MSU Recalibrations Using Simultaneous Nadir Overpasses For Climate Trend Studies

The MSU channel 2 on board nine different NOAA polar-orbiting satellites has been measuring the mid-tropospheric temperature for more than 25 years. It offers a unique opportunity for providing possible answers to the question of whether the troposphere is warming or cooling over the last several decades. However, the temperature trends derived from these measurements are under significant debate. In particular, different calibration algorithms, different merging methods for the multi-satellite time series, different treatments of the satellite orbital drift, and different modeling efforts for the diurnal cycle in the time series may all affect the MSU trend analyses. This study recalibrates the MSU channel 2 observations at level 0 using the post-launch simultaneous nadir overpass (SNO) dataset to construct a well-merged dataset for climate studies. We use the nonlinear calibration algorithm suggested by Mo to convert the MSU raw counts to the Earth-view radiance. The algorithm consists of the dominant linear responses of the MSU radiometer raw counts to the Earth-view radiance plus a weak quadratic term caused by an ‘imperfect' square-law detector. The cold space view and an on board warm target view are used as two reference calibration points. Uncertainties in the calibration algorithm are represented by a constant offset and errors in the coefficient for the nonlinear quadratic term. A SNO dataset that contains simultaneous nadir observations less than 2 minutes apart over the polar region for the nadir pixels is generated for all overlaps of nine NOAA satellites based on the method of Cao et al. The offset values and nonlinear coefficients are then determined by the regression of the SNO datasets. It is found that SNO provides a strong constraint for the calibration coefficients of different satellites--once the calibration coefficients for one satellite are determined from pre-launch information, all calibration coefficients for other satellites can be optimally determined from the SNO datasets. The nonlinear coefficients obtained from the SNO regressions double the values estimated from pre-launch calibration for some satellites, suggesting pre-launch calibration underestimates the nonlinear effect in the calibration algorithm. Applying these offset and nonlinear coefficients to the entire MSU observations at level 0, we obtain a well-merged MSU 1B dataset. A 5-day averaged (pentad) dataset is derived from the 1B data. The global ocean-averaged biases between satellite pairs for the pentad dataset are between 0.05 to 0.1K, an order of magnitude smaller than the biases with the NESDIS operational algorithm. The pentad data is then used to investigate the trend of the MSU channel 2 observations. It is found that the global ocean-averaged trend of the MSU channel 2 brightness temperature obtained from this well-merged pentad time series is 0.17 K decade-1 during 1987-2003.

Acknowledgments. The views, opinions, and findings contained in this report are those of the author(s) and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision.