MWHTS channel weight function calculation method based on neural network

Abstract

The invention discloses an MWHTS channel weight function calculation method based on a neural network, and the method comprises the steps: inputting an atmospheric parameter and an MWHTS observation altitude angle into a radiation transmission model RTTOV, and calculating the atmospheric transmittance from each layer in atmospheric stratification to a satellite load MWHTS; calculating an MWHTS channel weight function profile according to the atmospheric transmittance, and establishing a matching data set of the atmospheric parameters, the MWHTS observation elevation angle and the MWHTS channelweight function profile; training a BP neural network based on the matching data set, and establishing an optimal model for MWHTS channel weight function profile calculation based on the BP neural network for each channel of the MWHTS; establishing an MWHTS channel weight function maximum value sample as the output of the deep neural network; training the deep neural network by taking the atmospheric parameters and the MWHTS observation altitude angle as the input of the deep neural network, and establishing an optimal model of atmospheric stratification calculation in which the MWHTS channelweight function maximum value based on the deep neural network is located for each channel of the MWHTS. The method is high in calculation speed, small in calculation amount and simple and easy to operate.

Description

technical field [0001] The invention relates to a calculation method of a weight function of a MWHTS channel, in particular to a calculation method of a weight function of a MWHTS channel based on a neural network. Background technique [0002] Spaceborne microwave radiometers play an important role in the fields of atmospheric science such as numerical weather prediction, climate change research, and severe convective weather monitoring. The channel weight function is the theoretical basis for the channel setting of the spaceborne microwave radiometer receiver, and is an indicator of the sensitivity of each channel of the spaceborne microwave radiometer to different atmospheres. The atmospheric stratification corresponding to the maximum value of the channel weight function indicates that the channel is most sensitive to the layer of the atmosphere. In other words, the detection advantage of the channel is the atmospheric stratification where the maximum value of the channe...

AI Technical Summary

Problems solved by technology

However, for the current calculation method based on the physical mechanism of the channel weight function, when a large amount of spaceborne microwave radiation observation data is applied, the calculation amount of this method is large, and the calculation efficiency is low

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