Clear sky turbulent flux calculation algorithm, flight jolt intensity inversion method and system thereof

Abstract

The present application aims at the problem of low recognition efficiency of clear air turbulence in the prior art, and provides a clear air turbulence quantification algorithm, which is based on signal intensity level distribution data of each electromagnetic wave band of satellite remote sensing, calculates the air flow disorder degree by quantifying the signal intensity change degree, so as to obtain turbulence index level distribution and cloud cover probability level distribution data, and further improves the clear air turbulence recognition accuracy; then, modeling is carried out by combining signal source height and clear air turbulence quantification data and aircraft data, so as to obtain a flight jolt intensity inversion method and system with higher detection efficiency.

Description

Technical Field

[0001] This invention relates to the field of meteorology, and more specifically, to a clear-sky turbulence quantification algorithm, a flight turbulence intensity inversion method, and a system thereof. Background Technology

[0002] Turbulence is a major safety hazard in flight, causing severe shaking that can result in injury or death. Clear-air turbulence can occur in clear skies lacking any suspended matter (cloud droplets), making it impossible for pilots to observe accurately with the naked eye or using airborne rain radar.

[0003] Meteorological research indicates that upper-level turbulence is triggered by strong convective clouds, upper-level jet streams, vertical wind shear, and mountain ranges. Therefore, the airspace where turbulence may occur can be inverted from the atmospheric environment forecast field output by meteorological forecast models. However, the models themselves contain errors in their predictions of atmospheric environment evolution, and the accuracy of global-scale meteorological forecast models is insufficient. Therefore, they can only roughly predict the areas and times where turbulence may occur.

[0004] Based on meteorological observations and theories, weather forecasters can identify potential turbulence areas by analyzing cloud and water vapor distribution characteristics in meteorological satellite images. However, human judgment relies on experience and is therefore prone to significant errors. He et al. (2020) proposed using Gabor filters and grating unit operators in computer vision to directly identify gravity wave ripples from unprocessed satellite cloud images, but this method fails to identify smaller and weaker gravity waves, resulting in suboptimal performance. Furthermore, these techniques fail to quantify turbulence intensity or calculate turbulence height. Summary of the Invention

[0005] The purpose of this invention is to provide a clear-sky turbulence quantification algorithm, which can estimate the degree of airflow turbulence by quantifying the degree of horizontal local variation in the intensity of electromagnetic wave signals detected from Earth by remote sensing satellites, thereby improving the accuracy of turbulence identification; then, by combining the signal source altitude, clear-sky turbulence quantification data, and aircraft data for modeling, a flight turbulence intensity inversion method and system with higher detection efficiency are obtained.

[0006] The first aspect of this invention provides a clear-sky turbulence quantification algorithm for obtaining the horizontal distribution of turbulence index and the horizontal distribution of cloud cover probability, wherein the step of obtaining the horizontal distribution of turbulence index includes:

[0007] The electromagnetic band signal strength data in the satellite remote sensing data is obtained. The electromagnetic band signal strength data includes at least the signal strength data of the water vapor channel and the thermal infrared channel. The signal strength data of each channel is horizontally projected onto the coordinates of a plane map to obtain the horizontal distribution data of the signal strength of multiple channels.

[0008] The horizontal signal strength distribution data of each channel is subjected to high-pass filtering to obtain the local anomaly distribution data of the horizontal signal strength of each channel;

[0009] The degree of local diffusion in the local anomaly distribution data of the quantified horizontal signal intensity is used to derive the horizontal local smoothness index and the turbulence-free smoothness index;

[0010] The horizontal distribution of the turbulence index is obtained by taking the absolute value of the difference between the horizontal local smoothness index and the non-turbulent smoothness index.

[0011] Preferably, the signal strength data of the water vapor channel is electromagnetic band data with a wavelength of 5.2-8.5μm; the signal strength data of the thermal infrared channel is electromagnetic band data with a wavelength of 8.5-40.0μm.

[0012] Preferably, the local standard deviation, entropy, or variance of the local anomaly distribution data of the horizontal signal intensity is used as the local smoothness index.

