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

1. Introduction The warm season convection forecasting ( Done et al. 2004 ; Kain et al. 2008 ; Weisman et al. 2008 ) is one of the most challenging weather forecast problems (e.g., Fritsch and Carbone 2004 ; Jankov et al. 2005 ), which happens over the central United States from May through August, often under “weakly forced” conditions (e.g., Carbone et al. 2002 ). Weakly forced conditions often refer to certain upper-level instabilities but without the presence of a surface front (e

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

; however, modeling and forecasting floods caused by extreme precipitation, especially flash floods, is still very challenging ( Borga et al. 2011 ). The difficulty exists with two main aspects: modeling techniques and data acquisition. Hydrologic and hydraulic models have been used to model floods driven by rainfall data from various sources: gauges, radars, satellites, and numerical forecast models. Gauge rainfall data can be the most reliable as “true observation,” but it is “point” data and

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

1. Introduction Global-scale flood forecasting systems are currently being developed, evaluated, and improved ( Wu et al. 2014 ). These forecasting systems rely on two separate but equally important components: 1) global-scale, near-real-time precipitation estimates and 2) hydrological models that partition rainfall into runoff components and route runoff to predict streamflow fluctuations at various basin outlets. Multiple papers in the literature over the past 30 years document efforts 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

(IFloodS) in collaboration with the Iowa Flood Center (IFC) at The University of Iowa. This field campaign sought to enhance the understanding of flood-related rainfall processes and the prediction capability in flood forecasting as well as to support activities of Global Precipitation Measurement (GPM) Ground Validation (see, e.g., Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). A number of scientific instruments were deployed in central and northeastern Iowa to collect high

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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

provide global predictions of floods and other precipitation-induced natural hazards. The precipitation data allow us to evaluate (validate) space-based rainfall estimates across several spatial scales, while multiple stream gauges allow us to evaluate the predictive abilities of the hydrologic models used to convert rainfall into runoff and to forecast discharge and flooding. Petersen and Krajewski (2013) provide an overview of the IFloodS science objectives. The deployment of multiple weather

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

). The importance of radars for weather analysis, warnings, and forecasting has been proven throughout the years. However, as shown by various studies, the accuracy of SP radar QPE is limited, and applying this information in hydrology requires careful attention ( Smith et al. 1996 ; Baeck and Smith 1998 ; Borga 2002 ; Cunha et al. 2012 ; Berne and Krajewski 2013 ). With the goal of improving radar QPE, the NWS has implemented a few initiatives, one of the most recent ones being the upgrade of

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

; Trier et al. 2010 ). There have been previous modeling efforts to reproduce the diurnal cycle of precipitation; however, many global and regional models have experienced difficulties in capturing the nocturnal precipitation maxima, in particular over the Great Plains (e.g., Lee et al. 2008 , and references therein). Berbery and Rasmusson (1999) reported that the National Centers for Environmental Prediction (NCEP) Eta model forecast precipitation shows a dry bias over the central United States

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Bong-Chul Seo, Brenda Dolan, Witold F. Krajewski, Steven A. Rutledge, and Walter Petersen

for reference. a. IFC-SP product The Iowa Flood Center provides a real-time composite rain map with the grid spacing of approximately 500 m over the entire state of Iowa for the purpose of flood monitoring and forecasting. This composite rain map is constructed and updated every 5-min based on the reception of the real-time streaming radar Level II volume data using the Unidata Local Data Manager (LDM) and Internet Data Distribution (IDD) technology (e.g., Sherretz and Fulker 1988 ; Fulker et al

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

estimations at different physical layers through advancing the evapotranspiration (ET) estimation in SAC-HT by accounting for the effects of photosynthetically active radiation, soil moisture and vapor pressure deficits, and air temperature on ET. Empirical relationships are used to estimate these additional variables in an effort to reduce input data requirements to a level consistent with what is available for River Forecast Center operations. This new version is referred to as the SAC-HT for Enhanced

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

rainfall associated with ARs, we need to identify the most suitable rainfall product. Because the stage IV precipitation estimates are obtained from a number of weather radars that are part of the Next Generation Weather Radar (NEXRAD) network and from hourly and 6-hourly surface rain analysis data of 12 National Weather Service (NWS) River Forecasting Centers (RFCs) over the continental United States, our a priori expectation is that stage IV will be the product that most closely resembles the CPC

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