CN108957595B - Lightning forecasting method and system - Google Patents
Lightning forecasting method and system Download PDFInfo
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Abstract
The application relates to a lightning forecasting method and a lightning forecasting system. The method comprises the following steps: reading the latest electric field survey station data and radar observation data of the electric field station, and establishing a thunder and lightning approach prediction model according to the electric field survey station data and the radar observation data; the method for establishing the lightning imminence forecast model comprises the following steps: step a: setting an electric field amplitude threshold value and an electric field differential threshold value; step b: c, judging whether the read electric field test station data reach a set electric field amplitude threshold or an electric field differential threshold, and executing the step c if the electric field test station data reach the set electric field amplitude threshold or the electric field differential threshold; step c: d, judging whether the radar observation data before the current moment reaches a preset threshold value, and executing the step d if the radar observation data reaches the preset threshold value; step d: and generating a thunder early warning image-text product and sending a thunder early warning. The application realizes the early release of thunderstorm weather early warning, and improves the capability and level of thunder and lightning forecast early warning.
Description
Technical Field
The application belongs to the technical field of meteorological service, and particularly relates to a lightning forecasting method and a lightning forecasting system.
Background
Thunder is a weather phenomenon that discharge occurs between clouds or between cloud places in accumulated rain clouds, has the characteristics of transient large current, high voltage, strong electromagnetic radiation and the like, is often accompanied by strong wind, heavy rain, hail and even tornado, is one of serious meteorological disasters, is listed as one of the most serious natural disasters by united nations as early as the end of the last century, and is also called as public nuisance of the electronic information era.
With the rapid development of economy and improvement of living standard of people in China, the increasing of high-rise buildings and the wide application of various electronic products, the loss caused by thunder and lightning disasters is more and more serious, and even serious safety incidents such as personal casualties tend to be frequent. Taking Shenzhen as an example, in a low latitude region of Shenzhen, the occurrence of the thunderstorm caused by thermal convection is frequent, and the Shenzhen is one of the multiple thunderstorm regions in China.
The thunder and lightning forecasting system relates to the contents in many aspects such as data collection of lightning positioning and the like, construction of a database, comprehensive monitoring of thunder and lightning, establishment of a thunder and lightning concept model, application of a potential forecasting method, application of a thunder and lightning short-time forecasting and early warning technology and the like, and the researched and developed thunder and lightning forecasting method can obviously improve the levels of refinement, pertinence and high efficiency of a thunder and lightning forecasting and early warning product and reduce the disaster influence of thunder and lightning on personal safety of various industries and citizens.
The lightning disaster has a wide disaster-receiving range, and almost all industries are involved, so the requirements for comprehensive monitoring, forecasting, early warning and defense of lightning and the lightning disaster become more and more urgent. The generation of thunder is often closely related to the strong convection weather, and the occurrence of the strong convection weather mostly provides power conditions by the movement and the change of various medium and small-scale weather systems, usually only locally, and the life history of the generated thunder is short. Only by means of observation equipment, lightning is difficult to forecast in advance, and the requirements of citizens and various industries on lightning forecast early warning fine service are difficult to meet, so that the service effect is influenced.
Disclosure of Invention
The application provides a lightning forecasting method and a lightning forecasting system, which aim to solve at least one of the technical problems in the prior art to a certain extent.
In order to solve the above problems, the present application provides the following technical solutions:
a lightning prediction method comprising: reading the latest electric field survey station data and radar observation data of the electric field station, and establishing a thunder and lightning approach prediction model according to the electric field survey station data and the radar observation data; the method for establishing the lightning imminence forecast model comprises the following steps:
step a: setting an electric field amplitude threshold value and an electric field differential threshold value;
step b: c, judging whether the read electric field test station data reach a set electric field amplitude threshold or an electric field differential threshold, and executing the step c if the electric field test station data reach the set electric field amplitude threshold or the electric field differential threshold;
step c: d, judging whether the radar observation data before the current moment reaches a preset threshold value, and executing the step d if the radar observation data reaches the preset threshold value;
step d: and generating a thunder early warning image-text product and sending a thunder early warning.
The technical scheme adopted by the embodiment of the application further comprises the following steps: and setting a radar early warning threshold value according to the radar echo reflectivity factor, and establishing a radar data early warning threshold value calculation model.
The technical scheme adopted by the embodiment of the application further comprises the following steps: and carrying out electric field networking on the electric field stations in the set area range, establishing an area lightning early warning model, carrying out area lightning early warning in the set area range, and monitoring and early warning on the thunderstorm cloud change process in the set area range.
The technical scheme adopted by the embodiment of the application further comprises the following steps: and establishing a lightning short-time approach prediction model by comprehensively applying lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud group identification and tracking system, and outputting lightning short-time approach prediction series potential prediction products.
The technical scheme adopted by the embodiment of the application further comprises the following steps: identifying the thunderstorm monomers and the distribution condition of wind fields inside each thunderstorm monomer by using an image identification technology to generate a thunderstorm potential forecasting product in a thunderstorm influence area; and calculating the time when the thunder enters the specified place, the time when the thunder leaves the specified place and the time when the thunder affects the specified place by using the thunder affected area at the set moment through a space superposition analysis method, and outputting a thunder fixed point forecasting product.
Another technical scheme adopted by the embodiment of the application is as follows: a lightning forecast system comprises a lightning approach forecast model establishing module, wherein the lightning approach forecast model establishing module is used for reading the latest electric field survey station data and radar observation data of an electric field station and establishing a lightning approach forecast model according to the electric field survey station data and the radar observation data; the establishing of the lightning approach prediction model specifically comprises the following steps:
a threshold setting unit: the method is used for setting an electric field amplitude threshold value and an electric field differential threshold value;
a first threshold value judging unit: the second threshold judging unit is used for judging whether the radar observation data before the current moment reaches a preset threshold or not if the electric field survey station data reaches the set electric field amplitude threshold or the set electric field differential threshold;
a second threshold value judging unit: and the system is used for judging whether the radar observation data before the current moment reaches a preset threshold value, and if the radar observation data reaches the preset threshold value, generating a thunder early warning image-text product and sending a thunder early warning.
