A satellite-ground synchronized inversion method for mesoscale atmospheric dynamic parameters

Abstract

The present invention provides a satellite-ground synchronized inversion method for mesoscale atmospheric dynamic parameters, comprising the following steps: distributing multiple wind profiler radar stations in a study area; using an Aeolus satellite equipped with a Doppler wind lidar and flying along a set orbit to obtain satellite-ground synchronized wind profiler product data; and performing satellite-ground synchronized inversion of atmospheric dynamic parameters. The present invention utilizes new high-temporal and spatial resolution observational data and datasets, such as wind profiler radar, Aeolus satellites, Sunflower-8 satellites, and hourly reanalysis data from European centers, to construct a satellite-ground synchronized triangular observation network. The network inverts mesoscale atmospheric dynamic parameters, such as horizontal divergence and relative vorticity, to compensate for the shortcomings of wind profiler radar observations in remote areas and the lack of high-resolution atmospheric dynamic parameter observations before severe convection is triggered. This provides important observational support for monitoring, forecasting, and early warning of small and medium-scale convective weather.

Description

Technical Field

[0001] The present invention belongs to the field of mesoscale meteorological technology, and in particular relates to a satellite-ground synchronous inversion method for mesoscale atmospheric dynamic parameters. Background Art

[0002] Severe convective weather is a serious weather disaster. Its forecast accuracy depends largely on errors in numerical forecast models and initial observations. The latter, in turn, relies heavily on the availability of spatiotemporally continuous observations of atmospheric environmental variables such as wind, temperature, and humidity before the convective event is triggered. Numerous studies, both domestic and international, have demonstrated the crucial role of thermal instability and dynamic lift signals in the triggering of severe convection and its subsequent evolution.

[0003] Regarding the dynamic lift signal ahead of convection, most previous studies have derived dynamic parameters such as horizontal wind vertical shear and temperature advection from measured vertical wind profiles. Alternatively, they have used the triangulation method, a method based on three non-collinear sounding or wind profiler radar stations, to infer atmospheric dynamic parameters such as horizontal divergence and relative vorticity above the triangle using the latitude and longitude coordinates of each triangle's vertices and vertical wind profile data. This method avoids the errors introduced by interpolating the wind field to a grid. However, sounding data suffer from low observation frequency (operational soundings are only twice a day) and long distances between stations. Therefore, this method cannot continuously characterize the evolution of atmospheric dynamic structure characteristics. Furthermore, the high observation and maintenance costs of wind profiler radar stations have led to their sparse and uneven distribution, especially in economically underdeveloped or remote areas. These sites struggle to meet the triangulation method's requirements for sharp angles, equal sides, and close proximity between adjacent stations. Consequently, the horizontal divergence and vorticity inferred from horizontal winds observed by three wind profiler radars are subject to significant errors, making it difficult to capture the development of mesoscale convective systems at national or regional scales.

[0004] The Aeolus satellite, launched by the European Space Agency in 2018, carries a Doppler wind lidar capable of observing large-scale wind profiles. However, the Aeolus satellite can only provide wind profiles along its orbit and lacks the ability to construct triangles to invert atmospheric dynamic parameters such as horizontal divergence and relative vorticity.

[0005] Therefore, how to invert and obtain atmospheric dynamic parameter observation data with larger scale and denser spatial distribution is an urgent problem that needs to be solved. Summary of the Invention

[0006] In view of the defects of the existing technology, the present invention provides a satellite-ground synchronous inversion method for mesoscale atmospheric dynamic parameters, which can effectively solve the above problems.

[0007] The technical solution adopted in the present invention is as follows:

[0008] The present invention provides a satellite-ground synchronous inversion method for mesoscale atmospheric dynamic parameters, comprising the following steps:

[0009] Step 1: Distribute multiple wind profiler radar sites in a study area; each wind profiler radar site is equipped with a wind profiler radar, and the wind profiler radar is used to observe and obtain radar wind profiler product data; wherein the radar wind profiler product data includes the following parameters: observation time, wind profiler radar station location, and radar wind profiler observation data;

[0010] Step 2: The Aeolus satellite is equipped with a Doppler wind laser radar and flies along a set orbit. The projection of its flight trajectory on the ground forms the Aeolus satellite ground track. When it flies to a certain position, its projection on the ground forms a subsatellite point located above the Aeolus satellite ground track. The Aeolus satellite is used to observe and obtain Aeolus satellite wind profile product data. The Aeolus satellite wind profile product data includes the following parameters: observation time, subsatellite point position, wind profile observation data of the Aeolus satellite Mie channel, and wind profile observation data of the Aeolus satellite Rayleigh channel.

