Global numerical pattern data assimilation and effect evaluation method, device and equipment
By using data assimilation technology to assimilate atmospheric and ocean observation data during the initialization process of the global numerical model, the problem of error amplification caused by uncertainty in the global numerical model initialization process will lead to poor simulation accuracy is solved, and a higher accuracy is achieved. Simulation accuracy.
Patent Information
- Application Number
- CN202411351550.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-26
- Publication Date
- 2025-01-24
AI Technical Summary
There is uncertainty in the initialization process of global numerical models, which leads to amplification of errors, which leads to poor simulation accuracy.
By using the data assimilation model during the initialization of the global numerical pattern, it continues to assimilate the atmosphere and marine observation data to the mode, and dynamically update the boundary conditions to improve the accuracy of simulation.
The mode simulation results are continuously closer to the observation value, improving the simulation accuracy of the global numerical model, and a reduction of the simulation accuracy caused by the error release conference.
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Figure CN119355844A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of numerical weather forecasting, and in particular to a method, device and equipment for global numerical model data assimilation and effect evaluation. Background Art
[0002] Numerical weather forecast, or numerical weather prediction, numerical weather prediction, numerical forecast, is a method of making weather forecasts by using the current weather conditions as input and solving the atmospheric motion equations through numerical methods given the initial and boundary conditions, so as to predict the atmospheric state at future moments from the atmospheric state at the known initial moment. Relying on numerical weather forecasts, meteorological services can currently achieve seamless weather forecasts on time scales: short-term (0-several hours), short-term (1-3 days), medium-term (4-9 days) and extended period (10-30 days). There are currently several numerical models, specifically:
[0003] The representative model of regional mesoscale numerical model is WRF (Weather Research and Forecasting) numerical model. WRF numerical model is a new generation of mesoscale numerical model system jointly developed by the National Center for Atmospheric Research (NCAR), the National Center for Environmental Prediction (NCEP) and many universities, research institutes and business departments. It has advanced design concepts, and the model theme is written in Fortran90 language. It has the advantages of flexibility, effectiveness and a wide range of applicable platforms. Its characteristics are advanced data assimilation technology, powerful nesting capabilities and advanced physical processes, especially in the processing capabilities of convection and mesoscale precipitation. The WRF numerical model system mainly consists of three parts: preprocessing (WRF Preprocessing System, WPS), WRF main mode and post-processing (Post-Processing Programs, such as NCL, ARWpost, etc.). The WRF main mode system is the core part, which takes into account the complete physical process options, such as microphysics, cumulus convection, planetary boundary layer, land surface process, radiation and surface layer, etc. There are rich parameterization schemes to choose from in each physical process, which can generate the power, heat and water vapor sources and sinks required by the model, and have a forcing and driving effect on the power and heat processes of the model. However, the WRF numerical model is a traditional regional model with side boundary restrictions (two-way information communication is impossible inside and outside the regional grid), and cannot directly achieve ultra-high resolution simulation, and cannot capture turbulent motion in the atmospheric boundary layer.
[0004] The mesoscale numerical model in China is represented by the GRAPES_Meso (global / regionalassimilation and prediction system_Meso) numerical model. The mesoscale version of the GRAPES_Meso numerical model is a new generation of global / regional integrated numerical prediction system independently developed by the China Meteorological Science Academy. It is mainly composed of a fully compressible dynamic framework and a physical parameterization program. The GRAPES_Meso model is a model for short-term weather forecasting in China. Since 2000, it has been widely used in weather forecasting for more than 20 years. It can accurately forecast weather conditions under different conditions in the world and China, and has played an important role in the actual meteorological business of the country and the region. The dynamic framework of the GRAPES_Meso numerical model adopts vertical height terrain tracking coordinates, semi-implicit and semi-Lagrangian time convection schemes for time integral discretization, and Arakawa-C grid variable distribution and second-order precision central difference format and non-vertical discretization fluid static pressure approximation scheme are used in the horizontal space. The main physical processes included are radiation transfer, turbulent mixing, moist convection and grid-scale precipitation processes, land surface processes and sub-grid-scale terrain gravity wave drag, etc. The parameterization schemes describing each physical process can be freely combined and selected. However, the GRAPES_Meso numerical model cannot achieve global accurate simulation, there is still a gap with the world's mainstream numerical forecasting models, and it is highly dependent on the initial field and boundary conditions.
[0005] Large eddy simulation, represented by the LES (Large Eddy Simulation, LES) numerical model, introduces large eddy simulation technology into the atmospheric boundary layer. Large eddy simulation has been widely used in the simulation of boundary layer turbulence and has obtained a series of detailed and reliable high-resolution atmospheric turbulence information. The basic idea of large eddy simulation is that turbulent motion is composed of many vortices of different sizes. A sufficiently fine grid scale (below 100m) is used to separate large eddies and small eddies. Since large turbulent eddies carry most of the turbulent energy, they are directly simulated; while small-scale turbulent eddies are generated by nonlinear interactions. They are not directly related to the instability and boundary problems of motion, and they play a dissipative role, making them easier to parameterize and close. The earliest large eddy simulation studies were all aimed at typical convective boundary layers with obvious large eddy characteristics. With the improvement of computer technology, large eddy simulation has been extended to stable boundary layers and uneven underlying surfaces with more complex boundaries and less obvious large eddy characteristics. At the same time, the LES model has extremely high grid resolution and three-dimensional simulation effect on boundary layer turbulence. These characteristics make it very suitable for the study of various physical processes, such as deep and shallow convection, warm cloud precipitation convection, etc. Based on the unique advantages of LES technology in boundary layer research, more and more mesoscale models hope to introduce high-resolution boundary layer schemes (large eddy simulation mechanisms) into numerical forecast models, which greatly improves the simulation effect of the boundary layer of the mesoscale model. At the same time, it is also very critical to introduce compensation mechanisms for the inherent dissipation problems in the advection scheme of the mesoscale model. However, the LES numerical model consumes a lot of computing resources, has high computing costs, and needs to be coupled with the mesoscale numerical model, making the calculation difficult to achieve.
[0006] The operation of the above numerical models requires continuous external data input. Once the data input is interrupted, the model cannot continue to operate. Therefore, the forecasting ability of traditional regional meteorological numerical models depends to a large extent on the quality of external input data.
