Theses and Dissertations

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Changes in shortwave solar radiation under local and transported wildfire smoke plumes: implications for agriculture, solar energy, and air quality applications
(Colorado State University. Libraries, 2024) Corwin, Kimberley A., author; Fischer, Emily, advisor; Pierce, Jeffrey, committee member; Chiu, Christine, committee member; Corr-Limoges, Chelsea, committee member; Burkhardt, Jesse, committee member
The emission and transport of pollutants from wildfires is well-documented, particularly at the surface. However, smoke throughout the atmospheric column affects incoming shortwave solar radiation with potentially wide-ranging consequences. By absorbing and scattering light, smoke changes the amount and characteristics of shortwave radiation–a resource that controls plant photosynthesis, solar energy generation, and atmospheric photochemical reactions. In turn, these influence ecological systems as well as air quality and human health. This dissertation examines how wildfire smoke alters boundary layer and surface-level shortwave radiation in ways that are relevant for agricultural, energy, and air quality applications. First, I present an analysis of smoke frequency and smoke-driven changes in the total and diffuse fraction (DF) of photosynthetically active radiation (PAR; 400-700 nm) at the surface. I compare PAR and PAR DF on smoke-impacted and smoke-free days during the agricultural growing season from 2006 to 2020 using data from 10 ground-based radiation monitors and satellite-derived smoke plume locations. I show that, on average, 20% of growing season days are smoke-impacted and that smoke prevalence has increased over time (r = 0.60, p < 0.05). Smoke frequency peaks in the mid to late growing season (i.e., July, August), particularly over the northern Rocky Mountains, Great Plains, and Midwest. I find an increase in the distribution of PAR DF on smoke-impacted days, with larger increases at lower cloud fractions. On clear-sky days, daily average PAR DF increases by 10 percentage points when smoke is present. Spectral analysis of clear-sky days shows smoke increases DF (average: +45%) and decreases total irradiance (average: −6%) across six wavelengths measured from 368 to 870 nm. Optical depth measurements from ground and satellite observations both indicate that spectral DF increases and total spectral irradiance decreases with increasing smoke plume optical depth. My analysis provides a foundation for understanding smoke's impact on PAR, which carries implications for agricultural crop productivity under a changing climate. Second, I examine smoke's impact on two key measures used to assess a location's baseline solar resource availability for solar energy production: direct normal (DNI) and global horizontal (GHI) irradiance. I quantify smoke-driven changes in DNI and GHI at different spatial and temporal scales across the contiguous U.S. (CONUS) using radiative transfer model output and satellite-based smoke, aerosol, and cloud observations. Importantly, I expand the scale of previous studies on smoke and solar energy by including areas primarily affected by dilute, aged, transported smoke plumes in addition to areas with dense, fresh, local smoke plumes. I show that DNI and GHI decrease as smoke frequency increases at the state, regional, and national scale. DNI is more sensitive to smoke with sizable losses persisting downwind of fires. Although large reductions in GHI are possible close to fires, mean GHI declines minimally (< 5%) due to transported smoke. Overall, GHI–the main resource used for photovoltaic energy production–remains a relatively stable resource across most of CONUS even in extreme fire seasons, which is promising given U.S. solar energy goals. Third, I investigate smoke-driven changes in surface-level and boundary layer downwelling actinic flux (F↓)–a crucial component of determining the rate of photooxidation in the atmosphere. I present a case study of changes in F↓ at 550 nm (process validation) and 380 nm (NO2 photolysis) along a research flight through the California Central Valley during the 2018 Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE-CAN) aircraft campaign. F↓ was measured onboard via the HIAPER Airborne Radiation Package (HARP), and I use the National Center for Atmospheric Research (NCAR) Tropospheric Ultraviolet and Visible (TUV) Radiation Model to compute F↓ under smoke-free and smoke-impacted conditions. Modeling F↓ with TUV facilitates calculating the change in F↓ and provides a means of assessing F↓ at altitudes not sampled by the aircraft, such as the ground. I find that the smoke-impacted F↓ from TUV aligns closely with HARP observations: all modeled fluxes are within 20% of measurements at 550 nm and 85% are within 20% of measurements at 380 nm. The average modeled-to-measured ratios (F ↓550=0.96; F ↓380=0.89) indicate that TUV minorly underestimates the observed F↓. On average, observed F↓380 decreased 26%, 17%, and 9% at 0-0.5 km, 0.5-1 km, and 1-1.5 km, respectively, while TUV estimates larger reductions of 41%, 26%, and 19% at the same altitudes. At the ground-level, I calculate a 47% decrease in F↓380 using TUV, which is likely an upper bound given the model slightly underestimates observations. As wildfire smoke increases with climate change, understanding how smoke aloft changes photochemistry is increasingly important for constraining future air quality.
