Method and device for predicting area temperature, processor and readable storage medium

Abstract

The invention discloses a method and device for predicting regional temperature, a processor and a readable storage medium, and the method comprises the steps: determining a target numerical mode of a target region, the target numerical mode being used for carrying out simulated weather forecast on the target region; determining an initial field of a target numerical pattern according to a forecast result of the global numerical pattern; obtaining observation data of a plurality of ground meteorological stations corresponding to the target area; correcting the initial field of the target numerical pattern according to the observation data to obtain a corrected initial field of the target numerical pattern; and performing numerical simulation calculation according to the corrected initial field of the target numerical mode to determine the predicted temperature of the target area. The observation data of the ground meteorological station corresponding to the target area is assimilated into the target numerical mode, so that the ground temperature in the initial field in the target numerical mode is closer to the temperature field in the real atmosphere, the temperature deviation in the numerical mode is reduced, and the accuracy of the temperature prediction result is improved.

Description

Technical Field

[0001] The present application relates to the technical field of snow cover prediction for power grids, and in particular to a method, device, processor, and readable storage medium for predicting regional temperature. Background Art

[0002] Temperature is crucial to distinguishing between precipitation types such as rain, snow, and freezing rain. Under different temperature conditions, precipitation forms can differ significantly. For example, precipitation may appear as snow at low temperatures, while it may be rain at higher temperatures. The formation of snowfall involves the condensation of water vapor in the atmosphere into ice crystals or snowflakes, which usually occurs within a certain temperature range, so temperature is one of the important factors affecting the snowfall process. Accurate reflection of the spatial and temporal evolution of temperature is the key to predicting snowfall.

[0003] At present, numerical weather forecast models are mainly used to predict temperature. However, in order to maintain the convergence and stability of calculations, numerical models usually take truncation processing for drastic temperature changes, which makes it difficult for the model to accurately reflect the sudden change of temperature during rapid cooling. Therefore, the temperature predicted by the model during rapid cooling is usually too high, resulting in deviations in the prediction of snowfall areas, which cannot meet the needs of the power grid for prediction and early warning of blizzard disasters. Therefore, the existing technology has the problem of low accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, processor and machine-readable storage medium for predicting regional temperature, so as to solve the problem of low accuracy in the prior art.

[0005] In order to achieve the above-mentioned object, a first aspect of an embodiment of the present application provides a method for predicting regional temperature, the method comprising:

[0006] Determine a target numerical model for a target area, where the target numerical model is used to simulate a weather forecast for the target area;

[0007] Obtain forecast results from global numerical models;

[0008] Determine the initial field of the target numerical model based on the forecast results of the global numerical model;

[0009] Obtain observation data from multiple ground meteorological stations corresponding to the target area;

[0010] Correcting the initial field of the target numerical model according to the observed data to obtain a corrected initial field of the target numerical model;

[0011] Numerical simulation calculations are performed based on the initial field of the modified target numerical model to determine the predicted temperature of the target area.

[0012] In an embodiment of the present application, determining a target numerical pattern of a target area includes: obtaining an initial numerical pattern of the target area; and setting a multi-layer nested calculation grid in the initial numerical pattern according to geographic location information of the target area to obtain the target numerical pattern.

[0013] In an embodiment of the present application, the method further includes: acquiring regional data corresponding to each layer of computational grids; and performing grid discretization processing according to the regional data to set a simulation area of ​​a target numerical model.

[0014] In an embodiment of the present application, based on the forecast results of the global numerical model, determining the initial field of the target numerical model includes: extracting the meteorological element field from the forecast results, the meteorological element field is used to drive the target numerical model; interpolating the meteorological element field into a multi-layer nested computational grid to determine the initial field and boundary field of the target numerical model.

[0015] In an embodiment of the present application, the method further includes: constraining the radiation, convection, boundary layer and microphysical processes of the target numerical model according to a preset parameterized calculation scheme of each layer of the calculation grid.

[0016] In an embodiment of the present application, the initial field of the target numerical model is corrected according to the observation data to obtain the corrected initial field of the target numerical model, including: based on a data assimilation system, the observation data is assimilated in the physical grid space of the target numerical model to obtain the corrected initial field of the target numerical model.

