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A robust framework for estimating precipitable water vapor based on near-infrared remote sensing data and reanalysis data fusion (NRF) method
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DOI：10.1016/j.jag.2026.105305.png)
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Abstract
En 中文
High-resolution and spatially continuous precipitable water vapor (PWV) observations are essential for accurately characterizing weather and climate processes, especially in regions with strong horizontal water vapor gradients. While existing near-infrared (NIR) remote sensing PWV products offer fine spatial resolution, they suffer from limited accuracy and substantial data gaps in cloud-contaminated areas. Reanalysis data provide high accuracy and spatial continuity, but their relatively coarse resolution inevitably restricts their ability to capture sub-grid scale PWV variability. Therefore, this study proposed a NIR remote sensing and reanalysis data fusion (NRF) framework for large-scale PWV estimation, aiming to generate seamless 1 km resolution PWV with reanalysis-level accuracy. The NRF framework consists of three stages. First, a nonlinear bias calibration model (NBCM) based on machine learning was developed to effectively eliminate biases between clear-sky moderateresolution Imaging Spectroradiometer (MODIS) and ECMWF Reanalysis 5th Generation (ERA5) PWV. Second, an annual water vapor cycle multi-scale data fusion model (AVCM) was constructed to fill MODIS cloudcontaminated gaps by integrating the bias-corrected MODIS and ERA5 PWV. Third, a guided filtering enhancement model (GFEM) was developed to spatially enhance the fused result using spatially adjacent clearsky MODIS observations, improving spatial continuity and preserving fine-scale heterogeneity. Validation against ground-based global navigation satellite system (GNSS) PWV shows strong agreement, with a Pearson correlation coefficient of 0.98, bias of 0.13 mm, root mean square error of 1.78 mm, and Kling-Gupta Efficiency of 0.93. The NRF framework preserves the native 1 km resolution of MODIS while achieving full spatial coverage under all-weather conditions. By employing pixel-level modeling, it effectively eliminates the reliance on the spatial completeness of input images, thereby offering greater adaptability and flexibility for large-scale data fusion applications. The resulting high-quality PWV provides a robust foundation for advancing the understanding of climate processes and enables post-processing applications such as atmospheric delay correction in satellite-based geodetic and remote sensing systems.
Keywords:
Precipitable water vapor
Reanalysis data
Near-infrared remote sensing
Annual watervaporcycle
Fusion
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