An Enhanced Algorithm Including First Guess for Deriving Precipitable Water Vapor From MODIS NIR Observations in High-Latitude Regions

High-latitude regions are frequently challenging for observing precipitable water vapor (PWV) from satellite near-infrared (NIR) channels because of the high surface reflectance and large solar zenith angle. Satellite-observed NIR PWV retrievals also present much higher uncertainties under cloudy sky conditions than under clear sky conditions. In this work, we propose an improved neural network-based algorithm for the first time to retrieve PWV from Moderate Resolution Imaging Spectroradiometer (MODIS) NIR all-weather observations in high-latitude areas. The retrieval algorithm is developed based on ground-based Global Navigation Satellite System (GNSS)-sensed PWV estimates, together with ERA5-based first-guess PWV as well as several dependence factors associated with NIR PWV retrievals. The results show that the newly retrieved PWV estimates remarkably outperform operational MODIS-derived NIR PWV products, exhibiting an all-weather reduction in root-mean-square error (RMSE) of 78.32% from 7.15 to 1.55 mm compared with GNSS-observed reference PWV and 81.38% from 7.52 to 1.40 mm compared with radiosonde-observed reference PWV. The observational accuracy of all-weather PWV retrievals is also comparable to that of PWV retrievals under clear sky conditions, denoting the capability and effectiveness of the retrieval method. The retrieval approach presents much larger RMSE reductions compared to previous algorithms that do not use first-guess water vapor, implying that the addition of first-guess PWV contributes to generating improved PWV estimates from satellite-sensed NIR measurements. While the retrieval method is developed for deriving MODIS NIR water vapor in high-latitude regions, it also has significant potential to be applicable to other satellite sensors as well as other worldwide regions.