Open Access
From surface to tropopause: on the vertical structure of the tropical cyclone vortex
(Colorado State University. Libraries, 2024) DesRosiers, Alexander J., author; Bell, Michael M., advisor; Barnes, Elizabeth A., committee member; Rasmussen, Kristen L., committee member; Davenport, Frances V., committee member
The internal vortex structure of a tropical cyclone (TC) influences intensity change. Beneficial structural characteristics that allow TCs to capitalize on favorable environmental conditions are an important determinant as to whether a TC will undergo rapid intensification (RI) or not. Accurately forecasting RI is a significant challenge and past work identified characteristics of radial and azimuthal structure of the tangential winds which favor RI, but vertical structure has received less attention. This dissertation aims to define vertical structure in a consistent manner to improve our understanding of how it influences intensity change in observed and modeled TCs, as well as discern when strong winds are more likely to reach the surface with potential for greater impacts. Part 1 investigates the height of the vortex (HOV) in observed TCs and its potential relationships with intensity and intensification rate. As a TC intensifies, the tangential wind field expands vertically and increases in magnitude. Past work supports the notion that vortex height is important throughout the TC lifecycle. The Tropical Cyclone Radar Archive of Doppler Analyses with Recentering (TC-RADAR) dataset provides kinematic analyses for calculation of HOV in observed TCs. Analyses are azimuthally-averaged with tangential wind values taken along the radius of maximum winds (RMW). A threshold-based technique is used to determine the HOV. A fixed-threshold HOV strongly correlates with current TC intensity. A dynamic HOV (DHOV) metric quantifies vertical decay of the tangential wind normalized to its maximum at lower levels with reduced intensity dependence. DHOV exhibits a statistically significant relationship with TC intensity change with taller vortices favoring intensification. A tall vortex is always present in observed cases meeting a pressure-based RI definition in the following 24-hr period, suggesting DHOV may be useful to intensity prediction. In Part 2, numerical modeling simulations are utilized to discern mechanisms responsible for the observed relationships in Part 1. Vertical wind shear (VWS) can tilt the TC vortex by misaligning the low- and mid-level circulation centers which prevents intensification until realignment occurs. Both observed and simulated TCs with small vortex tilt magnitudes possess DHOV values consistent with those observed prior to RI. In aligned TC intensification, DHOV and intensity have a mutually increasing relationship, indicating the metric provides useful information about vertical structure in both tilted and aligned TCs. Vertical vortex growth during RI is sensitive to internal processes which strengthen the TC warm core in the upper-levels of the troposphere. Comparison of a TC simulated in the presence of a concentrated upper-level jet of VWS to a control simulation in quiescent flow indicates that disruption of intensification in the upper levels limits vortex height and intensity without appreciable low- to mid-level tilt. Part 3 focuses on decay of the TC wind field as it encounters friction near the surface in the planetary boundary layer (PBL). Surface winds are important to operational TC intensity estimation, but direct observations within the PBL are rare. Forecasters use reduction factors formulated with wind ratios (WRs) from winds observed by aircraft in the free troposphere and surface winds. WRs help reduce stronger winds aloft to their expected weaker values at the surface. Asymmetries in the TC wind field such as those induced by storm motion can limit the accuracy of static existing WR values employed in operations. A large training dataset of horizontally co-located wind measurements at flight level and the surface is constructed to train a neural network (NN) to predict WRs. A custom loss function ensures the model prioritizes accurate prediction of the strongest wind observations which are uncommon. The NN can leverage relevant physical relationships from the observational data and predict a surface wind field in real-time for forecasters with greater accuracy than the current operational method, especially in high winds.
Open Access
Climate model error in the evolution of sea surface temperature patterns affects radiation and precipitation projections
(Colorado State University. Libraries, 2024) Alessi, Marc J., author; Rugenstein, Maria A.A., advisor; Barnes, Elizabeth A., committee member; Maloney, Eric D., committee member; Willis, Megan D., committee member
Atmosphere-ocean general circulation models (AOGCMs) are the primary tool climate scientists use in predicting the effects of climate change. While they have skill in reproducing global-mean temperature over the historical period, they struggle to replicate recently observed sea surface temperature (SST) trend patterns. In this dissertation, we quantify the impact of potential future model error in SST pattern trends on projections of global-mean temperature and Southwest U.S. (SWUS) precipitation. We primarily use a Green's function (GF) approach to identify which SST regions are most relevant for changes in these variables. Our findings demonstrate significant sensitivity of both global-mean temperature and SWUS precipitation to the pattern of sea surface warming, meaning that a continuation of AOGCM error in SST trend patterns adds uncertainty to climate projections which are currently not accounted for. In Chapter 1, we quantify the relevance of future model error in SST to global-mean temperature projections through convolving a GF with physically plausible SST pattern scenarios that differ from the ones AOGCMs produce by themselves. We find that future model error in the pattern of SST has a significant impact on projections, such as increasing total model uncertainty by 40% in a high-emissions scenario by 2085. A reversal of the current cooling trend in the East Pacific over the next few decades could lead to a period of global-mean warming with a 60% higher rate than currently projected. These SST pattern scenarios work through a destabilization of the shortwave cloud feedback to affect temperature projections. In Chapter 2, we focus on near-term projections of precipitation in the SWUS. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce this observed SST trend, and they may continue doing so in the future. In this chapter, we assess the sensitivity of SWUS precipitation projections to future SST trends using a GF approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. In Chapter 3, we analyze SWUS precipitation sensitivity to SST patterns on long timescales (7+ years) according to a GF approach and a convolutional neural network (CNN) approach. The GF and CNN identify different SST regions as having greater influence on SWUS precipitation: the GF highlights the Central Pacific known from theory to be relevant, while the CNN highlights the South-Central Pacific. To determine if the South-Central Pacific has a physically meaningful and so far overlooked influence on SWUS precipitation, rather than just a statistical relationship, we force an atmosphere-only climate model with an SST anomaly inspired by an Explainable Artificial Intelligence (XAI) method. We find that SSTs in the South-Central Pacific influence SWUS precipitation through an atmospheric bridge dynamical pathway, justifying the CNN's sensitivity physically. The fact that we cannot fully trust the evolution of SST patterns in AOGCMs has many implications for the field of climate science and for how the world's governments and organizations respond to global warming. It is critical for climate change adaptation and mitigation assessments to consider this previously unaccounted for uncertainty in climate projections. Climate scientists can do this by developing SST pattern storylines based on theory, observations, and our understanding of the ocean-atmosphere system. If we fail to communicate known uncertainties for both global-mean and regional projections, the world could lose faith in the climate science community, resulting in less of a global response to climate change.
Open Access
Ice nucleating particles in the Arctic: measurement and source tracking
(Colorado State University. Libraries, 2024) Barry, Kevin Robert, author; Kreidenweis, Sonia, advisor; DeMott, Paul, advisor; van den Heever, Susan, committee member; Fischer, Emily, committee member; Trivedi, Pankaj, committee member
The Arctic landscape is rapidly changing in a warming climate, with sea ice melting and permafrost thawing. Its near-surface air temperature is warming 3.8 times faster than other regions around the world. This rapid warming is known as Arctic amplification. Clouds contribute to this amplification, with their presence and phase is important for determining the surface energy budget. Arctic mixed-phase clouds can last for several days but are not represented well in climate models. Special aerosols, called ice nucleating particles (INPs) trigger ice formation in the atmosphere at temperatures warmer than -38 °C, and thus are important for determining the initiation, lifetime, and radiative properties of these clouds. Observations of INPs, especially over the central Arctic, are limited, and many sources are unknown. This dissertation has the overarching goal of increasing understanding of Arctic INPs. This is achieved through first presenting a full year of INP measurements in the central Arctic, as well as a full year of their composition, using coincident sampling of bacteria and fungi to gain insight into airmass origin. Next, some of the potentially most active Arctic INP sources are explored. Permafrost, which was known previously to contain high levels of INPs, was tested for its activity and persistence in water, and ability to be aerosolized through bubble bursting over several weeks. Then, sources of INPs were surveyed in a region that is controlled by permafrost (a thermokarst landscape). This included field measurements of permafrost, vegetation, sediment, active layer soil, water, and aerosol samples. A high temperature heat test was developed as a diagnostic tool to differentiate sources. Coincidentally, clean working methods to measure INPs were optimized, as efforts to reduce contamination are needed to accurately sample in this region. The main findings from this work suggest a regionally relatively homogenous population of Arctic INPs at most times of year, which is encouraging for efforts to represent them in numerical models across scales and understand their changes in the future. Permafrost-sourced INPs showed high activity and were enhanced near the coast. Unexpectedly, other components of the thermokarst landscape were found to be rich, organic INP reservoirs, emphasizing that the Arctic tundra is a diverse collection of potential contributors to the aerosol.
Open Access
Measurement of low-altitude aerosol layers surrounding convective cold pool passage observed by uncrewed aircraft
(Colorado State University. Libraries, 2024) Heffernan, Brian, author; Kreidenweis, Sonia, advisor; Perkins, Russell, advisor; Pierce, Jeffrey, committee member; Jathar, Shantanu, committee member
Convectively generated cold pools can have myriad impacts on local aerosol concentrations. Passage of cold pools may loft dust, pollen or other aerosols from the surface, and precipitation and humidity changes accompanying cold pools also impact local aerosols in several ways. The vertical profile of aerosols can have important effects on meteorology, however, the effects of cold pools on the vertical distribution of aerosol are largely unstudied. During the BioAerosol and Convective Storms (BACS) field campaigns in the Colorado plains in spring of 2022 and 2023, Uncrewed Aircraft (UA) were utilized to observe the vertical profile of aerosol, and how this vertical profile may be affected by the passage of cold pools. UAs with mounted aerosol and meteorological instrument packages were deployed in a vertical column to profile different atmospheric variables. Flights were conducted before, during, and after the passage of cold pools, and UA data were contextualized using radiosonde measurements and surface-based aerosol and meteorological instruments. A discussion of the challenges of UA-mounted aerosol sampling is presented. Validation experiments were conducted to assess the reliability of UA-mounted Optical Particle Counters (OPCs), and analyzed to show that UA-mounted OPCs can provide reliable data under certain circumstances. Two primary issues are discussed in detail: sensor drift and suppressed OPC sampling flow. A calibration procedure was developed and utilized to address the issue of sensor drift, while suppressed OPC sample flow was addressed by removing all data below a determined critical threshold flow rate. These methodologies lead to the creation of a robust data product for the measurement of aerosol vertical profiles using UA-mounted OPCs. Using these OPC data, an analysis of the vertical profiles observed during the BACS campaign is provided, up to 350m above the surface. We find that a common feature of a post cold pool environment is a layer of enhanced submicron aerosol concentration measured 120m above the surface. This feature and its evolution are examined in detail for several case studies, and different possible explanations are presented. Potential causes of this observed feature include pollen-rupture, low temperature inversions trapping aerosol in a low stable layer of elevated aerosol concentration, and emission and/or deposition of aerosols, but these explanations each appear to be insufficient. This feature appears to be caused by the dynamics of the cold pool, which can entrain and redistribute airmasses from different levels of the atmosphere.
Open Access
Data-driven models for subseasonal cyclogenesis forecasts in the east Pacific and north Atlantic
(Colorado State University. Libraries, 2024) Carlo Frontera, Zaibeth, author; Barnes, Elizabeth A., advisor; Maloney, Eric, advisor; Anderson, G. Brooke, committee member
Tropical cyclones (TCs) are hazardous and financially burdensome meteorological events. Previous studies have revealed that longer timescale phenomena, including the El Niño Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and African Easterly Waves, influence TC development by modifying large-scale environmental conditions such as vertical wind shear, mid-level moisture, and sea surface temperatures. Statistical models have been developed to forecast TCs in the Atlantic and Pacific basins by incorporating information about ENSO and the MJO. Expanding on this work, we employ logistic regression (LR) and neural network (NN) models with an extended set of variables to predict cyclogenesis on subseasonal timescales for the east Pacific and Atlantic regions. These models utilize ENSO and MJO indices, along with other local environmental information, and demonstrate enhanced forecasting skill relative to models that only use TC climatology. Overall, the NN model shows superior performance compared to the LR model, retaining skill out to three weeks leadtime for the east Pacific, and out to four weeks for the Atlantic basin. The predictive capabilities of the model are demonstrated for the years 1983 and 2021. To gain insights into the decision-making process of the NN models, an AI explainability technique is employed to understand which features are considered important in making the predictions. For both basins, the addition of ENSO and MJO information prove to be essential for the superior forecast skill of the NN model.