[0011] Step 3: Satellite-ground synchronous inversion process:

[0012] Step 3.1: Select one wind profiler radar site from the multiple wind profiler radar sites in step 1 as the target wind profiler radar site S1, whose location is Where λ1 is the longitude of the target wind profiler radar site S1, is the latitude value of the target wind profile radar site S1; selects the target observation time T1, reads the radar wind profile product data, and obtains the radar wind profile observation data observed by the target wind profile radar site S1 at the target observation time T1;

[0013] Step 3.2: Preset the search radius R and time tolerance △T;

[0014] The search area is defined as Area, with the target wind profiler radar site S1 as the center point and the search radius R from the target wind profiler radar site S1. The time search range is [T1-△T, T1+△T]. The Aeolus satellite wind profiler product data determined in step 2 are searched to determine whether the Aeolus satellite ground track within the range of [T1-△T, T1+△T] passes through the search area. If not, perform satellite-ground synchronous inversion for the next wind profiler radar site. If yes, proceed to step 3.3.

[0015] Step 3.3: With the target wind profiler radar site S1 as the vertex, select two vertices, denoted as vertex S2 and vertex S3, from the Aeolus satellite ground track located in the search area Area and with the observation time in the range [T1-△T, T1+△T], so that the target wind profiler radar site S1, vertex S2, and vertex S3 form an equilateral triangle.

[0016] Step 3.4, using the cloud observation data of the Sunflower-8 satellite, determine the cloud coverage of the search area Area in the time range of [T1-△T, T1+△T]. If it is determined that there is cloud, obtain the wind profile observation data of the Mie channel of the Aeolus satellite at the vertex S2 in the range of [T1-△T, T1+△T] as the Aeolus satellite wind profile observation data of the vertex S2; obtain the wind profile observation data of the Mie channel of the Aeolus satellite at the vertex S3 in the range of [T1-△T, T1+△T] as the Aeolus satellite wind profile observation data of the vertex S3; then execute step 3.5;

[0017] If it is determined that there is no cloud, the wind profile observation data of the Aeolus satellite Rayleigh channel with the vertex S2 in the range of [T1-△T, T1+△T] is obtained as the Aeolus satellite wind profile observation data of the vertex S2; the wind profile observation data of the Aeolus satellite Rayleigh channel with the vertex S3 in the range of [T1-△T, T1+△T] is obtained as the Aeolus satellite wind profile observation data of the vertex S3; then step 3.5 is executed;

[0018] Step 3.5, get the position of vertex S2 and the position of vertex S3 Among them, λ2 and Represents the longitude and latitude values ​​of vertex S2; λ3 and Represents the longitude and latitude values ​​of vertex S3;

[0019] Preset the vertical detection range and number of vertical layers to be inverted, so as to obtain the heights of multiple continuous vertical standard layers within the vertical detection range;

[0020] Since the Aeolus satellite wind profiler observation data of vertex S2 determined in step 3.4, the Aeolus satellite wind profiler observation data of vertex S3 determined in step 3.4, and the radar wind profiler observation data observed by the target wind profiler radar site S1 determined in step 3.1 at the target observation time T1 have different temporal and spatial resolutions, the Aeolus satellite wind profiler observation data of vertex S2 at each vertical standard layer, the Aeolus satellite wind profiler observation data of vertex S3 at each vertical standard layer, and the radar wind profiler observation data of the target wind profiler radar site S1 at each vertical standard layer are obtained by interpolation calculation;

[0021] The wind profile observation data of Aeolus satellite at each vertical standard layer at vertex S2 includes the latitudinal wind speed component u2 and the longitudinal wind speed component v2;

[0022] The wind profile observation data of Aeolus satellite at each vertical standard layer at vertex S3 includes the latitudinal wind speed component u3 and the longitudinal wind speed component v3;

[0023] Perform vector decomposition on the radar wind profiler observation data of the target wind profiler radar station S1 at each vertical standard layer to obtain the latitudinal wind speed component u1 and the longitudinal wind speed component v1;

[0024] The position of vertex S2 The position of vertex S3 Target wind profiler radar site S1 location Since the three vertices S1S2S3 are not far apart, the equilateral triangle formed by S1 S2S3 is approximately treated as a plane triangle. A plane rectangular coordinate system is established with the position of S1 as the origin. The x-axis and y-axis are the east-west and north-south directions respectively. According to customary definition, east and north are the positive directions.

