CN112101639A - Airport traffic capacity analysis method and system based on multi-source meteorological data - Google Patents
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Abstract
The embodiment of the invention discloses an airport traffic capacity analysis method and system based on multi-source meteorological data, relates to an air traffic flow management technology, and can perform qualitative analysis and evaluation on the weather-influenced degree of the airport traffic capacity in a certain period of time in the future, reduce the working difficulty of flow management personnel and improve the analysis accuracy of the actual traffic capacity of an airport. The invention comprises the following steps: determining all time slices to be predicted of a target airport; extracting multi-source meteorological information from an information source, and acquiring meteorological elements corresponding to each time slice by using the multi-source meteorological information; aiming at each time slice, acquiring the influence degree of the meteorological elements under the time slice on the traffic capacity of the target airport according to the meteorological elements corresponding to the time slice and the classification parameters of the target airport; and according to the acquired influence degree, carrying out grading marking on each time slice of the target airport, and outputting a grading marking result to a display device. The method is suitable for automatic analysis of the airport traffic capacity.
Description
Technical Field
The invention relates to an air traffic flow management technology, in particular to an airport traffic capacity analysis method and system based on multi-source meteorological data.
Background
Extreme weather such as low visibility, low cloud, strong wind, thunderstorm and the like interferes with normal take-off and landing of flights, and the traffic capacity of airports is greatly influenced. The traditional airport weather products mostly mainly take message and plain language forms, only describe the real-time weather phenomenon in an area, or forecast the weather condition in a certain period of time in the future, and then the flow management personnel subjectively judge the influence degree of the weather on the airport traffic capacity by combining own experience, thereby making corresponding flow management and adjustment strategies.
The working mode increases a large workload for flow management personnel, multiple weather products need to be referred to at the same time, the influence degree of weather factors on the traffic capacity of the airport is analyzed only by manpower, time and labor are wasted, and objectivity and accuracy are difficult to guarantee.
In an application scene of air traffic flow management, which requires extreme safety and accuracy, the conventional method is difficult to accurately evaluate the influence degree of weather on the traffic capacity of the airport intuitively, timely and quantitatively, so that the working difficulty of flow management personnel is increased, and the analysis accuracy of the actual traffic capacity of the airport is also reduced.
Disclosure of Invention
The embodiment of the invention provides an airport traffic capacity analysis method and system based on multi-source meteorological data, which can perform qualitative analysis and evaluation on the degree of influence of weather on the airport traffic capacity in a certain period of time in the future, reduce the working difficulty of flow management personnel and improve the accuracy of analyzing the actual traffic capacity of an airport.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method, including:
s1, determining all time slices to be predicted of the target airport;
s2, extracting multi-source meteorological information from an information source, and acquiring meteorological elements corresponding to each time slice by using the multi-source meteorological information;
s3, aiming at each time slice, obtaining the influence degree of the meteorological elements under the time slice on the traffic capacity of the target airport according to the meteorological elements corresponding to the time slice and the classification parameters of the target airport;
and S4, according to the acquired influence degree, carrying out grading marking on each time slice of the target airport, and outputting the grading marking result to a display device.
In a second aspect, an embodiment of the present invention provides a system, including:
the meteorological data collection subsystem is used for determining all time slices to be predicted of the target airport;
the weather information processing subsystem is used for extracting multi-source weather information from an information source and acquiring weather elements corresponding to each time slice by using the multi-source weather information; aiming at each time slice, acquiring the influence degree of the meteorological elements under the time slice on the traffic capacity of the target airport according to the meteorological elements corresponding to the time slice and the classification parameters of the target airport; according to the obtained influence degree, carrying out grading marking on each time slice of the target airport;
and the display subsystem is used for outputting the result of the grading mark to the display equipment.
