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Abstract Previous research has shown that 3-km horizontal grid spacing simulations depicting clusters of cells often change to showing linear structures when grid spacing is refined to 1 km. This increase in linear structures at finer horizontal grid spacings may be due simply to the resolving of stronger vertical motion along the leading edge of the MCS cold pool resulting in more continuous zones of convection in higher-resolution runs. However, prior work has suggested that the cold pools themselves are stronger with finer grid spacing, enhancing lift to grow linear morphologies faster. In the present study, Cloud Model 1 was used to simulate an array of MCSs with varying wind profiles and a constant thermodynamic profile (Weisman–Klemp analytic sounding) at both 1- and 3-km horizontal grid spacings and with 50 and 100 vertical levels. A line of seven randomly spaced warm bubbles was used to initiate convection. In 1-km Δxsimulations, gravity waves dominated in initiating new convection for growth into lines, and the ascent associated with them was much greater than in 3-km runs. Upscale growth into lines in 3-km Δxsimulations was driven more by ascent caused by the collision of convective cold pools. 

Abstract The degree of improvement in convective representation in NWP with horizontal grid spacings finer than 3 km remains debatable. While some research suggests subkilometer horizontal grid spacing is needed to resolve details of convective structures, other studies have shown that decreasing grid spacing from 3–4 to 1–2 km offers little additional value for forecasts of deep convection. In addition, few studies exist to show how changes in vertical grid spacing impact thunderstorm forecasts, especially when horizontal grid spacing is simultaneously decreased. The present research investigates how warm-season central U.S. simulated MCS cold pools for 11 observed cases are impacted by decreasing horizontal grid spacing from 3 to 1 km, while increasing the vertical levels from 50 to 100 in WRF runs. The 3-km runs with 100 levels produced the deepest and most negatively buoyant cold pools compared to all other grid spacings since updrafts were more poorly resolved, resulting in a higher flux of rearward-advected frozen hydrometeors, whose melting processes were augmented by the finer vertical grid spacing, which better resolved the melting layer. However, the more predominant signal among all 11 cases was for more expansive cold pools in 1-km runs, where the stronger and more abundant updrafts focused along the MCS leading line supported a larger volume of concentrated rearward hydrometeor advection and resultant latent cooling at lower levels. 

Abstract A severe derecho impacted the Midwestern United States on 10 August 2020, causing over $12 billion (U.S. dollars) in damage, and producing peak winds estimated at 63 m s⁻¹, with the worst impacts in Iowa. The event was not forecast well by operational forecasters, nor even by operational and quasi-operational convection-allowing models. In the present study, nine simulations are performed using the Limited Area Model version of the Finite-Volume-Cubed-Sphere model (FV3-LAM) with three horizontal grid spacings and two physics suites. In addition, when a prototype of the Rapid Refresh Forecast System (RRFS) physics is used, sensitivity tests are performed to examine the impact of using the Grell–Freitas (GF) convective scheme. Several unusual results are obtained. With both the RRFS (not using GF) and Global Forecast System (GFS) physics suites, simulations using relatively coarse 13- and 25-km horizontal grid spacing do a much better job of showing an organized convective system in Iowa during the daylight hours of 10 August than the 3-km grid spacing runs. In addition, the RRFS run with 25-km grid spacing becomes much worse when the GF convective scheme is used. The 3-km RRFS run that does not use the GF scheme develops spurious nocturnal convection the night before the derecho, removing instability and preventing the derecho from being simulated at all. When GF is used, the spurious storms are removed and an excellent forecast is obtained with an intense bowing echo, exceptionally strong cold pool, and roughly 50 m s⁻¹surface wind gusts. 

Upscale convective growth remains a poorly understood aspect of convective evolution, and numerical weather prediction models struggle to accurately depict convective morphology. To better understand some physical mechanisms encouraging upscale growth, 30 warm-season convective events from 2016 over the United States Great Plains were simulated using the Weather Research and Forecasting (WRF) model to identify differences in upscale growth and non-upscale growth environments. Also, Bryan Cloud Model (CM1) sensitivity tests were completed using different thermodynamic environments and wind profiles to examine the impact on upscale growth. The WRF simulations indicated that cold pools are significantly stronger in cases that produce upscale convective growth within the first few hours following convective initiation compared to those without upscale growth. Conversely, vertical wind shear magnitude has no statistically significant relationship with either MCS or non-MCS events. This is further supported by the CM1 simulations, in which tests using the WRF MCS sounding developed a large convective system in all tests performed, including one which used the non-MCS kinematic profile. Likewise, the CM1 simulations of the non-upscale growth event did not produce an MCS, even when using the MCS kinematic profile. Overall, these results suggest that the near-storm and pre-convective thermodynamic environment may play a larger role than kinematics in determining upscale growth potential in the Great Plains. 

Abstract Nocturnal bow echoes can produce wind damage, even in situations where elevated convection occurs. Accurate forecasts of wind potential tend to be more challenging for operational forecasters than for daytime bows because of incomplete understanding of how elevated convection interacts with the stable boundary layer. The present study compares the differences in warm-season, nocturnal bow echo environments in which high intensity [>70 kt (1 kt ≈ 0.51 m s −1 )] severe winds (HS), low intensity (50–55 kt) severe winds (LS), and nonsevere winds (NS) occurred. Using a sample of 132 events from 2010 to 2018, 43 forecast parameters from the SPC mesoanalysis system were examined over a 120 km × 120 km region centered on the strongest storm report or most pronounced bowing convective segment. Severe composite parameters are found to be among the best discriminators between all severity types, especially derecho composite parameter (DCP) and significant tornado parameter (STP). Shear parameters are significant discriminators only between severe and nonsevere cases, while convective available potential energy (CAPE) parameters are significant discriminators only between HS and LS/NS bow echoes. Convective inhibition (CIN) is among the worst discriminators for all severity types. The parameters providing the most predictive skill for HS bow echoes are STP and most unstable CAPE, and for LS bow echoes these are the V wind component at best CAPE (VMXP) level, STP, and the supercell composite parameter. Combinations of two parameters are shown to improve forecasting skill further, with the combination of surface-based CAPE and 0–6-km U shear component, and DCP and VMXP, providing the most skillful HS and LS forecasts, respectively. 

The Great Plains low-level jet (LLJ) is a contributing factor to the initiation and evolution of nocturnal Mesoscale Convective Systems (MCSs) in the central United States by supplying moisture, warm air advection, and a source of convergence. Thus, the ability of models to correctly depict thermodynamics in the LLJ likely influences how accurately they forecast MCSs. In this study, the Weather Research and Forecasting (WRF) model was used to examine the relationship between spatial displacement errors for initiating simulated MCSs, and errors in forecast thermodynamic variables up to three hours before downstream MCS initiation in 18 cases. Rapid Update Cycle (RUC) analyses in 3 layers below 1500 m above ground level were used to represent observations. Correlations between simulated MCS initiation spatial displacements and errors in the magnitude of forecast thermodynamic variables were examined in regions near and upstream of both observed and simulated MCSs, and were found to vary depending on the synoptic environment. In strongly-forced cases, large negative moisture errors resulted in simulated MCSs initiating further downstream with respect to the low-level flow from those observed. For weakly-forced cases, correlations were weaker, with a tendency for smaller negative moisture errors to be associated with larger displacement errors to the right of the inflow direction for initiating MCSs. 

Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period.