Multi-radar synthesis device and method

Abstract

The present invention relates to a multi-radar synthesis method in a multi-radar synthesis device, the method comprising the steps of: receiving multi-radar data to be synthesized; decoding the multi-radar data by using a library provided in advance; generating plane position indicator (PPI) data by gridding the decoded multi-radar data and aligning same with the grid space of a numerical forecast model; generating constant altitude plane position indicator (CAPPI) data in which the multi-radar data is synthesized by means of PPI data; and converting the CAPPI data into a file of a preset format that is the input form of the numerical forecast model. Therefore, an observation gap due to beam shielding can be replaced with data of other radar points, and a radar synthesis field in which multi-radar data are synthesized can be generated in order to generate, as input data of the numerical forecast model, the multi-radar data collected by radars located at different points.

Description

Multi-radar synthesis device and method

[0001] The present invention relates to a multi-radar synthesis device and method for synthesizing multi-radar data to generate multi-radar data as input data for a numerical forecast model.

[0002] Floods, which are classified as meteorological disasters among natural disasters, occur the most frequently around the world, and it has been confirmed that most of the natural disasters that occurred in Korea over the past 10 years were flood damage.

[0003] The majority of floods in Korea are caused by heavy rainfall, and heavy rainfall accounts for approximately 40% of economic losses associated with natural disasters on the Korean Peninsula. Therefore, the need for reliable forecasting systems to mitigate the impact of floods is increasing.

[0004] In particular, the Korean Peninsula experiences heavy rain during the summer, with heavy rain having a rainfall area of ​​20 to 30 km in radius and lasting from several tens of minutes to several hours.

[0005] These characteristics lead to significant regional variations in precipitation intensity and amount, making heavy rainfall predictions challenging. To improve the accuracy of heavy rainfall predictions, data assimilation is necessary. This involves assimilating observational data with high spatiotemporal resolution into numerical forecast models to enhance forecast accuracy.

[0006] Radar provides three-dimensional distribution, intensity, and movement speed of atmospheric precipitation and cloud particles with high spatiotemporal resolution, which can improve the accuracy of precipitation prediction when input into numerical models.

[0007] However, the Korean Peninsula is a region where over 70% of its area is mountainous, and mountainous terrain frequently causes beam obstruction. Therefore, the Korea Meteorological Administration (KMA) operates ten radars to observe the entire Korean Peninsula with high spatial and temporal resolution. Therefore, a method is needed to generate this multi-radar data as input data for a numerical weather forecast model.

[0008] The present invention has been devised to solve the above problems, and the purpose of the present invention is to provide a multi-radar synthesis device and method capable of replacing an observation gap caused by beam shielding with data from another radar point, and generating a radar synthesis field by synthesizing multiple radar data to generate multiple radar data collected from radars located at different points as input data for a numerical forecast model.

[0009] According to one embodiment of the present invention for achieving the above object, a multi-radar synthesis method is provided, in a multi-radar synthesis device that synthesizes multi-radar data to generate multi-radar data as input data of a numerical weather forecasting model, the method comprising: receiving multi-radar data as a target of synthesis; decoding the multi-radar data using a library prepared in advance; generating PPI (Plane Position Indicator) data by gridding the decoded multi-radar data to match the grid space of the numerical weather forecasting model; generating CAPPI (Constant Altitude Plane Position Indicator) data in which the multi-radar data is synthesized through the PPI data; and converting the CAPPI data into a file in a preset format which is an input format of the numerical weather forecasting model.

[0010] And in the above decoding step, reflectivity and radial velocity can be extracted from the multiple radar data.

[0011] In addition, the step of generating the PPI data may include the steps of generating at least one copy radar beam at a position spaced apart from the central radar beam by a certain angle based on the central radar beam included in the decoded multiple radar data; the step of copying an observation value corresponding to the central radar beam and applying the copy radar beam; the step of determining a representative value for each grid area of ​​a certain size in the multiple radar data including the copy radar beam to which the observation value is applied; and the step of converting the spherical coordinate system of the multiple radar data into an rectangular coordinate system to grid the multiple radar data according to the grid area, and using the determined representative value as a grid value.

