Weather management system and weather management method

Abstract

To provide a weather management system using a weather phenomenon corresponding to observation data from a weather radar obtained from weather-related post information.SOLUTION: A weather management system of an embodiment comprises: an analysis unit that classifies observation data from a weather radar on the basis of a predetermined criterion, and outputs an analysis result based on the classification; and an alert control unit that outputs alert information on the basis of the analysis result. When the analysis result and a weather phenomenon grasped from weather-related post information that is posted from a poster terminal and that matches the observation data observed in the weather radar are different from each other, the analysis unit changes the analysis result on the basis of the weather phenomenon grasped from the weather-related post information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] An embodiment of the present invention relates to a technology that uses true values ​​(levels of accuracy) of meteorological phenomena corresponding to observation data of a weather radar, obtained by comparing observation results based on observation data observed by a weather radar with actual meteorological phenomena. [Background technology]

[0002] Weather radar emits radio waves (microwaves) and observes rain or snow within a specified range. It measures the distance to the rain or snow from the time it takes for the emitted radio waves to return, and can observe the intensity of the rain or snow from the strength of the returned radio waves.

[0003] In recent years, dual-polarized meteorological Doppler radar has been introduced, which uses radio waves that vibrate horizontally (horizontal polarization) and radio waves that vibrate vertically (vertical polarization) to make it possible to more accurately identify the type of precipitation particles in clouds and estimate the intensity of precipitation. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Park, H. -S and AV Ryzhkov, DS Zrnic and K. -E Kim 2009: The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D:Description and Application to an MCS. Wea. Forecasting., 24, 730-748 [Non-Patent Document 2] Hideaki Kagesawa (and 5 others), Title: "Proposal and Consideration of Sensing System Using Twitter", Published: July 2, 2014, URL: https: / / ipsj.ixsq.nii.ac.jp / ej / ?action=pages_view_main&active_action=repository_view_main_item_detail&item_id=104972&item_no=1&page_id=13&block_id=8 Summary of the Invention [Problem to be solved by the invention]

[0005] A weather management system is provided that uses weather phenomena corresponding to weather radar observation data obtained from weather-related posted information. [Means for solving the problem]

[0006] According to an embodiment, the weather management system includes an analysis unit that classifies observation data of a weather radar based on a predetermined criterion and outputs an analysis result based on the classification, and an alert control unit that outputs alert information based on the analysis result. When the analysis result differs from a meteorological phenomenon grasped from weather-related posted information posted from a poster terminal that matches the observation data observed by the weather radar, the analysis unit changes the analysis result based on the meteorological phenomenon grasped from the weather-related posted information. [Brief description of the drawings]

[0007] [Figure 1] 1 is a diagram illustrating a functional block and a network configuration of an information management device according to a first embodiment. [Diagram 2] 10 is a diagram for explaining a process of generating true value information in which meteorological phenomena identified from posted information are linked to meteorological phenomena corresponding to observation data in the first embodiment. FIG. [Diagram 3] 11 is a diagram for explaining a process of extracting weather-related posted information from posted information by using various types of evaluation information in the first embodiment. FIG. [Figure 4] FIG. 4 is a diagram showing a process flow executed by the information management device of the first embodiment. [Diagram 5] FIG. 11 is a diagram showing a functional block and network configuration of a weather management device according to a second embodiment. [Figure 6] FIG. 11 is a diagram for explaining a determination criterion change process according to the second embodiment. [Figure 7] FIG. 11 is a diagram showing a process flow executed by the weather management device of the second embodiment. [Figure 8] FIG. 13 is a diagram illustrating a functional block and a network configuration of an information management device according to a third embodiment. [Figure 9] FIG. 13 is a diagram for explaining a first function of classifying weather-related posted information based on insurance payment history and a second function of classifying weather-related posted information using a classification model in the third embodiment. [Figure 10] FIG. 13 is a diagram showing a processing flow of a first function executed by a classification control unit of the third embodiment. [Figure 11] FIG. 11 is a diagram illustrating a process flow executed by an information management device according to a third embodiment. [Figure 12] FIG. 11 is a diagram showing a process flow executed by a weather management device linked to an information management device in the third embodiment. [Figure 13] FIG. 13 is a diagram showing a functional block and network configuration of a weather management device according to a fourth embodiment. [Figure 14] FIG. 13 is a diagram showing a process flow executed by a weather management device of the fourth embodiment. [Figure 15] FIG. 13 is a diagram showing a process flow executed by a weather management device of the fourth embodiment. [Figure 16] FIG. 13 is a diagram showing a process flow executed by a weather management device of the fourth embodiment. [Figure 17] FIG. 13 is a diagram showing a functional block and network configuration of a weather management system according to a fourth embodiment. BEST MODE FOR CARRYING OUT THEINVENTION

[0008] Hereinafter, an embodiment will be described with reference to the drawings.

[0009] As mentioned above, weather radars such as dual-polarized meteorological Doppler radars make it possible to distinguish the types of precipitation particles in clouds and estimate the intensity of precipitation more accurately. However, the analysis results based on observational data observed by weather radars are merely "estimates" and may differ from the weather phenomena that actually occurred at the site. Precipitation particles are classified into categories such as wet snow, dry snow, ice crystals, drizzle, rain, hail, and heavy rain. For example, in an observation area where it was estimated based on observational data that "wet snow fell in this observation area," the weather phenomenon that actually occurred was "hail."

[0010] Therefore, the JMA is working to improve the accuracy of estimates based on observation data by combining data from weather radar observations with data from rain gauges and other observational instruments owned by the JMA, the Ministry of Land, Infrastructure, Transport and Tourism, and local governments across the country, and using meteorological phenomena actually observed at or near the site.

[0011] However, while sensor devices such as rain gauges and snow gauges can tell whether the amount of rain or snowfall was large or small, they cannot tell whether it was drizzle, wet snow, dry snow, hail, or sleet. In this regard, disdrometers (precipitation particle size and velocity distribution measuring devices) can observe drizzle, rain, hail, snow, etc., but disdrometers are mostly used for research purposes and are not deployed at the Japan Meteorological Agency's AMeDAS observation stations throughout the country. In particular, hail and sleet occur infrequently and are rarely observed on the ground, so promoting the deployment of disdrometers is not realistic.

[0012] In this way, weather phenomena that cannot be observed or are difficult to observe using the observation equipment at AMeDAS observation stations are difficult to feed back into analyses based on weather radar observation data, making it difficult to improve the accuracy of analyses based on weather radar observation data.

[0013] In addition to hail and other weather phenomena, wind gusts such as tornadoes and whirlwinds also occur infrequently and are rarely observed on the ground. Although it is possible to observe the occurrence of cumulonimbus clouds based on weather radar observation data and estimate the possibility of the occurrence of tornadoes and other phenomena, they occur infrequently and in limited, localized areas, making them difficult to observe with observation equipment.

[0014] Therefore, we developed a new technology to compare the observation results based on the observation data observed by weather radar with the actual weather phenomena, and provide the true value (level of accuracy) of the weather phenomena corresponding to the observation data of the weather radar. Specifically, we use the weather-related posted information posted from the poster's terminal to a web server that accepts posted information.

[0015] In addition, in a weather management device that classifies and analyzes weather radar observation data based on predetermined criteria, the criteria are changed based on the true value (level of accuracy) of a weather phenomenon based on posted information, for example by lowering the criteria, thereby preventing the occurrence of a specified weather phenomenon from being overlooked.

[0016] (First embodiment) 1 to 4 are diagrams for explaining a first embodiment. Fig. 1 is a diagram showing functional blocks and a network configuration of an information management device 100 according to the present embodiment.

[0017] The information management device 100 is provided with information related to observation data of the weather radar from the weather management device 200, and is provided with posted information posted by the contributor from the web server 300. The information management device 100 provides a function of extracting and generating, as true values ​​(correct labels), meteorological phenomena understood from the weather-related posted information posted by the contributor as meteorological phenomena corresponding to the observation data observed by the weather radar. In other words, it functions as a device that generates a data set of observation data and correct labels corresponding to the observation data.

[0018] Weather radar, for example, emits radio waves (microwaves) and observes rain or snow. It measures the distance to the rain or snow from the time it takes for the emitted radio waves to return, and observes the intensity of the rain or snow from the strength of the returned radio waves. As mentioned above, dual-polarized meteorological Doppler radar has also been introduced, and by using radio waves that vibrate horizontally and vertically (horizontal polarization, vertical polarization), it is possible to distinguish the type of precipitation particles in the clouds and estimate the intensity of the precipitation.

[0019] Dual-polarized meteorological Doppler radar can estimate the shape of precipitation particles from the ratio of their amplitudes. The larger the precipitation particles, the more air resistance they experience and the flatter they become. This is observed using horizontal and vertical polarization, and the shape of the precipitation particles is estimated from the amplitude ratio of the reflected waves. The intensity of rain can also be estimated from the phase difference. When radio waves travel through water, such as raindrops, their speed is slightly slower than when they travel through empty air. Taking advantage of the fact that the heavier the rain, the slower the speed of horizontally polarized waves, observations are made using horizontal and vertical polarization, and the intensity of rain is estimated from the phase difference of the reflected waves.

[0020] In estimating precipitation particles based on the amplitude ratio, categories such as wet snow, dry snow, ice crystals, drizzle, rain, graupel, hail, and heavy rain are prepared in advance. For example, if the amplitude ratio is equal to or greater than a predetermined value, the precipitation particles are determined (categorized) as hail. The amplitude ratio is set as the determination criterion, and the weather phenomenon in the observation area is estimated based on the observation data. Hail is defined as ice particles with a diameter of 5 mm or more that fall from cumulonimbus clouds, and ice particles with a diameter of less than 5 mm are defined as graupel.

