Search Results
. 2008 ). The ET method does not assimilate observations but instead uses an externally derived analysis to center the initial ensemble perturbations. Bishop et al. (2001) introduced ETKF as a generalization of the ET method to assimilate observations and estimate their effects on forecast error covariance. Both the ET and ETKF methods have also been used to estimate the effects of potential adaptively corrected data on high-impact forecasts ( Szunyogh et al. 2000 ; Majumdar et al. 2001 , 2002
. 2008 ). The ET method does not assimilate observations but instead uses an externally derived analysis to center the initial ensemble perturbations. Bishop et al. (2001) introduced ETKF as a generalization of the ET method to assimilate observations and estimate their effects on forecast error covariance. Both the ET and ETKF methods have also been used to estimate the effects of potential adaptively corrected data on high-impact forecasts ( Szunyogh et al. 2000 ; Majumdar et al. 2001 , 2002
nature run in the perfect-model experiments by the NNR fields. Since the NNR assimilated real observations, we assume that the NNR fields are an approximate estimate of the unknown true atmosphere (a quantitative validation of this assumption is beyond the scope of this research). Random noise with the same standard deviation used in the perfect-model experiments is added to simulate “NNR observations.” The density of observations remains the same as that in the perfect-model experiments. a. Effects
nature run in the perfect-model experiments by the NNR fields. Since the NNR assimilated real observations, we assume that the NNR fields are an approximate estimate of the unknown true atmosphere (a quantitative validation of this assumption is beyond the scope of this research). Random noise with the same standard deviation used in the perfect-model experiments is added to simulate “NNR observations.” The density of observations remains the same as that in the perfect-model experiments. a. Effects
and Cotton 2004 ), and have shown that CRM-simulated cloud fields are particularly sensitive to changes in the parameters that define particle size distributions. A number of recent studies have used data assimilation techniques to study the effects of variation in model physics parameters ( Aksoy et al. 2006 ; Tong and Xue 2008 ) or parameterization schemes ( Meng and Zhang 2007 ). Instead of seeking to understand the nature of the uncertainty, the primary goal of these data assimilation
and Cotton 2004 ), and have shown that CRM-simulated cloud fields are particularly sensitive to changes in the parameters that define particle size distributions. A number of recent studies have used data assimilation techniques to study the effects of variation in model physics parameters ( Aksoy et al. 2006 ; Tong and Xue 2008 ) or parameterization schemes ( Meng and Zhang 2007 ). Instead of seeking to understand the nature of the uncertainty, the primary goal of these data assimilation
