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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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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

the horizontal polarization ( Dietz et al. 2012 ). At the lower GMI frequencies, snowpack and its effect on the dielectric properties of the surface leads to a positive emission signal, whereas at the higher frequencies scattering tends to decrease emissivity ( Shahroudi and Rossow 2014 ). Munchak et al. (2020) demonstrate a high information content in the retrieved emissivities up to 89 GHz for TPW values under about 50 mm. To lessen the effects of scattering due to vegetation, deviations from

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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

every year, with an average total yearly accumulation of 405 cm (with a mean 24 h accumulation of 28 cm) for Lake Erie and 615 cm (with a mean 24 h accumulation of 47 cm) for Lake Ontario. Spaceborne radar datasets also highlight LES prevalence globally with distinct seasonal cycles and notable shallow convective maxima located over extended high-latitude oceanic regions ( Kulie and Milani 2018 ; Kulie et al. 2016 , 2020 ). The Kulie et al. (2016) and Kulie and Milani (2018) CloudSat studies

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Giuseppe Mascaro

metropolitan area ( Fig. 1a ). The majority of the region is part of the Sonoran Desert where elevation is low (from 200 to 600 m MSL), while a minor portion is located in the transition zone to the Colorado Plateau, where elevation reaches up to 2000 m MSL in the Mogollon Rim. Climate is arid and characterized by a strong seasonality affecting the rainfall regime. In the summer months from July to September, the occurrence of the North American monsoon leads to localized convective thunderstorms with high

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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

precipitation research and applications. Focus of IPC12 and challenges ahead IPC12 focused on three main themes: 1) global precipitation estimation from multiple sensors, 2) water cycle dynamics and predictive modeling at local to regional to global scales, and 3) hydrologic impacts of precipitation extremes and anticipated change. Given the challenges of climate variability and change, especially changes in precipitation extremes and seasonality, specific emphasis was placed on subseasonal to seasonal (S2S

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Allison E. Goodwell

and water resources. We hypothesized that this information-based spatial predictability varies seasonally and regionally across the continental United States, and in certain areas is associated with climate indices. For example, Goodwell and Kumar (2019) found that predictability due to the knowledge of lagged, or past, precipitation states has increased over much of the western United States, and decreased over much of the east. This study further addresses the directions linked to this

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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

the performance of PDIR-Now in capturing the seasonal cycle of precipitation, based on monthly precipitation, with GPCP 1DD precipitation being used as a baseline for evaluation. We carry out the analysis at two regions with distinct rainfall seasonal cycles. The first region (location 1) is a rectangular region bounded by the latitudes (1°–6°N) and the longitudes (16°–21°E). This region lies at the northwestern part of the Congo River basin with elevation in the range of 0–700 m above sea level

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

regions ( Fig. S4 ). For example, in Southeast Asia (China, Myanmar, Cambodia, Vietnam), all the datasets have a low to medium p 0 (0.25 < p 0 < 0.5) except for PERSN-CDR. In equatorial Africa, however, all the datasets have a low p 0 (0.02 < p 0 < 0.25) except those from the CPC, which have high values over Africa. This difference in the results of the two regions may be due to the sparse gauge network and interpolation effects between distant gauges in equatorial Africa. For regions north of

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Stephen E. Lang and Wei-Kuo Tao

used, but there is also another option to consider. Yanai et al. (1973) recognized that the column-integrated apparent heating over an area sufficient for a cloud ensemble minus the radiation effects is balanced by the net surface precipitation and the net surface heat fluxes as follows: where Q 1 is the apparent heat source, Q R is the radiative heating rate, P o is the surface precipitation rate, S o is the surface heat flux, g is gravity, and L is the latent heat of condensation

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