Using Dual-Regression to Produce 16-Day Average AIRS Soundings

Temperature and humidity profiles are needed to estimate surface radiation budget. The Clouds and the Earth’s Radiant Energy System (CERES) team uses temperature and humidity profiles from a reanalysis product produced by NASA’s Global Modeling and Assimilation Office for surface irradiance computations. Biases and drifts in temperature and humidity profiles in the reanalysis product result in biases and drifts in surface irradiances computed with them. One approach to correct biases and drifts in temperature and humidity profile is to use satellite observations, similar to assimilating instantaneous spectral radiances to correct modeled temperature and humidity profiles. In this work, we use mean spectral radiances to test the possibility of understanding biases in reanalysis mean temperature and humidity profiles. Specifically, we use 16-day mean Atmospheric Infrared Sounder (AIRS) radiances and only use clear-sky spectral radiances with a viewing zenith angle nadir to near-nadir. We use the dual-regression method (Smith et al. 2012) to test whether temperature and humidity profiles retrieved from the 16-day mean spectral radiances agree with the average of temperature and humidity profiles derived from instantaneous spectral radiances. When daytime and nighttime spectral radiances are averaged separately and daytime and nighttime retrievals are performed separately, temperature and humidity profiles derived from the mean spectral radiances agree well with mean temperature and humidity profiles derived from instantaneous spectral radiances. The agreement improves when clouds are further screened to compute 16-day mean clear-sky spectral radiances.

Reference

Smith, W. L., Sr., E. Weisz, S. Kireev, D. K. Zhou, Z. Li, and E. E. Borbas, 2012: Dual-regression retrieval algorithm for real-time processing of satellite ultraspectral radiances. J. Appl. Meteor. Climatol., 51, 1455–1476, https://doi.org/10.1175/JAMC-D-11-0173.1.