Cuomo, Joaquin M., authorAnderson, Chuck, advisorChandrasekar, V., advisorPallickara, Sangmi Lee, committee memberSuryanarayanan, Sid, committee member2020-09-072022-09-022020https://hdl.handle.net/10217/212034Weather nowcasting is heavily dependent on the observation and estimation of radar echoes. There are many different types of deployed nowcasting systems, but none of them based on machine learning, even though it has been an active area of research in the last few years. This work sets the basis for considering machine learning models as real alternatives to current methods by proposing different architectures and comparing them against other nowcasting systems, such as DARTS and STEPS. The methods proposed here are based on residual convolutional encoder-decoder architectures, and they reach the state of the art performance and, in certain scenarios, even outperform them. Different experiments are presented on how the model behaves when using recurrent connections, different loss functions, and different prediction lead times.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.radar echoes predictionnowcastingvideo predictionText