Meteorological data processing method, meteorological data processing device and electronic equipment

Abstract

The application discloses a meteorological data processing method, a meteorological data processing device and an electronic equipment, and relates to the technical field of meteorology. The method can reduce the error of meteorological data and improve the accuracy of meteorological data. The method comprises the following steps: acquiring base data observed by each weather radar; performing gridding on the base data to obtain first gridding data of an observation area of each weather radar; determining a blur range of radial velocity; performing velocity de-blurring processing on the radial velocity in the first gridding data according to the blur range; determining a shielding range of a reflectivity factor; performing shielding removal processing on the reflectivity factor in the first gridding data according to the shielding range; performing gridding on the first gridding data after the velocity de-blurring processing and the shielding removal processing to obtain second gridding data; merging the second gridding data of each weather radar to obtain meteorological gridding data of a whole area; and converting the meteorological gridding data into target data corresponding to a weather forecasting system, so that the weather forecasting system can use the target data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a meteorological data processing method and a meteorological data processing device. Background Technology

[0002] Radar meteorological technology refers to the use of radar to observe the atmospheric environment, obtain information such as cloud cover and precipitation, and thus predict future weather conditions. Weather forecasting systems can acquire data from radar observations and use this data to predict weather conditions, which are then displayed to users.

[0003] Radar observation data differs in format from weather forecasting system data, requiring weather forecasting systems to convert radar data, such as converting radar base data to LittleR format. Different weather forecasting systems cover different regions, typically processing only the observation data for their own forecast area. If forecast areas overlap, the observation data will be processed repeatedly, leading to low data processing efficiency. Furthermore, observation error is a crucial aspect of weather forecasting models' assimilation of meteorological observation data; having accurate and reliable observation error data can improve the forecasting accuracy of weather forecasting models. Summary of the Invention

[0004] This application provides a meteorological data processing method, a meteorological data processing device, and an electronic device, which can improve the processing efficiency of radar data.

[0005] In a first aspect, this application provides a meteorological data processing method, comprising: acquiring baseline data observed by various weather radars, the baseline data including radial velocities and reflectivity factors at multiple elevation angles; gridding the baseline data to obtain first grid data for the observation area of ​​each weather radar; determining the ambiguity range of the radial velocity, and performing velocity deambiguation processing on the radial velocity in the first grid data according to the ambiguity range; determining the occlusion range of the reflectivity factor, and performing occlusion removal processing on the reflectivity factor in the first grid data according to the occlusion range; gridding the first grid data after velocity deambiguation processing and occlusion removal processing again to obtain second grid data; merging the second grid data of each weather radar to obtain meteorological grid data for the entire area; and converting the meteorological grid data into target data corresponding to a weather forecasting system for use by the weather forecasting system.

[0006] This scheme grids the baseline data observed by weather radar to obtain regional gridded data. Based on the ambiguity range of radial velocity, velocity deambiguation processing is performed on the data within the ambiguity range, improving data accuracy and reliability and reducing weather forecast errors. Based on the obstruction range of reflectivity factors, reflectivity factors within the obstruction range are processed, reducing inaccurate reflectivity factor data. Then, the second gridded data from each radar can be merged to obtain meteorological gridded data for the entire region. This meteorological gridded data is then transformed by the weather forecasting system for direct use, avoiding further data processing by the weather forecasting system and reducing data processing steps, thus improving data processing efficiency. Furthermore, this application simultaneously processes the baseline data observed by each radar to obtain target data for the entire region. This target data can simultaneously provide data support for multiple weather forecasting systems, further improving data processing efficiency.

[0007] In one possible implementation of this application, determining the ambiguity range of the radial velocity includes: acquiring the scanning configuration information of the weather radar, the scanning configuration information including the maximum unambiguous velocity of the weather radar; acquiring the target grid points in the first grid data whose radial velocity at the elevation angle exceeds the maximum unambiguous velocity, thereby obtaining the ambiguity range.

[0008] In one possible implementation of this application, determining the fuzzy range of the radial velocity includes: acquiring adjacent grid points with different radial velocity directions in the regional grid point data; and determining whether the adjacent grid points belong to the fuzzy range based on the difference in the radial velocities of the adjacent grid points.

