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Report on Completion of Internship at the Japan Weather Association (IWASE, Course of Maritime Techmology and Logistics)
I participated in the internship for three months from Thursday 1 June to Tuesday 29 August, at the Japan Weather Association.
The work was mainly remote, consisting of about 2 to 3 days per week. In my tasks, I had the freedom to implement projects using Python. I had meetings with my supervisors once every two weeks to receive feedback and proceed with our work. Additionally, I presented the work plan and the final report to the employees.
In this project, I performed spatiotemporal interpolation of maritime winds. Specifically, I researched various papers and implemented a Deep Learning model based on them to enhance the resolution of wind speed and direction. Ultimately, there were still some challenges, but I believe I was able to create a relatively accurate model.
Furthermore, I learned about handling meteorological data and its characteristics. Meteorological data has a unique format compared to typical tabular or image data, so learning how to work with it in Python is valuable for my future research in meteorology-related fields.
I participated in this internship because I saw it as a great opportunity to gain experience in AI development related to my research field in meteorology. As a result, thanks to the flexible and comfortable working environment and the support of many individuals, I was able to carry out AI implementations smoothly, making it a valuable experience. Finally, I would like to express my sincere gratitude to everyone at the Japan Weather Association, as well as to all those who supported me during this internship.
IWASE, 1st year student, Course of Maritime Techmology and Logistics