Method, system and equipment for constructing three-dimensional water vapor field based on multi-site GNSS

Abstract

The invention relates to the technical field of atmospheric exploration, and particularly discloses a method, system and device for constructing a three-dimensional water vapor field based on a multi-site GNSS, and the method comprises the following steps: obtaining zenith total water vapor data of a plurality of GNSS sites in a research region; constructing a water vapor model layer by layer by using zenith total water vapor data of a single GNSS site and combining a same-region sounding layered water vapor observation value and a layered physical quantity predicted by a numerical mode; generating a water vapor vertical profile of each site by using a water vapor model and taking zenith total water vapor data of a single GNSS site as a vertical constraint; for each height layer in the research area, integrating same-layer water vapor data of all GNSS sites, and expanding the same-layer water vapor data into grid data through an optimization interpolation method; integrating the grid data of all height layers to generate a regional three-dimensional water vapor field; according to the method, a water vapor model is constructed layer by layer by fusing multi-source observation data and an artificial intelligence algorithm, and high-precision chromatography reconstruction of a regional three-dimensional water vapor field is realized by taking GNSS zenith total water vapor data as a vertical constraint.

Description

Technical Field

[0001] This invention relates to the field of atmospheric sounding technology, and more specifically to a method, system, and equipment for constructing a three-dimensional water vapor field based on multi-site GNSS. Background Technology

[0002] GNSS (Global Navigation Satellite System) is a collective term for satellite navigation systems such as GPS, GLONASS, Galileo, and BeiDou. As a satellite radio navigation system, it provides all-weather, high-precision positioning, navigation, and timing services. Since the early 1990s, when GPS technology was applied to the field of atmospheric remote sensing, GNSS meteorology (GNSS / MET) has emerged. Scientists at home and abroad have verified its feasibility in detecting water vapor and applying it to weather forecasting through ground-based GNSS meteorology experiments. However, ground-based GNSS has limitations; it can usually only obtain the total water vapor content (PWV) in the zenith direction of the station and cannot provide information on the vertical distribution of water vapor at the station. With the development of GNSS inversion technology for tilted path total water vapor content (SWV), water vapor information has been expanded from one-dimensional to two-dimensional, broadening the application of GNSS observation networks in frontal passages and water vapor change monitoring. Although SWV still lacks vertical distribution information, it can now invert the water vapor distribution over a region. As an international research frontier, GNSS water vapor tomography can provide three-dimensional water vapor distribution information, effectively improve the initial humidity field of short-term forecasts, and enhance the accuracy of numerical forecasts. Compared with traditional radiosonde methods, it has significant advantages in spatiotemporal resolution.

[0003] Applying existing GNSS water vapor tomography technology requires meeting three core conditions: first, a dense ground-based GNSS observation network with a certain geometric structure; second, dense sampling of atmospheric water vapor through simultaneous observation of multiple satellites, ensuring that the SWV observations of each signal path contain water vapor information for that path; and third, the use of tomographic inversion technology to reconstruct the three-dimensional structure of atmospheric water vapor by solving the water vapor information of each layer through a model. The current process for solving GNSS water vapor tomography information mainly includes: acquiring the PWV in the zenith direction of the station; inverting SWVs in different azimuths to assist in vertical distribution inversion; and finally inverting the three-dimensional water vapor information using the SWV data source.

