Elevation correction method and device for occultation ZTD data and application system

Abstract

The invention discloses an elevation correction method and device for occultation ZTD data and an application system, and the method comprises the steps: obtaining occultation data of a target area, and determining a corresponding year product day and an occultation ZTD value before correction according to the occultation data; matching the occultation point spatial position of the occultation data with the grid point of the ERA5 data, and determining a target grid point; determining ZTD elevation scaling factors corresponding to the annual day and the target grid point by using a pre-constructed ZTD elevation scaling factor model according to the annual day; according to the ZTD elevation scaling factor and the conversion relation between the uncorrected occultation ZTD value and the corrected occultation ZTD value, the uncorrected occultation ZTD value is corrected to the target elevation position, and the corrected occultation ZTD value is obtained. By correcting the occultation ZTD value before correction to the target elevation according to the corresponding ZTD elevation scaling factor, the problem that the ZTD value error is large due to the fact that the earth surface part of occultation data is missing can be effectively solved, and the occultation ZTD data precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of occultation data processing, and in particular to an occultation ZTD elevation correction method, device and application system. Background Art

[0002] The existing methods for calculating the Zenith Total Delay (ZTD) have their own advantages and applicable scenarios in terms of technical implementation, but they generally have certain limitations and shortcomings. Although the ground-based Global Navigation Satellite System (GNSS) method has the advantages of high precision and high temporal resolution, its spatial coverage is low and it is mainly concentrated on land and developed areas, resulting in the lack of observations in oceans and remote areas; although the sounding observation method is accurate in inverting ZTD, it has low temporal resolution and is only observed twice a day. In addition, the cost of sounding balloons is high, making it difficult to achieve large-scale observations; the numerical weather prediction model (NWP) uses meteorological data simulated by the model to calculate ZTD, but the disadvantage is that the accuracy of the simulated meteorological parameters is highly dependent on the model scheme, and the simulated meteorological parameters may deviate greatly from the actual ones, and the horizontal resolution is low, which performs poorly under complex terrain and small-scale weather systems.

[0003] In contrast, Radio Occultation (RO) technology has unique advantages in calculating ZTD. It not only has the ability to cover the world, but also performs well in oceans and remote areas. It can provide uniform spatial distribution data, and has high-precision and all-weather observation capabilities without the need for complex ground equipment. These characteristics make occultation data an important means of calculating ZTD, effectively making up for the shortcomings of other methods of calculating ZTD. However, occultation data are usually affected by factors such as data reception failures or terrain, resulting in abnormalities or missing data in some occultation data, which in turn makes the ZTD value calculated by occultation lower than the true value. Summary of the invention

[0004] The present application aims to solve the technical problem that the surface part of occultation data is missing, resulting in large errors in ZTD values, and provides a method and device for correcting the elevation of occultation ZTD data.

[0005] This application adopts the following technical solutions.

[0006] In a first aspect, an embodiment of the present application provides a method for correcting elevation of occultation ZTD data, comprising:

[0007] Obtaining occultation data of a target area, and determining a corresponding annual accumulation day according to the occultation data;

[0008] Determining a pre-corrected occultation ZTD value according to the occultation data;

[0009] Matching the spatial position of the occultation point of the occultation data with the grid points of the ERA5 data to determine the target grid points;

[0010] According to the annual day, using a pre-constructed ZTD elevation scaling factor model, determining a ZTD elevation scaling factor corresponding to the annual day and the target grid point;

[0011] According to the ZTD elevation scaling factor and the conversion relationship between the pre-corrected occultation ZTD value and the post-corrected occultation ZTD value, the pre-corrected occultation ZTD value is corrected to the target elevation to obtain the post-corrected occultation ZTD value.

[0012] In some possible implementations, determining a corrected pre-occultation ZTD value based on the occultation data includes:

[0013] Obtaining an atmospheric refractive index according to the occultation data;

[0014] Determine the corresponding vertical integrated ZTD value by integrating the atmospheric refractive index layer by layer from the ground height to the tropopause height;

[0015] Above the tropopause height, the corresponding SaastamoinenZTD value is determined using the Saastamoinen model;

[0016] The vertically integrated ZTD value and the Saastamoinen ZTD value are added to obtain the corrected pre-occultation ZTD value.

