JP2013054006A - Weather change prediction information providing system, weather change prediction information providing method, weather change prediction information providing program, and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a weather change prediction information providing system providing information for predicting given weather changes which locally generate and disappear in a short time in a mountainous area such as a mountain; a weather change prediction information providing method; a weather change prediction information providing program; and recording medium.SOLUTION: A weather change prediction information providing system 1 is equipped with at least an atmospheric pressure sensor 10; and includes a plurality of weather measuring devices 2 disposed to be scattered in a mountainous area, and a data processing device 4 for processing atmospheric pressure data measured by each of the plurality of the weather measuring devices 2. The data processing device 4 includes a weather data obtaining portion 32 for obtaining weather data from each of the plurality of the weather measuring devices 2; and a weather change prediction information producing portion 36 for producing weather change prediction information for predicting local weather change generated by the change of the atmospheric pressure on a real-time basis, on the basis of the weather data obtained by the weather data obtaining portion 32.

Description

  The present invention relates to a weather fluctuation prediction information providing system, a weather fluctuation prediction information providing method, a weather fluctuation prediction information providing program, and a recording that provide useful information for performing highly accurate prediction regarding weather fluctuation in a mountainous area such as a mountainous area. It relates to the medium.

  AMeDAS is an abbreviation for “Automated Meteorological Data Acquisition System” and refers to “Regional Meteorological Observation System”. In order to closely monitor the weather conditions such as rain, wind, and snow in terms of time and area, it is important for the prevention and mitigation of weather disasters by automatically observing precipitation, wind direction / wind speed, temperature and sunshine duration. Playing a role. AMeDAS started operation on November 1, 1974, and there are currently about 1300 observation stations nationwide for observing precipitation. Of these, in addition to precipitation, wind direction, wind speed, temperature, and sunshine duration are observed at about 850 locations (about 21 km intervals), and snow depth is also observed at about 290 locations in snowy regions. ing.

  The Japan Meteorological Agency issues weather forecasts from a wide range of weather information at weather stations covering the whole country or a wide range of weather information such as cloud movements sent from artificial satellites. In the case of such weather forecasts by the Japan Meteorological Agency, the observation mesh is large and the time mesh is large, and it is possible to predict rainfall in a wide area from the weather forecast. It is very difficult to predict rainfall in a very small area such as a factory with a large number of plant facilities. This is because if there are many unspecified factors such as the topography near the factory, a weather situation completely different from the weather forecast, such as a sunset, often occurs.

  Furthermore, sales trends in stores and usage conditions in outdoor facilities change according to climate characteristics such as temperature, humidity, and rainfall. Therefore, it is important to acquire and analyze the weather information of the area in real time in these businesses. It is also important for users to know what the local climate is in order to act comfortably in the local area.

  By the way, weather information over a wide area can be obtained free of charge from the weather forecast of the Japan Meteorological Agency, but detailed weather information and analysis results have to be obtained from specialized service providers and are expensive.

  In the conventional weather information collection and distribution method, a content provider distributes weather information to a user by obtaining weather information of a relatively wide range from a meteorological association or the like using a meteorological satellite, AMeDAS, a weather radar, or the like. In this case, since the obtained weather information covers a relatively wide area, the weather information of the pinpoint area that the user really wants to obtain cannot be obtained and the user's needs cannot be met. Currently.

  The general public obtains meteorological information, pollen information, etc. from the “Amedas” etc. established by the Japan Meteorological Agency through TV, radio, etc., but this information is a fairly wide range of general information and is not necessarily used. It could not be said to be detailed information closely related to a specific area or area expected by a person. Although it is possible to acquire such information by the conventional method as described above, the introduction of a new observation device and communication equipment requires enormous costs. In addition, if you want to distribute regional information to individuals, or if you want to distribute earthquake or volcanic prediction information to each home, you need to install a receiving device in each home, and you will have to pay considerable expenses. It will be tough.

  Patent Document 1 states that “the tendency of the atmospheric pressure data is obtained based on current and past atmospheric pressure data collected from a plurality of weather sensors (hygrometer, barometer, solar radiation meter, rain gauge, wind direction / anemometer, etc.). A trend extracting means for predicting atmospheric pressure data at a future predicted time based on the trend atmospheric pressure data, a minimal area data storage means for storing minimal area atmospheric pressure data specific to a predetermined minimal area, and a past atmospheric pressure in advance One or more barometric pressure data out of the trend barometric pressure data, the predicted barometric pressure data, and the current barometric pressure data generated by the trend extracting unit, and the confidence level function information storage unit that stores the confidence level function information related to rainfall generated from the data Using the minimum region atmospheric pressure data and the certainty function information, the reliability of rainfall for each atmospheric pressure data is obtained, and the certainty of rainfall is calculated. If to determine, and a prediction judgment means for obtaining a final rainfall confidence, rainfall prediction apparatus characterized by predicting the precipitation of minimum area "it has been proposed.

  Patent Document 2 states that “means for generating atmospheric pressure data by observing physical quantities related to weather (generating data on at least one of indoor temperature, outdoor temperature, indoor humidity, outdoor humidity, atmospheric pressure, wind speed, wind direction, and rainfall)” And a weather information providing server connected to the Internet for storing the atmospheric pressure data, and a client (computer) for extracting the atmospheric pressure data from the weather information providing server via the Internet and performing a predetermined process. Meteorological information processing device characterized by the above has been proposed.

  Patent Document 3 states that “a plurality of information terminals in areas distant from each other are connected to server information devices via communication lines, and information is transferred between these information terminals and between the information terminals and the server. It is the Internet, and a sensor for weather observation (detecting atmospheric pressure, temperature, humidity, illuminance, rainfall, cloud height, wind direction, wind speed, etc.) and a timer are connected to the information terminal, and the obtained atmospheric pressure data is A graph program for displaying a time function graph is provided, and the server is provided with a storage device for storing atmospheric pressure data, map data, and an atmospheric pressure program for creating an atmospheric pressure weather map, and the server is accessed from the information terminal on the Internet. Barometric pressure data is transmitted, and barometric pressure data of any one of the sensors is received and displayed in a graph, and the server stores the memory data. A meteorological observation network system is proposed in which a wide-area atmospheric pressure weather map using the map data is created from the atmospheric pressure data stored in a device using the atmospheric pressure program, and the information terminal receives and displays them. ing.

  In Patent Document 4, “a meter reading device that transmits a measured meter reading value to a center via a communication system is provided, and is connected to the meter reading device, and at least one of temperature, humidity, atmospheric pressure, noise, presence / absence of rainfall, and rainfall amount. There has been proposed a sensor device for observing such observation data, wherein the observation data is allowed to be transmitted to the center together with the meter reading value and stored in the meter reading device.

  Patent Document 5 states that “a plurality of mobile objects that are loaded with weather observation terminals capable of observing weather and transmit atmospheric pressure data, a plurality of user terminals that transmit and receive the atmospheric pressure data and the atmospheric pressure data; and the user A plurality of mobile units comprising: a content server that manages search and distribution of the atmospheric pressure data requested from a terminal; and the Internet that connects the plurality of mobile units with the plurality of user terminals and the content server. Transmits information from various sensors loaded in the car and position information and time information from the GPS receiver to the content server via a mobile phone at a fixed time period, and the content server moves the plurality of movements. Meteorological information received from the body is processed and accumulated for each district point and time and distributed in response to inquiries from the plurality of user terminals. Meteorological terminal air temperature, air pressure, humidity, illuminance, rainfall, weather information collection distribution method "is proposed, which is a sensor for outputting the detected sensor data either atmospheric conditions of lightning.

  Therefore, since the weather information collection and distribution method can transmit the latest position data and barometric pressure data at each district point where each mobile object is traveling, highly accurate weather forecasts etc. for pinpoint districts can be sent to many users. Can be provided easily.

  Patent Document 6 includes “a plurality of portable terminals, an information processing center that collects information from these portable terminals, processes and distributes the information, and a communication network that performs communication between the portable terminal and the information processing center. In the information collection / distribution system, the portable terminal is configured to specify a position specifying unit for specifying its own location, an information collecting unit for collecting information, an output of the position specifying unit and an output of the information collecting unit. A transmission means for transmitting to the center; and a display means for displaying information from the information processing center, wherein the information processing center is a server for managing input / output information and information for storing and managing information from the portable terminal A database and transmission means for transmitting the information in the information database to the mobile terminal, and the position specifying means is a current status of the mobile terminal. Is collected as longitude data and latitude data, and the information collecting means is weather information measuring means for measuring weather information such as temperature, humidity, atmospheric pressure, ultraviolet intensity, pollen concentration, etc.・ "Distribution system" has been proposed. Therefore, it is possible to provide specific area information and detailed information.

  In Patent Document 7, “information detected by information detection means (information for detecting vehicle speed and position, vehicle wiper operation information, outside air temperature information, and atmospheric pressure information) installed in many vehicles” is disclosed. Are collected from each of the numerous vehicles, and the collected information is organized as regional information for each preset specific region and / or information type, and the regional information is wired, wireless, or via a specific medium, etc. A system for collecting and organizing information using vehicles characterized by being provided to users has been proposed.

