A GNSS single-station real-time atmospheric profile monitoring method, system and device

Abstract

The application discloses a kind of GNSS single station real-time atmospheric profile monitoring method, system and equipment, it is related to GNSS atmospheric detection field.The present application includes: real-time processing the observation data of single GNSS station, obtains the water vapor information carried by GNSS signal;The space region where GNSS station is located is discretized, and the slant-path water vapor observation value of GNSS side edge signal is calculated using water vapor height factor model;Establish the observation equation set of water vapor pressure parameter, and the atmospheric water vapor pressure parameter information is solved using optimization algorithm, and the precision of water vapor pressure parameter is evaluated;Based on meteorological physics function relationship, construct atmospheric specific humidity, atmospheric wet refractive index, atmospheric water vapor density and other meteorological parameter inversion model;Finally, using the meteorological data of GNSS station and collocated sounding station, the external precision of a variety of atmospheric water vapor profile is tested.The present application uses single GNSS station, realizes the all-weather, high-precision inversion of a variety of atmospheric parameter vertical profile.

Description

Technical Field

[0001] This invention belongs to the field of GNSS atmospheric sounding technology, specifically relating to a method, system, and equipment for real-time atmospheric profile monitoring of a single GNSS station. Background Technology

[0002] Atmospheric water vapor is a significant greenhouse gas, exhibiting complex spatial distribution and substantial temporal variations. The high spatiotemporal evolution of atmospheric water vapor can easily trigger severe weather events such as torrential rains and global climate anomalies, posing a significant threat to human life, property, and the ecological environment. There is an urgent need to develop high-resolution spatiotemporal atmospheric water vapor detection technologies to reconstruct a detailed three-dimensional atmospheric water vapor field, providing scientific and effective data support for the study of global climate change and small- and medium-scale extreme weather events.

[0003] Global Navigation Satellite System (GNSS) water vapor detection technology is an emerging method for atmospheric water vapor detection in recent years. With its advantages of high precision, low cost, and all-weather operation, this technology has been widely applied in meteorological research. With the construction and development of various GNSS stations worldwide, GNSS water vapor detection technology is becoming one of the important means of upper-space and temporal water vapor detection.

[0004] Among existing GNSS water vapor detection technologies, GNSS water vapor tomography is the only GNSS three-dimensional water vapor detection method capable of reconstructing a high spatiotemporal resolution three-dimensional atmospheric water vapor field. This technique utilizes oblique path water vapor observations provided by multiple GNSS stations within a local GNSS network to discretize the tropospheric study area into regular voxel units. Based on the positional relationships of the GNSS oblique path water vapor observations within the three-dimensional tomographic model framework, the water vapor content of each voxel unit is inverted, forming a reconstructed, highly detailed spatiotemporal atmospheric water vapor field. Numerous scholars have used this technique to effectively invert regional three-dimensional atmospheric water vapor fields based on GNSS station network data from different cities / provinces, including Shanghai, Wuhan, Nanjing, Xuzhou, Guangdong Province, and Hunan Province.

[0005] However, the aforementioned studies primarily reconstruct three-dimensional water vapor density profiles or three-dimensional wet refractive index profiles, failing to reconstruct the more crucial atmospheric water vapor pressure profiles and atmospheric specific humidity profiles. Furthermore, the aforementioned field experiments were all conducted based on regional GNSS networks composed of multiple GNSS stations. From a global perspective, the distribution of GNSS stations is extremely uneven, with most regions having very few or only one or two GNSS stations. Water vapor tomography techniques based on GNSS networks are simply unapplicable to most areas, making it impossible to reconstruct atmospheric water vapor profile information using a single GNSS station. This significantly limits the application scope and potential of GNSS technology for atmospheric water vapor profile detection globally. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, and equipment for real-time atmospheric profile monitoring of a single GNSS station, which can solve the problems in the prior art that it is impossible to reconstruct atmospheric water vapor profiles and difficult to reconstruct atmospheric water vapor pressure profiles and atmospheric specific humidity profiles using a single GNSS station.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0008] A method for real-time atmospheric profile monitoring at a single GNSS station includes the following steps:

