Dr. Yanmeng Bi Profile

Dr. Yanmeng Bi

at China Meterological Administration

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Publications (3)

Proceedings Article | 8 November 2014 Paper

Spectral parameters and signal-to-noise ratio requirement for TANSAT hyper spectral remote sensor of atmospheric CO2

Qian Wang, Zhong-Dong Yang, Yan-Meng Bi

Proceedings Volume 9259, 92591T (2014) https://doi.org/10.1117/12.2067572

KEYWORDS: Carbon dioxide, Spectral resolution, Sensors, Signal to noise ratio, Atmospheric modeling, Transmittance, Atmospheric sensing, Absorption, Spectroscopy, Satellites

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With the stable increase of carbon dioxide (CO₂) concentrations, space based measurements of CO² concentration in lower atmosphere by reflected sunlight in near infrared band has become a hot research topic at present. Recently, the instruments sensitive to total CO₂column data in near-surface have become available through the SCIAMACHY instrument on ENVISAT and TANSO-FTS on GOSAT. The developing hyper spectral CO₂ detector in China carried by TANSAT will be launched in late 2015. Hyper spectral CO₂detector is designed to provide global measurements of CO₂ in lower troposphere. It employs high resolution spectra of reflected sunlight taken simultaneously in near-infrared CO2 (1.61μm and 2.06μm) and O2 (0.76μm) bands. Associating climate change with the observation requirements of carbon sources and sinks, the feasibility of making CO2 column concentration measurements with high-resolution and high-precision is studied by high resolution atmosphere radiation transfer model. The effects of key specifications of the hyper spectral CO₂detector such as spectral resolution, sampling ratio and sign-to-noise ratio (SNR) on CO₂detection are analyzed combining the scientific requirements of CO₂measurements of China. The typical characteristics of hyper spectral CO2 detector on TANSAT are grating spectrometer and array-based detector. To achieve the column averaged atmospheric CO2 dry air mole fraction (XCO₂) precision requirements of 1×10^-6-4×10^-6, hyper spectral CO₂ detector should provide high resolution at first to resolve CO₂ absorption lines from continuous spectra of reflected sunlight. Compared to a variety of simulated spectral resolutions, the spectral resolution of hyper spectral CO₂ detector on TANSAT can resolve CO₂ spectral features and maintain the moderate radiance sensitivity. Since small size array detector-based instruments may suffer from undersampling of the spectra, the influences of spectral undersampling to CO₂absorption spectra are studied, the results indicate that sampling ratio should exceed 2 pixels/FWHM to ensure the accuracy of CO₂spectrum. Signal-to-noise ratio is one of the most important parameters of hyper spectral CO₂ detectors to ensure the reliability of CO₂signal. SNR requirements of CO₂ detector to different detection precisions are explored based on the radiance sensitivity factors. The results show that it is difficult to achieve the SNR to detect 1×10^-6-4×10^-6CO2 concentration change in the boundary layer by solar shortwave infrared passive remote sensing, limited by the instrument development at present. However, the instrument SNR to detect 1% change in the CO₂ column concentration is attainable. The results of this study are not only conductive to universal applications and guides on developing grating spectrometer, but also helpful to have a better understanding of the complexity of CO₂ retrieval.

Proceedings Article | 8 November 2014 Paper

Impacts of aerosol and albedo on TanSat CO2 retrieval using the near infrared CO2 bands

Yanmeng Bi, Zhongdong Yang, Songyan Gu, Qian Wang, Ling Gao, Jie Chen

Proceedings Volume 9259, 925915 (2014) https://doi.org/10.1117/12.2066123

KEYWORDS: Aerosols, Carbon dioxide, Scattering, Atmospheric particles, Atmospheric modeling, Absorption, Satellites, Spectral resolution, MODIS, Near infrared

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Proceedings Article | 12 May 2005 Paper

Remote sensing of atmospheric water vapor in the region of southwest China using GPS

Yanmeng Bi, Jietai Mao, Meihua Wang, Chengcai Li

Proceedings Volume 5832, (2005) https://doi.org/10.1117/12.619754

KEYWORDS: Global Positioning System, Humidity, Remote sensing, Meteorology, Climatology, Temperature metrology, Atmospheric sensing, Atmospheric modeling, Atmospheric monitoring, Environmental sensing

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