Method for monitoring macro and micro structures of aircraft icing clouds based on gpm satellite observations

Abstract

The application discloses a kind of aircraft icing cloud macro-microstructure monitoring methods based on GPM satellite observation, wherein method is by processing the dual-frequency precipitation radar data of GPM satellite, the macro-microstructure characteristics of aircraft icing cloud system are comprehensively analyzed.Use the unique observation ability of GPM satellite to penetrate cloud, obtain the vertical distribution information of cloud phase, based on this, the vertical gradient change of microphysical parameter in cloud can be accurately captured, the fine monitoring of macro-microstructure of aircraft icing cloud system and particle spectrum characteristics in cloud is realized.The application breaks through the limitation that traditional satellite observation can only monitor cloud top characteristics, provides scientific basis for aircraft icing early warning and prevention by comprehensively monitoring the structure in icing cloud, and can significantly improve the aviation flight safety guarantee capability.

Description

Technical Field

[0001] This invention relates to the field of monitoring and display technology related to aircraft icing, specifically to a method for monitoring the macro- and micro-structures of aircraft icing clouds based on GPM satellite observations. Background Technology

[0002] Aircraft icing is the process by which ice forms on the surface of an aircraft due to the freezing of supercooled water or the direct sublimation of water vapor. This icing phenomenon disrupts the streamlined design of the aircraft, thus significantly and adversely affecting its flight performance. Mild icing may affect the stability and handling of the aircraft, while severe icing can lead to communication system interruptions, instrument failures, and even stalls, causing catastrophic flight accidents and endangering the lives of passengers and crew.

[0003] The GPM satellite carries a dual-frequency precipitation radar (DPR). Through the Ku-band and Ka-band radars of the DPR, it performs three-dimensional scanning of precipitation particles in clouds, obtaining information on the vertical distribution of liquid water, ice crystals, and precipitation within the clouds. Its advantage lies in its ability to penetrate non-precipitating cloud layers to directly obtain information on the paths of liquid water, ice water, and the phase state of precipitation particles. 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 properties of aircraft icing cloud systems.

[0004] The study of the characteristics of water condensates within aircraft icing clouds, especially the spectral features of supercooled water particles, is of great significance for ensuring flight safety. While traditional observation methods have limitations in acquiring microphysical parameters within clouds, the GPM satellite, equipped with advanced observation equipment, possesses unique advantages that allow for effective monitoring of cloud conditions. Its data boasts high-precision observation, second-level sampling, and meter-level vertical resolution, enabling it to capture vertical gradient changes in parameters, simultaneously acquire multiple types of data to reveal interaction mechanisms, and provide verification data for the accuracy of satellite inversion algorithms.

[0005] By comprehensively analyzing the macroscopic and microscopic characteristics of clouds based on GPM satellite data, the macroscopic and microscopic structures of aircraft icing clouds and the particle spectrum characteristics within the clouds can be identified more accurately, providing theoretical and empirical support for improving aircraft icing monitoring and early warning capabilities. Given the current national emphasis on aviation safety, enhancing aircraft icing monitoring and early warning capabilities is an urgent and crucial task for ensuring flight safety and responding to major national needs.

[0006] Currently, domestic monitoring of aircraft icing cloud structure and supercooled water particle spectrum characteristics mainly relies on the Fengyun series of geostationary satellites. Among them, the Fengyun-4 satellite is the operational model of my country's second-generation geostationary meteorological satellite. This satellite can perform minute-level regional scans of China and surrounding areas.

[0007] In terms of cloud parameter inversion, the Fengyun-4 satellite can provide key parameters such as cloud top height (CTH), cloud top temperature (CTT), and cloud phase classification. These parameters are of great significance for identifying the macroscopic characteristics of icy cloud tops. However, it should be noted that the observations of the Fengyun satellite are mainly focused on cloud top characteristics. Its optical / infrared payloads are limited by their penetration capabilities, making it difficult to directly obtain the vertical distribution and particle spectrum characteristics of water condensates (such as cloud droplets, ice crystals, and supercooled water) within the cloud.

