GNSS occultation data one-dimensional variational retrieval method and device
By incorporating the content of liquid water and ice in clouds from ERA5 reanalysis into the GNSS occultation data inversion, the inversion process was optimized, the problem of neglecting the contribution of liquid water and ice to the refractive index in clouds was solved, and the inversion accuracy was improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 航天天目(重庆)卫星科技有限公司
- Filing Date
- 2024-12-24
- Publication Date
- 2026-05-26
AI Technical Summary
Existing variational inversion techniques for GNSS occultation data neglect the contribution of liquid water and ice water content in clouds to the refractive index, resulting in systematic biases in the inversion results.
Using cloud liquid water and ice water content provided by ERA5 reanalysis data, the cloud locations were interpolated to GNSS occultation data locations using a one-dimensional variational inversion method to correct the bias between observations and the background field and optimize the cost function in the inversion process.
It significantly reduces the deviation between the observed refractive index and the background refractive index, and improves the inversion accuracy of GNSS occultation data, especially in the equatorial convergence zone.
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Figure CN119716932B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of satellite remote sensing technology, specifically relating to a one-dimensional variational inversion method and apparatus for GNSS occultation data. Background Technology
[0002] GNSS occultation detection of the Earth's atmosphere is a unique lateral remote sensing method. It uses a receiver mounted on a low-Earth orbit satellite to receive dual-frequency signals from navigation satellites, and combines this with satellite orbital positioning and velocity information to calculate the signal delay (the delay of the actual signal compared to an ideal vacuum). Based on the signal delay, the refractive index of the Earth's atmosphere is calculated, and then atmospheric parameters such as temperature, humidity, and pressure are obtained.
[0003] Calculating Earth's temperature, humidity, and pressure elements from atmospheric refractive index requires an observation operator to establish the relationship between the observed variable (refractive index) and temperature, humidity, and pressure. Existing observation operators are as follows:
[0004] x = (T, P, P) w );
[0005]
[0006] Where P is air pressure (in hPa) and T is temperature (in K), P w It is the water vapor pressure (unit: hPa).
[0007] The above observation operators neglect the influence of cloud liquid water content and ice water content on the refractive index, assuming that their contribution to the atmospheric refractive index at microwave frequencies is negligible. However, existing studies have pointed out that neglecting the contribution of cloud liquid water content and ice water content to the refractive index will introduce systematic biases in the data inversion process.
[0008] For example, the article "Application of One-Dimensional Variational Method to Invert the Atmospheric Temperature and Humidity Profiles of GPS Occultations" (Bi Yanmeng, Liao Mi, Zhang Peng, Ma Gang, et al., published in Acta Physica Sinica, Vol. 62, No. 15 (2013) 159301) mentions that the refractive index of GPS occultation observations is a function of atmospheric temperature and humidity. The one-dimensional variational method (1DVAR) can simultaneously invert atmospheric temperature and humidity profiles from refractive index data. This method was tested using COSMIC 2011 occultation observation data from the Chinese region, with ECMWF atmospheric profiles used as the background field. The inversion results showed good consistency with matching radiosonde observations. This article focuses on the impact of the non-ideal gas effect on the temperature and humidity inversion errors in the one-dimensional variational inversion method. Comparison results from different months show that the non-ideal gas effect has a systematic impact on the atmospheric profiles inverted by occultation. Considering this effect can improve the bias of temperature inversion by 0.1 K and the bias of humidity inversion by about 0.5%. As one of the few satellite remote sensing data in numerical weather prediction data assimilation that does not require bias correction, the correction of non-ideal gas effects is undoubtedly very important.
[0009] However, like other existing technologies, the aforementioned articles neglect the contribution of cloud liquid water and ice content to the refractive index. The unbiasedness of observation errors and background field errors is a crucial assumption in variational inversion. Ignoring the contribution of cloud liquid water and ice content to the refractive index renders the unbiased assumption invalid, severely impacting the inversion results. Therefore, incorporating cloud liquid water and ice content and correcting biases are essential for variational inversion of GNSS occultation data. Summary of the Invention
[0010] A brief overview of embodiments of this application is provided below to offer a basic understanding of certain aspects of this application. It should be understood that this overview is not an exhaustive summary of this application. It is not intended to identify key or essential parts of this application, nor is it intended to limit the scope of this application. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.
