Numerical forecasting mode rainfall fusion correction method considering multivariate environment information

Abstract

The invention provides a numerical forecasting mode rainfall fusion correction method considering multivariate environmental information, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining the output data of a numerical forecasting mode of a target region and rainfall station rainfall observation data; interpolating the output data to a rainfall station to obtain rainfall forecast data corresponding to the output data; calculating rainfall deviation of the rainfall station according to the rainfall observation data and the rainfall forecast data of the rainfall station; based on the rainfall deviation of the rainfall station and a multivariate environment variable for rainfall fusion correction, calculating a rainfall deviation predicted value of a numerical forecasting mode grid point by using a multi-scale geographically weighted regression model; and according to the rainfall deviation prediction value and the output data, obtaining fused rainfall data. According to the method, the spatial precision and timeliness of rainfall forecasting can be improved, the deviation of a traditional rainfall forecasting model can be effectively reduced, and a more accurate rainfall forecasting result is provided.

Description

Background Art

[0002] Precipitation forecasting has always been an important research topic in meteorology and is widely used in many fields such as meteorological warning, disaster prevention, and agricultural irrigation. With the advancement of numerical weather prediction (NWP) technology, numerical forecast models (such as global meteorological models and regional meteorological models) have become the main precipitation forecasting tools. However, due to the complexity and variability of atmospheric processes, numerical weather forecasting still faces many challenges in precipitation forecasting, especially the accuracy of local and short-term precipitation forecasts.

[0003] Existing numerical forecast models mainly rely on physical parameterization schemes to simulate atmospheric processes, such as cloud physics, precipitation formation processes, radiation and convection. However, due to the high complexity and unpredictability of these physical processes, the precipitation forecasts output by the models often deviate in practical applications. For example, under some complex terrain and atmospheric conditions, the precipitation forecast errors of numerical forecast models are large, resulting in insufficient spatial accuracy of the forecasts, especially in mountainous areas and coastal areas, where the spatial distribution and variation of precipitation are more complex and are often affected by local factors.

[0004] In addition to numerical weather forecast models, precipitation observation data from rain gauges and remote sensing data have been used as important auxiliary data sources to improve precipitation forecasts. Traditional precipitation forecasting methods, such as empirical statistical regression models and weighted average methods, can reduce numerical forecast errors to a certain extent, but these methods often ignore the impact of geographical features and local meteorological variables on precipitation forecasts, making it difficult to achieve high-precision precipitation forecasts.

[0005] Therefore, how to effectively integrate data from different sources (such as numerical forecast data, ground observation data, and remote sensing data) and fully consider geographical and meteorological factors remains an urgent problem to be solved. Summary of the invention

[0006] In order to overcome the problems existing in the related art, the present invention provides a precipitation fusion correction method of a numerical forecast model taking into account multiple environmental information.

[0007] According to a first aspect of an embodiment of the present invention, a numerical forecast model precipitation fusion correction method taking into account multiple environmental information is provided, and the numerical forecast model precipitation fusion correction method taking into account multiple environmental information includes: Obtain output data of numerical forecast models and precipitation observation data from rain gauge stations in the target area; interpolating the output data to the rainfall station to obtain precipitation forecast data corresponding to the output data; Calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data; Based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction, a multi-scale geographically weighted regression model is used to calculate the precipitation deviation prediction value of the numerical forecast model grid point; The fused precipitation data is obtained according to the precipitation deviation prediction value and the output data.

[0008] In some exemplary embodiments of the present invention, based on the above scheme, the output data of the numerical forecast model of the target area It is calculated by: in, represents the precipitation produced by cumulus convection process, represents the precipitation generated by cloud microphysical processes, Represents the amount of precipitation produced by shallow convection processes.

