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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.
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 …