Hybrid assimilation method for microwave hygrometer data coupled with earth surface information control variable

Abstract

The invention discloses a microwave hygrometer data hybrid assimilation method for coupling earth surface information control variables. The method comprises the following steps: adding the earth surface information control variables of a set disturbance part into a hybrid assimilation system; obtaining a group of ensemble forecasting fields by using short-time ensemble forecasting, calculating expanded earth surface variable ensemble disturbance, and obtaining flow dependent background error information of earth surface information; based on the introduced earth surface information control variable, different forms of analysis increment of the earth surface variable and the atmospheric variable are constructed, so that the earth surface variable and the atmospheric variable are directly influenced by flow-dependent background error information of the earth surface information; and data assimilation of the microwave hygrometer is carried out based on a hybrid assimilation method of a coupling earth surface information control variable. According to the method, other atmospheric variables are influenced and optimized by introducing earth surface information control variables, and mode variable information is updated and optimized, so that a better analysis field is obtained, and the utilization rate of satellite data is improved.

Description

Technical Field

[0001] The invention relates to a microwave hygrometer data hybrid assimilation method coupled with surface information control variables, belonging to the technical field of numerical simulation and data assimilation. Background Art

[0002] In modern numerical weather forecasting and climate prediction, surface information, as a key element of surface-atmosphere interaction, directly affects the evolution of surface energy and water exchange and boundary layer processes. Surface information such as surface humidity, temperature, and soil moisture plays an important role in the formation and development of weather systems. For example, surface humidity affects the intensity of surface evaporation and latent heat flux, and is a key factor in the triggering of convection and precipitation; soil moisture and surface temperature affect the surface sensible heat flux and local atmospheric stability, further regulating the development of atmospheric circulation and weather systems. Research on the application of surface information in weather and climate systems is still mainly focused on the use of ocean surface observation data. The brightness temperature (TB) observed by satellites is highly sensitive to ocean and land surface parameters (such as sea surface, surface temperature and wind speed) and atmospheric information (such as water vapor, clouds and precipitation). The application research of surface information in satellite data assimilation has yet to be developed. Traditional assimilation systems pay insufficient attention to near-surface variables such as surface humidity in satellite data assimilation, making it difficult to fully reflect the dynamic characteristics of surface processes, resulting in low representation accuracy in the analysis field. At the same time, traditional control variable settings (such as temperature, humidity, and wind field) usually ignore the interaction between surface information and atmospheric information, weakening the influence of surface information in the assimilation system. Summary of the invention

[0003] Purpose of the invention: In view of the problems and shortcomings in the prior art, the present invention provides a hybrid assimilation method for microwave hygrometer data coupled with surface information control variables. The Hybrid En3DVar (ensemble / variational hybrid) hybrid assimilation method is used to expand the application of surface information control variables to the microwave hygrometer data assimilation system, strengthen the coupling between surface information and atmospheric information and their influence, improve weather system analysis and forecasting, and improve the accuracy of numerical weather forecasting and climate prediction.

[0004] Technical solution: A hybrid assimilation method of microwave hygrometer data coupled with surface information control variables. Based on the hybrid assimilation method, the surface information in satellite data is used in combination with the extended surface information control variables, using the dynamically changing surface information flow dependent background error information to improve the analysis and forecast of surface information and atmospheric information in the model, including the following contents: S1, random perturbation generates ensemble members, and adds surface information control variables of the ensemble perturbation part to the Hybrid En3DVar hybrid assimilation system.

[0005] S2, using short-term ensemble forecasts to obtain a set of ensemble forecast fields, by calculating the ensemble mean and standard deviation of each variable in the ensemble members, and then calculating the extended surface variable ensemble disturbance, the flow-dependent background error information of the surface information is obtained.

[0006] S3, based on the introduced surface information control variables, construct different forms of analysis increments of surface variables and atmospheric variables, so that both are directly affected by the flow-dependent background error information of the surface information, thereby reflecting the coupling characteristics of surface information and atmospheric information, and enhancing the assimilation system's ability to describe the coupling process between the surface and the atmosphere.