[0017] In some possible implementations, the method for determining the conversion relationship includes:

[0018] Determine the ERA5-ZTD value based on the ERA5 data;

[0019] According to the ERA5-ZTD value, a variation trend of the ERA5-ZTD value with elevation variation is obtained;

[0020] The ERA5-ZTD value is fitted using a negative exponential function according to the changing trend of the ERA5-ZTD value with the change of elevation, so as to determine the conversion relationship.

[0021] In some possible implementations, the expression of the conversion relationship is as follows:

[0022]

[0023] Among them: ZTD B is the corrected ZTD value after occultation, ZTDA To correct the pre-occultation ZTD value, H B is the target elevation value, H A is the lowest elevation value covered by the occultation data; H s is the ZTD elevation scaling factor.

[0024] In some possible implementations, the method for constructing the ZTD elevation scaling factor model includes: determining the elevation scaling factor by calculating the ERA5-ZTD value of each grid point in the ERA5 data at different annual accumulation days and different elevations, and determining the change trend of the ZTD elevation scaling factor, wherein the change trend of the ZTD elevation scaling factor presents a change in an annual cycle and a semi-annual cycle;

[0025] According to the changing trend of the ZTD elevation scaling factor, a time series fitting model is used to fit the ZTD elevation scaling factor corresponding to each annual day and each grid point, and the coefficients of the time series fitting model are determined using the least squares method to finally obtain the ZTD elevation scaling factor model.

[0026] In a second aspect, an embodiment of the present application provides an elevation correction device for occultation ZTD data, comprising:

[0027] An occultation data acquisition module is used to obtain occultation data of a target area and determine a corresponding annual accumulation day according to the occultation data;

[0028] An occultation ZTD value determination module is used to determine the pre-correction occultation ZTD value according to the occultation data;

[0029] A ZTD elevation scaling factor determination module is used to match the spatial position of the occultation point of the occultation data with the grid point of the ERA5 data to determine the target grid point; according to the annual accumulation day, a pre-constructed ZTD elevation scaling factor model is used to determine the ZTD elevation scaling factor corresponding to the annual accumulation day and the target grid point;

[0030] The ZTD elevation correction module is used to correct the pre-corrected occultation ZTD value to the target elevation and obtain the post-corrected occultation ZTD value according to the ZTD elevation scaling factor and the conversion relationship between the pre-corrected occultation ZTD value and the post-corrected occultation ZTD value.

[0031] In the third aspect, in order to improve the accuracy of atmospheric precipitable water monitoring based on the corrected occultation ZTD, the embodiment of the present application further provides an application system for occultation ZTD data, including an elevation correction device for occultation ZTD data, a meteorological data acquisition module, and an atmospheric precipitable water monitoring module as provided in the second aspect;

[0032] The elevation correction device of the occultation ZTD data is used to obtain the corrected occultation ZTD value;

[0033] The meteorological data acquisition module is used to determine the surface temperature and surface pressure value of the target point;

[0034] The atmospheric precipitable water monitoring module is used to determine the dry delay ZHD value separated from the corrected occultation ZTD value according to the surface pressure value, latitude and altitude of the target point; determine the wet delay ZWD value according to the corrected occultation ZTD value and the dry delay ZHD value; determine the atmospheric weighted average temperature according to the surface temperature of the target point; determine the conversion factor of the atmospheric precipitable water according to the atmospheric weighted average temperature; and determine the atmospheric precipitable water PWV of the target point according to the conversion factor and the wet delay ZWD value.

[0035] Compared with the prior art, the elevation correction and device of the occultation ZTD data provided in the embodiment of the present application can effectively solve the problem of large ZTD value errors caused by the missing surface part of the occultation data, by correcting the occultation ZTD value before correction to the target elevation according to the corresponding ZTD elevation scaling factor, thereby improving the accuracy of the occultation ZTD data.

[0036] Compared with the prior art, the application system of the occultation ZTD data provided in the embodiment of the present application monitors the atmospheric precipitable water based on the corrected occultation ZTD value, collects a variety of meteorological data of the target point, and combines the corrected occultation ZTD value to more comprehensively and accurately determine parameters such as the atmospheric dry delay value, wet delay value, and atmospheric weighted average temperature, thereby improving the calculation accuracy of the atmospheric precipitable water. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are for explanation purposes only and are not intended to limit the scope of the present application in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only for illustration purposes and are used to help understand the present application. They do not specifically limit the shapes and proportional dimensions of the components of the present application. Under the guidance of the present application, those skilled in the art can select various possible shapes and proportional dimensions to implement the present application according to specific circumstances. In the drawings:

[0038] Figure 1 A schematic flow chart of a method for correcting the elevation of occultation ZTD data provided in an embodiment of the present application;

[0039] Figure 2 (a) to Figure 2 (c) is a schematic diagram showing the fitting effect of ERA5-ZTD values ​​in three regions of the application example;

[0040] Figure 3 (a) to Figure 3 (c) is a schematic diagram of the fitting residuals of the ERA5-ZTD values ​​in the three regions of the application example;

[0041] Figure 4 (a) to Figure 4 (c) is a scatter diagram showing ZTD elevation scaling factors determined for grid points in three regions according to an embodiment of the application;

[0042] Figure 5 (a) to Figure 5 (c) is a schematic diagram of the Fourier analysis results of the ZTD elevation scaling factors determined for the grid points in three regions according to the embodiment of the application;

[0043] Figure 6 (a) to Figure 6 (b) is a schematic diagram showing the accuracy of the pre-corrected occultation ZTD value and the post-corrected occultation ZTD value in the embodiment of the present application;

[0044] Figure 7 This is a schematic diagram of the BIAS accuracy of correcting the pre-occultation ZTD value and the sounding station ZTD value in the embodiment of the present application;

[0045] Figure 8 This is a schematic diagram of the BIAS accuracy of the corrected occultation ZTD value and the sounding station ZTD value in the embodiment of the present application;

[0046] Fig. 9 This is a schematic diagram of the RMSE accuracy of the corrected pre-occultation ZTD value and the sounding station ZTD value in the embodiment of the present application;

[0047] Fig.10 This is a schematic diagram of the RMSE accuracy of the corrected occultation ZTD value and the sounding station ZTD value in the embodiment of the present application;

[0048] Fig.11 It is a schematic diagram of the BIAS accuracy of the corrected pre-occultation ZTD value and the ZTD value of the Lutai Network GNSS station in the embodiment of the present application;

[0049] Fig.12 It is a schematic diagram of the BIAS accuracy of the corrected ZTD value of the occultation and the ZTD value of the Lutai Network GNSS station in the embodiment of the present application;

[0050] Fig.13 This is a schematic diagram of the RMSE accuracy of the corrected pre-occultation ZTD value and the ZTD value of the Lutai Network GNSS station in the embodiment of the present application;

[0051] Fig.14 It is a schematic diagram of the RMSE accuracy of the corrected ZTD value of the occultation and the ZTD value of the land-based GNSS station in the embodiment of the present application;

[0052] Fig.15 A schematic diagram of the structure of an elevation correction device for occultation ZTD data provided in an embodiment of the present application;

[0053] Fig.16A schematic diagram of the application system structure of the occultation ZTD data provided in the embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0055] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more features.

[0056] The missing data of the near-surface occultation are mainly due to signal attenuation, terrain shielding, multipath effects and geometric characteristics of occultation observation. In addition, the complexity of near-surface atmospheric conditions and the limitations of the inversion algorithm also make it difficult to fully cover the data. These factors result in some occultation data only covering the high-altitude part, while the missing data of the surface part leads to a low calculated ZTD value.

[0057] The first embodiment of the present application provides a method for correcting the elevation of occultation ZTD data, such as Figure 1 As shown, the following steps are included:

[0058] Step S101, obtaining occultation data of a target area, and determining a corresponding annual day according to the occultation data.

[0059] Day of Year (abbreviated as DOY), also known as accumulated days, refers to the number of days counted sequentially from January 1 of the current year to the specified date.

[0060] Occultation data are often marked with years and days, which are used to assist in recording and analysis, so as to better maximize the value of the data and serve related scientific research and applications.

[0061] Step S102: determining the pre-corrected occultation ZTD value according to the occultation data.

[0062] Step S103: Match the spatial position of the occultation point of the occultation data with the grid point of the ERA5 data to determine the target grid point.

[0063] Step S104: According to the annual accumulation day, the ZTD elevation scaling factor corresponding to the annual accumulation day and the target grid point is determined by using a pre-constructed ZTD elevation scaling factor model.

[0064] Step S105, according to the ZTD elevation scaling factor and the conversion relationship between the pre-corrected occultation ZTD value and the post-corrected occultation ZTD value, the pre-corrected occultation ZTD value is corrected to the target elevation to obtain the post-corrected occultation ZTD value.