[0007] The global numerical model is different from the traditional regional meteorological numerical models mentioned above. It couples multiple system models such as the atmosphere, ocean, land, and sea ice through couplers, continuously transfers matter and energy between the systems, and simulates the movement of the global atmosphere for several years to decades. It has no external data input, but continuously adjusts the atmospheric state within the model to achieve equilibrium, which is the cold start process. However, the global coupled climate system model also has its shortcomings. First, due to the lack of external data input, the global climate system model needs a long time to run to reach a state of equilibrium; second, the global numerical model itself has uncertainties, and various errors are prone to occur in the process of solving the numerical model. If there is no real observation data to constrain it, after a long period of operation, the model error may be infinitely magnified, thereby reducing the accuracy of the global numerical model forecast.
[0008] In addition, numerical models are easily affected by computational models. The initial fields, boundary conditions, parameterization schemes, etc. introduced during the calculation will cause computational errors. Chaos theory also makes it difficult for numerical models to make accurate long-term forecasts. Therefore, the forecast results of numerical models often have systematic deviations from actual observations.
[0009] In summary, there are the following problems in using global numerical models for forecasting:
[0010] 1. Existing global numerical models can simulate the global atmospheric state and changes, taking into account the interaction between the atmosphere and other systems such as land and ocean. However, global numerical models often have coarse resolution and poor simulation accuracy.
[0011] 2. Global numerical models have their own uncertainties, and various errors are easily generated in the process of solving numerical models. If there is no real observation data to constrain them, after a long period of operation, the model errors may be infinitely magnified, and the weather simulation effect cannot be evaluated, thereby reducing the accuracy of global numerical model forecasts.
[0012] Therefore, there is an urgent need for a dynamic tuning and effect evaluation method for the global numerical model initialization process. Summary of the invention
[0013] In view of this, the present invention provides a global numerical model data assimilation and effect evaluation method, device and equipment to solve the problem of uncertainty in the global numerical model itself. If there is no real observation data to constrain it, various errors are likely to occur in the process of solving the numerical model and the errors are easily amplified, ultimately leading to poor simulation accuracy.
[0014] In a first aspect, the present invention provides a global numerical model data assimilation and effect evaluation method, the method comprising:
[0015] Build a data assimilation model;
[0016] Obtain atmospheric and ocean observation data;
[0017] Based on the data assimilation model and atmospheric observation data, atmospheric data assimilation is continuously performed during the initialization of the global numerical model to obtain a global numerical model with atmospheric assimilation added;
[0018] Based on the data assimilation model, atmospheric observation data and ocean observation data are continuously assimilated during the initialization of the global numerical model, and the global numerical model with the atmosphere and ocean assimilated is obtained;
[0019] The simulation effects of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmospheric and ocean assimilation on the preset weather elements are evaluated respectively.
[0020] The global numerical model data assimilation and effect evaluation method provided by the present invention can continuously assimilate atmospheric observation data and ocean observation data into the global numerical model by using a data assimilation model during the initialization operation of the global numerical model, so that the model simulation results can be continuously brought closer to the observed values, and the boundary conditions during the operation of the model can be dynamically updated and optimized, so as to continuously improve the accuracy of the global numerical model simulation. By using data assimilation technology during the initialization of the global numerical model, global analysis field data that is closer to the actual atmospheric state can be formed, providing more accurate initial field and boundary field data for subsequent model forecasts, and solving the problem that the global numerical model itself has uncertainty. If there is no real observation data to constrain it, various errors are likely to be generated in the numerical model solution process and the errors are likely to be amplified, which ultimately leads to the problem of poor simulation accuracy.
[0021] In an optional implementation, establishing a data assimilation model includes:
[0022] A data assimilation model for observational forcing terms is established based on the incremental analysis process and using a time-varying relaxation approximation algorithm.
[0023] In an optional implementation, the data assimilation model of the observational forcing term is expressed by the following formula:
[0024]
[0025] Among them, x is the prediction variable, x obs is the observed value of the forecast variable, α(x obs -x) is the observational forcing term, α is the assimilation coefficient, and F(x) is the model prediction equation group.
[0026] The global numerical model data assimilation and effect evaluation method provided by the present invention is based on an incremental analysis process and adopts a time-varying relaxation approximation algorithm to establish a data assimilation model of observational forcing terms. Compared with the traditional data assimilation model structure, it is simpler. By adding observational values as forcing terms in the global numerical model equation group, the model simulation effect can converge to the observational values in a short time, thereby obtaining an analysis field that is closer to the actual atmospheric state, thereby providing more accurate initial field data for subsequent model forecasts.
[0027] In an optional implementation, acquiring atmospheric observation data and ocean observation data includes:
[0028] Obtain FNL reanalysis data as atmospheric observation data;
[0029] Obtain GODAS ocean reanalysis data as ocean observation data.
[0030] The global numerical model data assimilation and effect evaluation method provided by the present invention uses the FNL (Final Reanalysis Data, FNL) reanalysis data produced every 6 hours with a spatial resolution of 1°×1° jointly produced by the National Center for Meteorological and Environmental Prediction (NCEP) of the United States and the National Center for Atmospheric Research (NCAR) of the United States for atmospheric observation data, and uses the GODAS (the Global Ocean Data Assimilation System, GODAS) ocean reanalysis data for ocean observation data. The most advanced global data assimilation system and the most complete observation database are used to perform quality control and assimilation processing on observation data from various sources, thereby obtaining a complete set of reanalysis data sets, which not only contains a wide variety of meteorological elements and has global large-scale data, but also can extend for a long period of time, and is a comprehensive data set, which provides conditions for subsequent data assimilation of global numerical models.
[0031] In an optional implementation, atmospheric data assimilation is continuously performed during the initialization operation of the global numerical model based on the data assimilation model and the atmospheric observation data, and the global numerical model after adding atmospheric assimilation is obtained, including:
[0032] Obtain atmospheric temperature and wind speed and direction data from atmospheric observation data;
[0033] Calculate atmospheric virtual temperature based on atmospheric temperature;
[0034] During the initialization operation of the global numerical model at a preset time, the atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature are continuously input into the global numerical model at a first preset time interval based on the data assimilation model to assimilate the atmospheric data, and obtain the global numerical model after adding atmospheric assimilation.