[0009] In some example embodiments of the present invention, based on the above scheme, before calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data, the precipitation fusion correction method of the numerical prediction model taking into account the multivariate environmental information further includes: According to the longitude and latitude information of the rainfall station, determine the row and column where the rainfall station is located in the numerical forecast model grid; According to the row and column indexes of the grid points in the numerical forecast model grid, the precipitation forecast data corresponding to the target rainfall station is obtained.

[0010] In some example embodiments of the present invention, based on the aforementioned scheme, the multivariate environmental variables used for precipitation fusion correction include geographic variables and meteorological variables, and the geographic variables include elevation, slope, and distance data to the coastline in a digital elevation model; The meteorological variables include wind speed and surface temperature in the land surface data assimilation system data of the China Meteorological Administration as global independent variables.

[0011] Based on the precipitation deviation of the rainfall stations and the multivariate environmental variables used for precipitation fusion correction, the multi-scale geographically weighted regression model is used to calculate the precipitation deviation prediction values ​​of the numerical forecast model grid points, including: interpolating the multivariate environmental variables used for precipitation fusion correction to the numerical forecast model grid points and the rain gauge station, respectively, to obtain the first multivariate environmental variables after the interpolation grid points and the second multivariate environmental variables after the interpolation rain gauge station; Based on the precipitation deviation of the rain gauge station and the second multivariate environmental variable, using a multiscale geographically weighted regression model, calculating the model coefficient of the multivariate environmental variable of the rain gauge station; The model coefficients are interpolated to the numerical forecast model grid points, and combined with the first multivariate environmental variable, the precipitation deviation prediction value of the numerical forecast model grid points is calculated.

[0012] In some example embodiments of the present invention, based on the above scheme, based on the precipitation deviation of the rain gauge station and the second multivariate environmental variable, using a multiscale geographically weighted regression model, calculating the model coefficient of the multivariate environmental variable of the rain gauge station includes: in, is the response variable, indicating the precipitation deviation at the rain gauge station; is a covariate, representing the second multivariate environmental variable; The representative bandwidth is No. Model coefficients for each rainfall station; Represents the spatial geographical location of the rainfall station; is the model regression residual, Represents the total number of rainfall stations.

[0013] In some example embodiments of the present invention, based on the above scheme, the model coefficients are interpolated to the numerical forecast model grid points, and combined with the first multivariate environmental variable, the precipitation deviation prediction value of the numerical forecast model grid points is calculated. include: in, is the precipitation observation value; For precipitation forecast data; For location The corresponding geographic variables in the first multivariate environmental variable; For location The corresponding The regression coefficient of the geographical variable; is the total number of geographical variables, For location The corresponding A global meteorological variable in the first multivariate environmental variable; For location The corresponding The regression coefficient of the meteorological variable; is the total number of meteorological variables, is the random error of the model.

[0014] In some exemplary embodiments of the present invention, based on the above scheme, the fused precipitation data is obtained according to the precipitation deviation prediction value and the output data. include: in, Represents output data, Represents the precipitation deviation prediction value.

[0015] According to a second aspect of an embodiment of the present invention, there is provided a device according to the above-mentioned numerical forecast model precipitation fusion correction method taking into account multiple environmental information, comprising: A data acquisition module is used to obtain the output data of the numerical forecast model of the target area and the precipitation observation data of the rain gauge station; An interpolation processing module, used for interpolating the output data to a ground rainfall station to obtain precipitation forecast data corresponding to the output data; A deviation calculation module, used for calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data; A deviation prediction module is used to calculate the precipitation deviation prediction value of the numerical prediction model grid point based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction using a multi-scale geographically weighted regression model; The result output module is used to obtain fused precipitation data according to the precipitation deviation prediction value and the output data.

[0016] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the numerical forecast model precipitation fusion correction method taking into account multiple environmental information in the first aspect is implemented.

[0017] According to a fourth aspect of an embodiment of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for fusion correction of precipitation of a numerical forecast model taking into account multivariate environmental information in the first aspect is implemented.

