GNSS occultation data one-dimensional variation inversion method and device

Abstract

The invention discloses a GNSS occultation data one-dimensional variation inversion method and device, and the method comprises the steps: background field preparation: carrying out the horizontal interpolation of temperature, air pressure and water vapor pressure from a lattice point field to the longitude and latitude positions of occultation data, carrying out the interpolation of the liquid water content and ice water content in cloud to the longitude and latitude positions of the occultation data, and carrying out the calculation of a background field; a background field profile required by inversion is obtained; and inputting observation data and a background field: inputting occultation data, the background field profile, the observation error covariance matrix and the background field error covariance matrix into a one-dimensional variation inversion system. A liquid water content item and an ice water content item are added into an observation operator to calculate deviation between observation data and background field data, distribution characteristics of the deviation are counted, and the average value of refractive index deviation between the two kinds of data after liquid water and ice water are added is remarkably reduced.

Description

Technical Field

[0001] The present application belongs to the field of satellite remote sensing technology, and specifically relates to a one-dimensional variational inversion method and device for GNSS occultation data. Background Art

[0002] GNSS occultation detection of the Earth's atmosphere is a unique limb-type remote sensing detection method. It receives dual-frequency signals from navigation satellites through receivers carried by low-orbit satellites, and calculates the signal delay (the delay of the real signal compared to the signal in an ideal vacuum state) by combining satellite orbit positioning and velocity information. The refractive index of the Earth's atmosphere is calculated based on the signal delay, and then the temperature, humidity, pressure and other elements of the Earth's atmosphere are obtained.

[0003] Calculating the temperature, humidity and pressure elements of the earth from the atmospheric refractive index requires an observation operator to establish the connection between the observed variable (refractive index) and the temperature, humidity and pressure. The existing observation operators are as follows:

[0004] x=(T,P,P w );

[0005]

[0006] Where P is the air pressure (in hPa), T is the temperature (in K), P w is the water vapor pressure (in hPa).

[0007] In the above observation operators, the effects of liquid water content and ice water content in clouds on the refractive index are ignored, and it is believed that their contribution to the atmospheric refractive index at microwave frequencies can be ignored. However, existing studies have pointed out that ignoring the contribution of liquid water content and ice water content in clouds to the refractive index will introduce systematic biases in the data inversion process.

[0008] For example, the article "Inversion of GPS Occultation Atmospheric Temperature and Humidity Profiles Using One-Dimensional Variational Method" (Bi Yanmeng, Liao Mi, Zhang Peng, Ma Gang, et al., published in Acta Phys. Sin. Vol. 62, No. 15 (2013) 159301) mentioned that the refractive index of GPS occultation observations is a function of the atmospheric temperature and humidity state. The one-dimensional variational method (1DVAR) can be used to simultaneously invert the atmospheric temperature and humidity profiles from the refractive index data. This method was tested using COSMIC 2011 regional occultation observation data in China. The background field used the ECMWF atmospheric profile. The inversion results were consistent with the matching sounding observations. This article focuses on the impact of non-ideal gas effects on the temperature and humidity inversion errors in the one-dimensional variational inversion method. The comparison results of different months show that the non-ideal gas effect has a systematic impact on the atmospheric profile inverted by occultation. Considering this effect can improve the temperature inversion bias by 0.1K and the relative humidity by about 0.5%. As one of the few satellite remote sensing data that do not require bias correction in numerical weather forecast data assimilation, the correction of non-ideal gas effect is undoubtedly very important for occultation data.

[0009] However, the above article, like other existing technologies, ignores the contribution of liquid water content and ice water content in the cloud to the refractive index. The unbiased observation error and background field error are both important assumptions in variational inversion. Ignoring the contribution of liquid water content and ice water content in the cloud to the refractive index makes the unbiased assumption in the variational inversion process invalid, which will seriously affect the inversion effect. Therefore, it is very important to introduce the liquid water and ice water content in the cloud and correct the bias for the variational inversion of GNSS occultation data. Summary of the invention

[0010] A brief summary of embodiments of the present application is provided below to provide a basic understanding of some aspects of the present application. It should be understood that the following summary is not an exhaustive summary of the present application. It is not intended to determine the key or important parts of the present application, nor is it intended to limit the scope of the present application. Its purpose is merely to provide certain concepts in a simplified form as a preface to a more detailed description discussed later.

[0011] The purpose of this application is to improve the existing GNSS occultation data variational inversion technology based on the cloud liquid water content and cloud ice water content provided by the ERA5 reanalysis data and to enhance the inversion accuracy.

