CN109917424B - Residual error correction method for troposphere delay in NWP (N-WP) inversion under multi-factor constraint - Google Patents
Residual error correction method for troposphere delay in NWP (N-WP) inversion under multi-factor constraint Download PDFInfo
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Abstract
The invention discloses a residual error correction method for NWP inversion troposphere delay under multi-factor constraint, which comprises the steps of firstly obtaining continuous year of ZTD of all continuously-operated reference stations in an operation area and continuous year of reanalysis data provided by the NWP in the area, inverting the ZTD of the continuously-operated reference stations in the area by using an integral method, then fitting the residual error of the NWP inversion ZTD based on a polynomial fitting method of a minimum absolute residual error method, obtaining the NWP forecast data at the current moment and initial coordinates of an observation station, calculating the residual error of the corrected NWP _ ZTD at the current moment by using a residual error model, inverting the initial value of the NWP _ ZTD at the current moment by using the NWP forecast data at the current moment, and adding the initial value of the NWP _ ZTD and the corrected residual error of the NWP _ ZTD to obtain the NWP _ ZTD with accurate current moment. The method is firstly modeled according to the relation between the temperature, humidity, position and other multi-factors and the time sequence of the troposphere residual error, and the estimation precision of the troposphere delay is improved to be a centimeter level by correcting the troposphere delay residual error of the NWP inversion.
Description
Technical Field
The invention relates to a residual error correction method for troposphere delay, in particular to a residual error correction method for NWP inversion troposphere delay under multi-factor constraint, which utilizes the change rule modeling of a plurality of meteorological factor time sequences to correct the residual error of the troposphere delay inverted by a numerical weather prediction model (NWP), thereby improving the convergence speed and the positioning precision of Precise Point Positioning (PPP) and long-distance baseline real-time precise dynamic positioning (RTK), and belongs to the technical field of global satellite navigation and positioning.
Background
Tropospheric delay is one of the main error sources for Global Navigation Satellite System (GNSS) Navigation and positioning, and its influence on electromagnetic wave signals is non-dispersive refraction. Due to the influence of troposphere refraction, the propagation path difference s of electromagnetic waves can reach 2.3m in the zenith direction, and can reach 20m when the satellite height angle is 10 degrees, so that the ambiguity convergence speed and the positioning accuracy of GNSS precise single-point positioning and medium-long distance baseline differential positioning are severely limited.
In the prior art of GNSS data processing, three methods are summarized to attenuate tropospheric delay: 1. the Zenith Troposphere Delay (ZTD) is estimated through an empirical model, but the ZTD estimated by the empirical model has poor precision, and the maximum error can reach 20 cm; 2. ZTD is solved as a parameter in mathematical models together with other unknown parameters (e.g., site coordinates and ambiguity). However, tropospheric delay is strongly correlated with position, and the equation requires strong satellite geometric constraints and long-time observation to obtain a stable solution; 3. ZTD is inverted using reanalysis or forecast data of a numerical weather forecast model.
At present, the residual error of the NWP forecast data inversion ZTD is about +/-6 cm and fluctuates, the average residual error and the root mean square value (RMS) are about 1cm and 3cm respectively, and the method cannot be directly used for GNSS precise positioning. In GNSS precision positioning solution, the ZTD is usually used as an initial value, and the residual error of the ZTD is used as an unknown number, and is solved together with parameters such as ambiguity and position. At this time, the accuracy of NWP inversion ZTD directly affects the convergence speed of ambiguity. In order to realize real-time PPP or long-distance RTK, it is necessary to improve the accuracy of NWP inversion ZTD, i.e., correct the residual error of the NWP inversion ZTD.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a residual error correction method for NWP inversion troposphere delay under multi-factor constraint, which is used for solving the technical problem of poor troposphere delay precision obtained by the existing method.
In order to solve the problems existing in the background technology, the method for correcting the residual error of the troposphere delay in the NWP inversion under the multi-factor constraint comprises the following steps:
the first step is to obtain the continuous year ZTD of all the continuously running reference stations in the operation area, to be recorded as IGS _ ZTD, and to eliminate the gross error, the data can be obtained at the central website of the continuously running reference station network of each province and city.
