An ensemble-variational hybrid assimilation system and method based on a large meteorological model

Abstract

The present invention belongs to the field of atmospheric science and technology and discloses an ensemble-variational hybrid assimilation system and method based on a large meteorological model. This system employs a physically constrained method (RANDOMMV) to randomly perturb the initial atmospheric field. Based on the powerful deduction capabilities of the large artificial intelligence meteorological model, ensemble members are rapidly generated to ensure member diversity and reduce sampling errors. On this basis, an ensemble-variational hybrid assimilation method is employed, combining the dynamic ensemble background error covariance with the static background error estimated using the NMC method to construct an ensemble-variational hybrid assimilation system for the effective assimilation of multi-source observational data. The present invention ensures the physical consistency and stability of AI ensemble forecast members. It combines the long-term averaged static error information derived from the difference field of historical forecast data with the flow-dependent background error information of the ensemble forecast; this dynamic and static combination enhances the robustness of the hybrid assimilation system.

Description

Technical Field

[0001] The present invention belongs to the field of atmospheric science technology, and in particular relates to an ensemble-variational hybrid assimilation system and method based on a large meteorological model. Background Art

[0002] The accuracy of weather forecasts depends on both the accuracy of the forecast model and the quality of the initial values. Obtaining the optimal initial field has always been a key challenge in both research and operations. Operational numerical forecast centers often use data assimilation techniques, combining model priors with observational information to find the optimal estimate of state variables.

[0003] Currently, common assimilation methods can be divided into two categories: variational assimilation and ensemble assimilation. Variational assimilation facilitates the assimilation of multiple observational data and is computationally inexpensive. However, the background error covariance it employs is static and isotropic, and therefore does not accurately reflect actual model errors. Ensemble assimilation, such as the ensemble Kalman filter, utilizes multiple ensemble members to reflect the uncertainty of the initial field. However, ensemble methods are significantly affected by sampling errors. Especially for convective systems with rapid spatiotemporal evolution, large model errors can lead to significant deviations in the ensemble error covariance, limiting the effectiveness of observational data assimilation.

[0004] The ensemble-variational hybrid assimilation method combines the advantages of ensemble assimilation and variational assimilation methods. It retains the advantages of variable correlation and "flow dependence" in the EnKF method, and uses static background error covariance to better deal with sampling error and model error problems. It is widely used in numerical weather forecasting of global and regional models.

[0005] Current ensemble-variational hybrid assimilation techniques face challenges and drawbacks: they require a large number of ensemble members to more accurately capture the flow-dependent nature of the background error covariance and characterize the uncertainty of initial values in the atmospheric system, thereby reducing random errors and improving the accuracy of the background error covariance, thereby enhancing assimilation performance. However, a larger number of ensemble members also requires greater computational resources and time, impacting operational forecast efficiency.

[0006] Current solutions to this problem include: using localization techniques (Houtekamer and Mitchell, 2001), ensemble resampling techniques (Anderson et al., 2007), and time-lagged ensemble techniques (Wang et al., 2017) to mitigate sampling errors caused by insufficient ensemble members; and using parallel computing and high-performance computing techniques (Michalakes and Vachharajani, 2008) to accelerate the generation of ensemble members. While these methods have optimized computing resources and efficiency to a certain extent, with the increasing demand for forecast accuracy and resolution, the increase in ensemble members is inevitable, and the computational bottleneck problem still exists.

[0007] Therefore, the technical problem that the existing technology urgently needs to solve is how to quickly obtain a large number of reliable ensemble members in the hybrid assimilation method to more accurately characterize the flow dependence and uncertainty characteristics of the background error covariance, thereby improving the assimilation effect.

