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Christina Kumler-Bonfanti, Jebb Stewart, David Hall, and Mark Govett

describes work using DL to perform image segmentation for the detection of tropical and extratropical cyclone ROI. The automated detection of significant ROIs is potentially valuable for improving data assimilation and model initialization for numerical weather prediction models, among many other possible applications. For example, Fig. 1 shows a water vapor image produced by an older generation NOAA Geostationary Operational Environmental Satellites (GOES). In this figure, red boxes indicate ROIs for

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

). The goal of the NN parameterization ( Rasp et al. 2018 ; Gentine et al. 2018 ) is to emulate how the embedded SAM models vertically redistribute temperature (approximately Q 1 ) and water vapor (approximately Q 2 ) in response to given coarse-grained conditions from the host model’s primitive equation dynamical predictions (i.e., temperature profile, water vapor profile, meridional velocity profile, surface pressure, insolation, surface sensible heat flux, and surface latent heat flux; all

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

standpoint of precipitation, having significant impacts on human activities, are also the areas that have the least amount of data to constrain estimates of the current atmospheric state. One approach is radiance assimilation (RA), which has the advantage of being physically based, making it simpler to interpret. Okamoto et al. (2019) , Honda et al. (2018a , b ), and Sawada et al. (2019) tested assimilation of Himawari-8 water vapor absorption bands, finding improvements for heavy rain cases

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

stability is determined by its vertical profile alone (stable if θ υ increases with height and unstable otherwise), and specific humidity is important because it is the total mass concentration of water vapor. We interpolate all five variables to heights from 0 to 12 km AGL ( Table 3 ). We use ground-relative heights here to prevent the use of underground height levels, which contain data extrapolated from the lowest atmospheric levels. We split both MYRORSS and GridRad examples into training

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

://www.ecmwf.int/sites/default/files/elibrary/2017/17282-satellite-monitoring-convective-storms-forecasters-point-view.pdf . Wang , P. K. , 2003 : Moisture plumes above thunderstorm anvils and their contributions to cross-tropopause transport of water vapor in midlatitudes . J. Geophys. Res. , 108 , 4194 , https://doi.org/10.1029/2002JD002581 . 10.1029/2002JD002581 Wang , P. K. , 2007 : The thermodynamic structure atop a penetrating convective thunderstorm . Atmos. Res. , 83 , 254 – 262 , https://doi.org/10.1016/j.atmosres.2005

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Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

_F strategy. Finally, the Prec_water05 scheme performed worst for most statistics, especially during fall and winter times (e.g., Fig. 10 ), distantly followed by the Short_reg scheme. Studies suggest that there is a weak relationship between precipitable water and precipitation in most regions (e.g., Rao and Da Silva Marques 1984 ; Teixeira and Satyamurty 2007 ), presumably because of the complex regimes of water vapor and rainfall, which are generally regulated by the ocean on the east, a steep

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

, A. A. , S. Gopal , and A. H. Strahler , 1998 : Forward and inverse modeling of canopy directional reflectance using a neural network . Int. J. Remote Sens. , 19 , 453 – 471 , https://doi.org/10.1080/014311698216099 . 10.1080/014311698216099 Aires , F. , W. B. Rossow , N. A. Scott , and A. Chédin , 2002 : Remote sensing from the infrared atmospheric sounding interferometer instrument: 2. Simultaneous retrieval of temperature, water vapor, and ozone atmospheric profiles

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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

misfit to observations, spatial features coherence, and interparameters correlations) to those generated by traditional physical approaches. For example, Boukabara et al. (2019a) showed that the total precipitable water vapor (TPW) retrieved from microwave brightness temperatures by AI captures all the main features of the NWP analyses. The most striking advantage of many AI approaches is efficiency. For example, while it takes about 2 h to process a full day of the Advanced Technology Microwave

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