Method of determining estimated hot and extremely hot periods, method of generating estimated hot and extremely hot period map, concrete construction method, method of producing concrete structures, estimated hot and extremely hot period output device, estimated hot and extremely hot period map generation device, estimated hot and extremely hot period output program, estimated hot and extremely hot period map generation program, and recording medium

Abstract

To provide a method, device, program and recording medium for generating estimated hot and extremely hot period maps by accurately determining estimated hot and extremely hot periods with high resolution for each region, reflecting effects of recent global warming.SOLUTION: An estimate hot and extremely hot period output system 100 disclosed herein is configured to compute estimated daily smoothed normal values of daily mean temperatures during the last 10 years in a region X from a data set comprising meteorological data including meteorological observation dates and temperatures on the observation dates at each of multiple weather observation points A as well as topological factors of each of the multiple weather observation points A and the region X.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a method for determining estimated hot weather and extreme heat periods, a method for generating an estimated hot weather and extreme heat period map, a concrete construction method, a method for manufacturing a concrete structure, an estimated hot weather and extreme heat period output device, an estimated hot weather and extreme heat period map generation device, an estimated hot weather and extreme heat period output program, an estimated hot weather and extreme heat period map generation program, and a recording medium. [Background technology]

[0002] High-temperature environments accelerate concrete hydration, causing excessively rapid setting, potentially shortening the time available for a series of steps, from pouring to compaction. As a result, inadequate measures can lead to cold joints, which can result in reduced structural strength and durability. In addition, insufficient moist curing can lead to rapid drying and a loss of strength. The temperature environment during transportation and pouring is particularly known to have a significant impact on concrete quality, and the above-mentioned problems are more likely to occur when the average daily temperature exceeds 25°C. Furthermore, with the recent rise in summer temperatures and the widespread use of higher-strength concrete, problems caused by high-temperature environments and increased heat of hydration are more likely to become apparent. Therefore, from the perspective of ensuring quality, appropriate measures are required for hot-weather concrete from the time of production through to the early stages of hardening. In response to this situation, the Architectural Institute of Japan's "Standard Specifications for Construction Works, Commentary, JASS 5: Reinforced Concrete Work" (hereinafter referred to as "JASS 5") and "Guidelines for Hot Weather Concreting, Commentary" (hereinafter referred to as "Hot Weather Guidelines") define the "hot season" (hereinafter referred to as the "hot season") when "hot weather concreting work" is applicable, and the "extremely hot season" (hereinafter referred to as the "extremely hot season") when temperatures are particularly high during the hot season (Non-Patent Documents 1 and 2). The hot season is defined as the period when the daily smoothed mean daily temperature (daily smoothed normal value) exceeds 25.0°C. The previous version of the guidelines (Summer Season Guidelines, 2nd Edition, revised in September 2000) used the normal values ​​of daily mean temperatures over a 30-year period. However, due to the effects of climate change in recent years, the number of days for the applicable period predicted based on the 30-year normal values ​​has begun to deviate significantly from the actual number of days. Therefore, the summer guidelines (3rd edition), revised in July 2019, stipulate that a smoothed value should be calculated from the average daily temperature over the most recent 10 years and used as the basis for the applicable period.

[0003] The above-mentioned mid-summer and extreme heat periods are basically set based on actual measurement data at the location where construction will actually be carried out (or the observation station closest to the construction location), but it is often not easy to go back in time and obtain actual measurement data at the construction site. Therefore, JASS 5 lists the names of representative local governments (major cities), including the locations of meteorological stations and the Japan Meteorological Agency's Regional Weather Observation System (AMeDAS), to provide guidelines for the mid-summer and extreme heat periods. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] "Standard Specifications for Construction Works and Commentary JASS 5 Reinforced Concrete Works 2022" Published by the Architectural Institute of Japan, November 2022, pp. 475-479 [Non-patent document 2] "Guidelines and Commentary for Hot Weather Concreting," Architectural Institute of Japan Publishing, July 2019, pp. 16-18 Summary of the Invention [Problem to be solved by the invention]

[0005] The summer guidelines indicate the hottest periods for major cities, but none of them cover the entire prefecture. Furthermore, the major cities are among the largest in their respective prefectures, and the proportion of artificially covered land area relative to the total land area is large, making it difficult to extrapolate such representative values ​​to other regions. Data from meteorological observatories and observation stations such as AMeDAS is available throughout the country, but it is point data and may not be applicable to other regions in the same way. Furthermore, because weather conditions are greatly affected by factors such as altitude, there are likely to be large regional differences even within the same city or town. In response to this issue, the Japan Meteorological Agency (JMA) has released meteorological analysis data (hereafter referred to as "mesh normals") on a 1-km grid to bridge the spatial gaps between observation points. These data are 30-year normals for each weather data item from 1991 to 2020 (mesh normals 2020). Therefore, by using these mesh normals, it is possible to estimate the hot and extreme heat periods even in areas without meteorological observation stations. However, as mentioned above, current guidelines, taking into account the effects of global warming in recent years, stipulate that the hot and extreme heat periods should be determined based on the average daily temperatures over the past 10 years. Therefore, even if hot and extreme heat periods are estimated using the above mesh normals, which are 30-year normals, there is a high possibility that they will not match the actual hot and extreme heat periods. Furthermore, due to the effects of global warming in recent years, the longer the data used in the calculation, the greater the discrepancy between the actual hot and extreme heat periods.

[0006] The present invention aims to provide a method for determining estimated hot weather and extreme heat periods, a method for generating an estimated hot weather and extreme heat period map, an estimated hot weather and extreme heat period output device, and an estimated hot weather and extreme heat period map generation device, which accurately determine estimated hot weather and extreme heat periods for each region with high resolution and by reflecting the effects of recent global warming. Another object of the present invention is to provide a program and a recording medium that cause a computer to function as the device. Another object of the present invention is to provide a concrete work method and a method for manufacturing a concrete structure that determine whether or not concrete work should be considered hot weather concreting work based on the estimated hot weather and extreme hot weather periods determined as described above. [Means for solving the problem]

[0007] The inventors have conducted extensive research to solve the above problems. As a result, they have found that by using observed meteorological data for a specific region and taking into account topographical factors, etc., it is possible to accurately determine the estimated hot and extreme heat periods for each region, including regions where meteorological data is not observed, with high resolution and while reflecting the effects of recent global warming. They have also found that this can contribute to improving the efficiency of the work of determining the estimated hot and extreme heat periods and streamlining construction planning. The present invention was completed through further research based on these findings.

[0008] That is, the above-mentioned problems of the present invention have been solved by the following means. [1] Multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n A method for determining estimated hot and extreme heat periods in area X, which calculates estimated daily smoothed normal values ​​of daily mean temperature for the past 10 years in area X from a dataset including the above and each of the topographic factors of area X, and determines estimated hot and extreme heat periods in area X. [2] The data set is n and the method for determining the estimated hot and extremely hot periods according to [1] above, including each urban factor of region X. [3] The region X is one of the mesh divisions corresponding to the reference region mesh, The method for determining the estimated hot and extreme heat periods according to [1] or [2] above, wherein the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years in the region X are calculated as follows: Two or more meteorological observation points A in order of proximity to the center of the area X i At each meteorological observation point A i The published daily average temperature for the mesh area containing the weather observation point A i Calculate the difference between the daily average temperature calculated from the weather data observed at each of the two or more meteorological observation points A for the past 10 years and the daily average temperature smoothed normal value published for the mesh division of the area X, and calculate the difference between the daily average temperature smoothed normal value published for the mesh division of the area X and the daily average temperature smoothed normal value published for the mesh division of the area X. i Corrections are made based on the differences using an inverse distance weighting method according to the distance to the target. [4] Two or more meteorological observation points A in descending order of distance from the center of the region X i The method for determining an estimated hot weather / extremely hot weather period according to [3] above, wherein the number of [5] The method for determining the estimated hot season and extreme heat season described in [3] or [4] above, wherein the correction to the published daily smoothed normal values ​​of the daily mean temperature in the mesh division of the region X is performed using the following (Equation 1) and (Equation 2).