[0013] Preferred methods for obtaining the turbulence-free smoothness index include:

[0014] Extract grid points in the local anomaly distribution data of horizontal signal intensity that have a local standard deviation, variance, or entropy less than a specified value, and use them as laminar flow regions.

[0015] Obtain all smoothness indices for the laminar flow region, and use the average smoothness index of the laminar flow region as the non-turbulent smoothness index.

[0016] Preferably, the method for obtaining the horizontal distribution of cloud coverage probability includes the following steps:

[0017] Local anomaly distribution data of horizontal signal intensity in the water vapor channel and thermal infrared channel were acquired respectively.

[0018] By comparing the local anomaly distribution data of the horizontal signal intensity of the water vapor channel and the local anomaly distribution data of the horizontal signal intensity of the thermal infrared channel within the same region, a similarity index is calculated.

[0019] After standardizing the similarity index, the horizontal distribution of cloud coverage probability is obtained.

[0020] Preferably, the similarity index calculation methods include the Mean Absolute Difference (MAD) algorithm, the Structural Similarity Algorithm (SSIM) algorithm, or the Normalized Set Algorithm (NCC) algorithm.

[0021] A second aspect of the present invention provides a method for inverting flight turbulence intensity, which uses the clear-sky turbulence diffraction algorithm used in the first aspect to obtain horizontal distribution data of turbulence index and horizontal distribution data of cloud cover probability. The method for inverting flight turbulence intensity includes:

[0022] A flight turbulence intensity inversion model was established, which can use the clear-sky turbulence diffraction algorithm to obtain horizontal distribution data of turbulence index and horizontal distribution data of cloud cover probability.

[0023] Historical flight data was selected and input into the flight turbulence intensity inversion model for training and validation, resulting in a mature flight turbulence intensity inversion model.

[0024] Input real-time flight data, retrieve the horizontal distribution data of turbulence index and cloud cover probability, the peak height distribution data of standardized weight function, and the height distribution data of several specified percentages from the real-time flight data, and obtain the turbulence prediction information and turbulence prediction intensity on the flight path.

[0025] Preferably, the specific steps for selecting historical flight data to input into the flight turbulence intensity inversion model for training and verification include:

[0026] Turbulence observation sample collection, the turbulence observation sample includes C n A non-turbulent sample, C n A sample of weak turbulence, C n A moderate turbulence observation sample and C n A severely turbulent observation sample, C n The number of times moderate turbulence occurred; no turbulence, slight turbulence, moderate turbulence, and severe turbulence were converted into turbulence acceleration;

[0027] The latitude and longitude of the turbulence observation samples were converted into brightness temperature horizontal distribution data coordinates, and the horizontal distribution of turbulence index and cloud cover probability of each channel within a radius of R kilometers of the same time range of the turbulence observation samples were extracted.

[0028] The vertical weight function of water vapor channel at the same time and location for the turbulent observation sample is calculated based on the conventional radiative transfer model. The peak height of the function and the height of several specified percentages are then calculated using the vertical weight function.

[0029] The horizontal distribution of turbulence index for each channel, the horizontal distribution of cloud cover probability, the peak height of the function, and several specified percentage heights are input into the flight turbulence intensity inversion model for training.

[0030] The turbulence acceleration output from the training was verified against the textual description of turbulence intensity from historical flight data.

[0031] Preferably, historical flight data includes at least aircraft type, flight time, flight altitude, flight latitude and longitude, and turbulence intensity; real-time flight data includes at least aircraft type, flight time, flight altitude, and flight latitude and longitude.

[0032] A third aspect of the present invention provides a flight turbulence intensity inversion system, obtained using any of the flight turbulence intensity inversion methods in the second aspect, the flight turbulence intensity inversion system comprising:

[0033] The model building module is used to build a flight turbulence intensity inversion model.

[0034] The model training module selects historical flight data as input to train and validate the flight turbulence intensity inversion model, thereby obtaining a mature flight turbulence intensity inversion model.

