• 대기
• /
• 제30권4호
• /
• pp.405-420
• /
• 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.

• 복식문화연구
• /
• 제31권5호
• /
• pp.607-626
• /
• 2023

This study analyzes consumer fashion purchase patterns from a big data perspective. Transaction data from 1 million transactions at two Korean fashion brands were collected. To analyze the data, R, Python, the SPADE algorithm, and network analysis were used. Various consumer purchase patterns, including overall purchase patterns, seasonal purchase patterns, and age-specific purchase patterns, were analyzed. Overall pattern analysis found that a continuous purchase pattern was formed around the brands' popular items such as t-shirts and blouses. Network analysis also showed that t-shirts and blouses were highly centralized items. This suggests that there are items that make consumers loyal to a brand rather than the cachet of the brand name itself. These results help us better understand the process of brand equity construction. Additionally, buying patterns varied by season, and more items were purchased in a single shopping trip during the spring season compared to other seasons. Consumer age also affected purchase patterns; findings showed an increase in purchasing the same item repeatedly as age increased. This likely reflects the difference in purchasing power according to age, and it suggests that the decision-making process for pur- chasing products simplifies as age increases. These findings offer insight for fashion companies' establishment of item-specific marketing strategies.

• 한국환경과학회지
• /
• 제17권11호
• /
• pp.1243-1253
• /
• 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.

• 한국수산과학회지
• /
• 제26권5호
• /
• pp.438-445
• /
• 1993

1991년 가을에는 1992년 여름 사이 아산만에서 otter trawl로 저어류 자료를 수집하여 저어류의 계절 및 정점간의 군집구조를 분석하였다. 출현한 34종 중, 참서대(Cynoglossus joyneri), 민태(Johnius belengeri), 곤어리(Thrissa koreana) 및 등가시치(Zoarces gillii)가 총 채집개체수의 $93\%$를 차지하였다. 위의 4종 중 부어류인 곤어리를 제외한 우점 3종은 저질이 세립질인 만 내부에서 생물량이 많았다. 각 계절 정점간 군집구조를 rank correlation을 이용하여 주성분 분석한 결과, 정점간에는 큰 차이가 없었고 계절에 따른 군집구조 변화는 뚜렷하였으며, 그 변화는 수온, 혹은 수온과 상관관계를 갖는 요인에 의하여 좌우됨을 알 수 있었다. 조사해역은 수온의 연교차가 크고, 조류에 의한 해수의 혼합이 활발하여 이에 적응한 소수종이 우점하고 이 우점저어류는 퇴적물의 입도에 따라 그 분포가 결정되지만, 출현 개체수가 적은 대부분의 종은 정점간에 큰 차이를 보이지 않고 계절에 따라 군집구조가 변하여 가는 것으로 보인다.

• 한국환경과학회지
• /
• 제17권7호
• /
• pp.719-732
• /
• 2008

The total number of flies collected was 4,864 with sex ratio of 32.8% from Suseungdae area, Geochang-gun, Gyeongnam, Korea. The fauna of fly was 35 species consisting of 13 Calliphoridae, 12 Muscidae, 8 Sarcophagidae, 1 Dryomyzidae and 1 Anthomyiidae. The number of genus and species of Calliphoridae and Muscidae of Suseungdae area were similarly found in the mountain and residential areas, however, those of Sarcophagidae was similar with that of residential area. Calliphoridae was the most dominant family at the survey sites with 60.4% of the total collected flies, and followed Muscidae 33.3% and Sarcophagidae 6.2%. The dominant species were constituted of 83.6% of the total flies and the order of collected flies was Chrysomyia pinguis (42.2%), Limnophora sp. (10.8%), Muscina angustifrons (8.6%), Calliphora lata (8.1%), Fannia scalaris (5.5%), Lucilia caesar (3.9%), and Boettcherisca peregrina (4.2%). The seasonal prevalence of flies was from the middle of March to the end of November, and the peak time of appearance was the middle of June, middle of September and middle of October. The highest peak of prevalence was the middle of October. The similarity index of the flies was above 80%, compared to Mt. Geonheung and landfill in Geochang-gun. The number of fly species was more affected by the mean air temperature. In the comparison of the seasonal prevalence and relative abundance of 7 predominant species among 35 species, each species was found to exhibit its specific characteristics and showed the pattern of species.

• 산업경영시스템학회지
• /
• 제39권1호
• /
• pp.25-30
• /
• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of Plant Biology
• /
• 제29권2호
• /
• pp.95-107
• /
• 1986

