Server, method, and computer program for expanding spatio-temporal resolution of weather data and providing same to terminal
The server expands weather data spatiotemporal resolution by converting and interpolating forecast values, addressing the limitations of current models to provide accurate, detailed weather information for specific regions.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- WEATHERI
- Filing Date
- 2024-11-07
- Publication Date
- 2026-05-15
AI Technical Summary
Current global weather models provide insufficient spatiotemporal resolution, limiting the ability to obtain detailed weather information for specific or small-scale regions, which is critical for accurate real-time climate monitoring and prediction.
A server that receives weather data, converts it into matrix data by longitude and latitude, expands the spatial resolution by dividing and interpolating forecast values at detailed parts, and transmits the data to a terminal, also expanding the temporal resolution by dividing forecast time intervals and interpolating values.
Enhances the accuracy of weather data by providing detailed spatiotemporal resolution, enabling precise real-time climate monitoring and prediction for specific regions.
Smart Images
Figure KR2024017529_15052026_PF_FP_ABST
Abstract
Description
Server, method, and computer program that expands the spatiotemporal resolution of weather data and provides it to a terminal
[0001] The present invention relates to a server, a method, and a computer program that receive weather data from a weather model, enlarge the spatiotemporal resolution, and provide it to a terminal.
[0002] Weather forecasting is performed using global weather models such as GFS and ECMWF, which enable the acquisition of weather data over large-scale spatial areas. These models are primarily used to monitor atmospheric conditions and forecast future weather, and the collected data is applied to various industrial sectors, including agriculture, energy management, aviation, and maritime transport.
[0003] In particular, the spatiotemporal resolution of meteorological data is a critical factor that significantly impacts the accuracy of real-time climate monitoring and prediction, and its importance is increasing as the impact of weather changes intensifies.
[0004] Meanwhile, the spatiotemporal resolution provided by current global weather models is structured around broad regional units. This is because resolution is often limited to reduce computational burden due to the inherent characteristics of the models, which limits the ability to obtain detailed weather information for specific or small-scale regions. Consequently, various industries seeking to utilize weather data are demanding an expansion of resolution, as the spatiotemporal resolution provided by existing models is insufficient.
[0005] [Prior Art Literature]
[0006] (Patent Document) Korean Registered Patent No. 2063358 (Registered Dec. 31, 2019)
[0007] The present invention aims to solve the problems of the aforementioned prior art, and the present invention seeks to expand the spatial resolution of weather data.
[0008] In addition, the present invention aims to expand the temporal resolution of weather data.
[0009] However, the technical problems that this embodiment aims to solve are not limited to the technical problems described above, and other technical problems may exist.
[0010] As a technical means for achieving the aforementioned technical problem, a server that expands the spatiotemporal resolution of weather data according to the first aspect of the present invention and provides it to a terminal comprises:
[0011] It may include a receiving unit that receives weather data having preset spatial resolution and temporal resolution from a weather model; a converting unit that converts the received weather data into matrix data including forecast values by longitude and latitude based on the received weather data; an expanding unit that expands the spatial resolution by dividing the longitude and latitude of the matrix data into more detailed parts than the preset spatial resolution and interpolating forecast values at the locations of the divided longitude and latitude; and a transmitting unit that transmits the weather data with expanded spatial resolution to a terminal.
[0012] A method for providing weather data with expanded spatiotemporal resolution to a terminal according to a second aspect of the present invention may include: receiving weather data having a preset spatial resolution and temporal resolution from a weather model; converting the received weather data into matrix data including forecast values by longitude and latitude based on the received weather data; dividing the longitude and latitude of the matrix data into more detailed values than the preset spatial resolution and interpolating forecast values at the locations of the divided longitude and latitude to expand the spatial resolution; and transmitting the weather data with expanded spatial resolution to a terminal.
