SNICAR-fx started with the idea of upgrading the biosnicar-py software, a python translation of SNICAR-ADv4 (SNow, ICe and Aerosols Radiative model), to a version that would be more portable and enable a flexible spectral range and resolution. Since then, it evolved into a distinct model with expanded functionality and its own strengths and assumptions.
- flexible spectral grid: between 200 and 5000nm with resolution >= 1nm
- light weight: no dependence on large optical properties databases
- fast: up to 50x faster in single-threaded runs depending on model configuration (number of layers in particular)
- directional capability: two-stream and multi-stream solvers
- specialized snow and ice features: liquid water content and empirical optical properties of glacial microbes
SNICAR-fx solves the 1-D unpolarized radiative transfer equation for a column of homogeneous layers of snow and/or ice. Each layer can be represented as a bulk medium made of ice grains or air bubbles, of which size should be larger than the wavelength because SNICAR-fx employs geometric optics assumptions to model single scattering properties, in contrast to SNICAR-ADv4 which uses Mie theory. Each layer has a specific surface area, water content and concentrations of various light absorbing particles. The incoming irradiance can be direct with a prescribed Solar Zenith Angle (SZA), or diffuse. Fresnel boundary layers can be incorporated between layers to account for the change in refractive index between air and ice when using the two-stream Delta-Eddingon solver ( Briegleb and Light 2007, Whicker et al. 2022 as in SNICAR-ADv4). A multi-stream delta-M solver employing the advanced matrix operator method is also available (Liu and Weng 2006, Liu and Weng 2013 as in CRTM), but does not support Fresnel layers to for now. SNICAR-fx is currently developed with a focus on melting environments and the radiative forcing of light absorbing particles. More specifically, the development currently targets melting weathering crust environments.
It is recommended to install snicar-fx via Github, with conda and pip.
Clone the repository and move into the snicar-fx directory
git clone https://github.com/openosmia/snicar-fx
cd snicar-fxCreate the conda environment with all required dependencies
conda env create -f environment.ymlActivate the new conda environment named snicarfx
conda activate snicarfxInstall the snicarfx software in editable (-e) mode, so that there is not need to re-install the package after local modifications. conda has already installed the dependencies at the previous step, so here pip only sets up the associated paths
pip install -e .Example scripts are provided in /examples. So far, only single runs using the two-stream solver are provided, but examples for batch runs are coming!
Original SNICAR equations (Two-stream Delta-Eddington formulation)
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Joseph, J. H., Wiscombe, W. J., & Weinman, J. A. (1976). The delta-Eddington approximation for radiative flux transfer. Journal of Atmospheric Sciences, 33(12), 2452-2459. DOI
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Wiscombe, W. J., & Warren, S. G. (1980). A model for the spectral albedo of snow. I: Pure snow. Journal of Atmospheric Sciences, 37(12), 2712-2733. DOI
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Flanner, M. G., Arnheim, J., Cook, J. M., Dang, C., He, C., Huang, X., ... & Zender, C. S. (2021). SNICAR-AD v3: A community tool for modeling spectral snow albedo. Geoscientific Model Development, 2021, 1-49. DOI
Two-stream adding-doubling solver with Fresnel layers
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Briegleb, P., & Light, B. (2007). A Delta-Eddington mutiple scattering parameterization for solar radiation in the sea ice component of the community climate system model. DOI
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Whicker, C. A., Flanner, M. G., Dang, C., Zender, C. S., Cook, J. M., & Gardner, A. S. (2021). SNICAR-ADv4: a physically based radiative transfer model to represent the spectral albedo of glacier ice. The Cryosphere, 2021, 1-36. DOI
Multi-stream advanced matrix operator method and adding solver (similar as in CRTM)
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Liu, Q., & Weng, F. (2006). Advanced Doubling–Adding Method for Radiative Transfer in Planetary Atmospheres. Journal of the Atmospheric Sciences, 63(12), 3459-3465. DOI
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Liu, Q., & Weng, F. (2013). Using advanced matrix operator (AMOM) in community radiative transfer model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1211-1218. DOI
