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  • IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission x
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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

1. Introduction Soil moisture estimates are valuable for numerous agricultural and hydrologic applications. At the point scale, estimates of field wetness for enhanced agricultural decision support enable real-time irrigation scheduling in drier climates ( Rao et al. 1988 ) and real-time estimates of potential field trafficability in more humid locations ( Coopersmith et al. 2014b ). At larger scales, soil moisture is an integral component of hydrologic storage and subsurface flows at the

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

hydrograph is the result of a collection of many internal basin processes ( Ivanov et al. 2010 ; Liang and Xie 2001 ). Several studies have pointed to soil moisture as a possible vehicle for describing these heterogeneous subbasin processes, particularly in respect to how streamflow is modulated ( Santanello et al. 2007 ; Campo et al. 2006 ; Wanders et al. 2014 ; Zamora et al. 2014 ). Some have turned to other variables such as evapotranspiration ( Rientjes et al. 2013 ; Immerzeel and Droogers 2008

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

.g., because of the presence of an upper-level trough and advection of potential vorticity). Numerous efforts have been undertaken to improve the warm season precipitation forecast (e.g., Gallus 1999 ; Gallus and Segal 2000 ; Carbone et al. 2002 ). Studies have demonstrated that choosing the convective scheme strongly affects simulated precipitation (e.g., Wang and Seaman 1997 ; Gallus 1999 ). Convective schemes are designed with different assumptions (e.g., mass flux and moisture adjustment) and are

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

vapor flux, and thermodynamic properties focus on this scale. It has been well documented that flooding events in the central United States are concentrated in late spring and early summer ( Wang and Chen 2009 ; Villarini et al. 2011a , b ; Smith et al. 2013 ) and that water vapor transported from the tropics is an important moisture source for precipitation and thus flooding events ( Rasmusson 1967 , 1968 , 1971 ; Trenberth and Guillemot 1996 ; Dirmeyer and Kinter 2010 ; Lavers and Villarini

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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

radars as well as numerous disdrometers and rain gauges, soil moisture sensors, and streamflow and stream-stage measuring devices while engaging in real-time data acquisition and ingestion into models requires an appropriate cyberinfrastructure. The term cyberinfrastructure, as defined in the National Science Foundation’s (NSF) landmark Atkins report ( Atkins et al. 2003 ), encompasses sensors, communication, computers, models, and people ( Newman et al. 2003 ; Bottum et al. 2008 ). Off

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

these high rainfall amounts and potential strong link between ARs and heavy rainfall over the central United States, it is reasonable to consider that some of the heavy rainfall events during IFloodS may have been associated with ARs. Quantifying the contribution from ARs is one of the primary objectives of the paper. Furthermore, to the best of our knowledge the distribution of rainfall accompanying ARs and its relationship with the strength of moisture transport within ARs over the central United

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

initial soil moisture conditions, we used values obtained through a spinup of the model with stage IV rainfall starting on 1 April, 4 weeks prior to the analyzed period. 5. Results In this section, we illustrate the proposed framework using data from IFloodS. We limit our consideration to three products. We use stage IV data as a reference and compare them with one radar-only product (IFC rainfall) and one satellite-based product (TMPA). Other radar rainfall products were evaluated by Cunha et al

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

larger coverage and finer spatiotemporal resolution have been available for use. Furthermore, with numerous contemporary and future missions such as NASA’s Global Precipitation Measurement (GPM; launched in 2014) and NASA’s Soil Moisture Active Passive (SMAP; launched in 2015), NASA offers the unique opportunity to better understand the physics of floods in order to develop a new generation of models, which will improve global flood forecasting. Moreover, powerful computing systems motivate modelers

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

mean annual scale cannot be directly applied to seasonal and finer time scales because of the larger uncertainty from soil moisture and surface water storage. The routing time estimation, seasonal agriculture practice, and other human regulation of the water systems make it more complicated to calculate the precipitation from streamflow and ET observations. Since the NLDAS-2 has the lowest bias (−4%) and the best model performance ( Table 2 ) in the long-term simulation, it is selected to

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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

soil moisture probes in the vicinity of the IFC network. The ASOS and AWOS data were collected with their original resolutions (i.e., 1 and 5 min, respectively) and accumulated over the designated time intervals (e.g., hourly). The use of the NWS COOP data was limited to the rain total and daily analyses because the network only reports data daily. 3. Methodology We provide the analysis procedures that are associated with product evaluation and error characterization with respect to multiple time

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