[0009] In one possible implementation of this application, determining the obstruction range of the reflectivity factor includes: acquiring historical scanning data of the weather radar; determining the beam obstruction rate of the weather radar at a preset elevation angle based on the historical scanning data; and when the beam obstruction rate exceeds an obstruction threshold, determining the grid points corresponding to the preset elevation angle in the regional grid data as the obstruction range of the reflectivity factor.

[0010] In one possible implementation of this application, the step of converting the meteorological grid data into target data corresponding to the weather forecasting system includes: interpolating the meteorological grid data according to the spatial resolution of the weather forecasting model to obtain target data that matches the spatial resolution.

[0011] In one possible implementation of this application, the step of converting the meteorological grid data into target data corresponding to the weather forecasting system includes: calculating the local variance of the meteorological grid data, using the local variance as the observation error of the meteorological grid data, and assimilating the meteorological grid data based on the observation error to obtain the target data.

[0012] In one possible implementation of this application, after converting the meteorological grid data into target data corresponding to the weather forecasting system, the method further includes: when receiving a data call request from the weather forecasting system, sending the target data to the weather forecasting system.

[0013] Secondly, this application provides a meteorological data processing device, comprising: a data acquisition module for acquiring basic data observed by various weather radars, the basic data including radial velocities and reflectivity factors at multiple elevation angles; a first gridding module for gridding the basic data to obtain first grid data for the observation area of ​​each weather radar; a deblurring module for determining the ambiguity range of the radial velocity and performing velocity deblurring processing on the radial velocity in the first grid data according to the ambiguity range; an occlusion removal processing module for determining the occlusion range of the reflectivity factor and performing occlusion removal processing on the reflectivity factor in the first grid data according to the occlusion range; a second gridding module for further gridding the first grid data after velocity deblurring and occlusion removal processing to obtain second grid data; a merging module for merging the second grid data from each weather radar to obtain meteorological grid data for the entire area; and a data output module for converting the meteorological grid data into target data corresponding to a weather forecasting system for use by the weather forecasting system.

[0014] Thirdly, this application provides an electronic device including a memory and one or more processors. The memory stores one or more computer programs, each including instructions that, when executed by the processor, cause the electronic device to perform the meteorological data processing method described in the first aspect.

[0015] Fourthly, this application provides a computer-readable medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the meteorological data processing method as described in the first aspect.

[0016] Fifthly, this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the meteorological data processing method as described in the first aspect.

[0017] Understandably, the beneficial effects achieved by the meteorological data processing device, electronic device, computer-readable medium, and computer program product provided above can be referred to the beneficial effects in the first aspect, and will not be repeated here. Attached Figure Description

[0018] Figure 1 A system architecture diagram of the meteorological data processing method provided in the embodiments of this application;

[0019] Figure 2 A flowchart illustrating the meteorological data processing method provided in the embodiments of this application;

[0020] Figure 3 This is another schematic flowchart of the meteorological data processing method provided in the embodiments of this application;

[0021] Figure 4 A framework diagram of the meteorological data processing apparatus provided in the embodiments of this application;

[0022] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. For example, "first chip" and "second chip" are only used to distinguish different chips and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply that they are different. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more.

[0024] It should be noted that "at the time of..." in the embodiments of this application can be either at the instant when a certain situation occurs, or for a period of time after the occurrence of a certain situation. The embodiments of this application do not make specific limitations on this.

[0025] Figure 1 A schematic diagram of the system architecture of an exemplary application environment in which a meteorological data processing method, a meteorological data processing device, and an electronic device of this embodiment can be applied is shown.

[0026] like Figure 1 As shown, system architecture 10 may include one or more weather radars, such as weather radar 11, weather radar 12, weather radar 13, etc.; electronic equipment 20 and weather forecasting system 30.

[0027] Weather radar 11, weather radar 12, and weather radar 13 can be various types of radar equipment such as Doppler CINRAD / SA radar and CINRAD / SD radar.

[0028] The electronic device 20 can be a personal computer, industrial computer, tablet computer or other terminal device, or a server that provides backend services; this embodiment does not impose any special limitations on the form of the electronic device.