[0009]

number

[0010]

number

[0011] however ΔT i :T i·10-year From T i·30-year The difference after subtracting (Weather observation point A i :Multiple weather observation points A n Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, and T i·10-year :Weather observation point A iThe daily average temperature is calculated from meteorological data for the past 10 years. i·30-year :Weather observation point A i (The published daily average temperature for the past 30 years in the mesh area containing the data) t x·10-year : Estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in region X t x·30-year : Daily smoothed normal values ​​of daily mean temperatures for the past 30 years published in the mesh division of Area X d i : From the center of area X to weather observation point A i Distance to k: Two or more meteorological observation points A in order of proximity to the center of region X i Number of [6] By the method for determining the estimated hot and extremely hot periods described in [1] to [5], multiple regions X n and generating a map based on the data of the estimated hot weather and extreme heat periods. [7] A concrete work method including determining whether or not concrete work to be carried out in the region X on a scheduled concrete pouring date is hot weather concreting work, based on an estimated hot weather / extremely hot weather period determined by the method for determining an estimated hot weather / extremely hot weather period described in [1] to [5]. [8] A method for manufacturing a concrete structure, comprising determining whether or not concrete work to be carried out in the region X on a scheduled concrete pouring date is hot weather concreting work, based on an estimated hot weather / extremely hot weather period determined by the method for determining an estimated hot weather / extremely hot weather period described in [1] to [5]. [9] Multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The topographical factors of each of the regions X are calculated from a dataset containing nThe multiple regions X are determined from the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years. n A memory unit in which data on each of the estimated hot and extreme heat periods is stored; The plurality of regions X n a location information input unit that accepts input of information about a specific location that exists in any one of the above; an extracting unit that extracts data on estimated hot and extreme heat periods for a specific region X that includes the specific location; an output unit that outputs data on the estimated hot and extreme heat periods for the specific region X; An estimated hot weather / extremely hot weather output device equipped with

[10] Multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n a dataset acquisition unit that acquires a dataset including each of the topographical factors; From the acquired dataset, the plurality of regions X n a calculation unit for calculating the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years for each of the above; The estimated daily smoothed normal values ​​are used to calculate the multiple regions X n an estimated hot and extreme heat period determination unit that determines each estimated hot and extreme heat period; The determined plurality of regions X n a map generating unit that generates a map based on the data of each of the estimated hot and extreme heat periods; an output unit that outputs the map; An estimated hot weather / extremely hot weather map generation device equipped with the above.

[11] The plurality of regions X n is one area among the mesh divisions corresponding to the reference area mesh, The plurality of regions X n The device according to [9] or

[10] , wherein the estimated daily smoothed normal value of the daily mean temperature for the past 10 years is calculated as follows: The multiple regions X n Each of the two or more meteorological observation points A in order of proximity to the central point of each of the iAt each meteorological observation point A i The published daily average temperature for the mesh area containing the weather observation point A i Calculate the difference between the daily average temperature calculated from the weather data observed in the past 10 years and the daily smoothed normal value, and n The daily smoothed normal values ​​of the daily mean temperature published in each mesh section of the multiple regions X n From each central point of each of the two or more meteorological observation points A i Corrections are made based on the differences using an inverse distance weighting method according to the distance to the target.

[12] The plurality of regions X n Two or more meteorological observation points A in order of proximity to the central point of each of i The device according to

[11] , wherein the number of

[13] The plurality of regions X n The device described in

[11] or

[12] , wherein the correction to the published daily smoothed normal value of the daily mean temperature in each mesh section is performed using the following (Equation 1) and (Equation 2).

[0012]

number

[0013]

number

[0014] however ΔT i :T i·10-year From T i·30-year The difference after subtracting (Weather observation point A i :Multiple weather observation points A n Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, and T i·10-year :Weather observation point A i The daily average temperature is calculated from meteorological data for the past 10 years.i·30-year :Weather observation point A i (The published daily average temperature for the past 30 years in the mesh area containing the data) t x·10-year : Estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in region X t x·30-year : Daily smoothed normal values ​​of daily mean temperatures for the past 30 years published in the mesh division of Area X d i : From the center of area X to weather observation point A i Distance to k: Two or more meteorological observation points A in order of proximity to the center of region X i Number of

[14] Computer, Multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The topographical factors of each of the regions X are calculated from a dataset containing n The multiple regions X are determined from the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years. n a storage means in which data on each of the estimated hot and extreme heat periods is stored; The plurality of regions X n a location information input means for receiving information input of a specific location that exists in any one of the above; an extraction means for extracting data on estimated hot and extreme heat periods for a specific region X that includes the specific location; an output means for outputting data on the estimated hot weather and extreme heat season for the specific region X; This is an estimated hot and extremely hot period output program.

[15] Computer, Multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X na dataset acquisition means for acquiring a dataset including each of the topographical factors; From the acquired dataset, the plurality of regions X n A means for calculating the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years for each of the above; The estimated daily smoothed normal values ​​are used to calculate the multiple regions X n a means for determining the estimated hot and extreme heat periods for each of the above; The determined plurality of regions X n a map generating means for generating a map based on the data of each of the estimated hot and extreme heat periods; This is a program that generates maps of estimated hot and extremely hot periods.

[16] The plurality of regions X n are the mesh area corresponding to the reference area mesh, The plurality of regions X n the program according to

[14] or

[15] , wherein the estimated daily smoothed normal value of the daily mean temperature for the past 10 years is calculated as follows: The multiple regions X n Each of the two or more meteorological observation points A in order of proximity to the central point of each of the i At each meteorological observation point A i The published daily average temperature for the mesh area containing the weather observation point A i Calculate the difference between the daily average temperature calculated from the weather data observed in the past 10 years and the daily smoothed normal value, and n The daily smoothed normal values ​​of the daily mean temperature published in each mesh section of the multiple regions X n From each central point of each of the two or more meteorological observation points A i Corrections are made based on the differences using an inverse distance weighting method according to the distance to the target.

[17] The plurality of regions X n Two or more meteorological observation points A in order of proximity to the central point of each of i The program according to

[16] above, wherein the number of

[18] The plurality of regions X n The program according to

[16] or

[17] , wherein the correction to the published daily smoothed normal value of the daily mean temperature in each mesh section is performed using the following (Equation 1) and (Equation 2).

[0015]

number

[0016]

number

[0017] however ΔT i :T i·10-year From T i·30-year The difference after subtracting (Weather observation point A i :Multiple weather observation points A n Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, and T i·10-year :Weather observation point A i The daily average temperature is calculated from meteorological data for the past 10 years. i·30-year :Weather observation point A i (The published daily average temperature for the past 30 years in the mesh area containing the data) t x·10-year : Estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in region X t x·30-year : Daily smoothed normal values ​​of daily mean temperatures for the past 30 years published in the mesh division of Area X d i : From the center of area X to weather observation point A i Distance to k: Two or more meteorological observation points A in order of proximity to the center of region X i Number of

[19] A computer-readable recording medium having the program according to any one of

[14] to

[18] recorded thereon.

[0018] In this invention and this specification, the term "hot season" refers to the "period during which concrete work carried out during the hot season is applicable" (i.e., the period during which appropriate measures such as using low-temperature materials, avoiding direct sunlight, and using retarding admixtures are required during concrete work). Furthermore, in this invention, the term "extremely hot season" refers to the "period during which concrete work carried out during the extremely hot season is applicable" (i.e., the period during which, in addition to the above-mentioned measures, appropriate measures such as using low-temperature groundwater during concrete work, using low-heat binders, selecting a ready-mix concrete plant with cooling equipment, selecting a ready-mix concrete plant with short transport times to the site, and cooling the concrete using liquid nitrogen, etc.) In the present invention and this specification, the term "hot weather / extreme heat" means "hot weather and / or extreme heat." [Effects of the Invention]

[0019] The method for determining the estimated hot weather and extreme heat periods of the present invention, the method for generating the estimated hot weather and extreme heat period map, The extreme heat period output device and estimated hot weather / extreme heat period map generation device determine estimated hot weather / extreme heat periods for each region with high resolution and with high accuracy, reflecting the effects of global warming in recent years, and can output estimated hot weather / extreme heat periods for specific regions based on this determination, and can also generate estimated hot weather / extreme heat period maps based on this determination. Furthermore, the estimated hot weather / extremely hot weather period output program, estimated hot weather / extremely hot weather period map generation program, and recording medium of the present invention can cause a computer to function as the above-mentioned device. Furthermore, the concrete work method or the method for manufacturing a concrete structure of the present invention makes it possible to determine whether or not the concrete work is to be hot weather concreting when carrying out the concrete work. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 is a diagram showing an example of a relationship between four meteorological observation points A and an area X. As shown in FIG. [Figure 2] FIG. 2 is a diagram showing a schematic diagram of an example of a system including the estimated hot weather / extremely hot weather output device of the present invention. [Figure 3] FIG. 3 is a functional block diagram showing the functional configuration of the estimated hot weather / extremely hot weather period output device of the present invention and the estimated hot weather / extremely hot weather period map generation device of the present invention. [Figure 4] FIG. 4 is a flowchart showing a processing flow in which the estimated hot weather / extremely hot weather period output device of the present invention outputs the estimated hot weather / extremely hot weather period. [Figure 5] FIG. 5 is an image showing an example of an output result in which estimated hot weather period data for an area including a specific location is output by the estimated hot weather period / extremely hot weather period output device of the present invention. [Figure 6] FIG. 6 is a flowchart showing a processing flow in which the estimated hot weather / extremely hot weather map generating device of the present invention outputs an estimated hot weather / extremely hot weather map. [Figure 7] FIG. 7 is a map showing the estimated summer season obtained in the example. [Figure 8] FIG. 8 is a map showing the estimated extreme heat period obtained in the examples. [Figure 9] FIG. 9 is a graph showing the consistency between the estimated daily smoothed normal values ​​at each meteorological observation point obtained in the example and the daily smoothed normal values ​​based on the actual measured values ​​at each meteorological observation point. DETAILED DESCRIPTION OF THE INVENTION

[0021] A preferred embodiment of the present invention will be described below, but the present invention is not limited to the following embodiment except as defined by the present invention.

