A short-term weather forecast method for civil aviation based on extrapolation of weather radar echoes

Abstract

The application discloses a kind of civil aviation weather short-time prediction methods based on weather radar echo extrapolation, by combining the optical flow method of pyramid algorithm to construct the motion vector field of radar echo map of radar wave, on the basis of motion vector field, extrapolation is obtained to predict the radar echo map of future time, and fusion ADS-B terminal system is displayed and early warning.The method realizes 2 hours extrapolation prediction of weather radar echo, helps weather, control and flight personnel to grasp the reference of weather;Realize a weather, control and flight organic integration of diversified platform, conducive to weather, control, flight personnel to establish consistent scene consciousness, guarantee flight safety.Control and flight personnel can effectively, timely obtain the latest meteorological information and dangerous weather warning information, and combine with ADS-B technology, according to aircraft track and meteorological warning information, guide aircraft to avoid dangerous weather, so as to improve airspace utilization.

Description

Technical Field

[0001] This invention relates to the field of weather forecasting, specifically to a short-term weather forecasting method for civil aviation based on weather radar echo extrapolation. Background Technology

[0002] Currently, the lack of integration between air traffic control and meteorological information in the air traffic control system means that during severe weather, controllers can only consult meteorologists by telephone. This verbal communication method lacks direct understanding of severe weather conditions, and pilots cannot effectively and promptly grasp the ever-changing weather. Therefore, developing a system that integrates weather radar information with ADS-B information is of great significance. This system, designed for controllers and pilots, provides them with a meteorological information access platform that is organically integrated with meteorological data. This allows controllers and pilots to effectively and promptly obtain the latest meteorological information and hazardous weather warnings. Combined with ADS-B technology, it guides aircraft to avoid hazardous weather based on flight paths and weather warnings, thereby improving airspace utilization. Summary of the Invention

[0003] To address the aforementioned shortcomings in the existing technology, this invention provides a short-term weather forecasting method for civil aviation based on weather radar echo extrapolation.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0005] A short-term weather forecasting method for civil aviation based on weather radar echo extrapolation includes the following steps:

[0006] S1. Acquire radar echo data from multiple radars and ADS-B terminal system data, and preprocess the radar echo data to obtain a radar echo map.

[0007] S2. Calculate the motion vector field of the radar echo map obtained in step S1 using the optical flow method combined with the pyramid algorithm;

[0008] S3. Based on the radar echo map motion vector field obtained in step S2, extrapolate to obtain the predicted radar echo map for future time intervals.

[0009] S4. The predicted radar echo map obtained in step S3 is fused with the ADS-B terminal system data, alarm thresholds are set, and the data is displayed through a graphical interface.

[0010] Furthermore, the method by which S1 preprocesses the radar echo data to obtain the radar echo map is as follows:

[0011] The radar echo data was preprocessed using bilinear interpolation and Lambert projection. The radar echo data images from multiple radars were then stitched together, and echo images of thunderstorm activity were obtained based on empirical thresholds.

[0012] Furthermore, step S2 specifically includes the following steps:

[0013] S21. Reduce the size of the stitched radar echo data map obtained in step S1 proportionally.

[0014] S22. Use the pyramid algorithm to correct the pixels in the reduced radar data echo map to obtain the reduced optical flow field.

[0015] S23. Using the optical flow method to absolutely reduce the optical flow field, displacement is estimated and the accurate optical flow field is calculated iteratively to obtain the motion vector field of the radar echo map.

[0016] Furthermore, step S22 specifically includes the following steps:

[0017] S221. Set the initial pixel displacement. Calculate the estimated position of the initial pixel in the next frame:

[0018]

[0019] in, X represents the estimated position of the initial pixel in the next frame, and X represents the position of the initial pixel in the current frame.

[0020] S22 specifically includes the following steps:

[0021] S221. Set the initial pixel displacement. Calculate the estimated position of the initial pixel in the next frame:

[0022]

[0023] in, X represents the estimated position of the initial pixel in the next frame, and X represents the position of the initial pixel in the current frame.

[0024] S222. Calculate the intermediate variables of the optical flow field based on the radar echo field displacement. The calculation method is as follows:

[0025]

[0026]

[0027] Where Δb(X) and A(X) are intermediate variables of the optical flow field, and A1(X) are the coefficients of the symmetric matrix corresponding to the pixels of the first frame radar echo image. These are the symmetric matrix coefficients corresponding to the pixels in the next frame of the radar echo image after displacement estimation. b1(X) represents the vector coefficients corresponding to the next frame of radar echo image after displacement estimation, and b1(X) represents the vector coefficients corresponding to the first frame of radar echo image.

