Airflow estimation method, airflow estimation system, and airflow estimation program

Abstract

To provide an air current estimation method capable of estimating the occurrence of abnormal weather with high accuracy.SOLUTION: An air current estimation method obtains weather data obtained by prediction or observation of a wind state at a weather prediction point that is present within an air current analysis target area; performs air current analysis of the air current analysis target area by using the weather data; and estimates an air current at an air current estimation point by using a prediction value at the weather prediction point which is obtained from the weather data, a ratio between a value that indicates an average air current state at the weather prediction point which is obtained by both the weather data and the air current analysis for the air current analysis target area and a value that indicates an average air current state at the air current estimation point, and a value indicating a sudden air current change at the air current estimation point which is obtained by the air current analysis.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] Embodiments of the present invention relate to an airflow estimation method, an airflow estimation system, and an airflow estimation program.

Background Art

[0002] Conventionally, systems for analyzing weather such as wind conditions are known. In such systems, airflow analysis is performed using weather data provided from the outside such as the Japan Meteorological Agency as input data. The results of the airflow analysis can be used in various scenarios affected by the weather. For example, it can be used in scenarios for estimating the conditions of transportation systems such as railways and roads.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In places such as railways and roads, for example, when an abnormal weather occurs in which the wind speed suddenly changes rapidly due to the approach of a typhoon or the like, transportation systems may be greatly affected. However, since conventional systems can only provide wide-area weather analysis data or weather observation data, it is difficult to accurately estimate the occurrence of such abnormal weather.

[0005] The problem to be solved by the present invention is to provide an airflow estimation method, an airflow estimation system, and an airflow estimation program capable of accurately estimating the occurrence of abnormal weather.

Means for Solving the Problems

[0006] One embodiment of the airflow estimation method involves acquiring meteorological data in which wind speed is predicted or observed at weather forecast points located within the airflow analysis target area, performing airflow analysis on the airflow analysis target area using the meteorological data, and estimating the airflow at the airflow estimation point using the predicted values ​​of the weather forecast points obtained from the meteorological data, the ratio of a value indicating the average airflow conditions at the weather forecast points obtained from the meteorological data and airflow analysis of the airflow analysis target area to a value indicating the average airflow conditions at the airflow estimation point, and a value indicating a sudden change in airflow at the airflow estimation point obtained from the airflow analysis. [Effects of the Invention]

[0007] According to this embodiment, it becomes possible to estimate the occurrence of extreme weather events with high accuracy. [Brief explanation of the drawing]

[0008] [Figure 1] This block diagram shows a schematic configuration of an airflow estimation system according to one embodiment. [Figure 2] This is a flowchart showing the procedure for the analysis operation of an airflow estimation system according to one embodiment. [Figure 3] This figure illustrates the analysis operation of an airflow estimation system according to one embodiment. [Figure 4] This is a schematic diagram illustrating the analytical operation of an airflow estimation system according to one embodiment. [Figure 5] This graph shows an example of airflow analysis data. [Figure 6] This graph shows an example of wind speed changes during a typhoon at an airflow estimation point. [Figure 7] This graph shows an example of a wind speed trend waveform at a weather forecasting location and an airflow estimation location. [Modes for carrying out the invention]

[0009] Embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are not intended to limit the present invention.

[0010] Figure 1 is a block diagram showing a schematic configuration of an airflow estimation system according to one embodiment. The airflow estimation system 1 shown in Figure 1 comprises a data input device 100 and a diagnostic evaluation device 200. The data input device 100 and the diagnostic evaluation device 200 are connected to each other via a communication network 300.

[0011] First, the configuration of the data input device 100 will be described. The data input device 100 has an online data input unit 110 and an offline data input unit 120. Weather data 111 is input to the online data input unit 110 from an external source.

