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Russ S. Schumacher

Abstract

In this study, idealized numerical simulations are used to identify the processes responsible for initiating, organizing, and maintaining quasi-stationary convective systems that produce locally extreme rainfall amounts. Of particular interest are those convective systems that have been observed to occur near mesoscale convective vortices (MCVs) and other midlevel circulations. To simulate the lifting associated with such circulations, a low-level momentum forcing is applied to an initial state that is representative of observed extreme rain events. The initial vertical wind profile includes a sharp reversal of the vertical wind shear with height, indicative of observed low-level jets.

Deep moist convection initiates within the region of mesoscale lifting, and the resulting convective system replicates many of the features of observed systems. The low-level thermodynamic environment is nearly saturated, which is not conducive to the production of a strong surface cold pool; yet the convection quickly organizes into a back-building line. It is shown that a nearly stationary convectively generated low-level gravity wave is responsible for the linear organization, which continues for several hours. New convective cells repeatedly form on the southwest end of the line and move to the northeast, resulting in large local rainfall amounts. In the later stages of the simulated convective system, a cold pool does develop, but its interaction with the strong reverse shear at low levels is not optimized for the maintenance of deep convection along its edge. A series of sensitivity experiments shows some of the effects of hydrometeor evaporation and melting, planetary rotation, and the imposed mesoscale forcing.

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Russ S. Schumacher

Abstract

On 31 May 2013, a supercell thunderstorm initiated in west-central Oklahoma and produced a deadly tornado. This convection then grew upscale, with a nearly stationary line developing early on 1 June that produced very heavy rainfall and caused deadly flash flooding in the Oklahoma City area. Real-time convection-allowing (Δx = 4 km) model forecasts used during the Mesoscale Predictability Experiment (MPEX) provided accurate guidance regarding the timing, location, and evolution of convection in this case. However, attempts to simulate this event at higher resolution degraded the forecast, with the primary supercell failing to initiate and the evolution of the overnight MCS not resembling the observed system. Experiments to test the dependence of forecasts of this event on model resolution show that with grid spacing smaller than 4 km, mixing along the dryline in northwest Texas was more vigorous, causing low-level dry air to move more quickly eastward into Oklahoma. This drying prevented the supercell from initiating near the triple point in the higher-resolution simulations. Then, the lack of supercellular convection and its associated cold pool altered the evolution of subsequent convection. Whereas in observations and the 4-km forecast, a nearly stationary MCS developed parallel to, but displaced from, the supercell’s cold pool, the higher-resolution simulations instead had a faster-moving squall line that produced less rainfall. Although the degradation of convective forecasts at higher resolution is probably unusual and appears sensitive to the choice of boundary layer parameterization, these findings demonstrate that how numerical models treat boundary layer processes at different grid spacings can, in some cases, have profound influences on predictions of high-impact weather.

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Russ S. Schumacher

Abstract

Using a method for initiating a quasi-stationary, heavy-rain-producing elevated mesoscale convective system in an idealized numerical modeling framework, a series of experiments is conducted in which a shallow layer of drier air is introduced within the near-surface stable layer. The environment is still very moist in the experiments, with changes to the column-integrated water vapor of only 0.3%–1%. The timing and general evolution of the simulated convective systems are very similar, but rainfall accumulation at the surface is changed by a much larger fraction than the reduction in moisture, with point precipitation maxima reduced by up to 29% and domain-averaged precipitation accumulations reduced by up to 15%. The differences in precipitation are partially attributed to increases in the evaporation rate in the shallow subcloud layer, though this is found to be a secondary effect. More importantly, even though the near-surface layer has strong convective inhibition in all simulations and the convective available potential energy of the most unstable parcels is unchanged, convection is less intense in the experiments with drier subcloud layers because less air originating in that layer rises in convective updrafts. An additional experiment with a cooler near-surface layer corroborates these findings. The results from these experiments suggest that convective systems assumed to be elevated are, in fact, drawing air from near the surface unless the low levels are very stable. Considering that the moisture differences imposed here are comparable to observational uncertainties in low-level temperature and moisture, the strong sensitivity of accumulated precipitation to these quantities has implications for the predictability of extreme rainfall.

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Russ S. Schumacher

Abstract

This study makes use of operational global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) to examine the factors contributing to, or inhibiting, the development of a long-lived continental vortex and its associated rainfall. From 25 to 30 June 2007, a vortex developed and grew upscale over the southern plains of the United States. It was associated with persistent heavy rainfall, with over 100 mm of rain falling in much of Texas, Oklahoma, Kansas, and Missouri, and amounts exceeding 300 mm in southeastern Kansas. Previous research has shown that, in comparison with other rainfall events of similar temporal and spatial scales, this event was particularly difficult for numerical models to predict.

