由 武, 聪聪 A New Deep-Learning-Assisted Global Water Vapor Stratification Model for GNSS Meteorology: Validations and Applications
一种新的深度学习辅助全球水汽分层模型用于GNSS气象学：验证与应用
引文格式 | Citation:
ZHANG W, GOU J, MÖLLER G, et al. A New Deep-Learning-Assisted Global Water Vapor Stratification Model for GNSS Meteorology: Validations and Applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-14. DOI:10.1109/TGRS.2024.3479778.
IEEE Transactions on Geoscience and Remote Sensing（中科院1区Top，IF：8.6）
深度学习 全球导航卫星系统 全球水汽分层模型 分层可降水量 无线电探空仪
Abstract | 摘要
Layer precipitable water (LPW), a water vapor product similar to precipitable water vapor (PWV), reports partial moisture content within a specified vertical range. Compared with PWV data, the latest LPW products can describe more refined distributions and variations in water vapor in the troposphere. Global Navigation Satellite Systems (GNSSs), as a powerful water vapor sensing tool, only provide the opportunity to retrieve all-weather PWV, not LPW products. To this end, we develop the first deep-learning-assisted, global water vapor stratification (GWVS) model to estimate the GNSS LPW within any given vertical range. The proposed model is trained and tested using the global radiosonde data, with the training and testing root mean square error (RMSE) of 0.94 and 1.10 mm for radiosonde LPW, indicating the excellent generalization of the GWVS model. Furthermore, the model is comprehensively validated using the data from the two regional GNSS networks and one global network. The RMSEs of the predicted GNSS LPW from the three GNSS networks compared with the co-located radiosonde LPW are 1.52, 1.80, and 1.54 mm, respectively. To study potential applications, we use the model-derived GNSS LPW products to calibrate Geostationary Operational Environmental Satellite-16 (GOES-16) LPW products and improve the GNSS water vapor tomography technique. Results show that the accuracy of three GOES-16 LPW products is improved by 31.3%, 23.3%, and 17.9%, respectively, and the RMSE of the tomography results is reduced from 2.28 to 1.67 g/m³ . Both validation and application results highlight that the GWVS model retrieves the required GNSS LPW products and provides additional value for water-vapor-related studies.
层析可降水量（LPW）是一种与整层大气可降水量（PWV）相似的水汽产品，用于报告特定垂直范围内的局部水汽含量。与PWV数据相比，最新的LPW产品能够更精细地描述对流层水汽的分布和变化。全球导航卫星系统（GNSS）作为一种强大的水汽探测工具，目前仅能提供全天候PWV反演数据，尚无法直接获取LPW产品。为此，我们首次开发了基于深度学习的全球水汽层析（GWVS）模型，用于估算任意给定垂直范围内的GNSS LPW。该模型基于全球无线电探空仪数据进行训练与测试，对探空仪LPW的训练集和测试集均方根误差（RMSE）分别为0.94毫米和1.10毫米，表明GWVS模型具有优异的泛化能力。此外，我们利用两个区域性GNSS网络和一个全球网络的观测数据对模型进行了全面验证。相较于共址无线电探空仪LPW数据，三个GNSS网络反演LPW的RMSE分别为1.52毫米、1.80毫米和1.54毫米。在应用研究方面，我们利用该模型生成的GNSS LPW产品对地球静止轨道环境业务卫星-16（GOES-16）的LPW产品进行校准，并改进了GNSS水汽层析技术。结果表明：三种GOES-16 LPW产品的精度分别提升了31.3%、23.3%和17.9%，层析结果的RMSE从2.28克/立方米降至1.67克/立方米。验证与应用结果共同表明，GWVS模型能够有效反演所需的GNSS LPW产品，并为水汽相关研究提供附加价值。