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A method for inversion of offshore surface air temperature

A temperature and inversion technology, applied in neural learning methods, biological neural network models, design optimization/simulation, etc., can solve problems that consume a lot of manpower and material resources, and achieve improved inversion accuracy, efficiency, and training efficiency and accuracy Effect

Active Publication Date: 2022-08-02
HARBIN ENG UNIV
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AI Technical Summary

Problems solved by technology

[0004] Traditional sea-air data processing methods require a lot of manpower and material resources, and these methods are no longer applicable in today's ever-increasing data volume

Method used

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  • A method for inversion of offshore surface air temperature
  • A method for inversion of offshore surface air temperature
  • A method for inversion of offshore surface air temperature

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Embodiment Construction

[0042] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0043] The invention proposes a method for inverting the air temperature on the offshore surface by establishing a training model based on a cyclic neural network. The specific implementation of the method includes key contents such as determining the type of input and output parameters of the model, data preprocessing method, weight initialization method, and improvement of the cyclic neural network structure. The offshore surface air temperature method of the present invention uses the BPTT algorithm as the inversion method, and the execution process is as follows figure 1 shown.

[0044] A new method for inversion of offshore surface air temperature proposed by the present invention specifically includes the following steps:

[0045] Step 1: Select a sea area and extract and preprocess the data in the area.

[0046] Since there are many ...

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Abstract

The invention discloses a method for inversion of offshore surface air temperature, which specifically includes step 1: selecting a sea area and extracting and preprocessing data in the area. Step 2: Determine the input and output parameters, and establish an inversion model of offshore surface temperature based on a recurrent neural network. Step 3: Weight initialization. Step 4: Add L 2 Parametric paradigm penalty. Step 5: Initialize the BPTT algorithm. Step 6: Forward propagation to obtain predicted values. Step 7: Backpropagation updates the connection weights. Step 8: Calculate the loss function. Step 9: Return to store the network parameters Compared with most of the offshore surface temperature methods only use linear regression and shallow neural network for modeling, the present invention uses a deep neural network for modeling and training, thereby improving the accuracy of offshore surface temperature inversion. precision.

Description

technical field [0001] The invention relates to a method for inversion of air temperature on an offshore surface, and belongs to the technical field of research on atmospheric and oceanographic data processing methods. Background technique [0002] The offshore surface air temperature is a very important but difficult to obtain air-sea parameter. Since there is a certain correlation between the air-sea parameters, the relevant air-sea parameters can be used to obtain the near-sea surface air temperature by means of inversion. The inversion of offshore surface air temperature is beneficial to the study of ocean-atmosphere interaction and to people's understanding of climate change. At the same time, obtaining the near-sea surface temperature by means of inversion can also provide support for the multi-faceted research on the marine atmosphere, and contribute to the development of marine resources development, marine environmental protection and other fields. [0003] Big da...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/084G06N3/045
Inventor 高峰周诗楠刘厂郭少彬
Owner HARBIN ENG UNIV
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