Direct assimilation method and device for ground-based microwave radiometer data

Abstract

The application provides a direct assimilation method and device for ground-based microwave radiometer data, wherein the assimilation method comprises the following steps: establishing an all-weather observation operator for ground-based microwave radiometer observation data; establishing a tangent linear adjoint model of the all-weather observation operator for ground-based microwave radiometer observation data; performing cloud scene identification on the ground-based microwave radiometer observation data to obtain a cloud scene identification result; establishing a background field covariance matrix and an observation error matrix according to the cloud scene identification result, a numerical prediction model background field and the all-weather observation operator; and determining a numerical prediction model analysis field according to the background field covariance matrix, the observation error matrix, the all-weather observation operator and the tangent linear adjoint model of the all-weather observation operator. The method can realize direct assimilation of ground-based microwave radiometer data in a numerical prediction model, thereby effectively avoiding the problems of inversion product error in indirect assimilation and repeated use of model background field information.

Description

Technical Field

[0001] This invention relates to the field of ground-based remote sensing technology, and in particular to a method and apparatus for the direct assimilation of ground-based microwave radiometer data. Background Technology

[0002] Atmospheric profiles refer to the distribution data of oxygen, water vapor, and other trace gases in the atmosphere at different altitudes. Atmospheric temperature and humidity profiles are important physical quantities of the Earth system and crucial parameters in numerical weather prediction models. Providing high-precision temperature and humidity profiles as initial fields to numerical weather prediction models can significantly improve the accuracy of numerical forecasts. Currently, atmospheric temperature and humidity profile data are often acquired by deploying weather balloons. While weather balloon data offers high accuracy, the detection cost is high, and the temporal resolution is low, typically only once or twice a day, thus failing to capture information on the diurnal variation of temperature and humidity profiles. In addition, satellite-borne instrument observations also contain atmospheric temperature and humidity profile information. However, satellite observations are often contaminated by surface data, making it impossible to accurately obtain near-surface atmospheric temperature and humidity.

[0003] Ground-based microwave radiometers offer an alternative to overcome the limitations of radiosonde and satellite detection. Ground-based microwave radiometer data are less affected by the Earth's surface and have high temporal resolution, providing minute-level atmospheric data. Therefore, applying ground-based microwave radiometer data to data assimilation is of great significance for improving the accuracy of numerical model forecasts.

[0004] However, current technologies for assimilating ground-based microwave radiometer (GBM) data primarily employ indirect assimilation. The main principle involves establishing a connection between GBM observations and atmospheric temperature and humidity at different layers through fitting or neural networks, thereby retrieving atmospheric temperature and humidity profiles from the GBM observations. Finally, these retrieved profiles are used as observational data and coupled into the numerical weather prediction (NMM) model assimilation system for data assimilation. Indirect assimilation has several drawbacks: First, its effectiveness depends heavily on the accuracy of the inversion algorithm. Improving NMM forecasting performance requires a more precise atmospheric temperature and humidity inversion algorithm. Second, in indirect assimilation, the NMM background field is used as the initial field in both the inversion and assimilation algorithms, leading to signal reuse and impacting assimilation efficiency. Furthermore, GBM data contains not only atmospheric temperature and humidity signals but also other atmospheric information (such as air pressure and gas concentration), making it difficult for indirect assimilation to extract all the information contained in the observations.

[0005] Given these drawbacks of indirect assimilation schemes, operational satellite observation data assimilation systems internationally have adopted direct assimilation. The main principle of direct assimilation is to combine the model background field covariance matrix, observation operators, and observation operator error matrices, and adjust the model background field through variational iteration under error convergence conditions to obtain the model analysis field. Compared to indirect assimilation, direct assimilation allows for a more physically continuous adjustment of the model background field, while avoiding errors in inversion products and the problem of reusing model background field information. However, for ground-based microwave radiometer data, due to the lack of observation operators, indirect assimilation has been used. Summary of the Invention

[0006] This invention provides a method for the direct assimilation of ground-based microwave radiometer data, enabling the direct assimilation of ground-based microwave radiometer data in numerical weather prediction models. The method includes:

[0007] Establish an all-weather observation operator for ground-based microwave radiometer observation data;

[0008] Establish a tangent linear adjoint model for all-weather observation operators based on ground-based microwave radiometer observation data;

[0009] Cloud scene identification was performed on ground-based microwave radiometer observation data to obtain cloud scene identification results;

[0010] Based on the background field of the numerical prediction model and the all-weather observation operator of the cloud scene recognition results, the background field covariance matrix and the observation error matrix are established.