[0028] S223. Iterate using the intermediate variables of the obtained optical flow field to obtain the motion vector field of the radar echo map.

[0029] Furthermore, step S3 specifically includes the following steps:

[0030] S31. Calculate the radar echo movement trajectory at future times using the semi-Lagrange method;

[0031] S32. The radar echo movement trajectory of the future time calculated in S31 is superimposed with the radar echo map identified in the current time to obtain the predicted radar echo map.

[0032] Furthermore, the specific calculation method for calculating the radar echo movement trajectory at future times using the semi-Lagrange method in S31 is as follows:

[0033] S311. Divide the forecast period τ into multiple intervals, represented as follows:

[0034] τ=N×Δt

[0035] Where N is the number of intervals, and Δt is the time length of each interval;

[0036] S312. Superimpose the pixel displacements within each interval to obtain the extrapolated image;

[0037] S313. Iterate the displacement of the pixels in each interval to obtain the radar echo movement trajectory for future times.

[0038] Furthermore, the specific calculation method for iteratively calculating the displacement of pixels within each interval in S313 is as follows:

[0039]

[0040] Among them, a n+1 This represents the displacement obtained in a single time interval after iteration. For pixels in the optical flow field The velocity at point n is the number of iterations.

[0041] The present invention has the following beneficial effects:

[0042] (1) Achieving 2-hour extrapolation forecasts of weather radar echoes helps meteorologists, air traffic controllers, and flight personnel to provide a reference for understanding the weather;

[0043] (2) To realize a diversified platform that organically integrates meteorology, air traffic control and flight operations, which is conducive to meteorological, air traffic control and flight personnel establishing a consistent situational awareness and ensuring flight safety.

[0044] (3) It enables air traffic controllers and flight personnel to obtain the latest meteorological information and hazardous weather warning information in a timely manner, and, in combination with ADS-B technology, guide aircraft to avoid hazardous weather based on aircraft tracks and meteorological warning information, thereby improving the utilization rate of airspace. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the process of a short-term weather forecasting method for civil aviation based on weather radar echo extrapolation according to the present invention.

[0046] Figure 2 This is a schematic diagram of the system architecture of an embodiment of the present invention.

[0047] Figure 3 This is a flowchart of the data center processing according to an embodiment of the present invention. Detailed Implementation

[0048] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0049] A short-term weather forecasting method for civil aviation based on weather radar echo extrapolation, such as... Figure 1 As shown, it includes the following steps:

[0050] S1. Acquire radar echo data from multiple radars and ADS-B terminal system data, and preprocess the radar echo data to obtain a radar echo map.

[0051] 1. Radar data

[0052] By combining radar data reflectivity products provided by the National Meteorological Administration, multiple radar images were mosaicked. Based on empirical thresholds for airport operations, echo images of thunderstorm activity were obtained, and these images were then extrapolated using the extrapolation method studied in this paper. Preprocessing before mosaicking utilized bilinear interpolation and the Lambert projection method.

[0053] 2. ADS-B terminal system data

[0054] The ADS-B ground station receives information such as aircraft call sign, latitude and longitude, altitude, speed, and heading, updating the data every second. Based on this, the target's movement trajectory and attitude can be plotted on a map in real time, and the above information can be selectively displayed as tags following the target.

[0055] S2. Calculate the motion vector field of the radar echo map obtained in step S1 using the optical flow method combined with the pyramid algorithm.

[0056] Optical flow is a method that primarily constructs optical flow equations by analyzing the changes in grayscale values ​​between adjacent image frames. This equations are then used to calculate the changes in motion between the two frames, thus obtaining the motion vector of the target object between them. In short, it's used to calculate the displacement of a target object between two images; when the time interval between the two frames is sufficiently small, the displacement can be considered as velocity.

[0057] The core problem of optical flow lies in the optical flow constraint equation. Based on different assumptions, conditions are introduced to construct equations and complete the calculation of optical flow. However, all optical flow methods must comply with the following basic assumptions: (1) Brightness remains constant. That is, the brightness of the same target does not change when it moves between different frames. This is the assumption of the basic optical flow method (which all optical flow methods must satisfy) and is used to obtain the basic equation of optical flow; (2) Time is continuous or the motion is "small motion". That is, the change of time will not cause a drastic change in the target position, and the displacement between adjacent frames should be relatively small. This is also an indispensable assumption of optical flow. Based on the first basic condition, the basic constraint equation necessary for all optical flow methods can be obtained: Suppose that pixel I(x, y, t) represents the gray value in the first frame, where t represents its time dimension. In the next frame, it moves a distance of (dx, dy) in time dt. Since it is the same pixel, based on the first assumption mentioned above, we assume that the light intensity of the pixel before and after the motion is constant, that is:

[0058] I(x,y,t)=I(x+dx,y+dy,t+dt)

[0059] Expanding the above equation using Taylor series, we get:

[0060]

[0061] Where ε represents a second-order infinitesimal term, which can be ignored. Combining the above two equations, we get...

