• Atmosphere
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• v.27 no.1
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• pp.93-104
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• 2017

The effect of Advanced Microwave Sounding Unit-A (AMSU-A) observations on the short-range forecast in East Asia (EA) was investigated for the Northern Hemispheric (NH) summer and winter months, using the Forecast Sensitivity to Observations (FSO) method. For both periods, the contribution of radiosonde (TEMP) to the EA forecast was largest, followed by AIRCRAFT, AMSU-A, Infrared Atmospheric Sounding Interferometer (IASI), and the atmospheric motion vector of Communication, Ocean and Meteorological Satellite (COMS) or Multi-functional Transport Satellite (MTSAT). The contribution of AMSU-A sensor was largely originated from the NOAA 19, NOAA 18, and MetOp-A (NOAA 19 and 18) satellites in the NH summer (winter). The contribution of AMSU-A sensor on the MetOp-A (NOAA 18 and 19) satellites was large at 00 and 12 UTC (06 and 18 UTC) analysis times, which was associated with the scanning track of four satellites. The MetOp-A provided the radiance data over the Korea Peninsula in the morning (08:00~11:30 LST), which was important to the morning forecast. In the NH summer, the channel 5 observations on MetOp-A, NOAA 18, 19 along the seaside (along the ridge of the subtropical high) increased (decreased) the forecast error slightly (largely). In the NH winter, the channel 8 observations on NOAA 18 (NOAA 15 and MetOp-A) over the Eastern China (Tibetan Plateau) decreased (increased) the forecast error. The FSO provides useful information on the effect of each AMSU-A sensor on the EA forecasts, which leads guidance to better use of AMSU-A observations for EA regional numerical weather prediction.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.3
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• pp.177-185
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• 2012

Wave field measurements were made over a period of 18 days to study the spatial distribution of incident wave on seaweed tarm field around Gumil-up Sea, Wando, Korea. These measured data were compared with data from the Geomun-do ocean weather/wave observation buoy. A numerical simulation model that combined the offshore design wave with the seasonal normal incoming wave was used to study the incident wave distribution surrounding a seaweed farm. The results are summarized as follows. (1) On-site wave measurements showed that the major relationship between maximum and significant wave height was $H_{max}=1.6H_{1/3}$. (2) Offshore incident wave energy reaching the coast was greatly influenced by the wind direction. A north wind reduced the incident wave energy and a south wind increased it. (3) The calculated maximum wave height under the design wave boundany conditions was in the range of 4~5 m and the reduction in the incident wave height ratio ranged from approximately 38.1% to 47.6% at Gumil-up Sea. Under normal wave conditions, the maximum wave heights were 3.6~4.0 m in summer and 2.3~2.7 m in winter while the reduction in the incident wave height ratio was about 41.8% to 49.1%. (4) The sea state in the southern area of Gumil-up was the most affected by ocean waves, whereas the sea state in the northern area was very stable. The significant wave ratio in the south was about six times that in the north.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.22 no.3
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• pp.118-134
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• 2017

The radiation observation data will be used importantly in research field such as climatology, weather, architecture, agro-livestock and marine science. The Ieodo Ocean Research Station (IORS) is regarded as an ideal observatory because its location can minimize the solar radiation reflection from the surrounding background and also the data produced here can serve as a reference data for radiation observation. This station has the potential to emerge as a significant observatory and join a global radiation observation group such as the Baseline Surface Radiation Network (BSRN), if the surrounding of observatory is improved and be equipped with the essential radiation measuring instruments (pyaranometer and pyrheliometer). IORS has observed the solar radiation using a pyranometer since November 2004 and the data from January 1, 2005 to December 31, 2015 were analyzed in this study. During the period of this study, the daily mean solar radiation observed from IORS decreased to $-3.80W/m^2/year$ due to the variation of the sensor response in addition to the natural environment. Since the yellow sand and fine dust from China are of great interest to scientists around the world, it is necessary to establish a basis of global joint response through the radiation data obtained at the Ieodo as well as at Sinan Gageocho and Ongjin Socheongcho Ocean Research Station. So it is an urgent need to improve the observatory surrounding and the accuracy of the observed data.

