Sea surface temperature calculating method and computer-readable recording medium storing program for executing the same

Abstract

To provide a sea surface temperature calculating method in which buoy data obtained by observation of a sea surface temperature of an area of interest by a buoy is combined with satellite data, and a computer-readable recording medium that stores a program for executing the method.SOLUTION: A method includes: a buoy data input step of at least one processor inputting buoy data obtained by observation of a sea surface temperature of an area of interest by a buoy; a satellite data input step of at least one processor inputting satellite data obtained by observation of the sea surface temperature of the area of interest by a satellite; a data combining step of: at least one processor combining the buoy data with the satellite data, thereby deriving a sea surface temperature of a blank zone where a sea surface temperature is blank in the satellite data, the blank zone being adjacent to land.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for calculating sea surface temperature, which uses buoy data observed from a buoy to derive sea surface temperature in a blank area where sea surface temperature is blank in satellite data as the area is adjacent to land, and a computer-readable recording medium having a program for executing the method recorded thereon. [Background technology]

[0002] Sea surface temperature is one of the most important variables for understanding ocean-atmosphere phenomena, such as ocean-atmosphere interactions and climate change. Global sea surface temperatures calculated from satellite-observed brightness temperatures have been applied not only to weather forecasts but also to climate forecasts. Infrared sensors mounted on polar and geostationary orbit satellites can provide global sea surface temperature fields with relatively high spatial resolution (polar orbits) and time resolution (geostationary orbits).

[0003] Algorithms for calculating sea surface temperature (SST) using brightness temperatures observed by satellites have been proposed over the past few decades. For example, the multi-channel sea surface temperature (MCSST) algorithm and the nonlinear sea surface temperature (NLSST) algorithm are representative empirical regression algorithms, and based on these, various algorithms are being developed to calculate sea surface temperature more accurately.

[0004] However, the algorithm for calculating sea surface temperatures (SST) using brightness temperatures observed by satellites creates areas in the satellite data where there are blanks for SST as the area comes close to land. This is because the coast, where the sea meets the land, has an unspecified and complex shape, and there are technical limitations to accurately observing it over long distances. In fact, for people on land who work in jobs that are sensitive to the climate, such as fishing, the sea surface temperature in the middle of a vast ocean is of no use. Instead, the sea surface temperature in the area closest to land is a fairly useful indicator.

[0005] Therefore, there is an urgent need in this technical field to develop a technology that can derive the sea surface temperature of the area closest to land that cannot be observed by satellite. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent No. 10-2016977 [Patent Document 2] Korean Patent No. 10-1575847 Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention has been made to solve the above problems, and an object of the present invention is to provide a method for calculating sea surface temperature in which buoy data observed from a buoy in an area of ​​interest is combined with satellite data so that the sea surface temperature of an area closest to land that cannot be observed by satellite can be derived, and a computer-readable recording medium having a program for executing the method recorded thereon.

[0008] The technical problems that the present invention aims to achieve are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the art from the description of the present invention. [Means for solving the problem]

[0009] In order to achieve the above object, the sea surface temperature calculation method of the present invention includes a buoy data input step in which at least one processor inputs buoy data in which sea surface temperatures in an area of ​​interest are observed from a buoy, a satellite data input step in which the at least one processor inputs satellite data in which sea surface temperatures in the area of ​​interest are observed from a satellite, and a data combination step in which the at least one processor combines the buoy data with the satellite data to derive sea surface temperatures in blank areas where sea surface temperatures are blank in the satellite data as the area is adjacent to land.

[0010] According to one aspect of the present invention, there is provided a computer-readable recording medium having a program recorded thereon for executing a sea surface temperature calculation method. Effect of the Invention

[0011] According to the present invention, there is a remarkable effect that the sea surface temperature of the blank area where the sea surface temperature is blank in the satellite data can be derived as the area approaches land. This allows people who work in the fishing industry to know the sea surface temperature adjacent to land, which is useful for their business that is directly related to their livelihood.

[0012] In addition, the present invention has the advantage of being able to minimize the amount of calculations by dividing satellite data into a plurality of pixels, grouping them, and arranging buoy data after leaving only representative pixels for each group.

