Channel Selection, Data Simulation, and Parameter Inversion of Ground-Based Hyperspectral Microwave Radiometer

Rui Wang, Yuxun Wang, Wei Yan, Wen Lu, Shuo Ma, Xianbin Zhao and Chengming Gu

Mathematical Problems in Engineering, 2019, vol. 2019, 1-10

Abstract:

To make up for the deficiency in the accuracy of temperature profile observation of existing ground-based microwave radiometers, the application of hyperspectral techniques to the microwave band was attempted. To develop a ground-based hyperspectral microwave radiometer, we must first select the detection channel. According to the degrees of signal freedom (DFS) based on information content of atmospheric temperature, the current study selected 200 channels containing 80% of the information to be selected from 343 candidate channels with oxygen absorption bands of 50~70 GHz, 110~130 GHz, and 415~435 GHz. At the same time, a sensitivity analysis was performed on DFS, in which the variation of background field errors had less influence on the channel selection, but the variation of observation errors significantly affected the information content of the channel. In 2015, the BP neural network method was used to simulate the atmospheric temperature profile environment in Kunming and compare it with seven temperature detection channels selected from the currently used microwave radiometer RPG~HATPR0~G3. The inversion results indicate the following: (1) Selecting the channel with 80% information content does not reduce the inversion accuracy of the temperature profile. (2) The 200 channels selected are relative to the 7 channels of RPG~HATPR0~G3. The accuracy of inversion is increased by 0.5 K at the height of 0~8 km and increased by 0.5~1.2 K in the range of 8~10 km.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4846378

DOI: 10.1155/2019/4846378

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