Identifying Cloud-Uncontaminated AIRS Spectra from Cloudy FOV Based on Cloud-Top Pressure and Weighting Functions

Abstract

An effort is made to increase the number of Advanced Infrared Sounder (AIRS) cloud-uncontaminated infrared data for regional mesoscale data assimilation and short-term quantitative precipitation forecast (QPF) applications. The cloud-top pressure from Moderate Resolution Imaging Spectroradiometer (MODIS) is utilized in combination with weighting functions (WFs) to develop a channel-based cloudy-data-removal algorithm. This algorithm identifies ?clear channels? for which the brightness temperature (BT) values are not cloud contaminated. A channel-dependent cutoff pressure (COP) level is first determined based on the structure of the WF of each channel. It is usually below the maximum WF level. If the cloud top (as identified by a MODIS cloud mask) is above (below) the COP level of a channel, this channel is then deemed cloudy (clear) and removed (retained). Using this algorithm, a sizable increase of cloud-uncontaminated AIRS data can be obtained. There are more usable domain points for those channels with higher COP levels. A case study is conducted. It is shown that instead of having less than 20% AIRS clear-sky observations, the algorithm finds 80% (58%) of the AIRS pixels on which there are channels whose COP levels are at or above 300 hPa (500 hPa) and the BT data in these channels at these pixels are cloud uncontaminated. Such a significant increase of the usable AIRS cloud-uncontaminated data points is especially useful for regional mesoscale data assimilation and short-term QPF applications.