[0008] In the industrial application of ensemble-variational hybrid assimilation technology, computing resources and time costs have brought about the following major technical problems, which have limited the practical application efficiency and promotion of this technology:

[0009] 1. Excessive Computing Resource Consumption: Hybrid assimilation methods rely on a large number of ensemble members to accurately estimate the background error covariance and capture the uncertainty in the atmospheric system. However, the process of generating and updating a large number of ensemble members requires enormous computing resources, especially in operational applications with high-resolution forecasts and rapid updates. This high computing demand significantly increases operating costs, making it unaffordable for many enterprises and research institutions.

[0010] 2. Inadequate real-time and timeliness: Real-time and timeliness are crucial in operational forecasting. However, existing hybrid assimilation methods require significant computational time, resulting in delays in data assimilation and forecast output, slowing down the forecast system's response speed. This is particularly challenging in fields requiring rapid response, such as weather forecasting, climate simulation, and aerospace, directly impacting the effectiveness of operational forecasts.

[0011] 3. Computational bottlenecks impact system scalability: As forecast model accuracy and resolution requirements increase, the number of ensemble members required also increases. Even with optimization strategies such as localization, ensemble resampling, and parallel computing, computational bottlenecks persist and become increasingly severe with increasing model complexity. Existing technologies struggle to balance forecast accuracy, resolution, and computational cost, limiting their scalability in high-precision forecast systems.

[0012] 4. High hardware requirements and high costs: Solving computing bottlenecks requires high-performance computing clusters or advanced parallel computing technologies, which place high demands on hardware. This, especially for large-scale industrial applications, requires a large number of servers and computing resources, increasing both initial investment and maintenance costs. This is a significant barrier for organizations with limited budgets.

[0013] 5. Method complexity raises the technical barrier to entry: While effective, techniques such as ensemble resampling and time-lagged ensembles increase system complexity and require a high level of specialized technical expertise. This high barrier to entry makes it more difficult to apply hybrid assimilation technology within enterprises, hindering its widespread adoption. Summary of the Invention

[0014] In response to the problems existing in the prior art, the present invention provides an ensemble-variational hybrid assimilation system and method based on a large meteorological model.

[0015] The present invention is implemented as follows: an ensemble-variation hybrid assimilation method based on a large meteorological model includes:

[0016] S1, initial perturbation of global model reanalysis data;

[0017] S2, deduce the initial value after disturbance using a large meteorological model to generate an ensemble forecast;

[0018] S3, calculate the ensemble perturbation and calculate the ensemble background error covariance;

[0019] S4, prepares the model background field, observation data, and static background error covariance, and implements assimilation analysis based on the ensemble-variation hybrid assimilation framework;

[0020] S5, weather forecast based on physical models or machine learning models.

[0021] Furthermore, S1 specifically includes preparing global model analysis or forecast fields, such as ERA5 (European Centre for Medium-Range Weather Forecasts, ECMWF), the Global Forecasting System (GFS), jointly published by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), JRA-55 (Japan Meteorological Agency), and CRA reanalysis data (China Meteorological Administration, National Meteorological Information Center). By perturbing the initial values, a large number of initial ensemble members can be generated.

[0022] The specific initial value perturbation scheme is as follows: for a set of ensemble forecasts derived from an AI meteorological model, the ensemble mean is regarded as the true value, and the ensemble member minus the ensemble mean is regarded as the ensemble forecast error, thereby performing ensemble error covariance estimation to obtain the co-correlation between different model variables. These ensemble forecasts can be AI meteorological model forecast results at the same time with different forecast time periods driven by GEFS. The initial field is perturbed according to the ensemble error covariance matrix calculated above. The random perturbation on each grid point will affect the surrounding grids and other variables through the rules in the ensemble error covariance matrix, thereby ensuring the physical consistency of the ensemble members after the perturbation. This method is similar to the RANDOMCV method, and both imply physical balance constraints. The difference is that the RANDOMCV method is based on the spatial background error covariance of the control variable (CV, control variable), while the method proposed in the present invention is based on the forecast error covariance of the model variable (MV, model variable) space. Therefore, the invention subsequently refers to the perturbation method as the RAMDOMMV method.

