Data and model coupling analysis method for marine drift trajectory prediction

Abstract

The invention relates to a data and model coupling analysis method oriented to marine drift trajectory prediction. The method comprises the following steps: actually measured drifting track data collection and marine environment data preparation, drifting track prediction numerical simulation experiments, different marine environment data set space-time analysis, multi-dimensional drifting track prediction precision evaluation, precision evaluation result statistical analysis and FSLE analysis based on a prediction track and an actually measured track, multi-factor sensitivity analysis and applicability evaluation of the environmental data and the prediction model in the region. According to the method, the environment data and physical model coupling analysis evaluation capability in drift track prediction is improved, the application of different modes of data in the drift track prediction field is promoted, and the further regional data refinement work is guided. The analysis and evaluation method provided by the invention can more comprehensively verify the real performance of the related prediction mode in the region, and is beneficial to improving the overall reliability of trajectory prediction.

Description

technical field [0001] The invention relates to a data and model coupling analysis method for sea drift track prediction. This method analyzes and evaluates the prediction model formed by the coupling of multi-source marine environmental data and various drift trajectory prediction physical models through a full-factor analysis framework, so as to determine a reliable prediction model for unpowered floating object drift trajectory that can be applied in different sea areas , while testing the application potential of the coupling results of environmental data and predictive models. Background technique [0002] In recent years, my country has carried out a series of work related to the construction and development of islands and reefs, offshore commercial operations, and marine scientific research in the South China Sea (Cheng et al. 2019). In this context, the actual demand for the prediction model of unpowered floating objects drifting at sea in the South China Sea is inc...

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Problems solved by technology

If the relevant factors are forcibly incorporated into the model when the data itself lacks rationality or poor accuracy, it will bring unpredictable disturbances to the model

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