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The NEXRAD program is deploying a network of approximately 160 weather radars throughout the United States and at selected overseas sites. The WSR-88D systems provide highly sensitive, fine-resolution measurements of reflectivity, mean radial velocity, and spectrum width data and generate up to 39 categories of analysis products derived from the base data every five to ten minutes. This paper provides an overview of the analysis products that are available on the WSR-88D systems. Primary uses and limitations of these products are discussed, and several examples are presented. A brief description of the WSR-88D system, including primary components, antenna scanning strategies, and product dissemination plans is also included.
The NEXRAD program is deploying a network of approximately 160 weather radars throughout the United States and at selected overseas sites. The WSR-88D systems provide highly sensitive, fine-resolution measurements of reflectivity, mean radial velocity, and spectrum width data and generate up to 39 categories of analysis products derived from the base data every five to ten minutes. This paper provides an overview of the analysis products that are available on the WSR-88D systems. Primary uses and limitations of these products are discussed, and several examples are presented. A brief description of the WSR-88D system, including primary components, antenna scanning strategies, and product dissemination plans is also included.
Abstract
An examination of severe wind-producing mesoscale convective systems that occur in environments of very limited moisture is presented. Such systems, herein referred to as low-dewpoint derechos (LDDs), are difficult to forecast as they form in regions where the level of convective instability is well below that normally associated with severe convective weather. Using a dataset consisting of 12 LDDs that affected various parts of the continental United States, composite surface and upper-level analyses are constructed. These are used to identify factors that appear to be associated with LDD initiation and sustenance. It is shown that LDDs occur in mean kinematic and thermodynamic patterns notably different from those associated with most derechos. LDDs typically form along or just ahead of cold fronts, in the exit region of strong, upper-level jet streaks. Based on the juxtaposition of features in the composite analysis, it appears that linear forcing for ascent provided by the front, and/or ageostrophic circulations associated with the jet streak, induce the initial convective development where the lower levels are relatively dry, but lapse rates are steep. This convection subsequently grows upscale as storm downdrafts merge. The data further suggest that downstream cell propagation follows in the form of sequential, downwind-directed microbursts. Largely unidirectional wind profiles promote additional downwind-directed storm development and system sustenance until the LDD ultimately moves beyond the region supportive of forced convective initiation.
Abstract
An examination of severe wind-producing mesoscale convective systems that occur in environments of very limited moisture is presented. Such systems, herein referred to as low-dewpoint derechos (LDDs), are difficult to forecast as they form in regions where the level of convective instability is well below that normally associated with severe convective weather. Using a dataset consisting of 12 LDDs that affected various parts of the continental United States, composite surface and upper-level analyses are constructed. These are used to identify factors that appear to be associated with LDD initiation and sustenance. It is shown that LDDs occur in mean kinematic and thermodynamic patterns notably different from those associated with most derechos. LDDs typically form along or just ahead of cold fronts, in the exit region of strong, upper-level jet streaks. Based on the juxtaposition of features in the composite analysis, it appears that linear forcing for ascent provided by the front, and/or ageostrophic circulations associated with the jet streak, induce the initial convective development where the lower levels are relatively dry, but lapse rates are steep. This convection subsequently grows upscale as storm downdrafts merge. The data further suggest that downstream cell propagation follows in the form of sequential, downwind-directed microbursts. Largely unidirectional wind profiles promote additional downwind-directed storm development and system sustenance until the LDD ultimately moves beyond the region supportive of forced convective initiation.
Abstract
There has been a recent wave of attention given to atmospheric bores in order to understand how they evolve and initiate and maintain convection during the night. This surge is attributable to data collected during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. A salient aspect of the PECAN project is its focus on using multiple observational platforms to better understand convective outflow boundaries that intrude into the stable boundary layer and induce the development of atmospheric bores. The intent of this article is threefold: 1) to educate the reader on current and future foci of bore research, 2) to present how PECAN observations will facilitate aforementioned research, and 3) to stimulate multidisciplinary collaborative efforts across other closely related fields in an effort to push the limitations of prediction of nocturnal convection.
Abstract
There has been a recent wave of attention given to atmospheric bores in order to understand how they evolve and initiate and maintain convection during the night. This surge is attributable to data collected during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. A salient aspect of the PECAN project is its focus on using multiple observational platforms to better understand convective outflow boundaries that intrude into the stable boundary layer and induce the development of atmospheric bores. The intent of this article is threefold: 1) to educate the reader on current and future foci of bore research, 2) to present how PECAN observations will facilitate aforementioned research, and 3) to stimulate multidisciplinary collaborative efforts across other closely related fields in an effort to push the limitations of prediction of nocturnal convection.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