[0012] The meteorological data 111 includes, for example, at least one of the following: Japan Meteorological Agency forecast data, weather forecast GPV (Grid Point Value) data, weather forecast SCW site (Super C Weather) data, weather reanalysis data such as ERA-5, wind condition data constructed from past actual measurement data, weather observation data such as AMeDAS, and data from WRF (Weather Research and Forecasting) analysis results such as numerical weather models. The Japan Meteorological Agency forecast data is wind condition forecast data predicted by the Japan Meteorological Agency. The weather forecast GPV data is past and future weather forecast data calculated by a supercomputer at grid points predetermined on a map. The ERA-5 data is data used by the European Centre for Medium-Range Weather Forecasts for weather forecasting. WRF is a forecasting model for predicting wind conditions such as wind speed and wind direction. The meteorological data 111 may also be machine learning forecast data obtained by machine learning using at least one of the above-mentioned data as training data. Furthermore, the training data may be added sequentially and the machine learning model may be updated.

[0013] Meanwhile, the offline data input unit 120 receives external inputs such as map data 121 and railway line data 122. Map data 121 is data related to the topography, such as the map and elevation of the area to be analyzed for airflow, as well as information on structures such as buildings and elevated structures. Railway line data 122 is data indicating the locations of railway tracks and stations.

[0014] Next, the configuration of the diagnostic evaluation device 200 will be described. The diagnostic evaluation device 200 includes a communication unit 210, an operation unit 220, a display unit 230, a control unit 240, and a storage unit 250. Hereinafter, each unit of the diagnostic evaluation device 200 will be described.

[0015] The communication unit 210 functions as a communication interface when communicating with the data input device 100 via the communication network 300.

[0016] The operation unit 220 receives user operation inputs. The operation unit 220 has an input device such as a keyboard or a mouse, for example.

[0017] The display unit 230 displays various images based on the control of the control unit 240. The display unit 230 has a display device such as a liquid crystal display, for example.

[0018] The control unit 240 includes a data acquisition unit 241, an airflow analysis unit 242, and a wind speed estimation unit 243. Hereinafter, each unit will be described.

[0019] The data acquisition unit 241 acquires various data from the data input device 100 via the communication unit 210.

[0020] The airflow analysis unit 242 calculates wind conditions such as wind speed and wind direction using the data acquired by the data acquisition unit 241.

[0021] The wind speed estimation unit 243 is an example of an airflow estimation unit and estimates the wind speed at the airflow estimation point.

[0022] The storage unit 250 has an airflow analysis database 251. The airflow analysis database 251 is a database showing the results of pre-analyzing the wind conditions along the railway line. The airflow analysis database 251 includes airflow analysis data calculated by the airflow analysis unit 242 and the like.

[0023] The operation of the airflow estimation system 1 described above will now be explained with reference to Figure 2. Figure 2 is a flowchart showing the operation procedure of the airflow estimation system 1 according to this embodiment.

[0024] As shown in Figure 2, first, data necessary for wind condition estimation is input to the data input device 100 from an external organization (step S1). The input data is classified into online data input unit 110 and offline data input unit 120 according to its characteristics. Online data input unit 110 receives online data whose data values ​​change over time, i.e., meteorological data 111. On the other hand, offline data input unit 120 receives constant offline data whose data values ​​do not change over time, i.e., map data 121 and railway line data 122.

[0025] Next, the airflow analysis unit 242 of the diagnostic evaluation device 200 performs airflow analysis (step S2). Here, the operation of step S2 will be explained in detail with reference to Figures 3 and 4. Figure 3 is a diagram illustrating the analysis operation of the airflow estimation system 1. Figure 4 is a schematic diagram illustrating the analysis operation of the airflow estimation system 1.

[0026] In step S2, as shown in Figure 3, the airflow analysis unit 242 first creates a wide-area analysis model using map data 121 (step S21). The wide-area analysis model includes topographic models such as the elevation of mountainous areas and plains within the wide-area area A1, which includes the area along the railway line R shown in Figure 4.

[0027] Next, the airflow analysis unit 242 inputs meteorological data 111 or wind speed data for each altitude based on meteorological data 111 into the wide-area analysis model to perform airflow analysis of wide-area A1 (step S22). The meteorological data 111 input in step S22 is, for example, forecast data from the Japan Meteorological Agency. Wide-area A1 includes not only the wind condition estimation area, which includes the railway R and its surrounding area, but also areas outside the wind condition estimation area. Therefore, the input meteorological data 111 includes not only meteorological data within the wind condition estimation area but also meteorological data outside the wind condition estimation area.

[0028] Step S22 can be achieved by pre-installing existing airflow analysis software, such as a k-ε model or LES model, on the diagnostic evaluation device 200, and having the airflow analysis unit 242 operate according to the program of that airflow analysis software.

[0029] Next, the airflow analysis unit 242 creates a local analysis model using the map data 121 and the railway line data 122 (step S23). In step S23, the wide area A1 is divided into local areas A2, for example, near the railway line. The local analysis model also includes a topographic model for each local area A2. The topographic models for local areas A2 show more detailed data, such as the location and height of buildings, than the topographic model for the wide area A1. In addition, the railway R exists across multiple local areas A2.

[0030] Next, the airflow analysis unit 242 inputs the results of the airflow analysis of the wide area A1 performed in step S22 into the local analysis model and performs airflow analysis for each local area (step S24). The airflow analysis data showing the analysis results for each local area A2 is stored in the airflow analysis database 251. Step S24, like step S22, can be achieved by the airflow analysis unit 242 operating according to the program of the airflow analysis software pre-installed on the diagnostic evaluation device 200.

[0031] Figure 5 is a graph showing an example of airflow analysis data for local area A2. In Figure 5, the horizontal axis shows the wind speed at a certain point, and the vertical axis shows the elevation at a certain point. In the graph shown in Figure 5, under normal conditions, the wind speed increases sharply in the positive direction as the elevation increases. In contrast, under emergency conditions, the wind speed initially increases in the negative direction until the elevation reaches a certain value, and then increases in the positive direction. Thus, the pattern of change in airflow analysis data differs between normal and emergency situations. For this reason, the airflow analysis data for normal conditions and the airflow analysis data for emergency situations may be stored in separate airflow analysis databases 251. In this case, the airflow analysis data can be classified and managed according to the wind conditions.

[0032] In this embodiment, in order to create airflow analysis data with high accuracy, the target area for airflow analysis is narrowed in stages from a wide area A1 to a local area A2, a method known as nesting. However, if data accuracy can be ensured, airflow analysis data may be created using a combination of wide-area and local airflow analysis, wide-area analysis only, or local airflow analysis only. When airflow analysis data is created using only wide-area or local airflow analysis, nesting airflow analysis becomes unnecessary, thus reducing the time required for airflow analysis and alleviating the processing load.

[0033] In this embodiment, by performing the airflow analysis described above, airflow analysis data such as wind speed and wind direction in the wind condition estimation area including the railway R is stored in the airflow analysis database 251 in advance.

[0034] Once the database creation of the airflow analysis data is complete, as shown in Figure 2, the wind speed estimation unit 243 estimates the wind speed at the airflow estimation point using the meteorological data 111 and the data read from the airflow analysis database 251 (step S3). The operation of step S3 will now be explained in detail.

[0035] In step S3, the wind speed estimation unit 243 calculates the estimated wind speed E at the airflow estimation point P2 shown in Figure 4 using the following equation (1). The wind speed estimation range can be selected, for example, by the user operating the control unit 220. Wind speed estimate E = predicted value F × average wind speed ratio α × gust coefficient β (1)

[0036] In equation (1) above, the predicted value F is the predicted wind speed value for weather forecast point P1a included in the weather data 111. If there are multiple weather forecast points P1 around airflow estimation point P2, in order to improve the accuracy of the wind speed estimate E, for example, the predicted wind speed value of the weather forecast point P1a closest to airflow estimation point P2 is used as the predicted value F. Alternatively, a predicted value F interpolated from weather forecast points P1 around airflow estimation point P2 may be used.

[0037] The average wind speed ratio α is an example of the ratio between a value representing the average airflow conditions at weather forecast point P1a and a value representing the average airflow conditions at airflow estimation point P2. In this embodiment, the average wind speed ratio α is a dimensionless coefficient calculated as the ratio of the average wind speeds between weather forecast point P1a and airflow estimation point P2, as shown in equation (2) below. Average wind speed ratio α = 2nd average wind speed AV2 / 1st average wind speed AV1 (2)

[0038] In equation (2), the first mean wind speed AV1 is the average value of the wind speed within a predetermined calculation step or time obtained from the airflow analysis at weather forecast point P1a. For example, Figure 7 shows the wind speed trend waveforms at weather forecast point P1a and airflow estimation point P2 within a predetermined time obtained from the airflow analysis. The first mean wind speed AV1 is the average value of the wind speed trend waveform at weather forecast point P1a.