Seasonal changes in pool size, inflow rates in biomass and phosphorus, and the efficiency of phosphorus use in the stand of three populations (Helianthus tuberosus, Artemisia princeps and Phalaris arundinacea) in the basin of the Mt. Geumoh were investigated. During the early growing period, in the three species populations the relative size of the phosphorus pool of population was larger then that of its biomass pool, but that of the phosphorus pool of belowground part decreased more rapidly than that of its biomass pool. In the A. princeps and P. arundinacea populations, the phosphorus inflow rate was markedly high during the soil thaw in early spring and its seasonal change pattern was different from that of the biomass production rate, showing two peaks in March and June. But in the H. tuberosus population, the two seasonal change patterns were alike. The annual biomass production was 2283 gDM m-2 in the H. tuberosus, 1884 m-2 in the A. princeps and 1879 gDM m-2 in the P. arundinacea population, and the annual phosphorus inflow was 11.35, 9.63 and 7.60 gP m-2, respectively. The P. arundinacea population showed the smallest LAI peak(5.4 in early June), and the largest NAR peak (36.9 gDM m-2wk-1) RGR peak (0.15g g-1 wk-1) among the three species populations. The seasonal change patterns in whole plant EPU of the three species populations showed the bell shape, but the annual EPU values among them were markedly different. It was noticed that the population with the highest RGR showed the highest EPU among the three species populations while the population with the lowest RGR showed the lowest EPU among them.

• 한국대기환경학회지
• /
• 제32권2호
• /
• pp.208-215
• /
• 2016

Pinic acid (PA) and cis-pinonic acid (CPA) in the atmospheric particulate matter with an aerodynamic diameter of less than or equal to a nominal $10{\mu}m$ ($PM_{10}$) were analyzed for the samples collected during the period of April 2010 to April 2011 at Jongro in Seoul. Both pinic acid and cis-pinonic acid showed higher seasonal average concentrations in summer (PA; $18.9ng/m^3$, CPA; $16.0ng/m^3$) than winter (PA; $5.3ng/m^3$, CPA; $5.9ng/m^3$). They displayed a seasonal pattern associated with temperature reflecting the influence on emissions of ${\alpha}-pinene$ and ${\beta}-pinene$ from conifers and their photochemical reaction. These results were confirmed through Pearson correlation coefficient between CPA, PA and $O_3+NO_2$, temperature. CPA was only correlated with n-alkanes ($C_{29}$, $C_{31}$, $C_{33}$) from biogenic source. PA was correlated with n-alkanes ($C_{29}$, $C_{31}$, $C_{33}$), n-alkanoic acid ($C_{20}$, $C_{22}$, $C_{24}$) from biogenic source and n-alkanes ($C_{28}$, $C_{30}$, $C_{32}$), and n-alkanoic acid ($C_{16}$, $C_{18}$) from anthropogenic source. These results showed that the formation of PA and CPA from ${\alpha}-pinene$ and ${\beta}-pinene$ is related to organic compounds from biogenic source. And it is possible for PA to be effected by organic compounds from anthropogenic source.

• 대한원격탐사학회지
• /
• 제19권3호
• /
• pp.217-221
• /
• 2003

The Measurement of Pollution in the Troposphere (MOPITT) instrument is an eight-channel gas correlation radiometer that launched on the Earth Observing System (EOS) Terra spacecraft in 1999. Its main objectives are to measure carbon monoxide (CO) and methane (CH4) concentrations in the troposphere. This study analyzes tropospheric carbon monoxide distributions using MOPITT data and compare with ozone distributions in Northeast Asia. In general, seasonal CO variations are characterized by a peak in spring and decrease in summer. Also, this study revealed that the seasonal cycles of CO are maximum in spring and minimum in summer with average concentrations ranging from 118ppbv to 170ppbv. The monthly average of CO shows a similar profile to those of O3. This fact clearly indicates that the high concentration of CO in spring is caused by two possible causes: the photochemical CO production in the troposphere, or the transport of the CO in the northeast Asia. The CO and $O_3$ seasonal cycles in the Northeast Asia are influenced extensively by the seasonal exchange of the different types of air mass due to the Asian monsoon. The continental air masses contain high concentrations of $O_3$ and CO due to higher continental background concentrations and sometimes due to the contribution of regional pollution. In summer the transport pattern is reversed. The Pacific marine air masses prevail over Korea, so that the marine air masses bring low concentrations of CO and $O_3$, which tend to give the apparent minimum in summer.

• 한국지구과학회지
• /
• 제42권6호
• /
• pp.605-622
• /
• 2021

Seasonal forecasting has numerous socioeconomic benefits because it can be used for disaster mitigation. Therefore, it is necessary to diagnose and improve the seasonal forecast model. Moreover, the model performance is partly related to the ocean model. This study evaluated the hindcast performance in the upper ocean of the Global Seasonal Forecasting System version 5-Global Couple Configuration 2 (GloSea5-GC2) using a multivariable integrated evaluation method. The normalized potential temperature, salinity, zonal and meridional currents, and sea surface height anomalies were evaluated. Model performance was affected by the target month and was found to be better in the Pacific than in the Atlantic. An increase in lead time led to a decrease in overall model performance, along with decreases in interannual variability, pattern similarity, and root mean square vector deviation. Improving the performance for ocean currents is a more critical than enhancing the performance for other evaluated variables. The tropical Pacific showed the best accuracy in the surface layer, but a spring predictability barrier was present. At the depth of 301 m, the north Pacific and tropical Atlantic exhibited the best and worst accuracies, respectively. These findings provide fundamental evidence for the ocean forecasting performance of GloSea5.