The NCEP Climate Forecast System Version 2

Journal of Climate - Tập 27 Số 6 - Trang 2185-2208 - 2014
Sujata Saha1, Shrinivas Moorthi1, Xingren Wu2, Jiande Wang3, Sudhir Nadiga2, Patrick Tripp2, David Behringer1, Yu-Tai Hou1, Hui-ya Chuang1, Mark Iredell1, Michael Ek1, Jesse Meng2, Rongqian Yang2, Malaquías Peña2, Huug van den Dool4, Qin Zhang4, Wanqiu Wang4, Mingyue Chen4, Emily Becker5
1Environmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland
2I. M. Systems Group, Inc., Rockville, Maryland
3Science Systems and Applications, Inc., Largo, Maryland
4Climate Prediction Center, NOAA/NWS/NCEP, College Park, Maryland
5Wyle Lab, Inc., Arlington, Virginia

Tóm tắt

Abstract

The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

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