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30
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
Singular vector perturbation growth in a primitive equation model with moist physics
 J. Atmos. Sci
, 1999
"... Finitetime growth of perturbations in the presence of moist physics (specifically, precipitation) is investigated using singular vectors (SVs) in the context of a primitive equation regional model. Two difficulties appear in the explicit consideration of the effect of moist physics when studying su ..."
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Cited by 16 (0 self)
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Finitetime growth of perturbations in the presence of moist physics (specifically, precipitation) is investigated using singular vectors (SVs) in the context of a primitive equation regional model. Two difficulties appear in the explicit consideration of the effect of moist physics when studying such optimal growth. First, the tangentlinear description of moist physics may not be as straightforward and accurate as for dryadiabatic processes; second, because of the consideration of moisture, the design of an appropriate measure of growth (i.e., norm) is subject to even more ambiguity than in the dry situation. In this study both of these problems are addressed in the context of the moist version of the National Center for Atmospheric Research Mesoscale Adjoint Modeling System, version 2, with emphasis on the second problem. Leading SVs are computed in an iterative fashion, using a Lanczos algorithm, for three norms over an optimization interval of 24 h; these norms are based on an expression related to (total) perturbation energy. The properties of these SVs are studied for a case of explosive cyclogenesis and a case of summer convection. The consideration of moisture leads to faster growth of perturbations than in the dry situation, as well as to the appearance of new growing structures. Apparently, moist processes provide for new mechanisms of error growth and do not simply lead to a modulation of SVs obtained with the dry version of the model. Consequently, consideration of the linearized moist processes is essential for revealing all structures that might potentially grow in a moist primitive equation model. In the context of this investigation growth rates depend more on the choice of the basic state and linearized model (moist vs dry) than on the choice of the norm (moist vs dry total energy norm). A reference is cited that supports the validity of the moist tangentlinear SV perturbation growth studied here in the nonlinear regime. 1.
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.
2005: Vertical Structure of Midlatitude Analysis and Forecast Errors
 Mon. Wea. Rev
"... The dominant vertical structures for analysis and forecast errors are estimated in midlatitudes using a small ensemble of operational analyses. Errors for fixed locations in the central North Pacific and eastern North America are selected for comparing errors in regions with relatively low and high ..."
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Cited by 5 (1 self)
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The dominant vertical structures for analysis and forecast errors are estimated in midlatitudes using a small ensemble of operational analyses. Errors for fixed locations in the central North Pacific and eastern North America are selected for comparing errors in regions with relatively low and high observation density, respectively. Results for these fixed locations are compared with results for zonal wavenumber 9, which provides a representative sample of baroclinic waves. This study focuses on deviations from the ensemble mean for meridional wind and temperature at 40°N; these quantities are chosen for simplicity and because they capture dynamical and thermodynamical aspects of midlatitude baroclinic waves. Results for the meridional wind show that analysis and forecast errors share the same dominant vertical structure as the analyses. This structure peaks near the tropopause and decays smoothly toward small values in the middle and lower troposphere. The dominant vertical structure for analysis errors exhibits upshear tilt and peaks just below the tropopause, suggesting an asymmetry in errors of the tropopause location, with a bias toward greater errors for downward tropopause displacements. The dominant vertical structure for temperature analysis errors is distinctly different from temperature analyses. Analysis errors have a sharp peak in the lower troposphere, with a secondary structure near the tropopause, whereas forecast errors and
2010: Naval Research Laboratory multiscale targeting guidance for TPARC and TCS08.Wea
 Forecasting
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FourDimensional Data Assimilation and Numerical Weather Prediction Abstract
, 2003
"... All forecast models, whether they represent the state of the weather, the spread of a disease, or levels of economic activity, contain unknown parameters. These parameters may be the model’s initial conditions, its boundary conditions, or other tunable parameters which have to be determined. Four di ..."
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All forecast models, whether they represent the state of the weather, the spread of a disease, or levels of economic activity, contain unknown parameters. These parameters may be the model’s initial conditions, its boundary conditions, or other tunable parameters which have to be determined. Four dimensional variational data assimilation (4DVar) is a method of estimating this set of parameters by optimizing the fit between the solution of the model and a set of observations which the model is meant to predict. The four dimensional nature of 4DVar reflects the fact that the observation set spans not only three dimensional space, but also a time domain. Although the method of 4DVar described in this report is not restricted to any particular system, the application described here has a Numerical Weather Prediction (NWP) model at its core, and the parameters to be determined are the initial conditions of the model. The purpose of this report is to give a survey covering assimilation of Doppler radar wind data into a highresolution NWP model. Some associated problems, such as sensitivity to small variations in the initial conditions or due to small changes in
Sensitivity of Tropical Cyclone Forecasts as Revealed by Singular Vectors
, 2005
"... Singular vector (SV) sensitivity, calculated using the adjoint model of the U.S. Navy Operation Global Atmosphere Prediction System (NOGAPS), is used to study the dynamics associated with tropical cyclone evolution. For each modelpredicted tropical cyclone, SVs are constructed that optimize perturb ..."
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Singular vector (SV) sensitivity, calculated using the adjoint model of the U.S. Navy Operation Global Atmosphere Prediction System (NOGAPS), is used to study the dynamics associated with tropical cyclone evolution. For each modelpredicted tropical cyclone, SVs are constructed that optimize perturbation energy within a 20 ° by 20 ° latitude/longitude box centered on the 48h forecast position of the cyclone. The initial SVs indicate regions where the 2day forecast of the storm is very sensitive to changes in the analysis. Composites of the SVs for straightmoving cyclones and nonstraightmoving cyclones that occurred in the Northern Hemisphere during its summer season in 2003 are examined. For both groups, the initialtime SV sensitivity exhibits a maximum within an annulus approximately 500 km from the center of the storms, in the region where the potential vorticity gradient of the vortex first changes sign. In the azimuthal direction, the composite initialtime SV maximum for the straightmoving group is located in the rear right quadrant with respect to the storm motion. The composite based on the nonstraightmoving cyclones does not have a preferred quadrant in the vicinity of the storms and has larger amplitude away from the cyclones compared with the straightmoving storms, indicating more environmental influence on these storms. For both groups, the maximum initial sensitive areas are collocated with regions of flow moving toward the storm. While the initial SV maximum is located where the potential vorticity gradient changes sign, the final SV maximum is located where the potential vorticity gradient is a maximum. Examinations of individual cases demonstrate how SV sensitivity can be used to identify specific environmental influences on the storms. The relationship between the SV sensitivity and the potential vorticity is discussed. The results support the utility of SVs in applications to phenomena beyond midlatitude baroclinic systems. 1.
European Centre for MediumRange Weather Forecasting, Reading,
"... Following Lorenz’s seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz’s work and how it has influenced the ways in which we seek to represent uncertainty in f ..."
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Following Lorenz’s seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz’s work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decisionmaking on adaptation and mitigation.
Part V: THE ENSEMBLE PREDICTION SYSTEM
, 2003
"... 1 Part V: ‘The ensemble prediction system (CY23R4)’ ..."