[0008] Although the Fengyun series of geostationary satellites plays a crucial role in monitoring aircraft icing clouds in my country, as passive remote sensing platforms in geostationary orbit, they primarily rely on receiving solar radiation reflected from cloud tops or their own emitted thermal infrared radiation for detection. 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 shortcomings in the satellite's icing cloud monitoring:

[0009] First, regarding the detection of microphysical parameters within clouds, geostationary satellites struggle to directly acquire the vertical distribution characteristics of supercooled water, which are crucial for aviation safety. The spatial distribution of supercooled water droplets exhibits significant stratification; multiple supercooled water enrichment zones may exist at different altitudes. Satellites can only extrapolate these characteristics using indirect parameters such as cloud top temperature, unlike active remote sensing devices that can directly detect the vertical distribution and particle spectrum characteristics of cloud condensates (such as cloud droplets, ice crystals, and supercooled water). Second, in terms of cloud particle phase identification, existing geostationary satellite observations struggle to accurately distinguish the mixing ratio of supercooled water droplets and ice crystals within clouds. Icing clouds often contain mixed phase regions of "supercooled water-ice crystals," and this mixing directly affects the icing rate and ice shape characteristics. Summary of the Invention

[0010] To address the aforementioned issues, this 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 (GPM) satellite remote sensing data, a multi-dimensional observation-based analysis system for aircraft icing cloud characteristics is constructed.

[0011] Technical solution:

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

[0013] Step 1: Acquire GPM satellite 2ADPR data and create a dataset;

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

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

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

[0017] As a preferred option, the phase classification algorithm is as follows: perform an integer operation on the phase value of 2ADPR, divide the phase value by 100 and then round down. When the integer value is 0, the particle phase is classified as solid; when the integer value is 1, the particle phase is classified as mixed phase; and when the integer value is 2, the particle phase is classified as liquid.

[0018] As a preferred option, the phase classification algorithm also includes: adding a 0°C height line to the vertical distribution map, and determining that when the integer value is 2 and it is above 0°C, it is a cold water enrichment area.

[0019] As a preferred option, the median mass diameter D m The calculation method is as follows: , where variables M3 and M4 are the third and fourth moments of the particle spectrum distribution, respectively.

[0020] Preferably, the particle number concentration N w The calculation method is as follows: , where the variable ρ w The density of liquid water is 1 g / cm³. 3 W represents the liquid water content, and D represents the liquid water content. m The median diameter is the mass.

[0021] As a preferred approach, the comprehensive analysis includes: first, examining the phase distribution of particles at different altitudes and the corresponding radar echo intensity from a macroscopic perspective to determine the degree of cloud system development and summarize the macroscopic structural characteristics under different events; then, studying the number and size characteristics of particles at different altitudes and their correlation with phase transitions from a microscopic and particle spectrum perspective to reveal the connection between particle spectrum characteristics and aircraft icing; finally, establishing a structural model and a particle spectrum characteristic model by integrating macroscopic and microscopic results to provide a basis for aircraft icing early warning and aviation safety assurance.

[0022] This invention also discloses a macro- and micro-structure monitoring system for aircraft icing clouds based on GPM satellite observations, comprising:

[0023] The data acquisition module is used to acquire 2ADPR data from the GPM satellite;

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

[0025] The analysis module is used to analyze the macroscopic and microscopic structural characteristics of aircraft icing clouds, and also to plot the radar reflectivity factor Z. e Number concentration N w and the median diameter D of the mass m Vertical distribution diagram.

[0026] Compared with existing aircraft icing cloud structure monitoring technologies, this invention has the following main advantages:

[0027] (1) This invention innovatively uses the satellite dual-frequency radar inversion data of GPM to realize the refined diagnosis and analysis of the macroscopic structural characteristics of icing clouds, the phase distribution of micro particles and the spectral parameters of supercooled water droplets, which can improve the accuracy and reliability of the early warning of icing risk of aircraft.

