Numerical model-based system for validating the effective range of artificial rainfall and method thereof

Abstract

An artificial rainfall effective range verification device using a numerical model comprising a processor that performs operations for at least one program of the present invention includes: an experimental data collection unit that collects basic data including seeding log data, weather data, and ion concentration data, and creates an integrated data set by aligning the collected basic data based on spatiotemporal criteria; a rainfall increase calculation unit that calculates a first rainfall amount under seeding performance conditions reflecting the seeding log data and the weather data using a numerical weather model, and a second rainfall amount under conditions where seeding is not performed based on the weather data, and generates rainfall increase data based on the difference between the first rainfall amount and the second rainfall amount; a sample analysis unit that analyzes the characteristics of ion concentration change according to time and space based on the ion concentration data to generate ion concentration change data related to the seeding material; a spatiotemporal alignment analysis unit that generates alignment data representing the temporal and spatial correspondence between the rainfall increase data and the ion concentration change data; and a seeding impact evaluation unit that calculates the artificial rainfall impact amount based on the alignment data and generates impact data.

Description

Technology Field

[0001] The present invention relates to an apparatus and method for verifying the effective range of artificial rainfall using a numerical model. Background Technology

[0002] With the recent increase in the frequency of droughts and the deepening imbalance in precipitation patterns due to climate change, various weather modification technologies for securing water resources are attracting attention, and artificial rainfall technology is being utilized as a practical alternative capable of inducing precipitation within a relatively short period. Artificial rainfall is achieved by spraying seeding substances, such as sodium chloride (NaCl), calcium chloride (CaCl2), and silver iodide (AgI), into the atmosphere, in which these seeding substances act as condensation nuclei or ice nuclei within the clouds to promote the growth of cloud particles.

[0003] Artificial rainfall technology is implemented by spraying seeding materials using aircraft, drones, rockets, or ground-launched systems, and its effectiveness can vary significantly depending on the seeding location, spraying altitude, the type and amount of input material, and meteorological conditions. Accordingly, during the design phase of artificial rainfall experiments, it is necessary to utilize numerical weather models to predict the potential for precipitation induction based on seeding conditions in advance. Furthermore, after the experiment, a process is required to verify the effectiveness by analyzing whether the seeding actually contributed to an increase in precipitation.

[0004] Conventional techniques for analyzing the effects of artificial rainfall primarily rely on numerical models to calculate differences in precipitation based on whether seeding is performed, while in some cases, methods comparing changes in precipitation distribution using weather radar observation data are employed. Although these methods can indirectly estimate whether precipitation increases under specific conditions, they have limitations in clearly verifying whether actual seeding material moved through the atmosphere and directly contributed to precipitation formation in a specific region.

[0005] In particular, since atmospheric precipitation formation is a nonlinear process involving the complex interaction of various meteorological factors, there is a problem in distinguishing whether the precipitation increase calculated by numerical models is due to actual seeding effects or natural atmospheric variability. Even in the case of radar-based analysis, limitations exist in directly verifying the influence of the seeding material itself, as it is limited to indirectly observing the size and distribution of precipitation particles.

[0006] Therefore, a new verification method is required that combines numerical model-based prediction results with the chemical composition analysis results of precipitation samples and can quantitatively verify the effects of artificial rainfall. The problem to be solved

[0007] The technical problem to be solved by the present invention is to provide an apparatus and method for verifying the effective range of artificial rainfall using a numerical model, which aligns rainfall increase data calculated through a numerical meteorological model with ion concentration change data of precipitation samples collected at actual ground observation points at the same spatiotemporal standard, and visualizes and provides the artificial rainfall influence and effective range based on the correspondence between the two phenomena. means of solving the problem

[0008] An artificial rainfall effective range verification device using a numerical model, comprising a processor that performs operations for at least one program according to an embodiment of the present invention, comprises: an experimental data collection unit that collects basic data including seeding log data, weather data, and ion concentration data, and creates an integrated data set by aligning the collected basic data based on spatiotemporal criteria; a rainfall increase calculation unit that calculates a first rainfall amount under seeding performance conditions reflecting the seeding log data and the weather data using a numerical weather model, and a second rainfall amount under conditions where seeding is not performed based on the weather data, and generates rainfall increase data based on the difference between the first rainfall amount and the second rainfall amount; a sample analysis unit that analyzes the characteristics of ion concentration change according to time and space based on the ion concentration data to generate ion concentration change data related to the seeding material; a spatiotemporal alignment analysis unit that generates alignment data representing the temporal and spatial correspondence between the rainfall increase data and the ion concentration change data; and a seeding impact evaluation unit that calculates the artificial rainfall impact amount based on the alignment data and generates impact data.

[0009] According to an embodiment, the experimental data collection unit may sort the seeding log data, the meteorological data, and the ion concentration data based on a time index and spatial coordinates, and combine the data corresponding to each time index and spatial coordinate into a single record to generate the integrated data set.

[0010] According to an embodiment, the rainfall amount calculation unit calculates the first rainfall amount and the second rainfall amount by setting the same initial conditions and boundary conditions for the seeding performance condition and the condition in which seeding is not performed, calculates the rainfall amount by calculating the difference between the first rainfall amount and the second rainfall amount for each time index and spatial grid index, and generates the rainfall amount data in the form of a multidimensional array by mapping the calculated rainfall amount based on the time index and spatial grid index.

[0011] According to an embodiment, the sample analysis unit may sort the ion concentration data for the tracking ion corresponding to the seeding material among the ion concentration data based on a time index and an observation point, and generate the ion concentration change data by calculating the amount of change in the ion concentration of the tracking ion over time at the same observation point.

[0012] According to an embodiment, the spatiotemporal alignment analysis unit determines the grid in which each observation point is located by corresponding the coordinates of each observation point to the spatial grid system of the numerical weather model based on observation point information included in the ion concentration change data, calculates the time difference between the time when the ion concentration change occurs at the observation point and the time when the rainfall amount in the determined grid increases by more than a preset reference value, and if the calculated time difference is within a preset time range, determines the temporal and spatial correspondence relationship between the ion concentration change and the rainfall increase to generate the alignment data.

[0013] According to an embodiment, the seeding impact evaluation unit may use the matching data to assign weights to at least one of an ion concentration-based impact based on the amount of change in ion concentration, a precipitation-based impact based on the magnitude of rainfall increase, a time-based impact corresponding to the time difference, and a space-based impact calculated based on the seeding location and wind direction, and calculate the artificial rainfall impact using a weighted sum method.

[0014] According to an embodiment, the seeding impact evaluation unit may select a grid in which the artificial rainfall impact is greater than or equal to a preset threshold as an impact area candidate, and generate impact data including data for the impact area candidate.

[0015] According to an embodiment, the processor may further include an effective range calculation unit that calculates an effective range representing the spatial range affected by artificial rainfall based on the influence data.

[0016] A method for verifying the effective range of artificial rainfall using a numerical model implemented by a seeding analysis device according to another embodiment of the present invention comprises: collecting basic data including seeding log data, weather data, and ion concentration data, and aligning the collected basic data based on spatiotemporal criteria to generate an integrated data set; calculating a first precipitation amount under seeding performance conditions reflecting the seeding log data and the weather data using a numerical weather model, and a second precipitation amount under conditions where seeding is not performed based on the weather data; generating increased rainfall data based on the difference between the first rainfall amount and the second rainfall amount; analyzing the characteristics of ion concentration change according to time and space based on the ion concentration data to generate ion concentration change data related to the seeding material; generating matching data representing the temporal and spatial correspondence between the increased rainfall data and the ion concentration change data; generating influence data by calculating the artificial rainfall influence based on the matching data; and calculating the effective range of artificial rainfall based on the influence data.

