Rainfall inversion method integrating GNSS (Global Navigation Satellite System) water vapor chromatography and water conservancy rain measurement radar

Abstract

The invention provides a rainfall inversion method integrating GNSS (Global Navigation Satellite System) water vapor chromatography and a water conservancy rain-measuring radar. The rainfall inversion method comprises the following steps: collecting GNSS data, water conservancy rain-measuring radar data, meteorological data and geographic information; estimating the atmospheric water vapor content SWV on the GNSS oblique signal path; performing three-dimensional water vapor chromatography to obtain a grid water vapor density value; identifying an inversion factor; and training the neural network and carrying out rainfall inversion. According to the rainfall inversion method established by the invention, GNSS water vapor chromatography and water conservancy rain measurement radar data are effectively utilized, the correlation between space-time gaseous water and liquid water is comprehensively considered, the rainfall inversion process is optimized, and a new thought is provided for refined inversion of rainfall intensity and spatial distribution.

Description

Technical Field

[0001] The invention relates to the field of water conservancy rainfall inversion, and in particular to a rainfall inversion method integrating GNSS water vapor tomography and water conservancy rainfall measuring radar. Background Art

[0002] Rainfall inversion is of great significance in many fields such as weather forecasting and disaster prevention and mitigation, water resources management, environmental monitoring, and water conservancy project planning. At present, traditional rainfall inversion methods are mainly based on statistical forecasting methods, methods based on numerical weather forecasts, and methods based on radar echo extrapolation, which have the problems of low inversion accuracy, low temporal resolution, and short rainfall forecast period. In order to make up for the problems of traditional rainfall inversion, in recent years, research has applied the ground-based Global Navigation Satellite System (GNSS) water vapor inversion technology to rainfall inversion, combined with remote sensing data to provide more meteorological information for rainfall inversion, effectively improve the rainfall inversion accuracy and temporal resolution, and improve the level of rainfall inversion.

[0003] GNSS water vapor inversion technology has the advantages of low cost, all-day, all-weather, high temporal and spatial resolution, and high precision, and can be used as a powerful supplement to traditional water vapor detection methods. GNSS two-dimensional water vapor inversion can obtain the overall water vapor content above the region, which is usually measured by atmospheric precipitable water vapor (PWV), that is, the total amount of water vapor contained in the air column per unit area. However, GNSS two-dimensional water vapor inversion cannot accurately reflect the distribution of water vapor in three-dimensional space, and cannot meet the needs of water vapor three-dimensional spatial content information and spatiotemporal change analysis.

[0004] The basic principle of GNSS water vapor tomography is to calculate the atmospheric water vapor content (Slant-Path Water Vapor, SWV) of the GNSS signal through a model function, divide the tomography area into three-dimensional grids in three-dimensional space to discretize, list the integral equation based on the GNSS signal slant path, discretize to each three-dimensional grid that the GNSS signal passes through, and form the tomography observation equation. When there is enough observation information, the water vapor density of each three-dimensional grid can be solved. Compared with two-dimensional water vapor inversion, water vapor tomography can achieve three-dimensional reconstruction of atmospheric water vapor distribution over the region. By revealing the spatiotemporal changes of the three-dimensional water vapor field, it helps to capture the accumulation and transport process of water vapor before rainfall, providing a more comprehensive and accurate scientific basis for rainfall inversion. However, the three-dimensional tomography technology has high computational complexity and is subject to many factors, including tomography data selection, tomography area geometry, tomography constraint setting, and GNSS observation network density. There are still certain challenges in spatial resolution, timeliness, reliability and stability in the results of three-dimensional water vapor tomography.

