CN114357770A - Troposphere chromatography method - Google Patents

Troposphere chromatography method Download PDF

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CN114357770A
CN114357770A CN202210005801.1A CN202210005801A CN114357770A CN 114357770 A CN114357770 A CN 114357770A CN 202210005801 A CN202210005801 A CN 202210005801A CN 114357770 A CN114357770 A CN 114357770A
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陈必焰
谭井树
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Abstract

The invention provides a troposphere chromatography method which comprises the steps of adding layered atmospheric degradable water amount observation value data obtained by a geostationary satellite aeolian cloud 4A on the basis of water vapor data measured by the existing GNSS, wherein the layered atmospheric degradable water amount observation value obtained by the aeolian cloud 4A comprises a low-layer water vapor observation value, a middle-layer water vapor observation value and a high-layer water vapor observation value, reasonably determining the weight ratio of the two types of water vapor data of the water vapor data measured by the GNSS and the layered atmospheric degradable water amount observation value data obtained by the aeolian cloud 4A, obtaining a more accurate troposphere chromatography model, and carrying out troposphere chromatography under the troposphere chromatography model. According to the invention, the layered water vapor data of the wind cloud satellite with high space-time resolution is added to the chromatographic model, so that the unsuitability of the traditional chromatographic model can be greatly reduced, and a more accurate chromatographic model of the troposphere is constructed, thereby obtaining better density distribution of the troposphere water vapor.

Description

Troposphere chromatography method
Technical Field
The invention belongs to the field of weather forecast, and particularly relates to atmospheric water vapor detection and a chromatographic method for a troposphere.
Background
Although the content of water vapor in the atmosphere is small, the water vapor is an important factor in global energy balance, and due to the phase change effect of the water vapor, various states such as rain, fog, cloud, snow and the like are important factors causing weather changes, and are also main driving forces for the formation and evolution of disastrous weather. In weather forecast, the success or failure of short-time approach forecast is determined by knowing the content of water vapor in the atmosphere, the change of the water vapor and the distribution conditions of time and space, and the accuracy of numerical forecast can be directly influenced by analyzing a humidity field in the atmosphere. The troposphere is the bottommost layer connected with the ground in the earth atmosphere, has the highest atmospheric density, and concentrates more than 90% of atmospheric water vapor. In the space geodetic survey, tropospheric delay has gradually become an important research direction of modern space geodetic technology, and due to the unstable characteristic of tropospheric water vapor in space and time, the signal delay caused by the water vapor becomes one of the main factors influencing the space geodetic survey accuracy. Therefore, the detection of the high-precision water vapor content has important significance for making more accurate weather forecast, strengthening early warning of extreme weather disasters and developing space geodetic research.
The traditional atmospheric water vapor detection method comprises the following steps: water vapor radiometers, radiosondes, weather satellite earth observation, and the like, and remote sensing of atmospheric water vapor content using Global Navigation Satellite System (GNSS) technology is an emerging atmospheric sounding technology developed in the nineties. When the satellite signal passes through the troposphere, it interacts with the atmospheric medium in contact, creating a delay effect in time and propagation path, i.e. refraction. An important meteorological parameter, namely the amount of atmospheric water reducible (PWV), is estimated by establishing a functional relationship between the delay amount and the refractive index of the atmosphere. As powerful supplement of the traditional atmospheric detection method, GNSS detection water vapor has the advantages of global coverage, real-time continuity, all weather, high precision and the like, and is an important means for modern water vapor detection. The water content reducible on the zenith only contains one-dimensional information of the water vapor content of the troposphere, and the vertical distribution information of the atmospheric water vapor content cannot be directly reflected, so that the application of the water content reducible on the zenith in meteorological research is limited. In 1992, based on tomographic techniques in the medical field, Bevis first proposed tropospheric tomography techniques, i.e., the reconstruction of tropospheric water vapor density images using the amount of rainfall (SWV) along the diagonal paths of different rays that penetrate the model space from various directions. The GNSS troposphere chromatography based water vapor four-dimensional distribution with high precision and high space-time density can be effectively obtained, and meanwhile, the GNSS troposphere chromatography based water vapor four-dimensional distribution based on the GNSS troposphere chromatography has the advantages of being low in cost, simple to operate, capable of monitoring all weather and the like, and provides possibility for mastering the space-time change of troposphere water vapor. Therefore, the research on the troposphere chromatography technology has important scientific significance and application value in the aspects of strengthening global atmospheric water vapor multi-dimensional dynamic change monitoring, promoting the development of modern weather forecast business and the like.