[0007] S4, based on the newly constructed hybrid assimilation method of coupled surface information control variables, the data of microwave hygrometer are assimilated to improve the comprehensive adjustment of microwave hygrometer to atmospheric information and surface information.

[0008] In S1, the surface information control variables of the collective disturbance part are added to the Hybrid En3DVar hybrid assimilation system, and the control variables of the collective flow dependency part are expanded. The expanded collective disturbance variables are:

[0009] is the atmospheric variable ensemble perturbation. is the extended set perturbation of surface variables.

[0010] In S2, on the basis of generating ensemble members by random disturbance, a set of ensemble forecast fields is obtained by using short-term ensemble forecast. The ensemble disturbance information of variables such as temperature, pressure, humidity, wind, cloud, and rain is obtained by calculating the ensemble mean and standard deviation of the variables of each ensemble member in the ensemble forecast field. Then the ensemble disturbance information of the extended surface variables (2-meter humidity, 2-meter temperature, 10-meter wind (U10, V10), etc.) is calculated. By calculating the mean and standard deviation of each variable between members, the degree of dispersion of surface variables in the ensemble members can be quantitatively characterized, and its role is to directly quantify the uncertainty of surface variables at different spatial positions and times. Based on the calculation results of the mean and standard deviation, the flow-dependent background error information is further obtained, which can dynamically reflect the spatiotemporal variation characteristics of the surface variable error with the evolution of the weather system. Different from the traditional static background error, the role of the flow-dependent background error information is reflected in two aspects: first, by associating the movement and development of the weather system (such as the front, convective activity), the non-uniform distribution of the error in space is characterized; second, it can capture the characteristics of the error evolution over time, thereby more accurately characterizing the error distribution and evolution law of the surface variable under a specific weather background. The formula for calculating the mean and standard deviation of surface information is as follows:

[0011] in represents the surface variable, K is the number of set members, is the average value of the surface variable. is the collective disturbance of surface variables, It represents the collective disturbance of the surface variable generated by the kth set member. stdv (Standard Deviation) is the standard deviation of the surface variable.

[0012] In S3, the analysis increment of the surface variables is defined in the Hybrid En3DVar hybrid assimilation system as:

[0013] is the surface information increment brought by the static background error covariance of 3DVar. Since the surface information is not introduced as a control variable in 3DVar, the present invention considers the set assimilation of flow dependence (only the surface information control variable is introduced in the set). The surface information increment of the static part Set to 0, K is the number of set members. The increment of the surface information variable brought by the flow-dependent error background information provided by the set. represents the control variable vector of the Kth set member, where the control variables include atmospheric variables (such as horizontal wind, radial wind, temperature, humidity, etc.) and surface information variables. When surface information is added to the control variables, the resulting surface variable set disturbance introduces new uncertainties and brings changes that will have an impact on the final atmospheric variables analysis increment.

[0014] The analysis increments of atmospheric variables (such as horizontal wind U, radial wind V, temperature T, specific humidity Q, etc.) defined in the Hybrid En3Dvar hybrid assimilation system are:

[0015] in It is the atmospheric information increment brought by the 3DVar static background error covariance. represents the atmospheric information increment brought by the collection, represents the collective disturbance of atmospheric variables produced by the kth ensemble member.

[0016] Surface information is also introduced into the observation operator, that is, variables related to the surface information are added as additional inputs in the calculation process of the observation operator.

[0017] The above contents S1 to S3, after expanding the surface information as a control variable, obtained an improved hybrid assimilation system, and the microwave hygrometer data were assimilated using the improved hybrid assimilation system.

[0018] In the process of assimilating microwave hygrometer data, the surface variable set perturbation is added as the input of the radiation transfer model on the basis of the traditional assimilation system, and the atmospheric variable set perturbation and surface variable set perturbation from the static background error covariance and the flow-dependent background error covariance are iteratively analyzed to obtain the analysis increments of each variable after the mutual influence and action of surface information and atmospheric information.

