CN113655483B - Method, system, equipment and medium for constructing weather radar reflectivity jigsaw data set - Google Patents
Method, system, equipment and medium for constructing weather radar reflectivity jigsaw data set Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
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- G—PHYSICS
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- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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Abstract
The invention provides a method, a system, equipment and a medium for constructing a weather radar reflectivity jigsaw data set, which are characterized in that the method for constructing the weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data by acquiring the 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, carrying out reflectivity conversion on 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 for the reflectivity missing grid points according to the weather radar reflectivity jigsaw data to be processed, interpolating and complementing the reflectivity missing grid points to obtain the weather radar reflectivity jigsaw data, and then, effectively improving the accuracy and the continuity of the weather radar reflectivity jigsaw data according to the weather radar reflectivity jigsaw data, further improving the accuracy of weather radar early warning on sudden strong disasters and providing convenience for researching the space-time variation of a weather system under a large scale.
Description
Technical Field
The invention relates to the technical field of weather, in particular to a Doppler weather radar reflectivity jigsaw data set construction method, a Doppler weather radar reflectivity jigsaw data set construction system, computer equipment and a storage medium.
Background
The sudden weather disasters can bring serious influence to the life production of people, the requirements of various industries on weather forecast, especially sudden strong disaster weather early warning, are higher and higher, and the comprehensive, timed, fixed-point and quantitative accurate weather disaster early warning is an important requirement for guaranteeing the stable development of life, property, economy and society of people. The weather radar is one of important equipment for detecting atmospheric water vapor, and the new-generation Doppler weather radar (CINRAD) has the characteristics of strong detection instantaneity, high altitude resolution and the like, can effectively detect the rainfall intensity and the time-space change trend, and plays an irreplaceable role in sudden strong disaster weather proximity prediction and early warning. With the gradual perfection of the construction of the new generation of the Doppler weather radar network in China, the combined detection of multiple radar networking can overcome the defects that a single radar has a small detection range, a weather system under a large scale cannot be detected and the like, so that the detection range is effectively enlarged, and the accuracy of an overlapping area is improved.
Most of the existing weather radar puzzles are obtained by puzzles fusion of single weather radar networking and processing overlapping areas such as a nearest neighbor method, a linear interpolation method and a Barnes method, although the weather radar monitoring area is enlarged to a certain extent, the detection range is limited, partial data on the radar puzzles are also lost due to the fact that the partial data are blocked by legend marks, and puzzles are inconvenient to use in a data format, so that weather workers are directly limited in researching weather systems under large scale.
Therefore, there is a need to provide a method for obtaining a weather radar tile that is free of data loss and convenient for direct research based on existing radar tiles.
Disclosure of Invention
The invention aims to provide a method for constructing a weather radar reflectivity jigsaw data set, which is characterized in that Cressman interpolation is adopted to optimize the reflectivity missing due to annotation coverage in Doppler weather radar reflectivity jigsaw data, and 0.01 degree x 0.01 degree gridding is utilized to construct a longitude and latitude net to construct the Doppler weather radar reflectivity jigsaw data set, so that the accuracy and the continuity of weather radar reflectivity jigsaw data are effectively improved, the accuracy of weather radar on sudden strong disaster weather forecast and early warning is further improved, and convenience is provided for researching space-time change of a weather system under a large scale.
In order to achieve the above objective, it is necessary to provide a method, a system, a computer device and a storage medium for constructing a weather radar reflectivity jigsaw data set.
In a first aspect, an embodiment of the present invention provides a method for constructing a weather radar reflectivity jigsaw dataset, the method including the steps of:
Acquiring the jigsaw data of the weather radar 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 marks comprise place name marks, boundary marks at all levels and river marks;
According to the reflectivity annotation RGB mapping table, performing reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed to obtain weather radar reflectivity jigsaw data to be processed;
Searching for reflectivity missing lattice points according to the to-be-processed weather radar reflectivity jigsaw data, and interpolating and complementing the reflectivity missing lattice 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 jigsaw data to be processed to obtain the reflectivity annotation RGB mapping table includes:
adopting ArcGIS software to respectively inquire RGB corresponding to each reflectivity and RGB corresponding to each annotation on a legend of the weather radar jigsaw data to be processed, and obtaining the legend reflectivity RGB and the annotation 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 jigsaw data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed includes:
obtaining 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 jigsaw data to be processed, and judging whether RGB of each grid point exists in the reflectivity annotation RGB mapping table;
If RGB of the lattice point does not exist in the reflectivity marking RGB mapping table, the corresponding lattice point is assigned to be a special reflectivity, otherwise, the corresponding lattice point is assigned to be a corresponding reflectivity or marking reflectivity according to the reflectivity marking RGB mapping table; the annotation reflectivity comprises place name annotation reflectivity, boundary annotation reflectivity at all levels and river annotation reflectivity.
