Reconstructing sea surface temperature and salinity fields in the northeastern Baltic from observational data, based on sub-regional Empirical Orthogonal Function (EOF) patterns from models

Abstract

Reconstruction of SST and SSS fields from observations is handled on the basis of EOF statistics, calculated in the northeastern Baltic over the model time series 2010-2015. Daily averages from the high-resolution (grid step 0.5 NM) sub-regional HBM model were spatially averaged over bins of 5x5 nautical miles. Three first modes cover 99% of variance of temperature and 61.4% of salinity. Based on the values of model EOF modes at observation points, observational amplitudes are determined from the least-square minimization of fitting errors. If these amplitudes are within adopted statistical limits, then we reconstruct the field by superposition of continuous model-based mode patterns multiplied by observational amplitudes. As shown by experiments with pseudo-observations (model values at these points reconstructed to the model grid and then compared with the original model data), reconstruction performance depends on the configuration of observation points in the model domain. Still, a few first modes usually produce acceptable results.