阅读说明：本技术 天气雷达反射率拼图数据集构建方法、系统、设备及介质 (Weather radar reflectivity jigsaw puzzle data set construction method, system, equipment and medium ) 是由 陈生 唐菁 于 2021-08-05 设计创作，主要内容包括：本发明提供了天气雷达反射率拼图数据集构建方法、系统、设备及介质,通过获取待处理天气雷达拼图数据,查询待处理天气雷达拼图数据的图例反射率RGB和注记RGB得到反射率注记RGB映射表,根据该映射表将待处理天气雷达拼图数据的所有格点的RGB进行反射率转换,得到待处理天气雷达反射率拼图数据,再根据待处理天气雷达反射率拼图数据查找反射率缺失格点,并对其进行插值补全得到天气雷达反射率拼图数据后,根据天气雷达反射率拼图数据构建天气雷达反射率拼图数据集的方法,有效提高天气雷达反射率拼图数据的准确性和连续性,进而提高天气雷达对突发性强灾害天气预报预警的精准性,为研究大尺度下天气系统的时空变化提供便利。(The invention provides a method, a system, equipment and a medium for constructing a weather radar reflectivity jigsaw data set, which effectively improve the accuracy and the continuity of the weather radar reflectivity jigsaw data and further improve the accuracy of the weather radar for forecasting and early warning sudden strong disaster weather by acquiring the weather radar jigsaw data to be processed, inquiring the legend reflectivity RGB and the notations RGB of the weather radar jigsaw data to obtain a reflectivity notation RGB mapping table, performing reflectivity conversion on the RGB of all grid points of the weather radar jigsaw data to be processed according to the mapping table to obtain the weather radar reflectivity jigsaw data to be processed, searching the reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed, performing interpolation completion on the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data, constructing the weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data, the method provides convenience for researching the space-time change of the weather system on a large scale.)

1. A method of constructing a weather radar reflectivity mosaic dataset, the method comprising the steps of:

acquiring weather radar jigsaw data to be processed;

inquiring the legend reflectivity RGB and the annotation RGB of the weather radar jigsaw data to be processed to obtain a reflectivity annotation RGB mapping table; the notes comprise place name notes, boundary line notes at all levels and river notes;

performing reflectivity conversion on RGB of all grid points of the weather radar mosaic data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity mosaic data to be processed;

searching reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed, and performing interpolation completion on the reflectivity missing grid points to obtain weather radar reflectivity jigsaw data;

and constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data.

2. The method for constructing a weather radar reflectivity tile data set according to claim 1, wherein the step of querying the legend reflectivity RGB and the notations RGB of the weather radar tile data to be processed to obtain a reflectivity notations RGB mapping table comprises:

adopting ArcGIS software to respectively query RGB corresponding to each reflectivity and RGB corresponding to each mark on a legend of the weather radar jigsaw data to be processed, and obtaining the legend reflectivity RGB and the mark RGB;

and establishing the reflectivity annotation RGB mapping table according to the legend reflectivity RGB and the annotation RGB.

3. The method of claim 1, wherein the step of performing reflectivity transformation on RGB of all grid points of the weather radar mosaic data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity mosaic data to be processed comprises:

acquiring RGB of all grid points of the weather radar jigsaw data to be processed by adopting matlab;

traversing all grid points of the weather radar mosaic data to be processed, and judging whether RGB of each grid point exists in the reflectivity annotation RGB mapping table;

if the RGB of the lattice point does not exist in the reflectivity annotation RGB mapping table, assigning the corresponding lattice point as a special reflectivity, otherwise, assigning the corresponding lattice point as a corresponding reflectivity or an annotated reflectivity according to the reflectivity annotation RGB mapping table; the mark reflectivity comprises a place name mark reflectivity, a boundary mark reflectivity at each level and a river mark reflectivity.

4. The method according to claim 1, wherein the step of finding the missing reflectivity grid points according to the to-be-processed weather radar reflectivity mosaic data and performing interpolation and completion on the missing reflectivity grid points to obtain the weather radar reflectivity mosaic data comprises:

traversing all the lattice points of the weather radar reflectivity jigsaw data to be processed, searching the lattice points assigned as the marked reflectivity, determining the corresponding lattice points as the reflectivity missing lattice points, and storing the lattice point indexes corresponding to the reflectivity missing lattice points and the marked reflectivity into a reflectivity missing array; the reflectivity missing grid points comprise place name reflectivity missing grid points, boundary reflectivity missing grid points of all levels and river reflectivity missing grid points;

traversing all lattice point indexes of the reflectivity-missing array, adopting a Cressman interpolation, setting a first influence radius, and calculating the reflectivity interpolation corresponding to the reflectivity-missing lattice points;

and updating the weather radar reflectivity jigsaw data to be processed by adopting the reflectivity interpolation of the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data.

5. The method of constructing a weather radar reflectivity tile data set according to claim 4, wherein constructing a weather radar reflectivity tile data set from the weather radar reflectivity tile data includes:

traversing the lattice point index of which the median of the reflectivity missing array is the place name marked reflectivity, adopting Cressman interpolation, setting a second influence radius, and performing interpolation optimization on the place name reflectivity missing lattice points of the updated weather radar reflectivity jigsaw data to obtain first interpolation optimized weather radar reflectivity jigsaw data;

traversing the grid point index of which the median of the reflectivity missing array is the reflectivity of the boundary marks at each level, adopting a Cressman interpolation, setting a third influence radius, and performing interpolation optimization on the reflectivity missing grid points of the boundary of each level of the first interpolation optimized weather radar reflectivity jigsaw data to obtain second interpolation optimized weather radar reflectivity jigsaw data;

traversing the lattice point index of which the median of the reflectivity missing array is the reflectivity of the river marking, adopting a Cressman interpolation, setting a fourth influence radius, and performing interpolation optimization on the river reflectivity missing lattice points of the second interpolation optimized weather radar reflectivity jigsaw data to obtain third interpolation optimized weather radar reflectivity jigsaw data;

traversing all the grid points of the third interpolation optimized weather radar reflectivity jigsaw data, searching the grid points with reflectivity values smaller than a preset reflectivity threshold value, adopting a Cressman interpolation, setting a fifth influence radius, calculating the reflectivity interpolation corresponding to the grid points, and updating the third interpolation optimized weather radar reflectivity jigsaw data to obtain the weather radar reflectivity jigsaw data set.