[0017] In an embodiment of the present application, a numerical simulation calculation is performed based on the initial field of the modified target numerical model to determine the predicted temperature of the target area, including: performing a cyclic assimilation calculation of the target numerical model based on the initial field of the modified target numerical model; during the cyclic assimilation calculation, acquiring observation data in real time, and inputting the real-time acquired observation data into the initial field of the modified target numerical model to obtain the predicted temperature of the target area.

[0018] A second aspect of an embodiment of the present application provides a processor configured to execute the above-mentioned method for predicting regional temperature.

[0019] A third aspect of an embodiment of the present application provides a device for predicting regional temperature, the device comprising:

[0020] A first determination module is used to determine a target numerical mode of a target area, where the target numerical mode is used to simulate a weather forecast for the target area;

[0021] The first acquisition module is used to obtain the forecast results of the global numerical model;

[0022] The second determination module is used to determine the initial field of the target numerical model according to the forecast result of the global numerical model;

[0023] The second acquisition module is used to acquire observation data of multiple ground meteorological stations corresponding to the target area;

[0024] A data correction module, used for correcting the initial field of the target numerical model according to the observed data to obtain the corrected initial field of the target numerical model;

[0025] The third determination module is used to perform numerical simulation calculations according to the initial field of the corrected target numerical model to determine the predicted temperature of the target area.

[0026] A fourth aspect of an embodiment of the present application provides a machine-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the above-mentioned method for predicting regional temperature is implemented.

[0027] The above technical scheme determines the target numerical model of the target area, the target numerical model is used to simulate the weather forecast for the target area, and then determines the initial field of the target numerical model based on the forecast results of the global numerical model, and simultaneously obtains the observation data of multiple ground meteorological stations corresponding to the target area, and then corrects the initial field of the target numerical model based on the observation data to obtain the corrected initial field of the target numerical model, and finally performs numerical simulation calculations based on the corrected initial field of the target numerical model to determine the predicted temperature of the target area. The present application assimilates the observation data of the ground meteorological stations corresponding to the target area into the target numerical model, so that the ground temperature in the initial field of the target numerical model is closer to the temperature field in the real atmosphere, thereby reducing the temperature deviation in the numerical model and improving the accuracy of the temperature prediction results.

[0028] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0030] Figure 1 A schematic flow chart of a method for predicting regional temperature provided in an embodiment of the present application;

[0031] Figure 2 A structural block diagram of a device for predicting regional temperature provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0033] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0034] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0035] Figure 1 A schematic diagram of a method for predicting regional temperature provided in an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for predicting regional temperature, which is described by taking the application of the method to a processor as an example. The method may include the following steps.

[0036] Step S101, determining a target numerical model of a target area, where the target numerical model is used to simulate a weather forecast for the target area.

[0037] Step S102, obtaining the forecast result of the global numerical model;

[0038] Step S103, determining the initial field of the target numerical model according to the forecast result of the global numerical model.

[0039] Step S104, obtaining observation data of multiple ground meteorological stations corresponding to the target area.

[0040] Step S105 , correcting the initial field of the target numerical model according to the observed data to obtain a corrected initial field of the target numerical model.

[0041] Step S106, performing numerical simulation calculations based on the initial field of the corrected target numerical model to determine the predicted temperature of the target area.

[0042] Specifically, we first need to identify the target area for forecasting, such as a country, a province, or a specific geographical area. Then, we select or construct a numerical model suitable for the area as the target numerical model. This numerical model should be able to accurately simulate the changes in meteorological elements such as atmospheric motion, temperature, and humidity in the area. The selection and construction of the target numerical model usually requires consideration of multiple factors, including the resolution of the model, the accuracy of the description of the physical process, and the limitations of computing resources.

[0043] Next, the initial field of the target numerical model is determined based on the forecast results of the global numerical model. The global numerical model is an atmospheric circulation model covering the entire world, which uses observational data and physical laws to simulate atmospheric movement. The forecast results of the global numerical model can be used as the initial conditions of the target numerical model. The forecast results usually include the distribution of variables such as atmospheric temperature, humidity, wind field, and air pressure. By "downscaling" these global-scale forecast results to the target area, a relatively accurate initial field can be provided for the target numerical model.

