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  • 1
    Online Resource
    Online Resource
    A Statistical Investigation of the Sensitivity of Ensemble-Based Kalman Filters to Covariance Filtering
    American Meteorological Society ; 2011
    In:  Monthly Weather Review Vol. 139, No. 9 ( 2011-09), p. 3036-3051
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 139, No. 9 ( 2011-09), p. 3036-3051
    Abstract: This paper investigates the effects of spatial filtering on the ensemble-based estimate of the background error covariance matrix in an ensemble-based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari–Cohn filter, which uses a fifth-order kernel function with a fixed localization length. Numerical experiments are carried out with the 40-variable Lorenz-96 model. The results of the experiments show that the nonparametric approach provides a more accurate estimate of the background error covariance matrix than the Gaspari–Cohn filter with any localization length. It is also shown that the Gaspari–Cohn filter tends to provide more accurate estimates of the covariance with shorter localization lengths. However, the analyses obtained by using longer localization lengths tend to be more accurate than those produced by using short localization lengths or the nonparametric approach. This seemingly paradoxical result is explained by showing that localization with longer localization lengths produces filtered estimates whose time mean is the most similar to the time mean of both the unfiltered estimate and the true covariance. This result suggests that a better metric of covariance filtering skill would be one that combined a measure of closeness to the sample covariance matrix for a very large ensemble with a measure of similarity between the climatological averages of the filtered and sample covariance.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/2011MWR3577.1
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2011
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    detail.hit.zdb_id: 202616-8
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  • 2
    Online Resource
    Online Resource
    Predictability of the Performance of an Ensemble Forecast System: Predictability of the Space of Uncertainties
    American Meteorological Society ; 2010
    In:  Monthly Weather Review Vol. 138, No. 3 ( 2010-03-01), p. 962-981
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 138, No. 3 ( 2010-03-01), p. 962-981
    Abstract: The performance of an ensemble prediction system is inherently flow dependent. This paper investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model gridpoint location ℓ. A local error covariance matrix 𝗣 ℓ is defined for each local region, and the diagnostics are applied to the linear space defined by the range of the ensemble-based estimate of 𝗣 ℓ. The particular diagnostics are chosen to help investigate the efficiency of in capturing the space of analysis and forecast uncertainties. Numerical experiments are carried out with an implementation of the local ensemble transform Kalman filter (LETKF) data assimilation system on a reduced-resolution [T62 and 28 vertical levels (T62L28)] version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere in a realistic setting are used in these experiments. It is found that (i) paradoxically, the linear space provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time; (ii) provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth (i.e., for cases of lower predictability); and (iii) the ensemble dimension (E dimension) is a reliable predictor of the performance of in predicting the space of forecast uncertainties.
    Type of Medium: Online Resource
    ISSN: 1520-0493 , 0027-0644
    URL: Article
    DOI: 10.1175/2009MWR3049.1
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2010
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
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  • 3
    Online Resource
    Online Resource
    Can a Moderate-Resolution Limited-Area Data Assimilation System Add Value to the Global Analysis of Tropical Cyclones?
    American Meteorological Society ; 2013
    In:  Monthly Weather Review Vol. 141, No. 6 ( 2013-06), p. 1866-1883
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 141, No. 6 ( 2013-06), p. 1866-1883
    Abstract: This study investigates the benefits of employing a limited-area data assimilation (DA) system to enhance lower-resolution global analyses in the northwest Pacific tropical cyclone (TC) basin. Numerical experiments are carried out with a global analysis system at horizontal resolution T62 and a limited-area analysis system at resolutions from 200 to 36 km. The global and limited-area DA systems, which are both based on the local ensemble transform Kalman filter algorithm, are implemented using a unique configuration, in which the global DA system provides information about the large-scale analysis and background uncertainty to the limited-area DA system. The limited-area analyses of the storm locations are, on average, more accurate than those from the global analyses, but increasing the resolution of the limited-area system beyond 100 km has little benefit. Two factors contribute to the higher accuracy of the limited-area analyses. First, the limited-area system improves the accuracy of the location estimates for strong storms, which is introduced when the background is updated by the global assimilation. Second, it improves the accuracy of the background estimate of the storm locations for moderate and weak storms. Improvements in the steering flow analysis as a result of increased resolution are modest and short lived in the forecasts. Limited-area track forecasts are more accurate, on average, than global forecasts, independently of the strength of the storms up to five days. This forecast improvement is due to the more accurate analysis of the initial position of storms and the better representation of the interactions between the storms and their immediate environment.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-12-00093.1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2013
    detail.hit.zdb_id: 2033056-X
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  • 4
    Online Resource
    Online Resource
    Wave energetics of the southern hemisphere of Mars
    Elsevier BV ; 2018
    In:  Icarus Vol. 309 ( 2018-07), p. 220-240
    Details
    In: Icarus, Elsevier BV, Vol. 309 ( 2018-07), p. 220-240
    Type of Medium: Online Resource
    ISSN: 0019-1035
    URL: Article
    DOI: 10.1016/j.icarus.2018.03.015