Open Access
Investigation of enhanced-reflectivity features embedded within a wintertime orographic cloud on 28-29 November 1984
(Colorado State University. Libraries, 1994) Baker, Ian T., author; Grant, Lewis O., advisor; Mielke, Paul W., committee member; Cotton, William R., committee member
A combination of aircraft, sounding, surface, vertically-pointing ku-Band radar and dual-channel radiometer data was used to investigate the microphysical characteristics of enhanced-reflectivity areas embedded within an orographic cloud in northwestern Colorado on 28-29 November 1984. The orographic cloud was associated with the passage of an open wave and upper-level front over the region, and embedded within the cloud were regularly-spaced areas of increased reflectivity as seen by the vertically-pointing radar. The radiometer observed a cyclical component on both the liquid and vapor channels when oriented in the vertical. Aircraft data reveal that there was supercooled liquid water in the cloud at levels as high as 41 kPa and as far as 55 km upwind of the barrier. 2D-C and 2D-P probe data indicated two crystal regimes, one where concentrations in individual size bins were larger and spectra were broader, indicating crystal growth. In the other, concentrations were smaller and size spectra were narrower. Radar data indicate that the enhanced-reflectivity regions were between 10-20 km apart, with a length dimension on the order of 5 km wide. It is believed that the presence of the enhanced-reflectivity areas is closely linked to the presence of a decoupled layer on the windward side of the barrier, and preliminary evidence points to a gravity-wave mechanism as a physical cause.
Open Access
Air quality impacts from unconventional oil and gas development
(Colorado State University. Libraries, 2024) Ku, I-Ting, author; Collett, Jeffrey L., Jr., advisor; Fischer, Emily V., committee member; Carlson, Kenneth H., committee member; Kreidenweis, Sonia, committee member
Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States raising concerns about associated air quality impacts. While significant effort has been made to quantify and limit methane emissions, relatively few observations have been made of emitted Volatile Organic Compounds (VOCs). Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local concentrations of air toxics and other VOCs during drilling and completions of new wells. With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. In Part 1, we report how emissions from Broomfield pre-production and production operations influence air toxics and other VOC concentrations at nearby locations. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C8-C10 n-alkanes during drilling operations highlighted the importance of VOC emissions from a synthetic drilling mud chosen to reduce odor impacts. More than 90 transient plumes were sampled and connected with specific UOGD operations. The chemical signatures of these plumes differed by operation type. Concentrations of individual, O&G-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations. In Part 2 OH reactivities (OHR) were calculated to examine the potential of emitted VOCs to contribute to regional ozone formation. NO2 was the largest contributor to OHR during winter when OHR values peaked, while VOCs dominated OH sinks during summer. Oxygenated VOCs and C3-C7 n-alkanes, closely associated with O&G activities, were primary contributors to OHR levels during the summer ozone season. In Part 3 we leverage observations from Broomfield and other Colorado O&G air quality studies to examine relationships between O&G emissions of methane and VOCs. A key goal is to determine whether more commonly measured methane emissions can serve as a surrogate to estimate emissions of less frequently measured compounds such as benzene, a key air toxic. While strong correlations are observed between benzene and methane emissions in some situations, considerable variability is observed in this relationship across locations and operations suggesting caution in assuming that reductions in methane emissions will yield proportionate reductions in releases of air toxics.
Open Access
Emissions, evolution, and transport of ammonia (NH₃) from large animal feeding operations: a summertime study in northeastern Colorado
(Colorado State University. Libraries, 2024) Juncosa Calahorrano, Julieta Fernanda, author; Fischer, Emily V., advisor; Collett, Jeffrey L., Jr., committee member; Pierce, Jeffrey R., committee member; Jathar, Shantanu H., committee member
The Transport and Transformation of Ammonia (TRANS2Am) airborne field campaign occurred over northeastern Colorado during the summers of 2021 and 2022. TRANS2Am measured ammonia NH3 emissions from cattle feedlots and dairies with the goal of describing the near-field evolution of the NH3 emitted from animal feeding operations. Most of the animal husbandry facilities in Colorado are co-located with oil and gas development within the Denver-Julesburg basin, an important source of methane (CH4) and ethane (C2H6) in the region. Leveraging TRANS2Am observations, this dissertation presents estimates of NH3 emissions ratios with respect to CH4 (NH3 EmR), with and without correction of CH4 from oil and gas, for 29 feedlots and dairies in the region. The data show larger emissions ratios than previously reported in the literature with a large range of values (i.e., 0.1 - 2.6 ppbv ppbv-1). Facilities housing cattle and dairy had a mean (std) of 1.20 (0.63) and 0.29 (0.08) ppbv ppbv-1, respectively. NH3 emissions have a strong dependency with time of day, with peak emissions around noon and lower emissions earlier in the morning and during the evening. Only 15% of the total ammonia (NHx) is in the particle phase (i.e., NH4+) near major sources during the warm summer months. Finally, estimates of NH3 emission rates from 4 optimally sampled facilities range from 4 - 29 g NH3 · h-1 · hd-1. This work also investigates the nearfield evolution of NH3 in five plumes from large animal husbandry facilities observed during TRANS2Am using a mass balance approach with CH4 as a conservative tracer in the timescales of plume transport. Since the plumes in TRANS2Am were not sampled in a pseudo-lagrangian manner, an empirical model is needed to correct for variations in summertime NH3 emissions as a function of local time (LT) (CF = 1.87ln(LT) - 3.95). Results from the mass balance approach show that the average summertime NH3 decay time below 80% and 60% against deposition in plumes from large animal feeding operations is ~1 and ~2 hours, respectively. Additionally, we present estimates of deposition/emission fluxes every 5 km downwind of the plume. We found that deposition almost always happens in the first 10 km from the emission source. Beyond that, the complex environmental exchange of NH3 between the atmosphere and the surface suggests that fresh NH3 emissions from small nearby sources, water bodies, and crops/soil could contribute to sufficient NH3 to switch the direction of the flux (to emission). Large uncertainties still remain in emission and deposition fluxes, shining light on the need for more measurements in the region. To our knowledge, this is the first study presenting NH3 evolution in the atmosphere using a conservative tracer and airborne measurements. The second goal of TRANS2Am was to investigate easterly wind conditions capable of moving agricultural emissions of ammonia (NH3) through urban areas and into the Rocky Mountains. TRANS2Am captured 6 of these events, unveiling important commonalities. 1) NH3 enhancements are present over the mountains on summer afternoons when easterly winds are present in the foothills region. 2) The abundance of summertime gas-phase NH3 is 1 and 2 orders of magnitude higher than particle-phase NH4+ over the mountains and major agricultural sources, respectively. 3) During thermally driven circulation periods, emissions from animal husbandry sources closer to the mountains likely contribute more to the NH3 observed over the mountains than sources located further east. 4) Transport of summertime plumes from major animal husbandry sources in northeastern Colorado westward across the foothills requires ~5 hours. 5) Winds drive variability in the transport of NH3 into nearby mountain ecosystems, producing both direct plume transport and recirculation. A similar campaign in other seasons, including spring and autumn, when synoptic scale events can produce sustained upslope transport, would place these results in context.
