미 퍼시픽 노스웨스트 국립 연구소(Pacific Northwest National Laboratory, PNNL)의 윤진호 박사가 오는 3월 8일 APEC 기후센터(APCC)를 방문한다. 그는 8일 오후 3시, APCC 에델바이스 회의실에서 “Challenges in climate prediction: regional downscaling efforts under the MRED project”라는 제목으로 발표를 진행할 예정이다.

윤진호 박사의 발표 제목과 abstract 등 상세 정보는 다음과 같다.
 

1. 발표자:　윤진호 박사 (Scientist, 퍼시픽 노스웨스트 국립 연구소 (PNNL))
 

2. 발표제목: Challenges in climate prediction: regional downscaling efforts under the MRED project

3. 일시: 15:00 PM on March 8th, 2013

4. 장소: The Edelweiss Room on the second floor, APCC

5. Abstract
 

Seasonal climate prediction has been operationally made using the Climate Forecast System (CFS) by NCEP/NOAA since August 2004. However, several challenges remain for its practical use. One of the major shortcomings of the CFS is its coarser spatial resolution. Two methods, dynamic and statistical downscaling, have been developed to enhance the usefulness of global climate forecasts. Statistical downscaling employs historical relationships between the CFS and observational data to create high-resolution forecast outputs. In contrast, limited area models can be forced by the initial and boundary conditions from the CFS to dynamically downscale global forecasts. A large community effort on dynamic downscaling is underway, under the Multi-RCM Ensemble Downscaling (MRED) project, a partnership with various research groups in the U.S. Six Regional Climate Models (RCMs) are used to dynamically downscale CFS seasonal predictions over the coterminous U.S. out to 5 months for the period from 1982 – 2003. In this talk, we will briefly review the statistical and dynamical downscaling methods, compare them to one another, and discuss what would be an optimal approach.

We will use drought prediction as an example to demonstrate how regional downscaling can add extra value to a global climate forecast. In an effort to predict drought in a more objective way, we developed a Standardized Precipitation Index (SPI) from the precipitation (P) seasonal forecasts from the CFS. Since then, the SPI has been widely used for monitoring drought by many operational centers around the world. Forecasting SPI can streamline our efforts in both monitoring and forecasting drought. The method for developing the SPI forecast and added values by regional downscaling will be discussed.