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Giuseppe Mascaro

several locations (e.g., Papalexiou and Koutsoyiannis 2013 ; Blanchet et al. 2016 ). If historical rainfall records are available at multiple sites, regional IDF curves are often generated by (i) spatially interpolating i ( T R , τ ) or parameters of the statistical distributions from local or at-site estimations, or (ii) applying regionalization techniques that merge rain gauges into homogeneous regions to increase robustness in the estimate of the statistical distribution parameters ( Hosking

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

variables or areas of the studied domain have delayed linear responses to the same signal with different delays. The spectral PCA (sPCA), through the phase (complex argument) information in the complex cross-spectral coefficients, also allows one to handle lagged correlations. Additionally, it offers the possibility to look for modes in specific frequency bands and is particularly potent at extracting wave-type modes and handling propagation effects (nonstationary waves). Many other methods rely on the

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Sarah Ringerud, Christa Peters-Lidard, Joe Munchak, and Yalei You

) have moved away from the empirical schemes. Regardless of formulation, the scattering signal forms the bulk of the information available to the retrieval over land simply due to the highly variable nature of the surface emissivity. Using coincident radar–radiometer observations from the predecessor Tropical Rainfall Measuring Mission (TRMM), Berg et al. (2006) showed that disagreement between rain-rate estimates from the active and passive sensors displayed regional patterns that could be

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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

1. Introduction Satellite remote sensing of precipitation is essential for science and society. With global precipitation estimates available from spaceborne platforms, it becomes feasible to assess water resources, monitor extreme events, and to gain and enhance scientific knowledge regarding precipitation processes at global, regional, and smaller scales ( Adler et al. 2009 ; Kirschbaum et al. 2017 ; Reed et al. 2015 ; Skofronick-Jackson et al. 2017 ; Sorooshian et al. 2011 ). Liquid rain

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

1. Introduction Seasonal prediction of regional hydroclimate is typically based on deterministic physical models or statistical techniques, yet both approaches exhibit limited predictive ability ( Wang et al. 2009 ; National Academies of Sciences, Engineering, and Medicine 2016 ). Precipitation predictions based on deterministic physical models (regional climate models) exhibit high uncertainty due to imperfect physical conceptualizations, sensitivity to initial and boundary conditions, and

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

. 2017 ; Sun et al. 2018 ). This paucity of ground-truth information (e.g., limited numbers of rain gauges) has led previous error modeling studies to note that some form of “regionalization” of error estimates or error model parameters would be necessary ( Gebregiorgis and Hossain 2014 , 2013 ; Tang and Hossain 2009 , 2012 ). Modeling SMPP errors regionally may reduce finescale variability in error structure, but the alternative is no error models in data-limited regions. In addition to

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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

time and from the millimeter scale of microphysical processes to regional and global scales in space. It also exhibits a large variability in magnitude and frequency, from low extremes resulting in prolonged droughts to high extremes resulting in devastating floods. Improving precipitation estimation and prediction has great societal impact for decision support in water resources management, infrastructure protection and design under accelerating climate extremes, quantifying water and energy

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Allison E. Goodwell

. Climate conditions, topography, and regional land–atmosphere feedbacks drive these aspects of temporal persistence and spatial synchronicity of precipitation, which in turn influence soil moisture, flows, and vegetation. For example, the direction, speed, and size of a storm event moving across a basin can impact downstream flows and ecohydrologic processes. Goodwell and Kumar (2019) explored temporal precipitation persistence and predictability, addressing the extent to which the knowledge of past

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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

) relationships. PDIR, however, corrects for the errors that frequently result from such an approach by calibrating the empirical relationships regionally based on monthly precipitation climatology ( Nguyen et al. 2020 ). In addition, the PDIR algorithm incorporates several techniques to further reduce estimation errors and uncertainties. Because PDIR-Now depends primarily on IR data, its main advantage is providing near-global precipitation estimates at a short latency (15–60 min), hence the acronym “Now

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

the statistical behavior of regional and global precipitation. Therefore, the answer to the question of how reliable these datasets are in representing precipitation extremes remains still vague. Precipitation is the main driver of terrestrial hydrology and therefore the most important input to hydrological models. Several sources of precipitation data exist, for example, ground measurements by precipitation gauges, remotely sensed data by radars and satellites, and reanalysis data that assimilate

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