Browsing by Author "Schumacher, Russ, advisor"
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Item Open Access Cold-season tornadoes: climatological, meteorological, and social perspectives(Colorado State University. Libraries, 2017) Childs, Samuel J., author; Schumacher, Russ, advisor; Rutledge, Steven, committee member; Trumbo, Craig, committee memberTornadoes that occur during the cold season, defined here as November–February (NDJF), pose many unique societal risks. For example, people can be caught off-guard because in general one does not expect severe weather and tornadoes during winter months. The public can also be unsuspecting of significant weather due to the bustle of major holidays like Thanksgiving, Christmas, and New Year's, when most people are concerned with family activities and not thinking about the weather. Cold-season tornadoes also have a propensity to be nocturnal and occur most frequently in the South and Southeastern U.S., where variable terrain, inadequate resources, and a relatively high mobile home density add additional social vulnerabilities. Over the period 1953–2015 within a study domain of (25-42.5°N, 75-100°W), some 937 people lost their lives as a result of NDJF tornadoes. Despite this enhanced societal risk of cold-season tornadoes in the South, very little attention has been given to their meteorological characteristics and climate patterns, and public awareness of their potential impacts is lacking. This thesis aims to greatly advance the current state of knowledge of NDJF tornadoes by providing an in-depth investigation from three different science perspectives. First, a climatology of all (E)F1-(E)F5 NDJF tornadoes is developed, spanning the period 1953–2015 within a domain of (25-42.5°N, 75-100°W), in order to assess frequency and spatial changes over time. A large increasing trend in cold-season tornado occurrence is found across much of the Southeastern U.S., with the greatest uptick in Tennessee, while a decreasing trend is found across eastern Oklahoma. Spectral analysis reveals a cyclic pattern of enhanced NDJF counts every 3-7 years, coincident with the known period for ENSO. Indeed, La Niña episodes are found to be correlated with NDJF tornado counts, although a stronger teleconnection correlation exists with the Arctic Oscillation (AO), which explains 25% of the variance in counts. A second perspective focuses on meteorological environments that characterize NDJF tornadoes through use of the NCEP/NCAR Reanalysis. Upon comparing the most tornadic and least tornadic cold seasons, it is found that active seasons are characterized by a large trough in the western U.S.; warm and moist conditions across the Southeast, likely due to an enhanced low-level jet transport from the western Gulf of Mexico; and enhanced 1000-500-hPa wind speed shear. The third perspective addressed in this thesis is that of social science. A case study of four tornado events from November 2016–February 2017, in which a post-event survey is disseminated to NWS meteorologists, broadcast meteorologists, and emergency managers, is carried out to assess strategies and barriers professionals face when communicating cold-season tornado risk and warnings to their respective communities. The survey also aims to shed light on the perceived levels of human preparedness, vulnerability, and resiliency from the professional's point of view. In addition to unique, case-specific challenges, the professionals expressed major barriers to communication due to inconsistency of messages and graphics, and an inability to give the public information on fine enough temporal and spatial scales. Each decision-making sector noted a high local vulnerability to tornadoes in general, mostly brought on by lack of education and/or resources. However, most professionals perceive their communities to be aware of cold-season tornado risk and thus adequately prepared and resilient when they occur. The survey results also confirm the desire and need for better collaboration among professionals, and with social scientists, in order to adequately educate and warn all sectors of society from tornado risk, especially those during times of year they are not typically expected. Harnessing all three perspectives presented in this study provides a much deeper understanding of NDJF tornadoes and their societal impacts, an understanding that serves to increase public awareness and ultimately save lives.Item Open Access Comparing precipitation estimates, model forecasts, and random forest based predictions for excessive rainfall(Colorado State University. Libraries, 2023) James, Eric, author; Schumacher, Russ, advisor; Bell, Michael, committee member; Van Leeuwen, Peter Jan, committee member; Morrison, Ryan, committee memberFlash flooding is an important societal challenge, and improved tools are needed for both real-time analysis and short-range forecasts. We present an evaluation of threshold exceedances of quantitative precipitation estimate (QPE) and forecast (QPF) datasets in terms of their degree of correspondence with observed flash flood events over a seven-year period. We find that major uncertainties persist in QPE for heavy rainfall. In general, comparison with flash flood guidance (FFG) thresholds provides the best correspondence, but fixed thresholds and average recurrence interval thresholds provide the best correspondence in certain regions of the contiguous US (CONUS). QPF threshold exceedances from the High-Resolution Rapid Refresh (HRRR) generally do not correspond as well as QPE exceedances with observed flash floods, except for the 1-h duration in the southwestern CONUS; this suggests that high-resolution model QPF may be a better indicator of flash flooding than QPE in some poorly observed regions. Subsequently, we describe a new random forest (RF) based excessive rainfall forecast system using predictor information from the 3-km operational