Physical Sciences and Mathematics Commons^™

In The Margins: Reconsidering The Range And Contribution Of Diazotrophs In Nearshore Environments, Corday R. Selden

OES Theses and Dissertations

Dinitrogen (N₂) fixation enables primary production and, consequently, carbon dioxide drawdown in nitrogen (N) limited marine systems, exerting a powerful influence over the coupled carbon and N cycles. Our understanding of the environmental factors regulating its distribution and magnitude are largely based on the range and sensitivity of one genus, Trichodesmium. However, recent work suggests that the niche preferences of distinct diazotrophic (N₂ fixing) clades differ due to their metabolic and ecological diversity, hampering efforts to close the N budget and model N₂ fixation accurately. Here, I explore the range of N₂ fixation …

Flight Data Of Airplane For Wind Forecasting, Astha Sharma

University of New Orleans Theses and Dissertations

This research solely focuses on understanding and predicting weather behavior, which is one of the important factors that affect airplanes in flight. The future weather information is used for informing pilots about changing flight conditions. In this paper, we present a new approach towards forecasting one component of weather information, wind speed, from data captured by airplanes in flight. We compare NASA’s ACT-America project against NOAA’s Wind Aloft program for prediction suitability. A collinearity analysis between these datasets reveals better model performance and smaller test error with NASA’s dataset. We then apply machine learning and a genetic algorithm to process …

Global Atmospheric Budget Of Acetone: Air-Sea Exchange And The Contribution To Hydroxyl Radicals, Siyuan Wang, Eric C. Apel, Rebecca H. Schwantes, Kelvin H. Bates, Daniel J. Jacob, Emily V. Fischer, Rebecca S. Hornbrook, Alan J. Hills, Louisa K. Emmons, Laura L. Pan, Shawn Honomichl, Simone Tilmes, Jean‐François Lamarque, Mingxi Yang, Christa A. Marandino, E. S. Saltzman, Warren J. De Bruyn, Sohiko Kameyama, Hiroshi Tanimoto, Yuko Omori, Samuel R. Hall, Kirk Ullmann, Thomas B. Ryerson, Chelsea R. Thompson, Jeff Peischl, Bruce C. Daube, Róisín Commane, Kathryn Mckain, Colm Sweeney, Alexander B. Thames, David O. Miller, William H. Brune, Glenn S. Diskin, Joshua P. Digangi, Steven C. Wofsy

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Acetone is one of the most abundant oxygenated volatile organic compounds (VOCs) in the atmosphere. The oceans impose a strong control on atmospheric acetone, yet the oceanic fluxes of acetone remain poorly constrained. In this work, the global budget of acetone is evaluated using two global models: CAM‐chem and GEOS‐Chem. CAM‐chem uses an online air‐sea exchange framework to calculate the bidirectional oceanic acetone fluxes, which is coupled to a data‐oriented machine‐learning approach. The machine‐learning algorithm is trained using a global suite of seawater acetone measurements. GEOS‐Chem uses a fixed surface seawater concentration of acetone to calculate the oceanic fluxes. Both …

Southwest Pacific Tropical Cyclone Frequency And Intensity Related To Observed And Modeled Geophysical And Aerosol Variables, Rupsa Bhowmick

LSU Doctoral Dissertations

The dissertation focuses on western region of Southwest Pacific Ocean (SWPO)

basin (135E - 180, and 5S - 35S) tropical cyclone (TC) climatology using observed

and modeled data. The classification-based machine learning approach

identifies the synoptic geophysical and aerosol environment favorable or unfavorable

for TC intensification and intensity change prior to landfall incorporating

observational and satellite data. A multiple poisson regression model with varying

temporal monthly lags was used to build a relationship between the number of

monthly TC days with basin wide average dust aerosol optical depth (AOD), sea

surface temperature (SST), and upper ocean temperature (UOT). This idea …

Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Department of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) …

Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey J. Wagner, Clay Koschnick, Steven J. Schuldt, Jada B. Williams, Kevin Hallinan

Faculty Publications

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined …

Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui

Student Research, Papers, and Creative Works

Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Deparment of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) …

Characterizing Regime-Based Flow Uncertainty, John L. Fioretti

Theses and Dissertations

The goal of this work is to develop a regime-based quantification of horizontal wind field uncertainty utilizing a global ensemble numerical weather prediction model. In this case, the Global Ensemble Forecast System Reforecast (GEFSR) data is utilized. The machine learning algorithm that is employed is the mini-batch K-means clustering algorithm. 850 hPa Horizontal flow fields are clustered and the forecast uncertainty in these flow fields is calculated for different forecast times for regions across the globe. This provides end-users quantified flow-based forecast uncertainty.

Estimating Abiotic Thresholds For Sagebrush Condition Class In The Western United States, Stephen Boyte, Bruce K. Wylie, Yingxin Gu, Donald J. Major

United States Geological Survey: Staff Publications

Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relatively little value to wildlife, livestock, and humans and function as fuel that increases fire frequency. The more land area covered by annual grasses, the higher the potential for fire, thus reducing the potential for native vegetation to reestablish, even when applying restoration treatments. Mapping …