[0035] The inversion module takes real-time flight data as input, retrieves the horizontal distribution data of the turbulence index and the horizontal distribution data of the cloud cover probability, the peak height distribution data of the standardized weight function, and the height distribution data of several specified percentages from the real-time flight data, and obtains the turbulence prediction information and turbulence prediction intensity along the flight path.

[0036] The beneficial effects of the embodiments of the present invention are:

[0037] 1. This invention introduces the horizontal distribution of signal intensity in infrared channels as a new indicator for turbulence assessment, especially the horizontal distribution of signal intensity in water vapor channels and thermal infrared channels. By calculating the degree of local variation in signal intensity of each channel, the degree of airflow turbulence can be inferred, thereby improving the accuracy of turbulence judgment.

[0038] 2. High-pass filtering is performed on the horizontal distribution data of signal strength in the water vapor channel, and the local smoothness of the horizontal distribution data of signal strength is quantified, so that the intensity of atmospheric gravity waves can be automatically and objectively obtained in the later stage and used as an indicator of turbulence intensity.

[0039] 3. Using the horizontal distribution of the turbulence index can identify other weather phenomena that can cause turbulence besides gravity waves, such as the laminar top fold region, which can effectively improve the efficiency of turbulence detection.

[0040] 4. It can be applied to machine learning / deep learning models for real-time turbulence intensity inversion, which is easy to implement and has high inversion accuracy. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the flight turbulence intensity inversion method based on satellite observation, according to an embodiment of the present invention.

[0043] Figure 2 This is a grayscale image of the brightness temperature distribution of a water vapor channel with a wavelength λ = 6.2 μm, according to an embodiment of the present invention.

[0044] Figure 3 This is a grayscale image of the local anomaly distribution of the horizontal brightness temperature of the water vapor channel in an embodiment of the present invention.

[0045] Figure 4 This is example data from the grayscale image of the brightness temperature distribution of the water vapor channel in an embodiment of the present invention;

[0046] Figure 5 To Figure 4 Example data: local anomaly distribution of water vapor channel signal strength obtained after high-pass filtering;

[0047] Figure 6 This is a grayscale map showing the horizontal distribution of the turbulence index for the water vapor channel.

[0048] Figure 7 Grayscale image of local anomaly distribution data of horizontal brightness temperature in thermal infrared channel;

[0049] Figure 8 A flowchart illustrating the calculation method for key datasets in the flight turbulence intensity inversion model;

[0050] Figure 9 This is a block diagram of a flight turbulence intensity inversion system. Detailed Implementation

[0051] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0052] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0053] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0054] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0055] It should be noted that "multiple" as mentioned in this article refers to two or more.

[0056] This invention leverages the characteristic that weather systems inducing upper-level turbulence cause disordered horizontal distribution of atmospheric water vapor. It provides a clear-sky turbulence quantification algorithm, performing local smoothness analysis on the horizontal signal intensity data distribution of electromagnetic bands (water vapor channels) detected by meteorological remote sensing satellites that originate from Earth and are absorbed by water vapor, to obtain the horizontal distribution of the turbulence index. Furthermore, taking advantage of the consistent horizontal variation between water vapor channel signals from clouds and thermal infrared channel signals, the horizontal distribution of cloud cover probability is obtained by comparing the similarity of local signal intensity variations across multiple channels. Finally, by combining the source altitude of each band signal with actual turbulence observation data and aircraft data, a flight turbulence intensity inversion method and real-time turbulence inversion system based on satellite observations are derived.

[0057] This technical solution can be widely applied in fields such as meteorological monitoring, weather forecasting, aviation weather forecasting, and atmospheric remote sensing.

[0058] First Embodiment

[0059] This embodiment provides a method for inverting flight turbulence intensity based on satellite observations. Please refer to [link to relevant documentation]. Figure 1 It includes the following steps:

[0060] S1: Establish a flight turbulence intensity inversion model;

[0061] The flight intensity turbulence inversion model in this embodiment is capable of using machine learning or deep learning to obtain the horizontal distribution data of turbulence index L for each infrared channel. c (λ, x, y) and horizontal distribution data of cloud cover probability P cloud (x,y) calculation, vertical weight function of water vapor channel, peak height h(w) max (x, y) and several specified percentages n th height The computational model. Specifically, XgBoost or GAN can be used for model selection.

