A method for acquiring long-term characteristics of meteorological elements of low-altitude different layers with ten-meter spatial resolution

Abstract

The application discloses a kind of long-term characteristics acquisition methods of meteorological elements of low-altitude different height layer with ten-meter spatial resolution, belong to the meteorological environment evaluation field of low-altitude aircraft route. Steps are as follows: S1, obtain the long-term historical meteorological three-dimensional data of the low-altitude route area covered in the RuiTu forecasting system;S2, convert the terrain and building white film data into the shape of the underlying surface under the model, generate the grid of the calculation domain;S3, coupling forms RuiTu-PALM system;S4, using RuiTu-PALM system, carry out long-term three-dimensional numerical simulation of ten-meter precision historical meteorological elements, obtain low-altitude meteorological multi-year calculation data of different vertical height layers with ten-meter horizontal resolution;S5, enhance the meteorological characteristics, establish ten-meter precision low-altitude meteorological multi-year enhancement database.The application can achieve ten-meter high spatial resolution, and has long-term representativeness and universality, and can be used to guide the planning and optimization of various low-altitude aircraft routes.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of meteorological environment evaluation of low-altitude aircraft routes, and particularly relates to a method for acquiring long-term characteristics of meteorological elements of different height layers of low-altitude. BACKGROUND

[0002] Under the background of global climate change, extreme weather events have become more frequent. Meteorological services, mainly including weather forecasting and disaster warning, have become an important support for the economic and social development of a country. The development of low-altitude economy also puts forward higher requirements for meteorological services, including carrying out high-temporal and high-spatial resolution low-altitude meteorological environment evaluation, more accurately evaluating the influence of buildings and street layouts on low-altitude meteorology, analyzing the long-term meteorological characteristics of low-altitude aircraft routes in various regions, evaluating the meteorological environmental safety of the routes, and thus scientifically, accurately and safely planning low-altitude aircraft routes.

[0003] Currently, methods for assessing the meteorological safety of low-altitude aircraft routes primarily fall into two categories: real-time monitoring and numerical simulation. Real-time monitoring methods utilize meteorological monitoring equipment to collect meteorological data during flight and generate flight route strategies based on the aircraft's meteorological data, flight parameters, and environmental parameters. However, these methods require the observation of numerous meteorological elements, including temperature, pressure, humidity, wind direction, and wind speed. These observations require a wide range of meteorological information at different altitudes and spatial locations, placing high demands on monitoring equipment and incurring high costs. Furthermore, meteorological stations are typically scarce near low-altitude aircraft routes, making high-density, high-spatial-resolution, three-dimensional real-time monitoring of the high-altitude meteorological environment impossible. Furthermore, the measurement accuracy of monitoring equipment can also hinder the accuracy of route planning strategies. Numerical simulation is another important method for assessing the meteorological safety of low-altitude routes. The existing technology (Zhong Ruomei, Wen Xiaohang, Xu Chenchen. Simulation and analysis of wind direction and speed of UAV routes in the Beijing-Tianjin-Hebei region based on high-resolution model [J]. Progress in Geography, 2021, 40(9): 1528-1539.) used the WRF model to simulate the wind speed and direction information of UAV low-altitude public routes in the Beijing-Tianjin-Hebei region from 2015 to 2019, and compared and analyzed the measured data from meteorological stations, providing safety recommendations for UAV low-altitude routes in the region from a meteorological perspective. However, due to the limitation of the spatial resolution of the model, this type of numerical simulation method can only achieve route meteorological assessment with a kilometer-level resolution. However, considering the complexity of the urban low-altitude environment and the fact that the operating aircraft are mainly light and small models, the total length of the route of low-altitude aircraft may be only a few hundred meters to 1-2 kilometers. Therefore, the route meteorological assessment with a kilometer-level resolution cannot meet the spatial accuracy requirements. The spatial resolution of the route meteorological simulation must be at least accurate to ten meters to meet the requirements. In addition, existing technologies (Dong Zexin, Wu Shuoyan, Ye Fang, et al., 2025. Research on numerical simulation of low-altitude wind shear based on CFD and LSTM technology of WRF model [J]. Plateau Meteorology, 44 (2): 546-562.) coupled the WRF model with the computational fluid dynamics model OpenFOAM and the long-term and short-term neural network method to simulate and analyze two wind shear processes at Lanzhou Zhongchuan Airport on April 15-16, 2021 at the hundred-meter level, and obtained the spatial distribution of the wind field over Zhongchuan International Airport with a hundred-meter resolution. However, this type of method is only applicable to high-altitude meteorological simulations in short-term individual cases, and its conclusions cannot represent the long-term characteristics of meteorological conditions on the route. Therefore, it is not universal and reliable, and cannot provide route planning guidance for different periods and different aircraft types in different seasons, months, altitudes, etc. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a method for acquiring the long-term characteristics of meteorological elements at different low-altitude layers with a spatial resolution of ten meters, which can provide route planning guidance for different periods and different aircraft types according to different seasons, months, altitudes, etc.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a method for acquiring long-term characteristics of meteorological elements at different altitudes at low altitudes with a spatial resolution of ten meters, comprising the following steps:

[0006] S1. Obtaining multiple years of three-dimensional meteorological historical data covering the low-altitude flight target area in the Ruitu forecast system, including precipitation, ground and high-altitude meteorological elements, and weather situation data;

[0007] S2. Generate the underlying surface shape of the PALM model based on the terrain and building blank model data of the target area, add the side and top surfaces of the computational domain to generate the computational domain mesh, and combine the geographical features and building distribution to divide the computational domain mesh using a combination of structured and unstructured grids;

[0008] S3. Extract meteorological information of the target area at multiple moments from the three-dimensional historical meteorological data calculated by the Ruitu system over many years, and remap the spatial grid to serve as the initial and boundary meteorological fields driving the PALM model calculation. Add the model underlying surface shape, computational domain, and computational grid based on the building white model data to form the Ruitu-PALM coupling system.

[0009] S4. Using the RITU-PALM coupling system, conduct long-term three-dimensional numerical simulation of historical meteorological data with a ten-meter accuracy in the target area, and obtain multi-year low-altitude meteorological data at different vertical heights with a horizontal resolution of ten meters in the target area;

[0010] S5. Enhance the meteorological characteristics of the multi-year low-altitude meteorological data calculated based on the Ruitu-PALM coupling system, integrate the low-altitude meteorological characteristics calculated by the Ruitu-PALM coupling system and the enhanced meteorological characteristics, and form a multi-year low-altitude meteorological enhancement database with a ten-meter accuracy in the target area.

[0011] Furthermore, the aforementioned method for acquiring long-term characteristics of meteorological elements at different low-altitude layers with a spatial resolution of ten meters also includes step S6, constructing a neural network for analyzing the spatiotemporal distribution characteristics of low-altitude meteorological elements, and using the ten-meter-accurate low-altitude meteorological multi-year enhanced database of step S5 to train the neural network to obtain a spatiotemporal distribution characteristic analysis model of low-altitude meteorological elements.

[0012] Further, the method for obtaining long-term characteristics of meteorological elements of low-altitude different height layers with a spatial resolution of ten meters further comprises the step S7 of inputting the meteorological data into a spatiotemporal distribution characteristic analysis model of low-altitude meteorological elements to obtain meteorological characteristics of different time lengths, and formulating corresponding routes according to characteristics of each type of aircraft.

[0013] Further, in the step S1, the low-altitude meteorological data of the target region with a time length of 1-3 years is obtained, the data has a horizontal resolution of 3 km, is integrated for 48 hours at different altitudinal height layers, and has an output frequency of 1 hour.

[0014] Further, in the step S2, a structured grid is used in a non-building adjacent area, and an unstructured grid is used in a building adjacent area, the grid lines in the structured grid are arranged along the directions of the rectangular coordinate system to form a regular grid structure, and the structured grid is suitable for simulation of regular geometric shapes.

[0015] The grid unit of the unstructured grid is in an arbitrary shape and is not arranged along the directions of the rectangular coordinate system, and the unstructured grid is suitable for simulation of irregular geometric shapes.

[0016] Further, the step S2 further comprises controlling and optimizing grid quality, including grid size, grid orthogonality and grid distortion, the grid quality control is realized by adjusting grid parameters and grid optimization algorithms, and the grid quality and the number of grids are verified.