[0022] [Method for determining estimated hot and extremely hot periods] One embodiment of the present invention is a method for detecting weather at a plurality of meteorological observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A nand each topographic factor of area X, calculates estimated daily smoothed normal values ​​of daily mean temperature for the past 10 years in area X, and determines estimated hot weather periods and extreme heat periods in area X (hereinafter also referred to as the determination method of the present invention). The determination method of the present invention uses estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in region X to determine estimated hot and extreme heat periods for periods that conform to the current summer guideline. Furthermore, because the estimated daily smoothed normal values ​​used are values ​​based on the past 10 years in region X, they can reflect the effects of recent global warming, etc.

[0023] The estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in area X can be calculated using at least the weather observation dates and weather data for those observation dates at multiple weather observation points A, as well as data on the topographic factors of multiple weather observation points A and area X. The calculation method will be described later. Unless otherwise specified, the provisions and preferred ranges of the determination method of the present invention can be applied to each invention in the other embodiments of the present invention described below. The same applies between each invention described below.

[0024] (Mid-hot season, extremely hot season) In the determination method of the present invention, the setting standards for the hot season and the extreme hot season can be determined appropriately taking into consideration various factors, such as the required quality (high-quality, regular, simple) and strength of the concrete, the concrete material used, the region where the concrete work is to be carried out (climate, etc.), and the construction technique of the concrete work. Typical setting standards for the hot season and the extreme hot season include those set forth in JASS 5 and the Summer Weather Guidelines. In this invention, the term "estimated hot weather and extreme heat periods" means that the daily smoothed normal values ​​used to calculate and determine hot weather and extreme heat periods are not based on observed values ​​(actual measured values) but on estimated (presumed) values.

[0025] (During hot and extremely hot periods based on the summer heat guidelines) As an example, the criteria for setting the above-mentioned summer guideline will be explained below. In the current summer guideline, a summer season is a period in which the daily average daily temperature over the past 10 years exceeds 25.0°C, and an extreme heat season is a period in which the daily average daily temperature over the past 10 years exceeds 28.0°C. The above-mentioned hot and extremely hot periods indicate the criteria for determining hot and extremely hot periods according to the current summer guidelines, which were revised in July 2019. If the summer guidelines or JASS 5 are revised in the future, for example, the hot and extremely hot periods can be determined according to the revised guidelines. In other words, the "hot and extremely hot periods" in the determination method of the present invention are determined based on the summer guidelines or JASS 5 at the time the present invention is implemented. The same applies to each of the following inventions.

[0026] Each element in the determination method of the present invention will be described in detail below.

[0027] -Weather Observation Point A- The meteorological observation point A is a point where meteorological data such as temperature is observed at point A. The meteorological observation point A is, for example, a point at which meteorological observations have been conducted for at least the past 10 years from the time when the determination method of the present invention was implemented, among points such as meteorological observatories, meteorological observation stations, weather stations, and the Japan Meteorological Agency's Area Meteorological Observation System (AMeDAS). In the determination method of the present invention, n The number of the meteorological observation points A is not particularly limited. For example, from the viewpoint of improving the accuracy of the multiple regression analysis described later, the number is preferably 500 or more, more preferably 700 or more, and even more preferably 900 or more. n The n simply indicates that there are multiple meteorological observation points A, and the multiple regions X n does not mean the same number.

[0028] -Region X- Region X is a region with a certain area that is divided into a specific range (for example, a city, ward, town, village, prefecture, or the whole of Japan). For example, multiple regions X nis a set of regions obtained by dividing the specific range into n regions. In the present invention and this specification, when simply referring to "region X", it means the plurality of regions X. n It means that the region is one of the multiple regions X. n It is assumed that at least the area where the meteorological observation point A does not exist is included. There are no particular restrictions on the size or shape of region X. From the perspective of estimating the hot and extreme hot periods with higher resolution, the area of ​​region X is set to 0.04 to 16 km². 2 It is preferable that the distance is 0.15 to 9 km. 2 It is more preferable that the distance is 0.5 to 4 km. 2 It is more preferable that the shape of the region X (the shape of the outer edge of each region X) is, for example, a rectangle (or a square) or a circle. n From the viewpoint of comprehensively arranging the regions X without gaps, the shape is preferably a rectangle (or a square), n It is more preferable that the outer edges of adjacent regions X contact each other. When region X is square, the length of one side is preferably 0.2 to 4 km, more preferably 0.4 to 3 km, and even more preferably 0.7 to 2 km. Furthermore, for example, the plurality of regions X n Each region in the map may correspond to each mesh division of the mesh normal values ​​published by the Japan Meteorological Agency (reference region mesh, tertiary region division, 1km mesh), or may correspond to a divided region mesh (1 / 2 region mesh, 1 / 4 region mesh). Each reference region mesh is assigned a mesh code.

[0029] -Dataset- The data set includes data from multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and each topographical factor of the area X. The data set also includes the plurality of meteorological observation points A, nand region X. In the present invention and this specification, the "plurality of meteorological observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n The "dataset including the data and each topographical factor of area X" may include already published daily smoothed normal values ​​of daily mean temperature calculated using the data and each factor (excluding already published daily smoothed normal values ​​of daily mean temperature calculated for the same period as the past 10 years that are the source range for calculating the estimated daily smoothed normal values ​​of daily mean temperature for area X). In other words, the dataset may be already published daily smoothed normal values ​​of daily mean temperature.

[0030] -Weather Data- The weather data includes data on the weather observation date and the temperature (including at least the daily mean temperature, and may also include the daily maximum temperature and daily minimum temperature) on that observation date observed at the weather observation point A. The weather data may be cumulative data covering at least the past 10 years or more, starting from the year in which the determination method of the present invention is implemented. It is preferable that the meteorological data include, in addition to the temperature data on the meteorological observation day, one or more of the following meteorological data: precipitation, maximum snow depth, snowfall amount, sunshine hours, global solar radiation, humidity, air pressure, wind direction, wind speed, clouds, visibility, and observations of atmospheric phenomena.

[0031] -Terrain factor- The topographical factors of meteorological observation point A and area X include, for example, latitude, longitude, coast, altitude, relief, land mass, west-east gradient, south-north gradient, southwest-northeast gradient, southeast-northwest gradient, gradient amount, east openness, west openness, south openness, north openness, openness, etc. As data for each of the topographical factors, for example, data from the altitude and slope 3rd mesh data (2011 edition) can be used. Furthermore, the "terrain factor of area X" may be, for example, the terrain factor at the center of area X, which may be used as a representative terrain factor of area X. Alternatively, for example, the average terrain factor at any points (for example, 16 points) within area X may be used.

[0032] -Urban factor- The dataset used to determine the estimated hot and extreme heat periods preferably further includes urban factors for meteorological observation point A and region X. Note that "urban factors for meteorological observation point A" specifically refers to urban factors for the region that includes meteorological observation point A. Examples of urban factors include artificial coverage. For example, land use tertiary mesh data (FY2014 edition, FY2021 edition) can be used as data on the urban factors.

[0033] - Estimated daily average temperature smoothed normal values ​​- In the determination method of the present invention, the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years in the region X are calculated by calculating the average daily mean temperature for the past 10 years from a plurality of meteorological observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and the topographical factors of region X, and region X, using multiple regression analysis or other methods (calculation method A). Alternatively, it may be calculated from the already published daily smoothed normal values ​​of daily mean temperature calculated using the dataset (excluding already published daily smoothed normal values ​​of daily mean temperature calculated for the same period as the past 10 years that are the source range for calculating the estimated daily smoothed normal values ​​of region X) (calculation method B). In the determination method of the present invention, the "past 10 years" preferably refers to the most recent past 10 years. Specifically, when the year in which the determination method of the present invention is implemented is set as year 0, the past 10 years preferably refers to a consecutive 10-year period from the 1st to 5th year to the 10th to 14th year, more preferably a consecutive 10-year period from the 1st to 3rd year to the 10th to 12th year, and even more preferably a consecutive 10-year period from the 1st to 10th year.

