Lightning information provision system, lightning prediction device, and lightning prediction method

Abstract

To provide a lightning information system capable of predicting lightning strikes with high accuracy and speed. [Solution] According to the embodiment, the lightning information provision system comprises a lightning prediction device and an information distribution device. The lightning prediction device comprises a data collection unit and a lightning prediction processing unit. The data collection unit collects weather-related data from data sources including a multi-parameter phased array weather radar. The lightning prediction processing unit calculates lightning prediction data based on the weather-related data. The information distribution device comprises a determination unit and an information distribution unit. The determination unit determines whether or not to issue a warning based on the lightning prediction data. The information distribution unit issues a warning based on the determination result of the determination unit.

Description

[Technical Field]

[0001] Embodiments of the present invention relate to a lightning information provision system, a lightning prediction device, and a lightning prediction method. [Background technology]

[0002] There is a great need from all sectors to accurately detect the location of lightning strikes currently occurring and to predict the locations and likelihood of lightning strikes in the near future. Weather radar plays a major role in observing not only lightning but all kinds of weather phenomena. For example, Patent Document 1 discloses an aviation weather system that contributes to the safety of the transportation industry.

[0003] One method for predicting lightning strikes using weather radar involves using radar reflectivity. Non-patent document 1 describes determining lightning activity when a region with radar reflectivity exceeding 40 dBZ occurs at an altitude around -10°C. Non-patent document 2 describes determining thunderclouds by integrating layers with high reflectivity.

[0004] Multiparameter radar can detect physical quantities such as particle shape (flatness ratio). The use of this type of information (multiparameter information) to detect lightning is also being considered. For example, Non-Patent Document 3 describes an example where lightning discharges were detected at the uppermost part of a hailstone distribution area, at the boundary with ice crystals, using particle discrimination information. Non-Patent Document 4 describes the negative interpolarization phase difference rate (K) in the upper atmosphere preceding lightning discharges. DP Examples are presented showing that the area expands.

[0005] As described above, the possibility of using multi-parameter information for lightning forecasting is being explored, but it has not yet reached the implementation stage. Furthermore, when using a rotating parabolic antenna, it takes about 5 minutes to observe the upper atmosphere with radar, making it difficult to observe the rapid development of thunderclouds. Therefore, attention is being drawn to multi-parameter phased array weather radar (MP-PAWR), which can acquire particle discrimination information at high speed. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Patent No. 6818569 [Non-patent literature]

[0007] [Non-Patent Document 1] "About Short-Term Lightning Prediction Technology", [online], Japan Meteorological Agency, [Retrieved October 15, 2024], Internet,<URL:https: / / www.jma.go.jp / jma / kishou / know / toppuu / 20part1 / 20-1-shiryo4.pdf> [Non-Patent Document 2] Yoshikawa, E., & Ushio, T. (2019). Tactical decision-making support information for aircraft lightning avoidance: Feasibility study in area of ​​winter lightning. Bulletin of the American Meteorological Society, 100(8), 1443-1452. [Non-Patent Document 3] Shikakura, S., Kikuchi, H., Yoshihara, Y., Yoshikawa, E., Nakamura, Y., Morimoto, T., & Ushio, T. (2023). Time-series changes in cloud particles and discharge processes using a dual-phased array weather radar and an LF lightning discharge device. Journal of Atmospheric Electricity, 42(2), 15-18. [Non-Patent Document 4] Wang, S., Wada, Y., Hayashi, S., Ushio, T., & Chandrasekar, V. (2024). Electrical alignment signatures of ice particles before intracloud lightning activity detected by dual-polarized phased array weather radar. Journal of Geophysical Research: Atmospheres, 129(7), e2023JD039942. [Overview of the project] [Problems that the invention aims to solve]

[0008] In recent years, existing lightning prediction technologies are often insufficient to meet diverse needs. Inaccurate information is unsafe, and even accurate information is meaningless if it takes too long to collect and distribute. Further technological development is desired to enhance safety in aircraft operations and disaster prevention, and to ensure the security of people's lives. Therefore, the objective is to provide a lightning information provision system, a lightning prediction device, and a lightning prediction method that can predict lightning with high accuracy and speed. [Means for solving the problem]

[0009] According to an embodiment, a lightning information providing system includes a lightning prediction device and an information distribution device. The lightning prediction device includes a data collection unit and a lightning prediction processing unit. The data collection unit collects weather-related data from a data source including a multi-parameter phased array weather radar. The lightning prediction processing unit calculates lightning prediction data based on the weather-related data. The information distribution device includes a determination unit and an information distribution unit. The determination unit determines whether to issue an alarm based on the lightning prediction data. The information distribution unit issues an alarm based on the result of the determination by the determination unit.

