Preprint Article Version 1 NOT YET PEER-REVIEWED

Bias Correction of Chinese Fengyun-3C Microwave Humidity and Temperature Sounder Measurements in Retrieval of Atmospheric Parameters

  1. Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
  2. University of Chinese Academy of Sciences, Beijing 100049, China
Version 1 : Received: 18 August 2016 / Approved: 19 August 2016 / Online: 19 August 2016 (09:22:31 CEST)

A peer-reviewed article of this Preprint also exists.

He, Q.; Wang, Z.; He, J. Bias Correction for Retrieval of Atmospheric Parameters from the Microwave Humidity and Temperature Sounder Onboard the Fengyun-3C Satellite. Atmosphere 2016, 7, 156. He, Q.; Wang, Z.; He, J. Bias Correction for Retrieval of Atmospheric Parameters from the Microwave Humidity and Temperature Sounder Onboard the Fengyun-3C Satellite. Atmosphere 2016, 7, 156.

Journal reference: Atmosphere 2016, 7, 156
DOI: 10.3390/atmos7120156

Abstract

The Microwave Humidity and Temperature sounder (MWHTS) on board the Fengyun (FY)-3C satellite measure the outgoing radiance form the Earth surface and atmospheric constituents. MWHTS makes measurements in the isolated oxygen absorption line near 118 GHz and the vicinity of strong water vapor line around 183 GHz, can provide fine vertical distribution structure of both atmospheric humidity and temperature. However, in order to obtain the accurate soundings of humidity and temperature by the physical retrieval method, bias between the observed radiance and those simulated by radiative transfer model from the background or first guess profiles must be correct. In this study, two bias correction methods are developed through the correlation analysis between MWHTS measurements and air mass identified by the first guess profiles of the physical inversion, one is the linear regression correction (LRC) and the other is neural networks correction (NNC), representing the linear and nonlinear nature between MWHTS measurements and air mass, respectively. Both correction methods have been applied to MWHTS observed brightness temperatures over the geographic area (180° W-180° E, 60° S-60° N). The corrected results are evaluated by the probability density function of the difference between corrected observations and simulated values and the root mean square error (RMSE) with respect to simulated observations. The numerical results show that the NNC method perform better, especially in MWHTS channels 1 and 7-9 whose peak weight function heights are close to the surface. In order to assess the effects of bias correction methods proposed in this study on the retrieval accuracy, a one-dimensional variational system was built and applied to the MWHTS uncorrected and corrected brightness temperatures to estimated atmospheric temperature and humidity profiles, The retrieval results show that the NNC has better performance which is to be expected. An indication of the stability and robustness of NNC method is given which suggests that the NNC method has promising application perspectives in the physical retrieval.

Subject Areas

FY-3C/MWHTS; linear regression correction; neural networks correction; one-dimensional variational algorithm; atmospheric temperature and humidity profiles

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