[0044] Furthermore, ground meteorological stations are an important part of meteorological observations, and they provide real-time meteorological data, such as temperature, humidity, wind speed, wind direction, precipitation, etc. In this step, it is necessary to collect observation data from multiple ground meteorological stations in the target area. These data will be used in the subsequent assimilation process to correct the initial field of the target numerical model. Assimilation is a process of integrating observational data into a numerical model. In one example, a data assimilation system (such as variational assimilation, ensemble Kalman filtering, etc.) can be used to assimilate the observational data of ground meteorological stations into the physical grid space of the target numerical model. This process adjusts the distribution of variables in the numerical model to make it more consistent with the observed data. Through the assimilation process, the errors in the initial field of the target numerical model can be corrected to improve the accuracy of the forecast.

[0045] Finally, the initial field of the corrected target numerical model is used for numerical simulation calculation. Numerical simulation calculation usually involves solving the atmospheric motion equations to predict the changes in the atmospheric state in the future. Through this process, the temperature prediction results of the target area in the future can be obtained. These prediction results can provide important references for weather forecasting, disaster prevention and mitigation, etc.

[0046] In this way, the method of assimilation of ground meteorological observation data is adopted to assimilate observation data such as the temperature of meteorological stations in the upstream area of ​​the cold air into the numerical model, so that the ground temperature in the initial field of the model is closer to the temperature field in the real atmosphere, thereby reducing the temperature deviation in the model and improving the numerical model's ability to predict blizzard disasters.

[0047] The above technical scheme determines the target numerical model of the target area, the target numerical model is used to simulate the weather forecast for the target area, and then determines the initial field of the target numerical model based on the forecast results of the global numerical model, and simultaneously obtains the observation data of multiple ground meteorological stations corresponding to the target area, and then corrects the initial field of the target numerical model based on the observation data to obtain the corrected initial field of the target numerical model, and finally performs numerical simulation calculations based on the corrected initial field of the target numerical model to determine the predicted temperature of the target area. The present application assimilates the observation data of the ground meteorological stations corresponding to the target area into the target numerical model, so that the ground temperature in the initial field of the target numerical model is closer to the temperature field in the real atmosphere, thereby reducing the temperature deviation in the numerical model and improving the accuracy of the temperature prediction results.

[0048] In an embodiment of the present application, determining a target numerical pattern of a target area includes: obtaining an initial numerical pattern of the target area; and setting a multi-layer nested calculation grid in the initial numerical pattern according to geographic location information of the target area to obtain the target numerical pattern.

[0049] Specifically, to determine the target numerical model for the target area, it is first necessary to select a basic numerical model suitable for the target area as the initial numerical model. This initial numerical model may be a general atmospheric circulation model covering a large range, or a numerical model optimized for a specific area or specific meteorological elements, such as WRF (Weather Research and Forecasting Model). When selecting the initial numerical model, factors such as its resolution, accuracy of physical process description, computational efficiency, and suitability for multi-layer nested calculations need to be considered.

[0050] Next, according to the geographical location information of the target area (such as longitude and latitude range, topography, climate characteristics, etc.), a multi-layer nested computational grid is set in the initial numerical model. Multi-layer nested computational grids are an effective method to improve the resolution and accuracy of numerical models. It can more finely describe the atmospheric motion process in the target area by gradually reducing the grid size and increasing the number of grids on the basis of the initial numerical model. In one example, setting up multi-layer nested computational grids generally includes four steps: determining the nesting level and grid size, designing the grid shape and layout, setting the grid boundary conditions, and performing grid optimization and verification. In this way, a target numerical model optimized and customized for the target area can be obtained.

[0051] In an embodiment of the present application, the method further includes: acquiring regional data corresponding to each layer of computational grids; and performing grid discretization processing according to the regional data to set a simulation area of ​​a target numerical model.

