Precipitation Fusion Correction Method for Numerical Prediction Model Considering Multiple Environmental Information

Abstract

The present invention provides a precipitation fusion correction method for numerical weather prediction models considering multiple environmental information, which relates to the field of meteorological monitoring technology. The method includes: obtaining the output data of the numerical weather prediction model for the target area and the precipitation observation data of rain gauges; interpolating the output data to the rain gauges to obtain precipitation forecast data corresponding to the output data; calculating the precipitation deviation of the rain gauges according to the precipitation observation data of the rain gauges and the precipitation forecast data; based on the precipitation deviation of the rain gauges and the multiple environmental variables for precipitation fusion correction, using a multi-scale geographically weighted regression model to calculate the predicted value of the precipitation deviation at the grid points of the numerical weather prediction model; and obtaining the fused precipitation data according to the predicted value of the precipitation deviation and the output data. The present invention can not only improve the spatial accuracy and timeliness of precipitation forecasts, but also effectively reduce the bias of traditional precipitation forecast models and provide more accurate precipitation prediction results.

Description

Background Art

[0002] Precipitation forecasting has always been an important research topic in meteorology and is widely used in multiple fields such as meteorological early warning, disaster prevention and control, and agricultural irrigation. With the progress of Numerical Weather Prediction (NWP) technology, numerical prediction 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 prediction still faces many challenges in precipitation forecasting, especially the accuracy problem of local area and short-term precipitation forecasting.

[0003] Existing numerical prediction 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 forecasting errors of numerical prediction models are relatively large, resulting in insufficient spatial accuracy of the forecasts. Especially in mountainous areas, coastal areas, etc., the spatial distribution and variation of precipitation are more complex and are often affected by local factors.

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

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

[0006] To overcome the problems existing in the related art, the present invention provides a precipitation fusion correction method for a numerical prediction model considering multiple environmental information.

[0007] According to the first aspect of the embodiments of the present invention, a precipitation fusion correction method for a numerical prediction model considering multiple environmental information is provided. The precipitation fusion correction method for a numerical prediction model considering multiple environmental information includes:

[0008] Obtain the output data of the numerical prediction model for the target area and the precipitation observation data from rain gauges;

[0009] Interpolate the output data to the rain gauges to obtain precipitation forecast data corresponding to the output data;

[0010] 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;

[0011] Based on the precipitation deviation of the rain gauge station and the multiple environmental variables for precipitation fusion correction, use a multi-scale geographically weighted regression model to calculate the predicted precipitation deviation value of the grid points of the numerical weather prediction model;

[0012] Obtain the fused precipitation data according to the predicted precipitation deviation value and the output data;

[0013] In some exemplary embodiments of the present invention, based on the foregoing solution, the output data of the numerical weather prediction model for the target area is calculated in the following manner:

[0014]

[0015] wherein, represents the precipitation generated by the cumulus convection process, represents the precipitation generated by the cloud microphysics process, represents the precipitation generated by the shallow convection process.

[0016] In some exemplary embodiments of the present invention, based on the foregoing solution, 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 numerical weather prediction model precipitation fusion correction method considering multiple environmental information further includes:

[0017] Determine the row and column where the rain gauge station is located in the grid of the numerical weather prediction model according to the longitude and latitude information of the rain gauge station;

[0018] Obtain the precipitation forecast data corresponding to the target rain gauge station according to the row and column indexes of the grid points in the grid of the numerical weather prediction model.

[0019] In some exemplary embodiments of the present invention, based on the foregoing solution, the multiple environmental variables for precipitation fusion correction include geographical variables and meteorological variables, and the geographical variables include elevation, slope and distance data to the coastline in the digital elevation model;

[0020] The meteorological variables include wind speed and surface temperature in the China Meteorological Administration Land Data Assimilation System data as global independent variables.

