Physically Based Thermal Infrared Snow/Ice Surface Emissivity for Fast Radiative Transfer Models

Abstract

Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system.

Ground‐Based Far Infrared Emissivity Measurements Using the Absolute Radiance Interferometer

“…The Nalli et al. (2023) is also shown with a range of grain sizes, of which the 200 micron size appears to agree best with the observations. For the FIR, the wavelengths are probably too large for the Mie scattering aspect of the Nalli model to be valid for grain sizes <50 microns.…”

“…(2016) (thin dashed and dotted black lines), Nalli et al. (2023) (thin blue dashed and dotted lines), and UCSB/Univ. Washington (thick dashed black line).…”

“…This difference is not accounted for by the ARI systematic uncertainty in Figure 14 Three reference emissivities from Huang et al (2016) are included, and each represents a different snow grain size at a view angle of 53°. The snow reference spectra provided by Nalli et al (2023) optimally combines theoretical concepts from the Wiscombe and Warren (1980) Model (WW80) for small particle sizes and from Hori et al (2013) for larger. The Nalli snow emissivity shown is specifically computed for a view angle of 45°and the grain radii sizes of 30, 200, and 500 microns.…”

“…(2016) and from Nalli et al. (2023), both of which were simulated to represent the proper view angle of 45°. The third reference is from UCSB and does not specify a view angle.…”

“…The snow reference spectra provided by Nalli et al. (2023) optimally combines theoretical concepts from the Wiscombe and Warren (1980) Model (WW80) (Warren & Wiscombe, 1980; Wiscombe & Warren, 1980) for small particle sizes and from Hori et al. (2013) for larger.…”