[0011] The analysis field of the numerical weather prediction model is determined based on the background field covariance matrix, observation error matrix, all-weather observation operator, and tangent linear adjoint model of all-weather observation operator.

[0012] In practice, an all-weather observation operator is established for ground-based microwave radiometer observation data, specifically including:

[0013] Atmospheric and cloud parameters are obtained from the background field of the numerical weather prediction model;

[0014] A rapid calculation model for atmospheric transmittance is established based on atmospheric parameters and line-by-line integration model.

[0015] A cloud particle scattering database was established based on cloud parameters and particle scattering algorithms.

[0016] The all-weather observation operator was determined based on the rapid atmospheric transmittance calculation model and cloud particle scattering database.

[0017] In specific implementation, a tangent linear adjoint model for all-weather observation operators based on ground-based microwave radiometer observation data is established, specifically including:

[0018] Based on the rapid calculation model of atmospheric transmittance, the derivatives of various atmospheric parameters are calculated to determine the tangent linear adjoint model of the rapid calculation model of atmospheric transmittance.

[0019] Based on the cloud particle scattering database, the cloud parameters are differentiated to determine the tangent linear adjoint model of the cloud particle scattering database;

[0020] Based on the tangent linear adjoint model of the rapid atmospheric transmittance calculation model and the tangent linear adjoint model of the cloud particle scattering database, the tangent linear adjoint model of the all-weather observation operator for ground-based microwave radiometer observation data is determined.

[0021] In practice, cloud scene identification is performed on ground-based microwave radiometer observation data to obtain cloud scene identification results, including:

[0022] The brightness temperature observation values ​​of each channel of the ground-based microwave radiometer were determined based on the observation data.

[0023] The cloud scattering index was established based on the brightness temperature observations of each channel of the ground-based microwave radiometer.

[0024] The cloud scene is identified by judging the threshold of the cloud scattering index, and the cloud scene identification result is obtained.

[0025] In practice, cloud scene identification is performed on ground-based microwave radiometer observation data to obtain cloud scene identification results, including:

[0026] Acquire cloud images generated by an all-sky imager, which is installed at the same site as a ground-based microwave radiometer;

[0027] Cloud scene recognition is performed using cloud images to obtain cloud scene recognition results.

[0028] In specific implementation, the step of establishing the background field covariance matrix and observation error matrix based on the cloud scene recognition results, the background field of the numerical weather prediction model, and the observation operator includes:

[0029] Determine the background field covariance matrix based on the background field of the numerical weather prediction model;

[0030] The observation error matrix is ​​determined based on the all-weather observation operator and the brightness temperature observation values ​​of each channel of the ground-based microwave radiometer.

[0031] In specific implementation, determining the observation error matrix based on the all-weather observation operator and the brightness temperature observation values ​​of each channel of the ground-based microwave radiometer specifically includes:

[0032] The simulated brightness temperature values ​​for each channel of the ground-based microwave radiometer are determined based on the all-weather observation operator;

[0033] The observation error is calculated in real time based on the simulated brightness temperature values ​​of each channel of the ground-based microwave radiometer and the observed values ​​of each channel of the ground-based microwave radiometer.

[0034] The observation error is corrected for deviation to obtain the observation error matrix, which satisfies a normal distribution.

[0035] In specific implementation, determining the numerical weather prediction model analysis field based on the background field covariance matrix, observation error matrix, all-weather observation operator, and tangent linear adjoint model of the all-weather observation operator includes:

[0036] Determine the derivatives of the simulated brightness temperature values ​​of each channel of the ground-based microwave radiometer with respect to atmospheric and cloud parameters;

[0037] The numerical prediction model background field, background field covariance matrix, observation error matrix, brightness temperature simulation values ​​of each channel of the ground-based microwave radiometer, and calculation results of the tangent adjoint mode are substituted into the numerical prediction model assimilation system. Variational iteration is performed on the numerical prediction model background field until convergence to obtain the numerical prediction model analysis field.

