Evaluation of hindcasts in subseasonal timescale of Paraná and São Francisco basins

• Grant number: 19/13528-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2019
• End date: July 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Meteorology
• Principal Investigator: Iracema Fonseca de Albuquerque Cavalcanti
• Grantee: Gabriel Alves Gazale
• Host Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovação (Brasil). São José dos Campos , SP, Brazil
• Associated research grant: 15/50687-8 - Climate services through knowledge co-production: a Euro-South American initiative for strengthening societal adaptation response to extreme events, AP.TEM

Abstract

The student will develop activities in the context of the CLIMAX project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative For Strengthening Societal Adaptation Response to Extreme Events. The project has three main objectives: 1. To increase the knowledge of processes associated with climate variability over South America, mainly southern and southeastern regions; 2. To do studies of prediction and predictability with results of numerical climate models; 3. To do studies of co-production and apply the studies in Agriculture and Energy sectors. The application to hydropower, which is the main energy source in Brazil, has the National Operator System (ONS) as a collaborator in the co-production. ONS uses weather forecasts up to 1 week and do a qualitative forecast and statistical analyses for weeks 2 to 4. The model precipitation forecasts are used as input to discharge models, which generate results that help in management of basins and hydroelectricity plants. The objective forecasts for the period of weeks 2 to 4 are important, mainly in cases of extreme precipitation (excess or deficit of rain). The purpose of this study is to verify if precipitation during these periods, in two basins used by ONS (Parana and São Francisco Basins) are reasonably predicted by models in this subseasonal timescale. The student will have opportunity of learning how to do data treatment, apply computational programs that he is using/learning in the graduation course and prepare himself to future activities on interfaces between computer science and meteorological analyses.