Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region
Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region
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DOI：
10.3390/rs13101884
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Jingjing Hu;Yansong Bao;Jian Liu;Hui Liu;G. Petropoulos;P. Katsafados;Liuhua Zhu;Xi Cai
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文献类型：
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作者：
Jingjing Hu;Yansong Bao;Jian Liu;Hui Liu;G. Petropoulos;P. Katsafados;Liuhua Zhu;Xi Cai
实时获取北极温湿度(RH)廓线对于北极气候研究和北极科学研究具有重要意义。然而，风云-3D的运算算法只考虑了60°N以内的区域，本文的创新之处在于提出了一种基于神经网络(NN)算法的新技术，可以从北极地区的风云-3D高光谱红外辐射大气探测(HIRAS)观测中实时地反演这些参数。考虑到北极地区很难获得大量的实际观测(如探空仪)，利用欧洲中期天气预报中心(ECMWF)的ERA5数据和HIRAS观测数据来训练神经网络。使用两个变量对亮温和训练目标进行分类：季节(暖季和冷季)和表面类型(海洋和陆地)。将基于神经网络的检索与ERA5数据和独立于神经网络训练集的无线电探空仪观测(RAOB)进行比较。结果表明：(1)在暖季和海洋上，NNS的反演精度普遍较高；(2)RAOB比较的廓线均方根误差(RMSE)一般略高于ERA5比较，但误差随高度的变化趋势是一致的；(3)与AIRS产品相比，NN方法反演的廓线更接近ERA5。结果表明，神经网络算法在晴空条件下利用HIRAS观测资料反演北极地区温湿度廓线具有潜在的时间和空间价值。因此，所提出的神经网络算法为从北极地区的HIRAS观测中可靠地反演温度和RH廓线提供了一条有价值的途径，在广泛的实际应用和研究调查中提供了具有实用价值的信息。总之，我们的工作对于扩大风云3D的业务实施范围从60°N到北极地区具有重要意义。
The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.