[0038] The present invention also provides a direct assimilation device for ground-based microwave radiometer data, the direct assimilation device comprising:

[0039] The observation operator establishment module is used to establish all-weather observation operators for ground-based microwave radiometer observation data;

[0040] The tangent linear adjoint model building module is used to build a tangent linear adjoint model for all-weather observation operators based on ground-based microwave radiometer observation data.

[0041] The cloud scene recognition module is used to perform cloud scene recognition on ground-based microwave radiometer observation data and obtain cloud scene recognition results.

[0042] The matrix building module is used to build the background field covariance matrix and the observation error matrix based on the cloud scene recognition results, the background field of the numerical weather prediction model and the all-weather observation operator.

[0043] The numerical weather prediction model analysis field determination module is used to determine the numerical weather prediction model analysis field based on the background field covariance matrix, observation error matrix, all-weather observation operator, and tangent linear adjoint model of all-weather observation operator.

[0044] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for direct assimilation of ground-based microwave radiometer data.

[0045] The present invention also provides a computer-readable storage medium storing a computer program for the direct assimilation method of the ground-based microwave radiometer data.

[0046] This invention provides a method, apparatus, computer equipment, and readable storage medium for the direct assimilation of ground-based microwave radiometer (BMT) data. The method includes: establishing an all-weather observation operator for BMT observation data; establishing a tangent linear adjoint model for the all-weather observation operator; performing cloud scene identification on the BMT observation data to obtain cloud scene identification results; establishing a background field covariance matrix and an observation error matrix based on the cloud scene identification results and the numerical weather prediction model background field and the BMT; and determining the numerical weather prediction model analysis field based on the background field covariance matrix, the observation error matrix, the all-weather observation operator, and the tangent linear adjoint model. This method enables the direct assimilation of BMT data into numerical weather prediction models, effectively avoiding errors in inversion products and the problem of reusing model background field information in indirect assimilation. Attached Figure Description

[0047] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some specific embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0048] Figure 1 This is a flowchart illustrating a method for the direct assimilation of ground-based microwave radiometer data according to a specific embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating the process of establishing an all-weather observation operator for ground-based microwave radiometer observation data according to a specific embodiment of the present invention.

[0050] Figure 3 This is a flowchart illustrating the process of establishing a tangent linear adjoint model for an all-weather observation operator based on ground-based microwave radiometer observation data, according to a specific embodiment of the present invention.

[0051] Figure 4 This is a flowchart illustrating cloud scene recognition by judging a threshold of cloud scattering index according to a specific embodiment of the present invention.

[0052] Figure 5 This is a schematic diagram of a process for cloud scene recognition using cloud images according to a specific embodiment of the present invention;

[0053] Figure 6 This is a flowchart illustrating the process of establishing the background field covariance matrix and the observation error matrix according to a specific embodiment of the present invention;

[0054] Figure 7 This is a flowchart illustrating the process of determining the observation error matrix according to a specific embodiment of the present invention;

[0055] Figure 8 This is a flowchart illustrating the process of determining the observation operator for the analysis field of a numerical weather prediction model according to a specific embodiment of the present invention.

[0056] Figure 9 This is a schematic diagram of a method for direct assimilation of ground-based microwave radiometer data according to a specific embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the specific embodiments of the present invention clearer, the specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative specific embodiments and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0058] like Figure 1 As shown, this invention provides a method for the direct assimilation of ground-based microwave radiometer data, used to achieve the direct assimilation of ground-based microwave radiometer data in numerical weather prediction models. The method for the direct assimilation of ground-based microwave radiometer data includes:

[0059] 101: Establish an all-weather observation operator for ground-based microwave radiometer observation data;

[0060] 102: Establish a tangent linear adjoint model for all-weather observation operators based on ground-based microwave radiometer observation data;

[0061] 103: Perform cloud scene identification on ground-based microwave radiometer observation data and obtain cloud scene identification results;

[0062] 104: Based on the background field of the numerical prediction model and the all-weather observation operator of the cloud scene recognition results, establish the background field covariance matrix and the observation error matrix;

[0063] 105: Determine the analysis field of the numerical weather prediction model based on the background field covariance matrix, observation error matrix, all-weather observation operator, and tangent linear adjoint model of the all-weather observation operator.