[0021] The web server 300 accepts posted information from the poster terminal T and provides a website that publishes the accepted posted information on the web. A poster is a user who is registered with the web server 300, and includes an unspecified number of posters. As an example, information posted to a social networking service (SNS) such as Twitter (registered trademark) or Facebook (registered trademark) can be used. In this case, posted information other than weather-related posted information is also stored in the web server 300.

[0022] As another example, information posted to a website that accepts weather-related posted information can be used. For example, a contributor can be a weather reporter or observer and post daily weather information. In this case, only weather-related posted information is stored in the web server 300.

[0023] The information management device 100 connects to one or more websites 300 to acquire weather-related posted information. The poster terminal T is a mobile terminal such as a multi-function mobile phone such as a smartphone or a tablet computer, and is equipped with a data communication function via an IP (Internet protocol) network or a mobile communication network, a calculation function (CPU, etc.), and a storage device (memory, auxiliary storage device, etc.).

[0024] The poster terminal T may also include a display control application such as a browser, a photographing device for photographing still images and / or moving images, a touch panel display input device, and a GPS device. As will be described later, the posted information posted from the poster terminal T includes the posted content as well as location information and time information acquired by the GPS device.

[0025] The weather forecasting device 400 provides a weather forecasting function using a weather forecasting model, for example, the meso model of the Japan Meteorological Agency or the meso weather model WRF, and provides forecast results of weather phenomena (snow, rain, hail, hail, etc.) in a forecast area and at a forecast time. The weather forecasting device 400 (weather forecasting model) is a well-known technology, and detailed description thereof will be omitted.

[0026] As shown in Fig. 1, an information management device 100 is connected to weather management information 200, a website 300, and a weather forecasting device 400 via an IP network or a dedicated line. A communication device 110 controls data communication between each device. The information management device 100 includes a control device 120 and a storage device 130. The control device 120 includes an information acquisition unit 121, an extraction unit 122, and a data management unit 123.

[0027] FIG. 2 is a diagram for explaining a process of generating true value information in which meteorological phenomena grasped from posted information are linked to meteorological phenomena corresponding to observation data in this embodiment.

[0028] Posted information posted to the web server 300 includes a posted article and / or a posted image, location information, time information, and poster information. A posted article is a posted text, and a posted image is an image taken by a photographing device provided in the poster terminal T, or an image taken by a photographing device other than the poster terminal T. The location information is the current location of the poster (poster terminal T) and may include address information corresponding to the current location. The posted time is date and time information when the web server 300 accepts the posted information. The poster information is a user identifier such as a user ID registered in the web server 300.

[0029] A contributor inputs a post article and a post image on a posting screen provided by the web server 300 through the contributor terminal T, and transmits the post information to the web server 300. The web server 300 accepts the post information transmitted from the contributor terminal T, and posts the post information on the posting website provided by the web server 300, making it publicly available.

[0030] As an example of the information management device 100 acquiring posted information, that is, collecting posted information, a mode of acquiring the posted information from the web server 300 that accepts and publishes posted information has been described, but the invention is not limited to this. For example, the posted information may be acquired via a search server (search site). The search site can collect posted information related to a search key such as a keyword from a plurality of different posting websites (a plurality of different websites 300). Thus, the information management device 100 can acquire posted information posted to the web server 300 from the website 300 or from a search site that can collect posted information from the website 300.

[0031] As shown in FIG. 2, the information acquisition unit 121 acquires weather-related posted information from a plurality of posted information items on the web server 300 based on the similarity of a keyword related to weather or an image related to weather. One or a plurality of weather-related posted information items can be acquired. For example, posted information including the keyword "hail" can be acquired as weather-related information, or posted information having a posted image similar to a reference image showing "hail" can be acquired as weather-related posted information. Although "hail" has been described as an example, weather-related posted information related to "graupel" can be collected in a similar manner. Note that an image recognition AI model can also be used as a process for extracting posted information having posted images related to "hail". For example, a group of images showing "hail" can be input as learning data in advance, and a learning process can be performed to generate a "hail image recognition" AI model. Then, the generated AI model can be used to calculate the similarity of the posted image to "hail", and posted information having a posted image with a similarity equal to or greater than a predetermined value can be acquired as weather-related information.

[0032] Next, the extraction unit 122 performs a matching process with the observation data of the weather radar. That is, the extraction unit 122 extracts weather-related posted information that matches the observation area and observation time of the observation data observed by the weather radar.

[0033] For example, the analysis result of the weather radar observation data, i.e., the estimation of precipitation particles based on the amplitude ratio, matches the observation area and observation time determined to be hail with the location information and posting time of the weather-related posted information related to "hail." The extraction unit 122 determines whether the location information and posting time of the weather-related posted information related to "hail" are within a predetermined range that is sufficiently close to the observation area and observation time, and extracts the weather-related posted information related to "hail" that is within the sufficiently close predetermined range.

[0034] The data management unit 123 links the weather-related posted information that matches the observation data as a meteorological phenomenon corresponding to the observation data. In other words, the data management unit 123 outputs the meteorological phenomenon identified from the weather-related posted information extracted by the extraction unit 122 as the meteorological phenomenon corresponding to the observation data.

[0035] The data management unit 123 outputs meteorological phenomena grasped from weather-related posted information as true values ​​(correct labels) of meteorological phenomena corresponding to the observation data, and can function as a data generation unit that generates a data set including the observation data and the true values ​​(correct labels) of the meteorological phenomena. This data set can be fed back to the meteorological management device 200 as supervised learning data, and can be used for tuning the analysis process of the observation data of the above-mentioned weather radar, the analysis algorithm, and the judgment criteria (thresholds) used in the analysis process. Note that, as long as the true values ​​(correct labels) of the meteorological phenomena can be linked to the corresponding observation data, it is not necessary to hold the observation data itself as a data set. For example, when feeding back to the meteorological management device 200, the information management device 100 (data management unit 123) can generate and provide a data set including the observation area, the observation time, and the true values ​​(correct labels) of the output meteorological phenomena.

[0036] In the above example, in estimating precipitation particles using observation data, a mode of matching observation data determined to be hail with meteorological posted information related to "hail" has been described, but for example, the observation area and observation time determined to be hail may be matched with the location information and posting time of meteorological posted information related to "hail", and "hail" may be linked as the true value (correct label) of the meteorological phenomenon to the observation data of the observation area and observation time determined to be hail. The same is also true vice versa.

[0037] In this way, the information management device 100 of this embodiment checks the meteorological phenomena estimated from the analysis results of the observation data using posted information that can be collected on the Web, and generates the meteorological phenomena understood from the weather-related posted information as true values ​​(correct answer labels) of the meteorological phenomena corresponding to the observation data.

[0038] In particular, for meteorological phenomena such as hail and sleet, which occur infrequently and are rarely observed on the ground, it will be possible to cross-check the results of analysis of observation data, thereby improving the accuracy of analysis based on weather radar observation data.

[0039] FIG. 3 is a diagram for explaining the process of extracting weather-related posted information from posted information by using various types of evaluation information according to this embodiment.

[0040] The information collecting unit 121 extracts weather-related posted information from a plurality of pieces of posted information, and can evaluate the reliability of the posted information and extract weather-related posted information having an evaluation above a certain standard. The information collecting unit 121 can include a first processing unit to a fourth processing unit.

[0041] As shown in FIG. 3, the first processing unit 121A of the information collection unit 121 extracts candidate posted information from a plurality of pieces of posted information posted to the web server 300 based on similarities between keywords related to weather or images related to weather (first processing).

[0042] The second processing unit 121B of the information collecting unit 121 generates a poster evaluation value for the poster of the candidate posted information based on a predetermined evaluation criterion (second process). The poster evaluation value can be calculated using evaluation parameters (evaluation criteria) such as the poster's number of past posts, the number of evaluations from other posters, and the number of achievements in which past posted information was adopted as a true value (correct label). The poster's number of past posts and the number of evaluations from other posters can be acquired from the posting history managed by the web server 300. The information management device 100 can accumulate posters of weather-related posted information that were adopted as a true value (correct label). The number of achievements in which past posted information was adopted as a true value (correct label) is stored in the storage device 130 as poster evaluation information 132.

[0043] The third processing unit 121C of the information collecting unit 121 calculates the number of pieces of posted information by other contributors similar to the candidate posted information extracted by the first processing unit 121A as the posting accuracy of the candidate posted information. Specifically, the third processing unit 121C extracts pieces of posted information by other contributors (similar posted information) that include location information and time information within a predetermined range close to the location information and time information of the candidate posted information and have similarity in keywords related to weather or images related to weather from the multiple pieces of posted information posted to the web server 300. The third processing unit 121C generates a posting accuracy evaluation value for the candidate posted information based on the extracted similar posted information (third processing).

[0044] The fourth processing unit 121D of the information collecting unit 121 evaluates the information accuracy of the candidate posted information extracted by the first processing unit 121A using the forecast information generated by the weather forecasting device 400. Specifically, the fourth processing unit 121D generates a weather event evaluation value for the candidate posted information based on the forecast information generated by a predetermined weather forecast model corresponding to the position information and time information of the candidate posted information (fourth processing). If the weather phenomenon in the position information and time information of the candidate posted information is similar to the weather information in the forecast information, the weather phenomenon evaluation value is calculated to be high, and if the weather phenomenon in the position information and time information of the candidate posted information is different from the weather information in the forecast information, the weather phenomenon evaluation value is calculated to be low.

