Novel troposphere refraction fusion correction method

Abstract

The invention relates to the technical field of sea surface height measurement, and particularly discloses a novel troposphere refraction fusion correction method, which comprises the following steps of GPT2W data calculation, ERA5 data set calculation and data fusion and correction. According to the method, the GPT2W model and the ERA5 data are combined, the temperature and the pressure are subjected to horizontal space and time sequence encryption, and the elevation angle change caused by troposphere refraction is corrected by using the temperature and pressure data with high temporal-spatial resolution, so that the sea surface height measurement precision is improved.

Description

Technical Field

[0001] The invention relates to the technical field of sea surface height measurement, and in particular to a novel tropospheric refraction fusion correction method. Background Art

[0002] At present, the atmospheric pressure and temperature in the GPT2, GPT2W and other models are generally used for tropospheric refraction correction. However, the temporal resolution and spatial resolution of the current models are low and cannot meet the needs of high-precision GNSS-IR sea surface height measurement. Therefore, the GPT2W model is combined with ERA5 data to encrypt the horizontal space and time series of temperature and pressure, and the high-temporal and spatial resolution temperature and pressure data are used to correct the altitude angle changes caused by tropospheric refraction, thereby improving the accuracy of sea surface height measurement. Summary of the invention

[0003] In view of the technical problems mentioned in the background technology, the present invention proposes a novel tropospheric refraction fusion correction method.

[0004] The present invention adopts the following technical solution:

[0005] A novel tropospheric refraction fusion correction method includes the following steps:

[0006] S1. Obtain the latitude, longitude and geodetic height of the measuring station, read the observation file and convert the date into annual cumulative days, and solve the GPT2W data accordingly to obtain the GPT2W grid I0 where the measuring station is located at the horizontal spatial resolution of 1°×1° on that day and convert it to the data of the measuring station height.

[0007] S2. Solve the ERA5 dataset and obtain the four points I closest to the station location in the ERA5 dataset within the range of I0. i (i=1,2,3,4) 24 data points in one day, and calculate the mean of the data at these four points respectively

[0008] S3, I0 data and I i (i=1,2,3,4) calculate the ratio P respectively i (i=1,2,3,4);

[0009] There are 16 small grids in the grid I0 area in the GPT2W data, and the four corner points I i (i=1,2,3,4) is ERA5 data, according to the measurement station and I i The Euclidean distances of the four points (i = 1, 2, 3, 4) are obtained by using the inverse distance weighted method. i A weighted data Q of (i=1,2,3,4) i(i=1,2,3,4);

[0010] The weighted data Q i With the ratio P i Multiply to get the desired data:

[0011] According to the time nearest matching principle, the temperature and pressure required for altitude angle correction within a day are allocated.

[0012] As a further technical solution of the present invention, in step S3, the I0 data and I i (i=1,2,3,4) calculate the ratio P respectively i (i=1,2,3,4), as shown in formula 1:

[0013] Formula 1:

[0014] As a further technical solution of the present invention, in step S3, according to the measuring station and I i The Euclidean distances of the four points (i = 1, 2, 3, 4) are obtained by using the inverse distance weighted method. i A weighted data Q of (i=1,2,3,4) i (i=1,2,3,4), as shown in formula 2:

[0015] Formula 2:

[0016] Where: l i is the Euclidean distance from the measuring station to the four points.

[0017] As a further technical solution of the present invention, in step S3, the weighted data Q i With the ratio P i The multiplication is the desired data, as shown in formula 3:

[0018] Formula 3: D = Q1P1 + Q2P2 + Q3P3 + Q4P4.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention combines the GPT2W model with the ERA5 data, encrypts the temperature and pressure in horizontal space and time series, and uses the temperature and pressure data with high spatiotemporal resolution to correct the altitude angle changes caused by tropospheric refraction, thereby improving the accuracy of sea surface height measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the technical principle of a new tropospheric refraction fusion correction method.

[0022] Figure 2This is the kernel density map of the TAR0 long time series height measurement results.

[0023] Figure 3 This is a short-term sea surface height measurement result diagram. DETAILED DESCRIPTION

[0024] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 The embodiment of the present invention provides a novel tropospheric refraction fusion correction method, comprising the following steps:

[0026] S1. Obtain the latitude, longitude and geodetic height of the measuring station, read the observation file and convert the date into annual cumulative days, and solve the GPT2W data accordingly to obtain the GPT2W grid I0 where the measuring station is located at the horizontal spatial resolution of 1°×1° on that day and convert it to the data of the measuring station height.

