Aircraft ice accretion monitoring method and equipment fusing millimeter wave cloud radar and ground-based microwave radiometer data

Abstract

The invention discloses an airplane ice accretion monitoring method fusing millimeter wave cloud radar and ground-based microwave radiometer data, which comprises the following steps: step 1, taking temperature profile and relative humidity profile data monitored by a ground-based microwave radiometer in a test area in real time as input quantities to obtain airplane ice accretion index values corresponding to all profile data; step 2, obtaining a reflectivity Z profile of liquid water particles in a test area and a total liquid water value LWP on an observation path; step 3, carrying out inversion to obtain a liquid water content LWC profile in the cloud on the observation path; step 4, taking an overlapped area of an area of which the temperature profile data of the foundation microwave radiometer is less than 0 DEG C and an area of which the inverted liquid water content data of the cloud is greater than 0, a supercooled water area in the cloud is formed; and step 5, analyzing whether the ice accumulation area in the step 1 and the supercooled water area in the step 4 are overlapped or not, if yes, representing that ice accumulation occurs in the air, and if not, representing that no ice accumulation occurs in the air. According to the method disclosed by the invention, a millimeter wave cloud radar and ground-based microwave radiometer active and passive combined detection technology is adopted, the total amount of liquid water and a water vapor profile in the airport airspace are detected, the distribution of supercooled liquid water in cloud in the airport airspace is inverted, and the distribution of a potential icing area of an aircraft is monitored in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft icing monitoring and early warning, and particularly relates to a method and device for aircraft icing monitoring that integrates millimeter-wave cloud radar and ground-based microwave radiometer data. The content involves aircraft icing index calculation, inversion of liquid water content in clouds based on the total liquid water content observed by the ground-based microwave radiometer and the reflectivity of the millimeter-wave cloud radar, etc. Background Art

[0002] The icing problem of aircraft affects flight safety, so countries around the world have always attached great importance to the aircraft icing problem. According to the statistical data of the Civil Aviation Administration of China, among the flight accidents caused by various weather reasons, icing has the greatest impact on aircraft flight safety. Doing a good job in aircraft icing monitoring can greatly reduce aircraft flight accidents. Therefore, it is urgent for us to conduct a detailed and in-depth theoretical study on aircraft icing to scientifically guide aviation flight safety.

[0003] The research methods for aircraft icing mainly include aircraft detection, numerical simulation, satellite data diagnosis and other methods. Among them, satellite remote sensing data can observe the icing area in real time and continuously over a large range, and is the main means for aircraft icing monitoring and identification. However, most satellite remote sensing retrieves the microphysical information of clouds in the icing area through visible light, infrared, microwave and other information of the satellite, which can provide relatively accurate cloud top information, lacks direct detection of cloud structure, and cannot provide reliable information below the cloud. Therefore, the resolution and accuracy of the retrieved aircraft icing information are low.

[0004] Direct aircraft detection is the most accurate method for aircraft icing monitoring. Flight icing reports can clearly and intuitively show aircraft icing events, but the spatio-temporal resolution of aviation flight reports is low, resulting in the inability to effectively monitor aircraft icing. Therefore, it is urgent to achieve effective real-time high-resolution monitoring of aircraft icing. Summary of the Invention

[0005] In order to solve the problem that it is difficult to monitor aircraft icing in real time all-weather, the present invention provides a method and device for aircraft icing monitoring that integrates millimeter-wave cloud radar and ground-based microwave radiometer data.