The problem of unsuitability of a troposphere chromatography model is a main reason influencing the accuracy of a chromatography result, and the conventional solution to the problem mainly comprises the following types:
optimizing a chromatography gridding division model: the chromatographic grid is divided according to the distribution of a ground GNSS station and a satellite constellation, or the chromatographic grid is divided in a self-adaptive mode by combining factors such as terrain and the like, so that the grid is penetrated by GNSS signal lines as much as possible, and the unsuitability of a chromatographic model is reduced. However, due to the "inverted cone" distribution characteristic of GNSS signal lines, this method has limited effect, and there is still a large number of grids that are not traversed by GNSS signal lines.
Add empirical constraints: according to the distribution characteristics of water vapor in the troposphere, empirical constraint conditions, mainly including horizontal constraint, vertical constraint, boundary constraint and the like, are added to participate in the calculation of the chromatographic equation so as to obtain a reliable chromatographic solution. However, due to the instability of the atmospheric system, the empirical constraint is often not consistent with the actual water vapor distribution, which results in the distorted chromatographic solution and difficulty in obtaining reliable chromatographic results.
Fusing multi-source water vapor information: non-GNSS water vapor information is used as strong constraint and introduced into a chromatographic model, so that the distortion effect of a calculation result caused by inaccurate empirical constraint conditions can be weakened. What has been introduced so far include: water vapor data which is analyzed or forecasted globally, PWV differential data which is measured by synthetic aperture radar interferometry, PWV images which are obtained by satellite sensors such as a medium-resolution imaging spectrometer, an atmospheric infrared detector and the like. These external moisture signals are all complete PWV signals that pass through the top of the tomographic model to the bottom grid of the model, which can improve the ill-posed nature of the tomographic model, but only to a limited extent, while easily increasing the redundancy of the tomographic equations.
Therefore, the traditional chromatographic model has a large number of grids which are not penetrated by signals, the chromatographic equation set is seriously ill-shaped, and the error of the chromatographic inversion result is large. The non-GNSS water vapor information of the chromatographic model fused with the external water vapor information is introduced into the chromatographic model as strong constraint, so that the distortion effect of a calculation result caused by inaccurate empirical constraint conditions can be weakened. Current studies use mostly the complete PWV signal across the top of the tomographic model to the bottom grid of the model. The signal contains the sum of all grid water vapor unknowns in the vertical direction penetrated by the signal, so that the unsuitability of a chromatographic model can be improved, but the improvement degree is limited, and the redundancy of a chromatographic equation set is easily increased.
Chinese patent CN201711033706.8 discloses a function-based three-dimensional water vapor detection method, which comprises the following steps: firstly, receiving and resolving observation data; secondly, resolving atmospheric tropospheric parameters; thirdly, calculating the content SWV of the atmospheric water vapor on the satellite signal inclined path; establishing a function base observation equation; fifthly, constructing a prior constraint equation; establishing a function-based three-dimensional water vapor chromatography model; seventhly, determining the weight ratio of various parameters in the function-based three-dimensional water vapor chromatography model; and eighthly, resolving parameters to be estimated and displaying results of the three-dimensional water vapor chromatographic model based on the function base. The method is based on a function-based observation equation, establishes a new function-based chromatographic model, ensures the spatial continuity of the water vapor density parameters to be estimated by introducing the function-based observation equation, reduces the number of the parameters to be estimated, enhances the stability of the chromatographic model structure, and ensures the precision and reliability of the water vapor result reconstruction. The method of the patent reduces the number of chromatography unknowns and effectively improves the unsuitability of a chromatography equation set. However, the patent uses a function base to describe the continuity of atmospheric water vapor in a horizontal space, but the actual water vapor distribution is complex and variable and cannot be completely described by a set of functions, so that a large error exists.
