Performance Analysis of the Temperature and Humidity Profiles Retrieval for FY-3D/MWTHS in Arctic Regions
Performance Analysis of the Temperature and Humidity Profiles Retrieval for FY-3D/MWTHS in Arctic Regions
复制标题
FY-3D/MWTHS北极地区温湿度廓线反演性能分析
DOI：
10.3390/rs14225858
复制
发表时间：
2022-11
期刊：
影响因子：
5
通讯作者：
Wenyu Wang
中科院分区：
文献类型：
--
作者：
Lanjie Zhang;Shengru Tie;Qiurui He;Wenyu Wang
极地的特殊地理位置增加了地表发射率模拟的难度，因此微波辐射计温湿廓线的物理反演算法主要集中在60°S ~ 60°N之间的区域。本文首先利用深度神经网络（DNN）和长短期记忆（LSTM）模型对北极地区FY-3D/MWHTS大气温湿廓线进行了真实的实时反演，并与物理反演算法进行了比较。机器学习模型的超参数使用网格搜索和10折交叉验证来确定。结果表明，与物理反演算法相比，DNN和LSTM模式对2021年6月海冰上空大气温湿廓线的反演精度更高，最大反演精度分别提高了约3.5 K和42%。在陆地上，DNN和LSTM模式2021年6月大气温度廓线的反演精度提高了约5 K。这两个模式的检索湿度的结果没有比较的物理反演算法，失败的湿度廓线反演在陆地上。此外，基于DNN和基于LSTM的模型使用独立的验证数据在2月，4月和9月的检索结果也在不同的表面类型进行了评估。两个模式反演的温度廓线的均方根误差均在4K以内，近地表除外;湿度廓线的均方根误差均在25%以内，2月份除外。由于秋季和冬季的发射率特性变化很大，9月份的温度廓线和2月份的湿度廓线与其他月份相比有所减少。结果表明，机器学习方法能够较好地评价FY-3D/MWHTS对北极地区大气温湿廓线的反演能力。
The special geographical location of the polar regions increases the difficulty of modeling surface emissivity, thus the physical retrieval algorithms of the temperature and humidity profiles for microwave radiometers mainly focus on the regions between 60°S and 60°N. In this paper, the deep neural networks (DNN) and long short-term memory (LSTM) models are first implemented to retrieve atmospheric temperature and humidity profiles in real time from FY-3D/MWHTS in Arctic regions and are compared with the physical retrieval algorithm. The hyperparameters of the machine learning models are determined using the grid search and 10-fold cross-validation. Results show that, compared with the physical retrieval algorithm, the retrieval accuracies of the atmospheric temperature and humidity profiles of the DNN and LSTM models in June 2021 are higher over sea ice, and the maximum retrieval accuracies are improved by about 3.5 K and 42%. Over land, the retrieval accuracies of the atmospheric temperature profiles for the DNN and LSTM models in June 2021 are improved by about 5 K. The retrieved humidity results for these two models are not compared with the physical retrieval algorithm, which fails for the humidity profile retrieval over land. In addition, the retrieval results of the DNN-based and LSTM-based models using the independent validation data in February, April, and September are also evaluated over different surface types. The RMSEs of the retrieved temperature profiles for the two models are within 4 K, except for the near-surface, and the humidity profiles are within 25%, except for in February. The temperature profiles in September and the humidity profiles in February are somewhat reduced compared to other months because of the highly variable emissivity properties in autumn and winter. Overall results show that the machine learning method can well-evaluate the retrieval capability of FY-3D/MWHTS of the atmospheric temperature and humidity profiles in Arctic regions.
登录
查看更多内容
影响因子：
4.9
作者：
M. McCarthy;P. Thorne;Holly A. Titchner
通讯作者：
M. McCarthy;P. Thorne;Holly A. Titchner
DOI：
10.3390/rs13101884
发表时间：
2021-05
期刊：
Remote. Sens.
影响因子：
--
作者：
Jingjing Hu;Yansong Bao;Jian Liu;Hui Liu;G. Petropoulos;P. Katsafados;Liuhua Zhu;Xi Cai
通讯作者：
Jingjing Hu;Yansong Bao;Jian Liu;Hui Liu;G. Petropoulos;P. Katsafados;Liuhua Zhu;Xi Cai
影响因子：
2.9
作者：
Gers, FA;Schmidhuber, J;Cummins, F
通讯作者：
Cummins, F
DOI：
10.3390/rs12030435
发表时间：
2020-01
期刊：
Remote. Sens.
影响因子：
--
作者：
Liuhua Zhu;Yansong Bao;G. Petropoulos;Peng Zhang;Fenglian Lu;Q. Lu;Ying Wu;Dandan Xu
通讯作者：
Liuhua Zhu;Yansong Bao;G. Petropoulos;Peng Zhang;Fenglian Lu;Q. Lu;Ying Wu;Dandan Xu
影响因子：
5.8
作者：
M. Lindskog;A. Dybbroe;R. Randriamampianina
通讯作者：
M. Lindskog;A. Dybbroe;R. Randriamampianina