The method and the system for analyzing the airport traffic capacity based on the multi-source meteorological data are mainly used for evaluating the airport traffic capacity of the multi-source meteorological data and can be widely applied to the field of air traffic management. And (4) aiming at each airport, establishing classification parameters of the degree grade of the influence of each meteorological element on the airport. And aiming at each evaluation time point, obtaining various meteorological element values at the moment from the multi-source meteorological information, evaluating the affected level of the airport according to the classification parameters, and finally selecting the maximum value of the affected level as the evaluation result of the airport at the moment. The method can qualitatively evaluate the degree of influence of weather on the airport traffic capacity, and provides reference and assistance for decision making of traffic management personnel. Compared with the traditional technical means, the method can accurately and qualitatively calculate the influence degree of each weather element on the traffic capacity of the airport, greatly reduces the workload of flow managers through the automatic analysis process, and obviously improves the working efficiency of the flow managers.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of the general logic of the method flow provided by the embodiment of the present invention.
FIG. 2 is a flowchart of airport trafficability assessment based on multi-source weather data in an embodiment of the invention.
Fig. 3 is an exploded view of wind along a runway and perpendicular to the runway in an embodiment of the present invention.
Fig. 4 is a schematic diagram of an airport trafficability assessment result based on multi-source meteorological data in an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The embodiment of the invention provides an airport traffic capacity analysis method based on multi-source meteorological data, which comprises the following steps:
and S1, determining all time slices to be predicted of the target airport.
Wherein, the target airport refers to the airport which needs to be monitored and analyzed.
And S2, extracting multi-source meteorological information from the information source, and acquiring meteorological elements corresponding to each time slice by using the multi-source meteorological information.
And S3, acquiring the influence degree of the meteorological elements under the time slice on the traffic capacity of the target airport according to the meteorological elements corresponding to the time slice and the classification parameters of the target airport for each time slice.
And S4, according to the acquired influence degree, carrying out grading marking on each time slice of the target airport, and outputting the grading marking result to a display device.
Further, in this embodiment, the method further includes obtaining a classification parameter of the target airport: determining a classification parameter E of the influence level of meteorological elements on the target airport by utilizing a reflectivity grade division standard and a minimum operation standard of airport blind landing, wherein the meteorological elements at least comprise: visibility V, cloud base height H, wind direction and wind speed W and combined reflectivity R, E ═ max (V, H, W, R), the value of E includes five integers 0 to 4, corresponds 5 influence levels.
Specifically, the meteorological elements include: the visibility impact level on airports is denoted by the classification sub-parameter V,
where v represents visibility.
The classification sub-parameter of the cloud base height impact level on the airport is denoted as H,
wherein h represents the cloud base height.
The classification sub-parameter of the wind speed versus airport impact level is denoted W,
wherein, the wind speed along the runway direction is u, and the wind speed vertical to the runway direction is V.
The classification sub-parameter of the combined reflectivity versus airport impact level is denoted R,
wherein r represents the average value of the combined reflectivity within the latitude and longitude extension specified range of the airport.
In a preferred embodiment, the average value of the combined reflectivity is represented within a range extending 8 km from the longitude and latitude of the airport.
The information source described in this embodiment includes: metal ambient Weather Report (Aviation Routine Weather Report), SPECI Report (Aviation specific Weather Report), TAF Report (Terminal airport Weather forecast), and short-term Weather forecast data of 0 to 9 hours.
Specifically, the S1 includes: taking the observation time of the current latest meteorological data as the initial prediction time t0Determining the forecast time slice set as T ═ T by taking one hour as the step length and the longest forecast time in the forecast meteorological data as the forecast span1，t2……t24}。
The S2 includes: obtaining time slices t respectivelyiThe visibility value, the cloud base height value, the wind speed value and the combined reflectance value are obtained from the latest METAR report, the combined reflectance value is obtained from the latest 0-9 hour short-term numerical weather forecast data, i is a positive integer and is more than or equal to 1 and less than or equal to 24.
If the forecast ending time of the latest FC (TAF (tag advertisement) message with the forecast validity period of 9 hours) message is tjThe latest FT message (TAF message with a prediction validity period of 24 hours) has a prediction end time tkFor a predicted time slice tiIf i is<j, the visibility value is obtained from the FC message, k is a positive integer and 0<k<24, j is a positive integer and 0<j<9. If j<i<And k, the visibility value is obtained from the latest FC message. If i>k, then the visibility value is set to 9999.
If the latest forecast ending time of the FC message is tj(0<j<9) The latest FT message forecast end time is tk(0<k<24) For a predicted time slice tiIf i is<j, the cloud base high value is obtained from the latest FC message. If j<i<And k, acquiring the cloud base height from the latest FC message. If i>k, then the cloud base high value is set to 9999.