[0009] Acquire observation data from a single GNSS station, process the GNSS data using data processing software, and determine the water vapor information of the GNSS signal;

[0010] Using the GNSS station as the center and length L as the radius, a cylindrical tropospheric region with height H is determined, and the cylindrical region is vertically and non-uniformly divided into k layers; the spatial relationship between the GNSS signal and the cylindrical region is determined, and the water vapor content of the GNSS side signal is calculated using the water vapor height factor model;

[0011] Based on the pressure intercept information of GNSS signals in different pressure layers, the observation equations for atmospheric water vapor pressure parameters are constructed, the constraint equations between different vertical layers are established, the equation set composed of the observation equations and the constraint equations is solved using the optimization algorithm, and the accuracy of the water vapor pressure parameters is evaluated.

[0012] Using physical functions of meteorological parameters such as water vapor pressure, specific humidity, wet refractive index, and water vapor density, inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density are constructed respectively.

[0013] External accuracy verification of various GNSS atmospheric water vapor profiles retrieved was performed using meteorological data from GNSS radiosonde stations located in parallel.

[0014] As a further preferred embodiment of the present invention, the step of processing GNSS data using data processing software to determine the water vapor information of the GNSS signal specifically involves: processing GNSS observations, precise ephemeris data, and clock errors using precise point positioning software to obtain the zenith tropospheric delay (ZTD) and atmospheric horizontal gradient of the GNSS station. and The tropospheric dry delay ZHD of the GNSS station was calculated using the Saastamoinen model. Then, the tropospheric wet delay ZWD was obtained by subtracting the tropospheric dry delay ZHD from the tropospheric delay ZTD. This was derived from the atmospheric horizontal gradient. and Extract the corresponding atmospheric humidity level gradients respectively. and The slant path water vapor content of GNSS signals was calculated using the SWV calculation formula.

[0015] As a further preferred embodiment of the present invention, the value of L ranges from 12.5 to 15 kilometers, the value of H ranges from 9 to 12 kilometers, and the value of k ranges from 10 to 15.

[0016] Furthermore, as a preferred embodiment of the present invention, the water vapor height factor model is as follows:

[0017] ;

[0018] in, Indicates the puncture height of the GNSS side signal. The elevation of atmospheric water vapor is represented by a value of 2 km, and the value of H ranges from 9 to 12 km.

[0019] Furthermore, as a preferred embodiment of the present invention, the optimization algorithm is as follows:

[0020] ;

[0021] Where e represents the atmospheric water vapor pressure at different pressure locations p during the inversion, and A and B represent the coefficient matrices corresponding to the observation equation and constraint equation, respectively. and These represent the weight matrices corresponding to the observation equation and the constraint equation, respectively. This represents a column vector composed of GNSS oblique path water vapor observations.

[0022] Furthermore, as a preferred embodiment of the present invention, the inversion models for the atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density vertical profiles are as follows:

[0023] ;

[0024] Where e represents the atmospheric water vapor pressure at different pressure points p during the inversion process. , and These represent specific humidity, water vapor density, and wet refractive index, respectively, and T represents atmospheric temperature. and This represents the atmospheric refractive index constant.

[0025] As a further preferred embodiment of the present invention, the distance range of the GNSS radiosonde station is 0~30km.

[0026] Furthermore, as a preferred embodiment of the present invention, the accuracy index used for the external accuracy inspection is the root mean square error (RMSE), calculated as follows:

[0027] ;

[0028] Where k represents the total number of vertical layers, and represents the inverted atmospheric water vapor parameter value of the i-th layer and the water vapor parameter value of the sounding station, respectively.

[0029] A GNSS single-station real-time atmospheric profile monitoring system includes:

[0030] The data acquisition and processing module is used to acquire observation data from a single GNSS station and process the GNSS observation data in real time to obtain water vapor information from the GNSS signal. The water vapor pressure profile inversion module is used to vertically stratify the tropospheric cylindrical region of the GNSS station, calculate the oblique path water vapor content of the GNSS side signal using a water vapor height factor model, construct observation equations for atmospheric water vapor pressure parameters based on the pressure intercept information of the GNSS signal in different pressure layers, establish constraint equations between different vertical layers, solve the equations using an optimization algorithm, and evaluate the accuracy of the water vapor pressure parameters. The multiple atmospheric profile inversion module is used to construct inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density, and perform external accuracy verification of the inverted multiple GNSS atmospheric water vapor profiles using meteorological data from juxtaposed radiosonde stations of the GNSS station.

