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Paulo Rodrigo Zanin and Prakki Satyamurty

: Discharge variability within the Amazon basin. IAHS Publ. , 296 , 21–30. Rozante , J. R. , D. S. Moreira , L. G. G. Gonçalves , and D. Vila , 2010 : Combining TRMM and surface observations of precipitation: Technique and validation over South America . Wea. Forecasting , 25 , 885 – 894 , . 10.1175/2010WAF2222325.1 Satyamurty , P. , C. P. W. Costa , and A. O. Manzi , 2012 : Moisture source for the Amazon basin: A study of contrasting

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Martyn P. Clark, Reza Zolfaghari, Kevin R. Green, Sean Trim, Wouter J. M. Knoben, Andrew Bennett, Bart Nijssen, Andrew Ireson, and Raymond J. Spiteri

B. G. Thomas , 1990 : Fixed grid techniques for phase change problems: A review . Int. J. Numer. Methods Eng. , 30 , 875 – 898 , . 10.1002/nme.1620300419 Wada , Y. , D. Wisser , and M. F. Bierkens , 2014 : Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources . Earth Syst. Dyn. , 5 , 15 – 40 , . 10.5194/esd-5-15-2014 Walker , A. P. , and Coauthors

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C. Kidd, P. Bauer, J. Turk, G. J. Huffman, R. Joyce, K.-L. Hsu, and D. Braithwaite

products were chosen, together with comparative data from the GPI, the ECMWF operational forecast model, surface radar, monthly Global Precipitation Climatology Centre (GPCC) gauge analysis, and U.K. national hourly gauge data. a. Satellite precipitation products 1) CMORPH technique The CMORPH technique was developed by Joyce et al. (2004) to exploit the fact that the retrievals of precipitation from PMW observations are better than those derived from IR techniques, although the IR data are capable

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Newsha K. Ajami, Qingyun Duan, Xiaogang Gao, and Soroosh Sorooshian

to suggest that multimodel ensemble simulations should be considered as an operational forecasting tool. The fact that the simple multimodel averaging approach such as the one used by Georgakakos et al. (2004) has led to more skillful and reliable simulations motivated us to examine whether more sophisticated multimodel combination techniques can result in consensus simulations of even better skill. Most hydrologists are used to the traditional contructionist approach, in which the goal of the

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Paul Block and Balaji Rajagopalan

standard deviation of σ ple can be generated and added to y pl to create an ensemble. The latter technique is implemented in this study. It is analogous to using the standard confidence/prediction interval equation from linear regression. Examples of ensemble forecasts in other work include Souza and Lall (2003) , Rajagopalan et al. (2005) , and Regonda et al. (2005 , 2006 ); x pl may be an observed data point or a new point. It is noteworthy to mention that for α = 1 and P = 1

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Zhiyong Liu, Ping Zhou, and Yinqin Zhang

( McKerchar and Delleur 1974 ; Kim and Barros 2001 ; Kisi and Cimen 2011 ; Nourani et al. 2014 ). These techniques have the advantages of minimum information requirements, ease of real-time implementation, and rapid development times ( Moradkhani et al. 2004 ). Although data-driven models may not be able to provide physical interpretations and insights into catchment processes, they usually produce relatively skillful and accurate forecasts ( Tiwari and Chatterjee 2010 ). Thus, they might be a suitable

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Augusto Getirana, Matthew Rodell, Sujay Kumar, Hiroko Kato Beaudoing, Kristi Arsenault, Benjamin Zaitchik, Himanshu Save, and Srinivas Bettadpur

hindcasts (i.e., forecasts of past events, or historical forecasts) up to 90 lead days in the future using near surface meteorological data from two different established seasonal forecasting techniques. Hindcasts are evaluated in terms of improvement to forecasted groundwater, when compared with those initialized by the reference simulation, also called the open loop (OL) simulation (i.e., no perturbation or assimilation applied). 2. Data and methods GRACE-based TWS is assimilated into the Catchment

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E. I. Nikolopoulos, N. S. Bartsotas, E. N. Anagnostou, and G. Kallos

rainfall is based on forecasted fields, while Zhang et al. (2013) used Weather Research and Forecasting Model simulations at 2-km resolution based on reanalysis data. Investigating the effectiveness of this approach based on forecasted precipitation allows us to evaluate its potential in real-time QPE. Second, Zhang et al. (2013) examined the application of the technique on CPC morphing technique (CMORPH) satellite estimates, while in this work we examine two additional real-time precipitation

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C. Lu, H. Yuan, E. I. Tollerud, and N. Wang

National Oceanic and Atmospheric Administration’s (NOAA) satellite data products, called Climate Prediction Center morphing technique (CMORPH; Joyce et al. 2004 ), which provides 6-hourly precipitation amounts on a 0.25° × 0.25° grid for a near-global domain. To compare with the GFS forecast fields, the CMORPH data are summed over 24 hours and aggregated on 1° × 1° grids. Figures 1b and 1e show the resulting averaged 24-h precipitation amounts estimated by CMORPH for winter and summer seasons

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Negin Hayatbini, Kuo-lin Hsu, Soroosh Sorooshian, Yunji Zhang, and Fuqing Zhang

.1° × 0.1° resolution over the chosen domain of 50°–50°S with 30-min time intervals ( Huffman et al. 2015 ). IMERG consists of algorithms from the Climate Prediction Center (CPC) morphing technique (CMORPH) from NOAA ( Joyce et al. 2004 ), the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) from NASA ( Huffman et al. 2007 ), and microwave-recalibrated PERSIANN-CCS ( Hong et al. 2004 ). The processing steps of PERSIANN-CCS algorithm include 1) cloud image

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