CN112034490A - NWP inversion troposphere delay improvement method - Google Patents
NWP inversion troposphere delay improvement method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G01S19/07—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections
Abstract
The invention discloses an improvement method for NWP inversion troposphere delay, which specifically comprises the following steps: 1, acquiring high-precision ZTD data of part of stations provided by International GNSS Service (IGS) in the global scope, and recording the data as IGS _ ZTD; 2, acquiring the continuous one-year layered meteorological data including air pressure, temperature, relative humidity, specific humidity and gravity potential of a corresponding station area from the re-analysis data ERA-Interim product of the European mesoscale weather forecast center; after the NWP data are obtained, firstly, the gravity potential parameters in the NWP data need to be subjected to preliminary processing, and finally the gravity potential parameters are converted into the ground height; 4, obtaining ZTD of the lattice point of the operation area by utilizing relevant meteorological data and a new integral method, and recording the ZTD as NWP _ ZTD; and 5, after calculating the ZTD of grid points around the measuring station on the height of the measuring station, interpolating the ZTD of the measuring station by a related interpolation method. The method of the invention obviously improves the inversion troposphere delay precision, can be applied to GNSS high-precision positioning, and can be applied to the global scope.
Description
Technical Field
The invention relates to an improvement method for NWP inversion troposphere delay, and belongs to the technical field of global satellite navigation and positioning.
Background
Tropospheric delay in the Global Navigation Satellite System (GNSS) generally refers to the signal delay that occurs when electromagnetic wave signals pass through an unionized neutral atmosphere with a height of 50km or less. Tropospheric delay is one of the main error sources that affect GNSS navigation positioning accuracy. Studies have shown that the Zenith Tropospheric Delay (ZTD) is about 2.3m, and the amount of Delay gradually increases as the satellite altitude decreases, and may reach 20m when the satellite altitude is 10 °.
To summarize, there are four methods available in the prior art for GNSS data processing to attenuate tropospheric delay, respectively: a model correction method, a difference method, a parameter estimation method, and a method using external data. The common models of the model correction method include Saastamoinen, Hopfield and Black, etc., and the model correction method is convenient to use but low in precision. The differential method is commonly used for relative positioning of short baselines in GNSS, but cannot be applied to absolute positioning and long-range baseline network RTK (RTK). The parameter estimation method is a relatively accurate method, in data processing, zenith wet delay or corrected zenith delay residual is taken as undetermined parameters and solved along with other unknown parameters, but the method needs long-time observation to realize convergence. With the improvement of the accuracy of Numerical Prediction data of meteorological observation, higher-accuracy tropospheric delay can be inverted by using reanalysis or Prediction data of a Numerical Weather Prediction (NWP) model, and the requirements of meter-level GNSS navigation positioning can be generally met.
Accuracy analysis of ECMWF/NCEP data calculation ZTD in Asia region of Chenkining et al, geophysical report 2012,55(05): 1541) -1548, indicated that average residual Error and Root Mean Square Error (RMSE) of ZTD calculated in Asia region using NWP re-analysis data were-10.0 and 27.0mm, respectively, and that RMSE tended to decrease with increasing station latitude. Research shows that in low-latitude areas, due to the fact that the water vapor content is rich in areas with higher latitude and close to the equator, the atmospheric motion and troposphere effect are severe, the inversion accuracy of the ZTD is poor, and for improving the accuracy of real-time Precise Point Positioning (PPP) or long-distance baseline network RTK (real time kinematic) in the global range, the inversion accuracy of the ZTD in the corresponding areas needs to be further improved.
Disclosure of Invention
The invention aims to improve an inversion method of a GNSS important error source to the troposphere delay in order to improve the satellite positioning precision, and provides an improvement method of NWP inversion troposphere delay for solving the technical problem of insufficient precision of the existing NWP inversion troposphere delay estimation method.
In order to solve the problems existing in the background technology, the invention provides an improved method for inverting troposphere delay by NWP, which comprises the following steps:
step 1: and acquiring reference data. High-precision ZTD data provided by an International GNSS Service (IGS) survey station in a global operating area is obtained and recorded as IGS _ ZTD, survey stations with gross errors and serious IGS _ ZTD data loss are removed, and the IGS _ ZTD data can be downloaded in an IGS official website.
