Meteorology Commons^™

Leveraging Deep Learning Recurrence And Attention Mechanisms For Flood Forecasting And Assessment, Elnaz Heidari

All Dissertations

Predicting how much water will flow in rivers and streams is important for managing floods, water supply, and the environment. Traditionally, government agencies have used complex models, such as the National Water Model (NWM), which simulate how much water moves through landscapes using physical laws and real-world data. However, recent advances in Artificial Intelligence (AI) have enabled new ways to make these predictions. This research explored whether AI-based models could predict river discharge more accurately. These AI models learn patterns from past data instead of relying only on physical rules. To find out how well they work, the AI models …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

A High-Resolution Daily Precipitation Fusion Framework Integrating Radar, Satellite, And Nwp Data Using Machine Learning Over South Korea, Hyoju Park, Hiroyuki Miyazaki, Menas Kafatos, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover …

Radar Precursors To Severe Weather Reports In Left-Moving Supercells, Eric A. Carothers

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

While much research has examined dual-polarimetric signatures of right-moving supercells, very little has been done with left-moving supercells. Given that left-moving supercells are thought to be disproportionate producers of large hail, understanding their internal dynamics is vitally important. This study examines differences and trends in the dual-polarimetric signatures of left-moving supercells to identify precursors to severe weather reports. A dataset of left-moving supercells associated with severe weather reports was created. These storms are processed with an automated analysis algorithm that identifies and quantifies the polarimetric signatures in each storm. A method for analysis of differences and trends in their dual-polarization …

Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick

Theses and Dissertations

The main contributions of this research is to add to the growing library of literature on the use of deep learning algorithms for the spatiotemporal prediction of dangerous atmospheric and hydrologic phenomena. Specifically, we develop novel attention-based and non-attention-based recurrent neural network frameworks to produce short-range sequential forecasts for lightning and tornado occurrences. Additionally, we introduce methods that account for and include error in the model tuning process to generate more reliable models. Furthermore, we have created a lightweight spatiotemporal tornadic prediction dataset that we plan to make publicly available. The first component of this research develops three novel spatiotemporal …

Unlocking Potential: Analyzing The Content, Style, Structure, And Interactivity Of Mesonets As Operational Dashboards, Savannah Olivas, Jeannette Sutton, Michele K. Olson

Emergency Preparedness, Homeland Security, and Cybersecurity Faculty Scholarship

Emergency managers need data and information to make life-saving decisions on behalf of the public. Operational dashboards, if designed appropriately, can provide this information in a central location and reduce cognitive demands during decision-making. Mesonet websites can serve as a type of operational dashboard that has the potential to provide the meteorological data necessary for emergency managers to make decisions. In this study, we use quantitative content analysis to examine the content, style, structure, and interactivity of 18 Mesonet websites from across the contiguous United States. We find that Mesonet websites vary in the type and amount of content they …

Applications Of Artificial Intelligence On Drought Impact Monitoring And Assessment, Beichen Zhang

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Drought, a prevalent and consequential natural disaster, poses widespread, indirect challenges across environmental and societal dimensions. Despite considerable focus on monitoring meteorological and hydrological drought and studying their characteristics, there is a gap in assessing its multifaceted impacts, especially on societal sectors. The dissertation comprises three research essays utilizing artificial intelligence to quantitatively study multi-dimensional drought impacts. The first essay leveraged deep learning and natural language processing to predict multi-dimensional drought impacts from textual datasets, including social media, news media, and citizen scientist reports. The findings demonstrate superior performance over traditional methods and unveil the spatial and temporal heterogeneity of …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Tornado Outbreak False Alarm Probabilistic Forecasts With Machine Learning, Kirsten Reed Snodgrass

Theses and Dissertations

Tornadic outbreaks occur annually, causing fatalities and millions of dollars in damage. By improving forecasts, the public can be better equipped to act prior to an event. False alarms (FAs) can hinder the public’s ability (or willingness) to act. As such, a probabilistic FA forecasting scheme would be beneficial to improving public response to outbreaks.