[0005] The water conservancy rain radar is an X-band radar that can generate real-time rainfall data of liquid water in the atmosphere from the ground to 2 km above the ground, directly describing the rainfall intensity information and serving the quantitative precipitation estimation and quantitative precipitation forecast in the water conservancy industry. However, the water conservancy rain radar also has certain disadvantages. Its number is small and its coverage is limited. The radar signal is easily blocked in complex terrain environments. Although radar networking can effectively alleviate the appearance of blind spots, the interference between radars and the complexity of data fusion in overlapping areas may still lead to inaccurate regional rainfall intensity and distribution information. In addition, the radar reflectivity is sensitive to the size and number of rainfall particles, which may lead to inaccurate rainfall intensity estimation. Summary of the invention

[0006] In order to overcome the deficiencies of existing rainfall inversion methods in terms of spatiotemporal resolution, accuracy, rainfall inversion, etc., the present invention provides a rainfall inversion method that integrates GNSS water vapor tomography and hydraulic rain radar, divides the tomography grid considering the consistency of the GNSS water vapor tomography results and the resolution of the hydraulic rain radar data, and makes full use of the advantages of GNSS three-dimensional water vapor tomography, such as high spatiotemporal resolution, and the ability to provide atmospheric water vapor content information at different height levels, as well as the ability of hydraulic rain radar to directly detect rainfall particles. The two types of data and the spatiotemporal characteristics of rainfall are learned through neural networks to achieve complementary advantages of the two types of data, and other data are used to perform high-precision spatiotemporal rainfall inversion, extend the rainfall forecast period, and improve rainfall inversion accuracy. The specific scheme of the present invention is as follows:

[0007] A rainfall inversion method integrating GNSS water vapor tomography and hydraulic rainfall radar comprises the following steps:

[0008] Step 1: Data collection: including GNSS data, water conservancy rainfall radar data, meteorological data and geographic information;

[0009] Step 2: Estimate the atmospheric water vapor content SWV on the GNSS slant signal path;

[0010] Step 3: Obtaining grid water vapor density value by three-dimensional water vapor tomography: According to the resolution of the water conservancy rainfall radar data, the tomography area is discretized and gridded to obtain n three-dimensional voxel blocks, and then a GNSS water vapor tomography model is constructed and solved based on the three-dimensional tomography algorithm to obtain the water vapor density value of each grid; the expression of the GNSS water vapor tomography model is:

[0011]

[0012] Where A is the coefficient matrix, representing the intercept information of the GNSS signal in each voxel block; B is the SWV observation value vector; X is the water vapor density vector; H is the coefficient matrix of horizontal constraints, V is the coefficient matrix of vertical constraints, I is the coefficient matrix of prior constraints, and C is the initial water vapor density value of the tomographic area;

[0013] Step 4: Identify inversion factors: Based on the water vapor density value of each grid, integrate in the vertical direction to obtain the atmospheric precipitable water volume (PWV) of each grid. Use PWV and its related data as well as water conservancy rainfall radar data as the main inversion factors, and use meteorological data and elevation data as auxiliary inversion factors to participate in rainfall inversion. Use linear regression method to analyze the correlation between each factor and the measured rainfall data to determine the actual rainfall inversion factors involved.

[0014] Step 5: Train the neural network and perform rainfall inversion: Use rainfall inversion factors and actual rainfall data as input layer data, and predicted rainfall as output layer data to build a neural network. Train the neural network, learn the spatiotemporal characteristics between rainfall inversion factors and measured rainfall, obtain the optimal model parameters, build a multi-factor rainfall inversion model, and perform rainfall inversion.

[0015] Further optimization, in step 1, the GNSS data includes GNSS raw observation data and IGS precise orbit clock data; the hydro-rainfall radar data includes hydro-rainfall radar base data Level 2 and product data Level 3, and the hydro-rainfall radar base data Level 2 includes reflectivity factor Z, differential reflectivity factor Z DR , differential propagation phase Differential propagation phase shift rate K DP The product data Level 3 includes surface rainfall inversion products, 0-2h surface rainfall forecast products, rainfall-related particle phase classification, and liquid water content data; the meteorological data includes measured rainfall, air pressure, temperature, and relative humidity; the geographic information includes elevation and longitude and latitude.

