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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, Saman Razavi, Guoqiang Tang, and John W. Pomeroy

events, and quantifies the likelihood of extremes to occur. More frequent and larger extremes with respect to the average precipitation occur when a heavy tail is observed in a particular region. Therefore, assessment of a tail’s heaviness is useful to understand the likelihood of extremes and thus guide risk management strategies. The tail function F ¯ X ⁡ ( x ) of random variable X is the complimentary cumulative distribution function of X . Several classifications of tail functions exist, yet

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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

4914) both DPR and GV-MRMS information are included. This selection criterion thus allows GPROF performance to be assessed, specifically if the algorithm can correctly convert GMI TBs into a physically realistic retrieval for this unique class of extreme winter precipitation. The analysis comprises the following components: GMI TB observational descriptions, visual comparisons of precipitation patterns, event detection and QPE assessments, statistical scores of detection ability, and a GPROF

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

limitations. In contrast, the use of machine learning has potential since climate information from the entire globe can in principle be used to inform the prediction. However, these techniques also face important practical limitations. First, because of the short record of observations and the large number of predictors, the number of degrees of freedom of the problem is vast, significantly increasing the risk of overfitting ( Ham et al. 2019 ). Second, strong spatiotemporal dependences among the

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Clément Guilloteau, Antonios Mamalakis, Lawrence Vulis, Phong V. V. Le, Tryphon T. Georgiou, and Efi Foufoula-Georgiou

interest, exhibiting a plethora of modes caused by different physical processes (e.g., solar forcing, oceanic/atmospheric circulations, land–atmosphere interactions, etc.), and imprinting themselves at various spatial and temporal scales. The accurate identification and modeling of the modes of the climate system is necessary for many key problems in geosciences, such as weather/climate prediction, attribution of extreme events and hazards, and assessment of climate change impacts. The comprehensive

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Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

.1007/978-3-319-09057-3_109 Carter , A. , 2018 : “Shouldn’t you have a right to know?” Daughter of landslide victim says warnings needed. News & Observer , 4 June , https://www.newsobserver.com/latest-news/article212367984.html . Cloke , H. L. , and F. Pappenberger , 2009 : Ensemble flood forecasting: A review . J. Hydrol. , 375 , 613 – 626 , https://doi.org/10.1016/j.jhydrol.2009.06.005 . 10.1016/j.jhydrol.2009.06.005 Dai , F. C. , C. F. Lee , and Y. Y. Ngai , 2002 : Landslide risk assessment and

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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

Twelfth International Precipitation Conference (IPCI2) What : Scientists from more than 60 countries met at the Twelfth International Precipitation Conference (IPCI2) to discuss the latest advances in precipitation estimation, prediction, and impact assessment and contemplate the challenges and opportunities that lie ahead. When : 19–21 June 2019 Where : Irvine, California Precipitation exhibits a large variability over a wide range of space and time scales: from seconds to years and decades in

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Shruti A. Upadhyaya, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, and Robert J. Kuligowski

observations and MWCOMB rates in order to detect and quantify precipitation at a spatial scale of ~2 km at nadir and 5-min temporal resolution across the conterminous United States (CONUS) (15 min across North and South America). The advancement in algorithms demands the assessment of their performance. Evaluating the accuracy of satellite precipitation products has always been one of the primary objectives of the International Precipitation Working Group (IPWG: http://www.isac.cnr.it/~ipwg/ ). This is

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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

due to the important role that satellite observations can play in preparedness, risk management, and, in turn, mitigating the devastating impacts of such events. Here, we examine the performance of PDIR-Now in capturing precipitation during Hurricane Harvey, which resulted in unprecedented rainfall accumulations of over 1.5 m that caused extensive flooding damage over the Houston metropolitan region, and an extreme rainfall event of thunderstorms that hit most of the Netherlands in June 2019, that

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