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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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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

D.-J. , 2009 : Characteristics of Reprocessed Hydrometeorological Automated Data System (HADS) hourly precipitation data . Wea. Forecasting , 24 , 1287 – 1296 , doi: 10.1175/2009WAF2222227.1 . Kitzmiller, D. , Miller D. , Fulton R. , and Ding F. , 2013 : Radar and multisensor precipitation estimation techniques in National Weather Service hydrologic operations . J. Hydrol. Eng. , 18 , 133 – 142 , doi: 10.1061/(ASCE)HE.1943-5584.0000523 . Krajewski, W. F. , and Smith J. A

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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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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

repetition time (PRT; Zrnić and Mahapatra 1985 ) and dual-PRF pulsing modes and can process data using either standard pulse pair or spectral mode techniques. An advanced signal processor computes the polarimetric estimates in multiple modes such as autocovariance and spectral processing ( Doviak and Zrnić 1993 ) at selectable range resolutions and range-oversampling ratios. At the default range resolution of 75 m, the radars have a clear air sensitivity of −5 dB Z at a range of 10 km, which allows the

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

remote sensing rainfall estimates and rain gauge measurements from different data sources. The datasets are the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) for research [TRMM 3B42, version 7 (TRMM 3B42-V7)] and for real time [TRMM 3B42RT, version 7 (TRMM 3B42RT-V7)] ( Huffman et al. 2007 ), the National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), Precipitation Estimation

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

parameterization; Decharme and Douville 2006 ; Voisin et al. 2008 ; Biemans et al. 2009 ; Seo et al. 2013 ; Nikolopoulos et al. 2013 ; W2014 ). REFERENCES Adam , J. C. , E. A. Clark , and D. P. Lettenmaie , 2006 : Correction of global precipitation products for orographic effects . J. Climate , 19 , 15 – 38 , doi: 10.1175/JCLI3604.1 . Alfieri , L. , P. Burek , E. Dutra , B. Krzeminski , D. Muraro , J. Thielen , and F. Pappenberger , 2013 : GloFAS—Global ensemble streamflow forecasting

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Merhala Thurai, Kumar Vijay Mishra, V. N. Bringi, and Witold F. Krajewski

. The time series depicts the MCS event of 26 May 2013, which lasted well over 8 h. Throughout the event, we see close resemblance between XPOL-5 measurements and each of the three 2DVDs within its coverage over all four parameters. The K dp and A h comparisons are of particular interest; their temporal fluctuations are remarkably similar between XPOL-5 and 2DVDs, lending confidence and credence to our data processing techniques. Fig . 4. The time series of XPOL-5 attenuation

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