A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations
A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations
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DOI：
10.1029/2001jd900085
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发表时间：
2001-07-27
影响因子：
4.4
通讯作者：
Rothstein, M
中科院分区：
文献类型：
--
作者：
Aires, F;Prigent, C;Rothstein, M
由于反问题的复杂性，对陆地上空微波观测数据进行分析以确定大气和地面参数的工作仍然有限。神经网络技术已经被证明是成功的基础上有效的检索方法的非线性情况下，然而，第一猜测估计，这是用于变分同化方法，以避免问题的解决方案的非唯一性或其他形式的解决方案的不规则性，到目前为止还没有被用于神经网络方法。在这项研究中，开发了一种神经网络方法，使用第一猜测。在神经网络和变分同化方法之间建立了概念桥梁。新的神经网络方法检索的表面皮肤温度，综合水汽含量，云液态水路径和19和85 GHz之间的地面微波发射率从特殊传感器微波成像仪观测。由于一致性原因，所有这些量的并行检索改善了结果。训练神经网络的数据库计算与辐射传输模型和全球收集的一致的表面和大气参数提取的国家环境预测中心再分析，从国际卫星云气候学项目的数据，并从微波发射率图集先前计算。神经网络反演的结果是令人鼓舞的。地球仪表面温度反演的理论均方根误差在晴空条件下为1.3 K，在多云情况下为1.6 K。水汽反演的理论RMS误差在晴朗条件下为3.8 kg m（-2），在多云条件下为4.9 kg m（-2）。云液态水路径的理论均方根误差为0.08kg·m ~（-2）。地面发射率反演的精度优于0.008，在晴朗的条件下，在多云的条件下，0.010。微波陆面温度反演是对红外反演的一个很有吸引力的补充：它可以产生陆面温度的时间记录。
The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of efficient retrieval methods for nonlinear cases; however, first guess estimates, which are used in variational assimilation methods to avoid problems of solution nonuniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first guess. Conceptual bridges are established between the neural network and variational assimilation methods. The new neural method retrieves the surface skin temperature, the integrated water vapor content, the cloud liquid water path and the microwave surface emissivities between 19 and 85 GHz over land from Special Sensor Microwave Imager observations. The retrieval, in parallel, of all these quantities improves the results for consistancy reasons. A database to train the neural network is calculated with a radiative transfer model and a global collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data, and from microwave emissivity atlases previously calculated. The results of the neural network inversion axe very encouraging. The theoretical RMS error of the surface temperature retrieval over the globe is 1.3 K in clear-sky conditions and 1.6 K in cloudy scenes. Water vapor is retrieved with a theoretical RMS error of 3.8 kg m(-2) in clear conditions and 4.9 kg m(-2) in cloudy situations. The theoretical RMS error in cloud liquid water path is 0.08 kg m(-2). The surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.