Theses and Dissertations

Permanent URI for this collectionhttps://hdl.handle.net/10217/100335

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Open Access
From sailors to satellites: investigating the maritime mystery of bioluminescent milky seas
(Colorado State University. Libraries, 2025) Hudson, Justin, author; Miller, Steven, advisor; Haddock, Steven, committee member; Maloney, Eric, committee member; Reardon, Kenneth, committee member; van Leeuwen, Peter Jan, committee member
Bioluminescence, the ability of living organisms to produce and emit light, has been a topic of human imagination and scientific study for millennia. Bioluminescence is observed in a myriad of forms in the ocean, among these bioluminescent displays milky seas stand out as perhaps one of the rarest, most poorly understood, and most awe-inspiring forms of bioluminescence. Milky seas are delineated from other more common forms of bioluminescence by their steady, non-flashing, eponymous white/green/gray glow which can cover 100,000 km2 of the nocturnal ocean surface for possibly months at a time. Poetic descriptions of milky seas by eyewitnesses have compared this phenomenon to an episode of the 'Twilight Zone', the biblical apocalypse, and an ocean haunted by spirts. Recent advances in spaceborne low-light imager technology, allowing milky seas to be identified remotely via satellite imagery, have greatly expanded our ability to study this phenomenon. Despite these technological advances and a modest compendium of published scientific literature on milky seas dating back to the 1700s, scientific understanding on milky seas has been historically limited by their remote, rare, and ephemeral nature. In addition, scientific research on milky seas has suffered the repeated loss of historical datasets. This dissertation presents a collection of research that seeks to understand the global/macroscale properties (e.g. distribution and timing) of milky seas as well as more local and intrinsic properties that inform on their predictability. Combining centuries of eyewitness accounts with recent satellite imagery, we reconstruct and build upon lost databases of milky sea observations. Leveraging this new and expanded database, we begin to address questions about milky sea occurrence, structure, and connection to the greater earth system. The scientific analysis enabled by this database and the plethora of modern atmospheric and oceanic datasets allows new connections between milky seas, the South Asian and Indo-Australian monsoons, the Indian Ocean Dipole, and the El Niño Southern Oscillation to be drawn. These connections, which serve as sources of predictability, guided this research toward the first known prediction of a milky sea event, and offer the potential for proactive in-situ sampling of a milky sea event which is necessary to fully answer questions pertaining to their composition and formation mechanisms. Furthermore, case study analysis of milky sea events near Java, Indonesia reveals insights into the physical processes that form, sustain, and eventually annihilate milky sea events. By way of this case study analysis, we test the natural flask hypothesis for milky seas, which postulates a physical mechanism for milky sea environments. Analysis of scatterometry data reveals the potential for coincident biological signals to be correlated with previously identified milky sea events, expanding the tools available to study and track the phenomenon from space across the lunar cycle and potentially overcome the limitations of current low-light visible observations.
Open Access
Conditions leading to extrinsic and intrinsic ecosystem change across large ensembles of climate futures
(Colorado State University. Libraries, 2025) Hueholt, Daniel M., author; Hurrell, James W., advisor; Barnes, Elizabeth A., advisor; Pierce, Jeffrey R., committee member; Lombardozzi, Danica, committee member
Natural climate variability and forced change influence ecosystems through the direct impacts of changing environmental conditions ("extrinsic change"), and by altering internal ecosystem dynamics ("intrinsic change"). While simulating complex ecosystems and species-level change remains challenging, Earth system models are often capable of capturing patterns of the regional-scale climate conditions which lead to ecological change. Investigating these climate conditions allows models to be leveraged in studying ecosystem change without requiring direct simulation of ecological processes. In this dissertation, we explore conditions driving extrinsic and intrinsic ecosystem change in large ensembles of climate futures with external forcings from anthropogenic warming and stratospheric aerosol injection, a hypothetical method of climate intervention. In the first project, we use the Community Earth System Model 2 Large Ensemble to describe how climate variability and change affect Arctic growing season warmth. Using a novel statistical metric, we find that many simulated Arctic ecoregions have already entered a state in which the warming trend dominates over internal variability. Storylines of cases where this "crossover" occurs earlier or later connect these events to coupled climate variability. The second study uses climate speeds--a metric of the rate of movement of thermal niches--to explore possible ecosystem impacts from design choices in stratospheric aerosol injection scenarios. We find highly distinct profiles of ecological risk in two simulations with similar global temperature targets but a 10-year delay in deployment. In the final study, we explore intrinsic change by using an ecological niche model to project future changes to habitat suitability for the Gyrfalcon (Falco rusticolus), a large predatory bird which is a top consumer in the tundra. Climate warming leads to a poleward contraction in suitability over the 21st century; a climate intervention scenario with global temperature reduction rapidly reverses overall trends but yields distinct regional patterns. Storyline methods reveal a substantial role for internal variability even under very strong external forcings. This dissertation provides new methods to use climate models to probe extrinsic and intrinsic ecosystem change, and reveals insights into potential ecological impacts from climate intervention methods.