[0023] Furthermore, the S2 specifically includes: deducing the initial value after disturbance to generate a future ensemble weather forecast; the deduction model is an AI meteorological large model, including Huawei's Pangu model, Google DeepMind's WeatherBench, NVIDIA's FourCastNet, and DeepMind's GraphCast.

[0024] Furthermore, the S3 specifically includes: obtaining a background field error covariance having flow dependence and multivariate variable correlation characteristics by means of an extended control variable method;

[0025] Furthermore, the S4 specifically includes: (1) preparing the model background field, which can be a global model reanalysis; (2) preparing observation data, including conventional observation data from GTS, such as sounding, buoy, aircraft reports, ship reports, ground station data, as well as meteorological satellites, radars, etc.; (3) preparing the estimated static background error covariance file; (4) based on the background error covariance provided by S3 to reflect the flow-dependent information of the actual weather, the hybrid assimilation analysis increment is obtained by minimizing the cost function, so that the observation information is better coordinated and transmitted between the model space and the multivariate under the action of the collective flow-dependent background error covariance, that is,

[0026]

[0027] Among them, the total assimilation analysis increment is, δx1 is the analysis increment related to the static background error covariance, is the kth set perturbation, K is the number of set members, α is the extended control variable; A is the diagonal matrix of the constrained set extended control variable α, which localizes the set error covariance; β1 and β2 are the weights of the static background error covariance (B) and the set background covariance, respectively, satisfying The third term on the right side of the equation is the observation-related term.

[0028] The background field superimposed analysis increment is the analysis field.

[0029] Furthermore, S5 specifically includes: using the analysis field obtained in S4 as the initial field to perform weather forecasting, which can be performed using a traditional numerical weather forecasting model or using an AI large model.

[0030] Furthermore, the calculation of the static background error covariance matrix in S4 can be performed by using historical forecast samples (forecast fields with different forecast time periods but at the same time) as background error samples, or by using ensemble forecast samples as background error samples and estimating them through GEN_BE software.

[0031] Another object of the present invention is to provide an ensemble-variation hybrid assimilation system based on a large meteorological model, comprising:

[0032] Initial value perturbation module, used to perform initial value perturbations on global model reanalysis data;

[0033] The generation module is used to deduce the initial value after the disturbance and generate the ensemble forecast;

[0034] Statistics module, used to calculate the collective disturbance and the statistical background error covariance of the collective;

[0035] Assimilation analysis module, used to prepare model background fields, observation data, static background error covariance, and implement assimilation analysis based on the ensemble-variation hybrid assimilation framework;

[0036] Weather forecast module, used to make weather forecasts based on physical models or machine learning models.

[0037] Another object of the present invention is to provide a computer device, which includes 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 ensemble-variational hybrid assimilation method based on the large meteorological model.

[0038] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the ensemble-variational hybrid assimilation method based on a large meteorological model.

[0039] Another object of the present invention is to provide an information data processing terminal, which is used to implement the ensemble-variation hybrid assimilation system based on the large meteorological model.

[0040] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0041] First, the present invention provides an ensemble-variational hybrid assimilation system and method based on an AI meteorological large model. By perturbing the initial field of the meteorological large model and performing deduction to generate an ensemble forecast, the flow-dependent background error covariance calculated from the ensemble forecast samples deduced from the meteorological large model is combined with the modeled static background error covariance of the three-dimensional variation, so that the ensemble-variational hybrid assimilation can be completed more efficiently to meet the needs of real-time forecasting.

[0042] (1) The ensembles in previous assimilation schemes all come from numerical weather forecasts. This patent generates ensemble members through an AI large model, which has a fast deduction speed and can make up for the lack of efficiency in traditional ensemble forecast calculations. It can generate larger ensemble samples within the allowed lag time, thereby meeting the real-time assimilation forecast requirements of the business while improving the accuracy of assimilation.

