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

convolution). Future work will use specialized ML-interpretation methods, such as those discussed in McGovern et al. (2019) , to compare physical relationships learned by the two models. In the future we also plan to adapt the models developed herein for an operational setting such as the HWT, where they could be evaluated in real time by forecasters. Although model development (training, validation, and testing) is computationally expensive, applying either trained CNN in real time takes ~5 min per

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

translating cloud-top features into features deep inside the cloud. In our future work we plan to try out GANs and compare results with CNNs in terms of accuracy versus blurriness. c. Examining the effective receptive field GREMLIN is a purely convolutional neural network, that is, it does not have any fully connected (aka dense) layers. This means that any individual output neuron, that is, any pixel of the estimated MRMS image, is connected to only a small group of input neurons corresponding to a

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

learning . Nature , 521 , 436 – 444 , https://doi.org/10.1038/nature14539 . 10.1038/nature14539 McGovern , A. , K. Elmore , D. Gagne , S. Haupt , C. Karstens , R. Lagerquist , T. Smith , and J. Williams , 2017 : Using artificial intelligence to improve real-time decision making for high-impact weather . Bull. Amer. Meteor. Soc. , 98 , 2073 – 2090 , https://doi.org/10.1175/BAMS-D-16-0123.1 . 10.1175/BAMS-D-16-0123.1 NASA MSFC , 2001 : AMSR-E Data Management Plan

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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

research topics such as water and carbon fluxes in agroecosystems, and land–atmosphere interactions. Second, we shed light on the effects of different cropping patterns on SM and seek alternative cropping patterns that may alleviate SM decline. This exploration will prompt decision making in ameliorating soil water stress and inform adaption plans in an increasingly water-scarce future. 2. Methods a. Neural network The neural network used in this work is a deep feed forward NN, which consists of

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