Further, the step of searching for the missing lattice points of the reflectivity according to the to-be-processed weather radar reflectivity jigsaw data and interpolating and complementing the missing lattice points of the reflectivity to obtain the weather radar reflectivity jigsaw data comprises the following steps:
traversing all lattice points of the weather radar reflectivity jigsaw data to be processed, searching the lattice points assigned as the annotation 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 annotation reflectivity into a reflectivity missing array; the reflectivity missing lattice points comprise place name reflectivity missing lattice points, boundary reflectivity missing lattice points at all levels and river reflectivity missing lattice points;
Traversing all grid point indexes of the reflectivity missing array, adopting Cressman interpolation, setting a first influence radius, and calculating reflectivity interpolation corresponding to the reflectivity missing grid points;
And updating the to-be-processed weather radar reflectivity jigsaw data by adopting the reflectivity interpolation of the reflectivity missing lattice 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 includes:
traversing the lattice index with the median value of the reflectivity missing array as the place name annotation reflectivity, adopting Cressman interpolation, setting a second influence radius, and carrying out interpolation optimization on the place name reflectivity missing lattice of the updated weather radar reflectivity jigsaw data to obtain first interpolation optimized weather radar reflectivity jigsaw data;
Traversing the grid point index with the median value of the reflectivity missing array being the marked reflectivity of each level boundary, adopting Cressman interpolation, setting a third influence radius, and performing interpolation optimization on the grid points with the reflectivity missing of each level boundary of the first interpolation optimized weather radar reflectivity jigsaw data to obtain second interpolation optimized weather radar reflectivity jigsaw data;
Traversing the grid point index with the median value of the reflectivity missing array being the river annotation reflectivity, adopting Cressman interpolation, setting a fourth influence radius, and carrying out interpolation optimization on the river reflectivity missing grid point of the second interpolation optimized weather radar reflectivity jigsaw data to obtain third interpolation optimized weather radar reflectivity jigsaw data;
traversing all lattice points of the third interpolation optimized weather radar reflectivity jigsaw data, searching lattice points with reflectivity value smaller than a preset reflectivity threshold value, adopting Cressman interpolation, setting a fifth influence radius, calculating the reflectivity interpolation corresponding to the lattice 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 jigsaw data set according to the weather radar reflectivity jigsaw data further includes:
and gridding the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, establishing a corresponding longitude and latitude network, and updating the weather radar reflectivity jigsaw data set.
Further, the step of meshing the weather radar reflectivity mosaic data in the weather radar reflectivity mosaic data set to establish a corresponding longitude and latitude network, and the step of updating the weather radar reflectivity mosaic 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 space relation of each control point;
calculating longitude and latitude coordinates of pixel points except for a control point of the weather radar reflectivity jigsaw data by adopting the coordinate transformation function relation;
And establishing a longitude and latitude network of the weather radar reflectivity jigsaw data according to 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 a second aspect, an embodiment of the present invention provides a weather radar reflectivity tile dataset construction system, the 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 marks comprise place name marks, boundary marks at all levels and river marks;
the conversion module is used for carrying out reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed;
The interpolation module is used for searching the reflectivity missing lattice points according to the to-be-processed weather radar reflectivity jigsaw data, and carrying out interpolation complementation on the reflectivity missing lattice points to obtain weather radar reflectivity jigsaw data;
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, embodiments of the present invention further provide a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above method.
The method realizes that RGB of each reflectivity and RGB of each annotation on a legend of weather radar jigsaw data to be processed are obtained by inquiring ArcGIS software, obtains a corresponding reflectivity annotation RGB mapping table, converts the RGB of all grid points of the weather radar jigsaw data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed, searches for a reflectivity missing grid point according to the weather radar reflectivity jigsaw data to be processed, interpolates and complements the reflectivity missing grid point by adopting Cressman interpolation to obtain the weather radar reflectivity jigsaw data, sequentially interpolates and optimizes the weather radar reflectivity jigsaw data according to the sequence of place name annotation, each level boundary annotation and river annotation, updates the weather radar reflectivity jigsaw data, and utilizes 0.01 degree x 0.01 degree gridding to establish a technical scheme of Doppler weather radar reflectivity jigsaw data set through a weft network. 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 weather radar on weather forecast and early warning of sudden strong disasters, and further provides convenience for researching space-time variation of a weather system under a large scale.
Drawings
Fig. 1 is a schematic diagram of an application scenario of a method for constructing a pattern data set of reflectivity of an weather radar in an embodiment of the present invention;
FIG. 2 is a flow chart of a method for constructing a set of data for a radar reflectivity tile in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of the data of the pending weather radar in the south China area (section) according to the embodiment of the present invention;
FIG. 4 is a flowchart of the step S12 in FIG. 2 for obtaining the reflectance annotation RGB mapping table of the weather radar jigsaw data to be processed;
FIG. 5 is a flowchart of step S13 in FIG. 2 to obtain the reflectivity mosaic data of the weather radar to be processed;
fig. 6a and b are respectively an overall effect schematic diagram and a detail effect schematic diagram of the RGB-converted weather radar jigsaw data of the south China area (part of the south China area);
FIG. 7 is a flowchart of step S14 in FIG. 2 to obtain weather radar reflectivity jigsaw data;
Fig. 8a and b are schematic views of interpolation effect details of the south China area shown in fig. 3 under two different influence radius R values;
FIG. 9 is a schematic flow chart of obtaining the weather radar reflectivity mosaic dataset through interpolation optimization in step S15 in FIG. 2;
Fig. 10a and b are respectively an overall effect schematic diagram and a detail effect schematic diagram of interpolation optimization of the weather radar jigsaw data Cressman to be processed in (part of) the south China area in fig. 3;
FIG. 11 is another flow chart of a method for constructing a set of data for radar reflectivity tiles of an antenna in accordance with an embodiment of the present invention;
FIG. 12 is a schematic diagram of the process of updating the weather radar reflectivity tile dataset by creating a longitude and latitude network of the weather radar reflectivity tile data at step S16 of FIG. 11;
FIG. 13 is a schematic diagram of a system for constructing a set of data sets for reflectivity tiles for an antenna radar in accordance with 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 objects, technical solutions and advantageous effects of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples, and it is apparent that the examples described below are part of the examples of the present application, which are provided for illustration only and are not intended to limit the scope of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The method for constructing the weather radar reflectivity jigsaw data set can be applied to a terminal or a server shown in figure 1. The terminal may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers and portable wearable devices, and the server may be implemented by a separate server or a server cluster formed by a plurality of servers. The server can adopt the Doppler weather radar reflectivity jigsaw data construction method to obtain the weather radar reflectivity jigsaw data set which is directly used for researching the space-time variation of the weather system under a large scale, 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 research and use by a terminal user.