[0034] <Calculation method A> Here is an example of a method for calculating the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years in region X using multiple regression analysis, etc. Multiple weather observation points A n A multiple regression analysis is performed on the relationship between meteorological data (smoothed normal values) such as temperature, precipitation, sunshine hours, and maximum snow depth for at least the past 10 years obtained above and parameters for topographic factors such as elevation, latitude, longitude, slope, distance to the coast, and openness of the area including the meteorological observation point, as well as parameters for urban factors such as artificial coverage, to create multiple regression equations that estimate various climatic values ​​(temperature, precipitation, global solar radiation, and snow depth) by element and month. Note that in this process, parameters with little influence on the multiple regression equation or variables that give outliers may be excluded, and the multiple regression analysis may be repeated. Using the above multiple regression equation, estimated daily smoothed normal values ​​for the daily mean temperature for Area X over the past 10 years can be obtained. The weather data (smoothed normal values) can be obtained by smoothing the daily normal values ​​of weather data from at least the past 10 years (KZ filter, 9-day moving average repeated three times).

[0035] <Calculation method B> Next, we will show an example of how to calculate the estimated daily mean temperature for the past 10 years for area X from the already published daily mean temperature normals for area X (excluding already published daily mean temperature normals calculated for the same period as the past 10 years that are the source range for calculating the estimated daily mean temperature normals for area X). In one example of the method, the region X is one of the mesh divisions corresponding to the reference region mesh, and the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years in the region X are calculated by calculating the average daily mean temperature for each of two or more meteorological observation points A in order of proximity to the center point of the region X. i At each meteorological observation point A i The published daily average temperature for the mesh area containing the weather observation point A iCalculate the difference between the daily average temperature calculated from the weather data observed at each of the two or more meteorological observation points A for the past 10 years and the daily average temperature smoothed normal value published for the mesh division of the area X, and calculate the difference between the daily average temperature smoothed normal value published for the mesh division of the area X and the daily average temperature smoothed normal value published for the mesh division of the area X. i The calculated distance is corrected based on each difference using an inverse distance weighting method according to the distance to the target point.

[0036] The published daily smoothed normals for daily mean temperatures can be mesh normals published by the Japan Meteorological Agency. The mesh normals for daily mean temperatures published by the Japan Meteorological Agency are calculated for each 1 km square (reference area mesh) using multiple regression analysis to estimate the statistical relationships between AMeDAS meteorological data (smoothed normals) at meteorological observation points, topographical factors such as latitude, longitude, elevation, and slope, and urban factors such as artificial coverage. The mesh normals are preferably the "2020 Mesh Normals." The 2020 Mesh Normals use meteorological data from 1991 to 2020 (smoothed normals) as meteorological data, elevation and slope tertiary mesh data (FY2011 edition) as topographical factors, and land use tertiary mesh data (FY2014 edition) as urban factors. In other words, the mesh normals for daily mean temperatures published by the Japan Meteorological Agency for Region X are equivalent to the estimated daily smoothed normals for Region X over the past 30 years.

[0037] The inverse distance weighting method is a technique for estimating a data value of a target point by averaging data values ​​of other points located near the target point, using the inverse of the distance to the point having the data value as a weight. As the inverse distance weighting method, a method commonly used for spatial interpolation can be selected, and the multiplier can be set to 1 to 3. From the viewpoint of reducing the load of calculation processing, the multiplier can also be set to 1. In the correction using the inverse distance weighting method, each of the two or more meteorological observation points A i The number is preferably 4 to 8, may be 4 to 6, or may be 4.

[0038] In the correction using the inverse distance weighting method, the estimated daily smoothed normal values ​​of daily mean temperature for the past 10 years in region X are preferably corrected using the following (Equation 1) and (Equation 2). Note that in the following (Equation 2), the already published daily smoothed normal values ​​of daily mean temperature are based on data for the past 30 years, and T i·30-year , t x·30-year However, the period for which the daily smoothed normal values ​​of daily mean temperature have already been published is not particularly limited as long as it is the same period as the past 10 years for the region X.

[0039]

number

[0040]

number

[0041] In the above (Equation 1) and (Equation 2), the parameters are as follows: ΔT i :T i·10-year From T i·30-year The difference after subtracting (However, at weather observation point A i :Multiple weather observation points A n Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, and T i·10-year :Weather observation point A i The daily average temperature is calculated from meteorological data for the past 10 years. i·30-year :Weather observation point A i (The published daily average temperature for the past 30 years in the mesh area containing the data) t x·10-year : Estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in region X t x·30-year : Daily smoothed normal values ​​of daily mean temperatures for the past 30 years published in the mesh division of Area X d i : From the center of area X to weather observation point A i Distance to k: Two or more meteorological observation points A in order of proximity to the center of region X i Number of

[0042] In the example shown in Figure 1, we will explain how to calculate the estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in area X using calculation method B. 1~4 (Weather observation point A 1~4 , i.e., k=4) exists in an area outside region X. Each weather observation point A 1~4 For example, let's assume that the data for Area X and Area X on April 1st is as shown in Table 1 below. While the example in Figure 1 shows a calculation method for April 1st, the estimated daily smoothed normal values ​​of the actual daily mean temperature are calculated for each day within the period. In the determination method of the present invention, the period of the data set used for determination can be a period that is likely to correspond to an actual hot or extreme heat period. For example, the estimated daily smoothed normal values ​​of the daily mean temperature for Area X over the past 10 years can be calculated based on data for the period from April 1st to October 31st.

[0043] [Table 1]

[0044] From the above (Equation 1), C is (1 / 3 + 1 / 4 + 1 / 6 + 1 / 12) = 10 / 12. Therefore, from the above (Equation 2), the estimated daily normal value for April 1 in region X is 20 + {(1 / 3) / (10 / 12)*1 + (1 / 4) / (10 / 12)*1 + (1 / 6) / (10 / 12)*2 + (1 / 12) / (10 / 12)*-1}=21[℃].

[0045] In addition, weather observation point A i The weather observation point A may be located in an area outside the area X or in an area inside the area X. i If the weather station A is located in the area inside the region X, iThe daily smoothed normal values ​​of daily mean temperatures for the past 10 years for region X may be used as the estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years for region X. In this case, for convenience, the term "estimated daily smoothed normal values" is used. However, if multiple regions X n When the determination method of the present invention is carried out for only one area X, the area X does not include a meteorological observation point A. i shall not be included.

[0046] [Method for generating estimated hot and extreme heat period maps] Another embodiment of the present invention is a method for determining whether a plurality of regions X n The method for generating an estimated hot weather / extremely hot weather map (hereinafter also referred to as the generation method of the present invention) determines estimated hot weather / extremely hot weather periods for each of the above and generates a map based on data on the estimated hot weather / extremely hot weather periods. The plurality of regions X n Each of the estimated hot and extremely hot periods is the estimated hot and extremely hot period of the region X determined by the determination method of the present invention. n This means that the estimated hot and extremely hot periods are obtained by performing the process n times for the number of times. n There are several weather observation points A n In this case, the daily smoothed normal values ​​of the daily mean temperature for the past 10 years at meteorological observation point A may be used as the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years for area X, which includes meteorological observation point A. The range of the map generated by the generation method of the present invention (multiple regions X n The range (the whole range) is not particularly limited. For example, it may be a range covering the whole of each city, ward, town, or village, a range covering the whole of a prefecture, or a range covering the whole of Japan. Furthermore, for example, the range may include overseas.

[0047] [Concrete construction methods, concrete building manufacturing methods] Yet another embodiment of the present invention is a method for concrete construction and a method for manufacturing a concrete structure. When concrete construction (concrete pouring) is carried out in a specific region X, for example, it can be determined whether the concrete construction is hot weather concreting based on the temperature on the construction day (concrete pouring day). However, if the hot weather and extreme heat period can be accurately predicted before the construction day, construction can proceed more smoothly. According to the concrete work method and concrete structure manufacturing method of the present invention, it is possible to determine whether or not the concrete work to be carried out in region X on the planned concrete pouring date should be hot weather concreting, based on the estimated hot weather / extremely hot weather period in region X where concrete is to be poured, as determined by the determination method of the present invention. Note that known hot weather concreting work can be applied as hot weather concreting work.