[0025] In the plane rectangular coordinate system, the coordinates of S1, S2, and S3 are: (x1, y1), (x2, y2), (x3, y3);

[0026] The east-west distance x3-x1 and the north-south distance y3-y1 between S3 and S1 are calculated using the following formula:

[0027]

[0028]

[0029] The east-west distance x2-x1 and the north-south distance y2-y1 between S2 and S1 are calculated using the following formula:

[0030]

[0031]

[0032] Where: R is the average radius of the earth, π is pi;

[0033] Substituting into the following formula, we can calculate the horizontal divergence D and relative vorticity within the triangle of each vertical standard layer:

[0034]

[0035]

[0036] Preferably, in step 3.4, the cloud coverage of the search area Area is determined within the time range [T1-△T, T1+△T] by the following method:

[0037] Obtain cloud data from the Sunflower-8 satellite in the search area Area within the time range [T1-△T, T1+△T]. If the ratio of the number of grid points with clouds to the total number of grid points in the search area Area is greater than 1 / 2, it is determined to be cloud-covered; otherwise, it is determined to be cloud-free.

[0038] The satellite-ground synchronous inversion method for mesoscale atmospheric dynamic parameters provided by the present invention has the following advantages:

[0039] The present invention utilizes new observation data and data sets with high temporal and spatial resolution, such as wind profiler radar, Aeolus satellite, Sunflower-8 satellite, and hourly reanalysis data from European centers, to construct a satellite-ground synchronized triangular observation network to invert mesoscale atmospheric dynamic parameters such as horizontal divergence and relative vorticity, in order to make up for the shortcomings of wind profiler radar observations in remote areas and the lack of observations of atmospheric dynamic parameters before the triggering of severe convection, and provide important observation support for small and medium-scale convective weather monitoring, forecasting, and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic flow chart of a satellite-ground synchronous inversion method for mesoscale atmospheric dynamic parameters provided by the present invention;

[0041] Figure 2 Provide the present invention with a ground track map of the Aeolus satellite;

[0042] Figure 3 A schematic diagram of the equilateral triangle construction method provided by the present invention;

[0043] Figure 4 The rainfall distribution across the country at a specific time (colored) and the Aeolus satellite ground track at that time were obtained based on observations from ground automatic stations;

[0044] Figure 5 This is the distribution map of the horizontal divergence of each altitude layer over the triangle in eastern Anhui at a specific time obtained based on the inversion of the triangle method. DETAILED DESCRIPTION

[0045] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] This method belongs to the field of mesoscale meteorology, specifically involving how to collaboratively utilize satellite and ground-based remote sensing data to carry out the inversion of atmospheric dynamic parameters. The relevant algorithms can be used for mesoscale convective weather monitoring and forecasting and early warning.

[0047] The present invention utilizes new observation data and data sets with high temporal and spatial resolution, such as wind profiler radar, Aeolus satellite, Sunflower-8 satellite, and hourly reanalysis data from European centers, to construct a satellite-ground synchronized triangular observation network to invert mesoscale atmospheric dynamic parameters such as horizontal divergence and relative vorticity, in order to make up for the shortcomings of wind profiler radar observations in remote areas and the lack of observations of atmospheric dynamic parameters before the triggering of severe convection, and provide important observation support for small and medium-scale convective weather monitoring, forecasting, and early warning.

[0048] refer to Figure 1 The present invention provides a method for satellite-ground synchronous inversion of mesoscale atmospheric dynamic parameters, comprising the following steps:

[0049] Step 1: Distribute multiple wind profiler radar sites in a study area; each wind profiler radar site is equipped with a wind profiler radar, and the wind profiler radar is used to observe and obtain radar wind profiler product data; wherein the radar wind profiler product data includes the following parameters: observation time, wind profiler radar station location, and radar wind profiler observation data;

[0050] Specifically, wind profiler radar, a new generation of ground-based atmospheric remote sensing systems, offers the advantages of continuous, unmanned operation, all-weather monitoring, and the ability to provide three-dimensional wind data in the lower atmosphere. In recent years, wind profiler radars have been widely deployed across China, providing real-time vertical wind profiles with a time resolution of six minutes.

