{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/156822"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/156822","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Adapting Transformer Encoder Architecture for Continuous Weather Datasets with Applications in Agriculture, Epidemiology and Climate Science","abstract":"This work introduces WeatherFormer, a transformer encoder-based model designed to robustly represent weather data from minimal observations. It addresses the challenge of modeling complex weather dynamics from small datasets, which is a bottleneck for many prediction tasks in agriculture, epidemiology, and climate science. Leveraging a novel pretraining dataset composed of 39 years of satellite measurements across the Americas, WeatherFormer achieves state-of-the-art performance in crop yield prediction and influenza forecasting. 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