[0028] (2) This invention breaks through the paradigm limitation 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) This invention analyzes the vertical structure of radar reflectivity using a spaceborne dual-frequency radar phase state identification algorithm, and combines it with particle spectrum parameter collaborative inversion technology to simultaneously obtain the phase state distribution and number concentration (N) within the cloud. w ) and effective diameter (D) m By using core parameters such as these, we can achieve multi-source comprehensive observation and analysis of the vertical structure and particle spectrum characteristics of water condensate within aircraft icing clouds. Attached Figure Description

[0030] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of the present invention;

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

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

[0034] Figure 4 The median diameter D observed by the GPM satellite on March 9, 2016. m Vertical distribution diagram;

[0035] Figure 5 The radar reflectivity factor Z observed by the GPM satellite on March 9, 2016. e Vertical distribution diagram. Detailed Implementation

[0036] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many different ways as described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention; therefore, the present invention is not limited to the specific embodiments disclosed below. Embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Example 1

[0037] This invention discloses a method for monitoring the macro- and micro-structure of aircraft icing clouds based on GPM satellite observations. The method processes dual-frequency precipitation radar data carried by the GPM satellite to comprehensively analyze the macro- and micro-structural characteristics of aircraft icing cloud systems. Utilizing the unique observational capabilities of the GPM satellite to penetrate cloud layers and obtain vertical distribution information of cloud phase states, it accurately captures vertical gradient changes in microphysical parameters within the cloud, achieving refined monitoring of the macro- and micro-structure of aircraft icing cloud systems and the particle spectrum characteristics within the cloud. This invention overcomes the limitation of traditional satellite observations, which can only monitor cloud top characteristics. By comprehensively monitoring the structure within icing clouds, it provides a scientific basis for aircraft icing early warning and prevention, significantly improving aviation flight safety capabilities.

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

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

[0040] Acquire 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: Particle Spectrum Drop Size Distribution (DSD) variable, Radar Reflectivity Factor (Z). e ) and Swath Level Variables (SLV). The SLV variables include particle number concentration (N... w ), median diameter of mass (D) m ); Particle spectrum variables include particle phase distribution, etc.

[0041] Step 2: Draw a vertical distribution diagram of particle phase states within the icy cloud system.

[0042] To investigate the vertical distribution characteristics of phase states within icy clouds, we extracted latitude and longitude information, as well as DSD variables containing phase information, from 2ADPR data from the GPM satellite. The particle phase state (Phase) was used to indicate the phase state of precipitation within the cloud. Its classification was calculated by dividing the phase value by 100 and rounding down. A value of 0 indicated a solid state (e.g., snow, hail), 1 indicated a mixed phase (e.g., sleet), and 2 indicated a liquid state. The 2ADPR data was processed using a phase classification algorithm, and vertical distribution maps of liquid, mixed, and solid states within the cloud were plotted based on the processing results. This clearly presents the vertical structure of phase states within the cloud, which helps in a deeper understanding of the evolution of microphysical processes within the cloud. Figure 2 The image shows a vertical distribution map of cloud phases based on 2ADPR data. In this embodiment, supercooled water areas are screened using temperature profiles. Specifically, a 0°C height line is added to the vertical distribution map. When the integer value is 2 and above 0°C, it is identified as a cold water enrichment area, and an ice accumulation warning can be activated as needed.

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

[0044] To deeply analyze the cloud precipitation physics 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. These variables specifically include particle number concentration Nw (i.e., the standardized intercept parameter, which effectively reflects the distribution characteristics of the number of particles of different sizes per unit volume), mass median diameter Dm (representing the mass-weighted average diameter, which can intuitively reflect the average size of the particle population), and radar reflectivity factor Ze (as an important indicator for measuring radar echo intensity, which can sensitively reflect the scattering characteristics of precipitation particles). The system also maps the Nw values ​​for areas where icing occurs. w D m and Z e Vertical distribution map, such as Figures 3 to 5 As shown.

