| Being the only real-time/delayed-time available three-dimensional global-scale ocean observation data, Argo data provides critical observation factual basis which contains a wealth of information resources for revealing the marine environment, temperature and salinity distribution and current structure, understanding and deepening of the marine environment features and climate change, and has important scientific significance. Aiming at the sparseness, fragments and discontinuousness of Argo data, the reanalysis research on fitting sparse data and reconstructing the three-dimensional temperature and salinity field is conducted. The surface current field is estimated and the three-dimensional current field is inversed based on the blending of Argo data and satellite remote sensing data. This study can provide a theoretical basis and technical means for exploring and optimizing the use of the Argo information resources and Argo data, and provide data and information support for deepening the research on the evolution of the marine environment and climate change and improving the numerical prediction capability of meteorological and oceanic numerical models.The main achievements and creative results are as follows:(1) Aiming at the small-scale detailed structures and characteristics of the sparse data like Argo data, the fractal interpolation method model is improved. By introducing genetic algorithm to the searching of fractal interpolation parameters (compression factor) to optimize the search, the objective optimization is achieved, and the rationality and effectiveness of the fractal interpolation method on the sparse data is improved.(2) A small sample interpolation method-Information diffusion interpolation is put forward. Based on the idea of fuzzy mapping, by fuzzy diffusing and mapping the sparse data points, this technique can achieve the probability interpolation from the limited data points to its neighboring regions points. In order to the limitations of the normal diffusion model on describing the asymmetric structure of data, the nonnormal diffusion model-the elliptical model and probability model is developed. This model is verified by comparing the results of interpolation experiments.(3) The reconstruction of three-dimensional ocean data field. Aiming at the deficiencies that exist in the Argo data and interpolation reconstruction Argo standardized grid products against the conventional space-time interpolation method, an improved space-time interpolation algorithm is developed, and the three-dimensional temperature and salinity model is conducted. This model can efficiently suppress the errors that exist in the conventional means.(4) Aiming at the common missing data in the actual marine field data and the conventional singular spectrum of the iterative parameter K, M selected subjectivity and poor computational efficiency, a new model parameter segmenting method is put forward. This method can avoid the model parameters into the local error convergence and thus a reasonable search for the global optimal solution can be got. The iterative computational efficiency and accuracy of the interpolation results can be improved and enhanced.(5) Based on the satellite altimeter data and satellite remote sensing of sea surface wind field QuikSCAT data, the inversed surface geostrophic flow and Ekman flow can be got by dealing with the flow model in different latitude segments using different ways to overcome the discontinuity near the equator.(6) Based on the Argo observational three-dimensional temperature salinity field, the three-dimensional ocean flow field is inversed using the P-vector method based on the isopycnal temperature and salinity constraints. This method makes up the actual ocean current observation difficulty and practical problems and deficiencies in available current data. The inversed three-dimensional flow field can objectively reproduce the performance of currents on the different depths. |
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