Theses and Dissertations

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Open Access
Impacts of historic anthropogenic aerosol forcing on large climate ensembles through the lens of poleward energy transport
(Colorado State University. Libraries, 2024) Needham, Michael Robert, author; Randall, David A., advisor; Rugenstein, Maria, committee member; van Leeuwen, Peter Jan, committee member; Rugenstein, Jeremy, committee member
In discussions of the human impact on Earth's climate, aerosols receive much less attention than greenhouse gases. And yet, the change in the global mean effective radiative forcing from anthropogenic aerosols was roughly of the same magnitude (but of opposite sign) as the change in greenhouse gases throughout much of the twentieth century. Aerosols also represent the largest uncertainty in the effective radiative forcing, due to their complex interactions with clouds and solar radiation. Complicating this even further, aerosols are relatively short-lived within the atmosphere, and thus exhibit a large degree of variability in space and time. This dissertation presents a set of studies which investigate the ways in which historic anthropogenic aerosols may have impacted the Earth's weather and climate, through the analysis of a large number historic climate model simulations which comprise so-called large ensembles. Analysis of these ensembles allows for the isolation of some forced signal (e.g., the influence of aerosols) from the noise (i.e., the background variability of the model). This leads to conclusions through the analysis of summary statistics across members of the ensemble population which would be impossible to make based on only one or a few simulations. In particular, these studies show that the emission of aerosol precursors from Europe and North America increased the northward transport of heat from the southern into the northern hemisphere in an ensemble of simulations performed with version 2 of the Community Earth System Model (CESM2). The additional heat transport was in excess of 0.25 PW. This is an increase of at least 4-5% compared to the baseline maximum transport of between 5-6 PW which occurs in the mid-latitudes. At latitudes away from these maxima, the increase was a much larger percentage of the total. This anomalous northward energy transport was accomplished by changes in both atmospheric and oceanic processes. These include a southward shift of the Intertropical Convergence Zone (ITCZ) associated with changes in the Hadley cells; an increase in the frequency of extratropical cyclones in the north Atlantic; a strengthening of the Atlantic Meridional Overturning Circulation (AMOC); as well as changes to multiple ocean processes across the Indo-Pacific. Comparison of these results to the literature indicates that this modeled response to aerosols in CESM2 is likely too large. Furthermore, analysis of two additional large ensembles reveals that this over-sensitivity of CESM2 cannot be due to some deficiency in the model. Instead, it is demonstrated that the difference is the result of changes to the historical emission estimates between phase 5 and phase 6 of the Coupled Model Intercomparison Project (i.e., CMIP5 and CMIP6). This finding leads to the hypothesis that the higher interannual variability associated with a change from decadal-scale CMIP5 emissions to annual-scale CMIP6 emissions is the ultimate cause of the overzealous response of the model. Testing this hypothesis likely will provide the most fertile ground for future work.
Open Access
Radiative feedbacks in tropical organized convection and the Madden-Julian oscillation
(Colorado State University. Libraries, 2024) Hsiao, Wei-Ting, author; Maloney, Eric D., advisor; Rugenstein, Maria A. A., committee member; Kummerow, Christian D., committee member; Randall, David A., committee member; Mueller, Nathaniel D., committee member
The organization of tropical deep convection is supported by radiative feedbacks, in which high clouds and moisture anomalies associated with convection imposes anomalous longwave (LW) radiative heating in the atmosphere, further supporting convection. Despite an abundance of studies using numerical simulations, the interactions between tropical convective organization, radiative feedbacks, and the large-scale atmospheric environment have not been comprehensively examined in real-world observations. The present dissertation examines such interactions among tropical mesoscale organized convection, radiative feedbacks, and the Madden-Julian oscillation (MJO) using a set of observation-derived data products, including retrievals using spaceborne satellites and ground-based precipitation radar, along with combined products and reanalyses. The main findings in each chapter are summarized as follows: (1) higher sea surface temperature and stronger low-level wind shear strength enhance tropical mesoscale convective activity, increasing cirrus cloud cover and LW heating generated per unit precipitation. (2) the estimation of LW cloud-radiative feedback (LW CRF), defined as the LW cloud-radiative heating produced per unit precipitation, is sensitive to the precipitation data set used. (3) radiatively driven circulation and the associated moistening effects in the MJO can be derived in a weak-temperature-gradient framework and a linear baroclinic model. The result suggests that LW heating moistens the MJO more efficiently than the total apparent heat source, while shortwave (SW) radiative effects dry the MJO. (4) The LW CRF of the MJO is spatially inhomogeneous, with stronger feedbacks over the tropical Indian ocean and to the northwest of Australia, but weaker feedbacks over the tropical western and central Pacific. The spatial pattern may be determined by the spatial distribution of preferred convective types and precipitation efficiency.
