Abstract

The reanalysis data produced by numerical weather prediction (NWP) models and data assimilation have been widely used for radiometer calibration. They provide atmospheric profiles that are necessary for radiative transfer simulation against observation. However, there are biases and uncertainties in the reanalysis due to NWP model mechanism, parameterization, boundary conditions, and assimilation skills. As spaceborne radiometer data have been used in deriving reanalyses, reanalyses are not independent of these radiometers and should be used with caution when used as reference for radiometer calibration. In addition, these data often have coarse spatial (~100 km horizontally) and temporal resolution (~6 h). An independent data set with high resolution can be very useful to diagnose reanalyses and might improve calibration. The Global Precipitation Measurement (GPM) core observatory measures atmospheric water signatures with an onboard radar and radiometer. A GPM data set including atmospheric water vapor, cloud liquid water, and precipitation has been produced based on observational retrieval with high spatiotemporal resolution (~5 km horizontally and 250 m vertically). We have developed a scheme to ingest the high-resolution GPM profiles and perform rigorous simulation and calibration taking into account the radiometer spectral response function, footprint size variation, and antenna pattern. GPM data exhibit different water vapor profiles and weighting functions from reanalyses. It produces overall consistent results of calibration as reanalyses and outperforms them in some aspects. The GPM profiles and our scheme are very useful and will be routinely applied to monitor Advanced Technology Microwave Sounder inflight status.

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