[0012] And in the step of determining the representative value, the representative value is determined by weighting the observation values ​​located within the grid area according to the angle and distance separated from the center of the grid area, the error of each observation value is calculated using the standard deviation of the observation values ​​within the grid area, and the final error value can be determined by adding the error due to the radar equipment to the standard deviation.

[0013] In addition, in the step of generating the above CAPPI data, the PPI data matched to the grid space of the above numerical forecast model can be vertically interpolated using the inverse distance weighting method.

[0014] And in the step of converting into a file of the above-described pre-set format, the reflectivity and radial velocity are aligned according to the latitude and longitude of the horizontal grid and the altitude in each grid, and the final error value can be stored together with the observed value.

[0015] Meanwhile, according to an embodiment of the present invention for achieving the above object, a multi-radar synthesis device is provided, which synthesizes multi-radar data to generate multi-radar data as input data of a numerical weather forecasting model, and which comprises: a communication unit which receives multi-radar data as a target of synthesis; a decoding unit which decodes the multi-radar data using a library prepared in advance; a PPI generation unit which generates PPI (Plane Position Indicator) data by gridding the decoded multi-radar data to match the grid space of the numerical weather forecasting model; a CAPPI generation unit which generates CAPPI (Constant Altitude Plane Position Indicator) data in which the multi-radar data is synthesized through the PPI data; and a conversion unit which converts the CAPPI data into a file in a preset format which is an input format of the numerical weather forecasting model.

[0016] According to one aspect of the present invention described above, by providing a multi-radar synthesis device and method, it is possible to replace an observation gap caused by beam blocking with data from another radar point, and to generate a radar synthesis field by synthesizing multi-radar data in order to generate multi-radar data collected from radars located at different points as input data for a numerical forecast model.

[0017] FIG. 1 is a drawing for explaining the configuration of a multi-radar synthesis device according to one embodiment of the present invention;

[0018] Figure 2 is a diagram showing the observation points of a radar that generates multiple radar data.

[0019] FIG. 3 is a drawing for explaining a process of generating a copy radar beam in a multi-radar synthesis device according to one embodiment of the present invention;

[0020] Figure 4 is a diagram illustrating the terrain elevation of data used in the numerical forecast model.

[0021] FIG. 5 is a drawing for explaining the final result generated by converting into the input form of a numerical forecast model in a multi-radar synthesis device according to one embodiment of the present invention.

[0022] FIG. 6 is a flowchart for explaining a multi-radar synthesis method according to one embodiment of the present invention, and

[0023] FIGS. 7 to 10 are diagrams for explaining PPI data and CAPPI data generated through a multi-radar synthesis method according to one embodiment of the present invention.

[0024] The following detailed description of the present invention refers to the accompanying drawings, which illustrate specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention, while different from each other, are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the present invention. Furthermore, it should be understood that the positions or arrangements of individual components within each disclosed embodiment may be modified without departing from the spirit and scope of the present invention. Accordingly, the following detailed description is not intended to be limiting, and the scope of the present invention is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled, if properly described. Like reference numerals in the drawings designate the same or similar functionality throughout the several aspects.

[0025] The components according to the present invention are defined by functional distinctions rather than physical distinctions, and can be defined by the functions each component performs. Each component may be implemented as hardware or program code and processing units that perform each function, and the functions of two or more components may be implemented by including them in a single component. Therefore, the names given to the components in the following embodiments are not intended to physically distinguish each component, but rather to suggest the representative functions performed by each component, and it should be noted that the technical spirit of the present invention is not limited by the names of the components.

[0026] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

[0027] FIG. 1 is a drawing for explaining the configuration of a multi-radar synthesis device (100) according to one embodiment of the present invention.

[0028] The multi-radar synthesis device (100, hereinafter referred to as the device) according to the present embodiment can synthesize multi-radar data to create input data for a numerical model with high spatiotemporal resolution to improve the accuracy of precipitation prediction.

