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Jackson Tan, Walter A. Petersen, Pierre-Emmanuel Kirstetter, and Yudong Tian


The Integrated Multisatellite Retrievals for GPM (IMERG), a global high-resolution gridded precipitation dataset, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scales and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final Run of IMERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5°) and accumulation periods (from 0.5 to 24 h) over a region in the southeastern United States. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multisatellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.

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Minda Le and V. Chandrasekar

rate. Brief algorithm descriptions are available in section 2 . The algorithm provides a surface snowfall flag (1 or 0 product) at each valid DPR Ku- and Ka-band matched footprint. Le et al. (2017) showed initial qualitative evaluations of the algorithm with promising results when compared to some of the Next Generation Weather Radars (NEXRAD; or WSR-88D). In this paper, we focus on performing more extensive ground validations in both qualitative and quantitative manner with NEXRAD, NASA

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Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

understand orographic modification of frontal precipitation processes but also to satisfy the need for further development and refinement of algorithms used to convert GPM’s satellite measurements to precipitation amounts in midlatitudes. The algorithms applied to TRMM satellite data over a nearly 17-yr period have been very successful for rain measurement and characterizing tropical convection ( Simpson 1988 ; Simpson et al. 1996 ; Kummerow et al. 1998 ; Zipser et al. 2006 ; Huffman et al. 2007

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Sarah D. Bang and Daniel J. Cecil

.S. National Weather Service to warn for a severe thunderstorm, and severe hail is often a harbinger of other violent weather to come ( Bluestein and Parker 1993 ; Johns and Hart 1998 ). Hailstorms’ destructive potential and their place at the upper reach of the intensity spectrum of convective precipitation drives the need within the meteorological community to establish global hail climatologies, which can be constructed using either ground-report-based or remotely sensed approaches. While there are

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Xiang Ni, Chuntao Liu, Daniel J. Cecil, and Qinghong Zhang

– 35 , doi: 10.1016/j.atmosres.2014.08.010 . 10.1016/j.atmosres.2014.08.010 Frisby , E. M. , and H. W. Sansom , 1967 : Hail incidence in the tropics . J. Appl. Meteor. , 6 , 339 – 354 , doi: 10.1175/1520-0450(1967)006<0339:HIITT>2.0.CO;2 . 10.1175/1520-0450(1967)006<0339:HIITT>2.0.CO;2 Guo , X. , D. Fu , X. Li , Z. Hu , H. Lei , H. Xiao , and Y. Hong , 2015 : Advances in cloud physics and weather modification in China . Adv. Atmos. Sci. , 32 , 230 – 249 , doi: 10

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

with satellite observations and physical constraints of the underlying processes, with fully realized dynamic interaction and feedback through explicit microphysics and mesoscale dynamics. Using an advanced ensemble data assimilation system developed for the NASA Unified Weather Research and Forecasting (NU-WRF; Peters-Lidard et al. 2015 ) Model, precipitation-sensitive microwave radiances are directly assimilated into a storm-scale NU-WRF simulation of the WAM. Assimilation of precipitation

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Liang Liao and Robert Meneghini

application to data from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite ( Iguchi et al. 2000 , 2009 ). The PR algorithm relies on a modification to the k – Z relation, where k is specific attenuation and Z is radar reflectivity factor, by a multiplicative ε factor that is used to adjust the initial k – Z relationship toward one that satisfies an independent path attenuation constraint ( Meneghini et al. 1983 ; Iguchi and Meneghini 1994 ). A similar

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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

1. Introduction NASA’s Global Precipitation Measurement (GPM) mission aims to advance understanding of Earth’s water and energy cycles and has a broader goal of improving prediction capability for high-impact weather and climate events in order to benefit society ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). Recent decades have seen tremendous precipitation science advancement, and there is now an unprecedented suite of space- and ground-based precipitation sensors in use around the

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Xiang Ni, Chuntao Liu, and Edward Zipser

1. Introduction Deep convection plays a crucial role in moisture and heat transfer in the tropics ( Riehl and Malkus 1958 ), and is a key component in organized convective systems ( Houze 1977 , 2004 ; Houze et al. 2015 ; Zipser 1977 ). They are commonly found in various kinds of weather systems, such as squall lines, hurricanes, and monsoons ( Xu and Zipser 2012 ; Jiang et al. 2011 ; Smull and Houze 1985 ). To quantify the role of the deep convection in latent heat release, it is

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Stephen E. Lang and Wei-Kuo Tao

observations and the thermal energy equation. More recently, Ahmed et al. (2016) built an algorithm to retrieve LH based on the sizes of convective and stratiform areas as well as their echo-top heights from a multiweek Weather Research and Forecasting (WRF) Model simulation using data from the Dynamics of the MJO (DYNAMO) field campaign in the Indian Ocean. In addition, the original TRMM-related algorithms have and will need to continue to evolve, especially with the expansion of TRMM’s successor, the

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