[0006] The present invention adopts the following technical solutions:

[0007] A method for aircraft icing monitoring that integrates millimeter-wave cloud radar and ground-based microwave radiometer data, the improvement lies in that it includes the following steps:

[0008] Step 1, taking the temperature profile and relative humidity profile data monitored in real time by the ground-based microwave radiometer in the test area as input quantities and substituting them into the following formula to obtain the aircraft icing index value I corresponding to all profile data:

[0009]

[0010] In the above formula, f is the relative humidity, T is the temperature, and the area where I≥0 is the icing area;

[0011] Step 2: Obtain the reflectivity Z profile of liquid water particles in the test area and the total liquid water content value LWP on the observation path:

[0012] In the reflectivity Z profile measured in real time by the millimeter-wave cloud radar in the test area, the information of ice-phase particles is removed to obtain the reflectivity Z profile of liquid water particles; the total liquid water content value LWP on the observation path monitored in real time by the ground-based microwave radiometer in the test area is extracted;

[0013] Step 3: Invert the liquid water content LWC profile in the cloud on the observation path:

[0014] Fuse the temperature profile data in Step 1 and the total liquid water content value LWP on the observation path in Step 2 into the reflectivity Z profile of liquid water particles, and invert the liquid water content LWC profile in the cloud on the observation path:

[0015]

[0016] In the above formula, i represents the i-th radar range bin, M represents the total number of radar range bins in the cloud, Fliq is the contribution ratio of liquid particles, b is a coefficient, and Δz represents the radar range bin length;

[0017] Step 4: Take the overlapping area of the area where the temperature profile data of the ground-based microwave radiometer is less than 0°C and the area where the inverted liquid water content data in the cloud is greater than 0 as the supercooled water area in the cloud;

[0018] Step 5: Analyze whether there is an overlap between the icing area in Step 1 and the supercooled water area in Step 4. If there is an overlap between the two, it means that icing occurs in the air. If there is no overlap, it means that there is no icing in the air.

[0019] Furthermore, the surface shapes of aircraft icing are divided into four types: clear ice, rime, horn ice, and frost.

[0020] Furthermore, in Step 1, the basis for dividing the icing intensity level is: I<0, no icing; 0≤I<5, light icing; 5≤I<8, moderate icing; I≥8, heavy icing.

[0021] An aircraft icing monitoring device that fuses data from a millimeter-wave cloud radar and a ground-based microwave radiometer, the improvement lies in including: a processor; a memory, in which executable instructions of the processor are stored; the processor is configured to execute the steps of the above method by executing the executable instructions.

[0022] The beneficial effects of the present invention are:

[0023] The method disclosed by the present invention adopts the active and passive combined detection technology of millimeter-wave cloud radar and ground-based microwave radiometer to detect the total amount of liquid water and water vapor profile in the airport airspace, invert the distribution of supercooled liquid water in the clouds in the airport airspace, and monitor the distribution of potential icing areas of aircraft in real time. It can provide an efficient monitoring means for aircraft icing monitoring in China, and play a great role in promoting the development of aircraft icing monitoring technology. At the same time, it is of great significance for improving the effectiveness of weapons and equipment such as aviation combat, radar, and communication. It can also be widely applied to various fields such as microwave remote sensing, detection, and battlefield perception, which has positive significance for improving the independent R & D level of microwave radiometers in China. Description of the Drawings

[0024] Figure 1 is a schematic flow chart of the method of the present invention;

[0025] Figure 2 is a schematic diagram for determining the supercooled water area;

[0026] Figure 3 is the simulation result of the aircraft icing index for 12 months of the whole year obtained by statistically analyzing the radiosonde data in Qingdao from 2005 to 2020;

[0027] Figure 4 is a schematic diagram of the occurrence probability of aircraft icing events for each month of the whole year obtained by statistically analyzing the radiosonde data in Qingdao from 2005 to 2020;

[0028] Figure 5 is a schematic diagram of the change of whether aircraft icing occurs over time determined by the radiosonde data and test equipment in Qingdao during the test period. Detailed Embodiments

[0029] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] Aircraft icing is a phenomenon that when an aircraft flies in a cloud and rain layer containing supercooled water droplets, ice layers accumulate on some parts of the fuselage surface when the surface temperature of the fuselage is less than 0°C. The supercooled water area is water that has reached the freezing point but still remains in a liquid state.