[0013] According to the third aspect of the present invention, the computer program may include a sequence of instructions that, when executed by a computing server, receive weather data having a preset spatial resolution and a time resolution from a weather model, convert the received weather data into matrix data including forecast values by longitude and latitude based on the received weather data, divide the longitude and latitude of the matrix data into more detailed parts than the preset spatial resolution, expand the spatial resolution by interpolating forecast values at the locations of the divided longitude and latitude, and transmit the weather data with the expanded spatial resolution to a terminal.
[0014] The above-described means for solving the problem are merely exemplary and should not be interpreted as intended to limit the invention. In addition to the exemplary embodiments described above, additional embodiments described in the drawings and the detailed description of the invention may exist.
[0015] According to any one of the means for solving the problem of the present invention described above, the present invention provides a configuration that converts weather data received from a server into matrix data including forecast values by longitude and latitude based on the data, divides the longitude and latitude of the matrix data into more detailed values than a preset spatial resolution, and interpolates forecast values at the locations of the divided longitude and latitude, thereby expanding the spatial resolution of the weather data.
[0016] In addition, the present invention can expand the time resolution by providing a configuration that divides the forecast time interval of weather data into more detailed segments than the preset time resolution and interpolates forecast values in the divided forecast time intervals.
[0017] FIG. 1 is a configuration diagram of a system that expands the spatiotemporal resolution of weather data and provides it to a terminal, according to one embodiment of the present invention.
[0018] FIG. 2 is a block diagram of the server shown in FIG. 1 according to one embodiment of the present invention.
[0019] FIG. 3 is an exemplary drawing for explaining a method of providing weather data to a terminal by enlarging the spatiotemporal resolution of the weather data by a server according to an embodiment of the present invention.
[0020] FIGS. 4a to 4d are exemplary drawings for explaining a method of providing weather data to a terminal by enlarging the spatiotemporal resolution of the weather data by a server according to an embodiment of the present invention.
[0021] FIG. 5 is a flowchart illustrating a method for providing weather data to a terminal by expanding its spatiotemporal resolution according to an embodiment of the present invention.
[0022] Embodiments of the present invention are described below in detail with reference to the attached drawings so that those skilled in the art can easily implement the invention. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.
[0023] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "electrically connected" with other components interposed between them. Furthermore, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.
[0024] In this specification, the term "part" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Additionally, one unit may be realized using two or more hardware, and two or more units may be realized by one hardware.
[0025] Some of the operations or functions described in this specification as being performed by a terminal or device may instead be performed by a server connected to said terminal or device. Likewise, some of the operations or functions described as being performed by a server may also be performed by a terminal or device connected to said server.
[0026] Hereinafter, an embodiment of the present invention will be described in detail with reference to the attached drawings.
[0027] FIG. 1 is a configuration diagram of a system that expands the spatiotemporal resolution of weather data and provides it to a terminal according to an embodiment of the present invention. Referring to FIG. 1, the system that expands the spatiotemporal resolution of weather data and provides it to a terminal may include a server (100), a terminal, and a weather model. Here, the terminal may be a plurality of terminals.
[0028] However, since the system that expands the spatiotemporal resolution of the weather data of FIG. 1 and provides it to a terminal is merely one embodiment of the present invention, the present invention is not limited to FIG. 1 and may be configured differently from FIG. 1 according to various embodiments of the present invention.
[0029] Generally, each component of the system that provides the weather data of FIG. 1 to a terminal by expanding its spatiotemporal resolution is connected through a network. A network refers to a connection structure that enables information exchange between each node, such as terminals and servers, and includes a Local Area Network (LAN), a Wide Area Network (WAN), the Internet (WWW: World Wide Web), wired and wireless data communication networks, telephone networks, wired and wireless television communication networks, etc. Examples of wireless data communication networks include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Bluetooth communication, infrared communication, ultrasonic communication, Visible Light Communication (VLC), and LiFi.
[0030] The server (100) can expand the weather data of the weather model and provide it to the terminal, and can receive weather data with preset spatial resolution and temporal resolution from the weather model.
[0031] A weather model refers to a numerical model that can simulate complex interactions between climate elements by simplifying them into physical and dynamic numerical equations to explain each element constituting the climate system, such as the atmosphere, ocean, and land surface, and can derive weather data through simulation.
