A method for the reconstruction of missing data based on an EOF decomposition has been applied to a large data set, a test case of Sea Surface Temperature satellite images of the Adriatic Sea. The EOF decomposition is realised with a Lanczos method, which allows optimising computational time for large matrices. The results show that the reconstruction method leads to accurate reconstructions as well as a low cpu time when dealing with realistic cases. The method has been tested with different amounts of missing data, artificially adding clouds ranging from 40% to 80% of data loss, and then compared to the same data set with no missing data. A comparison with in situ data has also been made. These validation studies show that results are robust, even when the amount of missing data is very high. The reconstruction of the data from the Adriatic Sea shows realistic features and a reliable temperature distribution. In addition, the method is compared to an Optimal Interpolation reconstruction. The results obtained with both methods are very similar. The main difference is the computational time, which is reduced nearly 30 times with the method presented here. Once the reconstruction has been performed, the EOF decomposition is analysed to show

B y E r i c P. c h a s s i g N E t, h a r l E y E . h u r l B u r t, E . J o s E P h M E t z g E r , o l E M a r t i N s M E d s ta d , J a M E s a . c u M M i N g s ,g E o r g E r . h a l l i w E l l , r a i N E r B l E c k , r E M y B a r a i l l E , The partnership represents a broad spectrum of the oceanographic community, bringing together academia, federal agencies, and industry/commercial entities, and spanning modeling, data assimilation, data management and serving, observational capabilities, and application of HYCOM prediction system outputs. In addition to providing real-time, eddy-resolving global-and basin-scale ocean prediction systems for the US Navy and NOAA, this project also offered an outstanding opportunity for NOAA-Navy collaboration and cooperation, ranging from research to the operational level. This paper provides an overview of the global HYCOM ocean prediction system and highlights some of its achievements. An important outcome of this effort is the capability of the global system to provide boundary conditions to even higherresolution regional and coastal models.Oceanography Vol. In addition to operational eddyresolving global-and basin-scale ocean prediction systems for the US Navy and NOAA, respectively, this project offered an outstanding opportunity for NOAA-Navy collaboration and cooperation ranging from research to the operational level.

The Data Interpolating Variational Analysis (Diva) is a method designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the combination of a particular methodology, based on the minimisation of a cost function, and a numerically efficient method, based on a finite-element solver. The cost function penalises the misfit between the observations and the reconstructed field, as well as the regularity or smoothness of the field. The method bears similarities to the smoothing splines, where the second derivatives of the field are also penalised.The intrinsic advantages of the method are its natural way to take into account topographic and dynamic constraints (coasts, advection, . . . ) and its capacity to handle large data sets, frequently encountered in oceanography. The method provides gridded fields in two dimensions, usually in horizontal layers. Three-dimension fields are obtained by stacking horizontal layers.In the present work, we summarize the background of the method and describe the possible methods to compute the error field associated to the analysis. In particular, we present new developments leading to a more consistent error estimation, by determining numerically the real covariance function in Diva, which is never formulated explicitly, contrarily to Optimal Interpolation. The real covariance function is obtained by two concurrent executions of Diva, the first providing the covariance for the second. With this improvement, the error field is now perfectly consistent with the inherent background covariance in all cases.A two-dimension application using salinity measurements in the Mediterranean Sea is presented. Applied on these measurements, Optimal Interpolation and Diva provided very similar gridded fields (correlation: 98.6%, RMS of the difference: 0.02). The method using the real covariance produces an error field similar to the one of OI, except in the coastal areas.

[1] Numerous climatologies are available at different resolutions and cover various parts of the global ocean. Most of them have a resolution too low to represent suitably regional processes and the methods for their construction are not able to take into account the influence of physical effects (topographic constraints, boundary conditions, advection, etc.). A high-resolution atlas for temperature and salinity is developed for the northeast Atlantic Ocean on 33 depth levels. The originality of this climatology is twofold: (1) For the data set, data are collected on all major databases and aggregated to lead to an original data collection without duplicates, richer than the World Ocean Database 2005, for the same region of interest. (2) For the method, climatological fields are constructed using the variational method Data-Interpolating Variational Analysis. The formulation of the latter allows the consideration of coastlines and bottom topography, and has a numerical cost almost independent on the number of observations. Moreover, only a few parameters, determined in an objective way, are necessary to perform an analysis. The results show overall good agreement with the widely used World Ocean Atlas, but also reveal significant improvements in coastal areas. Error maps are generated according to different theories and emphasize the importance of data coverage for the creation of such climatological fields. Automatic outlier detection is performed, and its effects on the analysis are examined. The method presented here is very general and not dependent on the region, hence it may be applied for creating other regional atlas in different zones of the global ocean.