[0016] Furthermore, the estimation of the atmospheric water vapor content SWV on the GNSS slant signal path in step 2 includes the following steps:

[0017] Step 2-1, solve the GNSS data based on the precise point positioning technology, use the VMF1 model as the zenith mapping function model, and obtain the zenith tropospheric total delay ZTD;

[0018] Step 2-2: Use the Saastamoinen model to calculate the zenith static delay ZHD, which is expressed as:

[0019]

[0020] Among them, P s is the air pressure value at the underlying surface of the measuring station, hPa; θ is the latitude of the measuring station; H is the height of the measuring station, m;

[0021] Step 2-3, calculate the zenith wet delay ZWD, in mm, using the expression:

[0022] ZED=ZTD-ZHD

[0023] Step 2-4: Calculate the oblique path wet delay SWD in mm using the expression:

[0024] SWD=M w (ε)×ZWD+M g (ε)×[G NS cos(α)+G EW sin(α)]+R

[0025] Among them, ε and α represent the satellite cut-off altitude angle and azimuth respectively; M w and M g Respectively represent the wet mapping function and the gradient mapping function; G NS and G EW denote the wet horizontal gradient terms in the north-south and east-west directions respectively; R denotes the residual term that is not modeled;

[0026] Step 2-5: Calculate the atmospheric water vapor content SWV on the oblique signal path in mm. The expression is:

[0027] SWV=Π×SWD

[0028] Among them, П is the atmospheric water vapor conversion coefficient, which is a dimensionless value, and its calculation formula is:

[0029]

[0030] Where ρ is the density of liquid water, kg / m 3 ; R v is the water vapor gas constant, its value is 461.495 J / (kg·K); k' 2 , k 3 is the atmospheric refractive index constant, whose values ​​are 16.48K / hPa and 3.776×10 5 K 2 / hPa; T m is the weighted mean temperature of the atmosphere, K;

[0031] Furthermore, in step three, the tomography area is discretized and gridded according to the resolution of the water conservancy rainfall radar data: including determining the layer height, horizontal resolution and vertical resolution of the tomography area, discretizing the geometric space and regional water vapor elements of the tomography area; setting the heights of the bottom and top of the grid to 0km and 11km respectively; the horizontal range tomography grid division needs to ensure that the horizontal resolution of the GNSS water vapor tomography is consistent with the horizontal resolution of the water conservancy rainfall radar; the tomography area grid division in the vertical direction adopts an uneven division method of dense at the bottom and sparse at the top based on the distribution characteristics of water vapor in the vertical direction, and the grid division of the tomography area below 2km is encrypted to ensure that the vertical resolution of the GNSS water vapor tomography is consistent with the vertical resolution of the water conservancy rainfall radar.

[0032] Furthermore, in step three, the horizontal constraint assumes that the water vapor value of a voxel block in the horizontal direction is the weighted average of the water vapor parameters of several adjacent voxel blocks in the same layer based on the Gaussian weighted function; the vertical constraint utilizes the exponentially decreasing change characteristics of water vapor in the vertical direction to construct the vertical constraint relationship between two adjacent layers; the prior constraint obtains the initial field of atmospheric water vapor as a constraint condition.

[0033] Furthermore, in step 4, the PWV related data include: PWV change amount and PWV change rate; the water conservancy rain measuring radar data include the basic data Level 2 and product data Level 3 of the water conservancy rain measuring radar.

[0034] Furthermore, in step five, the neural network: combines the convolutional neural network CNN and the long short-term memory neural network LSTM model structure, extracts the spatial characteristics between each rainfall inversion factor and the actual rainfall based on CNN, and combines the LSTM model to capture the temporal characteristics between each rainfall inversion factor and the actual rainfall, so as to enhance the ability of rainfall spatiotemporal inversion.

