Dr Luigi Renzullo

BSc (Mathematics & Computing); PostGradDipAppPhys; PhD

Dr Luigi Renzullo has built his career on exploring a role for earth observations in improving land and water management decision support, especially as constraints on biophysical modelling through techniques of model-data fusion and data assimilation. Before joining ANU Luigi spent 15 years at CSIRO where a large part of his work centred on water resources and the application of satellite-derived products for continental water balance. He was one of the recipient of CSIRO’s Impact from Science Medal in 2016 for the innovative research conducted through the engagement between CSIRO and Bureau of Meteorology to revolutionise national-scale water resources assessment and forecasting information products.

Since joining ANU and the IWF Dr Renzullo has focused his research on developing algorithms for improving real-time rainfall and soil moisture information products. On rainfall, he is exploring ways of improving prediction through merging of multiple data sources, which he does in collaboration with both the Bureau of Meteorology and India’s National Centre for Medium Range Weather Forecasting. On soil moisture, Luigi is exploring the methods of model-data assimilation to produce high-resolution root-zone soil moisture estimation across the continent, and he was successful in being awarded an ARC DP2020 on the topic with Fenner colleagues and a GRDC grant with University of Sydney colleagues.

Research interests

Dr Luigi Renzullo is a senior research scientist with over 14 years experience in areas such as: atmospheric correction of satellite data, hyperspectral sensing in the viticulture industry, landscape carbon and water balance modelling, and satellite rainfall and soil moisture applications for water resources assessments.

His interests are best summarised as "exploring the role that earth observations can play in land management decision support, especially as constraints on biophysical models through techniques of model-data fusion and data assimilation".

Keywords: satellite data; radiative transfer modelling; spectrometry; water cycle science; data assimilation