JP3439650B2 - Airport weather risk assessment processor - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technique for observing the weather of an airport and determining the degree of danger of the airport. In particular, the present invention relates to a device that integrates meteorological data from a plurality of observation devices and can comprehensively judge the degree of risk.

[0002]

2. Description of the Related Art When an airplane such as a passenger plane takes off and leaves,
Since the weather conditions around the airport have a great influence on their departure and arrival, it is extremely important to accurately understand the weather conditions. Therefore, the meteorological condition in a certain range including the airport premises has been conventionally observed using various methods. A meteorological radar and anemometer system have been used to observe such meteorological conditions.

In recent years, Doppler radar systems capable of measuring not only wind speed but also rainfall have been used to measure weather conditions in a certain range including airports. Such a Doppler radar for measuring airport weather is called an airport weather Doppler radar system.

FIG. 12 is an explanatory view showing a conventional weather observation operation using such an observation device and a state of an alarm issued to an airport based on the observation result. Observer 1 observes the weather at the airport, as shown in this figure.
0 is the above-mentioned airport weather Doppler radar system 12
Also, the weather conditions in a certain area centering on the airport are observed based on the meteorological data obtained from various meteorological instruments 14.
Then, the observer 10 determines whether or not it is dangerous for the aircraft to take off and land in a certain area around the airport. In other words, the observer makes a risk determination 16 on whether or not the aircraft may take off and land. As a result of this risk level judgment 16, if the warning notification 18 should be given to the airport, the observer 10 gives this warning notification 18 to the control room of the airport.

The observer 10 may observe the meteorological condition at a predetermined place of the airport, but in many cases, the person observing the meteorological condition at a meteorological observatory or the like observes the observer 10. Becomes

As described above, conventionally, a predetermined observer 10 individually obtains meteorological data obtained from various kinds of meteorological observation devices, and the observer 10 itself integrates and judges the data. The judgment 16 of No.

[0007]

[Problems to be Solved by the Invention] Conventional risk determination 1
6 was performed by the observer 10 manually collecting a plurality of meteorological data. Therefore, the determination 16 of the degree of risk is an extremely complicated work, and the burden on the observer 10 is heavy. Furthermore, if a more precise observation of the meteorological condition is attempted, the amount of meteorological data given to the observer 10 also increases, and the labor for making the accurate risk determination 16 increases.

The present invention has been made in view of the above problems, and integrates the meteorological data obtained from various meteorological observation devices with the data obtained from the airport meteorological Doppler radar system to automatically detect a dangerous area for takeoff and landing of an aircraft. To
It is to provide a device capable of making a notification.

[0009]

According to another aspect of the present invention, there is provided an airport meteorological hazard level determination processing device, comprising: storage means for storing a plurality of types of meteorological data including meteorological data having different types of physical quantities; An integrated determination processing device that determines the degree of risk for a certain area of the monitored area centering on the airport based on a plurality of types of meteorological data, wherein the integrated determination processing device includes each of the small areas that configure the monitored area. For the area, create a layer with a score normalized to the weather data for each weather data, create a risk level display layer that aggregates and adds the scores of the layer obtained for each weather data, The risk level of each of the small areas is determined based on the risk level display layer.

In the airport weather risk determination processing device of the present invention, the scoring in the integrated determination processing device is:
It is possible to do so by referring to the conversion table in which the relationship between the physical information and the degree of risk is set, and using the meteorological data as points.

In the airport weather risk determination processing device of the present invention, it is possible that the weather data includes radar weather data obtained from an airport weather radar system.

In the airport weather risk determination processing device of the present invention, it is possible that the weather data includes general weather data other than radar weather data.

In the airport weather risk determination processing device of the present invention, it is possible that the meteorological data includes near meteorological data of a near meteorological radar.

In the airport weather risk determination processing device of the present invention, the weather data may include wind speed distribution weather data obtained from a wind profiler system.

In the airport weather risk determination processing device of the present invention, the integrated determination processing device can give a score in accordance with the prediction of bad weather approaching the small area based on the radar weather data.

In the airport weather risk determination processing device of the present invention, the integrated determination processing device can give a score in accordance with the prediction of bad weather approaching the small area based on the neighborhood weather data.

In the airport weather risk determination processing apparatus of the present invention, the bad weather approach prediction in the integrated determination processing apparatus can be performed by tracking the movement of the rain cloud and predicting the position of the future rain cloud. Is.

In the airport meteorological danger degree judgment processing device of the present invention, the integrated judgment processing device increases the score of the region predicted that the rain cloud is located in the near future by a predetermined amount, and the region predicted that the rain cloud is located in the distant future. It is possible to increase the number of points less than the predetermined amount.

The airport weather risk determination processing device of the present invention can include display means for displaying the determination result of the integrated determination processing device in a manner of being superimposed on the map information of the airport.

In the airport weather risk determination processing device of the present invention, the display means can display the determination result of the integrated determination processing device in color for each monitoring area within a certain range centered on the airport. Is.

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

BEST MODE FOR CARRYING OUT THE INVENTION Preferred embodiments of the present invention will be described below with reference to the drawings.

Embodiment 1. FIG. 1 shows an explanatory diagram of a system centered on the airport weather risk determination processing device according to the present embodiment. As shown in FIG. 1, this device includes a centralized database device 20 (corresponding to a storage unit in claims), an integrated determination processing device 22, and data for displaying the result of the determination made by the integrated determination processing device 22 as data. And a display device 24.

