• 한국해양학회지
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• v.16 no.1
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• pp.1-11
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• 1981

The seasonal variations of surface current patterns in the Japan Sea were drawn out from the results of drift bottle experiments, current measurements and hydrographic observations during 1979∼1980. The North Korean Cold Current(NKCC) and the East Korean Warm Current(EKWC) were common features of circulation in the eastern sea of Korea. The intrusion of NKCC along the Korean coast became strong in summer(average velocity of 47.4cm/sec off Jumunjin and 23.4cm/sec near Jugbyeon) when the Tsushima Current was strong. But there was no indication of the NKCC in November 1979. Dynamic topography(August & November 1979) and satellite picture(November 1979) seemed to show the topographic steering of EKWC beginning off Janggigab. Drift bottles arrived at the Japaness coast were affected significantly by the strong Tsushima Current in summer and by the predominant northwesterlies in winter instead of weak current.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.43-48
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• 2001

Many applications in the areas of agricultural, hydrological and environmental resource management require data over very large areas and with a high imaging frequency - monitoring crop growth, water stress, seasonal wetland flooding and natural vegetation development. This precludes the use of fine resolution data (Landsat, Spot) on the grounds of cost, accessibility and low imaging frequency. Meteorological satellites have the potential to fill this need, given their very wide spatial coverage, and high repeat imaging. The Remote Sensing Unit (RSU) at the Zambia Meteorological Department routinely receives, processes and archives imagery from both Meteosat and NOAA AVHRR satellites. Here I wish to present some examples of applications of these data sets that arise from the RSU work - relationships between rainfall and vegetation development as assessed by satellite, derived information and seasonal patterns of flooding in the Barotse floodplain and the Kafue flats. I also wish to outline ways in which a more widespread use of this data by the Zambian institutions canbe achieved.

• Journal of Smart Tourism
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• v.2 no.3
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• pp.17-30
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• 2022

The tourism sector of the Maldives has seen rapid growth since its inception in 1972. One significant development is the transformation of the market composition in recent years. China has surpassed traditional European markets as the single largest source market. In this regard, this study seeks to assess the seasonality in the Maldivian tourism sector using a monthly dataset of visitor arrivals from 2003 to 2019. The seasonality ratio, the seasonality indicator, the Gini coefficient and the seasonal index were used to examine the seasonality patterns. The results of this study show that there are three distinct peaks (January to April, August, and November to December) and two off-peaks (May to July and September) periods. The findings also reveal that the rise of the Chinese market has significantly lessened the seasonality of Maldivian inbound tourism. Finally, some important implications are discussed.

• Ocean and Polar Research
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• v.44 no.2
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• pp.179-190
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• 2022

Data missing in optical satellite images caused by natural variations have been a crucial barrier in observing the status of marine surfaces. Although there have been many attempts to fill the gaps of non-observation, there is little research to analyze the ratio of missing grids to overall sea grids and their seasonal patterns. This report introduces the method of quantifying the distribution of missing points and then shows how the missing points have spatial correlation and seasonal trends. Both temporal and spatial integration methods are compared to assess the effectiveness of reducing missing data. The temporal integration shows more outstanding performance than the spatial integration. Moran's I and K-function with statistical hypothesis testing show that missing grids are clustered and there is a non-random distribution from daily integration. The result of the seasonality test for Moran's I through a periodogram shows dependency on full-year, half-year, and quarter-year periods respectively. These analysis results can be used to deduce appropriate integration periods with permissible estimation errors.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.7-16
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• 2000

This paper is focused on verifying time-space repetition of the highway accident and finding the their causes and deterrents. We classify all months into several seasonal groups, develop the model for each seasonal group and analyze the results of these models for Joong-bu highway. The existence of seasonal effect is verified by the analysis or self-organizing map and the accident indices. Agglomerative hierarchical cluster analysis which is used to decide the seasonal groups in accordance with accident patterns, winter group, spring-fall group. and summer group. The accident features of winter group are that the accident rate is high but the severity rate is low. while those of summer group are that the accident rate is low but the severity rate is high. Also, the regression model which is developed to identify the accident Pattern or each seasonal group represents that the season-related factors, such as the amount of rainfall, the amount of snowfall, days of rainfall, days of snowfall etc. are strongly related to the accident pattern of evert seasonal group and among these factors the traffic volume, amount of rainfall. the amount of snowfall and days of freezing importantly affect the local accident Pattern. So, seasonal effect should be considered to the identification of high-risk road section. the development of descriptive and Predictive accident model, the resource allocation model of accident in order to make safety management plan efficient.

• Atmosphere
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• v.30 no.4
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• pp.405-420
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• 2020

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.45 no.3
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• pp.262-270
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• 2012

Seaweeds provide habitats in which marine animals can spawn and develop, and serve as a food supply for algaegrazing species such as sea urchins and abalone. Recently, seaweed species have disappeared from coastal ecosystems, leaving barren ground, defined as habitats that have lost their algae forests and where coralline algae containing calcium carbonate components have become encrusted on rocks. The biological causes of barren ground include grazing by herbivores and excessive seaweed harvest. The environmental harm caused by the spread of barren ground includes accelerated eutrophication following the reduction in seaweed, which plays an important role in oceanic purification. In the present study, we identified the relationships between various seaweed species and the occurrence of barren ground. Subtidal benthic macroalgal flora and community structure were observed seasonally on barren ground along vertical transects of rocky shores of Bihwa, Samchuck, and the east coast of Korea from February to November 2006. Fifty-eight seaweed species were identified, including 7 green, 15 brown, and 36 red algae species. There were between 6 and 28 species among seasons. Over the whole study period, average seaweed biomass (g wet wt $m^{-2}$) was 241.90 g, with a seasonal range of 25.26 to 760.34 g. Seaweed biomass declined with increasing seawater depth and ranged between 91.26 and 422.08 g. The vertical distribution of algae was characterized by Undaria pinnatifida and Sargassum honeri at 5 m, S. honeri and U. pinnatifida at 10 m, and U. pinnatifida and Agarum clathratum at 15 m depth. Seasonal patterns in community indices were not found. Community indices showed different patterns along vertical shoreline gradients; the dominance index increased but the richness, evenness, and diversity indices decreased with seawater depth. Sea urchin density was 8 to 24 individ. $m^{-2}$ in Bihwa. These urchin populations had significantly aggregated spatial patterns and recurrent destructive grazing appeared to be occurring.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.33 no.3
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• pp.262-271
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• 2000

This study was carried out to clarify the spatial and temporal patterns of benthic environments and macrobenthos on the subtidal soft-bottom in Chonsu Bay. Seasonal surface water temperature was similar to the bottom layer, but freshwater discharges into the outlets dereased the surface salinity around the dyke in summer. Bottom dissolved oxygen was decreased deeply around the dyke and created the de-oxygenated layer during summer. Sediment grain size was consisted of finer at the neighboring of the dyke than the mouth of the bay. Organic matters including the sediment were decreased at the mouth of the bay. A total of 311 species ($769\;ind./m^2$) were identified. Polychaetes were the most abundant faunal group in the number of species and densities. The number of species revealed the spatial patterns that it was higher in the mouth of the bay, and their densities showed seasonal changes by mass recruitment occurred at the most of the area in summer, At this time, opportunistic species, Lumbrineris iongifolia and Theora fragilis, were also recruited massively. Chonsu Bay were classified into five station groups by the cluster analysis. The dominant species around the dyke were composed to opportunistic species, those in middle area were Sternaspis scutata, Paraplionospio pinnata, and those in the mouth of the bay were Mediomastus californiensis, Nephtys polybranchia. Seasonal fluctuations and spatial difference of environments seem to have influenced to the species compositions and affected to the stability of benthic ecosystems spatial-temporally In Chonsu nay.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.425-434
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• 2020

Background: Viral infection outbreaks are emerging public health concerns. They often exhibit seasonal patterns that could be predicted by the application of big data and bioinformatic analyses. Purpose: The purpose of this study was to identify trends in diarrhea-causing viruses such as rotavirus (Gr.A), norovirus G-I, and norovirus G-II in Cheonan, Korea. The identified related factors of diarrhea-causing viruses may be used to predict their trend and prevent their infections. Method: A retrospective analysis of 4,009 fecal samples from June 2010 to December 2019 was carried out at Dankook University Hospital in Cheonan. Reverse transcription-PCR (RT-PCR) was employed to identify virus strains. Information about seasonal patterns of infection was extracted and compared with local weather data. Results: Out of the 4,009 fecal samples tested using multiplex RT-PCR (mRT-PCR), 985 were positive for infection with Gr.A, G-I, and G-II. Out of these 985 cases, 95.3% (n = 939) were under 10 years of age. Gr.A, G-I, and G-II showed high infection rates in patients under 10 years of age. Student's t-test showed a significant correlation between the detection rate of Gr.A and the relative humidity. The detection rate of G-II significantly correlated with wind-chill temperature. Conclusion: Climate factors differentially modulate rotavirus and norovirus infection patterns. These observations provide novel insights into the seasonal impact on the pathogenesis of Gr.A, G-I, and G-II.

• Journal of Environmental Science International
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• v.17 no.11
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• pp.1243-1253
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• 2008

The seasonal variations of picoplankton including Prochlorococcus, Synechococcus and Picoeukayotes around Ulneung Island were investigated by flow cytometry in spring, summer and autumn in 2006. All groups of picoplankton showed clear seasonal patterns in population abundance. Among the group, Synechococcus showed the most prominent seasonal variation during the study period. The maximal abundance of Synechococcus occurred in summer and the lowest in autumn. The seasonal distribution of Prochlorococcus displayed the reverse tendency with that of Synechococcus. The abundance of Prochlorococcus ranged from $2.9{\times}10^3$ cells/ml in summer to $311{\times}10^3$ cells/ml in autumn. However, the seasonal distribution of Picoeukaryotes was shown to be relatively constant, and the maximal abundance was $81.5{\times}10^3$ cells/ml in summer. The highest abundance of Picoeukaryotes occurred in summer and the lowest in autumn and the seasonal distribution in abundance of Picoeukaryotes showed a similar trend with that of Synechococcus. The estimated total carbon biomass of picoplankton were ranged from $74.7\;mg\;C/m^2$ to $1,055.9\;mg\;C/m^2$. The highest total carbon biomass occurred in summer, but lowest occurred in autumn. The pattern of the contribution of three picoplankton to total autotrophic picoplankton carbon is different. The contribution of Synechococcus to total autotrophic picoplankton carbon is increased to 75%, but the contribution of Prochlorococcus dropped to 12% in summer. The contribution of Picoeukaryotes is ranged from 24% in summer to 72.5% in spring.