HRRR. Experiments exploring the use of spatial predictor information reveal the importance of averaging HRRR predictor fields across a spatial radius rather than using only information from sparse input grid points for regimes with small-scale excessive rain events. Tree interpreter results indicate that the forecast benefits of spatial aggregation stem from greater contributions provided by storm attribute predictors. Forecasts are slightly degraded when there is a mismatch between the trained RF model and the daily HRRR forecasts to which the model is applied, both in terms of initialization time and HRRR model version. Use of FFG as an additional predictor leads to forecast improvements, highlighting the potential of hydrologic information to contribute to forecast skill. In addition, averaging predictor information across several HRRR initializations leads to a statistically significant improvement in forecasts relative to using predictor fields from a single HRRR initialization. The HRRR-based RF has been evaluated at the annual Flash Flood and Intense Rainfall Experiment (FFaIR) over the past three years, with year-over-year improvements stemming from the results of sensitivity experiments. The HRRR-based RF represents an important baseline for future machine learning based excessive rainfall forecasts based on convection-allowing models.Item Open Access CSU-MLP GEFS day-1 "first-guess" excessive rainfall forecasts: aggregate evaluation and synoptic regimes of best- and worst-performing forecasts(Colorado State University. Libraries, 2022) Escobedo, Jacob A., author; Schumacher, Russ, advisor; van den Heever, Susan, committee member; Cooley, Daniel, committee memberForecasting excessive rainfall, particularly flash flood-producing rainfall, is an important problem that remains difficult due to the small spatial scales and varying temporal scales at which they occur. One important operational product that highlights areas for potential excessive rainfall and flash flood occurrences is the Excessive Rainfall Outlook (ERO) issued by the NOAA Weather Prediction Center (WPC), which provides outlooks for lead times of 1-3 days. To address the need for additional tools for WPC forecasters while forming a given ERO, the Colorado State University Machine Learning Probabilities (CSU-MLP) system, a probabilistic forecast system for excessive rainfall (and other convective hazards), was developed to produce forecasts to be used as a "first-guess" ERO. CSU-MLP employs the use of a random forest (RF) algorithm trained using NOAA's Second-Generation Global Ensemble Forecast System Reforecast (GEFS/R) and precipitation observations, while using the operational GEFS with the trained model to produce real-time forecasts. Initially developed as a medium range guidance (2-3 day lead time), CSU-MLP has produced day-1 forecasts that have been evaluated favorably during the 4-week Flash Flood and Intense Rainfall Experiment (FFaIR) in the summer of 2020. However, CSU-MLP day-1 forecasts have been observed to have daily forecast skill that can vary widely between days. This work will include an aggregate evaluation of CSU-MLP day-1 forecasts over a longer period of study (3 March 2019 – 15 October 2020) than that of FFaIR, and an identification of synoptic regimes for which these forecasts tend to perform at their best and worst. Results show that CSU-MLP day-1 forecasts are reliable, provide adequate discrimination of excessive rainfall events (AuROC =0.819), and have comparable performance, evaluated by use of the Brier skill score (BSS), to that of the ERO (CSU-MLP BSS = 0.081; ERO BSS = 0.085). However, CSU-MLP forecasts have a higher frequency of categorical probabilities (≥ 0.05) which results in larger variations of daily BSS. Synoptic regimes of best-performing daily forecasts reveal a tendency for these regimes to be characterized by moderate to strong large-scale forcing and relatively high low-level and column moisture. This would include warm-season regimes with moderate amplitude upper-level troughs, tropical cyclones, cut-off lows, and cool-season regimes where strong forcing is co-located near an abundant moisture source. Forecasts tend to perform worst when there is strong large-scale forcing and low-level and column moisture is relatively low, such as cool-season regimes with large amplitude troughs and surface cyclones but higher levels of atmospheric moisture are not present nor as widespread. This work has implications for WPC forecasters as they use the "first-guess" forecasts while developing the ERO for a given day, as well as implications for future CSU-MLP system model iterations and/or designs.Item Open Access Ensemble-based analysis of extreme precipitation events from 2007-2011(Colorado State University. Libraries, 2012) Lynch, Samantha, author; Schumacher, Russ, advisor; Johnson, Richard, committee member; Niemann, Jeffrey, committee memberFrom 2007 to 2011, 22 widespread, multiday rain events occurred across the United States. This study makes use of the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers of Environmental Prediction (NCEP), and the United Kingdom Office of Meteorology (UKMET) ensemble prediction systems (EPS) in order to assess their forecast skill of these 22 widespread, precipitation events. Overall, the ECMWF had a skillful forecast for almost every event, with an exception of the 25-30 June 2007 event, the mesoscale convective vortex (MCV) over the southern plains of the United States. Additionally, the ECMWF EPS generally outperformed both the NCEP and UKMET EPS. To better evaluate the ECMWF, two widespread, multiday precipitation events were selected for closer examination: 29 April-4 May 2010 and 23-28 April 2011. The 29 April-4 May 2010 case study used ECMWF ensemble forecasts to explore the processes responsible for the development and maintenance of a multiday precipitation event that occurred in early