[0045] Then, the information collecting unit 121 extracts weather-related posted information from the candidate posted information whose evaluation is equal to or greater than a predetermined value based on the poster evaluation value, the posting accuracy evaluation value, and the meteorological event evaluation value. For example, it is possible to extract candidate posted information whose evaluation values ​​are equal to or greater than a predetermined value as weather-related posted information, or to extract candidate posted information whose total of evaluation values ​​is equal to or greater than a predetermined value as weather-related posted information. In addition, it is possible to apply weight values ​​to the poster evaluation value, the posting accuracy evaluation value, and the meteorological event evaluation value, and to set the weight values ​​of the poster evaluation value and the posting accuracy evaluation value high and the weight value of the meteorological event evaluation value low to calculate the total evaluation value. In this case, the weight values ​​can be set arbitrarily.

[0046] The poster evaluation value, the posting accuracy evaluation value, and the weather event evaluation value may be configured to be composed of each evaluation value alone or in any combination. The information collection unit 121 may be configured to evaluate the candidate posted information by at least one of the poster evaluation value, the posting accuracy evaluation value, and the weather event evaluation value, or any combination thereof, and extract the weather-related posted information.

[0047] The above-mentioned extraction process of weather-related posted information using the evaluation value may not be applied depending on the characteristics of the posted information. For example, when the weather-related posted information is collected from a weather information posting web server 300 (website) that specializes in accepting posts of weather information, the posted information may be treated as posted information whose poster's reliability and information accuracy as posted information are guaranteed in advance, and the information management device 100 may not select the posted information based on each evaluation value. In other words, when the web server 300 that accepts the posted information selects posted information whose poster's reliability and information accuracy are guaranteed and makes it public on the web, the candidate posted information extracted by the first processing unit 121A may be extracted as it is as weather-related posted information.

[0048] FIG. 4 is a diagram showing a process flow executed by the information management device 100 of the present embodiment.

[0049] The web server 300 accumulates posted information from contributors via contributor terminals T. At this time, posted information other than that related to weather is also included.

[0050] The information management device 100 extracts candidate posted information from a plurality of pieces of posted information posted to the web server 300 based on similarities between keywords related to weather or images related to weather (S101). For example, the posted information stored in the web server 300 is searched for using keywords such as "hail" and "graupel" or sample images of "hail" and "graupel" as extraction keys. The extraction keys are arbitrarily set by the operator of the information management device 100.

[0051] The candidate posted information is posted information that matches either one of a keyword related to weather and a sample image, or posted information that matches both a keyword related to weather and a sample image.

[0052] Next, the information management device 100 performs the evaluation value calculation process described in Fig. 3 on the extracted candidate posted information (S102). The information management device 100 extracts weather-related posted information from one or more candidate posted information whose evaluation is equal to or greater than a predetermined value based on the calculated poster evaluation value, posting accuracy evaluation value, and meteorological event evaluation value (S103). For example, the candidate posted information with the highest evaluation result based on the evaluation value can be extracted as the weather-related posted information.

[0053] The information management device 100 acquires observation data from the weather management device 200. The information management device 100 extracts weather-related posted information that matches the observation area and observation time of the observation data observed by the weather radar (S104). Then, the information management device 100 outputs the meteorological phenomenon understood from the extracted weather-related posted information as the true value (correct label) of the meteorological phenomenon corresponding to the observation data (S105). The true value (correct label) of the meteorological phenomenon based on the output posted information is stored in the storage device 130 (S106).

[0054] As described above, the information management device 100 can also function as a data generator that generates a data set including observation data and true values ​​(correct labels) of meteorological phenomena. The information management device 100 can provide the data set to the weather management device 200 and the weather forecasting device 400 at the appropriate timing, which can be useful for improving the accuracy of the analysis process of the weather management device 200 and the prediction process of the weather forecasting device 400.

[0055] In the above-described processing steps of the information management device 100, the matching process centered on step S104 includes two modes.

[0056] First, based on the location information and time information of the weather-related posted information extracted in step S103, the information management device 100 (control device 120) can be configured to obtain observation data including the corresponding observation area and observation time from the weather management device 200, and match the two in step S104.

[0057] Secondly, the weather management device 200 can be configured to obtain in advance observation data that observes specific weather phenomena such as "hail" or "graupel" as analysis results, and in the process of extracting weather-related posted information from steps S101 to S103, extract weather-related posted information that corresponds to the observation area and observation time of the observation data.

[0058] In both processes, the weather-related posted information collected from the web server 300 is matched with the observation data using location information and time information, and the weather-related posted information that matches the observation area and observation time of the observation data observed by the weather radar is extracted, and this is output as the true value (correct label) of the weather phenomenon corresponding to the observation data.

[0059] Second embodiment 5 to 7 are diagrams for explaining the second embodiment. In this embodiment, a weather management device 200 analyzes observation data and provides a weather management function that outputs an alert based on the analysis result. At this time, the true value (correct answer label) of the weather phenomenon for the observation data generated by the information management device 100 of the first embodiment is fed back in real time. The weather management device 200 of this embodiment cooperates with the information management device 100 of the first embodiment to configure a weather management system.

[0060] 5 is a diagram showing the functional blocks and network configuration of the weather management device 200 of this embodiment. The weather management device 200 includes a communication device 210, a control device 220, and a storage device 230. The control device 220 includes an analysis unit 221, a judgment criterion control unit 222, an alert control unit 223, and a true value information acquisition unit 224.

[0061] The analysis unit 221 classifies the observation data of the weather radar based on a predetermined criterion, and outputs an analysis result based on the classification. As described above, the analysis unit 221 can perform precipitation particle estimation processing (analysis processing) based on the amplitude ratio, and categories (classifications) such as wet snow, dry snow, ice crystals, drizzle, rain, hail, and heavy rain are prepared in advance. The analysis unit 221 sets the amplitude ratio as the criterion, and estimates the meteorological phenomenon in the observation area based on the observation data.

[0062] The alert control unit 223 outputs alert information based on the analysis result of the analysis unit 221. For example, the alert information can be output to an information distribution system to which multiple users are registered, or to a web server that publishes information on a website, or the alert information can be provided to a user (user terminal) registered in the weather management device 200.

[0063] In this embodiment, a judgment criterion control unit 222 is provided, and the judgment criterion is changed based on the meteorological phenomenon (true value) grasped from the weather-related posted information corresponding to the observation data output from the information management device 100 of the first embodiment.

[0064] FIG. 6 is a diagram for explaining the determination criterion change process of the present embodiment.

[0065] The weather management device 200 performs precipitation particle estimation processing using weather radar observation data A. The observation data A is observed at 15:31 on January 5, 2023, in the observation area of ​​City A. The analysis unit 221 outputs a particle diameter of "3 mm" as an analysis result of the observation data A based on the judgment criteria. The analysis unit 221 outputs the particle diameter of "3 mm" and the meteorological phenomenon "hail" corresponding to the analysis result. The alert control unit 223 generates and outputs alert information such as "Hail may be falling in City A. Please be careful."

[0066] This observation data A is also provided to the information management device 100 in real time in parallel with the analysis process of the weather management device 200, and a process of generating a true value (correct label) of the meteorological phenomenon based on the weather-related posted information for the observation area and observation time is performed. Then, the true value (correct label) is linked to the observation data A and provided from the information management device 100 to the weather management device 200. The true value information acquisition unit 224 performs a process of providing the observation data A to the information management device 100 and a process of acquiring the true value (correct label) from the information management device 100.

[0067] When the judgment criteria control unit 222 receives the true value (correct label) of the observation data A, it performs a process of comparing the analysis result with the true value (correct label). If the result of the comparison process determines that the meteorological phenomenon in the analysis result differs from the true value (correct label) of the observation data A, the judgment criteria control unit 222 temporarily changes the judgment criteria. In the example of FIG. 6, the analysis result is "graupel" and the true value (correct label) is "hail", which are different. The judgment criteria control unit 222 changes the judgment criteria, assuming that the meteorological phenomenon based on the observation data A is the true value (correct label).

[0068] For example, the criteria for the precipitation particle estimation process is that when the amplitude ratio is equal to or greater than a predetermined value X but less than a predetermined value Y, it is determined to be "graupel," and when the amplitude ratio is equal to or greater than the predetermined value Y, it is determined to be "hail," and "graupel," etc. are output as the analysis result of the meteorological phenomenon. In the example of FIG. 6, the criteria control unit 222 outputs "graupel" as the analysis result because the amplitude ratio of the observation data A was equal to or greater than the predetermined value X but less than the predetermined value Y, but since "hail" actually fell, the criteria control unit 222 changes the predetermined value Y, which is the boundary threshold between "graupel" and "hail," to a predetermined value Y1. The predetermined value Y1 is a value smaller than the predetermined value Y, and the threshold change is performed to expand the area in the observation data that is determined to be "hail."

[0069] In this way, the threshold value is changed in the judgment criteria to temporarily narrow the area judged as "graupel" and widen the area judged as "hail." The changed judgment criteria are then applied to the analysis process of observation data B, which was observed after observation data A.

