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Adaptive Sampling With the Ensemble Transform . . .
, 2001
"... A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filt ..."
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Cited by 321 (19 self)
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A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filters in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observational resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences of observational networks to reduce forecast error variance. The ET KF was used by the National Centers for Environmental Prediction in the Winter Storm Reconnaissance missions of 1999 and 2000 to determine where aircraft should deploy dropwindsondes in order to improve 2472h forecasts over the continental United States. The ET KF may be applied to any wellconstructed set of ensemble perturbations. The ET KF
Assimilation of Standard and Targeted Observations within the Unstable Subspace of the ObservationAnalysisForecast Cycle System
 J. Atmos. Sci
"... In this paper it is shown that the flowdependent instabilities that develop within an observation–analysis– forecast (OAF) cycle and that are responsible for the background error can be exploited in a very simple way to assimilate observations. The basic idea is that, in order to minimize the analy ..."
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Cited by 16 (9 self)
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In this paper it is shown that the flowdependent instabilities that develop within an observation–analysis– forecast (OAF) cycle and that are responsible for the background error can be exploited in a very simple way to assimilate observations. The basic idea is that, in order to minimize the analysis and forecast errors, the analysis increment must be confined to the unstable subspace of the OAF cycle solution. The analysis solution here formally coincides with that of the classical threedimensional variational solution with the background error covariance matrix estimated in the unstable subspace. The unstable directions of the OAF system solution are obtained by breeding initially random perturbations of the analysis but letting the perturbed trajectories undergo the same process as the control solution, including assimilation of all the available observations. The unstable vectors are then used both to target observations and for the assimilation design. The approach is demonstrated in an idealized environment using a simple model, simulated standard observations over land with a single adaptive observation over the ocean. In the application a simplified form is adopted of the analysis solution and a single unstable vector at each analysis time whose amplitude is determined by means of the adaptive observation. The remarkable reduction of the analysis and forecast error obtained by
2006: Verification region selection and data assimilation for adaptive sampling
"... Adaptive or targeted observations supplement routine observations at a prespecified targeting time. Adaptive observation locations are selected to supplement routine observations in an attempt to minimize the forecast error variance of a future target forecast within some predefined verification re ..."
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Cited by 4 (2 self)
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Adaptive or targeted observations supplement routine observations at a prespecified targeting time. Adaptive observation locations are selected to supplement routine observations in an attempt to minimize the forecast error variance of a future target forecast within some predefined verification region (VR) at some predefined verification time. Ideally, the VR is placed in a location where unusually large forecast errors are likely. Here, we compare three methods of selecting VRs. A climatological method based on seasonal averages of forecast errors. An unconditioned method based on verification time ensemble spread and a conditioned method based on an Ensemble Transform Kalman Filter (ETKF) estimate of forecast error variance given the routine observations to be taken at the targeting time. To test the effectiveness of the three approaches, Observation System Simulation Experiments (OSSEs) on a chaotic barotropic flow were performed using an imperfect model. To test the sensitivity of our results to the type of forecast error covariance model used in the data assimilation (DA) scheme, two types of DA schemes were tested: An isotropic DA scheme and a hybrid DA scheme. For isotropic DA, correlations between vorticity forecast errors at any two points were solely a function of the distance between the points. For hybrid DA, the
2.7 ADAPTIVE OBSERVATION STRATEGIES WITH THE LOCAL ENSEMBLE TRANSFORM KALMAN FILTER
"... 1 “Targeted ” or “adaptive ” observation strategies to select the optimal location for observations added to the standard observing system are an ..."
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1 “Targeted ” or “adaptive ” observation strategies to select the optimal location for observations added to the standard observing system are an