Considering the ensemble members as different possible realizations of the evolution of the event, several methods are used to examine the processes that led to the development and maintenance of the long-lived vortex and its associated rainfall, and to its apparently limited predictability. Linear statistics are calculated to identify synoptic-scale flow features that were correlated to area-averaged precipitation, and differences between composites of “dry” and “wet” ensemble members are used to pinpoint the processes that were favorable or detrimental to the system’s development. The maintenance of the vortex, and its slow movement in the southern plains, are found to be closely related to the strength of a closed midlevel anticyclone in the southwestern United States and the strength of a midlevel ridge in the northern plains. In particular, with a weaker upstream anticyclone, the shear and flow over the incipient vortex are relatively weak, which allows for slow movement and persistent heavy rains. On the other hand, when the upstream anticyclone is stronger, there is stronger northerly shear and flow, which causes the incipient vortex to move southwestward into the high terrain of Mexico and dissipate. These relatively small differences in the wind and mass fields early in the ensemble forecast, in conjunction with modifications of the synoptic and mesoscale flow by deep convection, lead to very large spread in the resulting precipitation forecasts.

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Russ S. Schumacher

Abstract

Floods and flash floods are, by their nature, a multidisciplinary problem: they result from a convergence of atmospheric conditions, the underlying topography, hydrological processes, and the built environment. Research aimed at addressing various aspects of floods, on the other hand, often follows paths that do not directly address all of these fundamental connections. With this in mind, the NSF-sponsored Studies of Precipitation, Flooding, and Rainfall Extremes Across Disciplines (SPREAD) workshop was organized and held in Colorado during the summers of 2013 and 2014. SPREAD brought together a group of 27 graduate students from a wide variety of academic disciplines, but with the unifying theme being research interests in extreme precipitation or flooding. During the first meeting of the workshop, groups of graduate student participants designed interdisciplinary research projects that they then began work on over the intervening year, with the second meeting providing a venue to present their results. This article will outline the preliminary findings of these research efforts. Furthermore, the workshop participants had the unique and meaningful experience of visiting several locations in Colorado that had flooded in the past, and then visiting them again in the aftermath of the devastating 2013 floods. In total, the workshop resulted in several fruitful research activities that will advance understanding of precipitation and flooding. Even more importantly, the workshop fostered the development of a network of early-career researchers and practitioners who will be “multilingual” in terms of scientific disciplines, and who are poised to lead within their respective careers and across the scientific community.

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Gregory R. Herman and Russ S. Schumacher

Abstract

Fifteen years of forecasts from the National Oceanic and Atmospheric Administration’s Second-Generation Global Medium-Range Ensemble Reforecast (GEFS/R) dataset were used to develop a statistical model that generates probabilistic predictions of cloud ceiling and visibility. Four major airports—Seattle–Tacoma International Airport (KSEA), San Francisco International Airport (KSFO), Denver International Airport (KDEN), and George Bush Intercontinental Airport (KIAH) in Houston, Texas—were selected for model training and analysis. Numerous statistical model configurations, including the use of several different machine learning algorithms, input predictors, and internal parameters, were explored and verified through cross validation to develop skillful forecasts at each station. The final model was then compared with both probabilistic climatology-based forecasts and deterministic operational guidance. Results indicated significantly enhanced skill within both deterministic and probabilistic frameworks from the model trained in this study relative to both operational guidance and climatology at all stations. Probabilistic forecasts also showed substantially higher skill within the framework used than any deterministic forecast. Dewpoint depression and cloud cover forecast fields from the GEFS/R model were typically found to have the highest correspondence with observed flight rule conditions of the atmospheric fields examined. Often forecast values nearest the prediction station were not found to be the most important flight rule condition predictors, with forecast values along coastlines and immediately offshore, where applicable, often serving as superior predictors. The effect of training data length on model performance was also examined; it was determined that approximately 3 yr of training data from a dynamical model were required for the statistical model to robustly capture the relationships between model variables and observed flight rule conditions (FRCs).

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Russ S. Schumacher and Richard H. Johnson

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This study examines the radar-indicated structures and other features of extreme rain events in the United States over a 3-yr period. A rainfall event is defined as “extreme” when the 24-h precipitation total at one or more stations surpasses the 50-yr recurrence interval amount for that location. This definition yields 116 such cases from 1999 to 2001 in the area east of the Rocky Mountains, excluding Florida. Two-kilometer national composite radar reflectivity data are then used to examine the structure and evolution of each extreme rain event. Sixty-five percent of the total number of events are associated with mesoscale convective systems (MCSs). While a wide variety of organizational structures (as indicated by radar reflectivity data) are seen among the MCS cases, two patterns of organization are observed most frequently. The first type has a line, often oriented east–west, with “training” convective elements. It also has a region of adjoining stratiform rain that is displaced to the north of the line. The second type has a back-building or quasi-stationary area of convection that produces a region of stratiform rain downstream. Surface observations and composite analysis of Rapid Update Cycle Version 2 (RUC-2) model data reveal that training line/adjoining stratiform (TL/AS) systems typically form in a very moist, unstable environment on the cool side of a preexisting slow-moving surface boundary. On the other hand, back-building/quasi-stationary (BB) MCSs are more dependent on mesoscale and storm-scale processes, particularly lifting provided by storm-generated cold pools, than on preexisting synoptic boundaries.