May 2010, due to two successive quasi-stationary mesoscale convective systems. Locations in central Tennessee accumulated more than 483 millimeters (19 inches) of rain and the city of Nashville experienced a historic flash flood. Differences between ensemble members that correctly predicted heavy precipitation and those that did not were determined in order to determine the processes that were favorable or detrimental to the system's development. Statistical analysis was used to determine how synoptic-scale flows were correlated to area- averaged precipitation. For this particular case, the distribution of precipitation was found to be closely related to the strength of an upper-level trough in the central United States and an associated surface cyclone, with a weaker trough and cyclone being associated with more precipitation in the area of interest. The 23-28 April 2011 case study also used ECMWF ensemble forecasts to explore the processes responsible for the development and maintenance of a multiday precipitation event. This event was associated with persistent heavy rainfall, flooding more than six states lining the Mississippi River. In this case, the largest difference in the ensemble members' forecasts was the strength of the upper-level trough and associated occluded low, as well as the speed at which this system moved off to the east. These relatively small differences in the height field ultimately resulted in different forecasts of precipitation over the Mississippi Valley. This sensitivity to small-scale differences in the initial conditions highlights the importance of using ensembles for predicting the development of precipitation systems over both land and ocean. Comparison between the 29 April-4 May 2010 and 23-28 April 2011 widespread precipitation events provide information regarding which of the two case studies was better predicted in relation to both location and amount of precipitation. Heavy rainfall totals, exceeding the 100 and 150 mm threshold, were better anticipated for the 29 April-4 May 2010 event, while location of the precipitation was better predicted for the 23-28 April 2011 widespread rain event.Item Open Access Ensemble-based analysis of Front Range severe convection on 6-7 June 2012: forecast uncertainty and communication of weather information to Front Range decision-makers(Colorado State University. Libraries, 2014) Vincente, Vanessa, author; Schumacher, Russ, advisor; Johnson, Richard, committee member; Ramirez, Jorge, committee memberThe variation of topography in Colorado not only adds to the beauty of its landscape, but also tests our ability to predict warm season severe convection. Deficient radar coverage and limited observations make quantitative precipitation forecasting quite a challenge. Past studies have suggested that greater forecast skill of mesoscale convection initiation and precipitation characteristics are achievable considering an ensemble with explicitly predicted convection compared to one that has parameterized convection. The range of uncertainty and probabilities in these forecasts can help forecasters in their precipitation predictions and communication of weather information to emergency managers (EMs). EMs serve an integral role in informing and protecting communities in anticipation of hazardous weather. An example of such an event occurred on the evening of 6 June 2012, where areas to the lee of the Rocky Mountain Front Range were impacted by flash-flood-producing severe convection that included heavy rain and copious amounts of hail. Despite the discrepancy in the timing, location and evolution of convection, the convection-allowing ensemble forecasts generally outperformed those of the convection-parameterized ensemble in representing the mesoscale processes responsible for the 6-7 June severe convective event. Key features sufficiently reproduced by several of the convection-allowing ensemble members resembled the observations: 1) general location of a convergence boundary east of Denver, 2) convective initiation along the boundary, 3) general location of a weak cold front near the Wyoming/Nebraska border, and 4) cold pools and moist upslope characteristics that contributed to the backbuilding of convection. Members from the convection-parameterized ensemble that failed to reproduce these results displaced the convergence boundary, produced a cold front that moved southeast too quickly, and used the cold front for convective initiation. The convection-allowing ensemble also showed greater skill in forecasting heavy precipitation amounts in the vicinity of where they were observed during the most active convective period, particularly near urbanized areas. A total of 9 Front Range EMs were interviewed to research how they understood hazardous weather information, and how their perception of forecast uncertainty would influence their decision making following a heavy rain event. Many of the EMs use situational awareness and past experiences with major weather events to guide their emergency planning. They also highly valued their relationship with the National Weather Service to improve their understanding of weather forecasts and ask questions about the uncertainties. Most of the EMs perceived forecast uncertainty in terms of probability and with the understanding that forecasting the weather is an imprecise science. The greater the likelihood of occurrence (implied by a higher probability of precipitation) showed greater confidence in the forecast that an event was likely to happen. Five probabilistic forecast products were generated from the convection-allowing ensemble output to generate a hypothetical warm season heavy rain event scenario. Responses varied between the EMs in which products they found most practical or least useful. Most EMs believed that there was a high probability for flooding, as illustrated by the degree of forecasted precipitation intensity. Most