Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used.

水蒸气在地球的天气和气候过程以及能量传递中起着重要作用。已经开发了许多技术来监测可沉淀的水蒸气（PWV），但是联合使用不同的技术存在一些问题，包括系统偏差，不同的时空覆盖范围和不同数据集之间的分辨率。为了解决上述问题并提高数据利用率，我们建议使用广义回归神经网络（GRNN）融合来自全球导航卫星系统（GNSS），中分辨率成像光谱仪（MODIS）和欧洲中型研究中心的PWV范围天气预报重新分析5（ERA5）。该方法的核心思想是使用高质量的GNSS PWV通过构建的GRNN校准和优化相对较低质量的MODIS和ERA5 PWV。使用建议的方法，我们在2018年生成了400多个结合了GNSS，MODIS和ERA5 PWV的PWV图。结果显示，总体偏差，标准偏差（STD）和均方根（RMS）误差为0.0 mm，2.1改进的MODIS PWV为1.5mm和2.2mm，改进的ERA5 PWV为0.0mm，1.6mm和1.6mm。与原始的MODIS和ERA5 PWV相比，RMS的总改进为37.1％和15.8％。RMS改进主要来自MODIS PWV的偏置校准和ERA5 PWV的优化。它还表明原始的MODIS PWV倾向于大于GNSS PWV，而ERA5 PWV的偏差很小。经过校准和优化后，修改后的PWV与GNSS PWV之间的相关系数对于MODIS PWV为0.96，对于ERA5 PWV为0.98。所提出的方法还减小了准确性的时间和空间变化，从而生成均匀的PWV乘积。由于三个数据集之间的偏差已被很好地消除，并且数据准确性提高到了同一水平，因此它们很容易融合并共同使用。