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Assimilation Scheme (assimilation + scheme)

Distribution by Scientific Domains
Earth and Environmental Science100%

Kinds of Assimilation Scheme

  • data assimilation scheme
    		•

    Selected Abstracts

    Assimilating humidity pseudo-observations derived from the cloud profiling radar aboard CloudSat in ALADIN 3D-Var

    METEOROLOGICAL APPLICATIONS, Issue 4 2009
    Andrea Storto
    Abstract This paper describes an experimental procedure for assimilating CloudSat Cloud Profiling Radar (CPR) observations in ALADIN 3D-Var through the use of humidity pseudo-observations derived from a one-dimensional Bayesian analysis. Cloud data are considered as binary occurrences (,cloud' vs ,no-cloud'), which makes the approach feasible to be extended to other cloudiness observations, and to any other binary observation in general. A simple large-scale condensation scheme is used for projecting the prior information from a Numerical Weather Prediction model into cloud fraction space. Verification over a 1 month assimilation test period indicates a clear benefit of the pseudo-observation assimilation scheme for the limited CloudSat CPR data set, especially in terms of improved skill scores for dynamical parameters such as geopotential and wind. Copyright © 2008 Royal Meteorological Society [source]

    Three-dimensional spatial interpolation of surface meteorological observations from high-resolution local networks

    METEOROLOGICAL APPLICATIONS, Issue 3 2008
    Francesco Uboldi
    Abstract An objective analysis technique is applied to a local, high-resolution meteorological observation network in the presence of complex topography. The choice of optimal interpolation (OI) makes it possible to implement a standard spatial interpolation algorithm efficiently. At the same time OI constitutes a basis to develop, in perspective, a full multivariate data assimilation scheme. In the absence of a background model field, a simple and effective de-trending procedure is implemented. Three-dimensional correlation functions are used to account for the orographic distribution of observing stations. Minimum-scale correlation parameters are estimated by means of the integral data influence (IDI) field. Hourly analysis fields of temperature and relative humidity are routinely produced at the Regional Weather Service of Lombardia. The analysis maps show significant informational content even in the presence of strong gradients and infrequent meteorological situations. Quantitative evaluation of the analysis fields is performed by systematically computing their cross validation (CV) scores and by estimating the analysis bias. Further developments concern the implementation of an automatic quality control procedure and the improvement of error covariance estimation. Copyright © 2008 Royal Meteorological Society [source]

    Can 4D-Var use dynamical information from targeted observations of a baroclinic structure?

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 651 2010
    E. A. Irvine
    Abstract Targeted observations are generally taken in regions of high baroclinicity, but often show little impact. One plausible explanation is that important dynamical information, such as upshear tilt, is not extracted from the targeted observations by the data assimilation scheme and used to correct initial condition error. This is investigated by generating pseudo targeted observations which contain a singular vector (SV) structure that is not present in the background field or routine observations, i.e. assuming that the background has an initial condition error with tilted growing structure. Experiments were performed for a single case-study with varying numbers of pseudo targeted observations. These were assimilated by the Met Office four-dimensional variational (4D-Var) data assimilation scheme, which uses a 6 h window for observations and background-error covariances calculated using the National Meteorological Centre (NMC) method. The forecasts were run using the operational Met Office Unified Model on a 24 km grid. The results presented clearly demonstrate that a 6 h window 4D-Var system is capable of extracting baroclinic information from a limited set of observations and using it to correct initial condition error. To capture the SV structure well (projection of 0.72 in total energy), 50 sondes over an area of 1×106 km2 were required. When the SV was represented by only eight sondes along an example targeting flight track covering a smaller area, the projection onto the SV structure was lower; the resulting forecast perturbations showed an SV structure with increased tilt and reduced initial energy. The total energy contained in the perturbations decreased as the SV structure was less well described by the set of observations (i.e. as fewer pseudo observations were assimilated). The assimilated perturbation had lower energy than the SV unless the pseudo observations were assimilated with the dropsonde observation errors halved from operational values. Copyright © 2010 Royal Meteorological Society [source]

    The characteristics of Hessian singular vectors using an advanced data assimilation scheme

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 642 2009
    A. R. Lawrence
    Abstract Initial condition uncertainty is a significant source of forecast error in numerical weather prediction. Singular vectors of the tangent linear propagator can identify directions in phase-space where initial errors are likely to make the largest contribution to forecast-error variance. The physical characteristics of these singular vectors depend on the choice of initial-time metric used to represent analysis-error covariances: the total-energy norm serves as a proxy to the analysis-error covariance matrix, whereas the Hessian of the cost function of a 4D-Var assimilation scheme represents a more sophisticated estimate of the analysis-error covariances, consistent with observation and background-error covariances used in the 4D-Var scheme. This study examines and compares the structure of singular vectors computed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System using these two types of initial metrics. Unlike earlier studies that use background errors derived from lagged forecast differences (the NMC method), the background-error covariance matrix in the Hessian metric is based on statistics from an ensemble of 4D-Vars using perturbed observations, which produces tighter correlations of background-error statistics than in previous formulations. In light of these new background-error statistics, this article re-examines the properties of Hessian singular vectors (and their relationship to total-energy singular vectors) using cases from different periods between 2003 and 2005. Energy profiles and wavenumber spectra reveal that the total-energy singular vectors are similar to Hessian singular vectors that use all observation types in the operational 4D-Var assimilation. This is in contrast to the structure of Hessian singular vectors without observations. Increasing the observation density tends to reduce the spatial scale of the Hessian singular vectors. Copyright © 2009 Royal Meteorological Society [source]

