CN-122020570-A - Beidou atmosphere water content collaborative inversion method based on multi-source heterogeneous data fusion

CN122020570ACN 122020570 ACN122020570 ACN 122020570ACN-122020570-A

Abstract

The invention relates to the technical field of data processing, in particular to a Beidou atmosphere water content collaborative inversion method for multi-source heterogeneous data fusion, which comprises the steps of acquiring Beidou observation data, forecast mode data and ground meteorological data of a target monitoring station and adjacent monitoring stations; and calculating the water vapor advection intensity based on the spatial distribution difference of the total zenith delay of the target monitoring station and the adjacent monitoring station, generating self-adaptive cooperative weight by combining the water vapor advection intensity and the thermal power vertical decoupling index, dynamically weighting and fusing ground measured data and forecast mode data, and inverting the atmospheric water content by combining the atmospheric parameters. According to the method, the self-adaptive fusion driven by a physical mechanism effectively inhibits the interference of a reverse temperature layer and turbulence, and improves the accuracy of water vapor inversion.

Inventors

LUO XIANGANG
CAO SIWEN
ZHU FUKUN
Yang Daobo
YUE XIAODONG
SUN QIHUI
GUO WENQIAN
CHEN XI
WU JUN

Assignees

武汉中地云申科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (10)

1. A Beidou atmosphere water content collaborative inversion method for multi-source heterogeneous data fusion is characterized by comprising the following steps of: Acquiring Beidou satellite observation data, forecast mode data and ground actual measurement data of each of a target monitoring station and a neighboring monitoring station, extracting carrier phase post-test residual errors and zenith total delay from the Beidou satellite observation data, and extracting zenith statics delay from the ground actual measurement data; Calculating vertical gradient deviation of temperature along with the height change based on the temperature in the forecast mode data, and carrying out high integration on the vertical gradient deviation to determine a thermodynamic vertical decoupling index; acquiring historical statistical characteristics of the water vapor non-uniformity factors, constructing a distribution probability model based on statistical moment based on the historical statistical characteristics, and mapping the water vapor non-uniformity factors into a self-adaptive cooperative weight for adjusting the multi-source data fusion proportion; and carrying out weighted fusion on the temperature in the ground actual measurement data and the temperature in the forecast mode data by utilizing the self-adaptive cooperative weight, and inverting the atmospheric water content by combining zenith statics delay and zenith total delay.
2. The beidou atmospheric water content collaborative inversion method according to claim 1, wherein the thermodynamic vertical decoupling index is determined based on the following relationship: ; Wherein, the A thermodynamic vertical decoupling index for the target monitoring station, The ground altitude of the target monitoring station, For the top level of the troposphere, Is a height integral variable, representing the vertical height, For forecasting pattern data The temperature value at which the temperature is to be measured, As a vertical gradient of temperature with height, For a preset dry adiabatic taper rate constant, For forecasting pattern data The air pressure value at the position is equal to the air pressure value at the position, Is the air pressure value of the ground actual measurement data, As a sign of the absolute value of the sign, In order to integrate the infinitesimal, Is the standard atmospheric temperature.
3. The beidou atmospheric water content collaborative inversion method according to claim 1, wherein the water vapor advection strength is determined based on a vector synthesis method, comprising: Firstly, calculating the difference value of zenith total delay of a target monitoring station and zenith total delay between each adjacent monitoring station and the distance between the target monitoring station and each adjacent monitoring station, and taking the ratio of the difference value to the distance as a discrete gradient in the direction from the target monitoring station to the adjacent monitoring station; And projecting discrete gradients in directions from the target monitoring station to all adjacent monitoring stations to the east-west and north-south orthogonal coordinate axes by using a sine function and a cosine function, respectively accumulating to obtain two orthogonal components, and calculating the length of a synthesized vector of the two orthogonal components by using Euclidean norms to serve as the water vapor advection strength of the target monitoring station.
4. The beidou atmospheric water content collaborative inversion method according to claim 1, wherein the determination of the water vapor non-uniformity factor based on the comprehensive carrier phase post-test residual and water vapor advection strength and the thermodynamic vertical decoupling index is performed based on the following relation: ; Wherein, the Is the water vapor non-uniformity factor of the target monitoring station, A thermodynamic vertical decoupling index for the target monitoring station, For the total number of Beidou satellites which can be received by the target monitoring station, Is the serial number of the Beidou satellite, Received for the target monitoring station Carrier phase post-verification residual errors in Beidou satellite observation data corresponding to the Beidou satellites, Is the first The altitude of the individual beidou satellites, For the water vapor advection intensity of the target monitoring station, As a function of the sine of the wave, Is a preset standard residual error.