[0021] Based on the precipitation deviation of the rain gauge station and the multiple environmental variables for precipitation fusion correction, using a multi-scale geographically weighted regression model to calculate the predicted precipitation deviation value of the grid points of the numerical weather prediction model includes:

[0022] Interpolate the multivariate environmental variables for precipitation fusion correction to the grid points of the numerical weather prediction model and the rain gauges respectively, to obtain the first multivariate environmental variable after interpolating the grid points and the second multivariate environmental variable after interpolating the rain gauges;

[0023] Based on the precipitation deviation of the rain gauges and the second multivariate environmental variable, use the multi-scale geographically weighted regression model to calculate the model coefficients of the multivariate environmental variables of the rain gauges;

[0024] Interpolate the model coefficients to the grid points of the numerical weather prediction model, and combine with the first multivariate environmental variable to calculate the predicted value of the precipitation deviation of the grid points of the numerical weather prediction model.

[0025] In some exemplary embodiments of the present invention, based on the foregoing solution, calculating the model coefficients of the multivariate environmental variables of the rain gauges by using the multi-scale geographically weighted regression model based on the precipitation deviation of the rain gauges and the second multivariate environmental variable includes:

[0026]

[0027] Wherein, is the response variable, representing the precipitation deviation of the rain gauges; is the covariate, representing the second multivariate environmental variable; represents the -th model coefficient of the -th rain gauge with a bandwidth of represents the spatial geographical location of the rain gauge; is the model regression residual, represents the total number of rain gauges.

[0028] In some exemplary embodiments of the present invention, based on the foregoing solution, interpolate the model coefficients to the grid points of the numerical weather prediction model, and combine with the first multivariate environmental variable to calculate the predicted value of the precipitation deviation of the grid points of the numerical weather prediction model includes:

[0029]

[0030] Wherein, is the precipitation observation value; is the precipitation forecast data; is the -th geographical variable in the first multivariate environmental variable corresponding to the location ; is the -th regression coefficient of this geographical variable corresponding to the location ; is the total number of geographical variables, is the -th corresponding to the location a global meteorological variable in a first plurality of environmental variables; for a location corresponding regression coefficient of the meteorological variable; is the total number of meteorological variables, is the model random error.

[0031] In some exemplary embodiments of the present invention, based on the foregoing solution, fusion precipitation data is obtained according to the precipitation deviation prediction value and the output data including:

[0032]

[0033] wherein, represents the output data, represents the precipitation deviation prediction value.

[0034] According to a second aspect of the embodiments of the present invention, there is provided an apparatus for a precipitation fusion correction method of a numerical weather prediction model considering multiple environmental information as described above, including:

[0035] a data acquisition module, configured to acquire output data of a numerical weather prediction model for a target area and precipitation observation data of a rain gauge station;

[0036] an interpolation processing module, configured to interpolate the output data to a ground rain gauge station to obtain precipitation forecast data corresponding to the output data;

[0037] a deviation calculation module, configured to calculate a precipitation deviation of the rain gauge station according to the precipitation observation data of the rain gauge station and the precipitation forecast data;

[0038] a deviation prediction module, configured to calculate a precipitation deviation prediction value of a grid point of the numerical weather prediction model by using a multi-scale geographically weighted regression model based on the precipitation deviation of the rain gauge station and multiple environmental variables for precipitation fusion correction;

[0039] a result output module, configured to obtain fusion precipitation data according to the precipitation deviation prediction value and the output data.

[0040] According to a third aspect of the embodiments of the present invention, there is provided an electronic device, including: a processor; and a memory, where computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, the precipitation fusion correction method of the numerical weather prediction model considering multiple environmental information in the first aspect is implemented.

[0041] According to a fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the method for precipitation fusion correction of a numerical prediction model considering multi-source environmental information in the first aspect.

[0042] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0043] By fusing the output data of the numerical prediction model with the precipitation observation data of rain gauges, the present invention can effectively reduce the deviation of a single numerical prediction model and improve the accuracy of precipitation prediction; by introducing a multiscale geographically weighted regression model (MGWR), the single bandwidth assumption in traditional regression methods can be eliminated, and the spatial variability of precipitation can be better reflected. Especially under complex terrain and climate conditions, the MGWR model can dynamically adjust the regression coefficients according to local geographical features (such as elevation, slope, distance to the coastline) and global meteorological conditions (such as wind speed, surface temperature), so as to accurately correct the precipitation deviation and significantly improve the precipitation prediction accuracy in local areas.