First, the centralized database device 20 is a database device for integrating and storing meteorological data of various meteorological observation devices. As various weather observation devices, an airport weather Doppler radar system 12 is shown in FIG.
(Corresponding to the airport weather radar system in the claims)
, And various kinds of meteorological instruments 14 are shown, but it is of course preferable to use other observation devices.

Here, the airport meteorological Doppler radar system 12 is a device for emitting radio waves into the air and observing echoes from raindrops and clouds in the radial direction, and the phase of the echo and the emission. It is possible to convert the phase difference of radio waves as a velocity component of the echo and observe the moving velocity of the echo in the radial direction.

The various meteorological data stored in the centralized database device 20 are normalized by a predetermined score for each small area. Here, the small area will be described. In this system, a certain range of area centering on an airport is divided into grid-like small areas, and the degree of risk is judged for each small area.

FIG. 2 is an explanatory diagram showing the relationship between a "region" which is a region of a certain range centering on an airport and which is the target of weather observation, and "small regions" which constitute this region.
In this system, several tens of kilometers around the airport
A square area having a side of about 100 km is an area subject to meteorological observation (corresponding to "a certain range of surveillance area centered on an airport" in claims), and this area is
The area is managed by being divided into square areas (small areas) each having a side of several 100 m to several km. The area having one side of several hundred meters to several kilometers is read as a small area in the text, and the degree of risk is determined for each small area.

The various meteorological data obtained from various meteorological observation devices form one layer for each meteorological data. This layer is formed by scoring each small area. An illustration of such a layer is shown in FIG. In Figure 3,
Two layers, a wind speed layer 30 and a rainfall layer 32, are shown, but there are layers only for the types of observed meteorological data.

In the wind speed layer 30, for example, one point is corresponded to 1 m / s of wind speed, and the wind speed is converted into points.
Then, the wind speed is scored for each layer. In FIG. 3, each small area of the wind velocity layer 30 represents a numerical value with a score.

Similarly, in the rainfall amount layer 32, the rainfall amount is also expressed by being scored for each small area. For example,
It is converted into one point per mm of rainfall, and a score is given for each small area. Such scoring also serves as a normalization for comprehensively judging physical quantities with different units such as wind speed and rainfall. That is, the wind speed layer 30 is 0
Only a constant integer from 0 to 10 is attached to each small area, and also in the rainfall layer 32, only a constant integer from 0 to 10 is attached to each area. As a result, the wind speed and the rainfall amount can be integrated and judged.

In the present embodiment, the points are scored as described above and the normalized meteorological data are added for each small area, and a new layer formed of the addition result is created.
This layer is called the risk level display layer 34 in the present embodiment. FIG. 3 also shows a state of the risk level display layer 34. In the example shown in FIG. 3, only the wind speed layer 30 and the rainfall amount layer 32 are shown as the layers of the meteorological data, and the example in which the risk degree display layer 34 is generated from these two layers is shown. In FIG. 3, an example in which the risk level display layer 34 is generated from only two types of layers is shown for ease of explanation, but generally, the numerical values scored in a plurality of types of layers are aggregated (addition). By doing so, the risk level display layer 34 is generated.

As shown in this figure, the numerical values given to the respective small regions in the wind speed layer 30 and the numerical values given to the respective small regions in the rainfall layer 32 are simply added, and the danger level display layer 34 It is attached to each small area in as a numerical value showing the degree of risk.

The feature of this embodiment is that a plurality of meteorological data are scored for each small area,
This is to create a layer that represents the distribution of each meteorological data. Then, by adding the respective points attached to the small areas of the respective layers, the risk degree display layer 34 including the addition result in the respective small areas is formed. Therefore, the degree of risk can be calculated by integrating a plurality of meteorological data that are different physical quantities.

In the present embodiment, when the numerical value indicating the degree of danger attached to each small area of the danger degree display layer 34 is larger than a predetermined reference value, the aircraft cannot pass through the small area. It can be judged to be dangerous. Such a risk degree display layer 34 is displayed on the data display device 24 so as to be superimposed on a map of a region that is an object of weather observation centering on an airport. The user of this system can see which area is displayed by looking at the map centered on the airport shown on the data display device 24 and the risk level display layer 34 that is superimposed and displayed correspondingly. You can easily tell if it is dangerous.

The display of the risk level display layer 34 is as follows.
It is desirable to display each small area in a color according to its score. That is, the closer the value is to 0, the lighter the color (that is, closer to transparent), and the larger the value, the darker the color (for example, deep red). In this way, the degree of danger can be displayed in an intuitively easy-to-understand form based on the color density.

Of course, the data display device 24 according to the present embodiment has a map centering on an airport and a risk level display layer 3.
By displaying the wind velocity layer 30 and the rainfall amount layer 32 in addition to 4, the user can know the specific wind velocity and rainfall amount in each small area. It is also preferable to display the wind speed layer 30 and the risk degree display layer 34 at the same time together with the map centering on the airport. In this case, the wind speed layer 30
For example, if it is represented in blue and the value of the risk display layer 34 is represented in red, the value of each layer can be identified.

In the example described above, it is shown that the color density is changed according to the number of points in each small area when the risk level display layer 34 is displayed. In this case, it is also preferable to change not only the hue of the color but also the hue itself of the color when the attached numerical value is a predetermined value or more. For example, it is also preferable to change the color from yellow to red when the value is judged to be very large and extremely dangerous. As a result, it is possible to easily grasp not only the magnitude of the numerical value but also the fact that the predetermined reference value is exceeded.