[0035] The global numerical model data assimilation and effect evaluation method provided by the present invention continuously inputs atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature into the global numerical model at a first preset time interval through a data assimilation model during the initialization operation of the global numerical model at a preset time to perform atmospheric data assimilation, and obtains a global numerical model after adding atmospheric assimilation. The method can make the simulation results in the initialization process of the global numerical model continuously approach the observed values, and the boundary conditions in the operation process of the global numerical model can be dynamically updated and optimized, so as to continuously improve the accuracy of the global numerical model simulation.
[0036] In an optional implementation, based on the data assimilation model, the atmospheric observation data and the ocean observation data, atmospheric data assimilation and ocean data assimilation are continuously performed simultaneously during the initialization operation of the global numerical model, and the global numerical model after the atmospheric and ocean assimilation are simultaneously added includes:
[0037] Obtain sea surface temperature data from ocean observation data;
[0038] During the initialization operation of the global numerical model at a preset time, based on the data assimilation model, the atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature are continuously input into the global numerical model at a first preset time interval, and the ocean surface temperature data is continuously input at a second preset time interval. Atmospheric data assimilation and ocean data assimilation are performed simultaneously to obtain a global numerical model after the atmosphere and ocean assimilation are simultaneously added.
[0039] The global numerical model data assimilation and effect evaluation method provided by the present invention, based on the data assimilation model during the initialization operation process of the preset time, continuously inputs the atmospheric temperature, wind speed and direction data and the atmospheric virtual temperature into the global numerical model at a first preset time interval for atmospheric data assimilation, and simultaneously continuously inputs the ocean surface temperature data into the global numerical model at a second time interval for ocean data assimilation, so as to obtain a global numerical model after the atmosphere and ocean are assimilated at the same time. By using the data assimilation technology in the initialization process of the global numerical model, global analysis field data that is closer to the actual atmospheric state can be formed, so as to provide more accurate initial field and boundary field data for subsequent model forecasts.
[0040] In an optional embodiment, the preset weather elements include a 10m wind speed element, a 2m temperature element, a rainfall element, and a sea surface temperature element;
[0041] The simulation effects of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmospheric and ocean assimilation on the preset weather elements are evaluated separately, including:
[0042] The preset weather elements are simulated by using the global numerical model after adding the atmosphere assimilation and the global numerical model after adding the atmosphere and ocean assimilation. The 10m wind speed simulation results, 2m temperature simulation results, rainfall simulation results and sea surface temperature simulation results of the global numerical model after adding the atmosphere assimilation and the global numerical model after adding the atmosphere and ocean assimilation are as follows;
[0043] Obtain the 10m wind speed measured observation value, 2m temperature measured observation value, rainfall measured observation value and sea surface temperature measured observation value;
[0044] The preset evaluation indicators are used to compare the 10m wind speed simulation results of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation with the 10m wind speed observations, the 2m temperature simulation results with the 2m temperature observations, the rainfall simulation results with the rainfall observations, and the sea surface temperature simulation results with the sea surface temperature observations, to evaluate the simulation effect of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation on the preset weather elements.
[0045] The global numerical model data assimilation and effect evaluation method provided by the present invention uses preset indicators to evaluate the simulation effects of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmospheric and ocean assimilation on factors such as 10m wind speed, 2m temperature, rainfall and sea surface temperature. The use of data assimilation during the initialization of the global numerical model can make the model simulation effect continuously approach the measured observation value, and dynamically update and optimize the boundary conditions during the operation of the model, so as to continuously improve the accuracy of the global numerical model simulation and reach a balanced state faster than a cold start.
[0046] In a second aspect, the present invention provides a global numerical model data assimilation and effect evaluation device, the device comprising:
[0047] Model building module, used to build data assimilation model;
[0048] A data acquisition module, used to acquire atmospheric observation data and ocean observation data;
[0049] The atmospheric assimilation module is used to continuously assimilate atmospheric data during the initialization of the global numerical model based on the data assimilation model and atmospheric observation data, and obtain the global numerical model after adding atmospheric assimilation;
[0050] The atmosphere-ocean assimilation module is used to continuously assimilate atmospheric data and ocean data during the initialization of the global numerical model based on the data assimilation model, atmospheric observation data and ocean observation data, and obtain a global numerical model after adding the atmosphere and ocean assimilation at the same time;
[0051] The simulation effect evaluation module is used to evaluate the simulation effect of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmospheric and ocean assimilation on the preset weather elements.
[0052] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the global numerical model data assimilation and effect evaluation method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.
[0053] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the global numerical model data assimilation and effect evaluation method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS
[0054] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0055] Figure 1 is a flow chart of a global numerical model data assimilation and effect evaluation method according to an embodiment of the present invention;
[0056] Figure 2 is a flow chart of another global numerical model data assimilation and effect evaluation method according to an embodiment of the present invention;
[0057] Figure 3 is a flow chart of another global numerical model data assimilation and effect evaluation method according to an embodiment of the present invention;
[0058] Figure 4 is a flow chart of another global numerical model data assimilation and effect evaluation method according to an embodiment of the present invention;
[0059] Figure 5 is a schematic diagram of using an absolute error evaluation index to evaluate the 2m temperature simulation effect of a global numerical model after atmospheric assimilation according to an embodiment of the present invention;
[0060] Figure 6 is a schematic diagram of using a root mean square error evaluation index to evaluate the 2m temperature simulation effect of a global numerical model after atmospheric assimilation according to an embodiment of the present invention;
[0061] Figure 7 is a schematic diagram of using a correlation coefficient evaluation index to evaluate the 2m temperature simulation effect of a global numerical model after atmospheric assimilation according to an embodiment of the present invention;
[0062] Figure 8 It is a schematic diagram of using an absolute error evaluation index to evaluate the 2m temperature simulation effect of a global numerical model after assimilation of the atmosphere and ocean according to an embodiment of the present invention;
[0063] Fig. 9 It is a schematic diagram of using a root mean square error evaluation index to evaluate the 2m temperature simulation effect of a global numerical model after assimilation of the atmosphere and ocean according to an embodiment of the present invention;
[0064] Fig.10 It is a schematic diagram of using a correlation coefficient evaluation index to evaluate the 2m temperature simulation effect of a global numerical model after assimilation of the atmosphere and the ocean according to an embodiment of the present invention;
[0065] Fig.11 is a schematic diagram of data assimilation according to an embodiment of the present invention;
[0066] Fig.12 is a structural block diagram of a global numerical model data assimilation and effect evaluation device according to an embodiment of the present invention;
[0067] Fig.13 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION
[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.
