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  • 1
    In: Nature Neuroscience, Springer Science and Business Media LLC, Vol. 13, No. 9 ( 2010-9), p. 1082-1089
    Type of Medium: Online Resource
    ISSN: 1097-6256 , 1546-1726
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2010
    detail.hit.zdb_id: 1494955-6
    SSG: 12
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  • 2
    In: Monthly Weather Review, American Meteorological Society, Vol. 146, No. 8 ( 2018-08-01), p. 2639-2666
    Abstract: A 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 Experiment (DEEPWAVE), an international field campaign focused on gravity wave dynamics from 0 to 100 km, where an absence of reanalysis above 60 km inhibits research. Four experiments were performed from April to September 2014 and assessed for reanalysis skill above 50 km. A four-dimensional variational (4DVAR) run specified initial background error covariances statically. A hybrid-4DVAR (HYBRID) run formed background error covariances from an 80-member forecast ensemble blended with a static estimate. Each configuration was run at low and high horizontal resolution. In addition to operational observations below 50 km, each experiment assimilated 105 observations of the mesosphere and lower thermosphere (MLT) every 6 h. While all MLT reanalyses show skill relative to independent wind and temperature measurements, HYBRID outperforms 4DVAR. MLT fields at 1-h resolution (6-h analysis and 1–5-h forecasts) outperform 6-h analysis alone due to a migrating semidiurnal (SW2) tide that dominates MLT dynamics and is temporally aliased in 6-h time series. MLT reanalyses reproduce observed SW2 winds and temperatures, including phase structures and 10–15-day amplitude vacillations. The 0–100-km reanalyses reveal quasi-stationary planetary waves splitting the stratopause jet in July over New Zealand, decaying from 50 to 80 km then reintensifying above 80 km, most likely via MLT forcing due to zonal asymmetries in stratospheric gravity wave filtering.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 3
    In: Earth and Space Science, American Geophysical Union (AGU), Vol. 9, No. 6 ( 2022-06)
    Abstract: Northern mid‐latitude mesospheric cloud frequencies are reported from satellite observations that detect the brightest clouds there From 2007 to 2021 frequencies do not show a multi‐year trend or a dependence on the solar cycle but show extreme interannual variability July frequencies correlate with interannual variations in morning rocket launches, consistent with transport of exhaust modulated by tides
    Type of Medium: Online Resource
    ISSN: 2333-5084 , 2333-5084
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2022
    detail.hit.zdb_id: 2807271-6
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  • 4
    Online Resource
    Online Resource
    Elsevier BV ; 1980
    In:  Journal of the American Dietetic Association Vol. 76, No. 1 ( 1980-01), p. 41-45
    In: Journal of the American Dietetic Association, Elsevier BV, Vol. 76, No. 1 ( 1980-01), p. 41-45
    Type of Medium: Online Resource
    ISSN: 0002-8223
    Language: English
    Publisher: Elsevier BV
    Publication Date: 1980
    detail.hit.zdb_id: 2102813-8
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  • 5
    Online Resource
    Online Resource
    American Meteorological Society ; 2018
    In:  Monthly Weather Review Vol. 146, No. 7 ( 2018-07-01), p. 2247-2270
    In: Monthly Weather Review, American Meteorological Society, Vol. 146, No. 7 ( 2018-07-01), p. 2247-2270
    Abstract: The 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 maintain and more computationally scalable than earlier methods. In this paper, we compare the ability of the LETLM to predict the difference between two nonlinear trajectories of the Navy’s global weather prediction model at low resolution (2.5° at the equator) with that of the TLM currently used in the Navy’s four-dimensional variational (4DVar) data assimilation scheme. When compared to the pair of nonlinear trajectories, the traditional TLM and the LETLM have improved skill relative to persistence everywhere in the atmosphere, except for temperature in the planetary boundary layer. In addition, the LETLM was, on average, more accurate than the traditional TLM (error reductions of about 20% in the troposphere and 10% overall). Sensitivity studies showed that the LETLM was most sensitive to the number of ensemble members, with the performance gradually improving with increased ensemble size up to the maximum size attempted (400). Inclusion of physics in the LETLM ensemble leads to a significantly improved representation of the boundary layer winds (error reductions of up to 50%), in addition to improved winds and temperature in the free troposphere and in the upper stratosphere/lower mesosphere. The computational cost of the LETLM was dominated by the cost of ensemble propagation. However, the LETLM can be precomputed before the 4DVar data assimilation algorithm is executed, leading to a significant computational advantage.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 6
    Online Resource
    Online Resource
    Copernicus GmbH ; 2016
    In:  Atmospheric Chemistry and Physics Vol. 16, No. 13 ( 2016-07-07), p. 8193-8204
    In: Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 16, No. 13 ( 2016-07-07), p. 8193-8204
    Abstract: Abstract. Wind extraction from stratospheric ozone (O3) assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar) shallow water model (SWM) system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z), while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members), with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF). This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2–0.5 for small ensembles to 0.5–1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from  ∼  35 % with 25 and 50 members to  ∼  50 % with 1518 members. For the smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like) region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members), the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.
