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Neural Networks Approach to High Vertical Resolution Atmospheric Temperature Profile Retrieval from Spaceborne High Spectral Resolution Infrared Sounder Measurements

机译:神经网络方法从星载高光谱分辨率红外测深仪测量中获取高垂直分辨率大气温度廓线

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AIRS (Atmospheric Infra-Red Sounder) as NASA's first high spectral resolution sounding instrument provides both new and improved measurements of clouds, atmosphere, and land and oceans, with higher accuracy and higher resolution required by future weather and climate models. It will largely improve the deficiencies of the inability of current sounders (e.g. HIRS-3) to obtain high vertical resolution of retrieved atmosphere profiles. In this paper, temperature profiles with 1km vertical resolution at 100 pressure layers, from surface up to 0.005 hPa, were retrieved on different spectral bands and on different types of terrain in the middle latitude area by using a three-layered feed-forward neural networks with back-propagation algorithm. Results show that temperature profiles with accuracy of less than IK in 1 km thick tropospheric layers can be achieved by using AIRS data and neural networks method. And the Qinghai-Tibet Plateau has a measurably impact on the retrieval accuracy which is corresponding to the spectral bands used in performing retrievals. A promising approach to the elimination of this effect is to apply additional predictors which are non-satellite observed (e.g. surface altitude).
机译:AIRS(大气红外测深仪)是NASA的第一台高光谱分辨率测深仪,可提供新的和改进的云,大气,陆地和海洋测量值,并具有未来天气和气候模型所需的更高精度和更高分辨力。它将大大改善现有测深仪(例如HIRS-3)无法获得所获取的大气剖面的高垂直分辨率的缺陷。在本文中,通过使用三层前馈神经网络,在中纬度地区的不同光谱带和不同类型的地形上,从地表到0.005 hPa的100个压力层的垂直分辨率为1 km的温度分布图得到了。与反向传播算法。结果表明,使用AIRS数据和神经网络方法可以在1 km厚的对流层中获得精度低于IK的温度曲线。青藏高原对取回精度的影响可观,这与进行取回所使用的光谱带相对应。消除这种影响的一种有前途的方法是应用非卫星观测到的其他预测因子(例如,表面高度)。

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