And secondly, acquiring continuous one-year reanalysis data provided by the NWP of the area, wherein the data comprises air pressure, temperature, relative humidity and potential altitude, and can be obtained from the layered meteorological data of ERA-Interim (Jan1979-present) products in the reanalysis data of the European mesoscale weather forecasting center (ECMWF).
And thirdly, inverting the ZTD of the continuously running reference station in the area by using an integration method, and recording the ZTD as NWP _ ZTD. The formula for inverting ZTD by the layered integration method is as follows:
in the above formula, ZTDgridThe ZTD value of the grid point on the height of the measuring station, H _ IGS is the height of the reference station, H _ top is the height of the top layer of NWP data, N is the atmospheric refractive index, and is dimensionless, Nidenotes the atmospheric refractive index, Δ H, in the range of the i-th integration regioniIs the height within the range of the i-th integration region.
Wherein the approximate expression formula of the atmosphere refractive index N is as follows:
in the above formula, k1＝77.604K/mbar，k2＝64.79K/mbar，k3＝377600.0K2Mbar, P denotes pressure, e denotes water vapour pressure, in mbar, NtryDenotes the refractive index of the dry gas, NwetIndicating the refractive index of the wet gas.
And fourthly, under the constraints of relative humidity, temperature and position, fitting the residual error of the NWP inversion ZTD by adopting a polynomial fitting method based on a minimum absolute residual error method:
f(RH,T0)＝p00+p10*RH+p01*T0+p20*RH2+p11*RH*T0+p02*T0 2+p30*RH3+p21*RH2*T0+p12*RH*T0 2+p03*T0 3+p31*RH3*T0+p22*RH2*T0 2+p13*RH*T0 3+p04*T0 4
in the formula: RH stands for relative humidity in%; t is0Represents temperature in units of; p00、P10、P01、P20、P11、P02、P30、P21、P12、P03、P31、P22、P13、P04Is a coefficient of a polynomial; f (RH, T)0) Representing the fitted ZTD residuals.
And fifthly, acquiring NWP forecast data and the initial coordinates of the observation station at the current moment, and calculating the residual error of the corrected NWP _ ZTD at the current moment by using the residual error model obtained by fitting at the fourth step.
And sixthly, utilizing the NWP forecast data at the current moment and adopting an integral method to invert the initial value of the NWP _ ZTD at the current moment.
And seventhly, adding the initial value of the NWP _ ZTD and the corrected residual error of the NWP _ ZTD to obtain the NWP _ ZTD with accurate current time.
And eighthly, directly using the accurate NWP _ ZTD for precise single-point positioning and long-distance baseline real-time precise dynamic positioning in GNSS real-time precise positioning.
Compared with the prior art, the invention has the beneficial effects that:
firstly, modeling is carried out according to the relation between the temperature, humidity, position and other multi-factors and the time sequence of the troposphere residual error for the first time, the troposphere delay residual error of NWP inversion is corrected, and the estimation precision of the troposphere delay is improved to be a centimeter level.
Secondly, the method is applied to PPP and RTK, the ambiguity convergence time can be obviously shortened, and the positioning precision is improved.
Thirdly, because the NWP model can provide meteorological data covering the whole world, the method can estimate the ZTD of any point covered by the NWP model, and the application range is wide.
Drawings
FIG. 1 is a flow chart of the operation of the present invention.
Fig. 2 is a diagram of tropospheric delay before and after the SCIP station employs the method of the present invention.
Fig. 3 is a diagram of tropospheric delay before and after the PIN1 station uses the method of the present invention.
FIG. 4 is an RTK solution for a SCIP station using the method of the present invention.
Fig. 5 is an RTK solution situation in which the PIN1 station employs the method of the present invention.
Description of the drawings: FIGS. 2 (a) and (b) show comparison of ZTD residuals before and after correction by the method of the present invention in 2016 in SCIP station; fig. 3 (a) and (b) show comparison of ZTD residuals before and after correction by the method of the present invention in 2016 of PIN 1; FIG. 4 shows the results of the calculation of the standard RTK solution, the RTK solution under the standard NWP constraint and the RTK solution under the corrected NWP constraint respectively performed by using the SCIP station as the rover station and using a base line with a base line length of 148 km; fig. 5 shows the calculation results of the standard RTK solution, the RTK solution under the standard NWP constraint and the RTK solution under the corrected NWP constraint, which are respectively performed by using the PIN1 station as a rover and a base line with a base line length of 216 km.
