• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.953-968
• /
• 2018

In order to understand the change of surface water temperature in the East China Sea (ECS), this study analyzed the relationship between sea surface temperature (SST), air temperature (AT) and heat flux using satellite and model reanalysis data from 2003 to 2017. SST in the ECS showed the lowest (average : $13.72^{\circ}C$) in March and the highest (average : $28.12^{\circ}C$) in August. AT is highly correlated with SST and shows a similar seasonal change. In August, SST is higher than AT and then continuously higher than AT until winter. To analyze the change of the summer SST in the ECS, we used the SST anomaly value in August to classify the periods with positive (04', 06', 07', 13', 16', 17') and negative (03', 05', 08', 09', 10', 11', 12', 14', 15') values. Spatial similarity between the two periods indicates that SSTs are relatively larger variations in the northern part than in the southern part, and in the western part than in the eastern part in the study area. AT and net heat flux values also show similar changes with SST. However, the periods of the positive SST anomaly have the relatively increasing SST, AT and heat flux values compared to the periods of the negative SST anomaly in the summer season of the ECS. Although the change of SST in the summer season generally well correlates with AT, there were the periods when it was different from general trends between SST and AT (10', 12', 15', 16'). SST in August 2010 and 2012 decreased by $0.5^{\circ}C$ from AT. It suggests that the decreasing SST was considered to be caused by the effects of the typhoon passing through the study area. In August 2015, AT was relatively lower than SST (> $0.5^{\circ}C$), which is might be weakening of the East Asian Summer Monsoon. In August 2016, SST and AT show the highest values during the whole study periods, but SST is higher than AT (> $1^{\circ}C$). From satellite and heat flux data, the variations of SST have been shown to be relatively higher in the area of the expansion Changjiang Diluted Water (CDW) originated from the China coast. More research is needed to analyze this phenomenon, it is believed as not only the effect of rising AT but also the expansion of the low-salinity water.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.147-147
• /
• 2022

In January 2021 heavy flood affected South Kalimantan with causing many casualties. The heavy rainfall is predicted to be generated due to the ENSO (El Nino-Southern Oscillation). The weak La-Nina mode appeared to generate more convective cloud above the warmed ocean and result in extreme rainfall with high anomaly compared to past historical rainfall event. Subsequently, the antecedent soil moisture distribution showed to have an important role in generating the flood response. Saturated flow and infiltration excess mainly contributed to the runoff generation due to the high moisture capacity. The hydro-meteorological processes in this event were deeply analyzed using the coupled atmospheric model of Weather Research and Forecasting (WRF) and the hydrological model extension (WRF-Hydro). The sensitivity analysis of the flood response to the SST anomaly and the soil moisture capacity also compared. Result showed that although SST and soil moisture are the main contributors, soil moisture have more significant contribution to the runoff generation despite of anomaly rainfall occurred. Model performance was validated using the Global Precipitation Measurement (GPM) and Soil Moisture Operational Products System (SMOPS) and performed reasonably well. The model was able to capture the hydro-meteorological process of atmosphere and hydrological feedbacks in the extreme weather event.

• Ocean and Polar Research
• /
• v.23 no.4
• /
• pp.361-376
• /
• 2001

The characteristics of SST variability in the East Sea are analyzed using NOAA/AVHRR weekly SST data with about $0.18^{\circ}{\times}0.18^{\circ}$ resolution ($1981{\sim}2000$) and reconstructed historical monthly SST data with $2^{\circ}{\times}2^{\circ}$ resolution $(1950{\sim}1998)$. The distinct feature of wintertime SST is high variability in the western and eastern parts of $38^{\circ}{\sim}40^{\circ}$ latitudinal band, which are the northern boundary of warm current in the East Sea during winter. However, summertime SST exhibits variability with similar magnitude in the entire region of the East Sea. The analysis of remote correlation also shows that SST in the East Sea is closely correlated with that in the region of Kuroshio in winter, but in summer is related with that in the western and eastern regions of the same latitudes. From these results it is postulated that the SST variability in the East Sea may be related with the variations of East Korean Warm Current and Tsushima Warm Current in winter, but in summer probably with the variations of atmospheric components. In the analysis of ENSO related SST anomaly, a significant negative correlation between SST anomalies in the East Sea and SST anomalies in the tropical Pacific is found in the months of August-October (ASO). The SST in the ASO period shows more significant cooling in E1 $Ni\~{n}o$ events than warming in La $Ni\~{n}a$ events. Also, the regional analysis shows by the Student's t-test that the negative SST anomalies in the E1 $Ni\~{n}o$ events are more significant in the southwestern part of the East Sea.

