Method, device and medium for evaluating simulation performance of global climate models at the basin scale

Abstract

The present application discloses a method, device and medium for evaluating the simulation performance of a global climate model at the basin scale, relating to the technical field of evaluating the simulation performance of a global climate model for studying the impact of future climate change in a basin. The method includes: under corresponding evaluation criteria, performing a global-scale simulation performance evaluation based on ground meteorological variable observation data and ground meteorological variable simulation data of each global climate model to obtain a first subset of global climate models; performing a regional-scale simulation performance evaluation on the models in the first subset of global climate models based on upper-air meteorological variable observation data and upper-air meteorological variable simulation data of each global climate model to obtain a second subset of global climate models; and performing a basin-scale simulation performance evaluation on the models in the second subset of global climate models based on ground meteorological variable observation data and ground meteorological variable simulation data of each global climate model. The present application realizes a progressive and systematic evaluation of the simulation performance of a global climate model.

Description

Technical Field

[0001] The present application relates to the technical field of evaluating the simulation performance of global climate models for studying the impact of future climate change in river basins, and particularly to a method, device, and medium for evaluating the simulation performance of global climate models at the river basin scale. Background Art

[0002] Currently, global climate models (General Circulation Models, GCMs) are effective tools for global climate simulation and climate change assessment, and have now been widely used in the study of the impact of future climate change in river basins. However, different GCMs vary in model mechanism, initial conditions, resolution, parameterization scheme, etc., which results in significant differences in the accuracy of their output results at the river basin scale. Although the simulation accuracy of GCMs for surface air temperature and rainfall continues to improve, GCMs remain the largest source of uncertainty in the results of river basin climate change impact studies. It is generally believed that the matching degree between the historical simulation results of GCMs and the observed values in the same period is the only feasible method to measure the applicability of GCMs to river basin climate change impact. Therefore, selecting GCMs that accurately represent the climate characteristics of the river basin to generate future climate change scenarios has become an important means to improve the confidence of the results of future climate change impact studies.

[0003] In the study of the impact of river basin meteorological changes, the traditional evaluation of the simulation performance of GCMs often evaluates the average state simulation effect of some GCMs on surface meteorological variables (such as rainfall, temperature, etc.), ignoring the simulation of upper-air meteorological variables that affect surface observed meteorological elements and the dynamic simulation performance evaluation of surface observed meteorological elements. Also, due to the subjective selection problem of the GCMs to be evaluated, it is difficult to comprehensively, scientifically, and systematically evaluate the simulation performance of river basin GCMs, which leads to a reduction in the credibility of the results of climate change impact studies. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device, and medium for evaluating the simulation performance of global climate models at the river basin scale, and to achieve a progressive and systematic evaluation of the simulation performance of global climate models.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In the first aspect, the present application provides a method for evaluating the simulation performance of global climate models at the river basin scale, including the following multiple steps.

[0007] Obtain the observed data and the simulated data of the corresponding global climate model; when the observed data is the observed data of surface meteorological variables, the simulated data of the corresponding global climate model is the simulated data of surface meteorological variables of each global climate model; when the observed data is the observed data of upper-air meteorological variables, the simulated data of the corresponding global climate model is the simulated data of upper-air meteorological variables of each global climate model.

[0008] Taking the average state as the evaluation criterion, evaluate the simulation performance of each global climate model on a global scale according to the observed data of surface meteorological variables and the simulated data of surface meteorological variables of each global climate model, so as to obtain the first subset of global climate models; the simulation performance values of each global climate model in the first subset of global climate models all meet the first preset performance condition.

[0009] Taking the average state and correlation as the evaluation criterion, evaluate the simulation performance of each global climate model in the first subset of global climate models on a regional scale according to the observed data of upper-air meteorological variables and the simulated data of upper-air meteorological variables of each global climate model, so as to obtain the second subset of global climate models; the simulation performance values of each global climate model in the second subset of global climate models all meet the second preset performance condition.

[0010] Taking the average state, change trend, correlation and probability distribution as the evaluation criterion, evaluate the simulation performance of each global climate model in the second subset of global climate models on a basin scale according to the observed data of surface meteorological variables and the simulated data of surface meteorological variables of each global climate model.

[0011] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the method for evaluating the simulation performance of a global climate model at the basin scale.