[0031] A computer device includes: a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the above-described GNSS single-station real-time atmospheric profile monitoring method.

[0032] The GNSS single-station real-time atmospheric profile monitoring method, system, and equipment described in this invention, compared with existing technologies, have the following technical advantages:

[0033] This invention is based on various types of GNSS stations worldwide. It uses the observation data of a single GNSS station to invert the water vapor information of the GNSS signal, and then estimates the oblique path water vapor observation value corresponding to each GNSS station. Combined with the optimization algorithm, the observation equations of water vapor pressure parameters are solved to obtain the vertical profile information of atmospheric water vapor pressure, so as to effectively reconstruct the atmospheric water vapor profile of a single GNSS station.

[0034] This invention constructs a multi-meteorological parameter inversion model based on meteorological physical function relationships, including atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density, to achieve all-weather, high-precision inversion of the vertical profiles of various atmospheric parameters. Attached Figure Description

[0035] Figure 1A flowchart of a GNSS single-station real-time atmospheric profile monitoring method provided in an embodiment of the present invention;

[0036] Figure 2 This is a flowchart illustrating a GNSS single-station real-time atmospheric profile monitoring method provided in an embodiment of the present invention. Detailed Implementation

[0037] The present invention will be further explained in detail below with reference to the accompanying drawings, so that those skilled in the art can better understand and implement the present invention. However, the following examples are only used to explain the present invention and are not intended to limit the present invention.

[0038] See Figures 1-2 This invention provides a method for real-time atmospheric profile monitoring at a single GNSS station, comprising the following steps:

[0039] Step 1: Set up a single GNSS station within the monitoring area, collect observation data from the GNSS station, and synchronize precise ephemeris and clock error data. Process the collected data using precise point positioning software to obtain the zenith tropospheric delay (ZTD) and atmospheric horizontal gradient of the GNSS station. and The Saastamoinen model was used to calculate the tropospheric dry delay ZHD of the GNSS station, thereby determining the tropospheric wet delay ZWD of the GNSS station, and then the atmospheric horizontal gradient was used to calculate the wet delay ZWD. and Extract the corresponding atmospheric humidity level gradients respectively. and To calculate the water vapor content along the slant path of the GNSS signal, the Saastamoinen model can be calculated using formula (1):

[0040] (1);

[0041] in, , and These represent the latitude, elevation, and air pressure of the GNSS station, respectively.

[0042] The tropospheric wet delay ZWD can be calculated using formula (2):

[0043] (2);

[0044] The water vapor content along the slant path of the GNSS signal can be calculated using formula (3):

[0045] (3);

[0046] Where ele represents the elevation angle of the GNSS signal, and azi represents the azimuth angle of the GNSS signal. Represents the projection function of the moist atmosphere. This represents the atmospheric horizontal gradient projection function.

[0047] Step Two: Using the GNSS station as the center and length L as the radius, determine a cylindrical tropospheric region with height H. Divide the cylindrical region into k layers using a vertical non-uniform pressure stratification strategy. Determine the elevation information of the GNSS signal penetration points from the side of the cylindrical region. If the penetration height... If the height H of the cylindrical region is less than the GNSS height, the GNSS signal is considered a GNSS side signal, and the water vapor content along the oblique path of the signal is calculated using the water vapor height factor model. If the puncture height... If the height H of the cylindrical region is greater than or equal to the height H, proceed directly to step three.

[0048] Where L ranges from 12.5 to 15 kilometers, H ranges from 9 to 12 kilometers, and k ranges from 10 to 15 (integers).

[0049] The water vapor height factor model can be calculated using formula (4):

[0050] (4);

[0051] in, Indicates the puncture height of the GNSS side signal. The elevation representing the scale of atmospheric water vapor is taken as 2 km.