Step 2: and NWP data acquisition. In a European center for Medium-Range Weather features (ECMWF) reanalysis data ERA-Interim product, acquiring the continuous year layered meteorological data of the region in the same year as the step 1, wherein the meteorological parameters comprise air pressure, temperature, relative humidity and gravity potential.
And step 3: NWP data is initialized. After the NWP data is obtained, firstly, the gravity potential parameter in the NWP data needs to be primarily processed, and is converted into positive height, and the conversion formula of the potential and the positive height is as follows:
in the above formula, HIs justIs positive high, phi is the gravity potential, and the expression of the gravity acceleration is as follows:
g(B,H)＝9.80616(1-2.59×10-3cos2B)(1-3.14×10-7H)
in the above formula, B is the latitude of the earth, and H is the height of the earth.
After the gravity potential is converted into the positive height, in order to unify the elevation system with the known geodetic height of the survey station, the positive height needs to be converted into the geodetic height, and the conversion formula is as follows:
H＝His just+ξ
In the above formula, ξ is the height of the ground level and can be calculated according to the input time of the GTP2 model and the station survey information.
And 4, step 4: NWP _ ZTD calculation. And obtaining the ZTD of the operation area by utilizing relevant meteorological data and a new integral method, and recording the ZTD as NWP _ ZTD. The calculation formula is as follows:
wherein a is the bottom layer constant pressure surface, b is the constant pressure surface about 1km away from the ground, and the atmospheric refractive index between the i-th layer and the i +1 layer is delta NiThe difference in distance between the two layers is Deltasi；ZTDTOPThe ZTD value above the top layer of the grid point can be estimated by substituting the top layer atmospheric parameter into the related troposphere model. ZTDBOTThe ZTD below the bottom layer of grid points can be estimated by extrapolating meteorological parameters to the surface.
According to the irregular change of the atmospheric refractive index along with the height, corresponding to delta N in the formulaiFor two calculation methods, if the atmospheric refractive index is linearly attenuated, the following steps are performed:
ΔNi＝(Ni+Ni+1)/2
if the atmospheric refractive index decays exponentially, then:
the expression formula of the atmospheric refractive index N is as follows:
medium atmospheric refractive index constant k of the above formula1＝77.604K/mbar，k2＝64.79K/mbar，k3＝377600.0K2A/mbar; t is absolute temperature, e is water vapor pressure, pdIs a dry air pressure, pdThe calculation formula is as follows: pd＝P-e；
And 5: and after the ZTD of grid points around the measuring station on the height of the measuring station is calculated, the ZTD of the measuring station is interpolated by a related interpolation method.
As a preferred technical scheme of the invention, the troposphere model in the step 4 preferably adopts a Saastamoinen model which is simple and convenient, and the calculation result can meet the precision requirement.
As a preferred technical solution of the present invention, the interpolation method in step 5 preferably adopts a kriging interpolation method, which has high interpolation precision to ensure the precision of the final result.
Compared with the prior art, the invention has the beneficial effects that: firstly, compared with the original NWP integral model, the ZTD inversion precision of the integral model provided by the invention is obviously improved. Secondly, the NWP inversion troposphere delay integral model provided by the invention can invert high-precision ZTD, thereby accelerating the convergence speed of GNSS precision positioning and improving the positioning precision. Thirdly, because the NWP model can provide meteorological data covering the whole world, the method has wide application range and can estimate the ZTD of any point covered by the NWP model.
Drawings
FIG. 1 is a flow chart of the operation of the present invention.
Fig. 2 is a site map of 100 IGS test stations.
FIG. 3 is a chart comparing ZTD residuals before and after the test station employs the method of the present invention.
FIG. 4 is a comparison graph of RMSE of ZTD residuals before and after a test station employs the method of the present invention.
Description of the drawings: in fig. 3, (a) and (b) are the ZTD residuals before and after the correction by the method of the present invention in 2018 for 100 stations, respectively, and the average residual calculation method is to take the absolute value of the residual of one year for the stations and then average the absolute value; in fig. 4 (a) and (b) are the RMSEs of the ZTD residuals before and after correction by the method of the invention for 100 stations, respectively.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
To verify the effectiveness of the advanced NWP inversion troposphere delay method, high-precision ZTD products with sampling rate of 5 minutes for each year of 2018 continuous stations provided by 100 IGS in the global scope and hierarchical meteorological data of ERA-interam products in the re-analysis data of ECMWF corresponding to the stations are selected, the plane resolution is 0.125 degrees × 0.125 degrees, the vertical resolution is 37 layers (the height of the top layer is about 47km), and the time resolution is 6 hours. The station distribution is shown in figure 1.