Open Access
Aerosol and land surface impacts on tropical convective processes
(Colorado State University. Libraries, 2025) Leung, Gabrielle R., author; van den Heever, Susan C., advisor; Kreidenweis, Sonia M., committee member; Jathar, Shantanu, committee member; Miller, Steven D., committee member
In this three-part dissertation, we investigate the dynamical and microphysical processes that determine how tropical convective clouds respond to changes in aerosols and land surface properties. We focus on the variability in such processes across thermodynamic environments and cloud types. Using a combination of large eddy simulations (LES), long-term satellite observations, and Lagrangian object-tracking, we explore the physical mechanisms underlying these interactions. First, we investigate how aerosol–cloud–precipitation interactions influence convective transport and aerosol removal. We run a suite of sixteen LES with varying aerosol loadings and chemical compositions using the Regional Atmospheric Modeling System (RAMS). We find that increasing aerosol loading leads to increased convective transport of aerosol to the mid-troposphere and decreased aerosol removal through rainout. This means that in more polluted environments, not only is the aerosol loading larger than in pristine environments, but clouds are less able to regulate aerosol loadings via rainout. We further use tobac (tracking and object-based analysis of clouds), a cloud object-tracking algorithm, to explore shifts in the cloud population as a function of aerosol loading and type. We describe contrasting aerosol effects on rainfall from shallow cumulus and congestus clouds, leading to non-monotonic trends in domain rainfall. Decomposing these trends into cloud type-specific effects highlights the utility of Lagrangian approaches in elucidating processes driving varied aerosol–cloud interactions. Second, we explore the impact of widespread anthropogenically driven deforestation on cloud properties in the tropics. We use two decades of satellite data and statistical attribution methods to demonstrate that long-term deforestation in Southeast Asia robustly alters cloud properties. We also provide the first observational evidence that the magnitude of the cloud response to deforestation depends on the atmospheric environment, specifically on moisture and aerosol loading. These results emphasize that regional differences in climatology must be considered when assessing deforestation impacts on clouds and the climate system. Finally, we investigate the mechanisms driving land surface–cloud interactions using LES and cloud object-tracking. We conduct two sets of simulations over Borneo with identical atmospheric initial and boundary conditions but differing land cover to explore how land surface changes impact convection. We discuss how conversion of tropical forests to palm oil plantations influences the surface energy budget, driving robust decreases in sensible heat flux but enhanced evapotranspiration. We identify and track tens of thousands of clouds and show deforestation decreases region-wide shallow cloud cover but enhances cloudiness along deforestation boundaries via mesoscale vegetation breezes. We also discuss deforestation-driven changes to the sea breeze, deep convection, and precipitation. Our results demonstrate that shallow and deep convection are coupled to the surface through processes acting on different spatiotemporal scales. These findings emphasize that deforestation impacts vary spatially as well as diurnally. The research in this dissertation has advanced our understanding of the physical processes driving land–aerosol–cloud interactions and quantified how cloud populations shift in response to aerosol and land cover changes. Moreover, we have assessed when and where these shifts are the greatest and thus where perturbations to the aerosol environment and the land surface have the most significant impact for clouds, precipitation, and the broader Earth system.
Open Access
Smoky skies and polar air: aerosol microphysics across scales
(Colorado State University. Libraries, 2025) June, Nicole Ann, author; Pierce, Jeffrey R., advisor; Collett, Jeffrey L., Jr., committee member; Kreidenweis, Sonia M., committee member; Jathar, Shantanu H., committee member; Willis, Megan D., committee member
Atmospheric aerosol particles are important to understand as they have implications on climate and human health. These particles may be emitted directly or form in the atmosphere through secondary processes. In this dissertation, we focus on two systems of aerosol sources, microphysics, and chemistry: 1) wildfires and 2) the springtime marine Arctic. Biomass Burning Plume Injection Height: The magnitude of biomass burning impacts on air quality and climate are altered by the biomass burning plume injection height (BB-PIH). However, these alterations are not well-understood on a global scale. We present the novel implementation of BB-PIH in global simulations with an atmospheric chemistry model (GEOS-Chem) coupled with detailed TwO-Moment Aerosol Sectional (TOMAS) microphysics (GC-TOMAS). We conduct BB-PIH simulations under three scenarios: 1) all smoke is well-mixed into the boundary layer, and 2) and 3) smoke injection height is based on Global Fire Assimilation System (GFAS) plume heights. Elevating BB-PIH increases the simulated global-mean aerosol optical depth (10%) despite a global-mean decrease (1%) in near-surface PM2.5. Increasing the tropospheric column mass yields enhanced cooling by the global-mean clear-sky biomass burning direct radiative effect. However, increasing BB-PIH places more smoke above clouds in some regions; thus, the all-sky biomass burning direct radiative effect has weaker cooling in these regions as a result of increasing the BB-PIH. Elevating the BB-PIH increases the simulated global-mean cloud condensation nuclei concentrations at low-cloud altitudes, strengthening the global-mean cooling of the biomass burning aerosol indirect effect with a more than doubling over marine areas. Elevating BB-PIH also generally improves model agreement with the satellite-retrieved total and smoke extinction coefficient profiles. Our two-year global simulations with new BB-PIH capability enable understanding of the global-scale impacts of BB-PIH modeling on simulated air-quality and radiative effects, going beyond the current understanding limited to specific biomass burning regions and seasons. Aerosol Aging in Wildfire Smoke: The evolution of organic aerosol (OA) composition and aerosol size distributions within smoke plumes are uncertain due to variability in the rates of OA evaporation/condensation and coagulation within a plume. It remains unclear how the evolution varies across different parts of individual plumes. We use a large eddy simulation model coupled with aerosol-microphysics and radiation models to simulate the Williams Flats fire sampled during the FIREX-AQ field campaign. At aircraft altitude, the model captures observed aerosol changes through 4 hr of aging. The model evolution of primary OA (POA), oxidized POA (OPOA), and secondary OA (SOA) shows that >90% of the SOA formation occurs before the first transect (~40 min of aging). Lidar observations and the model show a significant amount of smoke in the planetary boundary layer (PBL) and free troposphere (FT), with the model having equal amounts of smoke in the PBL and FT. Due to faster initial dilution, PBL concentrations are more than a factor of two lower than the FT concentrations, resulting in slower coagulational growth in the PBL. A 20 K temperature decrease with height in the PBL influences faster POA evaporation near the surface, while net OA evaporation in the FT is driven by continued dilution after the first aircraft transect. Net OA condensation in the PBL after the first transect is the result of areas with higher OH concentration leading to OPOA formation. Our results motivate the need for systematic observations of the vertical gradients of aerosol size and composition within smoke plumes. Springtime Marine Arctic Sulfur Chemistry: Dimethyl sulfide (DMS) and methanethiol (MeSH) are important natural sources of sulfur to the atmosphere and influence the aerosol populations in the marine atmosphere. We use GC-TOMAS and data from the ARTofMELT field campaign to analyze sulfur chemistry in the Fram Strait during May and June 2023. We find that updating the model to include the emission of DMS from regions of partial ice cover improves model-observation