Open Access
Bridging human and artificial intelligence for skillful, trustworthy, and insightful seasonal-to-decadal climate prediction
(Colorado State University. Libraries, 2024) Rader, Jamin K., author; Barnes, Elizabeth A., advisor; Rasmussen, Kristen L., committee member; Hurrell, James W., committee member; Stevens-Rumann, Camille S., committee member
Seasonal-to-decadal climate variability is inherently difficult to predict and is intimately connected to human and natural systems worldwide. Skillful forecasts on two-month to ten-year timescales would enable proactive and informed decision-making for many industries, including fisheries, water management, and agriculture. Understanding the behavior of seasonal-to-decadal climate variability provides context for our changing environment. Neural networks, a class of artificial intelligence tools, are well-suited for exploring teleconnections, precursors, and patterns of variability, since they can identify complex relationships within immense quantities of data. Neural networks have traditionally been used as "black-box" models that produce predictions but are inherently difficult to explain. There has been a recent push to develop "interpretable" models that can be understood by human scientists. In this dissertation, I bridge human and artificial intelligence to leverage interpretable AI for skillful, trustworthy, and insightful prediction of seasonal-to-decadal climate variability. First, I show how interpretable neural networks can be used to optimize a simple forecasting method, analog forecasting. This approach highlights four precursor patterns for one-year forecasts of El Niño Southern Oscillation in the Tropical Pacific, West Pacific, Baja Coast region, and Tropical Atlantic. In addition, when making five-year forecasts of observed sea surface temperature variability in the North Atlantic, this optimized analog forecasting approach rivals the performance of an initialized decadal prediction system. Second, I design neural networks to learn patterns of internal variability and forced change. Using these neural networks, I perform climate change attribution for observed sea surface temperatures. Despite the unprecedented, record-high, global-mean sea surface temperature in 2023, our results suggest that much of this warming can be explained by internal variability, as anomalously cold conditions in 2021 and 2022 shifted to anomalously warm conditions in 2023. Third, I use neural networks to make decadal forecasts of the likelihood that annual-global-mean temperature exceeds 1.5˚C, a critical Paris Agreement temperature threshold. These forecasts predict that it is very likely that annual-global-mean temperature exceeds 1.5˚C in the next decade (2024-2033), serving as a harbinger for future climate change. These forecasts are consistent with dynamical initialized prediction systems, demonstrating that neural networks can provide skillful decadal forecasts at reduced computational expense. Neural networks are powerful tools for prediction, and facilitate deeper discovery of our chaotic, interconnected, predictable Earth.
Open Access
Investigating and mitigating errors in the remote sensing of maritime low clouds at night
(Colorado State University. Libraries, 2024) Turner, Jesse, author; Miller, Steven D., advisor; Kummerow, Christian D., committee member; Smith, Ryan G., committee member; Noh, Yoo-Jeong, committee member
Low clouds are ubiquitous to the world's oceans, affecting aviation, maritime transportation, and the structure and dynamics of the broader atmospheric system. Understanding the diurnal properties and distributions of these clouds requires an observing system capable of spanning vast regions of ocean devoid of surface-based observations. Here, earth observation satellite imagery provides potentially valuable information on cloud coverage over the oceans. The brightness temperature difference (BTD) between the longwave infrared (e.g., 11 µm) and shortwave infrared (e.g., 3.9 µm) window band measurements is commonly used as a first-order bi-spectral test to identify low clouds over the ocean at night. Occasionally, unusual patterns of clear-sky features in this BTD occur, giving rise to spurious false-positive cloud measurements. These confusing signals are caused by nuances of the atmospheric and surface emission sensitivity at these two wavelengths. Ideally, positive values in the 11 µm - 3.9 µm BTD are caused by actual low clouds, owing to slightly higher emissivity at the longwave IR compared to the shortwave IR. However, a clear-sky environmental scenario can mimic this signal: a warm and moist air mass over a cold region of water. These same environmental conditions are conducive to advection fog formation, compounding the interpretation of conventional infrared-based cloud detection in these regions. Moonlight reflectance, when available from the Day/Night Band on the Visible/Infrared Imaging Radiometer Suite (VIIRS), can help to disentangle cases of actual vs. false low cloud (FLC). This research examines cases from the United States east coast, the Mexico south coast, and the large-scale Gulf Stream to investigate the physical causes of false cloud signals. Insight gained from this research can help forecasters and researchers determine which physical regions are prone to false alarms, and in complement, which regions offer higher confidence for cloud detection. Further, this study uses numerical model data and radiative transfer simulations to estimate the positive signals caused solely by air mass over cold water effects. This simulation method lends insight on the global extent and frequency of nighttime maritime low cloud overstatement. Knowledge of the patterns of false signals in the IR BTD provides opportunities to improve products that depend on the nighttime low cloud test, such as fog and visibility warnings, sea-surface temperature cloud masking, and cloud climatologies used for climate research. The simulation also provides a novel predictive tool for anticipating potential regions of both false alarm low cloud and regions prone to advection fog formation.
