Performance of the line-by-line radiative transfer model (LBLRTM) for temperature and species retrievals: IASI case studies from JAIVEx

Abstract

Abstract. Presented here are comparisons between the Infrared Atmospheric Sounding instrument (IASI) and the "Line-By- Line Radiative Transfer Model" (LBLRTM). Spectral residuals from radiance closure studies during the IASI JAIVEx validation campaign provide insight into a number of spectroscopy issues relevant to remote sounding of temperature, water vapor and trace gases from IASI. In order to perform quality IASI trace gas retrievals the temperature and water vapor fields must be retrieved as accurately as possible. In general, the residuals in the CO2 ν2 region are of the order of the IASI instrument noise. However, outstanding issues in the CO2 spectral regions remain. There is a large residual ~−1.5 K in the 667 cm−1 Q-branch, and residuals in the CO2 ν2 and N2O/CO2 ν3 spectral regions that sample the troposphere are inconsistent, with the N2O/CO2 ν3 region being too negative (warmer) by ~0.6 K. Residuals on this lower wavenumber side of the CO2 ν3 band will be improved by line parameter updates, while future efforts to reduce the residuals reaching ~−0.5 K on the higher wavenumber side of the CO2 ν3 band will focus on addressing limitations in the modeling of the CO2 line shape (line coupling and duration of collision) effects. Brightness temperature residuals from the radiance closure studies in the ν2 water vapor band have standard deviations of ~0.2–0.3 K with some large peak residuals reaching ±0.5–1.0 K. These are larger than the instrument noise indicating that systematic errors still remain. New H2O line intensities and positions from Coudert have a significant impact on the retrieved water vapor, particularly in the upper troposphere where the water vapor retrievals are 10% drier when using line intensities from Coudert compared with HITRAN2004. In addition to O3, CH4, and CO, the high radiometric calibration of the IASI instrument combined with an accurate forward model allows for the detection of minor species with weak atmospheric signatures in the nadir radiances, such as HNO3 and OCS.

Emission Ratios for Ammonia and Formic Acid and Observations of Peroxy Acetyl Nitrate (PAN) and Ethylene in Biomass Burning Smoke as Seen by the Tropospheric Emission Spectrometer (TES)

“…TES retrievals of trace gas profiles are based on an optimal estimation approach (with a priori constraints) that minimizes the differences between the TES Level 1B spectra and a radiative transfer calculation that uses absorption coefficients calculated with the line-by-line radiative transfer model LBLRTM [20,[42][43][44][45]. Current Level 2 products from TES (V004) include retrieved profiles of CO, O 3 , H 2 O, HDO, and CH 4 .…”

Technical Note: An assessment of the accuracy of the RTTOV fast radiative transfer model using IASI data

“…In LBLRTM the CO 2 line shape includes the effects of line mixing for the P-R-branch and Q-branch in the υ2-region and υ3-region using first order line coupling parameters generated using the code by Niro et al, 2005. Since the model by Niro et al (2005) combines the effects of line mixing and duration of collision effects, the χ factor is set equal to 1. In addition, the CO 2 continuum is computed using the Niro et al (2005) coupling coefficients with a scaling factor of 0.75 applied to the ν3-region to agree with AIRS spectra in this spectral region (for details on the LBLRTM CO 2 continuum see Shepard et al, 2009). LBLRTM uses temperature dependent cross section data to model the absorption of heavy molecules.…”

IASI spectral radiance validation inter-comparisons: case study assessment from the JAIVEx field campaign

“…Simulated observations are based upon lineby-line (LBL)-based radiative transfer model (LBLRTM) (Clough et al, 2005;Shephard et al, 2009) calculations using the most recent code (i.e., Version 11.3) and best available estimate of surface and atmospheric state. Airborne FTS sensors, such as the NAST-I, serve as ideal validation sensors due to their higher spatial and spectral resolution (samescene) measurements which can then be degraded to best emulate that observed by the coincident satellite sensors.…”