[0004] However, current GNSS water vapor tomography technology still has significant shortcomings: First, a single water vapor station can only provide the total water vapor content value and cannot obtain the vertical profile of water vapor at a single station. This makes it difficult to analyze the vertical variation characteristics of water vapor in the troposphere, limiting a comprehensive understanding of the atmospheric humidity structure and thus weakening the ability to analyze and warn of extreme weather events. It is urgent to overcome this limitation through multi-site observations and advanced tomography techniques. Second, a dense ground-based GNSS observation network needs to be constructed. The limited information from a single station and the sparse signal paths easily lead to ill-conditioned inversion equations. A dense observation network (ideally with a station-to-station distance of 5-10 kilometers) can receive rich inversion information from multiple directions, improving the stability and reliability of the results. However, given the high cost of station construction, no province in China currently has the capability to build a dense GNSS water vapor monitoring network. Finally, current technologies face numerous challenges in practical applications: Although the inversion theory based on Radon transform discretizes the troposphere into voxels and assumes that water vapor parameters are constant, the uneven signal distribution is affected by satellite constellations and station networks, leading to an imbalance in the voxel signal distribution in the tomographic region, causing rank deficiency in the equation coefficient matrix, and affecting inversion stability; the multi-valued nature of observations makes solving the tomographic equations an ill-conditioned problem, increasing uncertainty and complexity; the lack of optimization methods for the equations limits the improvement of inversion accuracy; the differences in information content and accuracy of different types of equations involved in the construction process (such as tomographic observation equations and constraint equations) further exacerbate the complexity of inversion; at the same time, the lack of effective biased estimation methods means that the results do not fully reflect high-quality equation information, and the strong correlation between GNSS water vapor observations may cause parameter estimation bias in traditional iterative solution methods, all of which restrict the practical application of this technology in meteorological operations. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and device for constructing a three-dimensional water vapor field based on multi-site GNSS. By integrating multi-source observation data and artificial intelligence algorithms, a water vapor model is constructed layer by layer, and the total water vapor data at the GNSS zenith is used as a vertical constraint to achieve high-precision tomographic reconstruction of the regional three-dimensional water vapor field.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for constructing a three-dimensional water vapor field based on multi-site GNSS includes the following steps: Acquire zenith total water vapor data from multiple GNSS stations within the study area; Using zenith total water vapor data from a single GNSS station, combined with layered water vapor observations from radiosonde in the same region and layered physical quantities predicted by numerical models, a water vapor model is constructed layer by layer based on artificial intelligence algorithms; Using a water vapor model, the total zenith water vapor data of a single GNSS station is used as a vertical constraint to generate the vertical water vapor profiles for each station. For each altitude layer within the study area, the water vapor data of the same layer from all GNSS stations were integrated and expanded into grid data through optimized interpolation methods; By integrating grid data from all height layers, a regional three-dimensional water vapor field is generated, enabling water vapor tomography.

[0007] Furthermore, the artificial intelligence algorithms include: LightGBM, XGBoost, Random Forest, or ExtraTrees.

[0008] Furthermore, after generating the three-dimensional water vapor field in the region, the method further includes: verifying the three-dimensional water vapor field using water vapor data retrieved from regional radiosonde stratified water vapor or microwave radiometers, and optimizing the interpolation method based on the verification results.

[0009] The present invention also provides a system for performing a method for constructing a three-dimensional water vapor field based on multi-site GNSS, comprising: The data acquisition module is used to acquire zenith total water vapor data from multiple GNSS stations within the study area; The vertical profile construction module is used to construct a water vapor model layer by layer using the total zenith water vapor data of a single GNSS station, combined with the layered water vapor observations from radiosonde and the layered physical quantities predicted by numerical models in the same area, based on artificial intelligence algorithms; using the water vapor model, with the total zenith water vapor data of a single GNSS station as the vertical constraint, the vertical profile of water vapor for each station is generated. For each altitude layer within the study area, the water vapor data of the same layer from all GNSS stations are integrated and expanded into grid data through optimized interpolation methods; The 3D water vapor field generation module is used to integrate grid data from all height layers to generate a regional 3D water vapor field, enabling water vapor tomography.

[0010] Furthermore, the three-dimensional water vapor field generation module adopts an optimized interpolation method and integrates a verification unit to verify and optimize the interpolation results using radiosonde observations or microwave radiometer data.

[0011] The present invention also provides an electronic device, comprising: Memory, which stores computer programs; The processor executes the computer program to implement a method for constructing a three-dimensional water vapor field based on multi-site GNSS.