Open Access
Application of an interpretable prototypical-part network to subseasonal-to-seasonal climate prediction over North America
(Colorado State University. Libraries, 2024) Gordillo, Nicolas J., author; Barnes, Elizabeth, advisor; Schumacher, Russ, committee member; Anderson, Chuck, committee member
In recent years, the use of neural networks for weather and climate prediction has greatly increased. In order to explain the decision-making process of machine learning "black-box" models, most research has focused on the use of machine learning explainability methods (XAI). These methods attempt to explain the decision-making process of the black box networks after they have been trained. An alternative approach is to build neural network architectures that are inherently interpretable. That is, construct networks that can be understood by a human throughout the entire decision-making process, rather than explained post-hoc. Here, we apply such a neural network architecture, named ProtoLNet, in a subseasonal-to-seasonal climate prediction setting. ProtoLNet identifies predictive patterns in the training data that can be used as prototypes to classify the input, while also accounting for the absolute location of the prototype in the input field. In our application, we use data from the Community Earth System Model version 2 (CESM2) pre-industrial long control simulation and train ProtoLNet to identify prototypes in precipitation anomalies over the Indian and North Pacific Oceans to forecast 2-meter temperature anomalies across the western coast of North America on subseasonal-to-seasonal timescales. These identified CESM2 prototypes are then projected onto fifth-generation ECMWF Reanalysis (ERA5) data to predict temperature anomalies in the observations several weeks ahead. We compare the performance of ProtoLNet between using CESM2 and ERA5 data. We then demonstrate a novel approach for performing transfer learning between CESM2 and ERA5 data which allows us to identify skillful prototypes in the observations. We show that the predictions by ProtoLNet using both datasets have skill while also being interpretable, sensible, and useful for drawing conclusions about what the model has learned.
Open Access
Topographic and diurnal influences on storms associated with heavy rainfall in northern Colorado
(Colorado State University. Libraries, 2024) Douglas, Zoe A., author; Rasmussen, Kristen L., advisor; Bell, Michael M., committee member; Kampf, Stephanie K., committee member
Despite its profound impacts on agricultural and socioeconomical conditions globally, heavy rainfall is a high-impact weather phenomenon of which we have limited quantitative understanding and forecast skill. The Prediction of Rainfall Extremes Campaign in the Pacific (PRECIP) planned to observe the spectrum of heavy rainfall events in the moisture-rich environment of Taiwan and Japan during 2020, but was delayed until 2022 due to the global COVID-19 pandemic. As a result of this unanticipated delay, the PRECIP science team conducted the Preparatory Rockies Experiment for the Campaign in the Pacific ("PRE"-CIP), which observed precipitation over northern Colorado from May to August 2021 using Colorado State University's ground-based research radars and radiosondes. Extreme precipitation features are identified in the radar data and organized into storm modes based on prior research on the Tropical Rainfall Measuring Mission satellite's Precipitation Radar. An "ingredients-based" approach provides a theoretical framework to separate the storm modes into a spectrum of storm intensity and duration during the entire "PRE"-CIP field project, allowing us to connect storm modes to the topography, diurnal cycle, and overall rainfall characteristics in northern Colorado. While precipitation occurred from the mountains to the plains, the highest concentration of storm tracks calculated from all ground-based radar observations occurred over the Rocky Mountains, regardless of storm duration. The majority of storm tracks are of low intensity and short duration, with over 80% of tracked storms having lifetimes of 1 h or less, suggesting that the general population of warm-season precipitation in northern Colorado is short-lived and of weak intensity. When considering heavy rainfall-producing storms, deep convection is the most dominant storm mode in northern Colorado by up to three orders of magnitude over broader convective and stratiform systems. Deep convection most frequently occurred over the Rocky Mountains in the afternoon, while broader convective and stratiform systems most frequently occurred over the foothills and plains in the evening to nighttime hours. Therefore, diurnal forcing and orographic lift play important roles in the morphology of warm-season precipitation in northern Colorado, as has been seen in mountainous regions across the world. The frequent occurrence of deep convective storms directly over the Rocky Mountains, however, differs from the deep convective hotspots seen in the lowlands downstream of similarly large mountain barriers like the Andes and Himalayas. Ultimately, these radar-based analyses are important for the eventual comparison of heavy rainfall in a semi-arid midlatitude region (Colorado) and a moisture-rich tropical environment (Taiwan and Japan), thus providing an enhanced global understanding of the commonalities of heavy rainfall processes.
