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On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW Model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the preconvective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome. Significance Statement: Forecasts from operational numerical models are the foundation of weather forecasting. There are times when these models make forecasts that do not come true, such as 28 April 2019 when successive forecasts from the operational High-Resolution Rapid Refresh (HRRR) model predicted a supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Reasons for this forecast error are explored using numerical experiments. Results suggest that relatively small changes to the prestorm environment led to large differences in the evolution of storms on this day. This result emphasizes the challenges to operational severe weather forecasting and the continued need for improved use of all available observations to better define the atmospheric state given to forecast models. [ABSTRACT FROM AUTHOR]

Over a 2 1/2-day period beginning 0000 UTC 11 May 1982, 15 mesoscale convective systems (MCSs) developed and moved eastward across the moist axis located over the southern plains of the United States. While the 6-18-h lifetimes of each of these individual MCSs are not sufficiently long to influence the large-scale environment greatly, it is possible that the cumulative effects of the entire group of MCSs can produce significant changes in the large-scale flow patterns. This hypothesis is investigated using output from two runs of a sophisticated mesoscale model. One run includes the effects of convection, and the other does not. Results indicate that in low levels, the inflow of warm, moist air into the convective region is increased when convection is allowed in the model, enhancing the likelihood that convection will continue and thereby acting as a positive feedback mechanism. In upper levels, the convection acts as a Rossby wave source region and produces significant upper-level perturbations that cover at least a 50 [degrees] longitude spread. Convective effects also influence cyclogenesis since the MCSs strengthen the low-level baroclinicity and modify the phase relationship between pressure and thermal waves in the midlevels. Thus, it is clear that the effects of a persistent, mesoscale region of convection on the large-scale environment are substantial.

Participants to the workshop jointly sponsored by the National Meteorological Center and the National Severe Storms Laboratory from July 25-27, 1994 in Camp Springs, MD, proposed to conduct a one-year pilot study to test short-range ensemble forecasting (SREF) as an operation concept in weather forecasting. Speakers at the workshop suggested using analysis error as basis for initializing the model that will represent the SREF concept. The proceedings of the workshop and suggestions proposed by the speakers are presented.

Elevated mixed layers (EMLs) are an important factor in the development of springtime thunderstorms over the United States. EMLs can be considered a subset of a larger class, called residual layers, since the mean state variables are the same, at least initially, as those of the boundary layers in which EMLs are formed. It is possible, however, for boundary or residual layers that are not necessarily well mixed to be advected off regions of elevated terrain and overrun boundary layers forming over lower terrain. These layers are called elevated residual layers (ERLs); ERLs may form frequently in regions near mountains where terrain gradients exist. A simple slab mixed-layer model is used to examine how idealized ERL potential temperature profiles influence surface boundary-layer development. In addition, several regionally generated ERLs were observed over Phoenix, Arizona, during the Southwest Area Monsoon Project. These ERLs appear to have produced a change from moistening to entrainment-drying surface boundary-layer regimes. The thermodynamic structure of an ERL is determined by the processes that form the boundary layer, the timing and vertical extent of boundary-layer detachment from the elevated terrain relative to the diurnal heating cycle, and the vertical motion field (if any) accompanying the horizontal advection of the ERL away from the elevated terrain. Results suggest that the creation and evolution of ERLs may be important aspects of surface boundary-layer development in regions near and downstream of elevated terrain.

Horizontal convective rolls (HCRs) and cellular convection (cells) are frequently observed within the planetary boundary layer. Yet understanding of the evolution, seasonal variation, and characteristics of such boundary layer phenomena is limited as previous studies used observations from field experiments or satellites. As a result, little is known about the mean climatology and monthly variation of HCRs and cells. Polarimetric WSR-88D radar observations are used to develop a 10-yr April-September climatology in central Oklahoma including HCR and cell occurrence, duration, and aspect ratios as well as HCR orientation angles and wavelengths. Results indicate that HCRs or cells occur on over 92% of days without precipitation during the warm season. HCRs or cells typically form in midmorning and may persist throughout the day or transition between modes before dissipating around sunset. HCRs generally persist for 1-6 h with typical wavelengths of 2-10 km and most aspect ratios between 1 and 7. Rolls are often oriented within 108 of the mean boundary layer wind but can be as much as 308 off this direction. Mean HCR aspect ratios in this study remain constant during the afternoon, but decrease early in the day and increase late in the day, diverging from previous overland HCR studies. Cells generally persist for 2-6 h with aspect ratios of 1-6. These results should facilitate future studies on convection initiation, formation mechanisms of boundary layer organization, and model parameterization. [ABSTRACT FROM AUTHOR]

This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when instorm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones. [ABSTRACT FROM AUTHOR]