6. The method of claim 1, wherein the step of constructing a weather radar reflectivity tile data set from the weather radar reflectivity tile data further comprises:

gridding the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, establishing a corresponding graticule, and updating the weather radar reflectivity jigsaw data set.

7. The method of claim 6, wherein the step of gridding the weather radar reflectivity tile data in the weather radar reflectivity tile data set to create a corresponding graticule and updating the weather radar reflectivity tile data set comprises:

selecting a plurality of control points of the weather radar reflectivity jigsaw data;

establishing a coordinate transformation function relation according to the pixel point position and longitude and latitude spatial relation of each control point;

calculating longitude and latitude coordinates of pixel points of the weather radar reflectivity jigsaw data except the control points by adopting the coordinate transformation function relation;

and establishing a graticule of the weather radar reflectivity jigsaw data according to the longitude and latitude coordinates of all the pixel points of the weather radar reflectivity jigsaw data, and updating the weather radar reflectivity jigsaw data set.

8. A weather radar reflectivity mosaic dataset construction system, said system comprising:

the acquisition module is used for acquiring the weather radar jigsaw data to be processed;

the query module is used for querying the legend reflectivity RGB and the annotation RGB of the weather radar jigsaw data to be processed to obtain a reflectivity annotation RGB mapping table; the notes comprise place name notes, boundary line notes at all levels and river notes;

the conversion module is used for performing reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed according to the reflectivity mark RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed;

the interpolation module is used for searching the reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed and carrying out interpolation completion on the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data;

and the construction module is used for constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data.

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the computer program is executed by the processor.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.

Technical Field

The invention relates to the technical field of meteorology, in particular to a Doppler weather radar reflectivity jigsaw puzzle data set construction method, a Doppler weather radar reflectivity puzzle data set construction system, computer equipment and a storage medium.

Background

Sudden weather disasters can bring serious influence to life and production of people, the requirements of various industries on weather forecast, particularly sudden strong disaster weather early warning are higher and higher, and the realization of comprehensive, timed, fixed-point and quantitative accurate weather disaster early warning becomes an important requirement for guaranteeing the stable development of people's lives and properties and the economic society. The weather radar is one of important devices for detecting atmospheric water vapor, and a new generation Doppler weather radar (CIRAD) has the characteristics of strong detection real-time performance, high altitude resolution and the like, can effectively detect the rainfall intensity and the temporal and spatial variation trend, and plays an irreplaceable role in sudden strong-disaster weather close prediction and early warning. With the increasingly perfect construction of the new-generation Doppler weather radar network in China, the defects that a single radar is small in detection range and cannot detect a weather system under a large scale and the like due to the fact that multiple radars are used for networking and joint detection can be overcome, so that the detection range is effectively expanded, and the accuracy of an overlapped area is improved.

Most of the existing weather radar puzzles are obtained by performing puzzle fusion on a single weather radar network by adopting a method of processing an overlapped area such as a nearest neighbor method, a linear interpolation method, a Barnes method and the like, although a weather radar monitoring area is expanded to a certain extent, the detection range of the weather radar puzzles is still limited, partial data on the radar puzzles are lost due to being marked and blocked by legends, and the data format of the puzzles is inconvenient to be directly used, so that the research on a weather system under a large scale is directly limited by a meteorologist.

Therefore, it is desirable to provide a method for obtaining a weather radar jigsaw puzzle based on the existing radar jigsaw puzzle, which has no data loss and is convenient for direct research.

Disclosure of Invention

The invention aims to provide a construction method of a weather radar reflectivity jigsaw data set, which completes the reflectivity lost due to the coverage of a mark in Doppler weather radar reflectivity jigsaw data by adopting Cressman interpolation optimization, and constructs the Doppler weather radar reflectivity jigsaw data set by establishing a graticule at 0.01 degrees multiplied by 0.01 degrees to effectively improve the accuracy and the continuity of the weather radar reflectivity jigsaw data, further improves the accuracy of the weather radar in the forecast early warning of sudden strong disaster weather, and further provides convenience for researching the space-time change of a weather system under a large scale.

In order to achieve the above objects, it is necessary to provide a method, a system, a computer device and a storage medium for constructing a weather radar reflectivity mosaic dataset.

In a first aspect, an embodiment of the present invention provides a method for constructing a weather radar reflectivity mosaic dataset, where the method includes the following steps:

acquiring weather radar jigsaw data to be processed;

inquiring the legend reflectivity RGB and the annotation RGB of the weather radar jigsaw data to be processed to obtain a reflectivity annotation RGB mapping table; the notes comprise place name notes, boundary line notes at all levels and river notes;

performing reflectivity conversion on RGB of all grid points of the weather radar mosaic data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity mosaic data to be processed;

searching reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed, and performing interpolation completion on the reflectivity missing grid points to obtain weather radar reflectivity jigsaw data;

and constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data.