[0031] Aircraft icing is one of the dangerous weather conditions at high altitudes. It is mainly formed when an aircraft flies in clouds or precipitation areas, and the supercooled cloud droplets in the clouds or raindrops in the precipitation process freeze after colliding with the fuselage, or can also be directly formed by the sublimation of water vapor on the surface of the aircraft body. Aircraft icing will occur at the engine intake part and the windward part of the fuselage, and icing is more common at the engine intake, radar dome, leading edge of the wing, propeller, tail wing, etc.

[0032] Studies have shown that when the relative humidity of the atmospheric environment is less than 50%, the weather is clear and cloudless, and the probability of icing is almost zero; when the relative humidity of the atmospheric environment is greater than 50%, icing may occur; when the relative humidity of the atmospheric environment is close to 100%, precipitation may exist. In addition, considering that the temperature range of the atmospheric environment prone to icing is -14 to 0 °C.

[0033] The ground-based microwave radiometer is a portable passive remote sensing observation device that can perform real-time observations all day long. It has the ability to work independently and can work under almost various environmental conditions. It is mainly used for remote sensing of the atmospheric environment characteristics of the troposphere. It can accurately evaluate the potential of aircraft icing in the atmospheric environment. After analyzing the impact of aircraft icing on aircraft flight safety, the present invention proposes a method for monitoring aircraft icing by fusing millimeter-wave cloud radar and ground-based microwave radiometer data.

[0034] Example 1. This example discloses a method for monitoring aircraft icing by fusing millimeter-wave cloud radar and ground-based microwave radiometer data. Using the active and passive combined detection technology for aircraft icing, it can monitor the distribution of potential icing areas of the aircraft in real time, as Figure 1 shown, and specifically includes the following steps:

[0035] Step 1, take the temperature profile and relative humidity profile data monitored by the ground-based microwave radiometer in the test area as input quantities and substitute them into the following formula to obtain the aircraft icing index value I corresponding to all profile data:

[0036]

[0037] In the above formula, f is the relative humidity, T is the temperature. When f ranges from 50% to 100%, the icing index linearly increases from 0 to 100, which can represent the concentration and scale of water droplets; the growth rate of water droplets is fitted with the square of the temperature, and it reaches the maximum value of 1 at T = -7 °C, and the minimum values of 0 at T = -14 °C and 0 °C. The area where I ≥ 0 is the icing area;

[0038] The icing intensity usually refers to the thickness of the ice layer formed on the surface of the aircraft fuselage per unit time. The basis for dividing the icing intensity level is: I < 0, no icing; 0 ≤ I < 5, light icing; 5 ≤ I < 8, moderate icing; I ≥ 8, heavy icing. At present, according to the degree of harm of icing to aircraft operation, icing is divided into four levels (see the following table), namely trace icing, light icing, moderate icing and severe icing.

[0039] Table for dividing aircraft icing intensity levels

[0040] Ice accretion level Trace ice accretion Light ice accretion Moderate ice accretion Severe ice accretion Ice accretion thickness per unit time (mm / min) <0.6 0.6～1.0 1.1～2.0 >2.0 Ice layer thickness during flight (cm) ≤5.0 5.1～15.0 15.1～30.0 >30.0

[0041] This method for classifying icing intensity generally requires special detection devices to accurately measure. Trace icing and light icing are generally considered not to pose hazards to aircraft operation, while moderate icing or severe icing will have a greater impact and harm on the normal operation of aircraft. It must be pointed out that for the same cloud layer, the impact of icing on different aircraft flying through it is also different. Some pilots may think it is light icing, but when some aircraft fly through, they feel that the impact level reaches the level of moderate icing.

[0042] Generally, according to the surface shape, characteristics of aircraft icing and its impact on flight, aircraft icing can be divided into four types: clear ice, rime, glaze ice and frost.

