Three-dimensional atmospheric temperature profile inversion method and system based on DenseNet convolutional neural network

Abstract

The invention discloses a three-dimensional atmospheric temperature profile inversion method and system based on a DenseNet convolutional neural network, and belongs to the field of atmospheric microwave remote sensing. The method comprises the following steps: constructing a training data set according to a two-dimensional atmospheric observation brightness temperature image and a three-dimensional atmospheric temperature profile of an oxygen absorption frequency band; based on the training data set, training the DenseNet convolutional neural network until the DenseNet convolutional neural network is converged to obtain a trained network; and inputting a brightness temperature image to be inverted into the trained network, and outputting a three-dimensional atmospheric temperature profileobtained by inversion. A data set used for training takes two-dimensional brightness temperature images as units, each brightness temperature image covers a certain area on the earth, the whole dataset spans a long time interval, the generalization ability is greatly improved, and the inversion error is also reduced. The DenseNet convolutional neural network is large in layer number, the problemof gradient disappearance in the training process is avoided due to the dense connection structure of the DenseNet convolutional neural network, the DenseNet convolutional neural network is suitablefor complex inversion, data of three scenes of clear sky, cloud and rainy days can be directly inverted together, and time consumption is reduced.

Description

technical field [0001] The invention belongs to the technical field of atmospheric microwave remote sensing and detection, and more specifically relates to a three-dimensional atmospheric temperature profile inversion method and system based on a DenseNet convolutional neural network. Background technique [0002] Weather such as typhoon, heavy rain, and strong convection seriously threaten people's life safety because of their suddenness, and will have a serious impact on the social economy. As an important atmospheric meteorological element, the atmospheric temperature profile can be used for initial numerical weather prediction. The establishment of the field plays an important role in improving the performance of numerical weather prediction. Therefore, it is of great significance to invert the atmospheric temperature profile timely and accurately from satellite observation data. [0003] Atmospheric temperature detection is generally realized by spaceborne microwave ra...

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Problems solved by technology

[0005] Aiming at the problems of poor generalization ability and long time-consuming process of the existing 3D atmospheric temperature profile inversion method based on BP neural network, the present invention provides a 3D atmospheric temperature profile inversion method based on DenseNet convolutional neural network and system, the purpose of which is to simplify the temperature profile inversion process, shorten the inversion time, and improve the generalization ability of the inversion network, thereby improving the inversion accuracy

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