Diagnosis of vertical velocities with the QG omega equation: an examination of the errors due to sampling strategy

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This paper presents a detailed diagnostic analysis of hydrographic and current meter data from three, rapidly repeated, fine-scale surveys of the Almeria-Oran front.Instability of the frontal boundary, between surface waters of Atlantic and Mediterranean origin, is shown to provide a mechanism for significant heat transfer from the surface layers to the deep ocean in winter. The data were collected during the second observational phase of the EU funded OMEGA project on RRS Discovery cruise 224 during December 1996. High resolution hydrographic measurements using the towed undulating CTD vehicle, SeaSoar,. traced the subduction of Mediterranean Surface Water across the Almeria-Oran front. This subduction is shown to result from a significant baroclinic component to the instability of the frontal jet. The Qvector formulation of the omega equation is combined with a scale analysis to quantitatively diagnose vertical transport resulting from mesoscale ageostrophic circulation. The analyses are presented and discussed in the presence of satellite and airborne remotely sensed data; which provide the basis for a thorough and novel approach to the determination of observational error.

Section: ) Instability Of the Almeria-oran Frontmentioning

confidence: 99%

“…Following the Doppler shift like analogy of Allen et al (2000), then the apparent wavelength, λ a , from the in-situ FSS1-3 may be corrected by…”

Section: ) Instability Of the Almeria-oran Frontmentioning

confidence: 99%

Section: ) Vertical Velocitiesmentioning

confidence: 99%

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Mesoscale subduction at the Almeria–Oran front

Allen

Smeed

Tintoré

et al. 2001

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“…Although this is not usually true in practice, the contribution of observational and sampling errors can be evaluated rather independently of synopticity errors, even in the case that the latter are significantly larger than the first. This can occur, for instance, in the presence of fast moving disturbances (see for instance Allen et al, 2001). Synopticity errors will be quantified in Part II of this work, where a method for error reduction is also proposed.…”

Section: Introductionmentioning

confidence: 99%

Errors in dynamical fields inferred from oceanographic cruise data

Gomis

Pedder

2005

Diagnostic studies of ocean dynamics based on the analysis of oceanographic cruise data are usually quite sensitive to observation errors, to the station distribution and to the synopticity of the sampling. Here we present an error analysis of the first two sources (the third one is evaluated in Part II of this work). For observed variables and those linearly related to them, we use the Optimal Statistical Interpolation (OI) formulation. For variables which are not linearly related to observed variables (e.g., the vertical velocity), we carry out numerical experiments in a consistent way with OI statistics. Best results are obtained when some kind of scale selection or spatial filtering is applied in order to suppress small scales that cannot be properly resolved by the station distribution.

“…Gomis et al (2000) use a linear interpolation between surveys to examine the synopticity of a particular data set by comparing raw and pseudosynoptic analysed fields and derived quasigeostrophic (QG) vertical velocities fields. Allen et al (2001) examine the errors in diagnostic vertical velocities and vertical temperature fluxes due to sampling strategies by using the output of an idealised numerical channel model with strong advection. The balance between number of observations and synopticity can lead typically to errors of 85% in the estimation of net vertical heat flux.…”

Section: Introductionmentioning

confidence: 99%

A synopticity test of a sampling pattern in the Alboran Sea

Rixen¹,

Beckers²

2002

Hydrographic surveys are usually considered as synoptic, as if the corresponding data set had been sampled at the same time. However, this assumption might lead to strong biases in subsequent analyses. To obtain synoptic data at a given moment, we integrate a Lagrangian motion equation towards a 'modal sampling time', to relocate the sampling stations both in time and space, by combining objective analysis and geostrophic velocities, assumed to be either stationary or unstationary over the period of integration. The relocation algorithms are applied to a data set covering the entire Alboran Sea. The relocation of data points leads to a significant change on vertical velocities computed with the OMEGA equation. For validation purpose, the algorithm is also applied to a synthetic data set obtained from a 3D Primitive Equation (PE) model output. Raw and pseudosynoptic analysed temperature -salinity (T/S) fields and derived quasigeostrophic (QG) vertical velocities are compared. It is shown that the relocation method might correct significantly the errors arising from the synopticity hypothesis and that the sensitivity to the level of no-motion and the analysis parameters is less important than the nonsynopticity. D