Aircraft cumulus icing probability prediction method and device

Abstract

The invention provides an aircraft cumulus icing probability prediction method and device. The method comprises the following steps: acquiring forecast data of mesoscale numerical weather forecast in a three-dimensional space; wherein the forecast data comprises liquid water data of cloud drops, liquid water data of raindrops, air temperature and vertical airflow velocity; determining the icing probability of the first water drop based on the air temperature and the liquid water data of the cloud drop; determining the icing probability of the second water drop based on the air temperature, the liquid water data of the cloud drop and the liquid water data of the raindrop; wherein the median volume diameter of the first water drop is smaller than the median volume diameter of the second water drop; determining the icing probability of all particles in the three-dimensional space according to the icing probability of the first water drop and the icing probability of the second water drop, and determining the cumulus icing probability of the aircraft in the three-dimensional space according to the icing probability and the cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity. According to the method, the accuracy of icing forecasting of the aircraft in the cumulus cloud is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation meteorological monitoring, and in particular to a method and a device for predicting the probability of icing of cumulus clouds on an aircraft. Background Art

[0002] When an aircraft is flying in the clouds or in precipitation and encounters supercooled cloud droplets or raindrops, they will gather and form ice on certain parts of the aircraft surface, causing lift loss and increased drag, affecting the aircraft's maneuverability and stability. Therefore, transport aircraft must conduct high-risk and difficult natural icing certification test flights before mass production and delivery.

[0003] Natural icing test flights require finding icing areas that meet the outline conditions. The ability to monitor and forecast intermittent maximum icing conditions, that is, cumulus icing conditions, is the key basis for planning and commanding aircraft to cross cumulus clouds to conduct observation tests and test flights. Forecasting the aircraft's cumulus icing areas in advance can help plan and coordinate the aircraft's flight routes in advance.

[0004] In the related art, aircraft icing forecasts are usually based on the diagnosis of aircraft icing conditions based on numerical model results. For example, the International Civil Aviation Organization uses the IC icing index, CIP index or SFIP index to predict the probability of aircraft icing. However, since the physical quantities selected in the construction of the index threshold are too simple, there are a large number of false reports in actual business applications. In addition, the above-mentioned aircraft icing prediction algorithms are applicable to aircraft icing conditions in layered clouds. The selected cloud microphysical variables are usually only the liquid water content. However, flight tests of aircraft passing through cumulus clouds are relatively scarce, and the monitoring and forecasting of cumulus icing conditions lack specificity, resulting in inaccurate icing forecast results for aircraft in cumulus clouds. Summary of the invention

[0005] The present invention provides a method and device for predicting the probability of icing of aircraft in cumulus clouds, so as to solve the problem that the results of the prior art for predicting aircraft icing based on numerical model results are false alarms, and the method and device are only applicable to the icing forecast of aircraft in stratus clouds, but lack monitoring data on icing of cumulus clouds, resulting in inaccurate icing forecast results of aircraft in cumulus clouds, thereby improving the accuracy of icing forecast of aircraft in cumulus clouds.

[0006] The present invention provides a method for predicting the probability of cumulus icing of an aircraft, comprising: Obtaining forecast data of mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity; Determining the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplet; determining the freezing probability of the second water droplet based on the air temperature, the liquid water data of the cloud droplet and the liquid water data of the raindrop; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; The freezing probability of all particles in the three-dimensional space is determined according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and the cumulus icing probability of the aircraft in the three-dimensional space is determined according to the freezing probability and a cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

[0007] According to a method for predicting aircraft cumulus icing probability provided by the present invention, the liquid water data of the cloud droplets include the liquid water content and number concentration of the cloud droplets; The determining the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplet comprises: calculating a median volume diameter of the cloud droplets based on the liquid water content and the number concentration; calculating a first critical liquid water content based on the air temperature and the median volume diameter of the cloud droplets; The freezing probability of the first water droplet is obtained based on the liquid water content and the first critical liquid water content.

