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Near-surface air temperature inversion method based on temperature, emissivity and deep learning

A deep learning and air temperature technology, applied in neural learning methods, complex mathematical operations, biological neural network models, etc., can solve problems such as low universality, and achieve the effect of increasing universality and high inversion accuracy

Pending Publication Date: 2022-04-29
INST OF AGRI RESOURCES & REGIONAL PLANNING CHINESE ACADEMY OF AGRI SCI
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  • Abstract
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Problems solved by technology

[0005] In view of the above problems, the object of the present invention is to propose a near-surface air temperature inversion method based on temperature and emissivity and deep learning to solve the problem of low universality in the prior art

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  • Near-surface air temperature inversion method based on temperature, emissivity and deep learning
  • Near-surface air temperature inversion method based on temperature, emissivity and deep learning
  • Near-surface air temperature inversion method based on temperature, emissivity and deep learning

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Embodiment 1

[0029] according to figure 1 As shown, this embodiment proposes a near-surface air temperature inversion method based on temperature and emissivity and deep learning, including the following steps:

[0030] Step 1. Establish the heat radiation transfer equation

[0031] Determine the research area, obtain the satellite remote sensing sensor data and ERA5-Land data in the research area, establish a model data set based on the obtained satellite remote sensing sensor data and ERA5-Land data, and introduce auxiliary data into the model data set, and then obtain the data from the satellite remote sensing The thermal radiation transfer equation is established from the perspective of the sensor data, where the ERA5-Land data is generated by the European Center for Medium-Range Weather Forecast by reconstructing the ERA5 climate in the land part of the analysis data set. Decades of land variable reanalysis data are available at high resolution;

[0032] Step 2: Build an expert know...

Embodiment 2

[0065] This embodiment proposes a near-surface air temperature inversion method based on temperature and emissivity and deep learning, including the following steps:

[0066] Step 1. Establish the heat radiation transfer equation

[0067] Determine the research area, obtain the satellite remote sensing sensor data and ERA5-Land data in the research area, establish a model data set based on the obtained satellite remote sensing sensor data and ERA5-Land data, and introduce auxiliary data into the model data set, and then obtain the data from the satellite remote sensing The angle of the sensor data establishes the heat radiation transfer equation;

[0068] Step 2: Build an expert knowledge base

[0069] Based on the heat radiation transfer equation established in step 1, combined with the existing prior knowledge, an expert knowledge base is constructed, and then physical and logical reasoning is carried out through the constructed expert knowledge base to derive the parameter...

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Abstract

The invention provides a near-surface air temperature inversion method based on temperature, emissivity and deep learning, and the method comprises the four steps: building a thermal radiation transmission equation, constructing an expert knowledge base, constructing a high-precision database, and calculating and verifying an inversion result. Parameters required by inversion are determined through a radiation transfer mechanism, near-surface heat interaction influence is considered from the perspective of physics, and high inversion precision is realized by using surface temperature and emissivity as priori knowledge and using simulation data and acquired high-precision data, so that the advantages of a physical model and deep learning are fully utilized, and the method is suitable for large-scale popularization and application. According to the near-surface air temperature inversion method based on the DL-NN algorithm, a high-precision near-surface air temperature inversion result can be obtained by combining the near-surface air temperature and the DL-NN algorithm, and the DL-NN algorithm is adopted to process the ill-conditioned problem of the near-surface air temperature inversion and process the nonlinear relationship between the near-surface air temperature and the atmospheric average action temperature in different seasons and regions, so that the inversion precision is improved, and the universality is improved.

Description

technical field [0001] The invention relates to the technical field of near-surface air temperature inversion, in particular to a near-surface air temperature inversion method based on temperature, emissivity and deep learning. Background technique [0002] Near-surface air temperature usually refers to the atmospheric temperature about 2m above the ground. It is an important parameter for the interaction between the research area and the global atmosphere-earth system. It involves many related studies, such as global climate change, hydrology, atmosphere, and ecology. , agricultural production, urban heat island effect, and air pollution research all require near-surface air temperature as an input parameter. High-precision inversion of near-surface air temperature can better understand climate change or local disturbance and simulate complex surface processes. In addition, near-surface air temperature Air temperature also plays an important role in physical processes such ...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06F17/11G06N3/04G06N3/08G06F119/08
CPCG06F30/27G06F17/11G06N3/04G06N3/08G06F2119/08
Inventor 毛克彪杜宝裕孟飞郭中华曹萌萌袁紫晋
Owner INST OF AGRI RESOURCES & REGIONAL PLANNING CHINESE ACADEMY OF AGRI SCI
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