A big thanks to AWS reMARS (Amazon's global AI event) for the very nice article about our climate data products and our AI/ML journey with the Climate Intelligence (CLINT) research project.
The article is structured as an interview of Harilaos, our CEO, speaking about climate modeling, the importance of climate projections at high spatial resolutions and CLINT (short for climate intelligence), a new AI framework of machine learning techniques and algorithms to process big climate datasets for improving the detection, causation and attribution of extreme weather events. It can be summarized as follows:
The Intergovernmental Panel on Climate Change (IPCC) has unequivocally stated the impact of human-induced warming on the atmosphere, oceans, and land, emphasizing the need for urgent action. Climate policy makers, including the IPCC, are increasingly turning to machine learning, with the climate data factory, led by CEO Harilaos Loukos, playing a pivotal role in providing accessible climate information.
The climate data factory specializes in managing two types of model data: climate projections and climate forecasts. While climate projections support international climate negotiations, the startup enhances their spatial resolution for local risk assessments. Additionally, the climate data factory processes climate forecasts from operational sources, contributing to long-term forecasting. The startup focuses on data management and processing, offering skills and resources to make information ready for practical applications.
Loukos highlights concrete applications, such as consultants using high-resolution climate projections for local climate adaptation strategies and financial institutions utilizing data for mandatory risk assessments. The Climate Intelligence Project (CLINT), a four-year research initiative, aims to develop an AI framework for processing large climate datasets, with a focus on understanding extreme events. CLINT seeks to improve forecasting accuracy and minimize societal impacts, extending results to public and private entities through a demonstrator and software library. The climate data factory's algorithms prioritize optimization, resource allocation, and transparency through publications in reputable venues.
Read the full aws reMars article here.
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