Fusing Precipitable Water Vapor Data in CHINA at Different Timescales Using an Artificial Neural Network

Abstract

Global climate change has noticeable influences on the water vapor redistribution in China, which is embodied by the fact that both wetting and drying tendencies were observed across China. This poses the necessity to monitor and understand the water vapor evolution in China. However, observations of water vapor from different techniques are subjected to systematic biases, different spatiotemporal resolutions and coverages, and different accuracy, which would hamper their joint use, potentially leading to contradictory conclusions when using different techniques. Data fusion is a promising way to address this problem. Some scholars have proposed several methods to fuse multi-source PWV data in China region, such as the enhanced spatial and temporal adaptive reflectance fusion model, the hybrid PWV fusion model, and the linear calibration model. Although these models can produce PWV products with improved accuracy, they still have some shortcomings, such as no consideration for spatial or temporal variations in bias or inevitably impose some biases inaccurate information since assumptions made for interpolations are imperfect. In this study, we use the high-quality Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) to calibrate and optimize the Moderate-resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecasts ReAnalyses 5 (ERA5) PWV in 2018–2019 through a Generalized Regression Neural Network (GRNN) at annual, quarterly, and monthly timescales. Validation results demonstrate that modifying the MODIS and ERA5 PWV at the monthly timescale results in the best accuracy. In the monthly experiment, the average bias, standard deviation (STD), and root mean square (RMS) error of modified MODIS PWV are 0.0 mm, 2.6 mm, and 2.6 mm, respectively. The percentage improvement is as high as 50% in terms of RMS compared to the original MODIS PWV. It becomes 0.0 mm, 1.7 mm, and 1.7 mm for the modified ERA5 PWV and the percentage improvement is 40%. Since the biases among different products are well-calibrated and the accuracy of MODIS and ERA5 PWV is improved to the same level of GNSS PWV, we can fuse them by simply merging them. Finally, we generate a new product of PWV in China with a temporal resolution of 1 day, a spatial resolution better than 31 km, and an accuracy better than 2.7 mm, which will serve as a high-quality product for investigating the water vapor redistribution under a changing climate.

A Spatial PWV Retrieval Model Over Land for GCOM-W/AMSR2 Using Neural Network Method: A Case in the Western United States

“…Also, this research indicated that the land cover types in the spatial domain greatly influenced the retrieval model and analyzed the model performance on different land cover types. Xiong, et al [24] utilized a similar method to fuse GNSS, MODIS, and REA5 PWV data in CHINA at different timescales, and validation results demonstrated that modifying the MODIS and ERA5 PWV at the monthly timescale results had the best accuracy. Jiang, et al [25] applied the back propagation neural network (BPNN) method to combine AMSR2 and GNSS data to retrieve PWV on the global scale.…”

Fusion of CMONOC and ERA5 PWV Products Based on Backpropagation Neural Network

“…Popular data fusion methods use the advantages of the high precision and high temporal resolution of GNSS PWV to fuse or calibrate it with PWV products with high spatial resolution [20,21]. For example, Xiong et al and Zhang et al used a general regression neural network (GRNN) to fuse GNSS PWV and ERA5 PWV from mainland China and North America; the deviation between them was well calibrated, and high-precision PWV products were obtained [22,23]. Liu et al and Bai et al used a linear fitting method to correct MODIS PWV and obtained higher-accuracy PWV products on the basis of ensuring high resolution [24,25].…”