  Therefore, the information collection and organization system using vehicles collects detection information detected from a large number of vehicles traveling on roads, etc., analyzes and organizes all the collected information, and obtains regional information (regional, The information can be provided to the user in real time. In addition, the number of vehicles can be increased to increase the number of information collection sources, and various information can be detected as detection information. Therefore, there is little information bias, and detailed information can be provided.

  In Patent Document 8, “a positioning means for acquiring position information by GPS, a measuring means for measuring atmospheric pressure data, a transmitting means for transmitting observation information including the positional information, atmospheric pressure data and measurement time to an external device, Storage means for storing the atmospheric pressure data transmitted by the transmission means, wherein the measurement means comprises at least one of a temperature sensor, a humidity sensor, and an atmospheric pressure sensor, and the measurement means is arranged at predetermined time intervals. Alternatively, the atmospheric pressure data is continuously measured, and when the predetermined change occurs between the previous atmospheric pressure data stored in the storage means and the measured atmospheric pressure data, the atmospheric pressure data measured by the transmission means is included. An information terminal for collecting meteorological information characterized by transmitting observation information has been proposed.

  Therefore, since position information is acquired by GPS, atmospheric pressure data can be acquired quickly and easily at an arbitrary position, and weather in a narrow area where the information terminal is currently located can be predicted with high accuracy.

  As described above, in the radio mobile communication system, the cell radius of the base station is in the range of several hundred meters to several kilometers, and in the case of a cell radius of several kilometers, one cell is divided into a plurality of sectors. There are many cases. That is, the wireless mobile communication system can collect weather information in a region narrower than the section where AMeDAS is installed. If GPS, which is expected to become increasingly popular in the future, is mounted on mobile portable terminals, more detailed positioning is possible. In other words, by installing a weather sensor on each mobile terminal, using a mobile communication network and collecting weather sensor information, it is possible to collect weather information in a very narrow range at short intervals compared to AMeDAS. It is possible to make a short-term forecast accurately.

  The problem is that the reliability of the weather information observed by the weather sensor mounted on the mobile portable terminal is lower than the weather information observed at AMeDAS. Mobile mobile terminals are required to be smaller and cheaper, and it is difficult to mount a weather sensor with high accuracy and high price. In addition, when the mobile mobile device is in a bag or bag, when the mobile mobile device is placed in an air-conditioned room, or when the mobile mobile device is worn and affected by body temperature, observation by a weather sensor Conditions vary. It will be difficult to install high-precision and high-priced weather sensors in mobile handsets in the future, and how accurate the weather information collected by weather sensors will be, that is, of the collected weather information, It is important how to effectively remove weather information unnecessary for forecasting.

  Furthermore, it is difficult for the mobile portable terminal to transmit a large amount of observed weather information data communication. In a normal mobile communication system, packet communication is used for non-voice communication, and a fee system is charged according to the amount of information transmitted and received. Considering reduction of communication charges, reduction of communication traffic, etc., the amount of observed weather information communication data is preferably as small as possible.

  Here, if the communication frequency of the mobile portable terminal is reduced and the communication time interval is increased, the communication fee can be reduced and the communication traffic can be reduced. In AMeDAS, meteorological observation is conducted once every 10 minutes. However, in mobile communication, it is a relatively frequent transmission frequency that meteorological observation is performed once every 10 minutes and weather information is periodically transmitted each time. In addition, as described above, it is necessary to periodically transmit weather information at short intervals in terms of accurately performing weather forecasts at short intervals in a narrow area.

  Patent Document 9 states that “a weather prediction system including a mobile portable terminal and a communication host device, wherein the mobile portable terminal receives threshold information from the sensor unit that measures weather information and the communication host device. And an information control unit that discards weather information measured by the sensor unit based on threshold information received by the communication unit, and causes the communication unit to transmit weather information that has not been discarded to the communication host device The communication host device includes an external weather information database unit for storing at least one of external weather information other than the weather information measured by the sensor unit, seasonal information, and map information; Stores threshold information for discarding weather information measured by the sensor unit, which is calculated based on information stored in the weather information database unit. And a network interface for transmitting the threshold information stored in the threshold information storage unit to the mobile portable terminal and receiving weather information transmitted by the communication means. A weather forecasting system has been proposed.

  According to this configuration, the weather information can be measured by the sensor unit, and the first weather prediction can be performed in the first weather prediction unit based on the weather information. Furthermore, unnecessary weather information is discarded based on the threshold information stored in the first threshold information storage unit, the first weather prediction is performed based on the necessary small amount of weather information, and the first weather prediction output means is provided with the first Since the weather forecast can be output, the computing power required for the first weather forecast is reduced, and the mobile portable terminal capable of performing the first weather forecast with confidence is reduced in size and power consumption is reduced. can do. Furthermore, since the sensor unit measures minimum weather information, the amount of weather information transmitted by the communication means can be reduced.

  Appropriate allocation of functions for mobile mobile terminals and communication host devices, accurate weather forecasting, communication traffic can be reduced, and complex processing in mobile mobile terminals can be reduced. It is possible to provide a weather forecasting system that can provide a highly convenient weather forecasting service.

  Patent Document 10 states that “a transmission device is composed of a wirelessly connected sensor device and a transmission / reception device, an environmental state detection unit is provided in the sensor device, and a transmission unit is provided in the transmission / reception device. Environmental information supply system "has been proposed.

  Patent Document 11 states that “the above-mentioned data received by a central receiver that can receive meteorological / sea state measurement data and position data of each ship transmitted from ship-mounted radios of a large number of ships is collected and recorded in advance. It can calculate weather and sea state data at a number of fixed points at a given time, create grid data consisting of the relevant weather and sea state data body and header information, and further calculate isobar data etc. based on the grid data There has been proposed a “meteorological / sea state data real-time providing system including a database including an arithmetic circuit, a storage device capable of storing the arithmetic result, and a communication interface connectable to a communication line network”. In addition, each data received by the central receiver is subdivided into a mesh shape (the area is arbitrary, for example, 10 km square), but GIS (Geographic Information System) technology is used for each GPS position data corresponding to the sea area. [Similar example “AMeDAS”], which displays an isobaric line and the like on a map, and stores the result in a database.

JP 7-225284 A JP-A-10-132958 JP 2000-138978 A JP 2001-134882 A JP 2002-044289 A JP 2002-358321 A JP 2002-358599 A JP 2003-028967 A Japanese Patent Laying-Open No. 2005-300196 JP 2004-303125 A JP 2005-189165 A

  By the way, in recent years, the number of occurrences of local meteorological fluctuations that cause enormous damage such as torrential rain and tornadoes has increased, and it is desired to predict the occurrence location as a pinpoint. It is known that weather fluctuations such as torrential rain and tornadoes occur due to the rapid development of cumulonimbus clouds. 22 (A) to 22 (E) are schematic diagrams showing the mechanism of occurrence of concentrated heavy rain. 22 (A) to 22 (C) are in a developmental period, and an updraft is generated when a wind containing moist air hits a building or the like, or an updraft is generated by warming the air near the ground surface. Cumulonimbus clouds called cells develop. 22 (D) and 22 (E) are in the maturity period, and sufficiently grown raindrops fall on the ground and become heavy rain, generating a downdraft. FIG. 22 (F) is the decay period, the downdraft is stronger than the updraft, and the precipitation cell is converging. There may be a short time of about 30 minutes from when the cumulonimbus cloud starts to appear as shown in FIG. 22 (A) to when the heavy rain starts to occur as shown in FIG. 22 (D).

  The devices and systems described in Patent Documents 1 to 11 acquire data related to the weather using means such as sensors, but they occur locally such as torrential rains and tornadoes and disappear in a short time. No effective proposal has been made on how to predict the weather fluctuations.

  It is considered impossible to predict heavy rain using a radar or a rider that can capture raindrops, but raindrops are formed in the state shown in FIGS. 22 (B) and 22 (C). Even if the raindrops can be captured at this time, there may be only about 10 minutes before the occurrence of the heavy rain, and it cannot be an effective prediction method.

  The present invention has been made in view of the above problems, and according to some aspects of the present invention, it occurs locally in a mountainous area such as a mountainous area and disappears in a short time. It is possible to provide a weather fluctuation prediction information providing system, a weather fluctuation prediction information providing method, a weather fluctuation prediction information providing program, and a recording medium that provide information for predicting a given weather fluctuation.

  (1) The present invention is a weather fluctuation prediction information providing system that provides weather fluctuation prediction information for predicting local weather fluctuation caused by a change in atmospheric pressure in a mountainous area, and includes at least an atmospheric pressure sensor. A plurality of weather measurement devices distributed in the mountainous area, and a data processing device that processes weather data measured by each of the plurality of weather measurement devices, the data processing device, A weather data acquisition unit that acquires the weather data from each of a plurality of weather measurement devices, and a weather fluctuation prediction information generation unit that generates the weather fluctuation prediction information in real time based on the weather data acquired by the weather data acquisition unit And a weather fluctuation prediction information providing system.

  According to the present invention, by disposing a plurality of weather measurement devices in a mountainous area, weather data (including atmospheric pressure data) measured by each weather measurement device is acquired, and information on atmospheric pressure distribution in the mountainous area is obtained. Can be obtained. By processing this atmospheric pressure distribution information, it provides useful information that can be used to predict the occurrence of local weather fluctuations that occur due to changes in atmospheric pressure, particularly in mountainous areas where the weather changes easily. be able to.

