Cloud flagging and clearing using high-resolution infrared and microwave sounding data

The temperature profile retrieval performance of airborne and spaceborne atmospheric infrared sounders is significantly degraded by clouds. The impact due to two-level clouds is quantitatively assessed in two ways using simulated radiances from the Atmospheric InfraRed Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Microwave Humidity Sounder (MHS). First, cloud characterization (cloud flagging) techniques were developed to identify cloud-contaminated pixels. Second, a neural network was trained to estimate clear-column infrared radiances from cloud-contaminated 3×3 pixel clusters of infrared and microwave radiances (cloud clearing). This algorithm reduced the a priori radiance error due to clouds by at least a factor of three for those 4- and 15-micron sounding channels with weighting function peaks located from 0-10 km. Radiance cloud-clearing errors (RMS) for infrared channels with weighting functions peaking from 10 to 0 km ranged from 0.1 to 0.7 K for the 4-micron channels and from 0.1 to 1.2 K for the 15-micron channels. Combining microwave and infrared data at the pixel level always yielded results superior to infrared alone. It was found that microwave surface emissivity uncertainties are a key contributor to residual errors.