The technical scheme adopted by the embodiment of the application further comprises the following steps:
a radar data early warning threshold calculation model establishing module: the radar early warning threshold value calculation model is established according to the radar echo reflectivity factor;
the technical scheme adopted by the embodiment of the application further comprises the following steps:
the regional lightning early warning model building module comprises: the system is used for conducting electric field networking on electric field stations in a set area range, establishing an area lightning early warning model, conducting area lightning early warning in the set area range, and conducting monitoring and early warning on the thunderstorm cloud change process in the set area range.
The technical scheme adopted by the embodiment of the application further comprises the following steps:
the lightning short-time approach prediction model building module comprises: the method is used for comprehensively applying lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud cluster recognition and tracking system to establish a lightning short-time nowcasting model and output lightning short-time nowcasting series potential forecasting products.
The technical scheme adopted by the embodiment of the application further comprises the following steps:
the thunderstorm influence area lightning potential forecasting product generation module: the system is used for identifying the thunderstorm monomers and the distribution condition of wind fields in each thunderstorm monomer by using an image identification technology to generate a thunderstorm potential forecasting product in a thunderstorm influence area;
the thunder and lightning fixed point forecast product output module: the method is used for calculating the time when the thunder enters the appointed place, the time when the thunder leaves the appointed place and the time when the thunder affects the appointed place by utilizing the thunder affected area at the set moment through a space superposition analysis method, and outputting a thunder fixed point forecasting product.
Compared with the prior art, the embodiment of the application has the advantages that: the lightning forecasting method and the lightning forecasting system of the embodiment of the application establish a lightning approach forecasting model based on the atmospheric electric field and radar data, and network a plurality of atmospheric electric field station data and analyze the thunderstorm process moving through an observation area by combining the provided early warning method so as to realize early warning of one area and the whole thunderstorm process; meanwhile, lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud group identification and tracking system are comprehensively applied, lightning characteristics are statistically analyzed, and a lightning short-time nowcasting model is established by using forecasting factors, so that the thunder nowcasting is realized; in addition, the distribution conditions of the thunderstorm units and the wind fields in each thunderstorm unit are identified by using an image identification technology, and for a certain specified place, the single-point forecast and early warning of the radar are issued by using a lightning influence area at a certain moment and a space superposition analysis method. The method and the device improve the capability and level of lightning forecast early warning, and the thunderstorm weather early warning is released in advance for 45 minutes in nearly 3 years, so that the accuracy rate reaches more than 90%.
Drawings
FIG. 1 is a flow chart of a lightning forecasting method of an embodiment of the application;
FIG. 2 is a flow chart of a method for modeling lightning imminence prediction in accordance with an embodiment of the present application;
FIG. 3 is a schematic diagram of a series of products for short-term lightning nowcasting;
FIG. 4 is a schematic diagram of a lightning potential forecasting product in a thunderstorm influence area;
FIG. 5 is a schematic diagram of a lightning fixed-point forecast warning product;
fig. 6 is a schematic structural diagram of a lightning forecast system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Please refer to fig. 1, which is a flowchart illustrating a method for forecasting lightning according to an embodiment of the present application. The lightning forecasting method comprises the following steps:
step 100: reading the latest electric field survey station data and radar observation data of the electric field stations, and establishing a thunder and lightning approach prediction model according to the electric field survey station data and the radar observation data;
in step 100, the lightning activity often causes a significant change in the ground electric field, and the atmospheric electric field meter is a special precision device for measuring the change in the atmospheric electric field, and can reflect the change in the cloud charge around the electric field site. In non-thunderstorm weather, the electric field time series changes to be continuous and presents small electric field amplitudes. When thundercloud develops, the electrification process is gradually enhanced, charges are gradually accumulated, and the ground electric field strength is gradually enhanced to reach a certain amplitude and lasts for a period of time. Therefore, when the electric field reaches a certain amplitude, the thunderstorm cloud charges around the electric field station are represented to accumulate a certain amount of charge, and the probability of lightning is increased. When thunderstorm cloud takes place, the cloud ground that takes place in the cloud dodges etc. and discharges and can cause electric charge volume instantaneous change in the cloud, and the change of electric charge volume can be detected to atmosphere electric field appearance and appears as the jump of electric field time series. In the thunderstorm cloud process, cloud flashing accounts for the vast majority of lightning activities, and generally occurs 5-35 min before ground flashing. The electric field jumping phenomenon before the ground flash occurs is a precursor of lightning early warning, and can be described by a differential method. The atmospheric electric field instrument is a non-directional detection device, cannot detect the specific direction of the thunderstorm and is very sensitive to charge change caused by environmental factors; the weather radar can reflect the development conditions of a thunderstorm cloud structure, strong and weak convection and the like, and can perform positioning and lightning early warning functions on the thunderstorm cloud. Therefore, the lightning proximity prediction model is established by combining radar observation data and utilizing an electric field amplitude threshold value and electric field difference threshold value method, and the false alarm rate of electric field data early warning is reduced.
Fig. 2 is a flowchart illustrating a method for establishing a lightning nowcasting model according to an embodiment of the present disclosure. The method specifically comprises the following steps:
step 101: setting the electric field exceeding the set amplitude within the set time as an electric field amplitude threshold, and taking the electric field difference of which the number of times that the absolute value of the electric field difference exceeds the set threshold within 2 radar body scanning times reaches the set number of times as an electric field difference threshold;
in step 101, if a threshold value is simply set for the electric field amplitude threshold value to perform early warning, the early warning effect will be poor. The method and the device seek the optimal early warning index for the electric field data by counting a series of electric field amplitude threshold values and a series of early warning effects exceeding the threshold values within 18min (3 radar volume sweep time, 1080 electric field data). As a result, it was found that the optimum electric field amplitude threshold was reached when the ratio of the electric field exceeding 1kV/m reached 1/3 within 18 min.
The electric field jumping phenomenon is described by a difference method, and the difference of the atmospheric electric field time sequence intensity E is as follows:
in the formula (1), Et1(x, y, z) and Et2(x, y, z) are the electric fields at any two adjacent times, respectively.