  (2) In this weather fluctuation prediction information providing system, the weather fluctuation prediction information generation unit is configured to detect a change in atmospheric pressure in the mountainous area as at least part of the weather fluctuation prediction information based on the atmospheric pressure data included in the weather data. Information may be generated.

  Since it is possible to grasp the status of the atmospheric pressure changing in real time from the information on this atmospheric pressure change, it can be effectively used for predicting local weather fluctuations in mountainous areas.

  (3) In this weather fluctuation prediction information providing system, the weather fluctuation prediction information generation unit generates time-series image information representing the atmospheric pressure distribution in the mountainous area according to the atmospheric pressure as the atmospheric pressure change information. You may make it do.

  In this way, it is possible to very easily grasp the temporal change of the atmospheric pressure distribution in the mountainous area.

  (4) In this weather fluctuation prediction information providing system, the weather fluctuation prediction information generation unit calculates the atmospheric pressure gradients at a plurality of positions in the mountainous area based on the atmospheric pressure data included in the weather data, and the weather fluctuation You may make it produce | generate the information of the change of the atmospheric pressure gradient in the said mountainous area as at least one part of prediction information.

  Since there is a correlation between the atmospheric pressure gradient and the wind direction / wind speed, it is possible to know the approximate wind direction / wind velocity from the information of the change in the atmospheric pressure gradient. Therefore, by using the information on the atmospheric pressure gradient distribution together with the information on the atmospheric pressure distribution, it can be expected to improve the prediction accuracy of the occurrence of local weather fluctuations.

  (5) In this weather fluctuation prediction information providing system, the data processing device predicts the occurrence of the weather fluctuation based on the weather fluctuation prediction information, and gives warning information when the occurrence of the weather fluctuation is predicted. A weather fluctuation prediction unit to be generated may be further included.

  In this way, it is possible to automate the prediction of local weather fluctuations and provide alarm information related to weather fluctuations.

  (6) In this weather fluctuation prediction information providing system, the weather fluctuation prediction unit may predict the occurrence of the weather fluctuation by using geographical information of the mountainous area together with the weather fluctuation prediction information.

  Since the occurrence frequency of each weather fluctuation is considered to be related to the geography of the mountainous area, the prediction accuracy can be improved by predicting the weather fluctuation by adding the geographical information of the mountainous area to the weather situation.

  (7) In this weather fluctuation prediction information providing system, the weather fluctuation prediction unit generates the weather fluctuation using statistical information of weather conditions in which the weather fluctuation has occurred in the past in the mountainous area together with the weather fluctuation prediction information. You may make it perform prediction of.

  In this way, prediction accuracy can be improved by predicting weather fluctuations by adding past statistical information to weather conditions.

  (8) In this weather change prediction information providing system, the data processing device may further include a transmission control unit that performs control to transmit the weather change prediction information or the alarm information.

  In this way, it is possible not only to display information on weather fluctuations and alarm information on the monitor of the data processing apparatus, but also to automatically transmit it to a mountain hut or the like.

  (9) In this weather fluctuation prediction information providing system, at least a part of the weather measurement device may be installed in a mountain hut.

  If it does in this way, the weather information near a mountain hut can be acquired correctly. In addition, maintenance and management of the weather measurement device is facilitated.

  (10) In this weather fluctuation prediction information providing system, a part of the weather measurement device may be carried by a mountain climber.

  In this way, the accuracy of the complementary calculation of the atmospheric pressure and the atmospheric pressure gradient at each node of the observation mesh can be increased.

  (11) In this weather fluctuation prediction information providing system, the atmospheric pressure sensor has a pressure sensitive element that changes a resonance frequency according to atmospheric pressure, and outputs atmospheric pressure data according to the vibration frequency of the pressure sensitive element. May be.

  In general, barometers used for weather observation have a resolution of the order of hPa, whereas frequency change type barometric sensors measure Pa vibration frequency of a pressure sensitive element with a high frequency clock signal relatively easily. An order measurement resolution can be obtained. Further, the frequency change type atmospheric pressure sensor can detect the fluctuation amount of atmospheric pressure (change in atmospheric pressure) with high accuracy, whether the atmospheric pressure is changing slowly or suddenly. According to the present invention, by using a high-resolution frequency change type atmospheric pressure sensor, a slight change in atmospheric pressure in a short time is captured, and information for predicting weather fluctuation that occurs locally and disappears in a short time Can be provided. By analyzing this information, weather fluctuations can be accurately predicted.

  (12) In this weather fluctuation prediction information providing system, the pressure sensitive element may be a double tuning fork piezoelectric vibrator.

  By using a double tuning fork piezoelectric vibrator, a barometer with higher resolution can be realized.

  (13) The present invention is a weather fluctuation prediction information providing method for providing weather fluctuation prediction information for predicting a local weather fluctuation occurring in a mountainous area, comprising at least a barometric sensor, and distributed in the mountainous area. A weather data acquisition step for acquiring weather data from each of a plurality of weather measurement devices arranged in the same manner, and a weather change for generating the weather change prediction information in real time based on the weather data acquired in the weather data acquisition step Generating information on atmospheric pressure change in the mountainous area as at least part of the weather fluctuation prediction information based on the atmospheric pressure data included in the weather data in the weather fluctuation prediction information generation step. This is a method for providing weather fluctuation prediction information.

  (14) The present invention is a weather fluctuation prediction information providing program for providing weather fluctuation prediction information for predicting local weather fluctuation occurring in a mountainous area, comprising at least an atmospheric pressure sensor, and distributed in the mountainous area A meteorological data acquisition unit that acquires meteorological data from each of a plurality of meteorological measurement devices, and a meteorological variation that generates the meteorological variation prediction information in real time based on the meteorological data acquired by the meteorological data acquisition unit The computer functions as a prediction information generation unit, and the weather variation prediction information generation unit generates information on atmospheric pressure change in the mountainous area as at least a part of the weather variation prediction information based on the atmospheric pressure data included in the weather data. It is a program for providing weather fluctuation prediction information to be generated.

  (15) The present invention is a computer-readable recording medium on which the above-described weather fluctuation prediction information providing program is recorded.

1 is a schematic diagram of a weather fluctuation prediction information providing system according to an embodiment. The figure which shows the structural example of the weather fluctuation prediction information provision system of this embodiment. The figure which shows an example of the determination table. The figure which shows the structural example of the atmospheric | air pressure sensor of this embodiment. The schematic diagram of the cross section of the pressure sensor element of this embodiment. The schematic diagram of the cross section of the pressure sensor element of this embodiment. The bottom view which shows typically the vibration piece and diaphragm of this embodiment. The figure which shows an example of the flowchart of a whole process. The figure which shows an example of a virtual observation mesh. The figure which shows an example of the flowchart of the process which calculates an atmospheric pressure gradient. Explanatory drawing of the atmospheric pressure gradient vector between nodes. Explanatory drawing of the example of calculation of the atmospheric pressure gradient vector of each node. The figure which shows an example of the flowchart of the process which produces | generates weather fluctuation prediction information. The figure which shows an example of the display image of atmospheric | air pressure distribution roughly. The figure which shows an example of the display image of atmospheric | air pressure gradient distribution roughly. The figure which shows roughly an example of the display image of atmospheric | air pressure distribution and atmospheric | air pressure gradient distribution. The figure which shows an example of the flowchart of the prediction process of a weather fluctuation. The figure which shows notionally the relationship between the generation | occurrence | production process of torrential rain, and atmospheric pressure distribution and atmospheric pressure gradient distribution. The figure which shows the example of arrangement | positioning of the weather measurement apparatus in the modification 1. FIG. The figure which shows an example of the determination table in the modification 2. Explanatory drawing of the relationship between atmospheric pressure gradient and wind. Schematic showing the occurrence mechanism of concentrated heavy rain.

  DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. The embodiments described below do not unduly limit the contents of the present invention described in the claims. Also, not all of the configurations described below are essential constituent requirements of the present invention.

1. Overview of Weather Variation Prediction Information Providing System The weather variation prediction information providing system of this embodiment is information for predicting local meteorological variations that occur due to changes in atmospheric pressure in mountainous areas such as mountainous areas (hereinafter, “Weather forecast information”). Examples of local weather fluctuations caused by changes in atmospheric pressure include thunderstorms, torrential rains, tornadoes, and gusts.

  FIG. 1 is a diagram for explaining the outline of the weather fluctuation prediction information providing system of the present embodiment. As shown in FIG. 1, in the weather fluctuation prediction information providing system of this embodiment, a plurality of weather measurement devices 2 (indicated by white circles) are distributed in a mountainous area. The meteorological measurement device 2 is disposed, for example, in a mountain hut 5 or an observation station 6.

  Each meteorological measurement device 2 measures the weather at a constant period and transmits the measured weather data to a data processing device (not shown). The data processing device receives meteorological data from each meteorological measurement device 2, and generates meteorological fluctuation prediction information based on the received meteorological data. The data processing device may be installed in a mountain hut or an observation station. For example, a server connected to a communication network such as the Internet may be used as the data processing device.