[0013] In addition, the present invention has the advantage that buoy data observed from a buoy is pre-processed using a pre-set algorithm, thereby removing noise from the buoy data and deriving more accurate buoy data in blank areas of satellite data.

[0014] The effects of the present invention are not limited to the effects described above, and other effects not mentioned herein will be apparent to those skilled in the art from the detailed description and the claims. [Brief description of the drawings]

[0015] [Figure 1] 3 is a flowchart of a sea surface temperature calculation method according to the present invention. [Diagram 2] FIG. 2 is a diagram illustrating satellite data divided into a first pixel size according to an embodiment of the present invention. [Diagram 3] FIG. 2 illustrates a display of satellite data divided into second pixel sizes according to an embodiment of the present invention. [Figure 4] FIG. 1 illustrates a diagram illustrating pre-processing steps of buoy data according to an embodiment of the present invention. [Diagram 5] FIG. 13 is a diagram showing buoy data according to an input interval according to an embodiment of the present invention. [Figure 6] FIG. 13 is a diagram illustrating VU data that is shifted for calculation of standard deviation according to an embodiment of the present invention. [Figure 7] FIG. 13 is a diagram showing V-data for determining spike data according to an embodiment of the present invention. [Figure 8] 4 is a flow chart detailing the spike filtering steps according to one embodiment of the present invention; [Figure 9] 1A shows satellite data combined with buoy data according to an embodiment of the present invention, and FIG. 1B shows satellite data combined with pre-processed buoy data according to an embodiment of the present invention. [Figure 10] 13A shows satellite data combined with buoy data according to another embodiment of the present invention, and FIG. 13B shows satellite data combined with pre-processed buoy data according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] The terms used in this specification are generally used as widely as possible in consideration of the functions of the present invention, but may change depending on the intention of a person skilled in the art, precedents, the emergence of new technology, etc. In addition, in certain cases, the applicant may arbitrarily select terms, and in such cases, the meanings of the terms are described in detail in the detailed description of the invention. Therefore, the terms used in this specification are defined based on the meanings of the terms and the overall content of the present invention, rather than simply the names of the terms.

[0017] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention pertains. Terms as defined in commonly used dictionaries have the meaning consistent with the meaning they have in the context of the relevant art, and are not to be construed in an idealized or overly formal sense unless expressly defined in this application.

[0018] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, in which: FIG 1 is a flowchart of a method for calculating sea surface temperature according to the present invention;

[0019] Figure 2 is a diagram showing satellite data divided into a first pixel size according to an embodiment of the present invention. Figure 3 is a diagram showing satellite data divided into a second pixel size according to an embodiment of the present invention.

[0020] FIG. 4 is a diagram showing a veuic data pre-processing step (S200) according to an embodiment of the present invention. FIG. 5 is a diagram showing veuic data according to an input interval according to an embodiment of the present invention. FIG. 6 is a diagram showing veuic data shifted for calculating standard deviation according to an embodiment of the present invention. FIG. 7 is a diagram showing veuic data for determining spike data according to an embodiment of the present invention. FIG. 8 is a detailed flow chart of a spike filtering step (S230) according to an embodiment of the present invention.

[0021] Fig. 9 is a diagram showing satellite data combined with buoy data according to one embodiment of the present invention (a) and satellite data combined with pre-processed buoy data (b). Fig. 10 is a diagram showing satellite data combined with buoy data according to another embodiment of the present invention (a) and satellite data combined with pre-processed buoy data (b).

[0022] First, the present invention includes a computer-readable recording medium 120 having a program for executing the sea surface temperature calculation method recorded thereon. The recording medium 120 may be, for example, a CD, a DVD, a hard disk, a Blu-ray disk, a USB, a memory card, a ROM, etc. Furthermore, the sea surface temperature calculation method of the present invention may be implemented by at least one processor 110 in a computer device 100 reading the recording medium 120.

[0023] As shown in FIG. 1, the sea surface temperature calculation method of the present invention includes a buoy data input step (S100) in which at least one processor 110 inputs buoy data in which sea surface temperatures in an area of ​​interest are observed from a buoy, a satellite data input step (S300) in which the at least one processor 110 inputs satellite data in which sea surface temperatures in the area of ​​interest are observed from a satellite, and a data combination step (S400) in which the at least one processor 110 combines the buoy data with the satellite data to derive sea surface temperatures in blank areas where sea surface temperatures are blank in the satellite data as the area is adjacent to land.

