A CASE-STUDY IN 3-DIMENSIONAL INVERSE METHODS - COMBINING HYDROGRAPHIC, ACOUSTIC, AND MOORED THERMISTOR DATA IN THE GREENLAND SEA

A variety of measurements, including acoustic travel times, moored the rmistor time series, and hydrographic stations, were made in the Green land Sea during 1988-89 to study the evolution of the temperature held throughout the year. This region is of intense oceanographic interest because it is one of the few areas in the world where open-ocean conv ection to great depths has been observed. This paper describes how the various data types were optimally combined using linear, weighted lea st squares inverse methods to provide significantly more information a bout the ocean than can be obtained from any single data type. The app lication of these methods requires construction of a reference state, a statistical model of ocean temperature variability relative to the r eference state, and an analysis of the differing signal-to-noise ratio s of each data type. A time-dependent reference state was constructed from all available hydrographic data, reflecting !he basic seasonal va riability and keeping the perturbations sufficiently small so that lin ear inverse methods are applicable. Smoothed estimates of the vertical and horizontal covariances of the sound speed (temperature) variabili ty were derived separately for summer and winter from all available hy drographic and moored thermistor data. The vertical covariances were n ormalized before bring decomposed into eigenvectors, so that eigenvect ors were optimized to fit a fixed percentage of the variance at every depth. The 12 largest redimensionalized eigenvectors compose the verti cal basis of the model. A spectral decomposition of a 40-km correlatio n scale Gaussian covariance is used as the horizontal basis. The uncer tainty estimates provided by the inverse method illustrate the charact eristics of each dataset in measuring large-scale features during a di versely sampled time period in the winter of 1989. The acoustic data a lone resolve about 70% of the variance in the three-dimensional, 3-day average temperature field. The hydrographic data alone resolve approx imately 65% of the variance during the selected period but are much le ss dense or absent over most of the year. The thermistor array alone r esolves from 10% to 65% of the temperature variance, doing better near the surface where the most measurements were taken. The combination o f the complete 1988-89 acoustic, hydrographic, and thermistor datasets give three-dimensional temperature and heat content estimates that re solve on average about 90% of the expected variance during this partic ularly densely sampled time period.