[0012] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for evaluating the simulation performance of a global climate model at the basin scale is implemented.

[0013] According to the specific embodiments provided by this application, the following technical effects are achieved: This application provides a method, device, and medium for evaluating the simulation performance of global climate models at the basin scale, which is applicable to the study of the impact of basin climate change. Through the step-by-step screening of the first global climate model subset, the second global climate model subset, and the third global climate model subset, a progressive evaluation of the simulation performance is realized, gradually reducing the number of global climate models to be evaluated, thereby avoiding the problem of large data processing volume when evaluating all global climate models. Moreover, through the screening of the above three model subsets, a further and more detailed evaluation of better global climate models can be achieved, and the accuracy obtained can also be higher, avoiding the problem of deviation in the simulation performance evaluation results caused by the subjective selection of some GCMs by humans. In this application, the upper-air meteorological variable observation data that affects the surface observation meteorological elements is taken into account and the simulation performance evaluation of GCMs is considered, improving the reliability of the simulated data of surface meteorological variables. At the same time, four items, namely the mean state, change trend, correlation, and probability distribution, are set in the selection of evaluation criteria, which can ensure the robustness of the evaluation results of the global climate model simulation performance, help reduce the uncertainty in the construction of future climate scenarios in the basin, and improve the confidence level of the research results on the impact of future basin climate change.

[0014] In summary, this application is applicable to the study of the impact of basin climate change, can reduce the uncertainty in the simulation results of the impact of basin climate change, can comprehensively, scientifically, and reasonably evaluate the simulation performance of GCMs at the basin scale, and is crucial for improving the confidence level of the research on the impact of basin climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is an application environment diagram of a method for evaluating the simulation performance of a global climate model in an embodiment of this application.

[0017] Figure 2 It is a schematic flowchart of a method for evaluating the simulation performance of a global climate model provided in an embodiment of this application.

[0018] Figure 3 It is a schematic structural diagram of a computer device provided in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0020] This application is applicable to the evaluation of the simulation performance of global climate models for studying the impacts of basin climate change. Its main purpose is to reduce the uncertainty in the study of basin climate change impacts and improve the credibility of the research results on basin climate change impacts.

[0021] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0022] The method for evaluating the simulation performance of global climate models applicable to the study of basin climate change impacts provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, placed in the cloud or on other servers. The terminal 102 can send the observed data and the simulation data of the corresponding global climate model to the server 104. After receiving the data, the server 104 takes the average state as the evaluation criterion, and evaluates the simulation performance of each global climate model on a global scale according to the observed data of ground meteorological variables and the simulated data of ground meteorological variables of each global climate model to obtain the first subset of global climate models; taking the average state and correlation as the evaluation criterion, according to the observed data of upper-air meteorological variables and the simulated data of upper-air meteorological variables of each global climate model, evaluate the simulation performance of each global climate model in the first subset of global climate models on a regional scale to obtain the second subset of global climate models; taking the average state, change trend, correlation, and probability distribution as the evaluation criteria, according to the observed data of ground meteorological variables and the simulated data of ground meteorological variables of each global climate model, evaluate the simulation performance of each global climate model in the second subset of global climate models on a basin scale. The server 104 can feedback the evaluation results of the simulation performance of each global climate model obtained to the terminal 102. In addition, in some embodiments, the method for evaluating the simulation performance of global climate models at the basin scale can also be implemented separately by the server 104 or the terminal 102.

[0023] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, tablet computers, and IoT devices. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0024] In an exemplary embodiment, Figure 2 As shown, a method for evaluating the simulation performance of a global climate model is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 204.

[0025] Step 201, obtain observation data and corresponding simulation data of the global climate model; when the observation data is ground meteorological variable observation data, the corresponding simulation data of the global climate model is the ground meteorological variable simulation data of each global climate model; when the observation data is high-altitude meteorological variable observation data, the corresponding simulation data of the global climate model is the high-altitude meteorological variable simulation data of each global climate model.

[0026] In an exemplary embodiment of the present application, the process of acquiring observation data and corresponding global climate model simulation data includes the following steps (11) to (13), which specifically involve identifying the spatial scale range for evaluating GCMs simulation performance, data collection and pre-processing.