As can be seen from equation (1), the electric field timing difference value and the electric field difference value are related to Δ t. The strength of the thunderstorm cloud is represented not only by the amplitude of the electric field but also by the amplitude and frequency of the change of the electric field. Therefore, the electric field difference of which the number of times that the absolute value of the electric field difference exceeds the set threshold reaches the set number of times within 2 radar body scanning time is selected as the electric field difference threshold. Preferably, the electric field difference that the number of times that the absolute value of the electric field difference exceeds 0.15kV/m reaches 2 times within 2 radar volume sweep times is taken as the electric field difference threshold of the observation region, and the above numerical value can be set according to specific application.
Step 102: judging whether the read electric field station data reach a set electric field amplitude threshold or an electric field differential threshold, if so, executing a step 103, otherwise, continuing to execute the step 102;
in step 102, if the read electric field survey station data reaches a set electric field amplitude threshold or electric field differential threshold, it is considered that the amount of electric charge in the cloud around the electric field station is large at this time, and the probability of lightning occurrence is also large.
Step 103: judging whether the radar observation data before the moment reaches a preset threshold value, and executing step 104 if the radar observation data reaches the preset threshold value; otherwise, go to step 105;
step 104: generating a thunder early warning image-text product, storing the thunder early warning image-text product in a warehouse and sending a thunder early warning;
step 105: and continuously judging whether the radar observation data of the next radar body scanning time reaches a preset threshold value or not until a lightning early warning is sent out or the electric field threshold value is cancelled (if no electric field survey station data reaches a set electric field amplitude threshold value or an electric field difference threshold value within 30min after the electric field of a certain electric field station triggers the early warning, the electric field threshold value is considered to be cancelled).
In the embodiment of the application, lightning positioning data in the early warning range of each electric field station is used for detecting the lightning forecasting effect of the electric field station, and if no electric field station measuring data reaches a set electric field amplitude threshold value or electric field difference threshold value (namely no lightning occurs) within 30min after lightning occurs in a certain thunderstorm process and no radar echo greater than 30dBz exists in the early warning range of the electric field station, the thunderstorm is considered to be ended. Specifically, the following processing needs to be performed on the lightning location data:
(1) screening out lightning data in the early warning range of each electric field station;
(2) deleting invalid lightning with amplitude parameter less than 10 kA;
(3) detecting each lightning by utilizing radar observation data, namely if no echo exists above 2km height (in order to avoid echoes such as ground objects) in an early warning range or no echo larger than 15dBz exists in all scanning time before and after the lightning, and the lightning is considered invalid when the electric field fluctuation is smooth and the amplitude is small;
(4) screening out the first lightning of each thunderstorm process of each electric field station.
Step 200: setting a radar early warning threshold value according to the radar echo reflectivity factor, and establishing a radar data early warning threshold value calculation model;
in step 200, research on the relation between radar echo reflectivity and lightning initial characteristics at different heights reveals that the radar echo reflectivity is reflected as a strong echo rising above a certain height when a thunderstorm occurs. For the lightning forecast by using radar observation data, the number of false early warnings can be reduced by increasing the radar reflectivity threshold, but the advance time between the detection of a thunderstorm primary signal and the first occurrence of a local lightning is also reduced; and vice versa. Because the data of the electric field station reflects the charge condition of thunderstorm cloud, the radar echo reflectivity factor of the radar with the height of 5km (the statistics of sounding data shows that the temperature area with the height of 5km about 0 ℃ is about, the temperature area with the height of 5km above 0-15 ℃ is a turnover temperature area which carries charges with different polarities after ice crystals and aragonite collide in a non-induction electrification mechanism and is a characteristic area of thunderstorm electrification) reaches a certain volume (library) to be used as a radar early warning threshold value.
Step 300: the method comprises the steps that electric field networking is conducted on a plurality of electric field stations in a certain area range, an area lightning early warning model is built, area lightning early warning in the area range is achieved, and monitoring and early warning are conducted on a thunderstorm cloud change process in the area range;
in step 300, the atmospheric electric field instrument of a single electric field station can only monitor the charge condition of thunderstorm clouds in the electric field station area in real time, and pre-warn the thunderstorm to a certain extent, but cannot monitor the moving direction and the occurrence position of the thunderstorm and cannot monitor the strength condition of the thunderstorm clouds at the next moment, so that the accuracy of short-time thunderstorm pre-warning by using the electric field station data of the single electric field station is low due to the limitation of the monitoring range of the single electric field station, and the atmospheric electric field instrument only belongs to monitoring of one point for the thunderstorm clouds in a large range.
This application is through networking a plurality of electric field websites in the certain limit to strengthen the function of atmosphere electric field appearance, and enlarge the monitoring range of electric field, improve the early warning ability of thunder and lightning. When a certain electric field station has no trigger field threshold value, the early warning range is represented by transparent gray; if an electric field site triggers an electric field threshold, the electric field site is represented by transparent yellow; if an electric field station triggers an electric field threshold and radar observation data of the electric field station also reach an early warning threshold, the electric field station sends out a lightning early warning and represents the lightning early warning by transparent orange; if a lightning occurs at a certain electric field site, it is represented in red. Therefore, the thunderstorm cloud and lightning occurrence development situation in the networking area can be visually presented, and the potential area where lightning occurs can be visually judged.
As the thunderstorm cloud moves from far to near, the electric field stations in the networking area successively respond, and the electric field station data and the radar observation data are used for early warning, so that the occurrence of lightning can be early warned in advance, and a good early warning effect is also achieved. Therefore, this application is through networking a plurality of electric field websites in the certain limit, enlarges the monitoring range of electric field, not only can realize monitoring the early warning to the "face" in the networking region, can realize continuity in time moreover, monitors the early warning to the whole process of thunderstorm cloud, has the advantage that single electric field website does not have.