[0065] Specifically, step S102 may include: obtaining the atmospheric refractive index according to the occultation data; determining the corresponding vertical integrated ZTD value by integrating the atmospheric refractive index layer by layer from the ground altitude to the tropopause altitude; determining the corresponding SaastamoinenZTD value above the tropopause altitude by using the Saastamoinen model; adding the vertical integrated ZTD value and the SaastamoinenZTD value to obtain the corrected pre-occultation ZTD value, which may be expressed as follows:

[0066]

[0067] Where, h0 is the ground height (m), h tro is the height of the tropopause (m), Φ is the latitude corresponding to the grid point, P and h are the pressure (hPa) and height (m) of the top layer, respectively, N is the atmospheric refractive index, N i is the atmospheric refractivity at the ith grid point, N i+1 is the atmospheric refractive index at the i+1th grid point, h i is the height of the ith grid point, h i+1 is the height of the i+1th grid point.

[0068] In practical applications, the collected occultation data can be the wetPf2 atmospheric humidity profile product in COSMIC-2 Level 2 (download address: https: / / data.cosmic.ucar.edu / gnss-ro / cosmic2 / nrt / level2 / ). Since COSMIC-2 data provides a calculated refractive index profile, the COSMIC-2 ZTD value can be directly calculated according to formula (1).

[0069] In some embodiments, the method for converting the relationship between the corrected pre-occultation ZTD value and the corrected post-occultation ZTD value includes: determining the ERA5-ZTD value based on the ERA5 data; obtaining the changing trend of the ERA5-ZTD value with the change of elevation based on the ERA5-ZTD value; fitting the ERA5-ZTD value with a negative exponential function based on the changing trend of the ERA5-ZTD value with the change of elevation, thereby determining the conversion relationship.

[0070] In the embodiment, the applicant uses the ERA5 grid data for the Chinese region, covering latitude 15°-55°N and longitude 70°E-135°E. The temporal resolution is 1h, the spatial resolution is 0.25°x0.25°, and the vertical resolution is 37 layers of standard atmospheric pressure. (Download address: https: / / cds.climate.copernicus.eu / datasets).

[0071] Since the atmospheric refractive index is not directly provided in the ERA5 data, it is necessary to first calculate the water vapor pressure e (hPa) through the air pressure P (hPa) and specific humidity q (g / kg), and then calculate the atmospheric refractive index N in combination with the temperature T (K).

[0072]

[0073] Among them, constants k1 = 77.6890 (K / hPa); k2 = 71.2952 (K / hPa); k3 = 375463 (K 2 / hPa).

[0074] The ERA5-ZTD value at each grid point can be obtained by accumulating the integrated refractive index layer by layer to the top layer, and adding the part calculated by the Saastamoinen model above the top layer, as shown in the above formula (1). By processing the ERA5 data with formula (1), the ERA5-ZTD value corresponding to each grid with a horizontal resolution of 0.25°x0.25° in China from 2019 to 2021 can be obtained.

[0075] The exponential function is used to fit the ERA5-ZTD value of the Chinese region calculated by ERA5:

[0076] ZTD(h)=a·e b·h (4) Where: ZTD(h) is the ZTD value at elevation h, e is the base of the natural logarithm, and a and b are the parameters of the exponential model.

[0077] In order to verify the applicability of the exponential function to the fitting of ERA5-ZTD values, the ZTD values ​​at the grid points in South China, Central China and North China were selected as references and fitted using the exponential function. Figure 2 (a) is a schematic diagram of the fitting effect of ERA5-ZTD values ​​in South China in the embodiment of the present application; Figure 2 (b) is a schematic diagram of the fitting effect of the ERA5-ZTD value in Central China in the embodiment of the present application; Figure 2 (c) is a schematic diagram of the fitting effect of ERA5-ZTD values ​​in North China in the embodiment of the present application; Figure 3 (a) is a schematic diagram of the fitting residuals of the ERA5-ZTD values ​​in South China in the embodiment of the present application; Figure 3 (b) is a schematic diagram of the fitting residuals of the ERA5-ZTD values ​​in Central China in the embodiment of the present application; Figure 3 (c) is a schematic diagram of the fitting residuals of the ERA5-ZTD values ​​in North China in the embodiment of this application. Figure 2 (a) to Figure 2 (c), and Figure 3 (a) to Figure 3 (c) It can be seen that the fitting effect of the negative exponential function is good and the accuracy is better.

