The article considers results of validation of a sea ice modeling data using a novel methodology on the vector algebra theoretical bases. The vectorial-algebraic approach developed and was in use in Russia for an anal...The article considers results of validation of a sea ice modeling data using a novel methodology on the vector algebra theoretical bases. The vectorial-algebraic approach developed and was in use in Russia for an analysis of time series of vector values--wind, currents ice drift etc. The vectorial-algebraic approach allows significantly compressing the initial information and most adequately describes the vector time series of full-scale and model data restricted by a set of statistical characteristics in the invariant form. For an express analysis of correlation of the modeling data and in situ （or satellite derived） data a system of simplified correlation invariant indicators was used. This methodology was applied for validation of vector values fields at the first time. The method gives an opportunity to describe a field of a vector correlation and detect an areas with different levels of correlation between model and in situ （or other reference） vector data. The work was carried out in the frame of the My Ocean Project （FP7）.展开更多
文摘The article considers results of validation of a sea ice modeling data using a novel methodology on the vector algebra theoretical bases. The vectorial-algebraic approach developed and was in use in Russia for an analysis of time series of vector values--wind, currents ice drift etc. The vectorial-algebraic approach allows significantly compressing the initial information and most adequately describes the vector time series of full-scale and model data restricted by a set of statistical characteristics in the invariant form. For an express analysis of correlation of the modeling data and in situ （or satellite derived） data a system of simplified correlation invariant indicators was used. This methodology was applied for validation of vector values fields at the first time. The method gives an opportunity to describe a field of a vector correlation and detect an areas with different levels of correlation between model and in situ （or other reference） vector data. The work was carried out in the frame of the My Ocean Project （FP7）.