[0043] Step 2: Obtain the reflectivity Z profile of liquid water particles in the test area and the total liquid water content value LWP on the observation path:

[0044] In the reflectivity Z profile measured by the millimeter-wave cloud radar in real time in the test area, the ice-phase particle information is removed to obtain the reflectivity Z profile of liquid water particles; the total liquid water content value LWP on the observation path monitored by the ground-based microwave radiometer in the test area is extracted;

[0045] Step 3: Invert to obtain the liquid water content LWC profile in the cloud on the observation path:

[0046] The basic idea of the liquid water content inversion algorithm in the cloud is that on the premise that the microwave radiometer outputs the accurate total liquid water content LWP on the vertical path, according to the vertical distribution of the reflectivity factor Z obtained by the millimeter-wave cloud radar, the total liquid water content is distributed to each height to obtain the LWC profile. However, the LWP obtained by the microwave radiometer is the total liquid water content in the vertical height, and the reflectivity Z measured by the radar contains the contributions of liquid particles and ice-phase particles. Therefore, a distribution model of ice-phase particles is established, and the information of ice-phase particles in the reflectivity Z is removed. The essence of this method is to use the LWP data observed by the microwave radiometer as a constraint to reduce the influence of the change of the a coefficient in the Z-LWC empirical relationship and the radar echo intensity error on the inversion result.

[0047] The key problem and difficulty in jointly inverting the LWC profile of mixed clouds using a microwave radiometer and a millimeter-wave cloud radar is how to determine the distribution of liquid and ice-phase particles and their proportions in the echo intensity. Temperature is one of the decisive factors for particles to present different phases. To jointly invert the LWC profile of mixed-phase clouds using a millimeter-wave radar and a microwave radiometer, it is necessary to extract the intensity information corresponding to cloud water from the echo intensity above the zero-degree layer and remove the echo intensity information of cloud ice. For this reason, according to the relationship between cloud water and cloud ice content and temperature proposed by Dunn in 2011, a distribution model of ice-phase particles with temperature is obtained (see the following table), and an LWC inversion method for mixed clouds is established.

[0048] Distribution Model of Ice-Phase Particles with Temperature

[0049] Liquid particles Mixed phase Ice-phase particles T>0℃ 0℃>T>-16℃ T<-16℃

[0050] Fuse the temperature profile data in Step 1 and the total liquid water content value LWP on the observation path in Step 2 into the reflectivity Z profile of liquid water particles, and inversely obtain the liquid water content LWC profile in the cloud on the observation path:

[0051]

[0052] In the above formula, i represents the i-th radar range bin, and LWC i represents the liquid water content (g / m 3 ) at the i-th radar range bin, M represents the total number of radar range bins in the cloud, Fliq is the contribution ratio of liquid particles, b is the coefficient in the selected Z-LWC empirical relationship, taking b = 0.5, and Δz represents the radar range bin length (m);

[0053] Step 4: Take the overlapping area of the area where the temperature profile data of the ground-based microwave radiometer is less than 0 °C and the area where the inversely obtained liquid water content data in the cloud is greater than 0 as the supercooled water area in the cloud;

[0054] Specifically, as Figure 2 shown, select the area where the temperature profile data of the ground-based microwave radiometer observed in real time in Step 1 is less than 0 °C and denote it as area "A"; at the same time, select the area where the liquid water content LWC profile data obtained in Step 3 is greater than 0 and denote it as area "B", and select the overlapping area of area "A" and area "B" and denote it as area "C". Area "C" is the supercooled water area in the cloud.

[0055] Step 5: Analyze whether there is an overlap between the icing area in Step 1 and the supercooled water area in Step 4. If there is an overlap between the two, it means that icing occurs in the air. If there is no overlap, it means that there is no icing in the air.