[0044] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings herein are incorporated into the specification and constitute a part of the present invention, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0046] Figure 1 A schematic diagram of a system architecture showing an exemplary application environment of a method and apparatus for precipitation fusion correction of a numerical prediction model considering multi-source environmental information that can apply the embodiments of the present invention;

[0047] Figure 2 A schematic flowchart showing the method for precipitation fusion correction of a numerical prediction model considering multi-source environmental information according to some embodiments of the present invention;

[0048] Figure 3 A schematic diagram showing an apparatus for precipitation fusion correction of a numerical prediction model considering multi-source environmental information according to some embodiments of the present invention;

[0049] Figure 4 A schematic diagram showing the structure of a computer system of an electronic device according to some embodiments of the present invention;

[0050] Figure 5Schematically shown is a schematic diagram of a computer-readable storage medium according to some embodiments of the present invention. Detailed implementation manners

[0051] Here, the exemplary embodiments will be described in detail, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0052] 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 "said" used in the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. 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 of the associated listed items.

[0053] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, such 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 "when" or "while" or "in response to determining".

[0054] Figure 1 Shown is a schematic diagram of the system architecture of an exemplary application environment of a numerical weather prediction model precipitation fusion correction method and device that can take into account multi-source environmental information and apply the embodiments of the present invention.

[0055] As Figure 1 shown, the system architecture 100 may include one or more of terminal devices such as a desktop computer 101, a portable computer 102, a smart phone 103, etc., 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, wireless communication links, or fiber optic cables, etc. The terminal device may be various electronic devices having data processing functions, and a display screen is provided on the electronic device for presenting rainfall prediction data to the user, including but not limited to the above-mentioned desktop computer, portable computer, smart phone, etc. It should be understood, Figure 1The numbers of the terminal devices, networks, and servers in [it] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. For example, server 105 can be a sub-server cluster composed of multiple sub-servers, etc.

[0056] The method for precipitation fusion correction of a numerical weather prediction model considering multiple environmental information provided by the embodiments of the present invention can generally be executed by a terminal device. Correspondingly, the device for precipitation fusion correction of a numerical weather prediction model considering multiple environmental information is generally set in the terminal device. However, those skilled in the art can easily understand that the method for precipitation fusion correction of a numerical weather prediction model considering multiple environmental information provided by the embodiments of the present invention can also be executed by server 105. Correspondingly, the device for precipitation fusion correction of a numerical weather prediction model considering multiple environmental information can also be set in server 105. No special limitation is made in this exemplary embodiment.

[0057] In addition, it should be understood that the method for precipitation fusion correction of a numerical weather prediction model considering multiple environmental information in the embodiments of the present invention can be configured as a software module. In some implementation scenarios, the precipitation fusion correction solution of the numerical weather prediction model considering multiple environmental information of the present invention can be deployed separately to perform rainfall prediction for different target regions. In other implementation scenarios, the precipitation fusion correction solution of the numerical weather prediction model considering multiple environmental information of the present invention can be deployed in other software as a functional module of the software, such as being deployed in rainfall analysis software. The present invention does not make special restrictions on the application manner of the method for precipitation fusion correction of a numerical weather prediction model considering multiple environmental information.

[0058] Next, the embodiments of the present invention will be described in detail.

[0059] As Figure 2 shown, Figure 2 is a flowchart of a method for precipitation fusion correction of a numerical weather prediction model considering multiple environmental information shown according to an exemplary embodiment of the present invention, including the following steps:

[0060] S210: Obtain the output data of the numerical weather prediction model for the target region and the precipitation observation data of the rain gauge stations;

[0061] S220: Interpolate the output data to the rain gauge stations to obtain precipitation forecast data corresponding to the output data;

[0062] S230: Calculate the precipitation deviation of the rain gauge stations according to the precipitation observation data of the rain gauge stations and the precipitation forecast data;

[0063] S240: Based on the precipitation deviation of the rain gauge stations and the multiple environmental variables for precipitation fusion correction, use a multi-scale geographically weighted regression model to calculate the predicted value of the precipitation deviation of the grid points of the numerical weather prediction model;

[0064] S250: Obtain the fused precipitation data based on the precipitation deviation prediction value and the output data.