[0039] Furthermore, the second mean wind speed AV2 is the average value of the wind speed trend waveform at airflow estimation point P2.

[0040] On the other hand, the gust coefficient β is an example of a value that indicates a sudden change in airflow at the airflow estimation point P2. In this embodiment, the gust coefficient β is a dimensionless coefficient calculated as the ratio of the maximum wind speed Vmax of the wind speed fluctuations obtained by airflow analysis at the airflow estimation point P2 to the second mean wind speed AV2, as shown in equation (3) below. Gust coefficient β = Maximum wind speed Vmax / Second mean wind speed AV2 (3)

[0041] The maximum wind speed Vmax is the maximum wind speed value obtained from the airflow analysis in step S2 described above over a predetermined time. For example, Vmax is the maximum wind speed of the wind speed trend waveform at airflow estimation point P2 within a predetermined time period obtained from the airflow analysis shown in Figure 7. Furthermore, to improve the accuracy of wind condition estimation, the estimated wind speed E may be calculated using the average wind speed ratio α and gust coefficient β obtained from the airflow analysis, based on the weather forecast wind direction at weather forecast point P1 or the weather forecast wind direction around airflow estimation point P2. The wind speed estimation unit 243 may also estimate the wind direction at the forecast point from the difference between the wind direction at the forecast point and the wind direction at the estimation point, based on the wind direction at the forecast point obtained from the airflow analysis. Therefore, in this specification, not only when the wind speed estimation unit 243 estimates wind speed, but also when it estimates both wind speed and wind direction, it is considered to be estimating airflow.

[0042] Finally, the display unit 230 displays the estimated wind speed E and wind direction calculated in step S3 as an image (step S4). In step S4, the display unit 230 displays the estimated wind speed E in association with the airflow estimation point P2 shown on the map data, for example, as shown in Figure 4, but the display format of the estimated wind speed E is not particularly limited.

[0043] The operations described in steps S1 to S4 can also be achieved by having a computer execute a program that processes each step. This program can also be recorded on a recording medium as software.

[0044] Figure 6 is a graph showing an example of wind speed changes during a typhoon at airflow estimation point P2. In Figure 6, the horizontal axis represents the date and time, and the vertical axis represents wind speed. The solid line shows the change in the observed maximum instantaneous wind speed actually observed at airflow estimation point P2. On the other hand, the dashed line shows the change in the estimated wind speed E calculated by the airflow estimation method of this embodiment.

[0045] In this embodiment, as described above, when estimating the wind conditions at airflow estimation point P2, the wind speed estimate E is calculated not only by performing airflow analysis on the meteorological data 111, but also by using the average wind speed ratio α and the gust coefficient β. Therefore, when the occurrence of extreme weather events with rapidly changing wind conditions, such as typhoons, is expected, the accuracy of the wind speed estimate E is improved because it is reflected in the average wind speed ratio α and the gust coefficient β. For example, Figure 6, which shows a comparison of observed and estimated values, confirms that a result with higher estimation accuracy can be obtained.

[0046] Therefore, according to this embodiment, it is possible to improve the accuracy of wind speed estimation by locally estimating the wind speed. This makes it possible to detect strong winds in abnormal situations in advance, and thus provides sufficient information for railway operators to make decisions when considering operational details.

[0047] In this embodiment, the area along the railway line is used as the wind condition estimation area. However, the wind condition estimation area is not limited to the area along the railway line; for example, it may be a road such as a highway. In this case, the airflow analysis unit 242 analyzes the wind conditions of the road.

