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contributor authorKleist, Daryl T.
contributor authorParrish, David F.
contributor authorDerber, John C.
contributor authorTreadon, Russ
contributor authorWu, Wan-Shu
contributor authorLord, Stephen
date accessioned2017-06-09T16:32:39Z
date available2017-06-09T16:32:39Z
date copyright2009/12/01
date issued2009
identifier issn0882-8156
identifier otherams-69714.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4211414
description abstractAt the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains. Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.
publisherAmerican Meteorological Society
titleIntroduction of the GSI into the NCEP Global Data Assimilation System
typeJournal Paper
journal volume24
journal issue6
journal titleWeather and Forecasting
identifier doi10.1175/2009WAF2222201.1
journal fristpage1691
journal lastpage1705
treeWeather and Forecasting:;2009:;volume( 024 ):;issue: 006
contenttypeFulltext


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