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David J. Stensrud

Abstract

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 a 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.

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David J. Stensrud

Abstract

Over a 2½-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 50° 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.

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David J. Stensrud
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David J. Stensrud

Abstract

The ability of deep monsoon convection to influence the larger-scale circulation over North America is investigated for a 6-day-long case study during the 2006 North American monsoon. Results from Rossby wave ray tracing and numerical simulations using the Advanced Research Weather Research and Forecasting model indicate that North American monsoon convection provides a source region for stationary Rossby waves. Two wave trains are seen in the numerical model simulations, with behaviors that agree well with expectations from theory and ray tracing. The shorter and faster-moving wave train moves eastward from the source region in Mexico and reaches the western Atlantic within 4 days. The longer and slower-moving wave train travels northeastward and reaches the coastal New England region within 6 days. An upstream tail of anticyclonic vorticity extends westward from the source region into the central Pacific Ocean.

The monsoon convection appears to help cut off the low-level anticyclonic flow by developing low-level southerly flow in the Gulf of Mexico and northerly flow in the eastern Pacific, as suggested in earlier global model studies. However, the stationary Rossby wave trains further alter the location and intensity of deep convection in locations remote from the monsoon. These results suggest that unless a numerical model can correctly predict monsoon convection, the ability of the model to produce accurate forecasts of the large-scale pattern and associated convective activity beyond a few days is in question. This result may be important for global climate modeling, since an inaccurate prediction of monsoon convection would lead to an inaccurate Rossby wave response.

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David J. Stensrud

Abstract

Low-level jets (LLJs) occur frequently in many parts of the world. These low-level wind speed maxima are important for both the horizontal and vertical fluxes of temperature and moisture and have been found to be associated with the development and evolution of deep convection. Since deep convective activity produces a significant amount of upper-level cloudiness and is responsible for a large fraction of the warm season rainfall in the United States, the relationship between LLJs and deep convection suggests that LLJs are important contributors to regional climate. Results from a number of past studies are reviewed, and the potential for data from the Atmospheric Radiation Measurement program to augment our understanding of low-level jets is discussed.

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Nusrat Yussouf and David J. Stensrud

Abstract

A postprocessing method initially developed to improve near-surface forecasts from a summertime multimodel short-range ensemble forecasting system is evaluated during the cool season of 2005/06. The method, known as the bias-corrected ensemble (BCE) approach, uses the past complete 12 days of model forecasts and surface observations to remove the mean bias of near-surface variables from each ensemble member for each station location and forecast time. In addition, two other performance-based weighted-average BCE schemes, the exponential smoothing method BCE and the minimum variance estimate BCE, are implemented and evaluated. Values of root-mean-squared error from the 2-m temperature and dewpoint temperature forecasts indicate that the BCE approach outperforms the routinely available Global Forecast System (GFS) model output statistics (MOS) forecasts during the cool season by 9% and 8%, respectively. In contrast, the GFS MOS provides more accurate forecasts of 10-m wind speed than any of the BCE methods. The performance-weighted BCE schemes yield no significant improvement in forecast accuracy for 2-m temperature and 2-m dewpoint temperature when compared with the original BCE, although the weighted BCE schemes are found to improve the forecast accuracy of the 10-m wind speed. The probabilistic forecast guidance provided by the BCE system is found to be more reliable than the raw ensemble forecasts. These results parallel those obtained during the summers of 2002–04 and indicate that the BCE method is a promising and inexpensive statistical postprocessing scheme that could be used in all seasons.

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Jidong Gao and David J. Stensrud

Abstract

The impact of assimilating radar reflectivity and radial velocity data with an intermittent, cycled three-dimensional variational assimilation (3DVAR) system is explored using an idealized thunderstorm case and a real data case on 8 May 2003. A new forward operator for radar reflectivity is developed that uses a background temperature field provided by a numerical weather prediction model for automatic hydrometeor classification. Three types of experiments are performed on both the idealized and real data cases. The first experiment uses radial velocity data only, the second experiment uses both radial velocity and reflectivity data without hydrometeor classification, and the final experiment uses both radial velocity and reflectivity data with hydrometeor classification. All experiments advance the analysis state to the next observation time using a numerical model prediction, which is then used as the background for the next analysis. Results from both the idealized and real data cases show that, assimilating only radial velocity data, the model can reconstruct the supercell thunderstorm after several cycles, but the development of precipitation is delayed because of the well-known spinup problem. The spinup problem is reduced dramatically when assimilating reflectivity without hydrometeor classification. The analyses are further improved using the new reflectivity formulation with hydrometeor classification. This study represents a successful first effort in variational convective-scale data assimilation to partition hydrometeors using a background temperature field from a numerical weather prediction model.

