning of a radar scheduled in advance;
The radar networking jigsaw data acquisition unit comprises: the minimum longitude, maximum longitude, minimum latitude and maximum latitude for taking the combined reflectance networking data range of live data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411230787.0A CN118732089B (en) | 2024-09-04 | 2024-09-04 | A method and device for coordinated dispatching of weather radar based on hail potential forecast |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411230787.0A CN118732089B (en) | 2024-09-04 | 2024-09-04 | A method and device for coordinated dispatching of weather radar based on hail potential forecast |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118732089A CN118732089A (en) | 2024-10-01 |
| CN118732089B true CN118732089B (en) | 2024-11-19 |
Family
ID=92869582
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411230787.0A Active CN118732089B (en) | 2024-09-04 | 2024-09-04 | A method and device for coordinated dispatching of weather radar based on hail potential forecast |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118732089B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118732087B (en) * | 2024-09-02 | 2024-11-19 | 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 (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110501760A (en) * | 2019-07-29 | 2019-11-26 | 中国气象局广州热带海洋气象研究所（广东省气象科学研究所） | A kind of hail identification and nowcasting method based on weather radar |
| CN115047463A (en) * | 2022-08-16 | 2022-09-13 | 成都远望科技有限责任公司 | Collaborative scanning scheduling method considering radar echo attenuation in rainfall area |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006054320A1 (en) * | 2006-11-17 | 2008-05-21 | Robert Bosch Gmbh | Method and device for detecting precipitation by means of radar |
| EP2148225B1 (en) * | 2008-07-22 | 2016-11-02 | Siemens Aktiengesellschaft | Method and arrangement for the forecast of wind-resources |
| JP6441276B2 (en) * | 2016-01-08 | 2018-12-19 | 株式会社東芝 | Estimation apparatus, estimation method, and estimation program |
| CN116430389A (en) * | 2023-05-18 | 2023-07-14 | 成都信息工程大学 | Multi-radar networking collaborative hail cloud evolution process stereoscopic observation method |
| CN116911526A (en) * | 2023-06-19 | 2023-10-20 | 成都信息工程大学 | Cooperative control method for weather radar networking |
-
2024
- 2024-09-04 CN CN202411230787.0A patent/CN118732089B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110501760A (en) * | 2019-07-29 | 2019-11-26 | 中国气象局广州热带海洋气象研究所（广东省气象科学研究所） | A kind of hail identification and nowcasting method based on weather radar |
| CN115047463A (en) * | 2022-08-16 | 2022-09-13 | 成都远望科技有限责任公司 | Collaborative scanning scheduling method considering radar echo attenuation in rainfall area |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118732089A (en) | 2024-10-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109447315B (en) | Multi-time space scale-based electric power meteorological numerical weather forecast method and device | |
| CN107703564B (en) | A rainfall prediction method, system and electronic device | |
| CN110579823B (en) | Method and system for forecasting short-term and temporary heavy rainfall | |
| CN118732089B (en) | A method and device for coordinated dispatching of weather radar based on hail potential forecast | |
| CN109738970A (en) | Method, device and storage medium for realizing lightning early warning based on lightning data mining | |
| CN116363601B (en) | Data acquisition and analysis method and system for pollution monitoring equipment | |
| CN110134907B (en) | Rainfall missing data filling method and system and electronic equipment | |
| CN104166171A (en) | Method and system for predicating thundercloud cover area based on lightning locating system | |
| CN117113680A (en) | Method and device for checking and analyzing large-range thunderstorm wind event | |
| CN110399634A (en) | A method and system for determining a forecast area based on weather system influence | |
| CN115471128A (en) | Big data-based device for predicting air quality trend | |
| Si et al. | The combined influence of background climate and urbanization on the regional warming in Southeast China | |
| CN118732087B (en) | Weather radar collaborative scheduling method and device based on thunderstorm high wind potential forecast | |
| CN114459531A (en) | Station building structure safety monitoring method and device based on meteorological information and electronic equipment | |
| CN113075751A (en) | Method and system for fusing observation data in short-term forecasting | |
| CN116626682B (en) | Multi-radar networking space profile conversion method, system, medium and equipment | |
| CN116151493B (en) | Traffic congestion prediction method and device based on head effect and recurrent neural network | |
| CN118172036A (en) | Hydraulic engineering intelligent operation and maintenance method and system based on digital twin | |
| CN116704366A (en) | Storm drop zone identification early warning method and device based on transform deep learning model | |
| CN112630597B (en) | Fault determination method, device, equipment and storage medium | |
| CN117907528A (en) | Method for predicting contaminant concentration | |
| CN115902812A (en) | Method, system, equipment and terminal for automatically distinguishing short-term rainstorm weather background | |
| Zhou et al. | Holiday travel pattern forecast based on machine learning algorithm | |
| Bharath et al. | Understanding the changing LULC and its effect on air quality through field-based measurement and model-based approach | |
| CN114492965A (en) | Precipitation short-term probability prediction method and system based on oblique ellipsoid 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 |