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Haonan Chen, V. Chandrasekar, and Renzo Bechini

Abstract

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

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Di Wu, Christa Peters-Lidard, Wei-Kuo Tao, and Walter Petersen

-time NU-WRF forecasts. By doing so, we would like to understand 1) if there is added value in high-resolution NU-WRF simulations using North American Mesoscale Forecast System (NAM) forcing and 2) whether there is a positive impact on precipitation forecasts with high-resolution surface initialization. We first describe the experimental design, including the modeling system, configuration, and evaluation datasets. Next, we present an evaluation of the precipitation forecasts based on an archive for

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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

, whose derived products include NEXRAD’s Stage IV precipitation product and the North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004 ) derived, “quasi-operational” precipitation values rather than directly observed estimates. For this study, it is advantageous to reduce the number of variables influencing soil moisture, as each parameter or variable adds complexity to the modeling structure. This approach requires that, at each location, we employ a model calibrated at a similar

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Huan Wu, Robert F. Adler, Yudong Tian, Guojun Gu, and George J. Huffman

-time product (TMPA-RP and TMPA-RT, respectively; Huffman et al. 2007 ), CMORPH and CMORPH gauge adjusted (CMORPH-adj; Joyce et al. 2004 ), NMQ/Q2 (or Q2; http://nmq.ou.edu ; Zhang et al. 2011 ), stage IV ( Lin and Mitchell 2005 ; Baldwin and Mitchell 1998 ), phase 2 of the North American Land Data Assimilation System (NLDAS-2; Mitchell et al. 2004 ; http://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing_download.php ), and CPC Unified (CPC-U; Xie et al. 2007 ; Chen et al. 2008 ). An additional radar

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Young-Hee Ryu, James A. Smith, Mary Lynn Baeck, Luciana K. Cunha, Elie Bou-Zeid, and Witold Krajewski

heavily on reanalysis fields to examine the atmospheric water cycle. These analyses inherently rely on model parameterization schemes, model resolution, and data sampling frequency. For example, Ryu et al. (2015) reported that North American Regional Reanalysis (NARR) considerably underestimates the diurnal variation in water vapor flux in the eastern United States, compared to observationally derived water vapor flux analyses. We use a new method proposed by Ryu et al. (2015) for computing

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Munir A. Nayak, Gabriele Villarini, and A. Allen Bradley

Dettinger M. D. , 2008 : Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the west coast of North America based on eight years of SSM/I satellite observations . J. Hydrometeor. , 9 , 22 – 47 , doi: 10.1175/2007JHM855.1 . Neiman, P. J. , Wick G. A. , Moore B. J. , Ralph F. M. , Spackman J. R. , and Ward B. , 2014 : An airborne study of an atmospheric river over the subtropical Pacific during WISPAR: Dropsonde budget-box diagnostics and

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Andrea Thorstensen, Phu Nguyen, Kuolin Hsu, and Soroosh Sorooshian

–monitoring networks have been providing local soil moisture measurements for years. As part of the Oklahoma Mesonet ( Brock et al. 1995 ), soil moisture–monitoring instruments have been deployed since 1996 ( Scott et al. 2013 ). The NOAA Hydrometeorology Testbed program has developed soil moisture observation networks in the Russian River and North Fork American River basins in California as well as the San Pedro River basin in Arizona ( Zamora et al. 2011 ). Since the 1990s, the U.S. Department of Agriculture

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