As described above, in the present embodiment, it is possible to process the data in an integrated manner by scoring each meteorological data which is a different physical quantity. In order to perform this scoring, for example, an example in which one point is converted per 1 m / s of wind speed has been shown above, but it is also preferable to provide a conversion table and use this table to convert to points.

An example of the contents of the conversion table is shown in FIG. As shown in this figure, the conversion table is a table that stores a plurality of sets of values of meteorological data and points given to the meteorological data. By referring to this table, when the wind speed is 0 or more and less than x1 m / s, the score y1 is added. If the wind speed is x1 or more and less than x2 m / s, a score y2 is added. Further, when the wind speed is x2 or more, x3 m / s
If it is less than, a score y3 is added.

The merit of providing such a table is as follows.
By rewriting the contents of this conversion table, the conversion rule can be easily changed. Furthermore, the degree of danger does not always change in proportion to the value of wind speed. For example, when the wind speed exceeds a predetermined standard, the risk may suddenly increase.

Therefore, when the wind speed value and the degree of danger are not linearly proportional to each other in this way, a conversion table is prepared to determine what score each wind speed value is converted to. It is desirable to configure so that it can be set freely for each wind speed.

Embodiment 2. An explanatory view of the airport weather risk determination processing device according to the second embodiment is shown in FIG.
As shown in this figure, the airport weather risk determination processing device according to the second embodiment also includes a centralized database device 20 that centrally stores a plurality of meteorological data, and a centralized database device 20 that stores the weather data. An integrated determination processing device 22 that performs integrated determination processing based on existing meteorological data is provided. Further, as in the case of FIG. 1 described above, a data display device 24 that displays the determination result as data is also provided.

Now, as described in the first embodiment,
The centralized database device 20 can store multiple types of weather data from various weather observation devices. Figure 5
In the example shown in, the weather data from the airport weather Doppler radar system 12 and the nearby weather radar 40 are stored in the centralized database device 20. The nearby weather radar 40 is a weather radar for observing a so-called weather condition such as a cloud state, and the centralized database device 2
0 is connected via a predetermined data link. The nearby weather radar 40 is capable of observing weather conditions in a wider area than a certain observation area centered on the airport as shown in FIG. As described above, the airport weather risk determination processing device according to the second embodiment tries to determine the risk more accurately by considering the weather in the area other than the area of the risk determination target. .

Also in the airport weather risk judgment processing device shown in FIG. 5, a layer in which a plurality of weather data stored in the centralized database device 20 are scored is created, and the score of each layer is added. Forming the risk level display layer 34 by doing is similar to the example shown in FIG.

The characteristic feature of the second embodiment is that the data link with the adjacent nearby weather radar 40
The approach of bad weather is added to the weighting factor. Hereinafter, how to consider the approach of the bad weather will be described.

FIG. 6 is an explanatory view showing a state of the near-by weather radar data layer 42 created based on the data obtained from the near-by weather radar 40 shown in FIG. This layer is also divided into small areas as shown in FIG. 2 and FIG. 3, and each small area is given a score, but this small area is not shown for convenience of explanation.

Below, a description will be given of scoring when the near-field weather radar data layer 42 is created.

First, the edge of bad weather such as a rain cloud is detected by using the data value itself obtained from the near weather radar 40. Specifically, this edge becomes a line representing the range of the rain cloud. In FIG. 6, the solid line represented by Z0 represents the edge of the current rain cloud, that is, the edge. Moreover, the broken line represented by Z-1 in FIG. 6 is the edge of the rain cloud at the time of the previous observation, that is, the edge. The observation time interval is about 15 to 30 minutes. In the following description, it is assumed that the observation time interval is 15 minutes, that is, the observation cycle is 15 minutes.
Even if the observation period is 30 minutes, the essence of the present invention does not change at all.

Now, in FIG. 6, the edge Z of this rain cloud
By comparing -1 and Z0, it can be seen that the rain cloud is moving to the right with respect to the neighborhood weather radar data layer 42 of FIG. In this way, the echo data obtained from the nearby weather radar 40 is simplified by detecting the edge.

Next, the distance between the edges Z-1 and Z0 is calculated. Then, by moving the edge Z0 by this distance with respect to the moving direction of the rain cloud, the edge that will be detected in the next observation cycle is predicted. In FIG. 6, this predicted edge is represented by Z + 1.
As shown in FIG. 6, this edge Z + 1
Is represented by a dotted line. When this prediction is performed, the observed edges Z-1 and Z0 are once linearly approximated, and the approximated edges are moved to obtain
The edges Z + 1 and Z + 2 that will be observed in the future are calculated. Edge Z + 1 is the edge that will be detected at the next observation time, and Z + 2 is the edge that will be detected at the next observation time as well.

Next, an edge Z newly predicted and provided
Scoring is performed based on +1 and Z + 2. As shown in FIG. 6, the area between edges Z0 and Z-1 is currently a rain cloud. Therefore, as shown in FIG. 6, scoring such as "6" or "8" is added in the same manner as in the example shown in FIG.

