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21
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
A Hybrid Ensemble Kalman Filter / 3DVariational Analysis Scheme
"... A hybrid 3dimensional variational (3DVar) / ensemble Kalman filter analysis scheme is demonstrated using a quasigeostrophic model under perfectmodel assumptions. Four networks with differing observational densities are tested, including one network with a data void. The hybrid scheme operates by ..."
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Cited by 123 (18 self)
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A hybrid 3dimensional variational (3DVar) / ensemble Kalman filter analysis scheme is demonstrated using a quasigeostrophic model under perfectmodel assumptions. Four networks with differing observational densities are tested, including one network with a data void. The hybrid scheme operates by computing a set of parallel data assimilation cycles, with each member of the set receiving unique perturbed observations. The perturbed observations are generated by adding random noise consistent with observation error statistics to the control set of observations. Background error statistics for the data assimilation are estimated from a linear combination of timeinvariant 3DVar covariances and flowdependent covariances developed from the ensemble of shortrange forecasts. The hybrid scheme allows the user to weight the relative contributions of the 3DVar and ensemblebased background covariances. The analysis scheme was cycled for 90 days, with new observations assimilated every 12 h...
A comparison of probabilistic forecasts from bred, singularvector, and perturbation observation ensembles
 MON. WEA. REV
, 2000
"... The statistical properties of analysis and forecast errors from commonly used ensemble perturbation methodologies are explored. A quasigeostrophic channel model is used, coupled with a 3Dvariational data assimilation scheme. A perfect model is assumed. Three perturbation methodologies are considere ..."
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Cited by 55 (7 self)
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The statistical properties of analysis and forecast errors from commonly used ensemble perturbation methodologies are explored. A quasigeostrophic channel model is used, coupled with a 3Dvariational data assimilation scheme. A perfect model is assumed. Three perturbation methodologies are considered. The breeding and singularvector (SV) methods approximate the strategies currently used at operational centers in the United States and Europe, respectively. The perturbed observation (PO) methodology approximates a random sample from the analysis probability density function (pdf) and is similar to the method performed at the Canadian Meteorological Centre. Initial conditions for the PO ensemble are analyses from independent, parallel data assimilation cycles. Each assimilation cycle utilizes observations perturbed by random noise whose statistics are consistent with observational error covariances. Each member’s assimilation/forecast cycle is also started from a distinct initial condition. Relative to breeding and SV, the PO method here produced analyses and forecasts with desirable statistical characteristics. These include consistent rank histogram uniformity for all variables at all lead times, high spread/ skill correlations, and calibrated, reducederror probabilistic forecasts. It achieved these improvements primarily because 1) the ensemble mean of the PO initial conditions was more accurate than the mean of the bred or
2001: Idealized Adaptive Observation Strategies for Improving Numerical Weather Prediction
 J. Atmos. Sci
, 1998
"... Adaptive sampling uses information about individual atmospheric situations to identify regions where additional observations are likely to improve weather forecasts of interest. The observation network could be adapted for a wide range of forecasting goals, and it could be adapted either by allocat ..."
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Cited by 26 (2 self)
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Adaptive sampling uses information about individual atmospheric situations to identify regions where additional observations are likely to improve weather forecasts of interest. The observation network could be adapted for a wide range of forecasting goals, and it could be adapted either by allocating existing observations differently or by adding observations from programmable platforms to the existing network. In this study, observing strategies are explored in a simulated idealized system with a threedimensional quasigeostrophic model and a realistic data assimilation scheme. Using simple error norms, idealized adaptive observations are compared to nonadaptive observations for a range of observation densities. The results presented show that in this simulated system, the influence of both adaptive and nonadaptive observations depends strongly on the observation density. For sparse observation networks, the simple adaptive strategies tested are beneficial: adaptive observations can, on average, reduce analysis and forecast errors more than the same number of nonadaptive observations, and they can reduce errors by a given amount using fewer observational resources. In contrast, for dense observation networks it is much more difficult to benefit from adapting observations, at least for the data assimilation method used here. The results suggest that the adaptive strategies tested are most effective when the observations are adapted regularly and frequently, giving the data assimilation system as many opportunities as possible to reduce errors as they evolve. They also indicate that ensemblebased estimates of initial condition errors may be useful for adaptive observations. Further study is needed to understand the extent to which the results from this idealized study apply to more complex, more realistic systems. 1.
other authors
 Biocontrol Sci Technol
, 1999
"... Differential effects of salen and manganesesalen complex (EUK8) on the regulation of cellular cadmium uptake and toxicity ..."