[0012] According to one aspect of the present application, a one-dimensional variational inversion method for GNSS occultation data is provided, which specifically comprises the following steps:

[0013] Background field preparation: interpolate the temperature, air pressure and water vapor pressure from the grid field horizontally to the longitude and latitude of the occultation data. At the same time, interpolate the liquid water content and ice water content in the cloud to the longitude and latitude of the occultation data to obtain the background field profile required for inversion.

[0014] Observation data and background field input: input observation data, background field profile, observation error covariance matrix and background field error covariance matrix into the one-dimensional variational inversion system; observation data is occultation data;

[0015] The one-dimensional variational inversion system includes: achieving the optimal estimation of the atmospheric parameter x by minimizing the cost function to find the minimum value. The minimization cost function J(x) (loss function) is expressed as follows:

[0016]

[0017] where x is the atmospheric state, x b is the background field data, y obs is the observation data, B is the background field error covariance matrix, H(x) is the observation operator, H(x) is the occultation detection value corresponding to the atmospheric parameter x, that is, the atmospheric refractive index; O is the observation error covariance matrix, and the observation deviation is expressed as: The background field deviation is expressed as: True is the true state of the atmosphere;

[0018] The cost function is modified accordingly:

[0019]

[0020] in is the background field deviation minus the observed deviation.

[0021] As an implementation scheme, the minimum value of the minimization cost function J(x) is solved specifically by an iterative method (Levenberg-Marquardt or quasi-Newton iterative method).

[0022] The scale parameter α of the cloud particles is much smaller than 1, which satisfies the Mie theory (Mie, 1908). Under the Rayleigh approximation, the scattering and absorption coefficients of the cloud particles for GNSS signals can be derived. The scale parameter α of the cloud particles is 2πr / λ, where r is the radius of the cloud particles and λ is the wavelength of the GNSS signal. The observation operator is expressed as:

[0023] x=(T,P,P w , W liquid , W ice );

[0024]

[0025] Where W liquid and W ice is the liquid water content and ice water content in the cloud, in gm-3; P is the air pressure, in hPa; T is the temperature, in K, P w It is the water vapor pressure, unit is hPa.

[0026] As an implementation scheme, the background field preparation step is to interpolate the temperature, air pressure, water vapor pressure, cloud liquid water content and ice water content in the ERA5 grid data into the occultation data.

[0027] This application uses the above scheme to use the grid data of ERA5 as the background field for one-dimensional variational inversion. First, the temperature, air pressure, and water vapor pressure need to be horizontally interpolated from the grid field to the longitude and latitude of the occultation data. In order to include liquid water and ice water content in the inversion process, this application also interpolates the liquid water content and ice water content in the cloud in the same way when interpolating temperature, air pressure, and water vapor pressure to obtain the background field profile data required for inversion. Then input the observation data, background field data, observation error covariance, and background data error covariance into the one-dimensional variational inversion system.

[0028] According to another aspect of the present application, a one-dimensional variational inversion device for GNSS occultation data is provided, comprising:

[0029] Background field preparation unit: interpolate the temperature, air pressure and water vapor pressure from the grid field horizontally to the longitude and latitude of the occultation data. At the same time, interpolate the liquid water content and ice water content in the cloud to the longitude and latitude of the occultation data to obtain the background field profile required for inversion.

[0030] Observation data and background field input unit: input occultation data, background field profile, observation error covariance matrix and background field error covariance matrix into the one-dimensional variational inversion system;

[0031] Among them, the one-dimensional variational inversion system includes: achieving the optimal estimation of the atmospheric parameter x by minimizing the cost function to solve the minimum value, and the minimization cost function J(x) is expressed as follows:

[0032]

[0033] where x is the atmospheric state, x b is the background field data, y obs is the observation data, B is the background field error covariance matrix, H(x) is the observation operator, H(x) is the occultation detection value corresponding to the atmospheric parameter x, that is, the atmospheric refractive index; O is the observation error covariance matrix, μ B-O is the background field deviation minus the observation deviation,

[0034] In summary, this application implements a one-dimensional variational inversion method and device for GNSS occultation data through the above-mentioned scheme, adds liquid water content terms and ice water content terms in the observation operator to calculate the deviation between the observation data and the background field data, and statistically analyzes the distribution characteristics of the deviation. After adding liquid water and ice water, the average value of the refractive index deviation between the two types of data is significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present application can be better understood by referring to the following description given in conjunction with the accompanying drawings, wherein the same or similar reference numerals are used throughout the drawings to represent the same or similar components. The accompanying drawings, together with the following detailed description, are included in and form a part of this specification, and are used to further illustrate the preferred embodiments of the present application and explain the principles and advantages of the present application. In the accompanying drawings:

[0036] Figure 1 This is the frequency distribution of the percentage deviation between the observed refractive index and the background field refractive index before (left figure) and after (right figure) adding the liquid water content and ice water content in the cloud in an embodiment of the present application. DETAILED DESCRIPTION

[0037] Embodiments of the present application will be described below with reference to the accompanying drawings. Elements and features described in one drawing or one embodiment of the present application may be combined with elements and features shown in one or more other drawings or embodiments. It should be noted that for the purpose of clarity, the representation and description of components and processes that are not related to the present application and are known to those of ordinary skill in the art are omitted in the drawings and descriptions.

[0038] The purpose of this application is to improve the existing GNSS occultation data variational inversion technology based on the cloud liquid water content and cloud ice water content provided by the ERA5 reanalysis data and to enhance the inversion accuracy.

[0039] As a specific embodiment, the present application provides a one-dimensional variational inversion method for GNSS occultation data.

[0040] The scale parameter of cloud particles (α = 2πr / λ, r is the radius of cloud particles, λ is the wavelength of GNSS signal) is much smaller than 1, which satisfies Mie theory (Mie, 1908). Under the Rayleigh approximation, the scattering and absorption coefficients of cloud particles for GNSS signals can be derived. The observation operator is expressed as:

[0041] x=(T,P,P w , W liquid , W ice );

[0042]

[0043] Where W liquid and Wice is the content of liquid water and ice water in clouds, with the unit of gm-3 (Zou and Yang, 2012).

[0044] ERA5 uses advanced numerical weather prediction models and a large amount of observational data to assimilate meteorological variables on a global scale, providing high-resolution and high-precision meteorological information. The high-resolution version of the ERA5 dataset (HRES) has a horizontal resolution of 31 kilometers and a vertical structure of 137 layers, covering the Earth's atmosphere from 0 to 80 km, and provides cloud liquid water content and cloud ice water content.

[0045] To use the ERA5 grid data as the background field for one-dimensional variational inversion, it is first necessary to horizontally interpolate the temperature, air pressure, and water vapor pressure from the grid field to the longitude and latitude of the occultation data. In order to incorporate liquid water and ice water content into the inversion process, this application also interpolates the liquid water content and ice water content in the cloud in the same way as the temperature, air pressure, and water vapor pressure interpolation to obtain the background field profile data required for inversion. The observation data, background field data, observation error covariance, and background data error covariance are then input into the one-dimensional variational inversion system. The key step of one-dimensional variational inversion is to minimize the loss function:

[0046]

[0047] where x is the atmospheric state, x b is the background field data, y obs is the observation data, B is the background field error covariance matrix, H(x) is the observation operator, H(x) is the occultation detection value corresponding to the atmospheric parameter x, that is, the atmospheric refractive index; O is the observation error covariance matrix. The key assumption for inversion using the variational method is that both the observation data and the background field data are unbiased estimates of the true value. If the unbiased assumption does not hold, the observation bias must be subtracted from the observation and background field. and background field deviation True is the true state of the atmosphere.

[0048] The cost function is modified accordingly:

[0049]

[0050] in is the background field deviation minus the observed deviation.

[0051] The systematic bias between the observation data and the background field data is estimated based on sufficient samples of GNSS occultation observation data and background field data, and this bias is removed in the cost function, thereby improving the inversion effect of GNSS occultation data.

[0052] As another specific embodiment, a one-dimensional variational inversion device for GNSS occultation data is also provided, comprising:

[0053] Background field preparation unit: interpolate the temperature, air pressure and water vapor pressure from the grid field horizontally to the longitude and latitude of the occultation data. At the same time, interpolate the liquid water content and ice water content in the cloud to the longitude and latitude of the occultation data to obtain the background field profile required for inversion.

