Abstract

Extensive measurements were made of the summertime Great Plains low-level jet (LLJ) in central Kansas during June and July 2015 as a component of the Plains Elevated Convection at Night (PECAN) field study. Here, the authors describe the early phase of the LLJ development on 20 June 2015. Half-hourly soundings were launched to monitor the progress of the jet. An airborne mission was also conducted using the University of Wyoming King Air research aircraft. Vertical sawtooth patterns were flown along a fixed track at 38.7°N between longitudes 98.9° and 100.3°W to document changes in the potential temperature and wind profiles. Ageostrophic winds during the LLJ formation were also assessed. In addition, a high-resolution numerical simulation of the 20 June 2015 LLJ case was conducted using the Weather Research and Forecasting Model. Observations and model results show that the early stage of development consisted of a rapid increase in wind speed in the hours just after sunset with less pronounced directional change. The LLJ evolution is similar to that expected from an inertial oscillation of the ageostrophic wind following the stabilization of the near-surface layer.

Abstract

The Great Plains low-level jet (LLJ) has long been associated with summertime nocturnal convection over the central Great Plains of the United States. Destabilization effects of the LLJ are examined using composite fields assembled from the North American Mesoscale Forecast System for June and July 2008–12. Of critical importance are the large isobaric temperature gradients that become established throughout the lowest 3 km of the atmosphere in response to the seasonal heating of the sloping Great Plains. Such temperature gradients provide thermal wind forcing throughout the lower atmosphere, resulting in the establishment of a background horizontal pressure gradient force at the level of the LLJ. The attendant background geostrophic wind is an essential ingredient for the development of a pronounced summertime LLJ. Inertial turning of the ageostrophic wind associated with LLJ provides a westerly wind component directed normal to the terrain-induced orientation of the isotherms. Hence, significant nocturnal low-level warm-air advection occurs, which promotes differential temperature advection within a vertical column of atmosphere between the level just above the LLJ and 500 hPa. Such differential temperature advection destabilizes the nighttime troposphere above the radiatively cooled near-surface layer on a recurring basis during warm weather months over much of the Great Plains and adjacent states to the east. This destabilization process reduces the convective inhibition of air parcels near the level of the LLJ and may be of significance in the development of elevated nocturnal convection. The 5 July 2015 case from the Plains Elevated Convection at Night field program is used to demonstrate this destabilization process.

Abstract

A case study of the kinematical and dynamical evolution of the summertime Great Plains low level jet (LLJ) is presented. Airborne radar altimetry was used to discern the x and y components of the geostrophic wind at three levels in the lower atmosphere throughout the LLJ episode. Results appear to confirm previous theoretical and numerical studies regarding the importance of the diurnal cycle of heating over sloping terrain in producing an oscillating horizontal pressure gradient force. Inertial turning of the LLJ as a result of frictional decoupling was also documented. It is concluded that the inertial oscillation resulting from the sudden decrease in friction in the lower atmosphere during the early evening is the dominant mechanism in forcing this example of a summertime Great Plains LLJ.

Abstract

Lake-effect snowstorms are often observed to manifest as dominant bands, commonly produce heavy localized snowfall, and may extend large distances inland, resulting in hazards and high societal impact. Some studies of dominant bands have documented concomitant environmental baroclinity (i.e., baroclinity occurring at a scale larger than the width of the parent lake), but the interaction of this baroclinity with the inland structure of dominant bands has been largely unexplored. In this study, the thermodynamic environment and thermodynamic and kinematic structure of simulated dominant bands are examined using WRF reanalyses at 3-km horizontal resolution and an innovative technique for selecting the most representative member from the WRF ensemble. Three reanalysis periods are selected from the Ontario Winter Lake-effect Systems (OWLeS) field campaign, encompassing 185 simulation hours, including 155 h in which dominant bands are identified. Environmental baroclinity is commonly observed during dominant-band periods and occurs in both the north–south and east–west directions. Sources of this baroclinity are identified and discussed. In addition, case studies are conducted for simulation hours featuring weak and strong along-band environmental baroclinity, resulting in weak and strong inland extent, respectively. These contrasting cases offer insight into one mechanism by which along-band environmental baroclinity can influence the inland structure and intensity of dominant bands: in the case with strong environmental baroclinity, inland portions of this band formed under weak instability and therefore exhibit slow overturning, enabling advection far inland under strong winds, whereas the nearshore portion forms under strong instability, and the enhanced overturning eventually leads to the demise of the inland portion of the band.