[0027] (11) Obtain initial surface meteorological variable observation data from multiple surface meteorological stations; obtain high-altitude meteorological variable data from the global NCEP reanalysis data set and use them as initial high-altitude meteorological variable observation data. Obtain initial surface meteorological variable simulation data and initial high-altitude meteorological variable simulation data from various global climate models that are contemporaneous with the initial high-altitude meteorological variable observation data.

[0028] Specifically, before obtaining the initial data, or after obtaining the initial data, it is necessary to determine the evaluation scope that may be used in the subsequent steps 202-204. The present application involves three evaluation scopes: global scale, regional scale and watershed scale. Among them, the spatial scope of the regional scale must be able to objectively reflect the regional atmospheric physical processes and characterize the spatiotemporal variation characteristics of regional ground meteorological variables. For example: when conducting a study on the impact of climate change in a certain basin in the North China Plain of China, the regional scale can be selected as the North China Plain. If the basin area only occupies a few GCMs grids, the evaluation of the simulation performance of ground meteorological variables of GCMs at the basin scale can be carried out at the regional scale.

[0029] Data collection includes rainfall and average temperature data of ground meteorological stations within the evaluation scope at the annual / monthly time scale. The collected data can be used as the initial observed data of ground meteorological variables. Data collection also includes collecting upper-air meteorological variable data in the NCEP reanalysis dataset at the annual / monthly time scale, which can be used as the initial observed data of upper-air meteorological variables. Data collection also includes collecting simulated values of ground rainfall, average temperature, and upper-air meteorological variables in GCMs at the annual / monthly time scale, and the time series of the data is synchronous with the ground observed data. Among them, the time series length of the above data is generally not less than 30 years.

[0030] (12) Using the bilinear interpolation method, match the spatial resolution of the initial simulated data of ground meteorological variables with the spatial resolution of the initial observed data of ground meteorological variables; using the bilinear interpolation method, match the spatial resolution of the initial simulated data of upper-air meteorological variables with the spatial resolution of the initial observed data of upper-air meteorological variables.

[0031] Through the above data processing of bilinear interpolation, the spatial resolutions of different datasets can be unified. In a practical application, when evaluating the simulation performance of GCMs ground meteorological variables in step 202 or step 204, the bilinear interpolation method can be used to match the spatial resolution of the GCMs ground meteorological variable simulation data collected in the above step (11) to the longitude and latitude of the ground observation meteorological stations, so as to achieve the matching of spatial resolution. When evaluating the simulation performance of GCMs upper-air meteorological variables in step 203, the bilinear interpolation method can be used to match the spatial resolution of the GCMs upper-air meteorological variable simulation data with the spatial resolution of the upper-air meteorological variable observed data in the NCEP reanalysis dataset.

[0032] (13) Perform arithmetic mean processing or spatial interpolation processing on the initial observed data of ground meteorological variables and the initial observed data of upper-air meteorological variables after the matching is completed to obtain the observed data; perform arithmetic mean processing or spatial interpolation processing on the initial simulated data of ground meteorological variables and the initial simulated data of upper-air meteorological variables after the matching is completed to obtain the simulated data of the global climate model. The obtained observed data and the corresponding simulated data of the global climate model can be the average values of ground meteorological variables or upper-air meteorological variables at the global scale, regional scale, or basin scale at the annual / monthly time scale.

[0033] In step 202, taking the average state as the evaluation criterion, evaluate the simulation performance of each global climate model on a global scale according to the observed data of ground meteorological variables and the simulated data of ground meteorological variables of each global climate model, so as to obtain the first subset of global climate models; the simulation performance values of each global climate model in the first subset of global climate models all meet the first preset performance conditions.

[0034] Specifically, the accuracy of simulation of the average state of the climate system on a global scale is a prerequisite for the successful application of GCMs in the study of the impact of climate change. The simulation performance evaluation is achieved by matching the average state of the simulated data of the ground meteorological variables of the GCMs to be evaluated with the average state of the corresponding ground meteorological variable observation data. After the simulation performance evaluation, some GCMs with poor simulation performance of the global scale climate average state (i.e., not meeting the first preset performance condition, such as the comprehensive score of the global climate model is less than the first preset value) can be excluded from the simulation performance evaluation process (this part of the global climate model has completed the simulation performance evaluation), and the remaining GCMs are used as the first global climate model subset for the high-altitude meteorological variable simulation performance evaluation work in step 203.