[0032] In this document, the weather model may be any one of the Global Forecast System (GFS), the European Centre for Medium Range Weather Forecasts (ECMWF), the Korea Weather Research and Forecasting Model (KWRF), the Japan Global Spectral Model (JGSM), and the Navy Operational Global Atmospheric Prediction System (NOGAPS), but is not limited thereto.
[0033] The server (100) can convert the weather data received from the weather model into matrix data (121a) including forecast values by longitude and latitude.
[0034] For example, the server (100) can convert the weather data received from the weather model into matrix data (121a) that includes forecast values by longitude based on each longitude on the vertical axis and forecast values by latitude based on each latitude on the horizontal axis.
[0035] The server (100) can convert the received weather data into matrix data (121a) at preset forecast time intervals and convert it into multiple matrix data (121) for each forecast time.
[0036] For example, the server (100) can convert multiple matrix data (121) for each forecast time using the vertical axis of longitude and latitude as the time axis.
[0037] Weather data can be information derived by weather models simulating the interactions between climate elements.
[0038] For example, weather data includes spatial resolution and temporal resolution, and specifically, may include at least one of a latitude range, a longitude range, a latitude-longitude interval, a number of longitude coordinates, a number of latitude coordinates, a forecast time, a time interval, a reception period, and a forecast value.
[0039] Spatial resolution can be an indicator of how finely a weather model can represent the weather data it simulates and predicts over a specific region.
[0040] It can be the smallest unit of the ground surface.
[0041] Temporal resolution can be an indicator of how often image data is obtained for a specific region that a weather model simulates and predicts.
[0042] The latitude and longitude ranges may be the range of the total area predicted by the weather model through simulation.
[0043] The latitude-longitude interval can be the angle between each latitude and between each longitude.
[0044] The number of longitude coordinates can be the total number of each longitude in the total area predicted by the weather model simulation.
[0045] The number of latitude coordinates can be the total number of each latitude in the total area predicted by the weather model simulation.
[0046] The forecast time can be the total length of time at the time resolution predicted by the weather model through simulation.
[0047] The time interval can be the period represented by the time resolution predicted by the weather model through simulation.
[0048] The reception period may be the period during which the weather model forecasts weather data derived from simulation.
[0049] The forecast value can be a value that a weather model can simulate to predict weather information between longitude and latitude coordinates.
[0050] For example, weather data information from weather models such as the Global Forecast System (GFS) and the European Centre for Medium Range Weather Forecasts (ECMWF) can be shown in Table 1 and Table 2.
[0051] 1 File format GRIB2 2 Latitude range -90–90 3 Longitude range -180–180 4 Latitude-Longitude interval 0.5 degrees 5 Number of longitude coordinates 7206 Number of latitude coordinates 3617 Forecast time 0h–384h 8 Hour interval 3 hour 9 Receive cycle 4 times / day (00, 06, 12, 18 UTC)
[0052] Table 1 shows the weather data information that can be received via GFS. Here, GRIB2 (GRIdded Binary) may be a file format used by programs in the weather / atmosphere / ocean field to store past and forecast weather data.
[0053] UTC stands for Coordinated Universal Time, which is the standard time used as an international standard.
[0054] 1 File format GRIB 2 2 Latitude range -90–90 3 Longitude range -180–180 4 Latitude-Longitude interval 0.4 degrees 5 Longitude coordinates 900 6 Latitude coordinates 451 7 Forecast time 0h–240h 8 Hour interval 3 hour 9 Receive cycle 2 times / day (00,12 UTC)
[0055] Table 2 shows the weather data information available from ECMWF. Looking at the number of longitude coordinates, latitude coordinates, and latitude-longitude intervals of the weather data from ECMWF (The European Centre for Medium Range Weather Forecasts), it can be seen that the spatial resolution is more detailed and larger than that of GFS (Global Forecast System).
