CN-115685253-B - PWV inversion method of GNSS auxiliary wind cloud satellite No. three

CN115685253BCN 115685253 BCN115685253 BCN 115685253BCN-115685253-B

Abstract

The invention discloses a PWV inversion method of a GNSS auxiliary cloud satellite No. three, which relates to the technical fields of GNSS meteorology and remote sensing water vapor inversion, and aims at the current situation that when a current cloud satellite No. three MERSI sensor inverts PWV, the atmospheric transmittance parameter is underestimated, and the accuracy of PWV products is low due to the fact that water vapor and atmospheric transmittance regression coefficients are selected empirically, the scheme is provided. The invention has reasonable design, utilizes the advantage of providing high-precision PWV by the GNSS, performs PWV inversion on FY3/MERSIL1 data with high spatial resolution, and realizes the quick acquisition of the high-precision high-space-time resolution PWV of the GNSS-assisted FY3 satellite.

Inventors

ZHAO QINGZHI
MA ZHI
GENG PENGFEI
YUAN RONGCAI

Assignees

西安科技大学

Dates

Publication Date: 20260508
Application Date: 20221014

Claims (4)

The PWV inversion method of the GNSS assisted wind cloud satellite No. three is characterized by comprising the following steps of: S1, preprocessing data of a third satellite (FY 3) MERSI L of the wind cloud and GNSS data; S2, calculating the atmospheric transmittance; S3, estimating regression coefficients and calculating channel water vapor; s4, summing the weighted average of the multi-channel PWV; s5, introducing a digital elevation model (Digital Elevation Model, DEM) to further correct deviation of the wind-cloud satellite inversion PWV; the regression fit relation of the atmospheric transmittance and PWV in the S3 is shown as a formula (3): in the formula, Refers to the calculated atmospheric precipitation amount on a per-channel basis, And (3) with Fitting coefficients; based on the exponential relation of the atmospheric transmittance and the atmospheric precipitation, introducing a high-precision GNSS PWV as a fitting model parameter and calculating the coefficient by taking seasonal factors into consideration, wherein a specific formula is shown as formula (4): in the formula, The PWV provided seasonally for the GNSS station, 、 Fitting coefficients obtained through a polynomial fitting function; calculating the channel water vapor values of different channels on each lattice point of FY3/MERSI L1 by using the obtained fitting coefficient, wherein the specific formula is as shown in formula (5): in the formula, Is the first The water vapor value of the channel, , ; Firstly, calculating Bias of PWV and FY3-L1 PWV corresponding to a GNSS site, constructing a polynomials of multiple secondary based on the position and the elevation of the GNSS site, wherein the calculation formula is as shown in formula (9): Wherein, the In order for the deviation to be a function of, Is a polynomial fit coefficient, 、 And The latitude, longitude and elevation of the GNSS site respectively, The number of the GNSS stations; Then using the grid longitude and latitude data and DEM grid elevation data provided by FY3/MERSI L1 to obtain based on the above Calculating the grid deviation value by fitting coefficient, and finally combining the deviation with the obtained value Adding to obtain the final PWV.
2. The PWV inversion method for GNSS assisted cloud satellite No. three according to claim 1, wherein S1 includes: The Cloud removal processing of FY3/MERSI L1 channel data is realized by utilizing the cloud_mask attribute in FY3-L2 PWV products, cloud Mask data is a six-byte integer array, the content of which is stored according to bit, the integer array is converted into binary array, and then a Cloud Mask is generated according to the relation between the bit and corresponding Cloud to realize Cloud removal; S1.2 space-time matching of GNSS site and FY3/MERSI L1 data, namely taking a certain range mean value of the corresponding FY3/MERSI L data of longitude and latitude of the GNSS site as matching data of the site and the FY3/MERSI L1 data, in addition, because the transit time of the GNSS data and the FY3/MERSI L data is different, the GNSS data and the FY3/MERSI L data need to be matched according to a certain time range, and the horizontal distance range and the time range are 0.15 DEG 0.15 DEG 30min。
3. The method for inverting the PWV of the GNSS assisted cloud satellite of claim 1, wherein the near infrared channel transmittance in S2 is calculated by a two-channel ratio method and a three-channel ratio method, and the difference between the two methods is that whether the type of the remote sensing ground object is single or not, the two-channel ratio method is shown as a formula (1) for the single ground object, and the three-channel ratio method is shown as a formula (2) for the complex ground object; in the formula, Is the transmittance of the air, the air is in the form of air, The number of the water vapor absorption channels is MERSI L data =17, 18, 19), And (3) with For a reflectance of MERSI two window channels, And (3) with 0.8 And 0.2, respectively.
4. The method for inverting PWV of GNSS assisted wind cloud satellite No. three according to claim 1, wherein 3 near infrared absorption channels of MERSI sensor are different in sensitivity to water vapor, 18 absorption channels are sensitive to dry atmosphere, 17 absorption channels and 19 absorption channels are gentle in sensitivity change along with the increase of water vapor amount, but compared with 17 absorption channels, sensitivity to water vapor is slightly higher, therefore, transmittance of the three absorption channels can represent the radiation attenuation caused by water vapor and weight can be calculated according to the radiation attenuation, and weighted average value of water vapor of the three absorption channels is calculated through weighted summation to obtain more accurate water vapor inversion result; the weight formula of each channel is calculated as shown in (6): in the formula, Is the first The water vapor weights corresponding to the channels are normalized, and the weights obtained by the 3 absorption channels are: in the formula, Is the first The water vapor weight after normalization of each channel, therefore, the final PWV obtained by inversion of the obtained channel water vapor value and the water vapor weight is as follows: PWV L1 ＝f 17 PWV 17 +f 18 PWV 18 +f 19 PWV 19 (8) in the formula, FY3-L1 PWV inverted for GNSS assisted FY3/MERSI L1.

