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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

: ABI algorithm theoretical basis Document for Daytime Cloud Optical and Microphysical Properties (DCOMP). NOAA/NESDIS/STAR Algorithm Theoretical Basis Doc., 61 pp., https://www.goes-r.gov/products/ATBDs/baseline/Cloud_DCOMP_v2.0_no_color.pdf . Wilks , D. S. , 2006 : Statistical Methods in the Atmospheric Sciences . 2nd ed. Academic Press, 627 pp. Williams , E. , V. Mushtak , D. Rosenfeld , S. Goodman , and D. Boccippio , 2005 : Thermodynamic conditions favorable to superlative

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

.noaa.gov/big-data-project ). The latency requirement is particularly extreme for short-term forecasting of hazardous weather. Yet, improvements in NWP are driven by computationally intensive advances in all aforementioned areas. Examples of specific improvements for global medium-range NWP will include: enhanced assimilation of satellite measurements, including radiances affected by clouds, precipitation, and surface properties [requiring more complete radiative transfer (RT) models accounting for these effects], and using

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

.html . Toms , B. A. , E. A. Barnes , and I. Ebert-Uphoff , 2020 : Physically interpretable neural networks for the geosciences: Applications to Earth system variability . J. Adv. Model. Earth Syst. , 12 , e2019MS002002, https://doi.org/10.1029/2019MS002002 . 10.1029/2019MS002002 Veerman , M. A. , R. Pincus , R. Stoffer , C. van Leeuwen , D. Podareanu , and C. C. van Heerwaarden , 2020 : Predicting atmospheric optical properties for radiative transfer computations using neural

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