[0017] A computer-readable storage medium storing at least one program according to another embodiment of the present invention may include a program configured to be executed by at least one processor of an electronic device and including instructions for performing a method for verifying the effective range of artificial rainfall using a numerical model. Effects of the invention

[0018] According to the apparatus and method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention, rainfall increase data calculated through a numerical meteorological model and ion concentration change data of precipitation samples collected at actual ground observation points are aligned at the same spatiotemporal standard, and the artificial rainfall influence and effective range can be visualized and provided based on the correspondence between the two phenomena.

[0019] Furthermore, according to the apparatus and method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention, the limitations of existing indirect verification methods caused by the uncertainty inherent in the numerical model itself and the limitations of observational data can be overcome. That is, by linking the results of ionic component analysis, which directly confirms the chemical characteristics of seeding materials contained in actual precipitation, with the results of the numerical model, the spatial range in which the artificial rainfall enhancement effect is effectively manifested can be verified and calculated with high precision.

[0020] In addition, according to the device and method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention, for example, by determining whether an observation point corresponds to the upwind or downwind side based on a seeding location and dynamically reflecting the time delay phenomenon due to physical diffusion to align causal relationships, a reliable technical basis can be provided for objectively calculating the amount of water resources in artificial rainfall experiments. Brief explanation of the drawing

[0021] FIG. 1 is a schematic block diagram of an artificial rainfall effective range verification system using a numerical model according to an embodiment of the present invention. FIG. 2 is a schematic block diagram of a seeding analysis device according to an embodiment of the present invention. FIG. 3 is a schematic block diagram of a processor according to an embodiment of the present invention. FIG. 4 is a flowchart illustrating a method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention. FIG. 5 is a conceptual diagram illustrating a method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention. FIGS. 6a and 6b are drawings illustrating a method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention. Specific details for implementing the invention

[0022] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but can be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the present invention, and the present invention is defined only by the scope of the claims.

[0023] The terms used in this specification are for describing embodiments and are not intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. The terms "comprises" and / or "comprising" as used in this specification do not exclude the presence or addition of one or more other components in addition to the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and "and / or" includes each of the mentioned components and all combinations of one or more. Although terms such as "first," "second," etc., are used to describe various components, these components are not limited by these terms. These terms are used merely to distinguish one component from another. Therefore, the first component mentioned below may be the second component within the technical scope of the invention.

[0024] Unless otherwise defined, all terms used herein (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0025] The terms “part” or “module” as used in the specification refer to software or hardware components, such as FPGAs or ASICs, and “part” or “module” perform certain roles. However, “part” or “module” is not limited to software or hardware. “Part” or “module” may be configured to reside in an addressable storage medium or configured to run on one or more processors. Thus, by example, “part” or “module” includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules,” or further separated into additional components and “parts” or “modules.”

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

[0027] FIG. 1 is a schematic block diagram of an artificial rainfall effective range verification system using a numerical model according to an embodiment of the present invention.

[0028] Referring to FIG. 1, the artificial rainfall effective range verification system (10) using a numerical model according to an embodiment of the present invention can link weather observation data and ion analysis data of precipitation samples with the results of the rainfall amount calculation of the numerical model and provide a service to quantitatively evaluate the spatial range in which the actual seeding effect has occurred.

[0029] In particular, the artificial rainfall effective range verification system (10) using a numerical model can determine the time delay between the point of increase in ion concentration and the point of increase in rainfall amount, or the downwind spreading distance, by matching the result value of the numerical weather model with the change in the concentration of tracking ions in space and time.

[0030] The artificial rainfall effective range verification system (10) using a numerical model can compensate for the structural uncertainty of existing numerical models that rely only on calculation results, and can provide validity for water resource quantity evaluation and experimental design by objectively calculating the artificial rainfall effective range.

[0031] To this end, the artificial rainfall effective range verification system (10) using a numerical model may include a seeding analysis device (100), a user terminal (200), a seeding execution device (300), a weather observation system (400), and a sample analysis device (500).

[0032] The seeding analysis device (100) is a device capable of hosting an online network and network addressing, and may be a platform operation server that provides platform services online.

[0033] For example, the seeding analysis device (100) operates an artificial rain analysis platform and can be built in an on-premise environment or implemented as a cloud computing model based on a Platform as a Service (PaaS) that can run and manage related applications.

[0034] The seeding analysis device (100) can perform network communication with a user terminal (200), a seeding execution device (300), a weather observation system (400), and a sample analysis device (500) to provide an artificial rainfall effective range verification service.

[0035] Here, the network communication refers to a connection structure capable of exchanging information between the seeding analysis device (100), the user terminal (200), the seeding execution device (300), the weather observation system (400), and the sample analysis device (500), and may refer to a local area network (LAN), a wide area network (WAN), the internet (WWW), a wired and wireless data communication network, a telephone network, a wired and wireless television communication network, a satellite communication network, etc.

[0036] The seeding analysis device (100) can calculate the artificial rainfall impact by linking the numerical model calculation result with the change in ion concentration, and can display a seeding impact evaluation map that visualizes the artificial rainfall impact on a user terminal (200).

[0037] The seeding impact assessment map includes spatial grid-based map data corresponding to the experimental target area, and the seeding analysis device (100) can display at least one of a rainfall distribution map derived based on numerical model calculation results, a seeding impact heatmap reflecting changes in ion concentration, a threshold-based valid range boundary map, and a time series comparison graph of rainfall and ion concentration in the form of a visual layer on the seeding impact assessment map.

[0038] Here, the experimental area refers to the spatial range where artificial rainfall numerical simulation and verification are performed, and can be gridded and set based on weather data received from a weather observation system (400). The user can set impact assessment scenarios by variably inputting seeding locations, types of tracking ions to be analyzed, weight setting conditions, or time delay conditions through the artificial rainfall analysis platform, and can compare verification results according to each scenario.

[0039] The seeding analysis device (100) can map the three-dimensional position trajectory where seeding was performed and the material emission information onto a grid system based on seeding log data collected from the seeding execution device (300) and display them on a seeding impact assessment map.

[0040] In addition, the seeding analysis device (100) is linked with the weather observation system (400) and the sample analysis device (500), and can map location information and measurement data for multiple observation points onto a grid system to display on a seeding impact assessment map.

[0041] Here, an observation point refers to a physical location established to collect precipitation samples from the ground and measure ion concentration. For example, based on the seeding location and wind direction information from meteorological data, the observation point may be divided into an upwind observation point, which serves as a reference point unaffected by the seeding material, and a downwind observation point, which is expected to be reached by the diffusion of the seeding material.

[0042] The seeding analysis device (100) displays location markers for each observation point and, depending on user input, can provide a graph of tracking ion concentration change and an increase in ion concentration relative to background concentration in the form of a pop-up or split screen. Through this, the user can spatially and intuitively determine in which direction the ionic material has diffused and been detected on the ground, centered on the seeding location.

[0043] In addition, the seeding analysis device (100) can determine the spatiotemporal correspondence between the increased rainfall amount section and the increased ion concentration section, and visualize it by distinguishing it with different colors, saturation, or identification markers according to the magnitude of the artificial rainfall influence, thereby providing a verification interface to the user to confirm the artificial rainfall effect.