confirmed perceiving uncertainty in the different forecast representations, sharing the idea that there is an inherent uncertainty that follows modeled forecasts. The long-term goal of this research is to develop and add reliable probabilistic forecast products to the "toolbox" of decision-makers to help them better assess hazardous weather information and improve warning notifications and response.Item Open Access From rain gauges to retweets: using diverse datasets to explore overlapping hazards and human experiences in landfalling tropical cyclones(Colorado State University. Libraries, 2021) Mazurek, Alexandra C., author; Schumacher, Russ, advisor; Henderson, Jen, committee member; Morrison, Ryan, committee member; Rasmussen, Kristen, committee memberLandfalling tropical cyclones (LTCs) are responsible for numerous hazards, including damaging winds, storm surge, inland flooding, and tornadoes. Furthermore, multiple hazards may threaten an area at the same time, which raises challenges from a prediction, warning operations, and human impacts standpoint. Previous research has approached overlapping tornado and flash flood events—which exemplify these challenges because the recommended protective actions can be in conflict—in continental systems from multidisciplinary perspectives, but less work has been done to explore these phenomena in LTC environments. Because LTCs also introduce other hazards, additional complexities may exacerbate already challenging circumstances. This work integrates meteorological and social sciences to broadly advance the understanding and implications of simultaneous flash flood and tornado events in LTCs. Part I of this thesis investigates the relationship between two predecessors to tornadoes and flash floods—meso- to storm-scale rotation and heavy rainfall rates, respectively—using observations. Motivated by previous work that has drawn linkages between these two processes in continental convective storms, this connection is explored in Tropical Storm Imelda, a system that was among the wettest LTCs on record to impact the contiguous United States (CONUS), producing rainfall accumulations in excess of 1000 mm when it made landfall on the western Gulf Coast in September 2019. First, a synoptic and mesoscale overview of the tropical cyclone (TC) is presented as motivation for its utility in examining overlapping embedded rotation and extreme rainfall rates. Then, rain gauges from a high-density observing network in southeast Texas are analyzed alongside polarimetric radar data to compare rainfall rates that occur in the presence of embedded rotation to those that occur when no rotation is evident on radar. According to these results, 5-minute rainfall rates that followed subjectively-identified meso- to storm-scale rotation on radar tended to be statistically significantly greater, and when accumulated over time, more than twice as much rainfall was recorded at gauge sites when rotation was present near the gauge compared to when there was no rotation located nearby. To further quantify the spatial and temporal relationships of embedded rotation and heavy rainfall rates, quantitative precipitation estimates (QPE) and rotation tracks from the Multi-Radar Multi-Sensor system are compared in time and space. A positive correlation was found to exist between the hourly-accumulated 0-2 km rotation tracks and hourly local gauge bias-corrected QPE, suggesting that more rain tends to fall in the presence of low-level rotation. In Part II of this thesis, social science methods are used to investigate another LTC: Hurricane Harvey (2017)—an unprecedented event that became the wettest LTC on record to impact CONUS and spawned over 50 tornadoes when it affected the western Gulf Coast. This work aims to explore the notion of experience as it evolves on Twitter in real-time during Harvey among a group of users who were located in areas that were impacted by the LTC and its overlapping hazards. Though a significant amount of research has investigated experience through surveying and interview techniques after LTCs occur, much less work has been done to study experience as it is shared live during an event or through the lens of social media. Using this motivation and drawing on the overarching theme of concurrent hazards, this research begins with a database of tweets composed during the period surrounding Hurricane Harvey that reference tornadoes and flash flooding. The sample is refined through a multi-step querying process, ultimately resulting in a group of 39 users who shared 158 tweets about "past events"—that is, events related to LTCs and/or the hazards that are associated with them. These tweets are thematically analyzed by individual users, by individual past events, and over time. The results of these analyses show that Twitter users referenced past events during Harvey for two main reasons: first, because the user has a personal connection to the event and second, because the past event is helping them to make sense of various aspects of the situation that is unfolding around them. Understanding what roles past events may play in a real-time crisis is useful to leaders and decision-makers, such as meteorologists, local politicians, and emergency managers, as it provides insight on the evolving needs and concerns of the public that they serve as they change and are modulated by various events that unfold throughout the overarching crisis.Item Open Access Investigation into a displacement bias in numerical weather prediction models' forecasts of mesoscale convective systems(Colorado State University. Libraries, 2013) Yost, Charles, author; Schumacher, Russ, advisor; van den Heever, Sue, committee member; Ramirez, Jorge, committee memberAlthough often hard to correctly forecast, mesoscale convective systems (MCSs) are responsible for a majority of warm-season, localized extreme rain events. This study investigates displacement