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18
Performance characteristics of a pseudooperational ensemble Kalman filter
, 2008
"... The 2yr performance of a pseudooperational (real time) limitedarea ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Repor ..."
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Cited by 18 (1 self)
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The 2yr performance of a pseudooperational (real time) limitedarea ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Reporting System (ACARS), and cloud motion vectors every 6 h on a domain that includes the eastern North Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data. Relative to operational forecasts, the forecast from the ensemblemean analysis has slightly larger errors in wind and temperature but smaller errors in moisture, even though satellite radiances are not assimilated by the EnKF. Timeaveraged correlations indicate that assimilating ACARS and cloud wind data with flowdependent error statistics provides corrections to the moisture field in the absence of direct observations of that field. Comparison with a control experiment in which a deterministic forecast is cycled without observation assimilation indicates that the skill in the EnKF’s forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. Furthermore, the ensemble variance is generally in good agreement with the ensemblemean error and the spread increases monotonically with forecast hour.
A NonGaussian Ensemble Filter Update for Data Assimilation
, 2009
"... A deterministic square root ensemble Kalman filter and a stochastic perturbed observation ensemble Kalman filter are used for data assimilation in both linear and nonlinear single variable dynamical systems. For the linear system, the deterministic filter is simply a method for computing the Kalman ..."
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Cited by 12 (1 self)
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A deterministic square root ensemble Kalman filter and a stochastic perturbed observation ensemble Kalman filter are used for data assimilation in both linear and nonlinear single variable dynamical systems. For the linear system, the deterministic filter is simply a method for computing the Kalman filter and is optimal while the stochastic filter has suboptimal performance due to sampling error. For the nonlinear system, the deterministic filter has increasing error as ensemble size increases because all ensemble members but one become tightly clustered. In this case, the stochastic filter performs better for sufficiently large ensembles. A new method for computing ensemble increments in observation space is proposed that does not suffer from the pathological behavior of the deterministic filter while avoiding much of the sampling error of the stochastic filter. This filter uses the order statistics of the prior observation space ensemble to create an approximate continuous prior probability distribution in a fashion analogous to the use of rank histograms for ensemble forecast evaluation. This rank histogram filter can represent nonGaussian observation space priors and posteriors and is shown to be competitive with existing filters for problems as large as global numerical weather prediction. The ability to represent nonGaussian distributions is useful for a variety of applications such as convectivescale assimilation and assimilation of bounded quantities such as relative humidity. 1.
Initiation of Ensemble Data Assimilation
 Tellus
, 2006
"... A specification of the initial ensemble in ensemble data is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but it is applicable to other ensemble data assimilation algorithms as well. Two new methods are considered ..."
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Cited by 9 (0 self)
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A specification of the initial ensemble in ensemble data is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but it is applicable to other ensemble data assimilation algorithms as well. Two new methods are considered: first, based on the use of the KardarParisiZhang (KPZ) equation to form sparse random perturbations, followed by spatial smoothing to enforce desired correlation structure, and second, based on spatial smoothing of initially uncorrelated random perturbations. Data assimilation experiments are conducted using a global shallowwater model and simulated observations. The two proposed methods are compared to the commonly used method of uncorrelated random perturbations. The results indicate that the impact of the initial correlations in ensemble data assimilation is beneficial. The rootmeansquare error rate of convergence of data assimilation is improved, and the positive impact of initial correlations is noticeable throughout the data assimilation cycles. The sensitivity to the choice of the correlation length scale exists, although it is not very high. The implied computational savings and improvement of the results may be important in future realistic applications of ensemble data assimilation.
The Analysis and Impact of Simulated HighResolution Surface Observations for Convective Storms with Ensemble Kalman Filter: Perfect Model Experiments
, 2007
"... A series of observing system simulation experiments (OSSEs) are performed using the ARPS model and its EnKF (ensemble Kalman filter) data assimilation system to investigate the impact of surface observations on the analysis and forecast of convective storms in addition to Doppler radar data. A truth ..."
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Cited by 4 (3 self)
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A series of observing system simulation experiments (OSSEs) are performed using the ARPS model and its EnKF (ensemble Kalman filter) data assimilation system to investigate the impact of surface observations on the analysis and forecast of convective storms in addition to Doppler radar data. A truth simulation is created for a supercell storm at a 2 km horizontal resolution. This storm is sampled using a radar emulator that assumes a standard WSR88D radar scanning mode and in the vertical uses a realistic radar beam weighting function. A single radar is located at different distances from the storm. Partly due to the earth curvature effect, the lowlevel coverage of radar data decreases as the radar distance increases, causing the loss of coverage on important lowlevel features including the cold pool and gust front. When the radar is located far away (185 and 115 km) from the main convective storm, clear positive impact on storm analysis and forecast is achieved by mesonetlike surface observations of 20 km spacings, and such impact increases when the station spacing increases to 12 or 6 km. Through the background error covariance estimated from the ensemble and through dynamical interactions in the prediction model, the surface observations not only correct the
A Hierarchical Ensemble Filter for Data Assimilation
, 2004
"... Applying small ensemble filters to models with a large number of state variables has traditionally required the heuristic specification of functions that limit the impact of an observation to some set of state variables that is ‘close’ to the observation in a sense that is not rigorously defined. As ..."