[0064] In practice, there are multiple implementation schemes for establishing an all-weather observation operator for ground-based microwave radiometer observation data. For example, step 101: establishing an all-weather observation operator for ground-based microwave radiometer observation data, such as... Figure 2 As shown, it can specifically include:

[0065] 201: Obtain atmospheric and cloud parameters based on the background field of the numerical weather prediction model;

[0066] 202: Based on atmospheric parameters and line-by-line integration model, establish a rapid calculation model for atmospheric transmittance;

[0067] 203: Establish a cloud particle scattering database based on cloud parameters and particle scattering algorithms;

[0068] 204: Determine the all-weather observation operator based on the rapid atmospheric transmittance calculation model and cloud particle scattering database.

[0069] In practice, there are multiple implementation schemes for establishing the tangent linear adjoint model of the all-weather observation operator for ground-based microwave radiometer observation data. For example, step 102: establishing the tangent linear adjoint model of the all-weather observation operator for ground-based microwave radiometer observation data can specifically include:

[0070] 301: Based on the rapid calculation model of atmospheric transmittance, the derivatives of various atmospheric parameters are calculated to determine the tangent linear adjoint model of the rapid calculation model of atmospheric transmittance.

[0071] 302: Based on the cloud particle scattering database, differentiate the cloud parameters to determine the tangent linear adjoint model of the cloud particle scattering database;

[0072] 303: Based on the tangent linear adjoint model of the rapid atmospheric transmittance calculation model and the tangent linear adjoint model of the cloud particle scattering database, determine the tangent linear adjoint model of the all-weather observation operator for ground-based microwave radiometer observation data.

[0073] In practice, there are multiple implementation schemes for cloud scene identification based on ground-based microwave radiometer observation data. For example, step 103: performing cloud scene identification on ground-based microwave radiometer observation data and obtaining cloud scene identification results can include:

[0074] 401: Determine the brightness temperature observation values ​​of each channel of the ground-based microwave radiometer based on the observation data;

[0075] 402: Establish the cloud scattering index based on the brightness temperature observations of each channel of the ground-based microwave radiometer;

[0076] 403: Cloud scene identification is performed by judging the threshold of cloud scattering index, and the cloud scene identification result is obtained.

[0077] For example, step 103: performing cloud scene identification on the ground-based microwave radiometer observation data to obtain cloud scene identification results may also include:

[0078] 501: Acquire cloud images generated by the all-sky imager, wherein the all-sky imager and the ground-based microwave radiometer are installed at the same site;

[0079] 502: Cloud scene recognition is performed using cloud images to obtain cloud scene recognition results.

[0080] In specific implementations, there are multiple implementation schemes for establishing the background field covariance matrix and the observation error matrix. For example, step 104: establishing the background field covariance matrix and the observation error matrix based on the cloud scene recognition results, the numerical weather prediction model background field, and the observation operator can include:

[0081] 601: Determine the background field covariance matrix based on the background field of the numerical weather prediction model;

[0082] 602: Determine the observation error matrix based on the all-weather observation operator and the brightness temperature observation values ​​of each channel of the ground-based microwave radiometer.

[0083] In practice, there are multiple implementation schemes for determining the observation error matrix. For example, step 602: determining the observation error matrix based on the all-weather observation operator and the brightness temperature observation values ​​of each channel of the ground-based microwave radiometer, specifically includes:

[0084] 701: Determine the simulated brightness temperature values ​​of each channel of the ground-based microwave radiometer based on the all-weather observation operator;

[0085] 702: Calculate the observation error in real time based on the simulated brightness temperature values ​​of each channel of the ground-based microwave radiometer and the observed values ​​of each channel of the ground-based microwave radiometer;

[0086] 703: Correct the observation error for deviation to obtain the observation error matrix, which satisfies a normal distribution.

[0087] In practice, there are multiple implementation schemes for determining the numerical weather prediction model analysis field. For example, step 105: determining the numerical weather prediction model analysis field based on the background field covariance matrix, observation error matrix, all-weather observation operator, and the tangent linear adjoint model of the all-weather observation operator can specifically include:

[0088] 801: Determine the derivatives of the simulated brightness temperature values ​​of each channel of the ground-based microwave radiometer with respect to atmospheric and cloud parameters;

[0089] 802: Substitute the numerical model background field, background field covariance matrix, observation error matrix, brightness temperature simulation values ​​of each channel of the ground-based microwave radiometer, and calculation results of the tangent adjoint mode into the numerical weather prediction model assimilation system, and perform variational iteration until convergence based on the numerical weather prediction model background field to obtain the numerical weather prediction model analysis field.

