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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

radars are equipped with an on-site uninterrupted power supply (UPS) and can be controlled and monitored remotely over the Internet. Essentially, each unit is a self-reliant system with an on-site, on-demand archiving capability for raw time series and polarimetric products, global positioning system (GPS)-enabled time and location information, and Internet access to archived data. The transmitter is magnetron based with a peak output power of 25 kW. The radars can operate in staggered pulse

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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

which we describe in this paper. We organized the paper as follows. Section 2 documents the main IFloodS campaign collaboration portal, which is the central site for the exchange of data and information among the team, and describes how it was used before and during the field campaign as well as for the subsequent distribution of quality-controlled IFloodS data to the public. Section 3 discusses the IFloodS Planning Platform, an interactive tool for campaign planning and instrument placement

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Haonan Chen, V. Chandrasekar, and Renzo Bechini

.0) presented in this paper has incorporated a region-based hydrometeor classification methodology. In addition, the specific rainfall relations have been upgraded based on real DSD observations collected during the NASA IFloodS field campaign. This paper also attempts to quantify rainfall errors introduced by radar beam broadening. Although the dual-polarization techniques provide us with better means for radar system calibration, data quality control, and rainfall estimation, the geometry of radar

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

the Next Generation Weather Radar (NEXRAD) network with dual-polarization (DP) capabilities. DP radars present many advantages over SP radars, including better characterization of hydrometeor types; enabling the identification of nonweather targets; the differentiation of rain, snow, and melting layer; and the detection of hail and heavy rain. These features allow the improvement of data quality control (QC) and QPE (e.g., Chandrasekar et al. 1990 ; Liu and Chandrasekar 2000 ; Illingworth et al

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

’s new super-resolution data for hydrologic applications . J. Hydrometeor. , 11 , 1191 – 1198 , doi: 10.1175/2010JHM1265.1 . Seo, B.-C. , Krajewski W. F. , and Villarini G. , 2012 : Rain gauge data quality control and combining data from different networks for hydrologic applications. 2012 Fall Meeting , San Francisco, CA, Amer. Geophys. Union, Abstract H41I-1278 . Seo, B.-C. , Dolan B. , Krajewski W. F. , Rutledge S. A. , and Petersen W. , 2015 : Comparison of single and dual

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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

in terms of the input variables and membership functions employed. These DP identification algorithms contain part of data quality control (see Table 1 ) and yield categories of nonprecipitation radar echoes (e.g., ground clutter and biological returns) as well as hydrometeor types. The comparison of NWS-DP and -SP products, as well as the effect of hydrometeor identification, is documented in Cunha et al. (2013) . The Stage IV product ( Lin and Mitchell 2005 ; Wu et al. 2012 ) consists of

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

, anomalous propagation effects are initially identified and eliminated using three-dimensional structure of the radar reflectivity Z h data (e.g., Steiner and Smith 2002 ) as a quality-control step. The DP variables are not used to classify nonprecipitating radar returns in this SP procedure. The quality-controlled, multielevation-angle data are then used to construct a two-dimensional reflectivity field by using the hybrid scan algorithm detailed in Seo et al. (2011) . To synchronize different

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

contaminated by bird migrations. Birds migrate northward over the central United States in spring, preferably in the nighttime to benefit from the southerly winds. We compare the meridional VAD winds and radiosonde winds for the five stations (Topeka, Lincoln, Omaha, Davenport, and Minneapolis) for the period of 2007–14 in appendix A . The VAD winds show positive biases in April–June relative to the radiosonde winds, and we perform simple quality control for VAD winds based on the analysis (see appendix

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

a water budget analysis method using independent observed streamflow data and an evapotranspiration (ET) dataset (detailed in section 5a ). Furthermore, the streamflow bias responding to precipitation bias is quantified at annual, seasonal, and daily scales, which will be useful for the quality evaluation for the GFMS real-time application in areas where precipitation data are lacking. Calibration of a hydrologic model can be conducted using the best precipitation estimation after the

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

-corrected polarimetric variables (a)–(c) Z h , (d)–(f) Z dr , (g)–(i) K dp , and (j)–(l) A h , compared with that of 2DVDs SN37, SN38, and SN70, respectively. 5. Rain-rate comparisons After validating our data processing and quality control procedures, we can now assess the four rain-rate estimators. To compare our results, we use three 2DVDs and 12 rain gauges (see Table 1 for location details of these instruments) as in situ references. a. Comparisons with 2DVDs In Fig. 5 , we show the time series

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