Further, the step of querying the legend reflectivity RGB and the annotation RGB of the weather radar mosaic data to be processed to obtain a reflectivity annotation RGB mapping table includes:

adopting ArcGIS software to respectively query RGB corresponding to each reflectivity and RGB corresponding to each mark on a legend of the weather radar jigsaw data to be processed, and obtaining the legend reflectivity RGB and the mark RGB;

and establishing the reflectivity annotation RGB mapping table according to the legend reflectivity RGB and the annotation RGB.

Further, the step of performing reflectivity conversion on RGB of all grid points of the weather radar mosaic data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity mosaic data to be processed includes:

acquiring RGB of all grid points of the weather radar jigsaw data to be processed by adopting matlab;

traversing all grid points of the weather radar mosaic data to be processed, and judging whether RGB of each grid point exists in the reflectivity annotation RGB mapping table;

if the RGB of the lattice point does not exist in the reflectivity annotation RGB mapping table, assigning the corresponding lattice point as a special reflectivity, otherwise, assigning the corresponding lattice point as a corresponding reflectivity or an annotated reflectivity according to the reflectivity annotation RGB mapping table; the mark reflectivity comprises a place name mark reflectivity, a boundary mark reflectivity at each level and a river mark reflectivity.

Further, the step of searching the reflectivity missing grid points according to the to-be-processed weather radar reflectivity jigsaw data, and performing interpolation completion on the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data includes:

traversing all the lattice points of the weather radar reflectivity jigsaw data to be processed, searching the lattice points assigned as the marked reflectivity, determining the corresponding lattice points as the reflectivity missing lattice points, and storing the lattice point indexes corresponding to the reflectivity missing lattice points and the marked reflectivity into a reflectivity missing array; the reflectivity missing grid points comprise place name reflectivity missing grid points, boundary reflectivity missing grid points of all levels and river reflectivity missing grid points;

traversing all lattice point indexes of the reflectivity-missing array, adopting a Cressman interpolation, setting a first influence radius, and calculating the reflectivity interpolation corresponding to the reflectivity-missing lattice points;

and updating the weather radar reflectivity jigsaw data to be processed by adopting the reflectivity interpolation of the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data.

Further, the step of constructing a weather radar reflectivity mosaic data set according to the weather radar reflectivity mosaic data comprises:

traversing the lattice point index of which the median of the reflectivity missing array is the place name marked reflectivity, adopting Cressman interpolation, setting a second influence radius, and performing interpolation optimization on the place name reflectivity missing lattice points of the updated weather radar reflectivity jigsaw data to obtain first interpolation optimized weather radar reflectivity jigsaw data;

traversing the grid point index of which the median of the reflectivity missing array is the reflectivity of the boundary marks at each level, adopting a Cressman interpolation, setting a third influence radius, and performing interpolation optimization on the reflectivity missing grid points of the boundary of each level of the first interpolation optimized weather radar reflectivity jigsaw data to obtain second interpolation optimized weather radar reflectivity jigsaw data;

traversing the lattice point index of which the median of the reflectivity missing array is the reflectivity of the river marking, adopting a Cressman interpolation, setting a fourth influence radius, and performing interpolation optimization on the river reflectivity missing lattice points of the second interpolation optimized weather radar reflectivity jigsaw data to obtain third interpolation optimized weather radar reflectivity jigsaw data;

traversing all the grid points of the third interpolation optimized weather radar reflectivity jigsaw data, searching the grid points with reflectivity values smaller than a preset reflectivity threshold value, adopting a Cressman interpolation, setting a fifth influence radius, calculating the reflectivity interpolation corresponding to the grid points, and updating the third interpolation optimized weather radar reflectivity jigsaw data to obtain the weather radar reflectivity jigsaw data set.

Further, the step of constructing a weather radar reflectivity mosaic data set according to the weather radar reflectivity mosaic data further includes:

gridding the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, establishing a corresponding graticule, and updating the weather radar reflectivity jigsaw data set.

Further, the step of gridding the weather radar reflectivity jigsaw puzzle data in the weather radar reflectivity jigsaw data set, establishing a corresponding graticule, and updating the weather radar reflectivity jigsaw data set includes:

selecting a plurality of control points of the weather radar reflectivity jigsaw data;

establishing a coordinate transformation function relation according to the pixel point position and longitude and latitude spatial relation of each control point;

calculating longitude and latitude coordinates of pixel points of the weather radar reflectivity jigsaw data except the control points by adopting the coordinate transformation function relation;

and establishing a graticule of the weather radar reflectivity jigsaw data according to the longitude and latitude coordinates of all the pixel points of the weather radar reflectivity jigsaw data, and updating the weather radar reflectivity jigsaw data set.

In a second aspect, an embodiment of the present invention provides a weather radar reflectivity mosaic dataset construction system, where the system includes:

the acquisition module is used for acquiring the weather radar jigsaw data to be processed;

the query module is used for querying the legend reflectivity RGB and the annotation RGB of the weather radar jigsaw data to be processed to obtain a reflectivity annotation RGB mapping table; the notes comprise place name notes, boundary line notes at all levels and river notes;

the conversion module is used for performing reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed according to the reflectivity mark RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed;

the interpolation module is used for searching the reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed and carrying out interpolation completion on the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data;

and the construction module is used for constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data.

In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.

In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the above method.