errors often observed by forecasters and researchers in the Global Forecast System (GFS) and the North American Mesoscale (NAM) models, in addition to the European Centre for Medium Range Weather Forecasts (ECMWF) and the 4-km convection allowing NSSL-WRF models. Using archived radar data and Stage IV precipitation data from April to August of 2009 to 2011, MCSs were recorded and sorted into unique six-hour intervals. The locations of these MCSs were compared to the associated predicted precipitation field in all models using the Method for Object-Based Diagnostic Evaluation (MODE) tool, produced by the Developmental Testbed Center and verified through manual analysis. A northward bias exists in the location of the forecasts in all lead times of the GFS, NAM, and ECMWF models. The MODE tool found that 74%, 68%, and 65% of the forecasts were too far to the north of the observed rainfall in the GFS, NAM and ECMWF models respectively. The higher-resolution NSSL-WRF model produced a near neutral location forecast error with 52% of the cases too far to the south. The GFS model consistently moved the MCSs too quickly with 65% of the cases located to the east of the observed MCS. The mean forecast displacement error from the GFS and NAM were on average 266 km and 249 km, respectively, while the ECMWF and NSSL-WRF produced a much lower average of 179 km and 158 km. A case study of the Dubuque, IA MCS on 28 July 2011 was analyzed to identify the root cause of this bias. This MCS shattered several rainfall records and required over 50 people to be rescued from mobile home parks from around the area. This devastating MCS, which was a classic Training Line/Adjoining Stratiform archetype, had numerous northward-biased forecasts from all models, which are examined here. As common with this archetype, the MCS was triggered by the low-level jet impinging on a stationary front, with the heaviest precipitation totals in this case centered along the tri-state area of Iowa, Illinois, and Wisconsin. Low-level boundaries were objectively analyzed, using the gradient of equivalent potential temperature, for all forecasts and the NAM analysis. In the six forecasts that forecasted precipitation too far to the north, the predicted stationary front was located too far to the north of the observed front, and therefore convection was predicted to initiate too far to the north. Forecasts associated with a northern bias had a stationary front that was too far to the north, and neutral forecasts' frontal locations were closer to the observed location.Item Open Access Land surface sensitivity of mesoscale convective systems(Colorado State University. Libraries, 2016) Tournay, Robert C., author; Schumacher, Russ, advisor; Vonder Haar, Thomas, advisor; van den Heever, Susan, committee member; Nelson, Peter, committee memberMesoscale convective systems (MCSs) are important contributors to the hydrologic cycle in many regions of the world as well as major sources of severe weather. MCSs continue to challenge forecasters and researchers alike, arising from difficulties in understanding system initiation, propagation, and demise. One distinct type of MCS is that formed from individual convective cells initiated primarily by daytime heating over high terrain. This work is aimed at improving our understanding of the land surface sensitivity of this class of MCS in the contiguous United States. First, a climatology of mesoscale convective systems originating in the Rocky Mountains and adjacent high plains from Wyoming southward to New Mexico is developed through a combination of objective and subjective methods. This class of MCS is most important, in terms of total warm season precipitation, in the 500 to 1300m elevations of the Great Plains (GP) to the east in eastern Colorado to central Nebraska and northwest Kansas. Examining MCSs by longevity, short lasting MCSs (<12 hrs), medium (12-15 hrs) and long lasting MCSs (>15 hrs) reveals that longer lasting systems tend to form further south and have a longer track with a more southerly track. The environment into which the MCS is moving showed differences across commonly used variables in convection forecasting, with some variables showing more favorable conditions throughout (convective inhibition, 0-6 km shear and 250 hPa wind speed) ahead of longer lasting MCSs. Other variables, such as convective available potential energy, showed improving conditions through time for longer lasting MCSs. Some variables showed no difference across longevity of MCS (precipitable water and large-scale vertical motion). From subsets of this MCS climatology, three regions of origin were chosen based on the presence of ridgelines extending eastward from the Rocky Mountains known to be foci for convection initiation and subsequent MCS formation: Southern Wyoming (Cheyenne Ridge), Colorado (Palmer divide) and northern New Mexico (Raton Mesa). Composite initial and boundary conditions were developed from reanalysis data, from which control runs of regional MCSs were made as well a series of idealized experiments with imposed large scale soil moisture (SM) anomalies to study to impact to each regional MCS on SM variations in initiation region as well down stream in the GP. Another idealized experiment was made to study the impact of varying the planetary boundary layer (PBL) parameterization in the context of the idealized SM variations. While the distribution of SM has a major impact on CAPE and the location and magnitude of CI, also important is the differences in shear driven by the differences in large scale SM, playing a major, and varying depending on where the regional MCSs interact with the shear anomalies. Utilizing a different PBL parameterization impacts the timing and amount of initial CI, impacting the total precipitation produced by the MCSs, but not nearly the magnitude of alteration to the MCS as varying the SM distribution. A climatology