Troposphere temperature and water vapor inversion method combining GNSS-RO and AMSR-E

Abstract

The embodiment of the invention discloses a troposphere temperature and water vapor inversion method combining GNSS-RO (Global Navigation Satellite System-Reverse Osmosis) and AMSR-E. A bending angle observation value of each ray path is obtained through calculation and inversion based on a GNSS-RO technology and serves as GNSS-RO observation data; an AMSR-E forward operator is obtained based on a radiation transfer model, and a brightness and temperature observation value is obtained through AMSR-E and serves as AMSR-E observation data; performing joint assimilation on the GNSS-RO observation data and the AMSR-E observation data by using a 1DVar algorithm which is improved by adding a physical constraint condition; iteratively solving the atmospheric state vector by adopting a Newton method; iterative solution of the atmospheric state vector is completed through a convergence criterion, and temperature and water vapor inversion of the stratified atmosphere is completed; the GNSS-RO provides temperature and humidity profile information with high vertical resolution, and the AMSR-E provides horizontal distribution information with high spatial resolution, so that the three-dimensional temperature and water vapor distribution of the atmosphere can be more comprehensively and accurately inverted by jointly using the data of the GNSS-RO and the AMSR-E.

Description

Technical Field

[0001] The invention relates to the technical field of tropospheric temperature and water vapor inversion, and in particular to a tropospheric temperature and water vapor inversion method combining GNSS-RO and AMSR-E. Background Art

[0002] Accurate temperature and water vapor distribution information is very important in atmospheric science research and weather forecasting. Although traditional ground-based observations and weather balloons can provide high-precision data for local areas, their coverage is limited and the cost is high. The development of satellite remote sensing technology has provided a new means for atmospheric observation. GNSS-RO and AMSR-E, as two important satellite remote sensing technologies, each has its own unique advantages and disadvantages.

[0003] GNSS-RO technology obtains information such as atmospheric temperature, pressure and humidity by analyzing the refraction changes of GNSS (Global Navigation Satellite System) signals when they pass through the Earth's atmosphere. Its main principle is to use the refraction and delay effects that occur when radio waves propagate in the atmosphere, and to invert the physical properties of the atmosphere by receiving signals transmitted by satellites and analyzing their changes. GNSS-RO technology has the advantages of global coverage, high vertical resolution and all-weather operation, and is not affected by clouds and precipitation. By measuring the time-varying phase of the received signal, GNSS-RO can calculate and invert the bending angle of each ray path to retrieve the vertical profile of the refractive index, which can be used to retrieve temperature and humidity using prior information. With its edge sounding geometry, GNSS-RO is able to provide global coverage with high vertical resolution (about 200 meters) observations. The advantages of GNSS-RO technology are high vertical resolution and high accuracy, but its horizontal resolution is low, making it difficult to provide horizontal distribution information over a large range.

[0004] AMSR-E is a microwave radiometer installed on an Earth observation satellite. It measures microwave radiation emitted by the Earth's surface and atmosphere to obtain information such as temperature, water vapor content, precipitation, sea ice cover, soil moisture, etc. Because microwaves can penetrate clouds, AMSR-E can observe in various weather conditions. Its data is widely used in meteorological research and forecasting. The advantages of AMSR-E technology are high horizontal resolution and coverage of large areas, but its vertical resolution is low and the depiction of vertical structure is not detailed enough.

[0005] Therefore, a more comprehensive, accurate and reliable tropospheric temperature and water vapor inversion method is urgently needed. Summary of the invention

[0006] The main purpose of the present invention is to provide a tropospheric temperature and water vapor inversion method combining GNSS-RO and AMSR-E, which can more comprehensively and accurately invert the three-dimensional temperature and water vapor distribution of the atmosphere.