[0018] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention can effectively reduce the deviation of a single numerical prediction model and improve the accuracy of precipitation forecast by fusing the output data of the numerical prediction model with the precipitation observation data of the rain gauge station. By introducing the multiscale geographically weighted regression model (MGWR), the single bandwidth assumption in the traditional regression method can be eliminated, and the spatial variability of precipitation can be better reflected. Especially under complex terrain and climatic conditions, the MGWR model can dynamically adjust the regression coefficient according to local geographical characteristics (such as elevation, slope, distance to the coastline) and global meteorological conditions (such as wind speed, surface temperature), thereby accurately correcting the precipitation deviation and significantly improving the precipitation prediction accuracy in local areas.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] Figure 1 A schematic diagram showing a system architecture of an exemplary application environment of a method and device for fusion correction of precipitation in a numerical forecast model taking into account multiple environmental information, to which an embodiment of the present invention can be applied; Figure 2 A schematic diagram of a flow chart of a numerical forecast model precipitation fusion correction method taking into account multi-environmental information according to some embodiments of the present invention is shown; Figure 3 A schematic diagram of a numerical forecast model precipitation fusion correction device taking into account multi-environmental information according to some embodiments of the present invention is schematically shown; Figure 4 A schematic diagram schematically shows a structure of a computer system of an electronic device according to some embodiments of the present invention; Figure 5 A schematic diagram of a computer-readable storage medium according to some embodiments of the present invention is schematically shown. DETAILED DESCRIPTION

[0022] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0023] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0024] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0025] Figure 1 A schematic diagram of a system architecture of an exemplary application environment of a method and device for fusion correction of precipitation in a numerical forecast model taking into account multiple environmental information, to which an embodiment of the present invention can be applied, is shown.

[0026] like Figure 1 As shown, the system architecture 100 may include one or more terminal devices such as a desktop computer 101, a portable computer 102, a smart phone 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables, etc. The terminal device may be any electronic device with data processing capabilities, which has a display screen for displaying rainfall forecast data to the user, including but not limited to the above-mentioned desktop computers, portable computers, smart phones, etc. It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a sub-server cluster composed of multiple sub-servers.

[0027] The numerical forecast model precipitation fusion correction method taking into account multiple environmental information provided in the embodiment of the present invention can generally be executed by a terminal device, and accordingly, the numerical forecast model precipitation fusion correction device taking into account multiple environmental information is generally set in the terminal device. However, it is easy for those skilled in the art to understand that the numerical forecast model precipitation fusion correction method taking into account multiple environmental information provided in the embodiment of the present invention can also be executed by the server 105, and accordingly, the numerical forecast model precipitation fusion correction device taking into account multiple environmental information can also be set in the server 105, which is not particularly limited in this exemplary embodiment.

[0028] In addition, it should be understood that the numerical forecast model precipitation fusion correction method taking into account multiple environmental information of the implementation mode of the present invention can be configured as a software module. In some implementation scenarios, the numerical forecast model precipitation fusion correction scheme taking into account multiple environmental information of the present invention can be deployed separately to forecast rainfall for different target areas. In other implementation scenarios, the numerical forecast model precipitation fusion correction scheme taking into account multiple environmental information of the present invention can be deployed in other software as a functional module of the software, such as deployed in rainfall analysis software. The present invention does not place any particular restriction on the application mode of the numerical forecast model precipitation fusion correction method taking into account multiple environmental information.

[0029] Next, the embodiments of the present invention are described in detail.

[0030] like Figure 2 As shown, Figure 2 The present invention is a flowchart of a method for fusion correction of precipitation in a numerical forecast model taking into account multiple environmental information according to an exemplary embodiment of the present invention, comprising the following steps: S210: Obtain output data of the numerical forecast model of the target area and precipitation observation data of the rain gauge station; S220: interpolating the output data to the rainfall station to obtain precipitation forecast data corresponding to the output data; S230: Calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data; S240: Based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction, a multi-scale geographically weighted regression model is used to calculate the precipitation deviation prediction value of the numerical prediction model grid point; S250: Obtain fused precipitation data according to the precipitation deviation prediction value and the output data.