[0035] Step 203, using average state and correlation as evaluation criteria, based on the upper-air meteorological variable observation data and the upper-air meteorological variable simulation data of each global climate model, evaluate the simulation performance of each global climate model in the first global climate model subset on a regional scale to obtain a second global climate model subset; the simulation performance values ​​of each global climate model in the second global climate model subset all meet the second preset performance condition.

[0036] Specifically, in the study of the impact of climate change on the hydrology and water environment of the basin, the meteorological elements that characterize climate change are ground rainfall and temperature. In terms of atmospheric physical processes, GCMs can realistically simulate the physical processes of the climate system, and the accurate simulation of ground rainfall and temperature by GCMs is affected by the simulation accuracy of high-altitude meteorological variables. Therefore, high simulation accuracy of high-altitude meteorological variables that affect the performance of temperature and ground rainfall simulation and are directly related is a necessary condition for good regional-scale temperature and rainfall simulation by GCMs. Therefore, in this step, high-altitude meteorological variables with significant correlation with ground rainfall and temperature are selected as high-altitude meteorological variables to be evaluated.

[0037] The simulation performance evaluation is achieved by matching the mean and annual distribution of the simulated data of the upper-air meteorological variables of the GCMs to be evaluated with the mean and annual distribution of the corresponding NCEP upper-air meteorological variable observation data. After the simulation performance evaluation, some GCMs with poor simulation performance of upper-air meteorological variables (i.e., not meeting the second preset performance condition, such as the comprehensive score of the global climate model is less than the second preset value) can be excluded from the simulation performance evaluation process, and the remaining GCMs are used as the second global climate model subset for the comprehensive evaluation of the simulation performance of ground meteorological variables in step 204.

[0038] The above steps 202 and 203 achieve a progressive screening based on the simulation performance evaluation of GCMs. In this process, GCMs with poor simulation performance are gradually eliminated according to a certain logic, and finally a set of GCMs suitable for the comprehensive evaluation of the simulation performance of surface meteorological variables of GCMs at the basin scale is selected. This progressive processing follows two basic logics: (1) In terms of spatial scale, a good simulation effect of the climate average state in a large-scale range (e.g., global scale) is a necessary condition for a good simulation effect of surface meteorological variables of GCMs at the basin scale; (2) In terms of mechanism process, a good simulation effect of upper-air meteorological variables that are significantly correlated with surface meteorological variables and have physical significance is a necessary condition for a good simulation effect of surface meteorological variables of GCMs at the basin scale.

[0039] Step 204, taking the average state, change trend, correlation, and probability distribution as evaluation criteria, evaluates the simulation performance of each global climate model in the second subset of global climate models at the basin scale according to the observed data of the surface meteorological variables and the simulated data of the surface meteorological variables of each global climate model.

[0040] In an exemplary embodiment of the present application, the average state is used to characterize the degree of coincidence between the simulated data of the global climate model and the corresponding observed data in terms of statistical indicators related to the average state within the first preset time scale; the corresponding evaluation indicators include the mean value, standard deviation, and root mean square error.

[0041] The change trend is used to characterize the degree of coincidence between the simulated data of the global climate model and the corresponding observed data in terms of the change trend and amplitude within the second preset time scale; the corresponding evaluation indicators include the rank statistic ( Z value) and the change amplitude ( Slope value), and the above two indicators are obtained based on the non-parametric Mann-Kendall trend test.

[0042] The correlation is used to characterize the degree of coincidence between the simulated data of the global climate model and the corresponding observed data in terms of the temporal and spatial distribution changes within the third preset time scale; the corresponding evaluation indicators include the intra-year distribution correlation coefficient and the annual-scale spatial correlation coefficient.

[0043] The probability distribution is used to characterize the degree of coincidence between the simulated data of the global climate model and the corresponding observed data in terms of the probability distribution within the fourth preset time scale; the corresponding evaluation indicators include the KL divergence, Sscore value (Significance score), and BS (Bier Score) value.