  The weather fluctuation prediction information generated by the data processing device is displayed on the monitor of the data processing device or transmitted to another display device for display. For example, the data processing device sends weather fluctuation prediction information to the display device installed in each mountain hut, so that the owner of the mountain hut or a climber who stopped by the mountain hut can make thunderstorms from the weather fluctuation prediction information displayed on the display device. The occurrence of local weather fluctuations can be predicted. Alternatively, the data processing device may predict the occurrence of local meteorological fluctuations according to a predetermined criterion, and transmit information on the prediction result to the display device of each mountain hut. Thereby, warning information can be quickly provided to a climber.

2. Configuration of Weather Variation Prediction Information Providing System FIG. 2 is a diagram showing a configuration of the weather variation prediction information providing system of this embodiment. The weather fluctuation prediction information providing system of the present embodiment may have a configuration in which some of the components (each unit) in FIG. 2 are omitted or other components are added.

  As shown in FIG. 2, the weather fluctuation prediction information providing system 1 according to the present embodiment includes a plurality of weather measurement devices 2 and a data processing device 4.

  Each of the plurality of weather measurement devices 2 includes at least an atmospheric pressure sensor 10 and is distributed in a mountainous area. As the atmospheric pressure sensor 10, a frequency change type that captures a change in pressure as a change in the frequency of the vibrator, a capacitance type that captures a change in pressure as a change in capacitance, and a change in pressure as a change in the resistance value of the piezoresistor. Sensors such as piezoresistive sensors can be applied.

  The meteorological measurement device 2 may include other sensors that measure temperature, humidity, wind speed, wind direction, and the like in addition to the atmospheric pressure sensor 10.

  The meteorological measurement device 2 measures the weather in real time with a period of the second order, and the measured meteorological data is transmitted by the transmission unit 12 with, for example, radio waves having a frequency assigned to each meteorological measurement device 2. Each weather measurement device 2 is assigned a different transmission frequency.

  The data processing device 4 includes a receiving unit 20, a processing unit (CPU: Central Processing Unit) 30, an operation unit 40, a storage unit 50, a recording medium 60, a display unit 70, and a transmission unit 80.

  The receiving unit 20 receives transmission data from each weather measurement device 2 while switching at a predetermined cycle so that the reception frequency becomes a transmission frequency assigned to each weather measurement device 2 in order, and demodulates the weather data. Then, the receiving unit 20 sends the demodulated weather data to the processing unit (CPU) 30.

  Note that the transmission unit 12 of each weather measurement device 2 transmits weather data in a time-sharing manner at different predetermined periodic timings using radio waves of the same transmission frequency, and the reception unit of the data processing device 4 20 may receive weather data in a time-sharing manner in synchronization with the transmission timing of each weather measurement device 2.

  The operation unit 40 is an input device including operation keys, button switches, and the like, and outputs an operation signal corresponding to an operation by a user to the processing unit (CPU) 30.

  The storage unit 50 stores programs, data, and the like for the processing unit (CPU) 30 to perform various calculation processes and control processes. In particular, the storage unit 50 of the present embodiment stores a determination table 52 that defines a correspondence relationship between a determination criterion for weather conditions and a determined weather variation. This criterion includes at least conditions relating to atmospheric pressure. For example, whether or not the amount of decrease in atmospheric pressure per predetermined time exceeds a predetermined threshold may be used as a criterion for predetermined weather fluctuations (for example, heavy rain). In addition, when the data processing device 4 can obtain temperature and humidity information, the criteria for the temperature and humidity may be included in the determination criteria. For example, whether or not the amount of decrease in atmospheric pressure per fixed time exceeds a predetermined threshold and whether the temperature is within a predetermined range may be used as a criterion for predetermined weather fluctuation (for example, thunderstorm).

  FIG. 3 is a diagram illustrating an example of the determination table 52. In the example of FIG. 3, it is shown that a thunderstorm may occur within a predetermined time when the criterion 1 is satisfied. Similarly, when the determination criteria 2, 3, and 4 are satisfied, it indicates that there is a possibility that heavy rain, tornado, and gust may occur within a predetermined time. Note that the degree of possibility of occurrence of each weather variation may be divided into a plurality of stages, and each determination criterion 1, 2, 3, 4,... May be subdivided into a plurality of determination criteria.

  The storage unit 50 is used as a work area of the processing unit (CPU) 30. Data input from the operation unit 40, programs and data read from the recording medium 60, and the processing unit (CPU) 30 performs various programs. It is also used for temporarily storing the calculation result and the like executed according to the above.

  The processing unit (CPU) 30 performs various calculation processes and control processes in accordance with programs stored in the storage unit 50 and the recording medium 60. Specifically, the processing unit (CPU) 30 receives weather data from the receiving unit 20 and performs various calculation processes. In addition, the processing unit (CPU) 30 performs various processes according to operation signals from the operation unit 40, processes for displaying various types of information on the display unit 70, and communication with an external device via the reception unit 20 and the transmission unit 80. Performs processing to control data communication.

  In particular, in the present embodiment, the processing unit (CPU) 30 includes a weather data acquisition unit 32, a weather variation prediction information generation unit 34, a weather variation prediction unit 36, and a transmission control unit 38 described below. However, the processing unit (CPU) 30 of the present embodiment may have a configuration in which some of these configurations (elements) are omitted or another configuration (element) is added.

  The meteorological data acquisition unit 32 performs a process of continuously acquiring meteorological data (including at least atmospheric pressure data) sent from the receiving unit 20 in association with the identification ID of the meteorological measurement device 2. Specifically, the weather data acquisition unit 32 receives each weather data, and stores the received weather data in the storage unit 50 in order in association with the identification ID assigned to each weather measurement device 2.

  The weather fluctuation prediction information generation unit 34 performs processing for generating weather fluctuation prediction information in a mountainous area in real time based on the weather data acquired by the weather data acquisition unit 32. In the present embodiment, the meteorological measurement device 2 is arranged in a mountain hut or the like, but since the position of the mountain hut is not regular, a regular observation mesh is not formed. Therefore, the meteorological fluctuation prediction information generation unit 34 of the present embodiment maps the virtual observation mesh to the map information of the mountainous area, and complements the meteorological data of each node of the observation mesh from the meteorological data from each meteorological measurement device 2. Then, weather forecast information on a virtual observation mesh is generated.

  The weather fluctuation prediction information generation unit 34 generates information on atmospheric pressure change in the mountainous area as at least part of the weather fluctuation prediction information based on the atmospheric pressure data included in the weather data acquired by the weather data acquisition unit 32. Also good.

  This atmospheric pressure change information may be, for example, graph information representing the temporal change in the atmospheric pressure value of each node of the virtual observation mesh, or time-series image information representing the atmospheric pressure distribution in the virtual observation mesh (in real time). Updated image information).

  Further, the weather fluctuation prediction information generation unit 34 calculates the atmospheric pressure gradients at a plurality of positions in the mountainous area based on the atmospheric pressure data included in the weather data acquired by the weather data acquisition unit 32, and at least one of the weather fluctuation prediction information. Information on changes in atmospheric pressure gradient in mountainous areas may be generated as a part. The position for calculating the atmospheric pressure gradient may be an arbitrary position, for example, the position of each node of the virtual observation mesh.

  The information on the change in the atmospheric pressure gradient may be graph information indicating the temporal change in the atmospheric pressure gradient of each node of the virtual observation mesh, or time-series image information indicating the distribution of the atmospheric pressure gradient in the virtual observation mesh ( Image information updated in real time).

  The weather fluctuation prediction unit 36 predicts the occurrence of the weather fluctuation in the mountainous area based on the weather fluctuation prediction information generated by the weather fluctuation prediction information generation unit 34, and if the occurrence of the weather fluctuation is predicted, alert information is provided. Generate the process. Specifically, the weather fluctuation prediction unit 36 refers to the determination table 52 stored in the storage unit 50, and determines each determination criterion included in the determination table 52 (determination criteria 1, 2, 3, 4 in the example of FIG. 3). ,...)), It is determined whether each weather fluctuation occurs within a predetermined time.

  Note that the weather fluctuation prediction unit 36 may generate alarm information including a predicted weather fluctuation occurrence position and a predicted occurrence time when the occurrence of the weather fluctuation is predicted.

  The transmission control unit 38 sends the weather variation prediction information generated by the weather variation prediction information generation unit 34 and the warning information generated by the weather variation prediction unit 36 to an external device (display device, portable terminal, etc.) via the transmission unit 80. Control to send.

  If the weather fluctuation prediction information providing system of the present embodiment is sufficient to provide the weather fluctuation prediction information, the weather fluctuation prediction unit 36 may be omitted.

  The recording medium 60 is a computer-readable recording medium. In particular, in the present embodiment, a weather fluctuation prediction information providing program for causing the computer to function as each of the above-described units is stored. Then, the processing unit (CPU) 30 according to the present embodiment executes a weather fluctuation prediction information providing program stored in the recording medium 60, whereby a weather data acquisition unit 32, a weather fluctuation prediction information generation unit 34, a weather fluctuation It functions as a prediction unit 36 and a transmission control unit 38. Alternatively, a weather change prediction information providing program is received from a server connected to a wired or wireless communication network via the communication unit 80 or the like, and the received weather change prediction information providing program is stored in the storage unit 50 or the recording medium 60. Then, the weather fluctuation prediction information providing program may be executed. However, at least a part of the weather data acquisition unit 32, the weather variation prediction information generation unit 34, the weather variation prediction unit 36, and the transmission control unit 38 may be realized by hardware (dedicated circuit).