[0029] To this end, the device (100) may include a communication unit (110), a decoding unit (130), a PPI generation unit (150), a CAPPI generation unit (170), and a conversion unit (190). In addition, the device (100) may be installed and executed with software (application) for performing a multi-radar synthesis method, and the communication unit (110), the decoding unit (130), the PPI generation unit (150), the CAPPI generation unit (170), and the conversion unit (190) may be controlled by the software (application) for performing the multi-radar synthesis method.

[0030] At this time, the device (100) may be a separate terminal or a module of the terminal. In addition, the configuration of the communication unit (110), decoding unit (130), PPI generation unit (150), CAPPI generation unit (170), and conversion unit (190) may be formed as an integrated module or may be formed of one or more modules. However, conversely, each configuration may be formed as a separate module.

[0031] In addition, the device (100) may be mobile or fixed. This device (100) may be in the form of a server or an engine, and may be called by other terms such as a device, an apparatus, a terminal, a UE (user equipment), an MS (mobile station), a wireless device, a handheld device, etc. In addition, the device (100) may execute or produce various software based on an operating system (OS), that is, a system. Here, the operating system is a system program for enabling software to use the hardware of the device, and may include all mobile computer operating systems such as Android OS, iOS, Windows Mobile OS, Bada OS, Symbian OS, and Blackberry OS, as well as computer operating systems such as Windows series, Linux series, Unix series, MAC, AIX, and HP-UX.

[0032] And although not shown in the drawing, the device (100) may further include a storage unit in which a program for performing a multi-radar synthesis method is recorded.

[0033] In addition, the data processed by the communication unit (110), decoding unit (130), PPI generation unit (150), CAPPI generation unit (170), and conversion unit (190) is temporarily or permanently stored, and may include a volatile storage medium or a non-volatile storage medium, but the scope of the present invention is not limited thereto.

[0034] And the storage unit can store data accumulated while performing a multi-radar synthesis method.

[0035] Additionally, the device (100) can receive and input variables required for generating a radar synthesis field in advance.

[0036] Specifically, variables required for the radar synthesis field can be input into a program stored in the storage unit using a name list. Input using this name list can be received by the communication unit (110) through a separate user terminal, or transmitted to the communication unit (110) through a separate input device provided in the device (100).

[0037] When using Fortran namelists, users can use the program efficiently according to their intentions, as they only need to modify the variables in the namelist file without having to directly modify and compile the program code when they need to change an option.

[0038] This namelist consists of a section for entering radar information and domain information.

[0039]

[0040] *The section for entering radar information may include the number of radars to be input, the path where the radar data is located, the name of each radar file, whether quality inspection has been performed, the path to the NetCDF (Network Common Data Form) file containing information about the model grid, information about the variables required in the NetCDF file, the path and name for saving the preprocessed data, the output format of the data, grid information such as the number of grids and resolution of the model, the coordinate transformation method, the reflectivity and radial velocity to be output, and information about the observation error. The data processing process can be controlled by these variables.

[0041] Meanwhile, the communication unit (110) according to the present embodiment can receive multiple radar data that is the target of synthesis.

[0042] The multi-radar data received by the communication unit (110) of this embodiment may be radar data that has undergone quality control in the UF (Universal Format) format provided by the Korea Meteorological Administration API (Application Programming Interface) data center (heep: / apihub.kma.go.kr / ).

[0043] Figure 2 is a diagram illustrating the observation points of a radar generating multiple radar data sets. To generate radar data, the radar emits radio waves into the atmosphere and observes the intensity, speed, and direction of precipitation signals that bounce off atmospheric water bodies such as rain, snow, and hail.

[0044] The Korea Meteorological Administration operates 10 radars with different observation points, as shown in Figure 2, and provides quality-controlled observation data in real time at intervals of approximately 5 minutes.

[0045] Therefore, the communication unit (110) according to the present embodiment can receive such quality-controlled observation data as multi-radar data that is the target of synthesis.