[0017] Further, the step S3 comprises the following substeps:

[0018] S3.1, according to the setting of the simulation domain in the input file of the PLAM model and the calculation parameter setting of the WRF system, the WRF historical meteorological data corresponding to the PALM model simulation domain range and the boundary is extracted, including air temperature, wind speed, wind direction and humidity at different times;

[0019] S3.2, the extracted WRF meteorological data is mapped to the grid, and the meteorological data is mapped to the calculation grid of the PALM model through spatial reconstruction relationship,

[0020] S3.3, the output format of the WRF system, i.e. a three-dimensional array, is converted into an input file in the identification format of the PALM model to drive the operation of the PALM model;

[0021] S3.4, the PALM model is dynamically operated, the extracted meteorological data of the boundary condition and the initial condition is dynamically updated with time, and the time variation of the mesoscale meteorological field reflecting the calculation of the WRF system is obtained;

[0022] S3.5. Based on the generated underlying surface shape bottom surface, computational domain, and computational domain hybrid grid of the simulation area, the extracted multi-year hourly simulation data of the Ruitu system are used to update the hourly boundary conditions and initial conditions to achieve the coupling between the Ruitu system and the PALM model.

[0023] Furthermore, the aforementioned step S5 includes enhancing the headwind speed feature, the crosswind speed feature, the wind shear feature, the turbulence intensity feature, and the turbulence feature;

[0024] The positive wind speed characteristic is enhanced and is calculated as follows:

[0025] ,

[0026] Where θ is the angle between the flight direction and the east direction, U and V are the easterly and northerly components of the wind speed, respectively;

[0027] The crosswind speed characteristic is enhanced and is calculated as follows:

[0028] ,

[0029] Where θ is the angle between the flight direction and the east direction, U and V are the easterly and northerly components of the wind speed, respectively;

[0030] The wind shear characteristic is enhanced and is calculated as follows:

[0031] S V = ΔWs / Δz, along the z direction,

[0032] S H = ΔWs / Δx, along the x-direction,

[0033] or S H = ΔWs / Δy, along the y direction,

[0034] Among them, S V is the vertical wind shear, S H is the horizontal wind shear, Ws is the wind speed, and Δ represents the difference;

[0035] The turbulence intensity characteristic is enhanced and is calculated as follows:

[0036] ,

[0037] Where u′ is the root mean square of the velocity pulsation, that is, the root mean square value of the wind speed fluctuation part, is the average wind speed, I is the turbulence intensity;

[0038] The bump feature is enhanced and calculated as follows:

[0039] E=1.25SH +0.25S V 2 +10.5

[0040] Among them, S H and S V They are horizontal wind shear and vertical wind shear respectively.

[0041] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0042] 1. Obtain long-term meteorological information at different low-altitude locations and altitudes in the study route area. This information has high spatial resolution (ten-meter level) and is representative and universal over the long term, and can be used to guide the planning and optimization of various low-altitude aircraft routes.

[0043] 2. More accurately and effectively formulate and plan routes for different types of scenarios and different low-altitude aircraft. For example, based on the meteorological information of different seasons, months, hours, and altitudes in the study area and the characteristics of different low-altitude aircraft, plan low-altitude flight routes for different types of scenarios (such as logistics, sightseeing, emergency rescue, etc.), adopt flexible route planning strategies, effectively avoid adverse weather conditions in different periods, improve flight safety and efficiency, and meet the specific needs of diverse low-altitude flight missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the present invention.

[0045] Figure 2 It is a schematic diagram of the lower surface shape of the building.

[0046] Figure 3 It is a schematic diagram of the hybrid grid distribution.

[0047] Figure 4 This is the humidity data profile at the northern boundary of the computational domain at a certain moment extracted by Python.

[0048] Figure 5 This is the wind field map at a height of 10m in the simulated study area at 00:00 on November 1, 2024.

[0049] Figure 6 It is a wind field diagram at different heights in the simulated study area.

[0050] Figure 7 This is a partially enlarged view of the wind field calculation results on March 22, 2025.

[0051] Figure 8 This is a schematic diagram of the long-term average characteristics of the wind field at a height of 10 meters in a certain district's starting area in July 2024. DETAILED DESCRIPTION

[0052] In order to better understand the technical content of the present invention, specific embodiments are given and described below with reference to the accompanying drawings.

[0053] Various aspects of the present invention are described herein with reference to the accompanying drawings, which show a number of illustrative embodiments. The embodiments of the present invention are not limited to those described in the accompanying drawings. It should be understood that the present invention can be implemented by any of the various concepts and embodiments described above, as well as the concepts and implementations described in detail below, because the concepts and embodiments disclosed herein are not limited to any particular implementation. In addition, some aspects disclosed herein may be used alone or in any appropriate combination with other aspects disclosed herein.

