Sea surface temperature calculation method and computer-readable recording medium having a program for executing the method recorded thereon

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 using buoy data observed from a buoy to derive sea surface temperature in blank areas where sea surface temperature is blank in satellite data due to proximity 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 interactions, climate change, and other ocean-atmosphere phenomena. 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 results in blank areas in the satellite data as the area approaches land. This is because the coast, where the sea meets the land, has an unspecified and complex shape, which means there are technical limitations to accurately measuring it over long distances. In fact, for people who work on land in climate-sensitive jobs such as fishing, the sea surface temperature in the middle of the 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 a pressing need in this technical field for 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 Registration 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 its object is to provide a method for calculating sea surface temperature in an area of ​​interest, in which buoy data observed from a buoy is combined with satellite data so that the sea surface temperature of the 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 above 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] 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 in the satellite data that are adjacent to the 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 method for calculating sea surface temperature. [Effects of the Invention]

[0011] The present invention has the remarkable effect of deriving sea surface temperatures in blank areas of satellite data that are often blank as the area approaches land, thereby enabling fishermen to know the sea surface temperatures adjacent to land, which is useful for their work 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, leaving only representative pixels for each group, and then arranging buoy data.

[0013] In addition, the present invention has the effect 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 those described above, and other effects not mentioned herein will be apparent to those skilled in the art from the detailed description and claims. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a flowchart of a method for calculating sea surface temperature according to the present invention. [Figure 2] FIG. 2 is a diagram illustrating satellite data divided into first pixel sizes according to an embodiment of the present invention. [Figure 3] FIG. 10 is a diagram illustrating satellite data divided into second pixel sizes according to an embodiment of the present invention. [Figure 4] FIG. 1 illustrates buoy data pre-processing steps according to one embodiment of the present invention. [Figure 5] FIG. 10 is a diagram showing buoy data according to an input interval according to an embodiment of the present invention. [Figure 6] FIG. 10 is a diagram illustrating buoy data being shifted for calculation of standard deviation according to one embodiment of the present invention. [Figure 7] FIG. 10 is a diagram showing buoy data for determining spike data according to an embodiment of the present invention. [Figure 8] 10 is a flowchart 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] 10A shows satellite data combined with buoy data according to another embodiment of the present invention, and FIG. 10B shows satellite data combined with pre-processed buoy data according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] The terms used in this specification are generally used and widely used in consideration of the functions of the present invention, but these may change depending on the intentions of those skilled in the art, legal precedents, the emergence of new technologies, etc. In addition, in certain cases, the applicant may arbitrarily select terms, and in such cases, their meanings will be 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 this 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: Figure 1 is a flowchart of a method for calculating sea surface temperature according to the present invention;

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

[0020] FIG. 4 is a diagram illustrating the buoy data preprocessing step (S200) according to one embodiment of the present invention. FIG. 5 is a diagram illustrating buoy data according to an input interval according to one embodiment of the present invention. FIG. 6 is a diagram illustrating buoy data shifted for calculating standard deviation according to one embodiment of the present invention. FIG. 7 is a diagram illustrating buoy data for determining spike data according to one embodiment of the present invention. FIG. 8 is a detailed flowchart of the spike filtering step (S230) according to one embodiment of the present invention.

[0021] 9A shows satellite data combined with buoy data according to one embodiment of the present invention, and FIG. 10B shows satellite data combined with buoy data according to another embodiment of the present invention, and FIG. 10B shows satellite data combined with buoy data according to another embodiment of the present invention, and FIG. 10C shows satellite data combined with buoy data according to another embodiment of the present invention, and FIG. 10B shows satellite data combined with preprocessed buoy data according to another embodiment of the present invention.

[0022] First, the present invention includes a computer-readable recording medium 120 having recorded thereon a program for executing the sea surface temperature calculation method. The recording medium 120 may be, for example, a CD, DVD, hard disk, Blu-ray disc, USB, memory card, ROM, etc. Furthermore, the sea surface temperature calculation method of the present invention can 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 due to proximity to land.

[0024] The area of ​​interest referred to in this invention is the area where the sea surface temperature is to be confirmed, and can 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 can observe the sea surface temperature near its installation location in one-minute increments. Multiple buoys can be installed in various locations in the area of ​​interest, and buoy data can include the coordinates of each buoy's installation location 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 an embodiment of the present invention may be 1 minute, and the input interval may be 10 minutes.