[0046] Meanwhile, the decoding unit (130) can decode multiple radar data using a pre-prepared library.

[0047] Typically, S-band radar data is provided as a UF format file, and a separate library is required to process the file. Accordingly, the decoding unit (130) according to the present embodiment can decode multiple radar data using the RSL library.

[0048] Accordingly, the decoding unit (130) according to the present embodiment can read a UF file using the RSL library and then extract reflectivity and radial velocity from multiple radar data.

[0049] Meanwhile, the PPI generation unit (150) can generate PPI (Plane Position Indicator) data by gridding the decoded multi-radar data and matching it with the grid space of the numerical forecast model.

[0050] The PPI generation unit (150) can generate a copy radar beam based on the central radar beam included in the decoded multi-radar data.

[0051] FIG. 3 is a drawing for explaining a process of generating a radiation radar beam in a PPI generation unit (150) according to one embodiment of the present invention.

[0052] As illustrated in FIG. 3, the PPI generation unit (150) can generate at least one copy radar beam at a location spaced apart by a certain angle from the central radar beam indicated by a bold arrow.

[0053] The radar beam does not propagate only in the central direction (bold arrow) shown in Figure 3, but also at a certain angle, i.e., the beam width. It will spread that much.

[0054] And then, since the radar antenna averages the waves emitted and returned while rotating to determine the observation value, this must also be taken into account when gridding.

[0055] Accordingly, the PPI generation unit (150) according to this embodiment has a beam width of half on the left and right of the central radar beam (thick arrow). At least one copy radar beam can be generated at a location spaced apart from each other. At this time, the PPI generation unit (150) can generate three copy radar beams on each side of the central radar beam (thick arrow), thereby generating a total of seven data, including the central radar beam (thick arrow).

[0056] And the PPI generation unit (150) can copy the observation value corresponding to the central radar beam (thick arrow) and apply it to the copied radar beam. The data value copied from the central radar beam (thick arrow), i.e., the location of the observation value, is indicated by a black dot on each arrow in Fig. 4.

[0057] In addition, the PPI generation unit (150) according to the present embodiment can determine a representative value for each grid area (G) of a certain size from multiple radar data including a radiation radar beam to which an observation value is applied.

[0058] To this end, the PPI generation unit (150) can determine a representative value by weighting the observation values ​​located within the grid area (G) according to the angle and distance from the center point (C) of the grid area (G).

[0059] Specifically, in order to convert radar data in a spherical coordinate system into an orthogonal coordinate system and determine the representative value within the grid area (G), the observation values ​​within the grid area (G) must be converted into the angle ( ) and distance ( ) should be weighted averaged.

[0060] To this end, the PPI generation unit (150) according to the present embodiment assumes a beam shape of a normal distribution in the beam (Ray) direction, and the distance in the gate (gate) direction can be weighted using the Cressman method.

[0061] Among the observations within the grid area (G), the observation value that is farthest from the center point (C) of the grid area (G) is the value of the boundary of the vertex of the grid area (G), so the distance from this value, i.e. the grid size Radius of influence of the ship Cressman method It can be placed as .

[0062] Accordingly, the PPI generation unit (150) determines that the final weight value is the weight in the ray direction. and the weight of the gate direction It can be done by multiplying.

[0063] And the PPI generation unit (150) generates each observation value ( within the grid area (G) as in the following mathematical expression 1) ) and weights ( ) and averaged to obtain the average value within the grid area (G) ( ) can be determined. Therefore, the closer the value is to the center point (C) of the grid area (G), the more it is weighted and has a greater influence on the representative value of the grid area (G).

[0064] [Mathematical Formula 1]

[0065]

[0066]

[0067]

[0068] And the PPI generation unit (150) converts the spherical coordinate system of the multi-radar data into an orthogonal coordinate system to grid the multi-radar data according to the grid area (G), and can use the determined representative value as the grid value.