[0044] A user terminal (200) can be connected to an artificial rainfall analysis platform to use a valid range verification service. According to an embodiment, the user terminal (200) may refer to a PC, a smartphone, a tablet PC, a mobile internet device (MID), an internet tablet, an IoT device, a desktop computer, a laptop computer, a workstation computer, etc., but is not limited thereto.

[0045] The user of the user terminal (200) can set an experimental area, which is a spatial range where artificial rainfall numerical simulation and verification are performed through the artificial rainfall analysis platform, and configure data alignment and impact assessment scenarios for the experimental area.

[0046] Specifically, to verify the spatiotemporal causal relationship between the calculation results of rainfall increase from a numerical model and actual observation data, the user may set up multiple verification scenarios by variably inputting at least one of the following: the type of tracking ion to be analyzed (e.g., calcium ions, chloride ions, etc.), conditions of the windward and leeward observation points, an acceptable time delay range between ion concentration and rainfall increase, and weighting conditions for each influence indicator.

[0047] For example, the user may set the first scenario as the reference scenario and configure the second scenario in parallel as the comparison scenario. Here, the first scenario is a scenario that assigns the highest weight to the net increase in the concentration of trace ions detected in actual precipitation samples, and the second scenario may be a scenario that assigns a high weight to the physical diffusion distance (space-based influence) considering wind speed and response time.

[0048] Through this, the user can compare and determine in three dimensions whether the effective range of artificial rainfall is derived in a way that most corresponds to the actual causal relationship of the weather phenomenon using the seeding analysis device (100).

[0049] The seeding device (300) is equipment that sprays actual condensation nuclei into the atmosphere for artificial rain experiments, and includes aircraft, unmanned aerial vehicles, rockets, or ground launch devices, and can collect multidimensional experimental data that will serve as the basis for verifying input variables and validity ranges of a numerical model.

[0050] The seeding execution device (300) can generate seeding log data by recording spatiotemporal data including location information and time information where seeding was performed, and material data including the type and amount of seeding material sprayed, through a mounted recording device.

[0051] Here, the seeding log data may include spatiotemporal data composed of three-dimensional position information including at least one of a time value that can identify the start and end times of a seeding event, latitude and longitude coordinates that continuously represent the movement trajectory of a seeding performing device (300), barometric altitude, GPS altitude, and radar altitude.

[0052] In addition, the seeding log data may further include material data consisting of material type information, which is identification information of the seeding material introduced in the artificial rain experiment, and emission information indicating the total sprayed mass, sprayed mass per unit time, number of launches, or spray amount per event.

[0053] In particular, material type information can be utilized as key reference data to specify the types of trace ions (e.g., chloride ions, calcium ions, etc.) that need to be identified through sample analysis at ground observation points.

[0054] The seeding execution device (300) can provide seeding log data to the seeding analysis device (100) through a real-time telemetry communication method or by uploading a structured file after the experiment ends.

[0055] The weather observation system (400) may be a server of a national weather agency such as the Korea Meteorological Administration, or a self-contained weather observation network server established in the experimental area, and may generate weather data by observing atmospheric conditions.

[0056] Weather data may include atmospheric state variables and weather radar observation data corresponding to a three-dimensional grid network of the experimental area. For example, weather data may include temperature, pressure, and water vapor mixing ratio defining the thermodynamic environment of the atmosphere, dynamic variables including three-dimensional wind vectors (wind direction and wind speed) determining the spatial diffusion of the seeding material, and microphysical hydromechanical data such as cloud water concentration, ice crystal concentration, and rainwater and snow distribution representing the natural cloud state before seeding application.

[0057] In particular, wind direction and wind speed information included in meteorological data can be used as basic data to determine whether a ground observation point corresponds to the windward or leeward side relative to the seeding location, and to calculate the physical reaction distance of the seeding material and the time delay per observation point.

[0058] In addition, the weather data may include weather radar data including radar reflectance, line-of-sight velocity, and dual-polarization observation variables to determine the three-dimensional distribution of precipitation clouds and the state of internal particles.

[0059] The sample analysis device (500) can quantitatively analyze the ion components contained in a precipitation sample collected during an artificial rainfall experiment and generate ion concentration data to determine whether a seeding material has been introduced. The sample analysis device (500) can receive a sample collected in the form of precipitation such as rain, snow, or hail, detect the ion components contained in the sample, and generate ion concentration data by calculating the concentration value for the ion components.

[0060] Specifically, the sample analysis device (500) can perform a filtration process to remove impurities or solid particles contained in the collected precipitation sample, or dilute or quantify the sample to a state suitable for analysis equipment. In addition, the sample analysis device (500) can maintain the temperature or chemical stability of the sample and can continuously process automatically collected samples at regular time intervals.

[0061] The sample analysis device (500) can separate ionic components from a pre-treated sample and quantitatively measure the concentration of each ionic component. For example, the sample analysis device (500) can apply a precision analysis method using ion chromatography, inductively coupled plasma analysis, or an electrochemical sensor, and the trace ion to be analyzed can be determined in correspondence with the seeding material.

[0062] For example, if the seeding material is calcium chloride (CaCl2), calcium ions (Ca 2+ ) and chloride ions (Cl - ) can be the primary target of analysis, and the concentration of these ions can be calculated in ppm or μg / L units over time.

[0063] The sample analysis device (500) can generate ion concentration data by combining metadata such as the time of sample collection, the collection location (e.g., latitude, longitude, or windward / leeward identification information), and the type of ion analyzed, along with the concentration value of each ion based on the analysis results. The ion concentration data can be stored in a structure arranged in chronological order or separated by observation point.

[0064] Additionally, the sample analysis device (500) can process samples collected from multiple observation points sequentially or analyze them in parallel to continuously analyze multiple samples. Through this, the sample analysis device (500) can obtain high-resolution time-series data of the trend of ion concentration change over time and track the movement and diffusion process of seeding material introduced by artificial rain.

[0065] FIG. 2 is a schematic block diagram of a seeding analysis device according to an embodiment of the present invention.

[0066] Referring to FIG. 2, a seeding analysis device (100) according to an embodiment of the present invention includes a processor (110), a memory (120), a communication interface (130), and a storage (140).

[0067] The processor (110) controls the overall operation of each component of the seeding analysis device (100). The processor (110) may be configured to include a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), a GPU (Graphic Processing Unit), or any form of processor (110) well known in the art of the present invention.

[0068] Additionally, the processor (110) can perform operations for at least one application or program for executing the method according to embodiments of the present invention, and the server (100) may have one or more processors (110).

[0069] According to an embodiment, the processor (110) may further include RAM (Random Access Memory, not shown) and ROM (Read-Only Memory, not shown) for temporarily and / or permanently storing signals (or data) processed within the processor (110). Additionally, the processor (110) may be implemented in the form of a system-on-chip (SoC) comprising at least one of a graphics processing unit, RAM, and ROM.

[0070] The memory (120) stores various data, commands, or information. The memory (120) may load a program for verifying the effective range of artificial rainfall using a numerical model from the storage (140) to execute a method according to various embodiments of the present invention. When a computer program is loaded into the memory (120), the processor (110) may perform the method by executing one or more instructions constituting the computer program. The memory (120) may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

[0071] The communication interface (130) supports wired or wireless communication of the seeding analysis device (100). Additionally, the communication interface (130) may support various communication methods other than internet communication. To this end, the communication interface (130) may be configured to include a communication module well known in the art of the present invention. In some embodiments, the communication interface (130) may be omitted.

[0072] Storage (140) can store computer programs non-temporarily. When operating a composite seeding artificial rain analysis platform through a seeding analysis device (100), storage (140) can store various information required for generation and processing during the execution of the process.