[0065] In S210, obtain the output data of the numerical weather prediction model for the target area and the precipitation observation data of the rain gauge stations.

[0066] The numerical weather prediction model adopted by the present invention includes the Weather Research and Forecasting Model (WRF).

[0067] WRF uses the hydrodynamic equations and the parameterization schemes of atmospheric physical processes to simulate the evolution of the atmospheric state. It can simulate the target area through a high-resolution grid, predict the changes of various physical processes in the atmosphere, and is especially suitable for short-term weather forecasting and the simulation of local meteorological processes. It has the characteristics of high resolution, parallel computing, and flexible grid configuration.

[0068] The present invention uses the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) as the background field data, sets parameters such as the grid resolution and physical parameterization scheme according to the specific research area requirements and climate change characteristics, and introduces multi-source observation data assimilation (that is, assimilating data from different observation systems, such as surface meteorological station data, satellite data, meteorological radar data, etc.) to further improve the initial field quality of the WRF model.

[0069] The WRF model based on multi-source observation data assimilation performs numerical simulation on a specific area, outputs the precipitation generated by the cumulus convection process, cloud microphysical process, and shallow convection process, and obtains the actual precipitation grid data through calculation (that is, the output data of the numerical weather prediction model for the target area). :

[0070]

[0071] Among them, represents the precipitation generated by the cumulus convection process, represents the precipitation generated by the cloud microphysical process, represents the precipitation generated by the shallow convection process.

[0072] The target area refers to a specific geographical area for precipitation prediction, which may be a country, region, or local area. Obtaining the output data of the numerical weather prediction model for the target area is to predict the precipitation situation in this area through the numerical weather forecasting model.

[0073] A rain gauge station is a site that actually measures precipitation and is usually located at a specific geographical location. The number of rain gauge stations is limited and they are distributed throughout the forecast area.

[0074] Numerical weather prediction models can provide weather forecasts over a large area, but their accuracy is insufficient in some local areas; the observational data of ground rain gauge stations can provide relatively accurate local information, but their spatial resolution is limited. By combining these two types of data, the forecast accuracy and resolution can be effectively improved, especially under complex terrain or climate conditions.

[0075] In S220, the output data is interpolated to the rain gauge stations to obtain precipitation forecast data corresponding to the output data.

[0076] Here, the output data can be interpolated to the rain gauge stations by means of linear interpolation, polynomial interpolation, spline interpolation, Kriging interpolation, etc.

[0077] In S230, according to the precipitation observation data of the rain gauge stations and the precipitation forecast data, the precipitation deviation of the rain gauge stations is calculated.

[0078] In some embodiments, before calculating the precipitation deviation of the rain gauge stations according to the precipitation observation data of the rain gauge stations and the corresponding precipitation forecast data, the numerical weather prediction model precipitation fusion correction method considering multi-source environmental information further includes:

[0079] According to the longitude and latitude information of the rain gauge stations, determine the row and column where the rain gauge stations are located in the numerical weather prediction model grid;

[0080] According to the row and column indices of the grid points in the numerical weather prediction model grid, obtain the precipitation forecast data corresponding to the target rain gauge stations.

[0081] WRF uses a grid-based method to simulate meteorological data, and each grid point corresponds to a specific spatial position. Therefore, it is necessary to map each rain gauge station to the row and column positions in the grid through 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, determine the row and column indices of the station in the grid:

[0082]

[0083]

[0084] In the formula, is the row where the rain gauge station is located in the WRF grid, is the column where the rain gauge station is located in the WRF grid, is the spatial resolution of the 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 rain gauge station, is the longitude of the rain gauge station.