[0078] Since the fitting of the ERA5-ZTD value shows that the negative exponential fitting effect is better, the ZTD values ​​ZTDA and ZTDB at different heights A and B and their conversion relationship can be derived by the following formula:

[0079]

[0080] Since b is relatively small in actual calculations, the conversion relationship between the corrected pre-occultation ZTD value and the corrected post-occultation ZTD value related to the ZTD elevation scaling factor can usually be written according to formulas (5) to (7), and the expression is:

[0081]

[0082] Where: ZTD B is the occultation ZTD value at the target elevation, that is, the corrected occultation ZTD value, ZTD A is the ZTD value calculated based on the occultation data, that is, the ZTD value before correction, H B is the target elevation value, H A is the lowest elevation value covered by the occultation data; H s is the elevation scaling factor (unit: m).

[0083] It can be understood that the latitude and longitude of the occultation point of the occultation data are matched to the corresponding target grid point, and the ZTD elevation scaling factor H corresponding to the annual accumulation day and the target grid point is obtained. s It can be expressed as

[0084] In a specific embodiment, a method for constructing a ZTD elevation scaling factor model includes: determining the elevation scaling factor by calculating the ERA5-ZTD value of each grid point in ERA5 at different annual days and different elevations, and determining the changing trend of the ZTD elevation scaling factor, wherein the changing trend of the ZTD elevation scaling factor presents annual and semi-annual cycles; according to the changing trend of the ZTD elevation scaling factor, a time series fitting model is used to fit the ZTD elevation scaling factors corresponding to each annual day and each grid point, and the coefficients of the time series fitting model are determined by the least squares method, so as to finally obtain the ZTD elevation scaling factor model.

[0085] Specifically, in the embodiment, the ERA5-ZTD value at the ERA5 target elevation and the ERA5-ZTD value at the reference elevation can be calculated, and the corresponding elevation values ​​can be used to calculate the corresponding ZTD elevation scaling factor H through the above formula (8): s .

[0086] The grid points in South China, Central China and North China are still selected, and the ZTD elevation scaling factors for 2019-2021 are calculated using ERA5 data. The ZTD elevation scaling factors for three years are smoothed, and then the spectrum domain is analyzed using Fourier spectrum transform. The schematic diagram of the ZTD elevation scaling factor scatter plot and Fourier analysis results is shown in Figure 2. Figure 4 (a)- Figure 5 (c) as shown. Figure 4 (a) is a scatter plot of the ZTD elevation scaling factors determined for the grid points in South China in the embodiment of the present application; Figure 4 (b) is a scatter plot of the ZTD elevation scaling factors determined for the grid points in the central China region in the embodiment of the present application; Figure 4 (c) is a scatter plot diagram showing the ZTD elevation scaling factors determined for the grid points in North China in the embodiment of the present application; Figure 5 (a) is a schematic diagram of the Fourier analysis results of the ZTD elevation scaling factor determined for the grid points in South China in an embodiment of the present application; Figure 5 (b) is a schematic diagram of the Fourier analysis results of the ZTD elevation scaling factor determined for the grid points in the central China region in the embodiment of the present application; Figure 5 (c) is a schematic diagram of the Fourier analysis results of the ZTD elevation scaling factor determined for the grid points in North China in the embodiment of the present application. Figure 4 (a)- Figure 5 (c) It can be seen that the ZTD elevation scaling factors of the three regions all show obvious annual periodicity, which may be affected by seasonality or climate, and the overall range basically fluctuates between 6.5 and 8.0 km. Fourier spectrum analysis shows that the three regions have at least one significant peak, indicating that the elevation scaling factors of the three regions have at least one major periodic component, which may show annual or semi-annual changes.

[0087] From the above analysis, it can be seen that the elevation scaling factor shows annual and semi-annual changes. Therefore, a similar time series fitting model is used to fit the ZTD elevation scaling factor corresponding to each annual day and each grid point. The time series fitting model is as follows:

[0088]

[0089] Where: is the ZTD elevation scaling factor of the ith grid, is the ZTD height scaling factor H of the i-th grid s The annual average, and is the fitting coefficient of the ith grid, and doy is the corresponding annual day.

[0090] The coefficients of the time series fitting model are determined using the least squares method, and finally the ZTD elevation scaling factor model is obtained, and the results can be stored in each corresponding grid.