[0031] In S210, output data of the numerical forecast model of the target area and precipitation observation data of the rain gauge station are obtained.

[0032] The numerical forecasting model adopted by the present invention includes the Weather Research and Forecasting Model (WRF).

[0033] WRF uses fluid mechanics equations and atmospheric physical process parameterization schemes to simulate the evolution of atmospheric states. It can simulate the target area through high-resolution grids and predict the changes in various physical processes in the atmosphere. It is particularly suitable for short-term weather forecasts and simulations of local meteorological processes. It has the characteristics of high resolution, parallel computing, and flexible grid configuration.

[0034] The present invention uses the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) as the background field data, sets parameters such as grid resolution and physical parameterization scheme according to the needs of a specific research area and the characteristics of climate change, and introduces multi-source observation data assimilation (i.e., assimilating data from different observation systems, such as ground meteorological station data, satellite data, meteorological radar data, etc.) to further improve the initial field quality of the WRF model.

[0035] The WRF model based on multi-source observation data assimilation performs numerical simulation on a specific area, outputting precipitation generated by cumulus convection process, cloud microphysical process and shallow convection process, and obtains actual precipitation grid data (i.e., output data of the numerical forecast model in the target area) through calculation. : in, represents the precipitation produced by cumulus convection process, represents the precipitation generated by cloud microphysical processes, Represents the amount of precipitation produced by shallow convection processes.

[0036] The target area refers to the specific geographical area for which precipitation forecasting is performed, which may be a country, region or local area. The output data of the numerical weather forecast model for the target area is obtained in order to predict the precipitation in the area through the numerical weather forecast model.

[0037] Rain gauges are the actual locations where precipitation is measured, usually at specific geographical locations. There are a limited number of rain gauges, and they are spread across the forecast area.

[0038] Numerical forecast models can provide large-scale weather forecasts, but their accuracy is insufficient in some local areas; ground rainfall station observation data can provide relatively accurate local information, but its spatial resolution is limited. By combining these two types of data, the forecast accuracy and resolution can be effectively improved, especially in complex terrain or climate conditions.

[0039] In S220, the output data is interpolated to the rainfall station to obtain precipitation forecast data corresponding to the output data.

[0040] Here, linear interpolation, polynomial interpolation, spline interpolation, Kriging interpolation and other methods can be used to interpolate the output data to the rainfall station.

[0041] In S230, the precipitation deviation of the rain gauge station is calculated based on the precipitation observation data of the rain gauge station and the precipitation forecast data.

[0042] In some embodiments, before calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data corresponding to the rain gauge station, the precipitation fusion correction method of the numerical prediction model taking into account multivariate environmental information further includes: According to the longitude and latitude information of the rainfall station, determine the row and column where the rainfall station is located in the numerical forecast model grid; According to the row and column indexes of the grid points in the numerical forecast model grid, the precipitation forecast data corresponding to the target rainfall station is obtained.

[0043] WRF uses a gridded approach to simulate meteorological data. Each grid point corresponds to a specific spatial location, so each rain gauge needs to be mapped to the row and column positions in the grid by longitude and latitude. By calculating the relationship between the longitude and latitude of the rain gauge station and the longitude and latitude range of the grid, the row and column index of the station in the grid is determined: In the formula, is the row where the rainfall station is located in the WRF grid, is the column where the rainfall station is located in the WRF grid, is the spatial resolution of WRF grid precipitation data, is the maximum latitude of the WRF grid, is the minimum longitude of the WRF grid, is the latitude of the rainfall station, is the longitude of the rainfall station.

[0044] According to the location of the rain gauge station (row and column index), the precipitation forecast data for the grid point can be extracted from the output data of the WRF model.

