Simulation of atmospheric profile retrieval sensitivity with cloud from hyperspectral infrared data

Abstract

In this paper the simulated space-based high spectral resolution atmospheric infrared sounder (AIRS) infrared radiances with different cloud top heights and effective cloud fractions are used to demonstrate the measurement sensitivity and atmospheric profile retrieval performance. The simulated cloudy retrieval of atmospheric temperature and moisture derived from the statistical eigenvector regression algorithm are analyzed with different effective cloud fractions and different cloud height. The temperature and humidity root-mean-square error with cloud fraction ranging from 0.1 to 1.0 (with interval of 0.1) for cloud height (200, 300, 500, 700 and 850 hPa) known perfectly and cloud height error of 50 hPa are computed. Results show that the root-mean-square error of retrieved temperature and the mixed ratio of water vapor below the cloud top increase with effective cloud fraction. The retrieval accuracy of the cloud height error of 50 hPa decrease comparing with the cloud height known perfectly, while the temperature retrieval is more sensitive to cloud height error than humidity retrieval.