    Use of potential vorticity for incremental data assimilation

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 621C 2006
    M. Wlasak
    Abstract Decomposing the mass and wind fields in a data assimilation scheme into balanced and unbalanced flow is part of the process of defining a covariance model. It is not uncommon to assume that the dynamic balanced part of the flow is approximated solely by the rotational part of the wind, which is obtained from a Helmholtz decomposition of the horizontal momentum (with an associated balanced pressure being diagnostically inferred from a balance equation, for example). The unbalanced flow is then represented by the divergence and the residual unbalanced pressure. The assumption that the rotational part of the momentum is a good approximation to the total balanced flow is only valid in certain regimes. We propose a new approach that incorporates flow regime dependence, where we assume that the balanced part of the flow is approximated instead by a linearized potential vorticity increment. We show the benefit of such a formulation in the context of shallow-water equations defined on a hemisphere. Copyright © 2006 Royal Meteorological Society [source]

    Model error and sequential data assimilation: A deterministic formulation

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 634 2008
    A. Carrassi
    Abstract Data assimilation schemes are confronted with the presence of model errors arising from the imperfect description of atmospheric dynamics. These errors are usually modelled on the basis of simple assumptions such as bias, white noise, and first-order Markov process. In the present work, a formulation of the sequential extended Kalman filter is proposed, based on recent findings on the universal deterministic behaviour of model errors in marked contrast with previous approaches. This new scheme is applied in the context of a spatially distributed system proposed by Lorenz. First, it is found that, for short times, the estimation error is accurately approximated by an evolution law in which the variance of the model error (assumed to be a deterministic process) evolves according to a quadratic law, in agreement with the theory. Moreover, the correlation with the initial condition error appears to play a secondary role in the short-time dynamics of the estimation error covariance. Second, the deterministic description of the model error evolution, incorporated into the classical extended Kalman filter equations, reveals that substantial improvements of the filter accuracy can be gained compared with the classical white-noise assumption. The universal short-time quadratic law for the evolution of the model error covariance matrix seems very promising for modelling estimation error dynamics in sequential data assimilation. Copyright © 2008 Royal Meteorological Society [source]

    The optimal density of atmospheric sounder observations in the Met Office NWP system

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 629 2007
    M. L. Dando
    Abstract Large numbers of satellite observations are discarded from the numerical weather prediction (NWP) process because high-density observations may have a negative impact on the analysis. In current assimilation schemes, the observation error covariance matrix R is usually represented as a diagonal matrix, which assumes there are no correlations in the observation errors and that each observation is an independent piece of information. This is not the case when there are strong error correlations and this can lead to a degraded analysis. The experiments conducted in this study were designed to identify the optimal density and to determine if there were circumstances when exceeding this density might be beneficial to forecast skill. The global optimal separation distance of Advanced TIROS Operational Vertical Sounder (ATOVS) observations was identified by comparing global forecast errors produced using different densities of ATOVS. The global average of the absolute forecast error produced by each different density was found for a 3-week period from December 2004 to January 2005. The results showed that, when using the Met Office NWP system with a horizontal model resolution of ,60 km, the lowest global forecast errors were produced when using separation distances of 115,154 km. However, localized regions of the atmosphere containing large gradients such as frontal regions may benefit from thinning distances as small as 40 km and therefore the global optimal separation distance is not necessarily applicable in these circumstances. Copyright © 2007 Royal Meteorological Society [source]

    A study on the optimization of the deployment of targeted observations using adjoint-based methods

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 583 2002
    Thierry Bergot
    Abstract A new adjoint-based method to find the optimal deployment of targeted observations, called Kalman Filter Sensitivity (KFS), is introduced. The major advantage of this adjoint-based method is that it allows direct computation of the reduction of the forecast-score error variance that would result from future deployment of targeted observations. This method is applied in a very simple one-dimensional context, and is then compared to other adjoint-based products, such as classical gradients and gradients with respect to observations. The major conclusion is that the deployment of targeted observation is strongly constrained by the aspect ratio between the length-scale of the sensitivity area and the length-scale of the analysis-error covariance matrix. This very simple example also clearly illustrates that the reduction of forecast-error variance is stronger for assimilation schemes which have a smaller characteristic length-scale. Finally, the KFS technique is applied in a diagnostic way (i.e. once the observations are done) to four FASTEX cases. For these cases, the reduction of the forecasterror variance is in agreement with the efficiency of targeted observations as previously studied. A preliminary step towards an operational use has been performed on FASTEX IOP18, and results seem to validate the KFS approach of targeting. Copyright © 2002 Royal Meteorological Society. [source]