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Cited by 3 (0 self)
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Applying small ensemble filters to models with a large number of state variables has traditionally required the heuristic specification of functions that limit the impact of an observation to some set of state variables that is ‘close’ to the observation in a sense that is not rigorously defined. As a step toward the development of nearly generic filter assimilation systems, an algorithm is developed that precludes the need to specify ‘localization’ functions when using small ensemble filters in large models. Localization has been required to ameliorate sampling error that arises when small ensembles are used to sample the statistical relation between an observation and a state variable. This sampling error can be reduced more rigorously by using a Monte Carlo technique to detect and reduce the impact of spurious sample correlations between an observation and model state variables. A method referred to as a 'hierarchical ensemble filter' is applied, where groups of identical ensemble filters are used to minimize sampling error in the ensembles. Unlike traditional ensemble filters, hierarchical filters can adapt to a wide array of ensemble sizes and observational error characteristics without a need for heuristic tuning. Hierarchical filters also allow observations to efficiently impact state variables, even when the notion of ‘distance’ between the observation and the state variables cannot be easily defined. For instance, defining
An Improved Data Reduction Tool in Support of the RealTime Assimilation of NASA Satellite Data Streams
"... Today’s research and operational forecast models and data assimilation systems have difficulty ingesting and utilizing large volumes of satellite data, in part due to prohibitively large computational costs, time constraints ..."
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Today’s research and operational forecast models and data assimilation systems have difficulty ingesting and utilizing large volumes of satellite data, in part due to prohibitively large computational costs, time constraints
ORIGINAL PAPER
"... The analysis and impact of simulated highresolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter ..."
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The analysis and impact of simulated highresolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter
unknown title
, 2006
"... An investigation into the application of an ensemble Kalman smoother to highdimensional geophysical systems ..."
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An investigation into the application of an ensemble Kalman smoother to highdimensional geophysical systems
unknown title
, 2005
"... The specification of the initial ensemble for ensemble data assimilation is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but is also applicable to other ensemble data assimilation algorithms. Two methods are cons ..."
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The specification of the initial ensemble for ensemble data assimilation is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but is also applicable to other ensemble data assimilation algorithms. Two methods are considered: the first is based on the use of the KardarParisiZhang (KPZ) equation to form sparse random perturbations, followed by spatial smoothing to enforce desired correlation structure, while the second is based on the spatial smoothing of initially uncorrelated random perturbations. Data assimilation experiments are conducted using a global shallowwater model and simulated observations. The two proposed methods are compared to the commonly used method of uncorrelated random perturbations. The results indicate that the impact of the initial correlations in ensemble data assimilation is beneficial. The rootmeansquare error rate of convergence of the data assimilation is improved, and the positive impact of initial correlations is notable throughout the data assimilation cycles. The sensitivity to the choice of the correlation length scale exists, although it is not very high. The implied computational savings and improvement of the results may be important in future realistic applications of ensemble data assimilation. 1.
1004 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 25 Representation Error of Oceanic Observations for Data Assimilation
, 2007
"... A simple approach to the estimation of representation error (RE) of sea level (�), temperature (T), and salinity (S) observations for ocean data assimilation is described. It is assumed that the main source of RE is due to unresolved processes and scales in the model. Therefore, RE is calculated as ..."
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A simple approach to the estimation of representation error (RE) of sea level (�), temperature (T), and salinity (S) observations for ocean data assimilation is described. It is assumed that the main source of RE is due to unresolved processes and scales in the model. Therefore, RE is calculated as a function of model resolution. The methods described here exploit the availability of mapped sea level anomalies (mSLAs) and alongtrack sea level anomalies (atSLAs). The mSLA fields or atSLA observations are regarded as the true ocean state. Here, they are averaged according to the resolution of the model grid, and the averaged field is taken as a representation of the true state on the given grid. The difference between the original data and the averaged field is then regarded as the RE for �. Subsequently, the RE is projected for � over depth using a standard technique, giving an estimate of the RE for T and S. Examples of RE estimates for an intermediateand highresolution global grid are presented. It is found that there is significant spatial variability in the RE for �, T, and S, with values that are typically greater than or comparable to measurement error, particularly in regions of strong mesoscale variability. 1.