SOURCE-LINKED INTELLIGENCE

Impacts of Climate Extremes from Mining of Online Texts

CORDIS · observation · Publication date unknown

ly in my ongoing ERC project. ICE-MOT builds upon the database of climate extremes developed within my ongoing ERC project. It further leverages the experience of my research group in data-driven and machine learning analyses for climate science. I will use this interdisciplinary knowledge base to provide standardised, complete and automatically updateable spatio-temporal impact information, including indirect and/or cascading impacts, and quantify the climate conditions associated with the recorded impacts. Moreover, the database’s automated data extraction and processing pipeline will make it easily scalable to multiple regions and climate extremes. This effort is timely: the recent EU strategy on adaptation to climate change explicitly seeks to “gather more and better data on climate-related risks and losses” as a key adaptation tool. Moreover, the climate extremes data gathered in my ongoing ERC project provides a perfect basis to test the innovative idea underlying the ICE-MOT database, an opportunity which should be rapidly exploited.

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recordType

award

status

CLOSED

region

EU

value

150000

unit

EUR