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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

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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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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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David J. Stensrud

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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 and Jidong Gao

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The assimilation of operational Doppler radar observations into convection-resolving numerical weather prediction models for very short-range forecasting represents a significant scientific and technological challenge. Numerical experiments over the past few years indicate that convective-scale forecasts are sensitive to the details of the data assimilation methodology, the quality of the radar data, the parameterized microphysics, and the storm environment. In this study, the importance of horizontal environmental variability to very short-range (0–1 h) convective-scale ensemble forecasts initialized using Doppler radar observations is investigated for the 4–5 May 2007 Greensburg, Kansas, tornadic thunderstorm event. Radar observations of reflectivity and radial velocity from the operational Doppler radar network at 0230 UTC 5 May 2007, during the time of the first large tornado, are assimilated into each ensemble member using a three-dimensional variational data assimilation system (3DVAR) developed at the Center for Analysis and Prediction of Storms (CAPS). Very short-range forecasts are made using the nonhydrostatic Advanced Regional Prediction System (ARPS) model from each ensemble member and the results are compared with the observations. Explicit three-dimensional environmental variability information is provided to the convective-scale ensemble using analyses from a 30-km mesoscale ensemble data assimilation system. Comparisons between convective-scale ensembles with initial conditions produced by 3DVAR using 1) background fields that are horizontally homogeneous but vertically inhomogeneous (i.e., have different vertical environmental profiles) and 2) background fields that are horizontally and vertically inhomogeneous are undertaken. Results show that the ensemble with horizontally and vertically inhomogeneous background fields provides improved predictions of thunderstorm structure, mesocyclone track, and low-level circulation track than the ensemble with horizontally homogeneous background fields. This suggests that knowledge of horizontal environmental variability is important to successful convective-scale ensemble predictions and needs to be included in real-data experiments.

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Nusrat Yussouf and David J. Stensrud

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A simple binning technique developed to produce reliable probabilistic quantitative precipitation forecasts (PQPFs) from a multimodel short-range ensemble forecasting system is evaluated during the cool season of 2005/06. The technique uses forecasts and observations of 3-h accumulated precipitation amounts from the past 12 days to adjust the present day’s 3-h quantitative precipitation forecasts from each ensemble member for each 3-h forecast period. Results indicate that the PQPFs obtained from this simple binning technique are significantly more reliable than the raw (original) ensemble forecast probabilities. Brier skill scores and areas under the relative operating characteristic curve also reveal that this technique yields skillful probabilistic forecasts of rainfall amounts during the cool season. This holds true for accumulation periods of up to 48 h. The results obtained from this wintertime experiment parallel those obtained during the summer of 2004. In an attempt to reduce the effects of a small sample size on two-dimensional probability maps, the simple binning technique is modified by implementing 5- and 9-point smoothing schemes on the adjusted precipitation forecasts. Results indicate that the smoothed ensemble probabilities remain an improvement over the raw (original) ensemble forecast probabilities, although the smoothed probabilities are not as reliable as the unsmoothed adjusted probabilities. The skill of the PQPFs also is increased as the ensemble is expanded from 16 to 22 members during the period of study. These results reveal that simple postprocessing techniques have the potential to provide greatly improved probabilistic guidance of rainfall events for all seasons of the year.

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Jidong Gao and David J. Stensrud

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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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David J. Stensrud and Nusrat Yussouf

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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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David J. Stensrud and Nusrat Yussouf

Abstract

A simple binning technique is developed to produce reliable 3-h probabilistic quantitative precipitation forecasts (PQPFs) from the National Centers for Environmental Prediction (NCEP) multimodel short-range ensemble forecasting system obtained during the summer of 2004. The past 12 days’ worth of forecast 3-h accumulated precipitation amounts and observed 3-h accumulated precipitation amounts from the NCEP stage-II multisensor analyses are used to adjust today’s 3-h precipitation forecasts. These adjustments are done individually to each of ensemble members for the 95 days studied. Performance of the adjusted ensemble precipitation forecasts is compared with the raw (original) ensemble predictions. Results show that the simple binning technique provides significantly more skillful and reliable PQPFs of rainfall events than the raw forecast probabilities. This is true for the base 3-h accumulation period as well as for accumulation periods up to 48 h. Brier skill scores and the area under the relative operating characteristics curve also indicate that this technique yields skillful probabilistic forecasts. The performance of the adjusted forecasts also progressively improves with the increased accumulation period. In addition, the adjusted ensemble mean QPFs are very similar to the raw ensemble mean QPFs, suggesting that the method does not significantly alter the ensemble mean forecast. Therefore, this simple postprocessing scheme is very promising as a method to provide reliable PQPFs for rainfall events without degrading the ensemble mean forecast.

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