[0069] In addition, the PPI generation unit (150) can calculate the error of each observation value by using the standard deviation of the observation values ​​within the grid area (G), and determine the final error value by adding the error due to the radar equipment to the standard deviation.

[0070] That is, when the PPI generation unit (150) averages and grids the PPI data, it can calculate the error value of each observation value by using the standard deviation of the observation values ​​within the grid area (G).

[0071] This is because if the distribution of observation data within the grid area (G) is wide, the reliability of the grid representative value is considered low. In other words, the error must be calculated based on the area where the radar beams overlap. Therefore, the PPI generation unit (150) calculates the standard deviation of the observation values ​​within the grid area according to the following mathematical expression 2. ) errors due to radar equipment ( ) can be added to determine the final error value.

[0072] [Equation 2]

[0073]

[0074]

[0075] Meanwhile, the CAPPI generation unit (170) can generate CAPPI (Constant Altitude Plane Position Indicator) data synthesized from multiple radar data using PPI data.

[0076] This CAPPI generation unit (170) can vertically interpolate gridded PPI data of multiple points that are aligned with the grid space of the numerical forecast model generated by the PPI generation unit (150) using an inverse distance weighting method.

[0077] The conversion unit (190) can convert CAPPI data into a file in a pre-set format, which is an input format for a numerical forecast model.

[0078] Specifically, the background field, which is the initial estimate, contains errors. If these errors are not corrected, the errors in the predicted field will be amplified during the integration process. Therefore, data assimilation, which inputs observation data collected during the preprocessing process into the model to correct the model's prediction errors, is essential.

[0079] Accordingly, the WRFDA (WRF Data Assimilation) system, a data assimilation program, was designed to read observation data in ASCII format or BUFR (Binary Universal Form for the Representation of meteorological data) format.

[0080] Figure 4 is a diagram illustrating the topographic elevation of data used in a numerical forecast model.

[0081] In order for the effect of data assimilation to be spatially uniform during radar data assimilation, the input data must be uniformly distributed. Therefore, a radar synthetic field with the same grid as the model's grid is required. For this purpose, the device (100) used geo-em data as illustrated in FIG. 4, and the geo-em data is terrain data of the Weather Research and Forecasting (WRF) model, which includes the model's grid and terrain elevation information. The WEF used in the device (100) according to the present embodiment is a community model developed by the National Center for Atmospheric Research (NCAR), and is used in various meteorological fields such as weather forecasting and phenomenon analysis.

[0082] Accordingly, the conversion unit (190) can convert CAPPI data into an ASCII file named ob.radar.

[0083] These ASCII files may contain metadata such as the number of input radars, the name of the radar, the observation latitude and longitude, the altitude, and the number of vertical layers of data.

[0084] And the conversion unit (190) can convert the CAPPI data so that the reflectivity and radial velocity produced by the decoding unit (130) are aligned according to the latitude and longitude of the horizontal grid and the altitude in each grid, and the final error value produced by the PPI generation unit (150) is stored together with the observed value.

[0085] Meanwhile, FIG. 5 is a drawing for explaining the final result generated by converting into the input form of a numerical forecast model in the device (100) according to the present embodiment.

[0086] And the WFRDA used in the conversion unit (190) must convert all input data into the Little-R format. The Little-R format is an ASCII-based observation file format and can be composed of a header and data as shown in Fig. 5.

[0087] The header may include information about the type of observation, date, number of observations, and location.

[0088] Data is entered in the order of altitude, radial velocity, and reflectivity, and can include variable values, QC status, and variable errors for each variable.

[0089] Therefore, the device (100) according to the present embodiment can synthesize PPI data from multiple points into CAPPI data with the same grid size as the numerical model, and then convert the data into an ASCII format file, which is a data assimilation input format. The multi-radar composite field generated by the device (100) according to the present embodiment can replace the observation gap caused by beam shielding with data from other radar points.