[0051] Real-time wind vertical profile product data files from a mesoscale wind profiler radar network can be formatted as txt. The file name includes the station number, observation date and time, product identifier, radar model, and other information. The file name is represented as Z_RADR_IIiii_WPRD_CAMS_NWQC_Product Identifier_Radar Model_QI_yyyyMMddhhmmss.TXT. Take the following file name as an example: Z_RADA_54511_WPRD_CAMS_NWQC_OOBS_LC_QI_20180601000000.TXT, where Z represents a domestic exchange file; RADA represents radar data; IIiii represents the wind profiler radar station ID; WPRD represents wind profiler radar data; yyyyMMddhhmmss represents observational universal time (where yyyy is year, mm is month, dd is day, hh is hour, mm is minute, and ss is second, the same below); LC represents the radar model identifier, indicating an L-band boundary layer wind profiler radar; and ROBS represents the wind profiler radar product identifier. This represents a real-time product data file.

[0052] The daily real-time wind vertical profile product data text files for each station in the wind profiler radar network are stored in compressed format in a folder with the same date. The folder name format is yyyymmdd (year-month-day, UTC), for example, 20180601, 20180602, etc. The number of compressed files in each folder corresponds to the number of stations that returned data that day. Subfolders are created within the folder, each named after the station number in the format IIIiii, for example, 54511, 54399, etc. The compressed files for each station's daily real-time wind vertical profile product data are unzipped into the corresponding folder. The time resolution of the real-time wind vertical profile product data text files is 6 minutes. If there are no missing observations, each station subfolder contains 240 observation files each day.

[0053] To select a wind profiler radar station at a specific time, read the station's basic parameters, such as latitude, longitude, and altitude, as well as the radar wind profiler observation data at that time. Then, perform vector decomposition of the horizontal wind at the sampling height to obtain the latitudinal and longitudinal wind speed components. If the observation file for a station at a specific time is missing, the column is set to the missing value NAN.

[0054] Step 2: The Aeolus satellite is equipped with a Doppler wind laser radar and flies along a set orbit. The projection of its flight trajectory on the ground forms the Aeolus satellite ground track. When it flies to a certain position, its projection on the ground forms a subsatellite point located above the Aeolus satellite ground track. The Aeolus satellite is used to observe and obtain Aeolus satellite wind profile product data. The Aeolus satellite wind profile product data includes the following parameters: observation time, subsatellite point position, wind profile observation data of the Aeolus satellite Mie channel, and wind profile observation data of the Aeolus satellite Rayleigh channel.

[0055] As a specific implementation method, the Fengshen satellite flies in a sun-synchronous orbit with an altitude of about 320 kilometers and a revisit period of 7 days. Figure 2 The red and blue lines represent the ground tracks corresponding to the ascending and descending orbits around 06:00 and 18:00 local solar time, respectively. The black dots represent the locations of the wind profiler radar sites.

[0056] Aeolus satellite can provide Aeolus satellite wind profile product data from the ground to 30km altitude with a vertical resolution of 0.25 to 2km.

[0057] Step 3: Satellite-ground synchronous inversion process:

[0058] Step 3.1: Select one wind profiler radar site from the multiple wind profiler radar sites in step 1 as the target wind profiler radar site S1, whose location is Where λ1 is the longitude of the target wind profiler radar site S1, is the latitude value of the target wind profile radar site S1; selects the target observation time T1, reads the radar wind profile product data, and obtains the radar wind profile observation data observed by the target wind profile radar site S1 at the target observation time T1;

[0059] Step 3.2: Preset the search radius R and time tolerance △T;

[0060] The search area is defined as Area, with the target wind profiler radar site S1 as the center point and the search radius R from the target wind profiler radar site S1 as the search radius R. R can be set to 2 meters. The time search range is [T1-△T, T1+△T]. The Aeolus satellite wind profiler product data determined in step 2 is searched to determine whether the Aeolus satellite ground track within the range [T1-△T, T1+△T] passes through the search area. If not, perform a ground-to-satellite synchronous inversion for the next wind profiler radar site. If yes, proceed to step 3.3.

[0061] Step 3.3: With the target wind profiler radar site S1 as the vertex, select two vertices, denoted as vertex S2 and vertex S3, from the Aeolus satellite ground track located in the search area Area and with the observation time in the range [T1-△T, T1+△T], so that the target wind profiler radar site S1, vertex S2, and vertex S3 form an equilateral triangle.