[0019] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the microwave hygrometer data hybrid assimilation method coupled with surface information control variables as described above are implemented.

[0020] A computer-readable storage medium stores a computer program for executing the microwave hygrometer data hybrid assimilation method coupled with surface information control variables as described above.

[0021] Beneficial effects: In the hybrid assimilation system, the present invention introduces surface information control variables to influence and optimize other atmospheric variables, updates and optimizes the model variable information to obtain a better analysis field, and also improves the utilization rate of satellite data. The assimilation system obtains a more accurate initial field under the mutual influence of atmospheric information and surface information, which improves subsequent forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the implementation of the coupled surface information control variable hybrid assimilation system of the present invention; Figure 2 The RMS error of the analysis field in two different hybrid assimilation systems (coupled with surface information and uncoupled with surface information) relative to the sounding data, where the blue line is the error under coupled surface information and the red line is the error under uncoupled surface information. (a), (b), (c), and (d) are the errors of zonal wind, meridional wind, temperature, and specific humidity, respectively. DETAILED DESCRIPTION

[0023] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0024] A hybrid assimilation method for microwave hygrometer data coupled with surface information control variables can not only enhance the description of the spatiotemporal distribution of surface information, but also update and optimize model variables through dynamic coupling between control variables, and enhance the consistency of surface information and atmospheric information. This is of great significance for improving the assimilation effect of satellite data, improving the quality of analysis fields, and improving the performance of weather forecasts and climate predictions. Especially in the study of complex weather systems (such as typhoons, heavy rains) or regional climate, the coupling of surface information is expected to provide more refined analysis and prediction capabilities, laying the foundation for further optimization of numerical models.

[0025] S1, add surface information control variables that can affect the ensemble perturbation part to the Hybrid En3DVar hybrid assimilation system.

[0026] Traditional control variables only include atmospheric variables such as wind speed, temperature, and humidity, and pay less attention to surface variables. If global control variables are added, it will be necessary to construct a static background error covariance calculation for the corresponding expanded control variables. Therefore, the present invention expands the control variables of the ensemble flow dependent part, and adds the calculation of surface information to the ensemble forecast disturbance field. In this way, surface information is effectively introduced into the control variable space, so that the interaction between the surface and the atmosphere is more comprehensively considered in the assimilation system. This expanded ensemble perturbation not only includes atmospheric variables, but also combines surface variables, reflecting the potential impact of surface information on atmospheric analysis increments and model variable updates, thereby enhancing the assimilation system's ability to capture the complex feedback mechanism between the surface and the atmosphere. The expanded ensemble perturbation is:

[0027] is the atmospheric variable ensemble perturbation. The extended set of surface variable disturbances mainly includes 2-meter humidity, 2-meter temperature, 10-meter wind (U10, V10) and other information.

[0028] S2, calculate the extended set perturbation of surface variables.

[0029] In the hybrid assimilation system coupled with surface information control variables, random perturbations are introduced in the control variable space using the RandomCV method to generate ensemble members. On this basis, the ensemble members are updated using short-term ensemble forecasts, and variables related to surface information are added to perform comprehensive calculations when calculating the ensemble mean and standard deviation, and then the expanded surface variable ensemble perturbations are calculated to obtain the flow-dependent background error information of the surface variables, thereby reflecting the error size and error change characteristics of the surface information in space and time. The perturbation information includes the atmospheric variable ensemble perturbations and the surface variable ensemble perturbations, which can reflect the mutual influence between variables when calculating the ensemble mean and standard deviation of the ensemble members. In this way, the model can comprehensively consider the interaction between atmospheric information and surface information, capture the feedback mechanism between the atmosphere and the surface, and reflect their synergistic effect in the assimilation system. The formula for calculating the mean and standard deviation of surface information is as follows:

[0030] in represents the surface variable, K is the number of set members, is the average value of the surface variable. is the collective disturbance of surface variables, It represents the collective disturbance of the surface variable generated by the kth set member. stdv (Standard Deviation) is the standard deviation of the surface variable.

