Aircraft accretion cloud macro-microstructure monitoring method based on GPM satellite observation

Abstract

The invention discloses an airplane accretion cloud macro-microstructure monitoring method based on GPM satellite observation, and the method comprehensively analyzes the macro-microstructure characteristics of an airplane accretion cloud system through processing the data of a dual-frequency precipitation radar carried by a GPM satellite. The unique observation capability of a GPM satellite is utilized to penetrate through a cloud layer, vertical distribution information of a phase state in the cloud is obtained, on the basis, vertical gradient changes of micro physical parameters in the cloud can be accurately captured, and fine monitoring of macro and micro structures of an ice accretion cloud system of an airplane and particle spectrum characteristics in the cloud is achieved. The method breaks through the limitation that traditional satellite observation can only monitor cloud top characteristics, provides a scientific basis for airplane icing early warning and prevention by comprehensively monitoring the structure in the icing cloud, and can significantly improve the aviation flight safety guarantee capability.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and display related to aircraft icing, and in particular to a method for monitoring the macro- and micro-structures of aircraft icing clouds based on GPM satellite observations. Background Art

[0002] Aircraft icing is the process by which ice forms on the fuselage due to freezing of supercooled water or direct condensation of water vapor. This icing phenomenon can disrupt the aircraft's streamlined design, significantly adversely affecting its flight performance. Minor icing can affect the aircraft's stability and controllability, while severe icing can disrupt communications, cause instrument failure, or even cause the aircraft to stall, resulting in a catastrophic accident and endangering the lives of passengers and crew.

[0003] The GPM satellite is equipped with a dual-frequency precipitation radar (DPR). Using the DPR's Ku-band and Ka-band radars, it conducts three-dimensional scanning of precipitation particles in clouds, obtaining vertical distribution information of liquid water, ice crystals, and precipitation within the clouds. Its advantage lies in its ability to penetrate non-precipitating clouds to directly obtain information on the liquid water path, ice water path, and precipitation particle phase. It can also reconstruct the three-dimensional structure of clouds to reveal the microphysical characteristics of different cloud systems, providing key data for studying the vertical structural characteristics and spatial distribution characteristics of aircraft icing cloud systems.

[0004] Studying the characteristics of hydrometeors within aircraft icing clouds, particularly the spectral signature of supercooled water particles, is of great significance for flight safety. The GPM satellite, equipped with advanced observation equipment, effectively monitors cloud microphysical parameters through its unique advantages, despite limitations in traditional methods. Its data, characterized by high-precision observations, second-level sampling, and meter-level vertical resolution, captures vertical gradients of parameters, simultaneously acquires multiple data types to reveal interaction mechanisms, and provides validation for the accuracy of satellite inversion algorithms.

[0005] Comprehensive analysis of cloud macro- and microscopic characteristics based on GPM satellite data can more accurately identify the macro- and microstructure of aircraft icing clouds and the particle spectrum characteristics within them, providing theoretical and empirical support for improving aircraft icing monitoring and early warning capabilities. At a time when China prioritizes aviation safety, improving aircraft icing monitoring and early warning capabilities is an urgent and crucial task to ensure flight safety and address major national needs.

[0006] Currently, domestic monitoring of aircraft icing cloud structure and supercooled water particle characteristics is primarily carried out through the Fengyun series of geostationary satellites. The Fengyun-4 satellite, the operational model of my country's second-generation geostationary meteorological satellite, can scan China and surrounding areas within minutes.

[0007] In terms of cloud parameter retrieval, the Fengyun-4 satellite can provide key parameters such as cloud top height (CTH), cloud top temperature (CTT), and cloud phase classification, which are important for identifying the macroscopic characteristics of icy cloud tops. However, it should be noted that Fengyun satellite observations primarily focus on cloud top characteristics. Due to the limited penetration capability of its optical / infrared payload, it is difficult to directly obtain the vertical distribution and particle spectral characteristics of hydrometeors (such as cloud droplets, ice crystals, and supercooled water) within the cloud.

