David D. Kuhl
United States Naval Research Laboratory
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Featured researches published by David D. Kuhl.
Monthly Weather Review | 2013
David D. Kuhl; Thomas E. Rosmond; Craig H. Bishop; Justin McLay; Nancy L. Baker
AbstractThe effect on weather forecast performance of incorporating ensemble covariances into the initial covariance model of the four-dimensional variational data assimilation (4D-Var) Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) is investigated. This NAVDAS-AR-hybrid scheme linearly combines the static NAVDAS-AR initial background error covariance with a covariance derived from an 80-member flow-dependent ensemble. The ensemble members are generated using the ensemble transform technique with a (three-dimensional variational data assimilation) 3D-Var-based estimate of analysis error variance. The ensemble covariances are localized using an efficient algorithm enabled via a separable formulation of the localization matrix. The authors describe the development and testing of this scheme, which allows for assimilation experiments using differing linear combinations of the static and flow-dependent background error covariances. The tests are ...
Journal of the Atmospheric Sciences | 2007
David D. Kuhl; Istvan Szunyogh; Eric J. Kostelich; Gyorgyi Gyarmati; D. J. Patil; Michael Oczkowski; Brian R. Hunt; Eugenia Kalnay; Edward Ott; James A. Yorke
Abstract In this paper, the spatiotemporally changing nature of predictability is studied in a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), a state-of-the-art numerical weather prediction model. Atmospheric predictability is assessed in the perfect model scenario for which forecast uncertainties are entirely due to uncertainties in the estimates of the initial states. Uncertain initial conditions (analyses) are obtained by assimilating simulated noisy vertical soundings of the “true” atmospheric states with the local ensemble Kalman filter (LEKF) data assimilation scheme. This data assimilation scheme provides an ensemble of initial conditions. The ensemble mean defines the initial condition of 5-day deterministic model forecasts, while the time-evolved members of the ensemble provide an estimate of the evolving forecast uncertainties. The observations are randomly distributed in space to ensure that the geographical distribution of t...
Monthly Weather Review | 2011
José Antonio Aravéquia; Istvan Szunyogh; Elana J. Fertig; Eugenia Kalnay; David D. Kuhl; Eric J. Kostelich
AbstractThis paper evaluates a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter (LETKF) data assimilation scheme. The assimilation strategy includes a mechanism to select the radiance observations that are assimilated at a given grid point and an ensemble-based observation bias-correction technique. Numerical experiments are carried out with a reduced (T62L28) resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The observations used for the evaluation of the assimilation strategy are AMSU-A level 1B brightness temperature data from the Earth Observing System (EOS) Aqua spacecraft. The assimilation of these observations, in addition to all operationally assimilated nonradiance observations, leads to a statistically significant improvement of both the temperature and wind analysis in the Southern Hemisphere. This result suggests that the LETKF, combined with the pro...
Monthly Weather Review | 2016
Sergey Frolov; Craig H. Bishop; Teddy Holt; James Cummings; David D. Kuhl
AbstractIn a strongly coupled data assimilation (DA), a cross-fluid covariance is specified that allows measurements from a coupled fluid (e.g., atmosphere) to directly impact analysis increments in a target fluid (e.g., ocean). The exhaustive solution to this coupled DA problem calls for a covariance where all available measurements can influence all grid points in all fluids. Solution of such a large algebraic problem is computationally expensive, often calls for a substantial rewrite of existing fluid-specific DA systems, and, as shown in this paper, can be avoided.The proposed interface solver assumes that covariances between coupled measurements and target fluid are often close to null (e.g., between stratospheric observations and the deep ocean within a 6-h forecast cycle). In the interface solver, two separate DA solvers are run in parallel: one that produces an analysis solution in the atmosphere, and one in the ocean. Each system uses a coupled observation vector where in addition to resident mea...
Monthly Weather Review | 2018
Stephen D. Eckermann; Jun Ma; K. W. Hoppel; David D. Kuhl; Douglas R. Allen; James A. Doyle; Kevin C. Viner; Benjamin Ruston; Nancy L. Baker; Steven D. Swadley; Timothy R Whitcomb; Carolyn A. Reynolds; Liang Xu; Natalie Kaifler; Bernd Kaifler; Iain M. Reid; Damian J. Murphy; Peter T. Love
AbstractA data assimilation system (DAS) is described for global atmospheric reanalysis from 0- to 100-km altitude. We apply it to the 2014 austral winter of the Deep Propagating Gravity Wave Exper...
Monthly Weather Review | 2017
Elizabeth Satterfield; Daniel Hodyss; David D. Kuhl; Craig H. Bishop
AbstractData assimilation schemes combine observational data with a short-term model forecast to produce an analysis. However, many characteristics of the atmospheric states described by the observations and the model differ. Observations often measure a higher-resolution state than coarse-resolution model grids can describe. Hence, the observations may measure aspects of gradients or unresolved eddies that are poorly resolved by the filtered version of reality represented by the model. This inconsistency, known as observation representation error, must be accounted for in data assimilation schemes. In this paper the ability of the ensemble to predict the variance of the observation error of representation is explored, arguing that the portion of representation error being detected by the ensemble variance is that portion correlated to the smoothed features that the coarse-resolution forecast model is able to predict. This predictive relationship is explored using differences between model states and thei...
Monthly Weather Review | 2018
Sergey Frolov; Douglas R. Allen; Craig H. Bishop; Rolf H. Langland; K. W. Hoppel; David D. Kuhl
AbstractThe local ensemble tangent linear model (LETLM) provides an alternative method for creating the tangent linear model (TLM) and adjoint of a nonlinear model that promises to be easier to mai...
Monthly Weather Review | 2018
Elizabeth A. Satterfield; Daniel Hodyss; David D. Kuhl; Craig H. Bishop
AbstractBecause of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble-derived covariance matrix is equal to the true error covariance matrix. Here, we describe...
Monthly Weather Review | 2017
Douglas R. Allen; Craig H. Bishop; Sergey Frolov; K. W. Hoppel; David D. Kuhl; Gerald E. Nedoluha
AbstractAn ensemble-based tangent linear model (TLM) is described and tested in data assimilation experiments using a global shallow-water model (SWM). A hybrid variational data assimilation system was developed with a 4D variational (4DVAR) solver that could be run either with a conventional TLM or a local ensemble TLM (LETLM) that propagates analysis corrections using only ensemble statistics. An offline ensemble Kalman filter (EnKF) is used to generate and maintain the ensemble. The LETLM uses data within a local influence volume, similar to the local ensemble transform Kalman filter, to linearly propagate the state variables at the central grid point. After tuning the LETLM with offline 6-h forecasts of analysis corrections, cycling experiments were performed that assimilated randomly located SWM height observations, based on a truth run with forced bottom topography. The performance using the LETLM is similar to that of the conventional TLM, suggesting that a well-constructed LETLM could free 4D vari...
Atmospheric Chemistry and Physics | 2013
Douglas R. Allen; K. W. Hoppel; Gerald E. Nedoluha; David D. Kuhl; Nancy L. Baker; L. Xu; T. E. Rosmond