Step 400: comprehensively applying lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud identification and tracking system to establish a lightning short-time nowcasting model and output lightning short-time nowcasting series potential prediction products;
in step 400, lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud identification and tracking system are comprehensively applied, lightning characteristics and forecasting factors are statistically analyzed, a lightning short-term proximity forecasting background service system is established by combining radar reflectivity, echo peak height, vertically accumulated liquid water and the like to reach a certain threshold value and area range, the system is deployed at a server side in a distributed mode based on a C/S mode and executed in a multithread concurrent mode, data analysis is completed, and lightning short-term proximity forecasting series potential forecasting products are output by combining a lightning short-term proximity forecasting model, specifically as shown in fig. 3, a lightning short-term proximity forecasting series product schematic diagram is shown. The specific principle and process for establishing the lightning short-time nowcasting model are as follows:
step 401: acquiring forecasting factors Q, H and VIL;
in step 401, the prediction factors Q, H and VIL are obtained in the following manner: the reflectivity factor values of 9 elevation angles, which are 0.5-19.5 degrees, are obtained by calling and decoding the latest generated radar base data of the Doppler radar in time. The maximum values of the elevation angles R of 0.5 °, 1.5 °, 2.4 °, 3.4 °, 4.3 ° and 6.0 ° are taken and converted from the polar format to the graticule format. And (4) calling the radar base data of the past two moments to perform the same operation, thereby obtaining three longitude and latitude grid data files of Q at different moments. And (3) acquiring the longitude and latitude grid data files of 3 continuous different body scans by adopting the same method for H and VIL, and automatically reading Q, H and VIL values in the longitude and latitude grid data files by using a program.
Step 402: adopting an extrapolation method PP method to extrapolate Q, H and VIL to obtain a lightning forecasting result of the next moment after the forecasting moment;
in step 402, the evolution of the echo usually changes from weak to strong and then from strong to weak in a weather course. If a linear equation is adopted, the evolution characteristic of the radar echo cannot be accurately and objectively reflected, and the characteristics of a quadratic equation are just in line with the real situation of the evolution of the echo, so that the extrapolation of the echo characteristic parameter by adopting the quadratic equation is scientific and reasonable. The specific method for extrapolating Q, H and VIL is as follows: respectively substituting Q, H and VIL of the forecast time in the previous 30 minutes, the previous 12 minutes and the forecast time into an equation, establishing an equation set, and solving coefficients in the equation; and substituting Q, H and VIL values of the forecast time into an equation to obtain the forecast values of Q, H and VIL 12 minutes after the forecast time of the same place. The forecasting values of Q, H and VIL are comprehensively judged and processed to obtain the lightning forecasting result 12 minutes after the forecasting time, so that the lightning condition of the next time can be forecasted.
And (3) solving the coefficients in the quadratic equation, namely, setting the future change of the factor to follow y to a + bx + c, obtaining 1 equation set consisting of 3 curve equations through Q at three different moments, and solving the equation of the extrapolated Q by solving each coefficient by using a least square method. In the same way, equations for extrapolating H and VIL can be derived, and predictions of Q, H and VIL at the next time can be made by varying the time. In addition, the Q, H and VIL values change rapidly because the convection cloud changes rapidly and intensely. In order to truly reflect the change condition of convection cloud and improve the accuracy of lightning intensity and drop point forecast, real-time lightning data detected by a lightning monitor in Guangdong province (or hong Kong astronomical stage) is used, namely when the lightning detector monitors that the lightning occurs in a certain area and all data of radar echo data partially or completely fail to reach the minimum critical value of the lightning, the radar echo data in the area are corrected in real time in time to reach the minimum standard of the occurrence of the lightning. In addition, the same method and means are adopted for forecasting the lightning intensity, and real-time lightning data are used for correcting radar echo data, Q, H and VIL values are calibrated, and then the real-time lightning data participate in the next time of extrapolation forecasting, so that the forecasting accuracy is improved.
Step 403: based on a cross correlation method, the contribution of Q, H and VIL to the probability and strength of lightning occurrence is utilized, the 3 elements are integrated, and the probability and the lightning strength of 1h and 3h lightning occurrence in the future are obtained by combining live condition correction monitored by a lightning monitoring and positioning system;
in step 403, after obtaining the life cycle and family spectrum of the thunderstorm cloud, the boundary correlation tracking technique performs linear extrapolation on the movement direction, movement speed and intensity change of the cloud according to the movement inertia of the thunderstorm cloud. The speed extrapolation adopts the principle of 'no fast slow in peace', and the area change mainly corrects the linear extrapolation prediction result according to the cloud cluster area expansion coefficient; the calculation of the intensity change needs to adopt different processing methods according to the size of the cloud cluster, the small-scale cloud cluster directly uses the average intensity statistical result, the large-scale cloud cluster is divided into grids according to an equivalent rectangular mode, the intensity change is calculated according to the grids, and the extrapolation result is corrected accordingly. The contribution of Q, H and VIL to the probability and strength of lightning occurrence is utilized to integrate the 3 elements, and the probability and the lightning strength of 1h and 3h lightning occurrence in the future are obtained by combining the live condition correction monitored by the lightning monitoring and positioning system. The units used for Q, H and VIL were dBz, km, kg/m2, respectively.
Step 404: writing the comprehensive result into a formulated product format, and outputting a forecast product of a thunderstorm influence area and a thunderstorm fixed-point forecast product.
Step 500: identifying the thunderstorm monomers and the distribution condition of wind fields inside each thunderstorm monomer by using an image identification technology to generate a thunderstorm potential forecasting product in a thunderstorm influence area;
in step 500, the generation of the lightning potential forecast product of the thunderstorm influence area specifically comprises the following steps: the method comprises the steps of identifying thunderstorm monomers with the intensity of more than 35BBZ one by utilizing an image identification technology, identifying the distribution condition of wind fields in each thunderstorm monomer, forecasting the path of the thunderstorm monomers by utilizing a radar echo forecasting technology, and outputting a thunderstorm potential forecasting product of a thunderstorm influence area in 0-2 hours in the future by adopting the forecasting method, wherein the thunderstorm potential forecasting product is specifically shown in figure 4.
Step 600: calculating the time when the thunder enters the designated place, the time when the thunder leaves the designated place and the time when the thunder affects the designated place by using a thunder affected area at a certain moment through a space superposition analysis method, and outputting a thunder fixed-point forecasting product;
in step 600, outputting the thunder and lightning fixed point forecast product specifically comprises: aiming at a certain appointed place, calculating the time when the thunder enters the appointed place, the time when the thunder leaves the appointed place and the time when the thunder affects the appointed place by using a thunder influence area at a certain moment through a space superposition analysis method, and issuing single-point forecast and early warning of the radar based on the time, wherein a product schematic diagram of the thunder fixed-point forecast early warning is shown in fig. 5.
Please refer to fig. 6, which is a schematic structural diagram of a lightning forecasting system according to an embodiment of the present application. The lightning forecasting system comprises a lightning approach forecasting model establishing module, a radar data early warning threshold value calculation model establishing module, an area lightning early warning model establishing module, a lightning short-time approach forecasting model establishing module, a lightning potential forecasting product generating module of a thunderstorm influence area and a lightning fixed-point forecasting product output module.
The lightning approach prediction model building module comprises: the system comprises a power station, a lightning proximity prediction model, a power station data acquisition module and a power station data acquisition module, wherein the power station data acquisition module is used for acquiring power station data of an electric field; the ground electric field is usually obviously changed due to lightning activity, and the atmospheric electric field instrument is special precision equipment for measuring the change of the atmospheric electric field and can reflect the change condition of cloud charges around an electric field station. In non-thunderstorm weather, the electric field time series changes to be continuous and presents small electric field amplitudes. When thundercloud develops, the electrification process is gradually enhanced, charges are gradually accumulated, and the ground electric field strength is gradually enhanced to reach a certain amplitude and lasts for a period of time. Therefore, when the electric field reaches a certain amplitude, the thunderstorm cloud charges around the electric field station are represented to accumulate a certain amount of charge, and the probability of lightning is increased. When thunderstorm cloud takes place, the cloud ground that takes place in the cloud dodges etc. and discharges and can cause electric charge volume instantaneous change in the cloud, and the change of electric charge volume can be detected to atmosphere electric field appearance and appears as the jump of electric field time series. In the thunderstorm cloud process, cloud flashing accounts for the vast majority of lightning activities, and generally occurs 5-35 min before ground flashing. The electric field jumping phenomenon before the ground flash occurs is a precursor of lightning early warning, and can be described by a differential method. The atmospheric electric field instrument is a non-directional detection device, cannot detect the specific direction of the thunderstorm and is very sensitive to charge change caused by environmental factors; the weather radar can reflect the development conditions of a thunderstorm cloud structure, strong and weak convection and the like, and can perform positioning and lightning early warning functions on the thunderstorm cloud. Therefore, the lightning proximity prediction model is established by combining radar observation data and utilizing an electric field amplitude threshold value and electric field difference threshold value method, and the false alarm rate of electric field data early warning is reduced.
In the embodiment of the present application, the module for establishing a lightning approach prediction model specifically includes:
a threshold setting unit: the electric field difference threshold is used for setting the electric field exceeding the set amplitude within the set time as an electric field amplitude threshold, and taking the electric field difference reaching the set times when the absolute value of the electric field difference within 2 radar body scanning times exceeds the set threshold; wherein, for the electric field amplitude threshold value, if simply set a threshold value to carry out the early warning, the early warning effect will be relatively poor. The method and the device seek the optimal early warning index for the electric field data by counting a series of electric field amplitude threshold values and a series of early warning effects exceeding the threshold values within 18min (3 radar volume sweep time, 1080 electric field data). As a result, it was found that the optimum electric field amplitude threshold was reached when the ratio of the electric field exceeding 1kV/m reached 1/3 within 18 min.
The electric field jumping phenomenon is described by a difference method, and the difference of the atmospheric electric field time sequence intensity E is as follows:
in the formula (1), Et1(x, y, z) and Et2(x, y, z) are the electric fields at any two adjacent times, respectively.
As can be seen from equation (1), the electric field timing difference value and the electric field difference value are related to Δ t. The strength of the thunderstorm cloud is represented not only by the amplitude of the electric field but also by the amplitude and frequency of the change of the electric field. Therefore, the electric field difference of which the number of times that the absolute value of the electric field difference exceeds the set threshold reaches the set number of times within 2 radar body scanning time is selected as the electric field difference threshold. Preferably, the electric field difference that the number of times that the absolute value of the electric field difference exceeds 0.15kV/m reaches 2 times within 2 radar volume sweep times is taken as the electric field difference threshold of the observation region, and the above numerical value can be set according to specific application.
A first threshold value judging unit: the electric field observation station data reading unit is used for judging whether the read electric field observation station data reaches a set electric field amplitude threshold value or an electric field difference threshold value, if the electric field observation station data reaches the set electric field amplitude threshold value or the electric field difference threshold value, the second threshold value judging unit judges whether the radar observation data before the moment reaches a preset threshold value, and if not, the first threshold value judging unit continues to judge whether the electric field observation station data reaches the set electric field amplitude threshold value or the electric field difference threshold value;
a second threshold value judging unit: the system is used for judging whether radar observation data before the moment reaches a preset threshold value or not, generating a thunder early warning image-text product if the radar observation data reaches the preset threshold value, storing the thunder early warning image-text product in a warehouse and sending a thunder early warning; otherwise, continuously judging the radar observation data of the next radar body scanning time through a third threshold value judging unit;
a third threshold value judging unit: and the method is used for continuously judging whether the radar observation data of the next radar body scanning time reaches a preset threshold value or not until a lightning early warning is sent out or the electric field threshold value is cancelled (if no electric field survey station data reaches a set electric field amplitude threshold value or an electric field difference threshold value within 30min after the electric field of a certain electric field station triggers the early warning, the electric field threshold value is considered to be cancelled). The method comprises the steps of detecting the lightning forecasting effect of each electric field station according to lightning positioning data in the early warning range of each electric field station; and in the early warning range of a certain electric field station, if no electric field station-measuring data reaches a set electric field amplitude threshold value or an electric field differential threshold value (namely no lightning occurs) within 30min after lightning occurs in a certain thunderstorm process and no radar echo greater than 30dBz exists, the thunderstorm is considered to be ended. Specifically, the following processing needs to be performed on the lightning location data:
(1) screening out lightning data in the early warning range of each electric field station;
(2) deleting invalid lightning with amplitude parameter less than 10 kA;
(3) detecting each lightning by utilizing radar observation data, namely if no echo exists above 2km height (in order to avoid echoes such as ground objects) in an early warning range or no echo larger than 15dBz exists in all scanning time before and after the lightning, and the lightning is considered invalid when the electric field fluctuation is smooth and the amplitude is small;
(4) screening out the first lightning of each thunderstorm process of each electric field station.