    Type of Medium: Online Resource
    ISSN: 1680-7324
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    detail.hit.zdb_id: 2092549-9
    detail.hit.zdb_id: 2069847-1
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  • 7
    Online Resource
    Online Resource
    American Meteorological Society ; 2017
    In:  Monthly Weather Review Vol. 145, No. 1 ( 2017-01-01), p. 97-116
    In: Monthly Weather Review, American Meteorological Society, Vol. 145, No. 1 ( 2017-01-01), p. 97-116
    Abstract: An 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 variational methods from dependence on conventional TLMs. This is a first demonstration of the LETLM application within a context of a hybrid-4DVAR system applied to a complex two-dimensional fluid dynamics problem. Sensitivity tests are included that examine LETLM dependence on several factors including length of cycling window, size of analysis correction, spread of initial ensemble perturbations, ensemble size, and model error. LETLM errors are shown to increase linearly with correction size in the linear regime, while TLM errors increase quadratically. As nonlinearity (or forecast model error) increases, the two schemes asymptote to the same solution.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2017
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 8
    Online Resource
    Online Resource
    American Meteorological Society ; 2018
    In:  Monthly Weather Review Vol. 146, No. 12 ( 2018-12-01), p. 4015-4038
    In: Monthly Weather Review, American Meteorological Society, Vol. 146, No. 12 ( 2018-12-01), p. 4015-4038
    Abstract: A regionally enhanced global (REG) data assimilation (DA) method is proposed. The technique blends high-resolution model information from a single or multiple limited-area model domains with global model and observational information to create a regionally enhanced analysis of the global atmospheric state. This single analysis provides initial conditions for both the global and limited-area model forecasts. The potential benefits of the approach for operational data assimilation are (i) reduced development cost, (ii) reduced overall computational cost, (iii) improved limited-area forecast performance from the use of global information about the atmospheric flow, and (iv) improved global forecast performance from the use of more accurate model information in the limited-area domains. The method is tested by an implementation on the U.S. Navy’s four-dimensional variational global data assimilation system and global and limited-area numerical weather prediction models. The results of the monthlong forecast experiments suggest that the REG DA approach has the potential to deliver the desired benefits.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 9
    Online Resource
    Online Resource
    American Meteorological Society ; 2020
    In:  Monthly Weather Review Vol. 148, No. 6 ( 2020-06-01), p. 2549-2566
    In: Monthly Weather Review, American Meteorological Society, Vol. 148, No. 6 ( 2020-06-01), p. 2549-2566
    Abstract: An ensemble-based linearized forecast model has been developed for data assimilation applications for numerical weather prediction. Previous studies applied this local ensemble tangent linear model (LETLM) to various models, from simple one-dimensional models to a low-resolution (~2.5°) version of the Navy Global Environmental Model (NAVGEM) atmospheric forecast model. This paper applies the LETLM to NAVGEM at higher resolution (~1°), which required overcoming challenges including 1) balancing the computational stencil size with the ensemble size, and 2) propagating fast-moving gravity modes in the upper atmosphere. The first challenge is addressed by introducing a modified local influence volume, introducing computations on a thin grid, and using smaller time steps. The second challenge is addressed by applying nonlinear normal mode initialization, which damps spurious fast-moving modes and improves the LETLM errors above ~100 hPa. Compared to a semi-Lagrangian tangent linear model (TLM), the LETLM has superior skill in the lower troposphere (below 700 hPa), which is attributed to better representation of moist physics in the LETLM. The LETLM skill slightly lags in the upper troposphere and stratosphere (700–2 hPa), which is attributed to nonlocal aspects of the TLM including spectral operators converting from winds to vorticity and divergence. Several ways forward are suggested, including integrating the LETLM in a hybrid 4D variational solver for a realistic atmosphere, combining a physics LETLM with a conventional TLM for the dynamics, and separating the LETLM into a sequence of local and nonlocal operators.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 10
    Online Resource
    Online Resource
    American Meteorological Society ; 2013
    In:  Monthly Weather Review Vol. 141, No. 8 ( 2013-08-01), p. 2740-2758
    In: Monthly Weather Review, American Meteorological Society, Vol. 141, No. 8 ( 2013-08-01), p. 2740-2758
    Abstract: The 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 performed for two months of summer and two months of winter using operational model resolution and the operational observational dataset, which is dominated by satellite observations. Results show that the hybrid mode data assimilation scheme significantly reduces the forecast error across a wide range of variables and regions. The improvements were particularly pronounced for tropical winds. The verification against radiosondes showed a greater than 0.5% reduction in vector wind RMS differences in areas of statistical significance. The verification against self-analysis showed a greater than 1% reduction from verifying against analyses between 2- and 5-day lead time at all eight vertical levels examined in areas of statistical significance. Using the Navy's summary of verification results, the Navy Operational Global Atmospheric Prediction System (NOGAPS) scorecard, the improvements resulted in a score (+1) that justifies a major system upgrade.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2013
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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