[0062] Step 3.4, using the cloud observation data of the Sunflower-8 satellite, determine the cloud coverage of the search area Area in the time range of [T1-△T, T1+△T]. If it is determined that there is cloud, obtain the wind profile observation data of the Mie channel of the Aeolus satellite at the vertex S2 in the range of [T1-△T, T1+△T] as the Aeolus satellite wind profile observation data of the vertex S2; obtain the wind profile observation data of the Mie channel of the Aeolus satellite at the vertex S3 in the range of [T1-△T, T1+△T] as the Aeolus satellite wind profile observation data of the vertex S3; then execute step 3.5;

[0063] If it is determined that there is no cloud, the wind profile observation data of the Aeolus satellite Rayleigh channel with the vertex S2 in the range of [T1-△T, T1+△T] is obtained as the Aeolus satellite wind profile observation data of the vertex S2; the wind profile observation data of the Aeolus satellite Rayleigh channel with the vertex S3 in the range of [T1-△T, T1+△T] is obtained as the Aeolus satellite wind profile observation data of the vertex S3; then step 3.5 is executed;

[0064] Specifically, the wind profile data from the Aeolus satellite's Rayleigh channel refers to wind data observed in an aerosol-free atmosphere, while the wind profile data from the Aeolus satellite's Mie channel refers to wind data derived from the backscatter signal of dense clouds caused by aerosols and clouds. In summer, the Rayleigh channel winds and the Mie channel winds can approximately represent the wind fields under clear and cloudy conditions, respectively.

[0065] The following method can be used to determine the cloud coverage of the search area within the time range [T1-△T, T1+△T]:

[0066] Obtain cloud data from the Sunflower-8 satellite in the search area Area within the time range [T1-△T, T1+△T]. If the ratio of the number of grid points with clouds to the total number of grid points in the search area Area is greater than 1 / 2, it is determined to be cloud-covered; otherwise, it is determined to be cloud-free.

[0067] Specifically, because the Mie channel wind product has a higher horizontal resolution (available observations) than the Rayleigh channel wind product, cloud observations from the Sunflower-8 satellite are required before constructing the triangular network to determine the cloud cover around the wind profiler radar site. This helps determine which wind product to use. A cloud top brightness temperature less than -30°C on Sunflower-8's channel 13 (central wavelength 10.4 microns) can be used as the cloud criterion. This selection is based on the fact that channel 13 is an infrared channel, which avoids the situation where no observations are available at night.

[0068] Specifically, we can select the Sunflower-8 satellite data within 10 minutes of the observation time, count the number of cloud grid points within 2°×2° around the wind profiler radar site, and if the number of cloud grid points accounts for more than 1 / 2 of the total number of grid points, then the area above the wind profiler radar site is determined to be cloudy, and use the Mie channel available observation data to construct a triangular network, otherwise use the Rayleigh channel wind data.

[0069] Step 3.5, get the position of vertex S2 and the position of vertex S3 Among them, λ2 and Represents the longitude and latitude values ​​of vertex S2; λ3 and Represents the longitude and latitude values ​​of vertex S3;

[0070] Preset the vertical detection range and number of vertical layers to be inverted, so as to obtain the heights of multiple continuous vertical standard layers within the vertical detection range;

[0071] Since the Aeolus satellite wind profiler observation data of vertex S2 determined in step 3.4, the Aeolus satellite wind profiler observation data of vertex S3 determined in step 3.4, and the radar wind profiler observation data observed by the target wind profiler radar site S1 determined in step 3.1 at the target observation time T1 have different temporal and spatial resolutions, the Aeolus satellite wind profiler observation data of vertex S2 at each vertical standard layer, the Aeolus satellite wind profiler observation data of vertex S3 at each vertical standard layer, and the radar wind profiler observation data of the target wind profiler radar site S1 at each vertical standard layer are obtained by interpolation calculation;

[0072] For example, due to the different observation altitudes of Aeolus satellite and wind profiler radar, they can be uniformly interpolated to an altitude layer with a bottom layer of 150 meters and an altitude resolution of 120 meters.

[0073] In the present invention, since the sub-satellite point trajectory is not far from the wind profiler radar, it can be approximated as a plane triangle. In addition, when using the triangle method to calculate dynamic parameters such as horizontal divergence and relative vorticity, the shape of the triangle will cause errors in the calculation results: the triangle constructed by the present invention is an equilateral triangle. Among them, if vertex S2 and vertex S3 are exactly sub-satellite points, the Aeolus satellite wind profiler observation data of the corresponding position and time can be directly obtained. If vertex S2 and vertex S3 are not sub-satellite points, the Aeolus satellite wind profiler observation data of the corresponding position and time can be obtained by interpolating the sub-satellite background wind field to vertex S2 and vertex S3.