The application provides a method, a system, a computer device and a storage medium for constructing a weather radar reflectivity jigsaw data set, by which the method is realized that the RGB of each reflectivity and the RGB of each mark on the legend of the obtained weather radar jigsaw data to be processed are inquired by ArcGIS software to obtain a corresponding reflectivity mark RGB mapping table, the RGB of all grid points of the weather radar jigsaw data to be processed is subjected to reflectivity conversion according to the reflectivity mark RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed, the reflectivity missing grid points are searched according to the weather radar reflectivity jigsaw data to be processed, the reflectivity missing grid points are subjected to interpolation completion by adopting a Cressman interpolation value to obtain the weather radar reflectivity jigsaw data, then the interpolation optimization is further sequentially carried out according to the sequence of the place name marks, the boundary marks at each level and the river marks to update the weather radar reflectivity jigsaw data, and establishing a Doppler weather radar reflectivity jigsaw puzzle data set by utilizing 0.01 degrees multiplied by 0.01 degrees gridding. Compared with the prior art, the method for constructing the weather radar reflectivity jigsaw data set effectively improves the accuracy and the continuity of the weather radar reflectivity jigsaw data, further improves the accuracy of the weather radar in the early warning of sudden strong disaster weather forecast, and further provides convenience for researching the space-time change of a weather system under a large scale.

Drawings

FIG. 1 is a schematic diagram of an application scenario of a construction method of a weather radar reflectivity mosaic dataset according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating a method for constructing a mosaic weather radar reflectivity data set according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of weather radar mosaic data to be processed in (part of) south China in an embodiment of the present invention;

FIG. 4 is a schematic flowchart illustrating the process of obtaining the reflectivity index RGB mapping table of the weather radar mosaic data to be processed in step S12 in FIG. 2;

FIG. 5 is a schematic flowchart illustrating the process of obtaining the weather radar reflectivity tile data to be processed in step S13 in FIG. 2;

fig. 6 a and b are schematic diagrams of the overall effect and the detailed effect of the weather radar mosaic data RGB after being processed in the south china area (part) in fig. 3;

FIG. 7 is a flowchart illustrating the weather radar reflectivity tile data obtained in step S14 of FIG. 2;

in fig. 8, a and b are schematic diagrams of interpolation effect details in the south china area shown in fig. 3 under two different influence radius R values, respectively;

FIG. 9 is a schematic flowchart of the step S15 in FIG. 2 for obtaining a weather radar reflectivity tile data set through interpolation optimization;

fig. 10 a and b are schematic diagrams of the overall effect and the detailed effect of the mosaic data Cressman interpolation optimization of the weather radar to be processed in the south china (part of china) in fig. 3, respectively;

FIG. 11 is a schematic flow chart of a method for constructing a weather radar reflectivity tile data set according to an embodiment of the present invention;

FIG. 12 is a schematic flow chart illustrating the process of creating a graticule of weather radar reflectivity mosaic data and updating a weather radar reflectivity mosaic data set in step S16 of FIG. 11;

FIG. 13 is a schematic structural diagram of a weather radar reflectivity mosaic dataset construction system according to an embodiment of the present invention;

fig. 14 is an internal structural view of a computer device in the embodiment of the present invention.

Detailed Description

In order to make the purpose, technical solution and advantages of the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments, and it is obvious that the embodiments described below are part of the embodiments of the present invention, and are used for illustrating the present invention only, but not for limiting the scope of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The method for constructing the weather radar reflectivity jigsaw puzzle data set can be applied to a terminal or a server shown in figure 1. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server can be implemented by an independent server or a server cluster formed by a plurality of servers. The server can obtain a weather radar reflectivity jigsaw data set directly used for researching the space-time change of a weather system under a large scale by adopting the Doppler weather radar reflectivity jigsaw data construction method, and the weather radar reflectivity jigsaw data set is used for subsequent research and analysis of the server or is sent to a terminal for a terminal user to research and use.

The weather radar jigsaw data to be processed acquired in the embodiment of the invention is a radar jigsaw with a data format of PNG format issued by the China weather administration, which comprises China and eight regions (northeast, northwest, southwest, China, east and south), and a series of processing is required before the PNG format data is used for researching a weather system of a certain region. The following embodiment will explain the construction method of the weather radar reflectivity mosaic data set in detail by using radar mosaic data released by the China weather administration.

In one embodiment, as shown in fig. 2, there is provided a weather radar reflectivity tile data set construction method, including the steps of:

s11, acquiring weather radar jigsaw puzzle data to be processed;

the weather radar mosaic data to be processed can be a two-dimensional mosaic obtained by performing mosaic fusion on a single weather radar network by adopting an overlapping area such as a nearest neighbor method, a linear interpolation method, a Barnes method and the like in principle. In order to obtain a more representative data set which is convenient for subsequent research, the radar jigsaw puzzle data to be processed obtained in the embodiment is a radar jigsaw puzzle in a large area on a two-dimensional jigsaw puzzle obtained by fusing the national weather bureau as shown in fig. 3, and a subsequent weather radar reflectivity jigsaw puzzle data set is constructed based on the data.