[0045] After obtaining the precipitation forecast data of the grid points, the precipitation deviation can be obtained by subtracting the precipitation forecast value from the precipitation observation data of the rain gauge station.

[0046] In S240, based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction, a multi-scale geographically weighted regression model is used to calculate the precipitation deviation prediction value of the numerical forecast model grid point.

[0047] Here, the multivariate environmental variables used for precipitation fusion correction include geographic variables and meteorological variables, wherein the geographic variables include elevation, slope and distance to the coastline data in the digital elevation model; The meteorological variables include wind speed and surface temperature in the land surface data assimilation system data of the China Meteorological Administration as global independent variables.

[0048] Elevation indicates the altitude of each location in the target area, which has an important impact on the distribution and intensity of precipitation. Highlands usually promote the rise of air and form precipitation. Slope indicates the degree of inclination of the surface, which is usually related to the undulation of the terrain. Areas with larger slopes may affect the movement of air currents and the formation of precipitation. The distance to the coastline describes the distance from each location to the nearest coastline. The climate and precipitation patterns in coastal areas are usually different from those in inland areas. Places closer to the coastline may be affected by the marine climate and have higher precipitation. Since the effects of elevation, slope, and distance to the coastline data are usually more local and closely related to factors such as terrain and geographical features, the scope of influence is usually limited to a certain geographical area. Therefore, the elevation, slope, and distance to the coastline data are used as local independent variables.

[0049] Wind speed is a key factor in meteorological systems and can affect weather and precipitation patterns over a large area. Wind speed changes are not only affected by local geographical conditions, but are also controlled by large-scale climate systems and atmospheric circulation, and have a large spatial expansion. Its impact on precipitation is regional or even global. For example, changes in wind speed in atmospheric circulation can regulate the distribution of precipitation in the entire region. The impact of surface temperature on precipitation is also global. Surface temperature directly affects evaporation, humidity levels, and the amount of water vapor in the air, which in turn affects the formation process of precipitation. It is widely affected by factors such as atmospheric circulation and seasonal changes, and the affected area may span multiple geographical regions. Therefore, wind speed and surface temperature are used as global independent variables.

[0050] Multiscale Geographically Weighted Regression (MGWR) is a geographically weighted regression model that can flexibly adjust the regression coefficients according to different spatial locations. Unlike traditional regression methods, the MGWR model not only considers the local scale (for example, the impact of local climate characteristics and geographical factors on precipitation), but also the global scale (such as the impact of regional meteorological conditions), ensuring that precipitation variability at different scales can be correctly captured. In addition, MGWR also adjusts the regression coefficients according to different spatial locations, so that data from different regions have different weights in the regression, thereby improving the correction of local precipitation bias. Specifically, MGWR can handle the variability of precipitation forecast data in geographic space according to local geographical and meteorological factors, thereby providing more accurate correction results.

[0051] By introducing multi-scale geographical and meteorological factors, the MGWR model can accurately correct the spatial variability in precipitation forecast data, especially for areas with complex terrain and large local climate changes, and can effectively improve the accuracy and reliability of precipitation forecasts.

[0052] On this basis, based on the precipitation deviation of the rainfall stations and the multivariate environmental variables used for precipitation fusion correction, the multi-scale geographically weighted regression model is used to calculate the precipitation deviation prediction values ​​of the numerical forecast model grid points, including: interpolating the multivariate environmental variables used for precipitation fusion correction to the numerical forecast model grid points and the rain gauge station, respectively, to obtain the first multivariate environmental variables after the interpolation grid points and the second multivariate environmental variables after the interpolation rain gauge station; Based on the precipitation deviation of the rain gauge station and the second multivariate environmental variable, using a multiscale geographically weighted regression model, calculating the model coefficient of the multivariate environmental variable of the rain gauge station; The model coefficients are interpolated to the numerical forecast model grid points, and combined with the first multivariate environmental variable, the precipitation deviation prediction value of the numerical forecast model grid points is calculated.