[0048] That is, the method of the present invention can be expressed as the following embodiments. The concrete work method of the present invention includes determining whether or not the concrete work to be carried out in the region X on the scheduled concrete pouring date is to be hot weather concreting work, based on the estimated hot weather / extremely hot weather period determined by the determination method of the present invention. The method for manufacturing a concrete structure of the present invention also includes determining whether or not the concrete work to be carried out in the region X on the scheduled concrete pouring date is to be hot weather concreting work, based on the estimated hot weather / extremely hot weather period determined by the determination method of the present invention.

[0049] [Estimated hot weather / extremely hot weather output device] Yet another embodiment of the present invention is a method for detecting a plurality of weather stations A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The topographical factors of each of the regions X are calculated from a dataset containing n The multiple regions X are determined from the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years. nA memory unit in which data on each of the estimated hot and extreme heat periods is stored; The plurality of regions X n a location information input unit that accepts input of information about a specific location that exists in any one of the above; an extracting unit that extracts data on estimated hot and extreme heat periods for a specific region X that includes the specific location; an output unit that outputs data on the estimated hot and extreme heat periods for the specific region X; The present invention relates to an estimated hot weather / extremely hot weather output device (hereinafter also referred to as the output device of the present invention), which includes the above.

[0050] FIG. 2 is a diagram schematically illustrating an example of an estimated hot weather / extremely hot season output system 100 including an output device 1 of the present invention. The system 100 shown in FIG. 2 includes an external device 3 in addition to the output device 1 of the present invention. In the system 100 shown in FIG. 2, the external device 3 transmits location information (specific location information) of a location for which an estimated hot weather / extremely hot season is desired to be calculated (for example, a location where concrete work is being carried out) to the output device 1 of the present invention. Upon receiving the information (having the information input thereto), the output device 1 of the present invention outputs the estimated hot weather / extremely hot season for the specific location and transmits it to the external device 3. 2 shows an example of a system 100 having an external device 3, but the embodiment of the present invention is not limited to the form shown in FIG. 2. For example, specific location information may be input directly to the output device 1 without going through the external device 3. Furthermore, the estimated hot weather / extremely hot weather period may be output in a manner different from that transmitted to the external device 3. For example, the estimated hot weather / extremely hot weather period may be output to the output device 1 itself.

[0051] The output device 1 of the present invention makes it possible to easily search for information on hot weather and extreme heat periods in a planned construction area (or location), for example, when constructing concrete. In other words, a user can determine whether hot weather concreting is necessary or not simply by inputting location information of the location where the concreting work will be performed or information about the area including that location into the output device 1 of the present invention.

[0052] 3 shows a functional block diagram illustrating the functional configuration of the output device 1 of the present invention. The output device 1 of the present invention includes at least a storage unit 10, a position information input unit 11, an extraction unit 12, and an output unit 13. Each component will be described in more detail below.

[0053] <Storage section> The memory unit 10 stores a plurality of meteorological observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The topographical factors of each of the regions X are calculated from a dataset containing n The multiple regions X are determined from the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years. n The data of the estimated hot weather and extreme heat periods stored in the storage unit 10 is stored for each of the plurality of regions X. n The estimated daily smoothed normal values ​​can be determined from the estimated daily smoothed normal values ​​in the above table, and can be determined, for example, by the determination method of the present invention. The estimated daily smoothed normal values ​​can also be determined by the data set acquisition unit, the estimated daily smoothed normal value calculation unit, and the estimated hot weather / extreme heat period determination unit in the estimated hot weather / extreme heat period map generation device described below. In other words, the output device of the present invention may have the above-mentioned components as necessary. The storage unit 10 may have, as hardware resources, memories such as RAM (Random Access Memory), ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), magnetic disks, recording media such as CD-ROMs, etc., and has the function of storing (recording, saving) the above-mentioned estimated hot weather and extreme heat season data using these.

[0054] Regarding the data of the estimated hot weather and extreme heat period stored in the storage unit 10, the range of the area in the data (multiple areas X nThe range (the whole area) is not particularly limited. For example, it may be a range covering the whole of each city, ward, town, or village, a range covering the whole of a prefecture, or a range covering the whole of Japan. Furthermore, for example, the range may include overseas. Note that if there are multiple regions X, n This usually includes the area that includes meteorological observation point A.

[0055] The estimated hot weather and extreme heat period data stored in the memory unit 10 is collected from multiple regions X n The estimated hot weather / extremely hot weather data may include data on the area of ​​the estimated hot weather / extremely hot weather, the first day of the estimated hot weather / extremely hot weather period, the last day of the estimated hot weather / extremely hot weather period, the total number of days in the estimated hot weather / extremely hot weather period, etc. Furthermore, the estimated hot weather / extremely hot weather data may include, for example, an estimated hot weather / extremely hot weather period map. The estimated hot weather / extremely hot weather period map may be, for example, a map generated by the generation method of the present invention or the estimated hot weather / extremely hot weather period map generation device of the present invention described below.

[0056] <Location information input section> The location information input unit 11 is a functional unit that receives location information used to extract estimated hot weather and extreme heat season data to be output by the output device 1 of the present invention. The location information input unit 11 may have hardware resources such as a keyboard, physical buttons, a mouse, or a touch panel. The location information input unit 11 may also be connected to another device (external device 3) via a network and receive specific location information input by that device. In the embodiment shown in FIG. 2, the location information input unit 11 functions as a unit that receives specific location information input to the external device 3. Examples of such external devices 3 include smartphones, tablet terminals, and personal computers. Alternatively, the current location may be identified using a global positioning system (GPS), and the identified information may be transmitted to the location information input unit 11.

[0057] The specific location information input to the location information input unit 11 is not particularly limited as long as it includes location information. For example, it may be location information such as an address, a landmark (such as a station name, a building name, or a scenic spot name), latitude, and longitude. In addition, if map information is displayed on the location information input screen, a location can be specified by placing a cursor or pointer on the map.

[0058] <Extraction part> The extraction unit 12 extracts the location information from the plurality of regions X stored in the storage unit 10 based on the location information received by the location information input unit 11. n It has the function of extracting the estimated hot weather and extreme heat period data for a specific region X that includes the location in question from the estimated hot weather and extreme heat period data for each of the above. The "location information received by the location information input unit" is n If the information directly identifies a specific region X in the area, the data on the estimated hot and extreme heat periods for the specific region X can be extracted using the information. n is an area in a mesh division corresponding to the reference area mesh, and the location information is a mesh code, the extraction unit 12 can extract data on the estimated hot weather and extreme heat period for a specific area X based on the mesh code. Furthermore, the "location information received by the location information input unit" is n If the input location information does not directly identify a specific region X in the received location information, the region X including the position (point x) in the received location information is identified, and the extraction unit 12 can extract data on the estimated hot weather and extreme heat periods for the specific region X based on the identified region X. For example, if the input location information is information on a specific point x, the specific region X including the point x is identified.

[0059] The extraction unit 12 has a processor such as a CPU (Central Processing Unit) for executing information processing.

[0060] <Output section> The output unit 13 is a functional unit that outputs data on the estimated hot weather and extreme heat seasons for the specific region X extracted by the extraction unit 12. For example, as shown in Fig. 2, the output unit 13 may have a function to transmit the data to an external device 3, and if connected to a display device such as a display, the output unit 13 can also output the data to the display device. The estimated hot weather / extremely hot weather data output by the output unit 13 includes, for example, at least data on the area division, the first day of the estimated hot weather / extremely hot weather period, the last day of the estimated hot weather / extremely hot weather period, the total number of days of the estimated hot weather / extremely hot weather period, etc. Furthermore, the estimated hot weather / extremely hot weather data may include, for example, an estimated hot weather / extremely hot weather map for a specific area X and its surrounding area. The estimated hot weather / extremely hot weather map may be, for example, a map generated by the generation method of the present invention.

[0061] Next, the processing flow performed by the output device 1 of the present invention will be described with reference to FIG.

[0062] (Step S101) The location information input unit 11 receives information on a specific location for outputting data on the estimated hot weather and extreme heat periods.

[0063] (Step S102) If the specific location accepted by the location information input unit directly identifies the specific area X, the extraction unit 12 proceeds to step S104. If the specific location does not directly identify the specific area X, the process proceeds to step S103, where the area that includes the specific location is identified, and the process proceeds to step S104.

[0064] (Step S104) The extraction unit 12 extracts the region X from the storage unit 10. n Extract the estimated hot and extremely hot periods for a specific region X from the estimated hot and extremely hot periods for the region.

[0065] (Step S105) The output unit 13 outputs the data of the estimated hot season and extreme heat season of the specific region extracted as described above.