[0090] In specific implementation, step 101: establishing an all-weather observation operator for ground-based microwave radiometer observation data, and step 102: establishing a tangent linear adjoint model for the all-weather observation operator for ground-based microwave radiometer observation data, refer to establishing a fast radiative transfer model and its tangent linear adjoint module for the ground-based microwave radiometer. This fast radiative transfer model uses the background field of the numerical weather prediction model as input parameters to simulate the brightness temperature observations of each channel of the ground-based microwave radiometer. This model can include rapid calculation of gas absorption transmittance, calculation of cloud optical properties, and solving of the radiative transfer equation.

[0091] Furthermore, the background field of the numerical forecast model is global gridded atmospheric data simulated by the numerical weather prediction model.

[0092] Furthermore, the rapid calculation of gas absorption transmittance specifically involves constructing predictive factors using atmospheric variables (such as temperature, water vapor, and air pressure), and fitting the gas absorption transmittance of the accurate model based on the predictive factors, thereby enabling the rapid calculation of transmittance using atmospheric variables.

[0093] Furthermore, the calculation of cloud optical properties specifically involves using particle scattering algorithms (such as Mie scattering, discrete dipole, or T-matrix algorithms) to calculate the cloud scattering effect from cloud water content and the effective radius of cloud particles.

[0094] Furthermore, the radiative transfer equation can be solved based on the known atmospheric transmittance and cloud optical properties, thereby calculating the brightness temperature at the observation angle of the ground-based microwave radiometer.

[0095] In practice, under clear-sky conditions, the core of the fast radiative transfer mode is based on the following radiative transfer equation:

[0096]

[0097] Where I(τ,μ) is the emissivity, i.e., the variable to be determined; τ is the optical thickness; μ is the zenith angle cosine; and B(T) is the atmospheric Planck function, which can be determined based on the atmospheric temperature and the instrument channel frequency.

[0098] By solving formula (1), the emissivity I observed by the ground-based microwave radiometer can be obtained. sim for:

[0099]

[0100] Where Γ 0,i Γ is the transmittance from the top of the atmosphere to the i-th layer of the atmosphere, where Γ 0,0 =1; N represents the number of atmospheric layers used in the radiative transfer mode; T i T represents the atmospheric temperature of the i-th layer; cosmicThis represents the incident temperature of the cosmic background.

[0101] According to formula (2), given the atmospheric temperature profile and the incident temperature of the cosmic background, it is only necessary to calculate the transmittance Γ. 0,i This allows us to simulate the emissivity observed by a ground-based microwave radiometer.

[0102] In the newly established fast radiative transfer mode, to quickly calculate transmittance, we assume:

[0103]

[0104] Where X k,i (P,T,q,μ) are predictors, which are polynomials constructed using atmospheric pressure P, atmospheric temperature T, atmospheric humidity q, and observed zenith angle θ; a k These are regression coefficients, which can be obtained by regressing the results of the line-by-line integration model using formula (3). The line-by-line integration model uses the Liebe 89 model. In the microwave band, we consider the transmittance of both dry air and water vapor, and use different combinations of predictor factors for fitting. Specific combinations of predictor factors are shown in Table 1.

[0105] Table 1. Combination of predictor factors for fast radiative transfer modes of ground-based microwave radiometers.

[0106] Serial Number Dry air predictor Water vapor predictor 1 sec(θ) <![CDATA[H2O_A / T]]> 2 sec(θ)×T <![CDATA[H2O_A / T×H2O <!-- 6 -->]]> 3 sec(θ)×(T2) <![CDATA[H2O_A / (T2)×H2O / (T2)]]> 4 T <![CDATA[H2O_A / T2]]> 5 sec(θ)×sec(θ) <![CDATA[H2O_A / (T2)×H2O]]> 6 T2 <![CDATA[H2O_A / (T2) 2 ]]> 7 Tz <![CDATA[H2O_A]]> 8 <![CDATA[H2O_A×DT]]> 9 <![CDATA[(sec(θ)×GAzp) 2 ]]> 10 sec(θ)×GAzp 11 sec(θ) 12 <![CDATA[H2O_A×H2O_S]]> 13 <![CDATA[H2O_S×H2O_S]]> 14 <![CDATA[H2OdH2OTzp]]>