of CI in the Rocky Mountains and adjacent high plains is made using a high resolution observational dataset. From this climatology, the sensitivity of CI to land surface variables, including SM and vegetation is studied. It was found that the timing of CI had a stronger relationship with SM, with earlier CI over wetter than average soils, with the greatest difference in May in the north of the domain, nearly all statistical significant values across regions from north to south in June and July with little difference in August in the northern regions. Outside of May, which showed a strong relationship of earlier CI over less vegetated regions, the relationship was similar, but weaker than, that between SM and CI timing. Examining the CAPE, CIN and PW at CI and null points reveal that the values are generally more conducive to CI over wet soils and anomalously vegetated areas at both CI and null points, with stronger difference in the high plains in the east of regions. Examining the covariance of SM and vegetation at CI points revealed that July and August showed expected covariance relationships with concurrently measured convective variables (i.e., high SM/vegetation associated with high CAPE and vice versa for low SM/vegetation) while May and June higher CAPE and CIN over low vegetation anomalies. A climatology of elevated mixed layers in the central GP was conducted, revealing that the greatest number of EMLS occurred in the northern GP. Back trajectories (BT) were conducted from the radiosonde point of detection for 18 and 36 hours, revealing that the BT point mean for days with severe weather were further west and south from the origin point. The SM and vegetation was sampled at the BT point, revealing a negative, significant correlation with EML depth when pooling the northern stations in 18-hr BTs, and a significant, negative correlation with EVI when pooling the southern sites. A modeling case study was conducted in which an idealized SM anomaly was imposed over the EML origin region. Experiments were also conducted to test the sensitivity of ML formation and EML transport using different PBL parameterizations. While the YSU PBL parameterization produced the deeper PBL over anonymously dry soils in the EML origin region, the EML was not transported to the east as it was in those experiments using the MYNN parameterization, impacting the timing and extent of precipitation in the model runs.Item Open Access Maximizing the utility of available root zone soil moisture data for drought monitoring purposes in the Upper Colorado River Basin and western High Plains, and assessing the interregional importance of root zone soil moisture on warm season water balance(Colorado State University. Libraries, 2016) Goble, Peter, author; Schumacher, Russ, advisor; Denning, Scott, committee member; Chávez, José, committee memberTo view the abstract, please see the full text of the document.Item Open Access Model post-processing for the extremes: improving forecasts of locally extreme rainfall(Colorado State University. Libraries, 2016) Herman, Gregory Reid, author; Schumacher, Russ, advisor; Barnes, Elizabeth, committee member; Cooley, Daniel, committee memberThis study investigates the science of forecasting locally extreme precipitation events over the contiguous United States from a fixed-frequency perspective, as opposed to the traditionally applied fixed-quantity forecasting perspective. Frequencies are expressed in return periods, or recurrence intervals; return periods between 1-year and 100-years are analyzed for this study. Many different precipitation accumulation intervals may be considered in this perspective; this research chooses to focus on 6- and 24-hour precipitation accumulations. The research presented herein discusses the beginnings of a comprehensive forecast system to probabilistically predict extreme precipitation events using a vast suite of dynamical numerical weather prediction model guidance. First, a recent climatology of extreme precipitation events is generated using the aforementioned fixed-frequency framework. The climatology created generally conforms with previous extreme precipitation climatologies over the US, with predominantly warm season events east of the continental divide, especially to the north away from major bodies of water, and primarily cool-season events along the Pacific coast. The performance of several operational and quasi-operational models of varying dynamical cores and model resolutions are assessed with respect to their extreme precipitation characteristics; different biases are observed in different modeling systems, with one model dramatically overestimating extreme precipitation occurrences across the entire US, while another coarser model fails to produce the vast majority of the rarest (50-100+ year) events, especially to the east of the Rockies where most extreme precipitation events are found to be convective in nature. Some models with a longer available record of model data are employed to develop model-specific quantitative precipitation climatologies by parametrically fitting right-skewed distributions to model precipitation data, and applying these fitted climatologies for extreme precipitation forecasting. Lastly, guidance from numerous models is examined and used to generate probabilistic forecasts for locally extreme rainfall events. Numerous methods, from the simple to the complex, are explored for generating forecast probabilities; it is found that more sophisticated methods of generating forecast probabilities from an ensemble of models can significantly improve forecast quality in every metric examined when compared with the most traditional probabilistic forecasting approach. The research concludes with the application of the forecast system to a recent extreme rainfall outbreak which impacted several regions of the United States.Item Open Access Projecting end-of-century