[0062] Please see below. Figure 8 The method for calculating the horizontal distribution of the turbulence index in the flight turbulence intensity inversion model is as follows:

[0063] a1: Obtain the horizontal distribution data of signal intensity in the water vapor / thermal infrared channel.

[0064] In this embodiment, the signal strength data reflects the strength of the satellite signal, such as microwave radiation intensity, irradiance, brightness, and brightness temperature. This embodiment uses brightness temperature as an example. The meteorological satellite data is decoded according to its data format using conventional methods. After reading the brightness temperature data of the water vapor / thermal infrared channel, the data is projected onto an arbitrary plane map coordinate system using conventional geometric equations and interpolation methods to obtain the horizontal distribution data of the brightness temperature of the water vapor / thermal infrared channel.

[0065] The water vapor channel contains brightness temperature data with wavelengths λ ranging from 5.2 μm to 8.5 μm. Please refer to [link / reference]. Figure 2 The image shows the horizontal distribution of brightness temperature in the water vapor channel at wavelength λ = 6.2 μm. This band reflects the horizontal distribution of water vapor content at commonly used cruising altitudes in civil aviation. The thermal infrared channel contains brightness temperature data for wavelengths λ between 8.5 μm and 40.0 μm; please refer to [link to relevant documentation]. Figure 7 The image shows the horizontal distribution of brightness temperature in the thermal infrared channel with a wavelength of λ = 10.8 μm. This band can reflect the temperature of the Earth's surface and clouds.

[0066] a2: High-pass filtering is applied to the brightness temperature distribution data for each channel. The brightness temperature of the thermal infrared channel mainly reflects cloud top temperature and altitude information. High-pass filtering of the water vapor channel can more easily and directly describe small-scale brightness temperature fluctuations that forecasters particularly want to examine, such as improving local brightness temperature changes in the water vapor channel, resulting in... Figure 3 The local anomaly distribution T′ of the horizontal brightness temperature of the water vapor channel is shown. b (λ,x,y), where T′ b The formula for calculating (λ,x,y) is:

[0067]

[0068] in, The local average brightness temperature is calculated using the following formula:

[0069]

[0070] Where N1 represents the length and width of the moving window used in the filtering process, and N1 is any positive integer. For example data on the brightness temperature horizontal distribution of the water vapor channel, please refer to [link to example data]. Figure 4 Please refer to the data after high-pass filtering. Figure 5 .

[0071] a3: Then calculate the local standard deviation σ respectively. i (λ, x, y), local variance σ i 2(λ, x, y) or entropy S i (λ, x, y) is the local smoothness index H i (λ, x, y), where:

[0072]

[0073]

[0074]

[0075] a4: Calculate the smoothness index H0(λ) in the turbulent region.

[0076] Grid points with local variance / local standard deviation / entropy less than a specified value in the local anomaly distribution data of the horizontal brightness temperature of the water vapor channel are extracted as laminar flow regions; for example, in this embodiment, grid points with local variance less than 0.01 are used as laminar flow regions.

[0077] Obtain all smoothness indices of the extracted laminar flow region, and use the average smoothness index of the laminar flow region as the non-turbulent smoothness index H0(λ).

[0078] a5: Take the absolute value of the difference between the horizontal local smoothness index and the non-turbulent smoothness index to obtain the horizontal distribution L of the turbulence index. i (λ, x, y), that is:

[0079] L i (λ, x, y) = |H i (λ, x, y) - H0(λ)|,

[0080] From this, we can obtain Figure 6 Grayscale image of the horizontal distribution of the turbulence index in the water vapor channel.