The feature of this embodiment is that the strength of this data area (in the example shown in FIG. 6, "6" is also applied to the area where the area marked "6" approaches. ”Or“ 8 ”). For example, the rain cloud has not yet advanced in the area between the edge Z0 and the edge Z + 1. Therefore, the area of this portion is added with, for example, "0" when the points are assigned as in the example shown in FIG.
On the other hand, in the present embodiment, the region marked with “6” is the region which is going forward, and therefore the region between the edge Z0 and the edge Z + 1 is marked with “5”. . Similarly, "7" is shown as shown in FIG. 6 for the area to which the area to which "8" is added will proceed. Thus, the edges Z0 and Z
The areas between +1 and +1 are also given points according to the points given to the approaching areas.

Now, the area between the edges Z0 and Z + 1 is the area that will become a rain cloud after 15 minutes. On the other hand, Z +
The area between 1 and Z + 2 is the area that will become a rain cloud after 30 minutes. Therefore, from the viewpoint of risk, the area between the edge Z + 1 and the edge Z + 2 is the edge Z0.
Should be lower than the area between and edge Z + 1. Therefore, in this embodiment, a high score is assigned to an area where a rain cloud, which is bad weather, will soon reach, and a low score is assigned to an area where it will take a long time to reach. ing. Therefore, as shown in FIG. 6, the area currently marked with “6” is marked with “5” in the area close to the area that is going to proceed, and “4” as the distance increases. , "And" 3 ". Similarly, for the area currently marked with "8", "7" is added to the area where the rain cloud will soon reach, and "6" and "5" as the distance increases.
The numbers are attached so that they become smaller gradually.

Of course, the areas that will be reached after the same time has passed are given points according to the numbers attached to the rain clouds that should reach them. That is, the area adjacent to the area currently marked with “6” is marked with “5” accordingly. On the other hand, for the area adjacent to the area currently marked with "8",
Accordingly, "7" is attached.

As described above, a score is attached to each area. In reality, each area as shown in FIG. 6 is a collection of small areas described in FIGS. 2 and 3,
For example, all the small areas included in the area with “5” shown in FIG. 6 will be added with “5”.

The near-field weather radar data layer 42 thus created in consideration of approaching bad weather is superposed on the airport weather Doppler radar data layer 44.

Then, the near weather radar data layer 4
For the part where 2 and the airport weather Doppler radar data layer 44 overlap, the numerical value attached to each small area is added, and the risk degree display layer 34 is created by the same principle as described in FIG. It As shown in the lower part of FIG. 6, the neighborhood weather radar data layer 42
And the airport weather Doppler radar data layer 44 are generally different in size (width). This is because the neighborhood weather radar data layer 42 is echo data obtained from the weather radar around the airport and a relatively wide range of echo data is obtained, whereas the data obtained from the airport weather Doppler radar system 12 However, since the number of this radar system is limited to the area near the airport, it is often smaller than the nearby weather radar data (only a narrow range of data is available). Therefore, it is considered that the airport weather Doppler radar data layer 44 has an area smaller than that of the neighborhood weather radar data layer 42 in many cases. Even when the areas are different as described above, the risk level display layer 34 is formed by adding the numerical values given to the small areas of the overlapped portions, as in the example shown in FIG. is there.

As shown in the upper part of FIG. 6, a work for calculating the edge Z + 1 and the edge Z + 2 of the future rain cloud by moving the edge by the distance between the edges Z-1 and Z0. In the text, the edge is referred to as “extending so that a cobweb is stretched”.

Now, referring to FIG. 6, the scoring in the near weather radar data layer 42 has been described. In the present embodiment, when the airport weather Doppler radar data layer 44 is created, the approach of bad weather is taken into consideration as in the neighborhood weather radar data layer 42 shown in FIG. Therefore, next, a method for creating the airport weather Doppler radar data layer 44 will be described with reference to FIG.
It will be described based on.

FIG. 7 shows the appearance of the airport weather Doppler radar data layer 44. Also in the airport weather Doppler radar data layer 44, the edge is detected from the value of the echo data and the echo data is simplified, as in the case of the near-field weather radar data layer 42 described in FIG. In this way, by detecting the edge, the edge of the rain cloud can be extracted as in the case shown in FIG.

Then, vertical integrated data of rainfall obtained by using the airport weather Doppler radar data system 12 (generally VIL (Vertical Integrate)
edLiquid)), the moving direction and the average moving speed are calculated. The center of the vertically integrated data of the rainfall amount is a region filled with black in FIG. 7. The moving direction and moving speed can be calculated for this center. The movement direction is shown in FIG.
Is indicated by an arrow.

Now, the moving direction obtained in this way,
The movement distance is used to predict the position of a future edge. For this prediction, each edge of the rain cloud is moved in the moving direction by the average moving speed. The position obtained as a result of the movement is the predicted position of the edge that will be located in the future. Note that in FIG. 7, an edge that can be detected in the future is Z + in FIG. 7 as in FIG.
It is represented as 1, Z + 2.

As described above, by shifting the edge by the average moving speed, the edge can be "extended so that a cobweb is stretched".

Next, scoring is performed in the same manner as in the example shown in FIG. 6 in accordance with the vertical integrated value of rainfall, that is, the intensity of the integrated value in the vertical integrated data of rainfall. For example, as shown in FIG. 7, for example, "5" is attached to the region where the center of the vertical direction cumulative data of the rainfall amount is the direction to which the rain cloud will soon reach. And
Even in the region located in the moving direction of the center of the vertical cumulative data of the rainfall amount, a smaller numerical value “4” is attached to the region at the farther position. Then, a smaller numerical value is given as the distance from the center of the vertical accumulated data of the present rainfall increases.

