This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:

Before merging diverse data, it is regularly necessary to interpolate one data set temporally in order to obtain simultaneity (even if the data sources were available in the same temporal resolution, the problem is still relevant in cases of data outage). Simple interpolation approaches tend to fail in case of noticeably propagating features (the meteorological systems then do not actually move through the interpolated imagery, but slowly vanish at the initial location while gradually appearing at the target location). Therefore- following suggestions of earlier publications where such a technique was used for the temporal interpolation of NWP fields in order to get higher-frequency data- complex empirical orthogonal functions are extracted from a series of 2-dimensional arrays. After interpolating the phase and amplitude components of the resulting functions, fields of the variable under consideration (i.e. in the present context: satellite-measured brightness temperature, radar reflectivities, NWP forecasts) can be reconstructed for every intermediate point of time. The paper describes some experiments carried out to assess the strengths and limitations of this method.

Sea surface temperature fields in the East Sea are composed of various spatial structures such as eddies, fronts, filaments, turbulent-like features and other mesoscale variations associated with the oceanic circulations of the East Sea. These complex SST structures have many spatial scales and evole with time. Semi-monthly averaged SST distributions based on extensive satellite observations of SSTs from 1990 through 1995 were constructed to examine the characteristics of their spatial and temporal scale variations by using statistical methods of multi-dimensional autocorrelation functions and spectral analysis. Two-dimensional autocorrelation functions in the central part of the East Sea revealed that most of the spatial SST structures are anisotropic in the shape of ellipsoids with minor axes of about 90–290 km and major axes of 100–400 km. Two dimensional spatial scale analysis demonstrated a consistent pattern of seasonal variation that the scales appear small in winter and spring, increase gradually to summer, and then decrease again until the spring of the next year. These structures also show great spatial inhomogeneity and rapid temporal change on time scales as short as a semi-month in some cases. The slopes in spectral energy density spectra of SSTs show characteristics quite similar to horizontal and geostrophic turbulence. Temporal spectra at each latitude are demonstrated by predominant peaks of one and two cycles per year in all regions of the East Sea, implying that SSTs present very strong annual and semi-annual variations. Keywords: ⋅Sea surface temperature, ⋅ semi-monthly averaged SST, ⋅ spatial and temporal scales, ⋅ autocorrelation functions, ⋅ seasonal variations, ⋅geostrophic turbulence.