  The recording medium 60 can be realized by, for example, an optical disk (CD, DVD), a magneto-optical disk (MO), a magnetic disk, a hard disk, a magnetic tape, or a memory (ROM, flash memory, etc.).

  The display unit 70 is a display device configured by an LCD (Liquid Crystal Display) or the like, and displays various types of information based on display signals input from the processing unit (CPU) 30. For example, each frame of the atmospheric pressure distribution image and the atmospheric pressure gradient distribution image is displayed on the display unit 70 in real time.

  In order to capture in real time a precursor of local weather fluctuation in a mountainous area by the weather fluctuation prediction information providing system 1 having such a configuration, it is desirable to use a Pa-order high-resolution sensor as the atmospheric pressure sensor 10. At present, frequency change type barometric sensors have higher resolution than capacitance type and piezoresistive type barometric sensors, and frequency change type barometric sensors can achieve resolutions of 1 Pa or less. is there.

  FIG. 4 is a diagram illustrating a configuration example of the frequency change type atmospheric pressure sensor 10. As shown in FIG. 4, the atmospheric pressure sensor 10 of this embodiment includes a pressure sensor element 100, an oscillation circuit 110, a counter 120, a TCXO (Temperature Compensated Crystal Oscillator) 130, an MPU (Micro Processing Unit) 140, a temperature sensor 150, and an EEPROM. (Electrically Erasable Programmable Read Only Memory) 160 and a communication interface (I / F) 170 are included. However, the atmospheric pressure sensor of the present embodiment may have a configuration in which some of the components (each part) in FIG. 4 are omitted or other components are added.

  The pressure sensor element 100 has a pressure-sensitive element of a method (vibration method) that uses a change in the resonance frequency of the resonator element. This pressure-sensitive element is a piezoelectric vibrator formed of a piezoelectric material such as quartz, lithium niobate, or lithium tantalate. For example, a tuning fork vibrator, a double tuning fork vibrator, an AT vibrator (thickness sliding) A resonator), a SAW resonator, or the like is applied.

  In particular, a double tuning fork type piezoelectric vibrator has a very large change in resonance frequency with respect to elongation / compression stress and a large variable range of the resonance frequency compared to an AT vibrator (thickness shear vibrator), etc. By using a tuning-fork type piezoelectric vibrator, a high-resolution barometric sensor capable of detecting a slight barometric pressure difference can be realized. Therefore, the atmospheric pressure sensor 10 of the present embodiment uses a double tuning fork type piezoelectric vibrator as a pressure sensitive element. In addition, excellent stability and the highest level of resolution and accuracy can be realized by selecting a quartz material having a high Q value and excellent temperature stability as the piezoelectric material.

  FIG. 5 is a schematic cross-sectional view of the pressure sensor element 100 of the present embodiment. FIG. 6 is a bottom view schematically showing the resonator element 220 and the diaphragm 210 of the pressure sensor element 100 of the present embodiment. In FIG. 6, the base 230 as a sealing plate is omitted. FIG. 5 corresponds to a cross section taken along line AA of FIG.

  The pressure sensor element 100 includes a diaphragm 210, a vibrating piece 220, and a base 230 as a sealing plate.

  The diaphragm 210 is a flat plate-like member having a flexible part that receives pressure and bends. The outer surface of the diaphragm 210 is a pressure receiving surface 214, and a pair of protrusions 212 are formed on the back side of the pressure receiving surface 214.

  The vibration piece 220 includes a vibration beam (beam) 222 and a pair of base portions 224 formed at both ends of the vibration beam 222. The vibrating beam 222 is formed between the pair of base portions 224 in a doubly supported beam shape. The pair of base portions 224 are fixed to a pair of protrusions 212 formed on the diaphragm 210, respectively. The vibration beam 222 is appropriately provided with an electrode (not shown), and the vibration beam 222 can be bent and vibrated at a constant resonance frequency by supplying a drive signal from the electrode. The vibrating piece 220 is formed of a piezoelectric material. Examples of the material of the vibrating piece 220 include piezoelectric materials such as quartz, lithium tantalate, and lithium niobate. The vibration piece 220 is supported by the frame portion 228 by the support beam 226.

  The base 230 is joined to the diaphragm 210 to form a cavity 232 with the diaphragm 210. By using the cavity 232 as a decompression space, the Q value of the resonator element 220 can be increased (the CI value can be reduced).

  In the pressure sensor element 100 having such a structure, the diaphragm 210 bends and deforms when pressure is applied to the pressure receiving surface 214. Then, since the pair of base portions 224 of the vibrating piece 220 are respectively fixed to the pair of protrusions 212 of the diaphragm 210, the distance between the base portions 224 changes according to the deformation of the diaphragm 210. That is, when pressure is applied to the pressure sensor element 100, tensile or compressive stress can be generated in the vibration beam 222.

  FIG. 7 is a schematic cross-sectional view of the pressure sensor element 100 and shows a state where the diaphragm 210 is deformed by the pressure P. FIG. FIG. 7 shows an example in which the deformation of the diaphragm 210 convex toward the inside of the element is caused by the action of the pressure (pressure P) from the outside to the inside of the pressure sensor element 100. In this case, the interval between the pair of protrusions 212 is increased. On the other hand, although not shown, when a force from the inside to the outside of the pressure sensor element 100 acts, the diaphragm 210 is deformed so as to protrude toward the outside of the element, and the distance between the pair of protrusions 212 becomes small. . Accordingly, tensile or compressive stress is generated in a direction parallel to the vibration beam 222 of the vibration piece 220 whose both ends are fixed to the pair of protrusions 212. In other words, the pressure applied in the direction perpendicular to the pressure receiving surface 214 is converted into stress in a linear direction parallel to the vibration beam 222 of the vibration piece 220 via the protrusion (support portion) 212.

  The resonance frequency of the vibration beam 222 can be analyzed as follows. As shown in FIGS. 5 and 6, when the length of the vibration beam 222 is l, the width is w, and the thickness is d, the equation of motion when the external force F acts in the long side direction of the vibration beam 222 is as follows: It is approximated by (1).

  In equation (1), E is the longitudinal elastic constant (Young's modulus), ρ is the density, A is the cross-sectional area of the vibrating beam (= w · d), g is the gravitational acceleration, F is the external force, y is the displacement, and x is the vibration. Each arbitrary position of the beam is represented.

  By solving the equation (1) by giving a general solution and boundary conditions, the following equation (2) of the resonance frequency when there is no external force is obtained.

Second moment ^{I =} dw 3/12, the cross-sectional area A = dw, from λI = 4.73, equation (2) can be modified as the following equation (3).

Accordingly, the resonance frequency f ₀ when the external force F = 0 is proportional to the beam width w and inversely proportional to the square of the length l.

When the resonance frequency f _F when the external force F is applied to the two vibrating beams is determined in the same procedure, the following equation (4) is obtained.

Second moment I = ^dw 3/12 from equation (4) can be modified as the following equation (5).

In Formula (5), _SF represents stress sensitivity (= K · 12 / E · (l / w) ² ), and σ represents stress (= F / (2A)).

From the above, when the force F acting on the pressure sensor element 100 is negative in the compression direction and positive in the expansion direction, the resonance frequency f _F decreases when the force F is applied in the compression direction, and the force F is expanded and contracted. When added, the resonance frequency f _F increases.

  Then, the linearity error caused by the pressure-frequency characteristic and the temperature-frequency characteristic of the pressure sensor element 100 is corrected using the polynomial shown in the following equation (6), whereby the pressure value P with high resolution and high accuracy is obtained. Can be obtained.

In Expression (6), f _n is a sensor normalized frequency, and is represented by f _n = (f _F / f ₀ ) ² . Further, t is a temperature, and α (t), β (t), γ (t), and δ (t) are expressed by the following equations (7) to (10), respectively.

  In Expressions (7) to (10), a to p are correction coefficients.

That is, by measuring the frequency of the output signal of the pressure sensor element 100, the vibration frequency of the vibration beam 220 (resonance frequency f _F when the force F acts) can be obtained, and the resonance frequency f ₀ measured in advance or corrected The pressure P can be calculated from the equation (6) using the coefficients a to p.

  Returning to FIG. 4, the oscillation circuit 110 outputs an oscillation signal obtained by causing the vibration beam 222 of the pressure sensor element 100 to oscillate at the resonance frequency.

  The counter 120 is a reciprocal counter that counts a predetermined period of the oscillation signal output from the oscillation circuit 110 with a high-accuracy clock signal output from the TCXO 130 serving as a reference clock source. However, the counter 120 may be configured as a direct counting frequency counter (direct counter) that counts the number of pulses of the oscillation signal of the pressure sensor element 100 at a predetermined gate time.

An MPU (Micro Processing Unit) 140 performs a process of calculating the pressure value P from the count value of the counter 120. Specifically, the MPU 140 calculates the temperature t from the detection value of the temperature sensor 150 and uses the correction coefficient values a to p stored in advance in the EEPROM 160 to calculate α ( t), β (t), γ (t), and δ (t) are calculated. Further, the MPU 140 uses the count value of the counter 120 and the value of the resonance frequency f ₀ stored in advance in the EEPROM 160 to calculate the pressure value P from Equation (6). Then, the pressure value P calculated by the MPU is output to the outside of the atmospheric pressure sensor 10 via the communication interface 170.