Open Access
The effects of land surface-atmosphere interactions within two convective storm regimes
(Colorado State University. Libraries, 2024) Ascher, Benjamin D., author; van den Heever, Susan C., advisor; Schumacher, Russ, committee member; McGrath, Daniel, committee member
Convective storms, which are driven in part by atmospheric thermodynamic instability, come in a range of shapes and sizes and bring a variety of impacts both at the surface and throughout the atmosphere. Often these storms initiate as a result of lifting within the Planetary Boundary Layer (PBL), the behavior of which is strongly affected by the characteristics of the land surface below them. To examine the effects of land surface properties on convective storm behavior and impacts, I have conducted two high-resolution mesoscale modeling studies. The first study examined the impact of Lake Huron on convective lake-effect snow over Lake Erie, while the second analyzed the effects of heterogeneous vegetation cover on deep convection in an idealized coastal environment. Our findings in the first study revealed that Lake Huron initiates lake-effect snow bands which persist over land between Lake Huron and Lake Erie and then reintensify after moving over Lake Erie. The persistent band "kickstarts" convection over Lake Erie and increases snowfall over and downwind of Lake Erie compared to when Lake Huron is not present. I also found that areas of snow-free land can act as a "brown lake" and initiate lake effect-like convection on their own. An area of snow-free land upwind of Lake Erie fulfilled a similar role to Lake Huron in enhancing convection and snowfall downwind of Lake Erie. Such findings may have important implications for improved short-term forecasting of the location and intensity of heavy snowfall. The results in our second study indicated that heterogeneous land surfaces enhance convective storm activity over certain vegetation types and suppress it over others. In particular, I found an increase in precipitation over forests surrounded by pasture lands and suburban regions, while the precipitation over the pasture and suburban regions is suppressed. I also discovered that circulations induced by these heterogeneous land surfaces appear to be more important to the location and timing of convection initiation than a sea breeze which forms in the simulations. Finally, I concluded that cold pools produced by convective storms reinforce the land surface-induced circulations, thereby allowing these circulations to collide in the center of the forested region, where they initiate intense convection which subsequently produces heavy rainfall.
Embargo
The abundance and sources of ice nucleating particles (INPs) within Alaskan ice fog
(Colorado State University. Libraries, 2024) Lill, Emily, author; Fischer, Emily V., advisor; Creamean, Jessie, advisor; Kreidenweis, Sonia, committee member; Wall, Diana, committee member
Fairbanks, Alaska often experiences low visibility due to air pollution. Low wind speeds and strong temperature inversions paired with local emissions from burning of wood, oil, gasoline, and coal lead to wintertime pollution events where concentrations of fine particulate matter (PM2.5) often reach 50 μg m-3, exceeding the Environmental Protection Agency (EPA) 24-hour National Ambient Air Quality Standard (NAAQS) of 35 μg m-3. When temperatures fall below -15°C and sufficient moisture is present, these pollution events can facilitate the formation of ice fog, further worsening air quality and visibility issues for aviation and transportation. The formation of ice crystals from supercooled droplets is aided by a small, but critical, number of aerosol particles that potentially act as ice nucleating particles (INPs). However, studies evaluating the quantities and sources of INPs during ice fog are limited. The Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign included the deployment of a suite of atmospheric measurements in January - February 2022 with the goal of better understanding atmospheric processes and pollution under cold and dark conditions. We report on measurements of particle composition, particle size, INP composition, and INP size during an ice fog period (29 January - 3 February). There was a 153% increase in coarse particulate matter (PM10) during the ice fog period, associated with a decrease in air temperature. Results also show a 58% decrease in INPs active at -15°C during the ice fog period, indicating that particles were scavenged by ice fog ice crystals, likely via nucleation. Peroxide and heat treatments were performed on INPs in order to determine the fraction of INPs that were biological, organic, or inorganic. One hypothesis consistent with the results of the peroxide treatments is that more efficient INPs derived from biological materials or organics that typically activate at warmer freezing temperatures may have been depleted during the ice fog event. The reduction in heat-labile INPs during the ice fog event was unexpected for Fairbanks in the winter due to the very low temperatures and limited biological aerosol sources. Aerosol compositional measurements corroborate the presence of INPs from biomass burning and road dust.
Open Access
Assimilation of geostationary, infrared satellite data to improve forecasting of mid-level, mixed-phase clouds
(Colorado State University. Libraries, 2009) Seaman, Curtis J., author; Vonder Haar, Thomas H., advisor
Mid-level, mixed-phase clouds (altocumulus and altostratus) are difficult to forecast due to the fact that they are generally thin and form in areas of weak vertical velocity where operational models typically have poor vertical resolution and poor moisture initialization. This study presents experiments designed to test the utility of assimilating infrared window and water vapor channels from the Geostationary Operational Environmental Satellite (GOES) instruments, Imager and Sounder, into a mesoscale cloud-resolving model to improve model forecasts of mid-level clouds. The Regional Atmospheric Modeling Data Assimilation System (RAMDAS) is a four-dimensional variational (4-DVAR) assimilation system used to test the viability of assimilating cloudy scene radiances into a cloud-free initial model state for one case of a long-lived, isolated altocumulus cloud over the Great Plains of the United States. Observations from one observation time are assimilated and significant innovations are achieved. Three experiments are performed: (1) assimilation of the 6.7 μm (water vapor) and 10.7 μm (window) channels from GOES Imager, (2) assimilation of the 7.02μm (water vapor) and 12.02 μm (window) channels from GOES Sounder, and (3) assimilation of the 6.7 μm channel from GOES Imager and the 7.02 μm channel from GOES Sounder. It is shown that the GOES Sounder channels provide more useful information than the GOES Imager channels due to increased sensitivity to the mid-troposphere. The decorrelation lengths and variance used in the background error covariance are varied and the impact on the results of the experiments is discussed. The effect of constraining the surface temperatures during assimilation of the window channels is also discussed. It is found that, in a cloud-free initial model state, the adjoint sensitivities are calculated on the assumption that there is no cloud, even with cloud in the satellite observations. This has a significant impact on the success of other 4-DVAR satellite data assimilation experiments.
