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Total Column Water Vapor From INSAT-3D: Assessments With Ground-Based GNSS Receivers and GMI Datasets at Different Temporal Scales

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The quality of the total column water vapor (TCWV) data retrieved from the Indian national satellite (INSAT) system series (INSAT-3D) and its variation at different temporal scales have been evaluated.… Click to show full abstract

The quality of the total column water vapor (TCWV) data retrieved from the Indian national satellite (INSAT) system series (INSAT-3D) and its variation at different temporal scales have been evaluated. The reference TCWV dataset is obtained from four ground-based global navigation satellite system (GNSS) receivers and over the entire Indian subcontinent from a global precipitation measurement (GPM) microwave imager (GMI) that uses global analysis (GANAL) model data. TCWV comparison of INSAT-3D, GPM-GMI with GNSS shows higher correlations for GPM-GMI than INSAT-3D at all temporal scales. Though GMI can reproduce observed TCWV variations better than the INSAT-3D, the large biases of both the datasets indicate errors in the magnitude. Seasonal and monthly comparisons at two locations in the southeast peninsular India region show large correlations and small bias in the northeast and small correlations and large biases in southwest monsoon months. Southeast peninsular India receives a significant amount of rainfall in the northeast monsoon, and large TCWV correlations indicate both the algorithms removing cloud pixels with high accuracy. Therefore, the errors in the TCWV are attributed to the estimated radiances at different spectral channels. The spatiotemporal variations of TCWV correlations and bias of INSAT-3D indicate the need for improving the data quality by validating the estimated radiances at different spectral channels and, in turn, the retrieved TCWV over various regions at different temporal scales.

Keywords: column water; gnss; total column; temporal scales; different temporal; water vapor

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2022

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