[0029] The weather forecasting system 30 can be a system or platform that provides weather forecast services to users, such as the weather conditions for the next three days or the next ten days. Depending on the user's needs, the weather forecasting system 30 can also provide other services, such as spatial distribution maps of radial velocity and spatial distribution maps of reflectivity factors.

[0030] This application provides a meteorological data processing method, which can be applied to the aforementioned electronic device 20. (See reference...) Figure 2 As shown, the meteorological data processing method may include the following steps:

[0031] S201: Acquire the baseline data observed by each weather radar, which includes radial velocity and reflectivity factor at multiple elevation angles.

[0032] S202: Grid the base data to obtain the first grid data of the observation area of ​​each weather radar.

[0033] S203: Determine the fuzzy range of the radial velocity, and perform velocity defuzzification processing on the radial velocity in the first grid data according to the fuzzy range.

[0034] S204: Determine the occlusion range of the reflectivity factor, and perform occlusion removal processing on the reflectivity factor in the first grid data according to the occlusion range.

[0035] S205: The first grid data after the speed deblurring process and the occlusion removal process is re-gridized to obtain the second grid data.

[0036] S206: Merge the second grid data from each weather radar to obtain meteorological grid data for the entire region.

[0037] S207: Convert the meteorological grid data into target data corresponding to the weather forecasting system for use by the weather forecasting system.

[0038] The meteorological data processing method provided in this application can determine the ambiguity range of weather radar, perform velocity deambiguation on the data within the ambiguity range, and improve the reliability and accuracy of radial velocity. Furthermore, it can determine the obstruction range of reflectivity factors, and perform obstruction removal processing on the reflectivity factors within the obstruction range, thereby improving the accuracy of reflectivity factors. By merging the second data after velocity deambiguation processing and obstruction removal processing from each radar, meteorological grid data for the entire region can be obtained. This meteorological grid data is then converted into target data for a weather forecasting system. This target data can be directly accessed by the weather forecasting system, simplifying the data processing process and improving data processing efficiency. Simultaneously, this regional meteorological grid data can provide data support for multiple weather forecasting systems, improving the availability of radar data.

[0039] The steps described above in this example implementation will now be explained in more detail.

[0040] In S201, the base data refers to the volume scan data of the weather radar across the entire S-band, specifically comprising two blocks: common data and radial data. The common data block includes public information such as the weather radar's site information and mission configuration information. For example, site information may include site number, site name, longitude, and latitude; mission configuration information may include mission information and configuration information. Mission information may include, for example, scan mission type, pulse width, scan start time, and number of scan layers, while configuration information may include, for example, scan speed, elevation angle, and maximum unambiguous velocity.

[0041] The radial data block may include radial data for different radial directions at various elevation angles, specifically radial velocity and reflectivity factor, and may also include other parameters such as echo intensity and velocity spectrum width. This embodiment does not impose any special limitations on these parameters.

[0042] Each region may include one or more weather radars; for example, the national region may include 106 new-generation Doppler weather radars. The baseline data scanned by the weather radar can be uploaded to a corresponding receiving device, which communicates with the weather radar via a receiving antenna. Electronic devices can acquire the baseline data from the receiving device via a network, thereby collecting weather radar baseline data for each region.

[0043] In S202, electronic equipment can grid the base data of each weather radar. Gridding refers to arranging the base data according to a certain spatial resolution. For example, in a space of 1km, the radial velocities at different elevation angles and radial distances are arranged into a grid distribution with intervals of 1km, resulting in the gridded data, i.e., the first grid data.

[0044] In S203, the fuzzy range of radial velocity refers to the region where the radial velocity is blurred. Based on this fuzzy range, velocity defuzzification can be performed on the first grid point data, thereby reducing data with large velocity errors and improving data quality. Velocity defuzzification can include correcting the radial velocity within the fuzzy range of the first grid point data.

[0045] In an exemplary implementation, the ambiguity range of radial velocity can be determined by the following steps: first, obtain the scanning configuration information of the weather radar, which includes the maximum unambiguous velocity of the weather radar; then, obtain the target grid points in the regional grid data whose radial velocity at the elevation angle exceeds the maximum unambiguous velocity, thus obtaining the ambiguity range.