S12, inquiring the legend reflectivity RGB and the annotation RGB of the weather radar jigsaw data to be processed to obtain a reflectivity annotation RGB mapping table; the notes comprise place name notes, boundary line notes at all levels and river notes;

the weather radar mosaic data to be processed has the basic reflectivity of the legend, the place name, the national boundary, the provincial boundary, the county boundary, the river and the like shown in fig. 3, and some mosaic data have the condition that the reflectivity is lost due to the fact that some areas are covered by the place name, the national boundary, the provincial boundary, the county boundary, the river and the like, so that the using effect of the mosaic data is influenced. In order to accurately and effectively extract the range of each annotation on the to-be-processed weather radar mosaic data, the reflectivity of each legend corresponding to the to-be-processed weather radar mosaic data and the RGB corresponding to each annotation need to be obtained in advance. Since the weather radar mosaic data to be processed is in PNG format, the step S12 of obtaining the reflectivity annotation RGB mapping table needs to obtain the reflectivity annotation RGB mapping table by means of a query tool, as shown in fig. 4, the step S queries the legend reflectivity RGB and the annotation RGB of the weather radar mosaic data to be processed, and includes:

s121, respectively inquiring RGB corresponding to each reflectivity and RGB corresponding to each mark on a legend of the weather radar jigsaw data to be processed by adopting ArcGIS software to obtain the legend reflectivity RGB and the mark RGB;

the ArcGIS software is a complete set of GIS (geographic Information system) platform products developed by Esri corporation for over 40 years of geographic Information system consultation and research and development experience. In this embodiment, a query function of an identity tool in the ArcGIS software is used to query RGB corresponding to each reflectivity and RGB corresponding to each label on the legend, and a method for obtaining corresponding RGB by querying using the query function of the identity tool is implemented by referring to the prior art, which is not limited herein.

And S122, establishing the reflectivity annotation RGB mapping table according to the legend reflectivity RGB and the annotation RGB.

The reflectivity note RGB mapping table is a one-to-one mapping table established according to the reflectivity and RGB and the corresponding relationship between the labels and RGB, such as: the reflectivity of 10dBz corresponds to RGB (1, 160, 246), the place name mark corresponds to RGB (104, 104, 104) and the like, so that the reflectivity conversion of RGB of each grid point of the weather radar mosaic data to be processed is facilitated, different marks are arranged on each mark, and the range of each mark is accurately distinguished and extracted.

In the embodiment, the reflectivity annotation RGB mapping table corresponding to the weather radar mosaic data to be processed is conveniently and effectively obtained by adopting the query function of the identity tool in the ArcGIS software, so that effective guarantee is provided for extracting each annotation based on the reflectivity annotation RGB mapping table and performing interpolation completion on each annotation shielding data.

S13, performing reflectivity conversion on RGB of all grid points of the weather radar jigsaw puzzle data to be processed according to the reflectivity mark RGB mapping table to obtain the weather radar reflectivity jigsaw puzzle data to be processed;

the weather radar reflectivity jigsaw data to be processed can be understood as that all grid points in the original weather radar jigsaw data to be processed are subjected to RGB conversion, RGB corresponding to each reflectivity on a legend in a reflectivity annotation RGB mapping table can be directly converted into the reflectivity, RGB corresponding to each annotation needs to be subjected to different annotation reflectivity assignments according to specific annotation types, the annotation reflectivity is only used for representing different annotations and does not represent true reflectivity data, and the following steps are adopted to perform interpolation completion on reflectivity missing grid points corresponding to all the annotations. Specifically, as shown in fig. 5, the step S13 of performing reflectivity conversion on RGB of all grid points of the weather radar mosaic data to be processed according to the reflectivity annotation RGB mapping table to obtain weather radar reflectivity mosaic data to be processed includes:

s131, acquiring RGB of all grid points of the weather radar mosaic data to be processed by adopting matlab;

after the weather radar mosaic data to be processed is determined, the RGB of each pixel in the picture basically does not change, and any method capable of obtaining RGB based on the picture can be adopted in principle to obtain the RGB of all grid points on the weather radar mosaic data to be processed. In the embodiment, matlab programming is preferably adopted to read the to-be-processed weather radar jigsaw data, and three matrixes with the same row and column as the to-be-processed weather radar jigsaw data are simply and conveniently obtained: and performing conversion in the following corresponding steps based on the obtained RGB of each grid point to obtain the weather radar reflectivity jigsaw puzzle data to be processed as shown in FIG. 6(a, B).

S132, traversing all grid points of the weather radar mosaic data to be processed, and judging whether RGB of each grid point exists in the reflectivity annotation RGB mapping table;

s133, if the RGB of the lattice points does not exist in the reflectivity annotation RGB mapping table, assigning the corresponding lattice points as special reflectivity, otherwise, assigning the corresponding lattice points as corresponding reflectivity or annotation reflectivity according to the reflectivity annotation RGB mapping table; the mark reflectivity comprises a place name mark reflectivity, a boundary mark reflectivity at each level and a river mark reflectivity. After the RGB of all grid points of the weather radar mosaic data to be processed is obtained through the above steps, corresponding reflectivity conversion needs to be performed by using a reflectivity annotation RGB mapping table, and in the actual RGB conversion process: if the RGB of the lattice point is equal to the RGB of a color corresponding to a certain reflectivity (dBZ) in the reflectivity notation RGB mapping table, assigning the lattice point as the corresponding reflectivity (dBZ); if the RGB of the lattice point is equal to the RGB of the place name notation, the lattice point is assigned as the place name notation reflectivity; if the RGB of the grid point is equal to the RGB of the boundary marks of each level in the reflectivity mark RGB mapping table, the grid point is assigned as the reflectivity of the boundary marks of each level; if the RGB of the lattice point is equal to the RGB of the river mark in the reflectivity mark RGB mapping table, assigning the lattice point as the reflectivity of the river mark; if the RGB of the lattice point is not equal to the RGB corresponding to each reflectivity on the legend or the RGB of each mark, the lattice point is assigned as the special reflectivity. It should be noted that, in this embodiment, only when the RGB of a grid point is equal to the RGB of a color corresponding to a certain reflectivity (dBZ) in the reflectivity annotation RGB mapping table, the reflectivity (dBZ) assigned to the grid point is a real available reflectivity, the reflectivities assigned to other place names, the reflectivities assigned to different levels of boundary lines, the reflectivities assigned to river and the special reflectivities are not real available reflectivity values, where the assignment is only used for identification, which is convenient for subsequently determining the reflectivity-missing grid points requiring interpolation using the interpolation algorithm and effectively distinguishing the various reflectivity-missing grid points, and the specific values of the reflectivity assigned to the place names, the reflectivities assigned to different levels of boundary lines, the reflectivity assigned to river and the special reflectivities can be flexibly set according to the use requirements without affecting the specific implementation effect of this embodiment, for example, the reflectivity assigned to place names is set to 1, the reflectivity assigned to river and the special reflectivities are set to be not affected by the different kinds of the real implementation effect of this embodiment The level boundary note reflectivity is set to 2, the river note reflectivity is set to 3, and the special reflectivity is set to 0, and this is not particularly limited.