  According to the pressure change type pressure sensor 10 having such a configuration, the vibration frequency of the pressure sensor element 100 is counted by a high-accuracy and high-frequency (for example, several tens of MHz) clock signal output from the TCXO 130 by the counter 120. Since the MPU 140 calculates the pressure value and corrects the linearity error by digital calculation processing, it is possible to obtain a pressure value (atmospheric pressure data) with high resolution and high accuracy in the order of Pa. Furthermore, since the atmospheric pressure sensor 10 can update the atmospheric pressure data with a period of the second order even if the count time is taken into consideration, it can capture a slight change in atmospheric pressure in a short time, and is suitable for real-time weather measurement. Yes.

  In this embodiment, the TCXO 130 is used as a reference clock source. However, the reference clock source is configured by an oscillation circuit that does not have a temperature compensation circuit, for example, a crystal oscillation circuit that includes an AT-cut crystal resonator. Also good. In this case, the pressure fluctuation detection accuracy is reduced by the absence of the temperature compensation circuit, but whether the reference clock source is the crystal oscillation circuit or the TCXO 130 depends on the cost of the prediction system and the prediction accuracy. The designer may select as appropriate.

3. Processing of weather fluctuation prediction information provision system [Overall processing]
FIG. 8 is a diagram illustrating an example of a flowchart of overall processing of the processing unit (CPU) 30 of the data processing device 4.

  First, when the data processing device 4 is activated, the processing unit (CPU) 30 sets a time variable t to 0 (S10).

  Next, the processing unit (CPU) 30 (the weather data acquisition unit 32) acquires the weather data measured by each weather measurement device 2 (S12).

  Next, the processing unit (CPU) 30 (the meteorological change prediction information generating unit 34) maps a virtual observation mesh (hereinafter simply referred to as “observation mesh”) to the map information of the mountainous area, and the weather acquired in step S12. Using the atmospheric pressure data included in the data, the atmospheric pressure of each node of the observation mesh at time t is calculated (S14). Specifically, the processing unit (CPU) 30 (the meteorological change prediction information generation unit 34), for example, as shown in FIG. 9A, position information of contour lines of mountainous areas, mountain huts, etc. (displayed with black circles), etc. The observation mesh 310 is mapped to the map information 300 having In the observation mesh, one rectangular section is formed by four nodes. The length of one side of each section (distance between nodes) is a value that can predict the occurrence of meteorological fluctuations with sufficient accuracy in consideration of the mountainous climate and other conditions (for example, about several hundred meters) ). FIG. 9B shows an image in which the observation mesh is mapped to the map information, and the processing unit (CPU) 30 (the weather fluctuation prediction information generating unit 34) displays the acquired atmospheric pressure data, mountain huts, etc. (displayed with black circles). ) Position information (position information of the meteorological measurement device 2), the atmospheric pressure of each node (indicated by a white circle) of the observation mesh is complementarily calculated. However, since the atmospheric pressure decreases by about 1 hPa every time the altitude increases by 10 m, the processing unit (CPU) 30 (the weather fluctuation prediction information generation unit 34) corrects the acquired atmospheric pressure data according to the installation altitude of the weather measurement device 2. After that, the air pressure of each node is complementarily calculated.

  Each weather measurement device 2 may transmit the position information together with the weather data to the data processing device 4, so that the data processing device 4 may acquire the position information of each weather measurement device 2, or each weather measurement device 2 The location information of the installation location (such as a mountain hut) may be stored in advance in the storage unit 50 of the data processing device 4 or the like.

  Next, the processing unit (CPU) 30 (the weather fluctuation prediction information generation unit 34) calculates the atmospheric pressure gradient of each node at time t from the atmospheric pressure of each node calculated in step S14 (S16).

  Next, the processing unit (CPU) 30 (weather change prediction information generating unit 34) calculates the atmospheric pressure of the mountainous area at time t from the atmospheric pressure of each node calculated in step S14 and the atmospheric pressure gradient of each node calculated in step S16. Distribution data and distribution data of atmospheric pressure gradient are generated (S18).

  Next, the processing unit (CPU) 30 (the meteorological variation prediction unit 36) predicts the occurrence of meteorological variation from the atmospheric pressure distribution data and the atmospheric pressure gradient distribution data up to time t generated in step S18, and if necessary. Warning information is generated (S20).

  Next, the processing unit (CPU) 30 transmits the atmospheric pressure distribution data, the atmospheric pressure gradient distribution data, and the warning information at time t to a display device such as a mountain hut (S22).

  Then, the processing unit (CPU) 30 increases the time variable t by Δt and repeats the processing of S12 to S22 until the processing ends (Y in S24).

  By such processing of the processing unit (CPU) 30, for example, assuming that Δt is 1 second, the distribution image of the atmospheric pressure and the atmospheric pressure gradient displayed on the display device is updated in real time every second.

[Calculation of atmospheric pressure gradient]
FIG. 10 is a diagram illustrating an example of a flowchart of the process of calculating the atmospheric pressure gradient of each node at time t (the process of S16 in FIG. 8).

First, the processing unit (CPU) 30 (weather change prediction information generation unit 34) calculates the atmospheric pressure gradient between all two adjacent nodes from the atmospheric pressure data at time t (S110). Here, two adjacent nodes are two nodes on each side of each section of the observation mesh. The atmospheric pressure gradient G between two adjacent nodes N ₁ and N ₂ is expressed as follows, assuming that the atmospheric pressure of the node N ₁ is P ₁ , the atmospheric pressure of the node N ₂ is P ₂ , and the distance between the nodes N ₁ and N ₂ is L. Calculated by equation (11).

Here, as shown in FIG. 11, the middle point of the nodes N ₁ and N ₂ is the starting point, the length according to the magnitude of the atmospheric pressure gradient G, and the direction according to the sign of the atmospheric pressure gradient G (from the higher atmospheric pressure) Pressure gradient vector g can be considered.

Then, the processing unit (CPU) 30 (weather change prediction information generating unit 34) sets a variable i to 0 (S112), adjacent to the node _{N i} and node _{N i} at time t between the nodes from the vector sum of the pressure gradient, calculate the pressure gradient at the node _{N i} (S114). Figure 12 (A), the node _{N i} and the node _{N i} 4 one node adjacent to _{N A, N B, N C} , respectively _g A the pressure gradient vector between the _{N D, g B, g C,} and _{g D,} as shown in FIG. 12 (B), the vector of the node _{N i} is pressure gradient vector _{g i of, g a + g B + g} C + g D (g a, g B, g C, g D (Sum). It can be considered the length of the pressure gradient vector g _i and node N _i pressure gradient G _i of.

  If there is a node for which the atmospheric pressure gradient has not been calculated (Y in S116), the processing unit (CPU) 30 (weather fluctuation prediction information generation unit 34) increases the variable i by 1 until there is no uncalculated node (S118). , S114 is repeated.

[Process of generating weather fluctuation forecast information]
FIG. 13 is a diagram illustrating an example of a flowchart of the process of generating the atmospheric pressure distribution data and the atmospheric pressure gradient distribution data at the time t (the process of S18 of FIG. 8).

  First, the processing unit (CPU) 30 (meteorological fluctuation prediction information generation unit 34) complementarily calculates the atmospheric pressure at the position between nodes from the atmospheric pressure and the atmospheric pressure gradient of each node at time t (S210).

  Next, the processing unit (CPU) 30 (the meteorological fluctuation prediction information generation unit 34) connects the positions of the same atmospheric pressure with a line from the calculation result of step S210, and generates isobaric data at time t (S212).

  Next, the processing unit (CPU) 30 (weather fluctuation prediction information generation unit 34) color-codes the isobaric data generated in step S212 according to the atmospheric pressure, and generates atmospheric pressure distribution data at time t (S214).

  Next, the processing unit (CPU) 30 (weather fluctuation prediction information generation unit 34) expresses the magnitude and direction of the atmospheric pressure gradient of each node with an arrow, and generates distribution data of the atmospheric pressure gradient at time t (S216).

  The atmospheric pressure distribution data generated in step S214 and the atmospheric pressure gradient distribution data generated in step S216 are converted into images and displayed on a display device such as a mountain hut. Thereby, the distribution of atmospheric pressure and atmospheric pressure gradient is visualized.

  FIG. 14 is a diagram schematically illustrating an example of a display image of atmospheric pressure distribution. In the example of FIG. 14, the same pattern portion has the same color. The processing unit (CPU) 30 (meteorological fluctuation prediction information generation unit 34) generates atmospheric pressure distribution data such that a portion with higher atmospheric pressure is red and a portion with lower atmospheric pressure is blue like a thermographic image, for example. Also good. For example, the area A1 is a portion having the highest atmospheric pressure and is displayed in red. The area A2 has a slightly lower atmospheric pressure than the area A1, and is displayed in orange. The area A3 has a slightly lower atmospheric pressure than the area A2, and is displayed in yellow. The area A4 has a slightly lower atmospheric pressure than the area A3, and is displayed in green. The area A5 has a slightly lower atmospheric pressure than the area A4, and is displayed in light blue. A region A6 is a portion having the lowest atmospheric pressure and is displayed in blue.

