Browsing by Author "Schumacher, Russ S., advisor"

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Open Access
A new post-processing paradigm? Improving high-impact weather forecasts with machine learning
(Colorado State University. Libraries, 2018) Herman, Gregory Reid, author; Schumacher, Russ S., advisor; Barnes, Elizabeth A., committee member; van den Heever, Susan C., committee member; Cooley, Daniel S., committee member; Hamill, Thomas M., committee member
High-impact weather comes in many different shapes, sizes, environments, and storm types, but all pose threats to human life, property, and the economy. Because of the significant societal hazards inflicted by these events, having skillful forecasts of the risks with sufficient lead time to make appropriate precautions is critical. In order to occur, these extreme events require a special conglomeration of unusual meteorological conditions. Consequently, effective forecasting of such events often requires different perspectives and tools than routine forecasts. A number of other factors make advance forecasts of rare, high-impact weather events particularly challenging, including the lack of sufficient resolution to adequately simulate the phenomena dynamically in a forecast model; model biases in representing storms, and which often become increasingly pronounced in extreme scenarios; and even difficulty in defining and verifying the high-impact event. This dissertation systematically addresses these recurring challenges for several types of high-impact weather: flash flooding and extreme rainfall, tornadoes, severe hail, and strong convective winds. For each listed phenomenon, research to more concretely define the current state of the science in analyzing, verifying, and forecasting the phenomenon. From there, in order to address the aforementioned persistent limitations with forecasting extreme weather events, machine learning-based post-processing models are developed to generate skillful, calibrated probabilistic forecasts for high-impact weather risk across the United States. Flash flooding is a notoriously challenging forecast problem. But the challenge is rooted even more fundamentally with difficulties in assessing and verifying flash flooding from observations due to the complex combination of hydrometeorological factors affecting flash flood occurrence and intensity. The first study in this dissertation investigates the multi-faceted flash flood analysis problem from a simplified framework considering only quantitative precipitation estimates (QPEs) to assess flash flood risk. Many different QPE-to-flash flood potential frameworks and QPE sources are considered over a multi-year evaluation period and QPE exceedances are compared against flash flood observations and warnings. No conclusive "best" flash flood analysis framework is clearly identified, though specific strengths and weaknesses of different approaches and QPE sources are identified in addition to regional differences in optimal correspondence with observations. The next two-part study accompanies the flash flood analysis investigation by approaching forecasting challenges associated with extreme precipitation. In particular, more than a decade of forecasts from a convection-parameterized global ensemble, the National Oceanic and Atmospheric Administration's Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) model, are used to develop machine learning (ML) models for probabilistic prediction of extreme rainfall across the conterminous United States (CONUS) at Days 2 and 3. Both random forests (RFs) and logistic regression models (LR) are developed, with separate models trained for each lead time and for eight different CONUS regions. Models use the spatiotemporal evolution of a host of different atmospheric fields as predictors in addition to select geographic and climatological predictors. The models are evaluated over four years of withheld forecasts. The models, and particularly the RFs, are found to compare very favorably with both raw GEFS/R ensemble forecasts and those from a superior global ensemble produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) both in terms of forecast skill and reliability. The trained models are also inspected to discern what statistical findings are identified through ML. Many of the findings quantify anecdotal knowledge that is already recognized regarding the forecast problem, such as the relative skill of simulated precipitation in areas where extreme precipitation events are associated with large-scale processes well resolved by the GEFS/R compared with areas where extreme precipitation predominantly occurs in association with convection in the warm-season. But more subtle spatiotemporal biases are also diagnosed, including a northern displacement bias in the placement of convective systems and a southern displacement bias in placing landfalling atmospheric rivers. The final extended study shifts weather phenomenon focus from extreme rainfall to severe weather: tornadoes, large hail, and severe convective winds. While both high-impact, the two classes of weather hazards share some commonalities and contrasts. While rainfall is directly forecast by dynamical weather models, most severe weather occurs on too small of spatial scales to be directly simulated by the same models. Consequently, unlike with extreme precipitation, when developing post-processed severe weather forecasts, there is no obvious benchmark for objectively determining whether and how much improvement the post-processing is yielding. A natural alternative, albeit much more stringent, benchmark is operational forecasts produced by human