According to the method for obtaining the weather radar reflectivity jigsaw puzzle data, which is obtained by the steps, the RGB conversion is carried out on all the lattice points of the original weather radar jigsaw puzzle data to be processed according to the reflectivity annotation RGB mapping table obtained by the steps, so that the extraction of the normal lattice points and the reflectivity missing lattice points of the reflectivity data is realized, the effective distinguishing of various reflectivity missing lattice points is also realized, the necessary technical support is provided for the subsequent interpolation completion of the reflectivity missing lattice points and the respective interpolation optimization according to the reflectivity missing lattice point types, and the accuracy and the continuity of the weather radar reflectivity jigsaw puzzle data obtained by processing are effectively ensured.

S14, searching reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed, and performing interpolation completion on the reflectivity missing grid points to obtain weather radar reflectivity jigsaw data;

the reflectivity-missing grid points are the grid points marked as the place name marked reflectivity, the boundary marked reflectivity of each level, the river marked reflectivity and the special reflectivity when the RGB conversion is performed in the above steps, and in order to ensure the continuity of the reflectivity data in the weather radar reflectivity jigsaw data to be processed, the reflectivity interpolation is performed on each reflectivity-missing grid point by using a Cressman interpolation algorithm in the embodiment. As shown in fig. 7, the step S14 of finding the missing reflectivity grid points according to the to-be-processed weather radar reflectivity mosaic data, and performing interpolation and completion on the missing reflectivity grid points to obtain the weather radar reflectivity mosaic data includes:

s141, traversing all grid points of the weather radar reflectivity jigsaw data to be processed, searching for grid points assigned as the marked reflectivity, determining corresponding grid points as the reflectivity missing grid points, and storing grid point indexes corresponding to the reflectivity missing grid points and the marked reflectivity into a reflectivity missing array; the reflectivity missing grid points comprise place name reflectivity missing grid points, boundary reflectivity missing grid points of all levels and river reflectivity missing grid points;

the reflectivity-missing array can be understood as an array specially storing reflectivity-missing grid point information on the weather radar reflectivity jigsaw puzzle data to be processed, and comprises grid point indexes of the reflectivity-missing grid points and corresponding special reflectivity or marked reflectivity, so that subsequent reflectivity interpolation processing can be conveniently carried out on the reflectivity-missing grid points.

S142, traversing all lattice point indexes of the reflectivity-missing array, adopting a Cressman interpolation, setting a first influence radius, and calculating the reflectivity interpolation corresponding to the reflectivity-missing lattice points;

wherein, the Cressman interpolation algorithm determines the weight coefficient w of each adjacent point according to the distance between each adjacent point and the interpolation point, and finally the value v of the interpolation point_(i,j)The weight coefficient is calculated according to each adjacent point, and the specific formula is as follows:

in the formula, v_(i,j)Is the value of the interpolation point, w_nIs a data point v_nThe weight coefficient of (2). And multiplying each data point in the influence radius R by a respective weight coefficient, and dividing the sum by the weight sum of all the data points after accumulating and summing to obtain the value of the interpolation point. The most important thing in the Cressman interpolation algorithm is the determination of the weight coefficient w, the farther the data point is from the interpolation point, the smaller the weight coefficient w is, otherwise, the more the weight coefficient w isThe larger the weight coefficient w is, the formula is as follows:

wherein R is the distance from the data point to the interpolation point, and R is the radius of influence; since it affects the radius R to be too large, it affects the distance relationship between each adjacent data point and the interpolation point, it is too small, or it results in insufficient number of adjacent data points, therefore, R is usually selected as a positive integer (e.g., 1, 2, 3, 4, … …, 10).

In order to ensure the reasonable and effective selection of the influence radius, the embodiment determines the influence radius used by each Cressman interpolation by the following method: a plurality of influence radius values are selected in advance, interpolation effects of different influence radii under different interpolation conditions are compared, namely whether obvious marks appear in interpolation regions under different influence radii is checked, no mark exists after interpolation, influence radius R values with relatively small errors of the overall interpolation result are determined as influence radii finally used, and interpolation effects of different influence radii are shown in fig. 8(a and b). The first influence radius used for interpolating all the lattice points in the reflectivity-missing array and the second influence radius, the third influence radius, the fourth influence radius and the fifth influence radius selected for further interpolation optimization of the lattice points with different types of reflectivity-missing are determined according to the method.

And S143, updating the weather radar reflectivity jigsaw data to be processed by adopting the reflectivity interpolation of the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data.

After the reflectivity interpolation of each reflectivity-missing grid point is obtained by the method, the reflectivity of the grid point corresponding to the weather radar reflectivity jigsaw data to be processed is updated by using the reflectivity interpolation, and the weather radar reflectivity jigsaw data after the reflectivity is supplemented is obtained. Theoretically, the weather radar reflectivity mosaic data obtained in the step has no reflectivity loss, the continuity of the reflectivity mosaic data is guaranteed to a certain extent, but in order to further guarantee the accuracy of the reflectivity mosaic data, the following steps are further adopted to further optimize the weather radar reflectivity mosaic data obtained in the step.

