MWHTS simulation brightness temperature calculation method based on deep neural network

Abstract

The invention discloses an MWHTS simulation brightness temperature calculation method based on a deep neural network. The MWHTS simulation brightness temperature calculation method comprises the steps: establishing a matching data set of an MWHTS observation brightness temperature and a climate data set in space and time; dividing the matching data set into a clear sky data set, a cloud data set and a rain data set according to the cloud water content, and respectively forming a corresponding analysis data set and a corresponding verification data set; training a deep neural network model by utilizing the three analysis data sets, inputting atmospheric parameters in the corresponding verification data sets into the trained deep neural network model, and calculating MWHTS simulation brightness temperature; inputting the atmospheric parameters in the three verification data sets into a radiation transmission model to calculate the MWHTS simulated brightness temperature, comparing the calculation precision with the MWHTS simulated brightness temperature calculation precision based on the deep neural network, and selecting MWHTS channels with higher precision to form an MWHTS simulatedbrightness temperature calculation result. According to the method, the interaction between microwaves and atmospheric molecules is modeled by using the deep neural network, the calculation precisionhigher than that of a business radiation transmission model RTTOV is obtained, and the operation is simple and easy to implement.

Description

technical field [0001] The invention relates to a calculation method for MWHTS simulated brightness temperature, which belongs to the technical field of microwave remote sensing, and in particular to a calculation method for MWHTS simulated brightness temperature based on a deep neural network. Background technique [0002] In the field of microwave remote sensing, the problems dealt with can be divided into two categories: forward modeling and inversion. For spaceborne microwave remote sensing, the so-called forward modeling is to calculate the brightness temperature observed by the spaceborne microwave radiometer by modeling the radiation transmission process of microwaves in the atmosphere, and the inversion is usually a mathematical calculation of the inversion of the radiation transmission model , that is, the process of obtaining atmospheric parameters by observing the brightness temperature with a spaceborne microwave radiometer. The radiation transfer model of micro...

AI Technical Summary

Problems solved by technology

Insufficient current understanding of the interaction between microwaves and atmospheric molecules is the main cause of errors in radiative transfer models

Especially under cloudy and rainy atmospheric conditions, the nonlinearity of the radiative transfer equation increases, and it is difficult for the radiative transfer model to model the scattering effect of cloud and rain, which leads to poor calculation accuracy of the simulated brightness temperature.

Images