[0056] The server (100) can divide the longitude and latitude of the converted matrix data (121a) into more detailed values than the preset spatial resolution, and expand the spatial resolution by interpolating forecast values at the locations of the divided longitude and latitude.
[0057] For example, the server (100) can expand the spatial resolution by dividing the intervals between each longitude and latitude of the transformation matrix data (121a) into intervals narrower than the preset intervals between each longitude and latitude, and interpolating forecast values at the locations of the divided longitudes and latitudes.
[0058] The server (100) can divide the forecast time interval of the weather data into more detailed segments than the preset time resolution and expand the time resolution by interpolating the forecast values into the divided forecast time intervals.
[0059] The server (100) can transmit weather data with expanded spatial resolution to the terminal.
[0060] Below, the operation of each component of the system that expands the spatiotemporal resolution of the weather data of Fig. 1 and provides it to a terminal will be explained in more detail.
[0061] FIG. 2 is a block diagram of the server (100) shown in FIG. 1 according to one embodiment of the present invention.
[0062] Referring to FIG. 2, the server (100) may include a receiving unit (110), a conversion unit (120), an amplification unit (130), and a transmission unit (140). However, the server (100) illustrated in FIG. 2 is merely one embodiment of the present invention, and various modifications are possible based on the components illustrated in FIG. 2.
[0063] The receiver (110) can receive weather data with preset spatial resolution and temporal resolution from a weather model.
[0064] The conversion unit (120) can convert the weather data received from the receiving unit (110) into matrix data (121a) including forecast values by longitude and latitude.
[0065] Matrix data (121a) has the horizontal axis as longitude and the vertical axis as latitude, and the longitude and latitude are used as coordinates, and may include forecast values for each coordinate of longitude and latitude.
[0066] Matrix data (121a) has the horizontal axis as longitude and the vertical axis as time, and the longitude and time as coordinates, and may include forecast values for each coordinate of longitude and time.
[0067] Matrix data (121a) has the horizontal axis as latitude and the vertical axis as time, and the latitude and time as coordinates, and can include forecast values for each coordinate of latitude and time.
[0068] For example, referring to FIG. 3, the conversion unit (120) can convert the longitude to a vertical axis at intervals of 0.5 and the latitude to a horizontal axis at intervals of 0.5 based on the weather data received from the receiving unit (110). The upper right area of the corresponding longitude and latitude can be the forecast value.
[0069] For example, if the longitude is 0.5 and the latitude is 0, the forecast value can be p1.
[0070] The conversion unit (120) can convert the received weather data into the matrix data (121a) at each preset forecast time interval and convert it into a plurality of matrix data (121) for each forecast time.
[0071] Additionally, for example, referring to FIG. 3, the conversion unit (120) can convert the matrix data (121a), which includes forecast values by longitude and latitude based on weather data received from the receiving unit (110) when the preset forecast time is 3 hours, into a plurality of matrix data (121) for 3 hours with a time axis perpendicular to the axes of longitude and latitude.
[0072] Returning to FIG. 2, the magnification unit (130) can divide the longitude and latitude of the matrix data (121a) converted from the conversion unit (120) into a more detailed spatial resolution than the preset spatial resolution, and expand the spatial resolution by interpolating forecast values at the locations of the divided longitude and latitude.
[0073] The enlargement section (130) can divide the latitude into more detailed parts than the preset spatial resolution using a double linear interpolation method, and enlarge the spatial resolution by interpolating forecast values to the positions of the divided longitude and latitude.
[0074] For example, referring to FIG. 4a, the magnification unit (130) divides the longitude and latitude of the matrix data (121a) converted from the conversion unit (120) into a more detailed range than the preset spatial resolution, and can derive forecast values at the locations of the divided longitude and latitude as shown in the following mathematical formula 1.
[0075]
[0076] Here, let W1 and W2 be the distances from point P to the sides of the rectangle along the horizontal axis, and h1 and h2 be the distances along the vertical axis. Let A, B, C, and D be the data values at four known points. Then, the data value at point P is calculated by Bilinear Interpolation as shown in the following Equation 1. where α=h1 / (h1+h2), β=h2 / (h1+h2), p=W1 / (W1+W2), and q=W2 / (W1+W2).
