scholarly journals Airflow Configurations of Warm Season Southerly Low-Level Wind Maxima in the Great Plains. Part I: Spatial and Temporal Characteristics and Relationship to Convection

2001 ◽  
Vol 16 (5) ◽  
pp. 513-530 ◽  
Author(s):  
Claudia K. Walters ◽  
Julie A. Winkler
2018 ◽  
Vol 146 (8) ◽  
pp. 2615-2637 ◽  
Author(s):  
Joshua G. Gebauer ◽  
Alan Shapiro ◽  
Evgeni Fedorovich ◽  
Petra Klein

AbstractObservations from three nights of the Plains Elevated Convection at Night (PECAN) field campaign were used in conjunction with Rapid Refresh model forecasts to find the cause of north–south lines of convection, which initiated away from obvious surface boundaries. Such pristine convection initiation (CI) is relatively common during the warm season over the Great Plains of the United States. The observations and model forecasts revealed that all three nights had horizontally heterogeneous and veering-with-height low-level jets (LLJs) of nonuniform depth. The veering and heterogeneity were associated with convergence at the top-eastern edge of the LLJ, where moisture advection was also occurring. As time progressed, this upper region became saturated and, due to its placement above the capping inversion, formed moist absolutely unstable layers, from which the convergence helped initiate elevated convection. The structure of the LLJs on the CI nights was likely influenced by nonuniform heating across the sloped terrain, which led to the uneven LLJ depth and contributed toward the wind veering with height through the creation of horizontal buoyancy gradients. These three CI events highlight the importance of assessing the full three-dimensional structure of the LLJ when forecasting nocturnal convection over the Great Plains.


2009 ◽  
Vol 22 (20) ◽  
pp. 5401-5420 ◽  
Author(s):  
Scott J. Weaver ◽  
Siegfried Schubert ◽  
Hailan Wang

Abstract Sea surface temperature (SST) linkages to central U.S. low-level circulation and precipitation variability are investigated from the perspective of the Great Plains low-level jet (GPLLJ) and recurring modes of SST variability. The observed and simulated links are first examined via GPLLJ index regressions to precipitation, SST, and large-scale circulation fields in the NCEP–NCAR and North American Regional Reanalysis (NARR) reanalyses, and NASA’s Seasonal-to-Interannual Prediction Project (NSIPP1) and Community Climate Model, version 3 (CCM3) ensemble mean Atmospheric Model Intercomparison Project (AMIP) simulations for the 1949–2002 (1979–2002 for NARR) period. Characteristics of the low-level circulation and its related precipitation are further examined in the U.S. Climate Variability and Predictability (CLIVAR) Drought Working Group idealized climate model simulations (NSIPP1 and CCM3) forced with varying polarities of recurring modes of SST variability. It is found that the observed and simulated correlations of the GPLLJ index to Atlantic and Pacific SST, large-scale atmospheric circulation, and Great Plains precipitation variability for 1949–2002 are robust during the July–September (JAS) season and show connections to a distinct global-scale SST variability pattern, one similar to that used in forcing the NSIPP1 and CCM3 idealized simulations, and a subtropical Atlantic-based sea level pressure (SLP) anomaly with a maximum over the Gulf of Mexico. The idealized simulations demonstrate that a warm Pacific and/or a cold Atlantic are influential over regional hydroclimate features including the monthly preference for maximum GPLLJ and precipitation in the seasonal cycle. Furthermore, it appears that the regional expression of globally derived SST variability is important for generating an anomalous atmospheric low-level response of consequence to the GPLLJ, especially when the SST anomaly is positioned over a regional maximum in climatological SST, and in this case the Western Hemisphere warm pool.


2006 ◽  
Vol 45 (1) ◽  
pp. 194-209 ◽  
Author(s):  
Da-Lin Zhang ◽  
Shunli Zhang ◽  
Scott J. Weaver

Abstract Although considerable research has been conducted to study the characteristics of the low-level jets (LLJs) over the Great Plains states, little is known about the development of LLJs over the Mid-Atlantic states. In this study, the Mid-Atlantic LLJ and its associated characteristics during the warm seasons of 2001 and 2002 are documented with both the wind profiler data and the daily real-time model forecast products. A case study with three model sensitivity simulations is performed to gain insight into the three-dimensional structures and evolution of an LLJ and the mechanisms by which it developed. It is found that the Mid-Atlantic LLJ, ranging from 8 to 23 m s−1, appeared at an average altitude of 670 m and on 15–25 days of each month. About 90% of the 160 observed LLJ events occurred between 0000 and 0600 LST, and about 60% had southerly to westerly directions. Statistically, the real-time forecasts capture most of the LLJ events with nearly the right timing, intensity, and altitude, although individual forecasts may not correspond to those observed. For a selected southwesterly LLJ case, both the observations and the control simulation exhibit a pronounced diurnal cycle of horizontal winds in the lowest 1.5 km. The simulation shows that the Appalachian Mountains tend to produce a sloping mixed layer with northeasterly thermal winds during the daytime and reversed thermal winds after midnight. With additional thermal contrast effects associated with the Chesapeake Bay and the Atlantic Ocean, the daytime low-level winds vary significantly from the east coast to the mountainous regions. The LLJ after midnight tends to be peaked preferentially around 77.5°W near the middle portion of the sloping terrain, and it decreases eastward as a result of the opposite thermal gradient across the coastline from the mountain-generated thermal gradient. Although the Mid-Atlantic LLJ is much weaker and less extensive than that over the Great Plains states, it has a width of 300–400 km (to its half-peak value) and a length scale of more than 1500 km, following closely the orientation of the Appalachians. Sensitivity simulations show that eliminating the surface heat fluxes produces the most significant impact on the development of the LLJ, then topography and the land–sea contrast, with its area-averaged intensity reduced from 12 m s−1 to about 6, 9, and 10 m s−1, respectively.


1997 ◽  
Vol 125 (9) ◽  
pp. 2176-2192 ◽  
Author(s):  
Raymond W. Arritt ◽  
Thomas D. Rink ◽  
Moti Segal ◽  
Dennis P. Todey ◽  
Craig A. Clark ◽  
...  

2020 ◽  
Vol 35 (1) ◽  
pp. 215-235 ◽  
Author(s):  
Kelsey M. Malloy ◽  
Ben P. Kirtman

Abstract Warm-season precipitation in the U.S. “Corn Belt,” the Great Plains, and the Midwest greatly influences agricultural production and is subject to high interannual and intraseasonal variability. Unfortunately, current seasonal and subseasonal forecasts for summer precipitation have relatively low skill. Therefore, there are ongoing efforts to understand hydroclimate variability targeted at improving predictions, particularly through its primary transporter of moisture: the Great Plains low-level jet (LLJ). This study uses the Community Climate System Model, version 4 (CCSM4), July forecasts, made as part of the North American Multi-Model Ensemble (NMME), to assess skill in reproducing the monthly Great Plains LLJ and associated precipitation. Generally, the CCSM4 forecasts capture the climatological jet but have problems representing the observed variability beyond two weeks. In addition, there are predictors associated with the large-scale variability identified through linear regression analysis, shifts in kernel density estimators, and case study analysis that suggest potential for improving confidence in forecasts. In this study, a strengthened Caribbean LLJ, negative Pacific–North American (PNA) teleconnection, El Niño, and a negative Atlantic multidecadal oscillation each have a relatively strong and consistent relationship with a strengthened Great Plains LLJ. The circulation predictors, the Caribbean LLJ and PNA, present the greatest “forecast of opportunity” for considering and assigning confidence in monthly forecasts.


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