Open Access
Investigating the potential of meltwater as a local source of ice nucleating particles in the central Arctic summer
(Colorado State University. Libraries, 2024) Mavis, Camille, author; Kreidenweis, Sonia, advisor; Creamean, Jessie, advisor; Pierce, Jeffrey, committee member; Peers, Graham, committee member
Due to climate change, the Arctic has crossed a threshold into positive feedbacks between sea-ice loss and increased absorption of solar radiation, causing warming up to four times the global average. Parameterizing the Arctic radiation budget to predict the new steady-state is paramount for guiding policies impacting future global socioeconomics and Arctic livelihoods. Arctic mixed-phase clouds (AMPCs) are a pillar in the feedback systems by modulating the surface energy budget, depending on the partitioning of cloudwater between ice and liquid phases that is sensitive to the concentration of ice nucleating particles (INPs) in the atmosphere. However, current observational gaps of central Arctic INP concentrations and sources may contribute to current challenges in resolving the controls on Arctic cloud ice content. The year-long expedition aboard the RV Polarstern from 2019 - 2020, entitled The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), was a highly coordinated interdisciplinary effort that provided a unique opportunity to observe INPs in the central Arctic. The Arctic summer is a unique period characterized by pristine aerosol conditions, in which emissions from local sources have an increased influence, potentially impacting the ubiquitous low-lying AMPCs. Thus, the summer is an ideal season for exploration of the potential importance of INPs from local sources, such as melt ponds. In this study, we used the Colorado State University (CSU) Ice Spectrometer and chemical treatments to determine the INP concentration and inferred composition in source samples of bulk sea water and meltwater from ponds and leads over the month of July. In addition, ambient aerosol filters were deployed both on the ship and on the ice, downwind of these meltwater features. We found that the concentration of INPs in meltwater was 10 times higher than in the mixed layer of the ocean, a surprising result since previous studies did not see a difference in the two source samples. The INPs in meltwater were capable of freezing at temperatures (T) ≥ −10 °C and were predominantly biological, based on our heating assay. Biological INPs capable of freezing at T ≥ −10 °C were present in 80 % of the on-ice aerosol samples. The alignment of slopes of the cumulative INP spectra between the meltwater and aerosol filter samples at T ≥ −15 °C suggested an influence from meltwater on the aerosol INPs at those temperatures. Similarities between aerosol INP sampled on the ice and on-board Polarstern suggested that the on-ice INP concentrations were likely influenced by a regional meltwater source signature, rather than being measurably impacted by a singular upwind pond. A relationship was observed between wind speed, supermicron particle counts, and on-ice aerosol INP populations active at warm (−15 °C) and cold (−25 °C) temperatures. A distinct on-ice aerosol sample containing no INPs active at T ≥ −15 °C was found to be influenced by southerly air over the ice-free ocean, emphasizing the potential impact meltwater may have as a unique source of warm temperature INPs in the central Arctic. These findings suggest that summertime central Arctic biological INP concentrations may increase if, as predicted, a spatio-temporal expansion of the melt season occurs in the near future. This increased INP concentration from local sources could impact central Arctic cloud microphysics, and thus their impact on the surface energy budget.
