• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Proceedings of the KSRS Conference
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• v.1
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• pp.146-149
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• 2006

Marine surface winds observed by two microwave sensors, SeaWinds and Advanced Microwave Scanning Radiometer (AMSR), on the Advanced Earth Observing Satellite-II (ADEOS-II) are evaluated by comparison with off-shore moored buoy observations. The wind speed and direction observed by SeaWinds are in good agreement with buoy data with root-mean-squared (rms) differences of approximately 1 m $s^{-1}$ and $20^{\circ}$, respectively. No systematic biases depending on wind speed or cross-track wind vector cell location are discernible. The effects of oceanographic and atmospheric environments on the scatterometry are negligible. The wind speed observed by AMSR also exhibited reasonable agreement with the buoy data in general with rms difference of 1.2 m $s^{-1}$. Systematic bias which was observed in earlier versions of the AMSR winds has been removed by algorithm refinements. Intercomparison of wind speeds globally observed by SeaWinds and AMSR on the same orbits also shows good agreements. Global wind speed histograms of the SeaWinds data and European Centre for Medium-range Weather Forecasts (ECMWF) analyses agree precisely with each other, while that of the AMSR wind shows slight deviation from them.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.72-74
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• 2022

Several types of data are collected from buoy due to the development of hardware technology.. However, the collected data are difficult to use due to errors including missing values and outliers depending on mechanical faults and meteorological environment. Therefore, in this study, linear interpolation is performed by adding the missing time data to enable machine learning to the insufficient meteorological data. After the linear interpolation, XGBoost and KNN-regressor, are used to forecast error data and suggested model is evaluated by using real-world data of a buoy.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.313-320
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• 2022

The light buoy installed on the sea is always flexible, because it is affected by the weather as well as passing vessels. The position of the light buoy can be cached through the AtoN AIS (Automatic Identification System) and RTU (Remote Terminal Unit). This study analyzed the position data of the light buoys for the last five years (2017-2021), as well as the distribution of the light buoys within the maximum separated position. As a result, there was a basic error of 17.9% in the position data. Additionally, the separated position error of 197 light buoys to be analyzed was 70.64%, and the AtoN RTU was worse than the AtoN AIS by equipment. On the other hand, as a result of the plotting the position data of the light buoy, it was classified into four types. The most common percussion center type, the percussion center dichotomous type in which the position is divided into two zones based on the chimney, the central movement type with a fluctuating center, and the drag type, in which the position is deviated from the center for a certain period. Except for Type-1, the type was determined according to the position at which the light buoy was installed. This study is the first to analyze the position data of the light buoy, and it is expected that it will contribute to the improvement of the quality of the position data of the light buoy.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.301-308
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• 2011

The temporal variations of methene emission and its relation to air temperature were investigated in Upo-swamp during June 2007 through July 2008. To perform this study, the methene emission and air temperature were observed using the buoy-type chamber and automatic weather observation system(AWS), respectively. The methene emissions were much during summertime(June~August). The maximum value(about 73.4 mg/$m^2$/hr) appeared at August. The emission diminished by degrees after August. The methene emissions were fewer from September to May of the following year. The peak value(73.4 mg/$m^2$/hr) of the methene emission is very much compared to that of rice pappy field known as about 28.7 mg/$m^2$/hr.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.355-361
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• 2023

Temperature of the Earth's surface is a crucial physical variable in understanding weather and atmospheric dynamics and in coping with extreme heat events that have a great impact on living organismsincluding humans. Thermalsensors on satellites have been a useful meansfor acquiring surface temperature information for wide areas on the globe, and thus characterization of its estimation uncertainty is of central importance for the utilization of the data. Among various factors that affect the estimation, the uncertainty caused by the algorithm itself has not been tested for the atmospheric environment of Korean vicinity. Thisstudy derivesthe uncertainty of the single-channel algorithm under the local atmospheric and oceanic conditions by using reanalysis data and buoy temperature data collected around Korea. Atmospheric profiles were retrieved from two types of reanalysis data, the fifth generation of European Centre for Medium-Range Weather Forecasts reanalysis of the global climate and weather (ERA5) and Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2) to investigate the effect of reanalysis data. MODerate resolution atmospheric TRANsmission (MODTRAN) was used as a radiative transfer code for simulating top of atmosphere radiance and the atmospheric correction for the temperature estimation. Water temperatures used for MODTRAN simulations and uncertainty estimation for the single-channel algorithm were obtained from marine weather buoyslocated in seas around the Korean Peninsula. Experiment results showed that the uncertainty of the algorithm varies by the water vapor contents in the atmosphere and is around 0.35K in the driest atmosphere and 0.46K in overall, regardless of the reanalysis data type. The uncertainty increased roughly in a linear manner as total precipitable water increased.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.350-357
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• 2023