Brief Description of the Drawings

[0010] [Figure 1] FIG. 1 is a system diagram showing an example of a lightning prediction system according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing an example of the lightning prediction device 10. [Figure 3] FIG. 3 is a diagram showing an example of the data flow in the lightning prediction system according to an embodiment. [Figure 4] FIG. 4 is a diagram for explaining bright band. [Figure 5] FIG. 5 is a diagram showing an example of a neural network. [Figure 6] FIG. 6 is a functional block diagram showing an example of the information distribution device 30.

Modes for Carrying Out the Invention

[0011] FIG. 1 is a system diagram showing an example of a lightning information providing system according to an embodiment. This lightning information providing system 1 includes a lightning prediction device 10 and an information distribution device 30 connected to a network N. A meteorological agency server 40, an on-site server 50 such as an airport observation radar, an MP-PAWR 60, and a database 70 are connected to the network N.

[0012] The Meteorological Agency server 40, the on-site server 50, and the MP-PAWR 60 acquire data related to meteorological phenomena that change moment by moment, and distribute it via the network N or store it in the database 70. The database 70 stores lightning strike data related to the occurrence of past lightning, etc.

[0013] The lightning prediction device 10 collects meteorological-related data from these data sources via the network N and predicts the occurrence of lightning. The obtained lightning prediction data is passed from the lightning prediction device 10 to the information distribution device 30 together with the lightning observation data, and an alarm is distributed to the user according to the situation. The alarm is distributed via the Web to the terminal devices 20P, smartphones 20T, etc. via the base station BS of the mobile communication network, for example.

[0014] FIG. 2 is a block diagram showing an example of the lightning prediction device 10. FIG. 3 is a diagram showing an example of the data flow in the lightning prediction system according to the embodiment.To explain the configuration of the lightning prediction system, refer to FIGS. 2 and 3.

[0015] In FIG. 2, the lightning prediction device 10 is a computer including a processor 12 such as a CPU (central processing unit) and a memory 13. The processor 12 realizes the functions of the lightning prediction device 10 according to the program read into the memory 13. The program is read by the media reading unit 14 from a program distributed online or on a recording medium M and installed in the HDD (Hard Disk Drive). Further, the lightning prediction device 10 is connected to a communication unit 15 connected to the network N, an input unit 16 such as a keyboard and a pointing device, and a display unit 17 such as a liquid crystal display.

[0016] The processor 12 includes a data collection unit 12a, an observation data storage unit 12b, a data processing unit 12c, a lightning prediction processing unit 12d, a lightning prediction data storage unit 12e, and a learning unit 12f as processing functions according to the embodiment.

[0017] The data collection unit 12a collects meteorological data from data sources, including the MP-PAWR60. As shown in Figure 3, the data sources for meteorological data include lightning observation data from the MP-PAWR60, the Japan Meteorological Agency, aircraft, and private weather companies. The data collection unit 12a collects observation data and various physical quantities from the MP-PAWR60, meteorological data (LFM, upper-air temperature information, etc.) periodically distributed from the Japan Meteorological Agency server 40, aircraft lightning strike data (PIREP (Pilot REPort) information, etc.), lightning observation data, or predicted values ​​of meteorological phenomena (GPV (Grid Point Value) data, etc.) via the network N. The collected meteorological data is stored in the observation data storage unit 12b.

