초록

o 국내외 연구 현황 파악 및 자료동화 기법 조사
o 우리나라에서 활용 가능한 기상관측 자료의 조사 및 활용 방법 연구
o LAPS 시스템의 한반도 적용 이식
o LAPS 의 자료 동화 기법을 이용한 각종 기상관측자료의 동화
o 수치예보 모델의 초기자료 제공을 위한 초기화 프로그램 작성 및 이를 이용한 사례 연구
o 고해상도 재분석 자료 생산을 위한 기반 구축
o 종합 자료동화 시스템의 현업화

Abstract ▼

In order to improve the accuracy of numerical weather prediction, it is essential to provide the better initial data to the prediction model. The development of fine mesh models need the reasonable hight resolution initial data. For the precise representation of the initial status of atmosphere, the

In order to improve the accuracy of numerical weather prediction, it is essential to provide the better initial data to the prediction model. The development of fine mesh models need the reasonable hight resolution initial data. For the precise representation of the initial status of atmosphere, the data assimilation technique should be developed. To shorten this development, the international collaboration with FSL (Forecast Systems Laboratory) which has the advanced technique of data assimilation is one of the efficient ways.
This report explains the LAPS (Local Analysis and Prediction System) which has been under development at the Forecast Systems Laboratory since the late 1980s. LAPS is a high- resolution analysis and forecasting system designed to run on a small computer using all relevant available data sources. The output from LAPS can be used not only as the initial data for the numerical model but also as analyzed data for nowcasting.
There are four main components to LAPS: data collection, analysis, prediction and visualization. In case of data collection, the observational data from various data sources ( GTS, GMS, Radar etc.) are converted to NetCDF format and also interpolated to LAPS grids using Barnes or cubic spline fit. The LAPS consists of 8 analysis processes including surface, temperature, wind, humidity, cloud, cloud-driven, precipitation, and soil. The prediction component of LAPS is now using several numerical models (MM5, ARPS, RAMS). And then the analysis and forecast output is displayed by NCAR graphic software.
Among them, LAPS analysis routine is the key point of this project. The following is the main part of LAPS data analysis and assimilation.
- Basically the Barnes 2-pass analysis scheme is used for prerequisite analysis III almost analysis in LAPS. And then a unique analysis technique IS inserted to every analysis process.
-In surface analysis, a variational method is used to constrain the pressure and wind fields to partially satisfy the full equations of motion. HSM (Horizontal Shape Matching) technique is also used to insert the GMS TBB data for surface analysis.
- In humidity analysis, the variational method using a satellite forward radiance model is used in LAPS for adjustments of the upper air humidity. The forward model produces a simulated radiance. And then a scaled coefficient is calculated by minimizing the difference between the observed radiance and estimated radiance from the forward model. Finally, the scale coefficient is applied to the specific humidity field at each pressure level.
- In wind analysis, to use the radar data, the tangential velocity retrival method is used. That estimates the tangential velocity from non-radar data. And then the estimated velocity is combined with radar radial velocity.
Finally a two-dimensional radar wind field is produced. The radar two-dimensional wind field from the above process, is inserted into the wind analysis.

This is the first attempt to produce the high resolution initial data using all observational data including asynoptic data. It is possible to analyze the small scale meteorological phenomena to use the high resolution observational data from LAPS.
In this study we have not applied to use some particular data yet, for example radar velocity or aircraft data. In the near future, all available observational data will be included in LAPS and this is also expected to improve the three-dimensional representation of meso-scale structure. So this system will be very helpful to adopt to all area of weather forecast.

목차 Contents

• 표지...1
• 제출문...2
• 요약문...3
• SUMMARY...7
• 목차...9
• CONTENTS...11
• Figure List...13
• Tabel List...16
• 제1장. 서론...17
• 제2장. 국내외 기술개발 현황...19
• 제1절. 국내 연구 현황...19
• 제2절. 국외 연구 현황...20
• 제3장. 연구개발수행 내용 및 결과...22
• 제1절. 기상관측자료의 객관분석 기법 조사...22
• 1. 개요...22
• 2. 객관 분석 기법의 종류...22
• 제2절. 기상관측자료...35
• 1. 지상 및 해양 기상 관측 자료...35
• 2. 고층 기상 관측 자료...39
• 3. 위성 기상 관측 자료...40
• 4. 레이더 기상 관측 자료...42
• 5. 수치예보 자료...45
• 제3절. 국지규모 기상자료 분석 및 동화 시스템(LAPS)...47
• 1. 개발 배경...47
• 2. LAPS 분석 과정...48
• 3. LAPS의 입력 자료...50
• 4. LAPS와 수치예보 모델과의 결합...80
• 5. LAPS를 이용한 몇가지 사례 연구...82
• 6. LAPS를 이용한 몇가지 사례 연구...82
• 7. LAPS의 실시간 운영...94
• 8. 소결론...97
• 제4장. 연구개발목표 달성도 및 대외 기여도...99
• 제5장. 연구개발결과의 활용 계획...101
• 제6장. 참고문헌...101