[0046] Scanning configuration information can be obtained from the base data of the weather radar. The maximum unambiguous velocity is an attribute parameter of the weather radar. When the velocity of a detected target exceeds this maximum unambiguous velocity, spectral overlap can easily occur, leading to confusion of the measured target velocity and making it difficult to distinguish the true velocity. After obtaining the maximum unambiguous velocity of the weather radar, grid points with radial velocities exceeding this maximum unambiguous velocity can be selected from the first grid point data as target grid points. The set of target grid points is then used as the ambiguity range. The radial velocities at the target grid points in the first grid point data are corrected to obtain the deambigued first grid point data.

[0047] In an exemplary embodiment, adjacent grid points with different radial velocity directions in the first grid point data are obtained; the difference in radial velocity between adjacent grid points can also determine whether the adjacent grid point belongs to the fuzzy range.

[0048] In this method, adjacent grid points with different radial velocity directions can be identified based on the direction of the radial velocity at each grid point. These grid points may be boundaries of fuzzy velocities or boundaries of convective shear regions. Furthermore, the boundaries of convective shear regions are excluded based on the magnitude of the radial velocities of two adjacent grid points to prevent the true data from being corrected. Specifically, the difference in radial velocities between adjacent grid points is calculated, and it is determined whether this difference meets a preset condition. This preset condition may refer to the absolute value of the difference being less than the maximum unfuzzy velocity, or it may refer to the absolute value of the difference being less than K times the maximum unfuzzy velocity. K can be determined according to the actual situation, for example, 80%, 70%, etc., and this implementation is not limited to this.

[0049] When the absolute value of the difference is less than K times the maximum unambiguous velocity, the pair of adjacent grid points can be determined to be true values. That is, the pair of adjacent grid points belongs to the convective shear region. When the absolute value of the difference is not less than K times the maximum unambiguous velocity, the adjacent grid points can be determined to be within the ambiguity range.

[0050] For example, when the absolute value of the difference between adjacent grid points is less than K times the maximum unambiguous velocity, it can be further determined whether these grid points belong to the true value. Specifically, the difference between each pair of adjacent grid points with different radial velocity directions is obtained, and all adjacent grid points whose absolute value of the difference is less than K times the maximum unambiguous velocity are identified. It is then determined whether these adjacent grid points form a closed region. If a closed region is formed, these adjacent grid points can be determined to belong to the true value and not to the ambiguous range. Conversely, if these adjacent grid points do not form a closed region, they can be determined to belong to the ambiguous range.

[0051] For example, the radial velocity at grid points within the fuzzy range can be compared with the analysis field, and the radial wind of the analysis field can be calculated based on the wind speed data of the analysis field. For ease of description, the grid points within the fuzzy range are called fuzzy grid points. Then, the radial wind of the analysis field is added to k maximum fuzzy velocities, and the calculated result is taken as the actual radial velocity at the fuzzy grid point. This ensures that the difference between the radial wind and the actual radial velocity at the fuzzy grid point is less than the maximum fuzzy velocity, thus completing the defuzzification. The data after velocity defuzzification is used as the data to be processed.

[0052] Understandably, velocity deblurring can also include other processing methods. For example, the radial velocity at a blurred grid point can be corrected based on the radial velocities of the surrounding grid points; for instance, the average radial velocity of the six surrounding grid points can be used as the actual radial velocity of that blurred grid point. Another example is deblurring by removing radial velocities within the blurred area, and so on.

[0053] By deblurring the speed, the accuracy of the observed data can be improved, which in turn helps to enhance the authenticity of meteorological data.

[0054] In an exemplary implementation, the local variance is determined based on the radial velocity and reflectivity factor at each grid point in the regional grid data. Radial velocities and reflectivity factors with local variances exceeding a threshold are treated as noise points and placed within the interference range.

[0055] After determining the clutter interference range, radial velocity and reflectivity factors within this range can be removed. The thresholds for radial velocity and reflectivity factors can be determined based on actual conditions, for example, 100 and 60 respectively. Removing noise points from the first grid data improves data accuracy and also enhances the stability of meteorological data assimilation in weather forecasting systems.