[0027] S2. Solve the ERA5 dataset and obtain the four points I closest to the station location in the ERA5 dataset within the range of I0. i (i=1,2,3,4) 24 data points in one day, and calculate the mean of the data at these four points respectively

[0028] S3, I0 data and I i (i=1,2,3,4) calculate the ratio P respectively i (i=1,2,3,4), as shown in formula 1:

[0029] Formula 1:

[0030] There are 16 small grids in the grid I0 area in the GPT2W data, and the four corner points I i (i=1,2,3,4) is ERA5 data, according to the measurement station and I i The Euclidean distances of the four points (i = 1, 2, 3, 4) are obtained by using the inverse distance weighted method. i A weighted data Q of (i=1,2,3,4) i (i=1,2,3,4), as shown in formula 2:

[0031] Formula 2:

[0032] Where: li is the Euclidean distance from the measuring station to the four points;

[0033] The weighted data Q i With the ratio P i The multiplication is the desired data, as shown in formula 3:

[0034] Formula 3: D = Q1P1 + Q2P2 + Q3P3 + Q4P4:

[0035] According to the time nearest matching principle, the temperature and pressure required for altitude angle correction within a day are allocated.

[0036] The present invention uses temperature and pressure data with a horizontal spatial resolution of 0.25°×0.25° and a temporal resolution of 1 h, which can provide more accurate temperature and pressure required for tropospheric refraction correction and improve the accuracy of GNSS-IR sea surface height measurement.

[0037] (1) The technology was verified over a period of one year using three stations: TAR0, PTLD and GOM1. The sea surface height results corrected using this technology were compared with those inverted without tropospheric refraction correction. The correlation coefficients (PCC) with the tide gauge station were all above 0.9, indicating that the corrected sea surface height results are highly correlated with the tide and can better reflect the trend of sea surface height changes. After correction, the root mean square error (RMSE) of TAR0 increased by 22.7%; the accuracy of PTLD increased by 16.83%; and the accuracy of GOM1 increased by 12.75%. Figure 2 .

[0038] (2) Three time periods, namely the 1st to 30th day, 150th to 180th day and 250th to 280th day of the year, were selected to solve the RMSE for verification. The average accuracy of the TAR0 station in different time periods increased by 22.99%, the PTLD station increased by 20.23%, and the GOM1 station increased by 14.85%. By comparison, it was found that the RMSE of the same station in different time periods was different, but they all fluctuated around the RMSE of the long time series. The average absolute values ​​of the RMSE differences in adjacent time periods without correction for TAR0, PTLD and GMO1 were 0.47cm, 1.27cm and 1.25cm, respectively, and the average absolute values ​​of the RMSE differences after NTRF correction were 0.19cm, 0.59cm and 1.24cm, respectively. This technology reduces the gap in RMSE between different time periods and reduces the fluctuation of RMSE in different time periods. Figure 2 In the figure, the yellow line is the tidal data, the blue dots are the sea surface height after NTRF correction, the red dots are the sea surface height without tropospheric refraction correction, and a, b and c are local enlarged images.

[0039] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

[0040] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A new tropospheric refraction fusion correction method, characterized in that: The following steps are involved: S1. Obtain the latitude, longitude and geodetic height of the measuring station, read the observation file and convert the date into annual cumulative days, and solve the GPT2W data accordingly to obtain the GPT2W grid I0 where the measuring station is located at the horizontal spatial resolution of 1°×1° on that day and convert it to the measuring station height data S2. Solve the ERA5 dataset and obtain the four points I closest to the station location in the ERA5 dataset within the range of I0. i (i=1,2,3,4) 24 data points in one day, and calculate the mean of the data at these four points respectively S3, I0 data and I i (i=1,2,3,4) calculate the ratio P respectively i (i=1,2,3,4); There are 16 small grids in the grid I0 area in the GPT2W data, and the four corner points I i (i=1,2,3,4) is ERA5 data, according to the measurement station and I i The Euclidean distances of the four points (i = 1, 2, 3, 4) are obtained by using the inverse distance weighted method. i A weighted data Q of (i=1,2,3,4) i (i=1,2,3,4); The weighted data Q i With the ratio P i Multiply to get the desired data: According to the time nearest matching principle, the temperature and pressure required for altitude angle correction within a day are allocated.

2. According to the novel tropospheric refraction fusion correction method of claim 1, it is characterized in that: In step S3, the I0 data is combined with the I i (i=1,2,3,4) calculate the ratio P respectively i (i=1,2,3,4), as shown in formula 1: Formula 1:

3. The novel tropospheric refraction fusion correction method according to claim 2 is characterized in that: In step S3, according to the measuring station and I i The Euclidean distances of the four points (i = 1, 2, 3, 4) are obtained by using the inverse distance weighted method. i A weighted data Q of (i=1,2,3,4) i (i=1,2,3,4), as shown in formula 2: Formula 2: Where: l i is the Euclidean distance from the measuring station to the four points.

4. The novel tropospheric refraction fusion correction method according to claim 3 is characterized in that: In step S3, the weighted data Q i With the ratio P i The multiplication is the desired data, as shown in formula 3: Formula 3: D = Q1P1 + Q2P2 + Q3P3 + Q4P4.

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