[0043] (2) This patent combines the physical constraint perturbation method with the meteorological large model deduction to ensure the physical consistency and stability of AI ensemble forecast members.

[0044] (3) The long-term average static background error information is derived based on the difference field of historical forecasts; the AI ensemble samples can provide dynamic flow-dependent ensemble error covariance; the combination of dynamic and static enhances the robustness of the hybrid assimilation system.

[0045] Second, the expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0046] (a) Improve forecast accuracy and efficiency: By combining AI meteorological models with ensemble-variational assimilation systems,

[0047] Improve the accuracy of extreme weather forecasts, optimize resource consumption, and reduce computing costs.

[0048] (b) Service industry applications: High-precision forecasts can serve industries such as agriculture, transportation, and energy that have a demand for accurate weather forecasts, thereby increasing market value and competitiveness.

[0049] (c) Promote technological innovation: Promote the application and development of AI in the field of meteorology, establish technological barriers, form world-leading intellectual property rights, and increase industrial added value.

[0050] (d) Supporting government disaster prevention and mitigation: providing reliable forecast data to governments and public institutions, optimizing decision-making support, and bringing social and economic benefits.

[0051] The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0052] (a) Adding perturbations based on physical constraints obtained from historical samples to the initial values of the AI large model;

[0053] (b) Apply the ensemble forecast of the AI meteorological model to ensemble-variational hybrid assimilation.

[0054] The technical solution of this invention solves a long-cherished but unsuccessful technical challenge: large-scale meteorological models are highly nonlinear, and random perturbations in the initial field can cause the forecast field to violate physical laws. To address this technical challenge, this patent adds physical constraints to the ensemble forecast of large-scale meteorological models to more accurately characterize the uncertainty of initial values and the flow-dependence of background error covariance in the atmospheric system. This allows for the establishment of an ensemble-variational hybrid assimilation system based on large-scale meteorological models, enabling rapid updates of assimilated forecasts.

[0055] Third, the technical solution of the present invention solves the following key problems in the prior art in industrial applications through an ensemble-variational hybrid assimilation method based on a large meteorological model, and achieves significant technological progress:

[0056] 1. Solve the problem of rapid generation of ensemble samples in hybrid assimilation methods

[0057] Hybrid assimilation methods require a large number of ensemble samples to reasonably characterize the ensemble background error covariance. In existing hybrid assimilation methods, ensemble forecasts are all derived from numerical forecast models, which are computationally expensive and affect operational forecast efficiency.

[0058] The technical progress of the present invention is as follows: by generating ensemble members through the AI large model, the deduction speed is fast, which can make up for the insufficient computational efficiency of traditional ensemble forecasting, generate a larger ensemble sample within the allowed lag time, greatly improve the assimilation efficiency, and meet the time requirements of real-time assimilation forecasting in business operations.

[0059] 2. Solve the problem of lack of physical constraints in ensemble forecasts of large AI models

[0060] In existing data-driven AI large models, the initial perturbations of the ensemble lack physical equilibrium constraints. Perturbation methods that lack physical constraints easily generate some ensemble members that do not conform to the principles of atmospheric dynamics or thermodynamics, affecting the accuracy and reliability of the ensemble forecast and making it difficult to meet the needs of data assimilation.

[0061] The technical progress of the present invention is: by adopting the forecast error covariance estimation of the AI ensemble forecast model variable space and combining the constraints of the physical model to perform initial value perturbation, the perturbed ensemble members have higher physical consistency, thereby improving the accuracy of the ensemble error covariance estimation.

[0062] 3. Significant technological progress in industrial applications

[0063] Improve forecast accuracy: The ensemble-variation hybrid assimilation method of the present invention significantly improves the initial value quality of the forecast system and the forecast uncertainty estimation, thereby improving the overall forecast accuracy.