[0053] The present invention uses a bilinear interpolation method to interpolate the multivariate environmental variables used for precipitation fusion correction to the numerical forecast model grid points and rainfall stations respectively. Bilinear interpolation is to obtain the interpolation result of the target point by weighted averaging the four adjacent data points, ensuring that data of different resolutions can be matched on the same grid for subsequent analysis.

[0054] Rain gauges refer to actual ground rainfall observation sites. Interpolation maps geographic elements (elevation, slope, distance to the coastline) and meteorological observation data (such as wind speed, temperature, etc.) to these sites to obtain independent variable values ​​consistent with the observed data.

[0055] Finally, using the first multivariate environmental variable after interpolation of grid points and the second multivariate environmental variable after interpolation of rain gauges, precipitation fusion correction is performed. The MGWR model will perform model regression based on the input meteorological and geographic independent variables to predict the deviation of the numerical model forecast precipitation in the target area and further obtain precipitation fusion data. These precipitation fusion data will provide important information support for weather warning, agricultural production, irrigation, disaster management, etc.

[0056] Based on the precipitation deviation of the rain gauge station and the second multivariate environmental variable, the model coefficient of the multivariate environmental variable of the rain gauge station is calculated by using a multiscale geographically weighted regression model, including: in, is the response variable, indicating the precipitation deviation at the rain gauge station; is a covariate, representing the second multivariate environmental variable; The representative bandwidth is No. Model coefficients for each rainfall station; Represents the spatial geographical location of the rainfall station; is the model regression residual, Represents the total number of rainfall stations.

[0057] In other words, by substituting the precipitation deviation data and geographic variables (elevation, slope, distance to the coastline) and meteorological variables (wind speed, surface temperature) into the above formula, the regression coefficient of the rain gauge station can be calculated. The regression coefficient represents the degree of correction of the precipitation deviation by geographic and meteorological variables at a specific rain gauge location. The MGWR model can obtain accurate regression coefficients at a local spatial scale, so that each station has a specific correction factor.

[0058] After obtaining the regression coefficients for each rainfall station, these regression coefficients need to be converted to the grid points of the entire weather research and forecasting model through interpolation methods to form model coefficients corresponding to each grid point. The interpolation process ensures that the spatial distribution of the regression coefficients is continuous and applicable to the entire grid area.

[0059] Based on the regression coefficients, geographic variables, and meteorological variables corresponding to the grid points, the precipitation deviation of each grid point can be calculated. The precipitation deviation is the difference between the WRF model forecast precipitation and the actual observation data, which is a way to evaluate the accuracy of the forecast model. In order to reduce the impact of data of different dimensions, the precipitation deviation can also be normalized so that the mean of the data is 0 and the variance is 1, thereby improving the stability and accuracy of the calculation.

[0060] Interpolating the model coefficients to the numerical forecast model grid points, and combining the first multivariate environmental variable to calculate the precipitation deviation prediction value of the numerical forecast model grid points includes: in, is the precipitation observation value; For precipitation forecast data; For location The corresponding geographic variables in the first multivariate environmental variable; For location The corresponding The regression coefficient of the geographical variable; is the total number of geographical variables, For location The corresponding A global meteorological variable in the first multivariate environmental variable; For location The corresponding The regression coefficient of the meteorological variable; is the total number of meteorological variables, is the random error of the model. The precipitation observation values ​​indicated are those corresponding to the precipitation grid of the numerical model.

[0061] The normalized precipitation deviation value can be used for further analysis and model correction. In the denormalization process, the true value of the normalized precipitation deviation is first obtained by interpolation, and its mean and variance are calculated. These means and variances will be used to denormalize the deviation in the precipitation forecast model grid to obtain the actual grid precipitation deviation prediction value.

[0062] In S250, fused precipitation data is obtained according to the precipitation deviation prediction value and the output data.

