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Minda Le and V. Chandrasekar

profiles. In other words, 97% of snow profiles have SI > 17, while 97% of rain profiles have SI ≤ 17. A snow flag is generated using the thresholds of the snow index as well as other auxiliary information such as 0°C isotherm and clutter-free height but only as constraints. This surface snowfall flag is a “0” or “1” product, where “0” represents no snow and “1” represents snow. In the GPM DPR level-2 algorithm, this flag is named as “flagSurfaceSnowfall” and is currently available in the experimental

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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

pulse of our planet. NASA supports and enables a diverse range of applications of Earth science data. In this context, “applications” refers to the use of satellite and airborne data and related products in decision-making for societal benefit ( Brown and Escobar 2014 ). The ultimate goals of these efforts are to effectively and efficiently identify, respond to, and address the needs of current and potential user groups by guiding how the application of satellite data, models, and products can be

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W.-K. Tao, T. Iguchi, and S. Lang

-resolving models (CRMs) are regularly used to build lookup tables (LUTs) for LH retrieval algorithms (see the review papers by Tao et al. 2006 , 2016a ). Currently, two different LH algorithms, the Goddard convective–stratiform heating (CSH) and the Japanese spectral latent heating (SLH), are being used to produce standard LH products for both the TRMM and GPM periods. Table 1 shows the key references, inputs, CRM-simulated cases, and the LUTs used in the CSH and SLH algorithms. The CSH- and SLH

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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

Hurricane Joaquin. HWRF Model forecast domains, as indicated by d01, d02, and d03 and HWRF data assimilation domains, as indicated by ghost d02 (black shaded area), and ghost d03 (pink shaded area) are also indicated. The current data assimilation system for HWRF is the NCEP GSI-based hybrid ensemble–3DVar system. The cost function used in the data assimilation system is defined as follows: where the first term is the background error term with a hybrid background error covariance, which is a sum of two

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

surface scheme ( Jiménez et al. 2012 ), the Noah land surface model ( Chen and Dudhia 2001 ), the Yonsei University (YSU) planetary boundary layer scheme ( Hong et al. 2006 ), and the Kain–Fritsch cumulus scheme ( Kain 2004 ). c. WRF 4D-Var system and precipitation data assimilation The WRF data assimilation (WRFDA) system, developed collaboratively by several agencies and institutes, is currently maintained by the National Center for Atmospheric Research (NCAR). This study uses the 4D-Var component

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

modeling system that represents cloud, precipitation, aerosol, and land process ( Peters-Lidard et al. 2015 ). It is based on the Advanced Research WRF ( Skamarock et al. 2008 ) with additional coupling to advanced Goddard physics packages, satellite simulators, and high-resolution satellite/reanalysis data to initialize boundary conditions. In this work the NU-WRF model simulations are carried out at storm scale with lateral boundary forcing from the Modern-Era Retrospective Analysis for Research and

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Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

satellite precipitation sensors have shown the value of satellite-based global rain observations ( Hou et al. 2014 ). Now there is great interest in hydrological investigations with global falling snow-rate products to help close the atmospheric inputs to the water cycle ( Field and Heymsfield 2015 ; Rodell et al. 2015 ) and to study snowpack patterns and growth ( Palerme et al. 2014 ; Boening et al. 2012 ; Margulis et al. 2016 ; Wrzesien et al. 2018 ). Currently there are two National Aeronautics

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Sarah D. Bang and Daniel J. Cecil

footprints containing hail exhibit nonuniform beamfilling (NUBF), in which a combination of hailing and nonhailing portions of the scene are represented. Though differing footprint sizes and different orbits complicate combination of microwave radiometer datasets from different satellite platforms, the microwave channels between 10 and 89 GHz are on board several weather satellites ( Aqua , MetOp-A and MetOp-B , TRMM, and GPM, among many others). The platforms currently in orbit provide near

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

et al. 2015 ; Nasseri et al. 2013 ; Sunyer et al. 2015b ; Tareghian and Rasmussen 2013 ). In this study, we prefer deterministic and gridded SD for two reasons. First, for daily SD, one of the scientific objectives is to reduce the uncertainty and represent daily variability in a deterministic manner. The probability distribution function of precipitation under a certain atmosphere circulation is usually related to the modeled mean values. Actually, some of the current stochastic SD methods

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E. F. Stocker, F. Alquaied, S. Bilanow, Y. Ji, and L. Jones

mission deserves special mention; less variation in the reported roll and yaw are shown in Fig. 2 during the orbit descent phase at the end of 2014 and into 2015. This feature is because the PR instrument science data were not available during that time span for estimated true roll. The current processing reverts effectively to assuming the near-zero onboard roll/yaw estimate in the absence of better data, so the minimum and maximum values are quite small. In reality the true variation would have

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