A radar data early warning threshold calculation model establishing module: the radar early warning threshold value calculation model is established according to the radar echo reflectivity factor; the research on the relation between the radar echo reflectivity and the lightning initial characteristics at different heights reveals that the radar echo reflectivity is inevitably reflected as a strong echo rising above a certain height when a thunderstorm occurs. For the lightning forecast by using radar observation data, the number of false early warnings can be reduced by increasing the radar reflectivity threshold, but the advance time between the detection of a thunderstorm primary signal and the first occurrence of a local lightning is also reduced; and vice versa. Because the data of the electric field station reflects the charge condition of thunderstorm cloud, the radar echo reflectivity factor of the radar with the height of 5km (the statistics of sounding data shows that the temperature area with the height of 5km about 0 ℃ is about, the temperature area with the height of 5km above 0-15 ℃ is a turnover temperature area which carries charges with different polarities after ice crystals and aragonite collide in a non-induction electrification mechanism and is a characteristic area of thunderstorm electrification) reaches a certain volume (library) to be used as a radar early warning threshold value.
The regional lightning early warning model building module comprises: the system comprises a plurality of electric field stations, a regional lightning early warning model, a monitoring and early warning module and a power supply module, wherein the electric field stations in a certain regional range are connected in an electric field network, the regional lightning early warning model is established, regional lightning early warning in the regional range is realized, and the thunderstorm cloud change process in the regional range is monitored and early warned; wherein, the atmosphere electric field appearance of single electric field website only can carry out real-time supervision to the thunderstorm cloud electric charge condition in the electric field website region, carries out the early warning to the thunder and lightning to a certain extent, but can't monitor the moving direction and the position of taking place of thunderstorm, also can't monitor the strong and weak condition of thunderstorm cloud of moment down, so owing to receive the restriction of the monitoring range of single electric field website, the accuracy that the electric field survey station data of application single electric field website carries out short-term thunder and lightning early warning is lower, it only belongs to the monitoring of a "point" to thunderstorm cloud on a large scale.
This application is through networking a plurality of electric field websites in the certain limit to strengthen the function of atmosphere electric field appearance, and enlarge the monitoring range of electric field, improve the early warning ability of thunder and lightning. When a certain electric field station has no trigger field threshold value, the early warning range is represented by transparent gray; if an electric field site triggers an electric field threshold, the electric field site is represented by transparent yellow; if an electric field station triggers an electric field threshold and radar observation data of the electric field station also reach an early warning threshold, the electric field station sends out a lightning early warning and represents the lightning early warning by transparent orange; if a lightning occurs at a certain electric field site, it is represented in red. Therefore, the thunderstorm cloud and lightning occurrence development situation in the networking area can be visually presented, and the potential area where lightning occurs can be visually judged.
As the thunderstorm cloud moves from far to near, the electric field stations in the networking area successively respond, and the electric field station data and the radar observation data are used for early warning, so that the occurrence of lightning can be early warned in advance, and a good early warning effect is also achieved. Therefore, this application is through networking a plurality of electric field websites in the certain limit, enlarges the monitoring range of electric field, not only can realize monitoring the early warning to the "face" in the networking region, can realize continuity in time moreover, monitors the early warning to the whole process of thunderstorm cloud, has the advantage that single electric field website does not have.
The lightning short-time approach prediction model building module comprises: the system is used for comprehensively applying lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud group recognition and tracking system to establish a lightning short-time approach prediction model and output lightning short-time approach prediction series potential prediction products; the method comprises the steps of comprehensively applying lightning positioning network data, Doppler radar data, mesoscale numerical mode data and a thunderstorm cloud cluster automatic identification and tracking system, carrying out statistical analysis on lightning characteristics and forecasting factors, establishing a lightning short-term proximity forecasting background service system by combining radar reflectivity, echo peak height, vertically accumulated liquid water and the like to reach a certain threshold value and area range, wherein the lightning short-term proximity forecasting background service system is deployed on a server side in a distributed mode based on a C/S mode, is executed in a multi-thread concurrent mode, completes data analysis, and outputs a lightning short-term proximity forecasting series potential forecasting product by combining a lightning short-term proximity forecasting model; the module for establishing the lightning short-term approach prediction model specifically comprises:
a forecast factor acquisition unit: for obtaining forecasting factors Q, H and VIL; the forecasting factors Q, H and VIL are obtained in the following manner: the reflectivity factor values of 9 elevation angles, which are 0.5-19.5 degrees, are obtained by calling and decoding the latest generated radar base data of the Doppler radar in time. The maximum values of the elevation angles R of 0.5 °, 1.5 °, 2.4 °, 3.4 °, 4.3 ° and 6.0 ° are taken and converted from the polar format to the graticule format. And (4) calling the radar base data of the past two moments to perform the same operation, thereby obtaining three longitude and latitude grid data files of Q at different moments. And (3) acquiring the longitude and latitude grid data files of 3 continuous different body scans by adopting the same method for H and VIL, and automatically reading Q, H and VIL values in the longitude and latitude grid data files by using a program.
A forecast result calculation unit: the method is used for carrying out extrapolation processing on Q, H and VIL by adopting an extrapolation method PP method to obtain a lightning forecasting result of the next moment after the forecasting moment; the evolution of the echo usually changes from weak to strong and then from strong to weak in the course of one weather. If a linear equation is adopted, the evolution characteristic of the radar echo cannot be accurately and objectively reflected, and the characteristics of a quadratic equation are just in line with the real situation of the evolution of the echo, so that the extrapolation of the echo characteristic parameter by adopting the quadratic equation is scientific and reasonable. The specific method for extrapolating Q, H and VIL is as follows: respectively substituting Q, H and VIL of the forecast time in the previous 30 minutes, the previous 12 minutes and the forecast time into an equation, establishing an equation set, and solving coefficients in the equation; and substituting Q, H and VIL values of the forecast time into an equation to obtain the forecast values of Q, H and VIL 12 minutes after the forecast time of the same place. The forecasting values of Q, H and VIL are comprehensively judged and processed to obtain the lightning forecasting result 12 minutes after the forecasting time, so that the lightning condition of the next time can be forecasted.