• Atmosphere
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• v.21 no.1
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• pp.45-55
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• 2011

A definition on the tropical cyclone (TC) that influenced the Korean Peninsula (KP), the KP-influence TC, is widely used in the TC communities, but its criterion is not clear mainly due to the ambiguity and subjectiveness of the term such as 'influence', which led to the inconsistent TC statistical analysis. This study suggests a definition and criterion on the TC approaching to the KP (KP-approach TC) additionally, which is more obvious and objective than the KP-influence TC. In this study, the criterion on the KP-approach TC is determined when the TC's center from the RSMC best track data encounters the box areas of $28^{\circ}N{\sim}40^{\circ}N$ and $120^{\circ}E{\sim}138^{\circ}E$. The range is chosen by finding a minimum area that includes all official KP-influence TCs except three TCs that affected the KP as a tropical depression (TD). Statistical analysis reveals that, among total 1,537 TCs that occur in the western North Pacific during 1951-2008, the KP-approach TC was 472, the KP-influence TC was 187, and the KP-landfall TC was 87. August was the month that the largest TCs approach and influence to the KP. Finally, this paper suggests to determine the KP-influence TC by the strong wind and heavy rain advisories in the KP based on the observation after the storm's passage.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.2
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• pp.141-152
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• 2022

As a method to understand the ecological habits around the artificial reef, various reports such as fishing gear survey, diving, sound survey, underwater CCTV and camera, etc. are reported. Among them, the sound survey method is carried out by installing an acoustic system on the ship and can be investigated regardless of the marine environment such as time constraints and turbidity. Such method, however, takes a lot of manpower and time as the ship travels at a constant speed. Investigations around artificial reefs are being conducted in an artificial way, and a lot of time and labor are consumed as such. Maritime buoys have been operated for various purposes such as route signs, weather observation, marine environment monitoring and defense monitoring for navigation safety in the past, but studies on monitoring systems for ecological habits and distribution of fish using marine buoys are remarkably insufficient. Therefore, this study aims to develop a system that allows users to directly monitor fish group detector data by estimating the distribution of fish groups around artificial reefs and using wireless communication at sea. In order to confirm the suitability of the maritime buoy used in this study, it was operated to compare data using LTE-equipped buoys capable of wireless communication and a data logger-type system buoy. Data transmission of buoys capable of LTE communication was carried out in a 10-minute ON, 10-minute OFF method due to the limitation of the power supply capacity, and data of the data logger-type buoy received full data. We compared and analyzed the data received from the two fish detectors. It is expected that real-time monitoring of the wireless buoy detection device using LTE will be possible through future research.

• 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.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.26 no.3
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• pp.254-264
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• 1990

Using the meteorological data, surface weather map, and oceanographic data for 5 years(1984-1988). I investigated the characteristics of the fog occurrence and the role of the inshore in Pusan about the fog occurrence. And the meteorological data and sea surface temperature(SST), which were observed in July, 1989 in Suyoung Bay, were compared with those in Pusan. The fogs in Pusan concentrate in May, June and July. And at fog occurrence time the principal wind directions are Southwest(SW) winds, which easily supply with water vapor, and a series of Northeast(NE) wind. At the fog days pressure patterns are pattern 7 in spring time (March, April, and May) and pattern 10 and pattern 13 in summer time (June, July, and August). Also the advection fog(sea fog) is closely related with the relationship between warm and cold advection in 850~700mb and cold and warm SST rather than the increase of the instability of atmosphere in 850~500mb. The fogs in Taegu, which is the inland region, mainly occur at dawn in fall time due to the strong night radiation fog. On the other hand in Pusan the coastal region, the fogs occur from late spring time to summer time (May, June, and July). Because there is the abundant supply of the water vapor from the ocean owing to a series of South(S) wind at this time. Then the atmosphere, which has high relative humidity, reaches easily the supersaturation by the radiation cooling. In Suyoung Bay and Pusan the meteorological observation data, SST and fog days are almost similar. And I think that the mechanism of the fog occurrence nearly accords with both regions.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.3
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• pp.111-121
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• 2013