If the forecast ending time of the latest FC message is tj(0<j<9) Latest FT newspaperThe forecast end time of the text is tk(0<k<24) For a predicted time slice tiIf i is<j, the wind speed value is obtained from the latest FC message. If j<i<And k, acquiring the wind speed value from the FT message. If i>k, the wind speed value is set to-9999.
For predicted time slice t0～t9And calculating the average value of the combined reflectivity within 8 km of the position of the airport according to the combined reflectivity forecast in 0-9 hours and the longitude and latitude information of the airport. If the airport is located beyond the geographical range of the combined reflectivity forecast, the reflectivity value is set to-9999.
The S3 includes: using tiVisibility value, cloud base height value, wind speed value and combined reflectance value in time slice, and selecting maximum value from the influence degree of each meteorological element on the target airport as tiThe influence degree of meteorological elements under the time slice on the traffic capacity of the target airport.
For example, in practical applications, a specific analysis process may be as shown in fig. 2, where:
step 1, for a selected airport, according to a reflectivity grade division standard and a lowest operation standard of airport blind landing, and combining the influence conditions of meteorological elements such as wind speed, wind direction and cloud on the take-off and landing of an airplane, formulating classification parameters E of different meteorological elements of the airport on the influence grade of the airport, if the visibility, the cloud bottom height, the wind direction, the wind speed and the combined reflectivity are respectively marked as V, H, W and R, a final value taking method of E is as follows: e ═ max (V, H, W, R).
Wherein, the size of the basic reflectivity reflects the size and the density distribution of precipitation particles in the meteorological target and is used for representing the strength of the meteorological target, and the unit of the data on the product is represented by dBZ. "dbz" is a physical quantity representing the radar echo intensity. "dbz" can be used to estimate rainfall and snowfall intensity and to predict the likelihood of the occurrence of disastrous weather such as hail, high winds, etc. Generally, the larger its value, the greater the possibility of rainfall and snowfall, and the stronger its intensity, the greater the possibility of occurrence of thunderstorm weather when its value is 40dbz or more, and the greater the possibility of occurrence of strong convection weather such as rainstorm, hail, strong wind, and the like when its value is 45dbz or more. Of course, in determining what weather occurs in particular, factors such as echo height, echo area, echo movement speed, direction, and evolution are considered in combination, in addition to echo intensity (dbz). "z" is the radar reflection factor, proportional to the sixth power of the raindrop spectral diameter, in mm6/m3(6,3 are both powers). "db" is decibel and can also be understood as an operator, and dbz is scaled with z as: dbz ═ 10log (z).
In step 1, the method can be specifically subdivided into:
step 1-1, selecting four meteorological elements of visibility, cloud base height, wind speed and direction and combined reflectivity as evaluation elements of the method according to the influence degree of the meteorological elements on the traffic capacity of the airport.
Step 1-2, setting classification parameters of the weather influence degree of the airport. The weather influence degree of the airport is recorded as E, and the values of E are respectively 0 (no influence), 1 (mild influence), 2 (moderate influence), 3 (severe influence) and 4 (extreme influence). According to the reflectivity grade division standard on the weather and the lowest operation standard of airport blind landing, by combining the influence conditions of meteorological elements such as wind speed, wind direction and cloud on the take-off and landing of the airplane, classification parameters of visibility, cloud bottom height, wind direction, wind speed and combined reflectivity on the influence grade of the airport are formulated and respectively recorded as V, H, W and R, and then the final value taking method of E is as follows:
E＝max(V,H,W,R)
step 1-2-1, a classification sub-parameter V of visibility influence level on the airport is formulated. For different visibility V (unit: m), V is calculated as follows:
step 1-2-2, a classification sub-parameter H of the influence level of the cloud base height on the airport is formulated. For different cloud base heights H (unit: m), the calculation method of H is as follows:
and 1-2-3, establishing a classification sub-parameter W of the influence level of the wind speed and the wind direction on the airport. As shown in fig. 3, according to the angle between the runway direction α and the wind direction β of the current airport, the wind speed s (unit m/s) is decomposed into the runway direction and the vertical runway direction, and the wind speed u (unit m/s) is obtained after the decomposition, and the wind speed v (unit m/s) is obtained in the runway direction and the vertical runway direction.