[0056] In step S204, the obstruction range of the reflectivity factor refers to the grid points in the first grid data where the reflectivity factor may be inaccurate. In an exemplary embodiment, when determining the obstruction range, historical scanning data of each weather radar can be obtained, and the beam obstruction rate of the weather radar at a preset elevation angle can be determined based on the historical scanning data; when the beam obstruction rate exceeds a preset obstruction threshold, the grid points corresponding to the preset elevation angle in the regional grid data can be determined as the obstruction range.

[0057] The preset elevation angle can be the radar's lower-level elevation angle, such as the first or second-level elevation angle. Since the radar's lower-level elevation angle is relatively low, the emitted signal may be blocked by objects such as buildings. The beam obstruction rate at each azimuth angle can be calculated based on historical scanning data for each azimuth angle. The specific calculation method is as follows:

[0058]

[0059] Where B(n) represents the degree of obstruction in the nth azimuth direction; W(|n|) represents the power ratio of the nth azimuth direction in the total beam power; respectively:

[0060]

[0061]

[0062] Where θ1 is the horizontal beam resolution, and its value is 0.95; The vertical beam resolution is 0.95; m is the obstruction label at the nth azimuth, ranging from -16 to 15. m is related to the elevation at that azimuth and is equal to the label value that the obstruction height can reach.

[0063] After calculating the beam obstruction rate at the preset elevation angle, it can be determined whether the beam obstruction rate exceeds the obstruction threshold. If it exceeds the obstruction threshold, it can be determined that the grid point corresponding to the preset elevation angle in the first grid point data belongs to the obstruction range. The obstruction threshold can be determined according to the actual situation, such as 90%.

[0064] In step S205, the first grid data after velocity deblurring and occlusion removal is further gridded to obtain the second grid data. In this embodiment, the radar observation data is gridded twice, and the resolution of the first grid data obtained from the first gridding can be higher than the resolution of the second grid data. For example, the resolution of the first grid data can be 500 meters, and the resolution of the second grid data can be 1 kilometer. By performing velocity deblurring and occlusion removal on the higher-resolution first grid data, the impact on grid accuracy during data processing can be reduced.

[0065] In an exemplary implementation, the data quality of the first grid point data can be further improved by removing ground clutter. Specifically, the echo intensity difference between adjacent elevation angles is determined, and echoes with a difference greater than 25 dBz and a radial velocity of zero or less than or equal to 0.25 m / s are identified as ground clutter ranges, and thus removed from the first grid point data. The processed first grid point data is then further gridded to obtain the second grid point data.

[0066] In S206, the second grid data of each radar is arranged according to the radar's location, elevation angle, and azimuth angle to obtain meteorological grid data for the entire region.

[0067] In S207, the resolution of the second grid data corresponding to different radars may be different. According to the spatial resolution required by the weather forecast mode, the nearest grid method can be used for interpolation. The second grid data corresponding to each radar is interpolated to obtain target data that meets the spatial resolution.

[0068] If the observation areas of multiple weather radars overlap, the base data from the overlapping weather radars can be merged.

[0069] For example, a weather forecasting system can forecast different meteorological variables, such as rainfall, wind speed, and temperature. Based on the desired weather forecasting model, the meteorological gridded data can be assimilated, thereby converting radar observation data into target data for the weather forecasting system. The purpose of assimilation is to obtain a model variable field that is as close as possible to the actual observation data in terms of spatial resolution by solving for the variable transformation function. A cost function can be predefined to describe the degree of deviation between the model variable field and the observed data and background field. By recursively minimizing this cost function, the variable transformation function with the minimum deviation is obtained. Then, the radar observation variables in the meteorological gridded data are transformed into the model variable field according to the determined variable transformation function.

[0070] To assimilate meteorological gridded data, its observation error needs to be determined first. For weather radar, observation error may originate from radar instrument errors, sampling errors, velocity ambiguity, etc. The observation error can be approximated by local sampling of radial velocity. Specifically, the observation error for each grid point is calculated using a local variance or standard deviation algorithm. For example, the mean can be calculated for every four grid points in the meteorological gridded data, and the standard deviation for each of those four grid points can be determined based on this mean, thus obtaining the observation error for each grid point. Alternatively, the local variance in the meteorological gridded data can be calculated as the observation error.