[0026] A satellite is an artificial object launched into the atmosphere and orbits the Earth in a circular or elliptical orbit. Depending on the purpose of use, satellites can be classified as satellites for communications, broadcasting, meteorology, science, navigation, Earth observation, technological development, and military purposes. That is, the satellite data may be in the form of an image including the area of ​​interest and its latitude and longitude. Furthermore, the satellite data may display the sea surface temperature of the ocean within the area of ​​interest in different colors. For example, as shown in FIG. 5, the satellite data indicates the land area of ​​Korea and the East Sea, South Sea, and West Sea as areas of interest. The horizontal axis of the satellite data represents longitudes 124° to 130° east, and the vertical axis represents latitudes 33° to 39° north. Furthermore, 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° Celsius, and the closer to red, the closer to 30° Celsius.

[0027] Normally, sea surface temperatures in an area of ​​interest can be determined using satellite data alone, but satellites that observe from long distances make it practically difficult to observe sea surface temperatures in areas adjacent to land or hidden by clouds.

[0028] [Example 1] Data integration using satellite data and buoy data To solve the above-mentioned problems, the data combining 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 first pixel sizes; 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 sizes and the satellite data divided into the first pixel sizes is further divided into second pixel sizes; and a grouping step (S430) in which the satellite data divided into the second pixel sizes is grouped according to a pre-set window size and the satellite data of all pixels except for the representative pixel PH for each group Gp is ​​deleted.

[0029] 2, the size of the first pixel is m×n, where m and n may be the same or different and are positive constants. For example, the size of the first pixel is 0.9×0.9, 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 horizontally.

[0030] As shown in FIG. 3, satellite data is divided into a size of first pixels, and sea surface temperature is displayed as 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 3x3. 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 significant effect that the amount of calculation can be minimized and the sea surface temperature in the blank area of ​​the satellite data can be derived.

[0033] The data combining step (S400) may further include a buoy data arranging step (S440) of arranging the buoy data in the satellite data divided into second pixel sizes according to the installation position of the buoy, and Kriging interpolation may be applied. The buoy data may include coordinates of the installation position of the buoy. Here, the coordinates may be two-dimensional coordinates including latitude and longitude.

[0034] The term "Kriging Interpolation" used in the present invention refers to a geostatistical method for predicting a characteristic value at a point of interest by linearly combining known surrounding values. That is, in the data combining step (S400), Kriging Interpolation is applied to satellite data divided into second pixel sizes, which are actual measurements, and buoy data arranged according to the installation positions of buoys, thereby deriving the sea surface temperature of blank areas in the satellite data where sea surface temperature is blank due to proximity to land.

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

[0036] In the buoy data pre-processing step (S200), an algorithm according to the area of ​​interest can 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 are less than or exceed a predetermined numerical range are deleted from the buoy data; a standard deviation filtering step (S220) in which a standard deviation is calculated for a predetermined number of buoy data from the buoy data, and buoy data that are less than or exceed a predetermined standard deviation range are deleted based on the standard deviation; a spike filtering step (S230) in which spike data are determined in the buoy data using a predetermined equation and then deleted; and a repetitive filtering step (S240) in which repetitive data that are repeated more than a predetermined number of times are determined in the buoy data and then deleted.

[0038] As shown in Figure 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, each time the most recent buoy data is input, the most recent 288 buoy data can be preprocessed. In addition, in the buoy data preprocessing 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 pre-set numerical range (Value Range) of the buoy data can be determined as normal data and a flag value (Flag) can be set to 1, and buoy data below or exceeding the pre-set numerical range (Value Range) can be determined as abnormal data and a flag value (Flag) can be set to 2. The pre-set numerical range (Value Range) can be between 5 degrees Celsius and 33 degrees Celsius in the case where the West Sea, East Sea, and South Sea adjacent to the territory of South Korea are 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 of the 288 buoy data. Buoy data within the set standard deviation range (STD Range) is determined as 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 as abnormal data, and the flag value (Flag) can be set to 3.

[0042] Next, as shown in Figures 7 and 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 standard deviation filtering step (S220) among the buoy data for one year and has a flag value of 1.

[0043] The spike filtering step (S230) may calculate the average Mean1 of a total of 12 buoy data, which are six buoy data input in the previous hour and six buoy data input in the following hour, for one buoy data T1. The spike filtering step (S230) may also calculate the difference between the maximum and minimum values ​​of the 12 buoy data. The spike filtering step (S230) may also set the minimum temperature difference, for example, 0.0001, and the larger value between the minimum temperature difference and the difference between the maximum and minimum values ​​may be set as R1. It may also 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 difference between the minimum temperature and the difference between the maximum and minimum values, T1 is one buoy data, and mean1 is the average of a total of 12 buoy data, which are the six buoy data input in the previous hour and the six buoy data input in the following hour, based on one buoy data T1.

[0046] In the spike filtering step (S230), if Equation 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 buoy data T2 among the 12 buoy data for the last two hours. In the case of the first and second times, one buoy data among the buoy data within the first time may be T2. In addition, in the spike filtering step (S230), T2 may be excluded from the 12 buoy 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. Furthermore, it may be determined whether T2 is spike data using the following Equation 2 (S232).