[0048] Although embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel system described herein can be implemented in a variety of other forms. Furthermore, various omissions, substitutions, and modifications can be made to the forms of the system described herein without departing from the spirit of the invention. The appended claims and equivalents are intended to include such forms and modifications included in the scope and spirit of the invention. [Explanation of symbols]

[0049] 100: Data input device 200: Diagnostic and evaluation device 242: Airflow Analysis Department 243: Wind speed estimation part

Claims

1. We obtain weather data that predicts or observes wind conditions at weather forecasting points located within the area targeted for airflow analysis. Using the aforementioned meteorological data, the area to be analyzed for airflow is subjected to airflow analysis. An airflow estimation method for estimating the airflow at an airflow estimation point, using the predicted value of the weather forecast point obtained from the weather data, the ratio of a value indicating the average airflow conditions at the weather forecast point and a value indicating the average airflow conditions at the airflow estimation point obtained from the weather data and airflow analysis of the airflow analysis target area, and a value indicating sudden changes in airflow at the airflow estimation point obtained from the airflow analysis, wherein the airflow at the airflow estimation point is estimated using the following: The ratio is the average wind speed ratio of the first average wind speed at the weather forecast point within a predetermined calculation step or time obtained from the airflow analysis to the second average wind speed at the airflow estimation point within the predetermined calculation step or time obtained from the airflow analysis. The value indicating the sudden change in airflow is the gust coefficient calculated as the ratio of the maximum wind speed at the airflow estimation point within the predetermined calculation step or time to the second average wind speed. An airflow estimation method that calculates an estimated wind speed by multiplying the predicted value by the average wind speed ratio and the gust coefficient.

2. The airflow estimation method according to claim 1, wherein the results of the airflow analysis are stored in a memory unit, and then the wind speed at the airflow estimation point is estimated.

3. The airflow estimation method according to claim 1, wherein the airflow estimation point is located along a railway line.

4. A data input device into which weather data predicting wind speed at weather forecast points located within the area targeted for airflow analysis is input, The diagnostic and evaluation device comprises a data input device and a diagnostic and evaluation device that is communicatively connected to the aforementioned data input device. The diagnostic and evaluation device is An airflow analysis unit that performs airflow analysis on the airflow analysis target area using the aforementioned meteorological data, An airflow estimation unit estimates the airflow at the airflow estimation point using the following: the predicted value of the weather forecast point obtained from the weather data; the ratio of a value indicating the average airflow conditions at the weather forecast point and a value indicating the average airflow conditions at the airflow estimation point obtained from the weather data and airflow analysis of the airflow analysis target area; and a value indicating a sudden change in airflow at the airflow estimation point obtained from the airflow analysis. It has, The ratio is the average wind speed ratio of the first average wind speed at the weather forecast point within a predetermined calculation step or time obtained from the airflow analysis to the second average wind speed at the airflow estimation point within the predetermined calculation step or time obtained from the airflow analysis. The value indicating the sudden change in airflow is the gust coefficient calculated as the ratio of the maximum wind speed at the airflow estimation point within the predetermined calculation step or time to the second average wind speed. The airflow estimation unit calculates an estimated wind speed value by multiplying the predicted value by the average wind speed ratio and the gust coefficient, and is an airflow estimation system.

5. We obtain weather data predicting wind speed at weather forecast points located within the area targeted for airflow analysis. Using the aforementioned meteorological data, the area to be analyzed for airflow is subjected to airflow analysis. An airflow estimation program for causing a computer to perform a process to estimate the airflow at the airflow estimation point, using the predicted value of the weather forecast point obtained from the weather data, the ratio between a value indicating the average airflow conditions at the weather forecast point and a value indicating the average airflow conditions at the airflow estimation point obtained from the weather data and the airflow analysis of the airflow analysis target area, and a value indicating sudden changes in airflow at the airflow estimation point obtained from the airflow analysis, wherein the program causes a computer to perform a process to estimate the airflow at the airflow estimation point. The ratio is the average wind speed ratio of the first average wind speed at the weather forecast point within a predetermined calculation step or time obtained from the airflow analysis to the second average wind speed at the airflow estimation point within the predetermined calculation step or time obtained from the airflow analysis. The value indicating the sudden change in airflow is the gust coefficient calculated as the ratio of the maximum wind speed at the airflow estimation point within the predetermined calculation step or time to the second average wind speed. An airflow estimation program that calculates an estimated wind speed by multiplying the predicted value by the average wind speed ratio and the gust coefficient.

Images