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Russ S. Schumacher and Thomas J. Galarneau Jr.

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Global ensemble forecasts from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) are used to quantify the magnitude of moisture transport into North America ahead of recurving tropical cyclones (TCs). Two cases in which a predecessor rain event (PRE) occurred ahead of the recurving TC—Erin (2007) and Ike (2008)—are analyzed, with ensemble members correctly predicting TC recurvature contrasted from those predicting the TC to weaken or turn southward. This analysis demonstrates that TC-related moisture transport can increase the total water vapor in the atmosphere over North America by 20 mm or more, and that the moisture transport takes place both in the boundary layer and aloft. The increased moisture does not always correspond to increased rainfall in the ensemble forecasts, however, as the location and strength of baroclinic zones and their attendant secondary circulations that can lift this moist air are also crucial to the development of heavy rains.

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Gregory R. Herman and Russ S. Schumacher

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A continental United States (CONUS)-wide framework for analyzing quantitative precipitation forecasts (QPFs) from NWP models from the perspective of precipitation return period (RP) exceedances is introduced using threshold estimates derived from a combination of NOAA Atlas 14 and older sources. Forecasts between 2009 and 2015 from several different NWP models of varying configurations and spatial resolutions are analyzed to assess bias characteristics and forecast skill for predicting RP exceedances. Specifically, NOAA’s Global Ensemble Forecast System Reforecast (GEFS/R) and the National Severe Storms Laboratory WRF (NSSL-WRF) model are evaluated for 24-h precipitation accumulations. The climatology of extreme precipitation events for 6-h accumulations is also explored in three convection-allowing models: 1) NSSL-WRF, 2) the North American Mesoscale 4-km nest (NAM-NEST), and 3) the experimental High Resolution Rapid Refresh (HRRR). The GEFS/R and NSSL-WRF are both found to exhibit similar 24-h accumulation RP exceedance climatologies over the U.S. West Coast to those found in observations and are found to be approximately equally skillful at predicting these exceedance events in this region. In contrast, over the eastern two-thirds of the CONUS, GEFS/R struggles to predict the predominantly convectively driven extreme QPFs, predicting far fewer events than are observed and exhibiting inferior forecast skill to the NSSL-WRF. The NSSL-WRF and HRRR are found to produce 6-h extreme precipitation climatologies that are approximately in accord with those found in the observations, while NAM-NEST produces many more RP exceedances than are observed across all of the CONUS.

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Gregory R. Herman and Russ S. Schumacher

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Quantitative precipitation estimate (QPE) exceedances of numerous different heavy precipitation thresholds—including spatially varying average recurrence interval (ARI) and flash flood guidance (FFG) thresholds—are compared among each other and against reported and warned flash floods to quantify existing deficiencies with QPEs and to identify best practices for using QPE for flash flood forecasting and analysis. QPEs from three different sources—NCEP Stage IV Precipitation Analysis (ST4), Climatology Calibrated Precipitation Analysis (CCPA), and Multi-Radar Multi-Sensor (MRMS) QPE—are evaluated across the United States from January 2015 to June 2017. In addition to evaluating different QPE sources, threshold types, and magnitudes, QPE accumulation interval lengths from hourly to daily are considered. Systematic errors with QPE sources are identified, including a radar distance dependence on extreme rainfall frequency in MRMS, spurious occurrences of locally extreme precipitation in the complex terrain of the West in ST4, and insufficient QPEs for many legitimate heavy precipitation events in CCPA. Overall, flash flood warnings and reports corresponded to each other far more than any QPE exceedances. Correspondence between all sources was at a maximum in the East and worst in the West, with ST4, CCPA, and MRMS QPE exceedances locally yielding maximal correspondence in the East, Plains, and West, respectively. Surprisingly, using a fixed 2.5 in. (24 h)−1 proxy outperformed shorter accumulation exceedances and the use of ARIs and FFGs. On regional scales, different ARI exceedances achieved superior performance to the selection of any fixed threshold; FFG exceedances were consistently too rare to achieve optimal correspondence with observed flash flooding.

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