Search Results (2)

In regression analysis, oftentimes a linear (or linearized) Gauss-Markov Model (GMM) is used to describe the relationship between certain unknown parameters and measurements taken to learn about them. As soon as there are more than enough data collected to determine a unique solution for the parameters, an estimation technique needs to be applied such as ‘Least-Squares adjustment’, for instance, which turns out to be optimal under a wide range of criteria. In this context, the matrix connecting the parameters with the observations is considered fully known, and the parameter vector is considered fully unknown. This, however, is not always the reality. Therefore, two modifications of the GMM have been considered, in particular. First, ‘stochastic prior information’ (p. i.) was added on the parameters, thereby creating the – still linear – Random Effects Model (REM) where the optimal determination of the parameters (random effects) is based on ‘Least Squares collocation’, showing higher precision as long as the p. i. was adequate (Wallace test). Secondly, the coefficient matrix was allowed to contain observed elements, thus leading to the – now nonlinear – Errors-In-Variables (EIV) Model. If not using iterative linearization, the optimal estimates for the parameters would be obtained by ‘Total Least Squares adjustment’ and with generally lower, but perhaps more realistic precision. Here the two concepts are combined, thus leading to the (nonlinear) ’EIV-Model with p. i.’, where an optimal estimation (resp. prediction) technique is developed under the name of ‘Total Least-Squares collocation’. At this stage, however, the covariance matrix of the data matrix – in vector form – is still being assumed to show a Kronecker product structure. Full article

Accurate measurement of temperature and salinity is a fundamental task with heavy implications in all the possible applications of the currently available datasets, for example, in the study of climate changes and modeling of ocean dynamics. In this work, the reliability of measurements obtained by oceanographic devices (eXpendable BathyThermographs, Argo floats and Conductivity-Temperature-Depth sensors) is analyzed by means of an intercomparison exercise. As a first step, temperature profiles from XBT probes, deployed by commercial ships crossing the Ligurian and Tyrrhenian seas during the Ship of Opportunity Program (SOOP), were matched with profiles from Argo floats quasi-collocated in space and time. Attention was then paid to temperature/salinity profiling Argo floats. Since Argo floats usually are not recovered and should last up to five years without any re-calibration, their onboard sensors may suffer some drift and/or offset. In the literature, refined methods were developed to post-process Argo data, in order to correct the response of their profiling CTD sensors, in particular adjusting the salinity drift. The core of this delayed-mode quality control is the comparison of Argo data with reference climatology. At the same time, the experimental comparison of Argo profiles with ship-based CTD profiles, matched in space and time, is still of great importance. Therefore, an overall comparison of Argo floats vs. shipboard CTDs was performed, in terms of temperature and salinity profiles in the whole Mediterranean Sea, under space-time matching conditions as strict as possible. Performed analyses provided interesting results. XBT profiles confirmed that below 100 m depth the accordance with Argo data is reasonably good, with a small positive bias (close to 0.05 °C) and a standard deviation equal to about 0.10 °C. Similarly, side-by-side comparisons vs. CTD profiles confirmed the good quality of Argo measurements; the evidence of a drift in time was found, but at a level of about E−05 unit/day, so being reasonably negligible on the Argo time-scale. XBT, Argo and CTD users are therefore encouraged to take into account these results as a good indicator of the uncertainties associated with such devices in the Mediterranean Sea, for the analyzed period, in all the climatological applications. Full article