[0007] To achieve the above objectives, the present application provides a tropospheric temperature and water vapor inversion method combining GNSS-RO and AMSR-E, the method comprising:

[0008] Based on GNSS-RO technology, the bending angle observation value of each ray path is calculated and inverted as GNSS-RO observation data;

[0009] The AMSR-E forward operator is derived based on the radiation transfer model, and the brightness temperature observations are obtained by AMSR-E as AMSR-E observation data;

[0010] The GNSS-RO observation data and the AMSR-E observation data are jointly assimilated by using the improved 1DVar algorithm with added physical constraints;

[0011] The Newton method is used to iteratively solve the atmospheric state vector; the iterative solution of the atmospheric state vector is completed through a convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

[0012] Optionally, the bending angle observation value of each ray path is obtained by calculating and inverting based on the GNSS-RO technology as GNSS-RO observation data, including:

[0013] The one-dimensional bending angle β(a) is calculated and inverted by the following formula as GNSS-RO observation data, where a is the influencing parameter:

[0014]

[0015] Where n is the refractive index, r is the distance from the center of curvature to a point on the ray path, and a is defined as rn of the tangent point position.

[0016] Optionally, the method further includes:

[0017] The physical constraint condition is added by using the Lagrange multiplier method on the basis of the objective function to obtain the improved 1DVar algorithm; the objective function of the improved 1DVar algorithm is expressed as:

[0018] L(x,λ)=J(x)+λ·g(x)

[0019] Among them, λ is the Lagrange multiplier, g(x) is the physical constraint condition representing the relationship between temperature and water vapor, and J(x) is the objective function of the 1DVar algorithm before improvement.

[0020] Optionally, the physical constraint condition is:

[0021]

[0022] Where q is the water vapor mixing ratio, RH is the relative humidity, and e s (T) is the saturated water vapor pressure at temperature T, and p is the air pressure.

[0023] Optionally, the iterative solution of the atmospheric state vector using the Newton method includes:

[0024] Set the initial state x of the atmospheric state vector x 0 =x b , and the initial Lagrange multiplier λ 0 ;

[0025] Calculate the gradient of the Lagrangian function with respect to x and λ at the kth iteration, respectively:

[0026]

[0027]

[0028] Taking x and λ as joint variables, construct the Hessian matrix of the system:

[0029]

[0030] The Hessian matrix at the kth iteration is calculated by the above formula:

[0031]

[0032] Solve the following linear system:

[0033]

[0034] Get the update direction d x and d λ ;

[0035] Update the atmospheric state variable x k+1 and the Lagrange multiplier λ k+1 :

[0036] x k+1 =x k +α k d x

[0037] λ k+1 =λ k +α k d λ

[0038] where αk is the step size, determined by line search.

[0039] Optionally, the convergence criterion includes:

[0040] Setting conditions T k -T k-1 <0.1K and e k -e k-1 <0.1hPa.

[0041] On the other hand, the present application provides a tropospheric temperature and water vapor inversion device, comprising:

[0042] The first observation module is used to calculate and invert the bending angle observation value of each ray path based on the GNSS-RO technology as GNSS-RO observation data;

[0043] The second observation module is used to derive the AMSR-E forward operator based on the radiation transfer model, and obtain the brightness temperature observation value from AMSR-E as the AMSR-E observation data;

[0044] A joint assimilation module, for jointly assimilating the GNSS-RO observation data and the AMSR-E observation data by using an improved 1DVar algorithm with added physical constraints;

[0045] The solution convergence module is used to iteratively solve the atmospheric state vector using the Newton method; the iterative solution of the atmospheric state vector is completed through the convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

[0046] On the other hand, the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the first aspect and any possible implementation method thereof.

[0047] On the other hand, the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes each step of the method described in the first aspect.

[0048] The present application provides a tropospheric temperature and water vapor inversion method combining GNSS-RO and AMSR-E, wherein the bending angle observation value of each ray path is obtained by calculation and inversion based on GNSS-RO technology as GNSS-RO observation data; the AMSR-E forward operator is derived based on the radiation transfer model, and the brightness temperature observation value is obtained by AMSR-E as AMSR-E observation data; the 1DVar algorithm improved by adding physical constraints is used to jointly assimilate the GNSS-RO observation data and the AMSR-E observation data; the atmospheric state vector is iteratively solved by Newton's method; the iterative solution of the atmospheric state vector is completed by the convergence criterion, and the temperature and water vapor inversion of the layered atmosphere is completed; compared with the traditional 1DVar inversion algorithm, this method can obtain more accurate and reliable tropospheric temperature and water vapor inversion results by adding physical constraints in the objective function, and GNSS-RO provides temperature and humidity profile information with high vertical resolution, while AMSR-E provides horizontal distribution information with high spatial resolution, and the three-dimensional temperature and water vapor distribution of the atmosphere can be further comprehensively and accurately inverted by jointly using the data of the two. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] in:

[0051] Figure 1 A schematic flow chart of a method for inverting tropospheric temperature and water vapor by combining GNSS-RO and AMSR-E provided in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of another tropospheric temperature and water vapor inversion method combining GNSS-RO and AMSR-E provided in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of the structure of a tropospheric temperature and water vapor inversion device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely 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 the field without creative work are within the scope of protection of the present application.

[0055] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0056] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] The purpose of this application is to provide a method for inverting tropospheric temperature and water vapor that takes into account both resolution and accuracy in the vertical and horizontal directions, specifically an improved 1DVar algorithm for inverting tropospheric temperature and water vapor by jointly assimilating GNSS-RO and AMSR-E. The method of the embodiment of the present application mainly consists in first obtaining RO and AMSR-E observations in the same time period, which are bending angle observations and brightness temperature observations, respectively, and improving the traditional 1DVar algorithm by adding physical constraints to the objective function, and then using Newton's method to iteratively solve, and finally completing the iterative solution of the atmospheric state vector (composed of temperature, pressure and water vapor pressure) through the set calculation convergence standard, and completing the temperature and water vapor inversion of the stratified atmosphere. GNSS-RO provides temperature and humidity profile information with high vertical resolution, while AMSR-E provides horizontal distribution information with high spatial resolution. By fusing the data of the two and adding physical constraints, the three-dimensional temperature and water vapor distribution of the atmosphere can be more comprehensively and accurately inverted.

[0058] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0059] See also Figure 1 , is a flow chart of a method for inverting tropospheric temperature and water vapor by combining GNSS-RO and AMSR-E provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0060] 101. Based on GNSS-RO technology, the bending angle observation value of each ray path is calculated and inverted as GNSS-RO observation data;

[0061] 102. The AMSR-E forward operator is derived based on the radiation transfer model, and the brightness temperature observation value is obtained by AMSR-E as the AMSR-E observation data;

[0062] 103. Use the improved 1DVar algorithm with added physical constraints to jointly assimilate the above GNSS-RO observation data and the above AMSR-E observation data;

[0063] 104. Newton's method is used to iteratively solve the atmospheric state vector; the iterative solution of the atmospheric state vector is completed through the convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

[0064] The method in the embodiment of the present application can be executed by a tropospheric temperature and water vapor inversion device, which can specifically be a terminal device.

[0065] The RO (Radio Occultation) technology mentioned in the embodiments of the present application is a technology that uses the refraction effect of satellite radio signals when passing through the atmosphere to invert physical parameters (such as temperature, humidity, air pressure, etc.) in the atmosphere. Radio occultation technology calculates and inverts the bending angle of the ray path by measuring the changes in radio signals from navigation satellites (such as GPS) when passing through the Earth's atmosphere. The bending angle can be inverted by analyzing the phase changes of the signal at multiple times, using physical models and numerical methods (such as Snell's law and atmospheric refraction models). This bending angle represents the path deviation of the signal due to refraction at different altitudes in the atmosphere.

[0066] In the embodiment of the present application, the AMSR-E forward operator H can be obtained based on the radiation transfer model. AMSR-E (Forward operator: an observation operator that maps the atmospheric state vector to the observation space), and the brightness temperature observations are obtained by AMSR-E.

[0067] Specifically, in the embodiment of the present application, the traditional 1DVar algorithm can be improved by adding physical constraints, and the improved algorithm can be used to jointly assimilate the above-mentioned RO and AMSR-E observation data.