[0073] The storage (140) may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0074] The bus (150) provides communication functions between components of the seeding analysis device (100). The bus (150) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0075] FIG. 3 is a schematic block diagram of a processor according to an embodiment of the present invention.

[0076] Referring to FIG. 3, the processor (110) includes an experimental data collection unit (111), an increase amount calculation unit (112), a sample analysis unit (113), a spatiotemporal alignment analysis unit (114), a seeding influence evaluation unit (115), and an effective range calculation unit (116), and each component is implemented by a software module, a hardware module, or a combination thereof, and may mean a unit that processes at least one function or operation by being connected through at least one communication path.

[0077] The experimental data collection unit (111) collects basic data for the entire process of verifying the effective range of artificial rainfall and can integrate weather observation information and sample analysis information to convert them into structured data that is spatially and temporally compatible. Here, the basic data may include at least one of seeding log data, weather data, and ion concentration data.

[0078] The experimental data collection unit (111) can collect seeding log data from a seeding execution device. Here, the seeding log data is information generated by a seeding execution device, such as an aircraft, unmanned aerial vehicle, rocket, or ground launch device, and may include a structured file (e.g., CSV, JSON, etc.) or an unstructured text form collected via a real-time communication method or a file upload method after the experiment ends.

[0079] For example, the seeding execution device can transmit seeding log data generated during seeding execution via a network at regular intervals or at the time of an event, and the experimental data collection unit (111) can receive and store the seeding log data in a streaming form.

[0080] For example, the experimental data collection unit (111) can upload and store seeding log data from a seeding execution device or an external storage medium after seeding is completed.

[0081] Seeding log data may include coordinate information of the location where seeding was performed, the time of seeding, the type of material used for seeding, and the amount of material input. In this case, the coordinate information may be provided in the form of latitude and longitude or 3D coordinates, and the time of seeding may be recorded as absolute time or relative time.

[0082] In addition, the seeding log data may include information such as the movement path, speed, altitude, operating status (on / off), injection duration, and injection interval of the seeding device; if multiple seedings are performed, it may include a seeding ID capable of individually identifying each seeding event. The seeding ID may be assigned based on the order of seeding or the type of seeding material, and multiple seeding events performed within the same experiment can be managed separately.

[0083] The experimental data collection unit (111) can collect weather data from a weather observation system. Weather data can be obtained from various observation means, such as ground observation stations, automatic weather observation equipment, upper-air weather observation equipment, and weather radar systems, and may have different spatial and temporal resolutions depending on the characteristics of each observation means.

[0084] In addition, meteorological data may include factors affecting the diffusion and movement of artificial rainfall, such as wind direction, wind speed, temperature, humidity, atmospheric pressure, and precipitation amount, and radar data collected from weather radar may include precipitation reflectance, precipitation intensity, spatial distribution of precipitation areas, cloud development status, and movement path.

[0085] The experimental data collection unit (111) can perform data matching processing to integrate weather data having different resolutions. For example, since radar data is provided in the form of a grid-based 2D or 3D array, the experimental data collection unit (111) can reconstruct the radar data to fit the spatial grid system of the numerical model or convert it into a value corresponding to a specific observation point. In addition, if the temporal resolution is different, the experimental data collection unit (111) can ensure temporal alignment between data by setting a common temporal standard and performing interpolation or resampling.

[0086] The experimental data collection unit (111) can collect ion concentration data generated from the sample analysis device. Here, the precipitation sample is a material collected from precipitation phenomena such as rain, snow, hail, etc., collected at an observation point, and may include a liquid or solid form sample capable of ion component analysis.

[0087] Additionally, the ion concentration data is data generated by a sample analysis device analyzing the ion concentration contained in the precipitation sample, and may include concentration values ​​corresponding to the time of sample collection, the collection location, and the type of ion to be analyzed. In particular, the ion concentration data may include concentration information for a plurality of trace ions corresponding to the seeding material, and the concentration of each ion may be recorded continuously over time or provided as discrete time point data.

[0088] The experimental data collection unit (111) can arrange ion concentration data in chronological order and reconstruct the data structure to analyze the trend of concentration change for a specific ion component. For example, by arranging ion concentration data measured at the same observation point along a time axis or rearranging them based on a spatial grid, it can be configured to enable spatiotemporal alignment analysis with the increase amount data in a subsequent step.

[0089] Additionally, the experimental data collection unit (111) can construct a data set for analyzing the correlation between multiple ionic components and can structure the data to determine whether a change in the concentration of a specific ion occurs simultaneously with other ions.

[0090] The experimental data collection unit (111) can integrate seeding log data, weather data, and ion concentration data to generate an integrated data set aligned according to the same spatiotemporal standard. To this end, the experimental data collection unit (111) can set a common time index based on time information included in each data and perform interpolation or resampling on data collected at different time intervals to convert them to the same temporal resolution.

[0091] The experimental data collection unit (111) can generate data aligned based on time and space by performing spatial alignment processing based on location information included in seeding log data, weather data, and ion concentration data.

[0092] For example, the coordinate information of the seeding location and observation point can be mapped to the spatial grid system used in the numerical model, so that each data point can be compared at the same grid unit. In this case, if a specific observation point is affected by multiple grids, a corresponding value can be calculated through weight-based interpolation between adjacent grids.

[0093] The experimental data collection unit (111) can organize an integrated data set to maintain connectivity between each data based on data aligned according to time and space standards. The experimental data collection unit (111) can manage seeding log data, weather data, and ion concentration data by combining them into a single record unit based on the same time index and spatial grid index.

[0094] According to an embodiment, the experimental data collection unit (111) can generate an integrated data set by combining weather condition information corresponding to the time and location of seeding by linking seeding log data and weather data. For example, by combining wind direction, wind speed, temperature, and humidity information at the location or adjacent grid at the time a specific seeding event occurs, an integrated data set can be provided that can analyze the relationship between seeding conditions and weather conditions.

[0095] According to another embodiment, the experimental data collection unit (111) may combine ion concentration data and weather data to form an integrated data set that can analyze the relationship between the change in ion concentration at each observation point and the weather conditions. For example, by storing the time of the change in ion concentration at a specific observation point together with the weather data at that time, a basis can be provided to analyze the correlation between the precipitation formation process and the change in ion concentration.

[0096] The rainfall calculation unit (112) can calculate the additional rainfall amount due to the artificial rainfall effect by calculating the natural rainfall and the rainfall induced by seeding separately, and by comparing the difference in rainfall amount depending on whether seeding is performed under the same weather conditions.

[0097] The precipitation amount calculation unit (112) can calculate a first precipitation amount for cases where seeding is performed and a second precipitation amount for cases where seeding is not performed, respectively, using a numerical weather model. Here, the numerical weather model can be configured based on a grid and can calculate precipitation amounts over time for multiple grids having a constant spatial resolution.

[0098] The precipitation amount calculation unit (112) can calculate a first precipitation amount by reflecting the seeding log data and weather data collected from the experimental data collection unit (111) when seeding is performed, and can calculate a second precipitation amount by setting a reference scenario excluding seeding conditions while maintaining the same weather conditions when seeding is not performed. At this time, the two scenarios may share the same initial conditions and boundary conditions.

[0099] The rainfall amount calculation unit (112) can calculate the first rainfall amount of the seeding execution scenario and the second rainfall amount of the reference scenario for each time index and spatial grid index, respectively, calculate the difference between the first rainfall amount and the second rainfall amount to calculate the rainfall amount in the corresponding grid, and generate rainfall amount data including the calculated rainfall amount.