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 1467991-7
    SSG: 16,12
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  • 5
    Online Resource
    Online Resource
    A Simulation Study Using a Local Ensemble Transform Kalman Filter for Data Assimilation in New York Harbor
    American Meteorological Society ; 2008
    In:  Journal of Atmospheric and Oceanic Technology Vol. 25, No. 9 ( 2008-09-01), p. 1638-1656
    Details
    In: Journal of Atmospheric and Oceanic Technology, American Meteorological Society, Vol. 25, No. 9 ( 2008-09-01), p. 1638-1656
    Abstract: Data assimilation approaches that use ensembles to approximate a Kalman filter have many potential advantages for oceanographic applications. To explore the extent to which this holds, the Estuarine and Coastal Ocean Model (ECOM) is coupled with a modern data assimilation method based on the local ensemble transform Kalman filter (LETKF), and a series of simulation experiments is conducted. In these experiments, a long ECOM “nature” run is taken to be the “truth.” Observations are generated at analysis times by perturbing the nature run at randomly chosen model grid points with errors of known statistics. A diverse collection of model states is used for the initial ensemble. All experiments use the same lateral boundary conditions and external forcing fields as in the nature run. In the data assimilation, the analysis step combines the observations and the ECOM forecasts using the Kalman filter equations. As a control, a free-running forecast (FRF) is made from the initial ensemble mean to check the relative importance of external forcing versus data assimilation on the analysis skill. Results of the assimilation cycle and the FRF are compared to truth to quantify the skill of each. The LETKF performs well for the cases studied here. After just a few assimilation cycles, the analysis errors are smaller than the observation errors and are much smaller than the errors in the FRF. The assimilation quickly eliminates the domain-averaged bias of the initial ensemble. The filter accurately tracks the truth at all data densities examined, from observations at 50% of the model grid points down to 2% of the model grid points. As the data density increases, the ensemble spread, bias, and error standard deviation decrease. As the ensemble size increases, the ensemble spread increases and the error standard deviation decreases. Increases in the size of the observation error lead to a larger ensemble spread but have a small impact on the analysis accuracy.
    Type of Medium: Online Resource
    ISSN: 1520-0426 , 0739-0572
    URL: Article
    DOI: 10.1175/2008JTECHO565.1
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2008
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    detail.hit.zdb_id: 48441-6
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  • 6
    Online Resource
    Online Resource
    A Technique for the Verification of Precipitation Forecasts and Its Application to a Problem of Predictability
    American Meteorological Society ; 2018
    In:  Monthly Weather Review Vol. 146, No. 5 ( 2018-05-01), p. 1303-1318
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 146, No. 5 ( 2018-05-01), p. 1303-1318
    Abstract: A new morphing-based technique is proposed for the verification of precipitation forecasts for which the location error can be described by a spatial shift. An adaptation of the structural similarity index measure (SSIM) of image processing to the precipitation forecast verification problem, called the amplitude and structural similarity index (ASSIM), is also introduced. ASSIM is used to measure both the convergence of the new morphing algorithm, which is an iterative scheme, and the amplitude and structure component of the forecast error. The behavior of the proposed technique, which could also be applied to other forecast parameters with sharp gradients (e.g., potential vorticity), is illustrated with idealized and realistic examples. One of these examples examines the predictability of the location of precipitation events associated with winter storms. It is found that the functional dependence of the average magnitude of the location error on the forecast lead time is qualitatively similar to that of the root-mean-square error of the fields of the conventional atmospheric state variables (e.g., geopotential height). Quantitatively, the average magnitude of the estimated location error is about 40 km at initial time, 110 km at day 1, 250 km at day 3, and 750 km at week 1, and it eventually saturates at about week 2.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-17-0040.1
    RVK:
    RA 1000
    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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  • 7
    Online Resource
    Online Resource
    On the Propagation of Information and the Use of Localization in Ensemble Kalman Filtering
    American Meteorological Society ; 2010
    In:  Journal of the Atmospheric Sciences Vol. 67, No. 12 ( 2010-12-01), p. 3823-3834
    Details
    In: Journal of the Atmospheric Sciences, American Meteorological Society, Vol. 67, No. 12 ( 2010-12-01), p. 3823-3834
    Abstract: Several localized versions of the ensemble Kalman filter have been proposed. Although tests applying such schemes have proven them to be extremely promising, a full basic understanding of the rationale and limitations of localization is currently lacking. It is one of the goals of this paper to contribute toward addressing this issue. The second goal is to elucidate the role played by chaotic wave dynamics in the propagation of information and the resulting impact on forecasts. To accomplish these goals, the principal tool used here will be analysis and interpretation of numerical experiments on a toy atmospheric model introduced by Lorenz in 2005. Propagation of the wave packets of this model is shown. It is found that, when an ensemble Kalman filter scheme is employed, the spatial correlation function obtained at each forecast cycle by averaging over the background ensemble members is short ranged, and this is in strong contrast to the much longer range correlation function obtained by averaging over states from free evolution of the model. Propagation of the effects of observations made in one region on forecasts in other regions is studied. The error covariance matrices from the analyses with localization and without localization are compared. From this study, major characteristics of the localization process and information propagation are extracted and summarized.