[0071] After obtaining the observation error, the gridded data is transformed into target data for the forecast model based on the observation error, reflectivity factor, and radial velocity at each grid point in the meteorological gridded data. The specific cost function used for assimilation is as follows:

[0072] J = J O +J b +J p (4)

[0073] The pattern variable obtained when J approaches zero can be used as the final result. Here, J... O This refers to the deviation between model variables and observed variables such as reflectivity factor and radial velocity. b J p These represent the deviation between the mode variables and the background field, and the constraint terms of the mode field, respectively.

[0074]

[0075] V i o and V represents the radial velocity and reflectivity factor of the i-th radar observation. i and q i Let represent the corresponding model variables, and F represent the variable transformation function from the model variables to radar data points. σ and τ represent the spatial and temporal domains of the assimilation window, respectively. η v and η q These represent the weighting coefficients of radial velocity and reflectivity factor to the model variables, respectively. These weighting coefficients are typically inversely proportional to the variance of the observation error; therefore, they can also be expressed as: η v =σ v -2 S v η q =σ q -2 S q Among them, σ v σ is the standard deviation of the observation error of the radial velocity; q η represents the standard deviation of the observation error of the reflectivity factor. v and η q The standard deviation of the observation error can be obtained, and thus the observation error can be obtained.

[0076] By recursively minimizing the cost function J, the variable transformation function F, which minimizes the bias, can be obtained. The determined variable transformation function F then converts radial velocity and reflectivity factors into model variables, such as meteorological variables like temperature, wind, and humidity.

[0077] The target data can include radial velocity, reflectivity factor, and observation error for each grid point across the entire area. When the weather forecasting system requires data, the target data can be sent to it. In this embodiment, the weather forecasting system does not need to process the data further; it can directly use the target data to determine meteorological variables and provide weather information to users.

[0078] For example, weather forecasting systems include various data formats, such as MDV format, NetCDF format, LittleR format, etc. Meteorological grid data can be converted according to the data format to obtain target data corresponding to the weather forecasting model.

[0079] According to the mesoscale numerical model system of the weather forecasting system, the target data can be continuously updated to ensure that the target data meets the requirements of the numerical model system. When a data request is received from the weather forecasting system, the target data is sent to the weather forecasting system.

[0080] Figure 3 A flowchart of a meteorological data processing method provided in this embodiment is shown. Figure 2 As shown, the method may further include:

[0081] S301: Reads base data via radar data interface. Electronic devices can obtain base data from radar observations by accessing the radar data interface.

[0082] S302: Perform data alignment processing on the base data. Base data from different regions can be aligned to obtain base data for elevation and azimuth angles arranged in a standard format.

[0083] S303: Grid the aligned base data. The gridded data can be meteorological gridded data.

[0084] S304: Perform speed deblurring on the gridded data.

[0085] S305: Perform occlusion removal processing on the gridded data.

[0086] For example, other data quality control processes can be performed on the gridded data to improve data quality. These include clutter removal and noise reduction, but this implementation is not limited to these examples.

[0087] S306: Perform interpolation on the data after deblurring and occlusion removal. Interpolation can improve the spatial resolution of meteorological grid data, making it more compatible with weather forecast models.

[0088] S307: Determine the observation error.

[0089] S308: Output Data Product. The gridded data from each radar unit are merged to obtain regional meteorological gridded data. This regional meteorological gridded data is then combined with observation errors to output as a data product. The data product refers to gridded data including observation errors, reflectivity factors, and radial velocity. For example, based on the frequency of weather radar data acquisition, the baseline data from each observation can be processed in parallel to obtain a data product updated according to the acquisition frequency.

[0090] Understandable. Figure 3 Each step in the process has been described in detail in the above embodiments and will not be repeated here.

[0091] Furthermore, this application also provides a meteorological data processing apparatus for performing the above-described meteorological data processing method.