[0008] According to a method for predicting the probability of icing of cumulus clouds on an aircraft provided by the present invention, the second water droplets include third water droplets and fourth water droplets, the median volume diameter of the third water droplets is smaller than the median volume diameter of the fourth water droplets; the liquid water data of the cloud droplets include the liquid water content and number concentration of the cloud droplets; the liquid water data of the raindrops include the liquid water content and number concentration of the raindrops; The determining of the freezing probability of the second water drop based on the air temperature, the liquid water data of the cloud drop and the liquid water data of the raindrop comprises: The second critical liquid water content is calculated based on the median volume diameter of the third water droplet and the air temperature, and the third critical liquid water content is calculated based on the median volume diameter of the fourth water droplet and the air temperature; wherein the median volume diameter of the third water droplet is determined based on the liquid water content and number concentration of the raindrop, and the median volume diameter of the fourth water droplet is determined based on the liquid water content and number concentration of the cloud droplet, and the liquid water content and number concentration of the raindrop; The freezing probability of the third water drop is obtained based on the liquid water content of the raindrop and the second critical liquid water content; the freezing probability of the fourth water drop is obtained based on the liquid water content of the raindrop, the liquid water content of the cloud drop and the third critical liquid water content.

[0009] According to a method for predicting the icing probability of cumulus clouds on an aircraft provided by the present invention, determining the icing probability of all particles in the three-dimensional space according to the icing probability of the first water droplet and the icing probability of the second water droplet comprises: When the air temperature is greater than or equal to 0° C., the freezing probability of all the particles is confirmed to be 0. When the freezing probability of the first water droplet or the freezing probability of the second water droplet is greater than 1, the freezing probability of all the particles is confirmed to be 1.

[0010] According to a method for predicting icing probability of cumulus clouds on an aircraft provided by the present invention, after determining the icing probability of all particles in the three-dimensional space according to the icing probability of the first water droplet and the icing probability of the second water droplet, the method further includes: Determining the maximum value of the freezing probability at each height above each horizontal grid point in the three-dimensional space according to the freezing probability of all particles; A two-dimensional horizontal distribution field is determined based on multiple icing probability maxima.

[0011] According to a method for predicting aircraft cumulus icing probability provided by the present invention, the cumulus response function comprises: ; in, is the cumulus response value, is the maximum value of the vertical airflow above each horizontal grid point in the three-dimensional space.

[0012] The present invention also provides a device for predicting the probability of cumulus icing on an aircraft, comprising: A data acquisition module, used to acquire forecast data of mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity; a first calculation module, configured to determine the freezing probability of a first water droplet based on the air temperature and the liquid water data of the cloud droplet; and determine the freezing probability of a second water droplet based on the air temperature, the liquid water data of the cloud droplet, and the liquid water data of the raindrop; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; The second calculation module is used to determine the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determine the cumulus icing probability of the aircraft in the three-dimensional space according to the freezing probability and a cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting the probability of cumulus icing of an aircraft as described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for predicting the probability of icing of cumulus clouds on an aircraft as described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for predicting the probability of icing of cumulus clouds on an aircraft as described above is implemented.

[0016] The method and device for predicting the icing probability of cumulus clouds of an aircraft provided by the present invention determine the icing probability of a first water droplet through air temperature and liquid water data of cloud droplets, determine the icing probability of a second water droplet according to air temperature, liquid water data of cloud droplets and liquid water data of raindrops, then determine the icing probability of all particles in three-dimensional space according to the icing probability of the first water droplet and the icing probability of the second water droplet, and determine the icing probability of cumulus clouds of an aircraft in three-dimensional space according to the icing probability and cumulus response function, thereby improving the accuracy of icing prediction of an aircraft in cumulus clouds. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is one of the flow charts of the method for predicting the probability of cumulus icing on an aircraft provided by the present invention.

[0019] Figure 2 It is a schematic diagram of the cumulus icing envelope in Appendix C of the airworthiness standard provided by the present invention.

[0020] Figure 3 It is a relationship diagram between the critical liquid water content for freezing, air temperature and cloud droplet MVD provided by the present invention.

[0021] Figure 4 It is a schematic diagram of a function curve of the cumulus response function provided by the present invention.

[0022] Figure 5 It is a combined reflectivity map output by the mesoscale weather forecast provided by the present invention.

[0023] Figure 6It is a schematic diagram of the distribution of the maximum icing probability of cumulus clouds on the combined reflectivity image provided by the present invention.

[0024] Figure 7 It is a radar reflectivity map output by the mesoscale weather forecast on the AA'' north-south section provided by the present invention.

[0025] Figure 8 It is a schematic diagram of cumulus icing probability on the north-south section of AA'' provided by the present invention.

