Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies

Abstract

Modern data assimilation algorithms depend on accurate infrared spectroscopy in order to make use of the information related to temperature, water vapor (H₂O), and other trace gases provided by satellite observations. Reducing the uncertainties in our knowledge of spectroscopic line parameters and continuum absorption is thus important to improve the application of satellite data to weather forecasting. Here we present the results of a rigorous validation of spectroscopic updates to an advanced radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM), against a global dataset of 120 near-nadir, over-ocean, nighttime spectra from the Infrared Atmospheric Sounding Interferometer (IASI). We compare calculations from the latest version of LBLRTM (v12.1) to those from a previous version (v9.4+) to determine the impact of spectroscopic updates to the model on spectral residuals as well as retrieved temperature and H₂O profiles. We show that the spectroscopy in the CO₂ ν₂ and ν₃ bands is significantly improved in LBLRTM v12.1 relative to v9.4+, and that these spectroscopic updates lead to mean changes of ~0.5 K in the retrieved vertical temperature profiles between the surface and 10 hPa, with the sign of the change and the variability among cases depending on altitude. We also find that temperature retrievals using each of these two CO₂ bands are remarkably consistent in LBLRTM v12.1, potentially allowing these bands to be used to retrieve atmospheric temperature simultaneously. The updated H₂O spectroscopy in LBLRTM v12.1 substantially improves the a posteriori residuals in the P-branch of the H₂O ν₂ band, while the improvements in the R-branch are more modest. The H₂O amounts retrieved with LBLRTM v12.1 are on average 14% lower between 100 and 200 hPa, 42% higher near 562 hPa, and 31% higher near the surface compared to the amounts retrieved with v9.4+ due to a combination of the different retrieved temperature profiles and the updated H₂O spectroscopy. We also find that the use of a fixed ratio of HDO to H₂O in LBLRTM may be responsible for a significant fraction of the remaining bias in the P-branch relative to the R-branch of the H₂O ν₂ band. There were no changes to O₃ spectroscopy between the two model versions, and so both versions give positive a posteriori residuals of ~ 0.3 K in the R-branch of the O₃ ν₃ band. While the updates to the H₂O self-continuum employed by LBLRTM v12.1 have clearly improved the match with observations near the CO₂ ν₃ band head, we find that these updates have significantly degraded the match with observations in the fundamental band of CO. Finally, significant systematic a posteriori residuals remain in the ν₄ band of CH₄, but the magnitude of the positive bias in the retrieved mixing ratios is reduced in...

Cross-track Infrared Sounder (CrIS) satellite observations of tropospheric ammonia

“…The b a holds the corresponding a priori values, and K b = ∂L/∂b is the Jacobian describing the dependency of the forward model radiance L on the vector b. The fast forward model OSS-CrIS (Moncet et al, 2008), which is built from the Line-By-Line Radiative Transfer Model (LBLRTM) (Clough et al, 2005;Shephard et al, 2009;Alvarado et al, 2013), is used for these retrievals.…”

Validation of the CrIS fast physical NH<sub>3</sub> retrieval with ground-based FTIR

“…In this study we use the NH 3 retrieval as described by . The retrieval is based on an optimal estimation approach (Rodgers, 2000) that minimizes the differences between CrIS spectral radiances and simulated forward model radiances computed from the Optimal Spectral Sampling method (OSS) OSS-CrIS (Moncet et al, 2008), which is built from the well-validated Line-By-Line Radiative Transfer Model (LBLRTM) (Clough et al, 2005;Shephard et al, 2009;Alvarado et al, 2013) and uses the HITRAN database (Rothman et al, 2013) for its spectral lines. The fast computational speed of OSS facilitates the operational production of CrIS-retrieved (level 2) products using an optimal estimation retrieval approach (Moncet et al, 2005).…”

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

“…The line-by-line radiative transfer model LBLRTM (Alvarado et al, 2013;Clough et al, 2005;Turner et al, 2004), which shares the physical basis as the MonoRTM used in the downwelling experiment, is used to compute upwelling infrared radiance from the original and corrected RH data. The LBLRTM computes very high-resolution radiance data; in order to match the 2378 AIRS channels, the monochromatic LBLRTM output is convolved with the AIRS instrument spectral response function for each of the 2378 AIRS channels.…”