[0074] For the missing values ​​of the low-level Aeolus satellite, the hourly reanalysis wind field data of the European center at the nearest grid point are used to replace them. They also need to be interpolated to an altitude layer with a bottom layer of 150 meters and an altitude resolution of 120 meters.

[0075] like Figure 3 , which is a schematic diagram of the equilateral triangle construction method. The red dots represent wind profiler radar sites, the black dashed line represents the Aeolus satellite ground track, the black five-pointed star represents the sub-satellite point, and the black dots represent the points on the Aeolus satellite track selected to construct the triangle.

[0076] The wind profile observation data of Aeolus satellite at each vertical standard layer at vertex S2 includes the latitudinal wind speed component u2 and the longitudinal wind speed component v2;

[0077] The wind profile observation data of Aeolus satellite at each vertical standard layer at vertex S3 includes the latitudinal wind speed component u3 and the longitudinal wind speed component v3;

[0078] Perform vector decomposition on the radar wind profiler observation data of the target wind profiler radar station S1 at each vertical standard layer to obtain the latitudinal wind speed component u1 and the longitudinal wind speed component v1;

[0079] The position of vertex S2 The position of vertex S3 Target wind profiler radar site S1 location Since the three vertices S1S2S3 are not far apart, the equilateral triangle formed by S1 S2S3 is approximately treated as a plane triangle. A plane rectangular coordinate system is established with the position of S1 as the origin. The x-axis and y-axis are the east-west and north-south directions respectively. According to customary definition, east and north are the positive directions.

[0080] In the plane rectangular coordinate system, the coordinates of S1, S2, and S3 are: (x1, y1), (x2, y2), (x3, y3);

[0081] The east-west distance x3-x1 and the north-south distance y3-y1 between S3 and S1 are calculated using the following formula:

[0082]

[0083]

[0084] The east-west distance x2-x1 and the north-south distance y2-y1 between S2 and S1 are calculated using the following formula:

[0085]

[0086]

[0087] Where: R is the average radius of the earth, π is pi;

[0088] Substituting into the following formula, we can calculate the horizontal divergence D and relative vorticity within the triangle of each vertical standard layer:

[0089]

[0090]

[0091] Therefore, the present invention couples the ground-based wind profiler radar remote sensing of a single station with the wind profile obtained by the space-borne Aeolus satellite to construct a satellite-ground synchronized triangle, and inverts to obtain atmospheric dynamic parameter observation data with a larger scale and denser spatial distribution.

[0092] Figure 4The following is the national rainfall distribution (colored) and the Aeolus satellite ground track (black dashed line) from 06:00 to 07:00 Beijing time on July 20, 2020, obtained based on observations from ground automatic stations. After 06:00 on July 20, 2020, precipitation occurred in Anhui, southern Jiangsu, Jiangxi, and northern Zhejiang, with the maximum hourly rainfall at some stations reaching 15mm. This was the time when the Aeolus satellite was passing, so this technology selected Gaochun Station in western Jiangsu, combined its wind profiler radar with the Aeolus satellite, and used the triangulation method to calculate the distribution of horizontal divergence at each altitude layer above the triangle, as shown in the figure below. Figure 5 shown. Figure 5 The distribution of horizontal divergence at each altitude layer over the triangle in eastern Anhui at 06:06 on July 20, 2020, obtained by inversion based on the triangle method. The red (blue) shadow represents divergence (convergence).

[0093] This indicates that the triggering of convection is inextricably linked to the dynamic uplift of low-level convergence and high-level divergence above it. Using the three-dimensional wind field inversions of convergence and divergence provided by wind profiler radars, we can analyze the changing dynamic conditions of the atmosphere before the triggering of severe convective weather, precisely capturing the upward motion within mesoscale networks and providing a basis for determining the occurrence and development of mesoscale convective systems.

[0094] Traditional operational sounding balloon observations, conducted twice daily, make it difficult to capture the subtle evolution of the atmosphere's thermodynamic conditions. Wind profiler radars, however, offer advantages such as all-weather continuous observations and a high degree of automation. They can provide real-time information on factors such as horizontal wind direction, horizontal wind speed, vertical velocity, and the refractive index structure constant, compensating for the discontinuous nature of sounding observations. However, due to the sparse and uneven spatial distribution of wind profiler radar sites, particularly in economically underdeveloped or remote areas like the west, it is even difficult to fully form an effective acute-angle triangle in key areas. This means that in some areas, only single-station wind profiler and wind shear products are available, with no access to atmospheric dynamics products such as horizontal divergence and vorticity. Alternatively, because three wind profiler radar sites are located far apart, the triangles they construct often fail to meet the acute angle requirement, resulting in large errors in the horizontal divergence and vorticity derived from the horizontal winds observed by the three wind profiler radars.