[0069] According to an embodiment of the present invention, an embodiment of a global numerical model data assimilation and effect evaluation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.
[0070] The embodiment of the present invention realizes dynamic optimization of model simulation results by continuously assimilating atmospheric and ocean observation data during the initialization operation of the global numerical model, so that the model simulation results are gradually approached to the observed values, and finally the simulation results closest to the actual observation state are obtained, providing more accurate initial field and boundary field data for subsequent model forecasts.
[0071] In this embodiment, a global numerical model data assimilation and effect evaluation method is provided, which can be used in mobile terminals, such as central processing units, servers, etc. Figure 1 is a flow chart of a method for global numerical model data assimilation and effect evaluation according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:
[0072] Step S101: establishing a data assimilation model.
[0073] Specifically, data assimilation technology mainly adds observational data sets to the model state through the consistency constraints of physical characteristics and time evolution laws, so that the initial state of the model is closer to the observational data (that is, as close as possible to the real atmospheric state), thereby producing better forecasting results. Commonly used data assimilation methods include time-varying nudging (nudging data assimilation method) and four-dimensional variational data assimilation method (4DVAR data assimilation method).
[0074] In this embodiment, a time-varying relaxation approximation method is used to establish a data assimilation model.
[0075] Step S102, acquiring atmospheric observation data and ocean observation data.
[0076] Specifically, atmospheric observation data can be used to observe changes in meteorological elements such as atmospheric temperature, humidity, Kia, wind speed, and precipitation, and to analyze weather change patterns and climate change trends. The ocean is the largest carbon storage reservoir on Earth and plays a key role in regulating the Earth's temperature. Ocean observation data can be used to observe ocean climate change, which can help better understand the mechanism of global climate change and provide a scientific basis for responding to climate change.
[0077] Step S103, based on the data assimilation model and the atmospheric observation data, atmospheric data assimilation is continuously performed during the initialization operation of the global numerical model to obtain a global numerical model after adding atmospheric assimilation.
[0078] Specifically, the data assimilation model is used to continuously add atmospheric observation data to the global numerical model for data assimilation processing, thus achieving the purpose of assimilating atmospheric observation data into the global numerical model.
[0079] Step S104, based on the data assimilation model, the atmospheric observation data and the ocean observation data, atmospheric data assimilation and ocean data assimilation are continuously performed simultaneously during the initialization operation of the global numerical model to obtain a global numerical model after the atmosphere and ocean assimilation are added simultaneously.
[0080] Specifically, a data assimilation model is used to continuously add atmospheric observation data and ocean observation data to the global numerical model at different time intervals for atmospheric and ocean data assimilation processing, and a global numerical model is obtained after the atmosphere and ocean are assimilated at the same time.
[0081] Step S105 , respectively evaluating the simulation effects of the global numerical model after adding the atmospheric assimilation and the global numerical model after adding both the atmospheric and ocean assimilation on the preset weather elements.
[0082] Specifically, the preset weather elements may include wind speed, temperature, rainfall, and sea surface temperature, etc., which are set according to actual conditions and are not specifically limited here. In this embodiment, the simulation results of the preset weather elements of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both the atmosphere and the ocean assimilation can be compared with the measured observation values of the preset weather elements by using evaluation indicators to evaluate the simulation effect of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both the atmosphere and the ocean assimilation on the preset weather elements.
[0083] The global numerical model data assimilation and effect evaluation method provided in this embodiment can continuously assimilate atmospheric observation data and ocean observation data into the global numerical model by using the data assimilation model during the initialization operation of the global numerical model, so that the model simulation results can be continuously brought closer to the observed values, and the boundary conditions during the model operation process can be dynamically updated and optimized, which can continuously improve the accuracy of the global numerical model simulation. By using data assimilation technology in the initialization process of the global numerical model, global analysis field data that is closer to the actual atmospheric state can be formed, providing more accurate initial field and boundary field data for subsequent model forecasts, solving the problem that the global numerical model itself has uncertainty. If there is no real observation data to constrain it, various errors are likely to be generated in the numerical model solution process and the errors are easily amplified, which ultimately leads to the problem of poor simulation accuracy.
[0084] In this embodiment, a global numerical model data assimilation and effect evaluation method is provided, which can be used in mobile terminals, such as central processing units, servers, etc. Figure 2 is a flow chart of a method for global numerical model data assimilation and effect evaluation according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:
[0085] Step S201: establishing a data assimilation model.
[0086] Specifically, the above step S201 includes:
[0087] Step a. Based on the incremental analysis updating (IAU) process and using the time-varying relaxation approximation algorithm, a data assimilation model with preset observational forcing terms is established, such as Figure 4 shown.
[0088] The basic formula of the nudging data assimilation method is as follows:
[0089]
[0090] Among them, x is the prediction variable, x obs is the observed value of the forecast variable, a(x obs -x) is the observational forcing term, α is the assimilation coefficient, and F(x) is the model prediction equation group. By setting the atmospheric observation data as a forcing term in the global numerical model at intervals of 6 hours, the model integration results can be forced to gradually converge to the observed values, so that x obs -x gradually approaches 0, the model integral gradually becomes stable, and finally forms a set of analysis field data close to the actual atmospheric state. Fig.11 shown.
[0091] The advantages of nudging data assimilation over the 4DVAR data assimilation method are as follows: The structure of the nudging assimilation method is simpler. By adding observations as forcing terms in the model prediction equations, the model simulation effect can converge to the observations in a short time, thereby obtaining an analysis field closer to the actual atmospheric state, thereby providing more accurate initial field data for subsequent model predictions. The use of the nudging data assimilation method can achieve faster convergence of the forced simulation effect, shorten the assimilation time, greatly save the computing cost, and improve the computing efficiency.