human exposure to eastern Colorado tornadoes and hailstorms: meteorological and societal perspectives(Colorado State University. Libraries, 2020) Childs, Samuel J., author; Schumacher, Russ, advisor; Demuth, Julie, committee member; Ojima, Dennis, committee member; Rasmussen, Kristen, committee member; Rutledge, Steven, committee memberThe eastern half of Colorado is one of the most active areas for hailstorms and tornadoes in the U.S. An average of 39 tornadoes and 387 severe hail reports are tallied each year over this domain, and a number of damaging events, particularly hailstorms, have occurred in recent years. In an era of climate change, it is of worth to project how the frequency, geography, and severity of tornadoes and hailstorms may change over time, and doing so on a localized scale can shed light on the small-scale complexities that broader analyses miss. It is important to consider both meteorological and non-meteorological effects when projecting the changing human risk and exposure to these hazards in the future, as human factors such as population growth means that more people may potentially be exposed to tornadoes and hailstorms regardless of how climate change may influence storm characteristics. As such, this doctoral study employs a multidisciplinary, multi-perspective approach to investigate how the tornado and severe hail footprint may change across eastern Colorado by the end of the 21st century, and in turn how the impacts on those who live and work in this area may be exacerbated. A baseline climatology of tornadoes and hailstorms across eastern Colorado is established using Storm Prediction Center data records. Both hazards show increasing frequency since the 1950s, but when the temporal range is limited to 1997–2017, tornado reports and days show decreasing trends while severe hail reports and days continue to show upward trends. Population bias is inherent in the data records of both hazards and manifests itself as a clustering of reports near urban centers and along major roadways where people live and travel. However, the increasing number of severe hail days and proportion of hail reported at larger sizes is less likely to be influenced by population growth and thus may have a meteorological origin. Convective parameters output from high-resolution dynamical downscaling simulations of control and future climate scenarios using the Weather and Forecasting model are used as proxies to create and compare synthetic tornado and hail reports between the two simulations. Up to three more severe hail days and one more tornado day per year on average by the period 2071–2100 is found, maximized in the north-central part of the domain. This result is combined with population projections from the Shared Socioeconomic Pathways in Tornado and Hail Monte Carlo models to simulate changes in the number of people living underneath tornado tracks and hail swaths by the year 2100. Human exposure evolution is sensitive to the overlap of population and hazard spatial footprints, but the model predicts worst-case scenarios of a 178% increase in exposure to severe hail and a 173% increase in exposure to tornadoes by the end of the 21st century. In addition, population effects outweigh meteorological effects when simulated independently. Some simulations yield a decreasing human exposure to severe hail due to the greatest projected increases in hailstorms over rural, agricultural land. This finding provides motivation for an interview study of eastern Colorado farmers and ranchers to measure perceptions of exposure and sensitivity to severe hail. Most interviewees view hailstorms as a common nuisance throughout eastern Colorado and are most concerned with small hail that falls in large volumes or is driven by a strong wind since these scenarios cause the most damage to crops. Respondents express anxiety and dejection toward hailstorms, as they can significantly affect crop yields and in turn impact their livelihoods and local economy. Understanding this agricultural perspective validates ongoing research into hail surface characteristics and can promote stronger partnerships between the forecasting and farming communities. The synthesis of results from this dissertation, with its unique localized look at the human and meteorological factors contributing to a changing exposure, can be of great worth to forecasters, urban planners, emergency managers, insurance agents, and other local decision-makers. Moreover, this work will help to educate the local public about the past, present, and future of tornadoes and severe hailstorms within eastern Colorado, with the aim of protecting lives and property from their negative impacts.Item Open Access Rain and RELAMPAGO: analysis of the deep convective storms of central Argentina(Colorado State University. Libraries, 2023) Kelly, Nathan Robert, author; Schumacher, Russ, advisor; Rasmussen, Kristen, committee member; Bell, Michael, committee member; Nelson, Peter, committee memberWhen, where and how much precipitation falls are fundamental questions to research interests spanning the weather to climate spectrum, yet are difficult to solve. The various methods used to answer "how much" each have sources of error, making it important to obtain knowledge about the characteristics of an individual dataset. This is especially true for rare events such as extreme precipitation. IMERG, TRMM 3B42, MERRA2 and ERA5 precipitation datasets were regridded to the same resolution and compared for 3-hourly heavy rainfall (99th and 99.9th percentile) in subtropical South America, which has some of the strongest convective storms on Earth. Seasonal and dirunal distribution are compared, with similar seasonal distributions between the datasets but at the diurnal scale MERRA2 and ERA5 show more afternoon events than TRMM and IMERG. Thermodynamic environments were compared with MERRA2 events tending to occur in more