[0024] The area of ​​interest referred to in the present invention is an area where the sea surface temperature is to be confirmed, and may include both land and sea, not just the sea. A buoy is a floating facility in the sea, equipped with various sensors and detectors, and capable of observing the sea surface temperature near its installation location in one-minute intervals. A number of buoys may be installed in various locations in the area of ​​interest, and buoy data may include the coordinates of the installation location of each buoy and the sea surface temperature near its installation location.

[0025] Here, in the buoy data input step (S100), the buoy data may be input at an input interval equal to the buoy observation interval or at an input interval different from the buoy observation interval. Without being limited to a specific method, the buoy observation interval according to the embodiment of the present invention may be 1 minute, and the input interval may be 10 minutes.

[0026] A satellite is an artificial object that is launched outside the atmosphere and flies mainly in a circular or elliptical orbit around the earth. Depending on the purpose of use, satellites can be classified into satellites for communication, broadcasting, meteorology, science, navigation, earth observation, technology development, and military. That is, the satellite data may be in the form of an image including the area of ​​interest and the latitude / longitude of the area of ​​interest. In addition, the satellite data may display the sea surface temperature of the ocean in the area of ​​interest in different colors. For example, as shown in FIG. 5, the satellite data has the land of Korea and the East Sea, South Sea, and West Sea as areas of interest. The horizontal axis of the satellite data displays 124 degrees to 130 degrees east longitude, and the vertical axis displays 33 degrees to 39 degrees north latitude. In addition, the satellite data displays the sea surface temperatures of the East Sea, South Sea, and West Sea in different colors. The closer to blue, the closer to 0 degrees Celsius, and the closer to red, the closer to 30 degrees Celsius.

[0027] Typically, sea surface temperatures in an area of ​​interest can be ascertained using satellite data alone, but satellites that make observations from long distances make it practically difficult to measure sea surface temperatures in areas adjacent to land or that are obscured by clouds.

[0028] [Example 1] Data integration using satellite data and buoy data In order to solve the above-mentioned problems, the data combination step (S400) may include a first division step (S410) in which a first grid G1 including a plurality of first pixels P1 is arranged in the satellite data and the satellite data is divided into a first pixel size, a second division step (S420) in which a second grid G2 including a plurality of second pixels P2 is arranged in the satellite data divided into the first pixel size and the satellite data divided into the first pixel size is further divided into a second pixel size, and a grouping step (S430) in which the satellite data divided into the second pixel size is grouped according to a previously set window size and the satellite data of all pixels except for the representative pixel PH for each group Gp is ​​deleted.

[0029] As shown in Fig. 2, the size of the first pixel is m x n, where m and n may be the same or different and are positive constants. For example, the size of the first pixel is 0.9 x 0.9, and the satellite data may be divided into 36 first pixels P1. That is, the satellite data may be arranged in a first grid Gd1 at intervals of 0.9 degrees latitude and 0.9 degrees height.

[0030] As shown in FIG. 3, the satellite data is divided into a size of a first pixel, and the sea surface temperature is displayed by brightness. Here, the size of the second pixel is k×h, where k and h may be the same or different and are positive constants. k and h are always smaller than m and n. For example, the size of the second pixel is 0.1×0.1, and the satellite data can be divided into 81 second pixels P2. That is, the satellite data can be arranged in a second grid Gd2 at intervals of 0.1 degrees latitude and 0.1 degrees height.

[0031] In addition, the size of the window referred to in the present invention may be set based on the size of the second pixel, and may be 3×3. The grouping step (S430) is characterized in that the second pixels of the groups Gp do not overlap. Most preferably, the representative pixel PH of each group Gp may be the second pixel located at the top left of each group Gp.

[0032] According to the present invention, by including the first division step (S410), the second division step (S420) and the third division step (S430), there is a remarkable effect that the amount of calculation can be minimized and the sea surface temperature of the blank area of ​​the satellite data can be derived.

[0033] The data combining step (S400) further includes a buoy data arranging step (S440) of arranging the buoy data according to the installation position of the buoy in the satellite data divided into the second pixel size, and is characterized in that Kriging Interpolation is applied. The buoy data may include coordinates for the installation position of the buoy. Here, the coordinates may be two-dimensional coordinates including latitude and altitude.