Abstract

Predicting the inland penetration of lake-effect long-lake-axis-parallel (LLAP) snowbands is crucial to public safety because LLAP bands can produce hazardous weather well downwind of the parent lake. Accordingly, hypotheses for the variation in inland penetration of LLAP-band radar echoes (InPen) are formulated and tested. The hypothesis testing includes an examination of statistical relationships between environmental variables and InPen for 34 snapshots of LLAP bands observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign. Several previously proposed predictors of LLAP-band formation or InPen demonstrate weak correlations with InPen during OWLeS. A notable exception is convective boundary layer (CBL) depth, which is strongly correlated with InPen. In addition to CBL depth, InPen is strongly correlated with cold-air advection in the upper portion of the CBL, suggesting that boundary layer destabilization produced by vertically differential cold-air advection may be an important inland power source for preexisting LLAP bands. This power production is quantified through atmospheric energetics and the resulting variable, differential thermal advection power (DTAP), yields reasonably skillful predictions of InPen. Nevertheless, an InPen model developed using DTAP is outperformed by an empirical model combining CBL depth and potential temperature advection in the upper portion of the CBL. This two-variable model explains 76% of the observed InPen variance when tested on independent data. Finally, implications for operational forecasting of InPen are discussed.

Abstract

Intense lake-effect snowstorms regularly develop over the eastern Great Lakes, resulting in extreme winter weather conditions with snowfalls sometimes exceeding 1 m. The Ontario Winter Lake-effect Systems (OWLeS) field campaign sought to obtain unprecedented observations of these highly complex winter storms.

OWLeS employed an extensive and diverse array of instrumentation, including the University of Wyoming King Air research aircraft, five university-owned upper-air sounding systems, three Center for Severe Weather Research Doppler on Wheels radars, a wind profiler, profiling cloud and precipitation radars, an airborne lidar, mobile mesonets, deployable weather Pods, and snowfall and particle measuring systems. Close collaborations with National Weather Service Forecast Offices during and following OWLeS have provided a direct pathway for results of observational and numerical modeling analyses to improve the prediction of severe lake-effect snowstorm evolution. The roles of atmospheric boundary layer processes over heterogeneous surfaces (water, ice, and land), mixed-phase microphysics within shallow convection, topography, and mesoscale convective structures are being explored.

More than 75 students representing nine institutions participated in a wide variety of data collection efforts, including the operation of radars, radiosonde systems, mobile mesonets, and snow observation equipment in challenging and severe winter weather environments.

“SuomiNet,” a university-based, real-time, national Global Positioning System (GPS) network, is being developed for atmospheric research and education with funding from the National Science Foundation and with cost share from collaborating universities. The network, named to honor meteorological satellite pioneer Verner Suomi, will exploit the recently shown ability of ground-based GPS receivers to make thousands of accurate upper- and lower-atmospheric measurements per day. Phase delays induced in GPS signals by the ionosphere and neutral atmosphere can be measured with high precision simultaneously along a dozen or so GPS ray paths in the field of view. These delays can be converted into integrated water vapor (if surface pressure data or estimates are available) and total electron content (TEC), along each GPS ray path. The resulting continuous, accurate, all-weather, real-time GPS moisture data will help advance university research in mesoscale modeling and data assimilation, severe weather, precipitation, cloud dynamics, regional climate, and hydrology. Similarly, continuous, accurate, all-weather, real-time TEC data have applications in modeling and prediction of severe terrestrial and space weather, detection and forecasting of low-altitude ionospheric scintillation activity and geomagnetic storm effects at ionospheric midlatitudes, and detection of ionospheric effects induced by a variety of geophysical events. SuomiNet data also have potential applications in coastal meteorology, providing ground truth for satellite radiometry, and detection of scintillation associated with atmospheric turbulence in the lower troposphere. The goal of SuomiNet is to make large amounts of spatially and temporally dense GPS-sensed atmospheric data widely available in real time, for academic research and education. Information on participation in SuomiNet is available via www.unidata.ucar.edu/suominet.

Abstract

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night.

To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings.