Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels

Abstract

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is an ultra-spectral satellite sensor with 8461 spectral channels. IASI spectra contain high information content on atmospheric, cloud, and surface properties. The instrument presents a challenge for using thousands of spectral channels in a physical retrieval system or in a Numerical Weather Prediction (NWP) data assimilation system. In this paper we describe a method of simultaneously retrieving atmospheric temperature, moisture, and cloud properties using all available IASI channels without sacrificing computational speed. The essence of the method is to convert the IASI channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Studies show that about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in the super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements. While no large-scale validation has been performed on any retrieval methodology presented in this paper, comparisons of the retrieved atmospheric profiles, sea surface temperatures, and surface emissivities with co-located ground-and aircraft-based measurements over four days in Spring 2007 over the South-Central United States indicate excellent agreement.Correspondence to: X. Liu (xu.liu-1@nasa.gov)

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

“…Radiative transfer algorithms for infrared sounders that include scattering by clouds and aerosols are now available (see for example Matricardi, 2005;Liu et al, 2009;De Souza-Machado et al, 2010;Liuzzi et al, 2016). These RTAs use accurate scattering algorithms, but initializing the cloud representation for retrievals is difficult and has not been used operationally.…”

“…The AIRS radiances contain information about the thermodynamic state of the atmosphere (temperature, humidity), trace gases (such as ozone), and surface parameters (Aumann and Pagano, 2002;Strow et al, 2003), as well as ice and water clouds (Kahn et al, 2003(Kahn et al, , 2005Wu et al, 2009) and large aerosol particles (mineral dust and volcanic ash) (De Souza-Machado et al, 2010;Clarisse et al, 2010), though we do not consider aerosols in this paper. We introduce our cloud-representation and scattering approach below and test it statistically against an existing RTA, the principalcomponent-based radiative transfer model (PCRTM) (Liu et al, 2006(Liu et al, , 2009) that has been supplemented with a full accounting of the cloud subgrid variability (maximum random overlap, or MRO) (Chen et al, 2013).…”

“…The description of existing cloud-representation and scattering codes for nadir infrared sounders include those found in (Zhou et al, 2005), (Liu et al, 2009), (Chen et al, 2013), (Ou et al, 2013), (Vidot et al, 2015), (Liuzzi et al, 2016), and (Griessbach et al, 2013) for infrared limb sounders; separate examples can also be found for dust and volcanic ash aerosols.…”

“…We benchmarked the SARTA-TwoSlab model versus the radiance simulator based on the principal-component-based radiative transfer model (PCRTM version 2.1) (Liu et al, 2006(Liu et al, , 2009 with full accounting of the cloud subgrid variability (Chen et al, 2013). PCRTM is a fast model that computes atmospheric optical depths based on the line-by-line radiative transfer model (LBLRTM; Liu et al, 2006).…”

Satellite-Based Atmospheric Infrared Sounder Development and Applications

“…However, its characterization in current reanalysis data and climate models is insufficient (Allan et al 2003;Iacono et al 2003;Solomon et al 2010;MacKenzie et al 2012). More recently, hyperspectral observation data from AIRS, IASI, and CrIS are being used for analyzing UTWV (Susskind et al 2003;Liu et al 2009;Divakarla et al 2014). UTWV products from hyperspectral sounders have also been applied to the study of seasonal variance in monsoon regions (Gettelman et al 2004), model assessment (Takahashi et al 2016), and water vapor feedback mechanisms.…”