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Kafe 바로가기주관연구기관 | 연세대학교 Yonsei University |
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보고서유형 | 3단계보고서 |
발행국가 | 대한민국 |
언어 | 한국어 |
발행년월 | 2015-03 |
과제시작연도 | 2014 |
주관부처 | 기상청 Korea Meteorological Administration(KMA) |
등록번호 | TRKO201500013458 |
과제고유번호 | 1365001842 |
사업명 | 기상기술개발사업 |
DB 구축일자 | 2015-08-15 |
키워드 | 호우.대류밴드.구름무리.수치예보.개념모형.heavy rainfall.convection bands.cloud clusters.numerical weather prediction.conceptual model. |
DOI | https://doi.org/10.23000/TRKO201500013458 |
1. 연구개발 목표:
- 대류밴드 호우시스템의 발생과 진화 과정에 대한 이해 정립과 예측 알고리즘 제시
- 구름무리 호우시스템의 발생 및 조직화 과정에 대한 이해 개선과 예측 개선 방안 제시
2. 연구 내용 및 결과:
1) 대류밴드 호우시스템에 대한 이해 발전과 예측 능력 개선
⦁2001~2010년의 24개 대류밴드 사례를 분석하여 발생 환경을 구분하였고, 유형별로 발생과정을 파악
- 발생환경: 중규모저기압과 연관된 정체전선, 정체전선, 연장된 기압골, 서태평양고기압 북서 가장자리의 강한 남서
1. 연구개발 목표:
- 대류밴드 호우시스템의 발생과 진화 과정에 대한 이해 정립과 예측 알고리즘 제시
- 구름무리 호우시스템의 발생 및 조직화 과정에 대한 이해 개선과 예측 개선 방안 제시
2. 연구 내용 및 결과:
1) 대류밴드 호우시스템에 대한 이해 발전과 예측 능력 개선
⦁2001~2010년의 24개 대류밴드 사례를 분석하여 발생 환경을 구분하였고, 유형별로 발생과정을 파악
- 발생환경: 중규모저기압과 연관된 정체전선, 정체전선, 연장된 기압골, 서태평양고기압 북서 가장자리의 강한 남서풍대, 등
- 유형별로 대류밴드의 발생과정과 진화 특징을 파악
⦁전형적 대류밴드의 발생 기구와 개념 모형을 제시
- 중규모저기압에 의한 발생과 elevated convergence line에 의한 발생 기구를 제시
⦁대류밴드의 수치예측 가능성 진단
- 24개 사례에 대한 수치예측 실험에서 6개 사례에서 밴드를 성공적으로 재현
⦁전형적 대류밴드 사례의 개념 모형에 근거하여 대류밴드 발생 예측 알고리즘을 제시
2) 구름무리 호우시스템에 대한 이해 발전과 예측 능력 향상
⦁2001~2010년의 45개 구름무리 호우 사례를 분석하여 호우 발생 과정을 제시하였음
- 전체 사례의 64 %(29개 사례)에서 중규모 저기압에 동반된 구름무리에 의해 호우 발생
⦁구름무리를 동반하는 중규모 저기압의 발생지, 이동 특성, 구조를 파악하였음
- 발생지 파악: 티벹 동쪽 인근 6개, 중국 내륙과 동부 16개, 황해 부근에서 7개 사례 발생
- 저기압의 이동 경로와 속도, 한반도 호우 시작 때의 저기압 구조 등을 파악하였음
⦁중규모 기압골에 의한 구름무리 호우시스템의 발생과 조직화 과정 파악
⦁중규모 저기압/구름무리 호우의 수치 예측 능력 평가
- 중규모 저기압의 신뢰성 있는 수치예측 가능성을 확인하였음
⦁중규모저기압/구름무리 호우의 예측 능력 향상 방안을 제시하였음
Ⅳ. Results
1. Improvement of understanding and capability of convection bands (CBs)
a. Classification of convection bands and characteristics of each group of bands
Twenty four episodes of CBs during the 10-year period of 2001-2010 are selected and they are classified based on formation en
Ⅳ. Results
1. Improvement of understanding and capability of convection bands (CBs)
a. Classification of convection bands and characteristics of each group of bands
Twenty four episodes of CBs during the 10-year period of 2001-2010 are selected and they are classified based on formation environment. And characteristics of each group of bands are investigated.
(1) Classification based on formation environment
∙ convection bands occurring along the stationary fronts associated with meso low
∙ convection bands associated with large scale stationary fronts
∙ convection bands occurring along extended synoptic-scale troughs
∙ convection bands occurring within the southwesterly belt along the western Pacific subtropical high (WPSH)
(2) Analysis of charateristics of each classified CB group
∙ Formation environment, structure, formation and evolution processes are investigated
(a) convection bands occurring along the stationary fronts associated with a meso low
∙ Meso low forms in association with a mesoscale convective system, and a front forms in its western side by deformation flows associated with it
∙ The front becomes stationary as the low slowly moves eastward along the northwestern rim of the WPSH, and convective systems develop by the lifting along the front
(b) Convection bands associated with large scale stationary fronts
∙ A large-scale stationary front or low-level convergence zone can be found in between the northwestern rim of the WPSH and a synoptic-scale cyclone to the north of the Korean peninsula
∙ Convective cells are initiated simultaneously along the entire band, or initiated in a part of the band gradually developing along the band
(c) Convection bands occurring along extended synoptic-scale troughs
∙ An elongated trough extended from a synoptic-scale cyclone to the northeast of the Korean peninsula develops along the northwestern side of the WPSH
∙ Convection bands develop in an elevated convergence line in between the trough axis and the WPSH
(d) convection bands occurring within the southwesterly belt along the western Pacific subtropical high (WPSH)
∙ Convective cells form and develop as they move within a homogeneous, strong southwesterly stream along the northwestern rim of the WPSH
∙ Continued formation of cells upstream of the early cells and band development by moving cells downstream side tends to form an elongated band parallel to the southwesterly
∙ Wind directions above 850 hPa are approximately uniform and multiple bands tend to occur
b. Understanding of band formation processes and a conceptual model
(1) Mechanism of CB formation
(a) Formation processes and mechanism for CBs over a quasi-stationary front associated with a meso low
∙ A quasi-stationary front forms to the west of an MCS induced meso low, and band of convective systems develop along the front
∙ Ciruclation associated with meso low and the southwesterly wind of the WPSH develop quasi-stationary front which is activated in the southern side of the meso low, and the updraft of the front causes the development of CBs
(b) Formation process and mechanism of CBs related with extended trough
∙ If the atmosphere is stable in the region with the surface wind shift in the large-scale convergence zone, the elevated convergence line is generated in the rim of the WPSH and the CBs are initiated simultaneously along with this convergence line.