[0044] The first preset time scale, the second preset time scale, the third preset time scale, and the fourth preset time scale that appear in the above four evaluation criteria can all be annual scales, or can be adjusted by relevant technical personnel according to needs. The evaluation indicators corresponding to the above four evaluation criteria can be divided into two categories. One category is the evaluation indicators that only describe the statistical characteristics of the GCMs simulation values, such as mean, standard deviation, rank statistic, and variation range. The other category is the evaluation indicators that describe the matching relationship between the GCMs simulation values and the ground observation values, such as root mean square error, intra-year distribution correlation coefficient, annual scale spatial correlation coefficient, Sscore value, BS value, KL divergence, etc. The evaluation indicators corresponding to the above four evaluation criteria are shown in Table 1 below.

[0045] Table 1

[0046]

[0047] In another exemplary embodiment of the present application, the process of evaluating the simulation performance of each global climate model includes the following steps (21)-(25).

[0048] (21) According to the evaluation criteria, determine the corresponding multiple evaluation indicators. For example, in step 202 above, the corresponding multiple evaluation indicators include mean, standard deviation, and root mean square error; in step 203, the corresponding multiple evaluation indicators include mean, standard deviation, root mean square error, intra-year distribution correlation coefficient, and annual scale spatial correlation coefficient; in step 204, the corresponding multiple evaluation indicators include mean, standard deviation, root mean square error, rank statistic, variation range, intra-year distribution correlation coefficient, annual scale spatial correlation coefficient, KL divergence, Sscore value, and BS value.

[0049] (22) For any global climate model, on a global scale, a regional scale, or a basin scale, calculate the evaluation scores under each evaluation indicator according to the corresponding observation data and the simulation data of the global climate model; since the data of each quantitative evaluation indicator have different units and dimensions and cannot be directly compared, it is necessary to standardize all evaluation indicators. The present application uses the extreme value processing method to standardize the original data of the evaluation indicators and assigns the evaluation indicator results measuring the GCM simulation performance to 0-10 points. Specifically, the calculation formula for the evaluation scores under each evaluation indicator is:

[0050] .

[0051] Among them, x´ ij is the evaluation score of the i-th evaluation indicator of the j-th global climate model; x ijis the value of the i-th evaluation index of the j-th global climate model, which can be the statistical value of the i-th evaluation index or the relative error with the observed value. Specifically, which type it belongs to is related to the type of the evaluation index; (x i ) min and (x i ) max are respectively the minimum and maximum values of the i-th evaluation index among all global climate models.

[0052] (23) Use the analytic hierarchy process to calculate the subjective weights of each evaluation index; use the entropy weight method to calculate the objective weights of each evaluation index. Specifically, since the subjective weights are greatly affected by human factors and the calculation results may deviate, the objective weights are introduced to correct and compensate for them.

[0053] The main steps for calculating the weights of each evaluation index by the analytic hierarchy process: Construct a judgment matrix A = (a il ) n×n , normalize the judgment matrix by columns to obtain a normalized matrix Add the elements in the same row of the normalized matrix to get a vector , then divide by the number n of evaluation indexes, and finally obtain the weights of specific evaluation indexes. The calculation formula is as follows:

[0054] .

[0055] .

[0056] .

[0057] Among them, a ij is the importance of the i-th evaluation index relative to the l-th evaluation index; zw i is the subjective weight of the i-th evaluation index; n is the number of evaluation indexes.

[0058] Use the entropy weight method to calculate the objective weights of each evaluation index, including:

[0059] .

[0060] .

[0061] .

[0062] Among them, x ij represents the value of the i-th evaluation index of the j-th global climate model; represents the characteristic proportion of the j-th global climate model under the i-th evaluation index, Ei represents the entropy value of the i-th evaluation index; kw i represents the objective weight of the i-th evaluation index, and s is the number of global climate models.

[0063] (24) Determine the corresponding combined weight according to the subjective weight and objective weight of each evaluation index. The calculation formula is:

[0064] ;

[0065] where, W i represents the combined weight of the i-th evaluation index; and respectively represent the relative importance degrees of the subjective weight and the objective weight, ; ; . In an application, it can take the value = = 0.5.

[0066] (25) Calculate the comprehensive score of the global climate model according to the combined weights and evaluation scores of all the evaluation indexes, and mark the comprehensive score as the simulation performance value.