agreement of DMS and MeSH by over half-an-order-of-magnitude. Model-observation agreement of MeSH is less than that of DMS suggesting model biases in MeSH emissions and/or lifetime. The model-observation agreement of DMS and MeSH varies depending on the oceanic DMS concentration dataset. The monthly oceanic DMS climatology has the lowest magnitude bias of atmospheric DMS during periods of ocean influence, while the daily oceanic DMS prediction by an artificial neural network has the most consistent bias for atmospheric DMS across the differing source regions. The primary DMS oxidation pathway in the model is OH-addition with 64% of the DMS oxidation occurring through that pathway in the campaign average; however, the model likely underestimates the importance of the BrO oxidation pathway due to biases in halogen sources and chemistry. During fog, the aqueous oxidation of DMS with O3 becomes important. The DMS oxidation product of DMSO is underestimated in the model due to the biases in DMS, wet deposition of DMSO, and biases in oxidants. Our results motivate the need for continued improvement of the representation of the sulfur budget in the marine Arctic. Aerosol Size and Composition in the Springtime Marine Arctic: Aerosol size and composition are key to understanding aerosol radiative effects as they impact aerosol lifetime, scattering and absorption properties, and ability to be cloud condensation nuclei. In this study, we aim to understand GC-TOMAS biases of aerosol size and composition during ARTofMELT. We conduct several sensitivity model simulations to determine the impact of blowing snow emissions, more vigorous wet-removal, a marine source of SOA precursor, and nucleation from organics with sulfuric acid on model-observation agreement. We find that there is likely an Arctic marine source of SOA precursor contributing to the OA mass and accumulation mode number concentrations during the campaign. However, the model has a high bias in OA mass and in the accumulation mode throughout the campaign, indicating the assumed model emission flux of the marine SOA precursor is high. There is limited ammonia in the region of the ship, limiting the new particle formation (NPF) through ternary nucleation. As a result, the simulations suggest the importance of the organics with sulfuric acid nucleation mechanism to explaining the observed NPF events. Lastly, we find that the removal of accumulation mode particles through drizzle in marine Arctic low-level clouds helps to reduce the overestimate of the accumulation mode, but increases the underestimate of the nucleation mode. Overall, despite continued efforts to understand the aerosol population in the Arctic, there remain deficits in the ability of a regional model to accurately represent the size and composition of aerosols.
Open Access
Understanding the ability of the Southern Ocean to influence the southeastern tropical Pacific
(Colorado State University. Libraries, 2025) Zheng, Yiyu, author; Rugenstein, Maria, advisor; van Leeuwen, Peter Jan, committee member; Hurrel, James W., committee member; Mcgrath, Daniel, committee member
The tropical Pacific plays a central role in the climate system and is linked to two major challenges in climate modeling: persistent biases in simulations and large inter-model spread in projections. Emerging studies show that the Southern Ocean has a remote influence on sea surface temperatures (SST) in the southeastern tropical Pacific through a teleconnection involving cloud feedbacks, oceanic upwelling, climatological winds, and wind-evaporation-SST feedback. This teleconnection has primarily been explored using perturbation experiments imposed on climate model simulations, leaving open questions about how it manifests in observations and fully coupled model outputs. This dissertation investigates the relationship between SSTs in the Southern Ocean (SO) and the southeastern tropical Pacific (SEP) using Coupled Model Intercomparison Project phase 6 (CMIP6) coupled model outputs. In Chapter 2, I analyze this relationship using pre-industrial control simulations and abrupt-CO2-forced simulations from 53 CMIP6 models. I find a robust positive SO-SEP relationship both within and across models, regardless of whether the climate system is forced by external CO2 or not. The inter-model spread of the positive SO-SEP relationship is attributed to the strength of shortwave cloud feedback and ocean heat uptake off the west coast of South America. In Chapter 3, I analyze 30-year SST trends over the historical period (1985–2014) using 42 CMIP6 models and multiple observational products. Most models simulate delayed warming trends in both the SO and SEP, failing to capture the observed cooling. These warming trends are positively related across models, even after removing the global-mean trend. Models underestimate both shortwave cloud feedback and ocean heat uptake variability off the west coast of South America, leading to opposing constraint effects: if I strengthen cloud feedback in climate models, it would enhance the SO-SEP relationship; if I strengthen ocean heat uptake variability, it would weaken the SO–SEP relationship. Furthermore, the strength of the SO-SEP relationship is positively associated with equilibrium climate sensitivity, linking this teleconnection to the higher climate sensitivity in CMIP6 models--the "hot model" problem. In Chapter 4, I assess the SO-SEP relationship on interannual timescales using 26 CMIP6 models and observations. Both models and observations show robust positive correlations, even after removing the effects of El Niño-Southern Oscillation (ENSO)-related variability, tropical decadal variability, and the forced response. The constraining effects of shortwave cloud feedback and ocean heat uptake variability remain consistent with the previous chapter. The observed SO-SEP correlation shows that the SO-SEP relationship is underestimated in models, pointing to a dominant role of cloud feedback over ocean heat uptake variability in affecting the strength of such a relationship. Together, these findings demonstrate that the SO-SEP relationship is an intrinsic and robust feature of the climate system. They underscore the importance of accurately simulating both shortwave cloud feedback and ocean heat uptake variability to improve this relationship in climate models, with implications for reducing the SST trend biases in climate simulations and for a warmer climate in projections.
Open Access
Dynamics of convective organization in African easterly waves observed during the NAMMA and CPEX-CV field campaigns
(Colorado State University. Libraries, 2025) Colón-Burgos, Delián, author; Bell, Michael M., advisor; Maloney, Eric, committee member; Davenport, Frances, committee member
The mechanisms that govern the organization of moist convection in weakly rotating flows such as tropical easterly waves are not fully understood. In this study we aim to better understand the dynamical processes that govern the convective organization at the meso-alpha scale, including the location, and intensity of deep convection, using NASA airborne field campaign and satellite observations. We employ a 3D variational analysis technique called SAMURAI in a vortex-centric approach, integrating ERA5 reanalysis and research aircraft observations of 20 African easterly wave (AEW) cases collected during NAMMA in 2006 and CPEX-CV in 2022. The SAMURAI analyses are centered on a potential vorticity (PV) centroid and show a low-level wave relative circulation across cases. Convection is classified from satellite imagery into three classes of shallow/moderate and deep to obtain a frequency of the occurrence relative to the PV centroid location. We find four clusters of organized deep convection denoted as minimal deep, southern, southwestern, and widespread, with the southwestern and widespread clusters associated with the greatest magnitudes of frequency. Results from a composite analysis reveal high PV and relative humidity (RH) at mid-levels were approximately co-located with the regions of low-level convergence and more frequent deep convection, particularly for the southwestern and widespread clusters. Waves with a higher frequency of deep convection are characterized by stronger PV and higher RH at mid-levels compared to waves with a lower frequency of deep convection. Further research is recommended to explore these relationships temporally to better determine the role of cause and effect between the PV and RH and deep convective organization.