[0012] According to specific embodiments provided by the present invention, the present invention has the following technical effects compared to the prior art: This invention uses GNSS zenith total water vapor data as a vertical constraint and combines radiosonde observations and layered physical quantities from numerical models to construct a layer-by-layer water vapor model. This effectively overcomes the limitations of insufficient vertical resolution and sparse radiosonde stations in traditional water vapor detection, significantly improving the inversion accuracy and reliability of single-site water vapor vertical profiles. For each altitude layer, an optimized interpolation method is used to expand discrete station data into grid data, solving the representativeness error problem caused by uneven spatial distribution of GNSS stations and enhancing the continuity and objectivity of regional water vapor horizontal distribution by integrating information from multiple stations at the same level. By superimposing grid data from all altitude layers to generate a regional three-dimensional water vapor field, this invention not only achieves a refined characterization of the spatiotemporal distribution of water vapor but also provides high spatiotemporal resolution water vapor field products for severe convective weather warnings and climate change research. Furthermore, the AI-driven layer-by-layer modeling and optimized interpolation strategy in this invention significantly improves the automation level and computational efficiency of water vapor tomography, giving it the potential for operational applications. This has significant practical value for improving the accuracy of weather forecasts, water cycle process research, and the ability to monitor severe weather. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0014] The method, system, and equipment for constructing a three-dimensional water vapor field based on multi-site GNSS according to the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the overall process of the method for constructing a three-dimensional water vapor field based on multi-site GNSS according to the present invention; Figure 2 This is a comparison chart of water vapor data and observation results from the 54511 station test set in Embodiment 1 of the present invention; wherein (a) is a graph of the root mean square error of the specific humidity test and the forecast deviation curve of the 54511 station test set; and (b) is a graph of the root mean square error of the relative humidity test and the forecast deviation curve of the 54511 station test set. Figure 3 This is a total water vapor distribution map based on monitoring data from different observation stations in Embodiment 1 of the present invention; Figure 4 The vertical profile of water vapor generated by the newly constructed water vapor model in Embodiment 1 of the present invention; wherein (a)-(h) are the vertical profiles of water vapor generated by the newly constructed water vapor model for the total water vapor monitored at different observation stations. Figure 5This is a schematic diagram of a three-dimensional water vapor field that most closely approximates the actual situation, constructed based on water vapor distribution on different isobaric surfaces in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram of a three-dimensional water vapor field constructed using different isobaric surfaces in Embodiment 1 of the present invention; where (a)-(h) are water vapor distribution diagrams of different isobaric surfaces, respectively. Detailed Implementation

[0015] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0016] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0017] Example 1 like Figure 1 As shown, the present invention provides a method for constructing a three-dimensional water vapor field based on multi-site GNSS, including the following steps: acquiring the total zenith water vapor data of multiple GNSS stations within the study area; Using zenith total water vapor data from a single GNSS station, combined with stratified water vapor observations from radiosonde and stratified physical quantities from numerical model forecasts in the same region, a water vapor model is constructed layer by layer based on artificial intelligence algorithms; the artificial intelligence algorithms include: LightGBM, XGBoost, Random Forest, or ExtraTrees.

[0018] Specifically, in this embodiment, when applying the lightGBM algorithm, for a model containing K trees, for sample x... i final predicted value It can be represented as the sum of all tree predictions, and its overall function is expressed as follows:

[0019] in: Water vapor model for the first i Predicted values ​​for each sample; K The total number of trees (i.e., the number of iterations); fk : No. k Independent tree functions; x i : No. i The feature vector of each input sample.

[0020] Using a water vapor model, the total zenith water vapor data of a single GNSS station is used as a vertical constraint to generate the vertical water vapor profiles for each station. In this embodiment, the vertical profile of water vapor at the GNSS station is constructed based on the total zenith water vapor, radiosonde water vapor observations, and numerical model forecasts. Specifically, the numerical model forecasts from the radiosonde observation station and adjacent stations, along with the total zenith water vapor at the GNSS station, are used as modeling factors, and radiosonde stratified water vapor (or microwave radiometer-derived water vapor) is used as the forecast object. Artificial intelligence (AI) algorithms are employed for modeling, constructing the water vapor model layer by layer. Based on the constructed model, water vapor profile data are calculated for other GNSS stations, and the total water vapor is used as a constraint to ultimately form the vertical profile of water vapor.

