Long-time sequence near-surface ozone inversion method based on neural network

Abstract

The invention discloses a long-time sequence near-surface ozone inversion method based on a neural network. The method comprises the following steps: S1, acquiring satellite remote sensing monthly-scale ozone column concentration data according to satellite remote sensing data; s2, performing multi-temporal interpolation calculation on the meteorological data of the ground monitoring meteorological station to obtain meteorological data with spatial distribution characteristics, wherein the meteorological data of the ground monitoring meteorological station comprises temperature, wind speed, air pressure, relative humidity and sunshine duration; s3, establishing a near-surface ozone inversion neural network model according to the satellite remote sensing monthly-scale ozone column concentration data and the meteorological data with the spatial distribution characteristics, and training the near-surface ozone inversion neural network model; and S4, performing simulation test on the near-surface ozone inversion neural network model. The inversion method is high in precision and simple to operate, can realize relatively accurate near-surface ozone concentration measurement, and can obtain a near-surface ozone concentration data set capable of accurately reflecting seasonal change, annual change and spatial distribution.

Description

technical field [0001] The invention relates to the field of remote sensing inversion, in particular to a neural network-based long-term near-surface ozone inversion method. Background technique [0002] Ozone is an important trace gas. About 90% of ozone in the atmosphere exists in the stratosphere, and only 10% exists in the troposphere. Stratospheric ozone can block ultraviolet rays and protect the earth's biosphere. However, near-surface ozone is a pollutants. Near-surface ozone is an important greenhouse gas, and the increase in ozone concentration will directly lead to surface warming; at the same time, as a highly reactive and highly oxidizing measuring gas, ozone will have certain effects on the respiratory system of humans and animals and plants. Damage, high concentration of ozone is very irritating to the eyes and respiratory tract, can cause respiratory diseases, and destroy the immune function of the human body; in addition, ozone can enter the crop body throug...

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Problems solved by technology

[0004] In view of the lack of spatial representation of site observation data and the differences between remote sensing observation data and near-surface ozone data, the present invention provides a neural network-based long-term near-surface ozone inversion method

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