[0031] S3, the analysis increment of the surface variables defined in the Hybrid En3DVar assimilation system is:

[0032] It is the surface information increment brought by the 3DVar static background error covariance. Since the surface information is not introduced in 3DVar, the surface information control variable is introduced in the form of a set, and the static background error covariance does not contain relevant information about the surface information. The increment of surface information is set to 0. K is the number of set members. represents the collective disturbance of the surface variables generated by the kth set member. The surface information increment brought by the flow-dependent background error information provided for the set. Represents the extended control variable vector of each ensemble member, which is used to describe the adjustment of the atmospheric state during the assimilation process. When the surface information control variable is introduced, The composition of has changed, no longer just including the original atmospheric control variables, but expanding to control variables containing surface information. Since surface information affects atmospheric processes, the introduction of surface information will have a feedback effect on atmospheric information, thereby changing the analysis increment of atmospheric variables. This means that the perturbation of the surface variable set not only affects the analysis increment of surface information, but also affects the analysis increment of other atmospheric variables, thereby updating and optimizing the model variables in the assimilation system.

[0033] S4, the analysis increment of atmospheric variables defined in the Hybrid En3Dvar hybrid assimilation system is:

[0034] in It is the atmospheric information increment brought by the 3DVar static background error covariance. represents the atmospheric information increment brought by the collection, represents the atmospheric variable collective disturbance generated by the kth ensemble member. Affected by the disturbance of the surface variable set, the analytical increment of atmospheric variables will also be affected by the surface information. By adjusting the analysis increment of atmospheric variables, the analysis results of model variables are affected, and the model variables are updated and optimized. This can not only reflect the coupling relationship between the atmosphere and the surface in the assimilation system, but also reflect the impact of surface information on atmospheric analysis results, thereby ensuring that the assimilation system can more comprehensively capture the feedback mechanism between the surface and the atmosphere.

[0035] S5, surface information is also introduced into the observation operator, that is, variables related to surface information are added as additional inputs in the observation operator calculation process. Based on the hybrid analysis increment calculation method of the newly constructed coupled surface information control variable in the hybrid assimilation system (the calculation method involved in formula (2) and formula (3)) and the improved hybrid assimilation system constructed by contents S1-S3 with the expansion of surface information as a control variable, the microwave hygrometer data are assimilated. In the process of assimilating microwave hygrometer data, the variable set perturbation obtained from the surface is added as the input of the radiation transfer model on the basis of the traditional assimilation system, and the atmospheric variable set perturbation and the surface variable set perturbation from the static background error covariance and the flow-dependent background error covariance are iteratively analyzed to obtain the analysis increments of each variable after the mutual influence and action of the surface information and the atmospheric information.

[0036] By comparing the increments of the analysis field variables in two different hybrid assimilation systems (coupled surface information and uncoupled surface information), it can be found that the increment of two-meter specific humidity in the uncoupled surface information is 0. After introducing the surface information control variable, the incremental information of the two-meter specific humidity can be reflected. This not only strengthens the adjustment of atmospheric variables, but also effectively captures the mutual influence between the surface and the atmosphere, making the analysis increment more accurate and comprehensive. This improvement shows that the addition of surface information enables the assimilation system to better reflect the coupling relationship between the surface and the atmosphere, thereby improving the analysis accuracy of meteorological variables. This result further verifies the importance of surface information in the meteorological assimilation system and its positive role in optimizing meteorological forecasts and improving analysis quality. Figure 2 The error values ​​of the analysis fields in two different hybrid assimilation systems (coupled surface information and uncoupled surface information) are shown in Figure 2. It can be seen that coupled surface information optimizes most variables.