[0054] refer to Figure 1 The present invention provides a method for acquiring long-term characteristics of meteorological elements at different altitude layers at low altitude with a spatial resolution of ten meters, comprising the following steps:

[0055] S1. Obtain the three-dimensional historical meteorological data covering the low-altitude flight target area in the Ruitu forecast system for many years, including precipitation, ground and low-altitude meteorological elements, and weather situation data.

[0056] The Ruitu Rapid Update Circular Assimilation Forecast System is a next-generation, seamless, multi-scale numerical meteorological forecast system that accounts for land surface and aerosol influences. The system integrates forecast data from automatic meteorological stations, weather radar, and numerical models. It utilizes rapid fusion technology for complex terrain and multi-source, multi-temporal and multi-scale meteorological data. It provides meteorological reanalysis data for my country from June 2022 to the present, with a horizontal resolution of 3km and vertically three-dimensional temperature and humidity (20 layers, 200-meter vertical resolution), and wind field (32 layers, 125-meter vertical resolution) within an altitude of 0-4km. Compared to other historical meteorological products (such as the US FNL data and Europe's ERA5 data), this data offers advantages in high resolution (horizontal accuracy of 3km) and a three-year archive of historical data. The Ruitu system is used in the present invention to obtain low-altitude meteorological data of the study area for a time period of 1-3 years. The data has a horizontal resolution of 3km, different altitude layers (32 layers), an integration of 48 hours, and an output frequency of 1 hour. It includes meteorological field products of physical quantities such as precipitation, ground and low-altitude meteorological elements, and weather conditions.

[0057] S2. Based on the terrain and building blank model data of the target area, the underlying surface shape of the PALM model is generated, and the side and top surfaces of the computational domain are added to generate the computational domain mesh. At the same time, combined with the geographical features and building distribution, the computational domain mesh is divided by combining structured and unstructured grids.

[0058] Based on the terrain and building white model data of the study area, the underlying surface shape required by the PALM model is generated, such as Figure 2 As shown. On this basis, the side and top surfaces of the computational domain are added to form the entire computational area. Then, combined with the geographical features and building distribution, the computational domain grid is divided using a method combining structured and unstructured grids, as shown in the figure. Figure 3 As shown. A structured grid is used away from buildings, while an unstructured grid is used in areas adjacent to buildings. In a structured grid, the grid lines are arranged along the rectangular coordinate system to form a regular grid structure, which is suitable for simulating regular geometric shapes. In contrast, the grid cells of an unstructured grid can be of any shape and do not need to be arranged along the rectangular coordinate system, which is suitable for simulating irregular geometric shapes. This structured and unstructured grid generation method ensures a sufficiently high grid density around buildings and in key areas (such as areas where low-level jets may occur), which can capture the details of the flow and improve the accuracy and reliability of the simulation.

[0059] At the same time, to ensure simulation accuracy, it is necessary to control and optimize mesh quality, including mesh size, mesh orthogonality, and mesh distortion. Mesh quality control is achieved by adjusting mesh parameters and using mesh optimization algorithms. Verification of mesh quality and mesh quantity is performed to ensure high-density, high-quality meshing around buildings and in critical areas (such as those where low-level jets may occur), thereby improving simulation accuracy and reliability.

[0060] S3. Extract meteorological information of the target area at multiple times from the multi-meteorological three-dimensional historical data calculated by the Ruitu system, and remap the spatial grid as the initial meteorological field and boundary meteorological field to drive the PALM model calculation. Add the model underlying surface shape, calculation domain and calculation grid based on the conversion of the building white model data to form the Ruitu-PALM coupling system.

[0061] Using self-written Python code, according to the settings of the simulation domain in the PALM input file (such as the regional latitude and longitude range, region size, grid resolution, and number of grids) and the calculation parameter settings of the Ruitu system (such as simulation region, nested grid, physical process parameterization scheme, etc.), the historical Ruitu meteorological data covering the PALM simulation domain range and the boundary locations are extracted, including temperature, wind speed, wind direction, humidity, etc. at different times. The extracted Ruitu meteorological data is then grid-mapped using Python scripts, and the data is mapped from the Ruitu calculation grid distribution to the PALM model calculation grid through spatial reconstruction relationships, such as Figure 4As shown. It is converted from the output format of the Ruitu system (three-dimensional array) to an input file in a format recognizable by PALM to drive the operation of the PALM model. During the operation of the PALM model, these extracted meteorological data for boundary conditions and initial conditions are dynamically updated over time, thereby reflecting the temporal changes in the mesoscale meteorological field calculated by the Ruitu system. At the same time, during the operation of the PALM model, the underlying surface shape bottom surface, computational domain, and computational domain hybrid grid of the simulation area generated in the previous step are also required. On this basis, the extracted Ruitu system's multi-year hourly simulation data is used to update the hourly boundary conditions and initial conditions, thereby realizing the coupling of the Ruitu system and the PALM model, forming a Ruitu-PALM coupled system.