[0026] Fig. 9 It is a reflectivity map output by the mesoscale weather forecast on the north-south section of BB'' provided by the present invention.

[0027] Fig.10 It is a schematic diagram of cumulus icing probability on the north-south section of BB'' provided by the present invention.

[0028] Fig.11 This is the second flow chart of the method for predicting the probability of cumulus icing on an aircraft provided by the present invention.

[0029] Fig.12 The present invention is a schematic diagram of the structure of the device for predicting the probability of cumulus icing on an aircraft.

[0030] Fig.13 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Combine the following Figure 1-Figure 12 The invention describes a method and device for predicting the probability of cumulus icing on an aircraft.

[0033] Figure 1 FIG. 1 is one of the flow charts of the method for predicting the probability of cumulus icing of aircraft provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 110, obtaining forecast data of mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity.

[0034] In this step, the three-dimensional space is an airborne area through which aircraft such as airplanes pass, for example, the three-dimensional space includes cumulus clouds above a certain area.

[0035] In this embodiment, the mesoscale numerical weather forecast service can be provided by a mesoscale weather system, which refers to a weather system with a horizontal scale ranging from 2km to 2000km. It can use numerical models to predict the evolution and characteristics of the weather, providing a scientific basis for meteorological forecasting and disaster prevention and mitigation.

[0036] In this embodiment, the forecast data includes the mixing ratio Q (unit: kg kg-1) and the number concentration N (unit: kg-1) of different types of hydrometeors; for example, the liquid water data of cloud droplets include the liquid water content and the number concentration in the cloud droplets, and the liquid water data of raindrops include the liquid water content and the number concentration in the raindrops.

[0037] Step 120: Determine the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplets; determine the freezing probability of the second water droplet based on the air temperature, the liquid water data of the cloud droplets and the liquid water data of the raindrops; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet.

[0038] In this step, the variables directly output by the mesoscale numerical weather prediction model contain the mixing ratio Q (in kg kg) of different types of hydrometeors. -1 ) and number concentration N (unit: kg -1 ), estimate the median volume diameter (MVD) of cloud droplets, raindrops and the total.

[0039] In this embodiment, the first water droplet is a small water droplet with an MVD of 15 to 50 μm, and the second water droplet is a large water droplet with an MVD of more than 50 μm.

[0040] Specifically, the liquid water data of the cloud droplets includes the liquid water content and number concentration of the cloud droplets; determining the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplets includes the following steps: (1) Calculate the median volume diameter of cloud droplets based on liquid water content and number concentration; In this example, the median volume diameter of cloud droplets is calculated by the following formula: : ; in, is the density of water; is the liquid water content in cloud droplets, is the number concentration in cloud droplets, k is a parameter determined by the particle spectrum, and the value of k is generally between 1 and 2.

[0041] (2) Calculate the first critical liquid water content based on air temperature and the median volume diameter of cloud droplets.

[0042] Figure 2 This is the schematic diagram of the cumulus icing envelope in Appendix C of the airworthiness standards. Figure 2 In the embodiment shown, the relationship between the small water droplet icing condition and MVD, liquid water content and air temperature has been given in the trial flight standards of my country. Since the relationship between the icing probability and the air temperature is not necessarily linear, the actual aircraft ambient temperature and the relationship between the icing probability and the air temperature are used. Figure 2 Dividing the critical temperature by the calculated value cannot accurately obtain a freezing probability value in the range of 0-1 (or 0-100%). Figure 2 It is difficult to combine with the output variables of mesoscale numerical weather forecasts to calculate the probability of icing.

[0043] Figure 3 is a relationship diagram between the critical liquid water content for freezing, air temperature and cloud droplet MVD provided by the present invention, Figure 3 It is through Figure 2 The results are obtained by digital extraction and coordinate conversion. Specifically, the critical temperature diagram in the "effective water droplet diameter (MVD)-liquid water content" coordinate system is converted into the critical liquid water content diagram in the "air temperature-MVD" coordinate system; Figure 2 Convert to Figure 3 The purpose is to make better use of Figure 2 Information in Figure 3 In the embodiment shown, the numerical relationship between different air temperatures, different MVDs and different critical liquid water contents for freezing is given; by converting the current air temperature and Substitution Figure 3 The first critical liquid water content is obtained by performing two-dimensional linear interpolation calculation on the data shown. .