[0068] The 1D variation method (1DVar) algorithm mentioned in the embodiments of the present application is a numerical method commonly used in atmospheric science and meteorology, especially in satellite remote sensing data processing and atmospheric state analysis. The main purpose of this algorithm is to obtain a more accurate estimate of the atmospheric state by integrating observational data and background information (usually from numerical weather forecast models).

[0069] The Advanced Microwave Scanning Radiometer-Earth Observing System sensor (AMSR-E) mentioned in the embodiments of the present application is an advanced microwave scanning radiometer that can obtain physical information about the earth's surface and atmosphere by receiving natural radiation (i.e., brightness temperature) emitted by the earth's surface and atmosphere in different microwave frequency bands. Different earth surface characteristics (such as water surface, ice surface, land, vegetation, etc.) and different atmospheric conditions (such as water vapor, clouds, precipitation) will produce different radiation characteristics in the microwave band, so by analyzing these radiations, many environmental parameters can be inverted.

[0070] In the embodiment of the present application, Newton's method can be used to iteratively solve the atmospheric state vector. Newton's method is a method for approximately solving equations in real and complex domains, which uses the first few terms of the Taylor series of function f(x) to find the root of the equation f(x)=0.

[0071] The above-mentioned convergence criterion can be set as needed to provide a convergence standard for iterative calculation, so as to iteratively solve the atmospheric state vector and complete the inversion of temperature and water vapor of the stratified atmosphere.

[0072] Specifically, in the above embodiment, in step 101 and step 102, RO and AMSR-E observation values ​​are first obtained, which are the bending angle observation value β and the brightness temperature observation value T respectively. b Then, in step 103 and step 104, the traditional 1DVar algorithm is improved by adding physical constraints to the objective function, and the objective function of the improved 1DVar algorithm is constructed by combining RO and AMSR-E observations. The Newton method is used to iteratively solve the atmospheric state vector x to be estimated. Finally, the iterative solution of the atmospheric state vector (composed of temperature, pressure and water vapor pressure) is completed through the convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

[0073] That is, in order to achieve the above purpose, the technical solutions in the embodiments of the present application can be summarized as follows:

[0074] The GNSS-RO and AMSR-E measurements are combined using an estimation method with additional physical constraints. By using RO bending angle and AMSR-E brightness temperature measurements instead of RO refractive index and water vapor radiometer (WVR) atmospheric precipitable water vapor (PWV) inversion, GNSS-RO provides high vertical resolution temperature and humidity profile information, while AMSR-E provides high spatial resolution horizontal distribution information. By adding physical constraints to the 1DVar objective function and fusing the two data using the improved 1DVar algorithm, the three-dimensional temperature and water vapor distribution of the atmosphere can be more comprehensively and accurately inverted.

[0075] In order to more clearly illustrate the method in the embodiment of the present application, another embodiment of the tropospheric temperature and water vapor inversion method combining GNSS-RO and AMSR-E is proposed below.

[0076] Figure 2 A flow chart of another method for inverting tropospheric temperature and water vapor by combining GNSS-RO and AMSR-E is provided in an embodiment of the present application, as shown in FIG. Figure 2 As shown, the method specifically includes:

[0077] Step 1: Based on GNSS-RO technology, the one-dimensional bending angle β(a) obtained by inversion is used as the GNSS-RO observation value, where a is the influencing parameter, and it is related to the atmospheric refractive index through the forward Abel equation:

[0078]

[0079] Where n is the refractive index, r is the distance from the center of curvature to a point on the ray path, and a is defined as rn at the point of tangency.

[0080] Alternatively, the above relationship between refractive index n and altitude can be calculated from the atmospheric temperature and water vapor profile:

[0081]

[0082] Where N = (n-1) × 10 6 , p is the pressure in millibars (mbar), e is the water vapor pressure (in mbar), and T is the temperature (in Kelvin).

[0083] Step 2: Derive the AMSR-E forward operator H based on the radiation transfer model AMSR-E , and the brightness temperature observation value T is obtained by AMSR-E b .