  In this way, by visualizing the atmospheric pressure distribution in the mountainous area with different colors, it is possible to visually grasp the temporal change of the atmospheric pressure distribution in the mountainous area very easily. Therefore, it is possible to easily identify the location (A6 in FIG. 14) where a local (small) cyclone (also referred to as “precipitation cell”) occurs.

  FIG. 15 is a diagram schematically showing an example of a display image of the atmospheric pressure gradient distribution. In the example of FIG. 15, an arrow (representing an atmospheric pressure gradient vector) is displayed at the position of each node, with a thickness corresponding to the magnitude of the atmospheric pressure gradient and a direction from the higher atmospheric pressure toward the lower atmospheric pressure.

  Thus, by visualizing the pressure gradient distribution in the mountainous area, it is possible to roughly know the wind information (wind direction / wind speed) correlated with the pressure gradient.

  Mountain hut owners and climbers who have stopped at the hut can use the visualized pressure distribution data and pressure gradient data to visually grasp temporal changes in pressure and wind, and predict the occurrence of weather fluctuations Can be expected to be performed accurately.

  As shown in FIG. 16, the atmospheric pressure distribution and the atmospheric pressure gradient distribution may be displayed in an overlapping manner.

[Prediction processing of weather fluctuation]
FIG. 17 is a diagram illustrating an example of a flowchart of the weather fluctuation prediction process (the process of S18 of FIG. 8).

  First, the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) determines the presence or absence of a local low pressure (precipitation cell) from the atmospheric pressure distribution data and the atmospheric pressure gradient distribution data up to time t (S310).

  When it is determined that there is no local low pressure (N in S312), the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) determines that there is no possibility that the weather fluctuation will occur within a predetermined time (S316). ), And the prediction process at time t ends.

  On the other hand, when the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) determines that there is a local low pressure (Y in S312), the position, strength, and movement of the local low pressure at time t. The direction, moving speed, etc. are calculated (S314).

  Next, the processing unit (CPU) 30 (the weather variation prediction unit 36) determines whether or not each weather variation is likely to occur within a predetermined time according to each determination criterion from the calculation result of step S314. (S318). For example, the processing unit (CPU) 30 (the meteorological fluctuation prediction unit 36) may determine whether or not the amount of change in atmospheric pressure over a certain period of time in a local low atmospheric pressure is greater than a predetermined threshold. The sudden drop in atmospheric pressure is thought to be related to the generation of updrafts during the cumulonimbus development. Therefore, for example, the occurrence of meteorological fluctuations accompanying the development of cumulonimbus clouds can be predicted by using as a criterion the determination that the amount of pressure drop during a certain period of time is greater than a predetermined threshold. However, in order to increase the accuracy of the prediction, the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) performs prediction by taking into consideration data other than atmospheric pressure (temperature and humidity data) based on the atmospheric pressure data. May be. Furthermore, the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) may subdivide the determination criteria and determine the possibility (probability) of occurrence of each weather fluctuation step by step.

  If the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) determines that there is no possibility that the weather fluctuation will occur within a predetermined time (N in S320), the prediction process at the time t is terminated.

  On the other hand, if the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) determines that there is a possibility that the weather fluctuation will occur within a predetermined time (Y in S320), it calculates the predicted occurrence position of the weather fluctuation, Warning information including information on the type of weather change and the predicted occurrence position is generated (S322), and the analysis process at time t is terminated. Note that the warning information may include information on the degree of probability (probability) that a weather change may occur.

FIG. 18 is a diagram conceptually showing the relationship between the generation process of concentrated heavy rain, which is one of weather fluctuations, and the atmospheric pressure distribution and the atmospheric pressure gradient distribution. FIG. 18A is an example of a pressure distribution and a pressure gradient distribution before the occurrence of a local low pressure. For example, the pressures of the nodes N _{1 to} N ₉ are substantially the same, and the pressure gradient is also substantially zero. ing. FIG. 18B is an example of the atmospheric pressure distribution and the atmospheric pressure gradient distribution in the development period of FIG. 22A, FIG. 22B, or FIG. 22C, and when a local low pressure is generated, node smaller pressure gradient toward the center of the low pressure from the low portion A1 with each node of pressure around the position close to the N ₈ between N ₅ and N ₈ is observed. Figure 18 (C) is an example of a pressure distribution and pressure gradient distribution of maturity FIG 22 (D) or FIG. 22 (E), the _{N 5} between the center of the cyclone nodes _{N 5} and _{N 8} While moving to a close position, a portion A2 where the atmospheric pressure is considerably low and a large atmospheric pressure gradient from each node toward the center of the low atmospheric pressure are observed. FIG. 18D is an example of the atmospheric pressure distribution and the atmospheric pressure gradient distribution in the decay period of FIG. 22F. The low atmospheric pressure disappears, and the atmospheric pressure difference and the atmospheric pressure gradient of the nodes N _{1 to} N ₉ become smaller.

  The processing unit (CPU) 30 (the weather fluctuation prediction unit 36), for example, from the state where the atmospheric pressure distribution and the atmospheric pressure gradient distribution as shown in FIG. 18A are observed, the atmospheric pressure distribution as shown in FIG. When the atmospheric pressure gradient distribution is observed, it can be determined that torrential rain occurs within a predetermined time. In addition, the processing unit (CPU) 30 (the weather fluctuation prediction unit 36) calculates a low-pressure movement path from the direction of the atmospheric pressure gradient vector of each node and the time change of the low-pressure part, thereby generating the position where the weather fluctuation occurs. And the occurrence time can be predicted.

  As described above, according to the weather fluctuation prediction information providing system of the present embodiment, the weather data measured by each weather measurement device 2 by distributing and arranging a plurality of weather measurement devices 2 in a mountainous area. (Including atmospheric pressure data) can be obtained to obtain information on atmospheric pressure distribution in mountainous areas. By processing this atmospheric pressure distribution information, it provides useful information that can be used to predict the occurrence of local weather fluctuations that occur due to changes in atmospheric pressure, particularly in mountainous areas where the weather changes easily. be able to.

  Further, according to the weather fluctuation prediction information providing system of the present embodiment, the atmospheric pressure distribution in the mountainous area is visualized and provided as a time-series image (an image that changes in real time), so by monitoring this image in real time The situation of the changing atmospheric pressure can be easily grasped and can be effectively used for predicting weather fluctuations.

  Further, since a general barometer is expensive, it is not practical to arrange a large number of barometers. In the present embodiment, the barometric sensor 10 is provided at low cost by using a semiconductor manufacturing technique. Therefore, a large number of weather measurement devices 2 can be distributed. Furthermore, by using the atmospheric pressure sensor 10 as a high-resolution frequency change type atmospheric pressure sensor on the order of Pa, it is possible to accurately capture slight atmospheric pressure changes before the occurrence of local weather fluctuations such as downbursts.

  By using the meteorological fluctuation prediction information providing system of this embodiment, for example, it is possible to capture the occurrence of local cyclones at the stage of FIG. 22A, which is the initial stage of the cumulonimbus development period. There is a possibility that the warning information can be transmitted with a time margin more than before until a weather change such as the above occurs.

4). The present invention is not limited to this embodiment, and various modifications can be made within the scope of the present invention.

[Modification 1]
In the present embodiment, each weather measurement device 2 is fixedly installed in a mountain hut or the like, but at least a part of the weather measurement device 2 may be installed on a moving body. For example, as shown in FIG. 19, the weather measurement device 2 may be installed in a mountain hut 5 or an observation station 6, and a mountain climber 7 (including a guide (guide) or the like) may carry the weather measurement device 2. . The meteorological measurement device 2 may be attached to a climbing cane or the like used by the climber 7. For example, a GPS (Global Positioning System) is mounted on the weather measurement device 2, the weather measurement device 2 transmits position information together with the weather data, and the data processing device 4 acquires the weather data in association with the position information. (Remember. In this way, the accuracy of the complementary calculation of the atmospheric pressure and the atmospheric pressure gradient at each node of the observation mesh can be increased.

[Modification 2]
Even if the meteorological conditions (atmospheric pressure, temperature, humidity, wind direction, wind speed, etc.) measured by the meteorological measurement device 2 are the same, whether or not a cumulonimbus that causes thunderstorms develops depends on the geography (topography, building) And the arrangement of trees). Therefore, the weather fluctuation prediction unit 36 may predict the occurrence of weather fluctuations such as thunderstorms using the geographical information of the mountainous area together with the weather fluctuation prediction information such as the change of the atmospheric pressure distribution and the change of the atmospheric pressure gradient distribution. . For example, the geographical information of the mountainous area may be stored in the storage unit 50, or the geographical information of the mountainous area is stored in a server on a communication network such as the Internet, and the data processing device 4 passes through the communication network. The geographic information may be acquired.

  In the present modification, for example, as shown in FIG. 20, the determination table 52 stored in the storage unit 50 defines the correspondence between the determination criterion A for the weather condition and the determination criterion B for the geographical condition and the determined weather variation. To do. The example of FIG. 20 indicates that a thunderstorm may occur within a predetermined time when the determination criterion A1 regarding the weather condition is satisfied and the determination criterion B1 regarding the geographical condition is satisfied. Similarly, if both the criterion A2 regarding the weather condition and the criterion B2 regarding the geographical condition are satisfied, there is a possibility that a heavy rain will occur within a predetermined time, and both the criterion A3 regarding the weather condition and the criterion B3 regarding the geographical condition are both If it is satisfied, a tornado may occur within a predetermined time, and if both the criterion A4 regarding weather conditions and the criterion 4 regarding geographical conditions are satisfied, a gust may occur within a predetermined time. Yes. In addition, the degree of possibility of occurrence of each weather change is divided into a plurality of stages, and each of the determination criteria A1, A2, A3, A4,... And each of the determination criteria B1, B2, B3, B4,. The criteria may be subdivided.