[0081] Please see below. Figure 8 The method for calculating the horizontal distribution of cloud cover probability in the flight turbulence intensity inversion model is as follows:

[0082] b1: Using the method disclosed in steps a1-2, obtain the local anomaly distribution data of horizontal brightness temperature for the water vapor channel and the thermal infrared channel, respectively. For details, please refer to [link to relevant documentation]. Figure 2 and Figure 7 ;

[0083] b2: Then, the similarity index is calculated for the local brightness temperature changes of the water vapor channel and the local brightness temperature changes of the thermal infrared channel in the same area;

[0084] Similarity indices for local anomaly distribution of horizontal brightness temperature include, but are not limited to:

[0085] ①Mean absolute difference The formula is:

[0086]

[0087] ② Structural similarity index SSIM(T′) b (λ1,x,y),T′ b (λ2,x,y)), its formula is:

[0088]

[0089] in,

[0090]

[0091]

[0092]

[0093] C n =(k n L) 2 ,

[0094]

[0095] k1 = 0.01, k2 = 0.03.

[0096] ③ Normalized set algorithm NCC(T′) b (λ1, x, y), T′ b (λ2, x, y)),

[0097] b3: After standardizing the similarity index, the horizontal distribution of cloud coverage probability P is obtained. cloud For (x,y), a standardization method can be to divide by the largest possible value.

[0098] Please see below. Figure 8 Vertical weight function and peak height h(w) of the flight turbulence intensity inversion model max (x, y) and several specified percentages n th height To determine the exact location of turbulence by combining vertical height, the calculation method is as follows:

[0099] C1: The vertical weight function of the water vapor channel is calculated from the signal sources of each water vapor channel at the time and location of turbulence occurrence;

[0100] C2: Peak height h(w) is calculated using the vertical weight function. max (x, y) and several specified percentages n th height

[0101] S2: Select historical flight data and input it into the flight turbulence intensity inversion model for training and validation to obtain a mature flight turbulence intensity inversion model. The specific training and validation methods are as follows:

[0102] S21: Historical turbulence observation sample collection.

[0103] Flight path data is extracted from historical flight data, such as captain's report PIREPs or onboard accelerometer data. This flight path data includes, but is not limited to, time, longitude, latitude, aircraft type, flight altitude, and a textual description of turbulence intensity. For the turbulence intensity descriptions, terms like "no turbulence," "minor turbulence," "moderate turbulence," or "severe turbulence" are converted to turbulence acceleration according to standard reference criteria, with the frequency of moderate turbulence occurrences used as the data balancing baseline C. n Retrieve C from historical flight data n One case without bumps, C n A case of slight turbulence, C n A moderately bumpy case and C n One severe turbulence case was used as a sample for historical turbulence observation.

[0104] S22: Convert the latitude and longitude of the turbulence observation samples into coordinates that match the brightness temperature horizontal distribution data, and extract the horizontal distribution L of the turbulence index of each channel within a radius of R kilometers of the turbulence observation samples at the same time range. i (λ, x, y) and the horizontal distribution of cloud cover probability P cloud (x,y).

[0105] The specific method is as follows: The latitude and longitude of each turbulence observation sample are converted into coordinate values ​​corresponding to the brightness temperature horizontal distribution data on the geographic coordinate system. Then, a conventional geometric algorithm is used to find four coordinate values ​​after shifting the turbulence observation sample by R kilometers in the northeast, southeast, southwest, and northwest directions. These coordinates are taken as the geographical range of the vicinity of the turbulence observation sample. The flight intensity turbulence inversion model calculates the horizontal distribution of the turbulence index and the horizontal distribution of cloud cover probability for each water vapor channel. Based on the coordinate values ​​and the time of occurrence of the turbulence observation sample, the horizontal distribution L of the turbulence index of the water vapor channel within the vicinity of R is retrieved. c (λ, x, y) and the horizontal distribution of cloud cover probability P cloud (x,y).

[0106] S23: Calculate the vertical weight function of the water vapor channel at the same time and location for the turbulent observation sample based on the conventional radiative transfer model, and calculate the peak height of the function and the height of several specified percentages through the vertical weight function.