[0095] Based on the above problems, the present invention provides a satellite-ground synchronized inversion method for mesoscale atmospheric dynamic parameters, which has the following advantages:

[0096] (1) Based on the observation of wind profiler radar network with high temporal resolution (6 minutes), the present invention adds top-down observation of Aeolus satellite to realize the complementary advantages of ground-based remote sensing and satellite remote sensing in spatial coverage, and then uses the horizontal wind inversion of synchronous observation of satellite and ground to deduce atmospheric dynamic parameters such as divergence and vorticity.

[0097] (2) One of the main innovations of the present invention is that, with the help of satellite-ground synchronous wind profiler observation, by constructing an equilateral triangle, the spatial encrypted observation of the wind profile is realized, and the atmospheric boundary layer and lower troposphere atmospheric dynamic parameters of a larger spatial scale are inverted, which to a certain extent solves the pain point problem of being unable to form an effective triangle due to the sparse and uneven spatial layout of wind profiler radar sites.

[0098] (3) One of the main innovations of the present invention is that it makes good use of the extremely strong cloud observation capability of the Sunflower-8 geostationary meteorological satellite and gives full play to the advantage of the Aeolus satellite in being able to invert the wind field in both cloudy and clear sky conditions, making the horizontal wind used in the calculation more accurate (for example, in cloudy conditions, the Mie wind of the Aeolus satellite should be selected instead of the Rayleigh wind).

[0099] (4) One of the main innovations of the present invention is that it optimizes the shape of triangles. This algorithm avoids the generation of obtuse triangles when constructing triangles, ensuring the optimal shape of triangles, thereby effectively reducing the error in calculating atmospheric dynamic parameters using the triangle method.

[0100] Therefore, the present invention, based on the three-dimensional wind data provided by wind profiler radar and Aeolus satellites, can monitor more detailed vorticity, divergence, and their spatiotemporal evolution in real time. This facilitates a more detailed analysis of the structure of mesoscale systems and the changing characteristics of atmospheric dynamic conditions before severe convective weather is triggered, providing observational support for research on boundary layer clouds, pre-convection signals, and convective triggering mechanisms. Combining multi-source observation data such as sounding observations, multi-wavelength Doppler weather radar, dual-polarization radar, and ground-based automatic station observations can achieve detailed observations of the three-dimensional atmospheric thermodynamic structure from clear skies to cloud formation and rain-inducing events, thereby seamlessly capturing the changing characteristics of the atmosphere's vertical dynamics, heat, and water vapor, providing an important reference for severe convective weather monitoring, early warning, and forecasting.

[0101] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A satellite-ground synchronous inversion method for mesoscale atmospheric dynamic parameters, characterized in that: The following steps are involved: Step 1: Distribute multiple wind profiler radar sites in a study area; each wind profiler radar site is equipped with a wind profiler radar, and the wind profiler radar is used to observe and obtain radar wind profiler product data; wherein the radar wind profiler product data includes the following parameters: observation time, wind profiler radar station location, and radar wind profiler observation data; Step 2: The Aeolus satellite is equipped with a Doppler wind laser radar and flies along a set orbit. The projection of its flight trajectory on the ground forms the Aeolus satellite ground track. When it flies to a certain position, its projection on the ground forms a subsatellite point located above the Aeolus satellite ground track. The Aeolus satellite is used to observe and obtain Aeolus satellite wind profile product data. The Aeolus satellite wind profile product data includes the following parameters: observation time, subsatellite point position, wind profile observation data of the Aeolus satellite Mie channel, and wind profile observation data of the Aeolus satellite Rayleigh channel. Step 3: Satellite-ground synchronous inversion process: Step 3.1: Select one wind profiler radar site from the multiple wind profiler radar sites in step 1 as the target wind profiler radar site S1, whose location is Where λ1 is the longitude of the target wind profiler radar site S1, is the latitude value of the target wind profile radar site S1; selects the target observation time T1, reads the radar wind profile product data, and obtains the radar wind profile observation data observed by the target wind profile radar site S1 at the target observation time T1; Step 3.2: Preset the search radius R and time tolerance △T; The search area is defined as Area, with the target wind profiler radar site S1 as the center point and the search radius R from the target wind profiler radar site S1. The time search range is [T1-△T, T1+△T]. The Aeolus satellite wind profiler product data determined in step 2 are searched to determine whether the Aeolus satellite ground track within the range of [T1-△T, T1+△T] passes through the search area. If not, perform satellite-ground synchronous inversion for the next wind profiler radar site. If yes, proceed to step 3.

