Aplicación de un método de interpolación en regiones costeras
Keywords:
Interpolation Method, Objective Analysis, Coastal Regions, OceanographyAbstract
Data reconstruction of non stationary heterogenic fields obtained at the study area is a process intrinsic in coastal studies, for that is necessary to implement interpolation techniques that minimize the involved error. In general, a measured variable in coastal regions presents gaps in spatial or temporal information. These variables are involved under other processes that evaluate other variables and parameters. The results could be used to solve system of equations that could propagate significative errors which can be accumulated at the intervenient systems. The Objective Analysis is an interpolation procedure based in the Gauss-Markov mapping that can provide answers to these needs. Some authors had applied this method in meteorological and oceanographic fields, besides that is a good data-analysis tool and a basis for the design of observational arrays. In this work we propose to analyze an implementation of this interpolation technique based in the Objective analysis (or mapping), applied to two datasets of a different character from coastal regions. The first dataset is a topographic 82 measurement from a tidal marsh using an instrument specifically designed for this purpose. The other dataset, temporally distributed, are current measurements on a tidal channel during a complete tidal cycle. The results are compared with the solution obtained with the Inverse Distance method trough the estimation of an error curve. This curve is constructed based in the progressive generation of randomly distributed gaps until cover a 60% of the analyzed data. After that, the fields were reconstructed with the two methods plotting the error curve obtained as a function of gap number. The results suggest that the error curves for the two datasets using the Objective Analysis is always less than the Inverse Distance method. From the estimations we can infer that the Objective Analysis method represents in a better way the behavior of the original data.
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Copyright (c) 2008 Jorge O. Pierini, Maximiliano G. Sassi, Gerardo M.E. Perillo
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