[0021] This embodiment specifically utilizes AI algorithms (such as LightGBM, XGBoost, Random Forest, ExtraTrees, etc.) to perform stratified water vapor modeling based on radiosonde data from Beijing station 54511, water vapor monitoring data from co-located GNSS stations, and CMA-BJ model forecast data. Based on the total water vapor from the GNSS station and the stratified physical quantities predicted by the station model, a model is constructed for the stratified water vapor data from radiosonde (54511). Based on long-term big data, AI algorithm modeling can effectively construct a relationship model between stratified observed water vapor, model forecasts, and total GNSS water vapor. Comparison of the water vapor data from the 54511 station test set (the last 20% of the total sample) constructed by the model with the observation results shows good fitting effects for both relative humidity and specific humidity, and good representativeness of the actual situation (e.g., Figure 2 (As shown). Furthermore, water vapor profiles were constructed for GNSS stations located in the Beijing area. The distribution of these water vapor profiles suggests that the results are generally reasonable (e.g., ...). Figure 3 and Figure 4 (As shown).

[0022] For each altitude layer within the study area, the water vapor data of the same layer from all GNSS stations are integrated and expanded into grid data through optimized interpolation methods; In this embodiment, the interpolation function will be selected from mature interpolation methods such as radial basis function (RBF), inverse distance weight (IDW), kriging, and spline.

[0023] By integrating grid data from all height layers, a regional three-dimensional water vapor field is generated, enabling water vapor tomography.

[0024] In this embodiment, firstly, the vertical water vapor profiles of all GNSS stations within the region are used as the data source. Within each isobaric layer, water vapor data from all stations are synthesized, and an optimized interpolation method is used to extend the station data into gridded data. Next, the three-dimensional gridded water vapor field is validated using radiosonde observations and microwave radiometer inversion data within the region. Based on the validation results, the interpolation method from stations to gridded data is optimized, ultimately constructing a three-dimensional water vapor field that most closely approximates the actual situation, thus achieving water vapor tomography (e.g., Figure 5 and Figure 6(As shown).

[0025] Compared with the existing GNSS water vapor chromatography technology, this application has the following technical advantages: For many years, existing GNSS water vapor tomography technology has revolved around the concept of "total zenith water vapor → inclined path water vapor → tomographic water vapor." However, the computational flow, timeliness, and accuracy of tomographic water vapor analysis still cannot meet the needs of meteorological operational applications. This invention adopts the concept of "total zenith water vapor → (single station) water vapor vertical profile → tomographic water vapor," constructing a three-dimensional water vapor field that meets meteorological operational requirements, realizing the transformation from the entire PWV layer of GNSS stations to a three-dimensional water vapor field. The water vapor tomography scheme developed in this study employs mature data processing technologies in each stage, has a simple process, and low computational resource requirements, thus meeting the needs of meteorological operational applications.

[0026] This invention adds vertical height-layered information to the total water vapor data at all individual stations within a GNSS water vapor monitoring network, expanding it into a vertical water vapor profile and transforming one-dimensional water vapor data into two-dimensional water vapor data. Then, within each height layer, point data is converted into grid data using interpolation methods. Finally, by integrating the grid data from each height layer, a three-dimensional water vapor field is constructed, achieving water vapor tomography. The application of data mining technology adds spatial information to the total water vapor data from the stations, eliminating the limitations of GNSS station total water vapor data in meteorological applications, improving the initial humidity field for short-term forecasts, expanding the application areas of the GNSS water vapor observation network, and enhancing the application value of the data.