  The weather fluctuation prediction unit 36 refers to the determination table 52 and compares, for example, the weather condition (part of the weather fluctuation prediction information) of each node of the observation mesh with the determination criterion A and each node obtained from the geographic information. Are compared with the criterion B, and the occurrence of each weather fluctuation is predicted.

  As in this modification, prediction accuracy can be improved by predicting weather fluctuations by taking into account the geographical information of mountainous areas in the weather conditions.

[Modification 3]
The weather conditions (atmospheric pressure, temperature, humidity, wind direction, wind speed, etc.) measured by the meteorological measurement device 2 are collated with statistical information on the weather conditions when weather changes have occurred in the past, thereby increasing the accuracy of forecasting weather fluctuations. be able to. Therefore, the meteorological fluctuation prediction unit 36 uses statistical information on meteorological conditions such as thunderstorms in the past in the mountainous area together with meteorological fluctuation prediction information such as changes in atmospheric pressure distribution and changes in atmospheric pressure gradient distribution. You may make it perform generation | occurrence | production prediction of a weather fluctuation. For example, the statistical information may be stored in the storage unit 50, or the statistical information is stored in a server on a communication network such as the Internet, and the data processing device 4 stores the statistical information via the communication network. You may make it acquire. This statistical information is updated using information such as weather conditions immediately before the occurrence of each weather fluctuation such as thunderstorms, torrential rains, tornadoes, and gusts. Then, in conjunction with the update of the statistical information, each determination criterion in the determination table 52 is also updated as necessary.

  The weather fluctuation prediction unit 36 refers to the determination table 52 and compares, for example, the weather conditions (part of the weather fluctuation prediction information) of each node of the observation mesh with the determination criteria linked to the statistical information, thereby generating each weather fluctuation. Predict.

  As in this modification, prediction accuracy can be increased by predicting weather fluctuations by adding past statistical information to weather conditions.

  Note that the weather fluctuation prediction unit 36 can predict the occurrence of weather fluctuation by using both geographic information and statistical information together with the weather fluctuation prediction information, thereby further improving the accuracy of weather fluctuation prediction.

[Modification 4]
The processing unit (CPU) 30 (the weather fluctuation prediction information generation unit 34) may generate wind distribution data instead of the atmospheric pressure gradient distribution data or together with the atmospheric pressure gradient distribution data. As shown in FIG. 21, the direction and strength of the wind near the ground or the sea can be obtained from the pressure gradient force, the friction force, and the Coriolis force. The frictional force is applied in the direction opposite to the direction of the wind. The Coriolis force works to the right in the right direction in the northern hemisphere and to the left in the southern hemisphere.

  The pressure gradient force F is calculated by the following equation (12), where G is the pressure gradient, m is the mass of the air mass, and ρ is the density of the air mass.

  If wind distribution data is converted into an image and displayed on a monitor, for example, gusts and the like can be intuitively easily understood.

  The present invention includes configurations that are substantially the same as the configurations described in the embodiments (for example, configurations that have the same functions, methods, and results, or configurations that have the same objects and effects). In addition, the invention includes a configuration in which a non-essential part of the configuration described in the embodiment is replaced. In addition, the present invention includes a configuration that exhibits the same operational effects as the configuration described in the embodiment or a configuration that can achieve the same object. Further, the invention includes a configuration in which a known technique is added to the configuration described in the embodiment.

DESCRIPTION OF SYMBOLS 1 Weather fluctuation prediction information provision system, 2 Weather measuring device, 4 Data processing device, 10 Atmospheric pressure sensor, 12 Transmitter, 20 Receiving part, 30 Processing part (CPU), 32 Weather data acquisition part, 34 Weather fluctuation prediction information generation part 36, weather change prediction unit, 38 transmission control unit, 40 operation unit, 50 storage unit, 52 determination table, 60 recording medium, 70 display unit, 80 transmission unit, 100 pressure sensor element, 110 oscillation circuit, 120 counter, 130 TCXO , 140 MPU, 150 Temperature sensor, 160 EEPROM, 170 Communication interface (I / F), 210 Diaphragm, 212 Protrusion, 214 Pressure receiving surface, 220 Vibration piece, 222 Vibration beam (beam), 224 Base, 226 Support beam, 228 frame Part, 230 base, 232 cavity, 3 00 Map information, 310 Observation mesh

Claims (15)

A weather fluctuation prediction information providing system for providing weather fluctuation prediction information for predicting local weather fluctuation caused by a change in atmospheric pressure in a mountainous area,
A plurality of meteorological measurement devices which are provided with at least an atmospheric pressure sensor and are distributed in the mountainous area;
A data processing device that processes weather data measured by each of the plurality of weather measurement devices,
The data processing device includes:
A weather data acquisition unit for acquiring the weather data from each of the plurality of weather measurement devices;
A weather fluctuation prediction information providing system comprising: a weather fluctuation prediction information generating section that generates the weather fluctuation prediction information in real time based on the weather data acquired by the weather data acquiring section. In claim 1,
The weather fluctuation prediction information generation unit
A weather fluctuation prediction information providing system that generates information on atmospheric pressure change in the mountainous area as at least part of the weather fluctuation prediction information based on atmospheric pressure data included in the weather data. In claim 2,
The weather fluctuation prediction information generation unit
A weather fluctuation prediction information providing system that generates time-series image information representing the atmospheric pressure distribution in the mountainous area according to the atmospheric pressure as the atmospheric pressure change information. In any one of Claims 1 thru | or 3,
The weather fluctuation prediction information generation unit
Based on the atmospheric pressure data included in the weather data, calculate the atmospheric pressure gradients at a plurality of positions in the mountainous area, and generate information on changes in the atmospheric pressure gradient in the mountainous area as at least part of the weather fluctuation prediction information, Weather change prediction information provision system. In any one of Claims 1 thru | or 4,
The data processing device includes:
A weather fluctuation prediction information providing system further including a weather fluctuation prediction unit that predicts occurrence of the weather fluctuation based on the weather fluctuation prediction information and generates warning information when the occurrence of the weather fluctuation is predicted. In claim 5,
The weather fluctuation prediction unit
A weather fluctuation prediction information providing system that predicts occurrence of the weather fluctuation by using geographical information of the mountainous area together with the weather fluctuation prediction information. In claim 5 or 6,
The weather fluctuation prediction unit
A weather fluctuation prediction information providing system for predicting the occurrence of the weather fluctuation using statistical information of weather conditions in which the weather fluctuation has occurred in the past in the mountainous area together with the weather fluctuation prediction information. In any one of Claims 1 thru | or 7,
The data processing device includes:
A weather fluctuation prediction information providing system further including a transmission control unit that performs control for transmitting the weather fluctuation prediction information or the alarm information. In any one of Claims 1 thru | or 8.
At least a part of the weather measurement device is a weather fluctuation prediction information providing system installed in a mountain hut. In any one of Claims 1 thru | or 9,
A part of the weather measurement device is a weather fluctuation prediction information providing system carried by a mountain climber. In any one of Claims 1 thru | or 10.
The atmospheric pressure sensor includes a pressure-sensitive element that changes a resonance frequency according to atmospheric pressure, and outputs atmospheric pressure data according to a vibration frequency of the pressure-sensitive element. In claim 11,
The weather pressure prediction information providing system, wherein the pressure sensitive element is a double tuning fork piezoelectric vibrator. A weather fluctuation prediction information providing method for providing weather fluctuation prediction information for predicting local weather fluctuation occurring in a mountainous area,
A meteorological data obtaining step for obtaining meteorological data from each of a plurality of meteorological measurement devices that are provided with at least an atmospheric pressure sensor and are distributed in the mountainous area;
Based on the meteorological data acquired in the meteorological data acquisition step, the meteorological variation prediction information generating step for generating the meteorological variation prediction information in real time, and
In the weather fluctuation prediction information generation step,
A weather fluctuation prediction information providing method for generating information on atmospheric pressure change in the mountainous area as at least part of the weather fluctuation prediction information based on atmospheric pressure data included in the weather data. A weather fluctuation prediction information providing program for providing weather fluctuation prediction information for predicting local weather fluctuation occurring in a mountainous area,
A meteorological data acquisition unit that includes at least an atmospheric pressure sensor and acquires meteorological data from each of a plurality of meteorological measurement devices distributed in the mountainous area;
Based on the meteorological data acquired by the meteorological data acquisition unit, the computer functions as a meteorological variation prediction information generation unit that generates the meteorological variation prediction information in real time,
The weather fluctuation prediction information generation unit
A weather fluctuation prediction information providing program for generating information on atmospheric pressure change in the mountainous area as at least a part of the weather fluctuation prediction information based on atmospheric pressure data included in the weather data.   The computer-readable recording medium which recorded the weather fluctuation prediction information provision program of Claim 14.