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.
In order to prove that the effectiveness of the NWP inversion troposphere delay residual correction method based on the multi-factor constraint on improving the NWP inversion troposphere delay accuracy is improved, two long baselines are selected from convective layer data of IGS MGEX network SCIP stations (32.91 degrees N and 118.49 degrees W) and PIN1 stations (33.61 degrees N and 116.46 degrees W) in 2015 and 2016 for testing, and data of SCIP stations, PIN1 stations and ROCK stations (34.24 degrees N and 118.68 degrees W) in 2016 for 1 month and 13 days for testing, one base line length is 148km (between the SCIP stations and the ROCK stations, wherein the ROCK stations are base stations and the SCIP stations are rover stations), and the other base line length is 216km (between the PIN1 stations and the ROCK stations, wherein the ROCK stations are base stations and the PIN1 stations are rover stations). Meanwhile, the European mesoscale weather forecasting center (ECMWF) of corresponding time of corresponding areas of three sites is selected to analyze the layered meteorological data of the ERA-Interim (Jan1979-present) product in the data, the planar resolution is 0.125 degrees multiplied by 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.
Example 1
Step one, acquiring continuous year-round ZTD of a SCIP station and a ROCK station 2015 at an IGS MGEX website, recording as IGS _ ZTD, and removing gross errors.
And secondly, acquiring layered meteorological data of an SCIP station and ROCK station region 2015 with plane resolution of 0.125 degrees multiplied by 0.125 degrees, vertical resolution of 37 layers and time resolution of 6 hours continuously for one year in an ERA-Interim (Jan1979-present) product in the re-analysis data of the European mesoscale weather forecasting center (ECMWF), wherein the meteorological parameters comprise air pressure, temperature, relative humidity and potential height.
And thirdly, inverting regional meteorological data of the SCIP station and the ROCK station by using an integral method to obtain ZTD of the SCIP station and the ROCK station in 2015, and recording the ZTD as NWP _ ZTD. The formula for inverting ZTD by the layered integration method is as follows:
in the formula ZTDgridThe ZTD value of the grid point on the height of the measuring station, H _ IGS is the height of the reference station, H _ top is the height of the top layer of NWP data, N is the atmospheric refractive index, and is dimensionless, Nidenotes the atmospheric refractive index, Δ H, in the range of the i-th integration regioniIs the height within the range of the i-th integration region.
In the formula, the approximate expression formula of the atmospheric refractive index N is as follows:
in the formula, k1＝77.604K/mbar，k2＝64.79K/mbar，k3＝377600.0K2Mbar, P denotes pressure, e denotes water vapour pressure, in mbar, NtryDenotes the refractive index of the dry gas, NwetIndicating the refractive index of the wet gas.
And fourthly, respectively fitting residual errors of the ZTD in 2015 of the SCIP station and the ROCK station NWP inversion by adopting a polynomial fitting method based on a minimum absolute residual error method under the constraints of relative humidity, temperature and position to obtain a residual error model as follows:
f(RH,T0)＝p00+p10*RH+p01*T0+p20*RH2+p11*RH*T0+p02*T0 2+p30*RH3+p21*RH2*T0+p12*RH*T0 2+p03*T0 3+p31*RH3*T0+p22*RH2*T0 2+p13*RH*T0 3+p04*T0 4
in the formula: RH stands for relative humidity in%; t is0Represents temperature in units of; p00、P10、P01、P20、P11、P02、P30、P21、P12、P03、P31、P22、P13、P04Is a coefficient of a polynomial; f (RH, T)0) Representing the fitted ZTD residuals.
And fifthly, acquiring NWP forecast data of the SCIP station and the ROCK station in 2016, and respectively calculating the residual error of the corrected NWP _ ZTD of the two stations at the current moment by using the residual error model obtained by fitting in the fourth step.