[0084] Step 3: Improve the traditional 1DVar algorithm by adding physical constraints, and use the improved algorithm to jointly assimilate RO and AMSR-E observation data. Specifically, the temperature, pressure, and water vapor pressure of each layer in the stratified atmosphere constitute the atmospheric state vector x to be estimated (to be inverted):

[0085]

[0086] Where p is pressure, T is temperature, and e is water vapor pressure. Relating equations (1) and (2), we can obtain the forward operator H that maps the state vector x to the GNSS-RO observation value (i.e., the one-dimensional bending angle β): RO .

[0087] The 1DVar algorithm inverts the atmospheric state by minimizing an objective function. The objective function is usually defined as:

[0088]

[0089] Where x is the atmospheric state vector to be inverted (including temperature and water vapor). b is the background state vector, which can be obtained from global weather analysis such as NCEP, MERRA-2 or ECMWF. B is the prior x b where y represents the RO and AMSR-E observations. H(x) is the observation operator that maps the state vector x to the observation space. R is the error covariance matrix of the observation y.

[0090] In the embodiment of the present application, the Lagrange multiplier method is used to add physical constraints based on the objective function shown in formula (4):

[0091] L(x,λ)=J(x)+λ·g(x) (5)

[0092] Among them, λ is the Lagrange multiplier, g(x) is the physical constraint condition representing the relationship between temperature and water vapor; the derivation idea of ​​the constraint equation g(x) is as follows:

[0093] ① The Clausius-Clapeyron equation describes the saturated water vapor pressure e s Relationship with temperature T:

[0094]

[0095] Among them, e s (T) is the saturated water vapor pressure at temperature T, e 0 is the reference temperature T 0 The saturated water vapor pressure under v is the latent heat of vaporization of water, R v is the gas constant for water vapor.

[0096] ②Relative humidity RH indicates the current water vapor pressure e relative to the saturated water vapor pressure e s Ratio:

[0097]

[0098] The water vapor mixing ratio q represents the mass of water vapor per unit of dry air:

[0099]

[0100] Here, p is the air pressure and e is the water vapor pressure.

[0101] ③ Combining equations (6), (7) and (8), we can get a constraint equation that represents the relationship between temperature and water vapor:

[0102]

[0103] The constraint equation represents the relationship between temperature T and water vapor mixing ratio q under given air pressure p and relative humidity RH.

[0104] Next, we jointly assimilate the RO and AMSR-E observations and consider the physical constraints shown in equation (9). We rewrite the objective function of the 1DVar algorithm shown in equation (4) into the form of equation (5):

[0105]

[0106] Where x is the atmospheric state vector to be inverted (including temperature and water vapor), λ is the Lagrange multiplier, and x b is the background state vector, which can be obtained from global weather analysis such as NCEP, MERRA-2 or ECMWF. B is the prior x b The error covariance matrix of Represents RO and AMSR-E observations. represents the forward operator (mapping the state vector x to the observation space y), It represents the error covariance matrix of the observed value y. q is the water vapor mixing ratio, RH is the relative humidity, e s (T) is the saturated water vapor pressure at temperature T, and p is the air pressure.

[0107] Step 4: Use Newton's method to iteratively solve the atmospheric state vector x. Specifically:

[0108] ①Set the initial state x 0 =x b and the initial Lagrange multiplier λ 0 ;

[0109] ② Calculate the gradient of the Lagrangian function with respect to x and λ at the kth iteration, which are:

[0110]

[0111] ③ Take x and λ as joint variables and construct the Hessian matrix of the system:

[0112]

[0113] The Hessian matrix at the kth iteration is calculated by the above formula:

[0114]

[0115] ④Solve the following linear system:

[0116]

[0117] Get the update direction d x and d λ ;

[0118] ⑤Update the atmospheric state variable x k+1 and the Lagrange multiplier λ k+1 :

[0119] x k+1 =x k +α k d x (16)

[0120] λ k+1 =λ k +α k d λ (17)

[0121] where α k is the step size, which can be determined by line search.

[0122] Among them, you can set the conditions T k -T k-1 <0.1K and e k -e k-1 <0.1 hPa was used as the convergence criterion, and the optimal solution was obtained when the convergence criterion was met.