And 1-2-4, establishing a classification sub-parameter R of the influence level of the combined reflectivity on the airport. According to the average value R (unit dBz) of the combined reflectivity in the range of 8 kilometers extended from the longitude and latitude where the airport is located, the method for calculating the R is as follows:
and 2, selecting an information source and determining all time slices to be predicted.
Step 2-1 the information source refers to the existing meteorological observation and forecasting products, including: METAR messages, SPECI messages, TAF messages and 0-9 hour short-term numerical weather forecast data.
The METAR report is a routine airport message which is issued once every hour or half an hour and mainly describes the real-time observation conditions of meteorological elements such as wind temperature, pressure and humidity in an airport area.
The format of the SPECI report is the same as that of the METAR report, and the SPECI report is issued emergently when meeting special weather.
The TAF report is an airport weather forecast message and describes weather conditions of an airport in a future time period. TAF is classified into FC and FT, the former forecasting a future 9-hour weather condition, updated every 3 hours, and the latter is a future 24-hour weather condition, updated every 6 hours.
The 0-9 hour short-term weather forecast data is a nationwide combined reflectivity forecast product, and the reflectivity intensity (updated once per hour) of the position of each airport can be calculated according to the longitude and latitude values of each airport.
Step 2-2, taking the observation time of the current latest observation meteorological data (updated once per hour) as the initial prediction time t0. Determining a forecast time slice set as T ═ T by taking one hour as a step length and taking the longest forecast time (24 hours) in forecast meteorological data as a forecast span1，t2……t24}。
And 3, for each time slice, obtaining the value of each meteorological element from the multi-source meteorological information.
Step 3-1, obtaining each time slice tiIs detected. For observation time slice t0And the visibility value is obtained from the latest METAR message.
If the forecast ending time of the latest FC message is tj(0<j<9) The forecast end time of the latest FT message is tk(0<k<24) For a predicted time slice tiIf i is<j, the visibility value is obtained from the FC message.
And if j < i < k, the visibility value is obtained from the FT message. If i > k, the visibility value is set to 9999.
Step 3-2, obtaining each time slice tiThe cloud base of (2) is high. For predicted time slice t0And the cloud base height is obtained from the METAR message.
If the forecast ending time of the latest FC message is tj(0<j<9) The forecast end time of the latest FT message is tk(0<k<24) For a predicted time slice tiIf i is<j, the cloud base height is obtained from the FC message.
And if j < i < k, acquiring the cloud base height from the FT message. If i > k, the cloud base high value is set to 9999.
Step 3-3, obtaining each time slice tiThe wind speed of (1). For predicted time slice t0And acquiring the wind speed value from the METAR message.
If the forecast ending time of the latest FC message is tj(0<j<9) The forecast end time of the latest FT message is tk(0<k<24) For a predicted time slice tiIf i is<j, then the wind speed value is reported from FCAnd (4) obtaining.
And if j < i < k, acquiring the wind speed value from the FT message. If i > k, the wind speed value is set to-9999.
Step 3-4, obtaining each time slice tiThe combined reflectivity value for that airport.
For predicted time slice t0～t9And calculating the average value of the combined reflectivity within 8 km of the position of the airport according to the combined reflectivity forecast in 0-9 hours and the longitude and latitude information of the airport.
If the airport is located beyond the geographical range of the combined reflectivity forecast, the reflectivity value is set to-9999.
And 4, calculating the influence level of the meteorological elements on the airport traffic capacity under the current time slice according to the classification parameters of the influence levels of different meteorological elements on the airport.
Step 4-1, for tiVisibility value, cloud bottom height value, wind speed value and combined reflectance value under the time slice are used for t according to the classification parameters of the airport influence levels of different meteorological elements set in the step 1iAnd (4) evaluating the influence level of each element on the airport during the time slice.
Step 4-2, selecting a maximum value from the influence degrees of all elements on the airport as tiThe level of influence of weather on the airport under the timeslice.
And 5, repeating the steps 3-4 until all the predicted time slices are calculated.