The weather radar jigsaw data to be processed obtained in the embodiment of the invention is a radar jigsaw with a PNG format issued by the Chinese weather bureau, and comprises Chinese and eight major areas (northeast, north China, northwest, southwest, china, east China and south China), and a series of processing is needed before the PNG format data is used for researching a weather system of a certain area. The following examples will illustrate in detail the method of constructing a weather radar reflectivity jigsaw dataset of the present invention with radar jigsaw data issued by the weather agency of China.
In one embodiment, as shown in fig. 2, there is provided a method for constructing a weather radar reflectivity tile dataset, comprising the steps of:
S11, acquiring to-be-processed weather radar jigsaw data;
The weather radar jigsaw data to be processed can be in principle two-dimensional jigsaw obtained by carrying out jigsaw fusion on overlapping areas such as a nearest neighbor method, a linear interpolation method, a Barnes method and the like on a single weather radar network. In order to obtain a more representative data set which is convenient for subsequent research and use, the weather radar jigsaw data to be processed obtained in the embodiment selects a radar jigsaw of a large area on a two-dimensional jigsaw obtained by fusion of the national weather service as shown in fig. 3, and constructs a subsequent weather radar reflectivity jigsaw data set 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 marks comprise place name marks, boundary marks at all levels and river marks;
The weather radar jigsaw data to be processed has the basic reflectivity and place names of the legend, the national boundary, the provincial boundaries line, the county boundary, the river and the like shown in fig. 3, and partial jigsaw data has the condition that certain areas are covered by marks such as the place names, the national boundary, the provincial boundaries line, the county boundary, the river and the like to cause reflectivity loss, so that the using effect of the weather radar jigsaw data is affected. In order to facilitate the accurate and effective extraction of the scope of each annotation on the weather radar jigsaw data to be processed in the following, various legend reflectances corresponding to the weather radar jigsaw data to be processed and RGB corresponding to the various annotations need to be obtained in advance. Since the weather radar jigsaw data to be processed is PNG format, a reflectivity annotation RGB mapping table needs to be obtained by means of a query tool, as shown in fig. 4, the step S12 of querying the legend reflectivity RGB and annotation RGB of the weather radar jigsaw data to be processed to obtain the reflectivity annotation RGB mapping table includes:
S121, adopting ArcGIS software to respectively inquire 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;
the ArcGIS software is a complete GIS (Geographic Information system) platform product developed by Esri company set of 40 years of geographic information system consultation and research and development experience. In this embodiment, the query function of the Identify tool in the ArcGIS software is adopted, so that RGB corresponding to each reflectivity and RGB corresponding to each mark on the query legend are queried, and specifically, how to query to obtain the corresponding RGB by using the query function of the Identify tool is only needed, and the method is realized by referring to the prior art, and is not particularly limited herein.
S122, according to the legend reflectivity RGB and the annotation RGB, the reflectivity annotation RGB mapping table is established.
The reflectivity annotation RGB mapping table is a one-to-one mapping table established according to the corresponding relation between each reflectivity and RGB and between each annotation and RGB, such as: the reflectivity of 10dBz corresponds to RGB (1, 160, 246), the place name marks correspond to RGB (104, 104, 104) and the like, so that reflectivity conversion is conveniently carried out on RGB of each grid point of the weather radar jigsaw data to be processed, different marks are arranged on each mark, and the range of each mark is accurately distinguished and extracted later.
In the embodiment, the reflectivity mark RGB mapping table corresponding to the weather radar jigsaw data to be processed is conveniently and effectively obtained by adopting the query function of the Identify tool in the ArcGIS software, and effective guarantee is provided for extracting each mark based on the reflectivity mark RGB mapping table and interpolating and complementing each mark shielding data.
S13, performing reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed;
The weather radar reflectivity jigsaw data to be processed can be understood as performing RGB conversion on all grid points in the original weather radar jigsaw data to be processed, RGB corresponding to each reflectivity in a legend in a reflectivity annotation RGB mapping table can be directly converted into the reflectivity, RGB corresponding to each annotation needs to be respectively assigned with different annotation reflectivities according to specific annotation types, the annotation reflectivities are only used for representing different annotations and do not represent true reflectivity data, and the following steps are adopted to perform interpolation and complementation on reflectivity missing grid points corresponding to all annotations. Specifically, as shown in fig. 5, the step S13 of performing reflectivity conversion on RGB of all grid points of the weather radar reflectivity jigsaw data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed includes:
s131, obtaining RGB of all grid points of the weather radar jigsaw data to be processed by adopting matlab;
After the weather radar jigsaw data to be processed is determined, the RGB of each pixel in the picture is basically unchanged, and in principle, any method capable of acquiring RGB based on the picture can be adopted to obtain RGB of all grid points on the weather radar jigsaw data to be processed. In this embodiment, matlab programming is preferably adopted to read the tile data of the weather radar to be processed, so that three matrices identical to the tile data rows and columns of the weather radar to be processed are simply and conveniently obtained: r matrix, G matrix and B matrix, namely RGB of each lattice point, and performing the following conversion corresponding to the steps based on the obtained RGB of each lattice point to obtain the weather radar reflectivity jigsaw data to be processed as shown in fig. 6 (a and B).
