2020
DOI: 10.37394/232015.2020.16.63
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Background Error in WRF Model

Abstract: WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desir… Show more

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Cited by 4 publications
(3 citation statements)
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References 22 publications
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“…All runs were initialized with 25-km NCEP GFS Model GRIB data results from regional NWP do not have the same quality for all areas within the domain. (Kutaladze et al, 2021). The results of model validation are not homogeneous inside domain and are highly dependent on the physical content of the synoptic process and complexity of relief.…”
Section: Methodsmentioning
confidence: 99%
“…All runs were initialized with 25-km NCEP GFS Model GRIB data results from regional NWP do not have the same quality for all areas within the domain. (Kutaladze et al, 2021). The results of model validation are not homogeneous inside domain and are highly dependent on the physical content of the synoptic process and complexity of relief.…”
Section: Methodsmentioning
confidence: 99%
“…Notably, the WRF model is frequently employed to calculate BE and perform DA experiments using model forecasts initialized at different times. Accurate estimation of BE is essential for the success of DA, as it ensures appropriate weighting to background information, implicitly considering observations [24]. Additionally, BEs are spatially correlated, enabling the propagation of observational information in three dimensions.…”
Section: Introductionmentioning
confidence: 99%
“…The process of background error estimation is very important, as it adds to the calculation of the adequate weight for the observations to be used in the DA [10]. Multiple meteorological variables are correlated regarding background errors, enabling the analysis to make multivariate adjustments that reflect the atmosphere's dynamic and physical equilibrium [11,12].…”
Section: Introductionmentioning
confidence: 99%