The Use of Digital Warping of Microwave Integrated Water Vapor Imagery to Improve Forecasts of Marine Extratropical Cyclones

Alexander, G. David; Weinman, J. A.; Schols, J. L.

doi:10.1175/1520-0493(1998)126<1469:tuodwo>2.0.co;2

Cited by 24 publications

(15 citation statements)

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“…The proposed method for constructing L is drawn from the nature of the expected displacement field. Displacements can be represented as smooth flow fields in many fluid flows and often arise from systematic and large-scale1 background flow errors, fore example see [1]. Smoothness naturally leads to a Tikhonov type formulation [11] and, in particular, L(q) is designed as a gradient and a divergence penalty term.…”

Section: Data Assimilation By Field Alignmentmentioning

confidence: 99%

Amplitude-Position Formulation of Data Assimilation

Ravela

2006

Computational Science – ICCS 2006

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Abstract. Classical formulations of data-assimilation perform poorly when forecast locations of weather systems are displaced from their observations. They compensate position errors by adjusting amplitudes, which can produce unacceptably "distorted" states, adversely affecting analysis, verification and subsequent forecasts. It is non-trivial to identify sources of position error, but correcting misplaced forecasts is essential for operationally predicting strong, localized weather events such as tropical cyclones. In this paper, we propose a method that accounts for both position and amplitude errors. The proposed method assimilates observations in two steps. The first step is field alignment, where the current model state is aligned with observations by adjusting a continuous field of local displacements, subject to certain constraints. The second step is amplitude adjustment, where contemporary assimilation approaches are used. Our method shows improvements in analyses, with sparse and uncertain observations.

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Section: Data Assimilation By Field Alignmentmentioning

confidence: 99%

Amplitude-Position Formulation of Data Assimilation

Ravela

2006

Computational Science – ICCS 2006

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“…Such features include fronts, thunderstorms, squall lines, hurricanes and precipitation, the height of the tropopause, and wild fires (e.g. Thiebaux et al, 1990;Jones and MacPherson, 1997;Alexander et al, 1998;Beezley and Mandel, 2008;Michel, 2010). A few attempts to include the positional error in the assimilation have been made.…”

Section: Introductionmentioning

confidence: 99%

“…A few attempts to include the positional error in the assimilation have been made. One such proposed technique developed by Alexander et al (1998), for the improvement of forecasts of features associated with marine cyclones, is based on manually identifying corresponding features. A warping technique was then used to warp the entire field to match the observations.…”

Section: Introductionmentioning

confidence: 99%

A new floating model level scheme for the assimilation of boundary‐layer top inversions: the univariate assimilation of temperature

Fowler

Bannister

Eyre

2011

Quart J Royal Meteoro Soc

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The assimilation of observations with a forecast is often heavily influenced by the description of the error covariances associated with the forecast. When a temperature inversion is present at the top of the boundary layer (BL), a significant part of the forecast error may be described as a vertical positional error (as opposed to amplitude error normally dealt with in data assimilation). In these cases, failing to account for positional error explicitly is shown to result in an analysis for which the inversion structure is erroneously weakened and degraded.In this article, a new assimilation scheme is proposed to explicitly include the positional error associated with an inversion. This is done through the introduction of an extra control variable to allow position errors in the a priori to be treated simultaneously with the usual amplitude errors. This new scheme, referred to as the 'floating BL scheme', is applied to the one-dimensional (vertical) variational assimilation of temperature. The floating BL scheme is tested with a series of idealised experiments and with real data from radiosondes.For each idealised experiment, the floating BL scheme gives an analysis which has the inversion structure and position in agreement with the truth, and outperforms the assimilation which accounts only for forecast amplitude error. When the floating BL scheme is used to assimilate a large sample of radiosonde data, its ability to give an analysis with an inversion height in better agreement with that observed is confirmed. However, it is found that the use of Gaussian statistics is an inappropriate description of the error statistics of the extra control variable.

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“…Forecast model bias is a persistent problem in numerical modeling and data assimilation (Dee and da Silva 1998). For instance, Alexander et al (1998) show that the fifth-generation Pennsylvania State UniversityNational Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) model has trouble accurately predicting the position of cyclones. A complicated structure such as a cyclone can be described using parameters such as the center of the storm's location and velocity and the cyclone's radius, elongation, and rotational speed (Matyas 2007).…”

Section: Introductionmentioning

confidence: 99%

Jet Alignment in a Two-Layer Quasigeostrophic Channel Using One-Dimensional Grid Warping

Beechler

Weiss

Duane

et al. 2010

Journal of the Atmospheric Sciences

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Because of position errors traditional methods of data assimilation can broaden and weaken jets or other flow structures leading to reduced forecast skill. Here a technique to assimilate properties of coherent structures is developed and tested. Focusing on jets, the technique identifies jets in both the modeled and observed fields and warps the model grid so that the jet positions are better aligned prior to further assimilation of observations. The technique is tested using optimal interpolation on the flow in a two-layer quasigeostrophic channel. The results show that a simple and fast jet position correction algorithm can significantly improve the skill of a 12-h forecast. Furthermore, the results indicate that this method of position correction maintains its utility when observations become sparse.

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The Use of Digital Warping of Microwave Integrated Water Vapor Imagery to Improve Forecasts of Marine Extratropical Cyclones

Cited by 24 publications

References 0 publications

Amplitude-Position Formulation of Data Assimilation

Amplitude-Position Formulation of Data Assimilation

A new floating model level scheme for the assimilation of boundary‐layer top inversions: the univariate assimilation of temperature

Jet Alignment in a Two-Layer Quasigeostrophic Channel Using One-Dimensional Grid Warping

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