[0092] Step S202, acquiring atmospheric observation data and ocean observation data.
[0093] Specifically, the above step S202 includes:
[0094] Step S2021, obtaining FNL reanalysis data as atmospheric observation data.
[0095] Specifically, the FNL (Final Reanalysis Data, FNL) atmospheric reanalysis data are used as atmospheric observation data.
[0096] Step S2022, obtaining GODAS ocean reanalysis data as ocean observation data.
[0097] Specifically, the Global Ocean Data Assimilation System (GODAS) ocean reanalysis data are used as ocean observation data.
[0098] Step S203: Based on the data assimilation model and the atmospheric observation data, atmospheric data assimilation is continuously performed during the initialization of the global numerical model to obtain a global numerical model after adding atmospheric assimilation. Figure 1Step S103 of the illustrated embodiment will not be described in detail here.
[0099] Step S204: Based on the data assimilation model, the atmospheric observation data and the ocean observation data, atmospheric data assimilation and ocean data assimilation are continuously performed during the initialization of the global numerical model, so as to obtain a global numerical model after adding the atmospheric and ocean assimilation. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.
[0100] Step S205, respectively evaluate the simulation effect of the global numerical model after adding the atmospheric assimilation and the global numerical model after adding the atmospheric and ocean assimilation on the preset weather elements. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.
[0101] The global numerical model data assimilation and effect evaluation method provided in this embodiment is based on the incremental analysis process and uses the time-varying relaxation approximation algorithm to establish a data assimilation model with preset observation forcing terms. Compared with the traditional data assimilation model structure, it is simpler. By adding observation values as forcing terms in the global numerical model equation group, the model simulation effect can converge to the observation value in a short time, thereby obtaining an analysis field closer to the actual atmospheric state, thereby providing more accurate initial field data for subsequent model forecasts. The atmospheric observation data uses the FNL reanalysis data with a spatial resolution of 1°×1° produced every 6 hours by the National Center for Meteorological and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) of the United States. The ocean observation data uses the GODAS ocean reanalysis data. The most advanced global data assimilation system and the most complete observation database are used to perform quality control and assimilation processing on observation data from various sources, thereby obtaining a complete set of reanalysis data data sets, which not only contains a wide variety of meteorological elements, has global data of a large range, but also can extend for a long period of time. It is a comprehensive data set, which provides conditions for subsequent data assimilation of global numerical models.
[0102] In this embodiment, a global numerical model data assimilation and effect evaluation method is provided, which can be used in mobile terminals, such as central processing units, servers, etc. Figure 3 is a flow chart of a method for global numerical model data assimilation and effect evaluation according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:
[0103] Step S301: Establish a data assimilation model. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.
[0104] Step S302: Acquire atmospheric observation data and ocean observation data. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.
[0105] Step S303, based on the data assimilation model and the atmospheric observation data, atmospheric data assimilation is continuously performed during the initialization operation of the global numerical model to obtain a global numerical model after adding atmospheric assimilation.
[0106] Specifically, the global numerical model in this embodiment uses the CAS FGOALS-f2 global coupled model (sea-air coupled model), the standard horizontal resolution of the model is 100 kilometers, which includes four fully coupled components, namely: atmosphere, ocean, land and sea ice, and its atmospheric component model is FAMIL2, which is characterized by using a finite volume method as a dynamic core and discretizing on a cubic sphere grid system. The cloud microphysical parameterization scheme used in FGOALS-f2 comes from GFDL (Geophysical Fluid Dynamics Laboratory, GFDL), and considers six types of water particles; the cumulus parameterization scheme uses a convective-analyzed precipitation scheme, which can calculate the microphysical processes in deep and shallow convective cumulus processes.
[0107] The above step S303 includes:
[0108] Step S3031, obtaining atmospheric temperature and wind speed and direction data from atmospheric observation data.
[0109] Specifically, atmospheric temperature and wind speed and direction data are obtained from atmospheric observation data.
[0110] Step S3032, calculating the atmospheric virtual temperature based on the atmospheric temperature.
[0111] Specifically, the atmospheric virtual temperature is calculated according to the following formula:
[0112] Tv=(1+0.608q)*T;
[0113] Among them, Tv is the atmospheric virtual temperature, T is the atmospheric temperature, and q is the specific humidity.
[0114] Step S3033, during the initialization operation of the global numerical model at a preset time, the atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature are continuously input into the global numerical model at a first preset time interval based on the data assimilation model to perform atmospheric data assimilation, and a global numerical model after adding atmospheric assimilation is obtained.
[0115] Specifically, in this embodiment, the global numerical model initialization operation process of the preset time is set to the global numerical model initialization operation time, which is 5 years (2018 to 2022); the first preset time interval is set to the atmospheric data assimilation time interval, which is 6 hours.
[0116] A data assimilation model is used to continuously input atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature into a global numerical model (CAS FGOALS-f2 global coupled model) every 6 hours for atmospheric data assimilation, and a global numerical model with atmospheric assimilation is obtained.
[0117] Step S304, based on the data assimilation model, the atmospheric observation data and the ocean observation data, atmospheric data assimilation and ocean data assimilation are continuously performed simultaneously during the initialization operation of the global numerical model to obtain a global numerical model after the atmosphere and ocean assimilation are added simultaneously.
[0118] Specifically, the above step S304 includes:
[0119] Step 3041, obtaining ocean surface temperature data from ocean observation data.
[0120] Specifically, sea surface temperature (SST) data is obtained from ocean observation data.
[0121] Step 3042, during the initialization operation of the global numerical model at a preset time, based on the data assimilation model, the atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature are continuously input into the global numerical model at a first preset time interval, and the ocean surface temperature data is continuously input at a second preset time interval to perform atmospheric data assimilation and ocean data assimilation at the same time, so as to obtain a global numerical model after the atmosphere and ocean assimilation are simultaneously added.
[0122] Specifically, in this embodiment, the global numerical model initialization operation process of the preset time is set to the global numerical model initialization operation time, which is 5 years (2018 to 2022); the second preset time is set to the ocean data assimilation time interval, which is 30 days.