[0044] (3) The freezing probability of the first water droplet is obtained based on the liquid water content and the first critical liquid water content.

[0045] In this embodiment, according to the cloud droplet liquid water content value The first critical liquid water content The ratio of the first drop of small water droplets is obtained to obtain the freezing probability of the first drop of small water droplets. , which is calculated by the following formula: ; where ρ is the air density.

[0046] This embodiment calculates the freezing probability of the first water droplet by comprehensively considering the liquid water content, number concentration, and air temperature in three-dimensional space, thereby achieving accurate prediction of the freezing of small water droplets in cumulus clouds.

[0047] In this embodiment, the second water droplet includes a third water droplet and a fourth water droplet, and the median volume diameter of the third water droplet is smaller than the median volume diameter of the fourth water droplet; the liquid water data of the cloud droplet includes the liquid water content and number concentration of the cloud droplet; the liquid water data of the raindrop includes the liquid water content and number concentration of the raindrop; In this embodiment, the third water droplets may be raindrops, or a mixture of cloud droplets and raindrops, and the fourth water droplets may be a mixture of cloud droplets and raindrops.

[0048] In this embodiment, determining the freezing probability of the second water drop based on the air temperature, the liquid water data of the cloud drop and the liquid water data of the rain drop comprises the following steps: (1) Calculating a second critical liquid water content based on the median volume diameter of the third water droplet and the air temperature, and calculating a third critical liquid water content based on the median volume diameter of the fourth water droplet and the air temperature; wherein the median volume diameter of the third water droplet is determined based on the liquid water content and number concentration of the raindrop, and the median volume diameter of the fourth water droplet is determined based on the liquid water content and number concentration of the cloud droplet, and the liquid water content and number concentration of the raindrop.

[0049] In this embodiment, the icing probability of the third water droplet and the fourth water droplet is calculated by referring to Appendix O of the Federal Aviation Regulations of the United States, Airworthiness Standards for Transport Category Aircraft (hereinafter referred to as Appendix O of FAR25). In this embodiment, if the MVD of the water droplet is ≥ 40 μm, the second critical liquid water content is calculated by the following formula: : =0.31-0.0046*T; Where T is the temperature in °C.

[0050] If the MVD of a water droplet is less than 40 μm, the third critical liquid water content is calculated by the following formula: : =0.26-0.0038*T.

[0051] (2) The freezing probability of the third water drop is obtained based on the liquid water content of the raindrop and the second critical liquid water content; the freezing probability of the fourth water drop is obtained based on the liquid water content of the raindrop, the liquid water content of the cloud drop and the third critical liquid water content.

[0052] Specifically, when the third water droplet is a raindrop and the MVD of the third water droplet is ≥ 100 μm, the freezing probability of the corresponding third water droplet is calculated by the following formula: ; in, is the freezing probability of raindrops, is the liquid water content in the raindrop; the MVD of the third water drop ( ) is calculated by the following formula: ; in, is the number concentration in the raindrop.

[0053] When the third water droplet is a mixture of cloud droplets and raindrops, and the MVD of the third water droplet is ≥ 40 μm, the freezing probability of the corresponding third water droplet is calculated by the following formula: ; in, is the mixed water content of cloud droplets and raindrops; the MVD of the third water droplet ( ) is calculated by the following formula: ; in, is the mixed number concentration of cloud droplets and raindrops; the freezing probability of the third water droplet is calculated by the following formula: .

[0054] When the fourth water droplet is a mixture of raindrops and raindrops, and the MVD of the fourth water droplet is less than 40 μm, the freezing probability of the corresponding fourth water droplet is calculated by the following formula: .

[0055] This embodiment calculates the freezing probability of the second water droplet by comprehensively considering the liquid water content, number concentration, and air temperature in three-dimensional space, thereby achieving accurate prediction of the freezing of large water droplets in cumulus clouds.

[0056] Step 130: determining the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determining the cumulus icing probability of the aircraft in the three-dimensional space according to the freezing probability and the cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

[0057] In this step, according to the freezing probability of the small water droplets and the two large water droplets obtained in the above step, the maximum value is taken at each three-dimensional space point to obtain the three-dimensional space variable P 总 , as the freezing probability of all particles.

