Cloud-State-Dependent Sampling in AIRS Observations Based on CloudSat Cloud Classification

Abstract

The precision, accuracy, and potential sampling biases of temperature T and water vapor q vertical profiles obtained by satellite infrared sounding instruments are highly cloud-state dependent and poorly quantified. The authors describe progress toward a comprehensive T and q climatology derived from the Atmospheric Infrared Sounder (AIRS) suite that is a function of cloud state based on collocated CloudSat observations. The AIRS sampling rates, biases, and center root-mean-square differences (CRMSD) are determined through comparisons of pixel-scale collocated ECMWF model analysis data. The results show that AIRS provides a realistic representation of most meteorological regimes in most geographical regions, including those dominated by high thin cirrus and shallow boundary layer clouds. The mean AIRS observational biases relative to the ECMWF analysis between the surface and 200 hPa are within 61 K in T and from 21 to 10.5 g kg 21 in q. Biases because of cloud-state-dependent sampling dominate the total biases in the AIRS data and are largest in the presence of deep convective (DC) and nimbostratus (Ns) clouds. Systematic cold and dry biases are found throughout the free troposphere for DC and Ns. Somewhat larger biases are found over land and in the midlatitudes than over the oceans and in the tropics, respectively. Tropical and oceanic regions generally have a smaller CRMSD than the midlatitudes and over land, suggesting agreement of T and q variability between AIRS and ECMWF in these regions. The magnitude of CRMSD is also strongly dependent on cloud type.

The Observed State of the Water Cycle in the Early Twenty-First Century

“…However, the AIRS sampling biases are largest in regions of deep convection and baroclinic activity. The global implications of these cloud-induced biases are discussed by Tian et al (2012Tian et al ( , 2013, Hearty et al (2014), and Yue et al (2013). AMSR-E water vapor sampling biases are small except under heavily precipitating conditions representing 2%-5% of all scenes.…”

An Assessment of SAPHIR Calibration Using Quality Tropical Soundings

“…Observations in the infrared (IR), in the 6.3-mm band [e.g., by the Atmospheric Infrared Sounder (AIRS), the High-Resolution Infrared Radiation Sounder (HIRS), the first-and second-generation imagers of Meteosat, and the Cross-Track Infrared Sounder (CrIS)], are highly attenuated by clouds. Consequently, climatologies of atmospheric relative humidity (RH) derived from IR observations are potentially subject to a clear-sky bias (Lanzante and Gahrs 2000;Fetzer et al 2006;John et al 2011;Yue et al 2013). Some analyses yield to include low cloud scenes for the free-tropospheric humidity estimation; these clouds having a negligible impact on the 6.3-mm radiances, which helps to increase the sampling (Brogniez et al 2006).…”

A multisensor, blended, layered water vapor product for weather analysis and forecasting

“…The normal for this prototype case is a monthly climatology of AIRS V5 Level 3 retrievals from [2003][2004][2005][2006][2007][2008]. Because the AIRS climatology is affected by biases in sampling due to avoidance of clouds (Yue et al 2013) and the MIRS retrievals are possible across a wider range of cloud opacity, the normal for this prototype case is a monthly climatology of AIRS V5 Level 3 retrievals from [2003][2004][2005][2006][2007][2008]. An LPW anomaly product must be further evaluated to reduce differences between the weather and climatology water vapor fields.…”