[0063] The present invention does not specifically limit the way in which this step can be implemented. For example, in some embodiments, weighted average method, difference method, least squares regression method, etc. can be used to obtain fused precipitation prediction data based on the precipitation deviation prediction value and the precipitation forecast data.

[0064] In an embodiment of the present invention, the fused precipitation data is obtained according to the precipitation deviation prediction value and the output data. include: in, Represents output data, Represents the precipitation deviation prediction value.

[0065] According to a second aspect of an embodiment of the present invention, a numerical forecast model precipitation fusion correction device taking into account multiple environmental information is also provided, referring to Figure 3As shown, the numerical forecast model precipitation fusion correction device taking into account multiple environmental information includes: The data acquisition module 310 is used to acquire the output data of the numerical forecast model of the target area and the precipitation observation data of the rain gauge station; An interpolation processing module 320 is used to interpolate the output data to the rainfall station to obtain precipitation forecast data corresponding to the output data; A deviation calculation module 330, configured to calculate the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data; The deviation prediction module 340 is used to calculate the precipitation deviation prediction value of the numerical prediction model grid point based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction using a multi-scale geographically weighted regression model; The result output module 350 is used to obtain fused precipitation data according to the precipitation deviation prediction value and the output data.

[0066] It should be noted that although several modules of the numerical forecast model precipitation fusion correction device taking into account multivariate environmental information are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules described above can be embodied in one module or unit. Conversely, the features and functions of one module described above can be further divided into multiple modules or submodules to be embodied.

[0067] In addition, in an exemplary embodiment of the present invention, an electronic device capable of implementing the above-mentioned numerical forecast model precipitation fusion correction method taking into account multiple environmental information is also provided.

[0068] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0069] Refer to the following Figure 4 An electronic device 400 according to such an embodiment of the present invention is described. Figure 4 The electronic device 400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0070] like Figure 4As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: the at least one processing unit 410, the at least one storage unit 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0071] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 performs the steps of various exemplary embodiments of the present invention described in the above “Exemplary Method” section. For example, the processing unit 410 can perform the following steps: Figure 2 S210 shown in: obtaining the output data of the numerical forecast model of the target area and the precipitation observation data of the rain gauge station; S220: interpolating the output data to the rain gauge station to obtain the precipitation forecast data corresponding to the output data; S230: calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data; S240: based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction, using the multi-scale geographically weighted regression model, calculating the precipitation deviation prediction value of the numerical forecast model grid point; S250: obtaining the fused precipitation data according to the precipitation deviation prediction value and the output data.

[0072] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0073] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0074] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0075] The electronic device 400 may also communicate with one or more external devices 470 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 through a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0076] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present invention.

[0077] In an exemplary embodiment of the present invention, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present invention is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps of various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present invention.

[0078] refer to Figure 5 As shown, a program product 500 for implementing the above-mentioned numerical forecast model precipitation fusion correction method taking into account multivariate environmental information according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In the present invention, the readable storage medium can be any tangible medium containing or storing a program, and the program can be used by or in combination with an instruction execution system, an apparatus or a device.

[0079] The program product may use any combination of one or more readable storage media. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0080] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0081] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0082] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.

[0083] Other embodiments of the invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. The invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0084] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A precipitation fusion correction method for numerical forecast models taking into account multiple environmental information, characterized in that: include: Obtain output data of numerical forecast models and precipitation observation data from rain gauge stations in the target area; interpolating the output data to the rainfall station to obtain precipitation forecast data corresponding to the output data; Calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data; Based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction, a multi-scale geographically weighted regression model is used to calculate the precipitation deviation prediction value of the numerical forecast model grid point; The fused precipitation data is obtained according to the precipitation deviation prediction value and the output data.

2. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 1, characterized in that: Output data of numerical prediction model in target area It is calculated by: in, represents the precipitation produced by cumulus convection process, represents the precipitation generated by cloud microphysical processes, Represents the amount of precipitation produced by shallow convection processes.

3. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 1, characterized in that: Before calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data, the precipitation fusion correction method of the numerical forecast model taking into account the multivariate environmental information further includes: According to the longitude and latitude information of the rainfall station, determine the row and column where the rainfall station is located in the numerical forecast model grid; According to the row and column indexes of the grid points in the numerical forecast model grid, the precipitation forecast data corresponding to the target rainfall station is obtained.

4. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 1, characterized in that: The multivariate environmental variables used for precipitation fusion correction include geographic variables and meteorological variables, and the geographic variables include elevation, slope and distance to the coastline data in the digital elevation model; The meteorological variables include wind speed and surface temperature in the land surface data assimilation system data of the China Meteorological Administration as global independent variables.

5. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 1, characterized in that: Based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction, the precipitation deviation prediction values ​​of the numerical forecast model grid points are calculated using the multi-scale geographically weighted regression model, including: interpolating the multivariate environmental variables used for precipitation fusion correction to the numerical forecast model grid points and the rain gauge station, respectively, to obtain the first multivariate environmental variables after the interpolation grid points and the second multivariate environmental variables after the interpolation rain gauge station; Based on the precipitation deviation of the rain gauge station and the second multivariate environmental variable, using a multiscale geographically weighted regression model, calculating the model coefficient of the multivariate environmental variable of the rain gauge station; The model coefficients are interpolated to the numerical forecast model grid points, and combined with the first multivariate environmental variable, the precipitation deviation prediction value of the numerical forecast model grid points is calculated.

6. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 5, characterized in that: Based on the precipitation deviation of the rain gauge station and the second multivariate environmental variable, the model coefficient of the multivariate environmental variable of the rain gauge station is calculated by using a multiscale geographically weighted regression model, including: in, is the response variable, indicating the precipitation deviation at the rain gauge station; is a covariate, representing the second multivariate environmental variable; The representative bandwidth is No. Model coefficients for each rainfall station; Represents the spatial geographical location of the rainfall station; is the model regression residual, Represents the total number of rainfall stations.

7. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 5, characterized in that: The model coefficients are interpolated to the numerical forecast model grid points, and combined with the first multivariate environmental variable, the precipitation deviation prediction value of the numerical forecast model grid points is calculated. include: in, is the precipitation observation value; For precipitation forecast data; For location The corresponding geographic variables in the first multivariate environmental variable; For location The corresponding The regression coefficient of the geographical variable; is the total number of geographical variables, For location The corresponding A global meteorological variable in the first multivariate environmental variable; For location The corresponding The regression coefficient of the meteorological variable; is the total number of meteorological variables, is the random error of the model.

8. The precipitation fusion correction method of numerical forecast model taking into account multiple environmental information according to any one of claims 1 to 7, characterized in that: According to the precipitation deviation prediction value and the output data, the fused precipitation data is obtained. include: in, Represents output data, Represents the precipitation deviation prediction value.

9. A device for the precipitation fusion correction method of numerical forecast model taking into account multi-environmental information according to any one of claims 1 to 7, characterized in that: include: A data acquisition module is used to obtain the output data of the numerical forecast model of the target area and the precipitation observation data of the rain gauge station; An interpolation processing module, used for interpolating the output data to the rainfall station to obtain precipitation forecast data corresponding to the output data; A deviation calculation module, used for calculating the precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data; A deviation prediction module is used to calculate the precipitation deviation prediction value of the numerical prediction model grid point based on the precipitation deviation of the rain gauge station and the multivariate environmental variables used for precipitation fusion correction using a multi-scale geographically weighted regression model; The result output module is used to obtain fused precipitation data according to the precipitation deviation prediction value and the output data.

10. An electronic device, characterized in that: include: processor; as well as A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the numerical forecast model precipitation fusion correction method taking into account multivariate environmental information as described in any one of claims 1 to 7.

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