[0058] In this embodiment, the cumulus response function is determined based on a common mathematical activation function (Sigmoid function), and the cumulus response function is a continuous function that can distinguish cumulus clouds at a typical maximum updraft velocity.

[0059] Specifically, the cumulus response functions include: ; in, is the cumulus response value, is the maximum value of the vertical airflow above each horizontal grid point in the three-dimensional space; it should be noted that for values ​​less than 10 -7 of Assign a value of 0; after multiplying the icing probability of undifferentiated cumulus by the cumulus response function, the icing probability of only retaining the cumulus part can be obtained, which is easy to calculate.

[0060] Figure 4 is a schematic diagram of a function curve of a cumulus response function provided by the present invention, Figure 4 In the embodiment shown, the cumulus response function is greater than the maximum updraft velocity at the grid point for more than 5 ms -1 is very close to 1, and the maximum updraft velocity at the grid point is 0.1 ms -1 The magnitude is close to 0 and is a continuous function that can distinguish cumulus clouds with typical maximum updraft speeds.

[0061] It should be noted that from the general understanding of meteorology and cloud precipitation physics, stratiform clouds are clouds formed by the uplift of the entire layer of the atmosphere, and the typical maximum value of the updraft is 0.1 ms. -1 Cumulus (convective cloud) is a cloud formed by convective motion under unstable atmospheric stratification conditions. The typical updraft in the initial stage can be 5 ms. -1 The typical maximum updraft of cumulus development and severe convective clouds is around 10 ms. -1 Magnitude; This embodiment can use the three-dimensional cumulus response function and the three-dimensional full particle icing probability P 总 Multiplying them together, we get the three-dimensional cumulus icing probability.

[0062] exist Figure 5 In the illustrated embodiment, the combined reflectivity forecast results in the mesoscale numerical weather forecast mode are included to characterize the approximate location and intensity of the predicted precipitation cloud system; Figure 5 Some echoes with strong centers can be seen in the image. There is also a relatively large echo between 115~116°E and 40~41°N, which is not easy to distinguish whether it is cumulus cloud based on the combined reflectivity alone (line segments AA'' and BB'' both represent north-south section lines).

[0063] In this embodiment, the maximum value is taken at each three-dimensional space point to obtain the three-dimensional space variable P 总 , as the freezing probability of all particles, the corresponding calculation formula is as follows: .

[0064] In this embodiment, when the air temperature is greater than or equal to 0° C., the freezing probability of all particles is confirmed to be 0, and when the freezing probability of the first water droplet or the freezing probability of the second water droplet is greater than 1, the freezing probability of all particles is confirmed to be 1.

[0065] Specifically, when the ambient temperature is greater than or equal to 0°C, the freezing probability P 总 Assign a value of 0; during the calculation process, if , or If the value of is greater than 1, they are assigned the value 1.

[0066] In this embodiment, the above It is two-dimensional data, which is expanded into a three-dimensional variable by setting its value at each height equal; then, the three-dimensional Fc is used with the three-dimensional total particle freezing probability P 总 By multiplying them, we can get the three-dimensional cumulus icing probability, which is calculated as follows: .

[0067] The method for predicting the icing probability of cumulus clouds of an aircraft provided by an embodiment of the present invention determines the freezing probability of a first water droplet through air temperature and liquid water data of cloud droplets, determines the freezing probability of a second water droplet according to air temperature, liquid water data of cloud droplets and liquid water data of raindrops, then determines the freezing probability of all particles in three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determines the freezing probability of cumulus clouds of the aircraft in three-dimensional space according to the freezing probability and cumulus response function, thereby improving the accuracy of icing forecasts for aircraft in cumulus clouds.

[0068] In some embodiments, after determining the freezing probability of all particles in the three-dimensional space based on the freezing probability of the first water droplet and the freezing probability of the second water droplet, the aircraft cumulus icing probability prediction method also includes: determining the maximum freezing probability at each height above each horizontal grid point in the three-dimensional space based on the freezing probability of all particles; and determining a two-dimensional horizontal distribution field based on multiple freezing probability maximum values.

[0069] In this embodiment, it is assumed that there is a three-dimensional array ice_probability for the freezing probability of each particle, and its dimension is (x, y, z), where x and y represent the coordinates of the horizontal grid points, and z represents the height; each element ice_probability[x, y, z] represents the freezing probability at the (x, y) position and z height; the maximum freezing probability is calculated by the following steps: (1) Initialize the maximum freezing probability array: (2) Create a two-dimensional array max_ice_probability with dimension (x, y) to store the maximum ice probability above each horizontal grid point.