[0090]

[0091] Meanwhile, FIG. 6 is a flowchart for explaining a multi-radar synthesis method according to an embodiment of the present invention. Since the multi-radar synthesis method according to an embodiment of the present invention is performed on a configuration substantially identical to that of the multi-radar synthesis device (100) illustrated in FIG. 1, the same components as those of the multi-radar synthesis device (100) illustrated in FIG. 1 are given the same drawing reference numerals, and repeated descriptions thereof will be omitted.

[0092] A multi-radar synthesis method according to the present embodiment is provided to generate multi-radar data as input data for a numerical forecast model, and for this purpose, includes a step of receiving multi-radar data (S110), a step of decoding multi-radar data (S130), a step of generating PPI data (S150), a step of generating CAPPI data (S170), and a step of converting CAPPI data (S190).

[0093] In the step of receiving multiple radar data (S110), the device (100) can receive multiple radar data that is the target of synthesis from the Korea Meteorological Administration.

[0094]

[0095] *In the step of decoding multiple radar data (S130), the device (100) can decode multiple radar data using a pre-prepared library.

[0096] In the step (S130) of decoding such multi-radar data, the device (100) can extract reflectivity and radial velocity from the multi-radar data.

[0097] In the step of generating PPI data (S150), the device (100) can generate PPI data that matches the grid space of the numerical forecast model by gridding the decoded multi-radar data.

[0098] In the step of generating such PPI data, the device (100) can generate at least one copy radar beam at a position spaced apart from the central radar beam by a certain angle based on the central radar beam included in the decoded multiple radar data.

[0099] And in the step of generating PPI data, the device (100) can copy the observation value corresponding to the central radar beam and apply it to the copy radar beam.

[0100] Additionally, in the step of generating PPI data, the device (100) can determine a representative value for each grid area of ​​a certain size from multiple radar data including a radiation radar beam to which observation values ​​are applied.

[0101] In the step of determining the representative value, the device (100) can determine the representative value by weighting the observation values ​​located within the grid area according to the angle and distance separated from the center of the grid area.

[0102] And in the step of determining the representative value, the device (100) can calculate the error of each observation value using the standard deviation of the observation values ​​within the grid area, and add the error due to the radar equipment to the standard deviation to determine the final error value.

[0103] In the step of generating PPI data, the device (100) converts the spherical coordinate system of the multi-radar data into an orthogonal coordinate system to grid the multi-radar data according to the grid area, and the determined representative value can be used as the grid value.

[0104] Meanwhile, in the step of generating CAPPI data (S170), the device (100) can generate CAPPI data synthesized from multiple radar data using PPI data.

[0105] Specifically, in the step (S170) of generating CAPPI data, the device (100) can vertically interpolate PPI data that matches the grid space of the numerical forecast model using an inverse distance weighting method to generate CAPPI data.

[0106] In the step of converting CAPPI data (S190), the device (100) can convert the CAPPI data into a file in a preset format, which is an input format for a numerical forecast model.

[0107] And in the step (S190) of converting CAPPI data, the device (100) can arrange the reflectivity and radial velocity according to the latitude and longitude of the horizontal grid and the altitude in each grid, and store the final error value together with the observed value.

[0108] FIGS. 7 to 10 are drawings for explaining PPI data and CAPPI data generated through a multi-radar synthesis method according to one embodiment of the present invention, and are PPI reflectivity data and CAPPI reflectivity data at 2200 UTC on August 10, 2021.

[0109] More specifically, Fig. 7 is a diagram showing the lowest elevation angle of the gridded PPI data at Gosan (SGN), Fig. 8 is a diagram showing the lowest elevation angle of the gridded PPI data at Seongsan (SSP), and Fig. 9 is a diagram showing the lowest elevation angle of the Jindo (JNI) points, and Fig. 10 is a diagram showing the composite CAPPI reflectivity field data at an altitude of 2.0 km.

[0110] As shown in Figures 7 to 9, beam blocking occurred behind Mt. Halla based on each radar point, resulting in an observation gap.

[0111] However, as shown in Fig. 10, in the CAPPI composite field generated through the multi-radar synthesis method according to the present embodiment, it can be seen that the data from each point fills in the observation gaps of other regions, and three-dimensional data over Jeju Island is formed.