[0008] While the Fengyun series of geostationary satellites play an important role in monitoring icing clouds for Chinese aircraft, as passive remote sensing platforms in geostationary orbit, they rely primarily on receiving solar radiation reflected from cloud tops or thermal infrared radiation emitted by the cloud itself. This observation method cannot penetrate the cloud layer to obtain information about the internal structure of the cloud. This physical limitation leads to several key deficiencies in satellite icing cloud monitoring:

[0009] First, when it comes to detecting microphysical parameters within clouds, geostationary satellites struggle to directly capture the vertical distribution of supercooled water, which is crucial for aviation safety. The spatial distribution of supercooled water droplets exhibits a distinct stratification, with multiple supercooled water-rich regions likely existing at different altitudes. Satellites can only infer this information using indirect parameters such as cloud top temperature, and are unable to directly detect the vertical distribution and particle size characteristics of hydrometeors (such as cloud droplets, ice crystals, and supercooled water) within clouds, as active remote sensing equipment can. Second, when it comes to identifying cloud particle phases, existing geostationary satellite observations struggle to accurately distinguish the mixing ratio of supercooled water droplets and ice crystals within clouds. Regions of mixed "supercooled water-ice crystal" phases often exist within icing clouds, and this mixing directly influences ice accretion rates and ice type characteristics. Summary of the Invention

[0010] Based on the above problems, the present invention discloses a method for monitoring the macro- and micro-structures of aircraft icing clouds based on GPM satellite observations. By utilizing Global Precipitation Measurement satellite remote sensing data, a system for analyzing the characteristics of aircraft icing clouds based on multi-dimensional observations is constructed.

[0011] Technical solution:

[0012] A method for monitoring the macro- and micro-structures of aircraft icing clouds based on GPM satellite observations comprises the following steps:

[0013] Step 1: Obtain GPM satellite 2ADPR data and establish a data set;

[0014] Step 2: Read the latitude and longitude information and DSD variables (where the DSD variables include cloud phase information) from the 2ADPR data. Use the phase classification algorithm to process the 2ADPR data and draw vertical distribution maps of liquid, mixed, and solid phases within the cloud based on the processing results to show the vertical structure of the cloud phase.

[0015] Step 3: Read the longitude and latitude information, radar reflectivity factor Ze, and paramDSD variables under the SLV data group (where the paramDSD variable covers the particle number concentration Nw and mass median diameter Dm) from the 2ADPR data. When drawing the vertical profile of the radar reflectivity factor Ze, particle number concentration Nw, and mass median diameter Dm, add the boundary lines between different phases in the vertical structure to more clearly show the characteristics of the vertical distribution of each parameter in different phases.

[0016] Step 4: Construct an icing risk field based on GPM satellite data and comprehensively analyze the macro- and micro-structures of the aircraft icing cloud system and the particle spectrum characteristics within the cloud.

[0017] Preferably, the phase state classification algorithm is: perform integer operation on the phase state value of 2ADPR, divide the phase state value by 100 and round down, when the integer value is 0, classify the particle phase state as solid, when the integer value is 1, classify the particle phase state as mixed phase, and when the integer value is 2, classify the particle phase state as liquid.

[0018] Preferably, the phase classification algorithm further comprises: adding a 0°C height line on the vertical distribution diagram, and when the integer value is 2 and is above 0°C, it is determined to be a cold water enrichment area.

[0019] As a preference, the mass median diameter D m The calculation method is: , where variables M3 and M4 are the third-order moment and fourth-order moment of particle spectral distribution, respectively.

[0020] Preferably, the particle number concentration N w The calculation method is: , where the variable ρ w is the density of liquid water, which is 1 g / cm 3 , W is the liquid water content, D m is the mass median diameter.