[0077] Referring to FIG. 4b, in matrix data (121a), the horizontal axis is latitude and the vertical axis is longitude, and forecast values (p1, p2, p3, p4) up to coordinates (1.1) with each interval of 0.5 can be expanded into matrix data (121a) with intervals between latitude and longitude of 0.01 days by applying bilinear interpolation.
[0078] When the values of A, B, C, and D in Equation 1 are denoted as p1, p2, p3, and p4, respectively, the longitude and latitude divided between the coordinate points of p1, p2, p3, and p4 can be used as coordinates, and the distance corresponding to the corresponding coordinate can be applied to Equation 1 to interpolate each forecast value.
[0079] The enlargement unit (130) can divide the forecast time interval of the weather data into more detailed segments than the preset time resolution and enlarge the time resolution by interpolating the forecast values into the divided forecast time intervals.
[0080] The enlargement section (130) can increase the number of the plurality of matrix data (121) by interpolating the forecast values in the divided forecast time intervals.
[0081] The enlargement section (130) can divide the latitude into more detailed parts than the preset time resolution using a double linear interpolation method, and enlarge the time resolution by interpolating forecast values to the divided longitude and latitude positions.
[0082] For example, referring to FIG. 4c, the horizontal axis is time and the vertical axis is longitude, and the forecast values (p1, p2, p3, p4) up to the coordinate (1.6) where the interval between longitudes is 0.5 and the interval between time is 3 can be expanded into matrix data (121a) divided into intervals between longitudes of 0.5 and intervals between time of 0.01 by applying Equation 1, which is a bilinear interpolation method.
[0083] For example, referring to FIG. 4d, the horizontal axis is time and the vertical axis is latitude, and the forecast values (p1, p2, p3, p4) up to the coordinate (1.6) where the interval between longitudes is 0.5 and the interval between time is 3 can be expanded into matrix data (121a) divided into intervals between latitudes of 0.5 and intervals between time of 0.01 by applying Equation 1, which is a bilinear interpolation method.
[0084] Returning to Fig. 2, the transmission unit (140) can transmit weather data with expanded spatial resolution to a terminal.
[0085] In addition, the transmission unit (140) can further transmit weather data with expanded time resolution to the terminal.
[0086] FIG. 5 is a flowchart illustrating a method for providing a terminal with expanded spatiotemporal resolution of weather data by a server (100) according to an embodiment of the present invention.
[0087] Referring to FIG. 5, in step S201, the server (100) can receive weather data from a weather model with preset spatial resolution and temporal resolution.
[0088] In step S202, the server (100) can convert the received weather data into matrix data (121a) including forecast values by longitude and latitude.
[0089] In step S203, the server (100) can divide the longitude and latitude of the matrix data (121a) into more detailed segments than the preset spatial resolution, and expand the spatial resolution by interpolating forecast values at the locations of the divided longitude and latitude.
[0090] In step S204, weather data with expanded spatial resolution can be transmitted to the terminal.
[0091] In the description above, steps S201 to S204 may be further divided into additional steps or combined into fewer steps, depending on an embodiment of the present invention. Additionally, some steps may be omitted as necessary, and the order between steps may be changed.
[0092] The method of providing a terminal with an enlarged spatiotemporal resolution of weather data described through FIGS. 1 to 5 may also be implemented in the form of a recording medium containing a computer program stored on a medium executed by a computer or instructions executable by a computer. Additionally, the method of providing a terminal with an enlarged spatiotemporal resolution of weather data described through FIGS. 1 to 5 may also be implemented in the form of a computer program stored on a medium executed by a computer.
[0093] One embodiment of the present invention may also be implemented in the form of a recording medium comprising computer-executable instructions, such as program modules executed by a computer. A computer-readable medium may be any available medium accessible by a computer and includes both volatile and non-volatile media, and both removable and non-removable media. Additionally, a computer-readable medium may include all computer storage media. A computer storage medium includes both volatile and non-volatile, removable and non-removable media implemented by any method or technique for storing information, such as computer-readable instructions, data structures, program modules, or other data.