[0035] Beneficial effects of the present invention:

[0036] The present invention proposes a rainfall inversion method that integrates GNSS water vapor tomography and hydraulic rain measuring radar, which can accurately capture the water vapor accumulation and transport process in the three-dimensional space before rainfall, comprehensively consider the relationship between spatial and temporal air water and liquid water, enhance the correlation between water vapor and rainfall, and combine the direct characterization of rainfall by hydraulic rain measuring radar to optimize the rainfall inversion process, improve the accuracy and spatiotemporal resolution of rainfall inversion, enhance the ability of rainfall forecasting, make up for the shortcomings of single data, extend the rainfall forecast period, and provide new ideas for the refined inversion of rainfall intensity and spatial distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flowchart of rainfall inversion based on the integrated GNSS water vapor tomography and hydraulic rain radar of the present invention;

[0038] Figure 2Schematic diagram of the rainfall inversion model based on GNSS water vapor tomography and hydraulic rainfall radar. DETAILED DESCRIPTION

[0039] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0040] Example 1

[0041] like Figure 1 The process shown is a rainfall inversion method integrating GNSS water vapor tomography and hydraulic rainfall radar, including the following steps:

[0042] Step 1: Data collection: including GNSS data, water conservancy rainfall radar data, meteorological data and geographic information; including the following operations:

[0043] (1.1) GNSS data collection

[0044] Obtain GNSS raw observation data and IGS precise orbit and clock error data.

[0045] (1.2) Water conservancy rainfall radar data collection

[0046] Get the basic data of water conservancy rainfall radar (Level 2), including reflectivity factor (Z), differential reflectivity factor (Z DR ), differential propagation phase Differential propagation phase shift rate (K DP ), product data (Level 3) includes surface rainfall inversion products, 0-2h surface rainfall forecast products, rainfall-related particle phase classification, liquid water content and other data.

[0047] (1.3) Other data collection

[0048] Obtain measured meteorological data such as rainfall, air pressure, temperature, relative humidity, etc.; obtain regional elevation information, longitude and latitude, and other geographic information.

[0049] Step 2: Estimate the atmospheric water vapor content SWV on the GNSS slant signal path, including the following operations:

[0050] (2.1) Based on the precise point positioning technology, the VMF1 model is used as the zenith mapping function model to obtain the zenith tropospheric total delay ZTD;

[0051] (2.2) The zenith static delay ZHD is calculated using the Saastamoinen model, and the expression is:

[0052]

[0053] Among them, P s is the air pressure value at the underlying surface of the measuring station, hPa; θ is the latitude of the measuring station; H is the height of the measuring station, m.

[0054] (2.3) Calculate the zenith wet delay ZWD in mm using the expression:

[0055] ZWD=ZTD-ZHD

[0056] (2.4) The oblique path wet delay SWD can be recovered by ZWD and horizontal wet gradient delay term, expressed as:

[0057] SWD=M w (ε)×ZWD+M g (ε)×[G NS cos(α)+G EW sin(α)]+R

[0058] Among them, ε and α represent the satellite cut-off altitude angle and azimuth respectively; M w and M g Respectively represent the wet mapping function and the gradient mapping function; G NS and G EW represent the wet horizontal gradient terms in the north-south and east-west directions respectively; R represents the residual term that is not modeled.

[0059] (2.5) Calculate the atmospheric water vapor content SWV in the oblique path in mm using the expression:

[0060] SWV=II×SWD

[0061] Among them, П is the atmospheric water vapor conversion coefficient, which is a dimensionless value, and its calculation formula is:

[0062]

[0063] Where ρ is the density of liquid water, kg / m 3 ; R v is the water vapor gas constant, its value is 461.495 J / (kg·K); k 2 ', k 3 is the atmospheric refractive index constant, whose values ​​are 16.48K / hPa and 3.776×10 5 K 2 / hPa; T m is the weighted mean temperature of the atmosphere, K.

[0064] There is an obvious linear relationship between the atmospheric weighted average temperature and the surface temperature, expressed as:

[0065] T m =a×T s b

[0066] The coefficients a and b in the formula can be calculated based on the long-term surface temperature T of the study area.s and the corresponding atmospheric weighted average temperature, and determine its value by the least squares method to establish the region T m Model.

[0067] Step 3: Obtain grid water vapor density value by three-dimensional water vapor tomography: According to the resolution of the water conservancy rainfall radar data, the tomography area is discretized and gridded to obtain n three-dimensional voxel blocks. Then, a GNSS water vapor tomography model is constructed and solved based on the three-dimensional tomography algorithm to obtain the water vapor density value of each grid. The specific operations are as follows:

[0068] (3.1) The horizontal area of ​​the tomography needs to be slightly larger than the GNSS station network to ensure that the GNSS signals of each period are included in the tomography area and the GNSS signals pass through the top of the layer. According to the empirical method, the block method is used to perform regional tomography modeling for the area below 11 km in the troposphere covered by the GNSS signal.