[0062]

[0063] Let u and v be the optical flow velocity vectors along the X and Y axes, respectively, then we get

[0064]

[0065] set up Let X, Y, and Z represent the partial derivatives of the gray level of a pixel in the image along the X, Y, and Z directions, respectively. In summary, we can obtain...

[0066] I x u+I y v+I t =0

[0067] Where Ix, Iy, and It can be obtained from image data, and (u, v) is the optical flow vector to be obtained. This equation is also known as the optical flow constraint equation.

[0068] Specifically, this embodiment includes the following steps:

[0069] S21. Reduce the size of the stitched radar echo data map obtained in step S1 proportionally.

[0070] The displacement of the echo image is reduced to meet the computational requirements of the optical flow method. The specific principle is to reduce the original image by a certain ratio (typically 0.5). Simultaneously, the motion of pixels in the image is reduced proportionally, thus achieving the condition that the optical flow method has relatively small displacement requirements. The optical flow field of the reduced image is then calculated. This reduced optical flow field can roughly represent the movement of the original image. For example, if the original image is 800*800 pixels with a motion of 40, after reduction it becomes 400*400 pixels with a motion of 20. The optical flow of one pixel in the reduced image can represent the approximate displacement of four pixels in the original image. Therefore, the optical flow field of the reduced image can be used as the initial optical flow field of the original image, and the original optical flow field can be calculated again, thereby improving the accuracy of the optical flow method. Typically, the image is reduced multiple times, so the optical flow field is calculated iteratively multiple times.

[0071] S22. The pixel points in the reduced radar data echo map are corrected using the pyramid algorithm to obtain the reduced optical flow field. Specifically, the optical flow field method is corrected by combining the pyramid algorithm and in order to better track rapidly changing pixels and improve the quality of the optical flow field, including the following steps:

[0072] S221. Set the initial pixel displacement. Calculate the estimated position of the initial pixel in the next frame:

[0073]

[0074] in, X represents the estimated position of the initial pixel in the next frame, and X represents the position of the initial pixel in the current frame.

[0075] S22 specifically includes the following steps:

[0076] S221. Set initial pixel displacement Calculate the estimated position of the initial pixel in the next frame:

[0077]

[0078] in, X represents the estimated position of the initial pixel in the next frame, and X represents the position of the initial pixel in the current frame.

[0079] S222. Calculate the intermediate variables of the optical flow field based on the radar echo field displacement. The calculation method is as follows:

[0080]

[0081]

[0082] Where Δb(X) and A(X) are intermediate variables of the optical flow field, and A1(X) are the coefficients of the symmetric matrix corresponding to the pixels of the first frame radar echo image. These are the symmetric matrix coefficients corresponding to the pixels in the next frame of the radar echo image after displacement estimation. b1(X) represents the vector coefficients corresponding to the next frame of radar echo image after displacement estimation, and b1(X) represents the vector coefficients corresponding to the first frame of radar echo image.

[0083] S223. Iterate using the intermediate variables of the obtained optical flow field to obtain the motion vector field of the radar echo map.

[0084] S23. Using the optical flow method, the displacement of the reduced optical flow field is estimated and the accurate optical flow field is calculated iteratively to obtain the motion vector field of the radar echo map.

[0085] S3. Based on the radar echo map motion vector field obtained in step S2, extrapolate to obtain the predicted radar echo map for future time intervals.

[0086] After obtaining the motion vector field of two radar echo images using the optical flow method, it is necessary to extrapolate the predicted radar echo image for future time intervals based on the known motion vector field. Specifically, this includes the following:

[0087] S31. Calculate the radar echo movement trajectory at future times using the semi-Lagrange method;

[0088] This method uses the Fameback optical flow method to calculate the optical flow field between two adjacent radar echo images. However, for the predicted radar echo image, it is necessary to infer the optical flow field for future time intervals based on the existing optical flow field. Since the optical flow method used in this method is a dense optical flow method, simply using linear extrapolation can easily lead to large errors when the forecast duration is long. To solve this problem, the project team introduced the semi-Lagrange method to extrapolate the radar echo image. The semi-Lagrange method can consider the rotational characteristics of the system in the radar echo image while considering the movement path, which is closer to the actual situation than linear extrapolation and can significantly improve the probability of prediction success.

