One-dimensional variational inversion method of atmospheric temperature and humidity profile

Abstract

The invention relates to an one-dimensional variational inversion method of atmospheric temperature and humidity profile, and the method develops an atmospheric temperature and water vapor mixing ratio profile variational inversion method based on Metop-A / IASI infrared hyperspectral data. Using temperature and water vapor detection channel data of an IASI hyperspectral sensor, combining CRTM mode and WRF mode forecasting technology, using a one-dimensional variational method, developing a satellite data quality control, background error covariance localization, observation error covariance calculation and the like, and developing an atmospheric temperature and water vapor mixing ratio profile variational inversion system; based on the method, high-precision detection of the atmospheric temperature and water vapor mixing ratio profile can be carried out by using the Metop-A / IASI infrared hyperspectral data.

Description

technical field [0001] The invention relates to a three-dimensional field remote sensing inversion method of atmospheric temperature and humidity based on hyperspectral infrared sensor data of polar-orbiting meteorological satellites, and belongs to the field of atmospheric science atmospheric remote sensing and atmospheric detection. Background technique [0002] Atmospheric temperature and humidity are important parameters of the atmosphere, which are of great significance for weather forecasting and short-term climate prediction. Due to the strong absorption of oxygen and water vapor in the infrared band, infrared remote sensing technology can be used to realize the vertical detection of atmospheric temperature and humidity. Satellite remote sensing atmospheric temperature and humidity research began in the middle of the last century. As early as 1956, people calculated the atmospheric temperature and humidity profile based on the observation of satellite thermal infrared...

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Problems solved by technology

[0003] Comparing these types of atmospheric temperature and humidity profile inversion methods, it is found that the eigenvector statistical inversion method has a fast calculation speed and a simple algorithm, but it does not consider the physical nature of the atmospheric radiative transfer process, so the inversion accuracy is not ideal

The neural network inversion method has a strong ability to deal with nonlinear problems and good fault tolerance, but it relies on sample training and requires a large sample training data set. It is difficult to establish a representative inversion model in practical work.

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