Global climate model simulation performance evaluation method and device under watershed scale and medium

Abstract

The invention discloses a global climate model simulation performance evaluation method and device under a watershed scale, and a medium, and relates to the technical field of global climate model simulation performance evaluation for future climate change influence research of a watershed. Performing global scale simulation performance evaluation according to the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model to obtain a first global climate model subset; according to the high-altitude meteorological variable observation data and the high-altitude meteorological variable simulation data of each global climate model, performing regional scale simulation performance evaluation on models in the first global climate model subset to obtain a second global climate model subset; and performing drainage basin scale simulation performance evaluation on the models in the second global climate model subset according to the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model. According to the invention, progressive and systematic evaluation of the simulation performance of the global climate model is realized.

Description

Technical Field

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

[0002] At present, General Circulation Models (GCMs) are effective tools for global climate simulation and climate change assessment, and are now widely used in the study of future climate change impacts on river basins. However, different GCMs differ in model mechanism, initial conditions, resolution, parameterization schemes, etc., which results in large differences in the accuracy of their output results at the river basin scale. Although the accuracy of GCMs in simulating surface temperature and rainfall continues to improve, GCMs are still the biggest source of uncertainty in the results of studies on the impact of climate change on river basins. It is generally believed that the matching degree between the historical simulation results of GCMs and the observed values ​​of the same period is the only feasible method to measure the applicability of GCMs to the impact of climate change on river basins. Therefore, selecting GCMs that accurately characterize the climate characteristics of the river basin to generate future climate change scenarios has become an important means to improve the confidence in the results of studies on the impact of future climate change.

[0003] In the study of the impact of meteorological changes on river basins, traditional GCMs simulation performance evaluation often evaluates the average simulation effects of ground meteorological variables (such as rainfall, temperature, etc.) on some GCMs, ignoring the simulation of high-altitude meteorological variables that affect ground-observed meteorological elements and the dynamic simulation performance evaluation of ground-observed meteorological elements. In addition, due to the subjective selection problem of the evaluated GCMs, it is difficult for the existing GCMs simulation performance evaluation to achieve a comprehensive, scientific and systematic assessment of the simulation performance of river basin GCMs, which reduces the credibility of the research results on the impact of climate change. Summary of the invention

[0004] The purpose of this application is to provide a method, device and medium for evaluating the simulation performance of a global climate model at a watershed scale, so as to achieve a progressive and systematic evaluation of the simulation performance of a global climate model.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides a method for evaluating the simulation performance of a global climate model at a watershed scale, comprising the following steps.

[0007] 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.

[0008] Taking the average state as the evaluation criterion, the simulation performance of each global climate model is evaluated on a global scale according to the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model, so as 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.

[0009] Taking average state and correlation as evaluation criteria, the simulation performance of each global climate model in the first global climate model subset is evaluated on a regional scale according to the upper-air meteorological variable observation data and the upper-air meteorological variable simulation data of each global climate model 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.

[0010] Taking average state, change trend, correlation and probability distribution as evaluation criteria, the simulation performance of each global climate model in the second global climate model subset is evaluated at the basin scale based on the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model.

[0011] In a second aspect, the present application provides 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 a method for evaluating the simulation performance of a global climate model at a watershed scale.

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

[0013] According to the specific embodiments provided by the present application, the present application has the following technical effects: the present application provides a method, device and medium for evaluating the simulation performance of global climate models at a watershed scale, which is suitable for studying the impact of climate change on watersheds, and realizes progressive evaluation of simulation performance through step-by-step screening of the first global climate model subset, the second global climate model subset and the third global climate model subset, thereby gradually reducing the number of global climate models being evaluated, thereby avoiding the problem of large data processing volume generated when all global climate models are evaluated. Moreover, through the screening of the above three model subsets, a further and more detailed evaluation of a better global climate model can be achieved, and the accuracy that can be obtained is also higher, avoiding the problem of deviation in simulation performance evaluation results caused by subjective selection of some GCMs. In the present application, the high-altitude meteorological variable observation data that affects the ground-observed meteorological elements is taken into account, and the evaluation of its simulation performance by GCMs is considered, so as to improve the reliability of the ground-observed meteorological variable simulation data. At the same time, four items, namely average state, change trend, correlation and probability distribution, are set in the selection of evaluation criteria, which can ensure the robustness of the final global climate model simulation performance evaluation results, help reduce the uncertainty of constructing future climate scenarios in the basin, and improve the confidence in the research results of future climate change impacts on the basin.

[0014] In summary, this application is suitable for the study of the impact of climate change on watersheds. It can reduce the uncertainty in the simulation results of the impact of climate change on watersheds, and can conduct a comprehensive, scientific and reasonable evaluation of the simulation performance of GCMs at the watershed scale, which is crucial to improving the confidence in the study of the impact of climate change on watersheds. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is an application environment diagram of a global climate model simulation performance evaluation method in one embodiment of the present application.