[0034] The Kriging Interpolation referred to in the present invention refers to a geostatistical method that predicts a characteristic value at a point of interest by linearly combining known surrounding values ​​in order to know the characteristic value. That is, in the data combination step (S400), Kriging Interpolation is applied to the satellite data divided into the size of the second pixel, which is the actual measurement value, and the buoy data arranged according to the installation position of the buoy, so that the sea surface temperature of the blank area where the sea surface temperature is blank in the satellite data as it is adjacent to the land can be derived.

[0035] [Example 2] Data integration using satellite data and pre-processed buoy data In order to solve the above-mentioned problems, the method further includes a buoy data pre-processing step (S200) in which the buoy data is pre-processed by the at least one processor 110 using a pre-set algorithm.

[0036] In the buoy data pre-processing step (S200), an algorithm according to the area of ​​interest may be set based on annual buoy data observed from a plurality of buoys in the area of ​​interest.

[0037] As shown in FIG. 4, the buoy data pre-processing step (S200) includes a numerical filtering step (S210) in which buoy data that is less than or exceeds a previously set numerical range is deleted from the buoy data, a standard deviation filtering step (S220) in which a standard deviation is calculated for a previously set number of buoy data in the buoy data, and buoy data that is less than or exceeds a previously set standard deviation range is deleted based on the standard deviation, a spike filtering step (S230) in which spike data is determined in the buoy data according to a previously set equation and then deleted, and a repetitive filtering step (S240) in which repetitive data that is repeated more than a previously set number of times in the buoy data is determined and then deleted.

[0038] As shown in Fig. 5, in the buoy data input step (S100), if the input interval is set to 10 minutes, 144 buoy data can be input for one day, 288 buoy data can be input for two days, and 52,560 buoy data can be input for one year. If two days are used as the basis, every time the most recent buoy data is input, 288 buoy data from the most recent buoy data can be pre-processed. In addition, in the buoy data pre-processing step (S200), a flag value (Flag) can be set to 1 for all input buoy data (S201).

[0039] Next, in the numerical filtering step (S210), buoy data within a previously set numerical range may be determined as normal data and a flag value may be set to 1, and buoy data below or exceeding the previously set numerical range may be determined as abnormal data and a flag value may be set to 2. The previously set numerical range may be between 5 degrees Celsius and 33 degrees Celsius in the case of the West Sea, East Sea, and South Sea adjacent to the Korean territory being areas of interest.

[0040] Next, in the standard deviation filtering step (S220), the standard deviation can be calculated for two days of buoy data. As shown in Fig. 6, if the standard deviation is calculated for 288 buoy data for the first and second days, the standard deviation filtering step (S220) can be updated by shifting 144 buoy data for one day and calculating the standard deviation for 288 buoy data for the second and third days.

[0041] In addition, the standard deviation filtering step (S220) is based on the standard deviation for the 288 buoy data. Buoy data within a set standard deviation range (STD Range) is determined to be normal data, and the flag value (Flag) can be set to 1. Buoy data below or exceeding the standard deviation range (STD Range) is determined to be abnormal data, and the flag value (Flag) can be set to 3.

[0042] Next, as shown in FIG. 7 and FIG. 8, the spike filtering step (S230) can determine spike data from buoy data that is determined to be normal data in the filtering step (S210) and the standard deviation filtering step (S220) among the buoy data for one year and has a flag value of 1.

[0043] In the spike filtering step (S230), an average Mean1 of a total of 12 VU data, which are 6 VU data input in the previous hour and 6 VU data input in the following hour, may be calculated for one VU data T1. Also, in the spike filtering step (S230), a difference between the maximum and minimum values ​​of the 12 VU data may be calculated. Furthermore, in the spike filtering step (S230), a minimum temperature difference, for example 0.0001, may be set, and the greater value between the minimum temperature difference and the difference between the maximum and minimum values ​​may be set to R1. Also, it may be determined whether T1 is spike data by the following Equation 1 (S231).

