Aircraft ice accretion intelligent identification and estimation method based on wind cloud meteorological satellite data

Abstract

The invention discloses an intelligent identification and estimation method for airplane ice accretion based on wind cloud meteorological satellite data, and relates to the technical field of airplane ice accretion identification. The airplane ice accretion intelligent identification and estimation method based on the wind cloud meteorological satellite data comprises the following specific steps: estimating historical ice accretion data; reading the MODIS historical data of the to-be-identified aircraft in the icing occurrence time period through MATLAB, and generating the judgment and result of icing in the corresponding region through an icing index algorithm; acquiring parameters; and ice accumulation probability judgment and ice accumulation intensity identification. According to the method, real-time meteorological data is acquired through a satellite, parameters most related to airplane icing are acquired according to related parameters such as ground albedo, relative azimuth angles and scattering indexes in the meteorological data, probability judgment and strength recognition are combined, the icing possibility and icing strength are accurately judged, and the accuracy of airplane icing is improved. And a more detailed and accurate decision basis is provided for pilots and air traffic control departments.

Description

Technical Field

[0001] This invention relates to the field of aircraft icing identification technology, specifically to an intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data. Background Technology

[0002] Aircraft icing is a relatively dangerous type of aviation weather. In order to better study aircraft icing, it is necessary to understand the conditions under which it occurs. The formation of aircraft icing is usually related to factors such as temperature, cloud cover, humidity, and precipitation.

[0003] Among existing methods, statistical forecasting, frost point method, dynamic warming method, and numerical weather prediction are common methods for forecasting aircraft icing. However, statistical forecasting, frost point method, and dynamic warming method, due to their relatively singular consideration of meteorological factors, result in unsatisfactory forecasting results and are now rarely used. Instead, numerical weather prediction or a combination of numerical weather prediction and empirical formulas for aircraft icing is more commonly used. The basic idea is to build a forecasting model based on aircraft icing data from aircraft reports using statistical and meteorological analysis methods, and then apply parameters such as temperature, relative humidity, cloud cover, and precipitation output from the numerical model to forecast the aircraft icing potential. However, numerical weather prediction requires high-performance computing platforms, which are typically expensive and require professional maintenance, making them unavailable to most operational units due to insufficient funding and manpower.

[0004] For example, the apparatus and method for identifying the risk zone of aircraft icing on flight routes and constructing its feature parameters disclosed in Chinese Patent Publication No. CN115860464A monitors the risk of icing on flight route data by extracting machine features, which reduces the rate of missed and false alarms, while greatly reducing the cost of cloud intelligence and improving monitoring efficiency.

[0005] The feature parameters take into account actual meteorological elements such as temperature, humidity, and wind data, and use reliable numerical analysis models to calculate the possible weather conditions at each grid point. Therefore, the feature parameters need to be calculated in advance, and the relevant values ​​require the support of computers with high-speed computing power and large memory capacity. However, they are still limited by the low accuracy of observation data. When the parameters input into the relevant values ​​have slight errors, it will lead to errors in aircraft icing identification and estimation. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data, which solves the problems mentioned in the background.

[0007] To achieve the above objectives, the present invention provides a method for intelligent identification and estimation of aircraft icing based on meteorological satellite data, comprising the following specific steps:

[0008] S1: Estimate historical icing data; Use MATLAB to read MODIS historical data for the icing period of the aircraft to be identified, and use the icing index algorithm to generate the judgment and result of icing in the corresponding area;

[0009] S2: Parameter acquisition; Cloud microphysical parameters are obtained by inverting meteorological satellite data of the aircraft to be identified, and the effective temperature T, effective optical thickness h, effective particle radius r, and cloud liquid water path L are obtained by using the Ic icing index algorithm model based on the cloud microphysical parameters.

[0010] S3: Icing probability assessment; based on cloud top temperature T top =273K distinguishes the cloud attributes of the area where the aircraft to be identified is located, and based on the effective optical thickness obtained in S2, performs icing prediction and obtains the probability result of icing occurrence;

[0011] S4: Icing intensity identification; The intensity of the icing region of the aircraft to be identified is estimated using the effective temperature T, effective particle radius r, and cloud liquid water path L obtained in S2.

[0012] A further improvement to the technical solution of the present invention is that the acquisition of cloud microphysical parameters in S2 also includes the following specific steps: during the flight of the aircraft, the larger the water droplets, the higher the cloud water density, or the higher the supercooled water content, the more conducive it is to the formation of ice.

[0013] Therefore, the effective particle radius obtained by inverting satellite data in this application can directly reflect the size of cloud droplets, while the cloud liquid water path L represents the amount of supercooled water. By analyzing and comparing a large number of aircraft icing reports with cloud microphysical parameters provided by satellites, it can be found that there is a correlation between icing intensity and cloud microphysical parameters.