And S15, constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data.

The method comprises the following steps of processing weather radar reflectivity jigsaw data to be processed through the steps, wherein the range of a part of the area needing interpolation is large, the ranges covered by different notes are different in size, when interpolation is carried out through the unified first influence radius, partial reflectivity data can be caused to be inaccurate, the using effect of a weather radar reflectivity jigsaw data set obtained by direct construction is further influenced, the size of the influence radius needs to be further changed, continuous interpolation optimization is carried out on different note coverage areas, and therefore the continuity and the accuracy of a radar reflectivity jigsaw are guaranteed. As shown in fig. 9, the step S15 of constructing a weather radar reflectivity tile data set according to the weather radar reflectivity tile data includes:

s151, traversing the lattice point index of which the median of the reflectivity missing array is the place name marked reflectivity, adopting Cressman interpolation, setting a second influence radius, and performing interpolation optimization on the place name reflectivity missing lattice points of the updated weather radar reflectivity jigsaw data to obtain first interpolation optimized weather radar reflectivity jigsaw data;

s152, traversing the median of the reflectivity missing array to be the grid point index of the reflectivity of the boundary marks of each level, adopting a Cressman interpolation, setting a third influence radius, and performing interpolation optimization on the boundary reflectivity missing grid points of each level of the first interpolation optimized weather radar reflectivity jigsaw data to obtain second interpolation optimized weather radar reflectivity jigsaw data;

s153, traversing the lattice point index of which the median of the reflectivity missing array is the river marking reflectivity, performing interpolation optimization on the river reflectivity missing lattice points of the second interpolation optimized weather radar reflectivity jigsaw data by adopting a Cressman interpolation and setting a fourth influence radius to obtain third interpolation optimized weather radar reflectivity jigsaw data;

s154, traversing all the grid points of the third interpolation optimized weather radar reflectivity jigsaw data, searching the grid points with the reflectivity values smaller than the preset reflectivity threshold value, adopting a Cressman interpolation, setting a fifth influence radius, calculating the reflectivity interpolation corresponding to the grid points, and updating the third interpolation optimized weather radar reflectivity jigsaw data to obtain the weather radar reflectivity jigsaw data set.

The size relationship among the first influence radius, the second influence radius, the third influence radius, the fourth influence radius and the fifth influence radius is in direct proportion to the size of the corresponding interpolation coverage area, namely the larger the R value of the influence radius is, the more the adjacent data points are, and if the interpolation area is larger, the larger R value needs to be selected to ensure that enough adjacent data points exist. Because the coverage of each type of note to the area is from small to large: the place name annotation, the boundary line annotation at each level and the river annotation correspond to the influence radiuses which are as follows from small to large: the first influence radius can be an average value of the second influence radius, the third influence radius, the fourth influence radius and the fifth influence radius, and the interpolation optimization is also performed by sequentially performing interpolation calculation according to the sequence from the small coverage area note to the large coverage area note.

According to the weather radar reflectivity jigsaw data obtained by performing reflectivity interpolation on all reflectivity missing grid points based on the first influence radius, the appropriate influence radius is further set according to the size of the coverage range of the notes respectively, continuous interpolation optimization is performed on different note coverage areas, the constructed weather radar reflectivity jigsaw data set is effectively guaranteed to have better continuity and accuracy, the reliability of the weather radar reflectivity jigsaw data set for research is further improved, and the specific effect is as shown in fig. 10(a and b).

In addition, in order to enable the constructed Doppler weather radar reflectivity jigsaw data set to be more convenient for researching the space-time change of a weather system under a large scale, after the weather radar reflectivity jigsaw data set with continuous and accurate reflectivity is obtained based on the Cressman interpolation, the longitude and latitude of each grid are further calculated by utilizing 0.01 degrees multiplied by 0.01 degrees for gridding, the longitude and latitude of each weather radar reflectivity jigsaw data set are established, and the Doppler weather radar reflectivity jigsaw data set is further optimized and updated. As shown in fig. 11, the step S15 of constructing a weather radar reflectivity tile data set according to the weather radar reflectivity tile data further includes:

s16, gridding the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, establishing a corresponding graticule, and updating the weather radar reflectivity jigsaw data set.

Specifically, the step S16 of gridding specifically refers to performing 0.01 ° × 0.01 ° gridding according to longitude and latitude standards, determining longitude and latitude coordinates of each grid point, specifically, as shown in fig. 12, gridding the weather radar reflectivity mosaic data in the weather radar reflectivity mosaic data set, establishing a corresponding longitude and latitude grid, and updating the weather radar reflectivity mosaic data set includes:

s161, selecting a plurality of control points of the weather radar reflectivity jigsaw puzzle data;

the control points are points with obvious characteristics, such as boundary points of the northmost side and the eastermost side in the provincial region boundary, or inflection points with small deformation of the provincial region boundary under any scale, and the number and the selection mode of the specific control points can be determined by self according to the actual application requirements in principle. In order to ensure the rational and effective setting of longitude and latitude coordinates of each grid point, the embodiment requires that the number of the control points is ensured to be more than 3, the control points are not selected too intensively, and the control points are distributed in the range of the original weather radar jigsaw puzzle data as uniformly as possible.