[0052] In this step, it is necessary to collect and process the regional data corresponding to the computational grids of each layer of the target numerical model. These regional data may include terrain height, land use type, vegetation cover, water distribution, population density and other information. These data are crucial for accurately simulating and predicting the changes in meteorological elements in the target area, because they can affect processes such as atmospheric movement, energy exchange and water cycle. After obtaining the regional data corresponding to each layer of computational grids, grid discretization processing is required. Grid discretization is the process of converting continuous regional data into data on discrete points (i.e., grid points). This usually involves interpolating regional data to the nodes of the computational grid to generate a gridded data set. Further, after completing the grid discretization processing, the simulation area of ​​the target numerical model can be set according to the processed gridded data set. The simulation area refers to the geographical range covered by the numerical model for simulation calculation, which is usually determined according to the geographical location, size and change characteristics of meteorological elements in the target area. In this way, through the above steps, the target numerical model and its simulation area suitable for meteorological simulation and forecasting in the target area can be set according to the regional data corresponding to each layer of computational grids, which is conducive to providing an important foundation and support for subsequent meteorological simulation and forecasting work.

[0053] In an embodiment of the present application, based on the forecast results of the global numerical model, determining the initial field of the target numerical model includes: extracting the meteorological element field from the forecast results, the meteorological element field is used to drive the target numerical model; interpolating the meteorological element field into a multi-layer nested computational grid to determine the initial field and boundary field of the target numerical model.

[0054] Specifically, the global numerical model is an atmospheric circulation model covering the global scope, which can simulate and predict the spatiotemporal distribution and changes of various meteorological elements (such as temperature, humidity, wind field, air pressure, etc.) in the atmosphere. In the embodiment of the present application, it is necessary to extract the meteorological element field related to the target area from the forecast results of the global numerical model. These meteorological element fields will serve as important input data for driving the target numerical model. After extracting the meteorological element field, it is necessary to interpolate these data into the multi-layer nested calculation grid set for the target numerical model. The interpolation process is the process of converting the continuous data in the forecast results of the global numerical model into data on discrete grid points. Further, after completing the interpolation calculation of the meteorological element field, the initial field and boundary field of the target numerical model can be determined according to the interpolation result. Wherein, the initial field refers to the atmospheric state distribution when the numerical model starts to simulate, and is usually composed of the interpolation results of the meteorological element field at the initial moment. The boundary field refers to the boundary conditions that interact with the external environment during the numerical model simulation process, and is usually composed of the interpolation results of the meteorological element field on the boundary area and the boundary conditions set according to the physical law.

[0055] Thus, through the above steps, the initial field and boundary field of the target numerical model are determined according to the forecast results of the global numerical model, which is conducive to providing important basic data and supporting conditions for subsequent meteorological simulation and forecasting work. At the same time, due to the use of multi-layer nested computational grid technology and precise interpolation methods, the target numerical model will be able to more accurately simulate and predict the change process of meteorological elements in the target area, and improve the accuracy and reliability of meteorological forecasts.

[0056] In an embodiment of the present application, the method further includes: constraining the radiation, convection, boundary layer and microphysical processes of the target numerical model according to a preset parameterized calculation scheme of each layer of the calculation grid.

[0057] It is understandable that at different levels of computational grids, different parameterized computational schemes may be needed to simulate and describe physical processes in the atmosphere due to differences in resolution, topography, climate characteristics, etc. These parameterized computational schemes are usually based on a large amount of observational data, experimental results, and theoretical research, and can more accurately reflect the characteristics and laws of various physical processes in the atmosphere.

[0058] Specifically, it is necessary to determine the corresponding preset parameterized calculation scheme according to the characteristics of each layer of computing grids and the requirements of the target numerical model, including the parameterization of radiation transfer process, convection process, boundary layer process and microphysical process. Among them, the radiation process is one of the important driving factors for energy exchange in the atmosphere and changes in the climate system. The convection process is one of the important ways of energy and material exchange in the atmosphere, which has an important impact on weather and climate changes. The boundary layer is an important area of ​​interaction between the atmosphere and the surface, which has an important impact on weather and climate changes. Microphysical processes refer to the generation, evolution and extinction process of particles such as water droplets and ice crystals in the atmosphere, which has an important impact on the formation and development of clouds and precipitation. In order to improve the simulation accuracy and forecasting ability of the target numerical model, the radiation, convection, boundary layer and microphysical processes of the target numerical model can be constrained according to the preset parameterized calculation scheme. This provides more accurate and reliable basic data and reference basis for weather forecasting and climate prediction.

[0059] In an embodiment of the present application, the initial field of the target numerical model is corrected according to the observation data to obtain the corrected initial field of the target numerical model, including: based on a data assimilation system, the observation data is assimilated in the physical grid space of the target numerical model to obtain the corrected initial field of the target numerical model.