[0052] Step 3: Construct observation equations for atmospheric water vapor pressure parameters based on the pressure intercept information of each GNSS signal in the vertical pressure stratification. Establish constraint equations for water vapor pressure of different pressure layers based on the vertical spatial positional relationship of the vertical stratification. Then, use an optimization algorithm to solve the equation set composed of the observation equations and constraint equations to retrieve the atmospheric water vapor pressure information and evaluate its internal consistency accuracy. The observation equations for atmospheric water vapor pressure parameters can be expressed by formula (5):

[0053] (5);

[0054] in, This represents the water vapor content along the slant path of the nth GNSS signal. This represents the atmospheric water vapor pressure parameter of the k-th pressure stratification interval. This represents the pressure intercept information of the nth GNSS signal in the kth pressure stratification interval. If the GNSS signal does not pass through this pressure stratification interval, the value is 0.

[0055] The constraint equations for the water vapor pressures of the different pressure layers can be expressed by formula (6):

[0056] (6);

[0057] in, and Let a and b represent the atmospheric water vapor pressure parameters of the k-th and (k-1)-th air pressure stratification intervals, respectively, and let a and b represent the constraint model coefficients of the atmospheric water vapor pressure parameters of these two layers.

[0058] The optimization algorithm can be calculated using formula (7):

[0059] (7);

[0060] Where e represents the atmospheric water vapor pressure at different pressure locations p during the inversion, and A and B represent the coefficient matrices corresponding to the observation equation and constraint equation, respectively. and These represent the weight matrices corresponding to the observation equation and the constraint equation, respectively. This represents a column vector composed of GNSS oblique path water vapor observations.

[0061] Step 4: Using the physical functions of water vapor pressure and meteorological parameters such as specific humidity, wet refractive index, and water vapor density, inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density are constructed respectively. The inversion models can be expressed by formula (8): (8);

[0062] Where e represents the atmospheric water vapor pressure at different pressure points p during the inversion process. , and These represent specific humidity, water vapor density, and wet refractive index, respectively, and T represents atmospheric temperature. and This represents the atmospheric refractive index constant.

[0063] Step 5: Use meteorological data from juxtaposed radiosonde stations of GNSS stations to perform external accuracy verification on various retrieved GNSS atmospheric water vapor profiles. The distance range of the juxtaposed radiosonde stations of the GNSS stations is 0~30km. The accuracy index used for the external accuracy verification is the root mean square error (RMSE), which can be calculated using formula (9):

[0064] (9);

[0065] Where k represents the total number of vertical layers, and represents the inverted atmospheric water vapor parameter value of the i-th layer and the water vapor parameter value of the sounding station, respectively.

[0066] This invention utilizes observation data from a single GNSS station, combined with a water vapor height factor model, to obtain oblique path water vapor observation values ​​for GNSS side signals. Then, it employs an optimization algorithm to solve the observation equations for water vapor pressure parameters. Finally, it constructs inversion models for multiple meteorological parameters, such as atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density, achieving all-weather, high-precision inversion of vertical profiles for various atmospheric parameters. This effectively compensates for the shortcomings of GNSS technology in inverting vertical profiles of multiple meteorological parameters.

[0067] Specific advantages include: The vertical profile of atmospheric water vapor pressure is reconstructed using a single GNSS station, enabling real-time monitoring of the vertical dynamic changes in atmospheric water vapor. This solves the key problem that traditional GNSS network water vapor tomography technology cannot reconstruct atmospheric water vapor profiles using a single station. Furthermore, a multi-meteorological parameter inversion model is constructed using meteorological physical function relationships, effectively inverting various water vapor profiles such as atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density. This greatly improves the shortcomings of traditional technologies in inverting vertical profiles of multiple meteorological parameters, effectively achieving all-weather real-time monitoring of multiple atmospheric parameter elements from a single GNSS station.

