• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.518-521
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• 2007

To retrieve Sea Surface Temperature(SST) from NOAA-AVHRR imagery the spilt window atmospheric correction algorithm is generally used. Recently, there have been various new algorithms developed to process these data, namely the variable-coefficient split-window, the R54 transmittance-ratio method, fixed-coefficient nonlinear algorithm, dynamic water vapour (DWV) correction method, Dynamic Water Vapour and Temperature algorithm (DWVT). We used MCSST (Multi-Channel Sea surface temperature) and NLSST(Non linear sea surface temperature) algorithms in this study. The study area is around the Korea sea area (Yellow Sea). We compared and analyzed with various methods by applying each Ocean in-situ data and satellite data. The primary aim of study is to verify and optimize algorithms. Finally, this study proposes an optimized algorithm for SST retrieval.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.236-241
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• 1998

The NOAA AVHRR remote sense SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the seas adjacent to Korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple 557 images, all of images must be aligned exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which yields automatic detections of cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$ 3$^{\circ}C$ as a criterion of SST anomalies). The remote sense SST data are tuned by comparing remote sense data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel. The SST anomalies are studied by statistical method. We found that the SST anomalies are rather persistent with time scales between 1 and 2 months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST Model fit of SST anomalies to the Markov process model yields that autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. We plan to improve our algorithms of automatic cloud pixel detection and prediction of future SST. Our algorithm is expected to be incorporated to the operational real time service of SST around Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.20-27
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• 2004

The hydro-dynamical and ecological coupled model were applied in the Tokyo Bay, to evaluate the flow pattern including water quality parameters and the distribution of biomass flux, and to compare with the results obtained from the satellite data during March 2001. The flow pattern and salinity distribution obtained from the present model were nearly identical with those of the previous studies. SST from NOAA/AVHRR was $2.5^{\circ}C$ higher than model results in the mouth of bay and $0.5^{\circ}C$ lower than model results in the inner bay, respectively. It was found that the concentration of chlorophyll-a estimated from SeaWiFS was considerably higher than that of model result, regardless similar distribution pattern. This disagreement will be studied through the more elaborate investigation in the future.

• Proceedings of the KSRS Conference
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• v.2
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• pp.1027-1030
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• 2006

Development of diurnal warming in the open Okhotsk Sea during the daytime and calm conditions was studied using sea surface temperature (SST) fields retrieved from NOAA AVHRR, Terra and Aqua MODIS, Aqua AMSR-E and ADEOS-II AMSR data. Sea surface wind fields were estimated from AMSR-E/AMSR measurements as well as were obtained from QuikSCAT scatterometer. Weak winds and cloudless conditions were observed in the central area of anticyclone, which moved slowly on 28-30 June 2003 east off Sakhalin. The area where the amplitude of the diurnal SST signal ${\Delta}T$ was significant also shifted slowly and had or circular or elongated shape. The ${\Delta}T$ was estimated relative to the SST values in the areas surrounding the centre of anticyclone where wind speed W exceeded 5- 6 m/s. The diurnal variations of SST, day-night differences were computed using NOAA-12 and NOAA-16 AVHRRderived data. Analysis of simultaneous SST and W fields showed that the increase of W from 0 to 5-6 m/s causes the decrease of ${\Delta}T$ to zero. Maximum warming exceeded $8^{\circ}C$ and was observed in the centre of anticyclone where W = 0 m/s. So strong heating was likely due to the increased chlorophyll a concentration in the area under study that follows from analysis of satellite ocean colour data.

• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.58-60
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• 2003

Altimeter(Topex/Poseidon) and AVHRR(NOAA) data were used to study the variations and correlations of Sea Level(SL) and Sea Surface Temperature (SST) in the North East Asian Seas from November 1993 to May 1998. This region is influenced simultaneously to continental and oceanic climate. SL and SST have increased gradually every year because the global warming, and presented usually a strong annual variations in Kuroshio extension region with the influence of bottom topography.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.3
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• pp.165-173
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• 2010

A cloud detection method is introduced to improve the reliability of NOAA/AVHRR Sea Surface Temperature (SST) data processed during the daytime and nighttime in the TeraScan System. In daytime, the channels 2 and 4 are used to detect a cloud using the three tests, which are spatial uniformity tests of brightness temperature (infrared channel 4) and channel 2 albedo, and reflectivity threshold test for visible channel 2. Meanwhile, the nighttime cloud detection tests are performed by using the channels 3 and 4, because the channel 2 data are not available in nighttime. This process include the dual channel brightness temperature difference (ch3 - ch4) and infrared channel brightness temperature threshold tests. For a comparison of daytime and nighttime SST images, two data used here are obtained at 0:28 (UTC) and 21:00 (UTC) on May 13, 2009. 6 parameters was tested to understand the factors that affect a cloud masking in and around Korean Peninsula. In daytime, the thresholds for ch2_max cover a range 3 through 8, and ch4_delta and ch2_delta are fixed on 5 and 2, respectively. In nighttime, the threshold range of ch3_minus_ch4 is from -1 to 0, and ch4_delta and min_ch4_temp have the fixed thresholds with 3.5 and 0, respectively. It is acceptable that the resulted images represent a reliability of SST according to the change of cloud masking area by each level. In the future, the accuracy of SST will be verified, and an assimilation method for SST data should be tested for a reliability improvement considering an atmospheric characteristic of research area around Korean Peninsula.