[0094] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.
[0095] The scope of the present invention is defined by the claims set forth below rather than by the detailed description, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention.
Claims
1. In a server that provides weather data to a terminal by expanding its spatiotemporal resolution, A receiver that receives weather data having preset spatial resolution and temporal resolution from a weather model; A conversion unit that converts the received weather data into matrix data including forecast values by longitude and latitude based on the above-mentioned weather data; An enlargement unit that divides the longitude and latitude of the above matrix data into more detailed segments than the above preset spatial resolution, and expands the spatial resolution by interpolating forecast values at the locations of the divided longitude and latitude; and A server comprising a transmission unit that transmits weather data with the above-mentioned spatial resolution enlarged to a terminal.
2. In Paragraph 1, The above conversion unit is a server that converts the received weather data into the matrix data at preset forecast time intervals based on the received weather data, and converts it into a plurality of matrix data for each forecast time.
3. In Paragraph 2, The above-mentioned magnification unit divides the forecast time interval of the weather data into more detailed segments than the above-mentioned preset time resolution, and expands the time resolution by interpolating forecast values into the divided forecast time intervals. The above transmission unit further transmits the weather data with the enlarged time resolution to the terminal, server 4. In Paragraph 3, The above-mentioned amplification unit is a server that increases the number of the plurality of matrix data by interpolating forecast values in divided forecast time intervals.
5. In Paragraph 1, The above matrix data is a server in which longitude and latitude are used as coordinates and a forecast value is included for each coordinate of longitude and latitude.
6. In Paragraph 5, The above-mentioned magnification unit further includes interpolating the spatial resolution using bilinear interpolation of forecast values for each coordinate, server 7. A method for providing weather data to a terminal by expanding its spatiotemporal resolution, A step of receiving weather data having preset spatial resolution and temporal resolution from a weather model; A step of converting the received weather data into matrix data including forecast values by longitude and latitude based on the above-mentioned weather data; A step of dividing the longitude and latitude of the above matrix data into more detailed segments than the above preset spatial resolution, and expanding the spatial resolution by interpolating forecast values at the locations of the divided longitude and latitude; and The step of transmitting the weather data with the above-mentioned spatial resolution enlarged to a terminal A method that includes 8. In Paragraph 7, A method in which the step of converting into the above matrix data is to convert into the above matrix data at each preset forecast time interval based on the received weather data, thereby converting into a plurality of matrix data for each forecast time.
9. In Paragraph 8, The above-mentioned expanding step divides the forecast time interval of the weather data into more detailed segments than the above-mentioned preset time resolution, and expands the time resolution by interpolating forecast values into the divided forecast time intervals. A method in which the step of transmitting to the above terminal is to further transmit weather data with expanded time resolution to the terminal.
10. In Paragraph 9, The method wherein the above-mentioned expanding step is to increase the number of the plurality of matrix data by interpolating forecast values into divided forecast time intervals.
11. In Paragraph 7, A method in which the above matrix data has longitude and latitude as coordinates and includes a forecast value for each coordinate of longitude and latitude.
12. In Paragraph 11, The method further comprises the above-mentioned magnification section interpolating the spatial resolution through bilinear interpolation of the forecast values for each coordinate.
13. A computer program stored on a computer-readable recording medium comprising a sequence of instructions that expand weather data and provide it to a terminal, When the above computer program is executed by a computing server, Receive weather data with preset spatial and temporal resolutions from a weather model, and Based on the received weather data above, it is converted into matrix data including forecast values by longitude and latitude, and The longitude and latitude of the above matrix data are divided into more detailed segments than the above preset spatial resolution, and forecast values are interpolated at the locations of the divided longitude and latitude to expand the spatial resolution, and A computer program stored on a computer-readable recording medium, comprising a sequence of instructions for transmitting weather data with enlarged spatial resolution to a terminal.