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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

observations and storm morphology, little, if any, room has been left for a potentially novel physically based approach to emerge. However, recent advances in deep learning methods with neural networks may offer perhaps not new but for the first time fully applicable models that could better exploit the information content in PMW observations. This study seeks to investigate such a possibility through the use of deep learning for both retrieving precipitation types and improving the performance of PMW

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Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, and Stephen Wright

predictability . J. Climate , 16 , 2752 – 2765 ,<2752:HDPFOT>2.0.CO;2 . 10.1175/1520-0442(2003)016<2752:HDPFOT>2.0.CO;2 Goncalves , A. R. , A. Banerjee , V. Sivakumar , and S. Chatterjee , 2017 : Structured estimation in high dimensions: Applications in climate. Large-Scale Machine Learning in the Earth Sciences , CRC Press, 13–32. 10.4324/9781315371740-2 Ham , Y.-G. , J.-H. Kim , and J.-J. Luo , 2019 : Deep learning for multi-year ENSO

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Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson

detection performance can also be influenced by snowfall regimes, with intense, deeper events accompanied by higher columnar water vapor amounts typically easier to detect than light and/or shallow snowfall events that occur in drier ambient conditions (e.g., Skofronick-Jackson et al. 2013 ). Several recent studies highlight different snowfall modes both from satellite ( Kulie and Milani 2018 ; Kulie et al. 2016 ; West et al. 2019 ; Kulie et al. 2020 ) and ground-based radar perspectives ( Pettersen

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Clément Guilloteau and Efi Foufoula-Georgiou

for deep learning algorithms). It is also independent of the distance metric used to compute the distances between the TB vectors. The only way to reduce this uncertainty is to add supplementary information to the vector of observed TBs. This may be achieved by using ancillary datasets, as for example environmental variables from reanalyses ( Ferraro et al. 2005 ; Ringerud et al. 2015 ; Kidd et al. 2016 ; Petković et al. 2018 ; Takbiri et al. 2019 ). While the current state

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Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

progress in GEO sensor technologies along with the advancements in machine learning (ML) techniques, such as support vector machines, random forests, artificial neural network (ANN), deep learning, the new generation of precipitation retrieval algorithms must outperform the current operational products ( Meyer et al. 2016 ; Kuligowski et al. 2016 ; Sadeghi et al. 2019 ; Upadhyaya et al. 2020 ). In recent years, many studies have been conducted to utilize the generation sensor information to improve

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Efi Foufoula-Georgiou, Clement Guilloteau, Phu Nguyen, Amir Aghakouchak, Kuo-Lin Hsu, Antonio Busalacchi, F. Joseph Turk, Christa Peters-Lidard, Taikan Oki, Qingyun Duan, Witold Krajewski, Remko Uijlenhoet, Ana Barros, Pierre Kirstetter, William Logan, Terri Hogue, Hoshin Gupta, and Vincenzo Levizzani

) forecasting (the gap between weather forecasts and seasonal climate predictions) using models and observations and assessment of uncertainty propagation to impact studies such as floods, droughts and ecological changes. IPC12 also aimed to provide a forum to explore new data analytic and machine learning (ML) methodologies, taking advantage of the unprecedented explosion of Earth observations from space and climate model outputs, for improved estimation and prediction. It also brought together scientists

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