Step 1: and acquiring reference data. High-precision ZTD products in 2018 years, provided by 100 IGS stations in the global range, are obtained and recorded as IGS _ ZTD, and stations with gross errors and serious IGS _ ZTD data loss are removed. IGS _ ZTD data may be downloaded at the IGS official website.
Step 2: and NWP data acquisition. And acquiring layered meteorological data of the 100 stations 2018 in an ERA-Interim product in the ECMWF reanalysis data, wherein the meteorological parameters comprise air pressure, temperature, relative humidity and gravity potential.
And step 3: NWP data is initialized. After the NWP data is obtained, firstly, the gravity potential parameter in the NWP data needs to be primarily processed, and is converted into positive height, and the conversion formula of the potential and the positive height is as follows:
in the above formula, HIs justIs positive high, phi is the gravity potential, and the expression of the gravity acceleration is as follows:
g(B,H)＝9.80616(1-2.59×10-3cos2B)(1-3.14×10-7H)
in the above formula, B is the latitude of the earth, and H is the height of the earth.
After the gravity potential is converted into the positive height, in order to unify the elevation system with the known geodetic height of the survey station, the positive height needs to be converted into the geodetic height, and the conversion formula is as follows:
H＝His just+ξ
In the above formula, ξ is the height of the ground level and can be calculated according to the input time of the GTP2 model and the station survey information.
And 4, step 4: NWP _ ZTD calculation. Obtaining grid ZTD of corresponding areas of 100 stations by utilizing relevant meteorological data and a new integral method for inversion, and recording the grid ZTD as NWP _ ZTD, wherein the calculation formula is as follows:
wherein a is the bottom layer constant pressure surface, b is the constant pressure surface about 1km away from the ground, and the atmospheric refractive index between the i-th layer and the i +1 layer is delta NiThe difference in distance between the two layers is Deltasi，ZTDTOPReferring to the ZTD value above the top layer of the grid points, the top layer atmospheric parameters can be substituted into the Saastamoinen model for estimation. ZTDBOTThe ZTD below the bottom layer of grid points can be estimated by extrapolating meteorological parameters to the surface.
According to the irregular change of the atmospheric refractive index along with the height, corresponding to delta N in the formulaiFor two calculation methods, if the atmospheric refractive index is linearly attenuated, the following steps are performed:
ΔNi＝(Ni+Ni+1)/2
if the atmospheric refractive index decays exponentially, then:
the expression formula of the atmospheric refractive index N is as follows:
medium atmospheric refractive index constant k of the above formula1＝77.604K/mbar，k2＝64.79K/mbar，k3＝377600.0K2A/mbar; t is absolute temperature, e is water vapor pressure, pdIs a dry air pressure, pdThe calculation formula is as follows: pd＝P-e；
And 5: and after calculating the NWP _ ZTD of grid points around the survey station at the height of the survey station, interpolating the ZTD of the survey station by a Krigin interpolation method.
And calculating the ZTD of 100 IGS sites in the global range, and comparing the ZTD with the IGS _ ZTD which is a high-precision ZTD product provided by IGS to obtain the precision of calculating the ZTD by the improved method. The indexes for assessing accuracy are the station ZTD average residual and RMSE.