[0107] In the presence of clouds, the radiative transfer equation is relatively complex, and its form is as follows:

[0108]

[0109] Based on clear-sky radiative transfer, the effect of cloud scattering also needs to be considered. Therefore, the single-scattering albedo ω and the scattering phase function P(μ,μ′) are added to the equation. Given the cloud water content and the effective radius of cloud particles, these parameters can be calculated using particle scattering algorithms (such as the Mie scattering algorithm, discrete dipole algorithm, and T-matrix algorithm). Equation (4) is a differential-integral equation, which is difficult to solve analytically. Currently, there are many numerical solutions for approximate solutions. Taking the discrete ordinate method as an example, the integral term in equation (4) can be discretized:

[0110]

[0111] Finally, equation (4) is transformed into a set of linear equations, thereby solving the atmospheric radiation transfer equation.

[0112] In practice, after establishing the all-weather observation operator for ground-based microwave radiometer observation data, it is also necessary to include the step of establishing the tangent linear adjoint module of the all-weather observation operator. This step is to differentiate the calculation process of the above-mentioned all-weather observation operator with respect to parameters such as atmospheric temperature, humidity, air pressure, and cloud water content, and finally calculate the derivative of the simulated brightness temperature value of each channel with respect to atmospheric parameters such as atmospheric temperature, humidity, and air pressure.

[0113] For the fast radiative transfer mode of the ground-based microwave radiometer, this mode uses the background field of the numerical model as input parameters to directly simulate the brightness temperature observation values ​​of each channel of the ground-based microwave radiometer, and calculates the derivatives of the simulated brightness temperature with respect to atmospheric parameters such as atmospheric temperature, humidity, and air pressure, providing technical support for the direct assimilation of ground-based microwave radiometer observations by the numerical prediction model.

[0114] In practice, step 103 primarily involves generating static atmospheric background field data. Specifically, this involves establishing a sufficiently representative background field covariance matrix using global atmospheric products from numerical weather prediction models. Simultaneously, the observation error matrices for each channel of the ground-based microwave radiometer are calculated using global atmospheric products from numerical weather prediction models and observational data. The background field covariance matrix and the observation error matrix can serve as static input data for variational assimilation.

[0115] Specifically, atmospheric product data from five consecutive years of numerical weather prediction models can be selected to ensure representativeness.

[0116] Specifically, the background field covariance matrix can be a two-dimensional covariance matrix calculated from the profile set of global atmospheric products in numerical weather prediction models, representing different variables at different vertical layers. These different variables include atmospheric temperature, humidity, and pressure profiles.

[0117] Specifically, the observation error matrix can be the standard deviation of the difference between the brightness temperature simulated in the fast radiative transfer mode and the brightness temperature observed by the ground-based microwave radiometer.

[0118] In practice, the deviation correction of observation errors mentioned in step 703 above refers to the correction of systematic errors in the observation. Specifically, this can be achieved by fitting the observed brightness temperature value with the simulated brightness temperature according to the scanning angle of the ground-based microwave radiometer, or by performing deviation correction through variational iteration.

[0119] The aforementioned fitting process refers to linearly fitting the simulated brightness temperature to the observed brightness temperature using the least squares method and saving the results as offline linear fitting coefficients. During assimilation, the observed brightness temperature values ​​are substituted into the fitted linear equation along the scanning angle of the ground-based microwave radiometer to correct for bias.

[0120] Specifically, based on weather condition information extracted from the atmospheric background field, the observation system bias b(β,x) for each observation frequency is calculated, and the brightness temperature observations for each frequency are corrected based on the system bias. Here, x is the air mass factor calculated based on the background field, and β is the correction coefficient corresponding to each air mass factor. The final corrected brightness temperature y is compared with the actual observed brightness temperature y. o The relationship can be represented as:

[0121]

[0122] Where N p The number of air mass factors is represented by the number of factors. Commonly used air mass correction factors include tropospheric atmospheric thickness, stratospheric atmospheric thickness, and surface temperature. The air mass factor correction coefficient β can be obtained by fitting the observed brightness temperature with the simulated brightness temperature, or by using variational iteration to correct the bias.

