A deep learning approach to fast radiative transfer

Stegmann, Patrick G.; Johnson, Benjamin; Moradi, Isaac; Karpowicz, Bryan; McCarty, Will

doi:10.1016/j.jqsrt.2022.108088

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i

A deep learning approach to fast radiative transfer

2022
By Stegmann, Patrick G. ; Johnson, Benjamin ; Moradi, Isaac ; ...
Source: Journal of Quantitative Spectroscopy and Radiative Transfer, 280, 108088

[PDF-4.37 MB]

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Journal Title:

Journal of Quantitative Spectroscopy and Radiative Transfer
Personal Author:

Stegmann, Patrick G. ; Johnson, Benjamin ; Moradi, Isaac ; Karpowicz, Bryan ; McCarty, Will

Stegmann, Patrick G. ; Johnson, Benjamin ; Moradi, Isaac ; Karpowicz, Bryan ; McCarty, Will Less -
NOAA Program & Office:

NWS (National Weather Service)
Description:

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.
Keywords:

[+]

Atomic And Molecular Physics, And Optics Radiation Spectroscopy
Source:

Journal of Quantitative Spectroscopy and Radiative Transfer, 280, 108088
DOI:

https://doi.org/10.1016/j.jqsrt.2022.108088
ISSN:

0022-4073
Format:

PDF
Publisher:

Elsevier BV
Document Type:

Journal Article
Funding:

Grant no. NA16NWS4620043 ; Grant no. NA18NWS4620043B ; Grant no. NA20NWS4620043

Grant no. NA16NWS4620043 ; Grant no. NA18NWS4620043B ; Grant no. NA20NWS4620043 Less -
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
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Library
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urn:sha-512:8e49a2c83d4262c69e0026551d22b53c2390021bf1a4a38d3391aaf2b1975ad0fbfb7d45e8d901192507db1cb4f5b0e21d32ceedafd5e1b50c6faa3a9e849a51
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https://repository.library.noaa.gov/view/noaa/64509/noaa_64509_DS1.pdf
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A deep learning approach to fast radiative transfer

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