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Edward I. Tollerud, Fernando Caracena, Steven E. Koch, Brian D. Jamison, R. Michael Hardesty, Brandi J. McCarty, Christoph Kiemle, Randall S. Collander, Diana L. Bartels, Steven Albers, Brent Shaw, Daniel L. Birkenheuer, and W. Alan Brewer

previous section by using observations as initialization [using the Local Analysis and Prediction System (LAPS) from the Global Systems Division of the Earth System Research Laboratory (ESRL); see Albers 1995 , Albers et al. 1996 , and Birkenheuer 1999 ] for retrospective Weather Research and Forecasting (WRF) Model runs. LAPS integrates data from a variety of meteorological observation systems producing analyses on 12-km- (utilized here) and 4-km-resolution grids over the IHOP_2002 experiment area

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John R. Mecikalski, Kristopher M. Bedka, Simon J. Paech, and Leslie A. Litten

be assessed from pixels with radar echoes present but no IR indicators of CI. Principal component analysis (PCA) was also performed as a means of estimating information content and redundancy in these IR data for CI nowcasting. a. Skill determination Four measures of forecasting skill (i.e., POD, FAR, TS, and HSS) are used to evaluate the MB06 CI nowcast products. The POD and FAR are defined as in Wilks (2006 , 264–265): POD = a /( a + c ); FAR is defined similarly as FAR = b /( a + b

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Margaret A. LeMone, Fei Chen, Mukul Tewari, Jimy Dudhia, Bart Geerts, Qun Miao, Richard L. Coulter, and Robert L. Grossman

. 2004 ). The eastern track is characterized by a mix of mostly grassland and winter wheat, with trees bordering many fields and waterways. The track extends across the eastern side of the Walnut River watershed southeast of Wichita and into the watershed to the east. The numerical simulations are done with the coupled Advanced Research Weather Research and Forecasting modeling system (ARW-WRF; Skamarock et al. 2005 ), initialized using the High-Resolution Land Data Assimilation System (HRLDAS

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Margaret A. LeMone, Fei Chen, Mukul Tewari, Jimy Dudhia, Bart Geerts, Qun Miao, Richard L. Coulter, and Robert L. Grossman

numerical simulations use the Advanced Research Weather Research and Forecasting (ARW-WRF) model ( Skamarock et al. 2005 ), coupled to the Noah land surface model (LSM), which was initialized using the National Center for Atmospheric Research (NCAR) High-Resolution Land Data Assimilation System (HRLDAS; Chen et al. 2007 ). The data were collected in southeast Kansas using aircraft, surface flux towers, and three radar wind profilers, during May–June, 2002, as part of the International H 2 O Project

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Roger M. Wakimoto and Hanne V. Murphey

1. Introduction There has been an increased emphasis placed on understanding the initiation of deep convection during the summer months when large-scale forcing is weak or absent (e.g., Wilson et al. 1998 ). Indeed, Olsen et al. (1995) have shown a dramatic drop in the ability to forecast convection during the summer when major precipitation events occur. The main reason for this difference in skill is that winter season precipitation events are predominately associated with

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Robin L. Tanamachi, Wayne F. Feltz, and Ming Xue

grid numerical simulations of the atmosphere over the Homestead site were performed using the Advanced Regional Prediction System (ARPS) model developed at the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma (OU). A detailed description of the model and its applications can be found in Xue et al. (2000 , 2001 , 2003 ). ARPS was used because real-time, high-resolution numerical predictions were performed as part of the forecasting component of IHOP_2002 ( Xue et

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Roger M. Wakimoto and Hanne V. Murphey

1. Introduction There have been important advances in short-term forecasts (nowcasts) of thunderstorm initiation during the warm season. These advances are critical as illustrated by Olsen et al. (1995) . They highlighted the pronounced drop in our predictive skill during the summer months when the precipitation totals are the greatest. The improvements in our understanding of thunderstorm formation are largely attributed to the recognition that storms frequently develop near boundary layer

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Diane Strassberg, Margaret A. LeMone, Thomas T. Warner, and Joseph G. Alfieri

reduction of grid size improved results down to about 10 km, but further reduction did not necessarily mean an improvement in forecast success. Their own 1.33-km-grid-size MM5 simulations of the flow in the Salt Lake City, Utah, area performed only slightly better than comparable models run on 30- and 40-km grids. They attributed some of the remaining discrepancy to still-unresolved effects at the smallest scales. De Rooy and Kok (2004) discussed the point measurement problem and dealt with it in a

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S. B. Trier, F. Chen, K. W. Manning, M. A. LeMone, and C. A. Davis

al. 1996 ; Pan et al. 1996 ; Giorgi et al. 1996 ; Bosilovich and Sun 1999 ; Xue et al. 2001 ; Anderson et al. 2003 ) models. Contrasting results on the role of soil wetness were found in studies of this period. Beljaars et al. (1996) concluded that less accurate precipitation forecasts were related to capping inversions above the PBL, which arose from strong sensible heating over anomalously dry soil located ∼1 day upstream. Viterbo and Betts (1999) found that more realistic soil moisture

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Steven E. Koch, Wayne Feltz, Frédéric Fabry, Mariusz Pagowski, Bart Geerts, Kristopher M. Bedka, David O. Miller, and James W. Wilson

amplitude ordering and a horizontal wavelength of 9.3 km, which compares favorably to the 8.3 km averaged from the FM-CW, MAPR, and UWKA data. By contrast, the very weak waves in the 2-km simulation have an average wavelength of 11.6 km. This is smaller than 6 times the grid spacing, so those waves are poorly resolved. The bore is also clearly present in the coarse and fine grid model forecasts. The antecedent inversion surface is abruptly lifted during the progression of the bore to the southeast at a

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