[0064] Optimizing the meteorological assimilation system: This invention innovatively proposes a perturbation scheme based on pattern variables, which has stronger adaptability and higher computational efficiency in large-scale data assimilation and ensemble forecasting, meeting the real-time application requirements of modern meteorological forecasting.

[0065] Wide application: The technical solution of the present invention is not only applicable to conventional weather forecasts, but can also be applied to extreme weather events, climate change monitoring and meteorological risk prediction in related industries, and has broad application prospects.

[0066] The present invention solves important problems in the prior art in the field of weather forecasting, achieves significant technological progress, and has high industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of an ensemble-variational hybrid assimilation system and method based on a large meteorological model provided by an embodiment of the present invention.

[0068] Figure 2 This is the technical route of the ensemble-variation hybrid assimilation system based on the large meteorological model provided by the embodiment of the present invention.

[0069] Figure 3 This is a conceptual diagram of the ensemble-variational hybrid assimilation method provided by an embodiment of the present invention.

[0070] Figure 4 This is a structural block diagram of an ensemble-variational hybrid assimilation system based on a large meteorological model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] like Figure 1 As shown, an embodiment of the present invention provides an ensemble-variation hybrid assimilation method based on a large meteorological model, comprising the following steps:

[0073] S1, initial perturbation of global model reanalysis data;

[0074] S2, deduce the initial value after disturbance and generate ensemble forecast;

[0075] S3, calculate the ensemble perturbation and calculate the ensemble background error covariance;

[0076] S4, prepares the model background field, observation data, and static background error covariance, and implements assimilation analysis based on the ensemble-variation hybrid assimilation framework;

[0077] S5, weather forecast based on physical models or machine learning models.

[0078] The present invention, based on an ensemble-variational hybrid assimilation method for large meteorological models, improves the accuracy and stability of weather forecasts by integrating ensemble forecasting and variational assimilation techniques in multiple steps. First, in step S1, the global model reanalysis data is subjected to initial value perturbations to generate multiple initial value scenarios. The initial value perturbation process simulates different initial conditions by imposing small random perturbations on the initial state of the atmosphere, thereby capturing the uncertainty of the atmospheric state. This step provides the initial conditions for subsequent ensemble forecasts, laying the foundation for predictions under different scenarios.

[0079] Next, in step S2, the perturbed initial values are fed into the meteorological model for deduction, generating a series of different ensemble forecasts. Each ensemble member represents the deduction result for a perturbed initial state, reflecting the weather evolution under different initial conditions. Through the deduction of multiple ensemble members, the evolution of weather conditions under different paths can be determined, forming a probabilistic forecast of future weather and providing the necessary data support for the calculation of the ensemble background error covariance.

[0080] In step S3, the system performs a statistical analysis of the ensemble forecast results, calculates the ensemble perturbation, and calculates the ensemble background error covariance. The background error covariance reflects the differences in atmospheric conditions between ensemble members and characterizes the error propagation characteristics between different atmospheric regions and variables. By calculating this error covariance, the system can better capture the uncertainty in the atmospheric background field and provide dynamic background error covariance support for hybrid assimilation analysis.

[0081] Step S4 is the core of the method, implementing assimilation analysis through an ensemble-variational hybrid assimilation framework. In this step, the system prepares the model background field, observational data, and static background error covariances. Based on the dynamic background error covariances obtained from the ensemble forecast, these are introduced into the variational assimilation process. Through this hybrid assimilation framework, the system can effectively fuse observational information and model background fields, correct errors in the ensemble forecast, and thus obtain a more accurate analysis of the atmospheric state. By combining dynamic and static covariances, the hybrid assimilation process effectively reduces the limitations of traditional single assimilation methods.

[0082] In the final step, S5, the system uses either physical or machine learning models to forecast future weather based on the assimilated analysis results. Physical models derive from the laws of atmospheric dynamics and thermodynamics, while machine learning models use historical data and model outputs for fitting and optimization, enhancing their ability to capture nonlinear and complex weather conditions. This combination results in more accurate forecasts, capturing both overall weather trends and subtle local variations.