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Cited by 8 (1 self)
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Differential effects of salen and manganesesalen complex (EUK8) on the regulation of cellular cadmium uptake and toxicity
Analysiserror statistics of a quasigeostrophic model using threedimensional variational assimilation
 MON. WEA. REV
, 2002
"... A perfect model Monte Carlo experiment was conducted to explore the characteristics of analysis error in a quasigeostrophic model. An ensemble of cycled analyses was created, with each member of the ensemble receiving different observations and starting from different forecast states. Observations w ..."
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Cited by 6 (1 self)
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A perfect model Monte Carlo experiment was conducted to explore the characteristics of analysis error in a quasigeostrophic model. An ensemble of cycled analyses was created, with each member of the ensemble receiving different observations and starting from different forecast states. Observations were created by adding random error (consistent with observational error statistics) to vertical profiles extracted from truth run data. Assimilation of new observations was performed every 12 h using a threedimensional variational analysis scheme. Three observation densities were examined, a lowdensity network (one observation � every 202 grid points), a moderatedensity network (one observation � every 102 grid points), and a highdensity network (� every 52 grid points). Error characteristics were diagnosed primarily from a subset of 16 analysis times taken every 10 days from a long time series, with the first sample taken after a 50day spinup. The goal of this paper is to understand the spatial, temporal, and some dynamical characteristics of analysis errors. Results suggest a nonlinear relationship between observational data density and analysis error; there was a much greater reduction in error from the low to moderatedensity networks than from moderate to high density. Errors in the analysis reflected both structured errors created by the chaotic dynamics as well as random observational errors. The correction of the background toward the observations reduced the error but also
2008b: Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF. Nonlin
 Proc. Geophys
"... A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVarAUS) is tested in a QG model, under perfect model conditions, with a fixed observational network, with and without observational noise. The AUS scheme, originally formulated to assimilate adaptive obser ..."
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Cited by 6 (5 self)
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A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVarAUS) is tested in a QG model, under perfect model conditions, with a fixed observational network, with and without observational noise. The AUS scheme, originally formulated to assimilate adaptive observations, is used here to assimilate the fixed observations that are found in the region of local maxima of BDAS vectors (Bred vectors subject to assimilation), while the remaining observations are assimilated by 3DVar. The performance of the hybrid scheme is compared with that of 3DVar and of an EnKF. The improvement gained by 3DVarAUS and the EnKF with respect to 3DVar alone is similar in the present model and observational configuration, while 3DVarAUS outperforms the EnKF during the forecast stage. The 3DVarAUS 1 algorithm is easy to implement and the results obtained in the idealized conditions of this study encourage further investigation toward an implementation in more realistic contexts. 1
Ensemble forecasting and data assimilation: two problems with the same solution?
 PREDICTABILITY OF WEATHER AND CLIMATE
, 2005
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6.11 COMPARISON OF ENSEMBLEBASED AND VARIATIONALBASED DATA ASSIMILATION SCHEMES IN A QUASIGEOSTROPHIC MODEL
"... Data assimilation estimates the best analysis state to initialize a numerical model by combining the information of the model forecast state and observations. A good data assimilation system can ..."
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Data assimilation estimates the best analysis state to initialize a numerical model by combining the information of the model forecast state and observations. A good data assimilation system can
6.4 USE OF THE BREEDING TECHNIQUE IN THE ESTIMATION OF THE BACKGROUND COVARIANCE MATRIX FOR A QUASIGEOSTROPHIC MODEL
"... It is well known that numerical weather predictions are sensitive to small changes in the initial conditions, i.e., a rapid growth of the initial errors can lead in a ..."
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It is well known that numerical weather predictions are sensitive to small changes in the initial conditions, i.e., a rapid growth of the initial errors can lead in a