5. The Beidou atmospheric water content collaborative inversion method according to claim 1, wherein mapping the water vapor non-uniformity factor into an adaptive collaborative weight for adjusting a multi-source data fusion ratio comprises: Obtaining the average value and standard deviation of the water vapor non-uniformity factor of the target monitoring station in the preset historical time period at the current moment, taking the average value and standard deviation as historical statistical characteristics, constructing a distribution probability model based on statistical moment based on the historical statistical characteristics, Wherein, the method comprises the steps of, Is the self-adaptive cooperative weight of the weight, Is the water vapor non-uniformity factor of the target monitoring station, And The mean and the standard deviation, respectively.
6. The Beidou atmospheric water content collaborative inversion method according to claim 1, wherein weighting and fusing the temperature in the ground actual measurement data and the temperature in the forecast mode data by utilizing the adaptive collaborative weights comprises: Calculating to obtain a first temperature by utilizing a Beviss formula based on the temperature in the ground measured data; Weighting the first temperature by using the self-adaptive cooperative weight to obtain a first weighted temperature, and weighting the second temperature by using 1 minus the self-adaptive cooperative weight to obtain a second weighted temperature; The sum of the first weighted temperature and the second weighted temperature is taken as a dynamic weighted temperature.
7. The Beidou atmospheric water content collaborative inversion method according to claim 6, wherein the method for inverting the atmospheric water content is as follows: Calculating the difference value of total zenith delay and zenith statics delay of the target monitoring station, as zenith wet delay of the target monitoring station, constructing a water vapor conversion coefficient model based on the zenith wet delay and the pre-acquired atmospheric physical parameters, replacing the temperature parameters in the water vapor conversion coefficient model by the water vapor conversion coefficient model to be dynamic weighted temperature so as to realize correction of the water vapor conversion coefficient model, and inverting and calculating the atmospheric water content by utilizing the corrected water vapor conversion coefficient model.
8. The beidou atmospheric water content collaborative inversion method according to claim 1, wherein after obtaining the beidou satellite observation data, the prediction mode data and the ground actual measurement data of each of the target monitoring station and the adjacent monitoring stations, the following alignment operation is further performed: For any one of the target monitoring station or the adjacent monitoring stations, keeping the time reference of the forecasting mode data and the ground measured data of the monitoring station consistent with the time reference of the Beidou satellite observation data, carrying out space matching processing on the forecasting mode data based on the geographic position of the monitoring station, and carrying out time interpolation processing on the forecasting mode data so as to ensure that the time resolution of the forecasting mode data is consistent with the time resolution of the Beidou satellite observation data.
9. The Beidou atmosphere water content collaborative inversion method according to claim 1, wherein the method for extracting carrier phase post-test residual errors and zenith total delay from Beidou satellite observation data is as follows: and for any one of the target monitoring station or the adjacent monitoring stations, acquiring Beidou satellite observation data corresponding to all Beidou satellites which can be received by the monitoring station, constructing a positioning observation model of the monitoring station based on the Beidou satellite observation data, setting zenith total delay as a parameter to be solved of the positioning observation model, resolving the positioning observation model by utilizing a parameter estimation algorithm to obtain zenith total delay of the monitoring station, substituting the zenith total delay into the positioning observation model to obtain a theoretical value of the Beidou satellite observation data corresponding to each Beidou satellite which can be received by the monitoring station, and determining a difference value between an actual value and the theoretical value of the Beidou satellite observation data as a carrier phase post-verification residual of the Beidou satellite observation data corresponding to the Beidou satellite.
10. The Beidou atmospheric water content collaborative inversion method of claim 1, wherein the method for extracting zenith statics delay from ground measured data is as follows: And for any one of the target monitoring station or the adjacent monitoring stations, acquiring the air pressure in the ground actually measured data of the monitoring station and the latitude and altitude of the monitoring station, and calculating the zenith statics delay of the monitoring station through a Saastamoinen model based on the air pressure, the latitude and the altitude.

