Abstract
Abstract. DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The approach is tested in a three years sea surface temperature data set over the Black Sea. The effect of the filter in the temporal EOFs is presented, as well as some examples of the improvement achieved with the filtering in the SST reconstruction, both compared to the DINEOF approach without filtering.
Highlights
DINEOF (Data INterpolating Empirical Orthogonal Functions) is a technique to reconstruct missing data in geophysical data sets using an EOF basis to infer the missing data (Beckers and Rixen, 2003)
Even in satellite measures taken with microwave-based sensors
We propose a filter that, applied to the temporal covariance matrix of the data before the EOF decomposition step in DINEOF, will reduce the temporal discontinuities in the temporal EOFs and in the reconstructed data set
Summary
DINEOF (Data INterpolating Empirical Orthogonal Functions) is a technique to reconstruct missing data in geophysical data sets using an EOF basis to infer the missing data (Beckers and Rixen, 2003) This technique is typically applied to satellite data with gaps due to, for example, clouds present in the atmosphere that impede the surface IR radiation to reach the satellite sensor. Mission) Microwave Imager (TMI) satellites, or the Advanced Microwave Scanning Radiometer (AMSR-E), http: //www.remss.com, Gentemann et al, 2004) large gaps can be present in rain zones and at days when the satellite swath does not completely cover the zone of study These clouds and gaps can obscure almost completely the studied domain, with a few clustered observations remaining in some parts of it. Typical cloud sizes are very large, covering most of the Black Sea at a time
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