[0014] Establish the cloud reflection function R(h; r, T, β) for the area where the aircraft to be identified is located, and base it on the escape function K(μ) and ground albedo A from satellite data. g Symmetry factor g, extrapolated length of light scattering q0, cosine μ of the zenith angle θ of the satellite, cosine μ0 of the zenith angle θ of the sun, relative azimuth angle □, scattering index k, spherical reflectivity A * From this, we can deduce the spectral distribution of cloud droplet particles n(r), cloud bottom height z1, cloud top height z2, and extinction coefficient per unit volume βext.

[0015] A further improvement to the technical solution of this invention lies in that the acquisition of cloud microphysical parameters in S2 further includes the following specific steps: through The effective optical thickness h is obtained, where βext is the extinction coefficient in the satellite data and H is the cloud thickness in the satellite data.

[0016] A further improvement to the technical solution of this invention lies in that the acquisition of cloud microphysical parameters in S2 further includes the following specific steps: through Obtain the effective particle radius r, where n(r) is the spectral distribution of cloud droplet particles and K(μ) is the escape function.

[0017] A further improvement to the technical solution of this invention lies in that the cloud liquid water path L also includes the following calculation process: assuming the atmospheric water vapor density in the area where the aircraft to be identified is ρ, the total water content in the vertical column volume per unit area of ​​the cloud, i.e., the cloud liquid water path L, is:

[0018]

[0019] A further improvement of the technical solution of this invention lies in that, in terms of physical morphology, clouds can be divided into ice clouds, water clouds, and mixed clouds. When the cloud layer is thin, aircraft generally do not face the risk of icing. However, when water clouds are present, it is necessary to assess the potential risk of icing from warm or cold rain clouds. Existing technologies rely solely on cloud top temperature for this assessment, ignoring the influence of ambient light on cloud penetration. Therefore, this application introduces an effective optical thickness h. In step S3, the effective optical thickness h is determined based on the cloud top temperature T. top =273K distinguishes cloud attributes in the area where the aircraft to be identified is located, including: T yop At >273K, the cloud type is warm water cloud, and the icing prediction is no icing for the entire range of effective optical thickness h values.

[0020] T top When the cloud layer is ≤273K and the cloud type is supercooled water cloud, the icing prediction is that there is icing when the effective optical thickness h>1.

[0021] T yop When the cloud layer is ≤273K and contains supercooled water clouds, the icing prediction for effective optical thickness h≤1 is that there is no icing.

[0022] T top When the K level is ≤273K and the cloud type is mixed water cloud, icing is predicted to occur in all cases where the effective optical thickness h>6.

[0023] T top When the cloud layer is ≤273K and contains mixed water clouds, the icing prediction for effective optical thickness h≤6 is that there is no icing.

[0024] A further improvement of the technical solution of the present invention is that, when the ice accumulation prediction result in S3 is that there is ice accumulation, if r > 5um, the probability result of ice accumulation is obtained by A = 0.147 × ln(L) - 0.084, and if r ≤ 5um, the probability result of ice accumulation is obtained by A = 0.138 × ln(L) - 0.024.

[0025] A further improvement to the technical solution of the present invention is that the intensity estimation in S4 specifically includes, through... In the formula, r is the effective particle radius, V is the flight speed of the aircraft to be identified, and ω is the air viscosity of the area where the aircraft to be identified is located.

[0026] A further improvement to the technical solution of this invention lies in the fact that aircraft icing requires three conditions to be met: the ambient temperature and the fuselage temperature must be below 0°C; cold water droplets must be present, and the content and size of the cold water droplets must meet the icing threshold. Existing research has found a clear linear relationship between the probability of icing and the effective radius and the path of the liquid water. Therefore, the probability of occurrence can be obtained using the difference method, and based on the icing prediction results, the intensity of possible icing can be estimated. In step S2, the cloud reflection function is used to establish... In the formula, the escape function K(μ) and the ground albedo A g Symmetry factor g, extrapolated length of light scattering q0, cosine μ of the zenith angle θ of the satellite, cosine μ0 of the zenith angle θ of the sun, relative azimuth angle □, scattering index k, spherical reflectivity A * All data are satellite data for the current region.