[0107] S24: Input the horizontal distribution of turbulence index of each channel, the horizontal distribution of cloud cover probability, the peak height of the function, and the height of several specified percentages into the flight turbulence intensity inversion model for training;

[0108] S25: Verify the turbulence acceleration output from the training data by comparing it with the textual description of turbulence intensity from historical flight data.

[0109] S3: After the flight intensity turbulence inversion model matures, input real-time flight data (including but not limited to aircraft type, flight altitude, and flight latitude and longitude), retrieve the horizontal distribution data of turbulence index and cloud cover probability within the path range of the real-time flight data, the peak height distribution data of the standardized weight function, and the height distribution data of several specified percentages, and invert to obtain the turbulence prediction information and turbulence prediction intensity / turbulence acceleration on the flight path.

[0110] Second Embodiment

[0111] This embodiment provides a flight turbulence intensity inversion system. Please refer to [link / reference]. Figure 9 The flight turbulence intensity inversion method disclosed in the first embodiment is used to predict the flight turbulence intensity. The flight turbulence intensity inversion system includes:

[0112] A1: Model building module, used to build a flight turbulence intensity inversion model.

[0113] The method for establishing the flight turbulence intensity inversion model is as follows:

[0114] Real-time meteorological satellite data files were decoded according to their data format using conventional methods, and the brightness temperature level distribution of each infrared channel was read. The data was then projected onto an arbitrary planar map coordinate system using conventional geometric equations and interpolation methods. Infrared light data from the same observation time were divided into two groups based on wavelength: the water vapor channel (electromagnetic band with wavelength λ between 5.2 μm and 8.5 μm) and the thermal infrared channel (electromagnetic band with wavelength λ between 8.5 μm and 40.0 μm).

[0115] Using a square particle of size N1 x N1 (N1 is an arbitrary positive integer) as a moving window, high-pass filtering is applied to the brightness temperature distribution of all channels in the real-time water vapor channel and thermal infrared channel to obtain the local horizontal brightness temperature anomaly distribution T′ of each infrared channel. b (λ,x,y) and calculate the local standard deviation σ. i (λ, x, y), variance σ i 2 (λ, x, y) or entropy S i (λ, x, y) is the local smoothness index H i(λ, x, y) is defined, and grid points with local variance / local standard deviation / entropy less than a specified value are labeled as laminar regions. The average smoothness index of the laminar regions is calculated as the smoothness index H0(λ) of the non-turbulent regions. Finally, the difference |H0(λ) between the local smoothness index and the smoothness index of the non-turbulent regions is used as the smoothness index. i (λ, x, y) - H0(λ)| represents the real-time local turbulence exponent horizontal distribution L i (λ, x, y).

[0116] Using a square particle of size N²xN² (N² is an arbitrary positive integer) as a moving window, the absolute difference (MAD), structural similarity index (SSIM), or normalized set algorithm (NCC) of the local anomaly distribution of horizontal brightness temperature in the same region for each water vapor channel and each thermal infrared channel in real-time data is calculated. These are then standardized by dividing by their maximum possible value to obtain the real-time cloud cover probability horizontal distribution P. cloud (x,y).

[0117] The vertically normalized weight function of the real-time signal source for each water vapor channel was calculated using conventional methods from real-time three-dimensional atmospheric environment forecasts or actual observation data at the same time, and the peak height h(w) of the real-time function was also calculated. max (x, y) and several specified percentages n th height

[0118] A2: Model training module, selects historical flight data to input into the flight turbulence intensity inversion model for training and verification, and obtains a mature flight turbulence intensity inversion model;

[0119] A3: Inversion module. Input real-time flight data, retrieve turbulence index horizontal distribution data, cloud cover probability horizontal distribution data, standardized weight function peak height distribution data, and height distribution data of several specified percentages from the real-time flight data, and obtain turbulence prediction information and turbulence prediction intensity on the flight path.

