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Yixin Wen, Terry Schuur, Humberto Vergara, and Charles Kuster

, space-based precipitation measurements often have underestimation issues ( Wen et al. 2018 ; Bartsotas et al. 2018 ). Weather radars provide perhaps the best opportunity to collect precipitation with both high spatial and temporal resolutions. The current NWS Weather Surveillance Radar–1988 Doppler (WSR-88D) network has dramatically increased our ability to observe high-resolution precipitation data in space and time. Technically, WSR-88Ds are capable of implementing scanning strategies that would

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Zhichang Guo and Paul A. Dirmeyer

of transitional climate zones in Europe as a consequence of global warming. The geographic locations of hot spot regions in turn might be changed since they follow the migrated transition zone. These studies have suggested that interannual variability of soil moisture could result in year-to-year variability in land–atmosphere coupling strength. The current study examines the interannual variation in land–atmosphere coupling strength and how it is related to soil moisture anomalies. This is of

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Shida Gao, Pan Liu, and Upmanu Lall

? How well can the leading modes of precipitation for the next season be predicted from the leading modes of the current season’s SST and IVT? Since the SST field represents the slowly evolving boundary conditions for the atmosphere and hence for IVT and precipitation, we are interested in seeing how the SST information translates into the seasonal statistics (specifically the mean field) of IVT and through that to the hemispheric precipitation, at the seasonal time scale. The covariation of these

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Clara Sophie Draper

-consuming model retuning to obtain these forecast benefits, since NWP boundary layer schemes tend to be heavily tuned to their current (deterministic) land model climatology. 5. Conclusions and recommendations A number of conclusions and recommendations can be drawn from this study, starting with those for offline land data assimilation systems. First, the standard method of adding soil moisture perturbations to each ensemble member at regular time intervals produces unrealistic spatial patterns in the

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Jian Zhang, Lin Tang, Stephen Cocks, Pengfei Zhang, Alexander Ryzhkov, Kenneth Howard, Carrie Langston, and Brian Kaney

1. Introduction High-resolution and high-accuracy quantitative precipitation estimation (QPE) is a key component for many applications in agriculture, manufacturing, flash flood detection, river flood prediction, water resource managements, and climate assessments. The observation of precipitation requires various remote sensing systems and networks encompassing surface rain/snow gauges, radar, and satellite. Among them, ground radar networks currently provide the highest spatial and temporal

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Terence J. Pagano, Duane E. Waliser, Bin Guan, Hengchun Ye, F. Martin Ralph, and Jinwon Kim

cell with the largest onshore IVT is designated as the landfall location of the AR object. An example of AR objects from the global AR detection algorithm for 0000 UTC 18 January 2012 is shown in Fig. 2a . Variables provided by the AR database and used in the current study include the AR’s shape, zonal and meridional IVT, and landfall location. Fig . 2. (a) Atmospheric river objects extracted from the Guan and Waliser (2015) detection algorithm at 0000 UTC on 18 Jan 2012. The red-outlined box

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Bong-Chul Seo, Witold F. Krajewski, and Alexander Ryzhkov

Wang et al. (2019) . In this paper, we describe the implementation of R ( A ) within our current real-time QPE framework based on R ( Z ) and comparatively assess the performance of the adapted R ( A ) and current R ( Z ) algorithms. In section 2 , we provide information on the spatial domain and data for WSR-88D radars, temperature sounding retrieved from the numerical weather prediction model analysis, and ground reference data used in this study. Section 3 describes our R ( A

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E. J. Barton, C. M. Taylor, C. Klein, P. P. Harris, and X. Meng

authors commented that further work was needed to assess sensitivity to sub-plateau-scale surface wetness patterns. The impact of soil moisture (SM) on moist convection has been recognized by numerous works (e.g., see reviews by Seneviratne et al. 2010 ; Santanello et al. 2018 ). SM affects the partitioning of surface fluxes into sensible and latent heat, which control planetary boundary layer (PBL) growth and moisture availability, respectively. Depending on the atmospheric stability profile

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He Sun, Fengge Su, Zhihua He, Tinghai Ou, Deliang Chen, Zhenhua Li, and Yanping Li

-elevation area in Asia centered on the Tibetan Plateau and the origin of major Asian rivers ( Fig. 1 ). Fig . 1. Topography and boundaries of nine upper river basins in the TP. UYE, UYA, UM, US, UB, UI, UAMD, USRD, and UYK denote the upper regions of the Yellow, Yangtze, Mekong, Salween, Brahmaputra, Indus, Amu Darya, Syr Darya, and Yarkant river basins, respectively. The green dots denote the national meteorological stations used in this study. Gridded datasets obtained by interpolating limited observations

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Eric A. Rosenberg, Andrew W. Wood, and Anne C. Steinemann

important for seasonal streamflow prediction. With about 90% of its area falling within this system, HHWM8 is a classic example of this scenario, but others (e.g., EGLC2, GBRW4, and NVRN5) contain wilderness areas as well, primarily at higher elevations near basin boundaries. Table 2. Summary statistics for the 24 basins in the study. Mean values were calculated over the calibration period. The annual runoff ratio is defined as the ratio of annual runoff to annual precipitation. Fig . 1. The 24 basins

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