[0068] This invention provides a GNSS single-station real-time atmospheric profile monitoring system, comprising:

[0069] The data acquisition and processing module is used to acquire observation data from a single GNSS station and process the GNSS observation data in real time to obtain water vapor information from the GNSS signal. The water vapor pressure profile inversion module is used to vertically stratify the tropospheric cylindrical region of the GNSS station, calculate the oblique path water vapor content of the GNSS side signal using a water vapor height factor model, construct observation equations for atmospheric water vapor pressure parameters based on the pressure intercept information of the GNSS signal in different pressure layers, establish constraint equations between different vertical layers, solve the equations using an optimization algorithm, and evaluate the accuracy of the water vapor pressure parameters. The multiple atmospheric profile inversion module is used to construct inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density, and perform external accuracy verification of the inverted multiple GNSS atmospheric water vapor profiles using meteorological data from juxtaposed radiosonde stations of the GNSS station.

[0070] This invention provides a computer device, including a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described GNSS single-station real-time atmospheric profile monitoring method.

[0071] The specific implementation method is as follows:

[0072] 1. Set up a single GNSS station within the monitoring area, collect observation data from the GNSS station, and synchronize precise ephemeris and clock error data. Process the collected data using precise point positioning software to obtain the zenith tropospheric delay (ZTD) and atmospheric horizontal gradient of the GNSS station. and The Saastamoinen model was used to calculate the tropospheric dry delay ZHD of the GNSS station, thereby determining the tropospheric wet delay ZWD of the GNSS station, and then the atmospheric horizontal gradient was used to calculate the wet delay ZWD. and Extract the corresponding atmospheric humidity level gradients respectively. and Calculate the water vapor content along the slant path of the GNSS signal.

[0073] 2. Using the GNSS station as the center and length L as the radius, define a cylindrical tropospheric region with height H. Divide the cylindrical region into k layers using a vertical non-uniform pressure stratification strategy. Determine the elevation information of the GNSS signal penetration points from the side of the cylindrical region. If the penetration height... If the height H of the cylindrical region is less than the GNSS height, the GNSS signal is considered a GNSS side signal, and the water vapor content along the oblique path of the signal is calculated using the water vapor height factor model. If the puncture height... If the height H of the cylindrical region is greater than or equal to the height of the cylindrical region, proceed directly to the next step.

[0074] 3. Based on the pressure intercept information of each GNSS signal in the vertical pressure stratification, construct the observation equations for atmospheric water vapor pressure parameters. Based on the vertical spatial positional relationship of the vertical stratification, establish the constraint equations for water vapor pressure of different pressure layers. Then, use the optimization algorithm to solve the equation set composed of the observation equations and constraint equations, retrieve the atmospheric water vapor pressure information, and evaluate its internal consistency accuracy.

[0075] 4. Using the physical functions of water vapor pressure and meteorological parameters such as specific humidity, wet refractive index, and water vapor density, inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density are constructed respectively.

[0076] 5. Use meteorological data from GNSS radiosonde stations to verify the external accuracy of various retrieved GNSS atmospheric water vapor profiles.

[0077] The specific implementation schemes described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific implementation schemes of the present invention and are not intended to limit the scope of the present invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present invention should fall within the scope of protection of the present invention.

Claims

1. A method for real-time atmospheric profile monitoring at a single GNSS station, characterized in that, Includes the following steps: Acquire observation data from a single GNSS station, process the GNSS data using data processing software, and determine the water vapor information of the GNSS signal; Using the GNSS station as the center and length L as the radius, a cylindrical tropospheric region with height H is determined, and the cylindrical region is vertically and non-uniformly divided into k layers; the spatial relationship between the GNSS signal and the cylindrical region is determined, and the water vapor content of the GNSS side signal is calculated using the water vapor height factor model; Based on the pressure intercept information of GNSS signals in different pressure layers, the observation equations for atmospheric water vapor pressure parameters are constructed, the constraint equations between different vertical layers are established, the equation set composed of the observation equations and the constraint equations is solved using the optimization algorithm, and the accuracy of the water vapor pressure parameters is evaluated. Using physical functions of water vapor pressure and meteorological parameters such as specific humidity, wet refractive index, and water vapor density, inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density are constructed respectively. External accuracy verification of various retrieved GNSS atmospheric water vapor profiles was performed using meteorological data from GNSS radiosonde stations located in parallel. The water vapor height factor model is as follows: ； in, Indicates the puncture height of the GNSS side signal. The elevation representing the scale of atmospheric water vapor is taken as 2 km; The inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density are as follows: ； Where e represents the atmospheric water vapor pressure at different pressure points p during the inversion process. , and These represent specific humidity, water vapor density, and wet refractive index, respectively, and T represents atmospheric temperature. and This represents the atmospheric refractive index constant.