[0048]

number

[0049] Here, N is 5, R2 is the larger of the difference between the minimum temperature and the difference between the maximum and minimum values, 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 Equation 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 buoy data T3 among the 12 buoy data for the last two hours. In the case of the n-1th time and the nth time, one buoy data among the buoy data within the nth time may be T3. In addition, in the spike filtering step (S230), T3 may be excluded from the 12 buoy 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 R3. Furthermore, 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 larger value between the difference between the minimum temperature and the difference between the maximum and minimum values, T3 is one buoy data point among the 12 buoy data points for the last 2 hours, and mean3 is the average of the data points excluding one buoy data point, T3, among the 12 buoy data points for the last 2 hours.

[0054] Next, the repeated filtering step (S240) repeatedly determines data from buoy data that has been determined as normal data in the numerical filtering step (S210) and the standard deviation filtering step (S220) and has a flag value of 1. 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 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 buoy data among the buoy data for 12 hours from T4, and reso is the temperature change value of 0.00001.

[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 can be set to 5. Otherwise, in the repeated filtering step (S240), the flag value Flag of one buoy data T4 can be set to 1.

[0059] Therefore, in the buoy data pre-processing step (S200), buoy data with flag values ​​of 2 to 5 are deleted and only buoy data with flag value of 1 is maintained, and only the pre-processed buoy data can be used in the data combining step (S400), which has the significant effect of enabling more accurate derivation of sea surface temperatures in blank areas.

[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 temperatures are blank in the ocean adjacent to land in the region of interest. As shown in Figures 9(a) to 10(a), by combining the buoy data with satellite data, it can be seen that sea surface temperatures in blank areas where sea surface temperatures are blank in the satellite data are derived as the area approaches land. Furthermore, as shown in Figures 9(b) to 10(b), by combining the preprocessed buoy data with satellite data, it can be seen that sea surface temperatures in blank areas where sea surface temperatures are blank in the satellite data are derived as the area approaches land.

[0062] These embodiments may include hardware, software, firmware, middleware, It may be implemented as microcode, hardware technology language, or any combination thereof. When implemented as software, firmware, middleware, or microcode, program code or code segments that perform the necessary tasks 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, being 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 with reference to limited examples and drawings, those skilled in the art will recognize that various modifications and variations may be made to the above description. For example, the described techniques may be performed in a different order 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 substituted or replaced by other components or equivalents, and still achieve suitable results.

[0065] Therefore, other embodiments, other examples, and equivalents of the claims are also construed as falling within the scope of the claims set forth below. [Explanation of symbols]

[0066] 100 Computer Equipment 110 processors 120 Recording Media Gd1 First lattice P1 First pixel Gd2 second lattice P2 Second pixel W Window Gp Group PH Representative pixel S100 Buoy data input step S200 buoy data preprocessing steps S210 Numerical filtering step S220 Standard deviation filtering step S230 Spike Filtering Step S240 Repeated filtering step S300 Satellite Data Input Step S400 Data Merge 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; and a data combining step by the at least one processor to combine the buoy data with satellite data, thereby deriving sea surface temperatures in blank areas where sea surface temperatures are blank in the satellite data as the blank areas are adjacent to the land. The data combining step includes: a first division step in which a first grid including a plurality of first pixels is arranged on the satellite data, and the satellite data is divided into pieces of 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 size of the first pixel, and the satellite data divided into the size of the first pixel is further divided into the size of the second pixel; a grouping step in which the satellite data divided into the second pixel size is grouped according to the previously set window size, and satellite data of all pixels except for representative pixels for each group is deleted; The method further includes a buoy data arrangement step of arranging the buoy data in the satellite data divided into the second pixel size according to a buoy installation position, Kriging interpolation is applied to the buoy data to be placed, As the area becomes adjacent to land, the sea surface temperature in the satellite data becomes blank. A method for calculating sea surface temperature.

2. The method further includes a buoy data preprocessing step in which the buoy data is preprocessed by the at least one processor using a predefined algorithm. The method for calculating sea surface temperature according to claim 1 .

3. The buoy data pre-processing step includes: a numerical filtering step in which buoy data that is below or exceeds a predetermined value range is deleted from the buoy data; a standard deviation filtering step in which a standard deviation is calculated for a predetermined 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 by a pre-defined formula in the buoy data and then deleted; and a repetitive filtering step of determining and then deleting repetitive data that is repeated more than a predetermined number of times in the buoy data. The method for calculating sea surface temperature according to claim 2.

4. A program for executing the sea surface temperature calculation method according to any one of claims 1 to 3 is recorded. A computer-readable recording medium.

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