[0123] In practice, based on the background field covariance matrix, observation error matrix, observation operator tangent linear adjoint module, and brightness temperatures of each channel after bias correction, variational iteration is performed on the background field of the numerical weather prediction model to minimize the bias-corrected observation error, ultimately obtaining the analysis field X of the numerical weather prediction model. a Specifically, taking four-dimensional variational as an example, the objective functional of its variational assimilation is defined as follows:

[0124]

[0125] The first item on the right, J B It is the background field item, X a and X b Let J represent the analysis field and the background field, respectively. B is the background field error covariance matrix. The second term on the right-hand side, J... O These are observation terms, where the subscript i represents different observation times, and M... 0→i Let H represent the forecast model integral from the initial time to the observation time, H represent the observation operator, R represent the observation error matrix, and y represent the brightness temperature observation value after bias correction.

[0126] like Figure 9 As shown, the present invention also provides a direct assimilation device for ground-based microwave radiometer data, the direct assimilation device for ground-based microwave radiometer data comprising:

[0127] The observation operator establishment module 901 is used to establish all-weather observation operators for ground-based microwave radiometer observation data;

[0128] The tangent linear adjoint model establishment module 902 is used to establish a tangent linear adjoint model for all-weather observation operators based on ground-based microwave radiometer observation data;

[0129] The cloud scene recognition module 903 is used to perform cloud scene recognition on ground-based microwave radiometer observation data and obtain cloud scene recognition results.

[0130] The matrix building module 904 is used to build the background field covariance matrix and the observation error matrix based on the cloud scene recognition results, the background field of the numerical weather prediction model and the all-weather observation operator.

[0131] The numerical weather prediction model analysis field determination module 905 is used to determine the numerical weather prediction model analysis field based on the background field covariance matrix, observation error matrix, all-weather observation operator, and tangent linear adjoint model of all-weather observation operator.

[0132] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for direct assimilation of ground-based microwave radiometer data.

[0133] The present invention also provides a computer-readable storage medium storing a computer program for the direct assimilation method of the ground-based microwave radiometer data.

[0134] In summary, the present invention provides a method, apparatus, computer equipment, and readable storage medium for the direct assimilation of ground-based microwave radiometer data. The method includes: establishing an all-weather observation operator for ground-based microwave radiometer observation data; establishing a tangent linear adjoint model for the all-weather observation operator for the ground-based microwave radiometer observation data; performing cloud scene identification on the ground-based microwave radiometer observation data to obtain cloud scene identification results; establishing a background field covariance matrix and an observation error matrix based on the cloud scene identification results and the numerical weather prediction model background field and the all-weather observation operator; and determining the numerical weather prediction model analysis field based on the background field covariance matrix, the observation error matrix, the all-weather observation operator, and the tangent linear adjoint model of the all-weather observation operator. This method can achieve direct assimilation of ground-based microwave radiometer data in numerical weather prediction models, thereby effectively avoiding the problems of inversion product errors and the reuse of model background field information in indirect assimilation.

[0135] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of direct assimilation of ground-based microwave radiometer data, characterized in that, The direct assimilation method of the ground-based microwave radiometer data comprises: obtaining atmospheric parameters and cloud parameters according to a numerical prediction model background field; establishing a fast calculation model of atmospheric transmittance according to the atmospheric parameters and a line-by-line integral model; establishing a cloud particle scattering database according to the cloud parameters and a particle scattering algorithm; determining an all-weather observation operator for the ground-based microwave radiometer observation data according to the fast calculation model of atmospheric transmittance and the cloud particle scattering database; deriving each parameter of the atmosphere according to the fast calculation model of atmospheric transmittance to determine a tangent linear adjoint model of the fast calculation model of atmospheric transmittance; deriving the cloud parameters according to the cloud particle scattering database to determine a tangent linear adjoint model of the cloud particle scattering database; determining a tangent linear adjoint model of the all-weather observation operator for the ground-based microwave radiometer observation data according to the tangent linear adjoint model of the fast calculation model of atmospheric transmittance and the tangent linear adjoint model of the cloud particle scattering database; carrying out cloud scene recognition on the ground-based microwave radiometer observation data to obtain a cloud scene recognition result; establishing a background field covariance matrix and an observation error matrix according to the cloud scene recognition result, the numerical prediction model background field and the all-weather observation operator; determining a numerical prediction model analysis field according to the background field covariance matrix, the observation error matrix, the all-weather observation operator and the tangent linear adjoint model of the all-weather observation operator.