Open Access
An object-based analysis of lightning characteristics in pre-tropical cyclogenesis environments
(Colorado State University. Libraries, 2025) Mesa, Nicholas A., author; Bell, Michael M., advisor; Rasmussen, Kristen L., committee member; van de Lindt, John, committee member
The Geostationary Lightning Mapper (GLM) on GOES-16 provides continuous, high-resolution data that enables a novel investigation of lightning attributes in pre-tropical cyclogenesis environments. An object-based framework, which provides additional spatiotemporal characteristics, was used to evaluate the area and optical energy of GLM lightning groups through the Tracking and Object-Based Analysis of Clouds (tobac) Python package. Applying tobac's compositing and tracking methods to GLM observations was first tested with a case of genesis (Tropical Storm Claudette (2021)) within range of the NEXRAD network. Collocated ground-based, dual-polarization radar observations suggested that small-area and low-energy lightning was indicative of stronger convection and updrafts via composite vertical radar profiles of tobac lightning features. The physical interpretations of these lightning attributes were then applied to lightning 72 hours prior to genesis and within 200 km of the best-track invest center for four developing disturbances (Claudette (2021), Ida (2021), Earl (2022), and Beryl (2024)). The presence of small-area and low-energy lightning, previously associated with stronger updrafts and convection, was seen at various times prior to genesis for all cases. Large-area and high-energy lightning was also identified for all cases at various times in the analysis period. Lightning was suggested to be modulated by deep-layer vertical wind shear, and multiple instances of electrified convection were noted to coincide with improvements in organization. More work is needed to evaluate these lightning attributes in a larger composite of pre-genesis disturbances. This work offers a novel characterization of oceanic lightning in pre-tropical cyclogenesis environments for the North Atlantic basin.
Open Access
Future projections of the 2011 Super Tornado Outbreak under global warming and stratospheric aerosol injection
(Colorado State University. Libraries, 2025) Summers, Bali, author; Hurrell, James W., advisor; Rasmussen, Kristen L., advisor; Davenport, Frances V., committee member
Disasters associated with hazardous convective weather including severe thunderstorms, tornadoes, strong winds, large hail, and flooding, have been increasing in both frequency and cost. Previous studies using convection-permitting regional models show that climate change is likely to produce a future with fewer weak thunderstorms but more strong storms through increases in both convective available potential energy and convective inhibition. To potentially mitigate some of the threatening impacts of global warming, climate intervention strategies aiming to offset anthropogenic surface warming are receiving increased attention. One proposed approach is stratospheric aerosol injection (SAI), in which reflective aerosol particles would be injected into the upper atmosphere to decrease a small percentage of the total incoming solar radiation, thereby reducing future rates of warming. Little to no research has been conducted on the impacts from this possible strategy on severe weather using a convection-permitting model. We conduct novel simulations of the 2011 Super Tornado Outbreak using a 4-km version of the Weather and Research Forecasting (WRF) model to examine how this severe weather outbreak might be different in the future under two greenhouse gas emission scenarios with and without SAI. We find broadly that numerous parameters closely related to storm severity increase in a future with climate change, while parameter changes are minimal under climate change with SAI. To the best of our knowledge, this is the first study to consider the effects of SAI on mesoscale processes using a model like WRF.
Open Access
Evaporative moisture sources of Colorado's Front Range: a case study of the exceptionally wet May-July season of 2023
(Colorado State University. Libraries, 2025) Humphreys, Katherine V., author; Keys, Patrick W., advisor; Schumacher, Russ S., committee member; Davenport, Frances V., committee member
In 2023, some of Colorado's eastern plains experienced its wettest three-month period (May - July) out of 129 years of record (Colorado Climate Center, 2024). This extreme precipitation led to flash flooding, road washouts, and significant property damage among Colorado communities along the Front Range including Denver, Boulder, and Fort Collins. Although much is known about the seasonality of precipitation in Colorado, few studies have explored the evaporative origin of precipitation in the Front Range. To better anticipate and understand extreme precipitation events across the Front Range, we investigated the evaporative origin of 2023's extreme precipitation and how it compares to moisture sources during the previous 23 years. Specifically, this study uses the Water Accounting Model 2 Layers (WAM2layers) and hourly ERA5 reanalysis data to quantify the sources of precipitation in Colorado's Front Range during the early summer of 2023 and over the past 23 years (2000-2023). Our moisture source analysis reveals that for the Front Range region in May-July of 2023: (1) the three primary moisture sources were the Pacific Ocean, the western United States, and Colorado itself, contributing just over 66.2% of total precipitation; (2) while these sources are historically dominant, terrestrial contributions and local moisture recycling (i.e., precipitation that recently evaporated from within the Front Range) accounted for a significantly larger share than in prior years; (3) moisture sources in May-July 2023 were a statistical outlier in terms of the magnitude of moisture contributed to the Front Range, forming a cluster of its own relative to the past 24 years; and (4) between the two most dominant modes of variability, 2023 aligns more with a basin-wide pulsing pattern rather than a north-south dipole pattern of moisture sources. This research provides new insights into the extreme rainfall in the summer of 2023 as well as the historical origins of warm-season precipitation in the Front Range.