[0066] FIG. 5 shows an example of inputting specific location information and outputting estimated hot weather data for region X using the estimated hot weather / extreme heat period output device of the present invention. When specific location information (e.g., an address or landmark) is input to the estimated hot weather / extreme heat period output device of the present invention via an external device 3, data on the estimated hot weather / extreme heat period for the specific region X that includes the specific location is extracted and integrated with map information, enabling the output of estimated hot weather data as shown in FIG. 5. In the example shown in FIG. 5, hot weather information (first day of the hot weather period, last day of the hot weather period, total number of hot weather days) for the mesh area (mesh code: 57403710) that includes Sendai Station is displayed. In addition, the hot weather periods for areas outside the mesh area are also displayed in different colors. In the map shown in FIG. 5, light-colored areas represent a total number of hot weather days of 1 to 15 days, and dark-colored areas represent a total number of hot weather days of 16 to 30 days. Additionally, the map can be moved up, down, left, or right to display hot weather information for a desired mesh area. 5 shows an example of displaying hot weather information, but it is also possible to output and display information on extremely hot weather in the same way, or to display both hot weather information and extremely hot weather information together. It is also possible to configure the system so that the user can select the display method. In this way, instead of predicting hot and extremely hot periods based on empirical rules as in the past, it is now possible to easily obtain hot and extremely hot period information (first day, last day, total number of days, etc.) for a mesh range simply by inputting specific location information, which allows for safer and smoother planning and construction of concrete work.

[0067] [Estimated hot weather and extreme heat period map generator] Yet another embodiment of the present invention is a method for detecting a plurality of weather stations A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n a dataset acquisition unit that acquires a dataset including each of the topographical factors; From the acquired dataset, the plurality of regions X na calculation unit for calculating the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years for each of the above; The estimated daily smoothed normal values ​​are used to calculate the multiple regions X n an estimated hot and extreme heat period determination unit that determines each estimated hot and extreme heat period; The determined plurality of regions X n a map generating unit that generates a map based on the data of each of the estimated hot and extreme heat periods; an output unit that outputs the map; The estimated hot weather / extremely hot weather map generating device (hereinafter also referred to as the generating device of the present invention) is provided with the above.

[0068] Fig. 3 shows a functional block diagram illustrating the functional configuration of the generating device 2 of the present invention. The generating device 2 of the present invention includes at least a data set acquiring unit 20, an estimated daily smoothed normal value calculating unit 21, an estimated hot weather / extremely hot weather period determining unit 22, a map generating unit 23, and an output unit 24. Each component will be described in more detail below.

[0069] <Dataset Acquisition Section> The dataset acquisition unit 20 is a functional unit that accepts datasets such as meteorological data input to the generation device 2 of the present invention. The dataset acquisition unit 20 may have hardware resources such as a keyboard, physical buttons, a mouse, and a touch panel. The dataset acquisition unit 20 may also be connected to another device (such as the external device 3) via a network and receive datasets input by that device. The data set includes data from multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The data set includes the topographical factors of the plurality of meteorological observation points A and B. n and preferably includes city factors for each of a plurality of regions X. In the present invention and this specification, the "plurality of meteorological observation points A nweather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The "dataset including each of the topographic factors" includes the daily smoothed normal values ​​of daily mean temperature calculated using each of the data and already published (however, if multiple regions X n The dataset may include already published daily smoothed normal values ​​of daily mean temperature calculated for the same period as the past 10 years from which the estimated daily smoothed normal values ​​of daily mean temperature are calculated. In other words, the dataset may include already published daily smoothed normal values ​​of daily mean temperature.

[0070] <Calculation of estimated daily smoothed normal values> The estimated daily smoothed normal value calculation unit 21 calculates the daily normal values ​​for multiple regions X based on the dataset acquired by the dataset acquisition unit 20. n The method for calculating the estimated daily smoothed normal is to calculate the estimated daily smoothed normal for multiple regions X. n The calculation method described in the determination method of the present invention can be applied except that the calculation is performed for a plurality of meteorological observation points A. n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The above-mentioned topographical factors may be calculated by multiple regression analysis or the like from a dataset including these factors, or may be calculated from the daily smoothed normal values ​​of daily mean temperature that have been calculated using the dataset and have already been published. The calculated data of the estimated daily smoothed normal values ​​may be stored in the storage unit 10 described above.

[0071] <Determining Estimated Hot and Extremely Hot Periods> The estimated hot weather / extremely hot weather period determination unit 22 determines the estimated daily smoothed normal values ​​calculated by the estimated daily smoothed normal value calculation unit 21, and calculates the estimated daily smoothed normal values ​​for the plurality of regions X. n The criteria for setting the estimated hot weather and extreme heat periods can be set appropriately as explained in the determination method of the present invention. The determined estimated hot weather / extremely hot weather period data may be stored in the storage unit 10 described above.

[0072] <Map Generation> The map generating unit 23 generates a map of the plurality of regions X determined by the estimated hot weather / extremely hot weather period determining unit 22. n This is a functional part that combines the data on estimated hot weather and extreme heat periods with map information to generate a map (estimated hot weather and extreme heat period map). For example, by combining the obtained data on a Geographic Information System (GIS), it is possible to create a map of all of Japan that is color-coded by the number of days in the period. The generated estimated hot weather / extremely hot weather map may be stored in the storage unit 10 described above.

[0073] <Output section> The output unit 24 outputs the estimated hot weather / extremely hot weather map generated by the map generation unit 23. For example, the generated map can be stored in Google Earth or transmitted to an external device.

[0074] Next, the processing flow performed by the generating device 2 of the present invention will be described with reference to FIG.

[0075] (Step S201) The data set acquisition unit 20 acquires data from a plurality of meteorological observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n A dataset containing each of the terrain factors is accepted.

[0076] (Step S202) The estimated daily smoothed normal value calculation unit 21 calculates the daily normal value for multiple regions X based on the dataset received by the dataset acquisition unit 20. n Calculate the estimated daily smoothed normal values ​​of

[0077] (Step S203) The estimated hot weather / extremely hot weather period determination unit 22 determines the average daily average annual value calculated by the estimated daily average annual value calculation unit 21 for multiple regions X n Based on the estimated daily smoothed normal values ​​of n Determine the estimated hot and extreme heat periods.

[0078] (Step S204) The map generating unit 23 generates a map of the plurality of regions X determined by the estimated hot weather / extremely hot weather period determining unit 22. n The estimated hot weather and extreme heat period data is combined with map information to generate a map (estimated hot weather and extreme heat period map).

[0079] (Step S205) The output unit 24 outputs the data of the estimated hot weather / extremely hot weather map generated as described above.

[0080] [Program to output estimated hot and extremely hot periods, program to generate estimated hot and extremely hot period maps] The output device of the present invention and the generation device of the present invention can be realized by a computer that operates under program control. That is, yet another embodiment of the present invention is a program that causes a computer to function as the output device of the present invention or the generation device of the present invention. The following two embodiments of this program can be given.

[0081] Computer, Multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n The topographical factors of each of the regions X are calculated from a dataset containing n The multiple regions X are determined from the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years. n a storage means in which data on each of the estimated hot and extreme heat periods is stored; The plurality of regions X n a location information input means for receiving information input of a specific location that exists in any one of the above; an extraction means for extracting data on estimated hot and extreme heat periods for a specific region X that includes the specific location; an output means for outputting data on the estimated hot weather and extreme heat season for the specific region X; This is an estimated hot and extremely hot period output program.

[0082] Computer, Multiple weather observation points A n weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A n and multiple regions X n a dataset acquisition means for acquiring a dataset including each of the topographical factors; From the acquired dataset, the plurality of regions X n A means for calculating the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years for each of the above; The estimated daily smoothed normal values ​​are used to calculate the multiple regions X n a means for determining the estimated hot and extreme heat periods for each of the above; The determined plurality of regions X n a map generating means for generating a map based on the data of each of the estimated hot and extreme heat periods; This is a program that generates maps of estimated hot and extremely hot periods.

[0083] Furthermore, another embodiment of the present invention includes a computer-readable recording medium having the above-described program recorded thereon. The form of the recording medium is not particularly limited, and examples thereof include a magnetic disk, a magneto-optical disk, an optical disk, and a flash memory. [Example]

[0084] The present invention will be described in more detail based on examples. The present invention is not to be construed as being limited to the following examples except as defined in the present invention.

[0085] Example 1 In Example 1, an estimated hot season map and an estimated extreme heat season map were created using the determination method and generation method of the present invention. The data used and the analysis procedure are shown below.

