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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

Free access
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
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

Open access
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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Carolina A. Bieri, Francina Dominguez, and David M. Lawrence

, including the Pantanal wetlands, the plains of Las Pampas, and the semiarid Gran Chaco. Fig . 1. Location of the La Plata basin (red shaded region) within South America. Major cities within the basin are labeled. The black rectangle corresponds to the region of altered SM in the DRY CESM simulation. This region is also used in EOF analysis, but with 1° extensions of the northern and southern boundaries (see text). Mean MERRA-2 root-zone SM wetness fraction and precipitation are also depicted for SESA

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Ayumi Fujisaki-Manome, Greg E. Mann, Eric J. Anderson, Philip Y. Chu, Lindsay E. Fitzpatrick, Stanley G. Benjamin, Eric P. James, Tatiana G. Smirnova, Curtis R. Alexander, and David M. Wright

with previous research using WRF at this horizontal grid spacing for this application (e.g., Shi and Xue 2019 ; Wright et al. 2013 ). Hourly updated Rapid Refresh ( Benjamin et al. 2016a ) fields were used as initial and lateral boundary conditions as currently applied in operations. A one-dimensional lake model implemented in WRF ( Oleson et al. 2013 ) was used for smaller inland lakes. Over the Great Lakes, the control lower boundary condition for the lake surface (i.e., lake

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