forecasters. Operational severe weather forecasts are produced by the Storm Prediction Center (SPC), but there is limited published verification of their outlooks quantifying their probabilistic skill. In the first part of this study, an extended record SPC severe weather outlooks were evaluated to quantitatively assess the state of operational severe weather forecasting, including strengths and weaknesses. SPC convective outlooks were found to decrease in skill with increased forecast lead time, and were most skillful for severe winds, with the worst performance for tornado outlooks. Many seasonal and regional variations were also observed, with performance generally best in the North and East and worst in the South and especially West. The second part of the study follows similar methodology to the extreme precipitation models, developing RF-based probabilistic forecast models forced from the GEFS/R for Days 1--3 across CONUS, analogous to the format in which SPC produces its convective outlooks. RF properties are inspected to investigate the statistical relationships identified between GEFS/R fields and severe weather occurrence. Like with the extreme precipitation model, RF severe weather forecasts are generated and evaluated from several years of withheld validation cases. These forecasts are compared alongside SPC outlooks and also blended with them to produce a combined forecast. Overall, by statistically quantifying relationships between the synoptic-scale environment and severe weather in a manner consistent with the community's physical understanding of the forecast problems, the RF models are able to demonstrate skill over SPC outlooks at Days 2 and 3, and can be blended with SPC outlooks to enhance skill at Day 1. Overall, multiple high-impact weather phenomena---extreme precipitation and severe weather---are investigated from verification, analysis, and forecasting standpoints. On verification and analysis, foundations have been laid both to improve existing operational products as well as better frame and contextualize future studies. ML post-processing models developed were highly successful in advancing forecast skill and reliability for these hazardous weather phenomena despite being developed from predictors of a coarse, dated dynamical model in the GEFS/R. The findings also suggest adaptability across a wide array of forecast problems, types of predictor inputs, and lead times, raising the possibility of broader applicability of these methods in operational numerical weather prediction.
Open Access
Effect of latent heating on mesoscale vortex development during extreme precipitation: Colorado, September 2013
(Colorado State University. Libraries, 2014) Morales, Annareli, author; Kreidenweis, Sonia M., advisor; Schumacher, Russ S., advisor; Ramirez, Jorge A., committee member
From 9-16 September 2013, a slow-moving cut-off low in the southwestern U.S. funneled unseasonal amounts of moisture to the Colorado Front Range, resulting in extreme precipitation and flooding. The heaviest precipitation during the September 2013 event occurred over the northern Colorado Front Range, producing a 7-day total of over 380 mm of rain. The flash flooding caused over $3 billion in damage to property and infrastructure and resulted in eight fatalities. This study will focus on the precipitation and mesoscale features during 11-12 September 2013 in Boulder, CO. During the evening of 11 September, Boulder experienced flash flooding as a result of high rain rates accumulating over 180 mm of rain in 6 hours. From 0400-0700 UTC 12 September, a mesoscale vortex (mesovortex) was observed to travel northwestward towards Boulder. This circulation enhanced upslope flow and was associated with localized deep convection. The mesovortex originated in an area common for the development of a lee vortex known as the Denver Cyclone. We hypothesize that this mesoscale vortex is not associated with lee vortex formation, such as the Denver Cyclone, but developed through the release of latent heat from microphysical process. The Advanced Research Weather Research and Forecast (ARW) model was used to 1) produce a control simulation that properly represented the evolution and processes of interest during the event and 2) test the importance of latent heating to the development and evolution of the mesovortex. The results from various latent heating experiments suggested that the mesovortex did not develop through lee vortex formation and the latent heat released just before and during the mesovortex event was important to its development. Results also showed latent heating affected the flow field, resulting in a positive feedback between the circulation, associated low-level jet, and convection leading to further upslope flow and precipitation development. Further experiments showed condensation of cloud water was the dominant microphysical process responsible for a positive vertical gradient in latent heating near the surface. This gradient led to potential vorticity generation; a similar mechanism to that of a mesoscale convective vortex, except closer to the surface. Finally, an experiment where the latent heating was reduced by half after 1800 UTC 11 September resulted in no mesovortex development and a substantial decrease in precipitation. The results from this study have relevant implications to the representation of microphysical processes in numerical weather prediction models. The capability to forecast the development of these mesovortices and their subsequent environmental and hydrological effects could be critical for decision makers and the public, given their association with high rain fall rates.