• Atmosphere
• /
• v.22 no.4
• /
• pp.465-472
• /
• 2012

Using various observed data, we examined the evolution of tropical Pacific sea surface temperature (SST) during 2011-2012, with focusing on the development of 2012 El Ni$\tilde{n}$o. It is observed that a La Ni$\tilde{n}$a event during 2011 was followed by a moderate El Ni$\tilde{n}$o during 2012 summer. The 2012 summer El Ni$\tilde{n}$o initiated near the west coast of South America on February 2012, and continued to expand westward till August. Given this evolutionary pattern, the 2012 summer El Ni$\tilde{n}$o can be categorized as 'Eastern Pacific (EP) El Ni$\tilde{n}$o' because Ni$\tilde{n}$o-3 index is greater than Ni$\tilde{n}$o-4 index, and it may be the first well-defined EP El Ni$\tilde{n}$o since 2001. On February 2012, this event was initiated mainly by the local air-sea interaction, and at the same time the ocean heat content was accumulated over the tropical western Pacific due to the easterly wind anomaly over the tropical western Pacific. Then, the accumulated heat content slowly propagates to the tropical eastern Pacific, which attributes to maintain El Ni$\tilde{n}$o state during 2012 summer. After August, the positive SST anomaly over the equatorial eastern Pacific decays possibly due to the exhausted heat content and the weakening of air-sea interaction, but the weak positive SST anomaly over the central Pacific remains till now (2012 November).

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1413-1414
• /
• 2003

Interannual variability in the patterns of satellitederived pigment concentrations, sea-level height anomaly, sea surface temperature anomaly, and zonal wind anomaly are observed during the 2002-2003 El Ni${\tilde{n}}$o. The largest spatial extent of the phytoplankton bloom was recovery from El Ni${\tilde{n}}$o over the equatorial Pacific. The evolution towards a warm episode (El Ni${\tilde{n}}$o) started from spring of 2002 and continued during January 2003, while equatorial Sea Surface Temperature Anomaly (SSTA) remained greater than +1$^{\circ}$C in the central equatorial Pacific. The EOS (Earth Observing System) and OSMI (Ocean Scanning Multispectral Imager) data are used for detection of dramatic changes in the patterns of pigment concentration during El Ni${\tilde{n}}$o.

• Ocean Science Journal
• /
• v.41 no.1
• /
• pp.1-10
• /
• 2006

Total sea surface temperature (SST) in a coupled GCM is diagnosed by separating the variability into signal variance and noise variance. The signal and the noise is calculated from multi-decadal simulations from the COLA anomaly coupled GCM and the interactive ensemble model by assuming both simulations have a similar signal variance. The interactive ensemble model is a new coupling strategy that is designed to increase signal to noise ratio by using an ensemble of atmospheric realizations coupled to a single ocean model. The procedure for separating the signal and the noise variability presented here does not rely on any ad hoc temporal or spatial filter. Based on these simulations, we find that the signal versus the noise of SST variability in the North Pacific is significantly different from that in the equatorial Pacific. The noise SST variability explains the majority of the total variability in the North Pacific, whereas the signal dominates in the deep tropics. It is also found that the spatial characteristics of the signal and the noise are also distinct in the North Pacific and equatorial Pacific.

• Journal of Environmental Science International
• /
• v.12 no.11
• /
• pp.1137-1144
• /
• 2003

To provide evidence that the changes in oceanic environmental conditions are useful indices for predicting stock structure and distribution of the Pacific saury (Cololabis saira), the body length compositions and catch per unit fishing effort were examined in relation to the sea surface temperature(SST) anomalies in the Tsushima Warm Current(TWC) region. The size of the fish became larger(smaller) than the average in the same size category during the season of higher SST(lower SST) as opposed to the normal SST. The year-to-year changes in body size caused by the changes in the environmental conditions led the stock to be homogeneous during the period of high stock level from the late 1950s to early 1970s and in the 1990s. The changes in body size manifested by higher(lower) occurrence rates of larger (smaller) sized groups in relation to temperature anomalies suggest that the changes in the environmental conditions affect the distribution and the structure of the stock in the TWC region. Therefore, if the SST anomaly derived from satellite data is large enough in the early spring months(Mar. or Apr.), it is possible to predict whether or not sea temperature will be favorable for large sized groups of saury at normal or slightly earlier time of commencement of the fishery in spring(Apr.∼June).