[0100] According to an embodiment, the rainfall amount calculation unit (112) can calculate the cumulative rainfall amount over time. For example, the total rainfall amount can be calculated by accumulating the rainfall amount of each grid for a specific time interval, and the sustainability of the seeding effect can be evaluated by calculating the change in rainfall amount over a certain period of time after seeding is performed.

[0101] In addition, the rainfall amount calculation unit (112) can calculate the rainfall amount corresponding to each seeding event by distinguishing them when there are multiple seeding events. For example, the seeding events can be distinguished based on the seeding ID included in the seeding log data, and a scenario for each seeding event can be individually set to calculate the rainfall amount in grid units.

[0102] Additionally, the rainfall amount calculation unit (112) can generate rainfall amount data in the form of a multidimensional array based on a time index and a spatial grid index, and can include rainfall amount values ​​corresponding to each grid.

[0103] The increased rainfall calculation unit (112) can correct the calculation results of the numerical weather model using weather data collected from the weather observation system. The increased rainfall calculation unit (112) can compare the actual rainfall observed for a specific region and a specific time interval with the second rainfall calculated through the numerical weather model, and calculate a correction coefficient based on the difference between the two values.

[0104] Specifically, the rainfall calculation unit (112) can calculate the difference between the second rainfall amount corresponding to each spatial grid or observation point and the actual rainfall amount, and generate a grid-specific correction coefficient based on the calculated difference. For example, if the actual rainfall amount at a specific grid is greater than the second rainfall amount of the numerical weather model, the correction coefficient for that grid can be set greater than 1, and conversely, if the measured rainfall amount is smaller, the correction coefficient can be set less than 1.

[0105] The increased rainfall calculation unit (112) can generate a corrected reference rainfall by applying a generated correction coefficient to the second rainfall amount. For example, the rainfall amount of a reference scenario calculated from a numerical weather model can be corrected by applying a grid-specific correction coefficient to the second rainfall amount. Then, the increased rainfall calculation unit (112) can calculate the increased rainfall amount by calculating the difference between the corrected second rainfall amount and the first rainfall amount.

[0106] The rainfall amount calculation unit (112) can convert the rainfall amount into visualization data. For example, it can convert the rainfall amount values ​​for each spatial grid into color values ​​to generate data in the form of a heatmap, or distinguish and display areas that are above a specific threshold.

[0107] The sample analysis unit (113) can extract ion concentration change characteristics based on ion concentration data of a precipitation sample received from a sample analysis device, and generate ion concentration change data related to a seeding material based on the extracted ion concentration change characteristics.

[0108] Specifically, the sample analysis unit (113) can receive ion concentration data for precipitation samples collected from multiple observation points from a sample analysis device. The ion concentration data may include the time of sample collection, the location of collection, the type of ion analyzed, and the concentration value of each ion, and may be managed in a sorted form according to chronological order.

[0109] Ion concentration data may include concentration information for multiple tracer ions corresponding to the seeding material. Here, tracer ions may refer to ionic components that are contained in or originate from the seeding material and are detectable in precipitation samples. For example, if the seeding material is calcium chloride (CaCl2), calcium ions (Ca 2+ ) and chloride ions (Cl - ) can be set as a tracking ion.

[0110] The sample analysis unit (113) can generate ion concentration change data by analyzing the characteristics of ion concentration change over time based on ion concentration data. For example, the sample analysis unit (113) can arrange ion concentration data measured at the same observation point according to a time index and identify the interval where the ion concentration increases in a specific time interval. In addition, the sample analysis unit (113) can calculate the amount of change in ion concentration by comparing the ion concentration value at a specific time point with the ion concentration value at a previous time point.

[0111] In addition, the sample analysis unit (113) can compare ion concentration values ​​between observation points and analyze the characteristics of ion concentration change according to space to generate ion concentration change data.

[0112] Specifically, the sample analysis unit (113) can identify an observation point located on the windward side of the seeding location based on wind direction information, and set the ion concentration measured at that point as the background concentration in a natural state unaffected by seeding. Here, the background concentration may refer to the ion concentration measured at the windward observation point unaffected by seeding.

[0113] Subsequently, the sample analysis unit (113) can calculate the increase in ion concentration purely caused by the seeding material by subtracting the background concentration calculated above from the ion concentration at the observation point located on the leeward side, and can filter out noise caused by natural ion component fluctuations by generating ion concentration change data based on this.

[0114] According to an embodiment, the sample analysis unit (113) can generate multi-ion-based ion concentration change data using ion concentration data for a plurality of trace ions. For example, a pattern in which a plurality of trace ions increase simultaneously in the same time interval can be extracted, and ion concentration change data including the extracted pattern information can be generated.

[0115] For example, the sample analysis unit (113) detects calcium ions (Ca) at a specific observation point. 2+ ) and chloride ions (Cl - When the concentration values ​​of ) increase simultaneously in the same time interval, a simultaneous change pattern between the two ions can be identified, and ion concentration change data including time information and spatial information corresponding to the simultaneous change pattern can be generated.

[0116] Additionally, the sample analysis unit (113) can calculate the change in concentration for each of the plurality of trace ions, identify a section in which the change in concentration for all of the plurality of trace ions increases above a reference value, and generate ion concentration change data including the start time, end time, and change in ion concentration of the section.

[0117] The sample analysis unit (113) can group the concentration change points of multiple trace ions into the same event when they occur within a certain time range from each other, and can generate ion concentration change data including time information, observation point information, and ion concentration change information corresponding to the grouped event.

[0118] The spatiotemporal alignment analysis unit (114) can align the rainfall amount data calculated based on a numerical model and the ion concentration change data generated by the sample analysis unit (113) so that they can be compared on the same standard, and can generate alignment data that indicates the temporal and spatial correspondence relationship between the rainfall amount data and the ion concentration change data. Here, the alignment data may include the time point of the ion concentration change, the time point of the rainfall amount change, the time difference between the two time points, and spatial correspondence information.

[0119] Specifically, the spatiotemporal alignment analysis unit (114) can receive grid-based rainfall data provided by the rainfall calculation unit (112) and ion concentration change data provided by the sample analysis unit (113). The rainfall data may be provided as a data set organized based on a spatial grid and a time index, and the ion concentration change data may be provided in the form of a data set according to time for each observation point.

[0120] The spatiotemporal alignment analysis unit (114) can generate alignment data by performing spatial alignment between the data sets. For example, the coordinate information of each observation point can be mapped to the spatial grid system of the numerical model, and the grid position corresponding to the observation point can be determined. In addition, if one observation point corresponds to multiple grids, the increased rainfall value corresponding to the point can be calculated through weight-based interpolation.

[0121] The spatiotemporal alignment analysis unit (114) can generate alignment data by performing interpolation or resampling to align rainfall data and ion concentration change data, which are composed of different time intervals, based on the same time index. Through this, the respective data sets of rainfall data and ion concentration change data can be converted into a state where they can be compared based on the same time standard.

[0122] The spatiotemporal alignment analysis unit (114) can determine a correspondence relationship by comparing the change patterns of ion concentration change data and rainfall amount data based on a certain time range. Here, the correspondence relationship may refer to a relationship in which the time difference between the time when the ion concentration change occurred and the time when the rainfall amount increased occurred falls within a preset time range, and it is determined that the two phenomena occurred due to the same seeding effect.