Open Access
Quantification of volatile organic compound emissions from unconventional oil and gas development
(Colorado State University. Libraries, 2024) Zhang, Weixin, author; Collett, Jeffrey L., Jr., advisor; Pan, Da, committee member; Pierce, Jeffrey R., committee member; Ham, Jay M., committee member
Oil and gas (O&G) development in the U.S. has accelerated in the past two decades, aided by unconventional extraction techniques including hydraulic fracturing and horizontal drilling. Potential environmental and health impacts of volatile organic compounds (VOCs) originating from O&G activities in populated regions have raised concerns. In Broomfield, Colorado, six new O&G well pads were approved for development in 2017 and an air monitoring program was established in October 2018 to collect weekly and later plume-triggered air samples. This study addresses the limited existing knowledge of activity-specific VOC emission rates from unconventional O&G development (UOGD), utilizing these observations and dispersion model simulations through emission inversion methods. Emissions are characterized from well drilling, hydraulic fracturing, coiled tubing/millout, flowback, and production operations. Substantial variations in average VOC emission rates, determined using weekly canister observations, are observed across different UOGD phases. Drilling and coiled tubing/millout operations exhibit the highest total VOC emission rates, attributed to hydrocarbon release from shale formations and drilling mud. In contrast, hydraulic fracturing gives lower emission rates, consistent with injection of fluids into the well during this operation, minimizing the probability of subsurface hydrocarbon emissions. Diesel-powered engines are identified as the primary ethyne sources during hydraulic fracturing. Production was characterized by lower VOC emission rates than pre-production phases but remains an important emission category due to its long duration (decades). Variations of emission rates within each phase highlight the complexity of factors and activities influencing emission rates, including, for example, vertical vs. horizontal drilling and periodic maintenance activities. VOC emission rates associated with drilling mud volatilization and hydraulic fracturing suggest that previously published emission estimates (EPA (2022), and Hecobian et al. (2019)) underestimate average VOC emission rates during these activities. Significantly lower emission rates during flowback compared to previous work (Hecobian et al., 2019) reveal how improved management practices, including tankless, closed-loop fluid handling systems have effectively reduced what used to be a dominant source of pre-production VOC emissions. Plume-triggered samples, capturing transient high-concentration plumes, reveal short-term VOC emission rates approximately an order of magnitude higher for drilling and flowback than determined from weekly samples. In the case of flowback, short-term emission pulses have been linked to periodic emptying of sand canisters used to trap fracking sand emerging from previously fracked wells.
Open Access
Effects of warming and stratospheric aerosol injection on tropical cyclone distribution and frequency in a high-resolution global circulation model
(Colorado State University. Libraries, 2024) Feder, Andrew, author; Randall, David, advisor; Hurrell, James, committee member; Rugenstein, Jeremy, committee member
Tropical cyclones (TCs) occur stochastically in any given TC season, with varying numbers and intensities within basins over time. Nevertheless, they arise out of fundamental laws of thermodynamics and fluid physics, and in recent years, as global circulation models (GCMs) have increased in spatial resolution, increasingly realistic TCs and TC distributions have emerged from them. Where prior research on TC climatologies has relied on proxies like Potential Intensity (PI) and synthetic storm models, the cyclones emerging from the dynamics of newer GCMs can now be analyzed directly, using native model variables. Such direct analysis may be particularly useful in studying possible global storm distributions under radically altered future climates, including high-emissions warming scenarios, and even those shaped by climate interventions. These interventions include various directed changes in global albedo, such as Stratospheric Aerosol Injection (SAI), with only limited precedent in the historical period. GCMs simulating realistic climate intervention scenarios, have not as of yet paired storm-resolving resolution with realistic intervention scenario construction. This has left gaps in our understanding as to how interventions might affect global storm/TC distributions, and whether ameliorating warming in this way could also substantially lessen related natural disaster risk profiles. In this paper, we utilize a new high-resolution model configuration to conduct experiments examining the effects of SAI, on tropical cyclones and global storm physics more broadly. These experiments are constructed based on prior work on SAI using the GLENS GCM ensemble (Tilmes et al. 2020; Danabasoglu 2019a,b). Our analysis centers on 3 10-year experiments conducted using 30-km grid spacing. These include a recent-past calibration run; the Intergovernmental Panel on Climate Change climate pathway SSP 8.5 (IPCC 2021), for the years 2090-2099, with no SAI; and SSP 8.5, with SAI having begun in 2020 to maintain a global temperature rise of no more than 1.5° C, also simulated for the years 2090-2099. With the resulting data sets, we deploy a novel TC-tracking algorithm to analyze resulting changes in storm tracks and properties. Based on our results for these different scenarios, we find that SAI, while in some ways restoring global storm patterns to a pre-warming state, may also create unique basin-scale TC distribution features and pose novel related hazards.