Description

PWV inversion method of GNSS auxiliary wind cloud satellite No. three Technical Field The invention relates to the technical fields of GNSS meteorology and remote sensing water vapor inversion, in particular to a PWV inversion method of a GNSS auxiliary wind cloud satellite No. three. Background Moisture is an important component of the terrestrial atmosphere, mainly in the lower layers of the troposphere. Although water vapor occupies only a small portion of the atmosphere, it varies widely in space and time. A great deal of latent heat is released or absorbed during the phase change process, so water vapor plays an important role in global energy balance, climate change process and the formation and evolution of disastrous weather. The atmospheric precipitation (Precipitable Water Vapor, PWV) is the precipitation produced by condensation of all water vapor in the air column of unit cross-sectional area from the ground to the top of the atmosphere as rain, and is commonly used to quantify the water vapor content in the atmosphere. PWV is an important research content of global navigation satellite system (Global Navigation SATELLITE SYSTEM, GNSS) meteorology, and has been widely applied to the fields of short-term weather early warning, long-term weather monitoring and the like. At present, a plurality of PWV inversion technologies such as numerical forecasting, satellite remote sensing, radio detection and the like have been developed, but the problems of low space coverage, time sequence deletion, poor product precision and the like exist all the time. Therefore, the water vapor inversion method using the fusion multi-technology has important significance for acquiring high-precision water vapor data. The high-precision PWV acquired by GNSS is often used for verification of the acquisition of PWV by other technologies, but the spatial resolution is not high due to the maldistribution of GNSS sites, and it is difficult to acquire the distribution of PWV in the whole area. Compared with the GNSS technology represented by the site, the satellite remote sensing technology has the advantages of wide coverage, high time resolution and the like in the aspect of acquiring PWV. For example, the medium-resolution spectrum imager (Medium Resolution SPECTRAL IMAGER, MERSI) carried by the wind-cloud satellite III can provide atmospheric water vapor information with spatial resolution of 1×1 km at the highest, but the inversion of the PWV has the defects of underestimation of atmospheric transmittance parameters and empirical selection of water vapor and atmospheric transmittance regression coefficients, so that the high-precision high-space-time resolution PWV can not be obtained quickly, and the application requirement of the high-precision PWV can not be met. Disclosure of Invention The invention aims to solve the problem that the accuracy of PWV products is low due to underestimation of atmospheric transmittance parameters and empirical selection of water vapor and atmospheric transmittance regression coefficients when a satellite MERSI sensor of the wind cloud three is used for inverting the PWV at present, and provides a PWV inversion method of GNSS auxiliary FY 3. In order to achieve the above purpose, the present invention adopts the following technical scheme: the PWV inversion method of the GNSS assisted wind cloud satellite No. three comprises the following steps: S1, preprocessing data of a third satellite (FY 3) MERSI L of the wind cloud and GNSS data; S2, calculating the atmospheric transmittance; S3, estimating regression coefficients and calculating channel water vapor; s4, summing the weighted average of the multi-channel PWV; and S5, introducing a digital elevation model (Digital Elevation Model, DEM) to further correct deviation of the wind cloud satellite inversion PWV. In a preferred embodiment, the S1 includes: The Cloud removal processing of FY3/MERSI L1 channel data is realized by utilizing the cloud_mask attribute in FY3-L2 PWV products, cloud Mask data is a six-byte integer array, the content of the six-byte integer array is stored according to bit, the integer array is converted into binary array, then a Cloud Mask is generated according to the relation between bit and corresponding Cloud to realize Cloud removal, and the storage content of the bit of the FY3/MERSI Cloud detection array is shown in the accompanying drawing 2 of the specification. S1.2 space-time matching of GNSS site and FY3/MERSI L1 data, namely taking a certain range mean value of the corresponding FY3/MERSI L data of longitude and latitude of the GNSS site as matching data of the site and the FY3/MERSI L1 data, in addition, because the transit time of the GNSS data and the FY3/MERSI L data is different, the GNSS data and the FY3/MERSI L data need to be matched according to a certain time range, and the horizontal distance range and the time range are 0.15 DEG0.15 DEG30min。 In a preferred embodiment, the near infrared chan