And sixthly, respectively inverting the initial values of the NWP _ ZTD of the two sites at the current moment by using the NWP forecast data of the SCIP station and the ROCK station in 2016 by adopting an integral method.
And seventhly, adding the initial value of the NWP _ ZTD and the corrected residual error of the NWP _ ZTD to obtain the NWP _ ZTD with accurate current time. The comparison of the residual errors before and after correction of the ZTD of the SCIP station in 2016 is shown in FIG. 2, wherein the graph (a) is the ZTD before correction and mainly floats up and down at the position of 20mm, and the graph (b) is the ZTD after correction and mainly floats up and down at the position of 0mm, so that the corrected ZTD is obviously improved in precision.
The satellite observation data file, the precise ephemeris file, the broadcast ephemeris file and the precise clock error file which are required by RTK resolving corresponding to the SCIP station and the ROCK station 2016, 1 month and 13 days are downloaded in an IGS MGEX website, the RTK resolving under the constraint of standard NWP and the RTK resolving under the constraint of corrected NWP are respectively carried out on a base line consisting of the SCIP station and the ROCK station by utilizing the accurately estimated NWP _ ZTD, as shown in the figure 4, under the three methods, the ambiguity resolving time is sequentially shortened, the resolving success rate is sequentially improved, and the superiority of the method is obviously reflected.
Example 2
Firstly, acquiring ZTD of a PIN1 station and a ROCK station 2015 continuously for one year at an IGS MGEX website, recording as IGS _ ZTD, and removing gross errors.
Secondly, acquiring layered meteorological data of a PIN1 station and a ROCK station region 2015 with the plane resolution of 0.125 degrees multiplied by 0.125 degrees, the vertical resolution of 37 layers and the time resolution of 6 hours continuously for one year in ERA-Interim (Jan1979-present) products in the re-analysis data of European mesoscale weather forecasting center (ECMWF), wherein the meteorological parameters comprise air pressure, temperature, relative humidity and potential height.
And thirdly, inverting regional meteorological data of the PIN1 station and the ROCK station by using an integral method to obtain ZTD of the SCIP station and the ROCK station in 2015, and recording the ZTD as NWP _ ZTD. The formula for inverting ZTD by the layered integration method is as follows:
in the above formula: ZTDgridThe ZTD value of the grid point on the height of the measuring station, H _ IGS is the height of the reference station, H _ top is the height of the top layer of NWP data, N is the atmospheric refractive index, and is dimensionless, Nidenotes the atmospheric refractive index, Δ H, in the range of the i-th integration regioniIs the height within the range of the i-th integration region.
Wherein, the approximate expression formula of the atmosphere refractive index N is as follows:
in the formula, k1＝77.604K/mbar，k2＝64.79K/mbar，k3＝377600.0K2Mbar, P denotes pressure, e denotes water vapour pressure, in mbar, NtryDenotes the refractive index of the dry gas, NwetIndicating the refractive index of the wet gas.
Fourthly, respectively fitting residual errors of the ZTD in 2015 of the PIN1 station and the NWP inversion of the ROCK station by adopting a polynomial fitting method based on a minimum absolute residual error method under the constraint of relative humidity, temperature and position to obtain a residual error model as follows:
f(RH,T0)＝p00+p10*RH+p01*T0+p20*RH2+p11*RH*T0+p02*T0 2+p30*RH3+p21*RH2*T0+p12*RH*T0 2+p03*T0 3+p31*RH3*T0+p22*RH2*T0 2+p13*RH*T0 3+p04*T0 4
in the above formula: RH stands for relative humidity in%; t is0Represents temperature in units of; p00、P10、P01、P20、P11、P02、P30、P21、P12、P03、P31、P22、P13、P04Is a coefficient of a polynomial; f (RH, T)0) Representing the fitted ZTD residuals.
And fifthly, acquiring NWP forecast data of the PIN1 station and the ROCK station in 2016, and respectively calculating the residual error of the corrected NWP _ ZTD of the two stations at the current moment by using the residual error model obtained by fitting in the fourth step.
And sixthly, respectively inverting the initial values of the NWP _ ZTD of the two stations at the current moment by using the NWP forecast data of the PIN1 station and the ROCK station 2016 by adopting an integral method.