(2) Conceptual models for the formation of CB
∙ Evolution of the elevated convergence line and the generation of CB
- Step 1(Initiation stage): elevated convergence line develops in the southern side of the surface wind shift zone. Early stage of the CB is shown along with the whole area of the elevated convergence zone.
- Step 2(development stage): surface wind shift zone moves southerly and arrives to the elevated convergence line. Elevated convergence line sharply ascends about 3 km from the ground. Drastic growth of the CB has been made by the strengthened elevated convergence zone, and during this process, latent heat release plays an important role for the enhancement of the elevated convergence line.
c. Numerical prediction experiment of the CB and heavy rainfalls
∙ Numerical prediction experiment(6-km) has been made for selected 24 cases, then ev aluate the qualitative prediction ability: 6 out of 24 cases reproduce acceptable CB.
∙ Additional experiment on the 2-km grid has been made for the 6 cases. In this experiment, rainfall rate is underestimated and the accuracy for the estimation of the heavy rainfall area is low.
d. Algorithm for nowcasting of CB
∙ Suggest criterion to predict CB formation related to extended trough based on the ca se study
∙ Suggestion for the algorithm for nowcasting of CB formation related with extended tr ough
2. Improvement of understanding and capability of cloud clusters (CCs)
Forth five episodes of cloud clusters during the 10-year period of 2001~2010 are analysed to identify the disturbances associated with cloud clusters and to find out the origin, movement, evolution and structure of the disturbances.
a. Type of disturbance associated with CCs
∙ CCs accompanied by meso low: 64 % (29 episodes) of 45 CC episodes are associated with meso lows, majority of which form in China
∙ CCs accompanied by mesocale troughs: 31 % of CC episodes are associated with mesos cale troughs which form mostly over the Yellow Sea and the west cost of the Korean peninsula
b. Origin, movement, evolution and structure of meso lows that accompany CCs over the Korean peninsula
∙ Origin of meso lows: meso low is initiated over the eastern side of the Tibetan plateau (TP) in 6 episodes, central and eastern China in 16 episodes, and the Yellow Sea in 7 episodes.
∙ Movement, evolution and structure of meso low:
- Meso lows initiated in the east of TP: Meso low forms in the southeast of TP in 5 episodes, moves with an average speed of 32.4 km/h toward the northeast. Depth of positive vorticity layer occurs below 400 hPa in general.
- Meso lows initiated over central and eastern China: Meso low moves with an average speed of 37.2 km/h toward the northeast or eastward. Depth of positive vorticity layer occurs below 400 hPa in general.
- Meso lows initiated over the Yellow Sea: Meso low forms over the YS near the east Chinese coast line. Heavy rainfall may occur soon after the development of the low. Depth of positive vorticity layer are relatively shallower than the other lows of the previous two groups.
c. Development of cloud clusters associated with a mesoscale trough
∙ Cloud clusters associated with mesoscale troughs tend to occur near the west coast with a simultaneous heavy rainfall
∙ Relationship between mesocale trough and organization of CCs are analyzed through case study. Development of a mesoscale trough and organization of the CC is mainly due to interaction between the mesoscale trough and convective system that are located in strong southwesterly wind
d. Numerical prediction experiments of meso lows/CCs and heavy rainfalls
∙ Evolution of meso lows is successfully simulated on both 6- and 2-km simulation
- Prediction of meso low has reliable prediction capability
∙ Reliability of prediction of heavy rainfall system and rainfall amount is poor
e. Derivation of strategy for improvement in the prediction of CCs and rainfalls
∙ Suggest a strategy for improvement in the prediction of CCs accompanied by meso-low which comprising 64 % of the CCs
∙ Judgement basis on the possibility of improvement in the prediction capability of heavy rainfall associated with CCs:
- Possibility of relatively reliable prediction of meso-lows associated with meso-low is ound
- In the meso low, the occurrence location of CCs is similar in most of the cases
∙ Strategy of prediction improvement:
- Numerical prediction experiment of meso lows generated over China and the Yellow Sea for operational weather prediction
- Improve the capability to diagnosis meso low generation over China and the Yellow Sea through the mesosclae diagnosis capability examination of RDAPS 12-km grid reanalysis data
→ Based on the numerical prediction experiments and diagnosis capability examination, wide-range heavy rainfall and meso low that have 48, 24, and 12 hours lead time can be predicted
- Additional studies are needed to compensate the quantitative rainfall prediction method
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총연구비 (DetailSeriesProject) : | - |
키워드(keyword) : | - |
과제수행기간(LeadAgency) : | - |
연구목표(Goal) : | - |
연구내용(Abstract) : | - |
기대효과(Effect) : | - |
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