[0027] Beneficial effects

[0028] Compared with existing technologies, the beneficial effects of this invention are that it obtains real-time meteorological data from satellites and, based on relevant parameters such as ground albedo, relative azimuth, and scattering index from the meteorological data, acquires the effective temperature T, effective optical thickness h, effective particle radius r, and cloud liquid water path L, which are most relevant to aircraft icing. Based on the acquired parameters, it combines probability judgment and intensity recognition to achieve intelligent recognition, more accurately judging the possibility and intensity of icing, and providing pilots and air traffic control departments with more detailed and accurate decision-making basis. Attached Figure Description

[0029] Figure 1 A schematic diagram of the structure of an intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data;

[0030] Figure 2 This is a schematic diagram of an intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data. Detailed Implementation

[0031] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0032] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0033] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, and elements well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0034] This invention provides a method for intelligent identification and estimation of aircraft icing based on Fengyun meteorological satellite data. The method includes the following specific steps:

[0035] S1: Estimate historical icing data; Use MATLAB to read MODIS historical data for the icing period of the aircraft to be identified, and use the icing index algorithm to generate the judgment and result of icing in the corresponding area;

[0036] S2: Parameter acquisition; Cloud microphysical parameters are obtained by inverting meteorological satellite data of the aircraft to be identified, and the effective temperature T, effective optical thickness h, effective particle radius r, and cloud liquid water path L are obtained by using the Ic icing index algorithm model based on the cloud microphysical parameters.

[0037] During flight, larger water droplets, higher cloud water density, or higher supercooled water content are more conducive to icing formation.

[0038] Therefore, the effective particle radius obtained by inverting satellite data in this application can directly reflect the size of cloud droplets, while the cloud liquid water path L represents the amount of supercooled water. By analyzing and comparing a large number of aircraft icing reports with cloud microphysical parameters provided by satellites, it can be found that there is a correlation between icing intensity and cloud microphysical parameters.

[0039] This involves establishing the cloud reflection function R(h; r, T, β) for the area where the aircraft to be identified is located, and using the escape function K(μ) and ground albedo A from satellite data. g Symmetry factor g, extrapolated length of light scattering q0, cosine μ of the zenith angle θ of the satellite, cosine μ0 of the zenith angle θ of the sun, relative azimuth angle □, scattering index k, spherical reflectivity A * From this, we can deduce the spectral distribution of cloud droplet particles n(r), cloud bottom height z1, cloud top height z2, and extinction coefficient per unit volume βext.

[0040] Based on the cloud's reflection function:

[0041]

[0042] In the formula, the escape function K(μ) and the ground albedo A g Symmetry factor g, extrapolated length of light scattering q0, cosine μ of the zenith angle θ of the satellite, cosine μ0 of the zenith angle θ of the sun, relative azimuth angle □, scattering index k, spherical reflectivity A * All data are satellite data for the current region.

[0043] The acquisition of cloud microphysical parameters in S2 also includes the following specific steps: through Obtain the effective particle radius r, where n(r) is the spectral distribution of cloud droplet particles and K(μ) is the escape function.

[0044] pass The effective optical thickness h is obtained, where βext is the extinction coefficient in the satellite data and H is the cloud thickness in the satellite data.

[0045] The cloud liquid water path L also includes the following calculation process: Assuming the atmospheric water vapor density in the area where the aircraft to be identified is located is ρ, the total water content in the vertical column volume per unit area in the cloud, i.e., the cloud liquid water path L, is:

[0046]

[0047] S3: Icing probability assessment; based on cloud top temperature T top =273K distinguishes the cloud attributes in the area where the aircraft to be identified is located, and based on the effective optical thickness obtained by S2, performs icing prediction and obtains the probability result of icing occurrence;

[0048] In terms of physical morphology, clouds can be divided into ice clouds, water clouds, and mixed clouds. When the cloud layer is thin, the aircraft generally does not face the danger of icing. However, when water clouds are present, it is necessary to determine the potential risk of icing to the aircraft from warm rain clouds and cold rain clouds. Existing technologies rely solely on cloud top temperature for judgment, ignoring the influence of ambient light on cloud penetration. Therefore, this application introduces an effective optical thickness h.

[0049] Based on cloud top temperature T top =273K distinguishes cloud attributes in the area where the aircraft to be identified is located, including: T top At >273K, the cloud type is warm water cloud, and the icing prediction is no icing for the entire range of effective optical thickness h values.

[0050] T yop When the cloud layer is ≤273K and the cloud type is supercooled water cloud, the icing prediction is that there is icing when the effective optical thickness h>1.

[0051] T top When the cloud layer is ≤273K and contains supercooled water clouds, the icing prediction for effective optical thickness h≤1 is that there is no icing.

[0052] T top When the K level is ≤273K and the cloud type is mixed water cloud, icing is predicted to occur in all cases where the effective optical thickness h>6.

[0053] T top When the cloud layer is ≤273K and contains mixed water clouds, the icing prediction for effective optical thickness h≤6 is that there is no icing.

[0054] In S3, if the prediction result of ice accumulation is that there is ice accumulation, and r > 5um, the probability of ice accumulation is obtained by A = 0.147 × ln(L) - 0.084. If r ≤ 5um, the probability of ice accumulation is obtained by A = 0.138 × ln(L) - 0.024.