[0070] The observation data B is observed at 15:42 on January 5, 2023 in the observation area of ​​City B (next to City A). At this time, the observation data B is the same observation data as the observation data A, and the analysis result using the judgment criteria before the change is determined to be "graupel." However, in the judgment criteria after the change based on the true value (correct answer label) of the observation data A, the analysis unit 221 outputs the meteorological phenomenon "graupel" as the analysis result of the observation data B. The alert control unit 223 generates and outputs alert information such as, for example, "Hail may be falling in City B. Please be careful," or "Hail may fall in City B soon. Please be careful." Since weather radar can observe precipitation particles in the sky, it may be possible to output alert information several minutes to several tens of minutes before they fall to the ground.

[0071] Here, the change in the judgment criteria can be applied to each observation area. For example, analysis processing can be performed on observation data C in an observation area far away from the observation area of ​​observation data A without changing the judgment criteria. On the other hand, the system can be configured to apply the changed judgment criteria to analysis processing on observation data B that is close to the observation area of ​​observation data A and was observed within a predetermined time from the observation time of observation data A.

[0072] Therefore, the judgment criteria can be changed for each observation area and observation time of the observation data corresponding to the true value provided by the information management device 100. A valid area and an expiration date are set for the changed value of the judgment criteria. That is, an area within a predetermined range from the observation area of ​​the observation data A corresponding to the true value (correct label) is set as the observation area to which the changed value applies, and a predetermined time (e.g., 30 minutes) from the observation time of the observation data A is set as the expiration date. When the expiration date has passed, the changed value is initialized, and thereafter, analysis processing is performed using the judgment criteria value before the change (default value).

[0073] FIG. 7 is a diagram showing a process flow executed by the weather management device 200 of this embodiment.

[0074] The weather radar performs continuous observation in a time series at a predetermined interval and outputs the observation data. When the weather management device 200 receives the observation data A of the weather radar, the weather management device 200 performs an analysis process of the observation data A (S201). At this time, the weather management device 200 provides the observation data A to be analyzed to the information management device 100.

[0075] The weather management device 200 performs the above-mentioned precipitation particle estimation process. For example, it classifies based on a predetermined judgment criterion, and outputs an analysis result based on the classification. The analysis result is stored in the storage device 230 (S202). The weather management device 200 outputs alert information based on the analysis result (S203).

[0076] The weather management device 200 receives true value information of the meteorological phenomenon for the observation data A from the information management device 100. Upon receiving the true value information from the information management device 100, the weather management device 200 starts a judgment criterion change process (S204). The weather management device 200 compares the analysis result of the observation data A with the received true value (correct label) information, and if it is determined that the meteorological phenomenon is the same, it does not perform a judgment criterion change process (NO in S205). On the other hand, if it is determined that the meteorological phenomenon is different (YES in S205), it changes the judgment criterion based on the true value (correct label) understood from the weather-related posted information corresponding to the observation data A (S206).

[0077] The weather management device 200 uses the determination reference value changed in step S206 to repeatedly perform analysis processing of the subsequent observation data of the weather radar (from NO in S208 to S201).

[0078] At this time, as described above, when the criteria control unit 222 changes the criteria based on a meteorological phenomenon identified from weather-related posted information corresponding to observation data A, it sets an effective area and effective time to which the changed criteria will be applied based on the observation area and observation time of the observation data A (S207). Then, in step S201, the analysis unit 221 determines whether the observation area and observation time of the subsequent observation data B are within a valid range corresponding to the set effective area and effective time, and if it is determined that they are within the valid range, applies the changed criteria to the analysis process of the subsequent observation data B.

[0079] In this embodiment, in the analysis processing of observation data in the weather management device 200, a true value (correct label) provided by the information management device 100 is applied in real time and reflected in the analysis processing of the subsequent observation data. In other words, when the analysis result of the observation data and the true value (correct label) based on the weather-related posted information differ from each other, the judgment criteria can be changed based on the true value (correct label) to make the analysis result of the subsequent observation data more accurate.

[0080] Hail, in particular, is a natural disaster that causes great damage. Even if the analysis results of observation data indicate that graupel is falling rather than hail, there may actually be cases where hail is falling (in the posted information). Therefore, in the subsequent analysis process of the observation data, even similar observation data that was determined to be graupel is determined to be hail more strictly as falling. In other words, the frequency with which it is determined to be hail is increased, and the number of times it is not determined to be hail is reduced. By configuring in this way, it is possible to easily issue alerts for meteorological phenomena that cause great damage as natural disasters, and to appropriately call attention to disaster prevention.

[0081] In addition, the area and time range to which the change in the judgment criteria is applied are set based on the observation area and observation time of the observation data that triggered the change in the judgment criteria. As described above, hail is a special meteorological phenomenon that is observed infrequently, and is not a meteorological phenomenon that occurs over a long period of time or over a wide area. Therefore, the change in the judgment criteria is applied to observation data within a radius of, for example, 50 km from the observation area of ​​the observation data that triggered the change in the judgment criteria and within two hours, making it easier to issue an alert for meteorological phenomena that cause serious damage as a natural disaster. With this configuration, it is possible to appropriately limit the area for which a hail alert is issued, while not unnecessarily expanding the area for which a hail alert is issued, thereby suppressing confusion caused by the alert.

[0082] Third embodiment 8 to 12 are diagrams for explaining the third embodiment. In contrast to the first embodiment, this embodiment has a function of classifying weather-related posted information based on insurance payment history. Note that the same components as those in the first embodiment are denoted by the same reference numerals in each drawing and will not be described.

[0083] 8 is a diagram showing functional blocks and a network configuration of the information management device 100 of this embodiment. The control device 120 of the information management device 100 further includes a classification control unit 124, and the storage device 130 stores classification related information 134.

[0084] 9 is a diagram for explaining a first function of classifying weather-related posted information based on insurance payment history and a second function of classifying weather-related posted information using a classification model in this embodiment. The first function is a function of generating learning data from past weather-related posted information and generating a classification model using the generated learning data. The second function is a function of performing a classification judgment process on weather-related posted information newly extracted by the extraction unit 122 using the generated classification model and associating the result of the classification judgment process with a meteorological phenomenon corresponding to the observation data output from the data management unit 123, i.e., a true value (correct label).

[0085] The insurance payment history is the history of insurance payments made by insurance companies for damages caused by meteorological phenomena such as hail, sleet, tornadoes, whirlwinds, etc. Insurance products include insurance products for individuals that cover natural disasters such as fire insurance, flood insurance, hail insurance, and vehicle insurance, as well as insurance products for businesses that cover natural disasters.

[0086] The history of insurance claims payments is the record of insurance companies paying claims based on claims from policyholders after grasping the damage situation, and includes information such as the payment item, location of damage, date and time of damage, and the person to whom payment is made.

[0087] The payment item is a meteorological phenomenon related to the occurrence of damage, and for example, "hail damage" is included as a payment item. The location of damage is the location where the payment target was damaged by hail (location information where the damage occurred), and is, for example, address information such as XX town, XX city, XX prefecture. The payment target is the asset that suffered damage and is the insured object. For example, in the case of vehicle insurance, it is the vehicle, and in the case of fire insurance, it is the house. This insurance payment history information is provided by the insurance company. The insurance payment history information can be obtained from the insurance company (the insurance company's server or terminal) via a network, or can be obtained as physical list information and input into the information management device 100 by the operation manager. The insurance payment history information is stored in the storage device 130 as classification related information 134.

[0088] In this embodiment, the insurance payment history information is used to classify the weather-related posted information into a meteorological phenomenon group with a high risk of damage (a meteorological phenomenon group with a history of insurance payment) and other groups (a meteorological phenomenon group with no history of insurance payment). That is, from multiple pieces of weather-related posted information, weather-related posted information corresponding to a damage location and date and time for which there was an insurance payment history is extracted, and information indicating that the meteorological phenomenon has a high risk of damage is associated with it. This makes it possible to distinguish meteorological phenomena with a high risk of damage based on insurance payment history (record), even for the same meteorological phenomenon. For example, weather-related posted information belonging to the meteorological phenomenon "hail" can be classified into weather-related posted information belonging to the meteorological phenomenon "hail (high risk of damage)" and other weather-related posted information.

[0089] Therefore, the classification control unit 124 associates (labels) the classification information "high damage risk" with the weather-related posted information corresponding to the damage location and date and time for which there was an insurance payment history. Then, when generating a true value (correct label), the data management unit 123 can perform control so that the classification information associated with the weather-related posted information used to generate the true value (correct label) is included. For example, the true value information 133 of meteorological events by observation data stored in the storage device 130 can be configured to include the classification information "high damage risk" in addition to the meteorological phenomenon based on the observation time, observation area, and weather-related posted information.

[0090] As described above, hail is ice grains with a diameter of 5 mm or more, but hail with a diameter of 10 mm is the same. However, the larger the diameter, the greater the damage, and the smaller the diameter, the less damage there may be. Therefore, even if the meteorological phenomenon of hail is the same, the damage (loss) caused by the meteorological phenomenon differs, so in this embodiment, insurance payment history information (damage record) is used as evidence of actual damage caused by the meteorological phenomenon "hail."

[0091] Then, the weather-related posted information corresponding to the past insurance payment history (the weather-related posted information used to generate the true value (correct label)) is accumulated as learning data. The learning data is stored in the storage unit 130 as classification related information 134.

[0092] For example, images and posted text of weather-related posted information that corresponds to past insurance payment history can be accumulated as learning data. The images are used as learning data for hail images that caused actual damage, and the posted text is used as learning data for text (keywords, etc.) related to hail that caused actual damage. For example, posted text containing keywords related to the hail itself or keywords related to the damage (loss) caused by hail, such as "It was about 30 mm in diameter," "About the size of a golf ball," "The hood of the car was dented," and "It broke through the roof of the garage," is used as learning data.