[0120] Based on real-time global aircraft data, including but not limited to longitude, latitude, aircraft type, flight altitude, and horizontal distribution of turbulence index in various water vapor channels within a radius of its location. c (λ, x, y), horizontal distribution of cloud cover probability P cloud (x,y), peak height distribution of standardized weight function h c (w max (x, y) and several specified percentages n th Height distribution Input a pre-trained machine learning or deep learning model and make it reproduce real-time turbulence acceleration.

[0121] Compared to existing technologies, the advantages of this invention include:

[0122] 1. Traditional methods typically use wind speeds derived from numerical models or inverted from satellite cloud imagery animation (AMV). However, both methods have significant errors and are not intuitive. This invention utilizes the principle that weather systems causing upper-level turbulence lead to significant changes in the spatial distribution of local water vapor. It determines that the brightness temperature distribution of water vapor channels reflects the stability of mid-to-high-altitude airflow. Therefore, the degree of airflow turbulence can be inferred by calculating the degree of local changes in the brightness temperature of relevant electromagnetic bands, thereby improving the accuracy of the inversion.

[0123] 2. This invention uses conventional high-pass filtering on infrared satellite data to enhance the identification of local water vapor variations. However, high-pass filtering fails to filter out local water vapor variations caused by clouds (especially benign upper-level cirrus clouds that generate less turbulence), thus failing to help forecasters distinguish water vapor variations in clear-sky areas. This technical solution utilizes the similarity of horizontal local variations in multi-band electromagnetic wave signals from clouds to identify and filter cloud water vapor variation signals. By quantifying local water vapor variations, it provides a method for identifying and determining at least one subset of clear-sky gravity waves, thus identifying areas with large local water vapor variations in cloudless areas and aiding in the identification of clear-sky turbulent regions.

[0124] 3. This invention quantifies the local smoothness of the brightness temperature horizontal distribution of water vapor-sensitive electromagnetic waves after high-pass filtering, and can quickly, automatically and objectively obtain the intensity of gravity waves as an indicator of turbulence intensity.

[0125] 4. Compared with He and others (2020) computer vision algorithms that are only used to identify gravity wave ripples, this invention can identify other weather phenomena that can cause turbulence besides gravity waves, such as the tropopause fold zone, effectively improving the turbulence detection rate.

[0126] 5. Compared with inversion based on numerical forecast model prediction results, this invention uses real-time turbulence observation as the initial field, which greatly improves the understanding of the actual turbulence situation. Combined with machine learning / deep learning models for real-time turbulence intensity inversion, it can effectively improve the accuracy of turbulence identification.

[0127] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0129] The units described as separate components may or may not be physically separate. As will be appreciated by those skilled in the art, the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0130] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0132] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A clear-sky turbulence quantification algorithm, characterized in that, The horizontal distribution of turbulence index and the horizontal distribution of cloud cover probability are obtained through a clear-sky turbulence quantification algorithm. The steps for obtaining the horizontal distribution of turbulence index include: The electromagnetic band signal strength data in the satellite remote sensing data is obtained. The electromagnetic band signal strength data includes at least the signal strength data of the water vapor channel and the thermal infrared channel. The signal strength data of each channel is horizontally projected onto the coordinates of a plane map to obtain the horizontal distribution data of the signal strength of multiple channels. The horizontal signal strength distribution data of each channel is subjected to high-pass filtering to obtain the local anomaly distribution data of the horizontal signal strength of each channel; The local diffusion degree of the local anomaly distribution data of the horizontal signal intensity is quantified to obtain the horizontal local smoothness index and the turbulence-free smoothness index. The method for obtaining the turbulence-free smoothness index includes: extracting grid points in the local anomaly distribution data of the horizontal signal intensity with local variance, standard deviation or entropy less than a specified value as laminar flow regions; obtaining all smoothness indices of the laminar flow regions, and taking the average smoothness index of the laminar flow regions as the turbulence-free smoothness index. The horizontal distribution of the turbulence index is obtained by taking the absolute value of the difference between the horizontal local smoothness index and the non-turbulence smoothness index. The method for obtaining the horizontal distribution of cloud coverage probability includes the following steps: Local anomaly distribution data of horizontal signal intensity in the water vapor channel and thermal infrared channel were acquired respectively. By comparing the local anomaly distribution data of the horizontal signal intensity of the water vapor channel and the thermal infrared channel in the same area, a similarity index is calculated. After standardizing the similarity index, the horizontal distribution of cloud coverage probability is obtained.

