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Christopher A. Davis, Barbara G. Brown, Randy Bullock, and John Halley-Gotway

. These mainly involve geometric properties of the objects, but also involve intensity as determined within the present context by the distribution of rainfall accumulation within an object. The goal of the present paper is a comparison, using MODE, of numerical forecasts made by two different models, the Nonhydrostatic Mesoscale Model (NMM) and the Advanced Research version of the Weather and Research Forecasting (WRF) model (ARW). Both models exist within the overarching WRF software framework, but

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Elizabeth E. Ebert

of a high density network—What did we learn? Int. Verification Methods Workshop, Montreal, QC, Canada, World Weather Research Programme. [Available online at ] . Davis, C. , Brown B. , and Bullock R. , 2006 : Object-based verification of precipitation forecasts. Part I: Methods and application to mesoscale rain areas. Mon. Wea. Rev. , 134 , 1772 – 1784 . 10.1175/MWR3145.1 Davis, C. , Brown B

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Christian Keil and George C. Craig

1. Introduction An assessment of the forecast quality of mesoscale numerical weather prediction models is crucial (i) for model development, identifying shortcomings and systematic errors of existing models; (ii) for the documentation of the improvement of forecasting systems in time; and (iii) for the ranking and selection of “good” ensemble members for probabilistic forecasting products and as a key element in novel data assimilation techniques in high-resolution numerical weather forecasting

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Elizabeth E. Ebert and William A. Gallus Jr.

on all sides to define a search area for the best forecast match. For the ICP idealized cases, the search distance was set to 5° latitude–longitude or the maximum dimension of the CRA, whichever was smaller. For the WRF forecasts examined in the ICP, the search distance was set to 240 km, the value used in Grams et al. (2006) in their study of central U.S. mesoscale convective systems, and the sensitivity to a range of different search distances was tested. Forecast rain features outside the

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David Ahijevych, Eric Gilleland, Barbara G. Brown, and Elizabeth E. Ebert

1. Introduction With advances in computing power, numerical guidance has become available on increasingly finer scales. Mesoscale phenomena such as squall lines and hurricane rainbands are routinely forecasted. While the simulated reflectivity field and precipitation distribution have more realistic spatial structure and can provide valuable guidance to forecasters on the mode of convective evolution ( Weisman et al. 2008 ), the traditional verification scores often do not reflect improvement

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Jason E. Nachamkin

accompanying observations. Two separate verification studies were conducted using thresholds of 1.27 and 12.7 mm (0.05 and 0.5 in.) to define larger mesoscale events and smaller convective events. Due to the small number of forecasts, the event size ranges were liberally defined to include as many samples as possible. These ranges were 100–1000 and 50–1000 contiguous grid points for the 1.27- and 12.7-mm thresholds, respectively. These definitions combined with large precipitation gradients resulted in

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Eric Gilleland, Johan Lindström, and Finn Lindgren

dynamics. Three configurations of the Weather Research and Forecasting (WRF; information online at ) model are compared with corresponding stage II analysis fields (i.e., used here as observations), all of which consist of 24-h accumulated precipitation interpolated to the same 601 × 501 grid with about 4-km resolution. Specifically, the models compared are the Nonhydrostatic Mesoscale Model (NMM), the Advanced Research WRF (ARW), and a model from the University of Oklahoma

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Keith F. Brill and Fedor Mesinger

degree of difficulty involved in achieving such bias-corrected forecasts. This assertion is speculative, requiring that the dHdA method approximate the effects of the systematic error removal algorithm used to perform the bias correction. Figure 3a shows the ETS (histogram bars), ETS CPR (lines), and bias (symbols) for the National Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM) model along with the same for the NCEP Global Forecast System (GFS) for 24-h QPFs at the 24-h

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Valliappa Lakshmanan and John S. Kain

models) are shown in Fig. 5 . The images cover the lower 48 states of the United States. The 4NCEP model forecast was produced at the National Centers for Environmental Prediction (NCEP) using a Weather Research and Forecasting (WRF) model whose core was a Nonhydrostatic Mesoscale Model ( Janjić et al. 2005 ) with 4.5-km grid spacing and 35 vertical levels. The 4NCAR model forecast was produced at the National Center for Atmospheric Research using the Advanced Research WRF (ARW; Skamarock et al

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Eric Gilleland, David Ahijevych, Barbara G. Brown, Barbara Casati, and Elizabeth E. Ebert

-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev. , 134 , 1772 – 1784 . 10.1175/MWR3145.1 Davis, C. A. , Brown B. G. , Bullock R. G. , and Halley Gotway J. , 2009 : The Method for Object-based Diagnostic Evaluation (MODE) applied to WRF forecasts from the 2005 Spring Program. Wea. Forecasting , 24 , 1252 – 1267 . 10.1175/2009WAF2222241.1 Dickinson, S. , and Brown R. , 1996 : A study of near-surface winds in marine

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