Therefore, there is a need in the art for a new tropospheric chromatography method.
Disclosure of Invention
In order to solve the problems and establish a more accurate troposphere chromatography modeling method and theory, the invention develops a geostationary satellite layered water vapor data troposphere chromatography method fusing high space-time resolution on the basis of absorbing the advantages of the existing additional external constraint water vapor chromatography, the method uses the high space-time resolution geostationary satellite layered water vapor data as chromatography constraint conditions for the first time, the unsuitability of a chromatography model is greatly reduced, and a chromatography product closer to the actual water vapor distribution can be obtained.
The invention provides a troposphere chromatography method which comprises the steps of adding layered atmospheric degradable water amount observation value data obtained by a geostationary satellite aeolian cloud 4A on the basis of water vapor data measured by the existing GNSS, wherein the layered atmospheric degradable water amount observation value obtained by the aeolian cloud 4A comprises a low-layer water vapor observation value, a middle-layer water vapor observation value and a high-layer water vapor observation value, reasonably determining the weight ratio of the two types of water vapor data of the water vapor data measured by the GNSS and the layered atmospheric degradable water amount observation value data obtained by the aeolian cloud 4A, obtaining a more accurate troposphere chromatography model, and carrying out troposphere chromatography under the troposphere chromatography model.
In a specific implementation mode, the two types of water vapor data are evaluated by utilizing sounding data, the ratio of the obtained medium errors is used as a weight ratio, and a multiplication algebraic reconstruction technology is adopted to solve a chromatographic equation set in the troposphere chromatographic model in an iterative manner.
In one particular embodiment, the initial water vapor density field required for the multiplicative algebraic reconstruction technique iteration is provided by the American weather forecasting center.
In a specific embodiment, establishing the more accurate tropospheric tomography model comprises the steps of:
along the path l from the GNSS receiver to the GNSS satellite signal line, the inclined path can reduce the water volume SWV and the water vapor density NwThe relationship between is expressed as an integral:
Figure BDA0003455450780000031
the integral for each SWV signal line is approximately:
SWV(i)＝∑jAS(i，j)x(j) (2)
wherein Δ S (i, j) is an intercept of the ith signal line passing through the jth grid, and x (j) is a water vapor density unknown number of the jth grid;
adding the layered PWV data inverted by the wind cloud 4A to a chromatographic model to form a constraint condition for carrying out combined chromatographic inversion; the wind cloud 4A layered PWV data is also subjected to equation establishment according to the formula (2), but the boundary of the layered PWV data is determined on the basis of the sigma pressure coordinate and needs to be converted into the geometric height of the chromatographic model; determining barometric pressure values at boundaries of hierarchical PWV data using fifth generation global re-analysis of surface barometric pressure provided by the European mid-term weather forecast center
Figure BDA0003455450780000041
Figure BDA0003455450780000042
Wherein sigmakSigma coefficients, specifically 1.0, 0.9, 0.7 and 0.3;
Figure BDA0003455450780000043
re-analyzing the earth surface pressure for the fifth generation of global atmosphere at the pixel central point of the ith wind cloud 4A water vapor image, namely ERA5 earth surface pressure;
then, utilizing ERA5 hierarchical potential data to interpolate to obtain the potential at the boundary of each hierarchical PWV data, and using the following formula to approximate the corresponding geometric height H:
H＝Φ/g (4)
phi is the potential at the boundary of each hierarchical PWV data, and g is the gravity acceleration;
and finally, introducing horizontal constraint, vertical constraint and top layer constraint to obtain a troposphere chromatographic function model fused with the layered water vapor of the wind cloud 4A:
Figure BDA0003455450780000044
wherein, BGNSS、BFYAnd V are coefficient matrices; x is an unknown number vector of water vapor density; SWVGNSSIs a GNSS SWV observation; LPWFYLayering PWV observation values for the wind clouds 4A, wherein the PWV observation values comprise low-layer water vapor observation values, middle-layer water vapor observation values and high-layer water vapor observation values; and then reasonably determining the weight ratio of wind cloud 4A layered PWV data to GNSS measured water vapor data.