Open Access
Satellite observations of oceanic high-latitude drizzle using a combined radar-radiometer retrieval
(Colorado State University. Libraries, 2024) Jones, Spencer R., author; Kummerow, Christian, advisor; Chiu, Christine, committee member; |Chandrasekaran, Venkatachalam, committee member|Grassotti, Christopher, committee member
The high latitude oceans are problematic for satellite estimations of precipitation due to the high frequency of occurrence of light drizzle and snowfall. Passive microwave radiometric observations are sensitive to integrated cloud water path and provide good sampling for robust statistics but have little skill in distinguishing precipitation onset from cloud water and cloud ice due to a lack of sensitivity to drop sizes when they are small. Spaceborne precipitation radars to date have lacked sensitivity to drizzle, and cloud radars have suffered from both the uncertainties inherent in Z-R relations and poor sampling due to nadir-only scans. This study combines coincident active and passive microwave observations from CloudSat's Cloud Profiling Radar (CPR) and the Advanced Scanning Microwave Radiometer (AMSR2) to resolve cloud and hydrometeor distribution parameters and to force consistency between the two independent sets of coincident observations. Consistency between the radar and radiometer is found by using an optimal estimation (OE) retrieval algorithm, a physics-based technique that simultaneously resolves the most likely atmospheric state given both radar and radiometer observations as well as a priori information. The OE algorithm uncertainties are estimated using a method that attempts to emulate the departure in observation space of retrieved states from the unknown true state. The focus on observational uncertainties and the accuracy obtained by using nondiagonal observational error covariance matrices allows the algorithm both to resolve states that are radiatively consistent and to reduce the level of nonuniqueness found in dealing with passive observations alone. The result is an estimation of drizzle frequency and intensity that are consistent with both the CPR and AMSR2 observations for the high latitude oceans. We find that zonal means of retrieved high-latitude drizzle below 0.25 mm hr-1 from these combined observations (0.263 mm day-1) falls slightly above those of CloudSat estimates (0.244 mm day-1), provided by the 2C-RAIN-PROFILE and 2C-SNOW-PROFILE products (Lebsock 2018; Wood and L'Ecuyer 2018), and far below that of radiometer-only estimates (0.920 mm day-1) provided by GPROF (Kummerow et al. 2015).
Open Access
The signature of the western boundary currents on tropospheric climate variability
(Colorado State University. Libraries, 2024) Larson, James, author; Hurrell, James, advisor; Thompson, David, advisor; Willis, Megan D., committee member
Oceanic western boundary currents play a crucial role in transporting heat poleward, thereby influencing the midlatitude climatological-mean climate and serving as an important role for midlatitude storm tracks that provide rainfall to land regions. It is not yet firmly established what role these oceanic currents play in influencing atmospheric variability. Characterized by the presence of mesoscale features such as oceanic eddies and sharp sea surface temperature (SST) gradients, the western boundary currents define a uniquely separate regime for air-sea interactions on climatic timescales relative to the rest of the ocean basins. In this study, simple but robust observational and modeling evidence reveals that anomalous precipitation and vertical motion co-vary with local SST anomalies in the western boundary currents, with a measurable influence extending into the upper troposphere. Periods of anomalously warm SSTs are associated with anomalous, co-located upward motion of > 0.02 Pa/s and precipitation anomalies of ~0.6 mm/day when averaged over a month. Yet, the standard resolution of most climate models, with grid cells on the order of 100 kilometers, fail to capture this co-variability. It is demonstrated that sharpening the horizontal resolution in both a climate model and in atmospheric reanalyses alters the spatial patterns both of sea surface temperature and of regional atmospheric processes. Given the significant influence of these western boundary currents on the broader regions surrounding them, climate projections conducted with grid cells coarser than 50 kilometers may overlook crucial processes.
Embargo
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.