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A multitask approach to the forecasting of precipitation extremes

Grant number: 18/09887-1
Support type:Scholarships in Brazil - Master
Effective date (Start): June 01, 2018
Effective date (End): November 30, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Cooperation agreement: IBM Brasil
Principal Investigator:Fernando José von Zuben
Grantee:Nicole Santos e Aguiar
Home Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Company:Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação (FEEC)
Associated research grant:17/19397-9 - A multitasking learning approach to precipitation extremes forecasting, AP.PITE

Abstract

Abnormal climate events, such as precipitation extremes, are becoming more frequent, widespread, and intense, imposing huge challenges to society, even in urban or rural areas. Therefore, we are facing an increasing demand for high-performance information forecasting services. In fact, the effectiveness of economical and safety policies is directly promoted by our ability to manage the risks and estimate the impacts associated with extreme events.Agricultural productivity is closely linked to climatic conditions, thus expanding the demand for reliable forecasting systems. The purpose is to avoid huge losses in production or even operational issues in transportation and storage. Heavy rainfall events tend to foment flood scenarios, responsible for wiping out entire crops over wide areas. Excess of water may also cause other negative impacts, such as waterlogging, anaerobicity and reduced plant growth.Earth system models (ESMs) trust on physical principles that govern climate behaviour, and can be used to perform projections of future climate conditions. Many ESMs have been proposed and their response may differ significantly. That is why researchers usually perform climate projections based on ensembles of ESMs, aiming at better accuracy and reduced uncertainty. The ensemble will follow a multitask learning (MTL) perspective, where MTL looks for an improved generalization performance by simultaneously learning related tasks. A structural representation of the information sharing among tasks is going to be properly explored to make more robust and accurate precipitation extremes forecasting, also including spatial gridding and specific statistical tool devoted to extreme events. (AU)