[0018] The data processing unit 12c extracts features related to the occurrence of lightning from the collected weather-related data. The lightning prediction processing unit 12d calculates lightning prediction data, which indicates the probability of future lightning strikes, based on the collected weather-related data. The lightning prediction data is calculated, for example, for the probability of lightning strikes occurring within one hour from the present time, for each grid region (3D or 2D) into which the space is divided into multiple parts. The grid (mesh) is set in units of, for example, 2km. Based on the features extracted by the data processing unit 12c, the lightning prediction processing unit 12d calculates lightning prediction data using statistical processing, for example, machine learning such as a neural network. The calculated lightning prediction data is stored in the lightning prediction data storage unit 12e.

[0019] The learning unit 12f creates a trained model for the lightning prediction processing unit 12d to use to calculate lightning prediction data. The created trained model is stored in memory 13 (trained model 13a). In other words, the learning unit 12f generates the trained model 13a by training a model that uses the features extracted by the data processing unit 12c as explanatory variables and the lightning prediction data as the target variable, based on lightning data 70a stored in the database 70.

[0020] In machine learning, it is important to extract effective features. In this embodiment, for example, the following features (1) to (6) are used. The thresholds TH1 to TH9 can be parameterized.

[0021] (1) Is the radar reflectance at an altitude of -10°C greater than or equal to the threshold TH1 [dBZ]? Lightning charge separation is thought to occur at an altitude of approximately -10°C. Therefore, a high radar reflectance at this altitude is related to the active formation of cumulonimbus clouds. In this embodiment, the radar reflectance at a predetermined altitude is used as the first feature (1).

[0022] (2) The vertically accumulated water content in the range of TH2 [km] above and below -10°C altitude is TH3 [g / m³]. 2 Is that all? A high vertically integrated moisture content near the altitude at which lightning charge separation occurs is associated with the formation of active cumulonimbus clouds. The altitude range to be searched and the vertically integrated moisture content can be parameterized. In this embodiment, the vertically integrated moisture content in a predetermined altitude range is used as the second feature (2).

[0023] (3) In the particle discrimination results within a range of TH4km above and below an altitude of -10°C, are hail and ice crystals in close proximity and separated? Also, what is the volume of each TH5[m³]? 3 ], TH6[m 3 Is that all? This is the volume of what is known as dry hail. Understanding the conditions of hail and ice crystals, which are important for charge separation in lightning, is related to the possibility of lightning occurring within a developed cumulonimbus cloud. In this embodiment, the determination result related to the particle discrimination result in a predetermined altitude range, and / or the volume of the region of each particle are used as the third feature quantity (3). More specifically, the third feature (3) indicates whether hail and ice crystals are in close proximity and separated in the region containing an altitude of -10°C. Furthermore, the third feature (3) represents the volume of the hail region and the volume of the ice crystal region when hail and ice crystals are in close proximity and separated. Also, the third feature (3) represents the altitude of the hail region.

[0024] (4) A negative K in the atmosphere below TH7°C DP with a volume of TH8 [m 3 or more, or in the upper atmosphere, when the region with a negative K DP expands, charge separation is considered to progress, which is related to the possibility of lightning strikes. In an embodiment, the volume of the region having a negative polarization phase difference change rate in the atmosphere below a predetermined temperature is used as the fourth feature quantity (4).

[0025] (5) Whether the minimum value of K DP due to the vertical distribution of K DP is TH9 [deg / km2] or less, or if the intensity of the negative K DP is strong, charge separation is considered to progress, which is related to the possibility of lightning strikes. In an embodiment, the minimum value of the polarization phase difference change rate in the vertical distribution is used as the fifth feature quantity (5).

[0026] (6) The temperature obtainable by MP-PAWR When special weather conditions are met, a radar image called a bright band may be observed. Mainly in winter, the altitude of the melting layer where ice crystals melt may decrease. Since the radar reflection intensity of the melting layer is extremely high, if the convection is gentle, a ring-shaped radar image centered on the radar device may be observed. This is called a bright band. In an embodiment, the temperature corresponding to the cloud base height calculated from the radius of the bright band generated in the radar image is used as the sixth feature quantity (6).