[0123] During the 5 years of the initial operation of the global numerical model, the atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature are continuously input into the global numerical model (CASFGOALS-f2 global coupled model) every 6 hours, and the ocean surface temperature data is continuously input into the global numerical model every 30 days for atmospheric data assimilation and ocean data assimilation, and a global numerical model is obtained after the atmosphere and ocean are assimilated at the same time.
[0124] Step S305 , respectively evaluating the simulation effects of the global numerical model after adding the atmospheric assimilation and the global numerical model after adding both the atmospheric and ocean assimilation on the preset weather elements.
[0125] Specifically, the preset weather elements include a 10m wind speed element, a 2m temperature element, a rainfall element, and a sea surface temperature element. The above step S305 includes:
[0126] Step S3051, using the global numerical model after adding atmospheric assimilation and the global numerical model after adding both the atmosphere and the ocean assimilation to simulate the preset weather elements respectively, to obtain the 10m wind speed simulation results, 2m temperature simulation results, rainfall simulation results and sea surface temperature simulation results of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both the atmosphere and the ocean assimilation.
[0127] Specifically, the global numerical model after adding atmospheric assimilation was used to simulate the 10m wind speed element, 2m temperature element, rainfall element and sea surface temperature element, and the first 10m wind speed simulation result, the first 2m temperature simulation result, the first rainfall simulation result and the first sea surface temperature simulation result were obtained respectively.
[0128] The 10m wind speed element, 2m temperature element, rainfall element and sea surface temperature element were simulated by adopting the global numerical model after simultaneously incorporating the atmosphere and ocean assimilation, and the second 10m wind speed simulation result, the second 2m temperature simulation result, the second rainfall simulation result and the second sea surface temperature simulation result were obtained respectively.
[0129] Step S3052, obtaining the 10m wind speed measured observation value, the 2m temperature measured observation value, the rainfall measured observation value and the sea surface temperature measured observation value.
[0130] Step S3053, using preset evaluation indicators to compare the 10m wind speed simulation results of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation with the 10m wind speed actual observation value, the 2m temperature simulation results with the 2m temperature actual observation value, the rainfall simulation results with the rainfall actual observation value, and the sea surface temperature simulation results with the sea surface temperature actual observation value, to evaluate the simulation effect of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation on the preset weather elements.
[0131] Specifically, the preset evaluation indicators are selected as a root mean square error evaluation indicator (RMSE), a correlation coefficient evaluation indicator, and an absolute error evaluation indicator.
[0132] Among them: The calculation formula and meaning of the root mean square error evaluation index are as follows:
[0133]
[0134] Where n is the number of weather elements; y i Simulate the effects of weather elements. It is the actual observed value of weather elements.
[0135] The root mean square error evaluation index is used to indicate the error that will occur during the simulation process. The smaller the RMSE value between the simulation effect and the actual observation value, the smaller the error, the closer the simulation effect is to the actual observation value, that is, the better the simulation effect.
[0136] The calculation formula and meaning of the correlation coefficient evaluation index are as follows:
[0137]
[0138] Among them, X i , Y i are the simulated effects of the ith weather element. are the actual observation values of the i-th weather element.
[0139] The correlation coefficient evaluation indicator is used to indicate the correlation between the simulation effect and the actual observation value. The value range is between [-1,1], where [-1,0] is a negative correlation and [0,1] is a positive correlation. The closer the correlation coefficient is to 1, the stronger the correlation between the simulation effect and the actual observation value, that is, the better the simulation effect.
[0140] The calculation and meaning of the absolute error evaluation index are as follows:
[0141] Directly calculate the difference between the simulation effect and the actual observation value at each time step (every 6 hours / every 30 days). The smaller the difference, the closer the simulation effect is to the actual observation, that is, the better the simulation effect.
[0142] The root mean square error evaluation index, correlation coefficient evaluation index and absolute error evaluation index were used to compare and evaluate the first 10m wind speed simulation result and the actual 10m wind speed observation value, the second 10m wind speed simulation result and the actual 10m wind speed observation value, the first 2m temperature simulation result and the 2m temperature measured observation value, the second 2m temperature simulation result and the 2m temperature measured observation value, the first rainfall simulation result and the rainfall measured observation value, the second rainfall simulation result and the rainfall measured observation value, the first sea surface temperature simulation result and the sea surface temperature measured observation value, and the second sea surface temperature simulation result and the sea surface temperature measured observation value.
[0143] For example, the 2m temperature element is taken as an example to carry out dynamic tuning and effect evaluation analysis of the global numerical model initialization process after adding atmospheric assimilation, that is, the root mean square error evaluation index, correlation coefficient evaluation index and absolute error evaluation index are used to evaluate the simulation effect of the global numerical model on the 2m temperature element after adding atmospheric assimilation. The schematic diagrams are shown as follows: Figure 5 , Figure 6 and Figure 7 As shown. Figure 5 , Figure 6 and Figure 7 It can be seen that compared with the model free integration (scheme (1)), after the atmospheric observation data is assimilated into the global numerical model during the model initialization process, the first 2m temperature simulation results quickly approach the measured observations, and the overall error between the simulation results and the measured observations is significantly reduced, while the correlation is significantly improved. Among them, the best initialization effect is the assimilation scheme (4) nudge_wind_T, which has the most obvious improvement in the simulation performance of the global numerical model for 2m temperature.
[0144] Taking the 2m temperature element as an example, the dynamic optimization and effect evaluation of the global numerical model initialization process after the atmosphere and ocean assimilation are simultaneously performed. Compared with the global numerical model after the atmospheric observation data is assimilated only during the global numerical model initialization operation, the global numerical model after the atmospheric assimilation is improved. Figure 8 , Fig. 9 and Fig.10 It can be seen that after adding the ocean observation data assimilation, the initialization simulation performance of the global numerical model is further improved. The second 2m temperature simulation results are closer to the actual observations, and the overall error between the simulation effect and the measured observations is further reduced. At the same time, the correlation is further improved. The assimilation scheme (4) nudge_wind_T still has the best model initialization effect.