[0089] The semi-Lagrange method exhibits good stability and is currently widely used in data forecasting and climate models. The semi-Lagrange advection formula is as follows:

[0090]

[0091] In the formula F represents the expected future radar echo map, F represents the existing radar echo map, t0 represents the current time, τ represents the expected time period, X represents the position vector, and α represents the displacement vector.

[0092] The semi-Lagrangian method combines the Eulerian and Lagrangian methods used in fluid mechanics to describe the motion of objects. Its core idea is to divide the forecast period τ into N time intervals, i.e., τ = N × Δt. The displacements within each time interval are then superimposed to obtain the extrapolated image, and the displacement in each time interval is obtained by multiplying the displacement by the velocity at the midpoint of the displacement per unit time.

[0093] The specific calculation method for the displacement of pixels within each interval is as follows:

[0094]

[0095] Where, an+1 is the displacement of a single time interval obtained after iteration, and Δt is a single time interval; For pixels in the optical flow field The speed at which the iteration occurs is given by n, which is the number of iterations. Too many iterations do not necessarily improve the accuracy of the results; therefore, in this embodiment, the number of iterations is chosen to be 3.

[0096] S32. The radar echo movement trajectory of the future time calculated in S31 is superimposed with the radar echo map identified in the current time to obtain the predicted radar echo map.

[0097] S4. The predicted radar echo map obtained in step S3 is fused with the ADS-B terminal system data, alarm thresholds are set, and the data is displayed through a graphical interface.

[0098] This method integrates meteorological and surveillance information into a single terminal for display, providing weather forecasts. Through a graphical interface, controllers can intuitively understand current weather and traffic conditions on a single screen, enabling them to rationally adjust aircraft flight paths, effectively avoid severe weather areas, and improve flight safety. The specific implementation method is as follows:

[0099] 1. System Construction

[0100] like Figure 2 As shown, meteorological information is crucial for air traffic controllers. Large airports typically have their own weather stations and weather radars. Smaller airports, those without weather radar, and even larger airports can access meteorological information from the meteorological bureau's radar. However, this meteorological information is typically presented to controllers by airport weather forecasters, causing transmission delays and, given the variability and complexity of weather, creating potential safety hazards for flights. This project addresses this by constructing a multi-source data processing platform that integrates meteorological information and ADS-B surveillance data into the platform's data processing center. Terminals can access data from the data center in real time via the network, allowing meteorological and surveillance data to be displayed on the same screen. This not only improves controller efficiency but also enhances flight safety. In the diagram, the ADS-B ground station receives ADS-B airborne messages from aircraft in real time, sending one message per second from each target to the data processing center, while meteorological data updates slightly more slowly. On the monitoring terminal, controllers can customize the display of meteorological information according to their needs.

[0101] 2. System Implementation

[0102] The ultimate goal of the system is to achieve the fusion display of meteorological data and ADS-B monitoring data. However, different equipment suppliers make it difficult to achieve open equipment interfaces and standardized data protocols. Therefore, the purpose of the data processing center is to achieve data format uniformity, data storage, and sharing. The data processing center acquires meteorological information, analyzes and organizes this information to form a unified data format, and sends it to the terminals logged into the data center in real time. The data processing center not only needs to receive ADS-B monitoring information but also process meteorological data, so the real-time computing power requirements of the data processing center are very high. When designing and constructing the data center, it is necessary to consider not only hardware but also software design to meet the system's requirements for real-time data processing, continuity, and transmission security. In the entire system, data processing is divided into two steps: the first step is data acquisition, processing, storage, and sharing, which is completed by the data processing center; the second step is the fusion display of meteorological and monitoring information, such as... Figure 3As shown in the data center processing flowchart, the data center constructs a server that integrates the processing of meteorological and ADS-B data. The server software functions in three modules: network data processing, data storage, and interface and log update. In the network data processing module, during system initialization, network transmission attribute parameters are configured, and processing threads for meteorological, ADS-B, and monitoring terminal login management are started. These threads are triggered by data events to ensure real-time data transmission. The monitoring terminal login management thread is mainly responsible for verifying monitoring terminal logins and handling logout responses. If a user successfully logs in, they are added to the monitoring terminal user information queue; if they log out, they are removed from the queue. The meteorological and ADS-B data processing threads primarily respond to received data, normalize and encrypt it, and then forward it to the monitoring terminal user queue. Simultaneously, the raw data is temporarily stored in a data buffer queue according to the database data structure. The data storage module is used to store raw ADS-B and meteorological data, preserving original data for subsequent fault investigation. In this module, the database and data structure are created during initialization, and a data buffer is allocated. The network processing thread caches all received data in this data buffer. When triggered by a timer signal, this thread is awakened and the data in the buffer is saved to the database. Since this module does not have high real-time requirements, it generally runs periodically in the background. The interface and log update module is implemented using timers. Its main function is to monitor system vulnerabilities arising from user logins and interactions with other modules of the server software, and to record and save this information through logs, providing data for subsequent software improvements and secure operation.