[0017] Figure 2 A flowchart of a method for evaluating the simulation performance of a global climate model provided in one embodiment of the present application.

[0018] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] This application is applicable to the evaluation of the simulation performance of global climate models in studies on the impact of climate change on river basins. Its main purpose is to reduce the uncertainty in studies on the impact of climate change on river basins and to enhance the credibility of the results of studies on the impact of climate change on river basins.

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The global climate model simulation performance evaluation method for basin climate change impact research provided in the embodiments of the present application can be applied to Figure 1 In the application environment 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 separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the observation data and the corresponding simulation data of the global climate model to the server 104. After receiving the data, the server 104 uses the average state as the evaluation criterion, and evaluates the simulation performance of each global climate model on a global scale according to the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model to obtain the first global climate model subset; using the average state and correlation as the evaluation criteria, based on the high-altitude meteorological variable observation data and the high-altitude 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 the second global climate model subset; using the average state, change trend, correlation and probability distribution as the 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 on a basin scale. The server 104 may feed back the obtained simulation performance evaluation results of each global climate model to the terminal 102. In addition, in some embodiments, the simulation performance evaluation method of the global climate model at the watershed scale may also be implemented by the server 104 or the terminal 102 alone.

[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 annual / monthly time scale rainfall and average temperature data of ground meteorological stations within the evaluation scope. The collected data can be used as initial ground meteorological variable observation data. Data collection also includes collecting high-altitude meteorological variable data in the NCEP reanalysis data set on an annual / monthly time scale. This data can be used as initial high-altitude meteorological variable observation data. Data collection also includes collecting GCMs ground rainfall and average temperature, high-altitude meteorological variable simulation values ​​on an annual / monthly time scale. The data time series is the same as the ground observation data. Among them, the time series length of the above data is generally not less than 30 years.

[0030] (12) Using a 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 a 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.

[0031] Through the above-mentioned bilinear interpolation data processing, the spatial resolution of different data sets 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 station, thereby achieving spatial resolution matching. When evaluating the simulation performance of GCMs high-altitude meteorological variables in step 203, the bilinear interpolation method can be used to match the spatial resolution of the GCMs high-altitude meteorological variable simulation data with the spatial resolution of the high-altitude meteorological variable observation data in the NCEP reanalysis data set.

[0032] (13) Perform arithmetic averaging or spatial interpolation processing on the initial surface meteorological variable observation data and the initial upper air meteorological variable observation data after matching to obtain observation data; perform arithmetic averaging or spatial interpolation processing on the initial surface meteorological variable simulation data and the initial upper air meteorological variable simulation data after matching to obtain simulation data of the global climate model. The obtained observation data and the corresponding simulation data of the global climate model can be the average value of the surface meteorological variables or the average value of the upper air meteorological variables at the global scale, regional scale or basin scale annual / month time scale.

[0033] Step 202, 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.

[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 realize the progressive screening based on the evaluation of GCMs simulation performance. This process gradually eliminates GCMs with poor simulation performance according to a certain logic, and finally selects a set of GCMs suitable for comprehensive evaluation of the simulation performance of surface meteorological variables of GCMs at the basin scale. This progressive processing follows two basic logics: (1) From the perspective of spatial scale, a good simulation effect of the average climate state in a large scale (for example, the global scale) is a necessary condition for a good simulation effect of surface meteorological variables of GCMs at the basin scale; (2) From the perspective of the mechanism process, a good simulation effect of high-altitude meteorological variables that are significantly correlated with and physically meaningful to surface meteorological variables is a necessary condition for a good simulation effect of surface meteorological variables of GCMs at the basin scale.

[0039] Step 204, using 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, evaluate the simulation performance of each global climate model in the second global climate model subset at the basin scale.

[0040] In an exemplary embodiment of the present application, 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 statistical indicators related to the average state within a first preset time scale; the corresponding evaluation indicators include mean, standard deviation and root mean square error.

[0041] The change trend is used to characterize the degree of consistency between the change trend and amplitude of the simulated data of the global climate model and the corresponding observed data within the second preset time scale; the corresponding evaluation indicators include rank statistics ( Z value) and the range of change ( Slope 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 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 corresponding evaluation indicators include the intra-annual distribution correlation coefficient and the annual scale spatial correlation coefficient.

[0043] 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 probability distribution within a fourth preset time scale; the corresponding evaluation indicators include 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, and can also be adjusted by relevant technical personnel as needed. 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 GCMs simulation values, such as mean, standard deviation, rank statistics, and variation range; the other category is the evaluation indicators that describe the matching relationship between GCMs simulation values ​​and ground observations, such as root mean square error, annual 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 can be shown in Table 1 below.