[0123] If the interval in which a change in ion concentration occurs and the interval in which an increase in rainfall occurs overlap with each other, or if they occur sequentially within a preset time difference range, the spatiotemporal alignment analysis unit (114) can determine that there is a significant correlation between the two phenomena and set the corresponding time region as a corresponding relationship.

[0124] At this time, the spatiotemporal alignment analysis unit (114) can reflect the time delay phenomenon that may occur between the time of increased ion concentration and the time of increased rainfall based on the distance and wind speed data from the seeding point to each observation point.

[0125] For example, at an observation point close to the seeding point, the timing of the increase in ion concentration and rainfall amount appears similar in real time, whereas at an observation point far away, the timing of the increase in ion concentration may appear delayed compared to the timing of the increase in rainfall amount in proportion to the travel time of the seeding material. Therefore, the spatiotemporal alignment analysis unit (114) can improve the accuracy of spatiotemporal alignment by differentially applying the above-mentioned preset time range to each point according to the distance and weather conditions of each observation point.

[0126] According to an embodiment, the spatiotemporal alignment analysis unit (114) may perform correction processing to reduce errors that may occur during the data alignment process. For example, the spatiotemporal alignment analysis unit (114) may remove change data below a certain threshold, perform smoothing to reduce noise between data, and exclude data if it contains outliers.

[0127] The seeding impact evaluation unit (115) can calculate the artificial rainfall impact for each observation point or grid based on the matching data generated by the spatiotemporal matching analysis unit (114) and generate impact data. Here, the artificial rainfall impact refers to an indicator that reflects whether the seeding material has arrived at a specific observation point or grid and whether an increase in precipitation has occurred, and may include at least one of an ion concentration-based impact, a precipitation-based impact, a time-based impact, and a space-based impact.

[0128] For example, the artificial rainfall impact can be calculated in the form of a normalized value between 0 and 1 or a score within a preset range, and may include data in which the larger the value, the greater the impact caused by artificial rainfall at that location.

[0129] Specifically, the seeding influence evaluation unit (115) can calculate an ion concentration-based influence corresponding to a change in ion concentration at a specific observation point using matching data. The seeding influence evaluation unit (115) determines the amount of change in ion concentration at a specific observation point using matching data, and if the amount of change in ion concentration at that point is greater than or equal to a reference value, it can calculate a relatively high ion concentration-based influence for that point, and can set the ion concentration-based influence to increase as the ion concentration increase rate increases.

[0130] The seeding influence evaluation unit (115) can calculate a precipitation-based influence corresponding to the amount of rainfall at a specific observation point using matching data. The seeding influence evaluation unit (115) calculates the value of rainfall at a grid corresponding to a specific observation point using matching data, and if the value of rainfall or the cumulative amount of rainfall is greater than or equal to a reference value, it can calculate a high precipitation-based influence for that point, and can set the precipitation-based influence to increase as the value of rainfall increases.

[0131] The seeding impact evaluation unit (115) can calculate a time-based impact corresponding to the time difference between the time of change in ion concentration and the time of change in rainfall amount using matching data. For example, a high time-based impact can be calculated when the time difference is within a preset range, and the time-based impact can be set to decrease as the time difference increases.

[0132] The seeding influence evaluation unit (115) can calculate a spatial-based influence corresponding to the location of the observation point using matching data. For example, based on seeding location and wind direction information, if the observation point is located on the leeward side, a high spatial-based influence can be calculated, and if it is located on the windward side, a relatively low spatial-based influence can be calculated.

[0133] The seeding impact evaluation unit (115) can calculate the reaction distance (RD), which represents the physical diffusion range, according to the following mathematical formula 1 using the average wind speed included in the weather data and the reaction time of the seeding material.

[0134]

[0135] Here, MWS refers to the average wind speed, and RT refers to the reaction time of the seeding material.

[0136] The seeding impact evaluation unit (115) further determines whether an observation point or grid is located within the calculated reaction distance in the downwind direction from the seeding location, and if it is located within the reaction distance, it can precisely reflect the reachability of the actual seeding material in the impact calculation by adjusting the weight of the space-based impact upward.

[0137] The seeding impact evaluation unit (115) can calculate the artificial rainfall impact for each observation point or grid using at least one of the ion concentration-based impact, precipitation-based impact, time-based impact, and space-based impact. For example, the seeding impact evaluation unit (115) can assign weights to each of the ion concentration-based impact, precipitation-based impact, time-based impact, and space-based impact and calculate the artificial rainfall impact using a weighted sum method. Here, the weights are pre-set values ​​and can be variably set according to experimental conditions.

[0138] The seeding impact evaluation unit (115) can generate impact data by sorting the artificial rainfall impact corresponding to each observation point or grid according to a time index and a spatial index. Here, the impact data refers to a data set containing the artificial rainfall impact corresponding to each observation point or grid, and can be configured based on a time index and spatial coordinates or a grid index. Additionally, the impact data may include time information and location information corresponding to the impact value, along with the artificial rainfall impact value calculated for each observation point or grid.

[0139] For example, the seeding impact evaluation unit (115) can generate impact data by configuring the artificial rainfall impact corresponding to a specific time index and a specific observation point or grid into a single record, and can configure the record in an array form for multiple time intervals.

[0140] According to an embodiment, the seeding impact evaluation unit (115) can generate impact data in the form of a multidimensional array by mapping artificial rainfall impact values ​​corresponding to each grid based on a spatial grid.

[0141] The effective range calculation unit (116) can calculate the spatial range affected by the artificial rainfall effect based on the artificial rainfall effect of each observation point or grid unit calculated by the seeding effect evaluation unit (115).

[0142] The valid range calculation unit (116) can use the influence data to select observation points or grids where the artificial rainfall influence is greater than or equal to a preset threshold as influence area candidates, and generate a set of influence area candidates including the selected influence area candidates.

[0143] The effective range calculation unit (116) can form an artificial rainfall effective range by spatially connecting the generated set of candidate influence areas. The effective range calculation unit (116) can perform clustering based on the connection relationship between adjacent grids to configure the continuously connected grids into an artificial rainfall effective range affected by a single artificial rainfall.

[0144] Here, the artificial rainfall effective range refers to an artificial rainfall effective range in which observation points or grids where the artificial rainfall influence level is above a threshold are combined so as to be spatially continuous, and can be defined in the form of an area composed of one or more connected sets of multiple grids or observation points.

[0145] For example, the effective range calculation unit (116) can generate multiple artificial rainfall effective ranges by grouping adjacent grids among the grids included in the set of candidate influence areas into a single cluster, and spatially separated areas can be distinguished as independent artificial rainfall effective ranges. In addition, the effective range calculation unit (116) can reduce noise by removing spatially isolated or low-connectivity grids from the artificial rainfall effective ranges.

[0146] The effective range calculation unit (116) can correct the artificial rainfall effective range by performing spatial interpolation. For example, if the effective range calculation unit (116) confirms a high artificial rainfall influence level in a specific grid, it can expand the influence level around the grid into a continuous area by interpolating.

[0147] Additionally, the effective range calculation unit (116) can reflect changes in the effective range of artificial rainfall based on a time index. For example, the effective range calculation unit (116) can calculate the effective range of artificial rainfall for each of the multiple time steps and arrange the effective ranges of artificial rainfall in chronological order to show changes in the effective range over time. Through this, the effective range calculation unit (116) can reflect the pattern of the artificial rainfall effect expanding or decreasing over time.

