Case-study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data

Abstract

ABSTRACT:The objective of the paper is to apply a novel radiative transfer model and a physical retrieval algorithm to hyperspectral data taken during the European Aqua Atmospheric Thermodynamics Experiment (EAQUATE) campaign. A principal-component-based radiative transfer model (PCRTM) is used to calculate projection coefficients of the radiance spectrum onto a set of predefined empirical orthogonal functions (EOFs) and associated derivatives with respect to the state vector. Instead of fitting channel radiances, the physical retrieval algorithm iteratively fits the principal component (PC) scores or the EOF projection coefficients of the observed radiance spectrum using the PCRTM as its forward model. Since the EOFs are orthonormal to each other, only a few PC scores are needed to capture the information content of the radiance spectrum, therefore reducing the computational time needed for running both the forward model and the inversion. This paper demonstrates the application of such a physical algorithm for retrieving atmospheric temperature, moisture and ozone profiles, and surface properties such as surface skin temperature and surface emissivity. The results have been compared with those obtained with a NAST-I channel-based physical retrieval algorithm and with those obtained from collocated radiosonde and LIDAR measurements.

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

“…Aires et al (2002) found that for the IASI instrument, an EOF number of 30 for each of the three bands (or 90 total) will give the best compression/de-noising statistics. We have reached a similar conclusion in our studies here and in Liu et al (2007). These super channels essentially contain all the information content of 8461 IASI channels, while having 84 times less data volume.…”

“…The CO profile EOFs are generated from the NCAR Mozart model (Kinnison et al, 2007). For the surface emissivity retrieval, we compress the surface emissivity into PC scores as well Liu et al, 2007). Since the spectral features of the surface emissivity are broad, there is no need to retrieve them at each channel frequency.…”

Inversion for atmospheric thermodynamical parameters of IASI data in the principal components space

“…the principal components (PC) transform (e.g. Jolliffe, 2002), the use of which to reduce the dimensionality of the radiative transfer equation has been exemplified by Masiello and Serio (2004) and Liu et al (2007Liu et al ( , 2009). Today, forward models for IASI capable of computing PC scores directly are available (see, for example, Masiello and Serio, 2004;Liu et al, 2006;Matricardi, 2010), which could speed up the process of adapting the assimilation process for IASI PC scores.…”

Retrieving Atmospheric Profiles Data in the Presence of Clouds from Hyperspectral Remote Sensing Data

“…The CC is used by the AIRS level 2 data processing algorithm and by the Joint Polar Satellite System (JPSS) Cross-track Infrared and Microwave Sounder Suite (CrIMSS) algorithm. At NASA Langley Research Center (LaRC) [5,6,7,8,9], we have developed a retrieval algorithm, which explicitly retrieves cloud properties together with other parameters such as atmospheric temperature, moisture, and trace gases profiles, surface skin temperature and emissivity. We will present results of testing the CrIMSS Environmental Data Record (EDR) operational algorithm and the LaRC CR method using IASI satellite data.…”