S132, traversing all grid points of the weather radar jigsaw data to be processed, and judging whether RGB of each grid point exists in the reflectivity annotation RGB mapping table;
S133, if RGB of the grid point does not exist in the reflectivity marking RGB mapping table, the corresponding grid point is assigned to be a special reflectivity, otherwise, the corresponding grid point is assigned to be a corresponding reflectivity or marking reflectivity according to the reflectivity marking RGB mapping table; the annotation reflectivity comprises place name annotation reflectivity, boundary annotation reflectivity at all levels and river annotation reflectivity. After RGB of all grid points of the weather radar jigsaw data to be processed are obtained through the steps, a reflectivity mark RGB mapping table is needed to be used for corresponding reflectivity conversion, and in the actual RGB conversion process: if RGB of a grid point is equal to RGB of a corresponding color of a certain reflectivity (dBZ) in the reflectivity annotation RGB mapping table, assigning the grid point as the corresponding reflectivity (dBZ); if RGB of the grid point is equal to RGB of the place name annotation, assigning the grid point as place name annotation reflectivity; if RGB of the grid point is equal to RGB of each level boundary mark in the reflectivity mark RGB mapping table, the grid point is assigned to be the reflectivity of each level boundary mark; if RGB of the grid point is equal to RGB of the river mark in the reflectivity mark RGB mapping table, the grid point is assigned to be the reflectivity of the river mark; if RGB of the grid point is equal to RGB corresponding to each reflectivity on the legend and is not equal to RGB of each mark, the grid point is assigned to be a special reflectivity. It should be noted that, in this embodiment, only when RGB of a lattice point is equal to RGB of a color corresponding to a certain reflectivity (dBZ) in the reflectivity marking RGB mapping table, the assigned reflectivity (dBZ) of the lattice point is a truly available reflectivity, and other place name marking reflectivities, boundary marking reflectivities at different levels, river marking reflectivities and special reflectivities are not truly available reflectivities, where the assignment is only used for identification, so that the subsequent determination of a reflectivity missing lattice point requiring the interpolation of reflectivity by using an interpolation algorithm and the effective distinction of various reflectivity missing lattice points are facilitated, where the specific values of the place name marking reflectivities, the boundary marking reflectivities at different levels, river marking reflectivities and special reflectivities can be flexibly set according to the use requirements without affecting the specific implementation effects of this embodiment, such as setting the place name marking reflectivities to 1, the boundary marking reflectivities at different levels to 2, setting the river marking reflectivities to 3 and the special reflectivities to 0, and not being particularly limited.
According to the RGB mapping table of the reflectivity mark obtained in the previous step, all grid points of the original weather radar jigsaw data to be processed are subjected to RGB conversion to obtain the corresponding weather radar reflectivity jigsaw data to be processed, so that the extraction of normal grid points and reflectivity missing grid points of the reflectivity data is realized, the effective distinction of various reflectivity missing grid points is realized, further interpolation is completed for the subsequent reflectivity missing grid points, and necessary technical support is provided for interpolation optimization according to the type of the reflectivity missing grid points, and the accuracy and the continuity of the weather radar reflectivity jigsaw data obtained through processing are effectively ensured.
S14, searching for reflectivity missing lattice points according to the to-be-processed weather radar reflectivity jigsaw data, and interpolating and complementing the reflectivity missing lattice points to obtain the weather radar reflectivity jigsaw data;
The reflectivity missing lattice points are lattice points marked as place name annotation reflectivity, boundary annotation reflectivity of each level, river annotation reflectivity and special reflectivity when RGB conversion is performed in the steps, and in order to ensure continuity of reflectivity data in the weather radar reflectivity jigsaw data to be processed, the embodiment adopts Cressman interpolation algorithm to perform reflectivity interpolation on each reflectivity missing lattice point. As shown in fig. 7, the step S14 of searching for the missing lattice points of reflectivity according to the to-be-processed weather radar reflectivity jigsaw data and interpolating and complementing the missing lattice points of reflectivity to obtain the weather radar reflectivity jigsaw data includes:
S141, traversing all lattice points of the weather radar reflectivity jigsaw data to be processed, searching lattice points assigned as the annotation reflectivity, determining corresponding lattice points as the reflectivity missing lattice points, and storing the lattice point indexes corresponding to the reflectivity missing lattice points and the annotation reflectivity into a reflectivity missing array; the reflectivity missing lattice points comprise place name reflectivity missing lattice points, boundary reflectivity missing lattice points at all levels and river reflectivity missing lattice points;
The reflectivity missing array can be understood as an array which is specially used for storing reflectivity missing lattice point information on the to-be-processed weather radar reflectivity jigsaw data, and comprises lattice point indexes of the reflectivity missing lattice points and corresponding special reflectivity or marked reflectivity, so that the reflectivity missing lattice points can be subjected to reflectivity interpolation processing conveniently.
S142, traversing all lattice point indexes of the reflectivity missing array, adopting Cressman interpolation, setting a first influence radius, and calculating reflectivity interpolation corresponding to the reflectivity missing lattice points;
the Cressman interpolation algorithm, that is, determining the weight coefficient w of each adjacent point according to the distance between each adjacent point and the interpolation point, and calculating the value v (i,j) of the final interpolation point according to the weight coefficient of each adjacent point, wherein the specific formula is as follows:
Where v (i,j) is the value of the interpolation point and w n is the weight coefficient of the data point v n. Each data point in the influence radius R is multiplied by a respective weight coefficient, and the sum is accumulated and divided by the weight sum of all the data points to obtain the value of the interpolation point. The most important in 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, otherwise, the larger the weight coefficient w, and the formula is as follows:
Where R is the distance from the data point to the interpolation point and R is the radius of influence; since the influence radius R is selected to be too large, which affects the distance relation between each adjacent data point and the interpolation point, is selected to be too small, or causes the number of adjacent data points to be insufficient, R is usually a positive integer (such as 1,2,3,4, … …, 10).