Description

Beidou atmosphere water content collaborative inversion method based on multi-source heterogeneous data fusion Technical Field The invention relates to the technical field of data processing. In particular to a Beidou atmosphere water content collaborative inversion method for multi-source heterogeneous data fusion. Background The atmospheric water content monitoring is a key link in short-term weather forecast, extreme disaster early warning and weather modification operation, utilizes a Global Navigation Satellite System (GNSS), particularly a Beidou satellite navigation system, to perform water vapor inversion, has become an important means for weather monitoring due to the advantages of high time resolution, all-weather operation and low cost, and has the core principle that the water vapor content is inverted by utilizing the delay amount generated by signals when passing through a troposphere and combining with weighted average temperature, and typical application scenes comprise an urban waterlogging early warning system, airport weather guarantee service and an agricultural drought resistance monitoring network. However, in the existing inversion method of the atmospheric water content, the linear regression model (such as Beviss formula) based on the ground temperature is mainly relied on, and the method implies a hypothesis that the temperature change of the ground can linearly reflect the temperature structure of the high-altitude atmosphere, but in a physical atmosphere environment with actual complexity, a reverse temperature layer or a strong vertical turbulence phenomenon often occurs, and at the moment, the ground temperature and the high-altitude temperature are unhooked, so that the linear model fails. The above problems can cause deviation of the weighted average temperature of the linear regression model based on the ground air temperature in strong convection weather, frontal border or reverse temperature weather, and the deviation reduces the accuracy of the inversion result of the atmospheric water content and reduces the accuracy of weather forecast. Therefore, a multi-source collaborative inversion method capable of sensing the stability of the vertical structure of the atmosphere, quantifying the horizontal turbulence and the advection degree in real time and adaptively adjusting the multi-source data fusion strategy according to the stability, the quantized horizontal turbulence and the advection degree is needed, and the problem that inversion accuracy is reduced under the complex atmospheric working condition in the prior art is solved. Disclosure of Invention In order to solve the problem that in the prior art, under complex atmospheric conditions such as reverse temperature, turbulence or strong advection, a linear regression model which is singly dependent on ground air temperature leads to inaccurate inversion results of atmospheric water content, the invention provides a Beidou atmospheric water content collaborative inversion method for multi-source heterogeneous data fusion, which comprises the following steps: Acquiring Beidou satellite observation data, forecast mode data and ground actual measurement data of each of a target monitoring station and a neighboring monitoring station, extracting carrier phase post-test residual errors and zenith total delay from the Beidou satellite observation data, and extracting zenith statics delay from the ground actual measurement data; Calculating vertical gradient deviation of temperature along with the height change based on the temperature in the forecast mode data, and carrying out high integration on the vertical gradient deviation to determine a thermodynamic vertical decoupling index; acquiring historical statistical characteristics of the water vapor non-uniformity factors, constructing a distribution probability model based on statistical moment based on the historical statistical characteristics, and mapping the water vapor non-uniformity factors into a self-adaptive cooperative weight for adjusting the multi-source data fusion proportion; and carrying out weighted fusion on the temperature in the ground actual measurement data and the temperature in the forecast mode data by utilizing the self-adaptive cooperative weight, and inverting the atmospheric water content by combining zenith statics delay and zenith total delay. According to the technical scheme, the deviation degree of the actual atmospheric vertical temperature decreasing rate and the ideal dry adiabatic state is quantified from a physical mechanism through calculating a thermodynamic vertical decoupling index, so that the thermal disjoint phenomenon generated by the ground and the air caused by a reverse temperature layer or strong convection is accurately identified, the vertical decoupling index is used as physical gating and is introduced into analysis of residual errors (micro turbulence) and network level horizontal gradients (macro advection) after sate