[0070] (3) Traverse the three-dimensional array; for each (x, y) position, traverse all heights z, find the maximum value in ice_probability[x, y, z], and store it in max_ice_probability[x, y]. This will give the corresponding two-dimensional horizontal distribution field, which allows technicians to observe the probability of ice formation above the horizontal position through the two-dimensional image of the horizontal distribution field.

[0071] Figure 6 is a schematic diagram of the distribution of the maximum icing probability of cumulus clouds on the combined reflectivity image provided by the present invention. Figure 6 In the illustrated embodiment, in the combined reflectivity forecast results in the mesoscale numerical weather forecast mode, the maximum cumulus icing probability shows that there is a cumulus icing probability near a plurality of cloud body locations with a smaller strong center range, but there is no cumulus icing probability at the above-mentioned large strong echo locations.

[0072] The aircraft cumulus icing probability prediction method provided by the embodiment of the present invention determines the maximum icing probability at each height above each horizontal grid point in the three-dimensional space through the icing probability of all particles, and then determines the two-dimensional horizontal distribution field based on multiple icing probability maximum values, so that technicians can intuitively analyze the icing probability at different positions in the cumulus through the two-dimensional horizontal distribution field.

[0073] In some embodiments, after determining the cumulus icing probability of the aircraft in three-dimensional space, the maximum cumulus icing probability of the cumulus icing probability in three-dimensional space is calculated, and a new two-dimensional horizontal distribution field is determined based on the maximum cumulus icing probability, so that technicians can observe the icing probability of the aircraft passing through different horizontal areas in the cumulus through the two-dimensional image of the horizontal distribution field.

[0074] Figure 7 It is the radar reflectivity map output by the mesoscale weather forecast on the north-south section of AA'' provided by the present invention. Figure 7 In the embodiment shown, for the AA'' section, it can be seen from the radar reflectivity image that the south side ( Figure 7 The left side of the center is a cloud body with strong echoes and obvious vertical extension, which is a typical structure of cumulus clouds (convective clouds). Figure 7 The thick black solid line in the figure is the 30 dBZ contour line, which is used to characterize the outline of the precipitation cloud area.

[0075] Figure 8 is a schematic diagram of cumulus icing probability on the north-south section of AA'' provided by the present invention. Figure 8 In the embodiment shown, the maximum icing probability of cumulus appears in the front side of the cumulus, and the front side of the cumulus is generally the area where the updraft is mainly distributed and supercooled water is enriched, so the calculation result is relatively reasonable. Figure 8 The layered cloud area does not show the cumulus icing probability, which means that the cumulus icing probability has been successfully retained, which is in line with the expectations of this application.

[0076] Fig. 9 It is the reflectivity map output by the mesoscale weather forecast on the north-south section of BB'' provided by the present invention. Fig. 9In the embodiment shown, for the BB'' section, it can be seen from the radar reflectivity image that the two cloud bodies in the middle are cloud bodies with strong echoes and obvious vertical extension, which is a typical structure of cumulus clouds (convective clouds), while the north side ( Fig. 9 The thick black solid line in the figure is the 30 dBZ contour line, which is used to characterize the outline of the precipitation cloud area.

[0077] Fig.10 is a schematic diagram of cumulus icing probability on the north-south section of BB'' provided by the present invention. Fig.10 In the embodiment shown, the application effect on the BB'' section is similar to that on the AA'' section, in that a large value of the cumulus icing probability is given in the front part of each cumulus cloud, and a cumulus icing probability value of 0 is displayed in the layered cloud area, indicating that the calculation result of the icing probability is relatively reasonable and in line with the expectations of this application.

