174 Retrieval of atmospheric and cloud property anomalies and their trend from temporally and spatially averaged infrared spectra observed from space

Monday, 7 July 2014
Seiji Kato, NASA Langley Research Center, Hampton, VA; and F. G. Rose, X. Liu, B. A. Wielicki, and M. G. Mlynczak

A surface, atmospheric and cloud (fraction, height, optical thickness, and particle size) property anomaly retrieval from highly averaged longwave spectral radiances is simulated using 28 years of reanalysis. Instantaneous nadir-view spectral radiances observed from an instrument on a 90° inclination polar orbit are computed. Spectral radiance changes caused by surface, atmospheric, and cloud property perturbations are also computed and used for the retrieval. This study's two objectives are: 1) to investigate whether or not separating clear-sky from cloudy-sky reduces the retrieval error and 2) to estimate the error in a trend of retrieved properties. This simulation differs from earlier studies in that annual 10° zonal cloud and atmospheric property anomalies defined as the deviation from 28-year climatological means are retrieved instead of the difference of these properties from two time periods. The root-mean-square (RMS) difference of temperature and humidity anomalies retrieved from all-sky radiance anomalies is similar to the RMS difference derived from clear-sky radiance anomalies computed by removing clouds. This indicates that the cloud property anomaly retrieval error does not affect the retrieved temperature and humidity anomalies. When retrieval errors are nearly random, the error in the trend of retrieved properties is small. Approximately 30% of 10° zones meet conditions that the true temperature and water vapor amount trends are within a 95% confidence interval of retrieved trends, and that the standard deviation of retrieved anomalies (σret) are within 20% of the standard deviation of true anomalies (σn). If σret/σn -1 is within ±0.2, 91% of the true trends fall within the 95% confidence interval of the corresponding retrieved trend. The simulation suggests that highly averaged longwave spectral radiances can be used to derive the trend of cloud and atmospheric properties.
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