[0021] As a preferred method, the comprehensive analysis includes: first, looking at the phase distribution of particles at different altitudes and the corresponding radar echo intensity from the perspective of macroscopic structure, judging the degree of cloud development and summarizing the macroscopic structural characteristics under different events; then, from the perspective of microscopic and particle spectrum characteristics, studying the number and size characteristics of particles at different altitudes and their correlation with phase transitions, revealing the connection between particle spectrum characteristics and aircraft icing; finally, integrating macroscopic and microscopic results to establish a structural model and a particle spectrum characteristic model, providing a basis for aircraft icing warning and aviation safety assurance.

[0022] The present invention also discloses a system for monitoring the macro- and micro-structures of aircraft ice clouds based on GPM satellite observations, comprising:

[0023] Data acquisition module, used to obtain 2ADPR data of GPM satellite;

[0024] The data processing module is used to process the 2ADPR data from the GPM satellite. It includes: a first processing unit, which reads the longitude and latitude information and the DSD variable covering the cloud phase information phase from the 2ADPR data, processes the 2ADPR data using a phase classification algorithm, and draws vertical distribution maps of liquid, mixed, and solid states in the cloud based on the processing results, thereby presenting the vertical structure of the cloud phase; a second processing unit, which reads the longitude and latitude information, the radar reflectivity factor Ze, and the paramDSD variable covering the particle number concentration Nw and the mass median diameter Dm in the SLV data group from the 2ADPR data, draws vertical profiles of the radar reflectivity factor Ze, the particle number concentration Nw, and the mass median diameter Dm, and adds the boundary lines between different phases in the vertical structure, thereby more clearly showing the characteristics of each parameter in the vertical distribution of different phases;

[0025] Analysis module, used to analyze the macro-microstructure characteristics of aircraft ice cloud system, and also used to draw radar reflectivity factor Z e , number concentration N w and mass median diameter D m Vertical distribution diagram of .

[0026] Compared with existing aircraft ice cloud structure monitoring technology, the present invention has the following advantages:

[0027] (1) This invention innovatively uses the GPM's onboard dual-frequency radar inversion data to achieve refined diagnostic analysis of the macroscopic structural characteristics of icing clouds, the microscopic particle phase distribution, and the supercooled water droplet spectrum parameters, which can improve the accuracy and reliability of aircraft icing risk warnings.

[0028] (2) This invention breaks through the limitations of traditional aircraft icing prediction research, which relies on temperature and humidity conditions to indirectly diagnose aircraft icing potential. By utilizing GPM dual-frequency radar satellite data, a macro- and micro-structure analysis system for icing cloud systems is constructed, providing strong support for more accurate prediction of icing conditions.

[0029] (3) The present invention uses a spaceborne dual-frequency radar phase recognition algorithm to analyze the vertical structure of radar reflectivity, and combines it with the collaborative inversion technology of particle spectrum parameters to simultaneously obtain the phase distribution and number concentration (N) in the cloud. w ) and effective diameter (D m ) and other core parameters to achieve multi-source comprehensive observation and analysis of the vertical structure and particle spectrum characteristics of hydrometeors in aircraft icing clouds. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 is a flow chart of the present invention;

[0032] Figure 2 This is the phase diagram of particles in the frontal cloud system observed by the GPM satellite on March 9, 2016;

[0033] Figure 3 is the particle number concentration N observed by the GPM satellite on March 9, 2016 w Vertical distribution diagram of

[0034] Figure 4 is the mean median diameter D observed by the GPM satellite on March 9, 2016 m Vertical distribution diagram of

[0035] Figure 5 is the radar reflectivity factor Z observed by the GPM satellite on March 9, 2016 e Vertical distribution diagram of . DETAILED DESCRIPTION

[0036] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth many specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many different ways than those described herein, and those skilled in the art can make similar modifications without violating the scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The following embodiments of the present invention are further described in detail with reference to the accompanying drawings. Example 1

