Recursive Filter Objective Analysis of Meteorological Fields: Applications to NESDIS Operational Processing

Hayden, Christopher M.; Purser, Robert James

doi:10.1175/1520-0450-34.1.3

Cited by 156 publications

(113 citation statements)

References 7 publications

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“…A Successive Correction Method with a Gaussian filter was used to model the covariances in time and in the zonal and meridional directions. The recursive filter is described in Hayden and Purser (1995). It was especially designed to provide a computationally efficient interpolation method capable of producing realistic results for datasets with spatial heterogeneities of coverage.…”

Section: Improving Modelled Currents With Observed Velocitiesmentioning

confidence: 99%

A strategy for producing refined currents in the Equatorial Atlantic in the context of the search of the AF447 wreckage

Drévillon

Greiner

Paradis³

et al. 2012

Ocean Dynamics

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On the night of 1st June 2009, a Rio-Paris Air France flight (AF447) disappeared in a highly variable and poorly observed part of the western tropical Atlantic Ocean. The first debris was located 5 days after the accident. Several reverse drift computations were conducted in order to define the likely position of the wreckage. Unfortunately, the performance of the operational ocean analyses available in the region of interest ranges from 80 to 100 km of positioning error after 5 days of inverse drift computation. In preparation of the third phase of research of the wreckage at sea, a series of numerical experiments was performed at Météo-France and Mercator Océan in an attempt to better compute the surface currents in the region and for the period of the accident of the AF447 (May and June 2009). Tailored high-resolution atmosphere and ocean reanalyses were first produced respectively at Météo-France and Mercator Océan. Several nested experiments were then performed with a small and flexible ocean model limited to the region of interest. The date of the initial conditions and the type of atmospheric forcing fields were varied in order to produce a small ensemble from which information on the sensitivity to these changes could be derived. Probabilistic and statistical combinations between model and observations were tested and a solution was finally selected by means of a comparison of drift computations with independent surface drift observations.

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Section: Improving Modelled Currents With Observed Velocitiesmentioning

confidence: 99%

A strategy for producing refined currents in the Equatorial Atlantic in the context of the search of the AF447 wreckage

Drévillon

Greiner

Paradis³

et al. 2012

Ocean Dynamics

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“…The horizontal transform U h is represented using recursive filters (Hayden and Purser, 1995;Purser et al, 2003) in WRF 3DVar. There are two free parameters associated with each variable for the recursive filter -the number of applications of the filter and the correlation length scale of the filter.…”

Section: Introductionmentioning

confidence: 99%

Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India

Madhu

Chandrasekar

2016

Ann. Geophys.

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Abstract. The background error covariance structure influences a variational data assimilation system immensely. The simulation of a weather phenomenon like monsoon depression can hence be influenced by the background correlation information used in the analysis formulation. The Weather Research and Forecasting Model Data assimilation (WRFDA) system includes an option for formulating multivariate background correlations for its three-dimensional variational (3DVar) system (cv6 option). The impact of using such a formulation in the simulation of three monsoon depressions over India is investigated in this study. Analysis and forecast fields generated using this option are compared with those obtained using the default formulation for regional background error correlations (cv5) in WRFDA and with a base run without any assimilation. The model rainfall forecasts are compared with rainfall observations from the Tropical Rainfall Measurement Mission (TRMM) and the other model forecast fields are compared with a high-resolution analysis as well as with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. The results of the study indicate that inclusion of additional correlation information in background error statistics has a moderate impact on the vertical profiles of relative humidity, moisture convergence, horizontal divergence and the temperature structure at the depression centre at the analysis time of the cv5/cv6 sensitivity experiments. Moderate improvements are seen in two of the three depressions investigated in this study. An improved thermodynamic and moisture structure at the initial time is expected to provide for improved rainfall simulation. The results of the study indicate that the skill scores of accumulated rainfall are somewhat better for the cv6 option as compared to the cv5 option for at least two of the three depression cases studied, especially at the higher threshold levels. Considering the importance of utilising improved flowdependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.

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“…Among RFs, the Gaussian RFs are particularly suitable for digital image processing [13] and applications of the scalespace theory [8], [15]. Gaussian RFs are an efficient computational tool for approximating Gaussian-based convolutions [3], [14], [10], [11], [12]. Gaussian RFs are mainly derived in three different ways: the Deriche strategy uses an approximation of the Gaussian function in the space domain [5]; the approximation procedure of Jin et al is carried out in the z-domain, i.e.…”

Section: Introductionmentioning

confidence: 99%

A K-iterated scheme for the first-order Gaussian recursive filter with boundary conditions

Cuomo¹,

Farina²,

Galletti³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Recursive Filters (RFs) are a well known way to approximate the Gaussian convolution and are intensively used in several research fields. When applied to signals with support in a finite domain, RFs can generate distortions and artifacts, mostly localized at the boundaries of the computed solution. To deal with this issue, heuristic and theoretical end conditions have been proposed in literature. However, these end conditions strategies do not consider the case in which a Gaussian RF is applied more than once, as often happens in several realistic applications. In this paper, we suggest a way to use the end conditions for such a K-iterated Gaussian RF and propose an algorithm that implements the described approach. Tests and numerical experiments show the benefit of the proposed scheme.

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Recursive Filter Objective Analysis of Meteorological Fields: Applications to NESDIS Operational Processing

Cited by 156 publications

References 7 publications

A strategy for producing refined currents in the Equatorial Atlantic in the context of the search of the AF447 wreckage

A strategy for producing refined currents in the Equatorial Atlantic in the context of the search of the AF447 wreckage

Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India

A K-iterated scheme for the first-order Gaussian recursive filter with boundary conditions

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