Medium-Range Forecast Sensitivity to the Background State and Background Error Statistics in a Global Data Assimilation and Prediction System

Byoung-Kwon Park, Seong-On Hwang, Song-You Hong


The increase in the medium-range forecast skill of global models is attributable to the improvement of the initial condition, model dynamics, and physics. In this study, we evaluated the role of the background state and background error statistics (BES) on a medium-range forecast testbed, using the Global/Regional Integrated Model system (GRIMs) that incorporates the National Center for Environmental Prediction (NCEP) Gridpoint Statistics Interpolation (GSI). In addition to the control run, in which the initial condition is obtained from the NCEP Global Data Assimilation System (GDAS) analysis, four additional experiments with different background states and BES are executed to evaluate the impacts of the background state and the BES on forecast skills. The standard background fields are produced from the sophisticated, higher resolution NCEP Global Data Assimilation System (GDAS), whereas the other background fields are produced from the GRIMs through cycle runs every 6 hours. Further, the two kinds of BES are calculated from the NCEP Global Forecast System (GFS) and the GRIMs, respectively. Evaluations are performed in August 2010, with the focus on the 500-hPa geopotential height and precipitation. The experiment simulated with results from high-quality background fields performs better than when using results of low-quality background fields with respect to the 500-hPa geopotential height Anomaly Correlation (AC). This is true for both North and South Hemisphere results. The impact of BES dose not responds much towards primitive forecast skills, but it influences the forecast skill after day 7. In contrast to the large-scale features, the forecast skills of precipitation show the overall improvement of land precipitation with the support of the cycle run.

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Earth Science Research   ISSN 1927-0542 (Print)   ISSN 1927-0550 (Online)
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