Delta-M truncation method
-Wiscombe, W. J. (1977). The delta–M method: Rapid yet accurate radiative flux calculations for strongly asymmetric phase functions. Journal of Atmospheric Sciences, 34(9), 1408-1422. DOI
Air bubble asymmetry parameter
- Kokhanovsky, A. A. (2002). Optical properties of bubbles. Journal of Optics A: Pure and Applied Optics, 5(1), 47. DOI
Ice grain single scattering properties (similar as in TARTES)
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Kokhanovsky, A. A. (2021). Snow optics. Springer. DOI
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Kokhanovsky, A. A., & Macke, A. (1997). Integral light-scattering and absorption characteristics of large, nonspherical particles. Applied optics, 36(33), 8785-8790. DOI
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Kokhanovsky, A., Brell, M., Segl, K., & Chabrillat, S. (2024). SNOWTRAN: a fast radiative transfer model for polar hyperspectral remote sensing applications. Remote Sensing, 16(2), 334. DOI
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Picard, G. and Libois, Q. (2024). Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model, Geosci. Model Dev., 17, 8927–8953. DOI
Ice refractive indices
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Cooper, M. G., Smith, L. C., Rennermalm, A. K., Tedesco, M., Muthyala, R., Leidman, S. Z., ... & Fayne, J. V. (2021). Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet. The Cryosphere, 15(4), 1931-1953. DOI
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Picard, G., Libois, Q., & Arnaud, L. (2016). Refinement of the ice absorption spectrum in the visible using radiance profile measurements in Antarctic snow. The Cryosphere, 10(6), 2655-2672. DOI
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Warren, S. G., & Brandt, R. E. (2008). Optical constants of ice from the ultraviolet to the microwave: A revised compilation. Journal of Geophysical Research: Atmospheres, 113(D14). DOI
Spectral irradiances (SWNB2 model runs)
- Flanner, M. G., Arnheim, J., Cook, J. M., Dang, C., He, C., Huang, X., ... & Zender, C. S. (2021). SNICAR-AD v3: A community tool for modeling spectral snow albedo. Geoscientific Model Development, 2021, 1-49. DOI
Light absorbing particles
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Black carbon: Flanner, M. G., Liu, X., Zhou, C., Penner, J. E., & Jiao, C. (2012). Enhanced solar energy absorption by internally-mixed black carbon in snow grains. Atmospheric Chemistry and Physics, 12(10), 4699-4721. DOI
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Ice algae: Chevrollier, L. A., Cook, J. M., Halbach, L., Jakobsen, H., Benning, L. G., Anesio, A. M., & Tranter, M. (2023). Light absorption and albedo reduction by pigmented microalgae on snow and ice. Journal of Glaciology, 69(274), 333-341. DOI
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Colorado mineral dust: Skiles, S. M., Painter, T., & Okin, G. S. (2017). A method to retrieve the spectral complex refractive index and single scattering optical properties of dust deposited in mountain snow. Journal of Glaciology, 63(237), 133-147. DOI
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Brown carbon: Kirchstetter, T. W., Novakov, T., & Hobbs, P. V. (2004). Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon. Journal of Geophysical Research: Atmospheres, 109(D21). DOI
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Snow algae: Chevrollier, L. A., Wehrlé, A., Cook, J. M., Pirk, N., Benning, L. G., Anesio, A. M., & Tranter, M. (2025). Separating the albedo-reducing effect of different light-absorbing particles on snow using deep learning. The Cryosphere, 19(4), 1527-1538. DOI
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Volcanic ashes: Flanner, M. G., Gardner, A. S., Eckhardt, S., Stohl, A., & Perket, J. (2014). Aerosol radiative forcing from the 2010 Eyjafjallajökull volcanic eruptions. Journal of Geophysical Research: Atmospheres, 119(15), 9481-9491. DOI
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Greenland dust: Cook, J. M., Tedstone, A. J., Williamson, C., McCutcheon, J., Hodson, A. J., Dayal, A., ... & Tranter, M. (2020). Glacier algae accelerate melt rates on the south-western Greenland Ice Sheet. The Cryosphere, 14(1), 309-330. DOI
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Sahara dust: Balkanski, Y., Schulz, M., Claquin, T., & Guibert, S. (2007). Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data. Atmospheric Chemistry and Physics, 7(1), 81-95. DOI
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Cryoconite: coming!
