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The boundary layer (BL) plays a critical role in regulating energy and moisture exchange between the surface and the free atmosphere. However, a significant in situ observational gap resides in [...]

This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance. [ABSTRACT FROM AUTHOR]

The severity, duration, and spatial extent of thunderstorm impacts are related to convective storm mode. This study assesses the skill of the High-Resolution Rapid Refresh Ensemble (HRRR-E) and its deterministic counterpart (HRRRv4) at predicting convective mode and storm macrophysical properties using 35 convective events observed during the 2020 warm season across the eastern United States. Seven cases were selected from each of five subjectively determined convective organization modes: tropical cyclones, mesoscale convective systems (MCSs), quasi-linear convective systems, clusters, and cellular convection. These storm events were assessed using an object-based approach to identify convective storms and determine their individual size. Averaged across all 35 cases, both the HRRR-E and HRRRv4 predicted storm areas were generally larger than observed, with this bias being a function of storm lifetime and convective mode. Both modeling systems also underpredicted the rapid increase in storm counts during the initiation period, particularly for the smaller-scale storm modes. Interestingly, performance of the HRRRv4 differed from that of the HRRR-E, with the HRRRv4 generally having a larger bias in total storm area than the HRRR-E due to HRRRv4 predicting up to 66% more storm objects than the HRRR-E. The HRRR-E accurately predicted the convective mode 65% of the time, with complete misses being very rare (<5% of the time overall). However, an evaluation of rank histograms across all 35 cases revealed that the HRRR-E tended to be underdispersive when predicting storm size for all but the MCS mode. [ABSTRACT FROM AUTHOR]

Two mixed-phase cloudy boundary layer events observed over the Arctic ice pack in autumn are extensively analyzed. The local dynamic and thermodynamic structure of the boundary layers is determined from aircraft measurements including analysis of turbulence, longwave radiative transfer, and cloud microphysics. The large-scale forcing is determined from the National Centers for Environmental Prediction reanalysis fields while mesoscale forcing is estimated from 40-km aircraft box patterns. The two cases differed somewhat in their local static stability, surface characteristics, and large-scale forcing. One case was characterized by a stably stratified cloudy boundary layer over a heterogeneous surface containing numerous open leads. The other case occurred over a fairly homogenous surface of multiyear ice and consisted of a surface-based stable layer surmounted by a low-level jet and a cloud-topped mixed layer. An important large-scale factor in the development of low clouds appears to have been water vapor advection. Low clouds formed irrespective of the sign of the large-scale vertical velocity. Observed flux profiles indicate that both cloudy boundary layers are cooled through turbulent eddies except at cloud top where entrainment of warm moist air aloft occurs. Maximum turbulent kinetic energy occurs near cloud top where turbulent motions are driven by strong radiative cooling (>-70 K [day.sub.-1]) and in the vicinity of the low-level jet where turbulence is shear induced. The presence of both liquid and ice in the cloud layers appears to be a nearly steady-state feature at temperatures between -13 [degrees] and -20 [degrees] C. Results of a simple condensed water budget indicate that these colloidally unstable mixed-phase clouds may be maintained through strong cloud-top radiative cooling. The isobaric cooling rate required to maintain the presence of both liquid and ice in a stratiform cloud is quite sensitive to variations in the highly uncertain concentration of ice-forming nuclei.

The simulations were forced using data from the High-Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS) obtained from the National Center for Environmental Prediction (NCEP) Central Operations (NCO). The nested WRF configuration used in this study featured a large-eddy-permitting, 111-m grid spacing mesh for the inner-most domain. Output from the WRF forecasts were processed on-the-fly using the Unified Post Processor (UPP). A THREDDS data server was coupled with a web-viewer to provide real-time graphics that were used to support UAS flight planning and decision making. The simulations ran in under six hours on the National Center for Atmospheric Research (NCAR) Cheyenne supercomputer using 59 cores (2124 processors). The simulations were run twice per day (12 runs total during the experiment) to support both next-day mission planning and day-of tactical guidance. The simulations provided a high-resolution depiction of the four-dimensional variability of weather conditions across the northern half of the San Luis Valley, Colorado, where UAS operations took place. The simulations were used to assess the possibility of a number of small UAS weather hazards including wind shift boundaries, vertical shear, strong thermals, turbulence intensity, fog, low ceilings, and thunderstorms. Details of the model configuration used to perform the simulations and the data processing steps used to produce the final grids of state variables and other sensible weather products (e.g., ceiling and visibility, turbulence) are given. A few examples of predictive capabilities of the modeling system are provided to illustrate the skill of the model at predicting fine-scale boundary layer structures and turbulence associated with drainage winds, up-valley flows, and convective storm outflows. A subset of the at data is available at the Zenodo data archive (https://zenodo.org/communities/lapse-rate/) while full dataset (see Pinto et al. 2020a) is archived on the NCAR Digital Asset Services Hub (DASH) and may be obtained at https://doi.org/10.5065/83r2-0579. [ABSTRACT FROM AUTHOR]

The authors' previous idealized, two-dimensional cloud resolving model (CRM) simulations of Arctic stratus revealed a surprising sensitivity to the concentrations of ice crystals. In this paper, simulations of an actual case study observed during the Beaufort and Arctic Seas Experiment are performed and the results are compared to the observed data. It is again found in the CRM simulations that the simulated stratus cloud is very sensitive to the concentration of ice crystals. Using midlatitude estimates of the availability of ice forming nuclei (IFN) in the model, the authors find that the concentrations of ice crystals are large enough to result in the almost complete dissipation of otherwise solid, optically thick stratus layers. A tenuous stratus can be maintained in the simulation when the continuous input of moisture through the imposed large-scale advection is strong enough to balance the ice production. However, in association with the large-scale moisture and warm advection, only by reducing the concentration of IFN to 0.3 of the midlatitude estimate values can a persistent, optically thick stratus layer be maintained. The results obtained from the reduced IFN simulation compare reasonably well with observations. The longwave radiative fluxes at the surface are significantly different between the solid stratus and liquid-water-depleted higher ice crystal concentration experiments. This work suggests that transition-season Arctic stratus can be very vulnerable to anthropogenic sources of IFN, which can alter cloud structure sufficiently to affect the rates of melting and freezing of the Arctic Ocean. The authors find that the Hallett-Mossop riming splintering mechanism is not activated in the simulations because the cloud droplets are very small and cloud temperatures are outside the range supporting efficient rime splintering. Thus, the conclusions drawn from the results presented in this paper may be applicable to only a limited class of Arctic stratus.