Here, a machine learning approach is employed to predict FA likelihood from Storm Prediction Center (SPC) tornado outbreak forecasts. A database of hit and FA outbreak forecasts spanning 2010 – 2020 was developed using historical SPC convective outlooks and the SPC Storm Reports database. Weather …

Evaluation Of Wave Contributions In Hurricane Irma Storm Surge Hindcast, Abram Musinguzi, Lokesh Reddy, Muhammad K. Akbar

Mechanical and Manufacturing Engineering Faculty Research

This paper evaluates the contribution of waves to the total predicted storm surges in a Hurricane Irma hindcast, using ADCIRC+SWAN and ADCIRC models. The contribution of waves is quantified by subtracting the water levels hindcasted by ADCIRC from those hindcasted by ADCIRC+SWAN, using OWI meteorological forcing in both models. Databases of water level time series, wave characteristic time series, and high-water marks are used to validate the model performance. Based on the application of our methodology to the coastline around Florida, a peninsula with unique geomorphic characteristics, we find that wave runup has the largest contribution to the total water …

Effect Of Trigonometric Transformations On The Machine Learning Prediction And Quality Control Of Air Temperature, Andrea Fenoglio, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Conducting effective quality control of weather observations in real time is vital to the 14th Weather Squadron’s mission of providing authoritative climate data. This study explored automated quality control of weather observations by applying multiple machine learning techniques to 43,487 surface weather observations from 5 years of data at a single location. Temperature predictors were evaluated using recursive feature elimination on linear regression and XGBoost algorithms, as well as using a neural network hyperparameter sweep. Modeling was repeated after calculating trigonometric transforms of temporal variables to give the models insight into the diurnal heating cycle of the Earth. All models …

Analysis Of Titan's Fluvial Features Using Numerical Modeling, Jeshurun Horton

Mechanical Engineering Undergraduate Honors Theses

River channels have been observed near the Huygens probe landing site on the surface of Titan, along with evidence of rounded water ice boulders transported through fluid flow. Evidence near the landing site suggests active flow of liquid methane, which has motivated the study of the effects of sediment load and channel sizes on Titan’s fluvial features. A numerical model is used to determine the viscosity, flow velocity, and critical boulder transport diameter based on channel size, slope, and a range of sediment concentrations. This model achieves two ends: first, observed boulder diameters are used to determine the ideal channel …

Theoretical And Observational Analysis Of Ice Particles For Improvement Of Ice Microphysical Models, Vanessa Przybylo

Legacy Theses & Dissertations (2009 - 2024)

Frozen hydrometeors can grow to acquire a multitude of shapes and sizes, which influence the distribution of mass within cloud systems. Aggregates have a variety of formations based on initial ice particle size, shape, falling orientation, and the number of particles that collect. This work employs the theoretical Ice Particle and Aggregate Simulator (IPAS) as a statistical tool to repetitively collect ice crystals to derive bulk aggregate characteristics.

The Effect Of Initial Conditions On The Weather Research And Forecasting Model, Aaron D. Baker

Electronic Theses and Dissertations

Modeling our atmosphere and determining forecasts using numerical methods has been a challenge since the early 20th Century. Most models use a complex dynamical system of equations that prove difficult to solve by hand as they are chaotic by nature. When computer systems became more widely adopted and available, approximating the solution of these equations, numerically, became easier as computational power increased. This advancement in computing has caused numerous weather models to be created and implemented across the world. However a challenge of approximating these solutions accurately still exists as each model have varying set of equations and variables to …

Correction Of Back Trajectories Utilizing Machine Learning, Britta F. Gjermo Morrison

Theses and Dissertations

The goal of this work was to analyze 24-hour back trajectory performance from a global, low-resolution weather model compared to a high-resolution limited area weather model in particular meteorological regimes, or flow patterns using K-means clustering, an unsupervised machine learning technique. The duration of this study was from 2015-2019 for the contiguous United States (CONUS). Three different machine learning algorithms were tested to study the utility of these methods improving the performance of the CFS relative to the performance of the RAP. The aforementioned machine learning techniques are linear regression, Bayesian ridge regression, and random forest regression. These results mean …