[0037] The present invention discloses a method for monitoring the macro- and micro-structures of aircraft icing clouds based on GPM satellite observations. The method comprehensively analyzes the macro- and micro-structural characteristics of aircraft icing clouds by processing dual-frequency precipitation radar data carried by the GPM satellite. The unique observation capabilities of the GPM satellite are utilized to penetrate the cloud layer and obtain vertical distribution information of the phase state within the cloud. Based on this, the vertical gradient changes of microphysical parameters within the cloud can be accurately captured, enabling refined monitoring of the macro- and micro-structures of aircraft icing clouds and the particle spectrum characteristics within the cloud. The present invention breaks through the limitation of traditional satellite observations that can only monitor cloud top characteristics. By comprehensively monitoring the structure within the icing cloud, it provides a scientific basis for aircraft icing warning and prevention, and can significantly improve aviation flight safety assurance capabilities.

[0038] A method for monitoring the macro- and microstructure of aircraft icing clouds based on GPM satellite observations, such as Figure 1 As shown, the following steps are included:

[0039] Step 1: Create a dataset based on GPM satellite 2ADPR data.

[0040] Obtain Dual-frequency Precipitation Radar Level 2A (2ADPR) data from the GPM satellite during the aircraft icing period. 2ADPR data includes the following core observation variables: Drop Size Distribution (DSD) variables, radar reflectivity factor (Z e ) and Swath Level Variables (SLV). The Swath Level Variables include the particle number concentration (N w ), mass median diameter (D m ); Particle spectrum variables include particle phase distribution (Phase), etc.

[0041] Step 2: Draw the vertical distribution diagram of particle phase in the ice cloud system.

[0042] In order to explore the vertical distribution characteristics of the phase state in icy clouds, we extracted longitude and latitude information and DSD variables containing phase information (phase) from the 2ADPR data of the GPM satellite. Among them, the particle phase (Phase) is used to indicate the phase information of precipitation in the cloud. Its classification calculation is based on rounding down the quotient after dividing the phase value by 100. When the integer value is 0, it indicates a solid state (such as snow, hail, etc.), when it is 1, it indicates a mixed phase (such as sleet, etc.), and when it is 2, it indicates a liquid state. The 2ADPR data are processed using a phase classification algorithm, and the vertical distribution maps of liquid, mixed and solid states in the cloud are drawn based on the processing results, so as to clearly present the vertical structure of the phase state in the cloud, which helps to deeply understand the evolution of microphysical processes in the cloud. Figure 2 The figure below shows the vertical distribution of cloud phases based on 2ADPR data. In this example, the temperature profile is combined with the temperature to screen for supercooled water areas. This means adding a 0°C line to the vertical distribution chart. When the integer value is 2 and lies above 0°C, it is identified as a cold water-rich area. At this point, an ice accumulation warning can be activated as needed.

[0043] Step 3: Draw the vertical distribution of radar reflectivity factor (Ze), number concentration (Nw), and mass median diameter (Dm).

[0044] In order to deeply analyze the cloud precipitation physical characteristics contained in the GPM satellite 2ADPR data, the system reads the 2ADPR data file and accurately diagnoses and calculates a series of key microphysical variables from it. These variables specifically include the particle number concentration Nw (i.e., the standardized intercept parameter, which can effectively reflect the number distribution characteristics of particles of different sizes per unit volume), the mass median diameter Dm (representing the mass-weighted mean diameter, which can intuitively reflect the average size of the particle group) and the radar reflectivity factor Ze (as an important indicator of radar echo intensity, which can sensitively reflect the scattering characteristics of precipitation particles). Mapping the N in the area where ice accumulation occurs w 、D m and Z e The vertical distribution diagram of Figures 3 to 5 shown.

[0045] Mass median diameter D m The calculation method is: , where variables M3 and M4 are the third-order moment and fourth-order moment of the particle spectral distribution, respectively. The M3 and M4 data are directly derived from the Level 2A product (2ADPR) of the GPM satellite DPR radar and are stored in the paramDSD variable set.

[0046] Particle number concentration N w The calculation method is: , where the variable ρ w is the density of liquid water, which is 1g / cm 3 , W is the liquid water content, D m is the mass median diameter.