    Type of Medium: Online Resource
    ISSN: 1520-0469 , 0022-4928
    URL: Article
    DOI: 10.1175/2010JAS3452.1
    RVK:
    UA 4800
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2010
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    SSG: 16,13
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  • 8
    Online Resource
    Online Resource
    Mechanisms for the Development of Locally Low-Dimensional Atmospheric Dynamics
    American Meteorological Society ; 2005
    In:  Journal of the Atmospheric Sciences Vol. 62, No. 4 ( 2005-04-01), p. 1135-1156
    Details
    In: Journal of the Atmospheric Sciences, American Meteorological Society, Vol. 62, No. 4 ( 2005-04-01), p. 1135-1156
    Abstract: The complexity of atmospheric instabilities is investigated by a combination of numerical experiments and diagnostic tools that do not require the assumption of linear error dynamics. These tools include the well-established analysis of the local energetics of the atmospheric flow and the recently introduced ensemble dimension (E dimension). The E dimension is a local measure that varies in both space and time and quantifies the distribution of the variance between phase space directions for an ensemble of nonlinear model solutions over a geographically localized region. The E dimension is maximal, that is, equal to the number of ensemble members (k), when the variance is equally distributed between k phase space directions. The more unevenly distributed the variance, the lower the E dimension. Numerical experiments with the state-of-the-art operational Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) at a reduced resolution are carried out to investigate the spatiotemporal evolution of the E dimension. This evolution is characterized by an initial transient phase in which coherent regions of low dimensionality develop through a rapid local decay of the E dimension. The typical duration of the transient is between 12 and 48 h depending on the flow; after the initial transient, the E dimension gradually increases with time. The main goal of this study is to identify processes that contribute to transient local low-dimensional behavior. Case studies are presented to show that local baroclinic and barotropic instabilities, downstream development of upper-tropospheric wave packets, phase shifts of finite amplitude waves, anticyclonic wave breaking, and some combinations of these processes can all play crucial roles in lowering the E dimension. The practical implication of the results is that a wide range of synoptic-scale weather events may exist whose prediction can be significantly improved in the short and early medium range by enhancing the prediction of only a few local phase space directions. This potential is demonstrated by a reexamination of the targeted weather observations missions from the 2000 Winter Storm Reconnaissance (WSR00) program.
    Type of Medium: Online Resource
    ISSN: 1520-0469 , 0022-4928
    URL: Article
    DOI: 10.1175/JAS3403.1
    RVK:
    UA 4800
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2005
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    detail.hit.zdb_id: 2025890-2
    SSG: 16,13
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  • 9
    Online Resource
    Online Resource
    Propagation of the Effect of Targeted Observations: The 2000 Winter Storm Reconnaissance Program
    American Meteorological Society ; 2002
    In:  Monthly Weather Review Vol. 130, No. 5 ( 2002-05), p. 1144-1165
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 130, No. 5 ( 2002-05), p. 1144-1165
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/1520-0493(2002)130〈1144:POTEOT〉2.0.CO;2
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2002
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 10
    Online Resource
    Online Resource
    A local ensemble Kalman filter for atmospheric data assimilation
    Stockholm University Press ; 2004
    In:  Tellus A: Dynamic Meteorology and Oceanography Vol. 56, No. 5 ( 2004-01-01), p. 415-
    Details
    In: Tellus A: Dynamic Meteorology and Oceanography, Stockholm University Press, Vol. 56, No. 5 ( 2004-01-01), p. 415-
    Type of Medium: Online Resource
    ISSN: 1600-0870
    URL: Article
    DOI: 10.3402/tellusa.v56i5.14462
    Language: Unknown
    Publisher: Stockholm University Press
    Publication Date: 2004
    detail.hit.zdb_id: 2026987-0
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