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  • IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission x
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Di Wu, Christa Peters-Lidard, Wei-Kuo Tao, and Walter Petersen

-time NU-WRF forecasts. By doing so, we would like to understand 1) if there is added value in high-resolution NU-WRF simulations using North American Mesoscale Forecast System (NAM) forcing and 2) whether there is a positive impact on precipitation forecasts with high-resolution surface initialization. We first describe the experimental design, including the modeling system, configuration, and evaluation datasets. Next, we present an evaluation of the precipitation forecasts based on an archive for

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

.g., Cunha et al. 2012 ; Seo et al. 2012 ; Ayalew et al. 2014 ). The model has the ability to estimate streamflow fluctuations for headwater basins as small as 0.1 km 2 and as large as the largest scales considered here, and it does not rely on calibrated parameters. Our paper is organized as follows. In section 2 , we review previous studies that use space-based precipitation estimates to force hydrologic models and, subsequently, to establish the need for a systematic data analysis framework

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

types of forcing data: precipitation and temperature. This research used the National Centers for Environmental Prediction (NCEP) NEXRAD stage IV rainfall data derived from multiple sensors (gauges and radars) over the CONUS. The reanalysis air temperature from phase 2 of the North American Land Data Assimilation System (NLDAS-2) available from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) was also used. Both forcing data are in 4-km, hourly, spatiotemporal resolution

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Young-Hee Ryu, James A. Smith, Mary Lynn Baeck, Luciana K. Cunha, Elie Bou-Zeid, and Witold Krajewski

9-km grid spacing and its one-way nested domain (d02 in Fig. 2 ) with a 3-km grid spacing. In the second run, after the first run is finished, we conduct a simulation with a 1-km grid spacing (d03 in Fig. 2 ) and its initial and boundary forcings are provided from the simulation of the 3-km gridded domain. The initial and boundary forcings are prepared by using the “ndown” tool in the ARW system. The reason for running the model twice separately is to constrain the initial and boundary

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Bong-Chul Seo, Witold F. Krajewski, Felipe Quintero, Mohamed ElSaadani, Radoslaw Goska, Luciana K. Cunha, Brenda Dolan, David B. Wolff, James A. Smith, Steven A. Rutledge, and Walter A. Petersen

) of the selected RR products. A direct evaluation or verification of the reference product at finer scale (e.g., hourly) was not feasible because of the lack of independent ground reference data at the required scale. Rain gauge and disdrometer data collected during IFloodS were all included in the RR uncertainty characterization and used in the reference product generation procedures. Therefore, in this section, we force a hydrologic model using the reference product and assess its predictive

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

-time product (TMPA-RP and TMPA-RT, respectively; Huffman et al. 2007 ), CMORPH and CMORPH gauge adjusted (CMORPH-adj; Joyce et al. 2004 ), NMQ/Q2 (or Q2; http://nmq.ou.edu ; Zhang et al. 2011 ), stage IV ( Lin and Mitchell 2005 ; Baldwin and Mitchell 1998 ), phase 2 of the North American Land Data Assimilation System (NLDAS-2; Mitchell et al. 2004 ; http://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing_download.php ), and CPC Unified (CPC-U; Xie et al. 2007 ; Chen et al. 2008 ). An additional radar

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

, twice the maximum record of 110 yr ( Smith et al. 2013 ). b. Data collection Precipitation from PERSIANN-CCS from 29 May to 25 June 2008 was collected to use as forcing data in the simulation. Additionally, real-time hourly gridded radar-estimated precipitation with no bias removal (4 km) NEXRAD Stage 2 or “RAD” data for the same time period was obtained from the University Corporation for Atmospheric Research (UCAR; http://data.eol.ucar.edu/codiac/dss/id=21.006 ) for a comparison to the satellite

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

applied at a variety of scales, given a set of land surface parameters, meteorological data, and an in situ network time series. Land surface parameters that impact surface soil moisture include topography, such as slope and aspect, soil type, and vegetation cover ( Jacobs et al. 2004 ; Joshi and Mohanty 2010 ). Among meteorological variables, precipitation plays the primary role in forcing soil moisture. Precipitation data can be obtained from a variety of sources, including the NEXRAD network

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Luciana K. Cunha, James A. Smith, Witold F. Krajewski, Mary Lynn Baeck, and Bong-Chul Seo

(under- or overestimation) for simulations based on different rainfall products. These maps can be used to flag regions for which we should question (or trust) model results. For example, when storm total differences for the outlet of the basin are as high as 53% (Merged-DP event 4) or as low as 80% underestimation (IFC-SP event 2), we should question our ability to provide good predictions for these events based on this rainfall forcing. The diagnostic evaluation presented in this study is only

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