[0123] In the above technical solution, in step one, it is proposed to use the inverted one-dimensional bending angle β(a) as the GNSS-RO observation value. Selecting the one-dimensional bending angle as the observation value instead of the refractive index can avoid negative observation deviations. Since the refractive index inversion is pathological under waveguide conditions, the thermodynamic state of the atmosphere cannot be determined by the bending angle alone. It is also proposed to use AMSR-E observations to provide the basic information required for the refractive index inversion under constrained waveguide conditions.

[0124] In the above technical solution, in step 2, it is proposed to derive the AMSR-E forward operator H based on the radiation transfer model. AMSR-E , and the brightness temperature observation value T is obtained by AMSR-E b .

[0125] In the above scheme, in step 3, the forward operator H from the state vector x to the GNSS-RO observation value (i.e., the one-dimensional bending angle β) is obtained from equations (1) and (2): RO , the traditional 1DVar algorithm is improved by adding physical constraints, and the objective function of the improved 1DVar algorithm is constructed by combining RO and AMSR-E observations.

[0126] In the above scheme, in step 4, the Newton method is used to iteratively solve the atmospheric state vector x (composed of the temperature, pressure and water vapor pressure of each layer in the stratified atmosphere) to be estimated, and a convergence criterion for the iterative calculation is given.

[0127] In the embodiment of the present application, physical constraints are added to the objective function of the traditional 1DVar algorithm to jointly assimilate GNSS-RO and AMSR-E observations (one-dimensional bending angle β, brightness temperature observation value T b ) In order to invert the tropospheric temperature and water vapor, the first and second steps are to obtain the RO and AMSR-E observations, which are the bending angle observation β and the brightness temperature observation T respectively. b Then, in steps three and four, the traditional 1DVar algorithm is improved by adding physical constraints to the objective function, and the objective function of the improved 1DVar algorithm is constructed by combining RO and AMSR-E observations. The Newton method is used to iteratively solve the atmospheric state vector x to be estimated. Finally, the iterative solution of the atmospheric state vector (composed of temperature, pressure, and water vapor pressure) is completed through the convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

[0128] Based on the idea of ​​combining RO with microwave radiometer (MWR) or infrared sounder (IR) soundings, several methods have been proposed to exploit these complementary measurements. For example, von Engeln et al. (von Engeln, A., Bühler, S., Kirchengast, G., and Künzi, K.: Temperature profile retrieval from surface to mesopause by combining GNSSRadio Occultation and Passive Microwave Limb Sounder Data, Geophys. Res. Lett., 28, 775-778, https: / / doi.org / 10.1029 / 2000GL011718, 2001) applied a best estimate method to determine the temperature profile from RO bending angle measurements and oxygen line radiation from a passive microwave limb sounder (MLS). In Borbas et al. (Borbás, Menzel, WP, Li, J., and Woolf, HM: Combining radio occultationrefractivities and IR / MW radiances to derive temperature and moisture profiles: A simulation study plus early results using CHAMP and ATOVS, J. Geophys. Res., 108, 4676, https: / / doi.org / 10.1029 / 2003JD003386, 2003) used a multivariate regression approach to estimate the atmospheric state based on coefficients trained from a set of RO refractivity and MWR or IR brightness temperature observations. Simulation results near the tropopause height showed that the RO-IR combination could improve the temperature and moisture retrieval results by 0.5 K and 2.5%, respectively, compared with infrared retrieval alone. Ho et al. (Ho, S., Kuo, Y., and Sokolovskiy S.: Improvement of the Temperature and Moisture Retrievals in the Lower Troposphere Using AIRS and GPS Radio Occultation Measurements, J. Atmos. Ocean. Tech., 24, 1726-1739, 2007) adopted a similar regression approach and extended the application to the lower troposphere by introducing Atmospheric Infrared Sounder (AIRS) observations to reduce the impact of RO refractivity inversion bias. In both studies, the regression coefficients were calculated from specific training data sets, which may have biased distributions due to their spatial and temporal locations. The nonlinear behavior between profile variables (temperature and water vapor) and observables (brightness temperature and refractivity) may also lead to errors in simple linear regression expressions.