And seventhly, adding the initial value of the NWP _ ZTD and the corrected residual error of the NWP _ ZTD to obtain the NWP _ ZTD with accurate current time. The comparison of residual errors before and after correction of the ZTD of 2016 of PIN1 by the method is shown in FIG. 3, wherein the graph (a) shows that the ZTD before correction is mainly focused on the upward and downward movement at the position of 20mm, and the graph (b) shows that the ZTD after correction is mainly focused on the upward and downward movement at the position of 0mm, so that the corrected ZTD has obviously improved precision.
The satellite observation data file, the precise ephemeris file, the broadcast ephemeris file and the precise clock error file which are required by RTK resolving corresponding to a PIN1 station and a ROCK station 2016, 1, 13 and are needed are downloaded at an IGS MGEX website, standard RTK resolving is respectively carried out on a base line consisting of the PIN1 station and the ROCK station by utilizing the accurately estimated NWP _ ZTD, RTK resolving under the constraint of standard NWP and RTK resolving under the constraint of corrected NWP are shown in FIG. 5, ambiguity resolving time is sequentially shortened under the three methods, resolving success rate is sequentially improved, and superiority of the method is obviously reflected.
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 (1)
1. The method for correcting the residual error of the troposphere delay in the NWP inversion under the multi-factor constraint is characterized by comprising the following specific steps of:
acquiring continuous year ZTDs of all continuously-operated reference stations in an operation area, recording the ZTDs as IGS _ ZTD, and eliminating gross errors, wherein the data can be acquired from a central website of a continuously-operated reference station network of each province and city;
secondly, acquiring continuous one-year reanalysis data provided by the NWP of the area, wherein the reanalysis data comprises air pressure, temperature, relative humidity and potential height, and the data can be acquired from the layered meteorological data of an ERA-Interim product in the reanalysis data of an European mesoscale weather forecasting center (ECMWF);
thirdly, inverting the ZTD of the continuously running reference station in the area by using an integral method, recording the ZTD as NWP _ ZTD, and inverting the ZTD by using a layered integral method according to a calculation formula as follows:
in the above formula, ZTDgridThe ZTD value of the grid point on the height of the measuring station, H _ IGS is the height of the reference station, H _ top is the height of the top layer of NWP data, N is the atmospheric refractive index, and is dimensionless, Nidenotes the atmospheric refractive index, Δ H, in the range of the i-th integration regioniIs the height within the range of the i-th integration zone,
wherein the approximate expression formula of the atmosphere refractive index N is as follows:
in the above formula, k1＝77.604K/mbar，k2＝64.79K/mbar，k3＝377600.0K2Mbar, P denotes pressure, e denotes water vapour pressure, in mbar, NtryDenotes the refractive index of the dry gas, NwetRepresents the refractive index of the wet gas;
and fourthly, under the constraints of relative humidity, temperature and position, fitting the residual error of the NWP inversion ZTD by adopting a polynomial fitting method based on a minimum absolute residual error method:
f(RH,T0)＝p00+p10*RH+p01*T0+p20*RH2+p11*RH*T0+p02*T0 2+p30*RH3+p21*RH2*T0+p12*RH*T0 2+p03*T0 3+p31*RH3*T0+p22*RH2*T0 2+p13*RH*T0 3+p04*T0 4
in the formula: RH stands for relative humidity in%; t is0Represents temperature in units of; p00、P10、P01、P20、P11、P02、P30、P21、P12、P03、P31、P22、P13、P04Is a coefficient of a polynomial; f (RH, T)0) Represents the fitted ZTD residuals;
fifthly, acquiring NWP forecast data and initial coordinates of the observation station at the current moment, and calculating the residual error of the corrected NWP _ ZTD at the current moment by using the residual error model obtained by fitting in the fourth step;
sixthly, utilizing the NWP forecast data at the current moment and adopting an integral method to invert an initial value of NWP _ ZTD at the current moment;
and seventhly, adding the initial value of the NWP _ ZTD and the corrected residual error of the NWP _ ZTD to obtain the NWP _ ZTD with accurate current time.
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