[0078] Fig.11 This is the second flow chart of the method for predicting the probability of cumulus icing on an aircraft provided by the present invention. Fig.11 In the illustrated embodiment, the forecast quantity of the three-dimensional space through which the aircraft passes in the next nth hour is first obtained through mesoscale numerical weather forecast, including the temperature, the liquid water content and number concentration of cloud droplets, the liquid water content and number concentration of raindrops, and the vertical airflow velocity; the freezing probability of small water droplets is calculated according to the temperature, the liquid water content and number concentration of cloud droplets, and the freezing probability of large water droplets is calculated according to the temperature, the liquid water content and number concentration of cloud droplets, and the liquid water content and number concentration of cloud droplets; the cumulus response function is constructed according to the vertical airflow velocity, and the cumulus freezing probability (three-dimensional) is calculated according to the total particle freezing probability (three-dimensional) and the cumulus response function; in addition, the maximum value of the freezing probability at each height above each horizontal grid point in the three-dimensional space is calculated by calculating the total particle freezing probability, and the maximum freezing probability (two-dimensional) is obtained. The maximum cumulus freezing probability (two-dimensional) corresponding to each horizontal grid point in the three-dimensional space is calculated by the cumulus freezing probability, which can be used to draw a two-dimensional horizontal distribution map to intuitively observe the freezing condition in the cumulus.

[0079] The aircraft cumulus icing probability prediction device provided by the present invention is described below. The aircraft cumulus icing probability prediction device described below and the aircraft cumulus icing probability prediction method described above can be referenced to each other.

[0080] Fig.12 FIG. 1 is a schematic diagram of the structure of the device for predicting the probability of cumulus icing on an aircraft provided by the present invention. Fig.12 As shown, the aircraft cumulus icing probability prediction device includes: a data acquisition module 1210, a first calculation module 1220 and a second calculation module 1230.

[0081] The data acquisition module 1210 is used to acquire the forecast data of the mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes the liquid water data of cloud droplets, the liquid water data of raindrops, the air temperature and the vertical air flow velocity; The first calculation module 1220 is used to determine the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplet; determine the freezing probability of the second water droplet based on the air temperature, the liquid water data of the cloud droplet and the liquid water data of the raindrop; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; The second calculation module 1230 is used to determine the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determine the cumulus icing probability of the aircraft in the three-dimensional space according to the freezing probability and the cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

[0082] The device for predicting the icing probability of cumulus clouds of an aircraft provided in an embodiment of the present invention determines the freezing probability of a first water droplet through air temperature and liquid water data of cloud droplets, determines the freezing probability of a second water droplet according to air temperature, liquid water data of cloud droplets and liquid water data of raindrops, then determines the freezing probability of all particles in a three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determines the freezing probability of cumulus clouds of an aircraft in the three-dimensional space according to the freezing probability and a cumulus response function, thereby improving the accuracy of icing forecasts for aircraft in cumulus clouds.

[0083] Fig.13 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Fig.13 As shown, the electronic device may include: a processor (processor) 1310 , a communication interface (Communications Interface) 1320 , a memory (memory) 1330 and a communication bus 1340 , wherein the processor 1310 , the communication interface 1320 , and the memory 1330 communicate with each other through the communication bus 1340 . The processor 1310 can call the logic instructions in the memory 1330 to execute the aircraft cumulus icing probability prediction method, which includes: obtaining forecast data of mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity; determining the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplets; determining the freezing probability of the second water droplet based on the air temperature, the liquid water data of the cloud droplets and the liquid water data of the raindrops; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; determining the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determining the cumulus icing probability of the aircraft in the three-dimensional space according to the freezing probability and the cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

[0084] In addition, the logic instructions in the above-mentioned memory 1330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0085] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the aircraft cumulus icing probability prediction method provided by the above-mentioned methods, and the method includes: obtaining forecast data of mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity; determining the freezing probability of a first water droplet based on the air temperature and the liquid water data of cloud droplets; determining the freezing probability of a second water droplet based on the air temperature, the liquid water data of cloud droplets and the liquid water data of raindrops; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; determining the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determining the cumulus icing probability of the aircraft in the three-dimensional space according to the freezing probability and the cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

[0086] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the aircraft cumulus icing probability prediction method provided by the above-mentioned methods, the method comprising: obtaining forecast data of a mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data comprises liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity; determining the freezing probability of a first water droplet based on the air temperature and the liquid water data of cloud droplets; determining the freezing probability of a second water droplet based on the air temperature, the liquid water data of cloud droplets and the liquid water data of raindrops; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; determining the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determining the cumulus icing probability of the aircraft in the three-dimensional space according to the freezing probability and the cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