[0047] Step 4: Based on the processing results of GPM satellite data, a comprehensive analysis of the macro- and micro-structures of the aircraft icing cloud system and the particle spectrum characteristics within the cloud is conducted.

[0048] In order to obtain the particle phase state and radar reflectivity factor (Z e ), number concentration (N w ) and mass median diameter (D m ) and conduct a comprehensive analysis: first, from the perspective of macroscopic structure, we look at the phase distribution of particles at different altitudes and the corresponding radar echo intensity to determine the degree of cloud development and summarize the macroscopic structural characteristics under different events; then, from the perspective of microscopic and particle spectrum characteristics, we study the number and size characteristics of particles at different altitudes and their correlation with phase transitions, revealing the connection between particle spectrum characteristics and aircraft icing; finally, we establish a structural model and a particle spectrum characteristic model by combining the macroscopic and microscopic results to provide a basis for aircraft icing warning and aviation safety assurance.

[0049] Specifically, from a macroscopic perspective, this embodiment constructs a three-dimensional cloud structure model based on the dual-frequency reflectivity data obtained by the GPM satellite DPR radar through a phase classification algorithm, including vertical layer analysis: dividing the liquid / mixed / solid phase zones above and below the 0° layer according to the phase classification algorithm; radar echo feature correlation: analyzing the reflectivity intensity of different altitude layers (Z e From the microstructure, this embodiment combines the paramDSD parameter to carry out multi-scale analysis, including particle group parameter inversion: through the mass median diameter D m Characterize the particle size distribution based on the normalized particle number concentration N w Quantify number concentration; Phase-particle size correlation: observe the mass median diameter D of liquid / mixed / solid phase regions m and particle number concentration N w The range, combined with the radar reflectivity factor Z e The height information characterized by the method is used to comprehensively analyze the number and size characteristics of particles at different altitudes and their correlation with phase transitions, revealing the connection between particle spectrum characteristics and aircraft icing accumulation.

[0050] Finally, in this example, a multi-parameter fusion aircraft ice accretion monitoring model based on GPM satellite observations is established. In this embodiment, the warning parameter RI is constructed, and the calculation formula is as follows:

[0051] ,

[0052] Where, 、 is the weight coefficient, and the subscript crit is the critical value.

[0053] While existing GPM satellite data is primarily used for near-surface precipitation monitoring, this application innovatively applies it to aircraft icing cloud monitoring in the aviation safety field, achieving the first penetrating monitoring of the three-dimensional structure of icing clouds using a spaceborne dual-frequency precipitation radar (DPR). Leveraging GPM's wide-area coverage and active detection capabilities, it provides three-dimensional phase information on the vertical structure of icing clouds, surpassing the limitations of traditional passive remote sensing, which can only obtain cloud top parameters, while also providing large-scale background and target screening support for aircraft observations. Example 2

[0054] This embodiment discloses a system for monitoring the macro- and microstructures of aircraft ice clouds based on GPM satellite observations, including:

[0055] Data acquisition module, used to obtain 2ADPR data of GPM satellite;

[0056] The data processing module is used to process the 2ADPR data from the GPM satellite. It includes: a first processing unit that reads the longitude and latitude information and the DSD variable (where the DSD variable contains the phase information within the cloud) from the 2ADPR data, processes the 2ADPR data using a phase classification algorithm, and draws a vertical distribution map of liquid, mixed, and solid states within the cloud based on the processing results, thereby presenting the vertical structure of the cloud phase;

[0057] The second processing unit reads the longitude and latitude information, radar reflectivity factor Ze, and the paramDSD variable under the SLV data set (where the paramDSD variable covers the particle number concentration Nw and mass median diameter Dm) from the 2ADPR data, draws vertical profiles of the radar reflectivity factor Ze, particle number concentration Nw, and mass median diameter Dm, and adds boundary lines between different phases in the vertical structure to more clearly show the characteristics of the vertical distribution of each parameter in different phases;

[0058] The analysis module is used to analyze the macro- and micro-structural characteristics of aircraft ice cloud systems, and is also used to draw vertical cross-sectional diagrams of radar reflectivity factor Ze, number concentration Nw, and mass median diameter Dm.