[0055] Aircraft icing requires three conditions to be met: the ambient temperature and the fuselage temperature must be below 0°C, and there must be cold water droplets with a content and size that meet the icing threshold. Existing research has found that the probability of icing is linearly related to the effective radius and the path of the liquid water. Therefore, the probability of icing can be obtained by using the difference method, and the intensity of possible icing can be estimated based on the icing prediction results.

[0056] The intensity estimation in S4 specifically includes, through... In the formula, r is the effective particle radius, V is the flight speed of the aircraft to be identified, and ω is the air viscosity of the area where the aircraft to be identified is located.

[0057] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding an intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0058] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0059] This invention provides a method for intelligent identification and estimation of aircraft icing based on meteorological satellite data. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for intelligent identification and estimation of aircraft icing based on Fengyun meteorological satellite data, characterized in that, The method includes the following specific steps: S1: Estimate historical icing data; Use MATLAB to read MODIS historical data for the icing period of the aircraft to be identified, and use the icing index algorithm to generate the judgment and result of icing in the corresponding area; S2: Parameter acquisition; Cloud microphysical parameters are obtained by inverting meteorological satellite data of the aircraft to be identified, and the effective temperature T, effective optical thickness h, effective particle radius r, and cloud liquid water path L are obtained by using the Ic icing index algorithm model based on the cloud microphysical parameters. S3: Icing probability assessment; based on cloud top temperature T top =273K distinguishes the cloud attributes of the area where the aircraft to be identified is located, and based on the effective optical thickness obtained in S2, performs icing prediction and obtains the probability result of icing occurrence; S4: Icing intensity identification; The intensity of the icing region of the aircraft to be identified is estimated using the effective temperature T, effective particle radius r, and cloud liquid water path L obtained in S2.

2. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 1, characterized in that, The acquisition of cloud microphysical parameters in S2 also includes the following specific steps: Establish the cloud reflection function R(h; r, T, β) for the area where the aircraft to be identified is located, and base it on the escape function K(μ) and ground albedo A from satellite data. g Symmetry factor g, extrapolated length of light scattering q0, cosine μ of the zenith angle θ where the satellite is located, cosine μ0 of the solar zenith angle θ, relative azimuth angle Scattering index k, spherical reflectivity A * From this, we can deduce the spectral distribution of cloud droplet particles n(r), cloud bottom height z1, cloud top height z2, and extinction coefficient per unit volume βext.

3. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 2, characterized in that, The acquisition of cloud microphysical parameters in S2 also includes the following specific steps: through The effective optical thickness h is obtained, where βext is the extinction coefficient in the satellite data and H is the cloud thickness in the satellite data.

4. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 3, characterized in that, The acquisition of cloud microphysical parameters in S2 also includes the following specific steps: through Obtain the effective particle radius r, where n(r) is the spectral distribution of cloud droplet particles and K(μ) is the escape function.

5. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 2, characterized in that, The cloud liquid water path L also includes the following calculation process: Assuming the atmospheric water vapor density in the area where the aircraft to be identified is located is ρ, the total water content in the vertical column volume per unit area in the cloud, i.e., the cloud liquid water path L, is:

6. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 1, characterized in that, In S3, based on the cloud top temperature T tpp =273K distinguishes cloud attributes in the area where the aircraft to be identified is located, including: T top At >273K, the cloud type is warm water cloud, and the icing prediction is no icing for the entire range of effective optical thickness h values. T top When the cloud layer is ≤273K and the cloud type is supercooled water cloud, the icing prediction is that there is icing when the effective optical thickness h>1. T top When the cloud layer is ≤273K and contains supercooled water clouds, the icing prediction for effective optical thickness h≤1 is that there is no icing. T top When the K level is ≤273K and the cloud type is mixed water cloud, icing is predicted to occur in all cases where the effective optical thickness h>6. T top When the cloud layer is ≤273K and contains mixed water clouds, the icing prediction for effective optical thickness h≤6 is that there is no icing.

7. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 6, characterized in that, In S3, if the predicted ice accumulation result indicates that ice accumulation is present, and r > 5 μm, the probability of ice accumulation is obtained by A = 0.147 × ln(L) - 0.084; if r ≤ 5 μm, the probability of ice accumulation is obtained by A = 0.138 × ln(L) - 0.

024.

8. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 4, characterized in that, The intensity estimation in S4 specifically includes, through... In the formula, r is the effective particle radius, V is the flight speed of the aircraft to be identified, and ω is the air viscosity of the area where the aircraft to be identified is located.

9. The intelligent identification and estimation method for aircraft icing based on Fengyun meteorological satellite data according to claim 2, characterized in that, The S2 is established based on the cloud's reflection function. In the formula, the escape function K(μ) and the ground albedo A g Symmetry factor g, extrapolated length of light scattering q0, cosine μ of the zenith angle θ where the satellite is located, cosine μ0 of the solar zenith angle θ, relative azimuth angle Scattering index k, spherical reflectivity A * All data are satellite data for the current region.

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