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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
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
resolution images: 1) a “cumulus cloud mask” using a combination of VIS and IR imagery to isolate cumuliform clouds ( Berendes et al. 2008 ) and 2) a cloud-motion-tracking scheme that emphasizes the identification of mesoscale flows associated with cumulus cloud motions ( Bedka and Mecikalski 2005 ; Bedka et al. 2008, manuscript submitted to J. Appl. Meteor. Climatol. ). These techniques isolate only the cumulus convection in geostationary imagery, track moving cumulus convection over time, and identify
resolution images: 1) a “cumulus cloud mask” using a combination of VIS and IR imagery to isolate cumuliform clouds ( Berendes et al. 2008 ) and 2) a cloud-motion-tracking scheme that emphasizes the identification of mesoscale flows associated with cumulus cloud motions ( Bedka and Mecikalski 2005 ; Bedka et al. 2008, manuscript submitted to J. Appl. Meteor. Climatol. ). These techniques isolate only the cumulus convection in geostationary imagery, track moving cumulus convection over time, and identify
displacement of the top of the boundary layer around the time of RME B was predicted using the technique of Koch and Clark (1999) . In this technique, the spatial pattern of vertical velocity pattern of the bore passage was modeled as a simple cosine function, and vertical displacement was estimated by temporal integration of this function. The resultant estimate of the maximum vertical displacement of the top of the boundary layer was 1260 m, greater than the observed increase ( Fig. 6b ) of 1100 m. At
displacement of the top of the boundary layer around the time of RME B was predicted using the technique of Koch and Clark (1999) . In this technique, the spatial pattern of vertical velocity pattern of the bore passage was modeled as a simple cosine function, and vertical displacement was estimated by temporal integration of this function. The resultant estimate of the maximum vertical displacement of the top of the boundary layer was 1260 m, greater than the observed increase ( Fig. 6b ) of 1100 m. At
installation in the Falcon adjacent to the DLR DIAL system. DIAL is an appropriate technique for the remote sensing of atmospheric trace gases such as water vapor. A DIAL emits short light pulses into the atmosphere at two distinct wavelengths. The online wavelength is tuned to the center of a molecular water vapor absorption line (around 927 nm in IHOP_2002). The offline wavelength is the reference and contains information about the aerosol load and cloud cover of the probed atmosphere. Combining both
installation in the Falcon adjacent to the DLR DIAL system. DIAL is an appropriate technique for the remote sensing of atmospheric trace gases such as water vapor. A DIAL emits short light pulses into the atmosphere at two distinct wavelengths. The online wavelength is tuned to the center of a molecular water vapor absorption line (around 927 nm in IHOP_2002). The offline wavelength is the reference and contains information about the aerosol load and cloud cover of the probed atmosphere. Combining both
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
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
Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc. , 85 , 253 – 277 . Weckwerth , T. M. , C. R. Pettet , F. Fabry , S. Park , M. A. LeMone , and J. W. Wilson , 2005 : Radar refractivity retrieval: Validation and application to short-term forecasting. J. Appl. Meteor. , 44 , 285 – 300 . Whiteman , D. N. , and Coauthors , 2006a : Raman lidar measurements during the International H 2 O Project. Part I: Instrumentation and analysis techniques. J
Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc. , 85 , 253 – 277 . Weckwerth , T. M. , C. R. Pettet , F. Fabry , S. Park , M. A. LeMone , and J. W. Wilson , 2005 : Radar refractivity retrieval: Validation and application to short-term forecasting. J. Appl. Meteor. , 44 , 285 – 300 . Whiteman , D. N. , and Coauthors , 2006a : Raman lidar measurements during the International H 2 O Project. Part I: Instrumentation and analysis techniques. J
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
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
1. Introduction Water vapor variability was the main focus of the International H 2 O Project (IHOP_2002), which took place in May–June 2002 over the southern Great Plains of the United States ( Weckwerth et al. 2004 ). This field project gathered together most of the techniques for measuring water vapor. We address water vapor variability at the mesoscale (scales larger than thermals, ranging from tens to a few hundreds of kilometers). Comparatively few investigations have considered this
1. Introduction Water vapor variability was the main focus of the International H 2 O Project (IHOP_2002), which took place in May–June 2002 over the southern Great Plains of the United States ( Weckwerth et al. 2004 ). This field project gathered together most of the techniques for measuring water vapor. We address water vapor variability at the mesoscale (scales larger than thermals, ranging from tens to a few hundreds of kilometers). Comparatively few investigations have considered this