[0045] Table 1

[0046] 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) to (25).

[0047] (21) Determine the corresponding multiple evaluation indicators according to the evaluation criteria. For example, in the above step 202, the corresponding multiple evaluation indicators include mean, standard deviation, root mean square error; in step 203, the corresponding multiple evaluation indicators include mean, standard deviation, root mean square error, intra-annual 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, change range, intra-annual distribution correlation coefficient, annual scale spatial correlation coefficient, KL divergence, Sscore value, and BS value.

[0048] (22) For any global climate model, at the global scale, regional scale or basin scale, the evaluation score under each evaluation indicator is calculated based on 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, they cannot be directly compared, and all evaluation indicators need to be standardized. This application uses the extreme value processing method to standardize the original data of the evaluation indicators and assigns 0 to 10 points to the evaluation indicator results that measure the GCM simulation performance. Specifically, the calculation formula for the evaluation score under each evaluation indicator is: .

[0049] Among them, x´ ij is the evaluation score of the ith evaluation indicator of the jth global climate model; x ij is the value of the ith evaluation index of the jth global climate model. This value can be the statistical value of the ith evaluation index or the relative error with the observed value. The specific type is related to the type of the evaluation index. (xi ) min and (x i ) max are the minimum and maximum values ​​of the ith evaluation index in all global climate models, respectively.

[0050] (23) 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. Specifically, since the subjective weight is greatly affected by human factors, the calculation result may be biased, so the objective weight is introduced to correct and compensate for it.

[0051] The main steps of calculating the weights of each evaluation index by analytic hierarchy process: 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: .

[0052] .

[0053] .

[0054] 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 indicators.

[0055] The entropy weight method is used to calculate the objective weight of each evaluation index, including: .

[0056] .

[0057] .

[0058] 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.

[0059] (24) According to the subjective weight and objective weight of each evaluation indicator, the corresponding combined weight is determined. The calculation formula is: ; Among them, W i represents the combined weight of the i-th evaluation index; and Respectively represent the relative importance of subjective weight and objective weight, ; ; In an application, the value = =0.5.

[0060] (25) Calculate a comprehensive score of the global climate model based on the combined weights and evaluation scores of all the evaluation indicators, and mark the comprehensive score as a simulation performance value.

[0061] In a practical application, for a single evaluation variable, such as ground meteorological variable-temperature or high-altitude meteorological variable-relative humidity at 500 hPa (that is, when the observation data is a single evaluation variable), the following formula is used to calculate the score Y of the global climate model of the single evaluation variable: .

[0062] in, represents the evaluation score of the i-th evaluation index, represents the combined weight of the i-th evaluation index; When the evaluation variable is multiple variables, for example, when the observation data is ground meteorological variable rainfall and temperature observation data, or multiple high-altitude meteorological variables (that is, when the observation data is multiple evaluation variables), the following formula is used to calculate the comprehensive score S of the global climate model of multiple evaluation variables: .

[0063] Among them, Y h represents the score corresponding to the simulation performance of the hth evaluation variable in the global climate model; represents the weight of the hth evaluation variable, which is determined by the method described in step (23); m represents the number of evaluation variables. When the evaluation ground meteorological variables are precipitation and temperature, m is equal to 2.

[0064] In summary, this application has realized the evaluation of GCMs simulation performance based on the progressive method, and then realized the comprehensive evaluation of the simulation performance of GCMs ground meteorological variables at the basin scale. Specifically, the ground meteorological and high-altitude meteorological variable observations and the corresponding GCMs simulation values ​​are used to calculate the values ​​of each evaluation index, and the evaluation index database is constructed, thereby realizing the determination of the comprehensive score of the global climate model. This application can conduct a comprehensive, scientific and systematic evaluation of the GCMs simulation performance involved in the study of the impact of climate change on the basin.

[0065] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. 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. 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 an external device. 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, a global climate model simulation performance evaluation method is implemented.

[0066] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure 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 certain components, or have a different arrangement of components.

[0067] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0068] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0069] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0070] 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 used 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 must comply with relevant regulations.

[0071] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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 may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0072] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0073] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0074] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this 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, the simulation performance of each global climate model in the second global climate model subset is evaluated at the basin scale based on the ground meteorological variable observation data and the ground meteorological variable simulation data of each global climate model.

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 average state is used to characterize the degree of agreement 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 change trend is used to characterize the degree of agreement between the change trend and amplitude of the simulated data of the global climate model and the corresponding observed data within a second preset time scale; The correlation is used to characterize the degree of agreement 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 agreement 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.

3. 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.

4. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 3, 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.

5. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 3, 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.

6. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 3, 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.

7. The method for evaluating the simulation performance of a global climate model at a watershed scale according to claim 3, 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.

8. 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.

9. 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 according to any one of claims 1 to 8.

10. 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 8 is implemented.

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