[0044]

number

[0045] Here, N is 5, R1 is the larger of the minimum temperature difference and the maximum and minimum temperature difference, T1 is one buoy data, and mean1 is the average of a total of 12 buoy data, which are the 6 buoy data input in the previous hour and the 6 buoy data input in the following hour based on one buoy data T1.

[0046] In the spike filtering step (S230), if the formula 1 is satisfied, one V data T1 is determined to be spike data and the flag value Flag can be set to 4. If not, the spike filtering step (S230) can further perform the following process.

[0047] In the spike filtering step (S230), the average Mean2 may be calculated from data excluding one VU data T2 among 12 VU data for the last two hours. In the case of the first and second times, one VU data among the VU data within the first time may be T2. Also, in the spike filtering step (S230), T2 may be excluded from the 12 VU data, and the difference between the maximum and minimum values ​​may be calculated. Furthermore, in the spike filtering step (S230), the greater value between the minimum temperature difference, for example 0.0001, and the difference between the maximum and minimum values ​​may be set to R2. Also, it may be determined whether T2 is spike data by the following Equation 2 (S232).

[0048]

number

[0049] Here, N is 5, R2 is the greater of the minimum temperature difference and the maximum and minimum temperature difference, T2 is one buoy data point among the 12 buoy data points for the first 2 hours, and mean2 is the average of the data points excluding one buoy data point, T2, among the 12 buoy data points for the first 2 hours.

[0050] In the spike filtering step (S230), if the formula 2 is satisfied, one V data T2 is determined to be spike data and the flag value Flag can be set to 4. If not, the spike filtering step (S230) can further perform the following process.

[0051] In the spike filtering step (S230), the average Mean3 may be calculated from data excluding one VU data T3 among 12 VU data for the last 2 hours. In the case of the n-1th time and the nth time, one VU data among the VU data within the nth time may be T3. In addition, in the spike filtering step (S230), T3 may be excluded from the 12 VU data, and the difference between the maximum value and the minimum value may be calculated. Furthermore, in the spike filtering step (S230), the greater value between the minimum temperature difference, for example 0.0001, and the difference between the maximum value and the minimum value may be set to R3. Also, it may be determined whether T3 is spike data by the following Equation 3 (S233).

[0052]

number

[0053] Here, N is 5, R3 is the greater of the minimum temperature difference and the maximum and minimum temperature difference, T3 is one buoy data item among the 12 buoy data items for the last 2 hours, and mean3 is the average of the data items excluding one buoy data item T3 among the 12 buoy data items for the last 2 hours.

[0054] Next, the repeated filtering step (S240) repeatedly determines data from buoy data that is determined to be normal data in the numerical filtering step (S210) and the standard deviation filtering step (S220) and has a flag value of 1 among the buoy data for one year. It is possible.

[0055] In the repeated filtering step (S240), a temperature change value, for example, 0.00001, may be set, and repeated data may be determined based on the temperature change value, as shown in Equation 4 below.

[0056]

number

[0057] Here, T4 is one piece of buoy data among the buoy data for one year that is determined to be normal data in the numerical filtering step (S210) and the standard deviation filtering step (S220) and has a flag value of 1, x is one piece of buoy data among the buoy data for 12 hours from T4, and reso is 0.00001, which is the temperature change value.

[0058] In the repeated filtering step (S240), if the formula 4 is satisfied, one buoy data T4 is determined to be repeated data and the flag value Flag may be set to 5. If not, the repeated filtering step (S240) may set the flag value Flag of one buoy data T4 to 1.

[0059] Therefore, the buoy data pre-processing step (S200) has the significant effect of deleting buoy data with flag values ​​of 2 to 5 and retaining only buoy data with flag value of 1, so that only the pre-processed buoy data can be used in the data combination step (S400), thereby enabling the sea surface temperature of the blank area to be derived more accurately.

[0060] That is, in the buoy data arrangement step (S440), the buoy data preprocessed in the buoy data preprocessing step (S200) can be arranged in the satellite data divided into the second pixel size according to the buoy installation position, and in the data combination step (S400), the preprocessed buoy data can be combined with the satellite data by applying Kriging Interpolation.

