Statistical Properties of Three-Hour Prediction “Errors” Derived from the Mesoscale Analysis and Prediction System

Dévényi, D.; Schlatter, Thomas

doi:10.1175/1520-0493(1994)122<1263:spothp>2.0.co;2

Cited by 10 publications

(3 citation statements)

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“…They also showed there how the same procedure can be used to estimate the vertical correlations of forecast and observation errors from multilevel observed-minus-forecast residuals. Additional applications involving anisotropic, multivariate, and non-separable covariance models have been described by Thi ebaux et al (1986Thi ebaux et al ( , 1990, Bartello and Mitchell (1992), and Devenyi and Schlatter (1994).…”

Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology

Dee¹,

Silva²

1999

Mon. Wea. Rev.

146

116

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The maximum-likelihood method for estimating observation and forecast error covariance parameters is described. The method is presented in general terms but with particular emphasis on practical aspects of implementation. Issues such as bias estimation and correction, parameter identi ability, estimation accuracy, and robustness of the method, are discussed in detail. The relationship between the maximum-likelihood method and Generalized Cross-Validation is brie y addressed. The method can be regarded as a generalization of the traditional procedure for estimating covariance parameters from station data. It does not involve any restrictions on the covariance models and can be used with data from moving observers, provided the parameters to be estimated are identi able. Any available a priori information about the observation and forecast error distributions can be incorporated into the estimation procedure. Estimates of parameter accuracy due to sampling error are obtained as a by-product.

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Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology

Dee¹,

Silva²

1999

Mon. Wea. Rev.

146

116

View full text Add to dashboard Cite

show abstract

“…The statistics of innovations, the differences between model forecasts and data, are an important component for data assimilation and evaluation of performance of numerical weather prediction (NWP) models (Rutherford, 1972;Hollingsworth and Lönnberg, 1986;Lönnberg and Hollingsworth, 1986;Thiebaux et al, 1986Thiebaux et al, , 1990Shaw et al, 1987;Daley 1991Daley , 1992Daley , 1993Bartello and Mitchell, 1992;Mitchell et al, 1993;Devenyi and Schlatter, 1994;Cohn, 1997;Xu and Wei, 2001a,b;Xu et al, 2007). Rawinsonde data have been the main source of innovation statistics and the rawinsonde observation errors are assumed to be spatially uncorrelated which produces simultaneous estimates of the total observationand model forecast-error covariance Lönnberg and Hollingsworth, 1986).…”

Section: Introductionmentioning

confidence: 99%

Atmospheric turbulence component of the innovation covariance

Frehlich

2008

Quart J Royal Meteoro Soc

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ABSTRACT:The innovation covariance is derived for general atmospheric turbulence conditions and for climatologies of upper-level turbulence based on aircraft data. Error is defined in terms of the effective spatial filter of the forecast model to produce a consistent definition of measurement error and model error. Calculations of the atmospheric turbulence component (observation sampling-error covariance) are performed for rawinsonde data and various forecast model resolutions. Significant contributions from the atmospheric turbulence field are produced, especially for the older forecast models.

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“…For example, 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. Thus, the assumption about the statistical structure of the forecast error must be examined, especially with an operational data assimilation system, e.g., Devenyi and Schlatter (1994) for MAPS (Mesoscale Analysis Prediction System) in FSL/NOAA, Hollingsworth et al (1986) for ECMWF data assimilation system.…”

Section: Introductionmentioning

confidence: 99%

Statistics of 6-hour Forecast Errors Derived from Global Data Assimilation System at the Central Weather Bureau in Taiwan

Hong¹

2001

Terr. Atmos. Ocean. Sci.

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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 dis tribution 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 approxi mate 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 attrib uted to the background errors and suggest that the model error is domi nated by large scales. The skewness and kurtosis have many local extremes, suggesting that observational errors dominate these higher order statistics.

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Statistical Properties of Three-Hour Prediction “Errors” Derived from the Mesoscale Analysis and Prediction System

Cited by 10 publications

References 0 publications

Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology

Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology

Atmospheric turbulence component of the innovation covariance

Statistics of 6-hour Forecast Errors Derived from Global Data Assimilation System at the Central Weather Bureau in Taiwan

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