2. The method for real-time atmospheric profile monitoring of a single GNSS station according to claim 1, characterized in that, The specific steps for processing GNSS data using data processing software to determine the water vapor information of the GNSS signal include: By processing GNSS observations, precise ephemeris data, and clock bias data using precise point positioning software, the zenith tropospheric delay (ZTD) and atmospheric horizontal gradient of the GNSS station are obtained. and ; The tropospheric dry delay ZHD of the GNSS station was calculated using the Saastamoinen model. Then, the tropospheric wet delay ZWD was obtained by subtracting the tropospheric dry delay ZHD from the tropospheric delay ZTD. From atmospheric horizontal gradient and Extract the corresponding atmospheric humidity level gradients respectively. and The slant path water vapor content of GNSS signals was calculated using the SWV calculation formula.

3. The method for real-time atmospheric profile monitoring of a single GNSS station according to claim 1, characterized in that, The value of L ranges from 12.5 to 15 kilometers, the value of H ranges from 9 to 12 kilometers, and the value of k ranges from 10 to 15.

4. The method for real-time atmospheric profile monitoring of a single GNSS station according to claim 1, characterized in that, The optimization algorithm is as follows: ； Where e represents the atmospheric water vapor pressure at different pressure locations p during the inversion, and A and B represent the coefficient matrices corresponding to the observation equation and constraint equation, respectively. and These represent the weight matrices corresponding to the observation equation and the constraint equation, respectively. This represents a column vector composed of GNSS oblique path water vapor observations.

5. The method for real-time atmospheric profile monitoring of a single GNSS station according to claim 1, characterized in that, The distance range of the GNSS radiosonde stations is 0~30km.

6. The method for real-time atmospheric profile monitoring of a single GNSS station according to claim 1, characterized in that, The accuracy index used for the external accuracy inspection is the root mean square error (RMSE), which is calculated using the following formula: ； Where k represents the total number of vertical layers, and represents the inverted atmospheric water vapor parameter value of the i-th layer and the water vapor parameter value of the sounding station, respectively.

7. A GNSS single-station real-time atmospheric profile monitoring system, characterized in that, include: The data acquisition and processing module is used to acquire observation data from a single GNSS station and process the GNSS observation data in real time to obtain water vapor information from the GNSS signal. The water vapor pressure profile inversion module is used to vertically stratify the tropospheric cylindrical region of a GNSS station and calculate the oblique path water vapor content of the GNSS side signal using the water vapor height factor model. Based on the pressure intercept information of GNSS signals in different pressure layers, the observation equations for atmospheric water vapor pressure parameters are constructed, the constraint equations between different layers in the vertical direction are established, the equations are solved using optimization algorithms, and the accuracy of the water vapor pressure parameters is evaluated. Multiple atmospheric profile inversion modules are used to construct inversion models for vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density. External accuracy verification of the inverted GNSS atmospheric water vapor profiles is performed using meteorological data from GNSS radiosonde stations located side-by-side. The water vapor height factor model is as follows: ； in, Indicates the puncture height of the GNSS side signal. The elevation representing the scale of atmospheric water vapor is taken as 2 km; The inversion models for the vertical profiles of atmospheric specific humidity, atmospheric wet refractive index, and atmospheric water vapor density are as follows: ； Where e represents the atmospheric water vapor pressure at different pressure points p during the inversion process. , and These represent specific humidity, water vapor density, and wet refractive index, respectively, and T represents atmospheric temperature. and This represents the atmospheric refractive index constant.

8. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, it implements a GNSS single-station real-time atmospheric profile monitoring method according to any one of claims 1 to 6.

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