Stock, Jason D., authorAnderson, Charles, advisorEbert-Uphoff, Imme, advisorPallickara, Shrideep, committee memberKummerow, Christian, committee member2021-09-062021-09-062021https://hdl.handle.net/10217/233704Vertical profiles of temperature and moisture as provided by radiosondes are of paramount importance to forecasting convective activity, yet the National Weather Service radiosonde network is spatially coarse and suffers from temporal paucity. Supplementary information generated by numerical weather prediction (NWP) models is invaluable---analysis and forecast profiles are available at a high sampling frequency and horizontal resolution. However, numerical models contain inherent errors and inaccuracies, and many of these errors occur near the surface and influence the short-term prediction of high impact events such as severe thunderstorms. For example, the convective available potential energy and the convective inhibition are highly dependent on the near-surface values of temperature and moisture. To address these errors and to create the most useful vertical profiles of temperature and moisture for severe weather nowcasting, we explore a machine learning approach to combine satellite and surface observations with an initial NWP profile. In particular, we explore deep learning to improve vertical profiles from an NWP model, which is the first known work to do so. Using initial profile predictions from the Rapid Refresh (RAP) model, corresponding surface products from the Real-Time Mesoscale Analysis (RTMA), and satellite data from the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager, we train variations of fully-connected and convolutional neural networks with custom knowledge guided loss functions to produce enhanced profiles. We evaluate the success of our approach by comparing estimates with ground truth radiosonde observations (RAOB)s and their derived indices for samples collected between January 1, 2017 and August 31, 2020. The proposed Residual U-Net architecture shows a 26.15% reduction in error over the profiles relative to the RAP errors, with the greatest improvements in the mid- to upper-level moisture. Furthermore, we detail the importance of the GOES-16 channels and assess our model under different meteorological conditions, finding: 1) no bias of seasonality; 2) training with additional samples, even in cloudy conditions, to be beneficial; and 3) sounding locations with more samples and higher initial errors to have greater improvement. As such, this work is targeted to aid forecasters concerned with severe convection make more precise predictions, thereby enhancing the nation's readiness, responsiveness, and resilience to high-impact weather events.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.numerical weather predictionartificial neural networksweather forecastingText