To ensure reasonable and efficient influence radius selection, the present embodiment determines the influence radius used for each Cressman interpolation using the following method: and (3) selecting a plurality of influence radius values in advance, comparing interpolation effects of different influence radii under different interpolation conditions, namely checking whether obvious mark marks appear in an interpolation area under different influence radii, determining an influence radius R value with relatively smaller overall interpolation result error as an influence radius which is finally used, wherein the interpolation effects of different influence radii are shown in fig. 8 (a) and fig. b). The first influence radius used when interpolation is carried out on all grid points in the reflectivity missing array, and the second influence radius, the third influence radius, the fourth influence radius and the fifth influence radius which are selected by further interpolation optimization on different types of reflectivity missing grid points are determined according to the method.
S143, updating the to-be-processed weather radar reflectivity jigsaw data by adopting the reflectivity interpolation of the reflectivity missing lattice points to obtain the weather radar reflectivity jigsaw data.
After the reflectivity interpolation of each reflectivity missing lattice point is obtained by adopting the method, the reflectivity of the lattice 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 complemented is obtained. In theory, the weather radar reflectivity jigsaw data obtained in the step has no reflectivity loss, and the continuity of the reflectivity jigsaw data is ensured to a certain extent, but in order to further ensure the accuracy of the reflectivity jigsaw data, the weather radar reflectivity jigsaw data obtained in the step is further optimized by adopting the following steps.
S15, constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data.
The weather radar reflectivity jigsaw data is obtained by processing the weather radar reflectivity jigsaw data to be processed through the steps, and because the range of the area needing interpolation is larger in part and the range covered by different marks is different in size, the interpolation is carried out by adopting the uniform first influence radius, the partial reflectivity data is inaccurate, the using effect of the weather radar reflectivity jigsaw data set directly constructed and obtained is influenced, the influence radius is required to be further changed, and the coverage areas of the different marks are continuously interpolated and optimized, so that the continuity and the accuracy of the radar reflectivity jigsaw are ensured. As shown in fig. 9, the step S15 of constructing a weather radar reflectivity mosaic data set according to the weather radar reflectivity mosaic data includes:
s151, traversing lattice point indexes with the median value of the reflectivity missing array being the place name annotation reflectivity, adopting Cressman interpolation, setting a second influence radius, and carrying out 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 grid point indexes with the median value of the reflectivity missing array being the marked reflectivity of each level boundary, adopting Cressman interpolation, setting a third influence radius, and performing interpolation optimization on the grid points with the reflectivity missing of each level boundary of the first interpolation optimized weather radar reflectivity jigsaw data to obtain second interpolation optimized weather radar reflectivity jigsaw data;
s153, traversing lattice indexes with the median value of the reflectivity missing array being the river annotation reflectivity, adopting 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;
S154, traversing all lattice points of the third interpolation optimized weather radar reflectivity jigsaw data, searching lattice points with reflectivity values smaller than a preset reflectivity threshold value, adopting Cressman interpolation, setting a fifth influence radius, calculating the reflectivity interpolation corresponding to the lattice points, and updating the third interpolation optimized weather radar reflectivity jigsaw data to obtain the weather radar reflectivity jigsaw data set.
The magnitude relation among the first influence radius, the second influence radius, the third influence radius, the fourth influence radius and the fifth influence radius is proportional to the magnitude of the corresponding interpolation coverage area, namely, the larger the influence radius R value is, the more adjacent data points are, and if the interpolation area is larger, the larger R value is required to be selected to ensure enough adjacent data points. The coverage range of the region of each type of mark is sequentially from small to large: the place name annotation, the boundary line annotation at each level and the river annotation correspond to the following influence radiuses from small to large in sequence: the fifth influence radius, the second influence radius, the third influence radius and the fourth influence radius, wherein 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 to sequentially perform interpolation calculation according to the sequence from coverage area small mark to coverage area big mark.
According to the embodiment of the application, the weather radar reflectivity jigsaw data obtained by carrying out reflectivity interpolation on all reflectivity missing lattice points based on the set first influence radius is further provided with the proper influence radius according to the size of the annotation coverage area, continuous interpolation optimization is carried out on the coverage areas of different annotations, the built 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 shown in fig. 10 (a) and fig. 10 (b).
In addition, in order to make the constructed Doppler weather radar reflectivity jigsaw data set more convenient for researching the space-time variation 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 Cressman interpolation, the longitude and latitude of each grid are calculated by further utilizing 0.01 degree x 0.01 degree gridding, the longitude and latitude net of each weather radar reflectivity jigsaw data is 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 mosaic data set according to the weather radar reflectivity mosaic data further includes:
s16, gridding the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, establishing a corresponding longitude and latitude network, and updating the weather radar reflectivity jigsaw data set.
Specifically, the gridding specifically means gridding according to longitude and latitude standards by 0.01 degree x 0.01 degree, and determining longitude and latitude coordinates of each grid point, and specifically, as shown in fig. 12, the step S16 of gridding the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, establishing a corresponding longitude and latitude network, and updating the weather radar reflectivity jigsaw data set includes:
S161, selecting a plurality of control points of the weather radar reflectivity jigsaw data;
The control points are points with obvious characteristics, such as the north-most boundary points and the east-most boundary points in the provincial boundary line, or inflection points with small deformation of the provincial boundary line under any scale, and the number and the selection mode of the specific control points can be determined according to practical application requirements in principle. In order to ensure reasonable and effective setting of longitude and latitude coordinates of each grid point, the embodiment requires that the number of control points is more than 3, the selection of the control points is not concentrated, and the control points are distributed in the range of original weather radar jigsaw data as uniformly as possible.