The invention has the advantages that:
according to the method, the accuracy of the troposphere chromatography result is improved by fusing the wind cloud layering water vapor model. According to the invention, the layered water vapor data of the wind cloud satellite with high space-time resolution is added to the chromatographic model, so that the unsuitability of the traditional chromatographic model can be greatly reduced, and a more accurate chromatographic model of the troposphere is constructed, thereby obtaining better density distribution of the troposphere water vapor. Compared with the traditional model, the fused wind cloud layered water vapor chromatographic model provided by the invention can obviously improve the chromatographic resolution precision. Therefore, the method can effectively make up for the theoretical defects of the traditional model and determine the water vapor density field structure more accurately.
Furthermore, compared with patent CN201711033706.8, the invention does not perform chromatography grid division in horizontal space, but the invention performs uniform grid division in horizontal space; the method improves the chromatographic equation unsuitability by reducing the number of chromatographic unknowns, and improves the unsuitability by increasing the actual observation constraint, namely increasing the number of equations.
Drawings
Fig. 1 is a schematic diagram of a troposphere chromatographic model fused with layered water vapor of a wind cloud 4A.
FIG. 2 is a root mean square error distribution plot of a water vapor chromatographic inversion performed using a conventional model in Hunan province.
FIG. 3 is a root mean square error distribution diagram of a Hunan province region for water vapor chromatographic inversion using the new model fused with the layered water vapor data of the present invention.
FIG. 4 is a comparison of the data from the Hunan Huai exploration station with the water vapor density vertical profile of the conventional chromatographic method and the novel chromatographic method of the present invention.
Fig. 5 is a comparison graph of data of an air detecting station in Chenzhou, Hunan, with a water vapor density vertical profile of a conventional chromatography method and the new chromatography method in the present invention.
Detailed Description
Troposphere: the layer of the atmosphere closest to the earth's surface concentrates about 75% of the mass of the atmosphere and more than 90% of the mass of water vapor.
Troposphere chromatography: and performing inversion calculation according to troposphere delay information obtained by ray scanning, and reconstructing an image of the water vapor density distribution rule in the range of the detected troposphere.
GNSS: the global positioning navigation system comprises four major systems of China Beidou, European Galileo, Russian glonass and American GPS.
The invention solves the problem of improving the unsuitability of the chromatographic model by fusing meteorological satellite water vapor products in troposphere chromatographic modeling. In the process of establishing a troposphere chromatographic model, the prior art generally discretizes a chromatographic region by regular square blocks, and assumes that the water vapor density of each square block is uniformly distributed and kept unchanged in a certain time. And then establishing a functional relation between corresponding grid unknowns and signal line inclined path rainfall (SWV) according to the grid punctured by the GNSS signals, and further forming a chromatography equation set to invert the density of the steam in the troposphere. However, due to the fixed position of the ground GNSS survey station and the omnidirectional distribution of the satellite constellation, the GNSS signal lines have the characteristic of inverted cone distribution, which causes a great amount of grids which are not penetrated by the signal lines in the chromatographic model, thereby causing the problem of the ill-qualification of the chromatographic equation set. In order to reduce the ill-qualification of the chromatographic equation set and obtain a chromatographic solution closer to the actual water vapor distribution, the joint chromatographic inversion can be performed by adding an external multi-source water vapor signal. At present, a vertical PWV signal passing through a grid from the top of the chromatographic model to the bottom of the chromatographic model, namely the total water-lowering amount of the zenith (total water-lowering volume) (TPW), is mainly used by the chromatographic model fusing multi-source water vapor information, and the signal comprises the sum of unknown water vapor numbers of all grids in the vertical direction after the vertical PWV signal passes through the vertical PWV signal. The constraint condition established by the external TPW signal can improve the unsuitability of the tomographic model, but the improvement degree is limited, and the constraint condition is easy to generate contradiction with the GNSS TPW positioned in the same plane grid, so that the redundancy of the tomographic equation set is increased.
