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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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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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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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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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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

Open access
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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Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Mohan Thota, Vineeth Valappil, Oliver Branch, Hans-Dieter Wizemann, Niranjan Kumar Kondapalli, Youssef Wehbe, Taha Al Hosary, Abdeltawab Shalaby, Noor Al Shamsi, and Hajer Al Naqbi

hand, the LULC gives information about the land’s physical type and how it is currently being used (i.e., urban, cropland, shrubland, desert, etc.), determining surface properties such as albedo, emissivity and roughness length. While the albedo and emissivity are normally estimated using remote sensing assets such as satellites (e.g., Giri 2012 ; Sun and Schulz 2015 ; Fritz et al. 2017 ; Rwanga and Ndambuki 2017 ), and sometimes through field surveys (e.g., D’Antona et al. 2008 ), the

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Alejandro Hermoso, Victor Homar, and Arnau Amengual

of convective parameterizations has been obtained for resolutions higher than 4 km (e.g., Kain et al. 2008 ; Weisman et al. 2008 ). This model configuration, as well as the initial and lateral boundary conditions (IC/LBCs) are taken from one ensemble member out of all the ensemble strategies investigated in Part II. In particular, the boundary conditions are extracted from the global ECMWF ensemble prediction system and soil parameters are downscaled from the control member of the ECMWF

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