[0060] It can be understood that the data assimilation system is a technology that combines observational data with numerical models to improve the initial field accuracy of numerical models. The core task of the data assimilation system is to assimilate the observational data in the physical grid space of the target numerical model. Specifically, first of all, it is necessary to collect various observational data related to the simulation area of ​​the target numerical model. Among them, the observational data can include data from ground observation stations (such as temperature, humidity, air pressure, wind speed, etc.), high-altitude detection data (such as sounding balloons, aircraft observations, etc.), satellite remote sensing data (such as cloud maps, precipitation estimates, etc.) and radar observation data. The observational data should be as comprehensive and accurate as possible, and cover different levels and time periods of the simulation area.

[0061] Furthermore, based on the data assimilation system, the initial field of the target numerical model is corrected according to the observation data. After assimilation processing, the data assimilation system will output the corrected initial field of the target numerical model. This corrected initial field of the target numerical model will be used as the new initial condition of the target numerical model for subsequent simulation and forecasting work. In this way, by correcting the initial field of the target numerical model according to the observation data, a more accurate and reliable initial field can be obtained, which is conducive to improving the simulation accuracy and forecasting ability of the numerical model, and providing more accurate and reliable basic data and reference basis for weather forecasting and climate prediction. At the same time, the application of the data assimilation system also enables the numerical model to make better use of observation data, improve data utilization and forecasting effect.

[0062] In an embodiment of the present application, a numerical simulation calculation is performed based on the initial field of the modified target numerical model to determine the predicted temperature of the target area, including: performing a cyclic assimilation calculation of the target numerical model based on the initial field of the modified target numerical model; during the cyclic assimilation calculation, acquiring observation data in real time, and inputting the real-time acquired observation data into the initial field of the modified target numerical model to obtain the predicted temperature of the target area.

[0063] It can be understood that cyclic assimilation calculation is a method that combines observational data with numerical models and continuously iterates and updates the initial field of the numerical model. Through cyclic assimilation calculation, new observational data can be introduced in real time during the simulation process, thereby continuously improving the simulation accuracy and forecasting ability of the numerical model.

[0064] In this step, it is necessary to start the cyclic assimilation calculation according to the initial field of the revised target numerical model. The cyclic assimilation calculation usually includes multiple assimilation cycles, each of which will integrate new observation data into the numerical model and update the initial field of the numerical model. In the process of cyclic assimilation calculation, it is necessary to obtain the observation data related to the target area in real time. These observation data may include data from ground observation stations, high-altitude detection data, satellite remote sensing data, and radar observation data. These data should be as comprehensive and accurate as possible, and be able to reflect the changes in the atmospheric state of the target area in a timely manner. After obtaining the real-time observation data, it is necessary to input these data into the initial field of the revised target numerical model. This process usually involves steps such as data interpolation, quality control, and the application of assimilation algorithms to ensure that the observation data can be accurately reflected in the initial field of the numerical model. After integrating the real-time observation data into the initial field of the revised target numerical model, the numerical simulation calculation continues. The numerical simulation calculation will simulate the changes in the atmospheric state of the target area through the numerical model based on the initial field of the revised target numerical model and the real-time observation data, and calculate the predicted temperature. After the numerical simulation calculation, the predicted temperature of the target area can be obtained. This predicted temperature will be used as the output of the numerical model for subsequent weather forecasting and climate prediction work.

[0065] Through the above steps, numerical simulation calculations can be performed based on the initial field of the corrected target numerical model to determine the predicted temperature of the target area. The application of cyclic assimilation calculations enables the numerical model to introduce new observation data in real time and continuously update the initial field, thereby improving the simulation accuracy and forecasting ability. At the same time, the integration of real-time observation data also enables the numerical model to better reflect the changes in the atmospheric state of the target area, providing more accurate and reliable basic data and reference basis for weather forecasts and climate predictions.

[0066] In a specific embodiment of the present application, a temperature assimilation prediction method integrating upstream area sounding data is provided. The method comprises the following steps:

[0067] (1) Select a suitable regional meteorological numerical forecast model according to the target temperature area to be predicted. Considering that the cold air affecting my country moves from north to south, the temperature of the upstream northern region has a great influence on the cooling process of the downstream southern region. Therefore, in the numerical model, the simulation area is expanded to the upstream northern region, and a multi-layer nested computational grid of the numerical model covering a large-scale area is designed.

