Research theme area:
This research is part of the RCGI project - 64 GHG project - D.1, which has the title: “Greenhouse gas emissions in the Amazon and data analysis system and services”. In this project, a multidisciplinary team will build a computer system that integrates data associated with the emission of greenhouse gases in the Amazon, which integrate land use changes, remote sensing of greenhouse gases, meteorological parameters, forest degradation data, internal measures. in situ in soil and in fluxes and concentrations of greenhouse gases in the Amazon. The project will integrate land use change data from MapBiomas, INPE deforestation and fire detection systems, remote sensing and in-situ measurement of greenhouse gases data in an open access computational platform using machine learning and artificial intelligence techniques, with web access.
Carbon fluxes in tropical forests are influenced by a set of meteorological variables (temperature, precipitation, direct and diffuse radiation, cloud cover, etc.), as well as atmospheric components such as aerosols, ozone and others. This proposal aims to study the effects of changes in the meteorology, on the net carbon assimilation (Net Ecosystem Exchange - NEE) by primary forest ecosystems in the Amazon. Aerosol measurements will be made with MODIS, MISR and CALIPSO sensors, as well as new sensors such as ESA's Sentinel 5. Remote sensing measurements will be validated with NASA/AERONET network solar photometer measurements and ground based measurements at the ATTO tower. Cloud cover will be measured with CloudSat, precipitation with TRMM (Tropical Rainfall Measuring Mission) and the radiation balance measured with the CERES (Clouds and the Earth’s Radiant Energy System) sensor. Carbon fluxes will be measured using eddy-correlation techniques on the ground, in the LBA program towers and also in the ATTO tower or derived from remote sensing using data from the OCO-2 satellite. Changes in temperature and relative humidity of the atmosphere due to the interaction of solar radiation with the high load of aerosols emitted from fires, will also be studied for several sites in the Amazon. In view of large-scale aerosol transport during fires, changes in carbon fluxes will be analysed across the Amazon region, where aerosols make important changes in the potential for forest ecosystems to absorb significant amounts of atmospheric CO2. The combination of this large set of measurements will be integrated with a computational Big Data approach and also using artificial intelligence and machine Learning algorithms. The aim is to quantify the various drivers that control carbon fluxes in Amazonia.
The applicant will contribute in line with the main objectives of the project:
Creation of databases for the spatial distribution of various meteorological components such as radiation, precipitation, cloud cover, water vapor column, liquid water content, precipitation, and vertical movements for all the Amazonia regions. Integration of these meteorological databases with patterns of land use change and greenhouse gas fluxes.
Requirements to fill the position:
This Post Doc fellowship is suitable for a highly motivated researcher with a background in physics, chemistry, engineering, computing or mathematics. Requires programming skills in MatLab or Python. Desirable knowledge of remote sensing techniques for aerosols, trace gases or land use change. English proficiency is required. Ability to collaborate and develop your work in large teams is required.
The candidate must have obtained a doctorate degree less than seven years ago, priority for candidates who have just completed the Doctorate, within the regular duration, with an excellent academic record in postgraduate studies.
INFORMATION ABOUT FELLOWSHIP:
This Postdoc fellowship is funded by FAPESP (The São Paulo Research Foundation). The fellowship will cover a standard maintenance stipend of R$ 7.373,10 per month.
Position: Post-Doctoral REF: 21PDR136
https://www.rcgi.poli.usp.br/opportunities-aplication/ AND APPLICATION AT REF 21PDR136 – Post Doctoral