[0062] S4. Using the Ruitu-PALM coupling system, a long-term three-dimensional numerical simulation of historical meteorological data with a ten-meter accuracy in the target area is carried out to obtain multi-year low-altitude meteorological data at different vertical height layers with a horizontal resolution of ten meters in the target area.

[0063] The Ruitu-PALM model is used, based on the many years of simulation data of the Ruitu forecast system, including temperature, humidity, wind speed, wind direction, etc., as the initial conditions and boundary conditions that change over time, to carry out multi-year low-altitude meteorological simulation calculations at the ten-meter level in the study area. Taking the starting area of ​​a certain district as an example, the parameters in the calculation process are set as follows: the simulation area range is 5600m×5600m×615m (need to cover the horizontal range and vertical height of the aircraft route), the horizontal resolution is set to 10m×10m, and the vertical resolution is 10m. During the calculation process, the terrain and building white model data of the study area are used, and the many years of hourly simulation data of the Ruitu forecast system are used to update the hourly boundary conditions. The calculation time range is 1-3 years, so as to obtain the three-dimensional information of the low-altitude meteorological field covering the low-altitude drone route area in the area, including meteorological elements such as wind speed, temperature, and humidity, see Figure 5 and Figure 6 Its horizontal and vertical spatial resolution is on the order of ten meters, and it contains meteorological information at different altitudes with a time span of 1-3 years. Figure 7 This is a partially enlarged view of the wind field calculation results on March 22, 2025. As shown in the figure, the calculation grid distribution shown in the figure has a grid spacing of 10 meters. It can be seen that the horizontal resolution of the data can reach 10 meters.

[0064] S5. Enhance the meteorological characteristics of the multi-year low-altitude meteorological data calculated based on the Ruitu-PALM coupling system, integrate the low-altitude meteorological characteristics calculated by the Ruitu-PALM coupling system and the enhanced meteorological characteristics, and form a multi-year low-altitude meteorological enhancement database with a ten-meter accuracy in the target area.

[0065] Integrate multiple years of data on meteorological variables (wind speed, wind direction, temperature, humidity, etc.) at different altitudes and on the ground calculated by the Ruitu-PALM model, remove or correct obviously erroneous values ​​(such as physically impossible temperatures and wind speeds), and handle missing values. Based on this, calculate other important meteorological factors that may affect the safety of low-altitude flight (headwind speed, crosswind speed, wind shear, turbulence intensity, and turbulence) to enhance meteorological characteristics. The calculation and definition of meteorological enhancement features include:

[0066] 1. Headwind speed: Headwind speed (m / s) refers to the wind speed directly ahead (i.e., in the same direction as the flight). The calculation formula for this characteristic is:

[0067] ,

[0068] Where θ is the angle between the flight direction and the east direction (unit: degree), U and V are the east component (unit: m / s) and north component (unit: m / s) of the wind speed, respectively.

[0069] 2. Crosswind speed: Crosswind speed (m / s) refers to the wind's velocity perpendicular to the flight direction, i.e., the lateral impact of the wind on the flight direction. The calculation formula for this feature is:

[0070] ,

[0071] Where θ is the angle between the flight direction and the east direction (unit: degree), U and V are the east component (unit: m / s) and north component (unit: m / s) of the wind speed, respectively.

[0072] 3. Wind shear: Wind shear refers to the rate of change of wind speed or direction in space (horizontally or vertically) (unit: s -1 ), usually divided into vertical wind shear (S V , unit: s -1 ) and horizontal wind shear (S H , unit: s -1 ). The calculation formula is:

[0073] S V = ΔWs / Δz, along the z direction,

[0074] S H = ΔWs / Δx, along the x-direction,

[0075] or S H = ΔWs / Δy , along the y direction,

[0076] Where Ws is the wind speed (in m / s), and Δ represents the difference. In the x-direction, ΔWs is W(x+1)-W(x). Vertical wind shear is often used to analyze the effect of altitude changes on wind speed, while horizontal wind shear is used to analyze wind speed variations at different locations at the same altitude.

