Print Email Facebook Twitter Diverse Explorations of Rainfall Nowcasting with TrajGRU Title Diverse Explorations of Rainfall Nowcasting with TrajGRU: Mitigating Smoothness and Fading Out Challenges for Longer Lead Times Author Zou, Yanghuan (TU Delft Civil Engineering & Geosciences) Contributor Schleiss, M.A. (mentor) Fioranelli, F. (graduation committee) Taormina, R. (graduation committee) Degree granting institution Delft University of Technology Programme Water Management Date 2023-10-31 Abstract Machine learning models offer promising potential in precipitation nowcasting. However, a common issue faced by many of these models is the tendency to produce blurry precipitation nowcasts, which are unrealistic. Previous research on the deep learning model - TrajGRU (Shi et al., 2017) indicated that data imbalance in radar images and the double-penalty effect of pixel-wise loss functions are underlying causes for this blurriness.In this thesis, we continue to explore various approaches to improve the predictive performance of TrajGRU. Our research has first investigated spatially and temporally enhanced loss functions to address the two remaining issues: data imbalance and double penalty. The second part of our research focuses on manifold optimizations within the model network, such as incorporating additional model inputs or increasing batch size, to understand the model’s limitations.Our results reveal that enhanced loss functions did not lead to predictive improvements and even resulted in undesired checkerboard patterns. Changes to the model network make a difference in the image sharpness and predictive rain evolution. Our visual analysis indicates that a larger batch size generates sharper rain field edges; predictions by using multiple parameter groups exhibit more rain dynamics. The incorporation with other transformed datasets introduces finer structures within rain fields. Although the blurriness has not been completely resolved, our study recommended future work can continue exploring the optimization in the TrajGRU network. Subject PrecipitationNowcastingDeep learningWeather RadarBlurrinessGRU To reference this document use: http://resolver.tudelft.nl/uuid:08ee6883-4804-4a3d-809d-7376fa6f41f0 Related dataset 4TU.ResearchData https://doi.org/10.4121/12437ba3-4cf4-47c4-928b-94dc9bdec663.v1 Part of collection Student theses Document type master thesis Rights © 2023 Yanghuan Zou Files PDF RainGuRu_thesis_Yanghuan_final.pdf 19.89 MB Close viewer /islandora/object/uuid:08ee6883-4804-4a3d-809d-7376fa6f41f0/datastream/OBJ/view