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

assimilating satellite observations to improve hurricane track and intensity predictions ( Pu et al. 2002 , 2008 ; Pu and Zhang 2010 ; Liu et al. 2012 ; Xu et al. 2013 ; Zou et al. 2013 ; Zhang and Pu 2014 ; Yang et al. 2016 ; Xu et al. 2016 ; Wu et al. 2016 ). Specifically, these previous studies found that satellite microwave sounders is particularly useful for understanding moist processes associated with hurricanes owing to its unique capability to depict precipitation structure and moisture

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

satellite sensors with a temporal resolution of 3 h and a spatial resolution of 0.25° × 0.25° covering 50°S to 50°N latitudes ( Huffman et al. 2007 ). This product uses a series of microwave and infrared estimates of precipitation and removes the bias using rain gauge observations. In addition, we use level-3 SMOS soil moisture retrieval at a spatial resolution of 25 km from the Barcelona Expert Centre, which is based on a level-2 SMOS orbital soil moisture dataset ( Kerr et al. 2010 ). To validate the

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