[0148] According to an embodiment, the effective range calculation unit (116) can correct the artificial rainfall effective range using wind direction information. For example, the effective range calculation unit (116) can set a wind direction vector based on the seeding location and determine whether the point corresponds to the downwind side or the upwind side by using the relationship with the position vector of each observation point or grid. For grids classified as downwind, the effective range calculation unit (116) can maintain the artificial rainfall effective range or reflect the artificial rainfall influence of the corresponding grid as is, and for grids classified as upwind, it can correct the effective range by excluding them from the artificial rainfall effective range or reducing the artificial rainfall influence.

[0149] According to another embodiment, the effective range calculation unit (116) may perform filtering on the artificial rain effective range. For example, the effective range calculation unit (116) may calculate the area of ​​each artificial rain effective range or the number of included grids, and if the value is less than a preset minimum reference value, the area may be removed.

[0150] The valid range calculation unit (116) can generate data in the form of a heatmap that displays the area corresponding to the artificial rain valid range as a color value, or generate visualization data that displays the area above a threshold value as a boundary line.

[0151] For example, the effective range calculation unit (116) may assign different colors or color intensities according to the magnitude of the artificial rain influence and correspond them to a spatial grid to generate visualization data in the form of a heatmap. At this time, areas with a high artificial rain influence value may be expressed in colors of high saturation or high brightness, and areas with a low influence value may be expressed in colors of low saturation or low brightness.

[0152] Additionally, the valid range calculation unit (116) can calculate a boundary line based on a grid or observation point included in the artificial rainfall valid range and generate visualization data including the boundary line. For example, by comparing whether adjacent grids are included in the valid range, the boundary between grids with different inclusion statuses can be extracted, and a boundary line in the form of a closed curve can be generated by connecting them.

[0153] The valid range calculation unit (116) can transmit visualization data to a user terminal or an external system, or output it through its own display.

[0154] FIG. 4 is a flowchart illustrating a method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention.

[0155] Referring to FIG. 4, the seeding analysis device (100) can generate an integrated data set by aligning seeding log data, weather data, and ion concentration data collected from a seeding execution device, a weather observation system, and a sample analysis device based on a spatiotemporal basis (S100). The seeding analysis device (100) can structure basic data for subsequent numerical model calculations and ion data verification by reconstructing heterogeneous observation and experimental data having different spatial resolutions and time collection cycles by interpolating and resampling them into a common time index and grid system.

[0156] In addition, the seeding analysis device (100) can calculate a first precipitation amount reflecting the seeding performance conditions and a second precipitation amount under reference conditions where seeding is not performed, respectively, using a numerical weather model (S110). The seeding analysis device (100) can quantitatively separate only the precipitation phenomenon that changes purely due to seeding in the same atmospheric environment by setting the weather data included in the integrated data set as initial and boundary conditions and simulating two independent scenarios in parallel based on whether or not seeding material is input.

[0157] In addition, the seeding analysis device (100) can generate grid-based additional rainfall data by calculating the difference between the first and second rainfall amounts (S120). The seeding analysis device (100) can precisely quantify the additional rainfall due to the artificial rainfall effect by calculating the rainfall deviation between the two scenarios for each time index and spatial grid index, and correcting the calculation result by feeding back actual measured rainfall data obtained from the weather observation system.

[0158] In addition, the seeding analysis device (100) can generate ion concentration data by extracting the characteristics of the concentration change of tracking ions over time based on the analysis results of the collected precipitation sample (S130). The seeding analysis device (100) can objectively determine whether the seeding material actually reaches the ground by filtering out, for example, natural chemical component fluctuation noise, by setting the background concentration at the upwind observation point determined based on wind direction information as a reference value and subtracting it from the concentration at the downwind observation point to calculate the net increase.

[0159] In addition, the seeding analysis device (100) can generate matching data by linking the temporal and spatial correspondence between the rainfall amount data and the ion concentration data (S140). The seeding analysis device (100) can complete structured data that proves the spatiotemporal correspondence between the increase in physical precipitation and the arrival of chemical seeding material by calculating the time difference between the time when the ion concentration increased at a specific point and the time when the rainfall amount increased at the corresponding grid, and by dynamically combining the causal relationship between the two data by reflecting the time delay phenomenon according to the separation distance and average wind speed.

[0160] In addition, the seeding analysis device (100) can generate grid-unit influence data by assigning weights to each evaluation indicator and summing them based on the generated matching data (S150). The seeding analysis device (100) can derive a three-dimensional artificial rainfall influence index that is cross-verified with actual observation data beyond the calculation results of a simple numerical model by performing complex calculations by applying user-set weights to each of the downwind position conditions considering the net increase in ion concentration, the magnitude of rainfall increase, the matched time difference, and the physical reaction distance.

[0161] In addition, the seeding analysis device (100) can calculate the final effective range of artificial rainfall by clustering candidate impact areas where the calculated impact data is greater than or equal to a preset threshold (S160). The seeding analysis device (100) can provide verification analysis information that objectively visualizes the spatial range where the artificial rainfall effect actually had a significant effect by clustering spatially adjacent effective grids and performing filtering and spatial interpolation processing to determine them in the form of a continuous area.

[0162] FIG. 5 is a conceptual diagram illustrating a method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention.

[0163] Referring to Fig. 5, there is a mature stage region within the cloud where precipitation is formed, and in the mature stage region, precipitation can occur through condensation or ice crystal formation processes induced by seeding. The mature stage region can be formed in a specific spatial section, and precipitation particles are generated in that region and fall to the surface.

[0164] In the seeding phase, seeding material may be released from a certain seeding height by an aircraft, unmanned aerial vehicle, or ground device, and the released seeding material may spread while moving downwind under the influence of wind direction and wind speed. As indicated by the arrow in the drawing, the seeding material moves along the direction of the wind and is distributed along the seeding path. At this time, the horizontal distance traveled from the point where the seeding material is released to a location that contributes to precipitation formation after a certain period of time can be defined as the reaction distance, and the reaction distance can be calculated in proportion to the product of the average wind speed and the reaction time.

[0165] In addition, as shown in the drawing, multiple observation points (A, B) may be placed on the surface, and each observation point may be affected by the seeding material at different times according to the movement path of the seeding material.

[0166] For example, an observation point (A) located on the windward side, which is the direction from which the wind blows relative to the seeding location, can be used as a reference point to measure the background concentration in a natural state unaffected by the seeding material. On the other hand, an observation point (B) located in the maturation stage area on the leeward side, which is the direction from which the wind blows, can be used as a verification point where an increase in ion concentration and precipitation occurs due to the direct diffusion effect of the seeding material.

[0167] The seeding analysis device (100) can estimate the spatial range reachable by the seeding material based on information regarding the seeding location, wind direction, and wind speed, by reflecting the movement characteristics and reaction distance concepts of the seeding material. Additionally, the seeding analysis device (100) can determine whether an increase in precipitation occurring within the reaction distance is due to seeding by using data on changes in ion concentration and rainfall amount measured at each observation point.

[0168] Furthermore, the seeding analysis device (100) can calculate the artificial rainfall influence based on the net increase in ion concentration and the increase in precipitation calculated at the downwind observation point (B), taking into account the positional relationship between observation points (A, B) and the direction of movement of the seeding material, and use this to derive the effective range in which the artificial rainfall actually had an effect.

[0169] FIGS. 6a and 6b are drawings illustrating a method for verifying the effective range of artificial rainfall using a numerical model according to an embodiment of the present invention.

[0170] Fig. 6a shows calcium ions (Ca) derived from calcium chloride seeding material. 2+ Figure 6b shows the change in concentration over time (bar graph) and the increase amount based on a numerical model (line graph), and chloride ions (Cl - It shows the same comparison results for ).

