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Kumar Vijay Mishra, Witold F. Krajewski, Radoslaw Goska, Daniel Ceynar, Bong-Chul Seo, Anton Kruger, James J. Niemeier, Miguel B. Galvez, Merhala Thurai, V. N. Bringi, Leonid Tolstoy, Paul A. Kucera, Walter A. Petersen, Jacopo Grazioli, and Andrew L. Pazmany

( Krajewski et al. 2003 ). More importantly, detailed observations of rainfall at the near-ground level remain undetected in S- and C-band radars at far ranges because of the sparsity of the operational networks. Therefore, it has become increasingly more common to use shorter wavelengths, such as those in the X and Ku bands, to complement the S- and C-band rainfall observations and monitor the precipitation variability at scales smaller (e.g., basin) than the available products from the longer

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

), Advanced Synthetic Aperture Radar (ASAR), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and Ocean Topography Experiment (TOPEX)/Poseidon radar altimeter can be used to estimate flood inundation, water level, and river discharge. Applications of satellite-based surface water measurements can be found in Hossain et al. (2014a , b) , Khan et al. (2014) , Alsdorf et al. (2007) , Bjerklie et al. (2005) , Brakenridge et al. (2007) , Bates et al. (1997) , and Behrangi et

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Haonan Chen, V. Chandrasekar, and Renzo Bechini

1. Introduction Building upon the success of Tropical Rainfall Measuring Mission (TRMM), the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) have embarked on the Global Precipitation Measurement (GPM) mission ( Hou et al. 2014 ). The GPM Core Observatory satellite, carrying the first spaceborne Dual-Frequency Precipitation Radar (DPR) operating at Ku and Ka band and the GPM Microwave Imager (GMI), was launched on 27 February 2014. The active

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

extension of this approach to the watershed-scale estimates where such sensors are not available. Additionally, recent work analyzing in situ networks has been deployed to validate satellite observations, comparing satellite estimates from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and other satellite estimates to the Soil Climate Analysis Network (SCAN) and the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) watersheds ( Reichle et al. 2011

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

; Peters-Lidard et al. 2007 ). The infrastructure can not only be directly coupled with the atmosphere, but it can also integrate high-resolution observations with the model forecasts to generate improved estimates of land surface conditions such as soil moisture, evaporation, snowpack, and runoff at 1-km and finer spatial resolutions and at 1-h and finer temporal resolutions. During the IFloodS campaign period, two sets of 48-h NU-WRF forecasts were produced twice a day initialized at 0000 and 1200

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

precipitation estimation (QPE) remains very challenging ( Kidd and Levizzani 2011 ). All existing methods for precipitation information estimation have both strengths and weaknesses, with significant uncertainties being reported even in ground-based radar and gauge observations (e.g., Adam et al. 2006 ; Clark and Slater 2006 ). In situ (gauge) observations measure precipitation directly, while they have weakness in spatial coverage and issues of undercatch during windy weather and occasional loss of

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Felipe Quintero, Witold F. Krajewski, Ricardo Mantilla, Scott Small, and Bong-Chul Seo

address the first component of using satellite observations to estimate global precipitation. Recent summaries of these efforts are discussed by Kucera and Lapeta (2014) , Qu and Powell (2013) , Gebremichael and Hossain (2010) , and Kidd and Levizzani (2010) , among others. A common thread in the literature is the acknowledgment of considerable uncertainty associated with space-based precipitation estimates ( Young et al. 2014 ; Maggioni et al. 2014 ; Gebregiorgis and Hossain 2014 ; Moazami et

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Ibrahim Demir, Helen Conover, Witold F. Krajewski, Bong-Chul Seo, Radosław Goska, Yubin He, Michael F. McEniry, Sara J. Graves, and Walter Petersen

that were distributed over a region of about 50 000 km 2 . Many of these instruments reported in real time or with only small (minutes) delays and deposited their data in relational databases that were relayed to other centers and users. Computational models were fed with input data from these databases, as well as satellite observations and other products, to provide output that was ready for inspection by the researchers. Crews of technical staff, assisted by students, frequently monitored the

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

Press, 496 pp . Huffman, G. J. , and Coauthors , 2007 : The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales . J. Hydrometeor. , 8 , 38 – 55 , doi: 10.1175/JHM560.1 . Joyce, R. J. , Janowiak J. E. , Arkin P. A. , and Xie P. , 2004 : CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution . J. Hydrometeor. , 5 , 487

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