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Dusanka Zupanski, Sara Q. Zhang, Milija Zupanski, Arthur Y. Hou, and Samson H. Cheung

satellite observations with dynamic consistency. However, assimilation of satellite precipitation-affected observations into numerical weather prediction (NWP) models posts special challenges, due to the difficulties in incorporating cloud physics and the precipitation process into observation operators in the data assimilation procedures and in estimating the forecast error covariance in cloudy and precipitation regions. Because of these difficulties, it has been a common practice in operational

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F. M. Ralph, E. Sukovich, D. Reynolds, M. Dettinger, S. Weagle, W. Clark, and P. J. Neiman

recommendation of this report was to establish a Hydrometeorology Testbed (HMT) to accelerate research and improvements in QPFs—particularly extreme QPFs—through better physical understanding, observations, numerical modeling, and decision support systems—all of which are critical elements of the operational forecast process for QPFs and are important for future climate services related to extreme events (e.g., National Weather Service 1999 ; Antolik 2000 ; Morss and Ralph 2007 ). This paper follows a

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H. Leijnse, R. Uijlenhoet, C. Z. van de Beek, A. Overeem, T. Otto, C. M. H. Unal, Y. Dufournet, H. W. J. Russchenberg, J. Figueras i Ventura, H. Klein Baltink, and I. Holleman

to preview the data by quick looks, which are stored with the data files in the database. The data are all stored in network common data form (netCDF) format (available online at http://www.unidata.ucar.edu/software/netcdf ) and comply with the Climate and Forecast (CF) Metadata Convention version 1.4 (available online at http://cf-pcmdi.llnl.gov ). The CDS has become operational in July 2009 and will be filled with many datasets from the operational continuous measurement program, measurements

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Jian Zhang and Youcun Qi

.49712455112 Steiner, M. , Houze R. A. Jr. , and Yuter S. E. , 1995 : Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor. , 34 , 1978 – 2007 . 10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2 Tabary, P. , 2007 : The new French operational radar rainfall product. Part I: Methodology. Wea. Forecasting , 22 , 393 – 408 . 10.1175/WAF1004.1 Tabary, P. , Desplats J. , Do Khac K. , Eideliman F. , Gueguen C

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Sandra E. Yuter, David A. Stark, Justin A. Crouch, M. Jordan Payne, and Brian A. Colle

in nature and are more typical for the region. While much can be learned from the study of atypically strong cross-barrier winds such as the 13–14 December 2001 IMPROVE II case, improvements to routine operational forecasts require detailed examination of more typical storms as well. A broader impact of determining the locations of precipitation frequency local maxima relates to the husbanding of limited observing instrument resources. Moving a subset of precipitation gauges to locations where

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Youcun Qi, Jian Zhang, Brian Kaney, Carrie Langston, and Kenneth Howard

VPRs. However, the local VPR approach is relatively expensive computationally and is not easily implemented for operational applications. Kitchen et al. (1994) described a correction scheme in which observational data, combined with simple parameterizations of the bright band and low-level orographic growth, were used to construct an idealized reflectivity profile at each pixel. A linear relationship between the area of the brightband peak and the “background” reflectivity factor {i.e., “[t

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Ali Behrangi, Bisher Imam, Kuolin Hsu, Soroosh Sorooshian, Timothy J. Bellerby, and George J. Huffman

1. Introduction High-quality precipitation data at fine time and space resolution have many hydrometeorological applications including flood forecasting, drought monitoring, disaster management, and initialization of numerical weather prediction models, among others. The current constellation of earth observing satellites allows global retrieval of precipitation data that complement ground precipitation observations from relatively sparse radar/gauge networks. While high

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Yudong Tian, Christa D. Peters-Lidard, and John B. Eylander

tested with real satellite-based data, but has not been employed to produce corrected data operationally. The formulation of the Bayesian scheme is given in section 2 . Data and methodology used in this study are described in section 3 . Results are presented in section 4 , followed by conclusions and a discussion in section 5 . 2. Formulation At a grid point, when both gauge measurements and satellite estimates are available, we denote their values as G i and S j , respectively. Then for the

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Jonathan J. Gourley, Scott E. Giangrande, Yang Hong, Zachary L. Flamig, Terry Schuur, and Jasper A. Vrugt

1. Introduction Weather radars sample the atmosphere at high spatial resolution over contiguous regions, whereas operational rain gauge networks collect rainfall nearly continuously at points. Despite the opportunity to capture the spatial variability of rainfall, a number of studies have identified and quantified errors in radar rainfall estimation. Relevant literature reviews of radar-based rainfall errors can be found in Wilson and Brandes (1979) , Austin (1987) , and Joss and Waldvogel

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