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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
Path planning of autonomous underwater vehicles (AUVs) for adaptive sampling
, 2005
"... Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constrain ..."
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Cited by 22 (4 self)
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Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic understanding, to improve forecast quality, or to sample regions of high interest. This work provides a new pathplanning scheme for the adaptive sampling problem. We define the pathplanning problem in terms of an optimization framework and propose a method based on mixed integer linear programming (MILP). The mathematical goal is to find the vehicle path that maximizes the line integral of the uncertainty of field estimates along this path. Sampling this path can improve the accuracy of the field estimates the most. While achieving this objective, several constraints must be satisfied and are implemented. They relate to vehicle motion, intervehicle coordination, communication, collision avoidance, etc. The MILP formulation is quite powerful to handle different problem constraints and flexible enough to allow easy extensions of the problem. The formulation covers single and multiplevehicle cases as well as singleand multipleday formulations. The need for a multipleday formulation arises when the ocean sampling mission is optimized for several days ahead. We first introduce the details of the formulation, then elaborate on the objective function and constraints, and finally, present a varied set of examples to illustrate the applicability of the proposed method. Index Terms—Adaptive sampling, Autonomous Ocean Sampling Network (AOSN), autonomous underwater vehicle (AUV), data
WMO/CAS/WWW SIXTH INTERNATIONAL WORKSHOP on TROPICAL CYCLONES Topic 3.3: Targeted observation and data assimilation in track prediction Rapporteur:
"... The objective of this report is to document recent progress since IWTC5 on the topic related to the Targeted observation and data assimilation in track prediction. The report begins by reviewing the background of targeted observations, followed by an introduction to the techniques specifically used ..."
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The objective of this report is to document recent progress since IWTC5 on the topic related to the Targeted observation and data assimilation in track prediction. The report begins by reviewing the background of targeted observations, followed by an introduction to the techniques specifically used for targeted observations and data assimilation to improve tropical cyclone track prediction. These
q 2002 American Meteorological Society
, 2000
"... The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by the National Centers for Environmental Prediction (NCEP), is described. ..."
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The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by the National Centers for Environmental Prediction (NCEP), is described.
ABSTRACT Title of Document: APPLICATIONS OF THE LETKF TO ADAPTIVE OBSERVATIONS, ANALYSIS SENSITIVITY, OBSERVATION IMPACT AND THE ASSIMILATION OF MOISTURE
"... In this thesis we explore four new applications of the Local Ensemble Transform Kalman Filter (LETKF), namely adaptive observations, analysis sensitivity, observation impact, and multivariate humidity assimilation. In each of these applications we have obtained promising results. In the adaptive obs ..."
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In this thesis we explore four new applications of the Local Ensemble Transform Kalman Filter (LETKF), namely adaptive observations, analysis sensitivity, observation impact, and multivariate humidity assimilation. In each of these applications we have obtained promising results. In the adaptive observation studies, we found that ensemble spread strategy, where adaptive observations are selected among the points with largest ensemble spread (with the constraint that observations cannot be contiguous in order to avoid clusters of adaptive observations) is very effective and close to optimal sampling. The application on simulated Doppler Wind Lidar (DWL) adaptive observation studies shows that 3DVar is as effective as LETKF with 10 % adaptive observations sampled with the ensemble spread strategy. With 2 % adaptive observations, 3DVar is not as effective as the LETKF. In the analysis sensitivity study, we proposed to calculate this quantity within the LETKF with low additional computational time. Unlike in 4DVar (Cardinali et al., 2004), in the LETKF, the computation is exact and satisfies the theoretical value limits (between 0 and 1). The results from simulated experiments show that the trace
Nonlinear Processes in Geophysics c○European Geophysical Society 2001
, 2000
"... Using adjoint sensitivity as a local structure function in variational data assimilation ..."
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Using adjoint sensitivity as a local structure function in variational data assimilation
Nonlinear Processes in Geophysics c○European Geophysical Society 2001 Sensitivity to observations applied to FASTEX cases
, 2000
"... Abstract. The concept of targeted observations was implemented during field experiments such as FASTEX, NORPEX or WSRP in order to cope with some predictability problems. The techniques of targeting used at that moment (adjointbased or ensemble transform methods) lead to quite disappointing results: ..."
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Abstract. The concept of targeted observations was implemented during field experiments such as FASTEX, NORPEX or WSRP in order to cope with some predictability problems. The techniques of targeting used at that moment (adjointbased or ensemble transform methods) lead to quite disappointing results: the efficiency of the additional observations deployed over sensitive areas did not turn out to remain consistent from one case to another. The influence of targeted observations on the forecasts could sometimes consist of strong improvements, or sometimes strong degradations. It turns out that the latter failure explains why the concept of optimal sampling arose. The efficiency of adaptive sampling appears to depend on the assimilation scheme that deals with the observations. It is then very useful to integrate the nature of the assimilation algorithm, as well as the deployment
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, 2007
"... Simple Doppler Wind Lidar adaptive observation experiments with 3DVar and an ensemble Kalman filter in a global primitive equations model ..."
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Simple Doppler Wind Lidar adaptive observation experiments with 3DVar and an ensemble Kalman filter in a global primitive equations model
3.3 THE EFFECT OF TOPOGRAPHY ON THE INITIAL CONDITION SENSITIVITY OF A MESOSCALE MODEL
"... Errors in NWP model forecasts are typically due to deficiencies in the model formulation, inaccuracies associated with the numerical integration techniques, and errors in the specification of initial conditions. This ..."
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Errors in NWP model forecasts are typically due to deficiencies in the model formulation, inaccuracies associated with the numerical integration techniques, and errors in the specification of initial conditions. This