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  • The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction x
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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

1. Introduction Soil moisture (SM) is an essential component of the Earth system. It affects the variability of the coupled energy (latent and sensible heat fluxes) and water fluxes (runoff and evapotranspiration) by modifying the partitioning of water and energy across the land–atmosphere interface ( Seneviratne et al. 2010 ). The effects of SM on evapotranspiration also impact temperature variability and may intrigue persistent heatwaves ( Fischer et al. 2007 ; Hirschi et al. 2011

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Noah D. Brenowitz, Tom Beucler, Michael Pritchard, and Christopher S. Bretherton

levels times the number of variables. However, the dominant process parameterized by ML schemes is moist atmospheric convection, which has well-known sensitivities to two environmental variables: the midtropospheric moisture and the lower-tropospheric stability (LTS). On the one hand, the intensity of convection increases exponentially with the former ( Bretherton et al. 2004 ; Rushley et al. 2018 ), perhaps because the buoyancy of an entraining plume is strongly controlled by the environmental

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Andrew E. Mercer, Alexandria D. Grimes, and Kimberly M. Wood

-level moisture may help distinguish RI from non-RI. Despite the coarse nature of the 1° GFSA input, the unsupervised learning approach still highlighted the inner-core region, where smaller-scale processes previously noted to be important for short-term RI would occur (e.g., Judt and Chen 2016 ). Fig . 5. Composite 300-hPa specific humidity on the 2° × 2° domain for the 20-kt/12-h RI definition: (a) the true positive composite, (b) the false positive composite, (c) the false negative composite, and (d) the

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John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, Anthony Wimmers, Jason Brunner, and Willard Bellon

moisture and instability associated with a 500-hPa shortwave trough, numerous storms developed in western Arizona on 23 September 2019. At 1631 UTC, the ICP was ≥50% for two of the storms, likely due to the presence of clear overshooting tops and moderate-to-strong brightness temperature gradients around the cloud-top edges near the primary overshoot region ( Fig. 13a ). By 1706 UTC, the westernmost storm had an expanded area of ICP ≥ 50%, while the eastern storm ICP decreased to <25% as cloud

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Ryan Lagerquist, Amy McGovern, Cameron R. Homeyer, David John Gagne II, and Travis Smith

, and much less near-surface moisture, than the GridRad sounding. The difference occurs because the MYRORSS dataset contains many more nontornadic storms than GridRad, allowing for more extreme nontornadic cases. Fig . 13. As in Fig. 12 , but for MYRORSS. 6. Summary and future work We used convolutional neural networks, a type of deep-learning method, to predict the probability that a storm will be tornadic in the next hour. The predictors were a proximity sounding and storm-centered radar image

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

. Okamoto , M. Kunii , and T. Miyoshi , 2019 : Assimilating every-10-minute Himawari-8 infrared radiance to improve convective predictability . J. Geophys. Res. Atmos. , 124 , 2546 – 2561 , . 10.1029/2018JD029643 Schmit , T. , M. Gunshor , G. Fu , T. Rink , K. Bah , and W. Wolf , 2010 : Cloud and Moisture Imagery Product (CMIP), version 2.3. NOAA NESDIS STAR GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Doc., 62 pp

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

example, the emissivity of the land surface must be estimated or specified for RT calculations, but depends in a complicated way on the soil type, soil moisture, vegetation type, vegetation health, and vegetative phenology. Consequently, in DA and forecast systems, we make approximations and tune our estimates and parameterizations, often in ad hoc ways that involve some assumptions. For real-time DA and forecast applications, fast versions of RT are generally used, and these rely on parameterizations

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