Accurate prediction of sea water temperature has been emphasized to make precise local weather forecast and to understand change of ecosystem. The Yellow Sea, which has turbid water and strong tidal current, is an unique shallow marginal sea. It is essential to include the effects of the turbidity and the strong tidal mixing for the realistic simulation of temperature distribution in the Yellow Sea. Evaluation of ocean circulation model response to vertical mixing scheme and turbidity is primary objective of this study. Three-dimensional ocean circulation model(Regional Ocean Modeling System) was used to perform numerical simulations. Mellor- Yamada level 2.5 closure (M-Y) and K-Profile Parameterization (KPP) scheme were selected for vertical mixing parameterization in this study. Effect of Jerlov water type 1, 3 and 5 was also evaluated. The simulated temperature distribution was compared with the observed data by National Fisheries Research and Development Institute to estimate model's response to turbidity and vertical mixing schemes in the Yellow Sea. Simulations with M-Y vertical mixing scheme produced relatively stronger vertical mixing and warmer bottom temperature than the observation. KPP scheme produced weaker vertical mixing and did not well reproduce tidal mixing front along the coast. However, KPP scheme keeps bottom temperature closer to the observation. Consequently, numerical ocean circulation simulations with M-Y vertical mixing scheme tends to produce well mixed vertical temperature structure and that with KPP vertical mixing scheme tends to make stratified vertical temperature structure. When Jerlov water type is higher, sea surface temperature is high and sea bottom temperature is low because downward shortwave radiation is almost absorbed near the sea surface.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.390-399
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• 2020

Underwater ambient noise was measured at the eastern and western costal sites of Jeju Island where the water depth was 20 m by a hydrophone moored at mid-depth (10 m) for 4 months. These eastern and western sites were selected as potential sites for offshore wind power generator and the current wave energy generator, respectively. Ambient noise was affected by environmental data such as wind and wave, which were collected from nearby weather stations and an observation station. Below 100 Hz, ambient noise was changed about 5 dB ~ 20 dB due to low and high tide. Below 1 kHz, wave and wind effects were the main source for ambient noise, varying up to 25 dB. Ambient noise was strongly influenced by wave at lower frequency and by wind at higher frequency up to over 1 kHz. The higher frequency range over 10 kHz was influenced by rainfall and biological sources, and the spectrum was measured about 10 dB higher than the peak spectrum level from Wenz curve at this frequency range.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.123-135
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• 2013

The reflectance observed in the visible channels of a geostationary meteorological satellite can be used to calculate the amount of cloud by comparing the reflectance with the observed solar radiation data at the ground. Using this, the solar radiation arriving at the surface can be estimated. This study used the Meteorological Imager (MI) reflectance observed at a wavelength of 675 nm and the Geostationary Ocean Color Imager (GOCI) reflectance observed at similar wavelengths of 660 and 680 nm. Cloudy days during a typhoon and sunny days with little cloud cover were compared using observation data from the geostationary satellite. Pixels that had more than 40% reflectance in the satellite images showed less than 0.3 of the cloud index and blocked more than 70% of the solar energy. Pixels that showed less than 15% reflectance showed more than 0.9 of the cloud index and let through more than 90% of the solar energy to the surface. The calculated daily accumulated solar radiation was compared with the observed daily accumulated solar radiation in 22 observatories of the Korean Meteorological Administration. The values calculated for the COMS and MTSAT MI sensors were smaller than the observation and showed low correlations of 0.94 and 0.93, respectively, which were smaller than the 0.96 correlation coefficient calculated for the GOCI sensor. The RMSEs of MTSAT, COMS MI and GOCI calculation results showed 2.21, 2.09, 2.02 MJ/$m^2$ in order. Comparison of the calculated daily accumulated results from the GOCI sensor with the observed data on the ground gave correlations and RMSEs for cloudy and sunny days of 0.96 and 0.86, and 1.82 MJ/$m^2$ and 2.27 MJ/$m^2$, respectively, indicating a slightly higher correlation for cloudy days. Compared to the meteorological imager, the geostationary ocean color imager in the COMS satellite has limited observation time and observation is not continuous. However, it has the advantage of providing high resolution so that it too can be useful for solar energy analysis.