Mesoscale vortex trajectory prediction method

A trajectory prediction and scale vortex technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as the difficulty of dynamic equations, achieve good memory, improve prior knowledge, and avoid modeling and settlement. effect of the process

Active Publication Date: 2020-09-22
HARBIN ENG UNIV
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Problems solved by technology

[0005]Aiming at the above-mentioned prior art, the technical problem to be solved by the present invention is to provide a method for predicting mesoscale vortex trajectory based on satellite altimetry data, to solve the problem caused by mesoscale vortex The difficulty of establishing dynamic equations caused by the randomness of motion and improving the prediction accuracy of the prediction model

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  • Mesoscale vortex trajectory prediction method

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Embodiment Construction

[0041] The invention proposes a method for predicting mesoscale vortex tracks by using satellite altimetry data and establishing a prediction model of BP neural network. The specific implementation process of this method includes downloading satellite altimetry data, format conversion, identifying and tracking mesoscale eddies, determining the number of input and output data, establishing a mesoscale vortex track prediction model based on BP neural network, and training the prediction model. .

[0042] combine figure 1 , a method for predicting mesoscale vortex trajectories based on satellite altimetry data proposed by the present invention, specifically includes the following steps:

[0043]Step 1: Obtain satellite altimetry data, generally in nc format, each sampling (grid) point contains absolute dynamic height, sea surface height anomaly, eastward component of absolute geostrophic flow field, northward component of absolute geostrophic flow field, geostrophic flow field ...

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Abstract

The invention discloses a mesoscale vortex trajectory prediction method. The method comprises the steps of downloading and reading satellite height measurement data, extracting mesoscale vortex characteristic parameters and position information, tracking mesoscale vortexes, establishing a mesoscale vortex trajectory prediction model based on a BP neural network, determining input and output data,training the BP neural network, and inputting the initial positions of the mesoscale vortexes into the trained prediction model for prediction. According to the invention, the BP neural network is used as a prediction model, and the tracked mesoscale vortex position information and the initial ground rotation flow velocity anomaly are used as model inputs, so that the short-term prediction of themesoscale vortex trajectory can be realized. Meanwhile, in the mesoscale vortex tracking process, the minimum distance method and the similarity method are combined, limitation on the mesoscale vortexamplitude is added, the error tracking rate is reduced, the tracking accuracy is guaranteed, and therefore the model prediction precision is improved.

Description

technical field [0001] The invention relates to a method for predicting mesoscale eddy tracks, in particular to a method for predicting mesoscale eddy tracks based on satellite altimetry data, and belongs to the technical field of marine mesoscale eddy motion research. Background technique [0002] Mesoscale vortex is an important mesoscale ocean phenomenon that exists widely in the world's oceans. In recent years, more and more attention has been paid to the field of marine environmental monitoring and security, and mesoscale vortex plays an important role in the safety of marine environment. Mesoscale eddies are affected by the complex ocean environment while rotating and moving. Identifying mesoscale eddies from satellite altimetry data is currently the main means of detecting mesoscale eddies. Due to the delay in satellite altimetry data, in order to study the movement of mesoscale eddies , not only to grasp its current position, but also to predict its future direction,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06K9/62G06N3/04G06N3/08
CPCG06F30/27G06N3/084G06N3/045G06F18/22Y02A90/10
Inventor 高峰田苗何忠杰刘厂
Owner HARBIN ENG UNIV
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