During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced a daily 10-member 4-km horizontal resolution ensemble forecast covering approximately three-fourths of the continental United States. Each member used the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model core, which was initialized at 2100 UTC, ran for 33 h, and resolved convection explicitly. Different initial condition (IC), lateral boundary condition (LBC), and physics perturbations were introduced in 4 of the 10 ensemble members, while the remaining 6 members used identical ICs and LBCs, differing only in terms of microphysics (MP) and planetary boundary layer (PBL) parameterizations. This study focuses on precipitation forecasts from the ensemble.The ensemble forecasts reveal WRF-ARW sensitivity to MP and PBL schemes. For example, over the 7-week experiment, the Mellor-Yamada-Janjić PBL and Ferrier MP parameterizations were associated with relatively high precipitation totals, while members configured with the Thompson MP or Yonsei University PBL scheme produced comparatively less precipitation. Additionally, different approaches for generating probabilistic ensemble guidance are explored. Specifically, a ''neighborhood'' approach is described and shown to considerably enhance the skill of probabilistic forecasts for precipitation when combined with a traditional technique of producing ensemble probability fields.

The PECAN field campaign assembled a rich array of observations from lower-tropospheric profiling systems, mobile radars and mesonets, and aircraft over the Great Plains during June-July 2015 to better understand nocturnal mesoscale convective systems and their relationship with the stable boundary layer, the low-level jet, and atmospheric bores.

The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation's depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.

This study evaluates forecasts of thermodynamic variables from five convection-allowing configurations of the Weather Research and Forecasting Model (WRF) with the Advanced Research core (WRF-ARW). The forecasts vary only in their planetary boundary layer (PBL) scheme, including three “local” schemes [Mellor–Yamada–Janjić (MYJ), quasi-normal scale elimination (QNSE), and Mellor–Yamada–Nakanishi–Niino (MYNN)] and two schemes that include “nonlocal” mixing [the asymmetric cloud model version 2 (ACM2) and the Yonei University (YSU) scheme]. The forecasts are compared to springtime radiosonde observations upstream from deep convection to gain a better understanding of the thermodynamic characteristics of these PBL schemes in this regime. The morning PBLs are all too cool and dry despite having little bias in PBL depth (except for YSU). In the evening, the local schemes produce shallower PBLs that are often too shallow and too moist compared to nonlocal schemes. However, MYNN is nearly unbiased in PBL depth, moisture, and potential temperature, which is comparable to the background North American Mesoscale model (NAM) forecasts. This result gives confidence in the use of the MYNN scheme in convection-allowing configurations of WRF-ARW to alleviate the typical cool, moist bias of the MYJ scheme in convective boundary layers upstream from convection. The morning cool and dry biases lead to an underprediction of mixed-layer CAPE (MLCAPE) and an overprediction of mixed-layer convective inhibition (MLCIN) at that time in all schemes. MLCAPE and MLCIN forecasts improve in the evening, with MYJ, QNSE, and MYNN having small mean errors, but ACM2 and YSU having a somewhat low bias. Strong observed capping inversions tend to be associated with an underprediction of MLCIN in the evening, as the model profiles are too smooth. MLCAPE tends to be overpredicted (underpredicted) by MYJ and QNSE (MYNN, ACM2, and YSU) when the observed MLCAPE is relatively small (large).

