<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>DSpace Collection:</title>
    <link>https://repository.kopri.re.kr/handle/201206/5210</link>
    <description />
    <pubDate>Tue, 07 Apr 2026 07:10:45 GMT</pubDate>
    <dc:date>2026-04-07T07:10:45Z</dc:date>
    <item>
      <title>An optimization of sea ice/snow cover related spatial information : D/B development and quality analysis</title>
      <link>https://repository.kopri.re.kr/handle/201206/9068</link>
      <description>Title: An optimization of sea ice/snow cover related spatial information : D/B development and quality analysis
Authors: Han, Kyung-Soo</description>
      <pubDate>Wed, 25 Jan 2017 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://repository.kopri.re.kr/handle/201206/9068</guid>
      <dc:date>2017-01-25T00:00:00Z</dc:date>
    </item>
    <item>
      <title>A Numerical Simulation Study of Strong Wind Events at Jangbogo Station, Antarctica</title>
      <link>https://repository.kopri.re.kr/handle/201206/6474</link>
      <description>Title: A Numerical Simulation Study of Strong Wind Events at Jangbogo Station, Antarctica
Authors: Kwon, Hataek; Kim, Baek-Min; Kim, Seong-Joong; Jeong, Jee-Hoon; Choi, Taejin; Park, Sangjong; Kim, Shin Woo
Abstract: 본 연구에서는 장보고 과학기지에 설치, 운영되고 있는 자동기상관측장비에서 관측된 풍속 자료를 분석하여 특징적인 강？사례를 선정하고, 이를 극지역 모의를 위해 개발된 Polar WRF 수치모형을 이용하여 모의하였다. 이를 통해 장보고 과학기지 주변의 강풍 사례의 원인을 분석하고, 관측자료와의 비교를 통해 재분석 자료와 중규모 수치모델을 이용한 역학적 상세화(downscaling) 기법을 이용하여 생산된 국지 바람장의 검증을 수행하였다. 관측자료와 수치모의자료 분석결과 순간 최대 풍속의 최고값을 보인 사례의 경우 로스해 부근에서 발생한 저기압의 영향으로, 일 평균최대풍속을보인 사례의 경우 지형의 영향을 많이 받는 활강풍 사례임을 확인하였다. Polar WRF를 이용한 각 사례 별 3km 해상도의 수치모의자료 분석 결과, 저기압의 영향을 많이 받은 강풍 사례의 경우 사례 기간에 대한 풍속, 지면기압의 변화 경향 및 크기를 관측과 매우 유사하게 성공적으로 모의하였으나 활강풍 사례의 경우 풍속의 강도 및 강풍모의 시점에 있어 저기압의 영향을 많이 받은 사례에 비해 관측과 다소 큰 차이를 보이며 전반적으로 낮은 모의 정확도를 보임을 확인하였다. 이는 주변 지형의 영향을 많이 받는 활강풍 사례의 경우 수치모델에서 사용된 지형의 정밀도에 따라 강풍의 모의 강도 및 위치에 영향을 더 많이 받은것으로 추정되며, 3km 해상도의 수치모의에서 사용한 30초 지형자료가 매우 급격한 변화와 복잡한 형태를 가진 장보고 기지 부근의 실제 지형을 충분히 반영하지 못한데서 기인한 것으로 판단 된다.</description>
      <pubDate>Sun, 01 Jan 2017 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://repository.kopri.re.kr/handle/201206/6474</guid>
      <dc:date>2017-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Assessing crop yield simulations driven by the NARCCAP regional climate models in the southeast United States</title>
      <link>https://repository.kopri.re.kr/handle/201206/5644</link>
      <description>Title: Assessing crop yield simulations driven by the NARCCAP regional climate models in the southeast United States
Authors: Shin, Dong Wook; Kim, Baek-Min; Oh, Ji-Hyun; Steve Cocke; Consuelo C. Romero; Guillermo A. Baigorria
Abstract: A set of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models is used in crop modeling systems to assess economically valuable agricultural production in the southeast United States, where weather/climate exerts strong impact on agriculture. The maize/peanut/ cotton yield amounts for the period of 1981？2003 are obtained in a regularly gridded (~20km) southeast U.S. using (a) observed, (b) a reanalysis, and (c) the NARCCAP Phase I multimodel data set. It is shown that the regional-climate model-driven crop yield amounts are better simulated than the reanalysis-driven ones. Multimodel ensemble methods are then adopted to examine their usefulness in improving the simulation of regional crop yield amounts and are compared to each other. The bias-corrected or weighted composite methods combine the crop yield ensemble members better than the simple compositemethod. In general, the weighted ensemble crop yield simulations match marginally better with the observed-weather-driven yields compared to those of the other ensemble methods.</description>
      <pubDate>Sun, 01 Jan 2017 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://repository.kopri.re.kr/handle/201206/5644</guid>
      <dc:date>2017-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Multiple aspects of northern hemispheric wintertime cold extremes as revealed by Markov chain analysis</title>
      <link>https://repository.kopri.re.kr/handle/201206/5693</link>
      <description>Title: Multiple aspects of northern hemispheric wintertime cold extremes as revealed by Markov chain analysis
Authors: Kim, Joo-Hong; Choi, Young-Sang; Kim, Hye-Sil; Kim, WonMoo
Abstract: High-impact cold extremes have continued to bring devastating socioeconomic losses in recent years. In order to explain the exposure to cold extremes more comprehensively, this study investigates multiple aspects of boreal winter cold extremes, i.e., frequency, persistence, and entropy (Markovian descriptors). Cold extremes are defined by the bottom 10th percentile of daily minimum temperatures during 1950？2014 over the northern hemisphere. The spatial and temporal distributions of Markovian descriptors during 65 years are examined. Climatological mean fields show the spatial coincidence of higher frequency, shorter persistence, and higher entropy of cold extremes, and vice versa. In regard to the temporal variations over six representative regions of North America, Europe, and Asia, all regions share a decreasing tendency of frequency with the increases in regional winter mean temperature. By contrast, persistence and entropy show their intrinsic decadal variability depending on regions irrespective of the regional temperature variability, which gives different information from frequency. Therefore, the exposure to cold extremes would not simply decrease with regional warming. Rather these results indicate that the descriptors with multiple aspects of the extremes would be needed to embrace the topical features as well as the holistic nature of cold extremes.</description>
      <pubDate>Sun, 01 Jan 2017 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://repository.kopri.re.kr/handle/201206/5693</guid>
      <dc:date>2017-01-01T00:00:00Z</dc:date>
    </item>
  </channel>
</rss>