[0027] Figure 4 is a diagram illustrating the bright band. Weather radars emit radio waves upwards, so when a melting layer descends, areas of strong reflection intensity (hatched areas) may appear as rings. In Figure 4, it can be seen that a melting layer is present above the Tokyo radar (Kashiwa radar) of the Japan Meteorological Agency, which covers the Tokyo metropolitan area, or above the Funabashi and Yokohama radars of the Ministry of Land, Infrastructure, Transport and Tourism. By observing the radius of the bright band, the altitude of the melting layer can be determined. In other words, based on the data obtainable with MP-PAWR, the altitude at which the temperature is approximately 0°C (cloud base altitude) can be calculated. This data can be used to accurately predict winter thunderstorms, a phenomenon different from summer thunderstorms. It can also be used as alternative data when other weather-related data cannot be obtained.

[0028] Features (1) to (6) are nodes that can be determined at the determination time. Based on the determination result of each feature, a neural network is pre-trained using past lightning conditions obtained from the lightning data 70a to determine whether lightning was detected at the determination time and in what area. Of course, all of features (1) to (6) may be used, or at least any of (1) to (6) may be used.

[0029] Figure 5 shows an example of a neural network. The neural network in Figure 5 is a so-called fully connected convolutional neural network (CNN), with an input layer (L in ), convolutional layer (L C ), pooling layer (L p ), hidden layer (kth: HL) k ), and output layer (L out ) is equipped with.

[0030] The input layer has multiple nodes (1) to (n), and when feature quantities (1) to (6) are input as input data (explanatory variables) to these nodes, lightning prediction data is output as the target variable. By repeatedly providing input data and updating the weights (parameters) of the connections between nodes so that the error between input and output is minimized, a trained model 13a (Figure 2) is generated. The lightning prediction processing unit 12d then inputs the feature quantities of various data obtained from the data source into the trained model 13a to obtain the latest lightning prediction data.

[0031] Note that the features (1) to (6) are just examples, and various other features can be used. By selecting a variety of nodes, it becomes possible to consider various factors, thereby improving the accuracy of lightning prediction or detection. Furthermore, the configuration of the lightning prediction model can also be various different networks, such as RNNs and Bayesian networks. In addition, it is thought that the feature thresholds should be changed for ground users and airborne (aircraft) users. Therefore, it is possible to generate multiple networks with different numbers of nodes and thresholds.

[0032] Figure 6 is a functional block diagram showing an example of an information distribution device 30. The information distribution device 30 comprises a determination unit 31 and an information distribution unit 32. The determination unit 31 determines the area where lightning is detected from the lightning prediction data and determines whether or not to issue a warning based on the lightning prediction data. That is, the determination unit 31 compares the lightning prediction data with lightning observation data from, for example, the Japan Meteorological Agency's lightning monitoring system, LIDEN (Lightning Detection Network system), and determines whether or not it is appropriate to notify the user of information regarding the occurrence of lightning (warnings, etc.). When the probability of lightning strikes rises above a threshold, the information distribution unit 32 notifies the user of this via the network N.

[0033] As described above, in this embodiment, observed values ​​(MP-PAWR data, lightning observation data, PIREP (Pilot REPort) information, etc.) and predicted values ​​(GPV (Grid Point Value) data, etc.) that form the basis for lightning detection are acquired via a network. Then, by combining the high spatiotemporal resolution data obtained from MP-PAWR, its analysis methods, and general meteorological data, the probability of lightning occurrence is predicted. In other words, by combining various physical quantities analyzed by a multi-parameter phased array weather radar with upper-air temperature information provided by the Japan Meteorological Agency, etc., it is possible to construct a system that accurately captures lightning precursors and notifies the user when lightning is detected. This enables lightning detection / prediction with high accuracy and sufficient lead time, and furthermore, provides a lightning information provision system that promptly notifies the user when lightning is actually detected.

[0034] By creating lightning prediction data and notifying users when a threshold is exceeded, it becomes possible to take prompt action to avoid or cancel lightning strikes. Specifically, it is expected to assist in making decisions on whether aircraft and rockets can enter cumulonimbus clouds, and by providing lightning detection information to ground-based airport staff, agricultural and fishing workers, and event venues, thereby aiding in risk assessment. Furthermore, through data analysis and notification systems using communication infrastructure, there is potential for use in weather-affected industries such as agriculture, fisheries, and operations.

[0035] Based on these considerations, the embodiment makes it possible to provide a lightning information provision system, a lightning prediction device, and a lightning prediction method that can predict lightning with high accuracy and speed.