[0145] Figures 5 to 10 The nudge_off in the data indicates the model free integration scheme (1), nudge_T indicates the assimilation scheme with atmospheric temperature added (2), nudge_wind indicates the assimilation scheme with wind speed and direction data added (3), nudge_wind_T indicates the assimilation scheme with atmospheric temperature and wind speed and direction data added (4), nudge_wind_Tv indicates the assimilation scheme with wind speed and direction data added and atmospheric virtual temperature added (5), and observation indicates the actual observation value of the preset weather element. The data assimilation scheme design is shown in Table 1 below.
[0146] Scheme (1) means that atmospheric and ocean observation data are not used to constrain the global numerical model initialization process, and data assimilation is not performed in the global numerical model, that is, the model is freely integrated.
[0147] Table 1
[0148] T Wind TV (1) nudge_off - - - (2) nudge_T √ - - (3) nudge_wind - √ - (4) nudge_wind_T √ √ - (5) nudge_wind_Tv - √ √
[0149] It should be noted that if Figure 4 As shown, after obtaining the global numerical model after adding atmospheric assimilation, the 10m wind speed element, the 2m temperature element, the rainfall element and the sea surface temperature element are simulated by using the global numerical model after adding atmospheric assimilation to obtain the first 10m wind speed simulation result, the first 2m temperature simulation result, the first rainfall simulation result and the first sea surface temperature simulation result. That is, the evaluation step is set after step S303.
[0150] After obtaining the global numerical model after simultaneously adding the atmosphere and ocean assimilation, the global numerical model after simultaneously adding the atmosphere and ocean assimilation is used to simulate the 10m wind speed element, 2m temperature element, precipitation element and sea surface temperature element, respectively, and the second 10m wind speed simulation result, the second 2m temperature simulation result, the second precipitation simulation result and the second sea surface temperature simulation result are obtained respectively.
[0151] The specific evaluation process is the same as step S305 and will not be repeated here.
[0152] The global numerical model data assimilation and effect evaluation method provided in this embodiment uses the nudging data assimilation method to continuously assimilate the atmospheric (FNL reanalysis data) and ocean (GODAS reanalysis data) observation data into the model at fixed time intervals (6 hours for atmospheric temperature, wind speed and wind direction, virtual temperature, and 30 days for sea surface temperature) during the 5-year initialization operation of the global numerical model simulation, and uses RMSE, correlation coefficient and absolute error indicators to evaluate the simulation effect of the global numerical model on 10m temperature, 2m temperature, rainfall, sea surface temperature and other elements after using different data assimilation schemes. The results show that using data assimilation in the initialization process of the global numerical model can make the model simulation results constantly approach the observed values, and dynamically update and optimize the boundary conditions during the model operation process, which can continuously improve the accuracy of the global numerical model simulation, and can reach a balanced state faster than cold start. By using data assimilation technology in the initialization process of the global numerical model, global analysis field data that is closer to the actual atmospheric state can be formed, providing more accurate initial field and boundary field data for subsequent model forecasts.
[0153] In this embodiment, a global numerical model data assimilation and effect evaluation device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.
[0154] This embodiment provides a global numerical model data assimilation and effect evaluation device, such as Fig.12 As shown, including:
[0155] The model building module 1201 is used to build a data assimilation model.
[0156] The data acquisition module 1202 is used to acquire atmospheric observation data and ocean observation data.
[0157] The atmospheric assimilation module 1203 is used to continuously perform atmospheric data assimilation during the initialization operation of the global numerical model based on the data assimilation model and the atmospheric observation data, so as to obtain the global numerical model after adding the atmospheric assimilation.
[0158] The atmosphere-ocean assimilation module 1204 is used to continuously perform atmospheric data assimilation and ocean data assimilation simultaneously during the initialization operation of the global numerical model based on the data assimilation model, atmospheric observation data and ocean observation data, and obtain a global numerical model after adding both the atmosphere and the ocean assimilation.
[0159] The simulation effect evaluation module 1205 is used to evaluate the simulation effect of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmospheric and ocean assimilation on the preset weather elements.
[0160] In some optional implementations, the model building module 1201 includes:
[0161] The model building unit builds a data assimilation model with preset observational forcing terms based on the incremental analysis process and using a time-varying relaxation approximation algorithm.
[0162] In some optional implementations, the data acquisition module 1202 includes:
[0163] Atmospheric observation data acquisition unit, used to obtain FNL reanalysis data as atmospheric observation data;
[0164] The ocean observation data acquisition unit is used to obtain GODAS ocean reanalysis data as ocean observation data.
[0165] In some optional implementations, the atmospheric assimilation module 1203 includes:
[0166] The first data extraction unit is used to obtain atmospheric temperature and wind speed and direction data from the atmospheric observation data.
[0167] The virtual temperature calculation unit is used to calculate the atmospheric virtual temperature based on the atmospheric temperature.
[0168] The atmospheric data assimilation unit is used to continuously input the atmospheric temperature, wind speed and direction data and atmospheric virtual temperature into the global numerical model at a first preset time interval based on the data assimilation model during the initialization operation of the global numerical model at a preset time to assimilate the atmospheric data, and obtain the global numerical model after adding atmospheric assimilation.
[0169] In some optional embodiments, the atmosphere-ocean assimilation module 1204 includes:
[0170] The second data extraction unit is used to obtain the ocean surface temperature data from the ocean observation data.
[0171] The ocean data assimilation unit is used to input the atmospheric temperature, wind speed and direction data and atmospheric virtual temperature into the global numerical model at a first preset time interval and the ocean surface temperature data at a second preset time interval based on the data assimilation model during the initialization operation of the global numerical model at a preset time, and simultaneously perform atmospheric data assimilation and ocean data assimilation to obtain a global numerical model after the atmosphere and ocean assimilation are added simultaneously.
[0172] In some optional implementations, the preset weather elements include a 10 m wind speed element, a 2 m temperature element, a rainfall element, and a sea surface temperature element, and the simulation effect evaluation module 1205 includes:
[0173] The simulation unit is used to simulate the preset weather elements by adopting the global numerical model after adding the atmospheric assimilation and the global numerical model after adding the atmosphere and ocean assimilation, and obtain the 10m wind speed simulation results, 2m temperature simulation results, rainfall simulation results and ocean surface temperature simulation results of the global numerical model after adding the atmospheric assimilation and the global numerical model after adding the atmosphere and ocean assimilation at the same time.