[0083] The ensemble-variation hybrid assimilation method of the present invention achieves high-precision weather forecasting through multi-step assimilation and the introduction of dynamic background errors.

[0084] As attached Figure 2 The ensemble-variational hybrid assimilation system based on the large meteorological model in this invention first uses a physically constrained method (RANDOMMV) to randomly perturb the initial atmospheric field. Leveraging the powerful inference capabilities of the AI-powered large meteorological model, it rapidly generates ensemble members to ensure diversity and reduce sampling error. Furthermore, an ensemble-variational hybrid assimilation method is employed, combining the dynamic ensemble background error covariance with the static background error estimated using the NMC method, to construct an ensemble-variational hybrid assimilation system for the efficient assimilation of multi-source observational data.

[0085] Specific steps:

[0086] S1 provided in the embodiments of the present invention specifically includes preparing global model analysis fields, such as ERA5 published by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Global Forecasting System (GFS) jointly published by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), JRA-55 published by the Japan Meteorological Agency, and CRA reanalysis published by the National Meteorological Information Center of the China Meteorological Administration. This patent will use ERA5 as an example for illustration. Initial value perturbations are then performed on the reanalysis data.

[0087] The specific initial value perturbation scheme is as follows: for a set of ensemble forecasts derived from an AI meteorological model, the ensemble mean is regarded as the true value, and the ensemble member minus the ensemble mean is regarded as the ensemble forecast error, thereby performing ensemble error covariance estimation to obtain the co-correlation between different model variables. These ensemble forecasts can be AI meteorological model forecast results at the same time with different forecast time periods driven by GEFS. The initial field is perturbed according to the ensemble error covariance matrix calculated above. The random perturbation on each grid point will affect the surrounding grids and other variables through the rules in the ensemble error covariance matrix, thereby ensuring the physical consistency of the ensemble members after the perturbation. This method is similar to the RANDOMCV method, and both imply physical balance constraints. The difference is that the RANDOMCV method is based on the spatial background error covariance of the control variable (CV, control variable), while the method proposed in the present invention is based on the forecast error covariance of the model variable (MV, model variable) space. Therefore, the perturbation method is named the RAMDOMMV method.

[0088] The S2 provided in the embodiment of the present invention specifically includes: deducing the initial value after the disturbance to generate a future ensemble weather forecast. The deduction model is an AI meteorological large model, such as Huawei's Pangu model, Google DeepMind's WeatherBench, NVIDIA's FourCastNet, DeepMind's GraphCast, etc. Note that the required input grids for different large models are not consistent. Therefore, the reanalysis data needs to be grid interpolated. Taking the Pangu model as an example, the initial ensemble members after the disturbance are sequentially used as the input of the Pangu model, and the ensemble forecast for the future time with the same number of members will be output.

[0089] S3 provided by the embodiment of the present invention specifically includes: obtaining a background field error covariance having flow dependence and multivariate correlation characteristics by means of an extended control variable method.

[0090] The S4 provided in the embodiment of the present invention specifically includes: (1) preparing a model background field, which can be a global model analysis field; (2) preparing observation data, which can come from conventional observation data of the GTS, such as sounding, buoy, aircraft report, ship report, ground station data, as well as meteorological satellites, radars, etc.; (3) preparing a static background error covariance, which can be generated by the NMC method, which has been introduced in the introduction of the NMC method S1; (4) based on the background error covariance provided by S3 to reflect the flow-dependent information of the actual weather, the hybrid assimilation analysis increment is obtained by minimizing the cost function, so that the observation information is better coordinated and transmitted between the model space and the multivariate under the action of the collective flow-dependent background error covariance, that is,

[0091]

[0092] Among them, the total assimilation analysis increment is, δx1 is the analysis increment related to the static background error covariance, is the kth set perturbation, K is the number of set members, α is the extended control variable; A is the diagonal matrix of the constrained set extended control variable α, which localizes the set error covariance; β1 and β2 are the weights of the static background error covariance (B) and the set background covariance, respectively, satisfying The third term on the right side of the equation is the observation-related term.