S162, establishing a coordinate transformation function relation according to the pixel point positions and longitude and latitude space relations of all the control points;
After the control points are determined, longitude and latitude coordinates of each control point can be determined through the prior art, and corresponding coordinate transformation function relation is established according to pixel points of each control point in the weather radar jigsaw data and the longitude and latitude coordinates corresponding to the pixel points, wherein the coordinate transformation function relation is specifically expressed as follows:
Wherein 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 -1(·)、q-1 (. Cndot.) is the corresponding coordinate transformation function relation, respectively.
S163, calculating longitude and latitude coordinates of pixel points except the control points of the weather radar reflectivity jigsaw data by adopting the coordinate transformation function relation;
After the coordinate transformation function relation is obtained according to the steps, traversing all pixel points in the weather radar reflectivity jigsaw data, and reversely pushing out longitude and latitude coordinates of other pixel points by using the coordinate transformation function relation to establish a corresponding longitude and latitude network. If the change of a weather system in a small area needs to be studied, the reflectivity of each grid point in the study area can be extracted from the established longitude and latitude network according to the longitude and latitude range of the study area so as to facilitate the subsequent study.
S164, building a longitude and latitude network of the weather radar reflectivity jigsaw data according to longitude and latitude coordinates of all pixel points of the weather radar reflectivity jigsaw data, and updating the weather radar reflectivity jigsaw data set.
According to the embodiment, after a plurality of control points with obvious characteristics are selected from the weather radar reflectivity jigsaw data, longitude and latitude coordinates of each control point are obtained, a direct corresponding relation between pixel position coordinates and longitude and latitude coordinates of each control point on an original image is utilized, a coordinate transformation function relation is established, longitude and latitude coordinates of other pixel points are reversely deduced, and then 0.01 degree x 0.01 degree gridding is carried out on any one of the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, longitude and latitude networks of corresponding data are established, and finally, a Doppler weather radar reflectivity jigsaw data set with continuous and accurate reflectivity is built and is beneficial to researching space-time change of a weather system under a large scale.
Although the steps in the flowcharts described above are shown in order as indicated by arrows, these steps are not necessarily executed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders.
In one embodiment, as shown in FIG. 13, there is provided a weather radar reflectivity tile dataset construction system, the system comprising:
The acquisition module 1 is used for acquiring the 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 marks comprise place name marks, boundary marks at all levels and river marks;
the conversion module 3 is used for carrying out reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed;
the interpolation module 4 is used for searching for reflectivity missing lattice points according to the to-be-processed weather radar reflectivity jigsaw data, and carrying out interpolation complementation on the reflectivity missing lattice points to obtain 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 limitation regarding the weather radar reflectivity mosaic data set construction system, reference may be made to the limitation of the weather radar reflectivity mosaic data set construction method hereinabove, and no further description is given here. The modules in the weather radar reflectivity mosaic data set construction system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Fig. 14 shows an internal structural 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 device includes a processor, a memory, a network interface, a display, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile 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 the operating system and computer programs in the non-volatile storage media. 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 weather radar reflectivity tile dataset construction method. 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, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be 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 merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements may be implemented, and that a particular computing device may include more or less components than those shown in the middle, or may combine some of the 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 processor implementing the steps of the above method when the computer program is executed.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of the above method.
In summary, the method for constructing the weather radar reflectivity jigsaw data set provided by the embodiment of the invention, the system, the computer equipment and the storage medium realize that RGB of each reflectivity and RGB of each annotation on a legend of weather radar jigsaw data to be processed obtained by inquiring by ArcGIS software are sequentially interpolated and optimized according to a place name annotation, each level of boundary annotation and river annotation sequence, after a corresponding reflectivity annotation RGB mapping table is obtained, RGB of all grid points of the weather radar jigsaw data to be processed is subjected to reflectivity conversion according to the reflectivity annotation RGB mapping table to obtain weather radar reflectivity jigsaw data to be processed, and after reflectivity missing grid points are searched according to the weather radar reflectivity jigsaw data to be processed, the weather radar reflectivity jigsaw data is obtained by interpolating the reflectivity missing grid points by Cressman, the weather radar reflectivity jigsaw data is further sequentially interpolated and optimized according to a place name annotation, each level of boundary annotation and river annotation sequence, and a technical scheme of building the weather radar reflectivity jigsaw data set through a weft net is established by utilizing 0.01 degree x 0.01 degree 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 and further improves the accuracy of weather radar on sudden strong disaster weather forecast early warning, but also provides convenience for researching the space-time change of a weather system under a large scale.
In this specification, each embodiment is described in a progressive manner, and all the embodiments are directly the same or similar parts referring to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. It should be noted that, any combination of the technical features of the foregoing embodiments may be used, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few preferred embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the application. It should be noted that modifications and substitutions can be made by those skilled in the art without departing from the technical principles of the present application, and such modifications and substitutions should also be considered to be within the scope of the present application. Therefore, the protection scope of the patent of the application is subject to the protection scope of the claims.