[0061] Looking at the satellite data according to one embodiment of Figure 2, it can be seen that there are many blank areas where sea surface temperature is blank in the sea adjacent to land for the region of interest. As shown in Figures 9(a) to 10(a), it can be seen that by combining the buoy data with satellite data, sea surface temperatures of blank areas where sea surface temperature is blank in the satellite data are derived as the land is adjacent. Also, as shown in Figures 9(b) to 10(b), it can be seen that by combining the pre-processed buoy data with satellite data, sea surface temperatures of blank areas where sea surface temperature is blank in the satellite data are derived as the land is adjacent.

[0062] These embodiments may include hardware, software, firmware, middleware, It may be embodied as microcode, hardware technology language, or any combination thereof. When embodied as software, firmware, middleware, or microcode, program code or code segments that perform the necessary operations may be stored in a computer readable storage medium and executed by one or more processors.

[0063] It should be noted that aspects of the subject matter described herein may be described in the general context of computer-executable instructions, such as program modules or components, executed by a computer. Generally, program modules or components include routines, programs, objects, and data structures that perform particular tasks or implement particular data types. Aspects of the subject matter described herein may be practiced in distributed computing environments where work is performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including memory storage devices.

[0064] Although the embodiments have been described above by way of limited embodiments and drawings, those skilled in the art will appreciate that various modifications and variations may be made from the above description. For example, the described techniques may be performed in a different manner than described, and / or the components of the described systems, structures, devices, circuits, etc. may be combined or combined in a different manner than described, or may be replaced or substituted by other components or equivalents, while still achieving suitable results.

[0065] Therefore, other embodiments, other examples, and equivalents of the claims are also intended to fall within the scope of the claims set forth below. [Explanation of symbols]

[0066] 100 Computer Equipment 110 Processor 120 Recording media Gd1 First grid P1 First pixel Gd2 Second lattice P2 Second pixel W Window Gp Group PH Representative pixel S100 Buoy data input step S200 buoy data pre-processing steps S210 Numerical filtering step S220 Standard deviation filtering step S230 Spike Filtering Step S240 Repeated filtering step S300 Satellite Data Entry Step S400 Data Binding Step S410 First division step S420 Second division step S430 Grouping step S440 Buoy data placement step

Claims

1. A buoy data input step in which buoy data obtained by observing sea surface temperatures in a region of interest from a buoy is input by at least one processor; a satellite data input step in which satellite data obtained by observing sea surface temperatures in the region of interest from a satellite is input by the at least one processor; A data combining step in which the buoy data is combined with satellite data by the at least one processor to derive sea surface temperatures of blank areas in which sea surface temperatures are blank in the satellite data as the blank areas are adjacent to the land; Includes A method for calculating sea surface temperature.

2. The data combining step includes: a first division step in which a first grid including a plurality of first pixels is arranged in the satellite data, and the satellite data is divided into pieces having a size of the first pixels; a second division step in which a second grid including a plurality of second pixels is arranged on the satellite data divided into the first pixel size, and the satellite data divided into the first pixel size is further divided into the second pixel size; A grouping step in which the satellite data divided into the second pixel size is grouped according to the already set window size, and the satellite data of all pixels except for the representative pixel for each group is deleted. The method for calculating sea surface temperature according to claim 1 .

3. The data combining step includes: The method further includes a buoy data arrangement step of arranging the buoy data according to a buoy installation position in the satellite data divided into the second pixel size, Kriging Interpolation is applied The sea surface temperature calculation method according to claim 2 .

4. The method further includes a buoy data pre-processing step in which the buoy data is pre-processed by the at least one processor using a pre-defined algorithm. The method for calculating sea surface temperature according to claim 1 .

5. The buoy data pre-processing step includes: A value filtering step in which buoy data that is less than or exceeds a value range that has already been set in the buoy data is deleted; A standard deviation filtering step in which a standard deviation is calculated for a pre-defined number of buoy data among the buoy data, and buoy data that is less than or exceeds a standard deviation range (STD range) set based on the standard deviation is deleted; A spike filtering step in which spike data is determined and then deleted from the V-data by a previously set formula; and a repeat filtering step of determining and then deleting repeat data that is repeated a preset number of times or more in the buoy data. The sea surface temperature calculation method according to claim 4.

6. A computer program for executing the method for calculating sea surface temperature according to any one of claims 1 to 5 is recorded on the computer. A computer-readable recording medium comprising:

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