[0085] According to the rain gauge station location (row and column indices), precipitation forecast data for this grid point can be extracted from the output data of the WRF model.

[0086] After obtaining the precipitation forecast data for the grid point, subtracting the precipitation forecast value from the precipitation observation data of the rain gauge station can yield the precipitation deviation.

[0087] In S240, based on the precipitation deviation of the rain gauge station and the multivariate environmental variables for precipitation fusion correction, using a multiscale geographically weighted regression model, calculate the predicted value of the precipitation deviation for the grid point of the numerical weather prediction model.

[0088] Here, the multivariate environmental variables for precipitation fusion correction include geographical variables and meteorological variables. The geographical variables include elevation, slope, and distance to the coastline in the digital elevation model;

[0089] The meteorological variables include wind speed and surface temperature in the China Meteorological Administration Land Data Assimilation System data as global independent variables.

[0090] Elevation represents the altitude of each location in the target area and has an important impact on the distribution and intensity of precipitation. Highlands usually promote the upward movement of air and form precipitation. Slope represents the degree of inclination of the ground surface and is usually related to the terrain undulation. Areas with larger slopes may affect the movement of airflows 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 larger precipitation amounts. Since the elevation, slope, and distance to the coastline data usually have a more local impact and are closely related to factors such as terrain and geographical features, and the influence range is usually limited to a certain geographical area, the elevation, slope, and distance to the coastline data are used as local independent variables.

[0091] Wind speed is a key factor in the meteorological system and can affect weather and precipitation patterns on a large scale. The change in wind speed is not only affected by local geographical conditions but also controlled by large-scale climate systems and atmospheric circulation, with a large spatial extensibility. Its impact on precipitation is regional or even global. For example, the change in wind speed in the atmospheric circulation can regulate the precipitation distribution 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, thereby affecting the precipitation formation process. 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.

[0092] The Multiscale Geographically Weighted Regression (MGWR) model is a geographically weighted regression model that can flexibly adjust regression coefficients according to different spatial locations. Different from traditional regression methods, the MGWR model not only considers the local scale (e.g., the influence of local climate characteristics and geographical factors on precipitation), but also considers the global scale (such as the influence of regional meteorological conditions), ensuring that precipitation variability at different scales can be correctly captured. In addition, MGWR adjusts regression coefficients according to different spatial locations, giving different weights to data in different regions during regression, thereby improving the correction of local precipitation bias. Specifically, MGWR can process the variability of precipitation forecast data in the geographical space based on local geographical and meteorological factors, thus providing more accurate correction results.

[0093] By introducing multiscale geographical and meteorological factors, the MGWR model can accurately correct the spatial variability in precipitation forecast data. Especially for regions with complex terrain and large local climate changes, it can effectively improve the accuracy and reliability of precipitation forecasts.

[0094] On this basis, based on the precipitation bias of rainfall stations and the multivariate environmental variables used for precipitation fusion correction, using the multiscale geographically weighted regression model, calculating the precipitation bias prediction value of the grid points of the numerical weather prediction model includes:

[0095] Interpolate the multivariate environmental variables used for precipitation fusion correction to the grid points of the numerical weather prediction model and rainfall stations respectively, to obtain the first multivariate environmental variable after interpolating the grid points and the second multivariate environmental variable after interpolating the rainfall stations;

[0096] Based on the precipitation bias of the rainfall stations and the second multivariate environmental variable, use the multiscale geographically weighted regression model to calculate the model coefficients of the multivariate environmental variables of the rainfall stations;

[0097] Interpolate the model coefficients to the grid points of the numerical weather prediction model, and combine with the first multivariate environmental variable to calculate the precipitation bias prediction value of the grid points of the numerical weather prediction model.

[0098] The present invention uses the bilinear interpolation method to interpolate the multivariate environmental variables used for precipitation fusion correction to the grid points of the numerical weather prediction model and rainfall stations respectively. Bilinear interpolation obtains the interpolation result of the target point by weighted averaging of four adjacent data points, ensuring that data with different resolutions can be matched on the same grid for subsequent analysis.

