Forecast Skill in Prediction of the East Asia Summer Monsoon using S2S DB Sang-Min Lee, Yu-Kyong Hyun, Hyun-Suk Kang and Young-Hwa Byun [email protected] Climate Research Division, National Institute of Meteorological Sciences, KMA East Asia Summer Monsoon (EASM) Subseasonal to Seasonal (S2S) DB Concept of Lead Time Weekly precipitation climatology of GPCP 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ㆍ ★ ★ ★ ★ ▲ ▲ ▲ ▲ ▲ ▲ ● ● ● ● ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ - S2S Hindcast DB • NCEP/CMA: Daily [ㆍ] • KMA/UKMO: 1, 9, 17, 25 (4/month) [★] • BOM: 1, 6, 11, 16, 21, 26 (6/month) [▲] • JMA: 10, 20, last day of month (3/month) [●] • ECMWF: Sunday and Wednesday a week (2/week) [■] Resolution Rfc Ens. Size Rfc Frequency Time range Rfc length KMA N216L85 (432x325) 3 4/month (1,9,17,25) d 0-60 1996-2009 BoM T47L17 (144x72) 33 6/month (1,6,11,16,21,26) d 0-62 1981-2013 CMA T106L40 4 daily d 0-60 1994-2014 ECMWF T639/319 L91 11 2/week d 0-46 past 20 years NCEP T126L64 4 day d 0-44 1999-2010 JMA T319L60 5 3/month d 0-33 1981-2010 240x121 (1.5°) 1 week 2000-2009 (10yrs) Meiyu Changma Baiu - EASM Period: from the first week to the last week with above +1 in the average of precipitation in EASM area - In average, the EASM starts in early June (23W) and ends in late August (35W), so that it continues during 13 weeks Forecast Skill of the accumulated PRCP Acknowledgement: This work has been supported by the Research and Development for KMA Weather, Climate, and Earth system Services - Lead Time: The Closest date of forecast • 1-week lead time • 2-week lead time • 3-week lead time Zonal Average Precipitation Climatology & Synoptic Characteristics of EASM Fractions Skill Score of Precipitation The East Asian Summer Monsoon (EASM) is characterized by the heaviest seasonal precipitation in South Korea, China and Japan. It is also an important water resources and the fluctuations of the monsoon are often associated with floods, droughts, and other climate extreme events. Recently, Subseasonal to Seasonal (S2S) numerical models have played a vital role in improvement forecast skill and understanding on the subseasonal to seasonal timescale on high-impact weather events.