[0045] Mass median diameter D m The calculation method is as follows: In this context, variables M3 and M4 represent the third and fourth moments of the particle spectrum distribution, respectively. The data for M3 and M4 are directly derived from the Level 2A product (2ADPR) of the GPM satellite DPR radar, and the data is stored in the paramDSD variable set.

[0046] Particle number concentration N w The calculation method is as follows: , where the variable ρ w The density of liquid water is 1 g / cm³. 3 W represents the liquid water content, and D represents the liquid water content. m The median diameter is the mass.

[0047] Step 4: Based on the processing results of GPM satellite data, comprehensively analyze the macro- and micro-structure of aircraft icing clouds and the particle spectrum characteristics within the clouds.

[0048] In obtaining the particle phase state and radar reflectivity factor (Z) within the cumulonimbus cloud system e ), number concentration (N) w ) and median diameter of mass (D) m After obtaining the vertical distribution map of the cloud system, a comprehensive analysis is conducted: First, the phase distribution of particles at different altitudes and the corresponding radar echo intensity are examined from a macroscopic perspective to determine the degree of cloud system development and summarize the macroscopic structural characteristics under different events; then, from a microscopic and particle spectrum perspective, the number and size characteristics of particles at different altitudes and their correlation with phase transitions are studied to reveal the connection between particle spectrum characteristics and aircraft icing; finally, a structural model and a particle spectrum characteristic model are established by integrating macroscopic and microscopic results to provide a basis for aircraft icing early warning and aviation safety assurance.

[0049] Specifically, from a macroscopic structural perspective, this embodiment constructs a three-dimensional cloud structure model based on dual-frequency reflectivity data acquired by the GPM satellite DPR radar and a phase classification algorithm. This includes vertical layering analysis: dividing the liquid / mixed / solid phase regions above and below the 0° layer according to the phase classification algorithm; and radar echo feature correlation: analyzing the reflectivity intensity (Z) at different altitudes. e The classification rules of the gradient (characterization). From a microstructural perspective, this embodiment combines the paramDSD parameters to conduct multi-scale analysis, including particle swarm parameter inversion: through the mass median diameter D m Characterizing particle size distribution based on normalized particle number concentration N w Quantitative number concentration; Phase-particle size correlation: Observing the median mass diameter D of the liquid / mixed / solid phase regions. m and particle number concentration N w The range, combined with the radar reflectivity factor Z e By comprehensively analyzing the altitude information, the number and size characteristics of particles at different altitudes, as well as their correlation with phase transitions, are revealed to show the relationship between particle spectrum characteristics and aircraft icing.

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

[0051] ,

[0052] In the formula, , is the weighting coefficient, and the subscript crit is the critical value.

[0053] In existing technologies, GPM satellite data is mainly used for near-surface precipitation monitoring. However, this application innovatively applies it to aircraft icing cloud monitoring in the field of aviation safety, achieving for the first time penetrating monitoring of the three-dimensional structure of icing clouds using a spaceborne dual-frequency precipitation radar (DPR). Based on GPM's wide-area coverage and active detection capabilities, it provides three-dimensional phase information of the vertical structure of icing clouds, which not only breaks through the limitation of traditional passive remote sensing that can only obtain cloud top parameters, but also provides large-scale background and target screening support for aircraft observation. Example 2

[0054] This embodiment discloses a macro- and micro-structure monitoring system for aircraft icing clouds based on GPM satellite observations, including:

[0055] The data acquisition module is used to acquire 2ADPR data from the GPM satellite;

[0056] The data processing module is used to process 2ADPR data from the GPM satellite, including: a first processing unit, which reads latitude and longitude information and DSD variables (where the DSD variables cover cloud phase information) 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 in the cloud based on the processing results, thereby presenting the vertical structure of cloud phase states;