[0086] <Dataset> The dataset used in this example is the mesh normal values ​​from the "Mesh Normal Values ​​2020" published by the Japan Meteorological Agency, which are already published daily smoothed normal values ​​of daily mean temperature.

[0087] <Multiple Weather Observation Points A n > Multiple meteorological observation points A used in this embodiment n As the data, 915 meteorological observation points used to calculate the mesh normal values ​​for 2020 were used.

[0088] <Multiple Regions X n > Multiple regions X used in this embodiment n As the grid, we used the grid corresponding to the reference area grid.

[0089] <Calculation of estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years> The above multiple weather observation points A n For each of the periods A (2011-2020), B (2012-2021), C (2013-2022), and D (2014-2023), the daily smoothed normal values ​​of the daily mean temperature were calculated. i Daily smoothed normal values ​​(T i·10-year ) and mesh normal values ​​(T i·30-year ) and the difference (ΔTi=T i·10-year -T i·30-year ) was calculated daily. Next, multiple regions X n For each of the four (k=4) meteorological observation points A in order of proximity to the center point of each region X, i The mesh normal value (t x·30-year ) is adjusted using the inverse distance weighting method according to the distance, and the estimated daily smoothed normal value of the daily mean temperature for the past 10 years for each region X (t x·10-year ) was calculated for each day. The following (Equation 1) and (Equation 2) were used for this correction.

[0090]

number

[0091]

number

[0092] In the above (Equation 1) and (Equation 2), the parameters are as follows: ΔT i :T i·10-year From T i·30-year The difference after subtracting (However, at weather observation point A i :Multiple weather observation points A n Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, and T i·10-year :Weather observation point A i The daily average temperature is calculated from meteorological data for the past 10 years. i·30-year :Weather observation point A i (The published daily average temperature for the past 30 years in the mesh area containing the data) t x·10-year : Estimated daily smoothed normal values ​​of daily mean temperatures for the past 10 years in region X t x·30-year : Daily smoothed normal values ​​of daily mean temperatures for the past 30 years published in the mesh division of Area X d i : From the center of area X to weather observation point A i Distance to k: Two or more meteorological observation points A in order of proximity to the center of region X i Number of

[0093] <Determining the estimated hot and extreme heat periods> Multiple Regions X nFor each of the above, estimated hot and extreme heat periods were determined using the estimated daily smoothed normal values ​​of daily mean temperature for each region X over the past 10 years obtained as described above. Note that hot periods are defined as periods when the estimated daily smoothed normal value of daily mean temperature over the past 10 years exceeds 25.0°C, and extreme heat periods are defined as periods within the above hot periods when the estimated daily smoothed normal value of daily mean temperature over the past 10 years exceeds 28.0°C.

[0094] <Map Generation> Multiple regions above X n Using data on estimated hot and extremely hot periods for each of these areas, we stored and plotted the estimated hot and extremely hot periods in a GIS, creating a detailed mesh map covering the estimated hot and extremely hot periods across the country. Arc GIS Pro was used to create the map. The results are shown in Figures 7 and 8.

[0095] Figures 7(a) to 7(d) show mesh maps of the hot season based on each of periods A, B, C, and D. Similarly, Figures 8(a) to 8(d) show mesh maps of the extreme heat season based on each of periods A, B, C, and D. In the maps shown in Figures 7(A) to 7(D) and 8(A) to 8(D), the estimated hot season and extreme heat season are shown in different colors every 15 days.

[0096] The maps in Figures 7 and 8 show the hot and extremely hot periods for each region across Japan with high resolution. Furthermore, in the maps shown in Figures 7 and 8, the estimated hot and extremely hot periods in many regions were longer than those calculated based on the mesh normal values ​​in the "Mesh Normal Values ​​2020" published by the Japan Meteorological Agency (not shown in the figures). Furthermore, the hot periods in Figure 7 and the extremely hot periods in Figure 8 tended to get longer in the order of period (A), period (B), period (C), and period (D), and their ranges tended to move northward. This indicates that the estimated hot and extremely hot periods obtained by the present invention are able to reflect the effects of recent global warming and other factors.

[0097] The hot season shown in Figure 7 was confirmed to be widespread from Tohoku to Okinawa for all periods A through D. Furthermore, while there was a slight decrease in the number of days covered during period B compared to the other periods, this was similar across Western Japan for all periods, while in northern Tohoku the area covered by the hot season has expanded significantly over the past few years. These findings suggest that the area covered by the hot season has expanded significantly around areas where the number of days covered by each hot season condition is 30 days or less, and that there is an increasing need to prepare for the hot weather. Furthermore, it was confirmed that the extreme heat periods shown in Figure 8 were limited to urban areas south of the Kanto region for all periods A to D. Furthermore, a change in the distribution pattern was confirmed, with an expansion of the areas where the applicable period applies on the Sea of ​​Japan side south of Hokuriku and in the Kanto region.

[0098] Example 2 In Example 2, the data obtained in Example 1 is used to calculate the number of regions X based on the past 10-year period from 2014 to 2023 (period D). n The consistency between the data for the daily smoothed normal values ​​of daily mean temperature at each of the above locations and the daily smoothed normal values ​​of daily mean temperature based on actual measurements at each meteorological observation point was assessed, and the accuracy of the estimated hot and extreme heat periods obtained by this invention, which are determined using the daily smoothed normal values ​​of daily mean temperature, was evaluated.

[0099] The nine meteorological observation points used were those that had meteorological observation stations that were not used in calculating the mesh normal values ​​for 2020. Details of each point are as shown in Table 2 below.

[0100] [Table 2]

[0101] Daily smoothed normal values ​​(normal values ​​[measured]) of daily mean temperatures at the nine locations were calculated from the actual measured values ​​for the past 10 years of period D (2014 to 2023) at the nine locations. Next, the daily smoothed normal values ​​(normal values ​​[measured]) of daily mean temperatures at the nine locations were calculated based on the past 10 years of period D (2014 to 2023) obtained in Example 1 above. n For the estimated hot and extreme heat periods in each of the above locations, we extracted the estimated daily smoothed normal values ​​(normal values ​​[estimated]) of the daily mean temperature for the mesh code (area X) that includes the above nine locations. At the nine locations, the difference between the estimated normal value and the measured normal value was calculated for each day, and the average value (average of estimated - measured) and standard deviation (standard deviation of estimated - measured) were calculated. For comparison, the difference between the mesh normal value for 2020 and the measured normal value was also calculated in the same way, and the average value and standard deviation were calculated for each. The results are shown in Table 3 below and Figure 9.

[0102] [Table 3]

[0103] Table 3 and Figure 9 show that, at all locations, the estimated daily smoothed normals obtained using this invention are roughly the same as the daily smoothed normals based on actual measurements at each location. Furthermore, at most locations, the estimated daily smoothed normals obtained using this invention have smaller discrepancies with the actual measurements than the daily smoothed normals based on the 2020 Mesh Normal. Because the estimated daily smoothed normals are based on data from the past 10 years, the longer the observation period included in Period D for the nine locations mentioned above, the more consistent the estimated values ​​are.

[0104] Due to the effects of global warming in recent years, the average daily temperature has tended to rise. This invention can determine estimated hot periods and extreme heat periods based on data from the past 10 years, and it has been shown that it is possible to determine estimated hot periods and extreme heat periods that better reflect the effects of global warming in recent years. [Explanation of symbols]

[0105] 1 Estimated hot weather and extreme heat output device 10 Storage section 11 Location information input section 12 Extraction part 13 Output section 2. Estimated hot weather and extreme heat map generator 20 Dataset Acquisition Section 21 Estimated daily smoothed normal calculation section 22 Estimated Hot and Extremely Hot Period Determination Section 23 Map Generation Unit 24 Output section 3 External Devices 100 Estimated summer and extreme heat output system

Claims

1. Multiple weather observation points A ｎ weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A ｎ and each topographic factor of region X, and calculates estimated daily smoothed normal values ​​of daily mean temperature for the past 10 years in region X, and determines estimated hot weather periods and extreme heat periods in region X.

2. The data set includes the plurality of meteorological observation points A ｎ and each of city factors of region X.

3. The region X is one of the mesh divisions corresponding to the reference region mesh, 3. The method for determining an estimated hot weather / extremely hot weather season according to claim 2, wherein the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years in the region X are calculated as follows: Two or more meteorological observation points A in order of proximity to the center of the area X ｉ At each meteorological observation point A ｉ The published daily average temperature for the mesh area containing the weather observation point A ｉ The difference between the daily average temperature calculated from the weather data observed at each of the two or more meteorological observation points A for the past 10 years and the daily average temperature calculated from the daily average temperature calculated from the weather data observed at each of the two or more meteorological observation points A for the past 10 years is calculated, and the difference between the daily average temperature calculated from ... ｉ Corrections are made based on the differences using an inverse distance weighting method according to the distance to the target.