[0171] Referring to FIGS. 6a and 6b, to explain the time delay reflection algorithm of the seeding analysis device (100), there is a time series graph comparing the change in tracking ion concentration observed in an actual artificial rainfall experiment with the change in rainfall amount of a numerical weather model.

[0172] The x-axis of the graph represents the observation time, the left y-axis represents the trace ion concentration, and the right y-axis represents the rainfall amount simulated in the numerical model, and different observation points (e.g., CPOS, JBO, SSO) can be distinguished and displayed by color.

[0173] At SSO observation points, the point at which the concentration of tracer ions (calcium ions and chloride ions) surges may appear temporally synchronized with the point at which rainfall increases in the numerical model. This implies that the simulation results of the numerical model's rainfall effect at SSO observation points match the changes in chemical composition resulting from the actual influx of seeding materials.

[0174] On the other hand, an analysis of the results from CPOS and JBO observation points reveals that while the rainfall amount calculated by the numerical model began to increase early, the point at which the concentration of tracking ions actually surged on the ground tended to be delayed by about one hour compared to the point at which the rainfall amount increased.

[0175] This time delay phenomenon means that the physical time required for seeding material to diffuse and reach the ground varies depending on the distance from the seeding location to each observation point and meteorological data (wind speed, etc.).

[0176] The seeding analysis device (100) can improve the accuracy of spatiotemporal alignment by not simply determining causality by comparing only data from the same time period one-to-one, but by differentially applying an allowable time delay range between the point of increase in ion concentration and the point of increase in rainfall amount according to the distance between observation points and weather conditions.

[0177] The steps of the method or algorithm for verifying the effective range of artificial rainfall using a numerical model described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0178] Although embodiments of the present invention have been described above, it is understood that those skilled in the art can make various modifications without departing from the scope of the claims of the present invention. Explanation of the symbols

[0179] 10: Artificial Rainfall Effective Range Verification System Using Numerical Models 100: Seeding analyzer 110: Processor 111: Experimental Data Collection Unit 112: Rainfall Calculation Unit 113: Sample Analysis Unit 114: Space-Time Matching Analysis Unit 115: Seeding Impact Evaluation Unit 116: Valid Range Calculation Unit 120: Memory 130: Communication Interface 140: Storage 150: Bus 200: User terminal 300: Seeding execution device 400: Weather observation system 500: Sample analysis device

Claims

Claim 1 An artificial rainfall effective range verification device using a numerical model comprising a processor that performs operations for at least one program, wherein the processor comprises: an experimental data collection unit that collects basic data including seeding log data, weather data, and ion concentration data, and aligns the collected basic data based on spatiotemporal standards to generate an integrated data set; an increase in rainfall calculation unit that calculates a first rainfall amount under seeding performance conditions reflecting the seeding log data and the weather data using a numerical weather model, and a second rainfall amount under conditions where seeding is not performed based on the weather data, and generates increase in rainfall data based on the difference between the first rainfall amount and the second rainfall amount; a sample analysis unit that analyzes the characteristics of ion concentration change according to time and space based on the ion concentration data to generate ion concentration change data related to the seeding material; and a spatiotemporal alignment analysis unit that generates alignment data representing the temporal and spatial correspondence relationship between the increase in rainfall data and the ion concentration change data. An artificial rainfall effective range verification device using a numerical model, comprising a seeding impact evaluation unit that calculates the artificial rainfall impact based on the above matching data and generates impact data, wherein the spatiotemporal matching analysis unit determines the grid in which each observation point is located by corresponding the coordinates of each observation point to the spatial grid system of the above numerical weather model based on observation point information included in the above ion concentration change data, calculates the time difference between the time when the ion concentration change occurs at the above observation point and the time when the rainfall amount in the determined grid increases by more than a preset reference value, and if the calculated time difference is within a preset time range, determines the temporal and spatial correspondence relationship between the ion concentration change and the rainfall amount increase to generate the above matching data. Claim 2 In claim 1, the experimental data collection unit sorts the seeding log data, the weather data, and the ion concentration data based on a time index and spatial coordinates, and combines the data corresponding to each time index and spatial coordinate into a single record to generate the integrated data set, thereby forming an artificial rainfall effective range verification device using a numerical model. Claim 3 An artificial rainfall effective range verification device using a numerical model, wherein the rainfall amount calculation unit calculates the first rainfall amount and the second rainfall amount by setting the same initial conditions and boundary conditions for the seeding performance condition and the seeding non-performance condition, calculates the rainfall amount by calculating the difference between the first rainfall amount and the second rainfall amount for each time index and spatial grid index, and generates the rainfall amount data in the form of a multidimensional array by mapping the calculated rainfall amount based on the time index and spatial grid index. Claim 4 In claim 1, the sample analysis unit is an artificial rainfall effective range verification device using a numerical model that sorts the ion concentration data for a tracking ion corresponding to the seeding material among the ion concentration data based on a time index and an observation point, and calculates the amount of change in ion concentration of the tracking ion over time at the same observation point to generate the ion concentration change data. Claim 5 delete Claim 6 In claim 1, the seeding influence evaluation unit assigns weights to at least one of an ion concentration-based influence based on the amount of change in ion concentration, a precipitation-based influence based on the magnitude of rainfall increase, a time-based influence corresponding to the time difference, and a space-based influence calculated based on the seeding location and wind direction using the matching data, and an artificial rainfall effective range verification device using a numerical model that calculates the artificial rainfall influence using a weighted sum method. Claim 7 In claim 6, the seeding influence evaluation unit selects a grid whose artificial rainfall influence is greater than or equal to a preset threshold as an influence area candidate, and an artificial rainfall effective range verification device using a numerical model that generates influence data including data for the influence area candidates. Claim 8 In claim 1, the artificial rainfall effective range verification device using a numerical model further comprises an effective range calculation unit that calculates an effective range representing the spatial range affected by artificial rainfall based on the influence data. Claim 9 A method for verifying the effective range of artificial rainfall using a numerical model implemented by a seeding analysis device comprises: collecting basic data including seeding log data, meteorological data, and ion concentration data, and aligning the collected basic data based on spatiotemporal criteria to generate an integrated data set; calculating a first precipitation amount under seeding performance conditions reflecting the seeding log data and the meteorological data using a numerical meteorological model, and a second precipitation amount under conditions where seeding is not performed based on the meteorological data; generating increased rainfall data based on the difference between the first rainfall amount and the second rainfall amount; analyzing the characteristics of ion concentration change according to time and space based on the ion concentration data to generate ion concentration change data related to the seeding material; generating matched data representing the temporal and spatial correspondence between the increased rainfall data and the ion concentration change data; and generating influence data by calculating the artificial rainfall influence based on the matched data. A method for verifying the effective range of artificial rainfall using a numerical model, comprising the step of calculating the effective range of artificial rainfall based on the above-mentioned influence data, wherein the step of generating the matching data comprises determining the grid in which each observation point is located by corresponding the coordinates of each observation point to the spatial grid system of the numerical meteorological model based on observation point information included in the above-mentioned ion concentration change data, calculating the time difference between the time when the ion concentration change occurs at the observation point and the time when the rainfall amount in the determined grid increases by more than a preset reference value, and if the calculated time difference is within a preset time range, determining the temporal and spatial correspondence relationship between the ion concentration change and the rainfall amount increase to generate the matching data. Claim 10 A computer-readable storage medium for storing at least one program, wherein the program is configured to be executed by at least one processor of an electronic device, and the program comprises instructions for performing the method of claim 9.

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