The embodiment also provides an airport traffic capacity analysis system based on multi-source meteorological data, which is used for realizing the method flow, the system can be deployed in an air traffic control center, such as an air traffic control center in a regional area, which is responsible for managing the air situations of a plurality of airports, and the display subsystem can be in butt joint with terminal equipment (such as a flow management personnel terminal) of a plurality of air traffic control personnel and output the results of the grading marks. As shown in fig. 1, the system includes:
and the meteorological data collection subsystem is used for determining all time slices to be predicted of the target airport.
And the meteorological data collecting subsystem is also used for acquiring the classification parameters of the target airport. Wherein, all the time slices to be measured of the target airport are determined by backward pushing the observation time of the latest meteorological observation data of the current airport by 24 hours. The meteorological data collection subsystem is used for collecting and sorting meteorological observation data of a plurality of target airports, and is mainly responsible for collecting and distributing multi-source meteorological data in practical application, and the meteorological data collection subsystem can be inquired to obtain the observation time of the latest METAR, so that all time slices to be measured are determined. Wherein, METAR and TAF are issued by airport weather station, and 0-9 hours short-term weather numerical weather forecast data are issued by civil aviation weather center.
The weather information processing subsystem is used for extracting multi-source weather information from an information source and acquiring weather elements corresponding to each time slice by using the multi-source weather information; aiming at each time slice, acquiring the influence degree of the meteorological elements under the time slice on the traffic capacity of the target airport according to the meteorological elements corresponding to the time slice and the classification parameters of the target airport; and according to the acquired influence degree, carrying out hierarchical marking on each time slice of the target airport.
And the display subsystem is used for outputting the result of the grading mark to the display equipment.
The meteorological data collection subsystem is further configured to obtain classification parameters of the target airport: determining a classification parameter E of the influence level of meteorological elements on the target airport by utilizing a reflectivity grade division standard and a minimum operation standard of airport blind landing, wherein the meteorological elements at least comprise: visibility V, cloud base height H, wind direction and wind speed W and combined reflectivity R, E ═ max (V, H, W, R), the value of E includes five integers 0 to 4, corresponds 5 influence levels.
Specifically, the classification parameters of the target airport are as follows: determining a reflectivity rating standard on the weather, a wind rating standard, a minimum operation standard of airport blind landing and the like. Initially with static parameters, subsequent product use may support user modification.
The meteorological information processing subsystem is further used for respectively obtaining visibility values, cloud base height values, wind speed values and combined reflectance values of the time slices ti, wherein the visibility values, the cloud base height values and the wind speed valuesThe method comprises the following steps of obtaining from the latest METAR report, obtaining a combined reflectance value from the latest 0-9 hour short-term weather forecast data, wherein i is a positive integer and is more than or equal to 1 and less than or equal to 24, and the information source comprises the following steps: METAR message, SPECI message, TAF message and 0-9 hour short-term weather forecast data, and taking observation time of current latest meteorological data as initial prediction time t0Determining the forecast time slice set as T ═ T by taking one hour as the step length and the longest forecast time in the forecast meteorological data as the forecast span1，t2……t24}；
The display subsystem is configured to paint different grading marks with corresponding colors, and output the grading marks distinguished by the colors to a display device, such as shown in fig. 4.
The technical idea of the invention is as follows: on the basis of the existing meteorological products, the influence of each meteorological element is comprehensively considered, the traffic capacity of the airport is qualitatively evaluated, and the influenced degree is visually reflected in different grades. The threshold value setting of the grading refers to the reflectivity grading standard on the weather and the lowest operation standard of airport blind landing respectively, and combines the influence conditions of meteorological elements such as wind speed, wind direction and cloud on the taking-off and landing of the airplane.
The specific implementation scheme of the embodiment is mainly used for airport traffic capacity assessment of multi-source meteorological data, and can be widely applied to the field of air traffic management. And (4) aiming at each airport, establishing classification parameters of the degree grade of the influence of each meteorological element on the airport. And aiming at each evaluation time point, obtaining various meteorological element values at the moment from the multi-source meteorological information, evaluating the affected level of the airport according to the classification parameters, and finally selecting the maximum value of the affected level as the evaluation result of the airport at the moment. The method can qualitatively evaluate the degree of influence of weather on the airport traffic capacity, and provides reference and assistance for decision making of traffic management personnel.