[0077] 4. Turbulence Intensity: Turbulence intensity (I) is a dimensionless parameter that measures the ratio of wind speed fluctuations to the average wind speed in the atmosphere. It is usually used to describe the degree of wind fluctuation and assess the degree of turbulence during flight. The higher the turbulence intensity, the more severe the turbulence during flight. It is defined as the ratio of the standard deviation of wind speed to the average wind speed, and the calculation formula is: , where u′ is the root mean square of velocity pulsation, that is, the root mean square value of the wind speed fluctuation part, in m / s. is the average wind speed in m / s.

[0078] 5. Bumps

[0079] Turbulence is typically defined as the violent up-and-down or side-to-side vibration of an aircraft caused by atmospheric turbulence during flight. Turbulence can be expressed using the Dutton empirical parameter (E, dimensionless), which is expressed as:

[0080] E=1.25S H +0.25S V 2 +10.5,

[0081] Among them S H and S V They are horizontal wind shear and vertical wind shear respectively.

[0082] After acquiring new meteorological features that may affect low-altitude aircraft flight through feature enhancement, the original low-altitude meteorological features calculated by the Ruitu-PALM coupling system (temperature, air pressure, wind speed, wind direction, humidity, visibility, etc.) are further integrated with enhanced meteorological features (headwind speed, crosswind speed, wind shear, turbulence intensity, and turbulence). These features are then stored in a structured database according to a unified time (e.g., hour, day, month) and spatial grid. This ultimately forms an enhanced low-altitude meteorological information database covering multiple years, 10-meter resolution, and the study area, including timestamps (year, month, day, hour), spatial coordinates (longitude, latitude, grid index), and various meteorological variable fields. This database contains not only the original low-altitude meteorological feature data calculated by the Ruitu-PALM coupling system, but also the enhanced meteorological feature data, providing a data foundation for subsequent low-altitude route planning, risk assessment, and other applications.

[0083] This embodiment also includes steps S6 and S7, wherein step S6 is: constructing a neural network for analyzing the spatiotemporal distribution characteristics of low-altitude meteorological elements, using the ten-meter-precision low-altitude meteorological multi-year enhanced database of step S5 to train the neural network, and obtaining a spatiotemporal distribution characteristic analysis model of low-altitude meteorological elements.

[0084] Applying mathematical statistical methods such as time series analysis, Fourier transform, and wavelet analysis to the multi-year enhanced low-altitude meteorological database, we identify the temporal and trend characteristics of key meteorological elements, such as wind speed, wind direction, temperature gradient, pressure variation, wind shear, and turbulence intensity, including hourly, diurnal, monthly, and seasonal variations. We also calculate statistical characteristics of these elements, such as mean, variance, extreme value, probability distribution function, spatial gradient, and covariance matrix. This allows us to obtain meteorological information characteristics for the study area at different time scales and at different low-altitude aircraft flight levels (0-2000m). These characteristics include high / low value areas, areas of dramatic gradient changes, changes in the frequency and intensity of extreme events (such as strong winds), the effect of valley wind direction shifts from morning to evening, and temperature hysteresis (such as radiative cooling delay). Furthermore, we incorporate classification algorithms from artificial intelligence technologies (such as K-Means and support vector machines (SVM)) to automatically identify and classify low-altitude meteorological conditions across the study area in each season. Figure 8 What is shown is the long-term characteristic map of the average 10-meter altitude wind field in the starting area of ​​a certain district in July 2024 obtained by this method. The map shows the areas with low and high long-term wind speeds at the 10-meter altitude layer in that month, which can be used for subsequent route planning of various low-altitude aircraft in July.

[0085] Step S7 is: inputting meteorological data into the temporal and spatial distribution characteristic analysis model of low-altitude meteorological elements to obtain meteorological characteristics of different time lengths, and then formulating corresponding routes according to the characteristics of each type of aircraft.