[0093] The background field superimposed analysis increment is the analysis field.

[0094] S5 provided in the embodiment of the present invention specifically includes: using the analysis field obtained in S4 as the initial field to perform weather forecasting, which can be performed using a traditional numerical weather forecasting model or using an AI large model.

[0095] In this embodiment, the background field in step 4 may be the model forecast field predicted at the previous moment S5, or may be a numerical model analysis / forecast field from other sources at the same moment.

[0096] The calculation of the static background error covariance matrix in step 4 can be estimated by using the historical forecast samples (forecast fields with different forecast time periods but at the same time) as the background error samples, or by using the ensemble forecast samples as the background error samples through the GEN_BE software.

[0097] like Figure 4 As shown, an embodiment of the present invention provides an ensemble-variation hybrid assimilation system based on a large meteorological model, including:

[0098] Initial value perturbation module, used to perform initial value perturbations on global model reanalysis data;

[0099] The generation module is used to deduce the initial value after the disturbance and generate the ensemble forecast;

[0100] Statistics module, used to calculate the collective disturbance and the statistical background error covariance of the collective;

[0101] Assimilation analysis module, used to prepare model background fields, observation data, static background error covariance, and implement assimilation analysis based on the ensemble-variation hybrid assimilation framework;

[0102] Weather forecast module, used to make weather forecasts based on physical models or machine learning models.

[0103] Another object of the present invention is to provide a computer device, which includes 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 ensemble-variational hybrid assimilation method based on the large meteorological model.

[0104] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the ensemble-variational hybrid assimilation method based on a large meteorological model.

[0105] Another object of the present invention is to provide an information data processing terminal, which is used to implement the ensemble-variation hybrid assimilation system based on the large meteorological model.

[0106] 1. Typhoon Path and Intensity Forecast: This invention can be used to improve typhoon path and intensity forecasts. High-precision ensemble forecast members generated by a large AI meteorological model, combined with variational assimilation methods, can rapidly assimilate multi-source observation data. This not only improves the accuracy of typhoon paths but also enables more accurate predictions of typhoon intensity changes, providing disaster prevention and mitigation departments with precise path and intensity information and supporting timely emergency response.

[0107] 2. Urban Short-Term Heavy Rainfall Warning: This invention improves early warning capabilities for short-term heavy rainfall events, specifically targeting the rapid development of urban rainstorms. Leveraging AI-generated ensemble forecasts, the system rapidly assimilates high-resolution observational data to accurately forecast precipitation amounts and distribution over the next several hours. This provides real-time warning data to urban drainage systems and traffic management departments, effectively preventing waterlogging and mitigating losses caused by heavy rainfall.

[0108] These two examples demonstrate that the ensemble-variational hybrid assimilation method based on large-scale meteorological models not only has important applications in weather forecasting but also plays a key role in other fields such as urban management and transportation. By adding physical constraints to AI large-scale model ensemble forecasts and combining them with advanced ensemble-variational hybrid assimilation, this technology can provide more accurate forecast support, helping to make research and actual operations in related fields more efficient and safer.

