The NCEP Climate Forecast System Version 2

Autor: Qin Zhang, Shrinivas Moorthi, Mingyue Chen, Malaquías Peña Mendez, Yu-Tai Hou, Michael Ek, M. Iredell, Jesse Meng, Sudhir Nadiga, Hui Ya Chuang, Patrick Tripp, Wanqiu Wang, Rongqian Yang, Jiande Wang, Xingren Wu, David Behringer, Emily Becker, Huug van den Dool, Suranjana Saha
Rok vydání: 2014
Předmět:
Zdroj: Journal of Climate. 27:2185-2208
ISSN: 1520-0442
0894-8755
Popis: 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.
Databáze: OpenAIRE
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