The surface circulation of the Caribbean Sea and Gulf of Mexico is studied using 13 years of satellite altimetry data. Variability in the Caribbean Sea is evident over several time scales. At the annual scale, sea surface height (SSH) varies mainly by a seasonal steric effect. Interannually, a longer cycle affects the SSH slope across the current and hence the intensity of the Caribbean Current. This cycle is found to be related to changes in the wind intensity, the wind stress curl, and El Niñ o-Southern Oscillation. At shorter time scales, eddies and meanders are observed in the Caribbean Current, and their propagation speed is explained by baroclinic instabilities under the combined effect of vertical shear and the b effect. Then the Loop Current (LC) is considered, focusing on the anticyclonic eddies shed by it and the intrusion of the LC into the Gulf of Mexico through time. Twelve of the 21 anticyclonic eddies observed to detach from the LC are shed from July to September, suggesting a seasonality in the timing of these events. Also, a relation is found between the intrusion of the LC into the Gulf of Mexico and the size of the eddies shed from it: larger intrusions trigger smaller eddies. A series of extreme LC intrusions into the Gulf of Mexico, when the LC is observed as far as 928W, are described. The analyses herein suggest that the frequency of such events has increased in recent years, with only one event occurring in 1993 versus three from 2002 to 2006. Transport through the Straits of Florida appears to decrease during these extreme intrusions.Corresponding author address: Aida Alvera-Azcá rate, AGO-GHER, University of Liè ge, Allé e du 6 Aoû t 17,

[1] An empirical orthogonal function-based technique called Data Interpolating Empirical Orthogonal Functions (DINEOF) is used in a multivariate approach to reconstruct missing data. Sea surface temperature (SST), chlorophyll a concentration, and QuikSCAT winds are used to assess the benefit of a multivariate reconstruction. In particular, the combination of SST plus chlorophyll, SST plus lagged SST plus chlorophyll, and SST plus lagged winds have been studied. To assess the quality of the reconstructions, the reconstructed SST and winds have been compared to in situ data. The combination of SST plus chlorophyll, as well as SST plus lagged SST plus chlorophyll, significantly improves the results obtained by the reconstruction of SST alone. All the experiments correctly represent the SST, and an upwelling/downwelling event in the West Florida Shelf reproduced by the reconstructed data is studied.Citation: Alvera-Azcárate, A., A. Barth, J.-M. Beckers, and R. H. Weisberg (2007), Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields,

The molecular structure of the previously reported species "[Fe(bdtbpza)Cl]" has been revealed by X-ray structure determination to be a ferrous dimer [Fe(bdtbpza)Cl](2) (2c) [bdtbpza = bis(3,5-di-tert-butylpyrazol-1-yl)acetate]. The syntheses of ferrous 2:1 complexes [Fe(bpza)(2)] (3a) and [Fe(bdtbpza)(2)] (3c) as well as ferric 1:1 complexes [NEt(4)][Fe(bpza)Cl(3)] (4a) and [NEt(4)][Fe(bdmpza)Cl(3)] (4b) [bpza = bis(pyrazol-1-yl)acetate, bdmpza = bis(3,5-dimethylpyrazol-1-yl)acetate] are reported. Complexes 3a, previously reported [Fe(bdmpza)(2)] (3b), and 3c are high-spin. No spin crossover to the low-spin state was observed in the temperature range of 5-350 K. 4a and 4b are synthesized in one step and in high yield from [NEt(4)](2)[Cl(3)FeOFeCl(3)]. 4a and 4b are iron(III) high-spin complexes. Crystallographic information: 2c (C(24)H(39)ClFeN(4)O(2).CH(2)Cl(2).CH(3)CN) is triclinic, P1, a = 12.171(16) A, b = 12.851(14) A, c = 13.390(13) A, alpha = 98.61(9) degrees, beta = 113.51(11) degrees, gamma = 108.10(5) degrees, Z = 2; 3a (C(8)H(7)Fe(0.5)N(4)O(2)) is monoclinic, P2(1)/n, a = 7.4784(19) A, b = 7.604(3) A, c = 16.196(4) A, beta = 95.397(9) degrees, Z = 4; 3c (C(24)H(39)Fe(0.5)N(4)O(2)) is monoclinic, P2(1)/n, a = 9.939(6) A, b = 18.161(10) A, c = 13.722(8) A, beta = 97.67(7) degrees, Z = 4; 4b (C(20)H(35)Cl(3)FeN(5)O(2)) is monoclinic, C2/c, a = 30.45(6) A, b = 12.33(2) A, c = 16.17(3) A, beta = 118.47(5) degrees, Z = 8.

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