Hail Detection Using Dual Polarization Weather Radar, Alfonso Ladino Rincon

International Programs

This poster highlights how active remote sensors such as weather radar are completely useful for hail detection given its feature and the information they produce. Hail detection is already well studied by the atmospheric scientific community and dual polarimetric variables values for hail signature are presented according to those advances. Then, a supervised classification technique is showed to illustrated how machine learning can be integrated to radar information for automatic hail detection. However, this fuzzy logic algorithm has the capability to distinguish between meteorological and non-meteorological echoes. This automatic information might help forecasters from National Weather Services – NWS to …

Environmental Discriminators For Significant Tornadoes And Hail In The U.S. Using Proximity Soundings, Cody Michael Converse

Graduate Research Theses & Dissertations

Over the last 50 years, the United States has experienced an increase in severe storm events that produced $1 billion in damages or greater. Much of this loss is attributed to significant tornadoes and hail associated with deep, moist convection. Improving forecasts for these significant events assist in mitigating the impacts of these events. Previous work has identified statistically significant environmental parameters associated with severe thunderstorms, but more research is needed in identifying statistically significant ingredients associated with environments that produce significant tornadoes and hail.

This thesis aims to answer the following question: “Can diagnostics commonly used to forecast for …

An Investigation Into Weather's Effect On Aerosol Particles Using Wrf And Mapss, Hayden Webb, Devin Smoot

Student Research

Aerosols are solid or liquid particles suspended in air or gas. Many processes contribute to increased aerosol particles in the atmosphere, such as winds, ocean waves, and industrial exhaust. Once suspended, particles can be carried thousands of miles before being returned to the ground by gravity or rain. Some particles can be suspended for several years and travel all around the world, these particles have the greatest impact on climate and weather.

Additional Data Via Autonomous Systems To Supplement Traditional Sparse Sources For Weather Forecasting And Atmospheric Science, Suzanne Weaver Smith

Commonwealth Computational Summit

No abstract provided.

Improved Storm Data Processing Through Parallel Computing Approaches, Shauna Smith

Journal of Undergraduate Research at Minnesota State University, Mankato

A previous research study conducted at Michigan Technological University by Dr. Deborah Nykanen and her colleague Dr. Daniel Harris analyzed storm data in order to develop algorithms that will allow coarse resolution rainfall forecasted by weather models to be optimally used in high resolution hydrology models with the goal of improving stream flow predictions and early detection algorithms that can be used to warn communities about potential flash floods. This research was performed by analyzing a series of independent radar images derived from Weather Surveillance Radar-1988 Doppler (WSR-88D) data obtained from Dr. James A. Smith at Princeton University using a …

Using Neural Networks To Provide Local Weather Forecasts, Andrew Culclasure

College of Graduate Studies: Theses & Dissertations

Artificial neural networks (ANNs) have been applied extensively to both regress and classify weather phenomena. While one of the core strengths of neural networks is rendering accurate predictions with noisy datasets, there is currently not a significant amount of research focusing on whether ANNs are capable of producing accurate forecasts of relevant weather variables from small-scale, imperfect datasets. Also, there is not a significant amount of research focusing on the forecasting performance of neural networks applied to weather datasets that have been temporally rolled-up from a base dataset. In this paper, a survey of existing research on applying ANNs to …

Retrieval Of Sub-Pixel-Based Fire Intensity And Its Application For Characterizing Smoke Injection Heights And Fire Weather In North America, David Peterson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

For over two decades, satellite sensors have provided the locations of global fire activity with ever-increasing accuracy. However, the ability to measure fire intensity, know as fire radiative power (FRP), and its potential relationships to meteorology and smoke plume injection heights, are currently limited by the pixel resolution. This dissertation describes the development of a new, sub-pixel-based FRP calculation (FRP_f) for fire pixels detected by the MODerate Resolution Imaging Spectroradiometer (MODIS) fire detection algorithm (Collection 5), which is subsequently applied to several large wildfire events in North America. The methodology inherits an earlier bi-spectral algorithm for retrieving sub-pixel …

A Google Earth-Based Framework For Visualization Of The Chesapeake Bay Operational Forecast System, Gary Lawson