The formula for the ZTD average residual is:
RMSE calculation formula for ZTD residuals is:
in the above formula, i is the year. The distribution of 100 stations is shown in fig. 2, the residual results are shown in fig. 3, and the RMSE results are shown in fig. 4. As can be seen from FIGS. 3 and 4, the ZTD inversion accuracy is obviously improved after the method is adopted. The concrete expression is as follows: the mean residual results before and after correction were 14.11 and 4.87mm, respectively, the RMSE results before and after modification were 20.84 and 15.57mm, respectively, and the mean residual and RMSE accuracies were improved by about 65.49% and 25.58%, respectively.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (3)
1. An improved method for NWP inversion troposphere delay is characterized by comprising the following specific steps:
step 1: acquiring reference data; acquiring high-precision ZTD data of a survey station provided by international GNSS service in a global scope, recording the high-precision ZTD data as IGS _ ZTD, and eliminating the survey station with gross error and serious IGS _ ZTD data loss, wherein the IGS _ ZTD data can be downloaded in an IGS official website;
step 2: NWP data acquisition; in the European meso-scale weather forecast center, analyzing the data ERA-Interim product, acquiring the hierarchical meteorological data of the region and the continuous year in the same year as the step 1, wherein the meteorological parameters comprise air pressure, temperature, relative humidity and gravity potential;
and step 3: initializing NWP data; after the NWP data is obtained, firstly, the gravity potential parameter in the NWP data needs to be primarily processed, and is converted into positive height, and the conversion formula of the potential and the positive height is as follows:
in the above formula, HIs justIs positive high, phi is the gravity potential, and the expression of the gravity acceleration is as follows:
g(B,H)＝9.80616(1-2.59×10-3cos2B)(1-3.14×10-7H)
in the above formula, B is the geodetic latitude, and H is the geodetic height;
after the gravity potential is converted into the positive height, in order to unify the elevation system with the known geodetic height of the survey station, the positive height needs to be converted into the geodetic height, and the conversion formula is as follows:
H＝His just+ξ
In the above formula, xi is the height of the ground level, and can be obtained by calculation according to the input time of the GTP2 model and the survey station information;
and 4, step 4: NWP _ ZTD calculation; obtaining the ZTD of the operation area by utilizing relevant meteorological data and a new integral method, recording the ZTD as NWP _ ZTD, and having the calculation formula as follows:
wherein a is the bottom layer constant pressure surface, b is the constant pressure surface about 1km away from the ground, and the atmospheric refractive index between the i-th layer and the i +1 layer is delta NiThe difference in distance between the two layers is Deltasi；ZTDTOPThe ZTD value on the top layer of the grid point can be substituted into the related troposphere model for estimation; ZTDBOTThe ZTD under the bottom layer of the grid point can be estimated by extrapolating meteorological parameters to the earth surface;
according to the irregular change of the atmospheric refractive index along with the height, corresponding to delta N in the formulaiFor two calculation methods, if the atmospheric refractive index is linearly attenuated, the following steps are performed:
ΔNi＝(Ni+Ni+1)/2
if the atmospheric refractive index decays exponentially, then:
the expression formula of the atmospheric refractive index N is as follows:
medium atmospheric refractive index constant k of the above formula1＝77.604K/mbar，k2＝64.79K/mbar，k3＝377600.0K2A/mbar; t is absolute temperature, e is water vapor pressure, pdIs a dry air pressure, pdThe calculation formula is as follows: pd＝P-e；
And 5: and after NWP _ ZTD of grid points around the survey station on the height of the survey station is calculated, the ZTD of the survey station is interpolated by a related interpolation method.
2. The NWP inversion tropospheric delay improvement method of claim 1, wherein the tropospheric model of step 4 is a Saastamoinen model.
3. The NWP inversion tropospheric delay improvement method of claim 1, wherein said interpolation method in step 5 is kriging interpolation.
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| CN113093241A (en) * | 2021-03-12 | 2021-07-09 | 东南大学 | Single-survey-station troposphere slope delay calculation method considering elevation angle |
| CN113639893A (en) * | 2021-06-29 | 2021-11-12 | 东南大学 | Multi-meteorological-factor-based near-earth weighted average temperature information acquisition method |
| CN113960635A (en) * | 2021-10-25 | 2022-01-21 | 山东科技大学 | Tropospheric delay correction method taking daily change into account |
| CN116626730A (en) * | 2023-07-24 | 2023-08-22 | 山东科技大学 | Marine region CORS enhanced PPP method considering NWP |
| CN113960635B (en) * | 2021-10-25 | 2024-04-30 | 山东科技大学 | Troposphere delay correction method considering daily variation |
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| CN113639893A (en) * | 2021-06-29 | 2021-11-12 | 东南大学 | Multi-meteorological-factor-based near-earth weighted average temperature information acquisition method |
| CN113960635A (en) * | 2021-10-25 | 2022-01-21 | 山东科技大学 | Tropospheric delay correction method taking daily change into account |
| CN113960635B (en) * | 2021-10-25 | 2024-04-30 | 山东科技大学 | Troposphere delay correction method considering daily variation |
| CN116626730A (en) * | 2023-07-24 | 2023-08-22 | 山东科技大学 | Marine region CORS enhanced PPP method considering NWP |
| CN116626730B (en) * | 2023-07-24 | 2023-10-10 | 山东科技大学 | Marine region CORS enhanced PPP method considering NWP |
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