Marine accidents frequently occur due to the unreasonable operation of ships excluded from ship departure control during marine special weather warnings within smooth water areas. Coastal wave buoys installed in smooth water areas are major reference indicators for ship departure control and can be seen as being directly connected to the safety of ships navigating smooth water areas and the coast. In this study, the location appropriateness of currently operating coastal wave buoys and additional installation in the smooth water areas were assessed by analyzing coastal marine accidents over the past 30 years (1991-2020), the main wind direction and wind speed of each major trading port, and the GICOMS ship track data in 2018. The study results showed that an additional coastal wave buoy should be installed at each of the major trading ports(Inchon Port, Pohang Port, Ulsan Port, and Busan Port) and that the location of the coastal wave buoy needs to be moved in the case of Busan Port. Based on various data analysis in this study, the suggestion for an additional installation and movement of the coastal wave buoy presented in this study is expected to contribute to improving the reliability of ship departure control and resolving safety blind spots.

• Journal of the Korean Institute of Navigation
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• v.16 no.3
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• pp.19-31
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• 1992

The Navigational System is the Fundamental System of Port Transportation System and comprises 3 Subsystems, say, the Waterway System, the Shiphandling System and the Support System. The Waterway System of Navigational System is the important and fundamental System for Traffic Safety inside the Port like a Car Road System on Land. This study aims to make a Guideline for the Optimal Waterway System of Port Development and Safety. The Conclusion of this Paper are drawn : 1) The complicated Shiphandling Operations should be avoided for the period of Physical night Time for eliminating the Human Errors. 2) For the Maneuverability and all-weather Combined Piloting the Inside Turn Point Buoy and Begin the-turn Buoy should be mounted with Racon(T) and Radar Reflector for foggy and bad weathers. 3) The Seabuoy located in the Approaching Area for Pilot Station and making Landfall should be mounted with Racon(G) and Morese A Light for giving a Hint of Pilot Station to the Captain on the Bridge, and these Equipments of Racon and Light should be operated normally and effectively even in a Heavy and stormy weathers. 4) A Basic Practical Expression, 1/2 L sin D, for calculating the Extra Width of Cutoff Turn Regions was derived Originally from the Viewpoint of Turn Maneuvers and Maneuverability of the Ship.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.33-36
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• 2007

We have persistently constructed gridded products of surface wind/wind stress over the world ocean using satellite scatterometer (ERS and Qscat). They are available for users as the Japanese Ocean Flux data sets with Use of Remote sensing Observation (J-OFURO) data together with heat flux components. Recently, a new version data of the Qscat/SeaWinds based on improved algorithm for rain flag and high wind-speed range have been delivered, and allowed us to reconstruct gridded product with higher spatial resolution. These products are validated by comparisons with in-situ measurement data by mooring buoys such as TAO/TRITON, NDBC and the Kuroshio Extension Observation (KEO) buoys, together with numerical weather prediction model products such as the NCEP-1 and 2. Results reveal that the new product has almost the same magnitude in mean difference as the previous version of Qscat product and much smaller than the NCEP-1 and 2. On the other hand, it is slightly larger root-mean-square (RMS) difference than the previous one and NCEPs for the comparison using the KEO buoy data. This may be due to the deficit of high wind speed data in the buoy measurement. The high resolution product, together with sea surface temperature (SST) one, is used to examine a new type of relationship between the lower atmosphere and upper ocean in the Kuroshio Extension region.

• Journal of Navigation and Port Research
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• v.46 no.1
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• pp.11-17
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• 2022

The high wind wave alert has a great impact on ships and maritime service workers navigating in the vicinity of Korea. This study aims to evaluate the appropriateness of the high wind wave alert by comparing and analyzing the observation data of major marine meteorological buoys with the high wind wave alerts in the coastal sea and offshore of the West Sea, South Sea, and the East Sea announced by the Korea Meteorological Administration for the past 11 years(2010-2020). As a result of comparing the daily, monthly, and annual statistics of the high wind wave alert and marine meteorological buoy observation data for each sea area by annual, monthly, and seasonal average, the accuracy of high wind wave alerts was very low across the entire sea area. In particular, it was analyzed that the accuracy in the coastal sea of the South Sea and Jejudo was the lowest in winter. It was confirmed that the accuracy of marine weather forecasts and alerts needs to be improved when considering the effects of the high wind wave alerts on fishing vessels, passenger ships operations and tourism, and marine leisure activities.