Open Access
Heatwaves been faking me out: evaluating 2-M temperature forecast errors when AI weather prediction models can't catch the heat
(Colorado State University. Libraries, 2025) Ennis, Kelsey E., author; Barnes, Elizabeth, advisor; Maloney, Eric, advisor; Anderson, Brooke, committee member
Extreme heat is the deadliest weather-related hazard in the United States. Furthermore, it is also increasing in intensity, frequency, and duration, making skillful forecasts vital to protecting life and property. Traditional numerical weather prediction (NWP) models struggle with extreme heat for medium-range and subseasonal-to-seasonal (S2S) timescales. Meanwhile, artificial intelligence-based weather prediction (AIWP) models are progressing rapidly. However, it is largely unknown how well AIWP models forecast extremes, especially for medium-range and S2S timescales. This study investigates 2-m temperature forecasts for 60 heat waves across the four boreal seasons and over four CONUS regions at lead times up to 20 days, using two AIWP models (Google GraphCast and Pangu-Weather) and one traditional NWP model (NOAA United Forecast System Global Ensemble Forecast System (UFS GEFS)). First, case study analyses show that both AIWP models and the UFS GEFS exhibit consistent cold biases on regional scales in the 5–10 days of lead time before heat wave onset. GraphCast is the more skillful AIWP model, outperforming UFS GEFS and Pangu-Weather in most locations. Next, the two AIWP models are isolated and analyzed across all heat waves and seasons, with events split between models' testing (2018–2023) and training (1979–2017) periods. There are cold biases before and during the heat waves in both models and all seasons, except Pangu-Weather in winter, which exhibits a mean warm bias before heat wave onset. Overall, results offer encouragement that AIWP models may be useful for medium-range and S2S prediction of extreme heat.
Open Access
AI-informed model analogs for subseasonal-to-seasonal prediction
(Colorado State University. Libraries, 2025) Landsberg, Jacob B., author; Barnes, Elizabeth, advisor; Schumacher, Russ, committee member; Ham, Jay, committee member
Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We explore the use of an interpretable AI-informed model analog forecasting approach, previously employed on longer timescales, to improve S2S predictions. Using an artificial neural network, we learn a mask of weights to optimize analog selection and showcase its versatility across three varied prediction tasks: 1) classification of Week 3-4 Southern California summer temperatures; 2) regional regression of Month 1 midwestern U.S. summer temperatures; and 3) classification of Month 1-2 North Atlantic wintertime upper atmospheric winds. The AI-informed analogs outperform traditional analog forecasting approaches, as well as climatology and persistence baselines, for deterministic and probabilistic skill metrics on both climate model and reanalysis data. We find the analog ensembles built using the AI-informed approach also produce better predictions of temperature extremes and exhibit more reliable forecast uncertainty. Finally, by using an interpretable-AI framework, we analyze the learned masks of weights to better understand S2S sources of predictability.
Open Access
The role of internal variability and external forcing on the emergence of compound extremes in the CESM2 large ensemble
(Colorado State University. Libraries, 2025) Dwyer, Ashley E., author; Barnes, Elizabeth, advisor; Hurrell, James, advisor; Davenport, Frances, committee member
Extreme hot and dry compound events pose significant hazards to human health, agriculture, and ecosystems, making it critical to better understand what drives their occurrence and spatiotemporal variability. Although the role of internal climate variability in driving compound events has been previously studied, we leverage a large ensemble to enable a more robust understanding of the response of hot and dry events to both large-scale internal climate variability and external forcing. We explore the influence of well-known large-scale climate modes including the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), and the North Atlantic Oscillation (NAO) on the occurrence of hot and dry compound events in the Community Earth System Model 2 Large Ensemble (CESM2-LE). We also investigate when anthropogenic changes in hot and dry compound events emerge from the noise of internal variability. Knowledge of drivers from an internal variability perspective combined with an understanding of greenhouse gas forced changes can aid in quantifying the predictability of extreme compound events on regional scales.
Open Access
Comparison of microphysical and topographical influences on warm season storm electrification between subtropical South America and Colorado
(Colorado State University. Libraries, 2025) Gregg, Mitchell L., author; Rasmussen, Kristen L., advisor; Schumacher, Russ S., committee member; Chandrasekar, V., committee member; Morrison, Ryan, committee member
Sixteen years of observations from the Tropical Rainfall Measurement Mission (TRMM) satellite's Precipitation Radar were key in identifying subtropical South America in the lee of the Andes as a global hotspot for convection, with frequent back-building over terrain producing intense convection yielding some of the highest lightning flash rates on Earth. These observations motivated the 2018 Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign, which sought to further investigate convection and electrification processes through the deployment of Colorado State University's CHIVO radar and a NASA Lightning Mapping Array (LMA). In 2021, the Preparatory Rockies Experiment for the Campaign in the Pacific ("PRE"-CIP) campaign took place in northern Colorado to study extreme precipitation and convection in the lee of the Rockies, deploying the same CHIVO radar near a permanent LMA network. CHIVO radar observations are used to identify discrete precipitation features and microphysical parameters key to storm electrification for approximately three months during the warm seasons in both Argentina and Colorado. LMA-observed lightning flashes are co-located with these precipitation features, allowing for analysis of the relationships between various microphysical parameters and lightning behavior throughout a storm's lifecycle, in both regions. The continuity in instrumentation, precipitation feature identification methods, and microphysical/hydrometeor identification calculations across both campaigns allows for the first direct comparison of microphysical drivers to electrification across a spectrum of storm modes between these two regions. Subtropical South America is characterized by systematically larger and taller convection with higher-altitude lightning flashes. LMA data from both campaigns demonstrates that lightning in Colorado occurs most frequently over the immediate plains east of the Rockies, while in South America, flashes occur more frequently over the foothills, highlighting the critical role of the Sierras de Córdoba, a secondary mountain range east of the Andes, in the back-building of convection observed in this region. Regressions developed between key microphysical parameters and lightning flash rates demonstrate that subtropical South American storms require significantly greater intense echoes and graupel volumes to produce similar lightning flash rates as storms in Colorado, suggesting a fundamental difference in electrification processes and the role of microphysical processes between these two regions. The fundamental differences in convective and lightning processes identified between these two regions demonstrates the need for future work to prioritize both microphysical and kinematic drivers in a more diverse sample of climate regions around the world.