Claims (9)
1. The method for constructing the weather radar reflectivity jigsaw data set is characterized by comprising the following steps of:
Acquiring the jigsaw data of the weather radar 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 marks comprise place name marks, boundary marks at all levels and river marks;
According to the reflectivity annotation RGB mapping table, performing reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed to obtain weather radar reflectivity jigsaw data to be processed; the grid point reflectivity in the weather radar reflectivity jigsaw data to be processed is one of special reflectivity, reflectivity and annotation reflectivity;
Searching for reflectivity missing lattice points according to the to-be-processed weather radar reflectivity jigsaw data, and interpolating and complementing the reflectivity missing lattice points to obtain weather radar reflectivity jigsaw data;
constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data;
The step of searching for the reflectivity missing lattice points according to the to-be-processed weather radar reflectivity jigsaw data and interpolating and complementing the reflectivity missing lattice points to obtain the weather radar reflectivity jigsaw data comprises the following steps:
traversing all lattice points of the weather radar reflectivity jigsaw data to be processed, searching the lattice points assigned as the annotation 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 annotation reflectivity into a reflectivity missing array; the reflectivity missing lattice points comprise place name reflectivity missing lattice points, boundary reflectivity missing lattice points at all levels and river reflectivity missing lattice points;
Traversing all grid point indexes of the reflectivity missing array, adopting Cressman interpolation, setting a first influence radius, and calculating reflectivity interpolation corresponding to the reflectivity missing grid points;
And updating the to-be-processed weather radar reflectivity jigsaw data by adopting the reflectivity interpolation of the reflectivity missing lattice points to obtain the weather radar reflectivity jigsaw data.
2. The method of claim 1, wherein the step of querying the legend reflectivity RGB and annotation RGB of the weather radar tile data to be processed to obtain a reflectivity annotation RGB mapping table comprises:
Adopting ArcGIS software to respectively inquire RGB corresponding to each reflectivity and RGB corresponding to each annotation on a legend of the weather radar jigsaw data to be processed, and obtaining the legend reflectivity RGB and the annotation RGB;
And establishing the reflectivity annotation RGB mapping table according to the legend reflectivity RGB and the annotation RGB.
3. The method for constructing a weather radar reflectivity mosaic data set according to claim 1, wherein the step of performing reflectivity conversion on RGB of all grid points of the weather radar reflectivity 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:
obtaining 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 jigsaw data to be processed, and judging whether RGB of each grid point exists in the reflectivity annotation RGB mapping table;
If RGB of the lattice point does not exist in the reflectivity marking RGB mapping table, the corresponding lattice point is assigned to be a special reflectivity, otherwise, the corresponding lattice point is assigned to be a corresponding reflectivity or marking reflectivity according to the reflectivity marking RGB mapping table; the annotation reflectivity comprises place name annotation reflectivity, boundary annotation reflectivity at all levels and river annotation reflectivity.
4. The method of constructing a weather radar reflectivity tile dataset of claim 1, wherein the step of constructing a weather radar reflectivity tile dataset from the weather radar reflectivity tile dataset comprises:
traversing the lattice index with the median value of the reflectivity missing array as the place name annotation reflectivity, adopting Cressman interpolation, setting a second influence radius, and carrying out interpolation optimization on the place name reflectivity missing lattice of the updated weather radar reflectivity jigsaw data to obtain first interpolation optimized weather radar reflectivity jigsaw data;
Traversing the grid point index with the median value of the reflectivity missing array being the marked reflectivity of each level boundary, adopting Cressman interpolation, setting a third influence radius, and performing interpolation optimization on the grid points with the reflectivity missing of each level boundary of the first interpolation optimized weather radar reflectivity jigsaw data to obtain second interpolation optimized weather radar reflectivity jigsaw data;
Traversing the grid point index with the median value of the reflectivity missing array being the river annotation reflectivity, adopting Cressman interpolation, setting a fourth influence radius, and carrying out interpolation optimization on the river reflectivity missing grid point of the second interpolation optimized weather radar reflectivity jigsaw data to obtain third interpolation optimized weather radar reflectivity jigsaw data;
traversing all lattice points of the third interpolation optimized weather radar reflectivity jigsaw data, searching lattice points with reflectivity value smaller than a preset reflectivity threshold value, adopting Cressman interpolation, setting a fifth influence radius, calculating the reflectivity interpolation corresponding to the lattice points, and updating the third interpolation optimized weather radar reflectivity jigsaw data to obtain the weather radar reflectivity jigsaw data set.
5. The method for constructing a weather radar reflectivity tile dataset of claim 1, wherein the step of constructing a weather radar reflectivity tile dataset from the weather radar reflectivity tile dataset further comprises:
and gridding the weather radar reflectivity jigsaw data in the weather radar reflectivity jigsaw data set, establishing a corresponding longitude and latitude network, and updating the weather radar reflectivity jigsaw data set.
6. The method of constructing a weather radar reflectivity tile dataset of claim 5, wherein the step of meshing the weather radar reflectivity tile data in the weather radar reflectivity tile dataset to create a corresponding longitude and latitude network, and updating the weather radar reflectivity tile dataset 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 space relation of each control point;
calculating longitude and latitude coordinates of pixel points except for a control point of the weather radar reflectivity jigsaw data by adopting the coordinate transformation function relation;
And establishing a longitude and latitude network of the weather radar reflectivity jigsaw data according to longitude and latitude coordinates of all pixel points of the weather radar reflectivity jigsaw data, and updating the weather radar reflectivity jigsaw data set.
