The Climate of Innovation: AI’s Growing Influence in Weather Prediction Patents and Its Future Prospects

Minjong Cheon and Changbae Mun ()
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Minjong Cheon: Center for Sustainable Environment Research, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Wolgok-dong, Seongbuk-gu, Seoul 02792, Republic of Korea
Changbae Mun: Department of Electrical, Electronic & Communication Engineering, Hanyang Cyber University, Seoul 04764, Republic of Korea

Sustainability, 2023, vol. 15, issue 24, 1-13

Abstract: As the severity of climate change intensifies, understanding and predicting weather patterns have become paramount. Major firms worldwide have recognized this urgency, focusing their innovative efforts on weather prediction. In line with this trend, this research delves into the intricate patterns of patent data within the realm of weather prediction from 2010 to 2023. The study unveils a standard timeline for patent grants in this domain, particularly noting a distinctive peak in grant durations between 1500 and 2000 days. The global landscape of weather prediction innovation is highlighted, pinpointing the United States, China, and Japan as pivotal contributors. A salient finding is the ascendant influence of artificial intelligence (AI) in this sector, underscored by the prevalence of AI-centric keywords such as “machine learning” and “neural network”. This trend exemplifies the ongoing paradigm shift toward data-driven methodologies in weather forecasting. A notable correlation was identified between patent trends and academic trends on platforms such as arXiv, especially concerning keywords such as “machine learning” and “deep learning”. Moreover, our findings indicate that the transformer network, given its rising prominence in deep learning realms, is predicted to be a future keyword trend in weather prediction patents. However, despite its insights, the study also grapples with limitations in its predictive modeling component, which aims at forecasting patent grant durations. Overall, this research offers a comprehensive understanding of the patent dynamics in weather prediction, illuminating the trajectory of technological advancements and the burgeoning role of AI. It holds implications for academia, industry, and policymaking in navigating the future of weather prediction technologies.

Keywords: artificial intelligence; data-driven approaches; patent analysis; text mining; weather prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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