[0091] In the embodiment, the ERA5 reanalysis data with high spatiotemporal resolution provided by the European Center for Medium-Term Weather Forecasts (ECMWF) is used to calculate the ZTD vertical profile information of each grid point in the Chinese region, and the ZTD value calculated based on the negative exponential function fitting is used with the elevation as a variable. The ZTD values ​​at different elevations are substituted into the negative exponential function to calculate the elevation scaling factor in each corresponding grid point in the Chinese region, and the elevation scaling factors of different grid points and annual accumulation days are modeled. By matching the elevation scaling factor in the grid point closest to the occultation point, combined with the elevation value of the target surface (i.e., the target elevation value), the occultation ZTD value of the missing near-surface data is corrected in elevation to reduce the error.

[0092] This application can reduce data deviations caused by elevation differences, thereby improving the accuracy of data in reflecting atmospheric water vapor and other related conditions at the corresponding elevation level, making subsequent meteorological analysis, research and related applications based on the data more reliable.

[0093] Figure 6 (a) is a schematic diagram showing the accuracy of correcting the pre-occultation ZTD value in the embodiment of the present application; Figure 6 (b) is a schematic diagram of the accuracy of the corrected ZTD value of the occultation in the embodiment of the present application; Figure 6 (a) to Figure 6 (b) It can be seen that there is a large deviation between the occultation ZTD and the sounding station ZTD before correction, the BIAS is basically between -40 and -20 cm, and the root mean square error RMSE is between 30 and 60 cm. The corrected BIAS has been significantly improved, turning positive in most months, close to 0 cm; and the RMSE is mostly concentrated between 2 cm and 8 cm, indicating that the accuracy of the corrected COSMIC-2 ZTD has been significantly improved.

[0094] Based on the national geographic information public service platform "Tiantu", the accuracy distribution map of the corrected occultation ZTD value and the sounding station ZTD value was drawn. The results are shown in the figure below. Figure 7-Figure 10 Based on the national geographic information public service platform "Tiantu", the accuracy distribution map of the corrected occultation ZTD value and the ZTD value of the land-based GNSS station was drawn. The results are shown in Figure 11-Figure 14 .

[0095] Depend on Figure 7-Figure 10 and Figure 11-Figure 14 It can be seen that the comparison between the occultation ZTD and the sounding station ZTD after correction is significantly better than before correction. Both BIAS and RMSE are significantly reduced, especially in the area with large deviations. The corrected results are more accurate, reflecting the significant improvement of data quality by the correction method. This shows that the error between the occultation ZTD and the sounding station ZTD can be effectively reduced through correction.

[0096] The elevation correction method for occultation ZTD data provided in the embodiment of the present application can improve the accuracy of occultation ZTD, and has important value in the fields of satellite data assimilation, water vapor distribution detection and satellite positioning and navigation. On the one hand, the corrected occultation ZTD can more accurately describe the tropospheric delay in the atmosphere, and provide high-quality initial field data for the assimilation of numerical weather forecast models (such as WRF and GFS), thereby improving the analysis capabilities of atmospheric temperature, humidity and air pressure, and improving the prediction accuracy of extreme weather events (such as typhoon paths and precipitation intensity); on the other hand, a more accurate occultation ZTD contributes to the positioning accuracy and convergence speed of PPP (precision single point positioning). This improvement makes occultation ZTD more widely valuable in monitoring, analysis and application.

[0097] Based on the same inventive concept as the elevation correction method for occultation ZTD data provided in the above embodiment, the embodiment of the present application also provides an elevation correction device for occultation ZTD data, such as Fig.15 As shown, it includes an occultation data acquisition module, an occultation ZTD value determination module, a ZTD elevation scaling factor determination module and a ZTD elevation correction module.

[0098] The occultation data acquisition module is used to obtain the occultation data of the target area and determine the corresponding annual accumulation day based on the occultation data.

[0099] The occultation ZTD value determination module is used to determine the pre-correction occultation ZTD value according to the occultation data.

[0100] The ZTD elevation scaling factor determination module is used to match the spatial position of the occultation point of the occultation data with the grid points of the ERA5 data to determine the target grid points; based on the annual accumulation day, the pre-constructed ZTD elevation scaling factor model is used to determine the ZTD elevation scaling factor corresponding to the annual accumulation day and the target grid point.

[0101] The ZTD elevation correction module is used to correct the pre-corrected occultation ZTD value to the target elevation and obtain the post-corrected occultation ZTD value according to the ZTD elevation scaling factor and the conversion relationship between the pre-corrected occultation ZTD value and the post-corrected occultation ZTD value.