[0093] The accumulated learning data is used in the generation process (learning process) of a classification model that determines whether newly extracted weather-related posted information corresponds to a weather phenomenon with a high risk of damage. The classification model calculates the image similarity and the similarity of the posted text based on keywords, etc. from the image and / or posted text of the newly extracted weather-related posted information, and classifies weather-related posted information with a similarity equal to or greater than a predetermined value into a "weather phenomenon with a high risk of damage group."

[0094] 10 is a diagram showing a processing flow of the first function executed by the classification control unit 124. The classification control unit 124 performs the following processing.

[0095] 1) A first process (S1001, S1002) of classifying past weather-related posted information extracted by the extraction unit 122 into a meteorological phenomenon group with a high risk of damage based on insurance payment history information including the location of the damage related to the insurance payment, the date and time of the damage, and the meteorological phenomenon related to the damage; 2) A second process (S1003) of storing the weather-related posted information classified into a high-risk weather phenomenon group as learning data in a predetermined storage area; 3) A third process (S1004) of generating a classification model for classifying weather-related posted information extracted by the extraction unit 122 in the future into a weather group with a high risk of damage, using the learning data.

[0096] Fig. 11 is a diagram showing a processing flow executed by the information management device 100, and corresponds to the processing flow of Fig. 4 of the first embodiment. In Fig. 11, steps S104A and S104B are added, and these steps correspond to a second function of the classification control unit 124.

[0097] The classification control unit 124 performs the following process on the weather-related posted information extracted in step S104. 4) A fourth process (S104A) of performing a classification determination process of determining whether or not the weather-related posted information extracted by the extraction unit 122 belongs to a meteorological phenomenon group with a high risk of damage, using the generated classification model; 5) A fifth process (S104B) of associating the result of the classification determination process with the meteorological phenomenon (true value (correct label)) corresponding to the observation data output from the data management unit 123.

[0098] Then, the classification judgment result by the classification model (identification information indicating that the meteorological phenomenon has a high damage risk) is added to the true value (correct label) output from the data management unit 123. In other words, the true value (correct label) output from the data management unit 123 is configured to have information on whether or not the meteorological phenomenon belongs to a meteorological phenomenon group with a high damage risk (S105).

[0099] Next, a case will be described in which a true value (correct label) including identification information indicating that the weather phenomenon has a high risk of damage according to this embodiment is applied to the second embodiment.

[0100] First, in the second embodiment, as shown in Fig. 6, the judgment criterion can be changed using a true value (correct label) provided by the information management device 100. Here, even if the true value (correct label) provided by the information management device 100 of this embodiment includes identification information indicating that the meteorological phenomenon has a high risk of damage, the weather management device 200 can change the judgment criterion regardless of whether the meteorological phenomenon has a high risk of damage.

[0101] On the other hand, the weather management device 200 can also control the change range of the judgment criterion to be different between a true value (correct label) belonging to a "weather phenomenon with high damage risk" and a true value (correct label) not belonging to a "weather phenomenon with high damage risk". For example, when changing the judgment criterion based on a true value (correct label) belonging to a "weather phenomenon with high damage risk" (S206), the change value can be changed to P1, and when changing the judgment criterion based on a true value (correct label) not belonging to a "weather phenomenon with high damage risk", the change value can be changed to P2 (P1>P2) which is smaller than P1.

[0102] 12, the alert control unit 223 of the weather management device 200 determines whether the true value (correct answer label) provided by the information management device 100 belongs to a "weather phenomenon with high damage risk" (S2004), and if it is determined that it belongs to a "weather phenomenon with high damage risk" (YES in S2004), the alert control unit 223 can control the alert output to output alert information (e.g., a warning alert) indicating that it is a weather phenomenon for which actual damage has been reported in the past (S2005). On the other hand, if it is determined that it does not belong to a "weather phenomenon with high damage risk" (NO in S2004), the alert control unit 223 can control the alert output to output normal alert information (S2006).

[0103] Therefore, even with the same alert output process, an alert can be output for cases where there is a high risk of damage, where there has been actual damage in the past, and, for example, a stronger recommendation to evacuate indoors can be made for hail that is actually falling or that is likely to fall in the future.

[0104] In addition, the output destination of the alert information can be controlled to be different depending on whether or not the weather phenomenon belongs to the "weather phenomenon with high damage risk". As described above, in the second embodiment, the alert information can be output to an information distribution system to which multiple users are registered or a web server that publishes information on a website. In this case, the alert control unit 223, for example, determines that there is an emergency when the weather phenomenon belongs to the "weather phenomenon with high damage risk" and provides the alert information directly to the terminal of the user registered in the information distribution system. When the weather phenomenon does not belong to the "weather phenomenon with high damage risk", the alert information can be output to the information distribution system or the web server, and the terminal of each user can receive the alert information via the information distribution system or the web server. Note that even if the weather phenomenon belongs to the "weather phenomenon with high damage risk", the output destination may not be changed, and the alert information belonging to the "weather phenomenon with high damage risk" may be provided to the information distribution system or the web server, and provided to the terminal of each user from the information distribution system or the web server.

[0105] In this manner, the information management device 100 of this embodiment classifies weather-related posted information used to generate true values ​​(correct labels) into meteorological phenomenon groups with high damage risks and other groups based on past insurance payment history. In other words, multiple weather-related posted information (true values ​​(correct labels)) are classified by risk level based on past insurance payment history. For example, even for true values ​​(correct labels) of the same meteorological phenomenon "hail," differences in risk level can be distinguished. This makes it possible to provide feedback data by risk level for analysis based on weather radar observation data, thereby further improving the accuracy of analysis.

[0106] Furthermore, the information management device 100 of this embodiment accumulates weather-related posted information classified into the "weather phenomena with high damage risk" group based on past insurance payment history as learning data, and generates a classification model that determines "weather phenomena with high damage risk" using the learning data. As a result, when applied to the second embodiment, the weather management device 200 can provide different alert outputs depending on the classification determination result of "weather phenomena with high damage risk" added to the true value (correct label), and can control the change value of the determination criterion to be different depending on the degree of danger. As a result, the weather management device 200 can appropriately alert people to disaster prevention in real time.

[0107] For example, when large, highly dangerous hail falls, it can scratch the hoods of cars and damage crops. If this could be known in advance, even at the last moment, it could be possible to reduce the damage by taking actions such as moving cars under roofs and covering crops with sheets. The same is true for meteorological phenomena other than hail. If it were possible to know in advance that a gust of wind would occur, it could be possible to prevent damage by suspending train service, closing highways, and ceasing work at height. Although normal alert output is an effective way to encourage actions to prevent damage, high-risk alert output, which has caused actual damage in the past, can be used as a means to strongly recommend actions to prevent damage.

[0108] In addition, from the perspective of insurance contracts, calling for action to prevent damage before it occurs also has the following advantages: Insurance companies that offer insurance products that cover damage through insurance can notify policyholders of alert information and have them take action to reduce damage, allowing the policyholder to prevent damage before it occurs and the insurance company to reduce insurance payments, which is beneficial for both parties.

[0109] Although the first to third embodiments have been described above, the information management device 100 can provide true values ​​(correct answer labels) of meteorological phenomena other than hail, such as tornadoes and whirlwinds. In this case, the weather-related posted information is posted information related to hail, tornadoes, or whirlwinds, and the information collection unit 121 can acquire the weather-related posted information containing tornadoes and whirlwinds as keywords and / or similar images from the web server 300. Then, the extraction unit 122 extracts the weather-related posted information that matches the observation area and observation time of the observation data of the tornado or whirlwind observed by the weather radar. The data management unit 123 can output the meteorological phenomenon of the tornado or whirlwind grasped from the extracted weather-related posted information as the meteorological phenomenon corresponding to the observation data.

[0110] One method for observing tornadoes and whirlwinds is meteorological Doppler radar observation. In addition to the location and strength of precipitation, the Doppler effect of radio waves reflected from precipitation particles carried by the wind can be used to measure the components of wind approaching and receding from the weather radar. This is called the Doppler speed. If the approaching speed is equal to or greater than a certain value and the receding speed is equal to or greater than a certain value, it can be determined that a wind vortex (rotation within a developed cumulonimbus cloud that causes a tornado; a mesocyclone) is occurring. For example, if the Doppler speed is equal to or greater than a certain value, it can be determined that a tornado is occurring.

[0111] In this way, the weather management device 200 can obtain the Doppler velocity observation data from the meteorological Doppler radar and perform an analysis process to determine whether a tornado or whirlwind will occur. The weather management device 200 can then provide the Doppler velocity observation data to the information management device 100.

[0112] Furthermore, the weather forecasting device 400 in the first embodiment can use a weather forecast model (prediction algorithm) of EHI (Energy-Helicity Index) for tornadoes and whirlwinds. In this case, the information collecting unit 121 can generate a weather event evaluation value for the candidate posted information as the fourth processing unit 121D based on prediction information generated by a weather forecast model of a tornado or whirlwind corresponding to the position information and time information of the candidate posted information. Then, the information collecting unit 121 can extract weather-related posted information from the candidate posted information in consideration of the weather event evaluation value (extract weather-related posted information from the candidate posted information in which the evaluation value of the occurrence prediction of a tornado or whirlwind is equal to or greater than a predetermined value). A known method other than EHI can be applied to the weather forecast model for tornadoes and whirlwinds.