Open Access
Insights from machine learning-based forecasts of convective hazards and environments
(Colorado State University. Libraries, 2025) Mazurek, Alexandra Callahan, author; Schumacher, Russ S., advisor; Rasmussen, Kristen L., committee member; van den Heever, Susan C., committee member; Chen, Haonan, committee member; Hill, Aaron J., committee member
Severe convective thunderstorms and their associated hazards are costly, damaging, and difficult to predict. Machine learning (ML) techniques are rapidly being developed and deployed in an effort to predict severe thunderstorms more quickly and with greater accuracy than traditional methods. With these developments, there is a need to understand how ML-based weather prediction systems rely on atmospheric data and generate their forecasts. This work probes a number of ML-based convective thunderstorm-related forecasts over the contiguous United States to 1) understand how they make their predictions, 2) diagnose where their strengths and deficiencies may lie, and 3) explore how well their predictions resemble physical characteristics of the atmosphere. The insights gleaned from this research aim to support operational use of ML-based forecast guidance. First, probabilistic ML-based forecasts of severe convective hazards (i.e., tornadoes, hail, and thunderstorm-driven winds) from the Colorado State University Machine Learning Probabilities (CSU-MLP) system are studied using an explainable machine learning technique known as Tree Interpreter (TI). TI provides context to the CSU-MLP forecasts by disaggregating its forecast probabilities into "contributions" by each of the environmental variables that are used to train the model. This technique allows one to see the extent to which each atmospheric "ingredient" contributes to the final predictions. Results of this work show that CSU-MLP uses environmental information to make its predictions in ways that resemble the climatology and environments of severe storms, and the values of the contributions generally scale with values of the environmental inputs, effectively enhancing the interpretability of the ML system. Second, CSU-MLP forecast performance is examined across different synoptic regimes in an effort to understand which types of environmental conditions tend to lead to skillful versus less-skillful forecast performance. Self organizing maps (SOMs), which are a type of ML, are employed to statistically diagnose regimes across two years of reanalysis data. The skill of day-2 CSU-MLP probabilistic tornado, wind, and hail forecasts are examined across the SOM-identified regimes. This work shows that SOMs are successful at identifying distinct atmospheric patterns using only surface-based convective available potential energy (SBCAPE) and vertical wind shear as inputs. At times, the best- and worst-performing CSU-MLP forecasts occur under highly similar atmospheric conditions, though the best-performing forecasts tend to be characterized by strong synoptic forcing and many storm reports. Third, forecast output from three deep learning weather prediction (DLWP) models, GraphCast, Pangu-Weather, and FourCastNetv2, is studied to investigate how well they model severe storm environments and capture convection-related parameters. This work explores both native and derived fields from 22 months of daily forecasts from these three models, all of which were initialized with input conditions from the Global Forecasting System (GFS). The output is compared to ERA-5 reanalysis and GFS forecasts, both broadly and for specific convective events. Overarching results from this study show that the DLWP model forecasts tend to be characterized by less moisture and greater instability compared to ERA-5. For specific events, the DLWP forecasts can reasonably capture convective environments at least a week in advance and are competitive with the GFS. However they tend to underforecast the vertical wind shear magnitude, and their limited vertical resolution can lead to overly smooth profiles that lack key details such as stable layers.
