Reducing Biases in Thermal Infrared Surface Radiance Calculations Over Global Oceans

Nalli, Nicholas R.; Jung, James A.; Knuteson, Robert O.; Gero, Jonathan; Dang, Cheng; Johnson, Billy E.; Zhou, Lihang

doi:10.1109/tgrs.2023.3248490

Cited by 3 publications

(6 citation statements)

References 64 publications

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“…(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).…”

Section: Ari Emissivity Analysismentioning

confidence: 93%

“…(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.…”

Section: Ari Emissivity Analysismentioning

confidence: 99%

“…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.…”

Section: Ari Emissivity Analysismentioning

confidence: 99%

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

Loveless,

Adler,

Best

et al. 2024

Earth and Space Science

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Far infrared (FIR) emission from the Earth's polar regions has become an area of increasing scientific interest and value. FIR emission is important for understanding Earth's radiative balance and improving global climate models, especially in rapidly changing Arctic conditions. Far‐infrared emission from Earth is not currently being monitored from space, except as part of broadband emission channels of Earth radiation budget measurements like those from the CERES project, and only limited measurements in the FIR spectrum exist. The Absolute Radiance Interferometer (ARI), developed as a prototype of the infrared spectrometer for CLARREO at the University of Wisconsin‐Madison, Space Science and Engineering Center, measures absolute spectrally resolved infrared (IR) radiance from 200 to 2,000 cm−1 (or 5–50 μm) at 0.5 cm−1 resolution with high accuracy (<0.1 K 3‐sigma brightness temperature at scene temperature). This instrument was taken into the field in Madison, Wisconsin, USA, during the winters of 2021 and 2022, where the weather can reach polar‐like conditions to measure high spectral resolution radiances of various sample types. Sample materials included water, snow, ice, evergreen leaves, dry grass, and sand, all characteristic of high latitude regions. Radiances collected from both a sky view and the sample view in clear‐sky conditions were used to retrieve FIR emissivity. This paper describes the ARI instrument configuration and capability for ground‐based measurements in the FIR region, and documents retrieved emissivities of various analyzed samples. The retrieved emissivity results are publicly available, and comparisons are made to simulated emissivity estimates.

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“…(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).…”

Section: Ari Emissivity Analysismentioning

confidence: 93%

“…(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.…”

Section: Ari Emissivity Analysismentioning

confidence: 99%

See 1 more Smart Citation

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

Loveless,

Adler,

Best

et al. 2024

Earth and Space Science

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“…Generally speaking, ocean surfaces are the simplest to model in the TIR-FIR spectral region, given that they are spatially uniform, with individual surface roughness elements, being large relative to the wavelength of radiation and their ensemble inclinations being known as a function of surface wind speed [27]. These unique features of ocean surfaces have allowed the development and use of physical models for over a decade [28]. Conversely, land surfaces are extremely inhomogeneous in terms of spatiotemporal uniformity, roughness, and optical properties, and thus, over land, empirically-derived merged atlases, for example CAMEL [21,22] have been implemented, and these include snow and ice surfaces.…”

Section: Surface Emissivity Considerations and Modeling Approachesmentioning

confidence: 99%

“…Assuming spherical particles (as in the WW80 Mie-scattering model) and disregarding shadowing effects, the mean inclination happens to be θ ≡ 45 • . Thus, while it may be "semi-empirical", it should nevertheless be kept in mind that this is a physically reasonable approach in lieu of having an observed statistical description of snow/ice facet slopes, as is (fortuitously) the case in ocean emissivity models [28,56].…”

Section: Hybrid Physical Modelmentioning

confidence: 99%

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

Nalli,

Dang,

Jung

et al. 2023

Remote Sensing

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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.

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Underwater Target Tracking Based on Hierarchical Software-Defined Multi-AUV Reinforcement Learning: A Multi-AUV Advantage-Attention Actor-Critic Approach

Zhu,

Han,

Lin

et al. 2024

IEEE Trans. on Mobile Comput.

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Reducing Biases in Thermal Infrared Surface Radiance Calculations Over Global Oceans

Cited by 3 publications

References 64 publications

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

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

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

Underwater Target Tracking Based on Hierarchical Software-Defined Multi-AUV Reinforcement Learning: A Multi-AUV Advantage-Attention Actor-Critic Approach

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