[0068] (2) Collect static data such as terrain height and land use in the calculation grid area, discretize it into grids, and complete the numerical model simulation area setting.

[0069] (3) Based on the large-scale forecast results of the global numerical model, the meteorological element field used to drive the regional numerical model is extracted and interpolated to the regional nested calculation grid points constructed previously to construct the regional numerical forecast initial field and lateral boundary field.

[0070] (4) Select appropriate parameterized calculation schemes for each layer of the numerical model grid and constrain the radiation, convection, boundary layer, microphysics and other processes of the numerical model.

[0071] (5) Collect data such as temperature, humidity, and air pressure at 2 meters near the ground from ground meteorological stations, and collect data such as temperature and wind speed at different altitudes from sounding data for data assimilation calculations.

[0072] (6) Using the data assimilation system, the station observation data obtained in the calculation area are assimilated in the physical grid space of the numerical model, and anisotropic information is introduced into the background error covariance, so that the initial field of the numerical model is closer to the real atmospheric characteristics.

[0073] (7) Use the initial field of the modified numerical model to carry out numerical simulation calculations, and continuously obtain observation data at new calculation times during the calculation process. At the beginning of the assimilation time window, use the last forecast as the initial field to integrate the forecast model to the end of the assimilation time window, and record the forecast variables (called background fields) for later calculations.

[0074] (8) Organize the observational data according to the time interval and preprocess them into a format suitable for assimilation.

[0075] (9) Using the background field at the same time as the observation, the model observation equivalent of all valid observations and the difference between them and the actual observation value, i.e., the observation background error, are calculated.

[0076] (10) Calculate the objective function (or cost function) and its gradient, and use an appropriate optimization algorithm to estimate the corrected values ​​of the state variables to obtain more accurate numerical prediction results.

[0077] (11) Conduct heavy snow forecast based on the revised temperature forecast.

[0078] In this way, by assimilating ground meteorological observation data, the observation data such as the temperature of the meteorological station in the upstream area of ​​the cold air are assimilated into the numerical model, making the ground temperature in the initial field in the model closer to the temperature field in the real atmosphere, thereby reducing the temperature deviation in the model and improving the numerical model's ability to predict blizzard disasters.

[0079] In a specific embodiment of the present application, the temperature and snowfall forecast process of a certain area is used as an example for explanation. A heavy snowfall occurred in the area from the evening of February 6 to noon of February 7, and the snowfall in local areas reached more than 20 mm. This specific embodiment uses the Advanced Research Weather Research and Forecasting (WRF-ARW) model for experimental testing, selects Euler mass coordinates, and adopts the Runge-Kutta third-order time integration scheme. According to the scope of the forecast area and the characteristics of the weather system, a multi-layer nested computational grid is designed to ensure that the resolution of the grid can meet the forecast accuracy requirements while considering the limitations of computing resources.

[0080] Furthermore, a suitable parameterized calculation scheme is selected for each layer of the numerical model grid to realize the constraints of the radiation, convection, boundary layer, microphysics and other processes of the numerical model. Table 1 shows the selection of physical parameterization schemes, and some physical parameterization schemes used in the numerical model in the embodiment of the present application are shown in Table 1.

[0081] Microphysical processes Thompson scheme Long-wave and short-wave radiation processes RRTMG Program Land surface processes Land surface processes Boundary layer processes Land surface processes

[0082] Furthermore, the observation data of the national and regional observation sites on the ground are collected, including meteorological element data such as temperature, humidity, and air pressure. For example, the ground air pressure and temperature physical quantities measured by the shutters of multiple national and regional observation sites corresponding to the region are obtained.

[0083] Then, the above-mentioned station observation data are assimilated in the physical grid space of the numerical model using the grid point statistical interpolation system based on the three-dimensional variational assimilation method. Among them, the grid point statistical interpolation (GSI) assimilation system is a new generation of data assimilation system developed by the National Centers for Environmental Prediction (NCEP) and the National Oceanic and Atmospheric Administration (NOAA) of the United States, which can realize the assimilation of multi-source observation data in the global or regional scope.