[0129] In order to reduce the refractive index deviation caused by waveguide in GNSS-RO, make full use of the high spatial resolution horizontal distribution information provided by AMSR-E observations, and eliminate the nonlinearity between profile variables (temperature and water vapor) and observables (brightness temperature and refractive index), the embodiment of the present application proposes the above-mentioned improved 1DVar algorithm for jointly assimilating GNSS-RO and AMSR-E to invert tropospheric temperature and water vapor. This method can obtain more accurate and reliable tropospheric temperature and water vapor inversion results than the traditional 1DVar inversion algorithm by adding physical constraints to the objective function. This method can also eliminate the GNSS-RO temperature and water vapor inversion deviation at the same time, reduce the error in the prior profile used in the inversion, and capture the small-scale vertical structure that AMSR-E cannot resolve.

[0130] The combined GNSS-RO and AMSR-E tropospheric temperature and water vapor inversion method in the embodiment of the present application makes full use of the complementary measurements of GNSS-RO and AMSR-E. By adding physical constraints to the traditional 1DVar algorithm objective function to jointly assimilate GNSS-RO and AMSR-E observations (one-dimensional bending angle and brightness temperature observations, respectively), the tropospheric temperature and water vapor inversion is achieved by fusing GNSS-RO and AMSR-E data, which can complement each other's advantages, make up for the shortcomings of a single technology, and obtain higher quality tropospheric temperature and water vapor inversion results. This inversion technology not only improves the spatial and temporal resolution of the data, but also eliminates the nonlinearity between profile variables (temperature and water vapor) and observables (brightness temperature and refractive index), while eliminating GNSS-RO temperature and water vapor inversion bias, reducing errors in the prior profiles used in the inversion, and capturing small-scale vertical structures that AMSR-E cannot resolve. The method in the embodiment of the present application breaks through the limitations of the single application of GNSS-RO or AMSR-E technology. Compared with using only RO or AMSR-E, it can better solve the complex thermodynamic structure in the lower troposphere (including thermodynamic structure with waveguide), and can effectively improve the inversion accuracy of temperature and water vapor in the troposphere. Moreover, compared with the previous technology of inverting the thermodynamic state of the atmosphere using the traditional 1DVar estimation method, the 1DVar method with additional physical constraints proposed in this application can obtain more accurate and reliable tropospheric temperature and water vapor inversion results.

[0131] The method in the embodiment of the present application has broad application prospects in the fields of weather forecasting, climate research, initialization of numerical weather forecast models, environmental monitoring, etc. It can provide more accurate information on the temperature and humidity structure of the atmosphere, help improve weather forecast models, and improve the accuracy and timeliness of forecasts. In addition, it also has important scientific value for studying atmospheric processes and understanding climate change mechanisms.

[0132] Based on the description of the aforementioned method embodiment, the embodiment of the present application also provides a tropospheric temperature and water vapor inversion device.

[0133] Figure 3 A schematic diagram of the structure of a tropospheric temperature and water vapor inversion device provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the device 300 includes:

[0134] A first observation module 310 is used to calculate and invert the bending angle observation value of each ray path based on the GNSS-RO technology as GNSS-RO observation data;

[0135] The second observation module 320 is used to obtain an AMSR-E forward operator based on the radiation transfer model, and obtain brightness temperature observation values ​​from AMSR-E as AMSR-E observation data;

[0136] A joint assimilation module 330, configured to jointly assimilate the GNSS-RO observation data and the AMSR-E observation data by using a 1DVar algorithm improved by adding physical constraints;

[0137] The solution convergence module 340 is used to iteratively solve the atmospheric state vector using the Newton method; the iterative solution of the atmospheric state vector is completed according to the convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

[0138] It can be understood that the relevant contents of each module in the above device have been described in detail in the aforementioned method embodiment, and the details can be referred to the contents in the method embodiment; that is, the tropospheric temperature and water vapor inversion device provided in the present application can be performed as follows: Figure 1 or Figure 2 Any steps in the illustrated embodiment will not be described in detail here.