marginal environments than TRMM 3B42 and ERA5 environments over much of the analyzed region. Overall the satellite datasets showed the highest amounts. Brief case studies are included to illustrate these differences, which reinforce that choice of dataset can be an important factor in precipitation research. How the precipitation falls is also addressed using a case study from the RELAMPAGO field program in Argentina. Many observations are available of this case, which occurred during the mobile operations period of the field program. Mobile surface stations, increased temporal resolution from fixed sounding sites, and six mobile sounding systems provide a high level of detail on the evolution of this storm system. Additionally, a trove of radar data and a GOES mesoscale sector are available. This case is demonstrative of a common occurrence in the region: a strong MCS (Mesoscale Convective System) over the Sierras de Córdoba mountain range. The extent of the backbuilding observed with this MCS was not predicted by the operation convective allowing models used for field program forecasting. To study this event two simulations are presented: one in which backbuilding of the MCS occurs and one where such backbuilding does not occur. The difference between these simulations is the number of vertical levels used in the model which impacts moisture availability upstream of the system via the effect of mountain wave downslope winds.Item Open Access Using convection-allowing ensembles to understand the predictability of extreme rainfall(Colorado State University. Libraries, 2016) Nielsen, Erik R., author; Schumacher, Russ, advisor; van den Heever, Susan, committee member; Ramirez, Jorge, committee memberThe meteorological community has well established the usefulness of ensemble-based numerical weather prediction for precipitation guidance, since trusting one possible atmospheric solution can lead to, in some cases, particularly bad forecasts for precipitation guidance, owing to inherent uncertainties in precipitation processes that make deterministic prediction impractical. However, continued predictive challenges associated with intense convective rainfall has led to an increasing need to determine the most effective use of these ensemble systems in high impact, extreme precipitation events. Further, it cannot be assumed that ensembles will evolve similarly in both extreme precipitation and more benign events, due to the importance and error growth associated with convective-scale motions. This error growth associated with the chaotic nature of moist convective dynamics can also serve to limit the predictability of an extreme rainfall event (known as intrinsic predictability), in addition to predictability limits imposed by deficiencies in observing systems and numerical models (known as practical predictability). This research will focus on using a recently developed, operationally based ensemble dataset, specifically the National Oceanic and Atmospheric Administration's (NOAA) Second Generation Global Medium-Range Ensemble Reforecast Dataset (Reforecast-2), to create downscaled ensemble reforecasts of the extreme precipitation events. Some events examined during the course of this research are the inland movement of tropical storm Erin in 2007 and flooding associated with mesoscale convective vortices in Arkansas in 2010 and San Antonio, Texas in 2013. The global reforecasts are used to force an ensemble of convection-allowing WRF-ARW numerical simulations for the purpose of evaluating ensemble-based precipitation forecasts associated with specific extreme rainfall events. Using these ensemble forecasts, we address several questions related to the practical versus the intrinsic predictability of the extreme rainfall events examined. Experiments that vary the magnitude of the perturbations to the initial and lateral boundary conditions (ICs and LBCs) reveal a seemingly proportional scaling of ensemble spread early in the simulations associated with the magnitude of the perturbation, but this scaling is not maintained throughout the simulations. Additionally, a diurnal cycle in ensemble spread growth is observed with large growth associated with afternoon convection, but the growth rate then reduced after convection dissipates the next morning rather than continuing to grow. The specific characteristics of the diurnal cycle, however, vary based upon region and flow regime. Lastly, the ensemble spread was found to be influenced by the size of the IC perturbations out to at least 48 hours. These spread evolution characteristics speak to the viability of running convection-allowing ensembles for prediction on multi-day timescales, since no saturation of the ensemble spread is seen despite extreme precipitation within the modeled time period. In addition to the overall ensemble characteristics, terrain-induced precipitation variability associated with the terrain feature known as the Balcones Escarpment, located in central Texas, is analyzed in multiple instances of heavy rainfall in San Antonio and the surrounding area. Simulations in which the Balcones Escarpment is removed reveal that when the synoptic to mesoscale forcing for heavy rainfall are in place over the Balcones Escarpment, the terrain does not directly affect the occurrence or magnitude of precipitation. It does affect the spatial distribution of the precipitation in a small but consistent way. This shift in precipitation associated with removing the Balcones Escarpment, when compared to a WRF-ARW ensemble for the event, is smaller than shifts associated with typical atmospheric variability. The combined results of these experiments demonstrate that downscaled ensemble NWP systems using readily available global ensemble forecasts can faithfully represent previously unresolved mesoscale features, precipitation totals, and depict ensemble-spread characteristics associated with moist convection.