[0086] According to different scenario types, priority indicators and meteorological conditions, low-altitude aircraft routes are planned for different scenario cases. There are mainly three scenario types, as shown in Table 1. The applicable conditions and key meteorological elements of different low-altitude flight application scenarios are shown in the table: (1) Logistics distribution. This task focuses on timeliness. Therefore, it is necessary to identify areas with strong "narrow channel effect" (such as crosswinds > 15m / s) in the study area, and generate safe paths by combining terrain detour algorithms to significantly reduce the average flight time. (2) Emergency rescue dispatch. This task focuses on material accessibility. Therefore, it is necessary to balance the "shortest path" and "meteorological risks" and avoid areas with poor visibility and high turbulence intensity to achieve the goal of significantly shortening the material transportation time in emergency rescue situations. (3) Sightseeing routes. This task focuses on stability. Therefore, it is necessary to avoid areas with long-lasting turbulence to reduce the fluctuation of passenger-perceived acceleration and achieve "feel-free flight" route planning. Among them, different application scenarios are suitable for different types of low-altitude aircraft (consumer-grade drones, industrial-grade drones, manned aircraft, traditional fixed-wing aircraft, helicopters, electric vertical take-off and landing aircraft eVTOL), and are suitable for different airspace altitudes (<120m, 120~300m, 300m~1000m).

[0087] After obtaining the long-term low-altitude meteorological characteristics of the study area with a horizontal resolution of 10m, the present invention can provide meteorological safety information with high temporal and spatial resolution in different scenarios and on different types of aircraft routes based on the low-altitude meteorological element data at different altitudes and directions in the study area, and give planning suggestions for aircraft routes.

[0088] Table 1

[0089] Application Scenario low-altitude aircraft Flight altitude focus on Key meteorological elements Logistics and distribution Consumer drones, industrial drones <120m Timeliness Headwind, crosswind emergency rescue Industrial drones, traditional fixed-wing aircraft, manned aircraft, 120~300m Accessibility of supplies Visibility, turbulence intensity Sightseeing routes Manned aircraft, helicopters, electric vertical take-off and landing (eVTOL) aircraft 300m~1000m stability Bumps

[0090] The above examples show that the present invention is superior to the existing technology in the following aspects: (1) The present invention is not limited to the safety assessment of aircraft routes during a short period of time, but can provide long-term meteorological information covering the route range. Therefore, its conclusions on route safety are representative, universal and reliable in the long term. (2) Unlike the previous route meteorological simulation with a resolution of hundreds of meters or kilometers, the horizontal spatial resolution of the route meteorological information obtained by this method can reach ten meters. Therefore, it can be applied to the planning of low-altitude flight routes of low-altitude aircraft, which are usually hundreds of meters or 1-2 kilometers. (3) The route meteorological information obtained by this method is complete in space and time, covering various meteorological factors that can affect the flight of low-altitude aircraft in different seasons, months, days, hours and different altitudes above different directions in the study area. Therefore, route planning can be carried out in different types of scenes, different periods and different types of aircraft.

[0091] While the application has been described by way of example with reference to certain embodiments thereof, it is to be understood that the application is not limited to the embodiments described above, but intrinsically extends to various modifications and changes in form and details. Therefore, the scope of the application should be determined not by the embodiments described above, but by the scope of the appended claims and their equivalents.

Claims

1. A method for acquiring long-term characteristics of meteorological elements at different altitudes at low altitudes with a spatial resolution of ten meters, characterized in that: The following steps are involved: S1. Obtaining multiple years of three-dimensional meteorological historical data covering the low-altitude flight target area in the Ruitu forecast system, including precipitation, ground and high-altitude meteorological elements, and weather situation data; S2. Generate the underlying surface shape of the PALM model based on the terrain and building blank model data of the target area, add the side and top surfaces of the computational domain to generate the computational domain mesh, and combine the geographical features and building distribution to divide the computational domain mesh using a combination of structured and unstructured grids; S3. Extract meteorological information at multiple moments in the target area from the three-dimensional historical meteorological data calculated by the Ruitu system over many years, and remap the spatial grid to serve as the initial and boundary meteorological fields driving the PALM model calculation. Then, add the model underlying surface shape, computational domain, and computational grid based on the building white model data to form the Ruitu-PALM coupling system. S4. Using the RITU-PALM coupling system, conduct long-term three-dimensional numerical simulation of historical meteorological data with a ten-meter accuracy in the target area, and obtain multi-year low-altitude meteorological data at different vertical heights with a horizontal resolution of ten meters in the target area; S5. Enhance the meteorological characteristics of the multi-year low-altitude meteorological data calculated by the Ruitu-PALM coupling system, and integrate the low-altitude meteorological characteristics calculated by the Ruitu-PALM coupling system with the enhanced meteorological characteristics to form a multi-year low-altitude meteorological enhancement database with a ten-meter accuracy in the target area; S6, constructing a neural network for analyzing the spatiotemporal distribution characteristics of low-altitude meteorological elements, using the ten-meter-precision low-altitude meteorological multi-year enhanced database from step S5 to train the neural network, and obtaining a spatiotemporal distribution characteristic analysis model for low-altitude meteorological elements; S7, input the meteorological data into the temporal and spatial distribution characteristic analysis model of low-altitude meteorological elements to obtain meteorological characteristics of different time lengths, and then formulate corresponding routes according to the characteristics of each type of aircraft.