[0112] Therefore, when a radar synthesis field is generated using a multi-radar synthesis method according to the present embodiment, an observation gap caused by beam shielding can be replaced with data from other radar points.

[0113] The multi-radar synthesis method of the present invention can be implemented in the form of program commands that can be executed by various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium can include program commands, data files, data structures, etc., either singly or in combination.

[0114] The program commands recorded on the above computer-readable recording medium may be specially designed and configured for the present invention or may be known and available to those skilled in the art of computer software.

[0115] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, and flash memory.

[0116] Examples of program instructions include not only machine language codes, such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

[0117] Although various embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and various modifications can be made by those skilled in the art without departing from the gist of the present invention as claimed in the claims. Furthermore, such modifications should not be understood individually from the technical idea or prospect of the present invention.

[0118] [Explanation of symbols]

[0119] 100: Multi-radar synthesis device 110: Communication unit

[0120] 130: Decoding unit 150: PPI generation unit

[0121] 170: CAPPI generation unit 190: Conversion unit

Claims

1. A multi-radar synthesis method in a multi-radar synthesis device that synthesizes the multi-radar data to generate multi-radar data as input data for a numerical forecast model. A step of receiving multiple radar data that is the target of synthesis; A step of decoding the above multi-radar data using a pre-prepared library; A step of generating PPI (Plane Position Indicator) data by gridding the above-decoded multi-radar data and matching it with the grid space of the numerical forecast model; A step of generating CAPPI (Constant Altitude Plane Position Indicator) data by synthesizing the multi-radar data using the above PPI data; and A multi-radar synthesis method, comprising a step of converting the above CAPPI data into a file in a preset format, which is an input format of the above numerical forecast model.

2. In paragraph 1, In the above decoding step, A multi-radar synthesis method for extracting reflectivity and radial velocity from the above multi-radar data.

3. In paragraph 2, The steps for generating the above PPI data are: A step of generating at least one copy radar beam at a position spaced apart from the central radar beam by a certain angle based on the central radar beam included in the decoded multi-radar data; A step of copying an observation value corresponding to the central radar beam and applying it to the copy radar beam; A step of determining a representative value for each grid area of ​​a certain size from multiple radar data including a radiation radar beam to which the above observation value is applied; and A multi-radar synthesis method, comprising the steps of converting the spherical coordinate system of the multi-radar data into an orthogonal coordinate system, gridding the multi-radar data according to the grid area, and using the determined representative value as a grid value.

4. In paragraph 3, In the step of determining the above representative value, The representative value is determined by weighting the observation values ​​located within the grid area according to the angle and distance from the center of the grid area, A multi-radar synthesis method that calculates the error of each observation value by using the standard deviation of the observation values ​​within the above grid area, and determines the final error value by adding the error due to radar equipment to the standard deviation.

5. In paragraph 4, In the step of generating the above CAPPI data, A multi-radar synthesis method that vertically interpolates PPI data that matches the grid space of the above numerical forecast model using an inverse distance weighting method.

6. In paragraph 5, In the step of converting to a file in the format set above, A multi-radar synthesis method, wherein the above reflectivity and radial velocity are sorted according to the latitude and longitude of the horizontal grid and the altitude in each grid, and the final error value is stored together with the observation value.

7. A multi-radar synthesis device that synthesizes the multi-radar data to create input data for a numerical forecast model. A communication unit that receives multiple radar data that is the target of synthesis; A decoding unit that decodes the above multi-radar data using a pre-prepared library; A PPI generation unit that generates PPI (Plane Position Indicator) data by gridding the above-decoded multi-radar data and matching it with the grid space of the numerical forecast model; A CAPPI generation unit that generates CAPPI (Constant Altitude Plane Position Indicator) data synthesized from the above multi-radar data using the above PPI data; and A multi-radar synthesis device including a conversion unit that converts the above CAPPI data into a file in a preset format, which is an input format of the above numerical forecast model.

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