[0099] A rain gauge station refers to an actual ground rain gauge observation site. Interpolation is used to map geographical elements (elevation, slope, distance to the coastline) and meteorological observation data (such as wind speed, temperature, etc.) to these stations to obtain independent variable values consistent with the observation data.

[0100] Finally, using the first multivariate environmental variable after interpolating the grid points and the second multivariate environmental variable after interpolating the rain gauge stations, precipitation fusion correction is carried out. The MGWR model will perform model regression based on the input meteorological and geographical independent variables, 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.

[0101] Based on the precipitation deviation of the rain gauge station and the second multivariate environmental variable, using the multi-scale geographically weighted regression model, calculating the model coefficients of the multivariate environmental variable of the rain gauge station includes:

[0102]

[0103] Among them, is the response variable, representing the precipitation deviation of the rain gauge station; is the covariate, representing the second multivariate environmental variable; represents the bandwidth as the th model coefficient of the rain gauge station; represents the spatial geographical location of the rain gauge station; is the model regression residual, represents the total number of rain gauge stations.

[0104] That is to say, substituting the precipitation deviation data, geographical variables (elevation, slope, distance to the coastline), and meteorological variables (wind speed, surface temperature) into the above formula, the regression coefficients of the rain gauge stations can be calculated. The regression coefficient represents the degree of correction influence of geographical and meteorological variables on the precipitation deviation at a specific rain gauge station location. Through the MGWR model, accurate regression coefficients can be obtained at the local spatial scale, so that each station has a specific correction factor.

[0105] After obtaining the regression coefficients of each rain gauge station, these regression coefficients need to be transformed to the grid points of the entire weather research and forecasting model through interpolation methods to form the 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.

[0106] Based on the regression coefficients corresponding to the grid points, geographical variables, and meteorological variables, the precipitation deviation of each grid point can be calculated. The precipitation deviation is the difference between the precipitation predicted by the WRF model and the actual observed data, and it is a way to evaluate the accuracy of the prediction model. To reduce the influence of data with 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.

[0107] Interpolating the model coefficients to the grid points of the numerical weather prediction model and combining with the first multivariate environmental variables, calculating the predicted value of the precipitation deviation at the grid points of the numerical weather prediction model includes:

[0108]

[0109] where, is the precipitation observation value; is the precipitation forecast data; is the location corresponding to the th geographical variable among the first multivariate environmental variables; is the location corresponding to the th regression coefficient of this geographical variable; is the total number of geographical variables, is the location corresponding to the th global meteorological variable among the first multivariate environmental variables; is the location corresponding to the th regression coefficient of this meteorological variable; is the total number of meteorological variables, is the random error of the model. The precipitation observation value represented by represents the precipitation observation value corresponding to the precipitation grid of the numerical model.

[0110] The normalized precipitation deviation value can be used for further analysis and model calibration. In the denormalization process, first, the true value of the normalized precipitation deviation is calculated by interpolation, and its mean and variance are calculated. These mean and variance will be used to denormalize the deviation in the precipitation forecast model grid, so as to obtain the actual grid precipitation deviation predicted value.

[0111] In S250, according to the predicted value of the precipitation deviation and the output data, the fused precipitation data is obtained.

[0112] The present invention does not specifically limit the implementation manner of this step. For example, in some embodiments, methods such as weighted average method, difference method, least squares regression method can be used to obtain the fused precipitation prediction data according to the predicted value of the precipitation deviation and the precipitation forecast data.

[0113] In an embodiment of the present invention, based on the precipitation deviation prediction value and the output data, fused precipitation data is obtained. It includes:

[0114]

[0115] Wherein, represents the output data, represents the precipitation deviation prediction value.