Open Access
Improving predictions and generating actionable forecast insights for downslope windstorms with machine learning
(Colorado State University. Libraries, 2025) Zoellick, Casey L., author; Schumacher, Russ, advisor; Rasmussen, Kristen, committee member; Barnes, Elizabeth, committee member; Nelson, Peter, committee member
Downslope windstorms are an extreme weather phenomenon characterized by accelerating winds down the lee slope of a mountain with gusts often exceeding 45 m s-1. These impact society through damage directly related to the high winds, ground transportation concerns in the vicinity of the windstorm, aviation impacts through the accompanying mountain wave turbulence, and fueling the rapid intensification and spread of wildfires such as the 2018 Camp Fire, the 2021 Marshall Fire, and the 2023 Lahaina Fire. Despite improvements in numerical weather prediction and observational datasets, predictability of these windstorms still rarely exceeds 12 hours further exacerbating their impacts. Recent advances have made machine learning (ML) more accessible to researchers and have shown promise in improving forecasts of other extreme weather phenomena. We first present models driven by two different types of ML architectures that classify wind events as moderate or high at three locations along the Rocky Mountain Front Range: Cheyenne, Wyoming; Fort Collins, Colorado; and Boulder, Colorado. The first type of architecture is the random forest (RF), which is comprised of multiple decision trees, and the second type is the convolutional neural network (CNN), which is a deep learning method that excels at image recognition. These models make forecasts at the Day 1 and Day 2 lead times based on predictors derived from a 12-km version of the WRF operated at Colorado State University. The results show improvement over the direct weather model forecasts. CNNs show enhanced event detection capability compared to the RFs but with a higher false alarm rate limiting their utility in some cases. Next, explainable artificial intelligence (XAI) techniques are presented. Feature importances indicate that the ML models rely on predictors at geographic locations that align with known atmospheric variables important to downslope wind forecast along the terrain. Also, a framework for reducing the dimensions of the predictor data and clustering these data with a Gaussian mixture model yields insights to the forecast ML models' performance and the synoptic conditions in which downslope windstorms along the Front Range occur. The ML models perform better in regimes characterized by prominent synoptic features such as cold air advection or the presence of the jet stream aloft. Lastly, we investigate whether increasing the resolution of the traditional weather model creating the ML predictors results in performance improvements. We use NOAA's High Resolution Rapid Refresh (HRRR) model to derive input predictors for newly trained CNNs and observe a decrease in false alarms that results in an overall performance boost over the direct HRRR forecasts. A case study on the Marshall Fire is conducted and indicates that the HRRR-based CNN is able to correctly forecast the subsequent downslope wind event before the wind event is explicitly depicted in the HRRR output itself. This study is an example of how ML fused with current weather models closes the forecast gap in these impactful weather phenomena with incomplete physical understandings.
Open Access
Insights from machine learning-based forecasts of convective hazards and environments
(Colorado State University. Libraries, 2025) Mazurek, Alexandra Callahan, author; Schumacher, Russ S., advisor; Rasmussen, Kristen L., committee member; van den Heever, Susan C., committee member; Chen, Haonan, committee member; Hill, Aaron J., committee member
Severe convective thunderstorms and their associated hazards are costly, damaging, and difficult to predict. Machine learning (ML) techniques are rapidly being developed and deployed in an effort to predict severe thunderstorms more quickly and with greater accuracy than traditional methods. With these developments, there is a need to understand how ML-based weather prediction systems rely on atmospheric data and generate their forecasts. This work probes a number of ML-based convective thunderstorm-related forecasts over the contiguous United States to 1) understand how they make their predictions, 2) diagnose where their strengths and deficiencies may lie, and 3) explore how well their predictions resemble physical characteristics of the atmosphere. The insights gleaned from this research aim to support operational use of ML-based forecast guidance. First, probabilistic ML-based forecasts of severe convective hazards (i.e., tornadoes, hail, and thunderstorm-driven winds) from the Colorado State University Machine Learning Probabilities (CSU-MLP) system are studied using an explainable machine learning technique known as Tree Interpreter (TI). TI provides context to the CSU-MLP forecasts by disaggregating its forecast probabilities into "contributions" by each of the environmental variables that are used to train the model. This technique allows one to see the extent to which each atmospheric "ingredient" contributes to the final predictions. Results of this work show that CSU-MLP uses environmental information to make its predictions in ways that resemble the climatology and environments of severe storms, and the values of the contributions generally scale with values of the environmental inputs, effectively enhancing the interpretability of the ML system. Second, CSU-MLP forecast performance is examined across different synoptic regimes in an effort to understand which types of environmental conditions tend to lead to skillful versus less-skillful forecast performance. Self organizing maps (SOMs), which are a type of ML, are employed to statistically diagnose regimes across two years of reanalysis data. The skill of day-2 CSU-MLP probabilistic tornado, wind, and hail forecasts are examined across the SOM-identified regimes. This work shows that SOMs are successful at identifying distinct atmospheric patterns using only surface-based convective available potential energy (SBCAPE) and vertical wind shear as inputs. At times, the best- and worst-performing CSU-MLP forecasts occur under highly similar atmospheric conditions, though the best-performing forecasts tend to be characterized by strong synoptic forcing and many storm reports. Third, forecast output from three deep learning weather prediction (DLWP) models, GraphCast, Pangu-Weather, and FourCastNetv2, is studied to investigate how well they model severe storm environments and capture convection-related parameters. This work explores both native and derived fields from 22 months of daily forecasts from these three models, all of which were initialized with input conditions from the Global Forecasting System (GFS). The output is compared to ERA-5 reanalysis and GFS forecasts, both broadly and for specific convective events. Overarching results from this study show that the DLWP model forecasts tend to be characterized by less moisture and greater instability compared to ERA-5. For specific events, the DLWP forecasts can reasonably capture convective environments at least a week in advance and are competitive with the GFS. However they tend to underforecast the vertical wind shear magnitude, and their limited vertical resolution can lead to overly smooth profiles that lack key details such as stable layers.