[0067] In an actual application, for a single evaluation variable, such as the surface meteorological variable - air temperature or the upper-air meteorological variable - relative humidity at 500 hPa in the upper air (i.e., when the observed data is a single evaluation variable), the following formula is used to calculate the score Y of the global climate model for this single evaluation variable:

[0068] .

[0069] where, represents the evaluation score of the i-th evaluation index, represents the combined weight of the i-th evaluation index;

[0070] When the evaluation variable is a multi-variable, such as when the observed data is the surface meteorological variable rainfall and air temperature observation data, or is multiple upper-air meteorological variables (i.e., when the observed data is multiple evaluation variables), the following formula is used to calculate the comprehensive score S of the global climate model for multiple evaluation variables:

[0071] .

[0072] where, Y h represents the score corresponding to the simulation performance of the h-th evaluation variable in the global climate model; represents the weight of the h-th evaluation variable, which is determined by the method described in step (23); m represents the number of evaluation variables. When the surface meteorological variables are precipitation and air temperature, m is equal to 2.

[0073] In summary, the present application realizes the progressive simulation performance evaluation of GCMs, and then realizes the comprehensive evaluation of the simulation performance of ground meteorological variables of GCMs at the basin scale. Specifically, the numerical values of each evaluation index are calculated by using the observed values of ground meteorological and upper-air meteorological variables and the corresponding simulation values of each GCM, and an evaluation index database is constructed. Furthermore, the determination of the comprehensive score of the global climate model is realized. The present application can comprehensively, scientifically and systematically evaluate the simulation performance of GCMs involved in the research on the impact of basin climate change.

[0074] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes the method for evaluating the simulation performance of the global climate model.

[0075] Those skilled in the art can understand that Figure 3 the structure shown in

[0076] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0077] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are realized.

[0078] In an exemplary embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps in the above method embodiments.

[0079] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0080] Those of ordinary skill in the art can understand that all or part of the processes of implementing the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0081] The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0082] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0083] In this text, specific examples are used to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for evaluating the simulation performance of a global climate model at a watershed scale, characterized in that: The method for evaluating the simulation performance of the global climate model at the basin scale includes: Obtaining observation data and corresponding simulation data of a global climate model; when the observation data is ground meteorological variable observation data, the corresponding simulation data of the global climate model is the ground meteorological variable simulation data of each global climate model; when the observation data is high-altitude meteorological variable observation data, the corresponding simulation data of the global climate model is the high-altitude meteorological variable simulation data of each global climate model; Taking the average state as the evaluation criterion, based on the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model, the simulation performance of each global climate model is evaluated on a global scale to obtain a first global climate model subset; the simulation performance values ​​of each global climate model in the first global climate model subset all meet the first preset performance condition; Taking average state and correlation as evaluation criteria, based on the upper air meteorological variable observation data and the upper air meteorological variable simulation data of each global climate model, the simulation performance of each global climate model in the first global climate model subset is evaluated on a regional scale to obtain a second global climate model subset; the simulation performance values ​​of each global climate model in the second global climate model subset all meet the second preset performance condition; Taking average state, change trend, correlation and probability distribution as evaluation criteria, based on the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model, the simulation performance of each global climate model in the second global climate model subset is evaluated at the basin scale; The average state is used to characterize the degree of consistency between the simulated data of the global climate model and the corresponding observed data in terms of average statistical characteristics within a first preset time scale; the changing trend is used to characterize the degree of consistency between the simulated data of the global climate model and the corresponding observed data in terms of changing trend and amplitude within a second preset time scale; the correlation is used to characterize the degree of consistency between the simulated data of the global climate model and the corresponding observed data in terms of temporal and spatial distribution changes within a third preset time scale; the probability distribution is used to characterize the degree of consistency between the simulated data of the global climate model and the corresponding observed data in terms of probability distribution within a fourth preset time scale.

2. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 1, characterized in that: The process of evaluating the simulation performance of each global climate model includes: According to the evaluation criteria, determining corresponding multiple evaluation indicators; For any global climate model, at a global scale, a regional scale or a river basin scale, based on the corresponding observation data and the simulation data of the global climate model, calculate the evaluation score under each evaluation indicator; The analytic hierarchy process is used to calculate the subjective weight of each evaluation index; the entropy weight method is used to calculate the objective weight of each evaluation index; According to the subjective weight and objective weight of each evaluation indicator, determine the corresponding combined weight; According to the combined weights and evaluation scores of all the evaluation indicators, a comprehensive score of the global climate model is calculated, and the comprehensive score is marked as a simulation performance value.

3. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 2, characterized in that: When the evaluation criterion is an average state, the corresponding evaluation indicators include mean, standard deviation and root mean square error; when the evaluation criterion is a change trend, the corresponding evaluation indicators include rank statistics and change range; when the evaluation criterion is correlation, the corresponding evaluation indicators include intra-year distribution correlation coefficient and annual scale spatial correlation coefficient; when the evaluation criterion is a probability distribution, the corresponding evaluation indicators include KL divergence, Sscore value and BS value.

4. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 2, characterized in that: The calculation formula for the evaluation score under each evaluation indicator is: ； Among them, x´ ij is the evaluation score of the i-th evaluation indicator of the j-th global climate model, x ij is the value of the i-th evaluation index of the j-th global climate model, (x i ) min and (x i ) max are the minimum and maximum values ​​of the ith evaluation index in all global climate models, respectively.

5. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 2, characterized in that: The analytic hierarchy process is used to calculate the subjective weight of each evaluation index, including: Construct the judgment matrix A = (a il ) n×n , the judgment matrix is ​​normalized according to the columns to obtain a normalized matrix , the normalized matrix Add the elements in the same row to get the vector , and then divided by the number of evaluation indicators n, and finally the weight of the specific evaluation indicator is obtained. The calculation formula is as follows: ； ； ； Among them, a il is the importance of the i-th evaluation index relative to the l-th evaluation index; zw i is the subjective weight of the i-th evaluation index; The entropy weight method is used to calculate the objective weight of each evaluation index, including: ； ； ； Among them, x ij represents the value of the i-th evaluation index of the j-th global climate model; represents the characteristic weight of the jth global climate model under the i-th evaluation index, E i represents the entropy value of the i-th evaluation index; kw i represents the objective weight of the ith evaluation index, and s is the number of global climate models; The formula for calculating the combined weight is: ； Among them, W i represents the combined weight of the i-th evaluation index; and They represent the relative importance of subjective weight and objective weight respectively, α+β=1.

6. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 2, characterized in that: The comprehensive score of the global climate model is calculated based on the combined weights and evaluation scores of all the evaluation indicators, including: When the observation data is a single evaluation variable, the score Y of the global climate model of the single evaluation variable is calculated using the following formula: ； in, represents the evaluation score of the i-th evaluation index, represents the combined weight of the i-th evaluation index; When the observation data is multiple evaluation variables, the comprehensive score S of the global climate model of multiple evaluation variables is calculated using the following formula: ； Among them, Y h represents the score corresponding to the hth evaluation variable in the global climate model; represents the weight of the hth evaluation variable; m represents the number of evaluation variables.

7. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 1, characterized in that: The process of obtaining observational data and corresponding global climate model simulation data includes: Obtain initial surface meteorological variable observation data from multiple surface meteorological stations; obtain high-altitude meteorological variable data from the global NCEP reanalysis data set and use them as initial high-altitude meteorological variable observation data; Acquire initial surface meteorological variable simulation data and initial high-altitude meteorological variable simulation data of various global climate models in the same period as the initial high-altitude meteorological variable observation data; Using bilinear interpolation method, the spatial resolution of the initial surface meteorological variable simulation data is matched with the spatial resolution of the initial surface meteorological variable observation data; using bilinear interpolation method, the spatial resolution of the initial high-altitude meteorological variable simulation data is matched with the spatial resolution of the initial high-altitude meteorological variable observation data; The initial ground meteorological variable observation data and the initial high-altitude meteorological variable observation data after matching are subjected to arithmetic averaging or spatial interpolation processing to obtain the observation data; the initial ground meteorological variable simulation data and the initial high-altitude meteorological variable simulation data after matching are subjected to arithmetic averaging or spatial interpolation processing to obtain the simulation data of the global climate model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for evaluating the simulation performance of a global climate model at a watershed scale as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the simulation performance of a global climate model at a watershed scale described in any one of claims 1 to 7 is implemented.

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