Computational Modeling & Simulation Engineering Theses & Dissertations

For the persons who live near and travel the waters of the Chesapeake Bay, the data provided by the Chesapeake Bay Operational Forecast System (CBOFS) is invaluable. The information provided includes measurements and forecasts of surface wind velocity, water current velocity, salinity levels, water level, and temperature. Currently, this information is freely available on the CBQ_FS website hosted by the National Oceanographic and Atmospheric Administration (NOAA). It is offered as Nowcast, measured data, and Forecast data and is visualized using 2D images which describe a subset of the data in an easy to read chart. However, if the data were …

A Differential Absorption Model For Remote Sensing Of Atmospheric Pressure, Shivam J. Shah

Electrical & Computer Engineering Theses & Dissertations

The goal of the project is to develop and test a "model based" radar processing strategy that is compatible with the concept of a "cognitive radar". The basic approach will be to develop a cognitive radar algorithm (genetic algorithm) based on the capabilities of an existing commercially available Software Radio. While the focus of this effort is the development of a candidate approach for genetic algorithm, the longer term goal would be to implement the approach using this software radio technology to provide a low cost radar processor. The proposed technology would use differential absorption radar working at the 50-56 …

Information Technology Implementation Decisions To Support The Kentucky Mesonet, D. Michael Grogan

Masters Theses & Specialist Projects

The Kentucky Mesonet is a high-density, mesoscale network of automated meteorological and climatological sensing platforms being developed across the commonwealth. Data communications, collection, processing, and delivery mechanisms play a critical role in such networks, and the World Meteorological Organization recognizes that “an observing system is not complete unless it is connected to other systems that deliver the data to the users.” This document reviews the implementation steps, decisions, and rationale surrounding communications and computing infrastructure development to support the Mesonet. A general overview of the network and technology-related research is provided followed by a review of pertinent literature related to …

Design Of A Software Framework Prototype For Scientific Model Interoperability, Eric Fritzinger, Sohei Okamoto

2010 Annual Nevada NSF EPSCoR Climate Change Conference

19 PowerPoint slides Session 2: Infrastructure Convener: Sergiu Dascalu, UNR Abstract: -What are models? -Mathematical models used to describe a system -E.g. Atmospheric, Oceanic, Ecological, etc… -Algorithmic calculations which take input and produce estimated results -Weather forecasting, global warming predictions, sea level estimations, etc… -Models are invaluable

Suitability Of Unidata Metapps For Incorporation In Platform-Independent User-Customized Aviation Weather Products Generation Software, Harmen P. Visser

Theses and Dissertations

The Air Force Combat Climatology Center (AFCCC) is tasked to provide long-range seasonal forecasts for worldwide locations. Currently, the best long-range temperature forecasts the weather community has are the climatological standard normals. This study creates a stepping-stone into the solution of long-range forecasting by finding a process to predict temperatures better than those using climatological standard normals or simple frequency distributions of occurrences. Northern Hemispheric teleconnection indices and the standardized Southern Oscillation index are statistically compared to three-month summed Heating Degree Days (HDDs) and Cooling Degree Days (CDDs) at 14 U.S. locations. First, linear regression was accomplished. The results showed …

Extensible Markup Language As A Weather Tool, Michael J. Calidonna

Theses and Dissertations

This thesis is a proof of concept work that will extend the Core Mapping Application Program Interface (CMAPI) components to include weather data. The CMAPI project is headed by Air Force Research Lab (AFRL)/Information Directorate Information Handling Branch (IFEB) at Rome labs in Rome, New York. This work extends the CMAPI project in two distinct areas. The first goal is to figure out how to overlay and display weather data on a dynamically linked Internet platform. This was accomplished by incorporating existing data from the Air Force Weather Agency (AFWA) into the CMAPI program in a static environment. The other …

Data Warehouse Techniques To Support Global On-Demand Weather Forecast Metrics, Meriellen C. Joga

Theses and Dissertations

Air Force pilots and other operators make crucial mission planning decisions based on weather forecasts; therefore, the ability to forecast the weather accurately is a critical issue to Air Force Weather (AFW) and its customers. The goal of this research is to provide Air Force Weather with a methodology to automate statistical data analysis for the purpose of providing on-demand metrics. A data warehousing methodology is developed and applied to the weather metrics problem in order to present an option that will facilitate on-demand metrics. On-line analytical processing (OLAP) and data mining solutions are also discussed.