[0113] The weather management device 200 of the second embodiment can perform the processes of outputting alert information and changing the judgment criteria for tornadoes and whirlwinds. In the judgment criteria change process, the threshold value of the Doppler speed for determining the occurrence of a tornado can be changed based on the true value (correct label) provided by the information management device 100. The function of the classification control unit 124 of the third embodiment can also be configured to target tornadoes, whirlwinds, and wind gusts. In this case, the insurance payment history information includes tornadoes, whirlwinds, and wind gusts as meteorological phenomena related to the occurrence of damage.

[0114] In addition, each of the devices 100, 200 in the first, second and third embodiments may be an apparatus configuration targeting any one of hail, graupel, tornadoes and whirlwinds, or may be an apparatus configuration targeting all of hail, graupel, tornadoes and whirlwinds, or any combination thereof.

[0115] (Fourth embodiment) 13 to 17 are diagrams for explaining the fourth embodiment. This embodiment is provided with a mechanism for improving the analysis function (weather detection function) of the weather management device 200 of the second embodiment by using weather-related posted information (true value information) provided by the information management device 100 of the first embodiment. The same components as those of the above two embodiments are denoted by the same reference numerals in each drawing and will not be described.

[0116] Fig. 13 is a diagram showing the functional blocks and network configuration of the weather management device 200A of this embodiment. The configuration is basically the same as that of Fig. 5, but the analysis unit 221 is configured as a weather detection unit 221A equipped with a weather detection model 221a. The weather detection model 221a performs a weather detection process that performs type discrimination processing of precipitation particles in clouds in the sky in the observation data based on a judgment criterion, and outputs the type of precipitation particles that will fall to the ground based on the result of the type discrimination processing.

[0117] That is, the weather detection model 221a (analysis unit 221) performs a type discrimination process (type estimation process) of precipitation particles based on the amplitude ratio observed by the weather radar, and classifies the precipitation particles into each type, such as wet snow, dry snow, ice crystals, drizzle, rain, hail, and heavy rain, according to the magnitude of the amplitude ratio. At this time, a judgment criterion is set for each type of precipitation particles, and the weather detection model 221a judges which type judgment criterion the amplitude ratio observed by the weather radar belongs to, and discriminates the type of one or more precipitation particles present in the clouds in the sky. Then, the weather detection model 221a outputs the type of precipitation particles falling to the ground based on the result of the type discrimination process of the precipitation particles in the clouds in the sky.

[0118] The information output from the weather detection unit 221A can be configured to include the type of precipitation particles falling on the ground determined by the weather detection model 221a (e.g., hail), and the location and time based on the observation area and observation time of the observation data. The location can also be configured to output the corresponding region (e.g., XX prefecture, XX city, XX town) based on the latitude and longitude of the observation area based on map information.

[0119] It may also be possible to output information indicating the reliability of the detected meteorological phenomenon together with the location and time. The reliability will be described later.

[0120] The judgment criteria control unit 222 is configured as a learning unit 222A of the weather detection model 221a. The learning unit 222A generates the weather detection model 221a, and also uses meteorological phenomena understood from weather-related posted information as learning data (correct answer labels), learns the types of precipitation particles that have actually fallen to the ground from the types of precipitation particles in the sky based on the observation data, and performs a learning process (tuning process) to change the judgment criteria.

[0121] Moreover, the weather management device 200A of this embodiment can be configured to include a setting unit 221B for setting priorities for multiple types of precipitation particles. Multiple different types of precipitation particles exist in the clouds in the sky, and for example, the distribution status of hail areas, sleet areas, snow areas, and rain areas in the clouds in the sky can be grasped from the observation data. In other words, multiple different types of precipitation particles exist in the clouds in the sky in three dimensions (latitude, longitude, and altitude).

[0122] On the other hand, the type of precipitation particles that actually fall on the ground needs to be captured in two dimensions (latitude and longitude). In this embodiment, the setting unit 221B controls so that a priority can be set for each type of precipitation particles. Then, the weather detection model 221a determines the type of each precipitation particle in the sky that exists within the observation area of ​​the observation data, and outputs the type of precipitation particles that fall on the ground in the order of the set priority when outputting the type of precipitation particles that fall on the ground. For example, it is assumed that the priority is set in the order of hail > sleet > snow > rain. When two precipitation particles, "hail" and "sleet", are detected, the weather detection model 221a can output "hail", which has a higher priority, as the type of precipitation particles that fall on the ground.

[0123] Although the embodiment of output control using the priority by the setting unit 221B has been described, the present invention is not limited to this. For example, control may be performed so that only "hail", which is a natural disaster that causes great damage, is output. In other words, the weather detection model 221a may be configured to output the type of precipitation particles that will fall to the ground when a specific weather phenomenon is detected. Furthermore, the priority setting may be changed at any timing. For example, during the weather detection process, the priority of snow may be set higher than that of hail, and "snow" may be preferentially output as a precipitation particle midway through the process.

[0124] As described above, the weather management device 200A of this embodiment includes a communication device 210, a control device 220, and a storage device 230, and the control device 220 includes a weather detection unit 221A (corresponding to an analysis unit 221) equipped with a weather detection model 221a, a setting unit 221B, a learning unit 222A (corresponding to a judgment criteria control unit 222), an alert control unit 223, and a true value information acquisition unit 224.

[0125] Next, the weather detection model 221a and the learning unit 222A will be described. Fig. 14 is a diagram showing a processing flow executed by the weather management device 200A. Fig. 14 corresponds to Fig. 7 of the second embodiment, and is a flowchart adapted to the processing of this embodiment. Steps S201a to S207a correspond to steps S201 to S207 in Fig. 7.

[0126] The weather detection model 221a is a functional unit that sequentially receives observation data observed by a weather radar, and uses the observation data as input to determine and output the current weather phenomenon (type of precipitation particles falling to the ground) (S201a). The weather detection unit 221A stores the output weather detection results in the storage device 230 (S202a), and the alert control unit 223 performs an alert output process (203a). In the example of Fig. 14, the setting unit 221B has previously set the priority (S202A).

[0127] The learning unit 222A performs a learning process using the learning data to generate the weather detection model 221a (S201A). The learning data is observation data that sets the weather-related posted information to a correct answer level, and is information that sets past observation data by a weather radar and the weather-related posted information corresponding to the observation data. In the example of FIG. 13, it corresponds to the observation data 231 and the true value information 233, and is stored in the storage device 230.

[0128] The learning unit 222A performs a first learning process (step S201A) for generating a weather detection model 221a using accumulated past learning data, and a second learning process for tuning (adjusting) the weather detection model 221a generated by the first learning process. The second learning process is a process for tuning (adjusting) a judgment criterion for classifying observation data into a plurality of precipitation particle types, updating the weather detection model 221a, and generating individual weather detection models 221a with different judgment criteria by tuning as described later.

[0129] The second learning process is divided into a real-time process (step S206a) that is executed in real time when weather-related posted information (true value information) is input, and a batch process (S210a) that suspends the second learning process even when weather-related posted information is input and executes the second learning process at a predetermined timing. The tuning methods include a first tuning method that executes only the real-time process, a second tuning method that uses both the real-time process and the batch process, and a third tuning method that executes only the batch process.

[0130] In the second tuning method, a second learning process is performed when weather-related posted information (true value information) is input during weather detection processing by the weather detection model 221a, and the judgment criteria are tuned in real time (S206a). Then, at any timing when the weather detection processing is not being performed, for example, during a maintenance period, the judgment criteria are retuned (re-learning process) using multiple observation data and weather-related posted information accumulated from after the previous maintenance to the current maintenance, and the weather detection model 221a is updated (S210a). Figure 14 shows the processing flow of this second tuning method.

[0131] Although the first tuning method does not perform the second learning process by batch processing, like the second tuning method, the second learning process is performed during the weather detection process to perform tuning, so that when the weather phenomenon understood from the observation data differs from the weather phenomenon understood from the weather-related posted information, the weather-related posted information is used as the correct label and the judgment criteria can be changed in real time. Therefore, the weather detection process can be performed on subsequent observation data using the tuned judgment criteria.

[0132] On the other hand, in the case of the third tuning method, the weather detection model 221a is not updated in real time. Therefore, when the weather phenomenon understood from the observation data differs from the weather phenomenon understood from the weather-related posted information, the output result (type of precipitation particles falling to the ground) of the weather detection unit 221A for the subsequent observation data does not reflect the weather-related posted information corresponding to the previous observation data as a correct label. Therefore, when the third tuning method is applied, a replacement process is performed to replace the type of precipitation particles output from the weather detection model 221a with the weather phenomenon (correct label) understood from the weather-related posted information corresponding to the previous observation data as a temporary measure until the second learning process is performed.

[0133] FIG. 15 is a diagram showing a process flow according to the third tuning method. The weather detection unit 221A judges whether the type of precipitation particles output from the weather detection model 221a based on the observation data is different from the weather phenomenon grasped from the weather-related posted information (S205a). If it is judged that the weather phenomena are different (YES in S205a), the weather detection unit 221A temporarily holds the feature amount of the observation data and the weather phenomenon grasped from the weather-related posted information (S2010) and turns on the replacement process flag (S2011). Note that the feature amount is information that represents the weather condition grasped from the observation data, for example, the distribution range of each precipitation particle and the weather condition in the clouds in the sky, such as the altitude at which the precipitation particles exist. Note that the feature amount extracted from the observation data can be calculated by a known method and can be expressed, for example, as a vector of parameters indicating the weather condition.

