An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach

Abstract

Precipitable water vapor (PWV) is among the key variables in the water and energy cycles, whereas current PWV products are limited by spatiotemporal discontinuity, low accuracy, and/or coarse spatial resolution. Based on two widely used global PWV products, i.e., satellite-based MODIS and reanalysis-based ERA5 products, here we propose a data fusion approach to generate PWV maps of spatiotemporal continuity and high resolution (0.01°, daily) for the Upper Brahmaputra River (UBR, referred to as the Yarlung Zangbo River in China) basin in the Tibetan Plateau (TP) during the monsoon period (May – September) from 2007 to 2013. Results show that the fused PWV estimates have good agreement with PWV estimates from nine GPS stations (i.e., correlation coefficient: 0.87–0.97, overall bias: −0.4–1.8 mm, and root-mean-square error: 1.1–2.0 mm), greatly improving the accuracy of the MODIS PWV product. The fused PWV maps of high spatial resolution provide detailed and reasonable spatial variations which are generally consistent with those from the MODIS estimates under confident clear conditions and ERA5. Mean monthly PWV estimates across the UBR basin vary from ~6 to ~12 mm during the study period, and for each month high PWV values are found along the UBR valley and at the basin outlet. The developed data fusion approach maximizes the potential of satellite and reanalysis-based PWV products for retrieving PWV and has the potential to be applied to other high-mountain regions. The generated PWV estimates for the UBR basin are valuable in understanding the water and energy cycles and in retrieving atmospheric and surface variables for the southern TP including the Himalaya.