Ocean current dynamic optimization forecasting model based on deep learning algorithm

Abstract

The invention discloses an ocean current dynamic optimization forecasting model based on a deep learning algorithm, and belongs to the technical field of ocean information forecasting. The fact that an island chain open sea area flow field is mainly controlled by an ocean mesoscale process is comprehensively considered, an optimal forecasting factor and a forecasting model are determined by utilizing a genetic algorithm; local change and spatial change rules of mesoscale vortexes are considered; the optimal forecasting factor of the model is ensured to respectively accord with the viewpoints of the Euler field and the Lagrange field; an operator fitting algorithm in a deep learning algorithm is combined with a dynamic forecasting method; the accuracy and the calculation speed of ocean current power forecasting are improved; for flow field forecasting of the ocean mesoscale process, on one hand, a forecasting model and a forecasting factor which are simple in model and high in forecasting precision are screened out according to an operator fitting algorithm and a genetic algorithm of a deep learning algorithm; and on the other hand, the physical significance of the forecasting modeland the optimization factor is defined by using the physical characteristics of the vortex, and the forecasting accuracy and reliability are high.

Description

technical field [0001] The invention relates to the technical field of ocean information forecasting, in particular to an ocean current power optimization forecast model based on a deep learning algorithm. Background technique [0002] Since the appearance of satellite altimeter in the 1970s, it has played a huge role in global ocean dynamic detection and research. The satellite altimeter is a spaceborne active microwave remote sensor, which consists of a pulse transmitter, a sensitive receiver and a precise clock. The pulse transmitter transmits a series of extremely narrow radar pulses from the sea surface to the sea surface, the receiver detects the electromagnetic wave signal reflected by the sea surface, and then the time interval ΔT between transmission and reception is accurately measured by the timing clock, and the distance from the center of mass of the altimeter to the satellite can be calculated. The calculation formula for the instantaneous sea surface distance...

AI Technical Summary

Problems solved by technology

However, there are three obvious problems in the current numerical model for the prediction of mesoscale processes: First, because the ocean model with eddy resolution has high requirements for computing resources, it usually needs to be calculated on a large computer cluster, and the single-computer calculation consumes a lot time

Second, there are still many uncertainties in the simulation of the vortex process by the numerical model at present, and it is necessary to further simulate and optimize the parameterization scheme of the mesoscale process

Numerical models need to correct the forecast results by assimilating a large number of quasi-real-time ocean observation data, so as to ensure the accuracy of ocean mesoscale process forecasts. Therefore, it is highly dependent on measured data and requires a large amount of calculation.

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