Validation of MODIS Terra, AIRS, NCEP/DOE AMIP-II Reanalysis-2, and AERONET Sun photometer derived integrated precipitable water vapor using ground-based GPS receivers over India

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Abstract

[1] Water vapor is an important and highly variable constituent in time and space; the knowledge of its variability is important in climate studies. In India, the ground observations of water vapor using conventional methods such as radiosonde are limited. In this paper, a comparison of hourly estimates of total column water vapor from Global Positioning System (GPS) with multisensor satellite is presented over three stations. We show quantitatively seasonal and monthly dependency of bias, standard deviation, root mean square error (RMSE), and the correlation coefficient between the water vapor data sets. The GPS and Aerosol Robotic Network (AERONET) water vapor show good agreement (R2 = 95%, RMSE 3.87 mm, GPS-AERONET bias = −2.63 mm). On the basis of multiple-year data, Moderate Resolution Imaging Spectroradiometer near-infrared (MODIS NIR) clear column product shows higher correlation (R2 = 89–93%) with GPS compared to infrared (IR) products (R2 = 82–84%). MODIS is found to be overestimating in NIR clear and IR products in all seasons over India where the magnitude of bias and RMSE show systematic changes from month to month. MODIS is significantly underestimating in NIR cloudy column products during summer and monsoon seasons. MODIS NIR clear column (R2 = 97%, RMSE 5.44 mm) and IR (R2 = 81%, RMSE 7.17 mm) water vapor show similar performance on comparison with AERONET data. The MODIS NIR cloudy column product shows no correlation with GPS. The GPS National Centers for Environmental Prediction/Department of Energy Atmospheric Model Intercomparison Project II (GPS-NCEP/DOE AMIP-II) Reanalysis-2 water vapor show R2 = 87%, 77%, and 60% (and RMSE of 8.39 mm, 6.97 mm, and 9.30 mm) over Kanpur, Hyderabad, and Bangalore, respectively. All the satellite water vapor shows systematic bias with month and season that is found to be sensitive to the sky conditions. The magnitude of bias is invariably larger during monsoon season with relatively more cloudy days and moist atmosphere. The errors in satellite estimation are found to be invariably more during wet compared to dry months. Statistical analysis shows that MODIS NIR clear column and Atmospheric Infrared Sounder (AIRS) daytime water vapor are more reliable compared to other satellite estimates (MODIS IR and AIRS nighttime) except during cloudy days.

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