[0037] Obviously, those skilled in the art should understand that the various steps of the microwave hygrometer data hybrid assimilation method coupled with surface information control variables of the above-mentioned embodiment of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

Claims

1. A microwave hygrometer data hybrid assimilation method coupled with surface information control variables, characterized in that: It includes the following: S1, random perturbation generates ensemble members, adding surface information control variables of the ensemble perturbation part to the hybrid assimilation system; S2, using the short-term ensemble forecast to obtain a set of ensemble forecast fields, by calculating the ensemble mean and standard deviation of each variable in the ensemble members, and then calculating the extended surface variable ensemble disturbance to obtain the flow-dependent background error information of the surface information; S3, based on the introduced surface information control variables, different forms of analysis increments of surface variables and atmospheric variables are constructed, so that both are directly affected by the flow-dependent background error information of the surface information; S4, based on the newly constructed hybrid assimilation method of coupled surface information control variables, the microwave hygrometer data are assimilated.

2. The microwave hygrometer data hybrid assimilation method coupled with surface information control variables according to claim 1 is characterized in that: In S1, the surface information control variable of the collective disturbance part is added to the hybrid assimilation system, and the control variable of the collective flow dependence part is expanded. The expanded collective disturbance variable is: （1） is the atmospheric variable ensemble perturbation; is the extended set perturbation of surface variables.

3. The microwave hygrometer data hybrid assimilation method coupled with surface information control variables according to claim 1 is characterized in that: In the S2, on the basis of randomly perturbed ensemble members, a set of ensemble forecast fields are obtained by using short-term ensemble forecasts; the ensemble disturbance information of the variables is obtained by calculating the ensemble mean and standard deviation of the variables of each ensemble member in the ensemble forecast field; then the extended ensemble disturbance information of the surface variables is calculated; by calculating the mean and standard deviation of each variable between members, the discrete degree of the surface variables in the ensemble members is quantitatively characterized, which is used to directly quantify the uncertainty of the surface variables at different spatial positions and times; based on the calculation results of the mean and standard deviation, the flow-dependent background error information is obtained, which can dynamically reflect the spatiotemporal variation characteristics of the surface variable errors as the weather system evolves; The formula for calculating the mean and standard deviation of surface information is as follows: in represents the surface variable, K is the number of set members, is the average value of the surface variable; is the collective disturbance of surface variables, represents the collective disturbance of the surface variables generated by the kth set member; stdv is the standard deviation of the surface variables.

4. The microwave hygrometer data hybrid assimilation method coupled with surface information control variables according to claim 1 is characterized in that: In S3, the analysis increment of the surface variables in the hybrid assimilation system is defined as: is the surface information increment brought by the static background error covariance of 3DVar. Since the surface information is not introduced as a control variable in 3DVar, the surface information increment of the static part Set to 0, K is the number of set members; The increment of surface information variables brought by the flow-dependent error background information provided to the ensemble; in represents the control variable vector of the Kth set member, where the control variables include atmospheric variables and surface information variables; The analytical increments for defining atmospheric variables in a hybrid assimilation system are: in It is the atmospheric information increment brought by the 3DVar static background error covariance. represents the atmospheric information increment brought by the collection, represents the atmospheric variable ensemble disturbance generated by the kth ensemble member; Surface information is also introduced into the observation operator, that is, variables related to the surface information are added as additional inputs in the calculation process of the observation operator.

5. The microwave hygrometer data hybrid assimilation method coupled with surface information control variables according to claim 1 is characterized in that: In the data assimilation of the microwave hygrometer, a new surface variable set perturbation is added as the input of the radiation transfer model, and the atmospheric variable set perturbation and the surface variable set perturbation from the static background error covariance and the flow-dependent background error covariance are iteratively analyzed to obtain the analysis increments of each variable after the surface information and the atmospheric information interact with each other.

6. A computer device, characterized in that: The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the microwave hygrometer data hybrid assimilation method coupled with surface information control variables as described in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the microwave hygrometer data hybrid assimilation method coupled with surface information control variables as described in any one of claims 1-5.

Images