Compared with the traditional technical means, the embodiment is convenient for fusing various airport meteorological products; and the influence degree of each weather element on the traffic capacity of the airport can be accurately and qualitatively calculated, the workload of flow managers is greatly reduced through the automatic analysis process, and the working efficiency of the flow managers is obviously improved.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points. The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An airport traffic capacity analysis method based on multi-source meteorological data is characterized by comprising the following steps:
s1, determining all time slices to be predicted of the target airport;
s2, extracting multi-source meteorological information from an information source, and acquiring meteorological elements corresponding to each time slice by using the multi-source meteorological information;
s3, aiming at each time slice, obtaining the influence degree of the meteorological elements under the time slice on the traffic capacity of the target airport according to the meteorological elements corresponding to the time slice and the classification parameters of the target airport;
and S4, according to the acquired influence degree, carrying out grading marking on each time slice of the target airport, and outputting the grading marking result to a display device.
2. The method of claim 1, further comprising obtaining classification parameters for the target airport:
determining a classification parameter E of the influence level of meteorological elements on the target airport by utilizing a reflectivity grade division standard and a minimum operation standard of airport blind landing, wherein the meteorological elements at least comprise: visibility V, cloud base height H, wind direction and wind speed W and combined reflectivity R, E ═ max (V, H, W, R), the value of E includes five integers 0 to 4, corresponds 5 influence levels.
3. The method of claim 2, wherein, in the meteorological element: the visibility impact level on airports is denoted by the classification sub-parameter V,
the classification sub-parameter of the cloud base height impact level on the airport is denoted as H,
the classification sub-parameter of the wind speed versus airport impact level is denoted W,
the classification sub-parameter of the combined reflectivity versus airport impact level is denoted R,
4. The method of claim 3, wherein r represents an average of the combined reflectivities over 8 kilometers of latitude and longitude in which the airport is located.
5. The method of claim 1, wherein the information source comprises: METAR messages, SPECI messages, TAF messages and 0-9 hour short-term numerical weather forecast data.
6. The method according to claim 1 or 5, wherein the S1 comprises:
taking the observation time of the current latest meteorological data as the initial prediction time t0Determining the forecast time slice set as T ═ T by taking one hour as the step length and the longest forecast time in the forecast meteorological data as the forecast span1，t2……t24}；
The S2 includes:
obtaining time slices t respectivelyiThe visibility value, the cloud base height value, the wind speed value and the combined reflectance value are obtained from the latest METAR report, the combined reflectance value is obtained from the latest 0-9 hour short-term numerical weather forecast data, i is a positive integer and is more than or equal to 1 and less than or equal to 24.
7. The method of claim 6, further comprising:
if the latest forecast ending time of the FC message is tjThe latest FT message forecast end time is tkFor a predicted time slice tiIf i is<j, the visibility value is obtained from the FC message, k is a positive integer and 0<k<24, j is a positive integer and 0<j<9; if j<i<k, the visibility value is obtained from the latest FC message; if i>k, then the visibility value is set to 9999;
if the latest forecast ending time of the FC message is tj(0<j<9) The latest FT message forecast end time is tk(0<k<24) For a predicted time slice tiIf i is<j, acquiring the cloud base high value from the latest FC message; if j<i<k, acquiring the cloud base height from the latest FC message; if i>k, the cloud base height value is set to be 9999;
if the forecast ending time of the latest FC message is tj(0<j<9) The forecast end time of the latest FT message is tk(0<k<24) For a predicted time slice tiIf i is<j, acquiring the wind speed value from the latest FC message; if j<i<k, acquiring a wind speed value from the FT message; if i>k, setting the wind speed value to-9999;
for predicted time slice t0～t9Calculating the average value of the combined reflectivity within 8 km of the position of the airport according to the combined reflectivity forecast in 0-9 hours and the longitude and latitude information of the airport; if the airport is located beyond the geographical range of the combined reflectivity forecast, the reflectivity value is set to-9999.