[0056] Specifically, select the different aircraft icing areas obtained in real time by the ground-based microwave radiometer in Step 1: 0 ≤ I < 5, light icing area, denoted as area "A"; 5 ≤ I < 8, moderate icing area, denoted as area "B"; I ≥ 8, heavy icing area, denoted as area "C". At the same time, select the supercooled water area obtained in Step 4 and denote it as area "D", and judge whether there is an overlap between area "D" and area "A", area "B", and area "C". If there is no overlap area between area "D" and area "A", area "B", and area "C", it means that there is no icing in the air; if there is an overlap area between area "D" and area "A", area "B", and area "C", the overlap area means that icing occurs in the air and the overlap areas with area "A", area "B", and area "C" respectively represent different intensities of icing.

[0057] As Figure 3 shown (from left to right, top to bottom are the icing index maps for January, February, March, April, …… December), the radiosonde data in the Qingdao area from 2005 to 2020 were statistically analyzed to obtain the simulation results of the aircraft icing index for 12 months of the whole year.

[0058] When analyzing the accuracy of monitoring aircraft icing by fusing millimeter-wave cloud radar and ground-based microwave radiometer, taking whether aircraft icing occurred in the radiosonde data in the test area during the test period as the reference source, the monitoring of aircraft icing by fusing millimeter-wave cloud radar and ground-based microwave radiometer was compared with the reference source. The main manifestations are as follows:

[0059] 1) Substitute the effective radiosonde data samples N at 8 am and 8 pm in the test area during the test period into the aircraft icing index calculation formula:

[0060]

[0061] All effective event samples N = S + P during the test period are obtained; S is the number of effective samples with aircraft icing occurring calculated from the radiosonde data icing index, and P is the number of effective samples with no aircraft icing occurring calculated from the radiosonde data icing index.

[0062] 2) Statistically analyze the number of samples M = R + Q that are correctly judged whether aircraft icing events occur in the effective aircraft icing monitoring events monitored by fusing millimeter-wave cloud radar and ground-based microwave radiometer during the test period and at the same time as the effective radiosonde data; R is the number of effective samples with aircraft icing occurring monitored by fusing millimeter-wave cloud radar and ground-based microwave radiometer and also with aircraft icing occurring calculated from the radiosonde data icing index, and P is the number of effective samples with no aircraft icing occurring monitored by fusing millimeter-wave cloud radar and ground-based microwave radiometer and also with no aircraft icing occurring calculated from the radiosonde data icing index.

[0063] Accuracy: It reflects the judgment ability of the classifier for the entire sample, that is, it can judge the positive as positive and the negative as negative. That is: when the effective radiosonde data judges that there is icing water, fusing millimeter-wave cloud radar and ground-based microwave radiometer monitor that there is aircraft icing, and when the effective radiosonde data judges that there is no icing water, fusing millimeter-wave cloud radar and ground-based microwave radiometer monitor that there is no aircraft icing.

[0064] 3) Calculate the accuracy W of monitoring aircraft icing by fusing millimeter-wave cloud radar and ground-based microwave radiometer data Z :

[0065] W Z = M / N = M / (S + P)

[0066] Define the false negative rate: the proportion of events missed in the icing water events monitored by fusing the millimeter-wave cloud radar and ground-based microwave radiometer data among the events of aircraft icing occurring obtained by calculating the icing index from radiosonde data.

[0067] 4) Calculate the false negative rate W of aircraft icing monitoring by fusing the millimeter-wave cloud radar and ground-based microwave radiometer data L :

[0068] W L =(S - R) / S

[0069] The following is an example to illustrate the effectiveness, feasibility, accuracy rate, and false negative rate of the method of the present invention. Collect historical radiosonde data around the test area and conduct statistical analysis of historical data. Statistic the historical radiosonde data in Qingdao from 2005 to 2020, and the probability statistical samples for each month of the whole year in Qingdao are as follows: January: 880, February: 857, March: 945, April: 917, May: 885, June: 821, July: 804, August: 833, September: 866, October: 882, November: 858, December: 843. The occurrence probabilities of aircraft icing events in 12 months of the whole year in Qingdao are as Figure 4 shown. According to the historical data analysis: Aircraft icing mostly occurs in January, February, July, August, September, and December.

