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

regularizer is computed from a large ensemble of a climate model, rather than the single realization of observations. Second, we benchmark the GTV model against two different classes of predictive models: 1) other regularized regression methods (LASSO and fused LASSO; Tibshirani et al. 2005 ) and 2) simple ordinary least squares using known teleconnection indices as predictors. Our analysis shows that constraining the predictive model by the spatiotemporal covariance of the predictors via a GTV

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Giuseppe Mascaro

many others). When rainfall records are available at sufficiently fine temporal resolutions at a given site, the most common approach to conduct local IDF analyses consists of estimating i ( T R , τ ) via frequency analysis of annual rainfall maxima at different τ with parametric statistical distributions. For example, as suggested by the extreme value theory ( Coles 2001 ), the generalized extreme value (GEV) distribution has been shown to be able to model well annual rainfall maxima at

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

regression analysis. Principal Component Analysis , Springer-Verlag, 129–155. 10.1007/978-1-4757-1904-8_8 Kaiser , H. F. , 1958 : The varimax criterion for analytic rotation in factor analysis . Psychometrika , 23 , 187 – 200 , . 10.1007/BF02289233 Kalnay , E. , and Coauthors , 1996 : The NCEP/NCAR 40-Year Reanalysis Project . Bull. Amer. Meteor. Soc. , 77 , 437 – 472 ,<0437:TNYRP>2.0.CO;2 . 10

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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

the precipitation-free scenes inferred from the DPR profiles, together with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) ( Gelaro et al. 2017 ) temperature and water vapor profile, the surface emissivity for GMI’s first nine (89 GHz and below) channels is estimated by the emissivity retrieval method of Mathew et al. (2008) with the successive order of interaction (SOI) radiative transfer model ( Heidinger et al. 2006 ), regardless of the surface. For

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Allison E. Goodwell

, or clouds. We focus on the information that can be obtained from knowledge of the past precipitation state at the central location c and neighboring locations. This focus on past precipitation rather than multivariate drivers enables a relatively simple analysis of a single gridded dataset. Moreover, we assume that past precipitation somewhat integrates these other drivers, as it directly captures the duration and movement of events. Depending on a storm’s size, shape, speed, and direction of

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

underestimate precipitation rates greater than 50 mm h −1 . Kuligowski et al. (2016) identified such quantification error characteristics in the earlier version of SCaMPR and attributed them to the algorithm structure (mean-square-error minimization of linear regression models). In the newer SCaMPR version for GOES-R a histogram matching method is adopted to overcome this limitation. However, our error budget analysis indicates that the error trend remains. It is attributed therefore to the training with

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Clément Guilloteau and Efi Foufoula-Georgiou

cloud type and precipitation type. For example, Prabhakara et al. (2000) , and later Gopalan et al. (2010) , used the minimum value and the standard deviation of the TB at 85 GHz within a 40 km neighborhood to estimate the convection fraction at the pixel of interest. However, these indices have only been used for unispectral or bispectral algorithms within a linear regression framework so far. What is proposed here is to exploit the information contained in the observed fields of TB by analyzing

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

phenomena in locations and at scales not previously possible. SMPPs use algorithms that merge passive microwave and infrared sensing data from multiple satellites (e.g., Kidd and Levizzani 2011 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Wright 2018 ). Commonly used SMPPs include the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), and the Precipitation Estimation from Remotely Sensed

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

supplement to PMW observations. It is therefore important to assess if convective/stratiform information can be inferred from the passive microwave information itself. Yet, despite sustained, decades-long effort to identify a robust link between PMW observations and convective fraction, only a few regression methods with modest skill are available. These methods largely utilize the spatial variability of the brightness temperature (Tb) of the high-frequency channels (e.g., 30 GHz and above). Thus, the

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