Intercalibration of GOES-11 and GOES-12 Water Vapor Channels with MetOp IASI Hyperspectral Measurements.

The calibrated radiances from geostationary water vapor channels play an important role for weather forecasting, data assimilation, and climate studies. Therefore, better understanding the data quality for radiance measurements and independently assessing their onboard calibrations become increasingly more important. In this study, the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral measurements on the polar-orbiting Meteorological Operation-A (MetOp-A) satellite are used to assess the calibration accuracy of water vapor channels on the Geostationary Operational Environmental Satellite-11 (GOES-11) and GOES-12 imagers with one year of data. The near-simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES imager spectral response functions (SRFs) to compare with GOES imager observations. Assuming that IASI is well calibrated and can be used as an on-orbit radiometric reference standard, then the GOES imager water vapor channels have an overall relative calibration bias to IASI of better than 0.3 K (with a standard deviation of ∼0.2 K) at the brightness temperature (BT) range of 240–260 K, which meets the design specification (1.0-K calibration accuracy for infrared channels). This study further demonstrates the technique of using hyperspectral radiance measurements in a polar-orbiting satellite to accurately assess broadband radiometer calibration of the GOES imager, which also provides an effective way for monitoring sensor performance over time. In addition, the potential of using the intercalibration results to integrate and merge data from different observing systems involving both IASI and different GOES imagers to create consistent, seamless global products is explored. The method presented here can potentially be applied to other instruments on both polar-orbiting and geostationary satellites for generating long-term time series. [ABSTRACT FROM AUTHOR]