A feature of this embodiment is that the number of points to be added is reduced when the direction in which the edge is extended is different from the moving direction of the center of the vertical accumulated data of rainfall. For example, in FIG. 7, for an area formed by an edge that moves in a direction that does not match the moving direction of the center of the vertical cumulative amount of rainfall data, an area that is located in the moving direction such as “4” is added. Numerical values smaller than the numerical values shown are attached.

As a matter of course, even in a direction different from the moving direction of the center of the vertical direction cumulative data of rainfall, smaller values are given as the distance from the rain cloud increases.

Further, in the present embodiment, points are subtracted rather than added (minus minus) in an area in which the direction in which the edge is extended deviates from the moving direction of the center of the vertical direction cumulative data of rainfall by 90 degrees or more. )is doing. This is because the case where the extension direction of the edge is different from the moving direction of the rain cloud means that the rain cloud moves away from the area. Therefore, the risk should be rather negative.

The airport weather Doppler radar data layer 44 is created based on the echo data obtained from the airport weather Doppler radar system 12 as described above. As described with reference to FIG. 7, in the creation of the airport weather Doppler radar data layer 44, as shown in FIG.
Similar to the above, the score is taken in consideration of the approach of bad weather (see FIG. 7).

As described above, the integrated determination processing device 22
Is the airport weather Doppler radar data layer 44,
The near-air weather radar data layer 42 was created. Then, the integrated determination processing device 22 adds the numerical values attached to the respective data layers created in this way for each small area, and as shown in FIG.
Create 4.

Then, it is determined that a small area having a large score in the risk level display layer 34 has a high risk level. It should be noted that the integrated determination processing device 22 outputs the risk display layer 34 to the data display device 24 and displays it in superimposition with the map data centering on the airport.
This is similar to the embodiment shown in FIG. Of course, it is also preferable that the integrated determination processing device 22 stores the created risk level display layer 34 in the central database device 20 and keeps a history of how the risk level has changed.

Next, the operation of the second embodiment will be described in more detail with reference to the flowchart. FIG. 8 shows a flowchart showing the operation of the airport weather risk determination processing device.

First, in step S8-1, the observation data of the airport weather Doppler radar is input.
That is, this data is data obtained from the airport weather Doppler data system 12.

In step S8-2, edge detection is performed as described above based on the observation data of the airport weather Doppler radar obtained from the airport weather Doppler radar system 12. This edge detection is executed to detect the edge of the rain cloud, for example. Then, as described above, the so-called "cobweb processing" is performed to predict the edge of the rain cloud that will be observed in the future. This is performed by moving the edge of the rain cloud in the moving direction of the rain cloud as described above. Furthermore, in step S8-2, this "cobweb processing" is performed.
Scoring is performed based on the edges of future rain clouds obtained by. This scoring is performed based on whether or not a rain cloud currently exists in the area, and also scoring in consideration of whether or not the rain cloud is approaching the area.

At step S8-3, the meteorological data is converted into layer data. This is a process of creating layer data, which is scored for each small area as shown in FIG. 3, based on the meteorological data obtained from the airport meteorological Doppler radar system 12.

The operation of creating the layer data based on the weather data obtained from the airport weather Doppler radar system 12 has been described above.
The same process is performed on the weather data obtained from 0 (see FIG. 5). Such processing is shown in steps S8-4 to S8-6 in FIG.

First, in step S8-4, the data obtained from the near weather radar 40 is input.

In step S8-5, like in step S8-2, edge detection such as rain clouds is performed based on the data obtained from the near weather radar 40, and "cobweb processing" is executed based on this edge detection. To be done. Further, similarly to step S8-2, scoring is performed for each small area. Similar to step S8-2, this scoring is performed in consideration of whether or not the rain cloud is approaching.

In step S8-6, layer data is created, and data with a score is created for each area as shown in FIG.

In this way, the layer data created in steps S8-3 and S8-6 are overlaid in step S8-10. This superposition is performed so as to match the positions on the map. That is,
As shown in FIG. 6 and the like, layer data created from meteorological data obtained from various meteorological observation devices do not always have the same position on the map. Therefore, it is necessary to match the layer data based on the meteorological data obtained from each observation device so that the positions on the map are matched.

In step S8-7, the numerical values attached to the small areas of the layer data that have been superimposed are added. By summing up by such addition, the risk degree display layer 34 as shown in FIG. 3 is created. This creation is specifically performed in step S8-8.

In step S8-9, the created risk degree display layer 34 is displayed on the data display device 24. As described above, it is preferable that the display be specifically superimposed on the map around the airport. By superimposing and displaying, the user of this device can easily understand which area has what kind of risk at present.

In particular, by displaying the risk level not in the numerical value but in the color of the density proportional to the numerical value, it is possible to obtain the device capable of easily visually determining the risk level.

Embodiment 3. In the first and second embodiments, the airport weather Doppler radar system 12,
A plurality of meteorological data obtained from various meteorological instruments 14 and the nearby weather radar 40 are stored in the centralized database device 20, and the degree of risk is comprehensively determined from a range of a certain range including the airport area based on the meteorological data. The device has been described. However, the degree of risk is such that a certain area including an airport is divided into a plurality of small areas, the degree of danger is represented by a numerical value for each small area, and the degree of danger is determined by this numerical value. In addition, in the embodiment described above, it is also shown that the risk degree is displayed on the data display device 24 in a superimposed manner with the map around the airport. In particular, we proposed that the numerical value indicating the degree of risk is not expressed as it is, but that it is displayed in the color of the density corresponding to the numerical value.

