Evaluation of Radiative Transfer Models With Clouds

Abstract

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm−1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm−1 at night are reasonably consistent with results at 900 cm−1. Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm−1 are inferior to those at 900 cm−1 for daytime calculations.

Using AIRS and ARM SGP Clear‐Sky Observations to Evaluate Meteorological Reanalyses: A Hyperspectral Radiance Closure Approach

“…Compared with the line‐by‐line radiative transfer model benchmark calculations, the root‐mean‐square errors for the PCRTM to calculate AIRS spectrum is less than 0.4 K (Liu et al, ). A recent intercomparison study of fast hyperspectral radiative transfer models for cloudy scenes (Aumann et al, ) also confirms the robust performance of the PCRTM compared to other fast radiative transfer models. The PCRTM‐based simulator by Chen, Huang, and Liu () is designated to interface the PCRTM with meteorological fields from both climate models and reanalyses in a flexible way and has been used in other published studies (e.g., Bantges et al, ; Huang et al, ; Pan et al, ).…”

An update on the RTTOV fast radiative transfer model (currently at version 12)

“…As for the IR, the Baran et al (2014) or scattering property datasets can be selected for ice cloud. The phase function is calculated from the asymmetry parameter following Baran et al (2001), and the Legendre expansion of the phase function is calculated internally in RTTOV.…”

Information content analysis for a novel TES-based hyperspectral microwave atmospheric sounding instrument

“…The HT-FRTC model has been used in several other airborne and spaceborne remote sensing studies (recently, it has been used for investigating a new high-resolution infrared instrument, Laser Heterodyne Radiometer (LHR) for numerical weather prediction application applications [24]. The line-HT-FRTC model has also been compared and validated with other radiative transfer models [32].…”

“…The matrix is complex in structure and it is the combination of background error standard deviation and vertical error correlations. It is an important component of the analysis as the information content will be calculated with respect to the background information [23][24][25][26][27][28][29][30][31][32][33]. As this study is mainly focused on temperature and water vapour measurements, the matrix used here only comprises of temperature ( ) and humidity ( ) errors.…”