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

forecasting. It is therefore necessary to conduct comprehensive assessments of the NWP’s ability to forecast heavy and highly variable rainfall regimes in tropical and near-tropical regions dominated by large mesoscale convective systems ( Mohr and Zipser 1996 ). The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) are two leading NWPs for medium-range weather forecasting at the global scale. In

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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

water demand, and thus SM could also be a crucial factor affecting socioeconomic conditions. Despite the criticality of SM in the Earth system, accurate estimation of large-scale soil moisture is still a challenge, mainly due to its rapid fluctuations and the lack of sufficient ground truth observations. Currently, most large-scale SM products are either retrieved from satellite data or produced from land surface models (LSMs). As an example of product derived from satellites, the European Space

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

systems such as NASA and MicroMAS) are not expanded in the text when first used. AE Autoencoder AI Artificial intelligence ANN Artificial neural network BT Brightness temperature CAE Convolutional autoencoders CAM Community Atmosphere Model CFS Climate Forecast System CIRAS CubeSat Infrared Atmospheric Sounder CNN Convolutional neural network CPC Climate Prediction Center CRM Cloud-resolving model DA Data assimilation DNN Deep-learning neural networks ECMWF European Centre for Medium-Range Weather

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Imme Ebert-Uphoff and Kyle Hilburn

-0372.1 . 10.1175/MWR-D-19-0372.1 Lapuschkin , S. , S. Wäldchen , A. Binder , G. Montavon , W. Samek , and K.-R. Müller , 2019 : Unmasking clever HANS predictors and assessing what machines really learn . Nat. Commun. , 10 , 1096 , . 10.1038/s41467-019-08987-4 Lee , Y.-J. , D. Hall , J. Stewart , and M. Govett , 2018 : Machine learning for targeted assimilation of satellite data . Joint European Conf. on Machine Learning and

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