[0092] like Figure 4 As shown, the meteorological data processing device 400 may include: a data acquisition module 401, used to acquire basic data observed by various weather radars, the basic data including radial velocity and reflectivity factor at multiple elevation angles; a first gridding module 402, used to grid the basic data to obtain first grid data of the observation area of ​​each weather radar; a deblurring module 403, used to determine the ambiguity range of the radial velocity and perform velocity deblurring processing on the radial velocity in the first grid data according to the ambiguity range; and an occlusion removal processing module 404, used to determine the reflectivity factor at multiple elevation angles. The occlusion range of the reflectivity factor is determined, and the reflectivity factor in the first grid data is removed based on the occlusion range. A second gridding module 405 is used to further grid the first grid data after the velocity deblurring and occlusion removal processes to obtain second grid data. A merging module 406 is used to merge the second grid data from each weather radar to obtain meteorological grid data for the entire region. A data output module 407 is used to convert the meteorological grid data into target data corresponding to the weather forecast model for use by the weather forecast system.

[0093] In one embodiment of this application, the de-ambiguity module 403 may specifically include: a configuration information acquisition module, which can be used to acquire the scanning configuration information of the weather radar, wherein the scanning configuration information includes the maximum unambiguous velocity of the weather radar; and a first ambiguity determination module, which can be used to acquire target grid points in the first grid point data whose radial velocity of the elevation angle exceeds the maximum unambiguous velocity, thereby obtaining the ambiguity range.

[0094] In one embodiment of this application, the defuzzification module 403 may specifically include: a velocity direction acquisition module, used to acquire adjacent grid points with different radial velocity directions in the regional grid point data; and a second fuzziness determination module, used to determine whether the adjacent grid points belong to the fuzziness range based on the difference in the radial velocities of the adjacent grid points.

[0095] In one embodiment of this application, the occlusion removal processing module 404 may specifically include: a historical data acquisition module, used to acquire historical scanning data of the weather radar; an occlusion rate determination module, used to determine the beam occlusion rate of the weather radar at a preset elevation angle based on the historical scanning data; and a third fuzzy determination module, used to determine the grid points corresponding to the preset elevation angle in the regional grid data as the occlusion range of the reflectivity factor when the beam occlusion rate exceeds the occlusion threshold.

[0096] In one embodiment of this application, the data output module 407 can be specifically used to: interpolate the meteorological grid data according to the spatial resolution of the weather forecast model to obtain target data that matches the spatial resolution.

[0097] In one embodiment of this application, the data output module 407 may also be specifically used to: calculate the local variance of the meteorological grid data, use the local variance as the observation error of the meteorological grid data, and perform assimilation processing on the meteorological grid data based on the observation error to obtain target data.

[0098] In one embodiment of this application, the meteorological data processing device 400 may further include: a data transmission module, used to send the target data to the weather forecast system when a data call request is received from the weather forecast system.

[0099] The specific details of each module or unit in the above-mentioned meteorological data processing device have been described in detail in the corresponding meteorological data processing methods, so they will not be repeated here.

[0100] This application also provides an electronic device. Figure 5 A schematic diagram of the structure of an electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0101] like Figure 5As shown, the electronic device 100 includes a central processing unit (CPU) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage section 108 into a random access memory (RAM) 103. The RAM 103 also stores various programs and data required for system operation. The CPU 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0102] I / O interface 105 can also connect to the following components: input section 106, such as a keyboard and mouse; output section 107, including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; storage section 108, including, for example, a hard disk; and communication section 109, including, for example, a network interface card such as a LAN card and a modem. Communication section 109 performs communication processing via a network such as the Internet. Drive 110 is also connected to I / O interface 105 as needed. Removable media 111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 110 as needed so that computer programs read from them can be installed into storage section 108 as needed.

[0103] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 109, and / or installed from removable medium 111. When the computer program is executed by central processing unit (CPU) 101, it performs the functions defined in the embodiments of this application.

[0104] For example, when the computer program is executed by the central processing unit (CPU) 101, it can perform the following: acquire basic data observed by each weather radar, the basic data including radial velocity and reflectivity factor at multiple elevation angles; grid the basic data to obtain first grid data of the observation area of ​​each weather radar; determine the ambiguity range of the radial velocity, and perform velocity deambiguation processing on the radial velocity in the first grid data according to the ambiguity range; determine the occlusion range of the reflectivity factor, and perform occlusion removal processing on the reflectivity factor in the first grid data according to the occlusion range; grid the first grid data after velocity deambiguation processing and occlusion removal processing again to obtain second grid data; merge the second grid data of each weather radar to obtain meteorological grid data for the entire area; and convert the meteorological grid data into target data corresponding to the weather forecast model for use by the weather forecast system.