2. The clear-sky turbulence diffraction algorithm according to claim 1, characterized in that, The signal strength data of the water vapor channel is electromagnetic band data with wavelengths between 5.2μm and 8.5μm; the signal strength data of the thermal infrared channel is electromagnetic band data with wavelengths between 8.5μm and 40.0μm.

3. The clear-sky turbulence diffraction algorithm according to claim 1, characterized in that, The local standard deviation or entropy and variance of the local anomaly distribution data of the horizontal signal intensity are used as the horizontal local smoothness index.

4. The clear-sky turbulence diffraction algorithm according to claim 1, characterized in that, The similarity index is calculated using methods including the Mean Absolute Difference (MAD) algorithm, the Structural Similarity Algorithm (SSIM) algorithm, or the Normalized Set Algorithm (NCC).

5. A method for inverting flight turbulence intensity, characterized in that, The flight turbulence intensity inversion method includes: A flight turbulence intensity inversion model is established, which can use any of the clear-sky turbulence quantification algorithms described in claims 1-4 to obtain horizontal distribution data of turbulence index and horizontal distribution data of cloud cover probability. Historical flight data is selected and input into the flight turbulence intensity inversion model for training and verification to obtain a mature flight turbulence intensity inversion model; Input real-time flight data, retrieve the horizontal distribution data of turbulence index and cloud cover probability, the peak height distribution data of standardized weight function, and the height distribution data of several specified percentages from the real-time flight data, and obtain the geographical information of turbulence prediction and turbulence prediction intensity along the flight path.

6. The flight turbulence intensity inversion method according to claim 5, characterized in that, The method for inputting historical flight data into the flight turbulence intensity inversion model for training and verification includes: Turbulence observation sample collection, the turbulence observation sample includes A non-turbulent sample A sample of slightly bumpy observations A moderate turbulence observation sample and A severely turbulent observation sample, the aforementioned The number of times moderate turbulence occurred; the terms no turbulence, slight turbulence, moderate turbulence, and severe turbulence were converted into turbulence acceleration; The latitude and longitude of the turbulence observation sample are converted into brightness temperature horizontal distribution data coordinates, and the horizontal distribution of turbulence index and cloud cover probability of each channel within a radius of R kilometers in the same time range of the turbulence observation sample are extracted. The vertical weight function of water vapor channel at the same time and location for the turbulent observation sample was calculated based on the conventional radiative transfer model. The peak height of the function and the height of several specified percentages were then calculated using the vertical weight function. The horizontal distribution of turbulence index of each channel, the horizontal distribution of cloud cover probability, the peak height of the function, and several specified percentage heights are input into the flight turbulence intensity inversion model for training. The turbulence acceleration output from the training was verified against the textual description of turbulence intensity from historical flight data.

7. The flight turbulence intensity inversion method according to claim 5 or 6, characterized in that, The historical flight data includes at least the aircraft type, flight time, flight altitude, flight latitude and longitude, and turbulence intensity; the real-time flight data includes at least the aircraft type, flight time, flight altitude, and flight latitude and longitude.

8. A flight turbulence intensity inversion system, established using the flight turbulence intensity inversion method according to any one of claims 5-7, characterized in that, The flight turbulence intensity inversion system includes: The model building module is used to build a flight turbulence intensity inversion model. The model training module selects historical flight data and inputs it into the flight turbulence intensity inversion model for training and verification, thereby obtaining a mature flight turbulence intensity inversion model. The inversion module takes real-time flight data as input, retrieves the horizontal distribution data of the turbulence index and the horizontal distribution data of the cloud cover probability, the peak height distribution data of the standardized weight function, and the height distribution data of several specified percentages from the real-time flight data, and obtains the turbulence prediction information and turbulence prediction intensity along the flight path.

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