7. A weather radar reflectivity tile dataset construction system, the 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 marks comprise place name marks, boundary marks at all levels and river marks;
The conversion module is used for carrying out reflectivity conversion on RGB of all grid points of the weather radar jigsaw data to be processed according to the reflectivity annotation RGB mapping table to obtain the weather radar reflectivity jigsaw data to be processed; the grid point reflectivity in the weather radar reflectivity jigsaw data to be processed is one of special reflectivity, reflectivity and annotation reflectivity;
The interpolation module is used for searching the reflectivity missing lattice points according to the to-be-processed weather radar reflectivity jigsaw data, and carrying out interpolation complementation on the reflectivity missing lattice points to obtain weather radar reflectivity jigsaw data;
The construction module is used for constructing a weather radar reflectivity jigsaw data set according to the weather radar reflectivity jigsaw data;
the method for obtaining the weather radar reflectivity jigsaw data comprises the steps of searching reflectivity missing lattice points according to the weather radar reflectivity jigsaw data to be processed, and interpolating and complementing the reflectivity missing lattice points to obtain the weather radar reflectivity jigsaw data, wherein the method comprises the following steps:
traversing all lattice points of the weather radar reflectivity jigsaw data to be processed, searching the lattice points assigned as the annotation 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 annotation reflectivity into a reflectivity missing array; the reflectivity missing lattice points comprise place name reflectivity missing lattice points, boundary reflectivity missing lattice points at all levels and river reflectivity missing lattice points;
Traversing all grid point indexes of the reflectivity missing array, adopting Cressman interpolation, setting a first influence radius, and calculating reflectivity interpolation corresponding to the reflectivity missing grid points;
And updating the to-be-processed weather radar reflectivity jigsaw data by adopting the reflectivity interpolation of the reflectivity missing lattice points to obtain the weather radar reflectivity jigsaw data.
8. 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 processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08240663A (en) * | 1995-03-02 | 1996-09-17 | Toshiba Corp | Rainfall monitoring system |
| US5742297A (en) * | 1994-11-04 | 1998-04-21 | Lockheed Martin Corporation | Apparatus and method for constructing a mosaic of data |
| JP2006030013A (en) * | 2004-07-16 | 2006-02-02 | Foundation Of River & Basin Integrated Communications Japan | Nationwide combined radar rainfall information providing system |
| WO2012036368A1 (en) * | 2010-09-13 | 2012-03-22 | 대한민국(기상청장) | System for monitoring rainfall and water level in real-time and monitoring method using same |
| CN105607062A (en) * | 2015-12-29 | 2016-05-25 | 南威软件股份有限公司 | Weather radar map analysis system |
| CN108680920A (en) * | 2018-04-28 | 2018-10-19 | 深圳市雅码科技有限公司 | A kind of hazard weather identification early warning system and method based on dual polarization radar |
| CN110766097A (en) * | 2019-11-04 | 2020-02-07 | 上海眼控科技股份有限公司 | Image processing method, image processing device, computer equipment and readable storage medium |
| CN111638497A (en) * | 2020-06-10 | 2020-09-08 | 上海眼控科技股份有限公司 | Radar data processing method, device, equipment and storage medium |
| CN112558022A (en) * | 2020-11-02 | 2021-03-26 | 广东工业大学 | Radar echo image processing method, system, device and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6862528B2 (en) * | 1999-04-27 | 2005-03-01 | Usengineering Solutions Corporation | Monitoring system and process for structural instabilities due to environmental processes |
-
2021
- 2021-08-05 CN CN202110894583.7A patent/CN113655483B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5742297A (en) * | 1994-11-04 | 1998-04-21 | Lockheed Martin Corporation | Apparatus and method for constructing a mosaic of data |
| JPH08240663A (en) * | 1995-03-02 | 1996-09-17 | Toshiba Corp | Rainfall monitoring system |
| JP2006030013A (en) * | 2004-07-16 | 2006-02-02 | Foundation Of River & Basin Integrated Communications Japan | Nationwide combined radar rainfall information providing system |
| WO2012036368A1 (en) * | 2010-09-13 | 2012-03-22 | 대한민국(기상청장) | System for monitoring rainfall and water level in real-time and monitoring method using same |
| CN105607062A (en) * | 2015-12-29 | 2016-05-25 | 南威软件股份有限公司 | Weather radar map analysis system |
| CN108680920A (en) * | 2018-04-28 | 2018-10-19 | 深圳市雅码科技有限公司 | A kind of hazard weather identification early warning system and method based on dual polarization radar |
| CN110766097A (en) * | 2019-11-04 | 2020-02-07 | 上海眼控科技股份有限公司 | Image processing method, image processing device, computer equipment and readable storage medium |
| CN111638497A (en) * | 2020-06-10 | 2020-09-08 | 上海眼控科技股份有限公司 | Radar data processing method, device, equipment and storage medium |
| CN112558022A (en) * | 2020-11-02 | 2021-03-26 | 广东工业大学 | Radar echo image processing method, system, device and storage medium |
Non-Patent Citations (5)
| Title |
|---|
| A Technology for Building a Seamless Mosaic Coverage of the Antarctica Coasts with Various-Time Images from Sentinel-1;Garkusha, I;《 2020 IEEE 40TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY》;20201028;第747-750页 * |
| Image Processing of Radar Mosaics for the Climatology of Convection Initiation in South China;Lanqiang Bai;《 Journal of Applied Meteorology and Climatology》;20200131;第59卷(第1期);第65-81页 * |
| 基于GIS的内蒙古中部地区多源雷达回波拼图;于雪涛;《科技导报》;20111031;第29卷(第28期);第37-42页 * |
| 基于GIS的雷达回波图系统的设计与实现;江峰;《中国优秀硕士学位论文全文数据库信息科技辑》;20160315;全文 * |
| 复杂地形下高分辨率降雨雷达网数据质量控制及雨量估测研究;李睿;《中国优秀博士学位论文全文数据库基础科学》;20210615;全文 * |
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