[0134] Then, when the weather detection process is performed on the subsequent observation data and the type is determined, in step S2012, it is confirmed whether or not the replacement process flag is ON. If the replacement process flag is ON, the process proceeds to step S2013, and the similarity between the feature amount of the subsequent observation data and the feature amount of the held previous observation data is determined. The similarity is determined, for example, by determining whether the feature amount of the subsequent observation data is a weather state at a certain distance from the feature amount (weather state vector) of the held previous observation data. If the feature amount (weather state vector) of the subsequent observation data is at a certain distance from the feature amount of the previous observation data, it can be determined that they are similar. This vector distance can be determined by applying a known method such as Euclidean distance, Manhattan distance, or Mahalanobis distance.

[0135] Then, in step S2014, if it is determined that the subsequent observation data is similar to the previous observation data, the weather detection unit 221A compares the correct label based on the previous weather-related posted information with the result of the weather detection process for the subsequent observation data. If the result of the weather detection process for the subsequent observation data differs from the correct label based on the previous weather-related posted information, the weather detection unit 221A outputs the type of precipitation particles by taking the correct label based on the previous weather-related posted information as correct. On the other hand, if it is determined that the subsequent observation data is not similar to the previous observation data, the weather detection unit 221A outputs the result of the weather detection process for the subsequent observation data.

[0136] With this configuration, even if the determination criteria (weather detection model 221a) are not changed in real time, it is possible to output a weather detection processing result that reflects the correct answer label based on the weather-related posted information in the previous stage in real time.

[0137] Next, as explained in the second embodiment above, in accordance with the aspect in which different judgment criteria are held, in this embodiment as well, the tuned weather detection model 221a can be generated as a separate weather detection model from the pre-tuning weather detection model 221a. Fig. 16 is a diagram showing a processing flow in which, in the real-time processing of the second learning process, multiple weather detection models with different judgment criteria are generated, and a corresponding weather detection model is selected for input observation data to perform weather detection processing.

[0138] 16, when the weather phenomenon understood from the observation data differs from the weather phenomenon understood from the weather-related posted information during the tuning process (YES in S205a), the learning unit 222A performs a learning process based on the currently used weather detection model 221a with the weather-related posted information as the correct answer label, and individually generates a weather detection model 221a (corresponding to a first weather detection model) with changed judgment criteria (S2061a).Then, the learning unit 222A sets a valid area and a valid time for the individually generated weather detection model 221a based on the observation area and observation time of the observation data (S2071a).

[0139] Then, when subsequent observation data is input, the weather detection unit 221A selects a weather detection model 221a that matches the observation area and observation time of the subsequent observation data from a plurality of weather detection models 221a having different effective areas and effective times (S2001a), and controls the weather detection processing for the subsequent observation data to be performed using the selected weather detection model 221a (S201a).

[0140] In addition, in step S2001a, if there is no weather detection model 221a that matches the observation area and observation time of the subsequent observation data, the weather detection model 221A can be configured to select the weather detection model 221a (reference model) generated in step 201A or re-learned in step S210a, and perform weather detection processing.

[0141] By configuring in this way, each of the multiple weather detection models can be switched and used. That is, the weather detection model 221a generated in step 201A or re-learned in step S210a can be configured as a reference model to be used for a long time. Each weather detection model 221a generated individually in real time processing in the second learning process can be applied as a model that can be used for a short time with at least one of a judgment criterion, an effective area, and an expiration date being different. Then, the weather detection unit 221A can improve the processing accuracy of the weather detection process by switching from a reference model to be used for a long time to a model to be used for a short time according to the observation data, or by switching between multiple models to be used for a short time. Note that when the effective time (expiration date) of the applied weather detection model 221a has passed, the weather detection unit 221A can be controlled to switch to the reference model and perform the weather detection process.

[0142] Here, the reliability output from the weather detection unit 221A will be described. The weather detection model 221a uses weather-related posted information used as learning data. Therefore, the reliability of the weather detection model 221a can be calculated based on each evaluation value related to the weather-related posted information of the first embodiment. Then, the reliability of the weather detection model 221a can be configured to be output together with the result of the weather detection process. The weather detection unit 221A can output information including, for example, the weather phenomenon "hail", the area "XX city, XX prefecture", and the reliability "80%".

[0143] Specifically, as described in the first embodiment, the weather-related posted information can be evaluated by a poster evaluation value, a posting accuracy evaluation value, and a weather event evaluation value. Therefore, the information management device 100 provides the true value information and each evaluation value of the weather-related posted information on which the true value information is based to the weather management device 200A. Then, the reliability of the weather detection model 221a can be calculated using the sum of each evaluation value of the weather-related posted information used as learning data, or any of the evaluation values. When performing the learning process using multiple different weather-related posted information, the average value or median value of the evaluation values ​​of the weather-related posted information can be calculated and used as the reliability of the weather detection model 221a. The reliability calculation method is arbitrary.

[0144] On the other hand, a highly reliable weather detection model 221a can be generated. That is, the evaluation value of the weather-related posted information can be used as an index for selecting weather-related posted information to be used as learning data. For example, the weather-related posted information accumulated for a certain period of time is sorted in order of reliability, and learning is performed in order of reliability. Then, the learning process is terminated when a desired result is obtained. For example, a weather detection process is performed using another observation data that outputs a type different from the correct label as sample test data, and it is verified whether the same type of meteorological phenomenon as the correct label is output, and when the verification result reaches a predetermined value or more, the learning process may be terminated. Also, the learning process may be configured to be performed using learning data having an evaluation value of a certain value or more. By configuring in this way, it is possible to suppress the learning process using learning data with low reliability as weather-related posted information, and to avoid overlearning of the weather detection model 221a.

[0145] The learning method of the weather detection model 221a, i.e., the method of tuning the judgment criteria by the learning unit 222A, can be performed by any one of the following means or a combination of two or more of the following means. The type of precipitation particles obtained from the weather detection model 221a through the type discrimination process of the observation data will be described as "A", and the type of precipitation particles (weather phenomenon) grasped from the weather-related posted information will be described as "C".

[0146] (A) From the past learning data learned up to now, observation data that is within a certain range of similarity from the weather phenomenon (weather condition) extracted from the input observation data is extracted. Then, the correct answer label associated with the extracted observation data is replaced from "A" to "C". In other words, as learning data, the correct answer label of the observation data that has a certain similarity is replaced with "C", the type of precipitation particles identified from the weather-related posted information. Then, using the group of observation data whose correct answer label has been replaced with "C" as learning data, a re-learning process is performed to update the weather detection model 221a.

[0147] The similarity can be determined by applying the similarity determination process that compares the feature amounts between the observation data described above. At this time, the threshold value for determining whether or not the similarity is within a certain range can be dynamically changed. For example, if the output result of the updated weather detection model 221a is still the type of precipitation particles "A", the threshold value for determining whether or not the similarity is within a certain range can be updated (increased), the distribution range to be applied as learning data can be increased, and the learning process can be performed again to update the weather detection model.

[0148] (B) In the case of weather detection processing using a probability density function or the like, the probability of each type (classification) of precipitation particles can be calculated based on the input observation data, and the type with the highest probability can be output. In this case, the learning unit 222A can increase the probability that the input observation data is classified as type "C" or decrease the probability that it is classified as type "A". The amount of increase and decrease in the probability can be a fixed value set in advance, or the value of the increase and decrease can be dynamically changed.

[0149] When increasing or decreasing the probability, an offset may be uniformly added or subtracted, or the probability density function itself may be changed. When changing the probability density function, the mean and variance of the probability density function may be changed, and the amount of change may be a preset value or a dynamically changed value. For example, to increase the probability, the mean value of the probability density function of type "C" is changed so that it approaches the input value of the probability density function extracted from the input observation data. Conversely, to decrease the probability, the mean value of the probability density function of type "A" is changed so that it moves away from the input value of the probability density function extracted from the input observation data. The result based on this updated probability can be the final output. If the result based on the updated probability is still type "A", the same operation may be repeated until the desired result is obtained.

[0150] (C) As in (B) above, a probability density function or the like can be used, and a probability density function or the like can be provided for each feature of the meteorological phenomenon (feature of a high-dimensional vector) extracted from the input observation data. In this case, the probability of each feature can be multiplied to calculate the final probability for each type. At this time, the probability of each feature can be increased uniformly, or the amount of probability to be dynamically increased can be allocated according to the result based on the probability. For example, a probability density function can be provided for feature 1, feature 2, and feature 3, and the probability of each feature is calculated for type "B". If the probability calculated from feature 1 is 0.99, the probability calculated from feature 2 is 0.91, and the probability calculated from feature 3 is 0.95, the probability of feature 2 is low, so the probability of feature 2 is increased significantly. The method of increasing or decreasing the probability can be the same as that in (B) above.

[0151] (D) When using the probability density function or the like in (B) above, if multiple weather-related posted information with high evaluation values ​​(reliability) is obtained, the probability density function may be configured to be updated using these multiple correct labels. For example, a Bayesian update method can be used in which the probability density function is used as a prior distribution and a posterior distribution is obtained from reliable results. In this case, the probability density function of type "C" is updated using this posterior distribution as a probability density function, and the result based on this updated probability can be used as the final output. If the result based on the updated probability still outputs type "A," the same operation can be repeated until the desired result is obtained.

[0152] In the above description, the weather management device 200 is linked to the separately configured information management device 100, but the present invention is not limited to this. For example, as shown in Fig. 17, the weather management device or weather management system can be configured with each functional unit and each piece of information of the information management device 100. In this case, the weather management system can be configured as follows.

