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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
Analysis of the singular vectors of the fullphysics FSU Global Spectral Model
 Tellus A
, 2005
"... Analysis of singular vectors (SV) is performed on the Florida State University Global Spectral Model (FSUGSM) which includes linearized full physics of the atmosphere. ..."
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Cited by 6 (1 self)
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Analysis of singular vectors (SV) is performed on the Florida State University Global Spectral Model (FSUGSM) which includes linearized full physics of the atmosphere.
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
Controlling the global weather
 Bull. Am. Meteorol. Soc
, 2002
"... T he earth’s atmosphere has been hypothesizedto be chaotic. Chaos implies that there is afinite predictability time limit no matter howwell the atmosphere is observed and modeled. It is generally accepted that this limit is typically 2 weeks for largescale weather systems (Lorenz 1982), although so ..."
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Cited by 4 (1 self)
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T he earth’s atmosphere has been hypothesizedto be chaotic. Chaos implies that there is afinite predictability time limit no matter howwell the atmosphere is observed and modeled. It is generally accepted that this limit is typically 2 weeks for largescale weather systems (Lorenz 1982), although some situations may be more or less predictable, and smaller scales are certainly less predictable. Chaos also implies sensitivity to small perturbations. The most realistic numerical weather prediction (NWP) models are very sensitive to initial conditions. It is therefore very likely that the atmosphere is also extremely sensitive to small perturbations. A series of such perturbations to the atmosphere might be devised to effectively control the evolution of the atmosphere, if the atmosphere is observed and modeled sufficiently well. We present a system architecture to control the global weather that might be implemented within a few decades. It is a dream of mankind to control the weather— not to make every day the same, but to protect lives and property. We believe that this dream is in fact a possibility. Just imagine: no droughts, no tornadoes, no snowstorms during rush hour, etc. We probably cannot eliminate hurricanes, but we might be able to control the paths of hurricanes, and essentially prevent hurricanes from striking population centers. Our goal is not to change the climate, but to control the precise timing and paths of weather systems. For example, eliminating hurricanes and the associated mixing of the upper layers of the ocean would presumably change the climate in many indirect ways. Because of the intensive coupling of the weather over different regions of the globe, nothing short of control of the global weather should be considered. The nation that controls its own weather will neces
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
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
1552 MONTHLY WEATHER REVIEW VOLUME 130 Using Improved BackgroundError Covariances from an Ensemble Kalman Filter for Adaptive Observations
, 2001
"... A method for determining adaptive observation locations is demonstrated. This method is based on optimal estimation (Kalman filter) theory; it determines the observation location that will maximize the expected improvement, which can be measured in terms of the expected reduction in analysis or fore ..."
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A method for determining adaptive observation locations is demonstrated. This method is based on optimal estimation (Kalman filter) theory; it determines the observation location that will maximize the expected improvement, which can be measured in terms of the expected reduction in analysis or forecast variance. This technique requires an accurate model for background error statistics that vary both in space and in time. Here, these covariances are generated using an ensemble Kalman filter assimilation scheme. A variant is also developed that can estimate the analysis improvement in data assimilation schemes where background error statistics are less accurate. This approach is demonstrated using a quasigeostrophic channel model under perfectmodel assumptions. The algorithm is applied here to find the supplemental rawinsonde location to add to a regular network of rawinsondes that will reduce analysis errors the most. The observation network is configured in this experiment so there is a data void in the western third of the domain. Onehundredmember ensembles from three data assimilation schemes are tested as input to the target selection procedure, two variants of the standard ensemble Kalman filter and a third perturbed observation (3DVAR) ensemble. The algorithm is shown to find large differences in the expected variance reduction depending on the observation location, the flow of the day, and the ensemble
Dynamic sensor network configuration in InfoSymbiotic systems using model singular vectors
"... Data assimilation is an important datadriven application (DDDAS) where measurements of the real system are used to constrain simulation results. This paper describes a methodology for dynamically configuring sensor networks in data assimilation systems based on numerical models of timeevolving di ..."
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Data assimilation is an important datadriven application (DDDAS) where measurements of the real system are used to constrain simulation results. This paper describes a methodology for dynamically configuring sensor networks in data assimilation systems based on numerical models of timeevolving differential equations. The proposed methodology uses the dominant model singular vectors, which reveal the directions of maximal error growth. New sensors are dynamically placed such as to minimize an estimation error energy norm. A shallow water test problem is used to illustrate our approach.
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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