[0102] The elevation correction device for occultation ZTD data combines the relationship between the occultation observation altitude and the surface characteristics to effectively supplement the ZTD of the occultation near the surface, effectively solving the problem of low ZTD values ​​due to the missing surface part of the occultation data, and significantly improving the application value of occultation ZTD in numerical weather forecasting and navigation positioning.

[0103] It can be understood that each module in the elevation correction device for occultation ZTD data provided in the embodiment can implement the steps of the elevation correction method for occultation ZTD data provided in the above embodiment.

[0104] In order to improve the accuracy of atmospheric precipitable water monitoring based on the corrected occultation ZTD, the present application embodiment also provides an application system for occultation ZTD data, such as Fig.16 As shown, it includes: an elevation correction device for occultation ZTD data, a meteorological data acquisition module and an atmospheric precipitable water monitoring module as provided in the above embodiments.

[0105] The elevation correction device of occultation ZTD data is used to obtain the corrected occultation ZTD value.

[0106] The meteorological data acquisition module is used to determine the surface temperature and surface air pressure values ​​of the target point.

[0107] The atmospheric precipitable water monitoring module is used to determine the dry delay ZHD value separated from the corrected occultation ZTD value according to the pressure value, latitude and altitude value of the target point; determine the wet delay ZWD value according to the corrected occultation ZTD value and the dry delay ZHD value; determine the atmospheric weighted average temperature according to the surface temperature of the target point; determine the conversion factor of atmospheric precipitable water according to the atmospheric weighted average temperature; and determine the atmospheric precipitable water PWV of the target point according to the conversion factor and the wet delay ZWD value.

[0108] Specifically, the corrected occultation ZTD value can be decomposed into wet delay (ZWD) and dry delay (ZHD).

[0109] As an example, the dry delay ZHD value separated from the corrected occultation ZTD value is determined based on the surface temperature, surface pressure and elevation of the target point, which can be calculated by a model such as the Saastamoinen model. The wet delay ZWD is obtained by the difference between the corrected occultation ZTD value and the dry delay ZHD.

[0110] According to the conversion factor and the wet delay value ZWD, the atmospheric precipitable water at the target point can be determined using the following formula:

[0111] PWV=П·ZWD (10), PWV is the abbreviation of Precipitable Water Vapor, which refers to the depth of liquid water that can be formed after all water vapor in the atmospheric column per unit area is completely condensed into liquid water. The unit is usually millimeter (mm). П is the conversion factor related to atmospheric precipitable water.

[0112] The conversion factor can be estimated as follows:

[0113]

[0114] Where k is the refractive constant of moist air, which is related to the water vapor pressure and is usually taken as 10 -6 . R d is the dry air gas constant, which is It represents the rate of energy change per kilogram of dry air per Kelvin temperature; g is the acceleration due to gravity, and the standard value is 9.80665m / s 2 . T m is the weighted average temperature of the atmosphere in Kelvin (K).

[0115] In the embodiment, the calculation method of the atmospheric weighted average temperature can be determined by the following formula:

[0116] T m =70.2+0.72·T s (12) Among them, T m is the weighted average temperature of the atmosphere (unit K), T s is the surface temperature at the target location (unit: K).

[0117] The application system of occultation ZTD data provided by the embodiment can correct the occultation ZTD value to the target elevation through the elevation correction device of the occultation ZTD data, thereby reducing the error caused by the elevation difference, so that the subsequent calculations and analyses based on the ZTD data are more accurate; the meteorological data acquisition module collects a variety of meteorological data of the target point, such as the surface temperature and air pressure value of the target point, and the geographical data of the target point such as latitude and elevation value, etc., and combines the corrected occultation ZTD value to more comprehensively and accurately determine the parameters such as the dry delay value, wet delay value and atmospheric weighted average temperature of the atmosphere, thereby improving the calculation accuracy of the atmospheric precipitable water. Based on the corrected accurate occultation ZTD value, the embodiment can more reliably invert the precipitable water (PWV), providing real-time support for weather forecasts and disaster warnings (such as typhoons and rainstorms).

[0118] The above is a detailed introduction to the elevation correction method, device and system for the occultation ZTD data provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the concept of the present application and should not be construed as a limitation on the protection scope of the present application.