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Nusrat Yussouf and David J. Stensrud

Abstract

The ability of a multimodel short-range bias-corrected ensemble (BCE) forecasting system, created as part of NOAA’s New England High Resolution Temperature Program during the summer of 2004, to obtain accurate predictions of near-surface variables at independent locations within the model domain is explored. The original BCE approach produces bias-corrected forecasts only at National Weather Service (NWS) observing surface station locations. To extend this approach to obtain bias-corrected forecasts at any given location, an extended BCE technique is developed and applied to the independent observations provided by the Oklahoma Mesonet. First, a Cressman weighting scheme is used to interpolate the bias values of 2-m temperature, 2-m dewpoint temperature, and 10-m wind speeds calculated from the original BCE approach at the NWS observation station locations to the Oklahoma Mesonet locations. These bias values are then added to the raw numerical model forecasts bilinearly interpolated to this same specified location. This process is done for each forecast member within the ensemble and at each forecast time. It is found that the performance of the extended BCE is very competitive with the original BCE approach across the state of Oklahoma. Therefore, a simple postprocessing scheme like the extended BCE system can be used as part of an operational forecasting system to provide reasonably accurate predictions of near-surface variables at any location within the model domain.

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Nusrat Yussouf and David J. Stensrud

Abstract

Observational studies indicate that the densities and intercept parameters of hydrometeor distributions can vary widely among storms and even within a single storm. Therefore, assuming a fixed set of microphysical parameters within a given microphysics scheme can lead to significant errors in the forecasts of storms. To explore the impact of variations in microphysical parameters, Observing System Simulation Experiments are conducted based on both perfect- and imperfect-model assumptions. Two sets of ensembles are designed using either fixed or variable parameters within the same single-moment microphysics scheme. The synthetic radar observations of a splitting supercell thunderstorm are assimilated into the ensembles over a 30-min period using an ensemble Kalman filter data assimilation technique followed by 1-h ensemble forecasts. Results indicate that in the presence of a model error, a multiparameter ensemble with a combination of different hydrometeor density and intercept parameters leads to improved analyses and forecasts and better captures the truth within the forecast envelope compared to single-parameter ensemble experiments with a single, constant, inaccurate hydrometeor intercept and density parameters. This conclusion holds when examining the general storm structure, the intensity of midlevel rotation, surface cold pool strength, and the extreme values of the model fields that are most helpful in determining and identifying potential hazards. Under a perfect-model assumption, the single- and multiparameter ensembles perform similarly as model error does not play a role in these experiments. This study highlights the potential for using a variety of realistic microphysical parameters across the ensemble members in improving the analyses and very short-range forecasts of severe weather events.

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David J. Stensrud and Nusrat Yussouf

Abstract

A multimodel short-range ensemble forecasting system created as part of a National Oceanic and Atmospheric Administration pilot program on temperature and air quality forecasting over New England during the summer of 2002 is evaluated. A simple 7-day running mean bias correction is applied individually to each of the 23 ensemble members. Various measures of accuracy are used to compare these bias-corrected ensemble predictions of 2-m temperature and dewpoint temperature with those available from the nested grid model (NGM) model output statistics (MOS). Results indicate that the bias-corrected ensemble mean prediction is as accurate as the NGM MOS for temperature predictions, and is more accurate than the NGM MOS for dewpoint temperature predictions, for the 48 days studied during the warm season. When the additional probabilistic information from the ensemble is examined, results indicate that the ensemble clearly provides value above that of NGM MOS for both variables, especially as the events become more unlikely. Results also indicate that the ensemble has some ability to predict forecast skill for temperature with a correlation between ensemble spread and the error of the ensemble mean of greater than 0.7 for some forecast periods. The use of a multimodel ensemble clearly helps to improve the spread–skill relationship.

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