[0011] The purpose of this application is to improve the variational inversion technique for existing GNSS occultation data based on the cloud liquid water content and cloud ice water content provided by ERA5 reanalysis data, thereby increasing the inversion accuracy.
[0012] According to one aspect of this application, a one-dimensional variational inversion method for GNSS occultation data is provided, specifically including the following steps:
[0013] Background field preparation: interpolate the temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data. At the same time, interpolate the liquid water content and ice water content in the cloud to the latitude and longitude of the occultation data to obtain the background field profile required for inversion.
[0014] Observational data and background field input: Input the observational data, background field profile, observation error covariance matrix, and background field error covariance matrix into the one-dimensional variational inversion system; the observational data is occultation data;
[0015] The one-dimensional variational inversion system includes: achieving the optimal estimation of atmospheric parameter x by minimizing the cost function and solving for its minimum value. The minimization cost function J(x) (loss function) is expressed as follows:
[0016]
[0017] Where x represents the atmospheric state, x b It's background field data, y obs The data consists of observational data, where B is the background field error covariance matrix, H(x) is the observation operator, and H(x) is the occultation detection value corresponding to the atmospheric parameter x, i.e., the atmospheric refractive index; O is the observation error covariance matrix, and the observation bias is expressed as: Background field bias is expressed as: True represents the actual state of the atmosphere;
[0018] The cost function is modified accordingly as follows:
[0019]
[0020] in This is the background field bias minus the observation bias.
[0021] As one implementation scheme, the minimum value of the cost function J(x) is specifically solved through iterative methods (Levenberg-Marquardt or quasi-Newton iterative methods).
[0022] The scale parameter α of particles in the cloud is much smaller than 1, satisfying Mie's theory (Mie, 1908). Under the Rayleigh approximation, the scattering and absorption coefficients of cloud particles for GNSS signals can be derived. Here, the scale parameter α of the cloud particles is α = 2πr / λ, where r is the radius of the cloud particle 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] Among them W liquid and W ice It represents the liquid water and ice water content in clouds, measured in gm⁻³; P is atmospheric pressure, measured in hPa; T is temperature, measured in K. w It is water vapor pressure, measured in hPa.
[0026] As one implementation approach, the background field preparation step involves interpolating the temperature, air pressure, water vapor pressure, cloud liquid water content, and ice water content from the ERA5 grid data into the occultation data.
[0027] This application employs the aforementioned scheme, using ERA5 gridded data as the background field for one-dimensional variational inversion. First, temperature, atmospheric pressure, and water vapor pressure need to be horizontally interpolated from the gridded field to the latitude and longitude positions of the occultation data. To incorporate liquid water and ice content into the inversion process, this application uses the same method to interpolate the liquid water and ice content in the clouds when interpolating temperature, atmospheric pressure, and water vapor pressure, obtaining the background field profile data required for the inversion. Then, the observation data, background field data, observation error covariance, and background data error covariance are input into the one-dimensional variational inversion system.
[0028] According to another aspect of this application, a one-dimensional variational inversion device for GNSS occultation data is provided, comprising:
[0029] Background field preparation unit: interpolates temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data. At the same time, it interpolates the liquid water content and ice water content in the cloud to the latitude and longitude of the occultation data to obtain the background field profile required for inversion.
[0030] Observational 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;
[0031] The one-dimensional variational inversion system includes: achieving the optimal estimation of atmospheric parameter x by minimizing the cost function and solving for its minimum value. The cost function J(x) is expressed as follows:
[0032]
[0033] Where x represents the atmospheric state, x b It's background field data, y obs The data consists of observational data, where B is the background field error covariance matrix, H(x) is the observation operator, and H(x) is the occultation detection value corresponding to the atmospheric parameter x, i.e., the atmospheric refractive index; O is the observation error covariance matrix, μ B-O The background field bias minus the observation bias.