2. The method of direct assimilation of ground-based microwave radiometer data according to claim 1, wherein, The cloud scene recognition on the ground-based microwave radiometer observation data to obtain a cloud scene recognition result comprises: determining brightness temperature observation values of each channel of the ground-based microwave radiometer according to the ground-based microwave radiometer observation data; establishing a cloud scattering index according to the brightness temperature observation values of each channel of the ground-based microwave radiometer; carrying out cloud scene recognition by judging a threshold value of the cloud scattering index to obtain a cloud scene recognition result.

3. The method of direct assimilation of ground-based microwave radiometer data according to claim 1, wherein, The cloud scene recognition on the ground-based microwave radiometer observation data to obtain a cloud scene recognition result comprises: obtaining a cloud image generated by a full-sky imager, the full-sky imager being erected at the same site as the ground-based microwave radiometer; carrying out cloud scene recognition by the cloud image to obtain a cloud scene recognition result.

4. The method of direct assimilation of ground-based microwave radiometer data according to claim 1, wherein, The establishment of a background field covariance matrix and an observation error matrix according to the cloud scene recognition result, the numerical prediction model background field and the observation operator comprises: determining the background field covariance matrix according to the numerical prediction model background field; determining the observation error matrix according to the all-weather observation operator and the brightness temperature observation values of each channel of the ground-based microwave radiometer.

5. The method of direct assimilation of ground-based microwave radiometer data according to claim 4, characterized in that, The determination of the observation error matrix according to the all-weather observation operator and the brightness temperature observation values of each channel of the ground-based microwave radiometer comprises: determining brightness temperature simulation values of each channel of the ground-based microwave radiometer according to the all-weather observation operator; real-time calculation of observation errors according to the brightness temperature simulation values of each channel of the ground-based microwave radiometer and the observation values of each channel of the ground-based microwave radiometer; bias correction of the observation errors to obtain an observation error matrix, the observation error matrix satisfying a normal distribution.

6. The method of direct assimilation of ground-based microwave radiometer data according to claim 5, wherein, The determination of a numerical prediction model analysis field according to the background field covariance matrix, the observation error matrix, the all-weather observation operator and the tangent linear adjoint model of the all-weather observation operator comprises: Derivatives of simulated brightness temperature of each channel of the ground-based microwave radiometer with respect to atmospheric parameters and cloud parameters are determined; The numerical prediction model background field, the background field covariance matrix, the observation error matrix, the simulated brightness temperature of each channel of the ground-based microwave radiometer and the calculation result of the tangent companion model are substituted into a numerical prediction model assimilation system, and a variation iteration is performed on the basis of the numerical prediction model background field until convergence is achieved, so as to obtain an analysis field of the numerical prediction model.

7. A device for direct assimilation of ground-based microwave radiometer data, characterized in that, The direct assimilation device of the ground-based microwave radiometer data comprises: An observation operator establishment module is configured to obtain atmospheric parameters and cloud parameters according to a numerical prediction model background field, establish an atmospheric transmittance fast calculation model according to the atmospheric parameters and a line-by-line integral model, establish a cloud particle scattering database according to the cloud parameters and a particle scattering algorithm, and determine an all-weather observation operator for ground-based microwave radiometer observation data according to the atmospheric transmittance fast calculation model and the cloud particle scattering database; A tangent companion model establishment module is configured to determine a tangent companion model of the atmospheric transmittance fast calculation model by deriving each atmospheric parameter according to the atmospheric transmittance fast calculation model, determine a tangent companion model of the cloud particle scattering database by deriving cloud parameters according to the cloud particle scattering database, and determine a tangent companion model of the all-weather observation operator for ground-based microwave radiometer observation data according to the tangent companion model of the atmospheric transmittance fast calculation model and the tangent companion model of the cloud particle scattering database; A cloud scene identification module is configured to identify a cloud scene of ground-based microwave radiometer observation data to obtain a cloud scene identification result; A matrix establishment module is configured to establish a background field covariance matrix and an observation error matrix according to the cloud scene identification result, a numerical prediction model background field and the all-weather observation operator; A numerical prediction model analysis field determination module is configured to determine a numerical prediction model analysis field according to the background field covariance matrix, the observation error matrix, the all-weather observation operator and the tangent companion model of the all-weather observation operator.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the direct assimilation method of ground-based microwave radiometer data according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program for implementing the direct assimilation method of ground-based microwave radiometer data according to any one of claims 1 to 6.

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