Open Access
Data-driven improvements to GPROF-based satellite snowfall retrievals with a focus on mountain snowfall
(Colorado State University. Libraries, 2025) Gonzalez, Ryan L., author; Kummerow, Christian, advisor; Liston, Glen, committee member; Chiu, Christine, committee member; Rasmussen, Kristen, committee member; Notaros, Branislav, committee member
Snowfall is a critical component of Earth's hydrological and climate system despite only 5% of Earth's annual precipitation falling as snow. Satellite-based snowfall estimates, particularly those obtained from the Global Precipitation Measurement (GPM) Microwave Imager (GMI), struggle to accurately estimate the total annual snowfall accumulations, especially in mountainous regions of the world. Part of the challenge is due to the reference precipitation used in the GMI-based algorithms, while radiometers struggle to distinguish between the microwave signatures of surface snowpack and snowfall. The aim of this dissertation is to evaluate the impact machine learning-based GMI retrievals have on snowfall estimates, explore how temperature and climatological adjustments to the reference precipitation can provide additional information to the retrieval, and asses if these changes lead to improved snowfall accumulations required for modeling the lifecycle of snow. A key objective of this study is to improve snowfall accumulation estimates in mountainous areas, where snowpack is a critical component of water storage. First, snowfall rates estimated from the Goddard Profiling Algorithm (GPROF) for GMI are compared using three types of GPROF algorithms: one Bayesian (GPROF V7) and two neural network versions (GPROF-NN 1D and GPROF-NN 3D). The highest detection and quantitative statistics are observed using GPROF-NN 3D with both neural network retrieval algorithms outperforming the Bayesian version. It is shown that artificial biases in the retrieval statistics can result from the selected threshold for snow/no-snow classification. Coincident in-situ snowfall and radar data are also used to evaluate the temperature dependency of the reflectivity-snowfall (Z-S) relationship and how it impacts the GPROF retrievals. Second, an evaluation of the three GPROF algorithms is conducted in the mountains of the western United States. Using data from a snow reanalysis dataset, water year snowfall accumulations from the Multi-Radar Multi-Sensor (MRMS) are adjusted to produce more realistic snowfall magnitudes and spatial patterns. These adjustments were found to decrease errors in snowfall accumulation estimates for all three retrieval algorithms, resulting in significant improvements when compared to independent SNOTEL observations. These results provide a positive outlook for snowfall retrievals in mountainous regions by incorporating additional information to the retrieval algorithm. Finally, a framework for incorporating satellite precipitation estimates into a snow evolution model in the western United States is presented that offers a flexible design to account for different study domains. The objective of this framework is to present an approach for deriving snow water equivalent (SWE) from satellite precipitation estimates given the difficulties of directly measuring SWE from passive microwave sensors. A UNet-based retrieval model is used to estimate precipitation at 30 minute time resolution across the currently available passive microwave and infrared sensors. The initial precipitation estimates were found to have a systematic bias across the study period, which, after correction, produced realistic spatial patterns of snow depth and snow water equivalent, but underestimated the magnitudes compared to two reference snow model simulations.
Open Access
Cold pool propagation and cold pool-land surface interactions
(Colorado State University. Libraries, 2025) Falk, Nicholas Michael, author; van den Heever, Susan C., advisor; Grant, Leah D., advisor; Bell, Michael M., committee member; Schumacher, Russ S., committee member; Venayagamoorthy, Subhas K., committee member
Convective cold pools are important components of the Earth system as they influence processes such as deep convective initiation, storm longevity and intensity, surface energy fluxes, and aerosol transport. The overarching goal of the research outlined in this dissertation is to investigate the propagation characteristics of cold pools, as well as the interactions between cold pools and the land surface. The three studies comprising this dissertation use field campaign observations and high-resolution numerical simulations to investigate these cold pool processes. The first study evaluates a popular density current propagation speed equation using a large, novel set of radiosonde and dropsonde observations. First, data from pairs of sondes launched inside and outside of cold pools, along with the theoretical density current propagation speed equation, are used to calculate sonde-based propagation speeds. Second, radar/satellite- based propagation speeds are calculated by manually tracking the propagation of cold pools and correcting for advection due to the background wind. Comparisons of the propagation speeds calculated in these different ways demonstrate that sonde-based and radar- based propagation speeds are strongly correlated for US High Plains cold pools, suggesting the density current propagation speed equation is appropriate for use in midlatitude continental environments. Sonde-based propagation speeds are largely insensitive to how cold pool depth is defined, since the preponderance of negative buoyancy is near the surface in cold pools. Sonde-based propagation speeds can vary by ~300% based on where and when the sondes were launched, suggesting sub-mesoscale variability could have a major influence on cold pool propagation. The impacts of topographic slope on daytime haboob propagation speeds and dust lofting are examined in the second study comprising this dissertation, along with how these impacts are modulated by surface roughness length. A suite of 40 idealized, large-eddy simulations are conducted with varied linear topographic slopes and surface roughness lengths. It is found that on flat ground, greater surface roughness increases drag on haboobs and causes haboobs to dissipate faster, thereby decreasing both haboob propagation speeds and associated dust lofting. As the topographic slope is increased, an upslope anabatic wind forms which causes downslope haboob propagation speeds to decrease and upslope haboob propagation speeds to be mostly unchanged. Anabatic winds act to loft dust as well, leading to increased masses of dust being lofted jointly by the haboob and anabatic wind as topographic slopes are increased. The third study investigates the individual and synergistic impacts of cold pools and land surface heterogeneity on convection initiation. Idealized large eddy simulations of deep convection over the Amazon rainforest are conducted testing realistic and homogenized vegetation, along with realistic and eliminated cold pools. Convection initiation is more frequent over forested than deforested areas due to more favorable thermodynamics. Heterogeneous vegetation aggregates storm initiation locations compared with homogeneous vegetation. Over heterogeneous vegetation, cold pools propagate into deforested regions, thereby initiating storms and disaggregating storm initiation locations. Convection initiation locations are randomly distributed over homogeneous vegetation, with or without cold pools, demonstrating that cold pools have minimal impacts on convection initiation locations over homogeneous vegetation. The findings of this dissertation research shed new light on fundamental cold pool processes and should be helpful for improving the representation of cold pools in forecast and climate models. Several avenues for future research are discussed.