[0069] (3.2) Discretization of the tomographic region mainly includes determination of the tomographic region layer height, horizontal resolution and vertical resolution, and discretization of the geometric space and regional water vapor elements of the tomographic region. The heights of the bottom and top of the grid are set to 0km and 11km respectively. Different grid division strategies for the tomographic region are different depending on the water conservancy rain radar data used. When using phased array rain radar data, the horizontal range of the tomographic region grid division adopts a uniform division method of 30×30m along longitude and latitude; when using mechanical rain radar data, a uniform division method of 75×75m along longitude and latitude is adopted. The horizontal range tomographic grid division needs to ensure that the horizontal resolution of the GNSS water vapor tomography is consistent with the horizontal resolution of the water conservancy rain radar. The tomographic region grid division in the vertical direction adopts an uneven division method of dense at the bottom and sparse at the top based on the distribution characteristics of water vapor in the vertical direction. The grid division of the tomographic region below 2km is encrypted to ensure that the vertical resolution of the GNSS water vapor tomography is consistent with the vertical resolution of the water conservancy rain radar.

[0070] (3.3) According to the discretization principle of the tomographic region, n three-dimensional voxel blocks can be obtained. Based on the voxel blocks, the functional relationship between water vapor parameters and SWV observation values ​​is established, and the tomographic observation equation group is constructed:

[0071] AX=B

[0072] Among them, A is the coefficient matrix, representing the intercept information of the GNSS signal in each voxel block; B is the SWV observation value vector; X is the water vapor density vector.

[0073] Due to the lack of sufficient observation data and the uneven spatial distribution of the GNSS signal tilt path, the observation equation constructed directly using GNSS signals has problems such as non-unique solutions and instability. It is necessary to introduce physical or priori constraint equations to constrain the horizontal direction, vertical direction, boundary water vapor information or initial water vapor density to eliminate the inadaptability of the observation equation.

[0074] (3.4) Construct tomographic constraint equations, including horizontal constraints, vertical constraints and prior constraints, and solve the water vapor tomography observation equations. Horizontal constraints are usually based on the Gaussian weighted function, assuming that the water vapor value of a certain voxel block in the horizontal direction is the weighted average of the water vapor parameters of several adjacent voxel blocks in the same layer; vertical constraints use the exponential decreasing change characteristics of water vapor in the vertical direction to construct the vertical constraint relationship between two adjacent layers; prior constraints generally use radiosondes, microwave radiometers and other methods to obtain the initial field of atmospheric water vapor as a constraint condition. Construct a GNSS water vapor tomography model, the expression is:

[0075]

[0076] Where H is the coefficient matrix of horizontal constraints, V is the coefficient matrix of vertical constraints, I is the coefficient matrix of prior constraints, and C is the initial water vapor density value of the tomography area obtained by radiosonde, microwave radiometer and other methods.

[0077] Step 4: Identify the inversion factor:

[0078] The water vapor density data of each grid obtained by three-dimensional water vapor tomography is integrated in the vertical direction to obtain the PWV data of each grid. The PWV, PWV change, PWV change rate, water conservancy rainfall radar reflectivity factor (Z), differential propagation phase shift rate (K) of each grid are determined. DP ) as the rainfall inversion factor that must be included, and combined with the basic data (Level 2) and product data (Level 3) of other water conservancy rainfall measuring radars as the main inversion factors, coordinated with air pressure, temperature, longitude, latitude, elevation and other data as auxiliary factors to participate in rainfall inversion. The linear regression method is used to analyze the correlation between each factor and the measured rainfall data to determine the rainfall inversion factors actually involved.

