Advances in simultaneous atmospheric profile and cloud parameter regression based retrieval from high‐spectral resolution radiance measurements

Abstract

[1] The dual-regression (DR) method retrieves information about the Earth surface and vertical atmospheric conditions from measurements made by any high-spectral resolution infrared sounder in space. The retrieved information includes temperature and atmospheric gases (such as water vapor, ozone, and carbon species) as well as surface and cloud top parameters. The algorithm was designed to produce a high-quality product with low latency and has been demonstrated to yield accurate results in real-time environments. The speed of the retrieval is achieved through linear regression, while accuracy is achieved through a series of classification schemes and decision-making steps. These steps are necessary to account for the nonlinearity of hyperspectral retrievals. In this work, we detail the key steps that have been developed in the DR method to advance accuracy in the retrieval of nonlinear parameters, specifically cloud top pressure. The steps and their impact on retrieval results are discussed in-depth and illustrated through relevant case studies. In addition to discussing and demonstrating advances made in addressing nonlinearity in a linear geophysical retrieval method, advances toward multi-instrument geophysical analysis by applying the DR to three different operational sounders in polar orbit are also noted. For any area on the globe, the DR method achieves consistent accuracy and precision, making it potentially very valuable to both the meteorological and environmental user communities.Citation: Weisz, E., W. L. Smith Sr., and N. Smith (2013), Advances in simultaneous atmospheric profile and cloud parameter regression based retrieval from high-spectral resolution radiance measurements,

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

“…This limits geographic sampling in a complicated way that could make these products problematic for climate statistics. We note here the regression-based single-footprint retrievals provide cloud top information (Weisz et al, 2013); similarly, cloud phase and cirrus effective diameter and optical thickness retrievals are generated at AIRS single-footprint resolution (Kahn et al, 2014) after the L2 thermodynamic retrievals are done -in a separate step that keeps all other retrieval variables constant.…”

“…Earlier single-footprint retrievals using eigenvalue regression methods have been used with these all-sky (cloud and clear) radiances (see for example Weisz et al, 2013) but these methods have no reliable error estimates for individual scenes. Existing NASA and NOAA operational retrieval systems for IR sounders use cloud clearing (Susskind et al, 1998, Susskind et al, 2003, and Gambacorta, 2013.…”

AIRS, IASI, and CrIS Retrieval Records at Climate Scales: An Investigation into the Propagation of Systematic Uncertainty

“…The UW dual-regression (DR) retrieval method (Smith et al 2012;Weisz et al 2013b) was originally developed for operational use in real-time environments, but it proves relevant here. The UW dual-regression (DR) retrieval method (Smith et al 2012;Weisz et al 2013b) was originally developed for operational use in real-time environments, but it proves relevant here.…”

“…The UW dual-regression (DR) retrieval method (Smith et al 2012;Weisz et al 2013b) was originally developed for operational use in real-time environments, but it proves relevant here. Far from being a simple linear regression (e.g., Smith et al 1970;Weisz et al 2007), the DR method employs a geophysically based cloud-top-pressure stratification approach to account for the nonlinear dependence of measured radiance on cloud altitude and atmospheric moisture. It can ingest and process radiance measurements from any of the operational hyperspectral sounders: AIRS on Aqua, CrIS on SNPP, and IASI on MetOp-A and MetOp-B (section 2).…”

“…It is a fast physical-statistical method for inverting hyperspectral radiance measurements into atmospheric profile, surface, and cloud parameters. This is described in more detail elsewhere (Weisz et al 2011Smith et al 2012). The DR algorithm is a public domain software tool released under the UW Community Satellite Processing Package (CSPP; N. Smith et al 2014).…”