[0139] In one embodiment of the present application, an electronic device is also provided. The electronic device may include a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the execution Figure 1 or Figure 2 Any step in the method embodiment shown. The electronic device may also include an input / output device, etc. In a specific implementation, the electronic device may be a terminal device, etc.

[0140] In one embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes any step in the above method embodiment.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for inverting tropospheric temperature and water vapor by combining GNSS-RO and AMSR-E, characterized in that: The method comprises: Based on GNSS-RO technology, the bending angle observation value of each ray path is calculated and inverted as GNSS-RO observation data; The AMSR-E forward operator is derived based on the radiation transfer model, and the brightness temperature observations are obtained by AMSR-E as AMSR-E observation data; The GNSS-RO observation data and the AMSR-E observation data are jointly assimilated by using the improved 1DVar algorithm with added physical constraints; The Newton method is used to iteratively solve the atmospheric state vector; the iterative solution of the atmospheric state vector is completed through a convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

2. The method for tropospheric temperature and water vapor inversion based on combined GNSS-RO and AMSR-E according to claim 1, characterized in that: The bending angle observation value of each ray path is obtained by calculation and inversion based on the GNSS-RO technology as GNSS-RO observation data, including: The one-dimensional bending angle β(a) is calculated and inverted by the following formula as GNSS-RO observation data, where a is the influencing parameter: Where n is the refractive index, r is the distance from the center of curvature to a point on the ray path, and a is defined as rn of the tangent point position.

3. The method for tropospheric temperature and water vapor inversion based on combined GNSS-RO and AMSR-E according to claim 2, characterized in that: The method further comprises: The physical constraint condition is added by using the Lagrange multiplier method on the basis of the objective function to obtain the improved 1DVar algorithm; the objective function of the improved 1DVar algorithm is expressed as: L(x,λ)=J(x)+λ·g(x) Among them, λ is the Lagrange multiplier, g(x) is the physical constraint condition representing the relationship between temperature and water vapor, and J(x) is the objective function of the 1DVar algorithm before improvement.

4. The method for tropospheric temperature and water vapor inversion based on combined GNSS-RO and AMSR-E according to claim 3 is characterized in that: The physical constraints are: Where q is the water vapor mixing ratio, RH is the relative humidity, and e s (T) is the saturated water vapor pressure at temperature T, and p is the air pressure.

5. The method for tropospheric temperature and water vapor inversion based on combined GNSS-RO and AMSR-E according to claim 4 is characterized in that: The method of iteratively solving the atmospheric state vector by Newton's method comprises: Set the initial state of the atmospheric state vector x0 = x b , and the initial Lagrange multiplier λ0; Calculate the gradient of the Lagrangian function with respect to x and λ at the kth iteration, respectively: Taking x and λ as joint variables, construct the Hessian matrix of the system: The Hessian matrix at the kth iteration is calculated by the above formula: Solve the following linear system: Get the update direction d x and d λ ; Update the atmospheric state variable x k+1 and the Lagrange multiplier λ k+1 : x k+1 =x k +a k d x l k+1 =λ k +a k d λ where α k is the step size, determined by line search.

6. The method for tropospheric temperature and water vapor inversion using combined GNSS-RO and AMSR-E according to claim 5, characterized in that: The convergence criteria include: Setting conditions T k -T k-1 <0.1K and e k -e k-1 <0.1hPa.

7. A tropospheric temperature and water vapor inversion device, characterized in that: include: The first observation module is used to calculate and invert the bending angle observation value of each ray path based on the GNSS-RO technology as GNSS-RO observation data; The second observation module is used to derive the AMSR-E forward operator based on the radiation transfer model, and obtain the brightness temperature observation value from AMSR-E as the AMSR-E observation data; A joint assimilation module, for jointly assimilating the GNSS-RO observation data and the AMSR-E observation data by using an improved 1DVar algorithm with added physical constraints; The solution convergence module is used to iteratively solve the atmospheric state vector using the Newton method; the iterative solution of the atmospheric state vector is completed through the convergence criterion, and the temperature and water vapor inversion of the stratified atmosphere is completed.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 6.

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