Abstract
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.
Highlights
With the increase in computing power, improvements to the numerical weather prediction (NWP) models are being actively pursued for initial fields, model physics, and increased spatial resolution
The more marked descent from the Root Mean Square of Difference (RMSD) of the Background to the RMSD of analysis has been more remarkable between Group B rather than Group A, demonstrating that the incorporated data assimilation (DA) system within the warm cycle could create the optimal state of the atmosphere with even a background field produced from lower resolution forecast model
To evaluate the sensitivity to the initial field, two experiments were simulated with the initial data that are assimilated with the high-resolution Global Data Assimilation System (GDAS) background fields, where background error statistics (BES) of Global Forecast System (GFS) and Global/Regional Integrated Model system (GRIMs) are set up, respectively
Summary
With the increase in computing power, improvements to the numerical weather prediction (NWP) models are being actively pursued for initial fields, model physics, and increased spatial resolution. Analysis fields generated by any data assimilation (DA) system are important, due to their role as the best possible estimates of nature and in the initial states for NWP weather forecasts. Associated error relates with future forecast error as the former evolves during the forecast time interval, directly determining the quality of the forecast This relationship may result from various sources of error associated with observations, a forecast model, and DA systems, such as observation screening, observation data error, quality control, background information, background error variances, the choice of control variables, imposed constraint, and so on. The amplitudes of the stream function, unbalanced temperature, and unbalanced surface pressure, are larger in the mid-latitudes than in the tropics, and larger in the Southern Hemisphere than in the Northern Hemisphere These results correspond with those reported by Wu et al (2002).
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