[0116] According to the second aspect of the embodiments of the present invention, there is also provided a precipitation fusion correction device for a numerical weather prediction model considering multi-source environmental information. Referring to Figure 3 as shown, the precipitation fusion correction device for a numerical weather prediction model considering multi-source environmental information includes:

[0117] A data acquisition module 310, configured to acquire the output data of the numerical weather prediction model for the target area and the precipitation observation data of the rain gauge station;

[0118] An interpolation processing module 320, configured to interpolate the output data to the rain gauge station to obtain precipitation forecast data corresponding to the output data;

[0119] 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;

[0120] A deviation prediction module 340, configured to calculate the precipitation deviation prediction value of the grid points of the numerical weather prediction model by using a multi-scale geographically weighted regression model based on the precipitation deviation of the rain gauge station and the multi-source environmental variables for precipitation fusion correction;

[0121] A result output module 350, configured to obtain fused precipitation data according to the precipitation deviation prediction value and the output data.

[0122] It should be noted that although several modules of the precipitation fusion correction device for a numerical weather prediction model considering multi-source environmental information are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more of the above-mentioned modules 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 sub-modules for embodiment.

[0123] In addition, in an exemplary embodiment of the present invention, there is also provided an electronic device capable of implementing the above-mentioned precipitation fusion correction method for a numerical weather prediction model considering multi-source environmental information.

[0124] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0125] The following refers to Figure 4 to describe the electronic device 400 according to such an embodiment of the present invention. Figure 4 The illustrated electronic device 400 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0126] As Figure 4 shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one of the above-mentioned processing units 410, at least one of the above-mentioned storage units 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0127] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 410, so that the processing unit 410 executes the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" part of the present invention. For example, the processing unit 410 can execute as Figure 2 shown in S210: obtaining output data of the target area numerical weather prediction model and precipitation observation data of a rain gauge station; S220: interpolating the output data to the rain gauge 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 multiple environmental variables for precipitation fusion correction, using a multi-scale geographically weighted regression model to calculate the precipitation deviation prediction value of the numerical weather prediction model grid points; S250: obtaining the fused precipitation data according to the precipitation deviation prediction value and the output data.

[0128] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 421 and / or a cache storage unit 422, and may further include a read-only storage unit (ROM) 423.

[0129] The storage unit 420 may also include a program / utilities 424 having a set (at least one) of program modules 425. Such program modules 425 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0130] The bus 430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0131] The electronic device 400 may also communicate with one or more external devices 470 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or may communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through the input / output (I / O) interface 450. Moreover, the electronic device 400 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 460. As shown in the figure, the network adapter 460 communicates with other modules of the electronic device 400 through the 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.

[0132] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by the way of software in combination with necessary hardware. Therefore, the technical solution according to the embodiments 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 may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0133] In an exemplary embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above method of the present invention. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes program code that, when the program product runs on a terminal device, is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present invention.

[0134] Reference Figure 5 As shown, a program product 500 for implementing the above precipitation fusion correction method of the numerical weather prediction model considering multi-source environmental information according to an embodiment of the present invention is described. It can be in the form of a portable compact disc read-only memory (CD-ROM) and includes program code, and can 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 that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0135] The program product can adopt any combination of one or more readable storage media. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0136] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's 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 can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0137] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0138] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or in a manner combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0139] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the claims.

[0140] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited 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 the output data of the numerical forecast model in the target area and the precipitation observation data of the rain gauge station. It is calculated by: in, represents the precipitation produced by cumulus convection process, represents the precipitation generated by cloud microphysical processes, It represents the precipitation produced by shallow convection process; 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 multiscale geographically weighted regression model is used to calculate the precipitation deviation prediction value of the numerical forecast model grid point; wherein the multivariate environmental variables used for precipitation fusion correction include geographical variables and meteorological variables, the geographical 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; 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: 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.

3. 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 value of the numerical forecast model grid point is 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.

4. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 3 is 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.

5. The precipitation fusion correction method of numerical forecast model considering multiple environmental information according to claim 3, 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.

6. The precipitation fusion correction method of numerical forecast model taking into account multiple environmental information according to any one of claims 1 to 5, 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.

7. 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 5, 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.

8. 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 5.

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