2. The method for acquiring long-term characteristics of meteorological elements at different low altitude layers with a spatial resolution of ten meters according to claim 1, characterized in that: Step S1 specifically includes: obtaining high-altitude meteorological data of the target area with a time length of 1-3 years, with a horizontal resolution of 3km, different altitude layers, integration for 48 hours, and an output frequency of 1 hour.

3. The method for acquiring long-term characteristics of meteorological elements at different low altitude layers with a spatial resolution of ten meters according to claim 1, characterized in that: In step S2, a structured grid is used in areas not adjacent to buildings, and an unstructured grid is used in areas adjacent to buildings. The grid lines in the structured grid are arranged along the direction of the rectangular coordinate system to form a regular grid structure, which is suitable for the simulation of regular geometric shapes; the grid cells of the unstructured grid are of arbitrary shape and are not arranged along the direction of the rectangular coordinate system, which is suitable for the simulation of irregular geometric shapes.

4. The method for acquiring long-term characteristics of meteorological elements at different low altitude layers with a spatial resolution of ten meters according to claim 3, characterized in that: Step S2 also includes: controlling and optimizing the mesh quality: mesh size, mesh orthogonality, and mesh distortion, achieving mesh quality control by adjusting mesh parameters and mesh optimization algorithms, and verifying the mesh quality and mesh quantity.

5. The method for acquiring long-term characteristics of meteorological elements at different low altitude layers with a spatial resolution of ten meters according to claim 4, characterized in that: Step S3 includes the following sub-steps: S3.

1. Extract the Ruitu historical meteorological data covering the PALM model simulation domain and its boundaries, including temperature, wind speed, wind direction, and humidity at different times, based on the simulation domain settings in the PLALM model input file and the Ruitu system calculation parameter settings. S3.

2. Grid mapping is performed on the extracted Ruitu meteorological data. The meteorological data is mapped from the computational grid distribution of Ruitu to the computational grid of the PALM model through spatial reconstruction relationship. S3.

3. Convert the output format of the Ruitu system, i.e., a three-dimensional array, into an input file in the PALM model recognition format to drive the operation of the PALM model; S3.

4. Perform dynamic operation of the PALM model, and dynamically update the meteorological data of the extracted boundary conditions and initial conditions over time to obtain the temporal changes of the mesoscale meteorological field calculated by the Ruitu system; S3.

5. Based on the generated underlying surface shape bottom surface, computational domain, and computational domain hybrid grid of the simulation area, the extracted Ruitu system’s multi-year hourly simulation data is used to update the hourly boundary conditions and initial conditions to achieve coupling between the Ruitu system and the PALM model.

6. The method for acquiring long-term characteristics of meteorological elements at different low altitude layers with a spatial resolution of ten meters according to claim 4, characterized in that: In step S5, the wind speed characteristics of the headwind, the crosswind speed characteristics, the wind shear characteristics, the turbulence intensity characteristics, and the turbulence characteristics are enhanced; The positive wind speed characteristic is enhanced and is calculated as follows: ， Where θ is the angle between the flight direction and the east direction, U and V are the easterly and northerly components of the wind speed, respectively; The crosswind speed characteristic is enhanced and is calculated as follows: ， Where θ is the angle between the flight direction and the east direction, U and V are the easterly and northerly components of the wind speed, respectively; The wind shear characteristic is enhanced and is calculated as follows: S V = ΔWs / Δz, along the z direction, S H = ΔWs / Δx, along the x-direction, or S H = ΔWs / Δy, along the y direction, Among them, S V is the vertical wind shear, S H is the horizontal wind shear, Ws is the wind speed; The turbulence intensity characteristic is enhanced and is calculated as follows: , Where u′ is the root mean square of the velocity pulsation, that is, the root mean square value of the wind speed fluctuation part, is the average wind speed, I is the turbulence intensity; The bump feature is enhanced and calculated as follows: E=1.25S H +0.25S V 2 +10.5 Among them, S H and S V They are horizontal wind shear and vertical wind shear respectively.

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