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.1011-1014
• /
• 2006

Coarse resolution (9 - 50 km pixels) Sea Surface Temperature satellite data are frequently considered adequate for open ocean research. However, coastal regions, including coral reef, estuarine and mesoscale upwelling regions require high-resolution (1-km pixel) SST data. The AVHRR SST data often suffer from navigation errors of several kilometres and still require manual navigation adjustments. The second serious problem is faulty and ineffective cloud-detection algorithms used operationally; many of these are based on radiance thresholds and moving window tests. With these methods, increasing sensitivity leads to masking of valid pixels. These errors lead to significant cold pixel biases and hamper image compositing, anomaly detection, and time-series analysis. Here, after manual navigation of over 40,000 AVHRR images, we implemented a new cloud filter that differs from other published methods. The filter first compares a pixel value with a climatological value built from the historical database, and then tests it against a time-based median value derived for that pixel from all satellite passes collected within ${\pm}3$ days. If the difference is larger than a predefined threshold, the pixel is flagged as cloud. We tested the method and compared to in situ SST from several shallow water buoys in the Florida Keys. Cloud statistics from all satellite sensors (AVHRR, MODIS) shows that a climatology filter with a $4^{\circ}C$ threshold and a median filter threshold of $2^{\circ}C$ are effective and accurate to filter clouds without masking good data. RMS difference between concurrent in situ and satellite SST data for the shallow waters (< 10 m bottom depth) is < $1^{\circ}C$, with only a small bias. The filter has been applied to the entire series of high-resolution SST data since1993 (including MODIS SST data since 2003), and a climatology is constructed to serve as the baseline to detect anomaly events.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.1023-1026
• /
• 2006

As it was shown recently, climate changes in Antarctica resulted in interannual trends of some climatic parameters like sea level pressure, surface air temperature, ice thickness and others. These tendencies have effect on the Southern Ocean meteorological and hydrological regime. The following remote sensing data: AVHRR MCSST data, satellite altimetry data (merged data of mission ERS-2, TOPEX/Poseidon, Jason-1, ENVISAT, GFO-1) are used to analyse the interannual and/or climatic tendency of sea surface temperature (SST) and sea level anomaly (SLA). According to the obtained results, SST has negative trend $-0.02{\pm}0.003^{\circ}C/yr$ for 24-yr record (1982-2005) and SLA has positive trend $0.01{\pm}0.005$ cm/yr for 24-yr record (1982-2005) and $0.24{\pm}0.026$ cm/yr for 12-yr record (1993-2005). However in some areas (for example, Pacific-Antarctic Ridge) SST and SLA tendencies are stronger $-0.065{\pm}0.007^{\circ}C/yr$ and $-0.21{\pm}0.05$ cm/yr, respectively.

• Ocean and Polar Research
• /
• v.34 no.4
• /
• pp.395-401
• /
• 2012

Ten-year AVHRR sea surface temperature data obtained in the Yellow Sea are put into EOF analyses. Temperature variation is predominated by the first mode which is associated with the seasonal fluctuation of temperature with annual range decreasing with the bottom depth. Since such a strong annual signal may mask the upwind or downwind flows occurring intermittently during the winter, only the data obtained during this season are put into EOF analyses. Every winter shows similar results. The first mode, explaining more than 90% of total variance, appears to be a part of the seasonal variation of temperature mentioned above. In the second mode, the time coefficient is well correlated with northerly winds to which the responses of the trough and shallow coastal areas are opposite to each other. A simple theoretical consideration suggests the following physical explanation: The northerly wind stress anomaly creates an upwind (downwind) flow over the trough (coastal) areas, which then induces a temperature increase (decrease) by advection of heat, and vice versa for the southerly wind stress anomaly. Hence, this paper provides further evidence of the intermittent upwind or downwind flows occurring in the Yellow Sea every winter.