Open Access
Insights into extreme short-term precipitation associated with supercells and mesovortices
(Colorado State University. Libraries, 2019) Nielsen, Erik R., author; Schumacher, Russ S., advisor; van den Heever, Susan C., committee member; Bell, Michael M., committee member; Niemann, Jeffrey D., committee member
Overall, this manuscript aims to holistically evaluate the relationship between rotation and extreme precipitation processes, since radar and rain-gauge observations in several flash flooding events have suggested that the heaviest short-term rainfall accumulations were associated with supercells or mesovortices embedded within larger convective systems. A specific subclass of these events, when tornadoes and flash floods are both concurrent and collocated (referred to here at TORFF events), present a unique set of concerns, since the recommended life-saving actions for each threat are contradictory. Given this, Chapter 2 aims to evaluate the climatological and meteorological characteristics associated with TORFF events over the United States. Two separate datasets, one based on overlapping tornado and flash flood warnings and the other based on observations, were used to arrive at estimations of the instances when a TORFF event was deemed imminent and verified to have occurred, respectively. These datasets, combined with field project data, were then used to discern the geographical and meteorological characteristics of recent TORFF events. The results show that TORFF scenarios commonly occur, are not easily distinguishable from tornadic events that fail to produce collocated flash flooding, and present difficult challenges both from the perspective of forecasting and public communication. The research in Chapter 3 strives to identify the influence that rotation has on the storm-scale processes associated with heavy precipitation. Five total idealized simulations of a TORFF event, where the magnitude of the 0-1 km shear was varied, were performed to test the sensitivity of precipitation processes to rotation. In the simulations with greater environmental low-level shear and associated rotation, more precipitation fell, both in a point maximum and area-averaged sense. Intense, rotationally induced low-level vertical accelerations associated with the dynamic nonlinear perturbation vertical pressure gradient force were found to enhance the low-to-mid level updraft strength, total vertical mass flux, and allowed access to otherwise inhibited sources of moisture and CAPE in the higher shear simulations. The dynamical accelerations, which increased with the intensity of the low-level shear, dominated over buoyant accelerations in the low levels and were responsible for inducing more intense, low-level updrafts that were sustained despite a stable boundary layer. Chapter 4 aims to explore how often extreme short-term rain rates in the United States are associated with storm-scale or mesoscale vortices, since significant low-level rotation does not always yield a tornado (i.e., not all extreme rainfall events are TORFFs). Five years of METAR observations and three years of Stage-IV analyses were obtained and filtered for hourly accumulations over 75 and 100 mm, respectively. Local dual-pol radar data was then obtained for the remaining events for the hour leading up to the METAR observation. Nearly 50% of the cases were associated with low-level rotation in high-precipitation supercells and/or mesoscale vortices embedded in more organized storm modes. These results support recent modeling results, presented in Chapter 3, suggesting that rotationally induced dynamic vertical pressure accelerations are important to the precipitation formation mechanisms that lead to extreme short-term rainfall rates. The upper Texas Coast, in and around the Houston, TX area, has experienced many intense TORFF events over the recent years. The research in Chapter 5 focuses on examining the horizontally heterogeneous environmental characteristics associated with one of those events, the Tax Day flood of 2016, which was identified as a "verified" TORFF event in Chapter 2. Radar and local mesonet rain gauge observations were used to examine the storm scale characteristics to identify the locations and structures of extreme rain rate producing cells. To supplement the observational based analysis above, a WRF-ARW simulation of the Tax Day flood in 2016, based upon a real-time forecast from the HRRR, was examined. Convective cells that produced the most intense short-term (i.e., sub-hourly to hourly) accumulations within the MCS were examined for the influence of any attendant rotation on both the dynamics and microphysics of the precipitation processes. Results show that the most intense rainfall accumulations, as in the observations analysis, are associated with rotating convective elements, and the results of this chapter confirm that the processes described in Chapter 3 apply outside of the idealized framework.
