Meteorology Commons^™

Seasonal Variability And Predictability Of Monsoon Precipitation In Southern Africa, Matthew F. Horan, Fred Kucharski, Moetasim Ashfaq

Faculty Publications

Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño–Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%–75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key …

Lightning Forecast From Chaotic And Incomplete Time Series Using Wavelet De-Noising And Spatiotemporal Kriging, Jared K. Nystrom, Raymond Hill, Andrew J. Geyer, Joseph J. Pignatiello Jr., Eric Chicken

Faculty Publications

Purpose: Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Design/Methodology/Approach: Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lighting prediction.

Findings: The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

Abstract © Emerald Publishing …

Atmospheric Meteorological Effects On Forecasting Daily Lightning Occurrence At Cape Canaveral Space Force Station, Jon Saul [*], Torrey J. Wagner, Eric G. Mbonimpa, Brent T. Langhals

Faculty Publications

As the Cape Canaveral Space Force Station and Kennedy Space Center increase their launch rate, any process that could assist in the automation of the currently-manual lightning forecast would be valuable. This work examines the possibility of machine-learning assistance with the daily lighting forecast which is produced by the 45th Weather Squadron. A dataset consisting of 34 lightning, pressure, temperature and windspeed measurements taken from 334 daily weather balloon (rawinsonde) launches in the timeframe 2012-2021 was examined. Models were created using recursive feature elimination on logistic regression and XGClassifier algorithms, as well as Bayesian and bandit optimization of neural network …

Feasibility Of Obtaining Surface Layer Moisture Flux Using An Ir Thermometer, Steven T. Fiorino, Lance Todorowski, Jaclyn Schmidt, Yogendra Raut, Jacob Margraf

Faculty Publications

This paper evaluates the feasibility of a method using a single hand-held infrared (IR) thermometer and a mini tower of wet and dry paper towels to psychometrically obtain surface layer temperature and moisture gradients and fluxes. Sling Psychrometers have long been standard measuring devices for quantifying the thermodynamics of near-surface atmospheric gas–vapor mixtures, specifically moisture parameters. However, these devices are generally only used to measure temperature and humidity at one near-surface level. Multiple self-aspirating psychrometers can be used in a vertical configuration to measure temperature and moisture gradients and fluxes in the first 1–2 m of the surface layer. This …

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 …

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 …

E700xd Portable Doppler Radar Energy Systems Analysis, Brandon M. Bailey [*], Torrey J. Wagner, Jada Williams

Faculty Publications

Occurring in industrialized nations, inexpensive and abundantly available power is routinely taken for granted. However, energy resilience and to a lesser extent price are key concerns when considering potential solutions for disaster response, disaster relief, or military operations. The Department of Defense (DoD) currently uses a 5 kW generator to power the E700XD portable Doppler radar system when grid power is unavailable [1]. While the radar has an approximate power consumption of 2.5 kW, there is a potential for higher demand due to weather conditions [2]. This paper examines the cost of operating a currently installed generator, compared to the …