And (3) solving the coefficients in the quadratic equation, namely, setting the future change of the factor to follow y to a + bx + c, obtaining 1 equation set consisting of 3 curve equations through Q at three different moments, and solving the equation of the extrapolated Q by solving each coefficient by using a least square method. In the same way, equations for extrapolating H and VIL can be derived, and predictions of Q, H and VIL at the next time can be made by varying the time. In addition, the Q, H and VIL values change rapidly because the convection cloud changes rapidly and intensely. In order to truly reflect the change condition of convection cloud and improve the accuracy of lightning intensity and drop point forecast, real-time lightning data detected by a lightning monitor in Guangdong province (or hong Kong astronomical stage) is used, namely when the lightning detector monitors that the lightning occurs in a certain area and all data of radar echo data partially or completely fail to reach the minimum critical value of the lightning, the radar echo data in the area are corrected in real time in time to reach the minimum standard of the occurrence of the lightning. In addition, the same method and means are adopted for forecasting the lightning intensity, and real-time lightning data are used for correcting radar echo data, Q, H and VIL values are calibrated, and then the real-time lightning data participate in the next time of extrapolation forecasting, so that the forecasting accuracy is improved.
Probability and intensity calculation unit: the method is used for integrating 3 elements by utilizing contributions of Q, H and VIL to lightning occurrence probability and strength based on a cross-correlation method, and combining live condition correction monitored by a lightning monitoring and positioning system to obtain the lightning occurrence probability and lightning strength of 1h and 3h in the future; after the life cycle and the genealogy of the thunderstorm cloud cluster are obtained, the boundary correlation tracking technology carries out linear extrapolation on the movement direction, the movement speed and the intensity change of the cloud cluster according to the movement inertia of the thunderstorm cloud cluster. The speed extrapolation adopts the principle of 'no fast slow in peace', and the area change mainly corrects the linear extrapolation prediction result according to the cloud cluster area expansion coefficient; the calculation of the intensity change needs to adopt different processing methods according to the size of the cloud cluster, the small-scale cloud cluster directly uses the average intensity statistical result, the large-scale cloud cluster is divided into grids according to an equivalent rectangular mode, the intensity change is calculated according to the grids, and the extrapolation result is corrected accordingly. The contribution of Q, H and VIL to the probability and strength of lightning occurrence is utilized to integrate the 3 elements, and the probability and the lightning strength of 1h and 3h lightning occurrence in the future are obtained by combining the live condition correction monitored by the lightning monitoring and positioning system. The units used for Q, H and VIL were dBz, km, kg/m2, respectively.
A product output unit: and the system is used for writing the comprehensive result into a formulated product format and outputting a forecast product of a thunderstorm influence area and a thunderstorm fixed-point forecast product.
The thunderstorm influence area lightning potential forecasting product generation module: the system is used for identifying the thunderstorm monomers and the distribution condition of wind fields in each thunderstorm monomer by using an image identification technology to generate a thunderstorm potential forecasting product in a thunderstorm influence area; the specific method for generating the lightning potential forecast product of the thunderstorm influence area comprises the following steps: identifying thunderstorm monomers with the intensity of more than 35BBZ one by using an image identification technology, identifying the distribution condition of a wind field in each thunderstorm monomer, forecasting the path of the thunderstorm monomers by using a radar echo forecasting technology, and outputting a thunderstorm potential forecasting product of a thunderstorm influence area in 0-2 hours in the future by using the forecasting method;
the thunder and lightning fixed point forecast product output module: the system is used for calculating the time when the thunder enters a specified place and the time when the thunder leaves the specified place by utilizing a thunder influence area at a certain moment through a space superposition analysis method and outputting a thunder fixed-point forecasting product; the specific output thunder and lightning fixed-point forecast product is as follows: aiming at a certain appointed place, the time when the thunder enters the appointed place, the time when the thunder leaves the appointed place and the time when the thunder affects the appointed place are calculated by a space superposition analysis method by utilizing a thunder affected area at a certain moment, and based on the time, single-point prediction and early warning of the radar are issued.
The lightning forecasting method and the lightning forecasting system of the embodiment of the application establish a lightning approach forecasting model based on the atmospheric electric field and radar data, and network a plurality of atmospheric electric field station data and analyze the thunderstorm process moving through an observation area by combining the provided early warning method so as to realize early warning of one area and the whole thunderstorm process; meanwhile, lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud group identification and tracking system are comprehensively applied, lightning characteristics are statistically analyzed, and a lightning short-time nowcasting model is established by using forecasting factors, so that the thunder nowcasting is realized; in addition, the distribution conditions of the thunderstorm units and the wind fields in each thunderstorm unit are identified by using an image identification technology, and for a certain specified place, the single-point forecast and early warning of the radar are issued by using a lightning influence area at a certain moment and a space superposition analysis method. The method and the device improve the capability and level of lightning forecast early warning, and the thunderstorm weather early warning is released in advance for 45 minutes in nearly 3 years, so that the accuracy rate reaches more than 90%.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A lightning prediction method, comprising: reading the latest electric field survey station data and radar observation data of the electric field station, and establishing a thunder and lightning approach prediction model according to the electric field survey station data and the radar observation data; the method for establishing the lightning imminence forecast model comprises the following steps:
step a: setting an electric field amplitude threshold value and an electric field differential threshold value; taking the electric field difference within 2 radar body scanning time, and taking the electric field difference with the number of times that the absolute value of the electric field difference exceeds a set threshold value and reaches the set number of times as an electric field difference threshold value;
step b: c, judging whether the read electric field test station data reach a set electric field amplitude threshold or an electric field differential threshold, and executing the step c if the electric field test station data reach the set electric field amplitude threshold or the electric field differential threshold;
step c: d, judging whether the radar observation data before the current moment reaches a preset threshold value, and executing the step d if the radar observation data reaches the preset threshold value;
step d: generating a thunder early warning image-text product and sending a thunder early warning;
the method further comprises the following steps: performing electric field networking on electric field stations in a set area range, establishing an area lightning early warning model, performing area lightning early warning in the set area range, and monitoring and early warning the thunderstorm cloud change process in the set area range;
and setting a radar early warning threshold value according to the radar echo reflectivity factor, and establishing a radar data early warning threshold value calculation model.
2. The lightning prediction method of claim 1, further comprising: and establishing a lightning short-time approach prediction model by comprehensively applying lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud group identification and tracking system, and outputting lightning short-time approach prediction series potential prediction products.
