Evaluation of Satellite and Reanalysis Precipitable Water Vapor Data Sets Against Radiosonde Observations in Central Asia

摘要

Central Asia is facing severe water shortages and conflicts. The spatiotemporal variations of precipitable water vapor (PWV) are important aspects in understanding the water cycle and water resources. However, station observations in central Asia are limited and the performance of satellite and reanalysis products of PWV in central Asia has not been evaluated. Based on radiosonde observations, we show evidence that the two satellite products, namely, Atmospheric Infrared Sounder‐only and Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit, are applicable to investigate the spatiotemporal characteristics of PWV in central Asia. The two satellite products can capture the main climatological features, annual cycle, and monthly variations of PWV in central Asia, with high correlations with radiosonde observations, although slightly underestimate PWV values by ?15% to 0%. All the eight current state‐of‐the‐art reanalysis data sets, including European Centre for Medium‐Range Weather Forecasts (ECMWF) interim reanalysis, the fifth generation ECMWF atmospheric reanalysis (ERA5), National Centers for Environmental Prediction (NCEP)1, NCEP2, Climate Forecast System Reanalysis, 55‐year modern Japanese Reanalysis Project, Modern Era Retrospective‐Analysis for Research and Applications (MERRA), and MERRA version 2 (MERRA2), can reasonably reproduce the spatiotemporal variations of PWV, although with an overestimation in spring, autumn, and winter and an underestimation in summer. ERA5 and MERRA2 (NCEP1 and NCEP2) perform better (poorer) compared with other reanalysis data sets. A skill‐weighted ensemble mean of reanalysis data sets is constructed based on the different performance of individual data sets. It is better for understanding the climatological spatial pattern than the equally weighted ensemble mean and individual reanalysis data sets, while ERA5 is suggested to be used for revealing the interannual variations of PWV in central Asia.