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

importance of extreme precipitation, the challenge of predicting it accurately, and the regional variations of what defines an “extreme” precipitation event, this paper recommends that unique regional precipitation thresholds be used in the future to assess extreme precipitation QPF performance. These regionally relevant QPF performance statistics can then be combined to evaluate the forecast performance of extreme precipitation events nationally. 2. Data and methodology overview Data from the November

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James A. Smith, Gabriele Villarini, and Mary Lynn Baeck

. 10.1175/2010MWR3315.1 Villarini, G. , Smith J. A. , Baeck M. L. , and Krajewski W. F. , 2011 : Examining regional flood frequency in the midwest U.S. J. Amer. Water Resour. Assoc. , in press . Vitolo, R. , Stephenson D. B. , Cook I. M. , and Mitchell-Wallace K. , 2009 : Serial clustering of intense European storms. Meteor. Z. , 18 , 411 – 424 . 10.1127/0941-2948/2009/0393 Waylen, P. , 1991 : Modeling the effects of tropical cyclones on flooding in the Santa Fe River

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Timothy J. Lang, Steven A. Rutledge, and Robert Cifelli

and LeMone 1989 ; Hu and Srivastava 1995 ; Rosenfeld and Lensky 1998 ; Atlas and Ulbrich 2000 , 2006 ; Ulbrich and Atlas 2007 ). These two archetypes have been invoked to explain, among other things, observed regional variability in latent heating profiles ( Tao et al. 2001 , 2006 , 2010 ) as well as the observed land–ocean contrast in lightning flash rates ( Orville and Spencer 1979 ; Zipser and Lutz 1994 ; Boccippio et al. 2000 ; Nesbitt et al. 2000 ; Toracinta et al. 2002 ; Xu et al

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Masamichi Ohba, Shinji Kadokura, Yoshikatsu Yoshida, Daisuke Nohara, and Yasushi Toyoda

effects of WPs on the composite anomalous local rainfall is also reflected in the spatial distribution of 150 mm day −1 frequency of occurrence ( Fig. 4b ). Fig . 4. (a) Cluster-averaged (composited) anomalies of precipitation and (b) frequency of occurrence (%) for 150 mm day −1 rainfall for each cluster. Climatology is also plotted at the bottom right in (b) for comparison. The relationship between the WPs and heavy rainfall represents a significant regional difference, potentially due to

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F. M. Ralph, T. Coleman, P. J. Neiman, R. J. Zamora, and M. D. Dettinger

.7 to 31.6 m 3 s −1 (hereafter cms) and on the Russian River from 55.6 to 159.5 cms (a factor of 5.3 and 2.8, respectively). When only those events with at least 45 mm of precipitation are considered (60 events), the average duration was 29 h ( Table 2 ). More so than the effects of increased maximum upslope IWV flux (7%) and average rain rates (16%), it is the 45% longer duration that led to a 68% increase in storm-total rainfall for this subset of cases ( Table 2 ). The difference between 29

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

patterns of precipitation intensity and frequency change for different environmental characteristics? Which environmental variables have the largest impact on increasing frequency and intensity of precipitation? Some background on Portland, Oregon, regional storm characteristics is provided in section 2 . Section 3 describes our datasets and methods. Section 4 describes the observed distributions of environmental variables. Section 5 illustrates the sensitivity of the precipitation patterns to

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

over CONUS for the training period, the bias reductions ranged from 60% to 80% for the former and from 47% to 63% for the latter. This suggests that our scheme can be used over other areas of the globe, where sparser networks are more common. However, when a network gets too sparse (e.g., 300 gauges), the scheme will leave “holes” of uncorrected regions ( Fig. 1 ). There are also some undesirable but explainable effects resulted from this correction procedure with the two sample datasets, including

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