[0174] The measured observation value acquisition unit is used to obtain the 10m wind speed measured observation value, the 2m temperature measured observation value, the rainfall measured observation value and the sea surface temperature measured observation value.
[0175] The evaluation unit is used to compare the 10m wind speed simulation results of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation with the 10m wind speed actual observation value, the 2m temperature simulation results with the 2m temperature actual observation value, the rainfall simulation results with the rainfall actual observation value, and the sea surface temperature simulation results with the sea surface temperature actual observation value, using preset evaluation indicators, so as to evaluate the simulation effect of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation on the preset weather elements.
[0176] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.
[0177] The global numerical model data assimilation and effect evaluation device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.
[0178] The embodiment of the present invention also provides a computer device having the above Fig.12 The global numerical model data assimilation and effect evaluation device shown.
[0179] See also Fig.13 , Fig.13 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.13 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.13 A processor 10 is taken as an example.
[0180] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.
[0181] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.
[0182] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0183] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.
[0184] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig.13 The example of connecting through bus is taken in the following.
[0185] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.
[0186] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.
[0187] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.
Claims
1. A global numerical model data assimilation and effect evaluation method, characterized in that: The method comprises: Build a data assimilation model; Obtain atmospheric and ocean observation data; Based on the data assimilation model and the atmospheric observation data, atmospheric data assimilation is continuously performed during the initialization operation of the global numerical model to obtain a global numerical model after adding atmospheric assimilation; Based on the data assimilation model, the atmospheric observation data and the ocean observation data are continuously assimilated into the atmospheric data and the ocean data during the initialization operation of the global numerical model, so as to obtain a global numerical model after the atmospheric and ocean assimilation are added simultaneously; The simulation effects of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmospheric and ocean assimilation on the preset weather elements are evaluated respectively.
2. The method according to claim 1, characterized in that The establishing of the data assimilation model comprises: A data assimilation model for observational forcing terms is established based on the incremental analysis process and using a time-varying relaxation approximation algorithm.
3. The method according to claim 2, characterized in that The data assimilation model of the observational forcing term is expressed by the following formula: Among them, x is the prediction variable, x obs is the observed value of the forecast variable, α(x obs -x) is the observational forcing term, α is the assimilation coefficient, and F(x) is the model prediction equation group.
4. The method according to claim 1, characterized in that: The acquisition of atmospheric observation data and ocean observation data comprises: Obtain FNL reanalysis data as atmospheric observation data; Obtain GODAS ocean reanalysis data as ocean observation data.
5. The method according to claim 1, characterized in that Based on the data assimilation model and the atmospheric observation data, atmospheric data assimilation is continuously performed during the initialization operation of the global numerical model, and the global numerical model after adding atmospheric assimilation includes: Obtain atmospheric temperature and wind speed and direction data from atmospheric observation data; Calculating an atmospheric virtual temperature based on the atmospheric temperature; During the initialization operation of the global numerical model at a preset time, the atmospheric temperature, wind speed and direction data and atmospheric virtual temperature are continuously input into the global numerical model at a first preset time interval based on the data assimilation model to perform atmospheric data assimilation, thereby obtaining a global numerical model after adding atmospheric assimilation.
6. The method according to claim 5, characterized in that Based on the data assimilation model, atmospheric observation data and ocean observation data are continuously assimilated during the initialization of the global numerical model, and the global numerical model after adding the atmosphere and ocean assimilation is obtained, including: Obtain sea surface temperature data from ocean observation data; During the initialization operation of the global numerical model at a preset time, based on the data assimilation model, the atmospheric temperature, wind speed and direction data, and atmospheric virtual temperature are continuously input into the global numerical model at a first preset time interval, and the ocean surface temperature data is continuously input into the global numerical model at a second preset time interval, and atmospheric data assimilation and ocean data assimilation are performed simultaneously to obtain a global numerical model after the atmosphere and ocean assimilation are simultaneously added.
7. The method according to claim 1, characterized in that The preset weather elements include 10m wind speed element, 2m temperature element, rainfall element and sea surface temperature element; The simulation effects of the global numerical model after adding the atmospheric assimilation and the global numerical model after adding the atmospheric and ocean assimilation on the preset weather elements are evaluated separately, including: The preset weather elements are simulated by using the global numerical model after adding the atmospheric assimilation and the global numerical model after adding the atmospheric and ocean assimilation at the same time, and the 10m wind speed simulation results, 2m temperature simulation results, rainfall simulation results and ocean surface temperature simulation results of the global numerical model after adding the atmospheric assimilation and the global numerical model after adding the atmospheric and ocean assimilation are obtained; Obtain the 10m wind speed measured observation value, 2m temperature measured observation value, rainfall measured observation value and sea surface temperature measured observation value; The preset evaluation indicators are used to compare the 10m wind speed simulation results of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation with the 10m wind speed actual observation value, the 2m temperature simulation results with the 2m temperature actual observation value, the rainfall simulation results with the rainfall actual observation value, and the sea surface temperature simulation results with the sea surface temperature actual observation value, so as to evaluate the simulation effect of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmosphere and ocean assimilation on the preset weather elements.
8. A global numerical model data assimilation and effect evaluation device, characterized in that: The device comprises: Model building module, used to build data assimilation model; A data acquisition module, used to acquire atmospheric observation data and ocean observation data; An atmospheric assimilation module is used to continuously perform atmospheric data assimilation during the initialization operation of the global numerical model based on the data assimilation model and the atmospheric observation data, so as to obtain a global numerical model after adding atmospheric assimilation; An atmosphere-ocean assimilation module is used to continuously perform atmospheric data assimilation and ocean data assimilation during the initialization operation of the global numerical model based on the data assimilation model, the atmospheric observation data and the ocean observation data, so as to obtain a global numerical model after the atmosphere and the ocean are assimilated at the same time; The simulation effect evaluation module is used to respectively evaluate the simulation effects of the global numerical model after adding atmospheric assimilation and the global numerical model after adding both atmospheric and ocean assimilation on preset weather elements.
9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the global numerical model data assimilation and effect evaluation method according to any one of claims 1 to 7 by executing the computer instructions.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the global numerical model data assimilation and effect evaluation method according to any one of claims 1 to 7.