In the third embodiment, an example will be described in which the meteorological data to be integrated includes meteorological data obtained from the wind profiler system 50. FIG. 9 is an explanatory diagram of the airport weather risk determination processing device according to the third embodiment. In the example shown in this figure, the weather data obtained from the airport meteorological Doppler radar system 12 and the airport anemometer system 14 is a central database similar to the airport meteorological risk determination processing device shown in FIG. It is stored in the device 20.

The characteristic feature of the third embodiment is that
In addition to the above-mentioned weather data, weather data obtained from the wind profiler system 50 is also stored in the centralized database device 20. Unlike other meteorological observation devices, this wind profiler system 50 is a device capable of knowing the altitude distribution of the wind speed in the horizontal direction of the atmosphere immediately above this device. In this way, by knowing the altitude distribution of the wind speed, it is possible to obtain substantially a large amount of meteorological data, and it is possible to determine the risk degree with higher density.

The meteorological data obtained from the wind profiler system 50 is first stored in the centralized database device 20, and a layer is assigned to each small area based on the meteorological data as shown in FIG. Information is created. This layer information is subject to addition similarly to the wind velocity layer 30 and the rainfall amount layer 32 as shown in FIG. Then, as a result of addition of these layers, the risk degree display layer 34 is generated.

Now, such a risk level display layer 34
Is created in the integrated determination processing device 22 in FIG. The risk level display layer 34 created in this manner is displayed on the data display device 24 in the same manner as in the above-described embodiment in a manner of being superimposed on the map information around the airport.

According to the present embodiment, by adding the wind profiler system 50, the data directly above the airport can be added to the weighting factors, and the risk can be determined more accurately.

Next, the operation of the airport weather risk determination processing apparatus according to the third embodiment will be described based on the flowchart shown in FIG.

The operation in steps S9-1 to S9-3 is exactly the same as the operation in steps S8-1 to S8-3 in FIG. In this operation, weather data obtained from the airport weather Doppler radar system 12 is stored in the centralized database device 20, edges are detected from the stored weather data (step S9-2), and layer data is formed based on this. This is a process (step S9-3).

The operations from step S9-4 to step S9-6 are operations for creating layer data based on the meteorological data obtained from the wind profiler system 50.

First, in step S9-4, the weather data is input from the wind profiler system 50. This weather data is stored in the centralized database device 20.

In step S9-5, edge detection of rain clouds and the like, cobweb processing, and scoring for each small area are performed based on the stored meteorological data, as in step S9-2.

In step S9-6, layer data is created based on the score of each small area.

The operation from step S9-7 to step S9-9 is an operation for creating layer data based on the data obtained from various meteorological instruments 14 (see FIG. 9), and the principle thereof is the above step S9-. 4 to step S9
It is exactly the same as -6. That is, step S9-7
In, the meteorological data from the various meteorological instruments 14 is input and stored in the centralized database device 20.

In step S9-8, data processing such as edge detection is performed based on the stored meteorological data.

In step S9-9, layer data is created based on the points assigned to each small area.

In step S9-10, the layer data obtained as described above are superposed.
This superposition is performed so that the positions on the map match.

Steps S9-11 to S9-1
The process up to 3 is exactly the same as the process from step S8-7 to step S8-9 in FIG.

That is, first, in step S9-11, the points added to the layer data that have been superimposed are totaled.

In step S9-12, the risk level display layer 34 is created on the basis of the totalized result.

In step S9-13, the created risk level display layer 34 is displayed on the data display device 24. It is desirable that this display be displayed overlapping with the map around the airport. It is also preferable to display the risk level in a color having a density corresponding to the score representing the risk level.

Fourth Embodiment Up to this point, an apparatus for integrally determining the degree of danger of airport weather based on the weather data obtained from a plurality of types of weather observation apparatuses has been described. In the present invention, in principle, any kind of weather data may be used as long as it is a plurality of kinds of weather data.

For example, FIG. 11 shows a flowchart showing an operation of creating layer data based on four kinds of meteorological data and superposing the layer data to create the risk degree display layer 34. The operation principle of the flowchart shown here is exactly the same as that of FIGS. 8 and 10. For example, from step S10-1 to step S
Up to 10-3, airport weather Doppler radar system 12
It is a process of creating layer data using meteorological data based on. Also, from step S10-4 to step S
Up to 10-6 is a process of creating layer data based on the meteorological data obtained from the near weather radar 40.

Further, from step S10-7 to step S
Up to 11-9 is a process for creating layer data based on the meteorological data obtained from the various meteorological instruments 14. Further, the operations from step S10-10 to step S10-12 are processing for creating layer data based on the meteorological data obtained from the wind profiler system 50.

Layer data is created on the basis of the meteorological data obtained from these four types of meteorological observation devices, and in step S11-13, the position on the map is matched and the superposition processing is performed.

Finally, the processing from step S10-14 to step S10-16 is the same as the processing from step S8-7 to step S8-9 in FIG. 8 or FIG. 10 or step S9-11 to step S9-. 1
This is exactly the same as the processing up to 3.

[0115]

As described above, according to the present invention, since the weather data is scored and the risk is determined based on the score, the weather risk of the airport can be determined.

Further, according to the present invention, the radar meteorological data obtained from the airport meteorological radar system and the other meteorological data are stored in the storage means, and the risk is judged based on these, so that the observer judges. However, it is possible to make an objective determination of the degree of risk.

Further, according to the present invention, each weather data is scored and the risk is judged by adding the scores. Therefore, different types of meteorological data can be easily integrated, and the risk can be comprehensively determined.

Further, according to the present invention, the scoring is performed by referring to the table. Therefore, it is possible to freely adjust the meteorological data, which is an actual physical quantity, and the points to be attached to it.

Further, according to the present invention, it is possible to obtain an apparatus which can determine the degree of danger by integrating the radar weather data obtained from the airport weather radar system and the weather data obtained from the nearby weather radar.

Further, according to the present invention, considering not only the present weather but also bad weather approaching the area,
The risk is being judged. Therefore, the risk can be determined more accurately.

Further, according to the present invention, the degree of risk is judged by scoring the meteorological data physical quantity and adding the score. Therefore, it is possible to easily integrate meteorological data of different kinds of physical quantities and determine the degree of risk.

According to the present invention, the points to be attached to the meteorological data, which are physical quantities, are determined by referring to the table. Therefore, the relationship between the physical quantity and the risk can be easily adjusted by changing the contents of this table.

Further, according to the present invention, by increasing the score of the region where the rain cloud is predicted to be located in the future, it is possible to obtain the airport weather risk determination processing device capable of increasing the risk of the region.

Further, according to the present invention, it is predicted whether or not the rain cloud will be located in that area in the future based on radar meteorological data obtained from the radar system. Therefore, VIL
It is possible to predict the exact location of the rain cloud by using such as.

Further, according to the present invention, increasing the number of points in the region where the rain cloud is predicted to be located in the future is the same as in the above invention, but the time until the rain cloud arrives, that is, the rain cloud arrives in the near future. The number of points to be increased is changed depending on whether or not the rain cloud arrives in the distant future. Therefore, a device capable of more accurately determining the degree of danger can be obtained.

Further, according to the present invention, not only the radar weather data obtained from the airport weather radar system but also the weather data representing the distribution of the wind speed obtained from the wind profiler system and further the general weather data are utilized. The risk is being judged. Therefore, a more accurate risk can be determined.

Further, according to the present invention, the meteorological data (radar meteorological data and general meteorological data) which are physical quantities are scored based on the physical quantities. Then, by adding these points, the degree of danger of the area is determined. As a result, it is possible to obtain a device that can integrate different kinds of physical quantities and can comprehensively judge the weather at the airport.

Further, according to the present invention, in order to make the meteorological data (radar meteorological data and general meteorological data) which are physical data into points, the points to be added are determined by referring to the table. Therefore, the relationship between the physical information and the risk can be flexibly set by changing the contents of this table.

Further, according to the present invention, not only the radar meteorological data obtained from the airport meteorological radar system, but also the meteorological data representing the distribution of the wind speed obtained from the wind profiler system is used to judge the degree of danger. . Therefore, a more accurate risk can be determined.

Further, according to the present invention, the meteorological data (radar meteorological data and meteorological data representing the wind speed distribution), which are physical quantities, are scored based on the physical quantities.
Then, by adding these points, the degree of danger of the area is determined. As a result, it is possible to obtain a device that can integrate different kinds of physical quantities and can comprehensively judge the weather at the airport.

Further, according to the present invention, in order to make the meteorological data (radar meteorological data and meteorological data representing the wind speed distribution) which are physical data into points, the points to be attached are determined by referring to the table. . Therefore, the relationship between the physical information and the risk can be flexibly set by changing the contents of this table.

Further, according to the present invention, since the determination result of the risk degree is displayed by being superimposed on the map information of the airport, it is possible to easily visually grasp which area and how much the risk degree is. It is possible to obtain a possible airport weather risk judgment processing device.

Further, since the degree of danger is represented by color, the degree of danger can be easily grasped visually.

Further, according to the present invention, the meteorological data obtained from various meteorological instruments is used as the general meteorological data. Therefore, it is possible to effectively use the wind direction and anemometer system that has been conventionally used for observing the weather at the airport.

[Brief description of drawings]

FIG. 1 is an explanatory diagram illustrating an operation of an airport weather risk determination processing device according to a preferred embodiment of the present invention.

FIG. 2 is an explanatory diagram showing a relationship between an area that is a target of observing weather at an airport and a small area that constitutes this area.

FIG. 3 is an explanatory diagram showing a state in which layer data is created based on meteorological data obtained from each meteorological observation device, and a risk level display layer is created by adding the points attached to this layer data. .

FIG. 4 is an explanatory diagram showing a state of a conversion table.

FIG. 5 is an explanatory diagram showing an operation of the airport weather risk determination processing device according to the second embodiment.

FIG. 6 is an explanatory diagram showing a situation in which points are assigned to the near weather radar data layer 42.

[Figure 7] Airport Meteorological Doppler Radar Data Layer 4
FIG. 4 is an explanatory diagram showing how points are assigned to 4;

FIG. 8 is a flowchart showing the operation of the airport weather risk determination processing device according to the second embodiment.

FIG. 9 is an explanatory diagram showing the operation of the airport weather risk determination processing device according to the third embodiment.

FIG. 10 is a flowchart showing the operation of the airport weather risk determination processing device according to the third embodiment.

FIG. 11 is a flowchart showing an operation of creating a plurality of layer data based on a plurality of meteorological data obtained from a plurality of meteorological observation devices and integrating them to determine a risk level.

FIG. 12 is an explanatory diagram showing how an observer determines a risk level based on weather conditions at an airport in the conventional technique.

[Explanation of symbols]

10 Observers, 12 Airport Doppler radar system, 14 Various weather instruments, 16 Risk assessment, 18
Alert notification, 20 centralized database device, 22 integrated judgment processing device, 24 data display device, 30 wind speed layer, 32 rainfall layer, 34 risk level display layer,
40 near weather radar, 42 near weather radar data layer, 44 airport weather Doppler radar data layer, 50 wind profiler system.

Continuation of front page (56) Reference JP-A-7-110385 (JP, A) JP-A-7-110378 (JP, A) JP-A-8-271649 (JP, A) JP-B-60-22749 (JP , B2) Patent 2519657 (JP, B2) Patent 2891338 (JP, B1) Kiyoyuki Hata, Koichi Hirashima, Kyosuke Hamazu, "Airport Meteorological Doppler Radar System", Mitsubishi Electric Technical Report, Japan, Mitsubishi Electric Technical Report, 1994 October 25, Volume 68, No. 10, p. 856 −859 Kiyoyuki Hata, Kyosuke Hamazu, Yoshitaka Tokumaru, Koichi Koriyama, “Detection of Wind Shear”, IEICE Technical Report, Japan, The Institute of Electronics, Information and Communication Engineers, July 31, 1992, 1st Volume 92, No. 170, p. 7-14 Koichi Hirashima, Kiyoyuki Hata, Masaru Wakabayashi, Kyousuke Hamazu, "Wide-range Radar Rain and Snow Gauge System Network", IEICE Technical Report, Japan, The Institute of Electronics, Information and Communication Engineers, August 30, 1991 Sun, Vol. 91, No. 204, p. 1-8 Tomoya Matsuda, Hiroyuki Hashiguchi, Shinichiro Watanabe, Toshio Wakayama, Toshitaka Tsuda, Takashi Sato, Yamanaka University, Mamoru Yamamoto, Takuji Nakamura, Shoichiro Fukao, "Milliwave Doppler Development of Radar and Its Initial Observation Results ", Atmospheric Symposium, Japan, Institute for Space Science, March 5, 1998, 12th, p. 42-45 (58) Fields surveyed (Int.Cl. ⁷ , DB name) G01W 1/00-1/18 G01S 13/00-13/95 JISST file (JOIS)

Claims (12)

(57) [Claims] 1. Includes meteorological data with different types of physical quantities
Storage means for storing a plurality of types of meteorological data, and an integrated determination processing device for determining a degree of danger with respect to a certain range of surveillance area centered on an airport based on the plurality of types of meteorological data stored in the storage means When, wherein the said integrated determination process unit, for each of the small regions constituting the monitoring area, normal weather data for each of the meteorological data
Create a layer in which the reduction has been scoring, each weather data
The points of the layer obtained for
Create a risk level display layer
Determining the risk of the respective small regions based on chromatography, airport weather risk assessment process and wherein the. 2. The airport weather risk determination process according to claim 1.
In the integrated determination processing device, the scoring is performed by the physical information processing device.
Refer to the conversion table in which the relation between information and risk is set.
An airport meteorological risk determination processing device characterized in that it is performed by scoring weather data . 3. The airport weather risk determination process according to claim 1.
It is a processing device, and the weather data is obtained from an airport weather radar system.
Characterized by including radar weather data
Meteorological risk determination processor. 4. The airport weather risk determination process according to claim 1.
As the weather data, a general device other than radar weather data is used.
Airport weather risk assessment characterized by including elephant data
Processing equipment. 5. The airport weather risk determination process according to claim 1.
And a near-field weather data of a near-air weather radar as the weather data.
Data processing equipment for airport weather risk
Place 6. The airport weather risk determination process according to claim 1.
And a wind profiler system as the meteorological data.
It includes wind speed distribution meteorological data obtained from
An airport weather risk judgment processing device. 7. The airport weather risk determination process according to claim 3.
The integrated determination processing device is a processing device based on the radar meteorological data.
Scored in accordance with the Ku the approaching prediction of evil weather to small area
An airport weather risk determination processing device characterized by the following. 8. The airport weather risk determination process according to claim 5.
And an integrated determination processing device based on the neighborhood meteorological data.
Score according to the forecast of bad weather approaching the small area
An airport weather risk determination processing device characterized by the following. 9. Airport weather hazard according to claim 7 or 8.
A degree determination processing device, the bad weather approach prediction in the integrated determination processing device is
By tracking the movement of clouds and predicting the location of future rain clouds
Airport weather risk assessment processing device characterized by being performed
Place 10. The airport weather risk determination according to claim 9.
The integrated determination processing device is a processing device, and when a rain cloud is located in the near future.
Increase the number of points in the predicted area by a certain amount to prevent rain clouds in the distant future.
Is less than the predetermined amount.
Airport weather risk assessment facility characterized by increasing
Processing equipment. 11. The method according to any one of claims 1 to 10.
In the above-mentioned airport weather risk judgment processing device, the judgment result of the integrated judgment processing device is overlapped with the airport map information.
An airport characterized by including display means for folding and displaying
Meteorological risk determination processor. 12. The airport weather risk judgment according to claim 11.
A fixed processing device, wherein the display means monitors a certain range around the airport.
The judgment result of the integrated judgment processing device is displayed in color for each area.
An airport weather risk determination processing device characterized by the following.