Abstract
The statistics of 6-hour forecast errors for z, u, and v derived from the global data assimilation system at the Central Weather Bureau in Taiwan are presented. One point moments, including mean, standard deviation, skewness, and kurtosis of the forecast errors, are calculated at radiosonde stations to evaluate the statistical properties and define how close the distribution of the forecast error is to the Gaussian distribution. The degree to which the analyses fit the observation is also examined. The overall evaluations with respect to different domains show that the lower order statistics, mean and standard deviation, are reasonable and comparable to the results of other operational centers. The higher order statistics show that the distributions of the forecast error form an approximate Gaussian distribution. The spatial distribution of the one point moment shows that the mean and standard deviation of forecast errors are sensitive to the orographic effect (e.g., the Tibetan Plateau), the Asia and North American monsoon activities, and the mid-latitude disturbances. The pattern of the mean and standard deviation exhibits large-scale variability, which may be attributed to the background errors and suggest that the model error is dominated by large scales. The skewness and kurtosis have many local extremes, suggesting that observational errors dominate these higher order statistics.
Highlights
The optimal interpolation (OI) analysis technique is based on the assumption that the deviations from a background field are normally distributed (Lorenc 1986)
The goals of this paper are (I) to introduce the data assimilation system (GFS) at Central Weather Bureau (CWB) and show that it works in a reasonable way by verifying the forecast and analysis against the observations; (2) to compare the statistics of forecast error and analy sis performance to other operational centers; (3) to investigate whether the statistical proper ties of the forecast error follow an approximate Gaussian distribution
The statistics of the forecasts errors for z, u, and v derived from the global data assimila tion system at the Central Weather Bureau in Taiwan were presented
Summary
The optimal interpolation (OI) analysis technique is based on the assumption that the deviations from a background field are normally distributed (Lorenc 1986). The background fields are usually provided by short range forecasts produced by an operational data assimila-. Nonlinearity of the dynamical or physical processes in the numerical model and gross errors due to measurement and data transmission problems could result in a non-Gaussian distribution of background and observation error. The goals of this paper are (I) to introduce the data assimilation system (GFS) at CWB and show that it works in a reasonable way by verifying the forecast and analysis against the observations; (2) to compare the statistics of forecast error and analy sis performance to other operational centers; (3) to investigate whether the statistical proper ties of the forecast error follow an approximate Gaussian distribution.
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