Open Access
Quasi-stationary, extreme-rain-producing convective systems associated with midlevel cyclonic circulations
(Colorado State University. Libraries, 2008) Schumacher, Russ Stanley, author; Johnson, Richard H., advisor
Observations and numerical simulations are used to investigate the atmospheric processes responsible for initiating, organizing, and maintaining quasi-stationary mesoscale convective systems (MCSs) that form in association with midlevel mesoscale convective vortices or cutoff lows. Six events were identified in which an MCS remained nearly stationary for 6-12 hours and produced excessive rainfall that led to significant flash flooding. Examination of individual events and composite analyses reveals that the MCSs formed in thermodynamic environments characterized by very high relative humidity at low levels, moderate convective available potential energy, and very little convective inhibition. In each case, the presence of a strong low-level jet (LLJ) led to a pronounced reversal of the wind shear vector with height. Convection was initiated by lifting associated with the interaction between the LLJ and the midlevel circulation. One of these events was examined in detail using numerical simulations. This MCS, which occurred on 6-7 May 2000 in eastern Missouri, produced in excess of 300 mm of rain in 9 hours and led to destructive flash flooding. Simulations indicate that the MCS was long-lived despite the lack of a cold pool at the surface. Instead, a nearly stationary low-level gravity wave helped to organize the convection into a quasi-linear system that was conducive to extreme local rainfall amounts. Additionally, the convective system acted to reintensify the midlevel MCV and also caused a distinct surface low pressure center to develop in both the observed and simulated system. To further understand the important processes in these MCSs, idealized simulations using a low-level lifting mechanism and a composite thermodynamic profile are employed. These simulations successfully replicate many of the features of the observed systems. The low-level environment is nearly saturated, which is not conducive to the production of a strong surface cold pool; yet the convection quickly organizes into a quasi-linear system that produces very heavy local rainfall. As in the May 2000 case, a low-level gravity wave was responsible for this organization. The upstream development of new convective cells is shown to result from the interaction of the reverse-shear flow with these waves.
Open Access
Properties of the tropical hydrologic cycle as analyzed through 3-dimensional k-means cluster analysis
(Colorado State University. Libraries, 2008) Rogers, Matthew Alan, author; Stephens, Graeme, advisor
As the primary locations of deep convective activity and unrestrained tropical wave dynamics, the tropical West Pacific and East Indian oceans are among the most important regions in the tropics. Given that most of the region consists of unpopulated expanses of ocean, observations of tropical atmospheric properties in this important region is exceptionally difficult. Only with the help of satellite observations are we capable of gleaning valuable data from this region, and our utilization of advanced analysis techniques allows us to gain more from these observations then would otherwise be possible. In that vein, this dissertation reports on the use of a unique statistical technique, long known to other fields of research, as applied to a combined-instrument satellite observation dataset over the warm pool region of the tropical West Pacific ocean. The statistical technique, known as k-means cluster analysis, is used to delineate self-similar populations of cloud type, hereafter referred to as cloud regimes, from frequency-distribution histograms of cloud-top height, cloud optical thickness, and rainfall amount. We will show that four primary cloud regimes exist in the tropical region discussed, that the four regimes vary primarily through differences in convective activity, and that these four cloud regimes exist in a coherent temporal structure that explains the long-observed variability in convective activity seen in the tropics. Combining this regime information with satellite observations, along with reanalysis data, we then examine the individual properties of each cloud regime. These observations give us the means to understand the forcings behind cloud regime change in the region. We confirm the structural properties of these regimes using analysis from a cloud-resolving model, and apply our new understanding of the mechanism behind this large-scale forcing to the governance of the tropical hydrologic cycle as a whole. The insights gained from this analysis have benefits to both the fields of atmospheric remote sensing, and of cloud- and climate modeling of the tropical atmosphere. Applications of this technique are of particular interest to researchers developing retrieval algorithms for latent heat profiles using active sensors such as the cloud-profiling radar aboard CloudSat.
Open Access
Making real time measurements of ice nuclei concentrations at upper tropospheric temperatures: extending the capabilities of the continuous flow diffusion chamber
(Colorado State University. Libraries, 2009) Richardson, Mathews, author; Kreidenweis, Sonia, advisor
Due to their ubiquity, cirrus clouds are important drivers of climate. Researchers have developed a parameterization that predicts the onset of homogeneous freezing for particles of varying chemical composition. This parameterization is widely used to model cold cloud formation, but the applicability of this parameterization to real atmospheric aerosol has yet to be determined. The field-ready version of Colorado State University's continuous-flow diffusion chamber (CFDC-1H) is one of the few instruments capable of measuring atmospheric ice nuclei concentrations in real time. In this study, we examined the operational limits of the CFDC-1H at low temperature through a series of controlled laboratory studies using (NH 4)2SO4 particles at different operating conditions. We found that residence time played a dominant role in the CFDC-1H's ability to detect the onset of freezing at conditions closer to those predicted. Numerical studies confirmed this and indicated that at warmer temperatures the inability of the CFDC-1H to observe freezing onset conditions as predicted was attributable to the inability of particles to dilute rapidly enough while at colder temperatures the limited availability of water vapor in conjunction with limited residence times inhibited cloud particle growth. The final portion of this study focused on measurements of the freezing onset conditions of an ambient aerosol. Using water uptake measurements, we found that the hygroscopicity (κ) of the ambient aerosol (0.1 to 0.2) was significantly lower than that of ammonium sulfate (0.6). However, as predicted by theory, there was no observably significant difference between the onset conditions of size-selected (NH4)2SO4 and size-selected ambient aerosol. Freezing activation curves for the total ambient aerosol indicated that size plays an important role in the fraction freezing and should be considered when making conclusions regarding chemical composition as a function of fraction freezing. The chemical composition of ice crystal residuals was dominated by mineral type elements and carbon containing particles, contrary to expectations. Further work is necessary for any conclusive statement regarding the chemical composition of the freezing nuclei.