S162, establishing a coordinate transformation function relation according to the pixel point position and the longitude and latitude spatial relation of each control point;

after the control points are determined, longitude and latitude coordinates of each control point can be determined through the prior art, and a corresponding coordinate transformation function relation is established according to the pixel point position of each control point in the weather radar mosaic data and the corresponding longitude and latitude coordinates, and is specifically expressed as follows:

in the formula, x and y are respectively the abscissa and the ordinate of a control point in the original weather radar reflectivity jigsaw data; a. b, respectively controlling longitude coordinates and latitude coordinates corresponding to the points; p is a radical of^-1(·)、q^-1The (-) values are the corresponding coordinate transformation function relations respectively.

S163, calculating longitude and latitude coordinates of pixel points of the weather radar reflectivity jigsaw puzzle data except for the control points by adopting the coordinate transformation function relation;

and traversing all pixel points in the weather radar reflectivity jigsaw data after the coordinate transformation function relation is obtained according to the steps, reversely deducing longitude and latitude coordinates of other pixel points by using the coordinate transformation function relation, and establishing a corresponding longitude and latitude network. If the change of a weather system of a certain small area needs to be researched, the reflectivity of each grid point in a research area can be extracted from the established longitude and latitude network according to the longitude and latitude range of the research area, so that the follow-up research is facilitated.

S164, establishing a longitude and latitude network of the weather radar reflectivity jigsaw data according to the longitude and latitude coordinates of all pixel points of the weather radar reflectivity jigsaw data, and updating the weather radar reflectivity jigsaw data set.

In the embodiment, after a plurality of control points with obvious characteristics are selected from the weather radar reflectivity jigsaw data, the longitude and latitude coordinates of each control point are obtained, the direct corresponding relation between the pixel position coordinates and the longitude and latitude coordinates of each control point on the original image is utilized to establish a coordinate transformation function relation, and the longitude and latitude coordinates of other pixel points are reversely deduced, so that the purpose of meshing any weather radar reflectivity jigsaw data in the weather radar reflectivity data set by 0.01 degrees multiplied by 0.01 degrees is achieved, the longitude and latitude network of corresponding data is established, and finally, the Doppler weather radar reflectivity jigsaw data set with continuous and accurate reflectivity is constructed and beneficial to researching the space-time variation of a weather system under a large scale is achieved.

It should be noted that, although the steps in the above-described flowcharts are shown in sequence as indicated by arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise.

In one embodiment, as shown in fig. 13, there is provided a weather radar reflectivity tile data set construction system, the system comprising:

the acquisition module 1 is used for acquiring weather radar jigsaw data to be processed;

the query module 2 is used for querying the legend reflectivity RGB and the annotation RGB of the weather radar jigsaw data to be processed to obtain a reflectivity annotation RGB mapping table; the notes comprise place name notes, boundary line notes at all levels and river notes;

the conversion module 3 is used for performing reflectivity conversion on RGB of all grid points of the weather radar jigsaw puzzle data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw puzzle data to be processed;

the interpolation module 4 is used for searching the reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed, and carrying out interpolation completion on the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data;

and the construction module 5 is used for constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data.

It should be noted that, for specific limitations of the weather radar reflectivity mosaic data set construction system, reference may be made to the above limitations of the weather radar reflectivity mosaic data set construction method, which is not described herein again. The various modules in the weather radar reflectivity mosaic dataset construction system described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

Fig. 14 shows an internal structure diagram of a computer device, which may be a terminal or a server in particular, in one embodiment. As shown in fig. 14, the computer apparatus includes a processor, a memory, a network interface, a display, and an input device, which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method of constructing a weather radar reflectivity tile dataset. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those of ordinary skill in the art that the architecture shown in FIG. 14 is only a block diagram of some of the structures associated with the present solution and is not intended to limit the computing devices to which the present solution may be applied, and that a particular computing device may include more or less components than those shown in the drawings, or may combine certain components, or have the same arrangement of components.

In one embodiment, a computer device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the steps of the above method being performed when the computer program is executed by the processor.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method.

To sum up, the method for constructing a weather radar reflectivity jigsaw data set, a system, a computer device and a storage medium according to the embodiments of the present invention, the method for constructing a weather radar reflectivity jigsaw data set realizes that RGB of each reflectivity and RGB of each mark on a legend of weather radar jigsaw data to be processed are obtained by querying through ArcGIS software, a corresponding reflectivity mark RGB mapping table is obtained, reflectivity conversion is performed on RGB of all grid points of the weather radar mosaic data to be processed according to the reflectivity mark RGB mapping table to obtain weather radar reflectivity mosaic data to be processed, reflectivity missing grid points are searched according to the weather radar reflectivity mosaic data to be processed, interpolation is performed on the reflectivity missing grid points by using a Cressman interpolation to obtain the weather radar reflectivity mosaic data, and then interpolation optimization is further performed sequentially according to the order of place name marks, boundary marks and river marks, updating weather radar reflectivity jigsaw data, and establishing a technical scheme of constructing a Doppler weather radar reflectivity jigsaw data set by utilizing 0.01 degrees multiplied by 0.01 degrees gridding. The method for constructing the weather radar reflectivity jigsaw data set not only effectively improves the accuracy and the continuity of the weather radar reflectivity jigsaw data, further improves the accuracy of the weather radar in the early warning of sudden strong disaster weather forecast, but also provides convenience for researching the space-time change of a weather system under a large scale.

The embodiments in this specification are described in a progressive manner, and all the same or similar parts of the embodiments are directly referred to each other, and each embodiment is described with emphasis on differences from other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. It should be noted that, the technical features of the embodiments may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express some preferred embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these should be construed as the protection scope of the present application. Therefore, the protection scope of the present patent shall be subject to the protection scope of the claims.