Recovery of mesoscale covariance using time-phased ensembles
Ok-Yeon Kim, Pukyung National University, Busan, Korea; and C. Lu, J. A. McGinley, and J. H. Oh
In most data-assimilation systems in the operational numerical weather forecast centers around the world, the background error covariance (BEC) is either computed off-line and/or modeled with some empirical fitting functions. When BEC is computed off-line or modeled, it requires a large and long-period statistical sample of verification of model forecasts and analyses (e.g., the well-known NMC method). Typically, such methods render a very smoothed, large-scale structure in BEC due to near-climatological averaging of day-to-day weather variances. There are questions whether a data-assimilation system will produce any realistic mesoscale structure in the analysis if such constructed BEC is used, and whether such an analysis suitable for generating a short-range numerical weather prediction of 1-2 days.
In this study, we present a method to compute BEC, which can be not only done effectively on-line (in the sense of parallel with model runs), but the BEC can also capture the detailed mesoscale structure. The method uses a set of time-phased ensembles. We will demonstrate how such BEC can be calculated within a frequent data-assimilation cycle, and also compare the covariance structure recovered using this method with that from the NMC method.
Extended Abstract (2.4M)
Poster Session 2, Wednesday Poster Viewing
Wednesday, 27 June 2007, 4:30 PM-6:30 PM, Summit C
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