Beating the Uncertainties: Ensemble Forecasting and Ensemble‐Based Data Assimilation in Modern Numerical Weather Prediction

Zhang, Hailing; Pu, Zhaoxia

doi:10.1155/2010/432160

Cited by 29 publications

(14 citation statements)

References 75 publications

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“…Therefore, the error covariance matrix can be calculated by integrating the ensemble of model states. The EnKF has attracted broad attention and has been widely used in atmospheric and oceanic data assimilation (Keppenne, 2000;Evensen, 2003;Lorenc, 2003;Miyoshi and Sato, 2007;Zhang and Pu, 2010;Cui et al, 2011;Deng et al, 2012). With great effort and intensive study in the last decade, the EnKF-based data assimilation technique has become mature, leading to the development of various EnKF schemes to address the issues affecting the standard EnKF, including the ensemble *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model

Yang¹,

Min

Tang

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThis work attempts to validate two modified Kalman gain algorithms by assimilating a single radar simulation data set into the Weather Research and Forecasting model using an Ensemble Square Root Filter. Emphasis is placed on the comparison of assimilation performance between the two modified algorithms against the classical Kalman gain algorithm when the measurement operator is non-linear. Three ideal storm-scale experiments, which are configured identically except for the different Kalman gain algorithms, are designed in parallel for this purpose. The results show that the first modified algorithm can result in a better simulation of a storm, as measured by the root mean square error (RMSE). The second algorithm can also, to some extent, reduce the RMSE of the simulation of some state vectors, but with little improvement of the estimation of storm intensity. Overall, our preliminary experiments indicate that the two modified Kalman gain algorithms can benefit the assimilation of complex numerical models when the measurement operators are non-linear, confirming the earlier theoretical analysis and the results of simple models. Further work is needed to evaluate the impact of the modified Kalman gain algorithms on the assimilation performance of ensemble-based methods.

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Section: Introductionmentioning

confidence: 99%

Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model

Yang¹,

Min

Tang

2015

Tellus A: Dynamic Meteorology and Oceanography

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“…One could imagine to choose a variational method, as used in WRF [ Huang et al , 2009], an ensemble Kalman filter [ Fujita et al , 2007], or using random draws from the background error covariance matrix to generate an initial ensemble [ Torn et al , 2006]. However, it is not trivial matter to extend these techniques to the convective scales, due to non‐Gaussian error statistics and large forecast errors in the small scales due to imperfectly modeled processes in the boundary layer and cloud physics formulations [ Zhang and Pu , 2010; Zupanski et al , 2011].…”

Section: Introductionmentioning

confidence: 99%

Accounting for initial condition uncertainties in COSMO‐DE‐EPS

et al. 2012

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[1] This study describes the experimental setup of an atmospheric ensemble prediction system (EPS) based on a high resolution numerical weather prediction model developed at Deutscher Wetterdienst (DWD). The focus is set on uncertainties of initial conditions, and how to combine them with perturbations of the boundary conditions and model physics variations, discussed in a previous investigation. Two ensemble setups are constructed: one with model physics diversity and variations of boundaries (BP ensemble) and one which also includes perturbations of initial conditions (IBP ensemble). Experimental ensemble forecasts of precipitation with a lead time of 24 hours, for a period of 29 days in the summer of 2009, are generated. Deterministic verification shows that the inclusion of the initial perturbations does not decrease the quality of the forecast, with individual members of the IBP and BP ensembles being statistically similar. The probabilistic scores of the IBP ensemble are better than those of the BP ensemble during the first 12 hours of the forecast, and afterwards both have similar performances. Similarly, the IBP ensemble provides more spread during the first 12 hours of the forecast, decreasing to levels comparable to the BP ensemble afterwards.

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“…Although many of the scientific articles handled the subject of uncertainty in transport models, the literature on quantifying uncertainty in the form of variances, standard deviations, confidence intervals, or other related measures is relatively limited [10]. Furthermore, the researchers who considered the quantification traffic forecasts focused on parameters of the model structure or inputs travel time and trip cost such as; Ashley [11], Lowe et al [12], Kroes [13], Leurent [14,15], de Jong et al [16], Zhang and Pu [17], Perrakis et al [18], Deng et al [19], etc. While the problem of the posterior distribution of the input data of the traffic assignment model did not subject to extensive studies to know the parametric variability of OD pairs.…”

Section: Modelling Of Traffic Flow Data (Prediction)mentioning

confidence: 99%

Uncertainty Quantification of the Traffic Assignment Model

Seger

Kisgyörgy

2020

Period. Polytech. Civil Eng.

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Forecasting of traffic flow in the traffic assignment model suffered to a wide range of uncertainties arising from different sources and exacerbating through sequential-stages of the travel demand model. Uncertainty quantification can provide insights into the level of confidence on the traffic assignment model outputs, and also identify the uncertainties of the input Origin-Destination matrix for enhancing the forecasting robustness of the travel demand model. In this paper, a systematic framework is proposed to quantify the uncertainties that lie in the Origin-Destination input matrix. Hence, this study mainly focuses on predicting the posterior distributions of uncertainty Origin-Destination pairs and correcting the biases of Origin-Destination pairs by using the inverse uncertainty quantification formulated through Least Squares Adjustment method. The posterior distributions are further used in the forward uncertainty quantification to quantify the forecast uncertainty of the traffic flow on a transport network. The results show the effectiveness of implementing the inverse uncertainty quantification framework in the traffic assignment model. And demonstrate the necessity of including uncertainty quantification of the input Origin-Destination matrix in future work of travel demand modelling.

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Beating the Uncertainties: Ensemble Forecasting and Ensemble‐Based Data Assimilation in Modern Numerical Weather Prediction

Cited by 29 publications

References 75 publications

Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model

Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model

Accounting for initial condition uncertainties in COSMO‐DE‐EPS

Uncertainty Quantification of the Traffic Assignment Model

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