On the basis of absorbing the advantages of the existing additional external constraint water vapor chromatography, the invention develops the high-space-time-resolution geostationary satellite layered water vapor data troposphere chromatography method, the method uses the high-space-time-resolution geostationary satellite layered water vapor data as the chromatography constraint condition for the first time, the unsuitability of a chromatography model is reduced to a greater extent, and a chromatography product closer to the actual water vapor distribution can be obtained.
This patent improves to traditional chromatographic model's not enough, and the aeolian geostationary satellite layering steam data that has used high spatial and temporal resolution has reduced chromatographic model's inadaptation as the chromatography constraint condition to a great extent, can obtain the chromatography product that is closer to actual steam distribution more. The principle and the process are as follows:
path l, SWV and water vapor density N along GNSS receiver to GNSS satellite signal linewThe relationship between can be expressed as an integral:
SWV＝∮lNwdl (1)
tropospheric tomography techniques can invert the spatial distribution of tropospheric water vapor density based on a series of SWVs in each direction of the chromatographic space. In the conventional chromatography method, a grid model is established in a chromatography region, and each grid in the chromatography model is an unknown number of water vapor density under the assumption that the water vapor density in each voxel is constant and uniformly distributed in a chromatography period. The integral for each SWV signal line can therefore be approximated as:
SWV(i)＝∑jΔS(i，j)x(j) (2)
wherein Δ S (i, j) is an intercept of the ith signal line passing through the jth grid, and x (j) is an unknown number of water vapor density of the jth grid.
The wind cloud fourth satellite (wind cloud 4A) is a second generation geostationary orbit (GEO) quantitative remote sensing meteorological satellite developed by the eighth research institute (Shanghai aerospace technology research institute) of China aerospace science and technology group company, and a three-axis stable control scheme is adopted, so that the continuous and stable operation of the wind cloud fourth satellite can greatly improve the detection level of the geostationary orbit meteorological satellite in China. From 5 months and 8 days and zero hours in 2018, users in China and Asia-Pacific areas can formally receive the data of the wind, cloud, four-number and A star.
On the basis of a traditional chromatographic model, the invention provides that layered PWV data inverted by a wind cloud 4A satellite is added to the chromatographic model to form a constraint condition for carrying out combined chromatographic inversion. The wind cloud 4A layered PWV data is also formulated according to equation (2), but since the wind cloud 4A is based on sigma pressure coordinates to determine the boundary of the layered PWV data, it needs to be converted into the geometric height of the tomographic model. The present invention uses the fifth generation of global atmospheric re-analysis (ERA5) surface barometric pressure provided by the European mid-range weather forecast center (ECMWF) to determine barometric pressure values at the boundary of hierarchical PWV data
Figure BDA0003455450780000062
Figure BDA0003455450780000063
Wherein sigmakSigma coefficients, specifically 1.0, 0.9, 0.7 and 0.3;
Figure BDA0003455450780000064
the ERA5 ground pressure is the central point of the pixel of the ith wind cloud 4A water vapor image.
Then, utilizing ERA5 hierarchical potential data to interpolate to obtain the potential at the boundary of each hierarchical PWV data, and using the following formula to approximate the corresponding geometric height H:
H＝Φ/g (4)
where Φ is the potential at each hierarchical PWV data boundary and g is the gravitational acceleration.
And finally, introducing horizontal constraint, vertical constraint and top layer constraint to obtain a troposphere chromatographic function model fused with the layered water vapor of the wind cloud 4A:
Figure BDA0003455450780000071
wherein, BGNSS、BFYAnd V are coefficient matrices; x is an unknown number vector of water vapor density; SWVGNSSIs a GNSS SWV observation; LPWFYAnd (4) layering PWV observation values for the wind clouds and 4A, wherein the PWV observation values comprise LOW-layer water vapor (LPW-LOW), middle-layer water vapor (LPW-MID) and HIGH-layer water vapor (LPW-HIGH). Fig. 1 is a schematic diagram of a troposphere chromatographic model fused with layered water vapor of a wind cloud 4A.