Open Access
Objective analysis of extreme precipitation events in diverse geographic regions
(Colorado State University. Libraries, 2018) Kelly, Nathan Robert, author; Schumacher, Russ S., advisor; Rasmussen, Kristen L., committee member; Nelson, Peter A., committee member
Extreme precipitation events are a focus of much research in the atmospheric science community today. These events are extraordinarily impactful to society, damaging critical infrastructure and in the worst cases taking lives. The factors that lead to these destructive events are not the same everywhere, dependent on each regions unique geography and climatology. There are two critical ingredients to precipitation: moisture and lift. However, there are many synoptic patterns that can combine these two ingredients in the right proportions, resulting in an extreme storm. This thesis addresses the relationship between lift and moisture, and relates these two variables to the patterns that produce them, in a way that can be applied to any region of the world. To accomplish this task the synoptic patterns must be categorized. This is done in an objective way, using a global reanalysis product (namely MERRA-2) so as to be applicable to any area around the globe. The period 1980 to 2016 is analyzed, and an extreme precipitation event is defined here as an event that exceeds the 99.9th percentile of running 24-hour precipitation sums. Two domains are analyzed, one covering Argentina, and another covering northeast Colorado and part of the high plains to the north and east. Principal Component Analysis (PCA) is the objective method employed to investigate the variability within extreme precipitation events. PCA gives an indication as to what variables input into the analysis have the most impact on the variability of the dataset as a whole. This allows for an analysis of what variables are most different in different extreme events and what variables are about the same across events. PCA is performed on two different sets of variables at each grid point in both the northern Colorado and Argentina. Two points are selected for further analysis herein; these are 40.5N 104.375W (near Greeley, Colorado) and 31.5S 63.75W (near Córdoba, Argentina). At the northern Colorado gridpoint it is clear that there are two very distinct modes of extreme 24 hour precipitation. The first is a convective mode that is characterized at upper levels by a large ridge aloft with a small embedded shortwave. The second is a synoptic mode commonly associated with the most intense snowstorms in the region; a cutoff low approaching from the southwest. The convective mode is associated with more precipitable water than the synoptic mode, whereas in the synoptic mode the upper air features are able to contribute significantly to the lifting of air and cause extreme precipitation with a relative dearth of moisture. In Argentina, the primary variability seems to be in the position of a surface trough in the lee of the Andes as a large scale upper level trough impinges on the Andes crest. The first mode has this lee trough more directly contributing to lift and allowing the low level jet and associated moisture to reach farther south. The second involves the position of the lee trough farther north, which allows the south Atlantic high to push flow from the Atlantic upslope into the Sierra de Córdoba, initiating convection. The overlap between 1-hour and 24-hour extremes is also explored for Argentina, confirming the convective nature of much of this precipitation and illustrating just how important these convective episodes are to the production of extreme precipitation.
Open Access
On the environments and dynamics of nocturnal mesoscale convective systems
(Colorado State University. Libraries, 2018) Hitchcock, Stacey, author; Schumacher, Russ S., advisor; Randall, David A., committee member; van den Heever, Susan C., committee member; Eykholt, Richard, committee member
The 2015 Plains Elevated Convection at Night (PECAN) field campaign was motivated by unanswered questions about key processes in elevated mesoscale convective systems (MCSs) and the difficulty in accurately forecasting them. During the campaign, 15 MCS environments were sampled by an array of instruments including radiosondes launched by fixed and mobile sounding teams. Cluster analysis of observed vertical profiles established three primary pre-convective categories. Only one of these groups fits well with the common conceptual model of nocturnal MCS environments where equivalent potential temperature increases in an elevated layer with the onset of the low-level jet (LLJ). Post-convective soundings demonstrate substantial variability, but cold pools were observed in nearly every PECAN MCS case. However, stronger, deeper stable layers appear to lead to structures where the largest cooling is observed above the surface. On 24-25 June 2015, a 'bow-and-arrow' MCS structure was observed in an environment with strong low-level stability. Previous work on the mechanisms that support the structure in the arrow region (also sometimes referred to as rearward off-boundary development or ROD) has relied on a combination of a surface cold pool and large scale ascent provided by the interaction of a LLJ with a baroclinic zone. A horizontally homogeneous simulation initialized with a near-storm pre-convective PECAN sounding from the 24-25 June 2015 produces nearly the same MCS structure in the absence of a surface cold pool. Instead, outflow takes on several different forms in different regions of the MCS. Ultimately, the ROD (or arrow) is most likely supported by gravity wave amplified by vertical wind shear over the same layer, and maintained by persistent downdrafts. The success of both MCS initiation and development of ROD despite the strong stable layer and idealized horizontally homogeneous initial conditions suggests that the interactions between convective outflow and a stable layer in a sheared environment are important in both of these processes. Very few studies to date have explored these interactions, and even less in 3D. A series of 2D and 3D experiments were designed to explore 1) What happens when a downdraft impacts a stable layer with and without shear? 2) What low-level shear profiles support MCS development in an idealized simulation with strong stability and why? 3) What shear characteristics are favorable for ROD development? Results indicate that strong low level shear is critical for sustaining convection, that low-level shear may be as important as stability in determining the effective inflow layer, and that upper level winds play a critical role in the development of ROD.