[0027] Example 2 The present invention also provides a system for performing the method of constructing a three-dimensional water vapor field based on multi-site GNSS in Embodiment 1, comprising: The data acquisition module is used to acquire zenith total water vapor data from multiple GNSS stations within the study area; The vertical profile construction module is used to construct a water vapor model layer by layer using the total zenith water vapor data of a single GNSS station, combined with the layered water vapor observations from radiosonde and the layered physical quantities predicted by numerical models in the same area, based on artificial intelligence algorithms; using the water vapor model, with the total zenith water vapor data of a single GNSS station as the vertical constraint, the vertical profile of water vapor for each station is generated. For each altitude layer within the study area, the water vapor data of the same layer from all GNSS stations are integrated and expanded into grid data through optimized interpolation methods; The 3D water vapor field generation module is used to integrate grid data from all height layers to generate a regional 3D water vapor field, enabling water vapor tomography.

[0028] The three-dimensional water vapor field generation module adopts an optimized interpolation method and integrates a verification unit to verify and optimize the interpolation results using radiosonde observations or microwave radiometer data.

[0029] The present invention also provides an electronic device, comprising: Memory, which stores computer programs; The processor executes the computer program to implement the method for constructing a three-dimensional water vapor field based on multi-site GNSS in Embodiment 1.

[0030] A computer-readable storage medium storing computer-executable instructions, the computer-executable instructions being used in the method for constructing a three-dimensional water vapor field based on multi-site GNSS in Embodiment 1.

[0031] The above description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a three-dimensional water vapor field based on multi-site GNSS, characterized in that, Includes the following steps: Acquire zenith total water vapor data from multiple GNSS stations within the study area; Using zenith total water vapor data from a single GNSS station, combined with layered water vapor observations from radiosonde and layered physical quantities predicted by numerical models in the same region, a water vapor model is constructed layer by layer based on artificial intelligence algorithms; Using a water vapor model, the total zenith water vapor data of a single GNSS station is used as a vertical constraint to generate the vertical water vapor profiles for each station. For each altitude layer within the study area, the water vapor data of the same layer from all GNSS stations were integrated and expanded into grid data through optimized interpolation methods; By integrating grid data from all height layers, a regional three-dimensional water vapor field is generated, enabling water vapor tomography.

2. The method for constructing a three-dimensional water vapor field based on multi-site GNSS according to claim 1, characterized in that, The artificial intelligence algorithms include: LightGBM, XGBoost, Random Forest, or ExtraTrees.

3. The method for constructing a three-dimensional water vapor field based on multi-site GNSS according to claim 1, characterized in that, After generating the three-dimensional water vapor field in the region, the method further includes: verifying the three-dimensional water vapor field using water vapor data retrieved from regional radiosonde stratified water vapor or microwave radiometers, and optimizing the interpolation method based on the verification results.

4. A system for constructing a three-dimensional water vapor field based on multi-site GNSS, used to execute the method for constructing a three-dimensional water vapor field based on multi-site GNSS as described in any one of claims 1-3, characterized in that, include: The data acquisition module is used to acquire zenith total water vapor data from multiple GNSS stations within the study area; The vertical profile construction module is used to construct a water vapor model layer by layer using the total zenith water vapor data of a single GNSS station, combined with the layered water vapor observations from radiosonde and the layered physical quantities predicted by numerical models in the same area, based on artificial intelligence algorithms; using the water vapor model, with the total zenith water vapor data of a single GNSS station as the vertical constraint, the vertical profile of water vapor for each station is generated. For each altitude layer within the study area, the water vapor data of the same layer from all GNSS stations are integrated and expanded into grid data through optimized interpolation methods; The 3D water vapor field generation module is used to integrate grid data from all height layers to generate a regional 3D water vapor field, enabling water vapor tomography.

5. The system for constructing a three-dimensional water vapor field based on multi-site GNSS according to claim 4, characterized in that, The three-dimensional water vapor field generation module adopts an optimized interpolation method and integrates a verification unit to verify and optimize the interpolation results using radiosonde observations or microwave radiometer data.

6. An electronic device, characterized in that, include: Memory, which stores computer programs; The processor, when executing the computer program, implements the method for constructing a three-dimensional water vapor field based on multi-site GNSS as described in any one of claims 1-3.

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