4. Two or more meteorological observation points A in descending order of distance from the center point of the region X ｉ The method for determining an estimated hot weather / extremely hot weather period according to claim 3, wherein the number of is 4 to 8.

5. 5. The method for determining an estimated hot season / extremely hot season according to claim 4, wherein the correction to the published daily smoothed normal values ​​of the daily mean temperature in the mesh division of the region X is performed using the following (Equation 1) and (Equation 2). [0011] [0012] however ΔT i :T i・10-year From T i・30-year The difference after subtracting (Weather observation point A ｉ : Multiple weather observation points A ｎ Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, T i・10-year : Weather observation point A ｉ The daily average temperature is calculated from meteorological data for the past 10 years. i・30-year : Weather observation point A ｉ (The published daily average temperature for the past 30 years in the mesh area containing the above data) t x・10-year : Estimated daily smoothed normal values ​​of daily mean temperature for the past 10 years in region X t x・30-year : Daily smoothed normal values ​​of daily mean temperature for the past 30 years published in the mesh division of Area X d i : From the center of area X to meteorological observation point A ｉ Distance to k: Two or more meteorological observation points A in order of proximity to the center point of area X ｉ Number of

6. The method for determining an estimated hot weather / extremely hot weather period according to any one of claims 1 to 5, ｎ and generating a map based on the data of the estimated hot weather and extreme heat periods.

7. A concrete work method comprising determining whether or not concrete work to be carried out in the region X on a scheduled concrete pouring date is to be hot weather concreting work, based on an estimated hot weather / extremely hot weather period determined by the method for determining an estimated hot weather / extremely hot weather period described in any one of claims 1 to 5.

8. A method for manufacturing a concrete structure, comprising determining whether or not concrete work to be carried out in the region X on a scheduled concrete pouring date is to be hot weather concreting work, based on an estimated hot weather / extremely hot weather period determined by the method for determining an estimated hot weather / extremely hot weather period described in any one of claims 1 to 5.

9. Multiple weather observation points A ｎ weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A ｎ and multiple regions X ｎ Each of the topographical factors of the plurality of regions X is calculated from a data set including the ｎ The plurality of regions X are determined from the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years. ｎ a memory unit in which data on each of the estimated hot and extreme heat periods is stored; The plurality of regions X ｎ a location information input unit that accepts input of information about a specific location that exists in any one of the above; an extraction unit that extracts data on estimated hot weather and extreme heat periods for a specific region X that includes the specific location; an output unit that outputs data on the estimated hot weather and extreme heat season for the specific region X; An estimated hot weather / extremely hot weather output device equipped with the above.

10. Multiple weather observation points A ｎ weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A ｎ and multiple regions X ｎ a dataset acquisition unit that acquires a dataset including each of the terrain factors; From the acquired dataset, the plurality of regions X ｎ an estimated daily smoothed normal value calculation unit that calculates estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years for each of the above; The estimated daily smoothed normal values ​​are used to calculate the multiple regions X ｎ an estimated hot weather / extreme heat period determination unit that determines each estimated hot weather / extreme heat period; The determined plurality of regions X ｎ a map generating unit that generates a map based on the data of each of the estimated hot weather and extreme heat periods; an output unit that outputs the map; An estimated hot weather / extremely hot weather map generation device comprising:

11. The plurality of regions X ｎ is one area among the mesh divisions corresponding to the reference area mesh, The plurality of regions X ｎ 11. The apparatus according to claim 9 or 10, wherein the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years are calculated as follows: The multiple regions X ｎ Each of the two or more meteorological observation points A in order of proximity to the central point of each of the ｉ At each meteorological observation point A ｉ The published daily average temperature for the mesh area containing the weather observation point A ｉ Calculate the difference between the daily average temperature calculated from the weather data observed in the past 10 years and the daily smoothed normal value, and ｎ The daily smoothed normal values ​​of the daily mean temperature published in each mesh section of the multiple regions X ｎ From each central point of each of the two or more meteorological observation points A ｉ Corrections are made based on the differences using an inverse distance weighting method according to the distance to the target.

12. Two or more meteorological observation points A in descending order of distance from the center point of the region X ｉ The device of claim 11, wherein the number of

13. The apparatus according to claim 12, wherein the correction to the published daily smooth normal values ​​of the daily mean temperature in the mesh division of the region X is performed using the following (Equation 1) and (Equation 2). [0013] [0014] however ΔT i :T i・10-year From T i・30-year The difference after subtracting (Weather observation point A ｉ : Multiple weather observation points A ｎ Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, T i・10-year : Weather observation point A ｉ The daily average temperature is calculated from meteorological data for the past 10 years. i・30-year : Weather observation point A ｉ (The published daily average temperature for the past 30 years in the mesh area containing the above data) t x・10-year : Estimated daily smoothed normal values ​​of daily mean temperature for the past 10 years in region X t x・30-year : Daily smoothed normal values ​​of daily mean temperature for the past 30 years published in the mesh division of Area X d i : From the center of area X to meteorological observation point A ｉ Distance to k: Two or more meteorological observation points A in order of proximity to the center point of area X ｉ Number of

14. Computer, Multiple weather observation points A ｎ weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A ｎ and multiple regions X ｎ Each of the topographical factors of the plurality of regions X is calculated from a data set including the ｎ The plurality of regions X are determined from the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years. ｎ a storage means in which data on each of the estimated hot and extreme heat periods is stored; The plurality of regions X ｎ a location information input means for receiving information input of a specific location that exists in any one of the above; an extraction means for extracting data on estimated hot weather and extreme heat periods for a specific region X that includes the specific location; an output means for outputting data on the estimated hot weather and extreme heat season for the specific region X; This is an estimated hot and extremely hot period output program that functions as a.

15. Computer, Multiple weather observation points A ｎ weather data including the weather observation date and the temperature on the observation date at each of the plurality of weather observation points A ｎ and multiple regions X ｎ a dataset acquisition means for acquiring a dataset including each of the terrain factors; From the acquired dataset, the plurality of regions X ｎ an estimated daily smoothed normal value calculation means for calculating an estimated daily smoothed normal value of each of the daily mean temperatures for the past 10 years; The estimated daily smoothed normal values ​​are used to calculate the multiple regions X ｎ a means for determining an estimated hot weather / extremely hot weather period for each of the above; The determined plurality of regions X ｎ a map generating means for generating a map based on the data of each of the estimated hot weather and extreme heat periods; This is a program that generates estimated hot and extremely hot periods maps.

16. The plurality of regions X ｎ are the mesh area corresponding to the reference area mesh, The plurality of regions X ｎ 16. The program according to claim 14 or 15, wherein the estimated daily smoothed normal values ​​of the daily mean temperature for the past 10 years are calculated as follows: The multiple regions X ｎ Each of the two or more meteorological observation points A in order of proximity to the central point of each of the ｉ At each meteorological observation point A ｉ The published daily average temperature for the mesh area containing the weather observation point A ｉ Calculate the difference between the daily average temperature calculated from the weather data observed in the past 10 years and the daily smoothed normal value, and ｎ The daily smoothed normal values ​​of the daily mean temperature published in each mesh section of the multiple regions X ｎ From each central point of each of the two or more meteorological observation points A ｉ Corrections are made based on the differences using an inverse distance weighting method according to the distance to the target.

17. Two or more meteorological observation points A in descending order of distance from the center point of the region X ｉ The program according to claim 16, wherein the number of

18. 18. The program according to claim 17, wherein the correction to the published daily smoothed normal values ​​of daily mean temperatures in the mesh divisions of the region X is performed using the following (Equation 1) and (Equation 2). [Equation 15] [0016] however ΔT i :T i・10-year From T i・30-year The difference after subtracting (Weather observation point A ｉ : Multiple weather observation points A ｎ Among them, two or more meteorological observation points are selected in order of proximity to the center of region X, T i・10-year : Weather observation point A ｉ The daily average temperature is calculated from meteorological data for the past 10 years. i・30-year : Weather observation point A ｉ (The published daily average temperature for the past 30 years in the mesh area containing the above data) t x・10-year : Estimated daily smoothed normal values ​​of daily mean temperature for the past 10 years in region X t x・30-year : Daily smoothed normal values ​​of daily mean temperature for the past 30 years published in the mesh division of Area X d i : From the center of area X to meteorological observation point A ｉ Distance to k: Two or more meteorological observation points A in order of proximity to the center point of area X ｉ Number of

19. A computer-readable recording medium on which the program according to claim 14 or 15 is recorded.

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