[0034] In summary, this application implements a one-dimensional variational inversion method and apparatus for GNSS occultation data through the above scheme. By adding liquid water content and ice water content terms to the observation operator, the deviation between the observation data and the background field data is calculated, and the distribution characteristics of the deviation are statistically analyzed. After adding liquid water and ice water, the average value of the refractive index deviation between the two types of data is significantly reduced. Attached Figure Description
[0035] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to denote the same or similar parts. These drawings, together with the following detailed description, are incorporated in and form part of this specification, and are used to further illustrate preferred embodiments of the application and explain the principles and advantages of the application. In the drawings:
[0036] Figure 1 The frequency distribution of the percentage deviation between the refractive index and the background field refractive index is observed before (left figure) and after (right figure) the addition of liquid water content and ice water content in the cloud in this embodiment of the application. Detailed Implementation
[0037] Embodiments of this application will now be described with reference to the accompanying drawings. Elements and features described in one drawing or embodiment of this application may be combined with elements and features shown in one or more other drawings or embodiments. It should be noted that, for clarity, representations and descriptions of components and processes unrelated to this application and known to those skilled in the art have been omitted from the drawings and description.
[0038] The purpose of this application is to improve the variational inversion technique for existing GNSS occultation data based on the cloud liquid water content and cloud ice water content provided by ERA5 reanalysis data, thereby increasing the inversion accuracy.
[0039] As a specific embodiment, this application provides a one-dimensional variational inversion method for GNSS occultation data.
[0040] The scale parameter of the cloud particles (α = 2πr / λ, where r is the radius of the cloud particle and λ is the wavelength of the GNSS signal) is much smaller than 1, satisfying Mie's theory (Mie, 1908). Under the Rayleigh approximation, the scattering and absorption coefficients of the cloud particles for the GNSS signal can be derived. The observation operator is expressed as:
[0041] x = (T, P, P) w W liquid W ice );
[0042]
[0043] Among them W liquid and Wice It refers to the liquid water content and ice water content in clouds, with units of gm-3 (Zou and Yang, 2012).
[0044] ERA5 utilizes advanced numerical weather prediction models and a large amount of observational data to assimilate meteorological variables globally, providing high-resolution and high-precision meteorological information. The high-resolution version of the ERA5 dataset (HRES) has a horizontal resolution of 31 km and a vertical structure of 137 layers, covering the Earth's atmosphere from 0 to 80 km, and providing information on cloud liquid water content and cloud ice water content.
[0045] Using ERA5 gridded data as the background field for one-dimensional variational inversion, the temperature, air pressure, and water vapor pressure first need to be horizontally interpolated from the gridded field to the latitude and longitude positions of the occultation data. To incorporate liquid water and ice content into the inversion process, this application uses the same method to interpolate the liquid water and ice content in the clouds when interpolating temperature, air pressure, and water vapor pressure, obtaining the background field profile data required for the inversion. Then, the observation data, background field data, observation error covariance, and background data error covariance are input into the one-dimensional variational inversion system. The key step in one-dimensional variational inversion is minimizing the loss function:
[0046]
[0047] Where x represents the atmospheric state, x b It's background field data, y obs Here, B is the observation data, B is the background field error covariance matrix, H(x) is the observation operator, and H(x) is the occultation detection value corresponding to the atmospheric parameter x, i.e., the atmospheric refractive index; O is the observation error covariance matrix. The key assumption for using variational methods for inversion is that both the observation data and the background field data are unbiased estimates of the true values. If the unbiased assumption does not hold, then observation bias must be subtracted from the observations and the background field. and background field deviation True represents the actual state of the atmosphere.
[0048] The cost function is modified accordingly as follows:
[0049]
[0050] in This is the background field bias minus the observation bias.
