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Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Legacy Theses & Dissertations (2009 - 2024)
Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.
The Role Of Ammonia In Atmospheric New Particle Formation And Implications For Cloud Condensation Nuclei, Arshad Arjunan Nair
The Role Of Ammonia In Atmospheric New Particle Formation And Implications For Cloud Condensation Nuclei, Arshad Arjunan Nair
Legacy Theses & Dissertations (2009 - 2024)
Atmospheric ammonia has received recent attention due to (a) its increasing trend across various regions of the globe; (b) the associated direct and indirect (through PM2.5) effects on human health, the ecosystem, and climate; and (c) recent evidence of its role in significantly enhancing atmospheric new particle formation (NPF or nucleation) rates. The mechanisms behind nucleation in the atmosphere are not fully understood, although over the last decade there have been significant developments in our understanding. This dissertation aims at improving our understanding of atmospheric ammonia in the atmosphere, its spatiotemporal variability, its role in atmospheric new particle formation, and …
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
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 …
Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui
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) …
Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui
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
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.
Urban Health Related Air Quality Indicators Over The Middle East And North Africa Countries Using Multiple Satellites And Aeronet Data, Maram El-Nadry, Wenzhao Li, Hesham El-Askary, Mohamed A. Awad, Alaa Ramadan Awad
Urban Health Related Air Quality Indicators Over The Middle East And North Africa Countries Using Multiple Satellites And Aeronet Data, Maram El-Nadry, Wenzhao Li, Hesham El-Askary, Mohamed A. Awad, Alaa Ramadan Awad
Mathematics, Physics, and Computer Science Faculty Articles and Research
Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth …
Characterization Of Tropical Cyclone Intensity Using Microwave Imagery, Amanda M. Nelson
Characterization Of Tropical Cyclone Intensity Using Microwave Imagery, Amanda M. Nelson
Theses and Dissertations
In the absence of wind speed data from aircraft reconnaissance of tropical cyclones (TCs), analysts rely on remote sensing tools to estimate TC intensity. For over 40 years, the Dvorak technique has been applied to estimate intensity using visible and infrared (IR) satellite imagery, but its accuracy is sometimes limited when the radiative effects of high clouds obscure the TC convective structure below. Microwave imagery highlights areas of precipitation and deep convection revealing different patterns than visible and IR imagery. This study explores application of machine learning algorithms to identify patterns in microwave imagery to infer storm intensity, particularly focusing …
Using Advanced Post-Processing Methods With The Hrrr-Tle To Improve The Prediction Of Cold Season Precipitation Type, Timothy Thielke
Using Advanced Post-Processing Methods With The Hrrr-Tle To Improve The Prediction Of Cold Season Precipitation Type, Timothy Thielke
Theses and Dissertations
In this study we explore advanced statistical methods with the operational High-Resolution Rapid Refresh Model (HRRR) Time-Lagged Ensemble (TLE) to improve the prediction of cold season precipitation type. TLEs are a computationally efficient method to provide a slightly improved probabilistic forecast as the differences between model runs are an approximation of initial condition uncertainty. We apply evolutionary programming, weight-decay bias correction, and Bayesian Model Combination with fifteen HRRR forecast variables that potentially relate to precipitation type for station locations in the contiguous United States that are along and to the east of 100 W longitude to obtain probabilistic precipitation type …
Automated Image Interpretation For Science Autonomy In Robotic Planetary Exploration, Raymond Francis
Automated Image Interpretation For Science Autonomy In Robotic Planetary Exploration, Raymond Francis
Electronic Thesis and Dissertation Repository
Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data.
This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces.
Two …