Open Access
Response of MCSs and low-frequency gravity waves to vertical wind shear and nocturnal thermodynamic environments
(Colorado State University. Libraries, 2019) Groff, Faith, author; Schumacher, Russ S., advisor; Adams-Selin, Rebecca D., advisor; Rasmussen, Kristen L., committee member; Nelson, Peter A., committee member
Low-frequency gravity waves have been found to both increase and decrease environmental favorability ahead of mesoscale convective systems (MCSs) based on their associated vertical motions. The strength and timing of these waves is determined by the internal dynamics of the MCS. This study investigates the sensitivities of MCSs to changes in the vertical wind and thermodynamic profiles through idealized cloud model simulations, highlighting how internal MCS processes impact low-frequency gravity wave generation, propagation and environmental influence. A common feature among all of the simulations is that fluctuations within the internal latent heating profile, the generation mechanism behind n = 1 (N1) waves, display concurrent cellularity with the MCS updrafts. Spectral analysis is performed on the rates of latent heat release, updraft velocity, and deep-tropospheric descent ahead of the convection as a signal for N1 wave passage. Results strongly suggest that perturbations in mid-level descent up to 100 km ahead of the MCS occur at the same frequency as N1 gravity wave generation due to fluctuations of latent heat release caused by the cellular variations of MCS updrafts. The introduction of deep vertical wind shear does not change this connection nor impact the lifecycle of daytime MCS updrafts and associated N1 wave generation, however within a nocturnal environment, the frequency of the cellularity of the updrafts increases, subsequently increasing the frequency of N1 wave generation. In response to surges of latent cooling within the lower half of the troposphere, n = 2 (N2) low-frequency gravity waves are generated, however this only occurs with cooling contributions from both evaporation and melting of hydrometeors. Results indicate that in environments with minimal upper-level wind shear atop more pronounced shear below, the N2 wave generation mechanisms and environmental influence behave similarly among daytime and nocturnal MCSs. Within environments that incorporate deep vertical wind shear, many of the N2 waves are strong enough to support cloud development ahead of the MCS as well as sustain and support convection within the domain.
Open Access
Synoptic through mesoscale environments of South American thunderstorms
(Colorado State University. Libraries, 2020) Piersante, Jeremiah Otero, author; Rasmussen, Kristen L., advisor; Schumacher, Russ S., advisor; Nelson, Peter A., committee member
Subtropical South America east of the Andes Mountains is a global hotspot for deep convection owing to frequent mesoscale convective systems (MCSs) that contribute to over 90% of the region's rainfall and produce severe weather including large hail, flash flooding, high winds, and tornadoes. Investigations of these high impact systems through the Tropical Rainfall Measuring Mission (TRMM) satellite's precipitation radar (PR) determined that unique orographic, synoptic, and mesoscale processes initiate and maintain larger and longer-lasting MCSs in subtropical South America relative to the United States. Prior to the initiation of convection, the South American low-level jet guided by the Andes Mountains advects warm and moist air southward from the Amazon basin into the subtropics. A deep mid-level trough simultaneously approaches the mountains from the west in most cases, inducing dry mid- to upper-level subsidence flow and creating a strong capping inversion over the moist air mass. This cap is overcome via terrain-induced lift by the Andes foothills and the Sierras de Córdoba, a secondary mountain range in northern Argentina, resulting in explosive convection. These unique topographic features often act as a platform for "back-building" in which convection remains tied to the western terrain as storms propagate eastward and grow upscale; the quasi-stationary nature of MCSs inflicts considerable damage to property and agriculture in Argentina. To improve the predictability and understanding of the physical mechanisms leading to dangerous MCSs in subtropical South America relative to those in the U.S., two studies within this thesis employ data from the recently conducted Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign focusing on 1) the comparison of biases in warm-season Weather Research and Forecasting model (WRF) forecasts in North and South America and 2) a synoptic through mesoscale analysis of the driving factors behind upscale growth in subtropical South America. The first study uses WRF output over North and South America verified against Stage IV analyses and radiosonde observations to contrast magnitudes and sources of forecast error between continents. It is found that the cumulus parameterization, which is most active during the warm-season, governs biases in North American precipitation forecasts. While both continents featured a mid-level dry bias, the South American bias is greater. The second study uses TRMM PR, ERA5 reanalysis, high-resolution soundings and GOES-16 infrared brightness temperature data to identify synoptic and mesoscale phenomena that induce upscale growth varying in size and season. Synoptic forcing decreased from large to small systems and from spring to summer, suggesting that terrain-induced lift is more important in the summer. Furthermore, a case study of an MCS that exhibited rapid upscale growth during RELAMPAGO highlights the role of southerly return flow associated with the western edge of the 850-hPa lee trough on low-level convergence, vertical wind shear, and thus convection initiation. These two studies are of importance to the atmospheric science community as they enhance the understanding of some of the world's most violent thunderstorms in a region that has been notably understudied. Knowledge gained can also be applied to similar regions whose convection is also modulated by orography and provide a greater understanding of convective processes on a global scale.