Cold pools are a key element in the organization of precipitating convective systems, yet knowledge of their typical surface characteristics is largely anecdotal. To help to alleviate this situation, cold pools from 39 mesoscale convective system (MCS) events are sampled using Oklahoma Mesonet surface observations. In total, 1389 time series of surface observations are used to determine typical rises in surface pressure and decreases in temperature, potential temperature, and equivalent potential temperature associated with the cold pool, and the maximum wind speeds in the cold pool. The data are separated into one of four convective system life cycle stages: first storms, MCS initiation, mature MCS, and MCS dissipation. Results indicate that the mean surface pressure rises associated with cold pools increase from 3.2 hPa for the first storms' life cycle stage to 4.5 hPa for the mature MCS stage before dropping to 3.3 hPa for the dissipation stage. In contrast, the mean temperature (potential temperature) deficits associated with cold pools decrease from 9.5 (9.8) to 5.4 K (5.6 K) from the first storms to the dissipation stage, with a decrease of approximately 1 K associated with each advance in the life cycle stage. However, the daytime and early evening observations show mean temperature deficits over 11 K. A comparison of these observed cold pool characteristics with results from idealized numerical simulations of MCSs suggests that observed cold pools likely are stronger than those found in model simulations, particularly when ice processes are neglected in the microphysics parameterization. The mean deficits in equivalent potential temperature also decrease with the MCS life cycle stage, starting at 21.6 K for first storms and dropping to 13.9 K for dissipation. Mean wind gusts are above 15 m s Ϫ1 for all life cycle stages. These results should help numerical modelers to determine whether the cold pools in high-resolution models are in reasonable agreement with the observed characteristics found herein. Thunderstorm simulations and forecasts with thin model layers near the surface are also needed to obtain better representations of cold pool surface characteristics that can be compared with observations.

Recent observational studies have shown that strong midlatitude mesoscale convective systems (MCSs) tend to decay as they move into environments with less instability and smaller deep-layer vertical wind shear. These observed shear profiles that contain significant upper-level shear are often different from the shear profiles considered to be the most favorable for the maintenance of strong, long-lived convective systems in some past idealized simulations. Thus, to explore the role of upper-level shear in strong MCS environments, a set of two-dimensional (2D) simulations of density currents within a dry, statically neutral environment is used to quantify the dependence of lifting along an idealized cold pool on the upper-level shear. A set of three-dimensional (3D) simulations of MCSs is produced to gauge the effects of the upper-level shear in a more realistic framework.Results from the 2D experiments show that the addition of upper-level shear to a wind profile with weak to moderate low-level shear increases the vertical displacement of parcels despite a decrease in the vertical velocity along the cold pool interface. Parcels that are elevated above the surface (1-2 km) overturn and are responsible for the deep lifting in the deep-shear environments, while the surface-based parcels typically are lifted through the cold pool region in a rearward-sloping path. This deep overturning helps to maintain the leading convection and greatly increases the size and total precipitation output of the convective systems in more complex 3D simulations, even in the presence of 3D structures. These results show that the shear profile throughout the entire troposphere must be considered to gain a more complete understanding of the structure and maintenance of strong midlatitude MCSs.

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During the 2007 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced convection-allowing forecasts from a single deterministic 2-km model and a 10-member 4-km-resolution ensemble. In this study, the 2-km deterministic output was compared with forecasts from the 4-km ensemble control member. Other than the difference in horizontal resolution, the two sets of forecasts featured identical Advanced Research Weather Research and Forecasting model (ARW-WRF) configurations, including vertical resolution, forecast domain, initial and lateral boundary conditions, and physical parameterizations. Therefore, forecast disparities were attributed solely to differences in horizontal grid spacing. This study is a follow-up to similar work that was based on results from the 2005 Spring Experiment. Unlike the 2005 experiment, however, model configurations were more rigorously controlled in the present study, providing a more robust dataset and a cleaner isolation of the dependence on horizontal resolution. Additionally, in this study, the 2- and 4-km outputs were compared with 12-km forecasts from the North American Mesoscale (NAM) model. Model forecasts were analyzed using objective verification of mean hourly precipitation and visual comparison of individual events, primarily during the 21- to 33-h forecast period to examine the utility of the models as next-day guidance. On average, both the 2- and 4-km model forecasts showed substantial improvement over the 12-km NAM. However, although the 2-km forecasts produced more-detailed structures on the smallest resolvable scales, the patterns of convective initiation, evolution, and organization were remarkably similar to the 4-km output. Moreover, on average, metrics such as equitable threat score, frequency bias, and fractions skill score revealed no statistical improvement of the 2-km forecasts compared to the 4-km forecasts. These results, based on the 2007 dataset, corroborate previous findings, suggesting that decreasing horizontal grid spacing from 4 to 2 km provides little added value as next-day guidance for severe convective storm and heavy rain forecasters in the United States.