8. The method according to claim 1, wherein the S3 includes:
using tiVisibility value, cloud base height value, wind speed value and combined reflectance value in time slice, and selecting maximum value from the influence degree of each meteorological element on the target airport as tiThe influence degree of meteorological elements under the time slice on the traffic capacity of the target airport.
9. An airport capacity analysis system based on multi-source meteorological data, comprising:
the meteorological data collection subsystem is used for determining all time slices to be predicted of the target airport;
the weather information processing subsystem is used for extracting multi-source weather information from an information source and acquiring weather elements corresponding to each time slice by using the multi-source weather information; aiming at each time slice, acquiring the influence degree of the meteorological elements under the time slice on the traffic capacity of the target airport according to the meteorological elements corresponding to the time slice and the classification parameters of the target airport; according to the obtained influence degree, carrying out grading marking on each time slice of the target airport;
and the display subsystem is used for outputting the result of the grading mark to the display equipment.
10. The system of claim 9, wherein the weather data collection subsystem is further configured to obtain classification parameters for the target airport: determining a classification parameter E of the influence level of meteorological elements on the target airport by utilizing a reflectivity grade division standard and a minimum operation standard of airport blind landing, wherein the meteorological elements at least comprise: visibility V, cloud base height H, wind direction and wind speed W and combined reflectivity R, E is max (V, H, W and R), and the value of E comprises five integers from 0 to 4 and corresponds to 5 influence levels;
the meteorological information processing subsystem is also used for respectively obtaining each time slice tiThe visibility value, the cloud base height value, the wind speed value and the combined reflectance value are obtained from the latest METAR report, the combined reflectance value is obtained from the latest 0-9 hour short-term numerical weather forecast data, i is a positive integer and is not less than 1 and not more than 24, and the information source comprises: METAR message, SPECI message, TAF message and 0-9 hour short-term weather forecast data, and taking observation time of current latest meteorological data as initial prediction time t0Determining the forecast time slice set as T ═ T by taking one hour as the step length and the longest forecast time in the forecast meteorological data as the forecast span1，t2……t24}；
And the display subsystem is used for smearing different grading marks with corresponding colors and outputting the grading marks distinguished by colors to display equipment.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283722A (en) * | 2021-05-13 | 2021-08-20 | 民航机场建设集团西南设计研究院有限公司 | Method and device for calculating meteorological load of airport runway |
| CN115188223A (en) * | 2022-06-08 | 2022-10-14 | 中国人民解放军空军研究院战场环境研究所 | Airplane take-off and landing safety index determination method based on airport weather live condition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108152866A (en) * | 2017-11-06 | 2018-06-12 | 南京航空航天大学 | The aviation metrological forecasting method for evaluating quality of flight amount is influenced based on weather |
| CN109409587A (en) * | 2018-10-09 | 2019-03-01 | 南京航空航天大学 | A kind of airport excavated based on weather data is into traffic flow forecasting method of leaving the theatre |
| CN109581543A (en) * | 2018-12-20 | 2019-04-05 | 深圳航空有限责任公司 | A kind of aeronautical meteorology message graphical representation method |
-
2020
- 2020-08-28 CN CN202010888584.6A patent/CN112101639A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108152866A (en) * | 2017-11-06 | 2018-06-12 | 南京航空航天大学 | The aviation metrological forecasting method for evaluating quality of flight amount is influenced based on weather |
| CN109409587A (en) * | 2018-10-09 | 2019-03-01 | 南京航空航天大学 | A kind of airport excavated based on weather data is into traffic flow forecasting method of leaving the theatre |
| CN109581543A (en) * | 2018-12-20 | 2019-04-05 | 深圳航空有限责任公司 | A kind of aeronautical meteorology message graphical representation method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283722A (en) * | 2021-05-13 | 2021-08-20 | 民航机场建设集团西南设计研究院有限公司 | Method and device for calculating meteorological load of airport runway |
| CN115188223A (en) * | 2022-06-08 | 2022-10-14 | 中国人民解放军空军研究院战场环境研究所 | Airplane take-off and landing safety index determination method based on airport weather live condition |
| CN115188223B (en) * | 2022-06-08 | 2024-05-03 | 中国人民解放军空军研究院战场环境研究所 | Airplane take-off and landing safety index determining method based on airport weather condition |
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