Embargo
Extreme rainfall mechanisms in Hurricane Fiona (2022)
(Colorado State University. Libraries, 2025) Nieves Jiménez, Angelie, author; Bell, Michael M., advisor; Schumacher, Russ, committee member; Anderson, Brooke, committee member
Hurricane Fiona's (2022) historical heavy precipitation devastated the Caribbean Island of Puerto Rico after it made landfall as a category 1 hurricane. Rainfall accumulation totals in southern interior region areas surpassed 900 mm during 18 – 19 September 2022. To analyze the rainfall mechanisms, we use output from the Hurricane Analysis and Forecast System (HAFS) configuration "B" modeling system and observations from the Puerto Rico Next Generation Weather Radar (NEXRAD) Level 2 Doppler radar and rain gauges around the island. Quantitative precipitation estimates from radar and rainfall measurements suggest that HAFSB simulated reasonably well the precipitation amounts and location. HAFSB track differences from the real trajectory contributed to discrepancies between the simulated and observed rainfall. We investigate three stages of the Hurricane Fiona rain event, each focusing on different processes. The first stage is associated with the primary eyewall and rainfall produced through boundary layer convergence. The second stage focuses on the principal rainband affecting the island and is associated with rainfall enhancement from vertical wind shear interactions with Fiona's potential vorticity. The third and final stage analyzes the enhancement of a "tail rainband" both over open water and the southern portion of the island as Hurricane Fiona kept strengthening west of Puerto Rico. Our findings support the hypothesis that evaporative cooling within inner core rainfall from stage one and two sets up a favorable environment for isentropic uplift to enhance rainfall production in stage three. Additional enhancements of the rainfall occurred over Puerto Rico's high terrain by orographic effects.
Open Access
Eulerian and Lagrangian analyses of bioaerosol transport in three deep convective storm morphologies
(Colorado State University. Libraries, 2025) Davis, Charles M., author; van den Heever, Susan C., advisor; Kreidenweis, Sonia M., committee member; Jathar, Shantanu, committee member
In this thesis, we investigate the entrainment and transport of aerosol particles in a representative isolated deep convective storm, supercell, and squall line using idealized high-resolution mesoscale model simulations. We focus our investigation on the extent to which air from rainy surface regions, which have been noted in the literature to be sites of aerosolization of biological particles, is able to enter and subsequently be transported within these storm morphologies. We also investigate the residence time in supersaturated environments experienced by these parcels as they are entrained. The first part of this study quantifies the magnitude and timing of entrainment of air from the surface, and from rainy surface regions specifically, in all three storm morphologies. We use inert tracer quantities to constrain the timing with which rainy (referred to as rain-sourced tracers) and other surface air (referred to as fixed-source tracers) is entrained into the storms, and the fraction of each storm's updraft that is composed of air from these regions. At its peak, the isolated convective storm entrains the greatest proportion of surface-based air seen in any of the storms. However, it also attains the smallest concentrations of rain-sourced tracer and the smallest proportion of rain-sourced tracer in its updraft, indicating that significantly less of its entrained surface air originates in regions of potential rain-induced aerosolization of bioaerosols. The squall line and supercell attain greater values of both these quantities and sustain them for longer periods, indicating that more air in their updrafts originates in rainy regions. For light rain (>= 1 mm/hr), the squall line and supercell entrain comparable concentrations of air from rainy regions, but for heavy rain (>= 40 mm/hr) the squall line entrains significantly more. The second part of this work investigates the specific pathways by which surface air is entrained into these storms as well as the environments experienced by entrained surface-based air parcels. We do this by calculating parcel trajectories using the output of the aforementioned mesoscale simulations, initializing air parcels at various times within each storm's life cycle, and separately evaluating the trajectories of parcels originating in rainy and non-rainy regions. The isolated convective storm simply moves over and entrains the parcels not initialized in rainy regions into its updraft directly by its strong surface convergence. The squall line and supercell entrain non-rainy parcels by gust-front lofting, in which the circulation at the leading edge of the cold pool lofts the parcels to a level at which they can be entrained by the updraft behind the gust front. The isolated convective storm entrains parcels originating in rainy regions via the horizontal vortical circulation in the head of the cold pool, which lofts them and redirects them towards the updraft. The squall line also entrains rainy parcels by this same circulation in its cold pool. The supercell, on the other hand, entrains rainy parcels from a relatively narrow region within and just outside of the leading edge of the forward-flank downdraft's cold pool via a combination of gust front lofting and the known phenomenon of the "recycling" of some negatively buoyant air from the forward-flank downdraft's cold pool into its updraft. We find that the time these entrained parcels spend in supersaturated environments is a strong function of storm morphology. Parcels entrained into the squall line spend nearly twice as long on average in supersaturated regions as entrained parcels in the other two storm types. This arises because the squall line parcels take longer to reach mid-levels after first being lofted by the circulation in the head of the cold pool. This longer transit time is due to the upshear tilt of the updraft, as well as from more complex 3D variations in the structure of the gust front and updraft.
Open Access
The impact of stratospheric aerosol injection: a regional case study
(Colorado State University. Libraries, 2025) Cohen, Sabrina L., author; Hurrell, James W., advisor; Keys, Patrick W., committee member; Lombardozzi, Danica L., committee member
The detrimental effects of anthropogenic climate change have become ubiquitous as global greenhouse gas emissions concentrations continue to increase. As a result, research into proposed climate intervention (CI) techniques to offset some of the most damaging effects of climate change is increasing, with the idea that CI could provide more time for humanity to pursue decarbonization. One of the most researched CI techniques is stratospheric aerosol injection (SAI), which would reflect a small portion of sunlight away from Earth to reduce or lower temperature increases. While many studies have analyzed SAI's potential global impacts on climate variables, such as temperature and precipitation, relatively few have examined regional impacts on variables more intimately tied to human well-being, such as crop productivity. Thus, using climate model data, we analyze the projected impacts of one future climate change and three SAI scenarios on four Global South regions already socioeconomically vulnerable to climate change: South Asia (SAS), East Asia (EAS), South Central America (SCA), and West Africa (WAF). We find that, in the SAI scenarios, heat extremes are reduced and wet season precipitation, soil moisture and crop productivity increase relative to the climate change scenario in all four regions. Further, SAI clearly ameliorates crop productivity losses produced by climate change in WAF and SCA, with less clear benefits in SAS and EAS. Our study indicates the potential for SAI (in the scenarios examined here) to alleviate some of climate change's adverse impacts on human welfare.

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