[0059] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for monitoring the macro- and micro-structure of aircraft icing clouds based on GPM satellite observations, characterized in that: The following steps are involved: Step 1: Obtain GPM satellite 2ADPR data and establish a data set; Step 2: Read the longitude and latitude information and the DSD variable covering the phase information of the cloud from the 2ADPR data, process the 2ADPR data using the phase classification algorithm, and draw the vertical distribution map of liquid, mixed and solid in the cloud based on the processing results to show the vertical structure of the cloud phase; Step 3: Read the latitude and longitude information and radar reflectivity factor Z from the 2ADPR data e The paramDSD variable, which covers the particle number concentration Nw and mass median diameter Dm under the SLV data set, plots the radar reflectivity factor Z e , particle number concentration N w and mass median diameter D m A vertical cross-section of the structure with the boundary lines between different phases added; Step 4: Construct an icing risk field based on GPM satellite data and comprehensively analyze the macro- and micro-structures of the aircraft icing cloud system and the particle spectrum characteristics within the cloud.

2. The method according to claim 1, characterized in that The phase state classification algorithm is as follows: perform integer operation on the phase state value of 2ADPR, divide the phase state value by 100 and round down, and classify the particle phase state as solid when the integer value is 0, classify the particle phase state as mixed when the integer value is 1, and classify the particle phase state as liquid when the integer value is 2.

3. The method according to claim 2, characterized in that The phase classification algorithm further includes: adding a 0°C height line on the vertical distribution diagram, and when the integer value is 2 and is above 0°C, it is determined to be a cold water enrichment area.

4. The method according to claim 1, wherein The mass median diameter D m The calculation method is: , where variables M3 and M4 are the third-order moment and fourth-order moment of particle spectral distribution, respectively.

5. The method according to claim 4, characterized in that The particle number concentration N w The calculation method is: , where the variable ρ w is the density of liquid water, which is 1 g / cm 3 , W is the liquid water content, D m is the mass median diameter.

6. The method according to any one of claims 1 to 5, characterized in that The comprehensive analysis includes: first, from the perspective of macroscopic structure, looking at the particle phase distribution and corresponding radar echo intensity at different altitudes, judging the degree of cloud development and summarizing the macroscopic structural characteristics under different events; then, from the perspective of microscopic and particle spectrum characteristics, studying the number and size characteristics of particles at different altitudes and their relationship with phase transitions, revealing the connection between particle spectrum characteristics and aircraft icing; finally, combining macroscopic and microscopic results to establish a structural model and a particle spectrum characteristic model.

7. A system for monitoring the macro- and micro-structures of aircraft icing clouds based on GPM satellite observations, characterized in that: include: Data acquisition module, used to obtain 2ADPR data of GPM satellite; The data processing module is used to process the 2ADPR data from the GPM satellite. It includes: a first processing unit, which reads the longitude and latitude information and the DSD variable covering the cloud phase information phase from the 2ADPR data, processes the 2ADPR data using a phase classification algorithm, and draws vertical distribution maps of liquid, mixed, and solid states in the cloud based on the processing results, thereby presenting the vertical structure of the cloud phase; a second processing unit, which reads the longitude and latitude information, the radar reflectivity factor Ze, and the paramDSD variable covering the particle number concentration Nw and the mass median diameter Dm in the SLV data group from the 2ADPR data, draws vertical profiles of the radar reflectivity factor Ze, the particle number concentration Nw, and the mass median diameter Dm, and adds the boundary lines between different phases in the vertical structure, thereby more clearly showing the characteristics of each parameter in the vertical distribution of different phases; Analysis module, used to analyze the macro-microstructure characteristics of aircraft ice cloud system, and also used to draw radar reflectivity factor Z e , number concentration N w and mass median diameter D m Vertical distribution diagram of .

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