• Proceedings of the KSRS Conference
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• v.2
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• pp.831-835
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• 2006

NOAA/AVHRR data were used to analyze sea surface temperatures (SSTs) and thermal fronts (TFs) in the Korean seas. Temporal and spatial analyses were based on data from 1993 to 2000. Harmonic analysis revealed mean SST distributions of $10{\sim}25^{\circ}C$. Annual amplitudes and phases were $4{\sim}11^{\circ}C$ and $210{\sim}240^{\circ}$, respectively. Inverse distributions of annual amplitudes and phases were found for the study seas, with the exception of the East China Sea, which is affected by the Kuroshio Current. Areas with high amplitudes (large variations in SSTs) showed 'low phases' (early maximum SST); areas with low amplitudes (small variations in SSTs) had 'high phases' (late maximum SST). Empirical orthogonal function (EOF) analyses of SSTs revealed a first-mode variance of 97.6%. Annually, greater SST variations occurred closer to the continent. Temporal components of the second mode showed higher values in 1993, 1994, and 1995. These phenomena seemed to the effect of El $Ni{\tilde{n}}o$. The Sobel edge detection method (SEDM) delineated four fronts: the Subpolar Front (SPF) separating the northern and southern parts of the East Sea; the Kuroshio Front (KF) in the East China Sea, the South Sea Coastal Front (SSCF) in the South Sea, and a tidal front (TDF) in the West Sea. Thermal fronts generally occurred over steep bathymetric slopes. Annual amplitudes and phases were bounded within these frontal areas. EOF analysis of SST gradient values revealed the temporal and spatial variations in the TFs. The SPF and SSCF were most intense in March and October; the KF was most significant in March and May.

• Ocean and Polar Research
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• v.23 no.2
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• pp.189-194
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• 2001

A practical algorithm for prediction of the sea surface temperatures (SST)from the satellite remote sensing data is presented in this paper. The fluctuations of SST consist of deterministic normals and stochastic anomalies. Due to large thermal inertia of sea water, the SST anomalies can be modelled by autoregressive or Markov process, and its near future values can be predicted provided the recent values of SST are available. The actual SST is predicted by superposing the pre-known SST normals and the predicted SST anomalies. We applied this prediction algorithm to the NOAA AVHRR weekly SST data for 18 years (1981-1998) in the seas adjacent to Korea (115-$145^{\circ}E$, 20-$55^{\circ}N$). The algorithm is applicable not only for prediction of SST in near future but also for nowcast of SST in the cloud covered regions.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.237-242
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• 1999

As one of the key parameters to determine $CO_2$ flux between air - sea interface, it is quite important to know p$CO_2$, which has involved much uncertainty, mainly due to the complex variations of sea surface p$CO_2$ and the paucity of samples, made in ocean. In order to improve the interrelationship between partial pressure (p$CO_2$) and different physical and biochemical parameters in global sea surface water, a new empirical relation is established to correlate and parameterize p$CO_2$ in the mixed layer using the data from recent WOCE cruises. Meanwhile, by new empirical relation, abundant historical hydrographic and nutrients ship data, Levitus data set and NOAA/AVHRR(SST), p$CO_2$ have been accumulated and applied. Then effort has to be made fur promotion of this study to correlate and parameterize p$CO_2$ in the mixed Layer with different physical and biochemical parameters. and further attribute this huge historical data sets and NOAA/AVHRR(SST) data to estimate p$CO_2$. In this paper we analyzed more interrelationship between the model and ship/satellite data set. Finally, the inter-annual variations of p$CO_2$ in sea are presented and discussed.

• Proceedings of the KSRS Conference
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• v.2
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• pp.880-883
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• 2006

Altimeter(Topex/Poseidon) and AVHRR(NOAA) data were used to study the variations and correlations of Sea Level(SL) and Sea Surface Temperature (SST) in the North East Asian Seas from November 1993 to May 1998. This region is influenced simultaneously to continental and oceanic climate as the border of the East Sea(Japan Sea). SL and SST have increased gradually every year because the global warming, and presented usually a strong annual variations in Kuroshio extension region with the influence of bottom topography.