[0054] Observation data and background field input unit: input occultation data, background field profile, observation error covariance matrix and background field error covariance matrix into the one-dimensional variational inversion system;

[0055] Among them, the one-dimensional variational inversion system includes: achieving the optimal estimation of the atmospheric parameter x by minimizing the cost function to solve the minimum value, and the minimization cost function J(x) is expressed as follows:

[0056]

[0057] where x is the atmospheric state, x b is the background field data, y obs is the observation data, B is the background field error covariance matrix, H(x) is the observation operator, H(x) is the occultation detection value corresponding to the atmospheric parameter x, that is, the atmospheric refractive index; o is the observation error covariance matrix, μ B-O is the background field deviation minus the observation deviation,

[0058] In summary, this application realizes a one-dimensional variational inversion method and device for GNSS occultation data through the above scheme, adds liquid water content terms and ice water content terms in the observation operator to calculate the deviation between the observation data and the background field data, and statistically analyzes the distribution characteristics of the deviation. After adding liquid water and ice water, the average value of the refractive index deviation between the two types of data is significantly reduced.

[0059] Figure 1 The frequency distribution of the percentage deviation between the observed refractive index and the background field refractive index before (left) and after (right) adding the liquid water content and ice water content in the clouds. Considering the contribution of the liquid water content and ice water content in the clouds to the atmospheric refractive index can effectively improve the inversion effect of GNSS occultation data, especially in the equatorial convergence zone, where the atmosphere is covered by deep convective clouds all year round. The absorption and scattering effect of cloud water on GNSS signals is most obvious. Considering its influence can also significantly improve the inversion effect of GNSS occultation data.

[0060] Although the present application has been disclosed above by describing the specific embodiments of the present application, it should be understood that all the above embodiments and examples are exemplary and non-restrictive. Those skilled in the art can design various modifications, improvements or equivalents of the present application within the spirit and scope of the appended claims. These modifications, improvements or equivalents should also be considered to be included in the protection scope of the present application.

Claims

1. A one-dimensional variational inversion method for GNSS occultation data, characterized by: The steps include: Background field preparation: interpolate the temperature, air pressure and water vapor pressure from the grid field horizontally to the longitude and latitude of the occultation data. At the same time, interpolate the liquid water content and ice water content in the cloud to the longitude and latitude of the occultation data to obtain the background field profile required for inversion. Observation data and background field input: input the occultation data, background field profile, observation error covariance matrix and background field error covariance matrix into the one-dimensional variational inversion system; Among them, the one-dimensional variational inversion system includes: achieving the optimal estimation of the atmospheric parameter x by minimizing the cost function to solve the minimum value, and the minimization cost function J(x) is expressed as follows: where x is the atmospheric state, x b is the background field data, y obs is the observation data, B is the background field error covariance matrix, H(x) is the observation operator, H(x) is the occultation detection value corresponding to the atmospheric parameter x, that is, the atmospheric refractive index; O is the observation error covariance matrix, μ B-O The background field deviation minus the observation deviation 2. The one-dimensional variational inversion method for GNSS occultation data according to claim 1, characterized in that: The minimum value of the minimization cost function J(x) is solved by an iterative method, wherein the iterative method is a Levenberg-Marquardt or quasi-Newton iterative method.

3. The one-dimensional variational inversion method for GNSS occultation data according to claim 1, characterized in that: The observation operator is expressed as: x=(T,P,P w ,W liquid ,W ice ); Where W liquid and W ice is the liquid water content and ice water content in the cloud, in gm-3; P is the air pressure, in hPa; T is the temperature, in K, P w It is the water vapor pressure, unit is hPa.

4. The one-dimensional variational inversion method for GNSS occultation data according to claim 1, characterized in that: In the background field preparation step, the temperature, air pressure, water vapor pressure, liquid water content in clouds and ice water content in the ERA5 grid data are interpolated into the occultation data.

5. A one-dimensional variational inversion device for GNSS occultation data, characterized by: include: Background field preparation unit: interpolate the temperature, air pressure and water vapor pressure from the grid field horizontally to the longitude and latitude of the occultation data. At the same time, interpolate the liquid water content and ice water content in the cloud to the longitude and latitude of the occultation data to obtain the background field profile required for inversion. Observation data and background field input unit: input the occultation data, background field profile, observation error covariance matrix and background field error covariance matrix into the one-dimensional variational inversion system; Among them, the one-dimensional variational inversion system includes: achieving the optimal estimation of the atmospheric parameter x by minimizing the cost function to solve the minimum value, and the minimization cost function J(x) is expressed as follows: where x is the atmospheric state, x b is the background field data, y obs is the observation data, B is the background field error covariance matrix, H(x) is the observation operator, H(x) is the occultation detection value corresponding to the atmospheric parameter x, that is, the atmospheric refractive index; O is the observation error covariance matrix, μ B-O The background field deviation minus the observation deviation

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