Open Access
On the role of warm rain clouds in the tropics
(Colorado State University. Libraries, 2008) Rapp, Anita Denise, author; Kummerow, Christian D., advisor
A combined optimal estimation retrieval algorithm has been developed for warm rain clouds using Tropical Rainfall Measuring Mission (TRMM) satellite measurements. The algorithm uses TRMM Microwave Imager (TMI) brightness temperatures that have been deconvolved to the 19-GHz field-of-view (FOV) to retrieve cloud liquid water path (LWP), total precipitable water, and wind speed. Resampling the TMI measurements to a common FOV is found to decrease retrieved LWP by 30%. These deconvolved brightness temperatures are combined with cloud fraction from the Visible Infrared Scanner (VIRS) to overcome the beam-filling effects and with rainwater estimates from the TRMM Precipitation Radar (PR). This algorithm is novel in that it takes into account the water in the rain and retrieves the LWP due to only the cloud water in a raining cloud, thus allowing the investigation of the effects of precipitation on cloud properties. The uncertainties due to forward model parameters and assumptions are computed and range from 1.7 K at 10 GHz to about 6K at the 37 and 85 GHz TMI channels. Examination of the sensitivities in the LWP retrieval shows that the cloud fraction information increases the retrieved LWP with decreasing cloud fraction and that the PR rainwater reduces retrieved LWP. Retrieval algorithm results from December 2005 to January 2006 show that warm rain cloud LWP and the ratio of warm rain cloud LWP to rainwater both decrease by 50% over sea surface temperatures (SST) ranging from 292 to 302 K in the tropical western Pacific due to increased precipitation efficiency depleting more of the cloud water at higher SSTs. The LWP retrieval developed in this study is also applied to study the influence of warm rain clouds on atmospheric preconditioning for deep convection associated with tropical depression-type disturbances (TDs). Results show that positive warm rain cloud LWP anomalies occur with positive low-level moistening and heating anomalies prior to TD events, but that there is little variation in the properties of non-raining clouds. The moistening by these clouds is also shown to influence the generation of convective available potential energy (CAPE) prior to deep convection.
Open Access
The optical, chemical, and physical properties of aerosols and gases emitted by the laboratory combustion of wildland fuels
(Colorado State University. Libraries, 2008) McMeeking, Gavin R., author; Kreidenweis, Sonia M., advisor
Biomass burning is a major source of trace gases and particles that have a profound impact on the atmosphere. Trace gases emitted by fires include the greenhouse gases CO2 and CH4, as well as CO and volatile organic compounds that affect air quality. Particle emissions affect climate, visibility, the hydrologic cycle, and human health. This work presents measurements of trace gas and aerosol emissions from a series of controlled laboratory burns for various plant species common to North America. Over 30 fuels were tested through ~250 individual burns during the Fire Laboratory at Missoula Experiment. Emission factors are presented as a function of modified combustion efficiency (MCE), a measure of the fire combustion conditions. The emissions of many trace gas and aerosol species depended strongly on MCE: smoldering-phase combustion dominated fires (low MCE) emitted roughly four times as much gas-phase hydrocarbon species and organic aerosols than flaming-phase dominated fires (high MCE). Inorganic aerosol emissions depended more strongly on plant species and components than on MCE. Flaming-phase dominated fires tended to produce aerosol with high mass fractions of strongly light-absorbing elemental carbon. Smoldering-phase fires produced aerosol with large mass fractions of more weakly light absorbing organic carbon, but this material was found to have a strong wavelength dependence of absorption, greater than the inverse wavelength relationship typically assumed for light absorbing aerosol. A two component model-featuring elemental carbon with a weak wavelength dependence but high mass-normalized absorption efficiency and organic carbon with a strong wavelength dependence but low mass-normalized absorption efficiency-is shown to represent the bulk absorption spectra of biomass burning aerosol. The results show that at wavelengths below ~450 nm, organic carbon light absorption could rival that of elemental carbon for aerosol dominated by organic carbon. If ignored, the light absorption by organic carbon can cause errors in predicted surface ultraviolet and visible radiation fluxes and photochemical photolysis rates in regions affected by biomass burning emissions. The dependence of spectral aerosol optical properties on combustion conditions means that fire behavior must be accurately assessed and predicted to ensure accurate emissions inventories and estimates of biomass burning atmospheric impacts.
Open Access
A study of the relationship between thunderstorm processes and cloud-top ice crystal size
(Colorado State University. Libraries, 2008) Lindsey, Daniel T., author; Johnson, Richard H., advisor
Satellite observations and numerical models are used to understand the physical mechanisms responsible for thunderstorms with varying cloud-top ice crystal sizes. Geostationary Operational Environmental Satellite (GOES) data are used to create a three-year climatology of cloud-top 3.9 µm reflectivity, a quantity which is closely correlated with particle size. Maximum mean values are found over the High Plains and Rocky Mountain regions of the U.S., suggesting that convection over that region tends to generate smaller anvil ice crystals than areas throughout much of the eastern U.S. To correct for preferred forward scattering by the cloud-top ice crystals, an effective radius retrieval using GOES is developed. Forward radiative transfer simulations are run for a wide range of cloud-top ice crystal sizes and sun-cloud-satellite scattering angles. The output is used to generate a lookup table, so that GOES-measured radiances may be used along with sun-satellite geometry to obtain an estimate for particle size. Validation of the retrieval shows that the assumed scattering properties perform quite well. To help explain the geographical variation in cloud-top ice crystal size, a composite analysis is performed in the High Plains region by averaging environmental conditions for days which produced both small and large ice crystal storms. Small ice is found to occur with relatively high based storms and steep mid-level lapse rates. Additionally, observational evidence from a pyrocumulonimbus event is presented to show the effect of low-level cloud condensation nuclei (CCN) on cloud-top ice crystal size. Model simulations using the Colorado State University Regional Atmospheric Modeling System (RAMS) are performed to help understand the physical mechanisms responsible for cloud-top ice crystal size. Through a series of sensitivity tests, it is found that larger low-level CCN concentrations lead to smaller anvil ice. In addition, as cloud-base temperature decreases (and cloud-base height increases), storm-top ice crystals get smaller. A weaker updraft strength is found to have very little effect on ice crystal size.