[0103] 3. Alarm

[0104] The alarm threshold is designed with a radar echo intensity greater than or equal to 30 dBz, an influence range less than or equal to 10 km, and an area greater than or equal to 30 km². 2 When an alarm is triggered, the system can issue an alarm message, which includes information such as time, location, range, intensity, and duration.

[0105] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0109] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A short-term weather forecasting method for civil aviation based on weather radar echo extrapolation, characterized in that, Includes the following steps: S1. Acquire radar echo data from multiple radars and ADS-B terminal system data, preprocess the radar echo data using bilinear interpolation algorithm and Lambert projection method, and stitch the preprocessed radar echo data into a radar echo map. S2. The pixel points in the reduced radar echo image are corrected using the pyramid algorithm to obtain the reduced optical flow field. The displacement of the reduced optical flow field is estimated using the optical flow method, and the accurate optical flow field is iteratively calculated to obtain the motion vector field of the radar echo image. This process includes the following steps: S21. Reduce the size of the stitched radar echo data map obtained in step S1 proportionally. S22. Using the pyramid algorithm to correct the pixels in the scaled-down radar data echo map, the scaled-down optical flow field is obtained, including the following steps: S221. Set initial pixel displacement Calculate the estimated position of the initial pixel in the next frame: in, X represents the estimated position of the initial pixel in the next frame, and X represents the position of the initial pixel in the current frame. S222. Calculate the intermediate variables of the optical flow field based on the radar echo field displacement. The calculation method is as follows: Where Δb(X) and A(X) are intermediate variables of the optical flow field, and A1(X) are the coefficients of the symmetric matrix corresponding to the pixels of the first frame radar echo image. These are the symmetric matrix coefficients corresponding to the pixels in the next frame of the radar echo image after displacement estimation. b1(X) represents the vector coefficients corresponding to the next frame of radar echo image after displacement estimation, and b1(X) represents the vector coefficients corresponding to the first frame of radar echo image. S223. Iterate using the intermediate variables of the obtained optical flow field to obtain the motion vector field of the radar echo map; S23. Using the optical flow method, the displacement of the reduced optical flow field is estimated and the accurate optical flow field is calculated iteratively to obtain the motion vector field of the radar echo map. S3. Based on the motion vector field, the radar echo movement trajectory for future times is extrapolated using the semi-Lagrange method, and the radar echo movement trajectory is superimposed with the existing echo map to generate a predicted radar echo map. This specifically includes the following steps: S31. Calculate the radar echo movement trajectory at future times using the semi-Lagrange method; The specific calculation method for calculating the radar echo movement trajectory at future times using the semi-Lagrange method in S31 is as follows: S311. Divide the forecast period τ into multiple intervals, represented as follows: τ=N×Δt Where N is the number of intervals, and Δt is the time length of each interval; S312. Superimpose the pixel displacements within each interval to obtain the extrapolated image; S313. Iterate the displacement of the pixels in each interval to obtain the radar echo movement trajectory of future time intervals. The specific calculation method for iteratively calculating the displacement of pixels within each interval in S313 is as follows: Among them, a n+1 This represents the displacement obtained after iteration for a single time interval; For pixels in the optical flow field The velocity at point n is the number of iterations. S32. The radar echo movement trajectory of the future time calculated in S31 is superimposed with the radar echo map identified in the current time to obtain the predicted radar echo map. S4. The predicted radar echo map is fused with the system data obtained by the ADS-B terminal system, alarm thresholds are set and displayed through a graphical interface, so that air traffic controllers and flight personnel can effectively and timely obtain the latest meteorological information and hazardous weather warning information, and combine it with ADS-B technology to guide aircraft to avoid hazardous weather.

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