Filtering noise from oceanographic data with some applications for the Kara and Black Seas

If a reconstruction process is reduced to the solution of ill-posed algebra ic systems, we suggest several procedures to improve the accuracy of recons truction from noisy and irregular data. These procedures transform ill-pose d equations to their well-posed analogies, thereby reducing both the contri bution of noise to the equation system and the condition number of the syst em matrix. One of the techniques, the so-called "regularizing filter", can be applied to observation samples of limited size when the ratio of the num ber of estimated field parameters to the number of field observations and n oise to signal ratio are under 0.5-0.6 and 4-5, respectively. Furthermore, the filter is constructed without any preliminary knowledge of low-order no ise statistics. The regularizing filter combined with a conventional functi on fitting procedure is illustrated through Linear mapping scalar oceanogra phic fields, such as the surface temperature in the Black Sea observed from the NOAA-11, SiO2 in the Kara Sea, cesium and chlorophyll in the Black Sea . Herein comparing our approach to optimal interpolation, generalized cross -validation and smoothing spline-interpolation is also given. (C) 2001 Else vier Science B.V. All rights reserved.