[0109] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0110] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. An ensemble-variation hybrid assimilation method based on a large meteorological model, characterized by: The following steps are involved: S1, initial perturbation of global model reanalysis data; S2, deduce the initial value after disturbance and generate ensemble forecast; S3, calculate the ensemble perturbation and calculate the ensemble background error covariance; S4, prepares the model background field, observation data, and static background error covariance, and implements assimilation analysis based on the ensemble-variation hybrid assimilation framework; S5, weather forecasting based on physical models or machine learning models; Said S1 further comprises: Prepare global model analysis or forecast fields, including ERA5 released by the European Centre for Medium-Range Weather Forecasts (ECMWF), GFS jointly released by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) of the United States, JRA-55 released by the Japan Meteorological Agency, and CRA reanalysis data released by the National Meteorological Information Center of the China Meteorological Administration; Perform initial value perturbations on the reanalysis data to generate multiple initial ensemble members; The initial value perturbation specifically adopts the RANDOMMV method, Specifically, for a set of ensemble forecasts derived from AI meteorological models, the ensemble mean is regarded as the true value, and the ensemble members minus the ensemble mean are regarded as the ensemble forecast error, so as to estimate the ensemble error covariance and obtain the co-correlation between different model variables; these ensemble forecasts are the forecast results of the AI meteorological model at the same time with different forecast time limits driven by GEFS; the initial field is perturbed according to the ensemble error covariance matrix of the model variable space, and the random perturbation on each grid point will affect the surrounding grids and other variables through the ensemble error covariance matrix, which implies physical balance constraints.

2. The ensemble-variational hybrid assimilation method based on a large meteorological model according to claim 1, characterized in that: The S2 specifically includes: deducing the initial value after disturbance to generate a future ensemble weather forecast; the deduction model is an AI meteorological large model, including Huawei's Pangu model, Google DeepMind's WeatherBench, NVIDIA's FourCastNet, and DeepMind's GraphCast.

3. The ensemble-variation hybrid assimilation method based on a large meteorological model according to claim 1, characterized in that: The S3 specifically includes: obtaining the background field error covariance with flow dependence and multivariate correlation characteristics by means of an extended control variable method.

4. The ensemble-variational hybrid assimilation method based on a large meteorological model according to claim 1, characterized in that: The S4 specifically includes: (1) preparing the model background field, global model analysis field or forecast field; (2) preparing observation data, which are conventional observation data from GTS; (3) preparing static background error covariance; (4) reflecting the flow-dependent information of the actual weather based on the background error covariance provided by the ensemble perturbation, and obtaining the hybrid assimilation analysis increment by minimizing the cost function, so that the observation information is better coordinated and transmitted in the model space and multivariate reduction under the action of the ensemble flow-dependent background error covariance, that is, Among them, the total assimilation analysis increment is, δx1 is the analysis increment related to the static background error covariance, is the kth set perturbation, K is the number of set members, α is the extended control variable; A is the diagonal matrix of the constrained set extended control variable α, which localizes the set error covariance; β1 and β2 are the weights of the static background error covariance (B) and the set background covariance, respectively, satisfying The third term on the right side of the equation is the observation-related term; The background field superimposed analysis increment is the analysis field.

5. The ensemble-variational hybrid assimilation method based on a large meteorological model according to claim 1, characterized in that: The S5 specifically includes: using the analysis field obtained in S4 as the initial field to perform weather forecasting, using a traditional numerical weather forecasting model to perform forecasting, or using an AI large model to perform forecasting.

6. A large-scale meteorological model-based ensemble-variational hybrid assimilation system implementing the large-scale meteorological model-based ensemble-variational hybrid assimilation method according to any one of claims 1 to 5, characterized in that: The ensemble-variation hybrid assimilation system based on the large meteorological model includes: Initial value perturbation module, used to perform initial value perturbations on global model reanalysis data; The generation module is used to deduce the initial value after the disturbance and generate the ensemble forecast; Statistics module, used to calculate the collective disturbance and the statistical background error covariance of the collective; Assimilation analysis module, used to prepare model background fields, observation data, static background error covariance, and implement assimilation analysis based on the ensemble-variation hybrid assimilation framework; Weather forecast module, used to make weather forecasts based on physical models or machine learning models.

7. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the ensemble-variational hybrid assimilation method based on a large meteorological model as described in any one of claims 1 to 5.

8. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the ensemble-variation hybrid assimilation system based on the large meteorological model as described in claim 6.

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