Claims

1. A method for correcting the elevation of occultation ZTD data, characterized in that: include: Obtaining occultation data of a target area, and determining a corresponding annual accumulation day according to the occultation data; Determining a pre-corrected occultation ZTD value according to the occultation data; Matching the spatial position of the occultation point of the occultation data with the grid points of the ERA5 data to determine the target grid points; According to the annual day, using a pre-constructed ZTD elevation scaling factor model, determining a ZTD elevation scaling factor corresponding to the annual day and the target grid point; According to the ZTD elevation scaling factor and the conversion relationship between the pre-corrected occultation ZTD value and the post-corrected occultation ZTD value, the pre-corrected occultation ZTD value is corrected to the target elevation to obtain the post-corrected occultation ZTD value.

2. The elevation correction method for occultation ZTD data according to claim 1, characterized in that: Determining a corrected pre-occultation ZTD value according to the occultation data includes: Obtaining an atmospheric refractive index according to the occultation data; Determine the corresponding vertical integrated ZTD value by integrating the atmospheric refractive index layer by layer from the ground height to the tropopause height; Above the tropopause height, the corresponding SaastamoinenZTD value is determined using the Saastamoinen model; The vertically integrated ZTD value and the Saastamoinen ZTD value are added to obtain the corrected pre-occultation ZTD value.

3. The elevation correction method for occultation ZTD data according to claim 1, characterized in that: The method for determining the conversion relationship includes: Determine the ERA5-ZTD value based on the ERA5 data; According to the ERA5-ZTD value, a variation trend of the ERA5-ZTD value with elevation variation is obtained; The ERA5-ZTD value is fitted using a negative exponential function according to the changing trend of the ERA5-ZTD value with the change of elevation, so as to determine the conversion relationship.

4. The elevation correction method for occultation ZTD data according to claim 3, characterized in that: The expression of the conversion relationship is as follows: Among them: ZTD B is the corrected ZTD value after occultation, ZTD A To correct the pre-occultation ZTD value, H B is the target elevation value, H A is the lowest elevation value covered by the occultation data; H s is the ZTD elevation scaling factor.

5. The elevation correction method for occultation ZTD data according to claim 1, characterized in that: The method for constructing the ZTD elevation scaling factor model includes: Determine the elevation scaling factor by calculating the ERA5-ZTD value of each grid point in the ERA5 data at different annual accumulation days and different elevations, and determine the change trend of the ZTD elevation scaling factor, wherein the change trend of the ZTD elevation scaling factor presents changes in an annual cycle and a semi-annual cycle; According to the changing trend of the ZTD elevation scaling factor, a time series fitting model is used to fit the ZTD elevation scaling factor corresponding to each annual day and each grid point, and the coefficients of the time series fitting model are determined using the least squares method to finally obtain the ZTD elevation scaling factor model.

6. An elevation correction device for occultation ZTD data, characterized in that: include: An occultation data acquisition module is used to obtain occultation data of a target area and determine a corresponding annual accumulation day according to the occultation data; An occultation ZTD value determination module, used to determine the pre-correction occultation ZTD value according to the occultation data; A ZTD elevation scaling factor determination module is used to match the spatial position of the occultation point of the occultation data with the grid point of the ERA5 data to determine the target grid point; according to the annual accumulation day, a pre-constructed ZTD elevation scaling factor model is used to determine the ZTD elevation scaling factor corresponding to the annual accumulation day and the target grid point; The ZTD elevation correction module is used to correct the pre-corrected occultation ZTD value to the target elevation and obtain the post-corrected occultation ZTD value according to the ZTD elevation scaling factor and the conversion relationship between the pre-corrected occultation ZTD value and the post-corrected occultation ZTD value.

7. Application system of occultation ZTD data, characterized by: include: The elevation correction device for occultation ZTD data, the meteorological data acquisition module and the atmospheric precipitable water monitoring module as claimed in claim 6; The elevation correction device of the occultation ZTD data is used to obtain the corrected occultation ZTD value; The meteorological data acquisition module is used to determine the surface temperature and surface pressure value of the target point; An atmospheric precipitable water monitoring module is used to determine the dry delay value separated from the corrected occultation ZTD value according to the surface pressure value, latitude and elevation value of the target point; The wet delay value is determined according to the corrected occultation ZTD value and the dry delay value; the atmospheric weighted average temperature is calculated according to the surface temperature; the conversion factor of the atmospheric precipitable water is determined according to the atmospheric weighted average temperature; and the atmospheric precipitable water at the target point is determined according to the conversion factor and the wet delay value.

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