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

[0058] The analysis module is used to analyze the macroscopic and microscopic structural characteristics of aircraft icing cloud systems, and also to plot vertical cross-sections of radar reflectivity factor Ze, number concentration Nw, and median mass 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 foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring macro-microstructure of aircraft icing clouds based on GPM satellite observations, characterized in that, The method comprises the following steps: Step 1, obtaining GPM satellite 2ADPR data and establishing a data set; Step 2, reading latitude and longitude information and DSD variables covering phase information phase in the 2ADPR data, processing the 2ADPR data by using a phase classification algorithm, and drawing vertical distribution diagrams of liquid, mixed and solid states in the cloud according to the processing results to present the vertical structure of the phase in the cloud; Step 3. Read the latitude and longitude information, radar reflectivity factor Z from 2ADPR data e and paramDSD variables encompassing the number concentration Nw, mass median diameter Dm under the SLV data set, plot the radar reflectivity factor Z e , the number concentration N w and the mass median diameter D m vertical profiles, while adding the boundary lines between different phases on the vertical structure; Step 4, constructing an ice accretion risk field based on GPM satellite data, comprehensively analyzing the macro-micro structure of the aircraft ice accretion cloud system and the particle spectrum characteristics in the cloud, and specifically, first observing the particle phase state distribution and the corresponding radar echo intensity at different height layers from the macro structure to judge the development degree of the cloud system and summarize the macro structure characteristics under different events; then from the micro and particle spectrum characteristics, the particle number, size characteristics at different height layers and the correlation with phase transition are studied to reveal the relationship between particle spectrum characteristics and aircraft ice accretion; finally, the structure model and particle spectrum characteristic model are established by comprehensively analyzing the macro-micro results.

2. The method of claim 1, wherein, The phase classification algorithm is that: the phase value of 2ADPR is subjected to integer operation, the quotient of the phase value divided by 100 is rounded down, when the integer value is 0, the particle phase is classified as solid state, when the integer value is 1, the particle phase is classified as mixed phase, and when the integer value is 2, the particle phase is classified as liquid state.

3. The method of claim 2, wherein, The phase classification algorithm further comprises: adding a 0℃ height line on the vertical distribution diagram, when the integer value is 2 and located above 0℃, it is determined as a cold water enrichment area.

4. The method of claim 1, wherein, The mass median diameter D m The calculation method is: wherein the variables M3 and M4 are the third and fourth moments of the particle size distribution, respectively.

5. The method of claim 4, wherein, The particle number concentration N w The calculation method is: where the variable p w is the liquid water density, which is 1 g / cm 3 W is the liquid water content, and D m is the mass median diameter.

6. An aircraft icing cloud macro-microstructure monitoring system based on GPM satellite observations, characterized in that, The method comprises: a data acquisition module for acquiring 2ADPR data of a GPM satellite; a data processing module for processing the 2ADPR data of the GPM satellite, comprising: a first processing unit for reading latitude and longitude information and DSD variables covering phase information phase in the 2ADPR data, processing the 2ADPR data by using a phase classification algorithm, and drawing vertical distribution diagrams of liquid, mixed and solid states in the cloud according to the processing results to present the vertical structure of the phase in the cloud; and a second processing unit for reading latitude and longitude information, radar reflectivity factor Ze and paramDSD variables of particle number concentration Nw and mass median diameter Dm under SLV data set from the 2ADPR data, drawing vertical profile diagrams of the radar reflectivity factor Ze, the particle number concentration Nw and the mass median diameter Dm, and adding boundary lines between different phases in the vertical structure, so as to more clearly show the characteristics of each parameter in the vertical distribution of different phases. An analysis module is used for analyzing macro-micro structure characteristics of the aircraft icing cloud system, and is also used for drawing a vertical distribution diagram of radar reflectivity factor Z e , number concentration N w and mass median diameter D m . Specifically, first, the particle phase distribution and corresponding radar echo intensity at different height layers are observed from the macro structure, the cloud system development degree is judged, and the macro structure characteristics under different events are summarized; second, the particle number, size characteristics at different height layers and the correlation with phase transition are studied from the micro and particle spectrum characteristics, and the correlation between the particle spectrum characteristics and the aircraft icing is revealed; finally, the structure model and the particle spectrum characteristic model are established by comprehensively considering the macro-micro results.

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