[0079] Step 5: Train the neural network and perform rainfall inversion:

[0080] (5.1) Neural network design, such as Figure 2As shown in the figure, the rainfall inversion factor and actual rainfall data are used as input layer data, and the predicted rainfall is used as output layer data. For example, the convolutional neural network (CNN) and long short-term memory neural network (LSTM) model structures can be combined to extract the spatial characteristics between each rainfall inversion factor and the actual rainfall based on CNN, and the LSTM model can be combined to capture the temporal characteristics between each rainfall inversion factor and the actual rainfall to enhance the ability of rainfall spatiotemporal inversion. In addition, other neural network structures and model algorithms can be explored on this basis and flexibly selected according to actual needs.

[0081] (5.2) Neural network training: divide the training set and the test set, use the training set sample data to train the neural network model, learn the spatial and temporal characteristics of rainfall inversion factors and measured rainfall, continuously adjust the model parameters, use the validation set data to evaluate the accuracy of the model's predicted rainfall data, determine the optimal model parameters, and establish a rainfall inversion model that integrates GNSS water vapor tomography and hydraulic rainfall radar to perform rainfall inversion.

[0082] (5.3) Model accuracy assessment: statistical calculation of the accuracy, error rate, root mean square error (RMSE) and mean absolute error (MAE) of rainfall inversion.

[0083] The above-mentioned embodiments are only partial embodiments of the present invention and cannot cover the whole of the present invention. Based on the above-mentioned examples and drawings, technical personnel in this field can obtain more implementation methods without paying creative work. Therefore, these implementation methods obtained without paying creative work should be included in the protection scope of the present invention.

Claims

1. A rainfall inversion method integrating GNSS water vapor tomography and hydraulic rainfall radar, characterized in that: The following steps are involved: Step 1: Data collection: including GNSS data, water conservancy rainfall radar data, meteorological data and geographic information; Step 2: Estimate the atmospheric water vapor content SWV on the GNSS slant signal path; Step 3: Obtaining grid water vapor density value by three-dimensional water vapor tomography: According to the resolution of the water conservancy rainfall radar data, the tomography area is discretized and gridded to obtain n three-dimensional voxel blocks, and then a GNSS water vapor tomography model is constructed and solved based on the three-dimensional tomography algorithm to obtain the water vapor density value of each grid; the expression of the GNSS water vapor tomography model is: Where A is the coefficient matrix, representing the intercept information of the GNSS signal in each voxel block; B is the SWV observation value vector; X is the water vapor density vector; H is the coefficient matrix of horizontal constraints, V is the coefficient matrix of vertical constraints, I is the coefficient matrix of prior constraints, and C is the initial water vapor density value of the tomographic area; Step 4: Identify inversion factors: Based on the water vapor density value of each grid, integrate in the vertical direction to obtain the atmospheric precipitable water volume (PWV) of each grid. Use PWV and its related data as well as water conservancy rainfall radar data as the main inversion factors, and use meteorological data and elevation data as auxiliary inversion factors to participate in rainfall inversion. Use linear regression method to analyze the correlation between each factor and the measured rainfall data to determine the actual rainfall inversion factors involved. Step 5: Train the neural network and perform rainfall inversion: Use rainfall inversion factors and actual rainfall data as input layer data, and predicted rainfall as output layer data to build a neural network. Train the neural network, learn the spatiotemporal characteristics between rainfall inversion factors and measured rainfall, obtain the optimal model parameters, build a multi-factor rainfall inversion model, and perform rainfall inversion.

2. The rainfall inversion method integrating GNSS water vapor tomography and hydraulic rain radar according to claim 1 is characterized in that: In step 1, the GNSS data includes GNSS raw observation data and IGS precise orbit clock data; the hydrological rain radar data includes hydrological rain radar base data Level 2 and product data Level 3, and the hydrological rain radar base data Level 2 includes reflectivity factor Z, differential reflectivity factor Z DR , differential propagation phase Differential propagation phase shift rate K DP The product data Level 3 includes surface rainfall inversion products, 0-2h surface rainfall forecast products, rainfall-related particle phase classification, and liquid water content data; the meteorological data includes measured rainfall, air pressure, temperature, and relative humidity; the geographic information includes elevation and longitude and latitude.

