Sensitivity Testing of Microwave Temperature Sounder-II Onboard the Fengyun-3 Satellite to Sea Surface Barometric Pressure Based on Deep Neural Network
Sensitivity Testing of Microwave Temperature Sounder-II Onboard the Fengyun-3 Satellite to Sea Surface Barometric Pressure Based on Deep Neural Network
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基于深度神经网络的风云三号卫星微波测温仪-II对海面气压灵敏度测试
DOI：
10.3390/rs14122839
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发表时间：
2022-06
期刊：
影响因子：
5
通讯作者：
Wenyu Wang
中科院分区：
文献类型：
--
作者：
Qiurui He;Zhenzhan Wang;Jiaoyang Li;Wenyu Wang
海面气压有助于计算海面透过率，因此，具有非零海面透过率的微波测温仪-II通道的观测结果包含了海面气压信息。同时，由于各通道之间的相关性，微波温度探测仪-II的所有通道对海面气压都很敏感。然而，由于通道间的相关性，传统的基于辐射传输模型的灵敏度测试方法不能表征微波测温仪-II对海面气压的敏感性。本文利用深度神经网络研究了大气参数与微波测温仪-II观测值之间的关系，建立了基于深度神经网络的微波测温仪-II模拟模型。在此基础上，提出了基于深度神经网络的微波测温仪-II对海面气压的灵敏度测试方法，并进行了灵敏度测试。实验结果表明，微波测温仪-II各通道对海面气压的敏感性被深神经网络测试方法捕获。此外，还利用微波测温仪-II进行了海面气压的反演试验，并对反演结果进行了分析，进一步验证了基于深度神经网络的试验方法的可行性。
Sea surface barometric pressure contributes to calculating the surface transmissivity so that.the observations of Microwave Temperature Sounder-II channels with non-zero surface transmissivity.contain the sea surface barometric pressure information. Meanwhile, all channels of Microwave.Temperature Sounder-II are sensitive to sea surface barometric pressure due to the correlation between.channels. However, the traditional sensitivity test method based on the radiative transfer model.cannot characterize the sensitivity of Microwave Temperature Sounder-II to sea surface barometric.pressure due to the correlations between channels. In this study, the relationship between atmospheric.parameters and Microwave Temperature Sounder-II observations is studied by a deep neural network,.and the deep neural network-based model for Microwave Temperature Sounder-II simulations is.established. Then, the deep neural network-based test method for the sensitivity of Microwave.Temperature Sounder-II to sea surface barometric pressure is developed, and the sensitivity test.experiments are carried out. The experimental results show that the sensitivity of all channels of.Microwave Temperature Sounder-II to sea surface barometric pressure is captured by the deep neural.network-based test method. In addition, the retrieval experiments of sea surface barometric pressure.using Microwave Temperature Sounder-II observations are carried out, and the retrieval results.further validate the feasibility of the deep neural network-based test method.
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