[0087] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0088] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the probability of cumulus icing on an aircraft, characterized in that: include: Obtaining forecast data of mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity; Determining the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplet; determining the freezing probability of the second water droplet based on the air temperature, the liquid water data of the cloud droplet and the liquid water data of the raindrop; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; The freezing probability of all particles in the three-dimensional space is determined according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and the cumulus icing probability of the aircraft in the three-dimensional space is determined according to the freezing probability and a cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

2. The method for predicting aircraft cumulus icing probability according to claim 1, characterized in that: The liquid water data of the cloud droplets include the liquid water content and number concentration of the cloud droplets; The determining the freezing probability of the first water droplet based on the air temperature and the liquid water data of the cloud droplet comprises: calculating a median volume diameter of the cloud droplets based on the liquid water content and the number concentration; calculating a first critical liquid water content based on the air temperature and the median volume diameter of the cloud droplets; The freezing probability of the first water droplet is obtained based on the liquid water content and the first critical liquid water content.

3. The method for predicting aircraft cumulus icing probability according to claim 1, characterized in that: The second water droplets include third water droplets and fourth water droplets, and the median volume diameter of the third water droplets is smaller than the median volume diameter of the fourth water droplets; the liquid water data of the cloud droplets include the liquid water content and number concentration of the cloud droplets; the liquid water data of the raindrops include the liquid water content and number concentration of the raindrops; The determining of the freezing probability of the second water drop based on the air temperature, the liquid water data of the cloud drop and the liquid water data of the raindrop comprises: The second critical liquid water content is calculated based on the median volume diameter of the third water droplet and the air temperature, and the third critical liquid water content is calculated based on the median volume diameter of the fourth water droplet and the air temperature; wherein the median volume diameter of the third water droplet is determined based on the liquid water content and number concentration of the raindrop, and the median volume diameter of the fourth water droplet is determined based on the liquid water content and number concentration of the cloud droplet, and the liquid water content and number concentration of the raindrop; The freezing probability of the third water drop is obtained based on the liquid water content of the raindrop and the second critical liquid water content; the freezing probability of the fourth water drop is obtained based on the liquid water content of the raindrop, the liquid water content of the cloud drop and the third critical liquid water content.

4. The method for predicting aircraft cumulus icing probability according to claim 1, characterized in that: Determining the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water drop and the freezing probability of the second water drop includes: When the air temperature is greater than or equal to 0° C., the freezing probability of all the particles is confirmed to be 0. When the freezing probability of the first water droplet or the freezing probability of the second water droplet is greater than 1, the freezing probability of all the particles is confirmed to be 1.

5. The method for predicting aircraft cumulus icing probability according to claim 1, characterized in that: After determining the freezing probabilities of all particles in the three-dimensional space according to the freezing probabilities of the first water droplets and the second water droplets, the method further includes: Determining the maximum value of the freezing probability at each height above each horizontal grid point in the three-dimensional space according to the freezing probability of all particles; A two-dimensional horizontal distribution field is determined based on multiple icing probability maxima.

6. The method for predicting aircraft cumulus icing probability according to claim 1, characterized in that: The cumulus response function includes: ； in, is the cumulus response value, is the maximum value of the vertical airflow above each horizontal grid point in the three-dimensional space.

7. An aircraft cumulus icing probability prediction device, characterized in that: include: A data acquisition module, used to acquire forecast data of mesoscale numerical weather forecast in three-dimensional space; wherein the forecast data includes liquid water data of cloud droplets, liquid water data of raindrops, air temperature and vertical airflow velocity; a first calculation module, configured to determine the freezing probability of a first water droplet based on the air temperature and the liquid water data of the cloud droplet; and determine the freezing probability of a second water droplet based on the air temperature, the liquid water data of the cloud droplet, and the liquid water data of the raindrop; wherein the median volume diameter of the first water droplet is smaller than the median volume diameter of the second water droplet; The second calculation module is used to determine the freezing probability of all particles in the three-dimensional space according to the freezing probability of the first water droplet and the freezing probability of the second water droplet, and determine the cumulus icing probability of the aircraft in the three-dimensional space according to the freezing probability and a cumulus response function; wherein the cumulus response function is determined based on the vertical airflow velocity.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for predicting the probability of cumulus icing on an aircraft according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the probability of cumulus icing on an aircraft according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the probability of cumulus icing on an aircraft according to any one of claims 1 to 6 is implemented.

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