3. The rainfall inversion method integrating GNSS water vapor tomography and hydraulic rain radar according to claim 1 is characterized in that: In step 2, estimating the atmospheric water vapor content SWV on the GNSS slant signal path includes the following steps: Step 2-1, solve the GNSS data based on the precise point positioning technology, use the VMF1 model as the zenith mapping function model, and obtain the zenith tropospheric total delay ZTD; Step 2-2: Use the Saastamoinen model to calculate the zenith static delay ZHD, which is expressed as: Among them, P s is the air pressure value at the underlying surface of the measuring station, hPa; θ is the latitude of the measuring station; H is the height of the measuring station, m; Step 2-3, calculate the zenith wet delay ZWD, in mm, using the expression: ZWD=ZTD-ZHD Step 2-4: Calculate the oblique path wet delay SWD in mm using the expression: SWD=M w (e)×ZWD+M g (e)×[G NS cos(α)+G EW sin(a)]+R Among them, ε and α represent the satellite cut-off altitude angle and azimuth respectively; M w and M g Respectively represent the wet mapping function and the gradient mapping function; G NS and G EW denote the wet horizontal gradient terms in the north-south and east-west directions respectively; R denotes the residual term that is not modeled; Step 2-5: Calculate the atmospheric water vapor content SWV on the oblique signal path in mm. The expression is: SWV=Π×SWD Among them, П is the atmospheric water vapor conversion coefficient, which is a dimensionless value, and its calculation formula is: Where ρ is the density of liquid water, kg / m 3 ; R v is the water vapor gas constant, whose value is 461.495 J / (kg·K); k'2 and k3 are atmospheric refractive index constants, whose values ​​are 16.48 K / hPa and 3.776×10 5 K 2 / hPa; T m is the weighted mean temperature of the atmosphere, K.

4. The rainfall inversion method integrating GNSS water vapor tomography and hydraulic rain radar according to claim 1 is characterized by: In step three, the tomography area is discretized and gridded according to the resolution of the water conservancy rainfall radar data: including determining the layer height, horizontal resolution and vertical resolution of the tomography area, discretizing the geometric space and regional water vapor elements of the tomography area; setting the heights of the bottom and top of the grid to 0km and 11km respectively; the horizontal range tomography grid division needs to ensure that the horizontal resolution of the GNSS water vapor tomography is consistent with the horizontal resolution of the water conservancy rainfall radar; the tomography area grid division in the vertical direction adopts an uneven division method of dense at the bottom and sparse at the top based on the distribution characteristics of water vapor in the vertical direction, and the grid division of the tomography area below 2km is encrypted to ensure that the vertical resolution of the GNSS water vapor tomography is consistent with the vertical resolution of the water conservancy rainfall radar.

5. The rainfall inversion method integrating GNSS water vapor tomography and hydraulic rain radar according to claim 1 is characterized by: In step three, the horizontal constraint assumes that the water vapor value of a voxel block in the horizontal direction is the weighted average of the water vapor parameters of several adjacent voxel blocks in the same layer based on the Gaussian weighted function; the vertical constraint uses the exponentially decreasing change characteristics of water vapor in the vertical direction to construct the vertical constraint relationship between two adjacent layers; the prior constraint obtains the initial field of atmospheric water vapor as a constraint condition.

6. The rainfall inversion method integrating GNSS water vapor tomography and hydraulic rain radar according to claim 1 is characterized by: In step 4, the PWV related data include: PWV change and PWV change rate; the water conservancy rain measuring radar data include the basic data Level 2 and product data Level 3 of the water conservancy rain measuring radar.

7. The rainfall inversion method integrating GNSS water vapor tomography and hydraulic rain radar according to claim 1 is characterized by: In step five, the neural network: combines the convolutional neural network CNN and the long short-term memory neural network LSTM model structure, extracts the spatial characteristics between each rainfall inversion factor and the actual rainfall based on CNN, and combines the LSTM model to capture the temporal characteristics between each rainfall inversion factor and the actual rainfall, so as to enhance the ability of rainfall spatiotemporal inversion.

Images