[0070] The test time for monitoring aircraft icing by fusing the millimeter-wave cloud radar and ground-based microwave radiometer is from December 1, 2023 to January 30, 2024. The test location is Qingdao. During the test period, there are samples at different times compared with the radiosonde, with a total of 108 groups of samples. After excluding the data where both the radiosonde and the equipment are invalid during the test period, a total of 85 groups of profile samples are obtained for comparison. The results are as Figure 5 shown.

[0071] Substitute the 85 groups of radiosonde data into the icing index calculation formula:

[0072]

[0073] Among the 85 groups of radiosonde profiles, 16 groups of profiles show icing; among the 85 groups of profiles in the equipment monitoring results, 23 groups of profiles show icing. The specific times are shown in the following table:

[0074] Schedule of the times when the radiosonde data and the test equipment respectively determine the occurrence of aircraft icing during the test period

[0075]

[0076]

[0077] The accurate probability of equipment monitoring is: (15 + 63) / 85 = 91.76% (the number of correctly monitored aircraft icing samples / the total number of valid samples obtained by sounding); the false alarm rate: 1 / 16 = 6.25%.

[0078] This embodiment also discloses an aircraft icing monitoring device that fuses millimeter-wave cloud radar and ground-based microwave radiometer data, including: a processor; a memory that stores executable instructions of the processor; the processor is configured to execute the steps of the above method by executing the executable instructions.

Claims

1. A method for monitoring aircraft ice accretion by integrating millimeter wave cloud radar and ground-based microwave radiometer data, characterized in that: The steps include: Step 1: Substitute the temperature profile and relative humidity profile data monitored in real time by the ground-based microwave radiometer in the test area as input into the following formula to obtain the aircraft icing index value I corresponding to all profile data: In the above formula, f is the relative humidity, T is the temperature, and the area where I ≥ 0 is the ice accumulation area; Step 2: Obtain the reflectivity Z profile of liquid water particles in the test area and the total amount of liquid water LWP on the observation path: In the reflectivity Z profile measured in real time by the millimeter-wave cloud radar in the test area, the ice phase particle information is removed to obtain the reflectivity Z profile of liquid water particles; the total amount of liquid water LWP on the observation path monitored in real time by the ground-based microwave radiometer in the test area is extracted; Step 3: Invert and obtain the LWC profile of the cloud liquid water content on the observation path: The temperature profile data of step 1 and the total amount of liquid water LWP on the observation path of step 2 are fused into the reflectivity Z profile of liquid water particles, and the liquid water content LWC profile of the cloud on the observation path is inverted: In the above formula, i represents the i-th radar range library, M represents the total number of radar range libraries in the cloud, and Fliq is the contribution ratio of liquid particles. b is the coefficient, Δz represents the radar range library length; Step 4: take the overlapping area where the ground-based microwave radiometer temperature profile data is less than 0°C and the area where the inverted cloud liquid water content data is greater than 0 as the supercooled water area in the cloud; Step 5, analyzing whether the ice accumulation area in step 1 overlaps with the supercooled water area in step 4. If there is overlap, it means that ice accumulation occurs in the air, and if there is no overlap, it means that no ice accumulation occurs in the air.

2. The aircraft ice accretion monitoring method according to claim 1, characterized in that: The surface shapes of aircraft ice accumulation are divided into four types: clear ice, rime, hairy ice and hoarfrost.

3. The aircraft ice accretion monitoring method according to claim 1, characterized in that: In step 1, the ice accretion intensity levels are divided into the following categories: I<0, no ice accretion; 0≤I<5, light ice accretion; 5≤I<8, moderate ice accretion; I≥8, severe ice accretion.

4. An aircraft ice accretion monitoring device integrating millimeter wave cloud radar and ground-based microwave radiometer data, characterized in that: include: processor; A memory in which executable instructions of the processor are stored; the processor is configured to perform the steps of the method of claim 1 by executing the executable instructions.

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