3. The lightning prediction method of claim 2, further comprising: identifying the thunderstorm monomers and the distribution condition of wind fields inside each thunderstorm monomer by using an image identification technology to generate a thunderstorm potential forecasting product in a thunderstorm influence area; and calculating the time when the thunder enters the specified place, the time when the thunder leaves the specified place and the time when the thunder affects the specified place by using the thunder affected area at the set moment through a space superposition analysis method, and outputting a thunder fixed point forecasting product.
4. A lightning forecast system is characterized by comprising a lightning approach forecast model establishing module, wherein the lightning approach forecast model establishing module is used for reading latest electric field station data and radar observation data of an electric field station and establishing a lightning approach forecast model according to the electric field station data and the radar observation data; the establishing of the lightning approach prediction model specifically comprises the following steps:
a threshold setting unit: the method is used for setting an electric field amplitude threshold value and an electric field differential threshold value; taking the electric field difference within 2 radar body scanning time, and taking the electric field difference with the number of times that the absolute value of the electric field difference exceeds a set threshold value and reaches the set number of times as an electric field difference threshold value;
a first threshold value judging unit: the second threshold judging unit is used for judging whether the radar observation data before the current moment reaches a preset threshold or not if the electric field survey station data reaches the set electric field amplitude threshold or the set electric field differential threshold;
a second threshold value judging unit: the system is used for judging whether radar observation data before the current moment reach a preset threshold value or not, and if the radar observation data reach the preset threshold value, generating a thunder early warning image-text product and sending a thunder early warning;
the system further comprises:
the regional lightning early warning model building module comprises: the system comprises a power grid, a power grid and the like, wherein the power grid is used for connecting the power grid to the power grid;
a radar data early warning threshold calculation model establishing module: and the radar early warning threshold value calculation model is established according to the radar echo reflectivity factor.
5. The lightning forecast system of claim 4, further comprising:
the lightning short-time approach prediction model building module comprises: the method is used for comprehensively applying lightning positioning network data, Doppler radar data, mesoscale numerical mode data and an automatic thunderstorm cloud cluster recognition and tracking system to establish a lightning short-time nowcasting model and output lightning short-time nowcasting series potential forecasting products.
6. The lightning forecast system of claim 5, further comprising:
the thunderstorm influence area lightning potential forecasting product generation module: the system is used for identifying the thunderstorm monomers and the distribution condition of wind fields in each thunderstorm monomer by using an image identification technology to generate a thunderstorm potential forecasting product in a thunderstorm influence area;
the thunder and lightning fixed point forecast product output module: the method is used for calculating the time when the thunder enters the appointed place, the time when the thunder leaves the appointed place and the time when the thunder affects the appointed place by utilizing the thunder affected area at the set moment through a space superposition analysis method, and outputting a thunder fixed point forecasting product.
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| CN110109195B (en) * | 2019-04-23 | 2021-03-09 | 宁波市镇海区气象局 | Lightning approach prediction method based on radar and sounding data |
| CN110532334A (en) * | 2019-08-22 | 2019-12-03 | 中国气象局气象探测中心 | A kind of intensity of lightning zoning methods and system |
| CN110727719B (en) * | 2019-10-16 | 2023-08-01 | 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） | Lightning positioning data assimilation method based on dynamic relaxation approximation |
| CN110618474B (en) * | 2019-10-24 | 2021-08-03 | 广东省气象公共安全技术支持中心 | Lightning monitoring and early warning method and system based on multi-source data |
| CN111175755A (en) * | 2020-02-21 | 2020-05-19 | 厦门大恒科技有限公司 | Lightning comprehensive detection early warning system and lightning detection method thereof |
| CN113533835A (en) * | 2020-04-20 | 2021-10-22 | 中国石油化工股份有限公司 | Thunder early warning system and method based on thunder and electric field detection technology |
| CN111521878B (en) * | 2020-05-28 | 2022-05-13 | 宁夏中科天际防雷检测有限公司 | Method for judging regional lightning stroke risk degree |
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| CN112764129B (en) * | 2021-01-22 | 2022-08-26 | 易天气(北京)科技有限公司 | Method, system and terminal for thunderstorm short-term forecasting |
| CN113064222B (en) * | 2021-03-09 | 2022-05-10 | 中国气象科学研究院 | Lightning early warning and forecasting method and system |
| CN113567763A (en) * | 2021-08-09 | 2021-10-29 | 厦门大恒科技有限公司 | Lightning early warning method and lightning early warning device |
| CN113791286B (en) * | 2021-09-13 | 2023-08-15 | 厦门大恒科技有限公司 | Lightning early warning method and lightning early warning equipment |
| CN113655295B (en) * | 2021-10-21 | 2022-01-04 | 南京信息工程大学 | Lightning intensity identification method based on radar detection data |
| CN113985148A (en) * | 2021-11-04 | 2022-01-28 | 湖南省气象灾害防御技术中心 | Thunder and lightning forecasting method and thunder and lightning forecasting equipment |
| CN114137637B (en) * | 2021-11-09 | 2024-03-01 | 国网山东省电力公司应急管理中心 | Thunderstorm center trace aggregate probability forecasting method based on lightning and radar data |
| CN116910491B (en) * | 2023-09-11 | 2024-01-23 | 四川弘和数智集团有限公司 | Lightning monitoring and early warning system and method, electronic equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106353605A (en) * | 2016-11-02 | 2017-01-25 | 安徽锦坤电子科技有限公司 | Pre-warning system used for lightening |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH02103488A (en) * | 1988-10-12 | 1990-04-16 | Mitsubishi Electric Corp | Thundercloud detection method |
| CN103337133B (en) * | 2013-06-14 | 2015-08-05 | 广东电网公司中山供电局 | Based on the electrical network Thunderstorm early warning system and the method that identify forecasting technique |
| CN106443212A (en) * | 2016-11-02 | 2017-02-22 | 安徽锦坤电子科技有限公司 | Lightning monitoring and early-warning system |
| CN107037269A (en) * | 2017-04-14 | 2017-08-11 | 云南电网有限责任公司电力科学研究院 | A kind of power transmission line lightning shielding method for early warning |
| CN107607794B (en) * | 2017-10-11 | 2024-05-24 | 贵州电网有限责任公司 | Electric network lightning early warning information release system and release method thereof |
-
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Patent Citations (1)
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