[0153] (1) The weather management system (weather management device 200A) includes an information management unit that acquires weather-related posted information from a plurality of posted information items posted to a web server that accepts posted information items from a poster terminal, and extracts the weather-related posted information items that match the observation area and observation time of observation data observed by a weather radar; a weather detection unit including a weather detection model that performs a type discrimination process for precipitation particles in clouds in the sky based on the observation data and outputs the type of precipitation particles that will fall to the ground based on the result of the type discrimination process; The system has a learning unit that uses meteorological phenomena identified from weather-related posted information as learning data, learns the types of precipitation particles that have actually fallen to the ground in relation to the types of precipitation particles in the sky based on the observation data, and generates a weather detection model.

[0154] The type discrimination process of the weather management system in (1) above can be configured as a process of classifying meteorological phenomena in the sky based on observation data into the corresponding type based on the judgment criteria set for each type of precipitation particles. The learning unit performs a learning process using the meteorological phenomena identified from the weather-related posted information corresponding to the observation data as the correct answer label, and tunes the judgment criteria. Furthermore, the weather management system in (1) above can have each function of the weather detection unit 221A and the learning unit 222A including the weather detection model 221a of this embodiment.

[0155] Furthermore, the replacement process (steps S201a to S208) described with reference to FIG. 15 can be configured as a weather management device that reflects true value information by the replacement process without changing the judgment criteria, and can be configured as follows.

[0156] (3) Weather control equipment: an analysis unit that classifies the observation data of the weather radar based on a predetermined judgment criterion and outputs an analysis result based on the classification; an information acquisition unit that acquires weather-related posted information corresponding to the observation data output from the information management device according to claim 1; the analysis unit performs a type discrimination process for the precipitation particles in the clouds above in the observation data based on the judgment criteria set for each type of precipitation particle, and outputs a type of precipitation particles falling to the ground based on a result of the type discrimination process; the analysis unit compares the weather phenomenon identified from the weather-related posted information corresponding to the observation data with the type of precipitation particles falling on the ground that has been output, and when it is determined that the weather phenomena are different, holds the observation data and the weather-related posted information that have been determined to be different weather phenomena; The analysis unit determines the similarity between the subsequent observation data and the retained observation data, and if it is determined that there is similarity between the subsequent observation data and the retained observation data, it compares the weather phenomenon understood from the retained weather-related posted information with the type of precipitation particles falling to the earth's surface output for the subsequent observation data, and if the weather phenomenon obtained from the subsequent observation data is different from the weather phenomenon understood from the retained weather-related posted information, it outputs the weather phenomenon understood from the retained weather-related posted information as the weather detection result for the subsequent observation data.

[0157] Furthermore, similarly to the example of FIG. 17, the weather management device or weather management system having each functional unit and each piece of information of the information management device 100 can be configured to have the following components.

[0158] (4) The weather management system includes an information management unit that acquires weather-related posted information from a plurality of pieces of posted information posted to a web server that accepts posted information from a poster terminal, and extracts the weather-related posted information that matches an observation area and an observation time of observation data observed by a weather radar; The weather detection unit performs a weather detection process that performs a type discrimination process for precipitation particles in clouds in the sky based on the observation data, and outputs the type of precipitation particles that will fall to the ground based on the results of the type discrimination process. The weather detection unit then compares the weather phenomenon identified from the weather-related posted information corresponding to the observation data with the type of precipitation particles falling to the ground output by the weather detection process, and if it is determined that the weather phenomena are different, stores the observation data and the weather-related posted information that are determined to be different weather phenomena; Furthermore, the weather detection unit determines the similarity between the subsequent observation data and the retained observation data, and if it is determined that there is similarity between the subsequent observation data and the retained observation data, it compares the weather phenomenon understood from the retained weather-related posted information with the type of precipitation particles falling to the earth's surface output by the weather detection process using the subsequent observation data as input, and if the weather phenomenon obtained from the subsequent observation data is different from the weather phenomenon understood from the retained weather-related posted information, it outputs the weather phenomenon understood from the retained weather-related posted information as the weather detection result for the subsequent observation data.

[0159] In this way, the weather management device or weather management system of (3) and (4) above can have an aspect of a system configuration that does not assume a learning process or the like that reflects true value information based on weather-related posted information in the weather detection process itself. For example, an existing system that performs weather detection processing can be provided with a replacement processing function that replaces the type of precipitation particles output in the weather detection process with a weather phenomenon (correct label) understood from the weather-related posted information corresponding to the previous observation data, while cooperating with the information management device 100 that manages the weather-related posted information. By configuring in this way, it is possible to improve the accuracy of weather detection without changing the weather detection process itself.

[0160] In addition, the alert output process of the alert control unit 223 may be configured to obtain a large amount of weather-related posted information with high reliability as a weather phenomenon. As described in the second embodiment, the alert control unit 223 can generate and output alert information such as "There is a possibility of hail falling in A city. Please be careful." At this time, a message such as "What kind of weather phenomenon is occurring in the vicinity of A city? Please post it." is inserted into the alert information. By configuring in this way, the information management device 100 can collect (collect) a large amount of weather-related posted information, and the weather management device 200A can obtain a large amount of learning data. In addition, when providing alert information to a user (user terminal) registered in the weather management device 200, a push notification can be sent to the user terminal so that a large amount of weather-related posted information can be collected. In this way, by introducing a mechanism for encouraging posting in conjunction with the alert output process, posts on weather phenomena with high reliability (certainty) can be obtained, and the accuracy of the weather detection model 221a can be improved.

[0161] Also, like the first to third embodiments, this embodiment can perform weather detection processing that can detect weather phenomena such as tornadoes and whirlwinds.

[0162] As described above, each of the functions constituting the above-mentioned information management device 100 and weather management device 200 can be realized by a program, and computer programs prepared in advance to realize each function are stored in an auxiliary storage device, and a control unit such as a CPU reads the program stored in the auxiliary storage device into a main storage device, and the control unit executes the program read into the main storage device, thereby operating the functions of each unit.

[0163] The above program can also be provided to a computer in a state in which it is recorded on a computer-readable recording medium. Examples of computer-readable recording media include optical disks such as CD-ROMs, phase-change optical disks such as DVD-ROMs, magneto-optical disks such as MO (Magneto Optical) and MD (Mini Disk), magnetic disks such as floppy (registered trademark) disks and removable hard disks, and memory cards such as Compact Flash (registered trademark), Smart Media, SD memory cards, and memory sticks. Also included as recording media are hardware devices such as integrated circuits (IC chips, etc.) specially designed and configured for the purpose of the present invention.

[0164] Although the embodiment of the present invention has been described, the embodiment is presented as an example and is not intended to limit the scope of the invention. This new embodiment can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0165] 100 Information management device 110 Communication equipment 120 Control device 121 Information Gathering Department 121A First Processing Section 121B Second Processing Section 121C Third Processing Section 121D 4th Processing Section 122 Extraction part 123 Data Management Department 124 Classification Control Section 130 Storage device 131 Posted Information 132 Poster Rating Information 133 True Value Information 134 Classification-related information 200,200A Weather Control Equipment 210 Communication equipment 220 Control device 221 Analysis Department 221A Weather Detection Unit 221a Weather Detection Model 221B Setting section 222 Criteria control section 222B Learning Department 223 Alert control section 224 True value information acquisition unit 230 Storage device 231 Observation Data 232 Judgment criteria information 233 True Value Information 234 Setting information 300 Web Server 400 Weather Forecasting Device T terminal (submitter's terminal)

Claims

1. an analysis unit that classifies the observation data of the weather radar based on a predetermined judgment criterion and outputs an analysis result based on the classification; an alert control unit that outputs alert information based on the analysis result, A weather management system characterized in that the analysis unit changes the analysis result based on the weather phenomenon identified from the weather-related posted information when the analysis result differs from the weather-related posted information posted from the poster's terminal and matches observation data observed by a weather radar.

2. the analysis unit holds, when the analysis result differs from a meteorological phenomenon grasped from the weather-related posted information posted from a poster terminal and matching observation data observed by a weather radar, the observation data determined to be a different meteorological phenomenon and the weather-related posted information; the analysis unit determines whether a feature quantity representing the weather condition of the subsequent observation data is at a certain distance from a feature quantity representing the weather condition of the retained observation data, and when it is determined that the feature quantity of the subsequent observation data is at a certain distance from the feature quantity of the retained observation data, compares the weather phenomenon identified from the retained weather-related posted information with an analysis result of the subsequent observation data, and when the analysis result of the subsequent observation data differs from the weather phenomenon identified from the retained weather-related posted information, outputs the weather phenomenon identified from the retained weather-related posted information as the analysis result for the subsequent observation data. The weather management system according to claim 1 .

3. A program executed by a computer, the program comprising: A first function of classifying the observation data of the weather radar based on a predetermined judgment criterion and outputting an analysis result based on the classification; A second function of outputting alert information based on the analysis result is realized. The first function is a program characterized by changing the analysis result based on the meteorological phenomenon identified from the weather-related posted information when the analysis result differs from the meteorological phenomenon identified from the weather-related posted information posted from a poster's terminal that matches observation data observed by a weather radar.

4. A weather management method, comprising: A first step of classifying observation data of a weather radar based on a predetermined judgment criterion and outputting an analysis result based on the classification; A second step of outputting alert information based on the analysis result is executed; The weather management method is characterized in that the first step is to change the analysis result based on the weather phenomenon identified from the weather-related posted information when the analysis result differs from the weather-related posted information posted from the poster's terminal and matches the observation data observed by a weather radar.

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