[0036] It should be noted that this invention is not limited to the embodiments described above. For example, in the embodiments, the probability of lightning strikes was determined, but it is also possible to calculate the probability of cumulonimbus clouds ceasing based on meteorological data and then calculate the probability of lightning ceasing based on this.

[0037] While embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications are permitted without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention, as well as within the scope of the claims and their equivalents. [Explanation of symbols]

[0038] 1...Lightning information provision system, 10...Lightning prediction device, 12...Processor, 12a...Data acquisition unit, 12b...Observation data storage unit, 12c...Data processing unit, 12d...Lightning prediction processing unit, 12e...Lightning prediction data storage unit, 12f...Learning unit, 13...Memory, 13a...Trained model, 14...Media reading unit, 15...Communication unit, 16...Input unit, 17...Display unit, 20P...Terminal device, 20T...Smartphone, 30...Information distribution device, 31...Determination unit, 32...Information distribution unit, 40...Japan Meteorological Agency server, 50...On-site server, 70...Database, 70a...Lightning data.

Claims

1. It is equipped with a lightning prediction device and an information distribution device, The lightning prediction device is A data acquisition unit that collects weather-related data from data sources including a multi-parameter phased array weather radar, A data processing unit that extracts features related to the occurrence of lightning from the aforementioned weather-related data, The system includes a lightning prediction processing unit that calculates lightning prediction data based on the aforementioned weather-related data and the extracted feature quantities. The aforementioned information distribution device is A determination unit that determines whether or not to issue a warning based on the lightning prediction data, The system includes an information distribution unit that issues the alarm based on the determination result of the determination unit, A lightning information provision system in which the aforementioned feature quantities are feature quantities relating to the particle discrimination result in a predetermined altitude range, and include a third feature quantity indicating whether or not hail and ice crystals are in close proximity and separated in a region including an altitude of -10°C.

2. The system further comprises a database for storing lightning data related to past lightning occurrences, The lightning prediction device is The system includes a learning unit that generates a trained model by training a model with the aforementioned features as explanatory variables and the lightning prediction data as the target variable, based on the lightning strike data. The lightning prediction processing unit, The lightning information provision system according to claim 1, wherein the extracted features are given to the trained model to calculate the lightning prediction data.

3. The feature quantity is further, The first feature is the radar reflectance at a predetermined altitude. The second feature is the vertically integrated moisture content within a predetermined altitude range. The fourth feature is the volume of the region with a negative interpolarization phase difference change rate in the atmosphere below a predetermined temperature. The fifth feature is the minimum value of the rate of change of the interpolarization phase difference in the vertical distribution, or The sixth feature is the temperature corresponding to the cloud base altitude, calculated from the radius of the bright bands that appeared in the radar image. The lightning information provision system according to claim 1, comprising at least one of the following.

4. The lightning information provision system according to claim 1, wherein the third feature quantity is the volume of the hail region and the volume of the ice crystal region when the hail and ice crystals are in close proximity and separated.

5. The lightning information providing system according to claim 4, wherein the third feature quantity is the altitude of the hail region.

6. A data acquisition unit that collects weather-related data from a data source including a multi-parameter phased array weather radar, A data processing unit that extracts features related to the occurrence of lightning from the aforementioned weather-related data, The system comprises a lightning prediction processing unit that calculates lightning prediction data based on the aforementioned weather-related data and the extracted feature quantities, A lightning prediction device, wherein the aforementioned feature quantity is a feature quantity relating to the particle discrimination result in a predetermined altitude range, and includes a third feature quantity indicating whether or not hail and ice crystals are in close proximity and separated in a region including an altitude of -10°C.

7. A process by which a computer collects weather-related data from a data source including a multi-parameter phased array weather radar, The process by which the computer extracts features related to the occurrence of lightning from the weather-related data, The computer comprises a process for calculating lightning prediction data indicating the probability of lightning occurrence based on the weather-related data and the extracted feature quantities, A lightning prediction method in which the aforementioned feature quantities are feature quantities relating to the particle discrimination result in a predetermined altitude range, and include a third feature quantity indicating whether or not hail and ice crystals are in close proximity in a region including an altitude of -10°C.

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