[0105] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-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 thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0107] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0108] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which include instructions that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0109] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A meteorological data processing method, characterized in that, include: Acquire basic data observed by various weather radars, including radial velocity and reflectivity factor at multiple elevation angles; The base data is gridded to obtain the first grid data of the observation area of ​​each weather radar; Determine the fuzzy range of the radial velocity, and perform velocity defuzzification processing on the radial velocity in the first grid data according to the fuzzy range; Determine the occlusion range of the reflectivity factor, and perform occlusion removal processing on the reflectivity factor in the first grid data according to the occlusion range; The first grid data after the speed deblurring process and the occlusion removal process is re-gridped to obtain the second grid data; The second grid data from each weather radar are merged to obtain meteorological grid data for the entire region; The meteorological grid data is converted into target data corresponding to the weather forecasting system for use by the weather forecasting system.

2. The meteorological data processing method according to claim 1, characterized in that, Determining the fuzzy range of the radial velocity includes: Obtain the scanning configuration information of the weather radar, which includes the maximum unambiguous speed of the weather radar; Obtain the target grid point in the first grid point data whose radial velocity at the elevation angle exceeds the maximum unambiguous velocity, and obtain the ambiguity range.

3. The meteorological data processing method according to claim 1, characterized in that, Determining the fuzzy range of the radial velocity includes: Obtain adjacent grid points with different radial velocity directions in the grid data of the region; Whether an adjacent grid point belongs to a fuzzy range is determined based on the difference in the radial velocity of the adjacent grid points.

4. The meteorological data processing method according to claim 1, characterized in that, Determining the occlusion range of the reflectivity factor includes: Acquire historical scan data from the weather radar; The beam obstruction rate of the weather radar at a preset elevation angle is determined based on the historical scanning data. When the beam obstruction rate exceeds the obstruction threshold, the grid points corresponding to the preset elevation angle in the regional grid data are determined as the obstruction range of the reflectivity factor.

5. The meteorological data processing method according to claim 1, characterized in that, The process of converting the meteorological grid data into target data corresponding to the weather forecasting system includes: Based on the spatial resolution of the weather forecast model, the meteorological grid data is interpolated to obtain target data that matches the spatial resolution.

6. The meteorological data processing method according to claim 1, characterized in that, The process of converting the meteorological grid data into target data corresponding to the weather forecasting system includes: Calculate the local variance of the meteorological grid data, use the local variance as the observation error of the meteorological grid data, and perform assimilation processing on the meteorological grid data based on the observation error to obtain the target data.

7. The meteorological data processing method according to claim 6, characterized in that, After converting the meteorological grid data into target data corresponding to the weather forecasting system, the process further includes: Upon receiving a data request from the weather forecast system, the target data is sent to the weather forecast system.

8. A meteorological data processing device, characterized in that, include: The data acquisition module is used to acquire the base data observed by each weather radar, which includes radial velocity and reflectivity factor at multiple elevation angles; The first gridding module is used to grid the base data to obtain the first grid data of the observation area of ​​each weather radar. The deblurring module is used to determine the fuzziness range of the radial velocity and perform velocity deblurring processing on the radial velocity in the first grid data according to the fuzziness range; The occlusion removal processing module is used to determine the occlusion range of the reflectivity factor and perform occlusion removal processing on the reflectivity factor in the first grid data according to the occlusion range. The second gridding module is used to re-grid the first grid data after the speed deblurring process and the occlusion removal process to obtain the second grid data. The merging module is used to merge the second grid data of each weather radar to obtain meteorological grid data for the entire region; The data output module is used to convert the meteorological grid data into target data corresponding to the weather forecasting system for use by the weather forecasting system.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing one or more computer programs, the one or more computer programs including instructions that, when executed by the electronic device, cause the electronic device to perform the meteorological data processing method according to any one of claims 1-7.

10. A computer-readable medium storing instructions, characterized in that, When the instruction is executed on an electronic device, the electronic device causes the electronic device to perform the meteorological data processing method according to any one of claims 1-7.

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