Considering the inconsistency of accuracy between wind cloud 4A layered PWV data and GNSS measured water vapor data, it is also necessary to reasonably determine the weight ratio of the two types of data. The method utilizes sounding data to evaluate two types of water vapor data respectively, and uses the obtained ratio of the medium errors as a weight ratio. The Multiplicative Algebraic Reconstruction Technique (MART) adopts an iterative mode to reconstruct an image, avoids matrix inversion, has the advantage of good convergence in a short time, and is a common method for solving the problem of the uncertainty of the tropospheric tomography equation. The method adopts MART iteration to solve the chromatographic equation set, and an initial water vapor density field required by the iteration is provided by the American meteorological environment forecasting center.
Based on the observation data of the Hunan province continuous operation tracking station network (HNCORS), the large-scale water vapor chromatography inversion of the Hunan province region is realized by using the fusion wind cloud layered water vapor chromatography method provided by the invention, and the result shows that the method can obviously improve the water vapor chromatography result precision. Fig. 2 is a root mean square error distribution diagram of a water vapor chromatography inversion performed by using a traditional model in the area of the Hunan province, and fig. 3 is a root mean square error distribution diagram of a water vapor chromatography inversion performed by using the new model fused with the layered water vapor data of the wind clouds in the area of the Hunan province. Both fig. 2 and fig. 3 are the root mean square error (RMSE error) of the chromatographic results of their models compared to the reanalyzed data of ERA 5.
As can be seen from the artwork of fig. 2 and 3, fig. 2 appears orange overall and fig. 3 appears blue overall. Specifically observing the data of all areas in Hunan province in the graph, the RMSE error of comparison with the re-analyzed data of ERA5 in most areas in Hunan province in the traditional model is 1.7-2.5 g/m3In large area of 1.8-2.3 g/m3In the meantime. The new model provided by the invention obtains the chromatography result in most areas of Hunan provinceThe RMSE error of the product compared with the re-analyzed data of ERA5 is 0.2-1.7 g/m3And especially centered at 0.5-1.3 g/m3In the meantime. Therefore, in most chromatographic regions, the new model and the new method obtain smaller RMSE than the traditional model, and the new method can improve the quality of the chromatographic result as a whole.
Fig. 4 and 5 show the results of comparing the chromatography in both places of huan, hunan and chen, hunan with the water vapor density vertical profiles of two sounding stations, respectively. It is evident from fig. 4 and 5 that the results of the new model and the new method are more consistent with the probe water profile. The statistical results show that the RMSE of the traditional model and method and the new model and method are 0.99 and 0.52g/m respectively compared with the Hunan Huai exploration station3The reduction amplitude of RMSE reaches 47.47%; compared with Chenzhou detection of empty stations in Hunan province, the RMSE is 1.47g/m of the traditional model3Reduced to 0.95g/m3The RMSE reduction was 35.37%. Therefore, the novel model and the novel method can obviously improve the accuracy of the chromatography result, and obtain the three-dimensional water vapor distribution which is more in line with the reality.
The traditional troposphere chromatography model only adds empirical horizontal constraint, vertical constraint and top layer constraint, and the chromatography result is not ideal. The complete PWV signals of the satellite with high spatial resolution are used as constraint conditions, so that the unsuitability of a chromatography equation set is reduced to a certain extent, and the accuracy of a chromatography result is improved. However, the full PWV signal has limited improvement degree to the chromatographic model, and it is easy to increase redundancy of the chromatographic equation set, so that the modeling accuracy of the chromatographic model is still not high. In addition, currently, research for applying geostationary satellite layered water vapor data with high spatial and temporal resolution to a tomography model is lacked. According to the invention, the layered water vapor data of the wind cloud satellite with high space-time resolution is added to the chromatographic model, so that the unsuitability of the traditional chromatographic model can be greatly reduced, and a more accurate chromatographic model of the troposphere is constructed, thereby obtaining better density distribution of the troposphere water vapor. If the method provided by the invention is applied to an actual case, the change information of the water vapor density space distribution can be well inverted, the accurate inversion of the water vapor density value at any point in the space is realized, and the accuracy of the chromatographic result is integrally improved compared with that of the traditional method. Based on a large number of comparative analyses, compared with a traditional model, the fusion wind cloud layered water vapor chromatography model provided by the invention can obviously improve the chromatography precision. Therefore, the method can effectively make up for the theoretical defects of the traditional model and determine the water vapor density field structure more accurately.
