974 resultados para gravimetric inversion


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Egger (2008) constructs some idealised experiments to test the usefulness of piecewise potential vorticity inversion (PPVI) in the diagnosis of Rossby wave dynamics and baroclinic development. He concludes that, ``PPVI does not help us to understand the dynamics of linear Rossby waves. It provides local tendencies of the streamfunction which are unrelated to the true ones. The same way, the motion of baroclinic waves in shear flow cannot be understood by using PPVI. Moreover, the effect of boundary temperatures as determined by PPVI is unrelated to the flow evolution.'' He goes further in arguing that we should not consider velocities as ``induced'' by PV anomalies defined by carving up the global domain. However, these conclusions partly reflect the limitations of his idealised experiments and the manner in which the PV components were partitioned from one another.

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The one-dimensional variational assimilation of vertical temperature information in the presence of a boundary-layer capping inversion is studied. For an optimal analysis of the vertical temperature profile, an accurate representation of the background error covariances is essential. The background error covariances are highly flow-dependent due to the variability in the presence, structure and height of the boundary-layer capping inversion. Flow-dependent estimates of the background error covariances are shown by studying the spread in an ensemble of forecasts. A forecast of the temperature profile (used as a background state) may have a significant error in the position of the capping inversion with respect to observations. It is shown that the assimilation of observations may weaken the inversion structure in the analysis if only magnitude errors are accounted for as is the case for traditional data assimilation methods used for operational weather prediction. The positional error is treated explicitly here in a new data assimilation scheme to reduce positional error, in addition to the traditional framework to reduce magnitude error. The distribution of the positional error of the background inversion is estimated for use with the new scheme.

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The field of Molecular Spectroscopy was surveyed in order to determine a set of conventions and symbols which are in common use in the spectroscopic literature. This document, which is Part 3 in a series, deals with symmetry notation referring to groups that involve nuclear permutations and the inversion operation. Further parts will follow, dealing inter alia with vibration-rotation spectroscopy and electronic spectroscopy.

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The dynamics of a polar low are examined using a piecewise potential vorticity (PV) inversion method. In previous studies of this and other polar lows, structural evolution has been described in terms of regions of anomalous PV. In this study the relative importance of different PV anomalies and the interactions between them have been quantified using PV diagnostics. The intensification of the polar low occurred in three stages (in contrast to previous studies of polar lows that have only identified two stages). The dynamical characteristics of stages one and two are consistent with the proposed type C cyclogenesis mechanism. A diabatically-generated lower-tropospheric PV anomaly dominated intensification after initial triggering by a positive upper-level PV anomaly. A phase tilt between the upper and lower levels was maintained through retardation of the positive upper-level anomaly by the effects of latent heat release. Stage three was a period of growth dominated by wind-induced surface heat exchange (WISHE), which contributed at least 18% to the amplitude of the mature surface polar low.

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This Study was designed to investigate impact of tannins on in vitro ruminal fermentation parameters as well as relationships between concentration and in vitro biological activity of tannins present in tree fruits. Dry and mature fruits of known phenolic content harvested from Acacia nilotica, A. erubescens, A. erioloba, A. sieberiana, Piliostigima thonningii and Dichrostachys cinerea tree species were fermented with rumen fluid in vitro with or without polyethylene glycol (PEG). Correlation between in vitro biological activity and phenolic concentration was determined. Polyethylene glycol inclusion increased Cumulative gas production from all fruit substrates. The largest Increase (225%) after 48 h incubation was observed in D. cinerea fruits while the least (12.7%) increase was observed in A. erubescens fruits. Organic matter degradability (48 h) was increased by PEG inclusion for all tree species except A. erubescens and P. thonningii. For D. cinerea fruits, colorimetric assays were poorly correlated to Increases In gas production due to PEG treatment. Ytterbium precipitable phenolics (YbPh) were also poorly correlated with response to PEG for A. erioloba and P. thonningii fruits. However, YbPh were strongly and positively correlated to the increase In Cumulative gas production due to PEG for A. erubescens and A. nilotica. Folin-Ciocalteau assayed phenolics (SPh) were not correlated to response to PEG in P. thonningii and A. sieberiana. It was Concluded that the PEG effect oil in vitro fermentation was closely related to some measures of phenolic concentration but the relationships varied with tree species.

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The aza-Darzens ('ADZ') reactions of N-diphenylphosphinyl ('N-Dpp') imines with chiral enolates derived from N-bromoacetyl 2S-2,10-camphorsultam proceed in generally good yield to give N-diphenylphosphinyl aziridinoyl sultams. However, the stereoselectivity of the reaction is dependent upon the structure of the imine substituent: when the chiral enolate was reacted with arylimines substituted in the ortho-position, mixtures of cis- and trans-2'R,3'R-aziridines were obtained, often with a complete selectivity in favour of the trans-isomer. (c) 2006 Elsevier Ltd. All rights reserved.

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Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

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We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals <110 gC m−2 year−1 for the synthetic case. Annual C flux estimates generated by participants generally agreed with gap-filling approaches using half-hourly data. The estimation of ecosystem respiration and GPP through MDF agreed well with outputs from partitioning studies using half-hourly data. Confidence limits on annual NEE increased by an average of 88% in the prediction year compared to the previous year, when data were available. Confidence intervals on annual NEE increased by 30% when observed data were used instead of synthetic data, reflecting and quantifying the addition of model error. Finally, our analyses indicated that incorporating additional constraints, using data on C pools (wood, soil and fine roots) would help to reduce uncertainties for model parameters poorly served by eddy covariance data.