[0084] In one example, the basic cost function for 3D variational can be defined as:

[0085]

[0086] Among them, x a is the analytical field, x b is the background field, H is the nonlinear observation operator that realizes the projection of the pattern variables into the observation space, y0 is the observation vector, B and R are the background error covariance matrix and the observation error covariance matrix respectively, J c It is a general constraint term used for water vapor and dynamic field adjustment.

[0087] Define the analysis increment field as x = x a -x b , then the above formula can be expressed as:

[0088]

[0089] Next, by introducing the linearized observation operator And define the new information vector y = y0-H(x b ), then the above formula can be written as:

[0090]

[0091] In the GSI assimilation system, define the variable y = B -1 x, preprocess the cost function to get:

[0092]

[0093] In the above cost function, the gradients with respect to x and y are expressed as:

[0094]

[0095] In this way, the final analysis field can be obtained by simultaneously performing iterative minimization using the conjugate gradient method.

[0096] Furthermore, a numerical model cyclic assimilation calculation is carried out, and new observation data is continuously acquired during the calculation process and introduced into the numerical simulation analysis field, and finally a revised temperature forecast structure is obtained. Specifically, in a specific embodiment of the present application, the WRF model assimilated ground temperature reported in a certain area from 20:00 on February 5 of a certain year is used to calculate the temperature when snowing at 2:00 on February 7 of the same year. The final calculation result shows that after assimilating the ground observation data, the calculated ground temperature in the area is compared with the observed value. The error is reduced to between -1 and 1°C, while in the model without assimilating the ground observation data, the calculated temperature in the area is about 3°C ​​higher. Therefore, after assimilating the temperature observation data, the temperature simulation in Yueyang area is greatly improved.

[0097] An embodiment of the present application also provides a processor configured to execute the method for predicting regional temperature in the above-mentioned embodiment.

[0098] Figure 2 This is a structural block diagram of a device for predicting regional temperature provided in an embodiment of the present application. Figure 2 As shown, the embodiment of the present application further provides a device 200 for predicting regional temperature, and the device 200 may include:

[0099] The first determination module 210 is used to determine a target numerical model of a target area, where the target numerical model is used to simulate a weather forecast for the target area.

[0100] The first acquisition module 220 is used to obtain the forecast results of the global numerical model.

[0101] The second determination module 230 is used to determine the initial field of the target numerical model according to the forecast result of the global numerical model.

[0102] The second acquisition module 240 is used to acquire observation data of multiple ground meteorological stations corresponding to the target area.

[0103] The data correction module 250 is used to correct the initial field of the target numerical model according to the observed data to obtain a corrected initial field of the target numerical model.

[0104] The third determination module 260 is used to perform numerical simulation calculations according to the initial field of the modified target numerical model to determine the predicted temperature of the target area.

[0105] The above-mentioned device 200 for predicting regional temperature determines the target numerical model of the target area, the target numerical model is used to simulate the weather forecast for the target area, and then determines the initial field of the target numerical model based on the forecast results of the global numerical model, and simultaneously obtains the observation data of multiple ground meteorological stations corresponding to the target area, and then corrects the initial field of the target numerical model based on the observation data to obtain the initial field of the corrected target numerical model, and finally performs numerical simulation calculations based on the initial field of the corrected target numerical model to determine the predicted temperature of the target area. The present application assimilates the observation data of the ground meteorological stations corresponding to the target area into the target numerical model, so that the ground temperature in the initial field in the target numerical model is closer to the temperature field in the real atmosphere, thereby reducing the temperature deviation in the numerical model and improving the accuracy of the temperature prediction results.

[0106] In one embodiment, the first determination module 210 is further used to: obtain an initial numerical pattern of the target area; and set multiple layers of nested calculation grids in the initial numerical pattern according to the geographical location information of the target area to obtain the target numerical pattern.

[0107] In one embodiment, the first determination module 210 is further used to: obtain the regional data corresponding to each layer of the computational grid; and perform grid discretization processing according to the regional data to set the simulation area of ​​the target numerical model.

[0108] In one embodiment, the first determination module 210 is also used to: extract meteorological element fields from the forecast results, the meteorological element fields are used to drive the target numerical model; interpolate the meteorological element fields into multi-layer nested computational grids to determine the initial field and boundary field of the target numerical model.