[0051] The systematic bias between the observation data and the background field data is estimated based on sufficient GNSS occultation observation data and background field data samples, 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: interpolates temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data. At the same time, it interpolates the liquid water content and ice water content in the cloud to the latitude and longitude of the occultation data to obtain the background field profile required for inversion.
[0054] Observational 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;
[0055] The one-dimensional variational inversion system includes: achieving the optimal estimation of atmospheric parameter x by minimizing the cost function and solving for its minimum value. The cost function J(x) is expressed as follows:
[0056]
[0057] Where x represents the atmospheric state, x b It's background field data, y obs The data consists of observational data, where B is the background field error covariance matrix, H(x) is the observation operator, and H(x) is the occultation detection value corresponding to the atmospheric parameter x, i.e., the atmospheric refractive index; o is the observation error covariance matrix, μ B-O The background field bias minus the observation bias.
[0058] In summary, this application realizes a one-dimensional variational inversion method and device for GNSS occultation data through the above scheme. By adding liquid water content and ice water content terms to the observation operator, the deviation between the observation data and the background field data is calculated, and the distribution characteristics of the deviation are statistically analyzed. 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 refractive index before (left) and after (right) the addition of cloud liquid water and ice water content. Considering the contribution of cloud liquid water and ice water content 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 thick convective cloud systems year-round. The absorption and scattering effects of cloud water on GNSS signals are most significant, and considering their influence has the most significant improvement on the inversion effect of GNSS occultation data.
[0060] Although this application has been disclosed above through the description of specific embodiments, it should be understood that all the embodiments and examples described above are exemplary and not restrictive. Those skilled in the art can devise various modifications, improvements, or equivalents to this application within the spirit and scope of the appended claims. Such modifications, improvements, or equivalents should also be considered to be included within the protection scope of this application.
Claims
1. A one-dimensional variational inversion method for GNSS occultation data, characterized in that: Includes the following steps: Background field preparation: interpolate the temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data. At the same time, interpolate the liquid water content and ice water content in the cloud to the latitude and longitude of the occultation data to obtain the background field profile required for inversion. Observational 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; The one-dimensional variational inversion system includes: achieving the minimum value of atmospheric parameters by minimizing the cost function. The optimal estimate, minimizing the cost function The statement is as follows: ； in It is an atmospheric state. It is background field data. It is observational data. The background field error covariance matrix, As an observation operator, Atmospheric parameters The corresponding occultation detection value, i.e., atmospheric refractive index; The observation error covariance matrix, Subtract observation bias from background field bias ; Add liquid water content and ice water content terms to the observation operator to calculate the deviation between the observation data and the background field data.
2. The one-dimensional variational inversion method for GNSS occultation data according to claim 1, characterized in that: The minimize cost function The minimum value is found through an iterative method, specifically the Levenberg-Marquardt or quasi-Newton iteration 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: ； ； in and It refers to the liquid water content and ice water content in clouds, with the unit being gm-3; It is air pressure, measured in hPa; It's temperature, in Kelvin (K). It is water vapor pressure, measured in 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, cloud liquid water content, and ice water content from the ERA5 grid data are interpolated into the occultation data.
5. A one-dimensional variational inversion device for GNSS occultation data, characterized in that: include: Background field preparation unit: interpolates temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data. At the same time, it interpolates the liquid water content and ice water content in the cloud to the latitude and longitude of the occultation data to obtain the background field profile required for inversion. Observational 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; The one-dimensional variational inversion system includes: achieving optimal estimation of atmospheric parameters by minimizing a cost function to find its minimum value; minimizing the cost function... The statement is as follows: ； in It is an atmospheric state. It is background field data. It is observational data. The background field error covariance matrix, As an observation operator, Atmospheric parameters The corresponding occultation detection value, i.e., atmospheric refractive index; The observation error covariance matrix, Subtract observation bias from background field bias ; Add liquid water content and ice water content terms to the observation operator to calculate the deviation between the observation data and the background field data.