논문성과
재난안전 R&D를 통해 발간된 논문 성과 입니다. 논문명을 클릭하면 상세 정보를 확인할 수 있습니다.
논문성과 : 번호, 성과발생년도, 성과발생부처, 성과사업명, 논문명, 학술지명, 재난안전 유형 순으로 나열되고 있습니다.
| 번호 |
성과발생년도 |
성과발생부처 |
성과사업명 |
논문명 |
학술지명 |
재난안전 유형 |
| 9355 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
The Tropical Transition in the Western North Pacific: The Case of Tropical Cyclone Peipah (2007)
|
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES |
1. 태풍
|
| 9354 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
Influence of vertical wind shear on wind- and rainfall areas of tropical cyclones making landfall over South Korea
|
PLOS ONE |
1. 태풍
|
| 9353 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
Explosive Cyclogenesis around the Korean Peninsula in May 2016 from a Potential Vorticity Perspective: Case Study and Numerical Simulations
|
ATMOSPHERE |
1. 지진해일
|
| 9352 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
Numerical Modeling of Meteotsunami-Tide Interaction in the Eastern Yellow Sea
|
ATMOSPHERE |
1. 지진해일
|
| 9351 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
Development of a Numerical Algorithm Considering Tide-Tsunami Interaction
|
JOURNAL OF COASTAL RESEARCH |
1. 지진해일
|
| 9350 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
Structural evolution of two-stage rifting in the northern East China Sea Shelf Basin
|
GEOLOGICAL JOURNAL |
1. 지진
|
| 9349 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
An analysis of the intraplate earthquake (2016M5.8_GY) that occurred in the Gyeongsang Basin in the SE of the Korean Peninsula, based on 3-D modelling of the gravity and magnetic field
|
GEOPHYSICAL JOURNAL INTERNATIONAL |
1. 지진
|
| 9348 |
2019 |
기상청 |
기상·지진See-At기술개발연구(R&D) |
Unique sodic-calcic skarn hosted by ultramafic rocks and albitite at the Ulsan skarn deposit, Gyeongsang Basin, South Korea
|
ORE GEOLOGY REVIEWS |
1. 지진
|
| 9347 |
2019 |
기상청 |
자연재해대응영향예보생산기술개발(R&D)(기상청) |
Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea
|
WATER |
1. 호우
|
| 9346 |
2019 |
기상청 |
기상업무지원기술개발연구(R&D) |
Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018
|
Advances in Meteorology |
1. 대설
|