3. Step 3.3: With the target wind profiler radar site S1 as the vertex, select two vertices, denoted as vertex S2 and vertex S3, from the Aeolus satellite ground track located in the search area Area and with the observation time in the range [T1-△T, T1+△T], so that the target wind profiler radar site S1, vertex S2, and vertex S3 form an equilateral triangle. Step 3.4, using the cloud observation data of the Sunflower-8 satellite, determine the cloud coverage of the search area Area in the time range of [T1-△T, T1+△T]. If it is determined that there is cloud, obtain the wind profile observation data of the Mie channel of the Aeolus satellite at the vertex S2 in the range of [T1-△T, T1+△T] as the Aeolus satellite wind profile observation data of the vertex S2; obtain the wind profile observation data of the Mie channel of the Aeolus satellite at the vertex S3 in the range of [T1-△T, T1+△T] as the Aeolus satellite wind profile observation data of the vertex S3; then execute step 3.5; If it is determined that there is no cloud, the wind profile observation data of the Aeolus satellite Rayleigh channel with the vertex S2 in the range of [T1-△T, T1+△T] is obtained as the Aeolus satellite wind profile observation data of the vertex S2; the wind profile observation data of the Aeolus satellite Rayleigh channel with the vertex S3 in the range of [T1-△T, T1+△T] is obtained as the Aeolus satellite wind profile observation data of the vertex S3; then step 3.5 is executed; Step 3.5, get the position of vertex S2 and the position of vertex S3 Among them, λ2 and Represents the longitude and latitude values ​​of vertex S2; λ3 and Represents the longitude and latitude values ​​of vertex S3; Preset the vertical detection range and number of vertical layers to be inverted, so as to obtain the heights of multiple continuous vertical standard layers within the vertical detection range; Since the Aeolus satellite wind profiler observation data of vertex S2 determined in step 3.4, the Aeolus satellite wind profiler observation data of vertex S3 determined in step 3.4, and the radar wind profiler observation data observed by the target wind profiler radar site S1 determined in step 3.1 at the target observation time T1 have different temporal and spatial resolutions, the Aeolus satellite wind profiler observation data of vertex S2 at each vertical standard layer, the Aeolus satellite wind profiler observation data of vertex S3 at each vertical standard layer, and the radar wind profiler observation data of the target wind profiler radar site S1 at each vertical standard layer are obtained by interpolation calculation; The wind profile observation data of Aeolus satellite at each vertical standard layer at vertex S2 includes the latitudinal wind speed component u2 and the longitudinal wind speed component v2; The wind profile observation data of Aeolus satellite at each vertical standard layer at vertex S3 includes the latitudinal wind speed component u3 and the longitudinal wind speed component v3; Perform vector decomposition on the radar wind profiler observation data of the target wind profiler radar station S1 at each vertical standard layer to obtain the latitudinal wind speed component u1 and the longitudinal wind speed component v1; The position of vertex S2 The position of vertex S3 Target wind profiler radar site S1 location Since the three vertices S1S2S3 are not far apart, the equilateral triangle formed by S1S2S3 is approximately treated as a plane triangle. A plane rectangular coordinate system is established with the position of S1 as the origin. The x-axis and y-axis are the east-west and north-south directions respectively. According to customary definition, east and north are the positive directions. In the plane rectangular coordinate system, the coordinates of S1, S2, and S3 are: (x1, y1), (x2, y2), (x3, y3); The east-west distance x3-x1 and the north-south distance y3-y1 between S3 and S1 are calculated using the following formula: The east-west distance x2-x1 and the north-south distance y2-y1 between S2 and S1 are calculated using the following formula: Where: R is the average radius of the earth, π is pi; Substituting into the following formula, we can calculate the horizontal divergence D and relative vorticity within the triangle of each vertical standard layer:

2. The satellite-ground synchronous inversion method for mesoscale atmospheric dynamic parameters according to claim 1 is characterized in that: In step 3.4, the cloud coverage of the search area Area is determined within the time range [T1-△T, T1+△T] by the following method: Obtain cloud data from the Sunflower-8 satellite in the search area Area within the time range [T1-△T, T1+△T]. If the ratio of the number of grid points with clouds to the total number of grid points in the search area Area is greater than 1 / 2, it is determined to be cloud-covered; otherwise, it is determined to be cloud-free.

Images