While the embodiments of the present invention have been described in detail with reference to the drawings, the embodiments of the present invention are not limited to the details of the embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present invention within the technical concept of the embodiments of the present invention.

Claims (4)

1. A troposphere chromatography method is characterized in that layered atmospheric degradable water amount observation value data obtained by a geostationary satellite Feng Yun 4A is added on the basis of water vapor data measured by an existing GNSS, the layered atmospheric degradable water amount observation value obtained by the Feng Yun 4A comprises a low-layer water vapor observation value, a middle-layer water vapor observation value and a high-layer water vapor observation value, the weight ratio of the two types of water vapor data of the water vapor data measured by the GNSS and the layered atmospheric degradable water amount observation value data obtained by the Feng Yun 4A is reasonably determined, a more accurate troposphere chromatography model is obtained, and troposphere chromatography is carried out under the troposphere chromatography model.
2. The method of claim 1, wherein the two types of water vapor data are evaluated by using sounding data, the ratio of the obtained median errors is used as a weight ratio, and a multiplicative algebraic reconstruction technique is adopted to iteratively solve a chromatographic equation set in the tropospheric layer chromatographic model.
3. The method of claim 1, wherein the initial steam density field required for the iterative multiplicative algebraic reconstruction technique is provided by the American weather forecasting center.
4. The method of any one of claims 1 to 3, wherein establishing the more accurate tropospheric tomography model comprises the steps of:
along a path l from a GNSS receiver to a GNSS satellite signal line, the relationship between the water-reducing amount SWV of the inclined path and the water vapor density Nw is expressed by an integral:
SWV＝∮lNwdl (1)
the integral for each SWV signal line is approximately:
SWV(i)＝∑jΔS(i，j)x(j) (2)
wherein Δ S (i, j) is an intercept of the ith signal line passing through the jth grid, and x (j) is a water vapor density unknown number of the jth grid;
adding the layered PWV data inverted by the wind cloud 4A to a chromatographic model to form a constraint condition for carrying out combined chromatographic inversion; the wind cloud 4A layered PWV data is also subjected to equation establishment according to the formula (2), but the boundary of the layered PWV data is determined on the basis of the sigma pressure coordinate and needs to be converted into the geometric height of the chromatographic model; determining barometric pressure values at boundaries of hierarchical PWV data using fifth generation global re-analysis of surface barometric pressure provided by the European mid-term weather forecast center
Figure FDA0003455450770000013
Figure FDA0003455450770000011
Wherein sigmakSigma coefficients, specifically 1.0, 0.9, 0.7 and 0.3;
Figure FDA0003455450770000012
re-analyzing the earth surface pressure for the fifth generation of global atmosphere at the pixel central point of the ith wind cloud 4A water vapor image, namely ERA5 earth surface pressure;
then, utilizing ERA5 hierarchical potential data to interpolate to obtain the potential at the boundary of each hierarchical PWV data, and using the following formula to approximate the corresponding geometric height H:
H＝Φ/g (4)
phi is the potential at the boundary of each hierarchical PWV data, and g is the gravity acceleration;
and finally, introducing horizontal constraint, vertical constraint and top layer constraint to obtain a troposphere chromatographic function model fused with the layered water vapor of the wind cloud 4A:
Figure FDA0003455450770000021
wherein, BGNSS、BFYAnd V are coefficient matrices; x is an unknown number vector of water vapor density; SWVGNSSIs a GNSS SWV observation; LPWFYLayering PWV observation values for the wind clouds 4A, wherein the PWV observation values comprise low-layer water vapor observation values, middle-layer water vapor observation values and high-layer water vapor observation values; and then reasonably determining the weight ratio of wind cloud 4A layered PWV data to GNSS measured water vapor data.
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