Open Access
Toward an improved understanding of the synoptic and mesoscale dynamics governing nocturnal heavy-rain-producing mesocale convective systems
(Colorado State University. Libraries, 2015) Peters, John M., author; Schumacher, Russ S., advisor; van den Heever, Sue, committee member; Johnson, Richard, committee member; Niemann, Jeffrey D., committee member; Weisman, Morris, committee member
In the first stage of this research, rotated principal component analysis was applied to the atmospheric fields associated with a large sample of heavy-rain-producing mesoscale convective systems (MCSs) that exhibited the training-line adjoining stratiform (TL/AS) morphology. Cluster analysis in the subspace defined by the leading two resulting principal components revealed two sub-types with distinct synoptic and mesoscale characteristics, which are referred to as warm-season type and synoptic type events respectively. Synoptic type events, which tended to exhibit greater horizontal extent than warm-season type events, typically occurred downstream of a progressive upper-level trough, along a low-level potential temperature gradient with the warmest air to the south and southeast. Warm-season type events on the other hand occurred within the right entrance region of a minimally-to-anticyclonically curved upper level jet streak, along a low-level potential temperature gradient with the warmest low-level air to the southwest. Synoptic-scale forcing for ascent was stronger in synoptic type events, while low-level moisture was greater in warm-season type events. Warm-season type events were frequently preceded by the passage of a trailing stratiform (TS) type MCS, while synoptic type events often occurred prior to the passage of a TS type system. An idealized modeling framework was developed to simulate a quasi-stationary heavy-rain-producing MCSs. A composite progression of atmospheric fields from warm season TL/AS MCSs was used as initial and lateral boundary conditions for a numerical simulation of this MCS archetype. A realistic TL/AS MCS initiated and evolved within a simulated mesoscale environment that featured a low-level jet terminus, maximized low-level warm air advection, and elevated maximum in convective available potential energy. The first stage of MCS evolution featured an eastward moving trailing-stratiform type MCS that generated a surface cold pool. The initial system was followed by rearward off-boundary development (ROD), where a new line of convective cells simultaneously re-developed north of the surface cold pool boundary. Backbuilding persisted on the western end of the new line, with individual convective cells training over a fixed geographic region. The final stage was characterized by a deepening and southward surge of the cold pool, resulting in the weakening and slow southward movement of the training line. The dynamics of warm season TL/AS MCSs are elucidated through the analysis of the idealized simulation, along with a simulation of an observed case. The environmental conditions external to the MCS contributed to the development of a new convective line west of the initial MCS, and displaced northward of the southwestern flank of the surface OFB. Southwesterly low-level flow was thermodynamically stabilized as it lifted over the southwestern OFB from a pattern of adiabatic cooling below latent heating. This flow traveled 80-100 km northeastward beyond the surface OFB to the point where large-scale lifting sufficiently destabilized the flow for deep convection. These factors explain the geographic offset of the second convective line from the surface OFB left by the forward-propagating MCS. Eventually the surface cold pool became sufficiently deep so that gradual ascent of parcels with moisture and instability over the feature began triggering new convection close to the OFB (rather than 80-100 km away from it), which eventually drove the system southward. These results suggest that large-scale environmental factors were predominantly responsible for the quasi-stationary behavior of the simulated MCS, though upscale convective feedbacks played an important role in the complexity of the convective evolution.