[0109] In one embodiment, the first determination module 210 is further used to constrain the radiation, convection, boundary layer and microphysical processes of the target numerical model according to a preset parameterized calculation scheme of each layer of calculation grid.

[0110] In one embodiment, the data correction module 240 is further used to: based on the data assimilation system, perform assimilation processing on the observation data in the physical grid space of the target numerical model to obtain the initial field of the corrected target numerical model.

[0111] In one embodiment, the third determination module 250 is also used to: perform cyclic assimilation calculations of the target numerical model based on the initial field of the revised target numerical model; during the cyclic assimilation calculations, acquire observation data in real time, and input the real-time acquired observation data into the initial field of the revised target numerical model to obtain the predicted temperature of the target area.

[0112] An embodiment of the present application also provides a machine-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the method for predicting regional temperature in the above-mentioned embodiment is implemented.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0117] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0118] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0119] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0120] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0121] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting regional temperature, characterized in that The method comprises: Determining a target numerical mode for a target area, wherein the target numerical mode is used to simulate a weather forecast for the target area; Obtain forecast results from global numerical models; Determining the initial field of the target numerical model according to the forecast result of the global numerical model; Acquire observation data of multiple ground meteorological stations corresponding to the target area; Correcting the initial field of the target numerical model according to the observed data to obtain a corrected initial field of the target numerical model; A numerical simulation calculation is performed based on the corrected initial field of the target numerical model to determine the predicted temperature of the target area.

2. The method according to claim 1, characterized in that The target numerical mode for determining the target area includes: Acquire an initial numerical mode of the target area; According to the geographical location information of the target area, multiple layers of nested calculation grids are set in the initial numerical model to obtain the target numerical model.

3. The method according to claim 2, characterized in that The method further comprises: Get the regional data corresponding to each layer of computational grid; Grid discretization processing is performed according to the regional data to set a simulation area of ​​the target numerical model.

4. The method according to claim 2, characterized in that: Determining the initial field of the target numerical model according to the forecast result of the global numerical model comprises: Extracting a meteorological element field from the forecast result, wherein the meteorological element field is used to drive the target numerical model; The meteorological element field is interpolated into the multi-layer nested computational grid to determine the initial field and boundary field of the target numerical model.

5. The method according to claim 2, characterized in that: The method further comprises: According to the preset parameterized calculation scheme of the calculation grid of each layer, the radiation, convection, boundary layer and microphysical processes of the target numerical model are constrained.

6. The method according to claim 1, characterized in that The step of correcting the initial field of the target numerical model according to the observed data to obtain a corrected initial field of the target numerical model comprises: Based on the data assimilation system, the observation data is assimilated in the physical grid space of the target numerical model to obtain a modified initial field of the target numerical model.

7. The method according to claim 1, characterized in that The performing of numerical simulation calculation according to the modified initial field of the target numerical model to determine the predicted temperature of the target area includes: Performing cyclic assimilation calculation of the target numerical model according to the corrected initial field; In the process of cyclic assimilation calculation, the observation data is acquired in real time, and the observation data acquired in real time is input into the initial field of the modified target numerical model to obtain the predicted temperature of the target area.

8. A processor, characterized in that: The method is configured to perform the method for predicting regional temperature according to any one of claims 1 to 7.

9. A device for predicting regional temperature, characterized in that The device comprises: A first determination module is used to determine a target numerical mode of a target area, wherein the target numerical mode is used to simulate a weather forecast for the target area; The first acquisition module is used to obtain the forecast results of the global numerical model; A second determination module is used to determine the initial field of the target numerical model according to the forecast result of the global numerical model; A second acquisition module is used to acquire observation data of multiple ground meteorological stations corresponding to the target area; A data correction module, used for correcting the initial field of the target numerical model according to the observed data to obtain a corrected initial field of the target numerical model; The third determination module is used to perform numerical simulation calculations according to the corrected initial field of the target numerical model to determine the predicted temperature of the target area.

10. A machine-readable storage medium storing a program or an instruction, characterized in that: When the program or the instruction is executed by a processor, the method for predicting regional temperature according to any one of claims 1 to 7 is implemented.

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